using OpenCVForUnity.CoreModule;
using OpenCVForUnity.UtilsModule;
using System;
using System.Collections.Generic;
using System.Runtime.InteropServices;

namespace OpenCVForUnity.ImgprocModule
{
    // C++: class Imgproc


    public class Imgproc
    {

        private const int IPL_BORDER_CONSTANT = 0;
        private const int IPL_BORDER_REPLICATE = 1;
        private const int IPL_BORDER_REFLECT = 2;
        private const int IPL_BORDER_WRAP = 3;
        private const int IPL_BORDER_REFLECT_101 = 4;
        private const int IPL_BORDER_TRANSPARENT = 5;
        private const int CV_INTER_NN = 0;
        private const int CV_INTER_LINEAR = 1;
        private const int CV_INTER_CUBIC = 2;
        private const int CV_INTER_AREA = 3;
        private const int CV_INTER_LANCZOS4 = 4;
        private const int CV_MOP_ERODE = 0;
        private const int CV_MOP_DILATE = 1;
        private const int CV_MOP_OPEN = 2;
        private const int CV_MOP_CLOSE = 3;
        private const int CV_MOP_GRADIENT = 4;
        private const int CV_MOP_TOPHAT = 5;
        private const int CV_MOP_BLACKHAT = 6;
        private const int CV_RETR_EXTERNAL = 0;
        private const int CV_RETR_LIST = 1;
        private const int CV_RETR_CCOMP = 2;
        private const int CV_RETR_TREE = 3;
        private const int CV_RETR_FLOODFILL = 4;
        private const int CV_CHAIN_APPROX_NONE = 1;
        private const int CV_CHAIN_APPROX_SIMPLE = 2;
        private const int CV_CHAIN_APPROX_TC89_L1 = 3;
        private const int CV_CHAIN_APPROX_TC89_KCOS = 4;
        private const int CV_THRESH_BINARY = 0;
        private const int CV_THRESH_BINARY_INV = 1;
        private const int CV_THRESH_TRUNC = 2;
        private const int CV_THRESH_TOZERO = 3;
        private const int CV_THRESH_TOZERO_INV = 4;
        private const int CV_THRESH_MASK = 7;
        private const int CV_THRESH_OTSU = 8;
        private const int CV_THRESH_TRIANGLE = 16;
        // C++: enum <unnamed>
        public const int CV_GAUSSIAN_5x5 = 7;
        public const int CV_SCHARR = -1;
        public const int CV_MAX_SOBEL_KSIZE = 7;
        public const int CV_RGBA2mRGBA = 125;
        public const int CV_mRGBA2RGBA = 126;
        public const int CV_WARP_FILL_OUTLIERS = 8;
        public const int CV_WARP_INVERSE_MAP = 16;
        public const int CV_CHAIN_CODE = 0;
        public const int CV_LINK_RUNS = 5;
        public const int CV_POLY_APPROX_DP = 0;
        public const int CV_CONTOURS_MATCH_I1 = 1;
        public const int CV_CONTOURS_MATCH_I2 = 2;
        public const int CV_CONTOURS_MATCH_I3 = 3;
        public const int CV_CLOCKWISE = 1;
        public const int CV_COUNTER_CLOCKWISE = 2;
        public const int CV_COMP_CORREL = 0;
        public const int CV_COMP_CHISQR = 1;
        public const int CV_COMP_INTERSECT = 2;
        public const int CV_COMP_BHATTACHARYYA = 3;
        public const int CV_COMP_HELLINGER = CV_COMP_BHATTACHARYYA;
        public const int CV_COMP_CHISQR_ALT = 4;
        public const int CV_COMP_KL_DIV = 5;
        public const int CV_DIST_MASK_3 = 3;
        public const int CV_DIST_MASK_5 = 5;
        public const int CV_DIST_MASK_PRECISE = 0;
        public const int CV_DIST_LABEL_CCOMP = 0;
        public const int CV_DIST_LABEL_PIXEL = 1;
        public const int CV_DIST_USER = -1;
        public const int CV_DIST_L1 = 1;
        public const int CV_DIST_L2 = 2;
        public const int CV_DIST_C = 3;
        public const int CV_DIST_L12 = 4;
        public const int CV_DIST_FAIR = 5;
        public const int CV_DIST_WELSCH = 6;
        public const int CV_DIST_HUBER = 7;
        public const int CV_CANNY_L2_GRADIENT = (1 << 31);
        public const int CV_HOUGH_STANDARD = 0;
        public const int CV_HOUGH_PROBABILISTIC = 1;
        public const int CV_HOUGH_MULTI_SCALE = 2;
        public const int CV_HOUGH_GRADIENT = 3;
        // C++: enum MorphShapes_c
        public const int CV_SHAPE_RECT = 0;
        public const int CV_SHAPE_CROSS = 1;
        public const int CV_SHAPE_ELLIPSE = 2;
        public const int CV_SHAPE_CUSTOM = 100;
        // C++: enum SmoothMethod_c
        public const int CV_BLUR_NO_SCALE = 0;
        public const int CV_BLUR = 1;
        public const int CV_GAUSSIAN = 2;
        public const int CV_MEDIAN = 3;
        public const int CV_BILATERAL = 4;
        // C++: enum cv.AdaptiveThresholdTypes
        public const int ADAPTIVE_THRESH_MEAN_C = 0;
        public const int ADAPTIVE_THRESH_GAUSSIAN_C = 1;
        // C++: enum cv.ColorConversionCodes
        public const int COLOR_BGR2BGRA = 0;
        public const int COLOR_RGB2RGBA = COLOR_BGR2BGRA;
        public const int COLOR_BGRA2BGR = 1;
        public const int COLOR_RGBA2RGB = COLOR_BGRA2BGR;
        public const int COLOR_BGR2RGBA = 2;
        public const int COLOR_RGB2BGRA = COLOR_BGR2RGBA;
        public const int COLOR_RGBA2BGR = 3;
        public const int COLOR_BGRA2RGB = COLOR_RGBA2BGR;
        public const int COLOR_BGR2RGB = 4;
        public const int COLOR_RGB2BGR = COLOR_BGR2RGB;
        public const int COLOR_BGRA2RGBA = 5;
        public const int COLOR_RGBA2BGRA = COLOR_BGRA2RGBA;
        public const int COLOR_BGR2GRAY = 6;
        public const int COLOR_RGB2GRAY = 7;
        public const int COLOR_GRAY2BGR = 8;
        public const int COLOR_GRAY2RGB = COLOR_GRAY2BGR;
        public const int COLOR_GRAY2BGRA = 9;
        public const int COLOR_GRAY2RGBA = COLOR_GRAY2BGRA;
        public const int COLOR_BGRA2GRAY = 10;
        public const int COLOR_RGBA2GRAY = 11;
        public const int COLOR_BGR2BGR565 = 12;
        public const int COLOR_RGB2BGR565 = 13;
        public const int COLOR_BGR5652BGR = 14;
        public const int COLOR_BGR5652RGB = 15;
        public const int COLOR_BGRA2BGR565 = 16;
        public const int COLOR_RGBA2BGR565 = 17;
        public const int COLOR_BGR5652BGRA = 18;
        public const int COLOR_BGR5652RGBA = 19;
        public const int COLOR_GRAY2BGR565 = 20;
        public const int COLOR_BGR5652GRAY = 21;
        public const int COLOR_BGR2BGR555 = 22;
        public const int COLOR_RGB2BGR555 = 23;
        public const int COLOR_BGR5552BGR = 24;
        public const int COLOR_BGR5552RGB = 25;
        public const int COLOR_BGRA2BGR555 = 26;
        public const int COLOR_RGBA2BGR555 = 27;
        public const int COLOR_BGR5552BGRA = 28;
        public const int COLOR_BGR5552RGBA = 29;
        public const int COLOR_GRAY2BGR555 = 30;
        public const int COLOR_BGR5552GRAY = 31;
        public const int COLOR_BGR2XYZ = 32;
        public const int COLOR_RGB2XYZ = 33;
        public const int COLOR_XYZ2BGR = 34;
        public const int COLOR_XYZ2RGB = 35;
        public const int COLOR_BGR2YCrCb = 36;
        public const int COLOR_RGB2YCrCb = 37;
        public const int COLOR_YCrCb2BGR = 38;
        public const int COLOR_YCrCb2RGB = 39;
        public const int COLOR_BGR2HSV = 40;
        public const int COLOR_RGB2HSV = 41;
        public const int COLOR_BGR2Lab = 44;
        public const int COLOR_RGB2Lab = 45;
        public const int COLOR_BGR2Luv = 50;
        public const int COLOR_RGB2Luv = 51;
        public const int COLOR_BGR2HLS = 52;
        public const int COLOR_RGB2HLS = 53;
        public const int COLOR_HSV2BGR = 54;
        public const int COLOR_HSV2RGB = 55;
        public const int COLOR_Lab2BGR = 56;
        public const int COLOR_Lab2RGB = 57;
        public const int COLOR_Luv2BGR = 58;
        public const int COLOR_Luv2RGB = 59;
        public const int COLOR_HLS2BGR = 60;
        public const int COLOR_HLS2RGB = 61;
        public const int COLOR_BGR2HSV_FULL = 66;
        public const int COLOR_RGB2HSV_FULL = 67;
        public const int COLOR_BGR2HLS_FULL = 68;
        public const int COLOR_RGB2HLS_FULL = 69;
        public const int COLOR_HSV2BGR_FULL = 70;
        public const int COLOR_HSV2RGB_FULL = 71;
        public const int COLOR_HLS2BGR_FULL = 72;
        public const int COLOR_HLS2RGB_FULL = 73;
        public const int COLOR_LBGR2Lab = 74;
        public const int COLOR_LRGB2Lab = 75;
        public const int COLOR_LBGR2Luv = 76;
        public const int COLOR_LRGB2Luv = 77;
        public const int COLOR_Lab2LBGR = 78;
        public const int COLOR_Lab2LRGB = 79;
        public const int COLOR_Luv2LBGR = 80;
        public const int COLOR_Luv2LRGB = 81;
        public const int COLOR_BGR2YUV = 82;
        public const int COLOR_RGB2YUV = 83;
        public const int COLOR_YUV2BGR = 84;
        public const int COLOR_YUV2RGB = 85;
        public const int COLOR_YUV2RGB_NV12 = 90;
        public const int COLOR_YUV2BGR_NV12 = 91;
        public const int COLOR_YUV2RGB_NV21 = 92;
        public const int COLOR_YUV2BGR_NV21 = 93;
        public const int COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21;
        public const int COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21;
        public const int COLOR_YUV2RGBA_NV12 = 94;
        public const int COLOR_YUV2BGRA_NV12 = 95;
        public const int COLOR_YUV2RGBA_NV21 = 96;
        public const int COLOR_YUV2BGRA_NV21 = 97;
        public const int COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21;
        public const int COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21;
        public const int COLOR_YUV2RGB_YV12 = 98;
        public const int COLOR_YUV2BGR_YV12 = 99;
        public const int COLOR_YUV2RGB_IYUV = 100;
        public const int COLOR_YUV2BGR_IYUV = 101;
        public const int COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV;
        public const int COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV;
        public const int COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12;
        public const int COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12;
        public const int COLOR_YUV2RGBA_YV12 = 102;
        public const int COLOR_YUV2BGRA_YV12 = 103;
        public const int COLOR_YUV2RGBA_IYUV = 104;
        public const int COLOR_YUV2BGRA_IYUV = 105;
        public const int COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV;
        public const int COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV;
        public const int COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12;
        public const int COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12;
        public const int COLOR_YUV2GRAY_420 = 106;
        public const int COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420;
        public const int COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420;
        public const int COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420;
        public const int COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420;
        public const int COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420;
        public const int COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420;
        public const int COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420;
        public const int COLOR_YUV2RGB_UYVY = 107;
        public const int COLOR_YUV2BGR_UYVY = 108;
        public const int COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY;
        public const int COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY;
        public const int COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY;
        public const int COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY;
        public const int COLOR_YUV2RGBA_UYVY = 111;
        public const int COLOR_YUV2BGRA_UYVY = 112;
        public const int COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY;
        public const int COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY;
        public const int COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY;
        public const int COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY;
        public const int COLOR_YUV2RGB_YUY2 = 115;
        public const int COLOR_YUV2BGR_YUY2 = 116;
        public const int COLOR_YUV2RGB_YVYU = 117;
        public const int COLOR_YUV2BGR_YVYU = 118;
        public const int COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2;
        public const int COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2;
        public const int COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2;
        public const int COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2;
        public const int COLOR_YUV2RGBA_YUY2 = 119;
        public const int COLOR_YUV2BGRA_YUY2 = 120;
        public const int COLOR_YUV2RGBA_YVYU = 121;
        public const int COLOR_YUV2BGRA_YVYU = 122;
        public const int COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2;
        public const int COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2;
        public const int COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2;
        public const int COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2;
        public const int COLOR_YUV2GRAY_UYVY = 123;
        public const int COLOR_YUV2GRAY_YUY2 = 124;
        public const int COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY;
        public const int COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY;
        public const int COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2;
        public const int COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2;
        public const int COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2;
        public const int COLOR_RGBA2mRGBA = 125;
        public const int COLOR_mRGBA2RGBA = 126;
        public const int COLOR_RGB2YUV_I420 = 127;
        public const int COLOR_BGR2YUV_I420 = 128;
        public const int COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420;
        public const int COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420;
        public const int COLOR_RGBA2YUV_I420 = 129;
        public const int COLOR_BGRA2YUV_I420 = 130;
        public const int COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420;
        public const int COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420;
        public const int COLOR_RGB2YUV_YV12 = 131;
        public const int COLOR_BGR2YUV_YV12 = 132;
        public const int COLOR_RGBA2YUV_YV12 = 133;
        public const int COLOR_BGRA2YUV_YV12 = 134;
        public const int COLOR_BayerBG2BGR = 46;
        public const int COLOR_BayerGB2BGR = 47;
        public const int COLOR_BayerRG2BGR = 48;
        public const int COLOR_BayerGR2BGR = 49;
        public const int COLOR_BayerRGGB2BGR = COLOR_BayerBG2BGR;
        public const int COLOR_BayerGRBG2BGR = COLOR_BayerGB2BGR;
        public const int COLOR_BayerBGGR2BGR = COLOR_BayerRG2BGR;
        public const int COLOR_BayerGBRG2BGR = COLOR_BayerGR2BGR;
        public const int COLOR_BayerRGGB2RGB = COLOR_BayerBGGR2BGR;
        public const int COLOR_BayerGRBG2RGB = COLOR_BayerGBRG2BGR;
        public const int COLOR_BayerBGGR2RGB = COLOR_BayerRGGB2BGR;
        public const int COLOR_BayerGBRG2RGB = COLOR_BayerGRBG2BGR;
        public const int COLOR_BayerBG2RGB = COLOR_BayerRG2BGR;
        public const int COLOR_BayerGB2RGB = COLOR_BayerGR2BGR;
        public const int COLOR_BayerRG2RGB = COLOR_BayerBG2BGR;
        public const int COLOR_BayerGR2RGB = COLOR_BayerGB2BGR;
        public const int COLOR_BayerBG2GRAY = 86;
        public const int COLOR_BayerGB2GRAY = 87;
        public const int COLOR_BayerRG2GRAY = 88;
        public const int COLOR_BayerGR2GRAY = 89;
        public const int COLOR_BayerRGGB2GRAY = COLOR_BayerBG2GRAY;
        public const int COLOR_BayerGRBG2GRAY = COLOR_BayerGB2GRAY;
        public const int COLOR_BayerBGGR2GRAY = COLOR_BayerRG2GRAY;
        public const int COLOR_BayerGBRG2GRAY = COLOR_BayerGR2GRAY;
        public const int COLOR_BayerBG2BGR_VNG = 62;
        public const int COLOR_BayerGB2BGR_VNG = 63;
        public const int COLOR_BayerRG2BGR_VNG = 64;
        public const int COLOR_BayerGR2BGR_VNG = 65;
        public const int COLOR_BayerRGGB2BGR_VNG = COLOR_BayerBG2BGR_VNG;
        public const int COLOR_BayerGRBG2BGR_VNG = COLOR_BayerGB2BGR_VNG;
        public const int COLOR_BayerBGGR2BGR_VNG = COLOR_BayerRG2BGR_VNG;
        public const int COLOR_BayerGBRG2BGR_VNG = COLOR_BayerGR2BGR_VNG;
        public const int COLOR_BayerRGGB2RGB_VNG = COLOR_BayerBGGR2BGR_VNG;
        public const int COLOR_BayerGRBG2RGB_VNG = COLOR_BayerGBRG2BGR_VNG;
        public const int COLOR_BayerBGGR2RGB_VNG = COLOR_BayerRGGB2BGR_VNG;
        public const int COLOR_BayerGBRG2RGB_VNG = COLOR_BayerGRBG2BGR_VNG;
        public const int COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG;
        public const int COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG;
        public const int COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG;
        public const int COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG;
        public const int COLOR_BayerBG2BGR_EA = 135;
        public const int COLOR_BayerGB2BGR_EA = 136;
        public const int COLOR_BayerRG2BGR_EA = 137;
        public const int COLOR_BayerGR2BGR_EA = 138;
        public const int COLOR_BayerRGGB2BGR_EA = COLOR_BayerBG2BGR_EA;
        public const int COLOR_BayerGRBG2BGR_EA = COLOR_BayerGB2BGR_EA;
        public const int COLOR_BayerBGGR2BGR_EA = COLOR_BayerRG2BGR_EA;
        public const int COLOR_BayerGBRG2BGR_EA = COLOR_BayerGR2BGR_EA;
        public const int COLOR_BayerRGGB2RGB_EA = COLOR_BayerBGGR2BGR_EA;
        public const int COLOR_BayerGRBG2RGB_EA = COLOR_BayerGBRG2BGR_EA;
        public const int COLOR_BayerBGGR2RGB_EA = COLOR_BayerRGGB2BGR_EA;
        public const int COLOR_BayerGBRG2RGB_EA = COLOR_BayerGRBG2BGR_EA;
        public const int COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA;
        public const int COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA;
        public const int COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA;
        public const int COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA;
        public const int COLOR_BayerBG2BGRA = 139;
        public const int COLOR_BayerGB2BGRA = 140;
        public const int COLOR_BayerRG2BGRA = 141;
        public const int COLOR_BayerGR2BGRA = 142;
        public const int COLOR_BayerRGGB2BGRA = COLOR_BayerBG2BGRA;
        public const int COLOR_BayerGRBG2BGRA = COLOR_BayerGB2BGRA;
        public const int COLOR_BayerBGGR2BGRA = COLOR_BayerRG2BGRA;
        public const int COLOR_BayerGBRG2BGRA = COLOR_BayerGR2BGRA;
        public const int COLOR_BayerRGGB2RGBA = COLOR_BayerBGGR2BGRA;
        public const int COLOR_BayerGRBG2RGBA = COLOR_BayerGBRG2BGRA;
        public const int COLOR_BayerBGGR2RGBA = COLOR_BayerRGGB2BGRA;
        public const int COLOR_BayerGBRG2RGBA = COLOR_BayerGRBG2BGRA;
        public const int COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA;
        public const int COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA;
        public const int COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA;
        public const int COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA;
        public const int COLOR_COLORCVT_MAX = 143;
        // C++: enum cv.ColormapTypes
        public const int COLORMAP_AUTUMN = 0;
        public const int COLORMAP_BONE = 1;
        public const int COLORMAP_JET = 2;
        public const int COLORMAP_WINTER = 3;
        public const int COLORMAP_RAINBOW = 4;
        public const int COLORMAP_OCEAN = 5;
        public const int COLORMAP_SUMMER = 6;
        public const int COLORMAP_SPRING = 7;
        public const int COLORMAP_COOL = 8;
        public const int COLORMAP_HSV = 9;
        public const int COLORMAP_PINK = 10;
        public const int COLORMAP_HOT = 11;
        public const int COLORMAP_PARULA = 12;
        public const int COLORMAP_MAGMA = 13;
        public const int COLORMAP_INFERNO = 14;
        public const int COLORMAP_PLASMA = 15;
        public const int COLORMAP_VIRIDIS = 16;
        public const int COLORMAP_CIVIDIS = 17;
        public const int COLORMAP_TWILIGHT = 18;
        public const int COLORMAP_TWILIGHT_SHIFTED = 19;
        public const int COLORMAP_TURBO = 20;
        public const int COLORMAP_DEEPGREEN = 21;
        // C++: enum cv.ConnectedComponentsAlgorithmsTypes
        public const int CCL_DEFAULT = -1;
        public const int CCL_WU = 0;
        public const int CCL_GRANA = 1;
        public const int CCL_BOLELLI = 2;
        public const int CCL_SAUF = 3;
        public const int CCL_BBDT = 4;
        public const int CCL_SPAGHETTI = 5;
        // C++: enum cv.ConnectedComponentsTypes
        public const int CC_STAT_LEFT = 0;
        public const int CC_STAT_TOP = 1;
        public const int CC_STAT_WIDTH = 2;
        public const int CC_STAT_HEIGHT = 3;
        public const int CC_STAT_AREA = 4;
        public const int CC_STAT_MAX = 5;
        // C++: enum cv.ContourApproximationModes
        public const int CHAIN_APPROX_NONE = 1;
        public const int CHAIN_APPROX_SIMPLE = 2;
        public const int CHAIN_APPROX_TC89_L1 = 3;
        public const int CHAIN_APPROX_TC89_KCOS = 4;
        // C++: enum cv.DistanceTransformLabelTypes
        public const int DIST_LABEL_CCOMP = 0;
        public const int DIST_LABEL_PIXEL = 1;
        // C++: enum cv.DistanceTransformMasks
        public const int DIST_MASK_3 = 3;
        public const int DIST_MASK_5 = 5;
        public const int DIST_MASK_PRECISE = 0;
        // C++: enum cv.DistanceTypes
        public const int DIST_USER = -1;
        public const int DIST_L1 = 1;
        public const int DIST_L2 = 2;
        public const int DIST_C = 3;
        public const int DIST_L12 = 4;
        public const int DIST_FAIR = 5;
        public const int DIST_WELSCH = 6;
        public const int DIST_HUBER = 7;
        // C++: enum cv.FloodFillFlags
        public const int FLOODFILL_FIXED_RANGE = 1 << 16;
        public const int FLOODFILL_MASK_ONLY = 1 << 17;
        // C++: enum cv.GrabCutClasses
        public const int GC_BGD = 0;
        public const int GC_FGD = 1;
        public const int GC_PR_BGD = 2;
        public const int GC_PR_FGD = 3;
        // C++: enum cv.GrabCutModes
        public const int GC_INIT_WITH_RECT = 0;
        public const int GC_INIT_WITH_MASK = 1;
        public const int GC_EVAL = 2;
        public const int GC_EVAL_FREEZE_MODEL = 3;
        // C++: enum cv.HersheyFonts
        public const int FONT_HERSHEY_SIMPLEX = 0;
        public const int FONT_HERSHEY_PLAIN = 1;
        public const int FONT_HERSHEY_DUPLEX = 2;
        public const int FONT_HERSHEY_COMPLEX = 3;
        public const int FONT_HERSHEY_TRIPLEX = 4;
        public const int FONT_HERSHEY_COMPLEX_SMALL = 5;
        public const int FONT_HERSHEY_SCRIPT_SIMPLEX = 6;
        public const int FONT_HERSHEY_SCRIPT_COMPLEX = 7;
        public const int FONT_ITALIC = 16;
        // C++: enum cv.HistCompMethods
        public const int HISTCMP_CORREL = 0;
        public const int HISTCMP_CHISQR = 1;
        public const int HISTCMP_INTERSECT = 2;
        public const int HISTCMP_BHATTACHARYYA = 3;
        public const int HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA;
        public const int HISTCMP_CHISQR_ALT = 4;
        public const int HISTCMP_KL_DIV = 5;
        // C++: enum cv.HoughModes
        public const int HOUGH_STANDARD = 0;
        public const int HOUGH_PROBABILISTIC = 1;
        public const int HOUGH_MULTI_SCALE = 2;
        public const int HOUGH_GRADIENT = 3;
        public const int HOUGH_GRADIENT_ALT = 4;
        // C++: enum cv.InterpolationFlags
        public const int INTER_NEAREST = 0;
        public const int INTER_LINEAR = 1;
        public const int INTER_CUBIC = 2;
        public const int INTER_AREA = 3;
        public const int INTER_LANCZOS4 = 4;
        public const int INTER_LINEAR_EXACT = 5;
        public const int INTER_NEAREST_EXACT = 6;
        public const int INTER_MAX = 7;
        public const int WARP_FILL_OUTLIERS = 8;
        public const int WARP_INVERSE_MAP = 16;
        // C++: enum cv.InterpolationMasks
        public const int INTER_BITS = 5;
        public const int INTER_BITS2 = INTER_BITS * 2;
        public const int INTER_TAB_SIZE = 1 << INTER_BITS;
        public const int INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE;
        // C++: enum cv.LineSegmentDetectorModes
        public const int LSD_REFINE_NONE = 0;
        public const int LSD_REFINE_STD = 1;
        public const int LSD_REFINE_ADV = 2;
        // C++: enum cv.LineTypes
        public const int FILLED = -1;
        public const int LINE_4 = 4;
        public const int LINE_8 = 8;
        public const int LINE_AA = 16;
        // C++: enum cv.MarkerTypes
        public const int MARKER_CROSS = 0;
        public const int MARKER_TILTED_CROSS = 1;
        public const int MARKER_STAR = 2;
        public const int MARKER_DIAMOND = 3;
        public const int MARKER_SQUARE = 4;
        public const int MARKER_TRIANGLE_UP = 5;
        public const int MARKER_TRIANGLE_DOWN = 6;
        // C++: enum cv.MorphShapes
        public const int MORPH_RECT = 0;
        public const int MORPH_CROSS = 1;
        public const int MORPH_ELLIPSE = 2;
        // C++: enum cv.MorphTypes
        public const int MORPH_ERODE = 0;
        public const int MORPH_DILATE = 1;
        public const int MORPH_OPEN = 2;
        public const int MORPH_CLOSE = 3;
        public const int MORPH_GRADIENT = 4;
        public const int MORPH_TOPHAT = 5;
        public const int MORPH_BLACKHAT = 6;
        public const int MORPH_HITMISS = 7;
        // C++: enum cv.RectanglesIntersectTypes
        public const int INTERSECT_NONE = 0;
        public const int INTERSECT_PARTIAL = 1;
        public const int INTERSECT_FULL = 2;
        // C++: enum cv.RetrievalModes
        public const int RETR_EXTERNAL = 0;
        public const int RETR_LIST = 1;
        public const int RETR_CCOMP = 2;
        public const int RETR_TREE = 3;
        public const int RETR_FLOODFILL = 4;
        // C++: enum cv.ShapeMatchModes
        public const int CONTOURS_MATCH_I1 = 1;
        public const int CONTOURS_MATCH_I2 = 2;
        public const int CONTOURS_MATCH_I3 = 3;
        // C++: enum cv.SpecialFilter
        public const int FILTER_SCHARR = -1;
        // C++: enum cv.TemplateMatchModes
        public const int TM_SQDIFF = 0;
        public const int TM_SQDIFF_NORMED = 1;
        public const int TM_CCORR = 2;
        public const int TM_CCORR_NORMED = 3;
        public const int TM_CCOEFF = 4;
        public const int TM_CCOEFF_NORMED = 5;
        // C++: enum cv.ThresholdTypes
        public const int THRESH_BINARY = 0;
        public const int THRESH_BINARY_INV = 1;
        public const int THRESH_TRUNC = 2;
        public const int THRESH_TOZERO = 3;
        public const int THRESH_TOZERO_INV = 4;
        public const int THRESH_MASK = 7;
        public const int THRESH_OTSU = 8;
        public const int THRESH_TRIANGLE = 16;
        // C++: enum cv.WarpPolarMode
        public const int WARP_POLAR_LINEAR = 0;
        public const int WARP_POLAR_LOG = 256;
        //
        // C++:  Ptr_LineSegmentDetector cv::createLineSegmentDetector(int refine = LSD_REFINE_STD, double scale = 0.8, double sigma_scale = 0.6, double quant = 2.0, double ang_th = 22.5, double log_eps = 0, double density_th = 0.7, int n_bins = 1024)
        //

        /**
         * Creates a smart pointer to a LineSegmentDetector object and initializes it.
         *
         * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
         * to edit those, as to tailor it for their own application.
         *
         * param refine The way found lines will be refined, see #LineSegmentDetectorModes
         * param scale The scale of the image that will be used to find the lines. Range (0..1].
         * param sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
         * param quant Bound to the quantization error on the gradient norm.
         * param ang_th Gradient angle tolerance in degrees.
         * param log_eps Detection threshold: -log10(NFA) &gt; log_eps. Used only when advance refinement is chosen.
         * param density_th Minimal density of aligned region points in the enclosing rectangle.
         * param n_bins Number of bins in pseudo-ordering of gradient modulus.
         * return automatically generated
         */
        public static LineSegmentDetector createLineSegmentDetector(int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps, double density_th, int n_bins)
        {


            return LineSegmentDetector.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_createLineSegmentDetector_10(refine, scale, sigma_scale, quant, ang_th, log_eps, density_th, n_bins)));


        }

        /**
         * Creates a smart pointer to a LineSegmentDetector object and initializes it.
         *
         * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
         * to edit those, as to tailor it for their own application.
         *
         * param refine The way found lines will be refined, see #LineSegmentDetectorModes
         * param scale The scale of the image that will be used to find the lines. Range (0..1].
         * param sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
         * param quant Bound to the quantization error on the gradient norm.
         * param ang_th Gradient angle tolerance in degrees.
         * param log_eps Detection threshold: -log10(NFA) &gt; log_eps. Used only when advance refinement is chosen.
         * param density_th Minimal density of aligned region points in the enclosing rectangle.
         * return automatically generated
         */
        public static LineSegmentDetector createLineSegmentDetector(int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps, double density_th)
        {


            return LineSegmentDetector.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_createLineSegmentDetector_11(refine, scale, sigma_scale, quant, ang_th, log_eps, density_th)));


        }

        /**
         * Creates a smart pointer to a LineSegmentDetector object and initializes it.
         *
         * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
         * to edit those, as to tailor it for their own application.
         *
         * param refine The way found lines will be refined, see #LineSegmentDetectorModes
         * param scale The scale of the image that will be used to find the lines. Range (0..1].
         * param sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
         * param quant Bound to the quantization error on the gradient norm.
         * param ang_th Gradient angle tolerance in degrees.
         * param log_eps Detection threshold: -log10(NFA) &gt; log_eps. Used only when advance refinement is chosen.
         * return automatically generated
         */
        public static LineSegmentDetector createLineSegmentDetector(int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps)
        {


            return LineSegmentDetector.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_createLineSegmentDetector_12(refine, scale, sigma_scale, quant, ang_th, log_eps)));


        }

        /**
         * Creates a smart pointer to a LineSegmentDetector object and initializes it.
         *
         * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
         * to edit those, as to tailor it for their own application.
         *
         * param refine The way found lines will be refined, see #LineSegmentDetectorModes
         * param scale The scale of the image that will be used to find the lines. Range (0..1].
         * param sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
         * param quant Bound to the quantization error on the gradient norm.
         * param ang_th Gradient angle tolerance in degrees.
         * return automatically generated
         */
        public static LineSegmentDetector createLineSegmentDetector(int refine, double scale, double sigma_scale, double quant, double ang_th)
        {


            return LineSegmentDetector.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_createLineSegmentDetector_13(refine, scale, sigma_scale, quant, ang_th)));


        }

        /**
         * Creates a smart pointer to a LineSegmentDetector object and initializes it.
         *
         * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
         * to edit those, as to tailor it for their own application.
         *
         * param refine The way found lines will be refined, see #LineSegmentDetectorModes
         * param scale The scale of the image that will be used to find the lines. Range (0..1].
         * param sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
         * param quant Bound to the quantization error on the gradient norm.
         * return automatically generated
         */
        public static LineSegmentDetector createLineSegmentDetector(int refine, double scale, double sigma_scale, double quant)
        {


            return LineSegmentDetector.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_createLineSegmentDetector_14(refine, scale, sigma_scale, quant)));


        }

        /**
         * Creates a smart pointer to a LineSegmentDetector object and initializes it.
         *
         * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
         * to edit those, as to tailor it for their own application.
         *
         * param refine The way found lines will be refined, see #LineSegmentDetectorModes
         * param scale The scale of the image that will be used to find the lines. Range (0..1].
         * param sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
         * return automatically generated
         */
        public static LineSegmentDetector createLineSegmentDetector(int refine, double scale, double sigma_scale)
        {


            return LineSegmentDetector.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_createLineSegmentDetector_15(refine, scale, sigma_scale)));


        }

        /**
         * Creates a smart pointer to a LineSegmentDetector object and initializes it.
         *
         * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
         * to edit those, as to tailor it for their own application.
         *
         * param refine The way found lines will be refined, see #LineSegmentDetectorModes
         * param scale The scale of the image that will be used to find the lines. Range (0..1].
         * return automatically generated
         */
        public static LineSegmentDetector createLineSegmentDetector(int refine, double scale)
        {


            return LineSegmentDetector.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_createLineSegmentDetector_16(refine, scale)));


        }

        /**
         * Creates a smart pointer to a LineSegmentDetector object and initializes it.
         *
         * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
         * to edit those, as to tailor it for their own application.
         *
         * param refine The way found lines will be refined, see #LineSegmentDetectorModes
         * return automatically generated
         */
        public static LineSegmentDetector createLineSegmentDetector(int refine)
        {


            return LineSegmentDetector.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_createLineSegmentDetector_17(refine)));


        }

        /**
         * Creates a smart pointer to a LineSegmentDetector object and initializes it.
         *
         * The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
         * to edit those, as to tailor it for their own application.
         *
         * return automatically generated
         */
        public static LineSegmentDetector createLineSegmentDetector()
        {


            return LineSegmentDetector.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_createLineSegmentDetector_18()));


        }


        //
        // C++:  Mat cv::getGaussianKernel(int ksize, double sigma, int ktype = CV_64F)
        //

        /**
         * Returns Gaussian filter coefficients.
         *
         * The function computes and returns the \(\texttt{ksize} \times 1\) matrix of Gaussian filter
         * coefficients:
         *
         * \(G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\)
         *
         * where \(i=0..\texttt{ksize}-1\) and \(\alpha\) is the scale factor chosen so that \(\sum_i G_i=1\).
         *
         * Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize
         * smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly.
         * You may also use the higher-level GaussianBlur.
         * param ksize Aperture size. It should be odd ( \(\texttt{ksize} \mod 2 = 1\) ) and positive.
         * param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as
         * {code sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8}.
         * param ktype Type of filter coefficients. It can be CV_32F or CV_64F .
         * SEE:  sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur
         * return automatically generated
         */
        public static Mat getGaussianKernel(int ksize, double sigma, int ktype)
        {


            return new Mat(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_getGaussianKernel_10(ksize, sigma, ktype)));


        }

        /**
         * Returns Gaussian filter coefficients.
         *
         * The function computes and returns the \(\texttt{ksize} \times 1\) matrix of Gaussian filter
         * coefficients:
         *
         * \(G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\)
         *
         * where \(i=0..\texttt{ksize}-1\) and \(\alpha\) is the scale factor chosen so that \(\sum_i G_i=1\).
         *
         * Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize
         * smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly.
         * You may also use the higher-level GaussianBlur.
         * param ksize Aperture size. It should be odd ( \(\texttt{ksize} \mod 2 = 1\) ) and positive.
         * param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as
         * {code sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8}.
         * SEE:  sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur
         * return automatically generated
         */
        public static Mat getGaussianKernel(int ksize, double sigma)
        {


            return new Mat(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_getGaussianKernel_11(ksize, sigma)));


        }


        //
        // C++:  void cv::getDerivKernels(Mat& kx, Mat& ky, int dx, int dy, int ksize, bool normalize = false, int ktype = CV_32F)
        //

        /**
         * Returns filter coefficients for computing spatial image derivatives.
         *
         * The function computes and returns the filter coefficients for spatial image derivatives. When
         * {code ksize=FILTER_SCHARR}, the Scharr \(3 \times 3\) kernels are generated (see #Scharr). Otherwise, Sobel
         * kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to
         *
         * param kx Output matrix of row filter coefficients. It has the type ktype .
         * param ky Output matrix of column filter coefficients. It has the type ktype .
         * param dx Derivative order in respect of x.
         * param dy Derivative order in respect of y.
         * param ksize Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7.
         * param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not.
         * Theoretically, the coefficients should have the denominator \(=2^{ksize*2-dx-dy-2}\). 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 .
         * param ktype Type of filter coefficients. It can be CV_32f or CV_64F .
         */
        public static void getDerivKernels(Mat kx, Mat ky, int dx, int dy, int ksize, bool normalize, int ktype)
        {
            if (kx != null) kx.ThrowIfDisposed();
            if (ky != null) ky.ThrowIfDisposed();

            imgproc_Imgproc_getDerivKernels_10(kx.nativeObj, ky.nativeObj, dx, dy, ksize, normalize, ktype);


        }

        /**
         * Returns filter coefficients for computing spatial image derivatives.
         *
         * The function computes and returns the filter coefficients for spatial image derivatives. When
         * {code ksize=FILTER_SCHARR}, the Scharr \(3 \times 3\) kernels are generated (see #Scharr). Otherwise, Sobel
         * kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to
         *
         * param kx Output matrix of row filter coefficients. It has the type ktype .
         * param ky Output matrix of column filter coefficients. It has the type ktype .
         * param dx Derivative order in respect of x.
         * param dy Derivative order in respect of y.
         * param ksize Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7.
         * param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not.
         * Theoretically, the coefficients should have the denominator \(=2^{ksize*2-dx-dy-2}\). 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 .
         */
        public static void getDerivKernels(Mat kx, Mat ky, int dx, int dy, int ksize, bool normalize)
        {
            if (kx != null) kx.ThrowIfDisposed();
            if (ky != null) ky.ThrowIfDisposed();

            imgproc_Imgproc_getDerivKernels_11(kx.nativeObj, ky.nativeObj, dx, dy, ksize, normalize);


        }

        /**
         * Returns filter coefficients for computing spatial image derivatives.
         *
         * The function computes and returns the filter coefficients for spatial image derivatives. When
         * {code ksize=FILTER_SCHARR}, the Scharr \(3 \times 3\) kernels are generated (see #Scharr). Otherwise, Sobel
         * kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to
         *
         * param kx Output matrix of row filter coefficients. It has the type ktype .
         * param ky Output matrix of column filter coefficients. It has the type ktype .
         * param dx Derivative order in respect of x.
         * param dy Derivative order in respect of y.
         * param ksize Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7.
         * 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 .
         */
        public static void getDerivKernels(Mat kx, Mat ky, int dx, int dy, int ksize)
        {
            if (kx != null) kx.ThrowIfDisposed();
            if (ky != null) ky.ThrowIfDisposed();

            imgproc_Imgproc_getDerivKernels_12(kx.nativeObj, ky.nativeObj, dx, dy, ksize);


        }


        //
        // C++:  Mat cv::getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma, double psi = CV_PI*0.5, int ktype = CV_64F)
        //

        /**
         * Returns Gabor filter coefficients.
         *
         * For more details about gabor filter equations and parameters, see: [Gabor
         * Filter](http://en.wikipedia.org/wiki/Gabor_filter).
         *
         * param ksize Size of the filter returned.
         * param sigma Standard deviation of the gaussian envelope.
         * param theta Orientation of the normal to the parallel stripes of a Gabor function.
         * param lambd Wavelength of the sinusoidal factor.
         * param gamma Spatial aspect ratio.
         * param psi Phase offset.
         * param ktype Type of filter coefficients. It can be CV_32F or CV_64F .
         * return automatically generated
         */
        public static Mat getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma, double psi, int ktype)
        {


            return new Mat(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_getGaborKernel_10(ksize.width, ksize.height, sigma, theta, lambd, gamma, psi, ktype)));


        }

        /**
         * Returns Gabor filter coefficients.
         *
         * For more details about gabor filter equations and parameters, see: [Gabor
         * Filter](http://en.wikipedia.org/wiki/Gabor_filter).
         *
         * param ksize Size of the filter returned.
         * param sigma Standard deviation of the gaussian envelope.
         * param theta Orientation of the normal to the parallel stripes of a Gabor function.
         * param lambd Wavelength of the sinusoidal factor.
         * param gamma Spatial aspect ratio.
         * param psi Phase offset.
         * return automatically generated
         */
        public static Mat getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma, double psi)
        {


            return new Mat(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_getGaborKernel_11(ksize.width, ksize.height, sigma, theta, lambd, gamma, psi)));


        }

        /**
         * Returns Gabor filter coefficients.
         *
         * For more details about gabor filter equations and parameters, see: [Gabor
         * Filter](http://en.wikipedia.org/wiki/Gabor_filter).
         *
         * param ksize Size of the filter returned.
         * param sigma Standard deviation of the gaussian envelope.
         * param theta Orientation of the normal to the parallel stripes of a Gabor function.
         * param lambd Wavelength of the sinusoidal factor.
         * param gamma Spatial aspect ratio.
         * return automatically generated
         */
        public static Mat getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma)
        {


            return new Mat(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_getGaborKernel_12(ksize.width, ksize.height, sigma, theta, lambd, gamma)));


        }


        //
        // C++:  Mat cv::getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1))
        //

        /**
         * 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 #erode,
         * #dilate or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as
         * the structuring element.
         *
         * param shape Element shape that could be one of #MorphShapes
         * param ksize Size of the structuring element.
         * param 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.
         * return automatically generated
         */
        public static Mat getStructuringElement(int shape, Size ksize, Point anchor)
        {


            return new Mat(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_getStructuringElement_10(shape, ksize.width, ksize.height, anchor.x, anchor.y)));


        }

        /**
         * 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 #erode,
         * #dilate or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as
         * the structuring element.
         *
         * param shape Element shape that could be one of #MorphShapes
         * param ksize Size of the structuring element.
         * 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.
         * return automatically generated
         */
        public static Mat getStructuringElement(int shape, Size ksize)
        {


            return new Mat(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_getStructuringElement_11(shape, ksize.width, ksize.height)));


        }


        //
        // C++:  void cv::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 \(\texttt{ksize} \times
         * \texttt{ksize}\) aperture. Each channel of a multi-channel image is processed independently.
         * In-place operation is supported.
         *
         * <b>Note:</b> The median filter uses #BORDER_REPLICATE internally to cope with border pixels, see #BorderTypes
         *
         * param src input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be
         * CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U.
         * param dst destination array of the same size and type as src.
         * param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ...
         * SEE:  bilateralFilter, blur, boxFilter, GaussianBlur
         */
        public static void medianBlur(Mat src, Mat dst, int ksize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_medianBlur_10(src.nativeObj, dst.nativeObj, ksize);


        }


        //
        // C++:  void cv::GaussianBlur(Mat src, Mat& dst, Size ksize, double sigmaX, double sigmaY = 0, int borderType = BORDER_DEFAULT)
        //

        /**
         * Blurs an image using a Gaussian filter.
         *
         * The function convolves the source image with the specified Gaussian kernel. In-place filtering is
         * supported.
         *
         * param src input image; the image can have any number of channels, which are processed
         * independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be
         * positive and odd. Or, they can be zero's and then they are computed from sigma.
         * param sigmaX Gaussian kernel standard deviation in X direction.
         * param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be
         * equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height,
         * respectively (see #getGaussianKernel for details); to fully control the result regardless of
         * possible future modifications of all this semantics, it is recommended to specify all of ksize,
         * sigmaX, and sigmaY.
         * param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         *
         * SEE:  sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
         */
        public static void GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_GaussianBlur_10(src.nativeObj, dst.nativeObj, ksize.width, ksize.height, sigmaX, sigmaY, borderType);


        }

        /**
         * Blurs an image using a Gaussian filter.
         *
         * The function convolves the source image with the specified Gaussian kernel. In-place filtering is
         * supported.
         *
         * param src input image; the image can have any number of channels, which are processed
         * independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be
         * positive and odd. Or, they can be zero's and then they are computed from sigma.
         * param sigmaX Gaussian kernel standard deviation in X direction.
         * param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be
         * equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height,
         * respectively (see #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:  sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
         */
        public static void GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_GaussianBlur_11(src.nativeObj, dst.nativeObj, ksize.width, ksize.height, sigmaX, sigmaY);


        }

        /**
         * Blurs an image using a Gaussian filter.
         *
         * The function convolves the source image with the specified Gaussian kernel. In-place filtering is
         * supported.
         *
         * param src input image; the image can have any number of channels, which are processed
         * independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be
         * positive and odd. Or, they can be zero's and then they are computed from sigma.
         * param sigmaX Gaussian kernel standard deviation in X direction.
         * 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:  sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
         */
        public static void GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_GaussianBlur_12(src.nativeObj, dst.nativeObj, ksize.width, ksize.height, sigmaX);


        }


        //
        // C++:  void cv::bilateralFilter(Mat src, Mat& dst, int d, double sigmaColor, double sigmaSpace, int borderType = BORDER_DEFAULT)
        //

        /**
         * 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 (&lt;
         * 10), the filter will not have much effect, whereas if they are large (&gt; 150), they will have a very
         * strong effect, making the image look "cartoonish".
         *
         * _Filter size_: Large filters (d &gt; 5) are very slow, so it is recommended to use d=5 for real-time
         * applications, and perhaps d=9 for offline applications that need heavy noise filtering.
         *
         * This filter does not work inplace.
         * param src Source 8-bit or floating-point, 1-channel or 3-channel image.
         * param dst Destination image of the same size and type as src .
         * param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive,
         * it is computed from sigmaSpace.
         * param sigmaColor Filter sigma in the color space. A larger value of the parameter means that
         * farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting
         * in larger areas of semi-equal color.
         * param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that
         * farther pixels will influence each other as long as their colors are close enough (see sigmaColor
         * ). When d&gt;0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is
         * proportional to sigmaSpace.
         * param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
         */
        public static void bilateralFilter(Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_bilateralFilter_10(src.nativeObj, dst.nativeObj, d, sigmaColor, sigmaSpace, 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 (&lt;
         * 10), the filter will not have much effect, whereas if they are large (&gt; 150), they will have a very
         * strong effect, making the image look "cartoonish".
         *
         * _Filter size_: Large filters (d &gt; 5) are very slow, so it is recommended to use d=5 for real-time
         * applications, and perhaps d=9 for offline applications that need heavy noise filtering.
         *
         * This filter does not work inplace.
         * param src Source 8-bit or floating-point, 1-channel or 3-channel image.
         * param dst Destination image of the same size and type as src .
         * param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive,
         * it is computed from sigmaSpace.
         * param sigmaColor Filter sigma in the color space. A larger value of the parameter means that
         * farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting
         * in larger areas of semi-equal color.
         * param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that
         * farther pixels will influence each other as long as their colors are close enough (see sigmaColor
         * ). When d&gt;0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is
         * proportional to sigmaSpace.
         */
        public static void bilateralFilter(Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_bilateralFilter_11(src.nativeObj, dst.nativeObj, d, sigmaColor, sigmaSpace);


        }


        //
        // C++:  void cv::boxFilter(Mat src, Mat& dst, int ddepth, Size ksize, Point anchor = Point(-1,-1), bool normalize = true, int borderType = BORDER_DEFAULT)
        //

        /**
         * Blurs an image using the box filter.
         *
         * The function smooths an image using the kernel:
         *
         * \(\texttt{K} =  \alpha \begin{bmatrix} 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ \hdotsfor{6} \\ 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1 \end{bmatrix}\)
         *
         * where
         *
         * \(\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} &amp; \texttt{when } \texttt{normalize=true}  \\1 &amp; \texttt{otherwise}\end{cases}\)
         *
         * 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.
         *
         * param src input image.
         * param dst output image of the same size and type as src.
         * param ddepth the output image depth (-1 to use src.depth()).
         * param ksize blurring kernel size.
         * param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
         * center.
         * param normalize flag, specifying whether the kernel is normalized by its area or not.
         * param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
         * SEE:  blur, bilateralFilter, GaussianBlur, medianBlur, integral
         */
        public static void boxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_boxFilter_10(src.nativeObj, dst.nativeObj, ddepth, ksize.width, ksize.height, anchor.x, anchor.y, normalize, borderType);


        }

        /**
         * Blurs an image using the box filter.
         *
         * The function smooths an image using the kernel:
         *
         * \(\texttt{K} =  \alpha \begin{bmatrix} 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ \hdotsfor{6} \\ 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1 \end{bmatrix}\)
         *
         * where
         *
         * \(\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} &amp; \texttt{when } \texttt{normalize=true}  \\1 &amp; \texttt{otherwise}\end{cases}\)
         *
         * 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.
         *
         * param src input image.
         * param dst output image of the same size and type as src.
         * param ddepth the output image depth (-1 to use src.depth()).
         * param ksize blurring kernel size.
         * param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
         * center.
         * param normalize flag, specifying whether the kernel is normalized by its area or not.
         * SEE:  blur, bilateralFilter, GaussianBlur, medianBlur, integral
         */
        public static void boxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_boxFilter_11(src.nativeObj, dst.nativeObj, ddepth, ksize.width, ksize.height, anchor.x, anchor.y, normalize);


        }

        /**
         * Blurs an image using the box filter.
         *
         * The function smooths an image using the kernel:
         *
         * \(\texttt{K} =  \alpha \begin{bmatrix} 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ \hdotsfor{6} \\ 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1 \end{bmatrix}\)
         *
         * where
         *
         * \(\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} &amp; \texttt{when } \texttt{normalize=true}  \\1 &amp; \texttt{otherwise}\end{cases}\)
         *
         * 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.
         *
         * param src input image.
         * param dst output image of the same size and type as src.
         * param ddepth the output image depth (-1 to use src.depth()).
         * param ksize blurring kernel size.
         * param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
         * center.
         * SEE:  blur, bilateralFilter, GaussianBlur, medianBlur, integral
         */
        public static void boxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_boxFilter_12(src.nativeObj, dst.nativeObj, ddepth, ksize.width, ksize.height, anchor.x, anchor.y);


        }

        /**
         * Blurs an image using the box filter.
         *
         * The function smooths an image using the kernel:
         *
         * \(\texttt{K} =  \alpha \begin{bmatrix} 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ \hdotsfor{6} \\ 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1 \end{bmatrix}\)
         *
         * where
         *
         * \(\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} &amp; \texttt{when } \texttt{normalize=true}  \\1 &amp; \texttt{otherwise}\end{cases}\)
         *
         * 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.
         *
         * param src input image.
         * param dst output image of the same size and type as src.
         * param ddepth the output image depth (-1 to use src.depth()).
         * param ksize blurring kernel size.
         * center.
         * SEE:  blur, bilateralFilter, GaussianBlur, medianBlur, integral
         */
        public static void boxFilter(Mat src, Mat dst, int ddepth, Size ksize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_boxFilter_13(src.nativeObj, dst.nativeObj, ddepth, ksize.width, ksize.height);


        }


        //
        // C++:  void cv::sqrBoxFilter(Mat src, Mat& dst, int ddepth, Size ksize, Point anchor = Point(-1, -1), bool normalize = true, int borderType = BORDER_DEFAULT)
        //

        /**
         * Calculates the normalized sum of squares of the pixel values overlapping the filter.
         *
         * For every pixel \( (x, y) \) in the source image, the function calculates the sum of squares of those neighboring
         * pixel values which overlap the filter placed over the pixel \( (x, y) \).
         *
         * The unnormalized square box filter can be useful in computing local image statistics such as the local
         * variance and standard deviation around the neighborhood of a pixel.
         *
         * param src input image
         * param dst output image of the same size and type as src
         * param ddepth the output image depth (-1 to use src.depth())
         * param ksize kernel size
         * param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel
         * center.
         * param normalize flag, specifying whether the kernel is to be normalized by it's area or not.
         * param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
         * SEE: boxFilter
         */
        public static void sqrBoxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_sqrBoxFilter_10(src.nativeObj, dst.nativeObj, ddepth, ksize.width, ksize.height, anchor.x, anchor.y, normalize, borderType);


        }

        /**
         * Calculates the normalized sum of squares of the pixel values overlapping the filter.
         *
         * For every pixel \( (x, y) \) in the source image, the function calculates the sum of squares of those neighboring
         * pixel values which overlap the filter placed over the pixel \( (x, y) \).
         *
         * The unnormalized square box filter can be useful in computing local image statistics such as the local
         * variance and standard deviation around the neighborhood of a pixel.
         *
         * param src input image
         * param dst output image of the same size and type as src
         * param ddepth the output image depth (-1 to use src.depth())
         * param ksize kernel size
         * param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel
         * center.
         * param normalize flag, specifying whether the kernel is to be normalized by it's area or not.
         * SEE: boxFilter
         */
        public static void sqrBoxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_sqrBoxFilter_11(src.nativeObj, dst.nativeObj, ddepth, ksize.width, ksize.height, anchor.x, anchor.y, normalize);


        }

        /**
         * Calculates the normalized sum of squares of the pixel values overlapping the filter.
         *
         * For every pixel \( (x, y) \) in the source image, the function calculates the sum of squares of those neighboring
         * pixel values which overlap the filter placed over the pixel \( (x, y) \).
         *
         * The unnormalized square box filter can be useful in computing local image statistics such as the local
         * variance and standard deviation around the neighborhood of a pixel.
         *
         * param src input image
         * param dst output image of the same size and type as src
         * param ddepth the output image depth (-1 to use src.depth())
         * param ksize kernel size
         * param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel
         * center.
         * SEE: boxFilter
         */
        public static void sqrBoxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_sqrBoxFilter_12(src.nativeObj, dst.nativeObj, ddepth, ksize.width, ksize.height, anchor.x, anchor.y);


        }

        /**
         * Calculates the normalized sum of squares of the pixel values overlapping the filter.
         *
         * For every pixel \( (x, y) \) in the source image, the function calculates the sum of squares of those neighboring
         * pixel values which overlap the filter placed over the pixel \( (x, y) \).
         *
         * The unnormalized square box filter can be useful in computing local image statistics such as the local
         * variance and standard deviation around the neighborhood of a pixel.
         *
         * param src input image
         * param dst output image of the same size and type as src
         * param ddepth the output image depth (-1 to use src.depth())
         * param ksize kernel size
         * center.
         * SEE: boxFilter
         */
        public static void sqrBoxFilter(Mat src, Mat dst, int ddepth, Size ksize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_sqrBoxFilter_13(src.nativeObj, dst.nativeObj, ddepth, ksize.width, ksize.height);


        }


        //
        // C++:  void cv::blur(Mat src, Mat& dst, Size ksize, Point anchor = Point(-1,-1), int borderType = BORDER_DEFAULT)
        //

        /**
         * Blurs an image using the normalized box filter.
         *
         * The function smooths an image using the kernel:
         *
         * \(\texttt{K} =  \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ \hdotsfor{6} \\ 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ \end{bmatrix}\)
         *
         * The call {code blur(src, dst, ksize, anchor, borderType)} is equivalent to `boxFilter(src, dst, src.type(), ksize,
         * anchor, true, borderType)`.
         *
         * param src input image; it can have any number of channels, which are processed independently, but
         * the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param ksize blurring kernel size.
         * param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
         * center.
         * param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
         * SEE:  boxFilter, bilateralFilter, GaussianBlur, medianBlur
         */
        public static void blur(Mat src, Mat dst, Size ksize, Point anchor, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_blur_10(src.nativeObj, dst.nativeObj, ksize.width, ksize.height, anchor.x, anchor.y, borderType);


        }

        /**
         * Blurs an image using the normalized box filter.
         *
         * The function smooths an image using the kernel:
         *
         * \(\texttt{K} =  \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ \hdotsfor{6} \\ 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ \end{bmatrix}\)
         *
         * The call {code blur(src, dst, ksize, anchor, borderType)} is equivalent to `boxFilter(src, dst, src.type(), ksize,
         * anchor, true, borderType)`.
         *
         * param src input image; it can have any number of channels, which are processed independently, but
         * the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param ksize blurring kernel size.
         * param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
         * center.
         * SEE:  boxFilter, bilateralFilter, GaussianBlur, medianBlur
         */
        public static void blur(Mat src, Mat dst, Size ksize, Point anchor)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_blur_11(src.nativeObj, dst.nativeObj, ksize.width, ksize.height, anchor.x, anchor.y);


        }

        /**
         * Blurs an image using the normalized box filter.
         *
         * The function smooths an image using the kernel:
         *
         * \(\texttt{K} =  \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ \hdotsfor{6} \\ 1 &amp; 1 &amp; 1 &amp;  \cdots &amp; 1 &amp; 1  \\ \end{bmatrix}\)
         *
         * The call {code blur(src, dst, ksize, anchor, borderType)} is equivalent to `boxFilter(src, dst, src.type(), ksize,
         * anchor, true, borderType)`.
         *
         * param src input image; it can have any number of channels, which are processed independently, but
         * the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param ksize blurring kernel size.
         * center.
         * SEE:  boxFilter, bilateralFilter, GaussianBlur, medianBlur
         */
        public static void blur(Mat src, Mat dst, Size ksize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_blur_12(src.nativeObj, dst.nativeObj, ksize.width, ksize.height);


        }


        //
        // C++:  void cv::stackBlur(Mat src, Mat& dst, Size ksize)
        //

        /**
         * Blurs an image using the stackBlur.
         *
         * The function applies and stackBlur to an image.
         * stackBlur can generate similar results as Gaussian blur, and the time consumption does not increase with the increase of kernel size.
         * It creates a kind of moving stack of colors whilst scanning through the image. Thereby it just has to add one new block of color to the right side
         * of the stack and remove the leftmost color. The remaining colors on the topmost layer of the stack are either added on or reduced by one,
         * depending on if they are on the right or on the left side of the stack. The only supported borderType is BORDER_REPLICATE.
         * Original paper was proposed by Mario Klingemann, which can be found http://underdestruction.com/2004/02/25/stackblur-2004.
         *
         * param src input image. The number of channels can be arbitrary, but the depth should be one of
         * CV_8U, CV_16U, CV_16S or CV_32F.
         * param dst output image of the same size and type as src.
         * param ksize stack-blurring kernel size. The ksize.width and ksize.height can differ but they both must be
         * positive and odd.
         */
        public static void stackBlur(Mat src, Mat dst, Size ksize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_stackBlur_10(src.nativeObj, dst.nativeObj, ksize.width, ksize.height);


        }


        //
        // C++:  void cv::filter2D(Mat src, Mat& dst, int ddepth, Mat kernel, Point anchor = Point(-1,-1), double delta = 0, int borderType = BORDER_DEFAULT)
        //

        /**
         * 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:
         *
         * \(\texttt{dst} (x,y) =  \sum _{ \substack{0\leq x' &lt; \texttt{kernel.cols}\\{0\leq y' &lt; \texttt{kernel.rows}}}}  \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\)
         *
         * 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 (~{code 11 x 11} or
         * larger) and the direct algorithm for small kernels.
         *
         * param src input image.
         * param dst output image of the same size and the same number of channels as src.
         * param ddepth desired depth of the destination image, see REF: filter_depths "combinations"
         * param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point
         * matrix; if you want to apply different kernels to different channels, split the image into
         * separate color planes using split and process them individually.
         * param anchor anchor of the kernel that indicates the relative position of a filtered point within
         * the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
         * is at the kernel center.
         * param delta optional value added to the filtered pixels before storing them in dst.
         * param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         * SEE:  sepFilter2D, dft, matchTemplate
         */
        public static void filter2D(Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_filter2D_10(src.nativeObj, dst.nativeObj, ddepth, kernel.nativeObj, anchor.x, anchor.y, delta, 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:
         *
         * \(\texttt{dst} (x,y) =  \sum _{ \substack{0\leq x' &lt; \texttt{kernel.cols}\\{0\leq y' &lt; \texttt{kernel.rows}}}}  \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\)
         *
         * 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 (~{code 11 x 11} or
         * larger) and the direct algorithm for small kernels.
         *
         * param src input image.
         * param dst output image of the same size and the same number of channels as src.
         * param ddepth desired depth of the destination image, see REF: filter_depths "combinations"
         * param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point
         * matrix; if you want to apply different kernels to different channels, split the image into
         * separate color planes using split and process them individually.
         * param anchor anchor of the kernel that indicates the relative position of a filtered point within
         * the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
         * is at the kernel center.
         * param delta optional value added to the filtered pixels before storing them in dst.
         * SEE:  sepFilter2D, dft, matchTemplate
         */
        public static void filter2D(Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_filter2D_11(src.nativeObj, dst.nativeObj, ddepth, kernel.nativeObj, anchor.x, anchor.y, 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:
         *
         * \(\texttt{dst} (x,y) =  \sum _{ \substack{0\leq x' &lt; \texttt{kernel.cols}\\{0\leq y' &lt; \texttt{kernel.rows}}}}  \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\)
         *
         * 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 (~{code 11 x 11} or
         * larger) and the direct algorithm for small kernels.
         *
         * param src input image.
         * param dst output image of the same size and the same number of channels as src.
         * param ddepth desired depth of the destination image, see REF: filter_depths "combinations"
         * param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point
         * matrix; if you want to apply different kernels to different channels, split the image into
         * separate color planes using split and process them individually.
         * param anchor anchor of the kernel that indicates the relative position of a filtered point within
         * the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
         * is at the kernel center.
         * SEE:  sepFilter2D, dft, matchTemplate
         */
        public static void filter2D(Mat src, Mat dst, int ddepth, Mat kernel, Point anchor)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_filter2D_12(src.nativeObj, dst.nativeObj, ddepth, kernel.nativeObj, anchor.x, anchor.y);


        }

        /**
         * 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:
         *
         * \(\texttt{dst} (x,y) =  \sum _{ \substack{0\leq x' &lt; \texttt{kernel.cols}\\{0\leq y' &lt; \texttt{kernel.rows}}}}  \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\)
         *
         * 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 (~{code 11 x 11} or
         * larger) and the direct algorithm for small kernels.
         *
         * param src input image.
         * param dst output image of the same size and the same number of channels as src.
         * param ddepth desired depth of the destination image, see REF: filter_depths "combinations"
         * param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point
         * matrix; if you want to apply different kernels to different channels, split the image into
         * separate color planes using split and process them individually.
         * the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
         * is at the kernel center.
         * SEE:  sepFilter2D, dft, matchTemplate
         */
        public static void filter2D(Mat src, Mat dst, int ddepth, Mat kernel)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_filter2D_13(src.nativeObj, dst.nativeObj, ddepth, kernel.nativeObj);


        }


        //
        // C++:  void cv::sepFilter2D(Mat src, Mat& dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor = Point(-1,-1), double delta = 0, int borderType = BORDER_DEFAULT)
        //

        /**
         * Applies a separable linear filter to an image.
         *
         * The function applies a separable linear filter to the image. That is, first, every row of src is
         * filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D
         * kernel kernelY. The final result shifted by delta is stored in dst .
         *
         * param src Source image.
         * param dst Destination image of the same size and the same number of channels as src .
         * param ddepth Destination image depth, see REF: filter_depths "combinations"
         * param kernelX Coefficients for filtering each row.
         * param kernelY Coefficients for filtering each column.
         * param anchor Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor
         * is at the kernel center.
         * param delta Value added to the filtered results before storing them.
         * param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         * SEE:  filter2D, Sobel, GaussianBlur, boxFilter, blur
         */
        public static void sepFilter2D(Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor, double delta, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernelX != null) kernelX.ThrowIfDisposed();
            if (kernelY != null) kernelY.ThrowIfDisposed();

            imgproc_Imgproc_sepFilter2D_10(src.nativeObj, dst.nativeObj, ddepth, kernelX.nativeObj, kernelY.nativeObj, anchor.x, anchor.y, delta, 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 .
         *
         * param src Source image.
         * param dst Destination image of the same size and the same number of channels as src .
         * param ddepth Destination image depth, see REF: filter_depths "combinations"
         * param kernelX Coefficients for filtering each row.
         * param kernelY Coefficients for filtering each column.
         * param anchor Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor
         * is at the kernel center.
         * param delta Value added to the filtered results before storing them.
         * SEE:  filter2D, Sobel, GaussianBlur, boxFilter, blur
         */
        public static void sepFilter2D(Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor, double delta)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernelX != null) kernelX.ThrowIfDisposed();
            if (kernelY != null) kernelY.ThrowIfDisposed();

            imgproc_Imgproc_sepFilter2D_11(src.nativeObj, dst.nativeObj, ddepth, kernelX.nativeObj, kernelY.nativeObj, anchor.x, anchor.y, 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 .
         *
         * param src Source image.
         * param dst Destination image of the same size and the same number of channels as src .
         * param ddepth Destination image depth, see REF: filter_depths "combinations"
         * param kernelX Coefficients for filtering each row.
         * param kernelY Coefficients for filtering each column.
         * param anchor Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor
         * is at the kernel center.
         * SEE:  filter2D, Sobel, GaussianBlur, boxFilter, blur
         */
        public static void sepFilter2D(Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernelX != null) kernelX.ThrowIfDisposed();
            if (kernelY != null) kernelY.ThrowIfDisposed();

            imgproc_Imgproc_sepFilter2D_12(src.nativeObj, dst.nativeObj, ddepth, kernelX.nativeObj, kernelY.nativeObj, anchor.x, anchor.y);


        }

        /**
         * Applies a separable linear filter to an image.
         *
         * The function applies a separable linear filter to the image. That is, first, every row of src is
         * filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D
         * kernel kernelY. The final result shifted by delta is stored in dst .
         *
         * param src Source image.
         * param dst Destination image of the same size and the same number of channels as src .
         * param ddepth Destination image depth, see REF: filter_depths "combinations"
         * param kernelX Coefficients for filtering each row.
         * param kernelY Coefficients for filtering each column.
         * is at the kernel center.
         * SEE:  filter2D, Sobel, GaussianBlur, boxFilter, blur
         */
        public static void sepFilter2D(Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernelX != null) kernelX.ThrowIfDisposed();
            if (kernelY != null) kernelY.ThrowIfDisposed();

            imgproc_Imgproc_sepFilter2D_13(src.nativeObj, dst.nativeObj, ddepth, kernelX.nativeObj, kernelY.nativeObj);


        }


        //
        // C++:  void cv::Sobel(Mat src, Mat& dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT)
        //

        /**
         * Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
         *
         * In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) separable kernel is used to
         * calculate the derivative. When \(\texttt{ksize = 1}\), the \(3 \times 1\) or \(1 \times 3\)
         * kernel is used (that is, no Gaussian smoothing is done). {code ksize = 1} can only be used for the first
         * or the second x- or y- derivatives.
         *
         * There is also the special value {code ksize = #FILTER_SCHARR (-1)} that corresponds to the \(3\times3\) Scharr
         * filter that may give more accurate results than the \(3\times3\) Sobel. The Scharr aperture is
         *
         * \(\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\)
         *
         * for the x-derivative, or transposed for the y-derivative.
         *
         * The function calculates an image derivative by convolving the image with the appropriate kernel:
         *
         * \(\texttt{dst} =  \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\)
         *
         * 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:
         *
         * \(\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\)
         *
         * The second case corresponds to a kernel of:
         *
         * \(\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\)
         *
         * param src input image.
         * param dst output image of the same size and the same number of channels as src .
         * param ddepth output image depth, see REF: filter_depths "combinations"; in the case of
         *     8-bit input images it will result in truncated derivatives.
         * param dx order of the derivative x.
         * param dy order of the derivative y.
         * param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
         * param scale optional scale factor for the computed derivative values; by default, no scaling is
         * applied (see #getDerivKernels for details).
         * param delta optional delta value that is added to the results prior to storing them in dst.
         * param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         * SEE:  Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
         */
        public static void Sobel(Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale, double delta, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_Sobel_10(src.nativeObj, dst.nativeObj, ddepth, dx, dy, ksize, scale, delta, borderType);


        }

        /**
         * Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
         *
         * In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) separable kernel is used to
         * calculate the derivative. When \(\texttt{ksize = 1}\), the \(3 \times 1\) or \(1 \times 3\)
         * kernel is used (that is, no Gaussian smoothing is done). {code ksize = 1} can only be used for the first
         * or the second x- or y- derivatives.
         *
         * There is also the special value {code ksize = #FILTER_SCHARR (-1)} that corresponds to the \(3\times3\) Scharr
         * filter that may give more accurate results than the \(3\times3\) Sobel. The Scharr aperture is
         *
         * \(\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\)
         *
         * for the x-derivative, or transposed for the y-derivative.
         *
         * The function calculates an image derivative by convolving the image with the appropriate kernel:
         *
         * \(\texttt{dst} =  \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\)
         *
         * 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:
         *
         * \(\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\)
         *
         * The second case corresponds to a kernel of:
         *
         * \(\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\)
         *
         * param src input image.
         * param dst output image of the same size and the same number of channels as src .
         * param ddepth output image depth, see REF: filter_depths "combinations"; in the case of
         *     8-bit input images it will result in truncated derivatives.
         * param dx order of the derivative x.
         * param dy order of the derivative y.
         * param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
         * param scale optional scale factor for the computed derivative values; by default, no scaling is
         * applied (see #getDerivKernels for details).
         * param delta optional delta value that is added to the results prior to storing them in dst.
         * SEE:  Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
         */
        public static void Sobel(Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale, double delta)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_Sobel_11(src.nativeObj, dst.nativeObj, ddepth, dx, dy, ksize, scale, delta);


        }

        /**
         * Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
         *
         * In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) separable kernel is used to
         * calculate the derivative. When \(\texttt{ksize = 1}\), the \(3 \times 1\) or \(1 \times 3\)
         * kernel is used (that is, no Gaussian smoothing is done). {code ksize = 1} can only be used for the first
         * or the second x- or y- derivatives.
         *
         * There is also the special value {code ksize = #FILTER_SCHARR (-1)} that corresponds to the \(3\times3\) Scharr
         * filter that may give more accurate results than the \(3\times3\) Sobel. The Scharr aperture is
         *
         * \(\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\)
         *
         * for the x-derivative, or transposed for the y-derivative.
         *
         * The function calculates an image derivative by convolving the image with the appropriate kernel:
         *
         * \(\texttt{dst} =  \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\)
         *
         * 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:
         *
         * \(\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\)
         *
         * The second case corresponds to a kernel of:
         *
         * \(\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\)
         *
         * param src input image.
         * param dst output image of the same size and the same number of channels as src .
         * param ddepth output image depth, see REF: filter_depths "combinations"; in the case of
         *     8-bit input images it will result in truncated derivatives.
         * param dx order of the derivative x.
         * param dy order of the derivative y.
         * param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
         * param scale optional scale factor for the computed derivative values; by default, no scaling is
         * applied (see #getDerivKernels for details).
         * SEE:  Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
         */
        public static void Sobel(Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_Sobel_12(src.nativeObj, dst.nativeObj, ddepth, dx, dy, ksize, scale);


        }

        /**
         * Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
         *
         * In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) separable kernel is used to
         * calculate the derivative. When \(\texttt{ksize = 1}\), the \(3 \times 1\) or \(1 \times 3\)
         * kernel is used (that is, no Gaussian smoothing is done). {code ksize = 1} can only be used for the first
         * or the second x- or y- derivatives.
         *
         * There is also the special value {code ksize = #FILTER_SCHARR (-1)} that corresponds to the \(3\times3\) Scharr
         * filter that may give more accurate results than the \(3\times3\) Sobel. The Scharr aperture is
         *
         * \(\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\)
         *
         * for the x-derivative, or transposed for the y-derivative.
         *
         * The function calculates an image derivative by convolving the image with the appropriate kernel:
         *
         * \(\texttt{dst} =  \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\)
         *
         * 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:
         *
         * \(\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\)
         *
         * The second case corresponds to a kernel of:
         *
         * \(\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\)
         *
         * param src input image.
         * param dst output image of the same size and the same number of channels as src .
         * param ddepth output image depth, see REF: filter_depths "combinations"; in the case of
         *     8-bit input images it will result in truncated derivatives.
         * param dx order of the derivative x.
         * param dy order of the derivative y.
         * param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
         * applied (see #getDerivKernels for details).
         * SEE:  Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
         */
        public static void Sobel(Mat src, Mat dst, int ddepth, int dx, int dy, int ksize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_Sobel_13(src.nativeObj, dst.nativeObj, ddepth, dx, dy, ksize);


        }

        /**
         * Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
         *
         * In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) separable kernel is used to
         * calculate the derivative. When \(\texttt{ksize = 1}\), the \(3 \times 1\) or \(1 \times 3\)
         * kernel is used (that is, no Gaussian smoothing is done). {code ksize = 1} can only be used for the first
         * or the second x- or y- derivatives.
         *
         * There is also the special value {code ksize = #FILTER_SCHARR (-1)} that corresponds to the \(3\times3\) Scharr
         * filter that may give more accurate results than the \(3\times3\) Sobel. The Scharr aperture is
         *
         * \(\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\)
         *
         * for the x-derivative, or transposed for the y-derivative.
         *
         * The function calculates an image derivative by convolving the image with the appropriate kernel:
         *
         * \(\texttt{dst} =  \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\)
         *
         * 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:
         *
         * \(\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\)
         *
         * The second case corresponds to a kernel of:
         *
         * \(\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\)
         *
         * param src input image.
         * param dst output image of the same size and the same number of channels as src .
         * param ddepth output image depth, see REF: filter_depths "combinations"; in the case of
         *     8-bit input images it will result in truncated derivatives.
         * param dx order of the derivative x.
         * param dy order of the derivative y.
         * applied (see #getDerivKernels for details).
         * SEE:  Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
         */
        public static void Sobel(Mat src, Mat dst, int ddepth, int dx, int dy)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_Sobel_14(src.nativeObj, dst.nativeObj, ddepth, dx, dy);


        }


        //
        // C++:  void cv::spatialGradient(Mat src, Mat& dx, Mat& dy, int ksize = 3, int borderType = BORDER_DEFAULT)
        //

        /**
         * Calculates the first order image derivative in both x and y using a Sobel operator
         *
         * Equivalent to calling:
         *
         * <code>
         * Sobel( src, dx, CV_16SC1, 1, 0, 3 );
         * Sobel( src, dy, CV_16SC1, 0, 1, 3 );
         * </code>
         *
         * param src input image.
         * param dx output image with first-order derivative in x.
         * param dy output image with first-order derivative in y.
         * param ksize size of Sobel kernel. It must be 3.
         * param borderType pixel extrapolation method, see #BorderTypes.
         *                   Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported.
         *
         * SEE: Sobel
         */
        public static void spatialGradient(Mat src, Mat dx, Mat dy, int ksize, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dx != null) dx.ThrowIfDisposed();
            if (dy != null) dy.ThrowIfDisposed();

            imgproc_Imgproc_spatialGradient_10(src.nativeObj, dx.nativeObj, dy.nativeObj, ksize, borderType);


        }

        /**
         * Calculates the first order image derivative in both x and y using a Sobel operator
         *
         * Equivalent to calling:
         *
         * <code>
         * Sobel( src, dx, CV_16SC1, 1, 0, 3 );
         * Sobel( src, dy, CV_16SC1, 0, 1, 3 );
         * </code>
         *
         * param src input image.
         * param dx output image with first-order derivative in x.
         * param dy output image with first-order derivative in y.
         * param ksize size of Sobel kernel. It must be 3.
         *                   Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported.
         *
         * SEE: Sobel
         */
        public static void spatialGradient(Mat src, Mat dx, Mat dy, int ksize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dx != null) dx.ThrowIfDisposed();
            if (dy != null) dy.ThrowIfDisposed();

            imgproc_Imgproc_spatialGradient_11(src.nativeObj, dx.nativeObj, dy.nativeObj, ksize);


        }

        /**
         * Calculates the first order image derivative in both x and y using a Sobel operator
         *
         * Equivalent to calling:
         *
         * <code>
         * Sobel( src, dx, CV_16SC1, 1, 0, 3 );
         * Sobel( src, dy, CV_16SC1, 0, 1, 3 );
         * </code>
         *
         * param src input image.
         * param dx output image with first-order derivative in x.
         * param dy output image with first-order derivative in y.
         *                   Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported.
         *
         * SEE: Sobel
         */
        public static void spatialGradient(Mat src, Mat dx, Mat dy)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dx != null) dx.ThrowIfDisposed();
            if (dy != null) dy.ThrowIfDisposed();

            imgproc_Imgproc_spatialGradient_12(src.nativeObj, dx.nativeObj, dy.nativeObj);


        }


        //
        // C++:  void cv::Scharr(Mat src, Mat& dst, int ddepth, int dx, int dy, double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT)
        //

        /**
         * 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
         *
         * \(\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\)
         *
         * is equivalent to
         *
         * \(\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER_SCHARR, scale, delta, borderType)} .\)
         *
         * param src input image.
         * param dst output image of the same size and the same number of channels as src.
         * param ddepth output image depth, see REF: filter_depths "combinations"
         * param dx order of the derivative x.
         * param dy order of the derivative y.
         * param scale optional scale factor for the computed derivative values; by default, no scaling is
         * applied (see #getDerivKernels for details).
         * param delta optional delta value that is added to the results prior to storing them in dst.
         * param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         * SEE:  cartToPolar
         */
        public static void Scharr(Mat src, Mat dst, int ddepth, int dx, int dy, double scale, double delta, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_Scharr_10(src.nativeObj, dst.nativeObj, ddepth, dx, dy, scale, delta, 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
         *
         * \(\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\)
         *
         * is equivalent to
         *
         * \(\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER_SCHARR, scale, delta, borderType)} .\)
         *
         * param src input image.
         * param dst output image of the same size and the same number of channels as src.
         * param ddepth output image depth, see REF: filter_depths "combinations"
         * param dx order of the derivative x.
         * param dy order of the derivative y.
         * param scale optional scale factor for the computed derivative values; by default, no scaling is
         * applied (see #getDerivKernels for details).
         * param delta optional delta value that is added to the results prior to storing them in dst.
         * SEE:  cartToPolar
         */
        public static void Scharr(Mat src, Mat dst, int ddepth, int dx, int dy, double scale, double delta)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_Scharr_11(src.nativeObj, dst.nativeObj, ddepth, dx, dy, scale, 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
         *
         * \(\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\)
         *
         * is equivalent to
         *
         * \(\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER_SCHARR, scale, delta, borderType)} .\)
         *
         * param src input image.
         * param dst output image of the same size and the same number of channels as src.
         * param ddepth output image depth, see REF: filter_depths "combinations"
         * param dx order of the derivative x.
         * param dy order of the derivative y.
         * param scale optional scale factor for the computed derivative values; by default, no scaling is
         * applied (see #getDerivKernels for details).
         * SEE:  cartToPolar
         */
        public static void Scharr(Mat src, Mat dst, int ddepth, int dx, int dy, double scale)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_Scharr_12(src.nativeObj, dst.nativeObj, ddepth, dx, dy, scale);


        }

        /**
         * 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
         *
         * \(\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\)
         *
         * is equivalent to
         *
         * \(\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER_SCHARR, scale, delta, borderType)} .\)
         *
         * param src input image.
         * param dst output image of the same size and the same number of channels as src.
         * param ddepth output image depth, see REF: filter_depths "combinations"
         * param dx order of the derivative x.
         * param dy order of the derivative y.
         * applied (see #getDerivKernels for details).
         * SEE:  cartToPolar
         */
        public static void Scharr(Mat src, Mat dst, int ddepth, int dx, int dy)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_Scharr_13(src.nativeObj, dst.nativeObj, ddepth, dx, dy);


        }


        //
        // C++:  void cv::Laplacian(Mat src, Mat& dst, int ddepth, int ksize = 1, double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT)
        //

        /**
         * 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:
         *
         * \(\texttt{dst} =  \Delta \texttt{src} =  \frac{\partial^2 \texttt{src}}{\partial x^2} +  \frac{\partial^2 \texttt{src}}{\partial y^2}\)
         *
         * This is done when {code ksize &gt; 1}. When {code ksize == 1}, the Laplacian is computed by filtering the image
         * with the following \(3 \times 3\) aperture:
         *
         * \(\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\)
         *
         * param src Source image.
         * param dst Destination image of the same size and the same number of channels as src .
         * param ddepth Desired depth of the destination image, see REF: filter_depths "combinations".
         * param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for
         * details. The size must be positive and odd.
         * param scale Optional scale factor for the computed Laplacian values. By default, no scaling is
         * applied. See #getDerivKernels for details.
         * param delta Optional delta value that is added to the results prior to storing them in dst .
         * param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         * SEE:  Sobel, Scharr
         */
        public static void Laplacian(Mat src, Mat dst, int ddepth, int ksize, double scale, double delta, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_Laplacian_10(src.nativeObj, dst.nativeObj, ddepth, ksize, scale, delta, 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:
         *
         * \(\texttt{dst} =  \Delta \texttt{src} =  \frac{\partial^2 \texttt{src}}{\partial x^2} +  \frac{\partial^2 \texttt{src}}{\partial y^2}\)
         *
         * This is done when {code ksize &gt; 1}. When {code ksize == 1}, the Laplacian is computed by filtering the image
         * with the following \(3 \times 3\) aperture:
         *
         * \(\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\)
         *
         * param src Source image.
         * param dst Destination image of the same size and the same number of channels as src .
         * param ddepth Desired depth of the destination image, see REF: filter_depths "combinations".
         * param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for
         * details. The size must be positive and odd.
         * param scale Optional scale factor for the computed Laplacian values. By default, no scaling is
         * applied. See #getDerivKernels for details.
         * param delta Optional delta value that is added to the results prior to storing them in dst .
         * SEE:  Sobel, Scharr
         */
        public static void Laplacian(Mat src, Mat dst, int ddepth, int ksize, double scale, double delta)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_Laplacian_11(src.nativeObj, dst.nativeObj, ddepth, ksize, scale, 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:
         *
         * \(\texttt{dst} =  \Delta \texttt{src} =  \frac{\partial^2 \texttt{src}}{\partial x^2} +  \frac{\partial^2 \texttt{src}}{\partial y^2}\)
         *
         * This is done when {code ksize &gt; 1}. When {code ksize == 1}, the Laplacian is computed by filtering the image
         * with the following \(3 \times 3\) aperture:
         *
         * \(\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\)
         *
         * param src Source image.
         * param dst Destination image of the same size and the same number of channels as src .
         * param ddepth Desired depth of the destination image, see REF: filter_depths "combinations".
         * param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for
         * details. The size must be positive and odd.
         * param scale Optional scale factor for the computed Laplacian values. By default, no scaling is
         * applied. See #getDerivKernels for details.
         * SEE:  Sobel, Scharr
         */
        public static void Laplacian(Mat src, Mat dst, int ddepth, int ksize, double scale)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_Laplacian_12(src.nativeObj, dst.nativeObj, ddepth, ksize, scale);


        }

        /**
         * 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:
         *
         * \(\texttt{dst} =  \Delta \texttt{src} =  \frac{\partial^2 \texttt{src}}{\partial x^2} +  \frac{\partial^2 \texttt{src}}{\partial y^2}\)
         *
         * This is done when {code ksize &gt; 1}. When {code ksize == 1}, the Laplacian is computed by filtering the image
         * with the following \(3 \times 3\) aperture:
         *
         * \(\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\)
         *
         * param src Source image.
         * param dst Destination image of the same size and the same number of channels as src .
         * param ddepth Desired depth of the destination image, see REF: filter_depths "combinations".
         * param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for
         * details. The size must be positive and odd.
         * applied. See #getDerivKernels for details.
         * SEE:  Sobel, Scharr
         */
        public static void Laplacian(Mat src, Mat dst, int ddepth, int ksize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_Laplacian_13(src.nativeObj, dst.nativeObj, ddepth, ksize);


        }

        /**
         * 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:
         *
         * \(\texttt{dst} =  \Delta \texttt{src} =  \frac{\partial^2 \texttt{src}}{\partial x^2} +  \frac{\partial^2 \texttt{src}}{\partial y^2}\)
         *
         * This is done when {code ksize &gt; 1}. When {code ksize == 1}, the Laplacian is computed by filtering the image
         * with the following \(3 \times 3\) aperture:
         *
         * \(\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\)
         *
         * param src Source image.
         * param dst Destination image of the same size and the same number of channels as src .
         * param ddepth Desired depth of the destination image, see REF: filter_depths "combinations".
         * details. The size must be positive and odd.
         * applied. See #getDerivKernels for details.
         * SEE:  Sobel, Scharr
         */
        public static void Laplacian(Mat src, Mat dst, int ddepth)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_Laplacian_14(src.nativeObj, dst.nativeObj, ddepth);


        }


        //
        // C++:  void cv::Canny(Mat image, Mat& edges, double threshold1, double threshold2, int apertureSize = 3, bool L2gradient = false)
        //

        /**
         * Finds edges in an image using the Canny algorithm CITE: Canny86 .
         *
         * The function finds edges in the input 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
         * &lt;http://en.wikipedia.org/wiki/Canny_edge_detector&gt;
         *
         * param image 8-bit input image.
         * param edges output edge map; single channels 8-bit image, which has the same size as image .
         * param threshold1 first threshold for the hysteresis procedure.
         * param threshold2 second threshold for the hysteresis procedure.
         * param apertureSize aperture size for the Sobel operator.
         * param L2gradient a flag, indicating whether a more accurate \(L_2\) 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 ).
         */
        public static void Canny(Mat image, Mat edges, double threshold1, double threshold2, int apertureSize, bool L2gradient)
        {
            if (image != null) image.ThrowIfDisposed();
            if (edges != null) edges.ThrowIfDisposed();

            imgproc_Imgproc_Canny_10(image.nativeObj, edges.nativeObj, threshold1, threshold2, apertureSize, L2gradient);


        }

        /**
         * Finds edges in an image using the Canny algorithm CITE: Canny86 .
         *
         * The function finds edges in the input 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
         * &lt;http://en.wikipedia.org/wiki/Canny_edge_detector&gt;
         *
         * param image 8-bit input image.
         * param edges output edge map; single channels 8-bit image, which has the same size as image .
         * param threshold1 first threshold for the hysteresis procedure.
         * param threshold2 second threshold for the hysteresis procedure.
         * param apertureSize aperture size for the Sobel operator.
         * \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) 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 ).
         */
        public static void Canny(Mat image, Mat edges, double threshold1, double threshold2, int apertureSize)
        {
            if (image != null) image.ThrowIfDisposed();
            if (edges != null) edges.ThrowIfDisposed();

            imgproc_Imgproc_Canny_11(image.nativeObj, edges.nativeObj, threshold1, threshold2, apertureSize);


        }

        /**
         * Finds edges in an image using the Canny algorithm CITE: Canny86 .
         *
         * The function finds edges in the input 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
         * &lt;http://en.wikipedia.org/wiki/Canny_edge_detector&gt;
         *
         * param image 8-bit input image.
         * param edges output edge map; single channels 8-bit image, which has the same size as image .
         * param threshold1 first threshold for the hysteresis procedure.
         * param threshold2 second threshold for the hysteresis procedure.
         * \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) 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 ).
         */
        public static void Canny(Mat image, Mat edges, double threshold1, double threshold2)
        {
            if (image != null) image.ThrowIfDisposed();
            if (edges != null) edges.ThrowIfDisposed();

            imgproc_Imgproc_Canny_12(image.nativeObj, edges.nativeObj, threshold1, threshold2);


        }


        //
        // C++:  void cv::Canny(Mat dx, Mat dy, Mat& edges, double threshold1, double threshold2, bool L2gradient = false)
        //

        /**
         * \overload
         *
         * Finds edges in an image using the Canny algorithm with custom image gradient.
         *
         * param dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3).
         * param dy 16-bit y derivative of input image (same type as dx).
         * param edges output edge map; single channels 8-bit image, which has the same size as image .
         * param threshold1 first threshold for the hysteresis procedure.
         * param threshold2 second threshold for the hysteresis procedure.
         * param 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 ).
         */
        public static void Canny(Mat dx, Mat dy, Mat edges, double threshold1, double threshold2, bool L2gradient)
        {
            if (dx != null) dx.ThrowIfDisposed();
            if (dy != null) dy.ThrowIfDisposed();
            if (edges != null) edges.ThrowIfDisposed();

            imgproc_Imgproc_Canny_13(dx.nativeObj, dy.nativeObj, edges.nativeObj, threshold1, threshold2, L2gradient);


        }

        /**
         * \overload
         *
         * Finds edges in an image using the Canny algorithm with custom image gradient.
         *
         * param dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3).
         * param dy 16-bit y derivative of input image (same type as dx).
         * param edges output edge map; single channels 8-bit image, which has the same size as image .
         * param threshold1 first threshold for the hysteresis procedure.
         * param threshold2 second threshold for the hysteresis procedure.
         * \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) 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 ).
         */
        public static void Canny(Mat dx, Mat dy, Mat edges, double threshold1, double threshold2)
        {
            if (dx != null) dx.ThrowIfDisposed();
            if (dy != null) dy.ThrowIfDisposed();
            if (edges != null) edges.ThrowIfDisposed();

            imgproc_Imgproc_Canny_14(dx.nativeObj, dy.nativeObj, edges.nativeObj, threshold1, threshold2);


        }


        //
        // C++:  void cv::cornerMinEigenVal(Mat src, Mat& dst, int blockSize, int ksize = 3, int borderType = BORDER_DEFAULT)
        //

        /**
         * 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.
         *
         * param src Input single-channel 8-bit or floating-point image.
         * param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as
         * src .
         * param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ).
         * param ksize Aperture parameter for the Sobel operator.
         * param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
         */
        public static void cornerMinEigenVal(Mat src, Mat dst, int blockSize, int ksize, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_cornerMinEigenVal_10(src.nativeObj, dst.nativeObj, blockSize, ksize, 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.
         *
         * param src Input single-channel 8-bit or floating-point image.
         * param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as
         * src .
         * param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ).
         * param ksize Aperture parameter for the Sobel operator.
         */
        public static void cornerMinEigenVal(Mat src, Mat dst, int blockSize, int ksize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_cornerMinEigenVal_11(src.nativeObj, dst.nativeObj, blockSize, 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.
         *
         * param src Input single-channel 8-bit or floating-point image.
         * param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as
         * src .
         * param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ).
         */
        public static void cornerMinEigenVal(Mat src, Mat dst, int blockSize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_cornerMinEigenVal_12(src.nativeObj, dst.nativeObj, blockSize);


        }


        //
        // C++:  void cv::cornerHarris(Mat src, Mat& dst, int blockSize, int ksize, double k, int borderType = BORDER_DEFAULT)
        //

        /**
         * Harris corner detector.
         *
         * The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and
         * cornerEigenValsAndVecs , for each pixel \((x, y)\) it calculates a \(2\times2\) gradient covariance
         * matrix \(M^{(x,y)}\) over a \(\texttt{blockSize} \times \texttt{blockSize}\) neighborhood. Then, it
         * computes the following characteristic:
         *
         * \(\texttt{dst} (x,y) =  \mathrm{det} M^{(x,y)} - k  \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\)
         *
         * Corners in the image can be found as the local maxima of this response map.
         *
         * param src Input single-channel 8-bit or floating-point image.
         * param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same
         * size as src .
         * param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ).
         * param ksize Aperture parameter for the Sobel operator.
         * param k Harris detector free parameter. See the formula above.
         * param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
         */
        public static void cornerHarris(Mat src, Mat dst, int blockSize, int ksize, double k, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_cornerHarris_10(src.nativeObj, dst.nativeObj, blockSize, ksize, k, borderType);


        }

        /**
         * Harris corner detector.
         *
         * The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and
         * cornerEigenValsAndVecs , for each pixel \((x, y)\) it calculates a \(2\times2\) gradient covariance
         * matrix \(M^{(x,y)}\) over a \(\texttt{blockSize} \times \texttt{blockSize}\) neighborhood. Then, it
         * computes the following characteristic:
         *
         * \(\texttt{dst} (x,y) =  \mathrm{det} M^{(x,y)} - k  \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\)
         *
         * Corners in the image can be found as the local maxima of this response map.
         *
         * param src Input single-channel 8-bit or floating-point image.
         * param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same
         * size as src .
         * param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ).
         * param ksize Aperture parameter for the Sobel operator.
         * param k Harris detector free parameter. See the formula above.
         */
        public static void cornerHarris(Mat src, Mat dst, int blockSize, int ksize, double k)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_cornerHarris_11(src.nativeObj, dst.nativeObj, blockSize, ksize, k);


        }


        //
        // C++:  void cv::cornerEigenValsAndVecs(Mat src, Mat& dst, int blockSize, int ksize, int borderType = BORDER_DEFAULT)
        //

        /**
         * Calculates eigenvalues and eigenvectors of image blocks for corner detection.
         *
         * For every pixel \(p\) , the function cornerEigenValsAndVecs considers a blockSize \(\times\) blockSize
         * neighborhood \(S(p)\) . It calculates the covariation matrix of derivatives over the neighborhood as:
         *
         * \(M =  \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 &amp;  \sum _{S(p)}dI/dx dI/dy  \\ \sum _{S(p)}dI/dx dI/dy &amp;  \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\)
         *
         * 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
         *
         * <ul>
         *   <li>
         *    \(\lambda_1, \lambda_2\) are the non-sorted eigenvalues of \(M\)
         *   </li>
         *   <li>
         *    \(x_1, y_1\) are the eigenvectors corresponding to \(\lambda_1\)
         *   </li>
         *   <li>
         *    \(x_2, y_2\) are the eigenvectors corresponding to \(\lambda_2\)
         *   </li>
         * </ul>
         *
         * The output of the function can be used for robust edge or corner detection.
         *
         * param src Input single-channel 8-bit or floating-point image.
         * param dst Image to store the results. It has the same size as src and the type CV_32FC(6) .
         * param blockSize Neighborhood size (see details below).
         * param ksize Aperture parameter for the Sobel operator.
         * param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
         *
         * SEE:  cornerMinEigenVal, cornerHarris, preCornerDetect
         */
        public static void cornerEigenValsAndVecs(Mat src, Mat dst, int blockSize, int ksize, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_cornerEigenValsAndVecs_10(src.nativeObj, dst.nativeObj, blockSize, ksize, borderType);


        }

        /**
         * Calculates eigenvalues and eigenvectors of image blocks for corner detection.
         *
         * For every pixel \(p\) , the function cornerEigenValsAndVecs considers a blockSize \(\times\) blockSize
         * neighborhood \(S(p)\) . It calculates the covariation matrix of derivatives over the neighborhood as:
         *
         * \(M =  \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 &amp;  \sum _{S(p)}dI/dx dI/dy  \\ \sum _{S(p)}dI/dx dI/dy &amp;  \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\)
         *
         * 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
         *
         * <ul>
         *   <li>
         *    \(\lambda_1, \lambda_2\) are the non-sorted eigenvalues of \(M\)
         *   </li>
         *   <li>
         *    \(x_1, y_1\) are the eigenvectors corresponding to \(\lambda_1\)
         *   </li>
         *   <li>
         *    \(x_2, y_2\) are the eigenvectors corresponding to \(\lambda_2\)
         *   </li>
         * </ul>
         *
         * The output of the function can be used for robust edge or corner detection.
         *
         * param src Input single-channel 8-bit or floating-point image.
         * param dst Image to store the results. It has the same size as src and the type CV_32FC(6) .
         * param blockSize Neighborhood size (see details below).
         * param ksize Aperture parameter for the Sobel operator.
         *
         * SEE:  cornerMinEigenVal, cornerHarris, preCornerDetect
         */
        public static void cornerEigenValsAndVecs(Mat src, Mat dst, int blockSize, int ksize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_cornerEigenValsAndVecs_11(src.nativeObj, dst.nativeObj, blockSize, ksize);


        }


        //
        // C++:  void cv::preCornerDetect(Mat src, Mat& dst, int ksize, int borderType = BORDER_DEFAULT)
        //

        /**
         * Calculates a feature map for corner detection.
         *
         * The function calculates the complex spatial derivative-based function of the source image
         *
         * \(\texttt{dst} = (D_x  \texttt{src} )^2  \cdot D_{yy}  \texttt{src} + (D_y  \texttt{src} )^2  \cdot D_{xx}  \texttt{src} - 2 D_x  \texttt{src} \cdot D_y  \texttt{src} \cdot D_{xy}  \texttt{src}\)
         *
         * 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:
         * <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;
         * </code>
         *
         * param src Source single-channel 8-bit of floating-point image.
         * param dst Output image that has the type CV_32F and the same size as src .
         * param ksize %Aperture size of the Sobel .
         * param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
         */
        public static void preCornerDetect(Mat src, Mat dst, int ksize, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_preCornerDetect_10(src.nativeObj, dst.nativeObj, ksize, borderType);


        }

        /**
         * Calculates a feature map for corner detection.
         *
         * The function calculates the complex spatial derivative-based function of the source image
         *
         * \(\texttt{dst} = (D_x  \texttt{src} )^2  \cdot D_{yy}  \texttt{src} + (D_y  \texttt{src} )^2  \cdot D_{xx}  \texttt{src} - 2 D_x  \texttt{src} \cdot D_y  \texttt{src} \cdot D_{xy}  \texttt{src}\)
         *
         * 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:
         * <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;
         * </code>
         *
         * param src Source single-channel 8-bit of floating-point image.
         * param dst Output image that has the type CV_32F and the same size as src .
         * param ksize %Aperture size of the Sobel .
         */
        public static void preCornerDetect(Mat src, Mat dst, int ksize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_preCornerDetect_11(src.nativeObj, dst.nativeObj, ksize);


        }


        //
        // C++:  void cv::cornerSubPix(Mat image, Mat& 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 described in CITE: forstner1987fast, and as shown on the figure below.
         *
         * ![image](pics/cornersubpix.png)
         *
         * Sub-pixel accurate corner locator is based on the observation that every vector from the center \(q\)
         * 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:
         *
         * \(\epsilon _i = {DI_{p_i}}^T  \cdot (q - p_i)\)
         *
         * 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:
         *
         * \(\sum _i(DI_{p_i}  \cdot {DI_{p_i}}^T) \cdot q -  \sum _i(DI_{p_i}  \cdot {DI_{p_i}}^T  \cdot p_i)\)
         *
         * 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:
         *
         * \(q = G^{-1}  \cdot b\)
         *
         * 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.
         *
         * param image Input single-channel, 8-bit or float image.
         * param corners Initial coordinates of the input corners and refined coordinates provided for
         * output.
         * param winSize Half of the side length of the search window. For example, if winSize=Size(5,5) ,
         * then a \((5*2+1) \times (5*2+1) = 11 \times 11\) search window is used.
         * param zeroZone Half of the size of the dead region in the middle of the search zone over which
         * the summation in the formula below is not done. It is used sometimes to avoid possible
         * singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such
         * a size.
         * param criteria Criteria for termination of the iterative process of corner refinement. That is,
         * the process of corner position refinement stops either after criteria.maxCount iterations or when
         * the corner position moves by less than criteria.epsilon on some iteration.
         */
        public static void cornerSubPix(Mat image, Mat corners, Size winSize, Size zeroZone, TermCriteria criteria)
        {
            if (image != null) image.ThrowIfDisposed();
            if (corners != null) corners.ThrowIfDisposed();

            imgproc_Imgproc_cornerSubPix_10(image.nativeObj, corners.nativeObj, winSize.width, winSize.height, zeroZone.width, zeroZone.height, criteria.type, criteria.maxCount, criteria.epsilon);


        }


        //
        // C++:  void cv::goodFeaturesToTrack(Mat image, vector_Point& corners, int maxCorners, double qualityLevel, double minDistance, Mat mask = Mat(), int blockSize = 3, bool useHarrisDetector = false, double k = 0.04)
        //

        /**
         * Determines strong corners on an image.
         *
         * The function finds the most prominent corners in the image or in the specified image region, as
         * described in CITE: Shi94
         *
         * <ul>
         *   <li>
         *    Function calculates the corner quality measure at every source image pixel using the
         *     #cornerMinEigenVal or #cornerHarris .
         *   </li>
         *   <li>
         *    Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are
         *     retained).
         *   </li>
         *   <li>
         *    The corners with the minimal eigenvalue less than
         *     \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
         *   </li>
         *   <li>
         *    The remaining corners are sorted by the quality measure in the descending order.
         *   </li>
         *   <li>
         *    Function throws away each corner for which there is a stronger corner at a distance less than
         *     maxDistance.
         *   </li>
         * </ul>
         *
         * The function can be used to initialize a point-based tracker of an object.
         *
         * <b>Note:</b> If the function is called with different values A and B of the parameter qualityLevel , and
         * A &gt; B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector
         * with qualityLevel=B .
         *
         * param image Input 8-bit or floating-point 32-bit, single-channel image.
         * param corners Output vector of detected corners.
         * param maxCorners Maximum number of corners to return. If there are more corners than are found,
         * the strongest of them is returned. {code maxCorners &lt;= 0} implies that no limit on the maximum is set
         * and all detected corners are returned.
         * param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
         * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
         * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
         * quality measure less than the product are rejected. For example, if the best corner has the
         * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
         * less than 15 are rejected.
         * param minDistance Minimum possible Euclidean distance between the returned corners.
         * param mask Optional region of interest. If the image is not empty (it needs to have the type
         * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
         * param blockSize Size of an average block for computing a derivative covariation matrix over each
         * pixel neighborhood. See cornerEigenValsAndVecs .
         * param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris)
         * or #cornerMinEigenVal.
         * param k Free parameter of the Harris detector.
         *
         * SEE:  cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
         */
        public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, bool useHarrisDetector, double k)
        {
            if (image != null) image.ThrowIfDisposed();
            if (corners != null) corners.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            Mat corners_mat = corners;
            imgproc_Imgproc_goodFeaturesToTrack_10(image.nativeObj, corners_mat.nativeObj, maxCorners, qualityLevel, minDistance, mask.nativeObj, blockSize, useHarrisDetector, 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 CITE: Shi94
         *
         * <ul>
         *   <li>
         *    Function calculates the corner quality measure at every source image pixel using the
         *     #cornerMinEigenVal or #cornerHarris .
         *   </li>
         *   <li>
         *    Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are
         *     retained).
         *   </li>
         *   <li>
         *    The corners with the minimal eigenvalue less than
         *     \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
         *   </li>
         *   <li>
         *    The remaining corners are sorted by the quality measure in the descending order.
         *   </li>
         *   <li>
         *    Function throws away each corner for which there is a stronger corner at a distance less than
         *     maxDistance.
         *   </li>
         * </ul>
         *
         * The function can be used to initialize a point-based tracker of an object.
         *
         * <b>Note:</b> If the function is called with different values A and B of the parameter qualityLevel , and
         * A &gt; B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector
         * with qualityLevel=B .
         *
         * param image Input 8-bit or floating-point 32-bit, single-channel image.
         * param corners Output vector of detected corners.
         * param maxCorners Maximum number of corners to return. If there are more corners than are found,
         * the strongest of them is returned. {code maxCorners &lt;= 0} implies that no limit on the maximum is set
         * and all detected corners are returned.
         * param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
         * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
         * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
         * quality measure less than the product are rejected. For example, if the best corner has the
         * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
         * less than 15 are rejected.
         * param minDistance Minimum possible Euclidean distance between the returned corners.
         * param mask Optional region of interest. If the image is not empty (it needs to have the type
         * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
         * param blockSize Size of an average block for computing a derivative covariation matrix over each
         * pixel neighborhood. See cornerEigenValsAndVecs .
         * param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris)
         * or #cornerMinEigenVal.
         *
         * SEE:  cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
         */
        public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, bool useHarrisDetector)
        {
            if (image != null) image.ThrowIfDisposed();
            if (corners != null) corners.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            Mat corners_mat = corners;
            imgproc_Imgproc_goodFeaturesToTrack_11(image.nativeObj, corners_mat.nativeObj, maxCorners, qualityLevel, minDistance, mask.nativeObj, blockSize, useHarrisDetector);


        }

        /**
         * Determines strong corners on an image.
         *
         * The function finds the most prominent corners in the image or in the specified image region, as
         * described in CITE: Shi94
         *
         * <ul>
         *   <li>
         *    Function calculates the corner quality measure at every source image pixel using the
         *     #cornerMinEigenVal or #cornerHarris .
         *   </li>
         *   <li>
         *    Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are
         *     retained).
         *   </li>
         *   <li>
         *    The corners with the minimal eigenvalue less than
         *     \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
         *   </li>
         *   <li>
         *    The remaining corners are sorted by the quality measure in the descending order.
         *   </li>
         *   <li>
         *    Function throws away each corner for which there is a stronger corner at a distance less than
         *     maxDistance.
         *   </li>
         * </ul>
         *
         * The function can be used to initialize a point-based tracker of an object.
         *
         * <b>Note:</b> If the function is called with different values A and B of the parameter qualityLevel , and
         * A &gt; B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector
         * with qualityLevel=B .
         *
         * param image Input 8-bit or floating-point 32-bit, single-channel image.
         * param corners Output vector of detected corners.
         * param maxCorners Maximum number of corners to return. If there are more corners than are found,
         * the strongest of them is returned. {code maxCorners &lt;= 0} implies that no limit on the maximum is set
         * and all detected corners are returned.
         * param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
         * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
         * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
         * quality measure less than the product are rejected. For example, if the best corner has the
         * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
         * less than 15 are rejected.
         * param minDistance Minimum possible Euclidean distance between the returned corners.
         * param mask Optional region of interest. If the image is not empty (it needs to have the type
         * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
         * param blockSize Size of an average block for computing a derivative covariation matrix over each
         * pixel neighborhood. See cornerEigenValsAndVecs .
         * or #cornerMinEigenVal.
         *
         * SEE:  cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
         */
        public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize)
        {
            if (image != null) image.ThrowIfDisposed();
            if (corners != null) corners.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            Mat corners_mat = corners;
            imgproc_Imgproc_goodFeaturesToTrack_12(image.nativeObj, corners_mat.nativeObj, maxCorners, qualityLevel, minDistance, mask.nativeObj, blockSize);


        }

        /**
         * Determines strong corners on an image.
         *
         * The function finds the most prominent corners in the image or in the specified image region, as
         * described in CITE: Shi94
         *
         * <ul>
         *   <li>
         *    Function calculates the corner quality measure at every source image pixel using the
         *     #cornerMinEigenVal or #cornerHarris .
         *   </li>
         *   <li>
         *    Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are
         *     retained).
         *   </li>
         *   <li>
         *    The corners with the minimal eigenvalue less than
         *     \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
         *   </li>
         *   <li>
         *    The remaining corners are sorted by the quality measure in the descending order.
         *   </li>
         *   <li>
         *    Function throws away each corner for which there is a stronger corner at a distance less than
         *     maxDistance.
         *   </li>
         * </ul>
         *
         * The function can be used to initialize a point-based tracker of an object.
         *
         * <b>Note:</b> If the function is called with different values A and B of the parameter qualityLevel , and
         * A &gt; B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector
         * with qualityLevel=B .
         *
         * param image Input 8-bit or floating-point 32-bit, single-channel image.
         * param corners Output vector of detected corners.
         * param maxCorners Maximum number of corners to return. If there are more corners than are found,
         * the strongest of them is returned. {code maxCorners &lt;= 0} implies that no limit on the maximum is set
         * and all detected corners are returned.
         * param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
         * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
         * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
         * quality measure less than the product are rejected. For example, if the best corner has the
         * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
         * less than 15 are rejected.
         * param minDistance Minimum possible Euclidean distance between the returned corners.
         * param mask Optional region of interest. If the image is not empty (it needs to have the type
         * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
         * pixel neighborhood. See cornerEigenValsAndVecs .
         * or #cornerMinEigenVal.
         *
         * SEE:  cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
         */
        public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask)
        {
            if (image != null) image.ThrowIfDisposed();
            if (corners != null) corners.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            Mat corners_mat = corners;
            imgproc_Imgproc_goodFeaturesToTrack_13(image.nativeObj, corners_mat.nativeObj, maxCorners, qualityLevel, minDistance, mask.nativeObj);


        }

        /**
         * Determines strong corners on an image.
         *
         * The function finds the most prominent corners in the image or in the specified image region, as
         * described in CITE: Shi94
         *
         * <ul>
         *   <li>
         *    Function calculates the corner quality measure at every source image pixel using the
         *     #cornerMinEigenVal or #cornerHarris .
         *   </li>
         *   <li>
         *    Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are
         *     retained).
         *   </li>
         *   <li>
         *    The corners with the minimal eigenvalue less than
         *     \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
         *   </li>
         *   <li>
         *    The remaining corners are sorted by the quality measure in the descending order.
         *   </li>
         *   <li>
         *    Function throws away each corner for which there is a stronger corner at a distance less than
         *     maxDistance.
         *   </li>
         * </ul>
         *
         * The function can be used to initialize a point-based tracker of an object.
         *
         * <b>Note:</b> If the function is called with different values A and B of the parameter qualityLevel , and
         * A &gt; B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector
         * with qualityLevel=B .
         *
         * param image Input 8-bit or floating-point 32-bit, single-channel image.
         * param corners Output vector of detected corners.
         * param maxCorners Maximum number of corners to return. If there are more corners than are found,
         * the strongest of them is returned. {code maxCorners &lt;= 0} implies that no limit on the maximum is set
         * and all detected corners are returned.
         * param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
         * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
         * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
         * quality measure less than the product are rejected. For example, if the best corner has the
         * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
         * less than 15 are rejected.
         * param minDistance Minimum possible Euclidean distance between the returned corners.
         * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
         * pixel neighborhood. See cornerEigenValsAndVecs .
         * or #cornerMinEigenVal.
         *
         * SEE:  cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
         */
        public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance)
        {
            if (image != null) image.ThrowIfDisposed();
            if (corners != null) corners.ThrowIfDisposed();
            Mat corners_mat = corners;
            imgproc_Imgproc_goodFeaturesToTrack_14(image.nativeObj, corners_mat.nativeObj, maxCorners, qualityLevel, minDistance);


        }


        //
        // C++:  void cv::goodFeaturesToTrack(Mat image, vector_Point& corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, bool useHarrisDetector = false, double k = 0.04)
        //

        public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, bool useHarrisDetector, double k)
        {
            if (image != null) image.ThrowIfDisposed();
            if (corners != null) corners.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            Mat corners_mat = corners;
            imgproc_Imgproc_goodFeaturesToTrack_15(image.nativeObj, corners_mat.nativeObj, maxCorners, qualityLevel, minDistance, mask.nativeObj, blockSize, gradientSize, useHarrisDetector, k);


        }

        public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, bool useHarrisDetector)
        {
            if (image != null) image.ThrowIfDisposed();
            if (corners != null) corners.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            Mat corners_mat = corners;
            imgproc_Imgproc_goodFeaturesToTrack_16(image.nativeObj, corners_mat.nativeObj, maxCorners, qualityLevel, minDistance, mask.nativeObj, blockSize, gradientSize, useHarrisDetector);


        }

        public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize)
        {
            if (image != null) image.ThrowIfDisposed();
            if (corners != null) corners.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            Mat corners_mat = corners;
            imgproc_Imgproc_goodFeaturesToTrack_17(image.nativeObj, corners_mat.nativeObj, maxCorners, qualityLevel, minDistance, mask.nativeObj, blockSize, gradientSize);


        }


        //
        // C++:  void cv::goodFeaturesToTrack(Mat image, Mat& corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat& cornersQuality, int blockSize = 3, int gradientSize = 3, bool useHarrisDetector = false, double k = 0.04)
        //

        /**
         * Same as above, but returns also quality measure of the detected corners.
         *
         * param image Input 8-bit or floating-point 32-bit, single-channel image.
         * param corners Output vector of detected corners.
         * param maxCorners Maximum number of corners to return. If there are more corners than are found,
         * the strongest of them is returned. {code maxCorners &lt;= 0} implies that no limit on the maximum is set
         * and all detected corners are returned.
         * param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
         * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
         * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
         * quality measure less than the product are rejected. For example, if the best corner has the
         * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
         * less than 15 are rejected.
         * param minDistance Minimum possible Euclidean distance between the returned corners.
         * param mask Region of interest. If the image is not empty (it needs to have the type
         * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
         * param cornersQuality Output vector of quality measure of the detected corners.
         * param blockSize Size of an average block for computing a derivative covariation matrix over each
         * pixel neighborhood. See cornerEigenValsAndVecs .
         * param gradientSize Aperture parameter for the Sobel operator used for derivatives computation.
         * See cornerEigenValsAndVecs .
         * param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris)
         * or #cornerMinEigenVal.
         * param k Free parameter of the Harris detector.
         */
        public static void goodFeaturesToTrackWithQuality(Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize, bool useHarrisDetector, double k)
        {
            if (image != null) image.ThrowIfDisposed();
            if (corners != null) corners.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            if (cornersQuality != null) cornersQuality.ThrowIfDisposed();

            imgproc_Imgproc_goodFeaturesToTrackWithQuality_10(image.nativeObj, corners.nativeObj, maxCorners, qualityLevel, minDistance, mask.nativeObj, cornersQuality.nativeObj, blockSize, gradientSize, useHarrisDetector, k);


        }

        /**
         * Same as above, but returns also quality measure of the detected corners.
         *
         * param image Input 8-bit or floating-point 32-bit, single-channel image.
         * param corners Output vector of detected corners.
         * param maxCorners Maximum number of corners to return. If there are more corners than are found,
         * the strongest of them is returned. {code maxCorners &lt;= 0} implies that no limit on the maximum is set
         * and all detected corners are returned.
         * param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
         * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
         * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
         * quality measure less than the product are rejected. For example, if the best corner has the
         * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
         * less than 15 are rejected.
         * param minDistance Minimum possible Euclidean distance between the returned corners.
         * param mask Region of interest. If the image is not empty (it needs to have the type
         * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
         * param cornersQuality Output vector of quality measure of the detected corners.
         * param blockSize Size of an average block for computing a derivative covariation matrix over each
         * pixel neighborhood. See cornerEigenValsAndVecs .
         * param gradientSize Aperture parameter for the Sobel operator used for derivatives computation.
         * See cornerEigenValsAndVecs .
         * param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris)
         * or #cornerMinEigenVal.
         */
        public static void goodFeaturesToTrackWithQuality(Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize, bool useHarrisDetector)
        {
            if (image != null) image.ThrowIfDisposed();
            if (corners != null) corners.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            if (cornersQuality != null) cornersQuality.ThrowIfDisposed();

            imgproc_Imgproc_goodFeaturesToTrackWithQuality_11(image.nativeObj, corners.nativeObj, maxCorners, qualityLevel, minDistance, mask.nativeObj, cornersQuality.nativeObj, blockSize, gradientSize, useHarrisDetector);


        }

        /**
         * Same as above, but returns also quality measure of the detected corners.
         *
         * param image Input 8-bit or floating-point 32-bit, single-channel image.
         * param corners Output vector of detected corners.
         * param maxCorners Maximum number of corners to return. If there are more corners than are found,
         * the strongest of them is returned. {code maxCorners &lt;= 0} implies that no limit on the maximum is set
         * and all detected corners are returned.
         * param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
         * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
         * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
         * quality measure less than the product are rejected. For example, if the best corner has the
         * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
         * less than 15 are rejected.
         * param minDistance Minimum possible Euclidean distance between the returned corners.
         * param mask Region of interest. If the image is not empty (it needs to have the type
         * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
         * param cornersQuality Output vector of quality measure of the detected corners.
         * param blockSize Size of an average block for computing a derivative covariation matrix over each
         * pixel neighborhood. See cornerEigenValsAndVecs .
         * param gradientSize Aperture parameter for the Sobel operator used for derivatives computation.
         * See cornerEigenValsAndVecs .
         * or #cornerMinEigenVal.
         */
        public static void goodFeaturesToTrackWithQuality(Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize)
        {
            if (image != null) image.ThrowIfDisposed();
            if (corners != null) corners.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            if (cornersQuality != null) cornersQuality.ThrowIfDisposed();

            imgproc_Imgproc_goodFeaturesToTrackWithQuality_12(image.nativeObj, corners.nativeObj, maxCorners, qualityLevel, minDistance, mask.nativeObj, cornersQuality.nativeObj, blockSize, gradientSize);


        }

        /**
         * Same as above, but returns also quality measure of the detected corners.
         *
         * param image Input 8-bit or floating-point 32-bit, single-channel image.
         * param corners Output vector of detected corners.
         * param maxCorners Maximum number of corners to return. If there are more corners than are found,
         * the strongest of them is returned. {code maxCorners &lt;= 0} implies that no limit on the maximum is set
         * and all detected corners are returned.
         * param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
         * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
         * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
         * quality measure less than the product are rejected. For example, if the best corner has the
         * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
         * less than 15 are rejected.
         * param minDistance Minimum possible Euclidean distance between the returned corners.
         * param mask Region of interest. If the image is not empty (it needs to have the type
         * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
         * param cornersQuality Output vector of quality measure of the detected corners.
         * param blockSize Size of an average block for computing a derivative covariation matrix over each
         * pixel neighborhood. See cornerEigenValsAndVecs .
         * See cornerEigenValsAndVecs .
         * or #cornerMinEigenVal.
         */
        public static void goodFeaturesToTrackWithQuality(Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize)
        {
            if (image != null) image.ThrowIfDisposed();
            if (corners != null) corners.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            if (cornersQuality != null) cornersQuality.ThrowIfDisposed();

            imgproc_Imgproc_goodFeaturesToTrackWithQuality_13(image.nativeObj, corners.nativeObj, maxCorners, qualityLevel, minDistance, mask.nativeObj, cornersQuality.nativeObj, blockSize);


        }

        /**
         * Same as above, but returns also quality measure of the detected corners.
         *
         * param image Input 8-bit or floating-point 32-bit, single-channel image.
         * param corners Output vector of detected corners.
         * param maxCorners Maximum number of corners to return. If there are more corners than are found,
         * the strongest of them is returned. {code maxCorners &lt;= 0} implies that no limit on the maximum is set
         * and all detected corners are returned.
         * param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
         * parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
         * (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
         * quality measure less than the product are rejected. For example, if the best corner has the
         * quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
         * less than 15 are rejected.
         * param minDistance Minimum possible Euclidean distance between the returned corners.
         * param mask Region of interest. If the image is not empty (it needs to have the type
         * CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
         * param cornersQuality Output vector of quality measure of the detected corners.
         * pixel neighborhood. See cornerEigenValsAndVecs .
         * See cornerEigenValsAndVecs .
         * or #cornerMinEigenVal.
         */
        public static void goodFeaturesToTrackWithQuality(Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality)
        {
            if (image != null) image.ThrowIfDisposed();
            if (corners != null) corners.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            if (cornersQuality != null) cornersQuality.ThrowIfDisposed();

            imgproc_Imgproc_goodFeaturesToTrackWithQuality_14(image.nativeObj, corners.nativeObj, maxCorners, qualityLevel, minDistance, mask.nativeObj, cornersQuality.nativeObj);


        }


        //
        // C++:  void cv::HoughLines(Mat image, Mat& lines, double rho, double theta, int threshold, double srn = 0, double stn = 0, double min_theta = 0, double max_theta = CV_PI)
        //

        /**
         * 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 &lt;http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm&gt; for a good explanation of Hough
         * transform.
         *
         * param image 8-bit, single-channel binary source image. The image may be modified by the function.
         * param lines Output vector of lines. Each line is represented by a 2 or 3 element vector
         * \((\rho, \theta)\) or \((\rho, \theta, \textrm{votes})\), where \(\rho\) is the distance from
         * the coordinate origin \((0,0)\) (top-left corner of the image), \(\theta\) is the line rotation
         * angle in radians ( \(0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\) ), and
         * \(\textrm{votes}\) is the value of accumulator.
         * param rho Distance resolution of the accumulator in pixels.
         * param theta Angle resolution of the accumulator in radians.
         * param threshold %Accumulator threshold parameter. Only those lines are returned that get enough
         * votes ( \(&gt;\texttt{threshold}\) ).
         * param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho.
         * The coarse accumulator distance resolution is rho and the accurate accumulator resolution is
         * rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these
         * parameters should be positive.
         * param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta.
         * param min_theta For standard and multi-scale Hough transform, minimum angle to check for lines.
         * Must fall between 0 and max_theta.
         * param max_theta For standard and multi-scale Hough transform, an upper bound for the angle.
         * Must fall between min_theta and CV_PI. The actual maximum angle in the accumulator may be slightly
         * less than max_theta, depending on the parameters min_theta and theta.
         */
        public static void HoughLines(Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn, double min_theta, double max_theta)
        {
            if (image != null) image.ThrowIfDisposed();
            if (lines != null) lines.ThrowIfDisposed();

            imgproc_Imgproc_HoughLines_10(image.nativeObj, lines.nativeObj, rho, theta, threshold, srn, stn, min_theta, max_theta);


        }

        /**
         * 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 &lt;http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm&gt; for a good explanation of Hough
         * transform.
         *
         * param image 8-bit, single-channel binary source image. The image may be modified by the function.
         * param lines Output vector of lines. Each line is represented by a 2 or 3 element vector
         * \((\rho, \theta)\) or \((\rho, \theta, \textrm{votes})\), where \(\rho\) is the distance from
         * the coordinate origin \((0,0)\) (top-left corner of the image), \(\theta\) is the line rotation
         * angle in radians ( \(0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\) ), and
         * \(\textrm{votes}\) is the value of accumulator.
         * param rho Distance resolution of the accumulator in pixels.
         * param theta Angle resolution of the accumulator in radians.
         * param threshold %Accumulator threshold parameter. Only those lines are returned that get enough
         * votes ( \(&gt;\texttt{threshold}\) ).
         * param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho.
         * The coarse accumulator distance resolution is rho and the accurate accumulator resolution is
         * rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these
         * parameters should be positive.
         * param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta.
         * param min_theta For standard and multi-scale Hough transform, minimum angle to check for lines.
         * Must fall between 0 and max_theta.
         * Must fall between min_theta and CV_PI. The actual maximum angle in the accumulator may be slightly
         * less than max_theta, depending on the parameters min_theta and theta.
         */
        public static void HoughLines(Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn, double min_theta)
        {
            if (image != null) image.ThrowIfDisposed();
            if (lines != null) lines.ThrowIfDisposed();

            imgproc_Imgproc_HoughLines_11(image.nativeObj, lines.nativeObj, rho, theta, threshold, srn, stn, min_theta);


        }

        /**
         * 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 &lt;http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm&gt; for a good explanation of Hough
         * transform.
         *
         * param image 8-bit, single-channel binary source image. The image may be modified by the function.
         * param lines Output vector of lines. Each line is represented by a 2 or 3 element vector
         * \((\rho, \theta)\) or \((\rho, \theta, \textrm{votes})\), where \(\rho\) is the distance from
         * the coordinate origin \((0,0)\) (top-left corner of the image), \(\theta\) is the line rotation
         * angle in radians ( \(0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\) ), and
         * \(\textrm{votes}\) is the value of accumulator.
         * param rho Distance resolution of the accumulator in pixels.
         * param theta Angle resolution of the accumulator in radians.
         * param threshold %Accumulator threshold parameter. Only those lines are returned that get enough
         * votes ( \(&gt;\texttt{threshold}\) ).
         * param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho.
         * The coarse accumulator distance resolution is rho and the accurate accumulator resolution is
         * rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these
         * parameters should be positive.
         * param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta.
         * Must fall between 0 and max_theta.
         * Must fall between min_theta and CV_PI. The actual maximum angle in the accumulator may be slightly
         * less than max_theta, depending on the parameters min_theta and theta.
         */
        public static void HoughLines(Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn)
        {
            if (image != null) image.ThrowIfDisposed();
            if (lines != null) lines.ThrowIfDisposed();

            imgproc_Imgproc_HoughLines_12(image.nativeObj, lines.nativeObj, rho, theta, threshold, srn, 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 &lt;http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm&gt; for a good explanation of Hough
         * transform.
         *
         * param image 8-bit, single-channel binary source image. The image may be modified by the function.
         * param lines Output vector of lines. Each line is represented by a 2 or 3 element vector
         * \((\rho, \theta)\) or \((\rho, \theta, \textrm{votes})\), where \(\rho\) is the distance from
         * the coordinate origin \((0,0)\) (top-left corner of the image), \(\theta\) is the line rotation
         * angle in radians ( \(0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\) ), and
         * \(\textrm{votes}\) is the value of accumulator.
         * param rho Distance resolution of the accumulator in pixels.
         * param theta Angle resolution of the accumulator in radians.
         * param threshold %Accumulator threshold parameter. Only those lines are returned that get enough
         * votes ( \(&gt;\texttt{threshold}\) ).
         * param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho.
         * The coarse accumulator distance resolution is rho and the accurate accumulator resolution is
         * rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these
         * parameters should be positive.
         * Must fall between 0 and max_theta.
         * Must fall between min_theta and CV_PI. The actual maximum angle in the accumulator may be slightly
         * less than max_theta, depending on the parameters min_theta and theta.
         */
        public static void HoughLines(Mat image, Mat lines, double rho, double theta, int threshold, double srn)
        {
            if (image != null) image.ThrowIfDisposed();
            if (lines != null) lines.ThrowIfDisposed();

            imgproc_Imgproc_HoughLines_13(image.nativeObj, lines.nativeObj, rho, theta, threshold, srn);


        }

        /**
         * 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 &lt;http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm&gt; for a good explanation of Hough
         * transform.
         *
         * param image 8-bit, single-channel binary source image. The image may be modified by the function.
         * param lines Output vector of lines. Each line is represented by a 2 or 3 element vector
         * \((\rho, \theta)\) or \((\rho, \theta, \textrm{votes})\), where \(\rho\) is the distance from
         * the coordinate origin \((0,0)\) (top-left corner of the image), \(\theta\) is the line rotation
         * angle in radians ( \(0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\) ), and
         * \(\textrm{votes}\) is the value of accumulator.
         * param rho Distance resolution of the accumulator in pixels.
         * param theta Angle resolution of the accumulator in radians.
         * param threshold %Accumulator threshold parameter. Only those lines are returned that get enough
         * votes ( \(&gt;\texttt{threshold}\) ).
         * 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.
         * Must fall between 0 and max_theta.
         * Must fall between min_theta and CV_PI. The actual maximum angle in the accumulator may be slightly
         * less than max_theta, depending on the parameters min_theta and theta.
         */
        public static void HoughLines(Mat image, Mat lines, double rho, double theta, int threshold)
        {
            if (image != null) image.ThrowIfDisposed();
            if (lines != null) lines.ThrowIfDisposed();

            imgproc_Imgproc_HoughLines_14(image.nativeObj, lines.nativeObj, rho, theta, threshold);


        }


        //
        // C++:  void cv::HoughLinesP(Mat image, Mat& lines, double rho, double theta, int threshold, double minLineLength = 0, double maxLineGap = 0)
        //

        /**
         * Finds line segments in a binary image using the probabilistic Hough transform.
         *
         * The function implements the probabilistic Hough transform algorithm for line detection, described
         * in CITE: Matas00
         *
         * See the line detection example below:
         * INCLUDE: snippets/imgproc_HoughLinesP.cpp
         * This is a sample picture the function parameters have been tuned for:
         *
         * ![image](pics/building.jpg)
         *
         * And this is the output of the above program in case of the probabilistic Hough transform:
         *
         * ![image](pics/houghp.png)
         *
         * param image 8-bit, single-channel binary source image. The image may be modified by the function.
         * param lines Output vector of lines. Each line is represented by a 4-element vector
         * \((x_1, y_1, x_2, y_2)\) , where \((x_1,y_1)\) and \((x_2, y_2)\) are the ending points of each detected
         * line segment.
         * param rho Distance resolution of the accumulator in pixels.
         * param theta Angle resolution of the accumulator in radians.
         * param threshold %Accumulator threshold parameter. Only those lines are returned that get enough
         * votes ( \(&gt;\texttt{threshold}\) ).
         * param minLineLength Minimum line length. Line segments shorter than that are rejected.
         * param maxLineGap Maximum allowed gap between points on the same line to link them.
         *
         * SEE: LineSegmentDetector
         */
        public static void HoughLinesP(Mat image, Mat lines, double rho, double theta, int threshold, double minLineLength, double maxLineGap)
        {
            if (image != null) image.ThrowIfDisposed();
            if (lines != null) lines.ThrowIfDisposed();

            imgproc_Imgproc_HoughLinesP_10(image.nativeObj, lines.nativeObj, rho, theta, threshold, minLineLength, 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 CITE: Matas00
         *
         * See the line detection example below:
         * INCLUDE: snippets/imgproc_HoughLinesP.cpp
         * This is a sample picture the function parameters have been tuned for:
         *
         * ![image](pics/building.jpg)
         *
         * And this is the output of the above program in case of the probabilistic Hough transform:
         *
         * ![image](pics/houghp.png)
         *
         * param image 8-bit, single-channel binary source image. The image may be modified by the function.
         * param lines Output vector of lines. Each line is represented by a 4-element vector
         * \((x_1, y_1, x_2, y_2)\) , where \((x_1,y_1)\) and \((x_2, y_2)\) are the ending points of each detected
         * line segment.
         * param rho Distance resolution of the accumulator in pixels.
         * param theta Angle resolution of the accumulator in radians.
         * param threshold %Accumulator threshold parameter. Only those lines are returned that get enough
         * votes ( \(&gt;\texttt{threshold}\) ).
         * param minLineLength Minimum line length. Line segments shorter than that are rejected.
         *
         * SEE: LineSegmentDetector
         */
        public static void HoughLinesP(Mat image, Mat lines, double rho, double theta, int threshold, double minLineLength)
        {
            if (image != null) image.ThrowIfDisposed();
            if (lines != null) lines.ThrowIfDisposed();

            imgproc_Imgproc_HoughLinesP_11(image.nativeObj, lines.nativeObj, rho, theta, threshold, minLineLength);


        }

        /**
         * Finds line segments in a binary image using the probabilistic Hough transform.
         *
         * The function implements the probabilistic Hough transform algorithm for line detection, described
         * in CITE: Matas00
         *
         * See the line detection example below:
         * INCLUDE: snippets/imgproc_HoughLinesP.cpp
         * This is a sample picture the function parameters have been tuned for:
         *
         * ![image](pics/building.jpg)
         *
         * And this is the output of the above program in case of the probabilistic Hough transform:
         *
         * ![image](pics/houghp.png)
         *
         * param image 8-bit, single-channel binary source image. The image may be modified by the function.
         * param lines Output vector of lines. Each line is represented by a 4-element vector
         * \((x_1, y_1, x_2, y_2)\) , where \((x_1,y_1)\) and \((x_2, y_2)\) are the ending points of each detected
         * line segment.
         * param rho Distance resolution of the accumulator in pixels.
         * param theta Angle resolution of the accumulator in radians.
         * param threshold %Accumulator threshold parameter. Only those lines are returned that get enough
         * votes ( \(&gt;\texttt{threshold}\) ).
         *
         * SEE: LineSegmentDetector
         */
        public static void HoughLinesP(Mat image, Mat lines, double rho, double theta, int threshold)
        {
            if (image != null) image.ThrowIfDisposed();
            if (lines != null) lines.ThrowIfDisposed();

            imgproc_Imgproc_HoughLinesP_12(image.nativeObj, lines.nativeObj, rho, theta, threshold);


        }


        //
        // C++:  void cv::HoughLinesPointSet(Mat point, Mat& lines, int lines_max, int threshold, double min_rho, double max_rho, double rho_step, double min_theta, double max_theta, double theta_step)
        //

        /**
         * Finds lines in a set of points using the standard Hough transform.
         *
         * The function finds lines in a set of points using a modification of the Hough transform.
         * INCLUDE: snippets/imgproc_HoughLinesPointSet.cpp
         * param point Input vector of points. Each vector must be encoded as a Point vector \((x,y)\). Type must be CV_32FC2 or CV_32SC2.
         * param lines Output vector of found lines. Each vector is encoded as a vector&lt;Vec3d&gt; \((votes, rho, theta)\).
         * The larger the value of 'votes', the higher the reliability of the Hough line.
         * param lines_max Max count of Hough lines.
         * param threshold %Accumulator threshold parameter. Only those lines are returned that get enough
         * votes ( \(&gt;\texttt{threshold}\) ).
         * param min_rho Minimum value for \(\rho\) for the accumulator (Note: \(\rho\) can be negative. The absolute value \(|\rho|\) is the distance of a line to the origin.).
         * param max_rho Maximum value for \(\rho\) for the accumulator.
         * param rho_step Distance resolution of the accumulator.
         * param min_theta Minimum angle value of the accumulator in radians.
         * param max_theta Upper bound for the angle value of the accumulator in radians. The actual maximum
         * angle may be slightly less than max_theta, depending on the parameters min_theta and theta_step.
         * param theta_step Angle resolution of the accumulator in radians.
         */
        public static void HoughLinesPointSet(Mat point, Mat lines, int lines_max, int threshold, double min_rho, double max_rho, double rho_step, double min_theta, double max_theta, double theta_step)
        {
            if (point != null) point.ThrowIfDisposed();
            if (lines != null) lines.ThrowIfDisposed();

            imgproc_Imgproc_HoughLinesPointSet_10(point.nativeObj, lines.nativeObj, lines_max, threshold, min_rho, max_rho, rho_step, min_theta, max_theta, theta_step);


        }


        //
        // C++:  void cv::HoughCircles(Mat image, Mat& circles, int method, double dp, double minDist, double param1 = 100, double param2 = 100, int minRadius = 0, int maxRadius = 0)
        //

        /**
         * 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: :
         * INCLUDE: snippets/imgproc_HoughLinesCircles.cpp
         *
         * <b>Note:</b> 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, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number
         * to return centers only without radius search, and find the correct radius using an additional procedure.
         *
         * It also helps to smooth image a bit unless it's already soft. For example,
         * GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help.
         *
         * param image 8-bit, single-channel, grayscale input image.
         * param circles Output vector of found circles. Each vector is encoded as  3 or 4 element
         * floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .
         * param method Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT.
         * param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if
         * dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has
         * half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5,
         * unless some small very circles need to be detected.
         * param minDist Minimum distance between the centers of the detected circles. If the parameter is
         * too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is
         * too large, some circles may be missed.
         * param param1 First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT,
         * it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller).
         * Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value
         * shough normally be higher, such as 300 or normally exposed and contrasty images.
         * param param2 Second method-specific parameter. In case of #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. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure.
         * The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine.
         * If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less.
         * But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles.
         * param minRadius Minimum circle radius.
         * param maxRadius Maximum circle radius. If &lt;= 0, uses the maximum image dimension. If &lt; 0, #HOUGH_GRADIENT returns
         * centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses.
         *
         * SEE: fitEllipse, minEnclosingCircle
         */
        public static void HoughCircles(Mat image, Mat circles, int method, double dp, double minDist, double param1, double param2, int minRadius, int maxRadius)
        {
            if (image != null) image.ThrowIfDisposed();
            if (circles != null) circles.ThrowIfDisposed();

            imgproc_Imgproc_HoughCircles_10(image.nativeObj, circles.nativeObj, method, dp, minDist, param1, param2, minRadius, 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: :
         * INCLUDE: snippets/imgproc_HoughLinesCircles.cpp
         *
         * <b>Note:</b> 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, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number
         * to return centers only without radius search, and find the correct radius using an additional procedure.
         *
         * It also helps to smooth image a bit unless it's already soft. For example,
         * GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help.
         *
         * param image 8-bit, single-channel, grayscale input image.
         * param circles Output vector of found circles. Each vector is encoded as  3 or 4 element
         * floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .
         * param method Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT.
         * param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if
         * dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has
         * half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5,
         * unless some small very circles need to be detected.
         * param minDist Minimum distance between the centers of the detected circles. If the parameter is
         * too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is
         * too large, some circles may be missed.
         * param param1 First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT,
         * it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller).
         * Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value
         * shough normally be higher, such as 300 or normally exposed and contrasty images.
         * param param2 Second method-specific parameter. In case of #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. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure.
         * The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine.
         * If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less.
         * But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles.
         * param minRadius Minimum circle radius.
         * centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses.
         *
         * SEE: fitEllipse, minEnclosingCircle
         */
        public static void HoughCircles(Mat image, Mat circles, int method, double dp, double minDist, double param1, double param2, int minRadius)
        {
            if (image != null) image.ThrowIfDisposed();
            if (circles != null) circles.ThrowIfDisposed();

            imgproc_Imgproc_HoughCircles_11(image.nativeObj, circles.nativeObj, method, dp, minDist, param1, param2, minRadius);


        }

        /**
         * 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: :
         * INCLUDE: snippets/imgproc_HoughLinesCircles.cpp
         *
         * <b>Note:</b> 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, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number
         * to return centers only without radius search, and find the correct radius using an additional procedure.
         *
         * It also helps to smooth image a bit unless it's already soft. For example,
         * GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help.
         *
         * param image 8-bit, single-channel, grayscale input image.
         * param circles Output vector of found circles. Each vector is encoded as  3 or 4 element
         * floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .
         * param method Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT.
         * param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if
         * dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has
         * half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5,
         * unless some small very circles need to be detected.
         * param minDist Minimum distance between the centers of the detected circles. If the parameter is
         * too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is
         * too large, some circles may be missed.
         * param param1 First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT,
         * it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller).
         * Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value
         * shough normally be higher, such as 300 or normally exposed and contrasty images.
         * param param2 Second method-specific parameter. In case of #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. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure.
         * The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine.
         * If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less.
         * But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles.
         * centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses.
         *
         * SEE: fitEllipse, minEnclosingCircle
         */
        public static void HoughCircles(Mat image, Mat circles, int method, double dp, double minDist, double param1, double param2)
        {
            if (image != null) image.ThrowIfDisposed();
            if (circles != null) circles.ThrowIfDisposed();

            imgproc_Imgproc_HoughCircles_12(image.nativeObj, circles.nativeObj, method, dp, minDist, param1, param2);


        }

        /**
         * 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: :
         * INCLUDE: snippets/imgproc_HoughLinesCircles.cpp
         *
         * <b>Note:</b> 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, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number
         * to return centers only without radius search, and find the correct radius using an additional procedure.
         *
         * It also helps to smooth image a bit unless it's already soft. For example,
         * GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help.
         *
         * param image 8-bit, single-channel, grayscale input image.
         * param circles Output vector of found circles. Each vector is encoded as  3 or 4 element
         * floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .
         * param method Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT.
         * param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if
         * dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has
         * half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5,
         * unless some small very circles need to be detected.
         * param minDist Minimum distance between the centers of the detected circles. If the parameter is
         * too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is
         * too large, some circles may be missed.
         * param param1 First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT,
         * it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller).
         * Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value
         * shough normally be higher, such as 300 or normally exposed and contrasty images.
         * 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. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure.
         * The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine.
         * If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less.
         * But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles.
         * centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses.
         *
         * SEE: fitEllipse, minEnclosingCircle
         */
        public static void HoughCircles(Mat image, Mat circles, int method, double dp, double minDist, double param1)
        {
            if (image != null) image.ThrowIfDisposed();
            if (circles != null) circles.ThrowIfDisposed();

            imgproc_Imgproc_HoughCircles_13(image.nativeObj, circles.nativeObj, method, dp, minDist, param1);


        }

        /**
         * 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: :
         * INCLUDE: snippets/imgproc_HoughLinesCircles.cpp
         *
         * <b>Note:</b> 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, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number
         * to return centers only without radius search, and find the correct radius using an additional procedure.
         *
         * It also helps to smooth image a bit unless it's already soft. For example,
         * GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help.
         *
         * param image 8-bit, single-channel, grayscale input image.
         * param circles Output vector of found circles. Each vector is encoded as  3 or 4 element
         * floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .
         * param method Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT.
         * param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if
         * dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has
         * half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5,
         * unless some small very circles need to be detected.
         * param minDist Minimum distance between the centers of the detected circles. If the parameter is
         * too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is
         * too large, some circles may be missed.
         * it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller).
         * Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value
         * shough normally be higher, such as 300 or normally exposed and contrasty images.
         * 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. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure.
         * The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine.
         * If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less.
         * But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles.
         * centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses.
         *
         * SEE: fitEllipse, minEnclosingCircle
         */
        public static void HoughCircles(Mat image, Mat circles, int method, double dp, double minDist)
        {
            if (image != null) image.ThrowIfDisposed();
            if (circles != null) circles.ThrowIfDisposed();

            imgproc_Imgproc_HoughCircles_14(image.nativeObj, circles.nativeObj, method, dp, minDist);


        }


        //
        // C++:  void cv::erode(Mat src, Mat& dst, Mat kernel, Point anchor = Point(-1,-1), int iterations = 1, int borderType = BORDER_CONSTANT, Scalar borderValue = morphologyDefaultBorderValue())
        //

        /**
         * 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:
         *
         * \(\texttt{dst} (x,y) =  \min _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\)
         *
         * 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.
         *
         * param src input image; the number of channels can be arbitrary, but the depth should be one of
         * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param kernel structuring element used for erosion; if {code element=Mat()}, a {code 3 x 3} rectangular
         * structuring element is used. Kernel can be created using #getStructuringElement.
         * param anchor position of the anchor within the element; default value (-1, -1) means that the
         * anchor is at the element center.
         * param iterations number of times erosion is applied.
         * param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         * param borderValue border value in case of a constant border
         * SEE:  dilate, morphologyEx, getStructuringElement
         */
        public static void erode(Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_erode_10(src.nativeObj, dst.nativeObj, kernel.nativeObj, anchor.x, anchor.y, iterations, borderType, borderValue.val[0], borderValue.val[1], borderValue.val[2], borderValue.val[3]);


        }

        /**
         * 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:
         *
         * \(\texttt{dst} (x,y) =  \min _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\)
         *
         * 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.
         *
         * param src input image; the number of channels can be arbitrary, but the depth should be one of
         * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param kernel structuring element used for erosion; if {code element=Mat()}, a {code 3 x 3} rectangular
         * structuring element is used. Kernel can be created using #getStructuringElement.
         * param anchor position of the anchor within the element; default value (-1, -1) means that the
         * anchor is at the element center.
         * param iterations number of times erosion is applied.
         * param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         * SEE:  dilate, morphologyEx, getStructuringElement
         */
        public static void erode(Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_erode_11(src.nativeObj, dst.nativeObj, kernel.nativeObj, anchor.x, anchor.y, iterations, borderType);


        }

        /**
         * 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:
         *
         * \(\texttt{dst} (x,y) =  \min _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\)
         *
         * 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.
         *
         * param src input image; the number of channels can be arbitrary, but the depth should be one of
         * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param kernel structuring element used for erosion; if {code element=Mat()}, a {code 3 x 3} rectangular
         * structuring element is used. Kernel can be created using #getStructuringElement.
         * param anchor position of the anchor within the element; default value (-1, -1) means that the
         * anchor is at the element center.
         * param iterations number of times erosion is applied.
         * SEE:  dilate, morphologyEx, getStructuringElement
         */
        public static void erode(Mat src, Mat dst, Mat kernel, Point anchor, int iterations)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_erode_12(src.nativeObj, dst.nativeObj, kernel.nativeObj, anchor.x, anchor.y, 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:
         *
         * \(\texttt{dst} (x,y) =  \min _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\)
         *
         * 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.
         *
         * param src input image; the number of channels can be arbitrary, but the depth should be one of
         * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param kernel structuring element used for erosion; if {code element=Mat()}, a {code 3 x 3} rectangular
         * structuring element is used. Kernel can be created using #getStructuringElement.
         * param anchor position of the anchor within the element; default value (-1, -1) means that the
         * anchor is at the element center.
         * SEE:  dilate, morphologyEx, getStructuringElement
         */
        public static void erode(Mat src, Mat dst, Mat kernel, Point anchor)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_erode_13(src.nativeObj, dst.nativeObj, kernel.nativeObj, anchor.x, anchor.y);


        }

        /**
         * 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:
         *
         * \(\texttt{dst} (x,y) =  \min _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\)
         *
         * 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.
         *
         * param src input image; the number of channels can be arbitrary, but the depth should be one of
         * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param kernel structuring element used for erosion; if {code element=Mat()}, a {code 3 x 3} rectangular
         * structuring element is used. Kernel can be created using #getStructuringElement.
         * anchor is at the element center.
         * SEE:  dilate, morphologyEx, getStructuringElement
         */
        public static void erode(Mat src, Mat dst, Mat kernel)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_erode_14(src.nativeObj, dst.nativeObj, kernel.nativeObj);


        }


        //
        // C++:  void cv::dilate(Mat src, Mat& dst, Mat kernel, Point anchor = Point(-1,-1), int iterations = 1, int borderType = BORDER_CONSTANT, Scalar borderValue = morphologyDefaultBorderValue())
        //

        /**
         * 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:
         * \(\texttt{dst} (x,y) =  \max _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\)
         *
         * 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.
         *
         * param src input image; the number of channels can be arbitrary, but the depth should be one of
         * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param kernel structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular
         * structuring element is used. Kernel can be created using #getStructuringElement
         * param anchor position of the anchor within the element; default value (-1, -1) means that the
         * anchor is at the element center.
         * param iterations number of times dilation is applied.
         * param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
         * param borderValue border value in case of a constant border
         * SEE:  erode, morphologyEx, getStructuringElement
         */
        public static void dilate(Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_dilate_10(src.nativeObj, dst.nativeObj, kernel.nativeObj, anchor.x, anchor.y, iterations, borderType, borderValue.val[0], borderValue.val[1], borderValue.val[2], borderValue.val[3]);


        }

        /**
         * 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:
         * \(\texttt{dst} (x,y) =  \max _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\)
         *
         * 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.
         *
         * param src input image; the number of channels can be arbitrary, but the depth should be one of
         * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param kernel structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular
         * structuring element is used. Kernel can be created using #getStructuringElement
         * param anchor position of the anchor within the element; default value (-1, -1) means that the
         * anchor is at the element center.
         * param iterations number of times dilation is applied.
         * param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
         * SEE:  erode, morphologyEx, getStructuringElement
         */
        public static void dilate(Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_dilate_11(src.nativeObj, dst.nativeObj, kernel.nativeObj, anchor.x, anchor.y, iterations, borderType);


        }

        /**
         * 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:
         * \(\texttt{dst} (x,y) =  \max _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\)
         *
         * 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.
         *
         * param src input image; the number of channels can be arbitrary, but the depth should be one of
         * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param kernel structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular
         * structuring element is used. Kernel can be created using #getStructuringElement
         * param anchor position of the anchor within the element; default value (-1, -1) means that the
         * anchor is at the element center.
         * param iterations number of times dilation is applied.
         * SEE:  erode, morphologyEx, getStructuringElement
         */
        public static void dilate(Mat src, Mat dst, Mat kernel, Point anchor, int iterations)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_dilate_12(src.nativeObj, dst.nativeObj, kernel.nativeObj, anchor.x, anchor.y, 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:
         * \(\texttt{dst} (x,y) =  \max _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\)
         *
         * 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.
         *
         * param src input image; the number of channels can be arbitrary, but the depth should be one of
         * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param kernel structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular
         * structuring element is used. Kernel can be created using #getStructuringElement
         * param anchor position of the anchor within the element; default value (-1, -1) means that the
         * anchor is at the element center.
         * SEE:  erode, morphologyEx, getStructuringElement
         */
        public static void dilate(Mat src, Mat dst, Mat kernel, Point anchor)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_dilate_13(src.nativeObj, dst.nativeObj, kernel.nativeObj, anchor.x, anchor.y);


        }

        /**
         * 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:
         * \(\texttt{dst} (x,y) =  \max _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\)
         *
         * 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.
         *
         * param src input image; the number of channels can be arbitrary, but the depth should be one of
         * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst output image of the same size and type as src.
         * param kernel structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular
         * structuring element is used. Kernel can be created using #getStructuringElement
         * anchor is at the element center.
         * SEE:  erode, morphologyEx, getStructuringElement
         */
        public static void dilate(Mat src, Mat dst, Mat kernel)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_dilate_14(src.nativeObj, dst.nativeObj, kernel.nativeObj);


        }


        //
        // C++:  void cv::morphologyEx(Mat src, Mat& dst, int op, Mat kernel, Point anchor = Point(-1,-1), int iterations = 1, int borderType = BORDER_CONSTANT, Scalar borderValue = morphologyDefaultBorderValue())
        //

        /**
         * Performs advanced morphological transformations.
         *
         * The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as
         * basic operations.
         *
         * Any of the operations can be done in-place. In case of multi-channel images, each channel is
         * processed independently.
         *
         * param src Source image. The number of channels can be arbitrary. The depth should be one of
         * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst Destination image of the same size and type as source image.
         * param op Type of a morphological operation, see #MorphTypes
         * param kernel Structuring element. It can be created using #getStructuringElement.
         * param anchor Anchor position with the kernel. Negative values mean that the anchor is at the
         * kernel center.
         * param iterations Number of times erosion and dilation are applied.
         * param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         * param borderValue Border value in case of a constant border. The default value has a special
         * meaning.
         * SEE:  dilate, erode, getStructuringElement
         * <b>Note:</b> The number of iterations is the number of times erosion or dilatation operation will be applied.
         * For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply
         * successively: erode -&gt; erode -&gt; dilate -&gt; dilate (and not erode -&gt; dilate -&gt; erode -&gt; dilate).
         */
        public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_morphologyEx_10(src.nativeObj, dst.nativeObj, op, kernel.nativeObj, anchor.x, anchor.y, iterations, borderType, borderValue.val[0], borderValue.val[1], borderValue.val[2], borderValue.val[3]);


        }

        /**
         * Performs advanced morphological transformations.
         *
         * The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as
         * basic operations.
         *
         * Any of the operations can be done in-place. In case of multi-channel images, each channel is
         * processed independently.
         *
         * param src Source image. The number of channels can be arbitrary. The depth should be one of
         * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst Destination image of the same size and type as source image.
         * param op Type of a morphological operation, see #MorphTypes
         * param kernel Structuring element. It can be created using #getStructuringElement.
         * param anchor Anchor position with the kernel. Negative values mean that the anchor is at the
         * kernel center.
         * param iterations Number of times erosion and dilation are applied.
         * param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         * meaning.
         * SEE:  dilate, erode, getStructuringElement
         * <b>Note:</b> The number of iterations is the number of times erosion or dilatation operation will be applied.
         * For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply
         * successively: erode -&gt; erode -&gt; dilate -&gt; dilate (and not erode -&gt; dilate -&gt; erode -&gt; dilate).
         */
        public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_morphologyEx_11(src.nativeObj, dst.nativeObj, op, kernel.nativeObj, anchor.x, anchor.y, iterations, borderType);


        }

        /**
         * Performs advanced morphological transformations.
         *
         * The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as
         * basic operations.
         *
         * Any of the operations can be done in-place. In case of multi-channel images, each channel is
         * processed independently.
         *
         * param src Source image. The number of channels can be arbitrary. The depth should be one of
         * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst Destination image of the same size and type as source image.
         * param op Type of a morphological operation, see #MorphTypes
         * param kernel Structuring element. It can be created using #getStructuringElement.
         * param anchor Anchor position with the kernel. Negative values mean that the anchor is at the
         * kernel center.
         * param iterations Number of times erosion and dilation are applied.
         * meaning.
         * SEE:  dilate, erode, getStructuringElement
         * <b>Note:</b> The number of iterations is the number of times erosion or dilatation operation will be applied.
         * For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply
         * successively: erode -&gt; erode -&gt; dilate -&gt; dilate (and not erode -&gt; dilate -&gt; erode -&gt; dilate).
         */
        public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_morphologyEx_12(src.nativeObj, dst.nativeObj, op, kernel.nativeObj, anchor.x, anchor.y, iterations);


        }

        /**
         * Performs advanced morphological transformations.
         *
         * The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as
         * basic operations.
         *
         * Any of the operations can be done in-place. In case of multi-channel images, each channel is
         * processed independently.
         *
         * param src Source image. The number of channels can be arbitrary. The depth should be one of
         * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst Destination image of the same size and type as source image.
         * param op Type of a morphological operation, see #MorphTypes
         * param kernel Structuring element. It can be created using #getStructuringElement.
         * param anchor Anchor position with the kernel. Negative values mean that the anchor is at the
         * kernel center.
         * meaning.
         * SEE:  dilate, erode, getStructuringElement
         * <b>Note:</b> The number of iterations is the number of times erosion or dilatation operation will be applied.
         * For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply
         * successively: erode -&gt; erode -&gt; dilate -&gt; dilate (and not erode -&gt; dilate -&gt; erode -&gt; dilate).
         */
        public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_morphologyEx_13(src.nativeObj, dst.nativeObj, op, kernel.nativeObj, anchor.x, anchor.y);


        }

        /**
         * Performs advanced morphological transformations.
         *
         * The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as
         * basic operations.
         *
         * Any of the operations can be done in-place. In case of multi-channel images, each channel is
         * processed independently.
         *
         * param src Source image. The number of channels can be arbitrary. The depth should be one of
         * CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
         * param dst Destination image of the same size and type as source image.
         * param op Type of a morphological operation, see #MorphTypes
         * param kernel Structuring element. It can be created using #getStructuringElement.
         * kernel center.
         * meaning.
         * SEE:  dilate, erode, getStructuringElement
         * <b>Note:</b> The number of iterations is the number of times erosion or dilatation operation will be applied.
         * For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply
         * successively: erode -&gt; erode -&gt; dilate -&gt; dilate (and not erode -&gt; dilate -&gt; erode -&gt; dilate).
         */
        public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (kernel != null) kernel.ThrowIfDisposed();

            imgproc_Imgproc_morphologyEx_14(src.nativeObj, dst.nativeObj, op, kernel.nativeObj);


        }


        //
        // C++:  void cv::resize(Mat src, Mat& dst, Size dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR)
        //

        /**
         * 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 {code src},{code dsize},{code fx}, and {code fy}. If you want to resize src so that it fits the pre-created dst,
         * you may call the function as follows:
         * <code>
         *     // explicitly specify dsize=dst.size(); fx and fy will be computed from that.
         *     resize(src, dst, dst.size(), 0, 0, interpolation);
         * </code>
         * If you want to decimate the image by factor of 2 in each direction, you can call the function this
         * way:
         * <code>
         *     // specify fx and fy and let the function compute the destination image size.
         *     resize(src, dst, Size(), 0.5, 0.5, interpolation);
         * </code>
         * To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to
         * enlarge an image, it will generally look best with #INTER_CUBIC (slow) or #INTER_LINEAR
         * (faster but still looks OK).
         *
         * param src input image.
         * param dst output image; it has the size dsize (when it is non-zero) or the size computed from
         * src.size(), fx, and fy; the type of dst is the same as of src.
         * param dsize output image size; if it equals zero ({code None} in Python), it is computed as:
         *  \(\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\)
         *  Either dsize or both fx and fy must be non-zero.
         * param fx scale factor along the horizontal axis; when it equals 0, it is computed as
         * \(\texttt{(double)dsize.width/src.cols}\)
         * param fy scale factor along the vertical axis; when it equals 0, it is computed as
         * \(\texttt{(double)dsize.height/src.rows}\)
         * param interpolation interpolation method, see #InterpolationFlags
         *
         * SEE:  warpAffine, warpPerspective, remap
         */
        public static void resize(Mat src, Mat dst, Size dsize, double fx, double fy, int interpolation)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_resize_10(src.nativeObj, dst.nativeObj, dsize.width, dsize.height, fx, fy, 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 {code src},{code dsize},{code fx}, and {code fy}. If you want to resize src so that it fits the pre-created dst,
         * you may call the function as follows:
         * <code>
         *     // explicitly specify dsize=dst.size(); fx and fy will be computed from that.
         *     resize(src, dst, dst.size(), 0, 0, interpolation);
         * </code>
         * If you want to decimate the image by factor of 2 in each direction, you can call the function this
         * way:
         * <code>
         *     // specify fx and fy and let the function compute the destination image size.
         *     resize(src, dst, Size(), 0.5, 0.5, interpolation);
         * </code>
         * To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to
         * enlarge an image, it will generally look best with #INTER_CUBIC (slow) or #INTER_LINEAR
         * (faster but still looks OK).
         *
         * param src input image.
         * param dst output image; it has the size dsize (when it is non-zero) or the size computed from
         * src.size(), fx, and fy; the type of dst is the same as of src.
         * param dsize output image size; if it equals zero ({code None} in Python), it is computed as:
         *  \(\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\)
         *  Either dsize or both fx and fy must be non-zero.
         * param fx scale factor along the horizontal axis; when it equals 0, it is computed as
         * \(\texttt{(double)dsize.width/src.cols}\)
         * param fy scale factor along the vertical axis; when it equals 0, it is computed as
         * \(\texttt{(double)dsize.height/src.rows}\)
         *
         * SEE:  warpAffine, warpPerspective, remap
         */
        public static void resize(Mat src, Mat dst, Size dsize, double fx, double fy)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_resize_11(src.nativeObj, dst.nativeObj, dsize.width, dsize.height, fx, fy);


        }

        /**
         * 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 {code src},{code dsize},{code fx}, and {code fy}. If you want to resize src so that it fits the pre-created dst,
         * you may call the function as follows:
         * <code>
         *     // explicitly specify dsize=dst.size(); fx and fy will be computed from that.
         *     resize(src, dst, dst.size(), 0, 0, interpolation);
         * </code>
         * If you want to decimate the image by factor of 2 in each direction, you can call the function this
         * way:
         * <code>
         *     // specify fx and fy and let the function compute the destination image size.
         *     resize(src, dst, Size(), 0.5, 0.5, interpolation);
         * </code>
         * To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to
         * enlarge an image, it will generally look best with #INTER_CUBIC (slow) or #INTER_LINEAR
         * (faster but still looks OK).
         *
         * param src input image.
         * param dst output image; it has the size dsize (when it is non-zero) or the size computed from
         * src.size(), fx, and fy; the type of dst is the same as of src.
         * param dsize output image size; if it equals zero ({code None} in Python), it is computed as:
         *  \(\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\)
         *  Either dsize or both fx and fy must be non-zero.
         * param fx scale factor along the horizontal axis; when it equals 0, it is computed as
         * \(\texttt{(double)dsize.width/src.cols}\)
         * \(\texttt{(double)dsize.height/src.rows}\)
         *
         * SEE:  warpAffine, warpPerspective, remap
         */
        public static void resize(Mat src, Mat dst, Size dsize, double fx)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_resize_12(src.nativeObj, dst.nativeObj, dsize.width, dsize.height, fx);


        }

        /**
         * 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 {code src},{code dsize},{code fx}, and {code fy}. If you want to resize src so that it fits the pre-created dst,
         * you may call the function as follows:
         * <code>
         *     // explicitly specify dsize=dst.size(); fx and fy will be computed from that.
         *     resize(src, dst, dst.size(), 0, 0, interpolation);
         * </code>
         * If you want to decimate the image by factor of 2 in each direction, you can call the function this
         * way:
         * <code>
         *     // specify fx and fy and let the function compute the destination image size.
         *     resize(src, dst, Size(), 0.5, 0.5, interpolation);
         * </code>
         * To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to
         * enlarge an image, it will generally look best with #INTER_CUBIC (slow) or #INTER_LINEAR
         * (faster but still looks OK).
         *
         * param src input image.
         * param dst output image; it has the size dsize (when it is non-zero) or the size computed from
         * src.size(), fx, and fy; the type of dst is the same as of src.
         * param dsize output image size; if it equals zero ({code None} in Python), it is computed as:
         *  \(\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\)
         *  Either dsize or both fx and fy must be non-zero.
         * \(\texttt{(double)dsize.width/src.cols}\)
         * \(\texttt{(double)dsize.height/src.rows}\)
         *
         * SEE:  warpAffine, warpPerspective, remap
         */
        public static void resize(Mat src, Mat dst, Size dsize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_resize_13(src.nativeObj, dst.nativeObj, dsize.width, dsize.height);


        }


        //
        // C++:  void cv::warpAffine(Mat src, Mat& dst, Mat M, Size dsize, int flags = INTER_LINEAR, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar())
        //

        /**
         * Applies an affine transformation to an image.
         *
         * The function warpAffine transforms the source image using the specified matrix:
         *
         * \(\texttt{dst} (x,y) =  \texttt{src} ( \texttt{M} _{11} x +  \texttt{M} _{12} y +  \texttt{M} _{13}, \texttt{M} _{21} x +  \texttt{M} _{22} y +  \texttt{M} _{23})\)
         *
         * 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.
         *
         * param src input image.
         * param dst output image that has the size dsize and the same type as src .
         * param M \(2\times 3\) transformation matrix.
         * param dsize size of the output image.
         * param flags combination of interpolation methods (see #InterpolationFlags) and the optional
         * flag #WARP_INVERSE_MAP that means that M is the inverse transformation (
         * \(\texttt{dst}\rightarrow\texttt{src}\) ).
         * param borderMode pixel extrapolation method (see #BorderTypes); when
         * borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to
         * the "outliers" in the source image are not modified by the function.
         * param borderValue value used in case of a constant border; by default, it is 0.
         *
         * SEE:  warpPerspective, resize, remap, getRectSubPix, transform
         */
        public static void warpAffine(Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (M != null) M.ThrowIfDisposed();

            imgproc_Imgproc_warpAffine_10(src.nativeObj, dst.nativeObj, M.nativeObj, dsize.width, dsize.height, flags, borderMode, borderValue.val[0], borderValue.val[1], borderValue.val[2], borderValue.val[3]);


        }

        /**
         * Applies an affine transformation to an image.
         *
         * The function warpAffine transforms the source image using the specified matrix:
         *
         * \(\texttt{dst} (x,y) =  \texttt{src} ( \texttt{M} _{11} x +  \texttt{M} _{12} y +  \texttt{M} _{13}, \texttt{M} _{21} x +  \texttt{M} _{22} y +  \texttt{M} _{23})\)
         *
         * 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.
         *
         * param src input image.
         * param dst output image that has the size dsize and the same type as src .
         * param M \(2\times 3\) transformation matrix.
         * param dsize size of the output image.
         * param flags combination of interpolation methods (see #InterpolationFlags) and the optional
         * flag #WARP_INVERSE_MAP that means that M is the inverse transformation (
         * \(\texttt{dst}\rightarrow\texttt{src}\) ).
         * param borderMode pixel extrapolation method (see #BorderTypes); when
         * borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to
         * the "outliers" in the source image are not modified by the function.
         *
         * SEE:  warpPerspective, resize, remap, getRectSubPix, transform
         */
        public static void warpAffine(Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (M != null) M.ThrowIfDisposed();

            imgproc_Imgproc_warpAffine_11(src.nativeObj, dst.nativeObj, M.nativeObj, dsize.width, dsize.height, flags, borderMode);


        }

        /**
         * Applies an affine transformation to an image.
         *
         * The function warpAffine transforms the source image using the specified matrix:
         *
         * \(\texttt{dst} (x,y) =  \texttt{src} ( \texttt{M} _{11} x +  \texttt{M} _{12} y +  \texttt{M} _{13}, \texttt{M} _{21} x +  \texttt{M} _{22} y +  \texttt{M} _{23})\)
         *
         * 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.
         *
         * param src input image.
         * param dst output image that has the size dsize and the same type as src .
         * param M \(2\times 3\) transformation matrix.
         * param dsize size of the output image.
         * param flags combination of interpolation methods (see #InterpolationFlags) and the optional
         * flag #WARP_INVERSE_MAP that means that M is the inverse transformation (
         * \(\texttt{dst}\rightarrow\texttt{src}\) ).
         * 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.
         *
         * SEE:  warpPerspective, resize, remap, getRectSubPix, transform
         */
        public static void warpAffine(Mat src, Mat dst, Mat M, Size dsize, int flags)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (M != null) M.ThrowIfDisposed();

            imgproc_Imgproc_warpAffine_12(src.nativeObj, dst.nativeObj, M.nativeObj, dsize.width, dsize.height, flags);


        }

        /**
         * Applies an affine transformation to an image.
         *
         * The function warpAffine transforms the source image using the specified matrix:
         *
         * \(\texttt{dst} (x,y) =  \texttt{src} ( \texttt{M} _{11} x +  \texttt{M} _{12} y +  \texttt{M} _{13}, \texttt{M} _{21} x +  \texttt{M} _{22} y +  \texttt{M} _{23})\)
         *
         * 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.
         *
         * param src input image.
         * param dst output image that has the size dsize and the same type as src .
         * param M \(2\times 3\) transformation matrix.
         * param dsize size of the output image.
         * flag #WARP_INVERSE_MAP that means that M is the inverse transformation (
         * \(\texttt{dst}\rightarrow\texttt{src}\) ).
         * 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.
         *
         * SEE:  warpPerspective, resize, remap, getRectSubPix, transform
         */
        public static void warpAffine(Mat src, Mat dst, Mat M, Size dsize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (M != null) M.ThrowIfDisposed();

            imgproc_Imgproc_warpAffine_13(src.nativeObj, dst.nativeObj, M.nativeObj, dsize.width, dsize.height);


        }


        //
        // C++:  void cv::warpPerspective(Mat src, Mat& dst, Mat M, Size dsize, int flags = INTER_LINEAR, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar())
        //

        /**
         * Applies a perspective transformation to an image.
         *
         * The function warpPerspective transforms the source image using the specified matrix:
         *
         * \(\texttt{dst} (x,y) =  \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} ,
         *      \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\)
         *
         * when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert
         * and then put in the formula above instead of M. The function cannot operate in-place.
         *
         * param src input image.
         * param dst output image that has the size dsize and the same type as src .
         * param M \(3\times 3\) transformation matrix.
         * param dsize size of the output image.
         * param flags combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the
         * optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation (
         * \(\texttt{dst}\rightarrow\texttt{src}\) ).
         * param borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
         * param borderValue value used in case of a constant border; by default, it equals 0.
         *
         * SEE:  warpAffine, resize, remap, getRectSubPix, perspectiveTransform
         */
        public static void warpPerspective(Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (M != null) M.ThrowIfDisposed();

            imgproc_Imgproc_warpPerspective_10(src.nativeObj, dst.nativeObj, M.nativeObj, dsize.width, dsize.height, flags, borderMode, borderValue.val[0], borderValue.val[1], borderValue.val[2], borderValue.val[3]);


        }

        /**
         * Applies a perspective transformation to an image.
         *
         * The function warpPerspective transforms the source image using the specified matrix:
         *
         * \(\texttt{dst} (x,y) =  \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} ,
         *      \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\)
         *
         * when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert
         * and then put in the formula above instead of M. The function cannot operate in-place.
         *
         * param src input image.
         * param dst output image that has the size dsize and the same type as src .
         * param M \(3\times 3\) transformation matrix.
         * param dsize size of the output image.
         * param flags combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the
         * optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation (
         * \(\texttt{dst}\rightarrow\texttt{src}\) ).
         * param borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
         *
         * SEE:  warpAffine, resize, remap, getRectSubPix, perspectiveTransform
         */
        public static void warpPerspective(Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (M != null) M.ThrowIfDisposed();

            imgproc_Imgproc_warpPerspective_11(src.nativeObj, dst.nativeObj, M.nativeObj, dsize.width, dsize.height, flags, borderMode);


        }

        /**
         * Applies a perspective transformation to an image.
         *
         * The function warpPerspective transforms the source image using the specified matrix:
         *
         * \(\texttt{dst} (x,y) =  \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} ,
         *      \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\)
         *
         * when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert
         * and then put in the formula above instead of M. The function cannot operate in-place.
         *
         * param src input image.
         * param dst output image that has the size dsize and the same type as src .
         * param M \(3\times 3\) transformation matrix.
         * param dsize size of the output image.
         * param flags combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the
         * optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation (
         * \(\texttt{dst}\rightarrow\texttt{src}\) ).
         *
         * SEE:  warpAffine, resize, remap, getRectSubPix, perspectiveTransform
         */
        public static void warpPerspective(Mat src, Mat dst, Mat M, Size dsize, int flags)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (M != null) M.ThrowIfDisposed();

            imgproc_Imgproc_warpPerspective_12(src.nativeObj, dst.nativeObj, M.nativeObj, dsize.width, dsize.height, flags);


        }

        /**
         * Applies a perspective transformation to an image.
         *
         * The function warpPerspective transforms the source image using the specified matrix:
         *
         * \(\texttt{dst} (x,y) =  \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} ,
         *      \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\)
         *
         * when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert
         * and then put in the formula above instead of M. The function cannot operate in-place.
         *
         * param src input image.
         * param dst output image that has the size dsize and the same type as src .
         * param M \(3\times 3\) transformation matrix.
         * param dsize size of the output image.
         * optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation (
         * \(\texttt{dst}\rightarrow\texttt{src}\) ).
         *
         * SEE:  warpAffine, resize, remap, getRectSubPix, perspectiveTransform
         */
        public static void warpPerspective(Mat src, Mat dst, Mat M, Size dsize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (M != null) M.ThrowIfDisposed();

            imgproc_Imgproc_warpPerspective_13(src.nativeObj, dst.nativeObj, M.nativeObj, dsize.width, dsize.height);


        }


        //
        // C++:  void cv::remap(Mat src, Mat& dst, Mat map1, Mat map2, int interpolation, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar())
        //

        /**
         * Applies a generic geometrical transformation to an image.
         *
         * The function remap transforms the source image using the specified map:
         *
         * \(\texttt{dst} (x,y) =  \texttt{src} (map_x(x,y),map_y(x,y))\)
         *
         * 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.
         *
         * param src Source image.
         * param dst Destination image. It has the same size as map1 and the same type as src .
         * param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 ,
         * CV_32FC1, or CV_32FC2. See #convertMaps for details on converting a floating point
         * representation to fixed-point for speed.
         * param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map
         * if map1 is (x,y) points), respectively.
         * param interpolation Interpolation method (see #InterpolationFlags). The methods #INTER_AREA
         * and #INTER_LINEAR_EXACT are not supported by this function.
         * param borderMode Pixel extrapolation method (see #BorderTypes). When
         * borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that
         * corresponds to the "outliers" in the source image are not modified by the function.
         * param borderValue Value used in case of a constant border. By default, it is 0.
         * <b>Note:</b>
         * Due to current implementation limitations the size of an input and output images should be less than 32767x32767.
         */
        public static void remap(Mat src, Mat dst, Mat map1, Mat map2, int interpolation, int borderMode, Scalar borderValue)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (map1 != null) map1.ThrowIfDisposed();
            if (map2 != null) map2.ThrowIfDisposed();

            imgproc_Imgproc_remap_10(src.nativeObj, dst.nativeObj, map1.nativeObj, map2.nativeObj, interpolation, borderMode, borderValue.val[0], borderValue.val[1], borderValue.val[2], borderValue.val[3]);


        }

        /**
         * Applies a generic geometrical transformation to an image.
         *
         * The function remap transforms the source image using the specified map:
         *
         * \(\texttt{dst} (x,y) =  \texttt{src} (map_x(x,y),map_y(x,y))\)
         *
         * 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.
         *
         * param src Source image.
         * param dst Destination image. It has the same size as map1 and the same type as src .
         * param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 ,
         * CV_32FC1, or CV_32FC2. See #convertMaps for details on converting a floating point
         * representation to fixed-point for speed.
         * param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map
         * if map1 is (x,y) points), respectively.
         * param interpolation Interpolation method (see #InterpolationFlags). The methods #INTER_AREA
         * and #INTER_LINEAR_EXACT are not supported by this function.
         * param borderMode Pixel extrapolation method (see #BorderTypes). When
         * borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that
         * corresponds to the "outliers" in the source image are not modified by the function.
         * <b>Note:</b>
         * Due to current implementation limitations the size of an input and output images should be less than 32767x32767.
         */
        public static void remap(Mat src, Mat dst, Mat map1, Mat map2, int interpolation, int borderMode)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (map1 != null) map1.ThrowIfDisposed();
            if (map2 != null) map2.ThrowIfDisposed();

            imgproc_Imgproc_remap_11(src.nativeObj, dst.nativeObj, map1.nativeObj, map2.nativeObj, interpolation, borderMode);


        }

        /**
         * Applies a generic geometrical transformation to an image.
         *
         * The function remap transforms the source image using the specified map:
         *
         * \(\texttt{dst} (x,y) =  \texttt{src} (map_x(x,y),map_y(x,y))\)
         *
         * 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.
         *
         * param src Source image.
         * param dst Destination image. It has the same size as map1 and the same type as src .
         * param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 ,
         * CV_32FC1, or CV_32FC2. See #convertMaps for details on converting a floating point
         * representation to fixed-point for speed.
         * param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map
         * if map1 is (x,y) points), respectively.
         * param interpolation Interpolation method (see #InterpolationFlags). The methods #INTER_AREA
         * and #INTER_LINEAR_EXACT are not supported by this function.
         * 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.
         * <b>Note:</b>
         * Due to current implementation limitations the size of an input and output images should be less than 32767x32767.
         */
        public static void remap(Mat src, Mat dst, Mat map1, Mat map2, int interpolation)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (map1 != null) map1.ThrowIfDisposed();
            if (map2 != null) map2.ThrowIfDisposed();

            imgproc_Imgproc_remap_12(src.nativeObj, dst.nativeObj, map1.nativeObj, map2.nativeObj, interpolation);


        }


        //
        // C++:  void cv::convertMaps(Mat map1, Mat map2, Mat& dstmap1, Mat& dstmap2, int dstmap1type, bool nninterpolation = false)
        //

        /**
         * 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()) \(\rightarrow\) (dstmap1.type(), dstmap2.type()) ) are
         * supported:
         *
         * <ul>
         *   <li>
         *  \(\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). 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.
         *   </li>
         * </ul>
         *
         * <ul>
         *   <li>
         *  \(\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). The same as above but
         * the original maps are stored in one 2-channel matrix.
         *   </li>
         * </ul>
         *
         * <ul>
         *   <li>
         *  Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same
         * as the originals.
         *   </li>
         * </ul>
         *
         * param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 .
         * param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix),
         * respectively.
         * param dstmap1 The first output map that has the type dstmap1type and the same size as src .
         * param dstmap2 The second output map.
         * param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or
         * CV_32FC2 .
         * param nninterpolation Flag indicating whether the fixed-point maps are used for the
         * nearest-neighbor or for a more complex interpolation.
         *
         * SEE:  remap, undistort, initUndistortRectifyMap
         */
        public static void convertMaps(Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type, bool nninterpolation)
        {
            if (map1 != null) map1.ThrowIfDisposed();
            if (map2 != null) map2.ThrowIfDisposed();
            if (dstmap1 != null) dstmap1.ThrowIfDisposed();
            if (dstmap2 != null) dstmap2.ThrowIfDisposed();

            imgproc_Imgproc_convertMaps_10(map1.nativeObj, map2.nativeObj, dstmap1.nativeObj, dstmap2.nativeObj, dstmap1type, 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()) \(\rightarrow\) (dstmap1.type(), dstmap2.type()) ) are
         * supported:
         *
         * <ul>
         *   <li>
         *  \(\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). 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.
         *   </li>
         * </ul>
         *
         * <ul>
         *   <li>
         *  \(\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). The same as above but
         * the original maps are stored in one 2-channel matrix.
         *   </li>
         * </ul>
         *
         * <ul>
         *   <li>
         *  Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same
         * as the originals.
         *   </li>
         * </ul>
         *
         * param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 .
         * param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix),
         * respectively.
         * param dstmap1 The first output map that has the type dstmap1type and the same size as src .
         * param dstmap2 The second output map.
         * param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or
         * CV_32FC2 .
         * nearest-neighbor or for a more complex interpolation.
         *
         * SEE:  remap, undistort, initUndistortRectifyMap
         */
        public static void convertMaps(Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type)
        {
            if (map1 != null) map1.ThrowIfDisposed();
            if (map2 != null) map2.ThrowIfDisposed();
            if (dstmap1 != null) dstmap1.ThrowIfDisposed();
            if (dstmap2 != null) dstmap2.ThrowIfDisposed();

            imgproc_Imgproc_convertMaps_11(map1.nativeObj, map2.nativeObj, dstmap1.nativeObj, dstmap2.nativeObj, dstmap1type);


        }


        //
        // C++:  Mat cv::getRotationMatrix2D(Point2f center, double angle, double scale)
        //

        /**
         * Calculates an affine matrix of 2D rotation.
         *
         * The function calculates the following matrix:
         *
         * \(\begin{bmatrix} \alpha &amp;  \beta &amp; (1- \alpha )  \cdot \texttt{center.x} -  \beta \cdot \texttt{center.y} \\ - \beta &amp;  \alpha &amp;  \beta \cdot \texttt{center.x} + (1- \alpha )  \cdot \texttt{center.y} \end{bmatrix}\)
         *
         * where
         *
         * \(\begin{array}{l} \alpha =  \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta =  \texttt{scale} \cdot \sin \texttt{angle} \end{array}\)
         *
         * The transformation maps the rotation center to itself. If this is not the target, adjust the shift.
         *
         * param center Center of the rotation in the source image.
         * param angle Rotation angle in degrees. Positive values mean counter-clockwise rotation (the
         * coordinate origin is assumed to be the top-left corner).
         * param scale Isotropic scale factor.
         *
         * SEE:  getAffineTransform, warpAffine, transform
         * return automatically generated
         */
        public static Mat getRotationMatrix2D(Point center, double angle, double scale)
        {


            return new Mat(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_getRotationMatrix2D_10(center.x, center.y, angle, scale)));


        }


        //
        // C++:  void cv::invertAffineTransform(Mat M, Mat& iM)
        //

        /**
         * Inverts an affine transformation.
         *
         * The function computes an inverse affine transformation represented by \(2 \times 3\) matrix M:
         *
         * \(\begin{bmatrix} a_{11} &amp; a_{12} &amp; b_1  \\ a_{21} &amp; a_{22} &amp; b_2 \end{bmatrix}\)
         *
         * The result is also a \(2 \times 3\) matrix of the same type as M.
         *
         * param M Original affine transformation.
         * param iM Output reverse affine transformation.
         */
        public static void invertAffineTransform(Mat M, Mat iM)
        {
            if (M != null) M.ThrowIfDisposed();
            if (iM != null) iM.ThrowIfDisposed();

            imgproc_Imgproc_invertAffineTransform_10(M.nativeObj, iM.nativeObj);


        }


        //
        // C++:  Mat cv::getPerspectiveTransform(Mat src, Mat dst, int solveMethod = DECOMP_LU)
        //

        /**
         * Calculates a perspective transform from four pairs of the corresponding points.
         *
         * The function calculates the \(3 \times 3\) matrix of a perspective transform so that:
         *
         * \(\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\)
         *
         * where
         *
         * \(dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\)
         *
         * param src Coordinates of quadrangle vertices in the source image.
         * param dst Coordinates of the corresponding quadrangle vertices in the destination image.
         * param solveMethod method passed to cv::solve (#DecompTypes)
         *
         * SEE:  findHomography, warpPerspective, perspectiveTransform
         * return automatically generated
         */
        public static Mat getPerspectiveTransform(Mat src, Mat dst, int solveMethod)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            return new Mat(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_getPerspectiveTransform_10(src.nativeObj, dst.nativeObj, solveMethod)));


        }

        /**
         * Calculates a perspective transform from four pairs of the corresponding points.
         *
         * The function calculates the \(3 \times 3\) matrix of a perspective transform so that:
         *
         * \(\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\)
         *
         * where
         *
         * \(dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\)
         *
         * param src Coordinates of quadrangle vertices in the source image.
         * param dst Coordinates of the corresponding quadrangle vertices in the destination image.
         *
         * SEE:  findHomography, warpPerspective, perspectiveTransform
         * return automatically generated
         */
        public static Mat getPerspectiveTransform(Mat src, Mat dst)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            return new Mat(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_getPerspectiveTransform_11(src.nativeObj, dst.nativeObj)));


        }


        //
        // C++:  Mat cv::getAffineTransform(vector_Point2f src, vector_Point2f dst)
        //

        public static Mat getAffineTransform(MatOfPoint2f src, MatOfPoint2f dst)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            Mat src_mat = src;
            Mat dst_mat = dst;
            return new Mat(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_getAffineTransform_10(src_mat.nativeObj, dst_mat.nativeObj)));


        }


        //
        // C++:  void cv::getRectSubPix(Mat image, Size patchSize, Point2f center, Mat& patch, int patchType = -1)
        //

        /**
         * Retrieves a pixel rectangle from an image with sub-pixel accuracy.
         *
         * The function getRectSubPix extracts pixels from src:
         *
         * \(patch(x, y) = src(x +  \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y +  \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\)
         *
         * where the values of the pixels at non-integer coordinates are retrieved using bilinear
         * interpolation. Every channel of multi-channel images is processed independently. Also
         * the image should be a single channel or three channel image. While the center of the
         * rectangle must be inside the image, parts of the rectangle may be outside.
         *
         * param image Source image.
         * param patchSize Size of the extracted patch.
         * param center Floating point coordinates of the center of the extracted rectangle within the
         * source image. The center must be inside the image.
         * param patch Extracted patch that has the size patchSize and the same number of channels as src .
         * param patchType Depth of the extracted pixels. By default, they have the same depth as src .
         *
         * SEE:  warpAffine, warpPerspective
         */
        public static void getRectSubPix(Mat image, Size patchSize, Point center, Mat patch, int patchType)
        {
            if (image != null) image.ThrowIfDisposed();
            if (patch != null) patch.ThrowIfDisposed();

            imgproc_Imgproc_getRectSubPix_10(image.nativeObj, patchSize.width, patchSize.height, center.x, center.y, patch.nativeObj, patchType);


        }

        /**
         * Retrieves a pixel rectangle from an image with sub-pixel accuracy.
         *
         * The function getRectSubPix extracts pixels from src:
         *
         * \(patch(x, y) = src(x +  \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y +  \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\)
         *
         * where the values of the pixels at non-integer coordinates are retrieved using bilinear
         * interpolation. Every channel of multi-channel images is processed independently. Also
         * the image should be a single channel or three channel image. While the center of the
         * rectangle must be inside the image, parts of the rectangle may be outside.
         *
         * param image Source image.
         * param patchSize Size of the extracted patch.
         * param center Floating point coordinates of the center of the extracted rectangle within the
         * source image. The center must be inside the image.
         * param patch Extracted patch that has the size patchSize and the same number of channels as src .
         *
         * SEE:  warpAffine, warpPerspective
         */
        public static void getRectSubPix(Mat image, Size patchSize, Point center, Mat patch)
        {
            if (image != null) image.ThrowIfDisposed();
            if (patch != null) patch.ThrowIfDisposed();

            imgproc_Imgproc_getRectSubPix_11(image.nativeObj, patchSize.width, patchSize.height, center.x, center.y, patch.nativeObj);


        }


        //
        // C++:  void cv::logPolar(Mat src, Mat& dst, Point2f center, double M, int flags)
        //

        /**
         * Remaps an image to semilog-polar coordinates space.
         *
         * deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG);
         *
         *
         * Transform the source image using the following transformation (See REF: polar_remaps_reference_image "Polar remaps reference image d)"):
         * \(\begin{array}{l}
         *   dst( \rho , \phi ) = src(x,y) \\
         *   dst.size() \leftarrow src.size()
         * \end{array}\)
         *
         * where
         * \(\begin{array}{l}
         *   I = (dx,dy) = (x - center.x,y - center.y) \\
         *   \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\
         *   \phi = Kangle \cdot \texttt{angle} (I) \\
         * \end{array}\)
         *
         * and
         * \(\begin{array}{l}
         *   M = src.cols / log_e(maxRadius) \\
         *   Kangle = src.rows / 2\Pi \\
         * \end{array}\)
         *
         * The function emulates the human "foveal" vision and can be used for fast scale and
         * rotation-invariant template matching, for object tracking and so forth.
         * param src Source image
         * param dst Destination image. It will have same size and type as src.
         * param center The transformation center; where the output precision is maximal
         * param M Magnitude scale parameter. It determines the radius of the bounding circle to transform too.
         * param flags A combination of interpolation methods, see #InterpolationFlags
         *
         * <b>Note:</b>
         * <ul>
         *   <li>
         *    The function can not operate in-place.
         *   </li>
         *   <li>
         *    To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
         *   </li>
         * </ul>
         *
         * SEE: cv::linearPolar
         */
        [Obsolete("This method is deprecated.")]
        public static void logPolar(Mat src, Mat dst, Point center, double M, int flags)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_logPolar_10(src.nativeObj, dst.nativeObj, center.x, center.y, M, flags);


        }


        //
        // C++:  void cv::linearPolar(Mat src, Mat& dst, Point2f center, double maxRadius, int flags)
        //

        /**
         * Remaps an image to polar coordinates space.
         *
         * deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags)
         *
         *
         * Transform the source image using the following transformation (See REF: polar_remaps_reference_image "Polar remaps reference image c)"):
         * \(\begin{array}{l}
         *   dst( \rho , \phi ) = src(x,y) \\
         *   dst.size() \leftarrow src.size()
         * \end{array}\)
         *
         * where
         * \(\begin{array}{l}
         *   I = (dx,dy) = (x - center.x,y - center.y) \\
         *   \rho = Kmag \cdot \texttt{magnitude} (I) ,\\
         *   \phi = angle \cdot \texttt{angle} (I)
         * \end{array}\)
         *
         * and
         * \(\begin{array}{l}
         *   Kx = src.cols / maxRadius \\
         *   Ky = src.rows / 2\Pi
         * \end{array}\)
         *
         *
         * param src Source image
         * param dst Destination image. It will have same size and type as src.
         * param center The transformation center;
         * param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too.
         * param flags A combination of interpolation methods, see #InterpolationFlags
         *
         * <b>Note:</b>
         * <ul>
         *   <li>
         *    The function can not operate in-place.
         *   </li>
         *   <li>
         *    To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
         *   </li>
         * </ul>
         *
         * SEE: cv::logPolar
         */
        [Obsolete("This method is deprecated.")]
        public static void linearPolar(Mat src, Mat dst, Point center, double maxRadius, int flags)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_linearPolar_10(src.nativeObj, dst.nativeObj, center.x, center.y, maxRadius, flags);


        }


        //
        // C++:  void cv::warpPolar(Mat src, Mat& dst, Size dsize, Point2f center, double maxRadius, int flags)
        //

        /**
         * Remaps an image to polar or semilog-polar coordinates space
         *
         *  polar_remaps_reference_image
         * ![Polar remaps reference](pics/polar_remap_doc.png)
         *
         * Transform the source image using the following transformation:
         * \(
         * dst(\rho , \phi ) = src(x,y)
         * \)
         *
         * where
         * \(
         * \begin{array}{l}
         * \vec{I} = (x - center.x, \;y - center.y) \\
         * \phi = Kangle \cdot \texttt{angle} (\vec{I}) \\
         * \rho = \left\{\begin{matrix}
         * Klin \cdot \texttt{magnitude} (\vec{I}) &amp; default \\
         * Klog \cdot log_e(\texttt{magnitude} (\vec{I})) &amp; if \; semilog \\
         * \end{matrix}\right.
         * \end{array}
         * \)
         *
         * and
         * \(
         * \begin{array}{l}
         * Kangle = dsize.height / 2\Pi \\
         * Klin = dsize.width / maxRadius \\
         * Klog = dsize.width / log_e(maxRadius) \\
         * \end{array}
         * \)
         *
         *
         * \par Linear vs semilog mapping
         *
         * Polar mapping can be linear or semi-log. Add one of #WarpPolarMode to {code flags} to specify the polar mapping mode.
         *
         * Linear is the default mode.
         *
         * The semilog mapping emulates the human "foveal" vision that permit very high acuity on the line of sight (central vision)
         * in contrast to peripheral vision where acuity is minor.
         *
         * \par Option on {code dsize}:
         *
         * <ul>
         *   <li>
         *  if both values in {code dsize &lt;=0 } (default),
         * the destination image will have (almost) same area of source bounding circle:
         * \(\begin{array}{l}
         * dsize.area  \leftarrow (maxRadius^2 \cdot \Pi) \\
         * dsize.width = \texttt{cvRound}(maxRadius) \\
         * dsize.height = \texttt{cvRound}(maxRadius \cdot \Pi) \\
         * \end{array}\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  if only {code dsize.height &lt;= 0},
         * the destination image area will be proportional to the bounding circle area but scaled by {code Kx * Kx}:
         * \(\begin{array}{l}
         * dsize.height = \texttt{cvRound}(dsize.width \cdot \Pi) \\
         * \end{array}
         * \)
         *   </li>
         * </ul>
         *
         * <ul>
         *   <li>
         *  if both values in {code dsize &gt; 0 },
         * the destination image will have the given size therefore the area of the bounding circle will be scaled to {code dsize}.
         *   </li>
         * </ul>
         *
         *
         * \par Reverse mapping
         *
         * You can get reverse mapping adding #WARP_INVERSE_MAP to {code flags}
         * \snippet polar_transforms.cpp InverseMap
         *
         * In addiction, to calculate the original coordinate from a polar mapped coordinate \((rho, phi)-&gt;(x, y)\):
         * \snippet polar_transforms.cpp InverseCoordinate
         *
         * param src Source image.
         * param dst Destination image. It will have same type as src.
         * param dsize The destination image size (see description for valid options).
         * param center The transformation center.
         * param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too.
         * param flags A combination of interpolation methods, #InterpolationFlags + #WarpPolarMode.
         * <ul>
         *   <li>
         *              Add #WARP_POLAR_LINEAR to select linear polar mapping (default)
         *   </li>
         *   <li>
         *              Add #WARP_POLAR_LOG to select semilog polar mapping
         *   </li>
         *   <li>
         *              Add #WARP_INVERSE_MAP for reverse mapping.
         *   </li>
         * </ul>
         * <b>Note:</b>
         * <ul>
         *   <li>
         *   The function can not operate in-place.
         *   </li>
         *   <li>
         *   To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
         *   </li>
         *   <li>
         *   This function uses #remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767.
         *   </li>
         * </ul>
         *
         * SEE: cv::remap
         */
        public static void warpPolar(Mat src, Mat dst, Size dsize, Point center, double maxRadius, int flags)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_warpPolar_10(src.nativeObj, dst.nativeObj, dsize.width, dsize.height, center.x, center.y, maxRadius, flags);


        }


        //
        // C++:  void cv::integral(Mat src, Mat& sum, Mat& sqsum, Mat& tilted, int sdepth = -1, int sqdepth = -1)
        //

        /**
         * Calculates the integral of an image.
         *
         * The function calculates one or more integral images for the source image as follows:
         *
         * \(\texttt{sum} (X,Y) =  \sum _{x&lt;X,y&lt;Y}  \texttt{image} (x,y)\)
         *
         * \(\texttt{sqsum} (X,Y) =  \sum _{x&lt;X,y&lt;Y}  \texttt{image} (x,y)^2\)
         *
         * \(\texttt{tilted} (X,Y) =  \sum _{y&lt;Y,abs(x-X+1) \leq Y-y-1}  \texttt{image} (x,y)\)
         *
         * Using these integral images, you can calculate sum, mean, and standard deviation over a specific
         * up-right or rotated rectangular region of the image in a constant time, for example:
         *
         * \(\sum _{x_1 \leq x &lt; x_2,  \, y_1  \leq y &lt; y_2}  \texttt{image} (x,y) =  \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\)
         *
         * 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(4,4,3,2) 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 .
         *
         * ![integral calculation example](pics/integral.png)
         *
         * param src input image as \(W \times H\), 8-bit or floating-point (32f or 64f).
         * param sum integral image as \((W+1)\times (H+1)\) , 32-bit integer or floating-point (32f or 64f).
         * param sqsum integral image for squared pixel values; it is \((W+1)\times (H+1)\), double-precision
         * floating-point (64f) array.
         * param tilted integral for the image rotated by 45 degrees; it is \((W+1)\times (H+1)\) array with
         * the same data type as sum.
         * param sdepth desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or
         * CV_64F.
         * param sqdepth desired depth of the integral image of squared pixel values, CV_32F or CV_64F.
         */
        public static void integral3(Mat src, Mat sum, Mat sqsum, Mat tilted, int sdepth, int sqdepth)
        {
            if (src != null) src.ThrowIfDisposed();
            if (sum != null) sum.ThrowIfDisposed();
            if (sqsum != null) sqsum.ThrowIfDisposed();
            if (tilted != null) tilted.ThrowIfDisposed();

            imgproc_Imgproc_integral3_10(src.nativeObj, sum.nativeObj, sqsum.nativeObj, tilted.nativeObj, sdepth, sqdepth);


        }

        /**
         * Calculates the integral of an image.
         *
         * The function calculates one or more integral images for the source image as follows:
         *
         * \(\texttt{sum} (X,Y) =  \sum _{x&lt;X,y&lt;Y}  \texttt{image} (x,y)\)
         *
         * \(\texttt{sqsum} (X,Y) =  \sum _{x&lt;X,y&lt;Y}  \texttt{image} (x,y)^2\)
         *
         * \(\texttt{tilted} (X,Y) =  \sum _{y&lt;Y,abs(x-X+1) \leq Y-y-1}  \texttt{image} (x,y)\)
         *
         * Using these integral images, you can calculate sum, mean, and standard deviation over a specific
         * up-right or rotated rectangular region of the image in a constant time, for example:
         *
         * \(\sum _{x_1 \leq x &lt; x_2,  \, y_1  \leq y &lt; y_2}  \texttt{image} (x,y) =  \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\)
         *
         * 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(4,4,3,2) 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 .
         *
         * ![integral calculation example](pics/integral.png)
         *
         * param src input image as \(W \times H\), 8-bit or floating-point (32f or 64f).
         * param sum integral image as \((W+1)\times (H+1)\) , 32-bit integer or floating-point (32f or 64f).
         * param sqsum integral image for squared pixel values; it is \((W+1)\times (H+1)\), double-precision
         * floating-point (64f) array.
         * param tilted integral for the image rotated by 45 degrees; it is \((W+1)\times (H+1)\) array with
         * the same data type as sum.
         * param sdepth desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or
         * CV_64F.
         */
        public static void integral3(Mat src, Mat sum, Mat sqsum, Mat tilted, int sdepth)
        {
            if (src != null) src.ThrowIfDisposed();
            if (sum != null) sum.ThrowIfDisposed();
            if (sqsum != null) sqsum.ThrowIfDisposed();
            if (tilted != null) tilted.ThrowIfDisposed();

            imgproc_Imgproc_integral3_11(src.nativeObj, sum.nativeObj, sqsum.nativeObj, tilted.nativeObj, sdepth);


        }

        /**
         * Calculates the integral of an image.
         *
         * The function calculates one or more integral images for the source image as follows:
         *
         * \(\texttt{sum} (X,Y) =  \sum _{x&lt;X,y&lt;Y}  \texttt{image} (x,y)\)
         *
         * \(\texttt{sqsum} (X,Y) =  \sum _{x&lt;X,y&lt;Y}  \texttt{image} (x,y)^2\)
         *
         * \(\texttt{tilted} (X,Y) =  \sum _{y&lt;Y,abs(x-X+1) \leq Y-y-1}  \texttt{image} (x,y)\)
         *
         * Using these integral images, you can calculate sum, mean, and standard deviation over a specific
         * up-right or rotated rectangular region of the image in a constant time, for example:
         *
         * \(\sum _{x_1 \leq x &lt; x_2,  \, y_1  \leq y &lt; y_2}  \texttt{image} (x,y) =  \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\)
         *
         * 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(4,4,3,2) 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 .
         *
         * ![integral calculation example](pics/integral.png)
         *
         * param src input image as \(W \times H\), 8-bit or floating-point (32f or 64f).
         * param sum integral image as \((W+1)\times (H+1)\) , 32-bit integer or floating-point (32f or 64f).
         * param sqsum integral image for squared pixel values; it is \((W+1)\times (H+1)\), double-precision
         * floating-point (64f) array.
         * param tilted integral for the image rotated by 45 degrees; it is \((W+1)\times (H+1)\) array with
         * the same data type as sum.
         * CV_64F.
         */
        public static void integral3(Mat src, Mat sum, Mat sqsum, Mat tilted)
        {
            if (src != null) src.ThrowIfDisposed();
            if (sum != null) sum.ThrowIfDisposed();
            if (sqsum != null) sqsum.ThrowIfDisposed();
            if (tilted != null) tilted.ThrowIfDisposed();

            imgproc_Imgproc_integral3_12(src.nativeObj, sum.nativeObj, sqsum.nativeObj, tilted.nativeObj);


        }


        //
        // C++:  void cv::integral(Mat src, Mat& sum, int sdepth = -1)
        //

        public static void integral(Mat src, Mat sum, int sdepth)
        {
            if (src != null) src.ThrowIfDisposed();
            if (sum != null) sum.ThrowIfDisposed();

            imgproc_Imgproc_integral_10(src.nativeObj, sum.nativeObj, sdepth);


        }

        public static void integral(Mat src, Mat sum)
        {
            if (src != null) src.ThrowIfDisposed();
            if (sum != null) sum.ThrowIfDisposed();

            imgproc_Imgproc_integral_11(src.nativeObj, sum.nativeObj);


        }


        //
        // C++:  void cv::integral(Mat src, Mat& sum, Mat& sqsum, int sdepth = -1, int sqdepth = -1)
        //

        public static void integral2(Mat src, Mat sum, Mat sqsum, int sdepth, int sqdepth)
        {
            if (src != null) src.ThrowIfDisposed();
            if (sum != null) sum.ThrowIfDisposed();
            if (sqsum != null) sqsum.ThrowIfDisposed();

            imgproc_Imgproc_integral2_10(src.nativeObj, sum.nativeObj, sqsum.nativeObj, sdepth, sqdepth);


        }

        public static void integral2(Mat src, Mat sum, Mat sqsum, int sdepth)
        {
            if (src != null) src.ThrowIfDisposed();
            if (sum != null) sum.ThrowIfDisposed();
            if (sqsum != null) sqsum.ThrowIfDisposed();

            imgproc_Imgproc_integral2_11(src.nativeObj, sum.nativeObj, sqsum.nativeObj, sdepth);


        }

        public static void integral2(Mat src, Mat sum, Mat sqsum)
        {
            if (src != null) src.ThrowIfDisposed();
            if (sum != null) sum.ThrowIfDisposed();
            if (sqsum != null) sqsum.ThrowIfDisposed();

            imgproc_Imgproc_integral2_12(src.nativeObj, sum.nativeObj, sqsum.nativeObj);


        }


        //
        // C++:  void cv::accumulate(Mat src, Mat& dst, Mat mask = Mat())
        //

        /**
         * Adds an image to the accumulator image.
         *
         * The function adds src or some of its elements to dst :
         *
         * \(\texttt{dst} (x,y)  \leftarrow \texttt{dst} (x,y) +  \texttt{src} (x,y)  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\)
         *
         * The function supports multi-channel images. Each channel is processed independently.
         *
         * The function cv::accumulate can be used, for example, to collect statistics of a scene background
         * viewed by a still camera and for the further foreground-background segmentation.
         *
         * param src Input image of type CV_8UC(n), CV_16UC(n), CV_32FC(n) or CV_64FC(n), where n is a positive integer.
         * param dst %Accumulator image with the same number of channels as input image, and a depth of CV_32F or CV_64F.
         * param mask Optional operation mask.
         *
         * SEE:  accumulateSquare, accumulateProduct, accumulateWeighted
         */
        public static void accumulate(Mat src, Mat dst, Mat mask)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();

            imgproc_Imgproc_accumulate_10(src.nativeObj, dst.nativeObj, mask.nativeObj);


        }

        /**
         * Adds an image to the accumulator image.
         *
         * The function adds src or some of its elements to dst :
         *
         * \(\texttt{dst} (x,y)  \leftarrow \texttt{dst} (x,y) +  \texttt{src} (x,y)  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\)
         *
         * The function supports multi-channel images. Each channel is processed independently.
         *
         * The function cv::accumulate can be used, for example, to collect statistics of a scene background
         * viewed by a still camera and for the further foreground-background segmentation.
         *
         * param src Input image of type CV_8UC(n), CV_16UC(n), CV_32FC(n) or CV_64FC(n), where n is a positive integer.
         * param dst %Accumulator image with the same number of channels as input image, and a depth of CV_32F or CV_64F.
         *
         * SEE:  accumulateSquare, accumulateProduct, accumulateWeighted
         */
        public static void accumulate(Mat src, Mat dst)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_accumulate_11(src.nativeObj, dst.nativeObj);


        }


        //
        // C++:  void cv::accumulateSquare(Mat src, Mat& dst, Mat mask = Mat())
        //

        /**
         * Adds the square of a source image to the accumulator image.
         *
         * The function adds the input image src or its selected region, raised to a power of 2, to the
         * accumulator dst :
         *
         * \(\texttt{dst} (x,y)  \leftarrow \texttt{dst} (x,y) +  \texttt{src} (x,y)^2  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\)
         *
         * The function supports multi-channel images. Each channel is processed independently.
         *
         * param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
         * param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
         * floating-point.
         * param mask Optional operation mask.
         *
         * SEE:  accumulateSquare, accumulateProduct, accumulateWeighted
         */
        public static void accumulateSquare(Mat src, Mat dst, Mat mask)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();

            imgproc_Imgproc_accumulateSquare_10(src.nativeObj, dst.nativeObj, mask.nativeObj);


        }

        /**
         * Adds the square of a source image to the accumulator image.
         *
         * The function adds the input image src or its selected region, raised to a power of 2, to the
         * accumulator dst :
         *
         * \(\texttt{dst} (x,y)  \leftarrow \texttt{dst} (x,y) +  \texttt{src} (x,y)^2  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\)
         *
         * The function supports multi-channel images. Each channel is processed independently.
         *
         * param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
         * param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
         * floating-point.
         *
         * SEE:  accumulateSquare, accumulateProduct, accumulateWeighted
         */
        public static void accumulateSquare(Mat src, Mat dst)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_accumulateSquare_11(src.nativeObj, dst.nativeObj);


        }


        //
        // C++:  void cv::accumulateProduct(Mat src1, Mat src2, Mat& dst, Mat mask = Mat())
        //

        /**
         * Adds the per-element product of two input images to the accumulator image.
         *
         * The function adds the product of two images or their selected regions to the accumulator dst :
         *
         * \(\texttt{dst} (x,y)  \leftarrow \texttt{dst} (x,y) +  \texttt{src1} (x,y)  \cdot \texttt{src2} (x,y)  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\)
         *
         * The function supports multi-channel images. Each channel is processed independently.
         *
         * param src1 First input image, 1- or 3-channel, 8-bit or 32-bit floating point.
         * param src2 Second input image of the same type and the same size as src1 .
         * param dst %Accumulator image with the same number of channels as input images, 32-bit or 64-bit
         * floating-point.
         * param mask Optional operation mask.
         *
         * SEE:  accumulate, accumulateSquare, accumulateWeighted
         */
        public static void accumulateProduct(Mat src1, Mat src2, Mat dst, Mat mask)
        {
            if (src1 != null) src1.ThrowIfDisposed();
            if (src2 != null) src2.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();

            imgproc_Imgproc_accumulateProduct_10(src1.nativeObj, src2.nativeObj, dst.nativeObj, mask.nativeObj);


        }

        /**
         * Adds the per-element product of two input images to the accumulator image.
         *
         * The function adds the product of two images or their selected regions to the accumulator dst :
         *
         * \(\texttt{dst} (x,y)  \leftarrow \texttt{dst} (x,y) +  \texttt{src1} (x,y)  \cdot \texttt{src2} (x,y)  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\)
         *
         * The function supports multi-channel images. Each channel is processed independently.
         *
         * param src1 First input image, 1- or 3-channel, 8-bit or 32-bit floating point.
         * param src2 Second input image of the same type and the same size as src1 .
         * param dst %Accumulator image with the same number of channels as input images, 32-bit or 64-bit
         * floating-point.
         *
         * SEE:  accumulate, accumulateSquare, accumulateWeighted
         */
        public static void accumulateProduct(Mat src1, Mat src2, Mat dst)
        {
            if (src1 != null) src1.ThrowIfDisposed();
            if (src2 != null) src2.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_accumulateProduct_11(src1.nativeObj, src2.nativeObj, dst.nativeObj);


        }


        //
        // C++:  void cv::accumulateWeighted(Mat src, Mat& dst, double alpha, Mat mask = Mat())
        //

        /**
         * 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:
         *
         * \(\texttt{dst} (x,y)  \leftarrow (1- \texttt{alpha} )  \cdot \texttt{dst} (x,y) +  \texttt{alpha} \cdot \texttt{src} (x,y)  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\)
         *
         * That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images).
         * The function supports multi-channel images. Each channel is processed independently.
         *
         * param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
         * param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
         * floating-point.
         * param alpha Weight of the input image.
         * param mask Optional operation mask.
         *
         * SEE:  accumulate, accumulateSquare, accumulateProduct
         */
        public static void accumulateWeighted(Mat src, Mat dst, double alpha, Mat mask)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();

            imgproc_Imgproc_accumulateWeighted_10(src.nativeObj, dst.nativeObj, alpha, mask.nativeObj);


        }

        /**
         * 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:
         *
         * \(\texttt{dst} (x,y)  \leftarrow (1- \texttt{alpha} )  \cdot \texttt{dst} (x,y) +  \texttt{alpha} \cdot \texttt{src} (x,y)  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\)
         *
         * That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images).
         * The function supports multi-channel images. Each channel is processed independently.
         *
         * param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
         * param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
         * floating-point.
         * param alpha Weight of the input image.
         *
         * SEE:  accumulate, accumulateSquare, accumulateProduct
         */
        public static void accumulateWeighted(Mat src, Mat dst, double alpha)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_accumulateWeighted_11(src.nativeObj, dst.nativeObj, alpha);


        }


        //
        // C++:  Point2d cv::phaseCorrelate(Mat src1, Mat src2, Mat window = Mat(), double* response = 0)
        //

        /**
         * 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 &lt;http://en.wikipedia.org/wiki/Phase_correlation&gt;
         *
         * Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed
         * with getOptimalDFTSize.
         *
         * The function performs the following equations:
         * <ul>
         *   <li>
         *  First it applies a Hanning window (see &lt;http://en.wikipedia.org/wiki/Hann_function&gt;) to each
         * image to remove possible edge effects. This window is cached until the array size changes to speed
         * up processing time.
         *   </li>
         *   <li>
         *  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.
         *   </li>
         *   <li>
         *  It then computes the cross-power spectrum of each frequency domain array:
         * \(R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\)
         *   </li>
         *   <li>
         *  Next the cross-correlation is converted back into the time domain via the inverse DFT:
         * \(r = \mathcal{F}^{-1}\{R\}\)
         *   </li>
         *   <li>
         *  Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to
         * achieve sub-pixel accuracy.
         * \((\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\)
         *   </li>
         *   <li>
         *  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.
         *   </li>
         * </ul>
         *
         * param src1 Source floating point array (CV_32FC1 or CV_64FC1)
         * param src2 Source floating point array (CV_32FC1 or CV_64FC1)
         * param window Floating point array with windowing coefficients to reduce edge effects (optional).
         * param response Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).
         * return detected phase shift (sub-pixel) between the two arrays.
         *
         * SEE: dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow
         */
        public static Point phaseCorrelate(Mat src1, Mat src2, Mat window, double[] response)
        {
            if (src1 != null) src1.ThrowIfDisposed();
            if (src2 != null) src2.ThrowIfDisposed();
            if (window != null) window.ThrowIfDisposed();
            double[] response_out = new double[1];
            double[] tmpArray = new double[2];
            imgproc_Imgproc_phaseCorrelate_10(src1.nativeObj, src2.nativeObj, window.nativeObj, response_out, tmpArray);
            Point retVal = new Point(tmpArray);
            if (response != null) response[0] = (double)response_out[0];
            return retVal;
        }

        /**
         * 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 &lt;http://en.wikipedia.org/wiki/Phase_correlation&gt;
         *
         * Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed
         * with getOptimalDFTSize.
         *
         * The function performs the following equations:
         * <ul>
         *   <li>
         *  First it applies a Hanning window (see &lt;http://en.wikipedia.org/wiki/Hann_function&gt;) to each
         * image to remove possible edge effects. This window is cached until the array size changes to speed
         * up processing time.
         *   </li>
         *   <li>
         *  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.
         *   </li>
         *   <li>
         *  It then computes the cross-power spectrum of each frequency domain array:
         * \(R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\)
         *   </li>
         *   <li>
         *  Next the cross-correlation is converted back into the time domain via the inverse DFT:
         * \(r = \mathcal{F}^{-1}\{R\}\)
         *   </li>
         *   <li>
         *  Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to
         * achieve sub-pixel accuracy.
         * \((\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\)
         *   </li>
         *   <li>
         *  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.
         *   </li>
         * </ul>
         *
         * param src1 Source floating point array (CV_32FC1 or CV_64FC1)
         * param src2 Source floating point array (CV_32FC1 or CV_64FC1)
         * param window Floating point array with windowing coefficients to reduce edge effects (optional).
         * return detected phase shift (sub-pixel) between the two arrays.
         *
         * SEE: dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow
         */
        public static Point phaseCorrelate(Mat src1, Mat src2, Mat window)
        {
            if (src1 != null) src1.ThrowIfDisposed();
            if (src2 != null) src2.ThrowIfDisposed();
            if (window != null) window.ThrowIfDisposed();

            double[] tmpArray = new double[2];
            imgproc_Imgproc_phaseCorrelate_11(src1.nativeObj, src2.nativeObj, window.nativeObj, tmpArray);
            Point retVal = new Point(tmpArray);

            return retVal;
        }

        /**
         * 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 &lt;http://en.wikipedia.org/wiki/Phase_correlation&gt;
         *
         * Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed
         * with getOptimalDFTSize.
         *
         * The function performs the following equations:
         * <ul>
         *   <li>
         *  First it applies a Hanning window (see &lt;http://en.wikipedia.org/wiki/Hann_function&gt;) to each
         * image to remove possible edge effects. This window is cached until the array size changes to speed
         * up processing time.
         *   </li>
         *   <li>
         *  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.
         *   </li>
         *   <li>
         *  It then computes the cross-power spectrum of each frequency domain array:
         * \(R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\)
         *   </li>
         *   <li>
         *  Next the cross-correlation is converted back into the time domain via the inverse DFT:
         * \(r = \mathcal{F}^{-1}\{R\}\)
         *   </li>
         *   <li>
         *  Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to
         * achieve sub-pixel accuracy.
         * \((\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\)
         *   </li>
         *   <li>
         *  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.
         *   </li>
         * </ul>
         *
         * param src1 Source floating point array (CV_32FC1 or CV_64FC1)
         * param src2 Source floating point array (CV_32FC1 or CV_64FC1)
         * return detected phase shift (sub-pixel) between the two arrays.
         *
         * SEE: dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow
         */
        public static Point phaseCorrelate(Mat src1, Mat src2)
        {
            if (src1 != null) src1.ThrowIfDisposed();
            if (src2 != null) src2.ThrowIfDisposed();

            double[] tmpArray = new double[2];
            imgproc_Imgproc_phaseCorrelate_12(src1.nativeObj, src2.nativeObj, tmpArray);
            Point retVal = new Point(tmpArray);

            return retVal;
        }


        //
        // C++:  void cv::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:
         * <code>
         *     // create hanning window of size 100x100 and type CV_32F
         *     Mat hann;
         *     createHanningWindow(hann, Size(100, 100), CV_32F);
         * </code>
         * param dst Destination array to place Hann coefficients in
         * param winSize The window size specifications (both width and height must be &gt; 1)
         * param type Created array type
         */
        public static void createHanningWindow(Mat dst, Size winSize, int type)
        {
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_createHanningWindow_10(dst.nativeObj, winSize.width, winSize.height, type);


        }


        //
        // C++:  void cv::divSpectrums(Mat a, Mat b, Mat& c, int flags, bool conjB = false)
        //

        /**
         * Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum.
         *
         * The function cv::divSpectrums performs the per-element division of the first array by the second array.
         * The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform.
         *
         * param a first input array.
         * param b second input array of the same size and type as src1 .
         * param c output array of the same size and type as src1 .
         * param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that
         * each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a {code 0} as value.
         * param conjB optional flag that conjugates the second input array before the multiplication (true)
         * or not (false).
         */
        public static void divSpectrums(Mat a, Mat b, Mat c, int flags, bool conjB)
        {
            if (a != null) a.ThrowIfDisposed();
            if (b != null) b.ThrowIfDisposed();
            if (c != null) c.ThrowIfDisposed();

            imgproc_Imgproc_divSpectrums_10(a.nativeObj, b.nativeObj, c.nativeObj, flags, conjB);


        }

        /**
         * Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum.
         *
         * The function cv::divSpectrums performs the per-element division of the first array by the second array.
         * The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform.
         *
         * param a first input array.
         * param b second input array of the same size and type as src1 .
         * param c output array of the same size and type as src1 .
         * param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that
         * each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a {code 0} as value.
         * or not (false).
         */
        public static void divSpectrums(Mat a, Mat b, Mat c, int flags)
        {
            if (a != null) a.ThrowIfDisposed();
            if (b != null) b.ThrowIfDisposed();
            if (c != null) c.ThrowIfDisposed();

            imgproc_Imgproc_divSpectrums_11(a.nativeObj, b.nativeObj, c.nativeObj, flags);


        }


        //
        // C++:  double cv::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 multiple-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 parameter.
         *
         * Also, the special values #THRESH_OTSU or #THRESH_TRIANGLE may be combined with one of the
         * above values. In these cases, the function determines the optimal threshold value using the Otsu's
         * or Triangle algorithm and uses it instead of the specified thresh.
         *
         * <b>Note:</b> Currently, the Otsu's and Triangle methods are implemented only for 8-bit single-channel images.
         *
         * param src input array (multiple-channel, 8-bit or 32-bit floating point).
         * param dst output array of the same size  and type and the same number of channels as src.
         * param thresh threshold value.
         * param maxval maximum value to use with the #THRESH_BINARY and #THRESH_BINARY_INV thresholding
         * types.
         * param type thresholding type (see #ThresholdTypes).
         * return the computed threshold value if Otsu's or Triangle methods used.
         *
         * SEE:  adaptiveThreshold, findContours, compare, min, max
         */
        public static double threshold(Mat src, Mat dst, double thresh, double maxval, int type)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            return imgproc_Imgproc_threshold_10(src.nativeObj, dst.nativeObj, thresh, maxval, type);


        }


        //
        // C++:  void cv::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:
         * <ul>
         *   <li>
         *    <b>THRESH_BINARY</b>
         *     \(dst(x,y) =  \fork{\texttt{maxValue}}{if \(src(x,y) &gt; T(x,y)\)}{0}{otherwise}\)
         *   </li>
         *   <li>
         *    <b>THRESH_BINARY_INV</b>
         *     \(dst(x,y) =  \fork{0}{if \(src(x,y) &gt; T(x,y)\)}{\texttt{maxValue}}{otherwise}\)
         * where \(T(x,y)\) is a threshold calculated individually for each pixel (see adaptiveMethod parameter).
         *   </li>
         * </ul>
         *
         * The function can process the image in-place.
         *
         * param src Source 8-bit single-channel image.
         * param dst Destination image of the same size and the same type as src.
         * param maxValue Non-zero value assigned to the pixels for which the condition is satisfied
         * param adaptiveMethod Adaptive thresholding algorithm to use, see #AdaptiveThresholdTypes.
         * The #BORDER_REPLICATE | #BORDER_ISOLATED is used to process boundaries.
         * param thresholdType Thresholding type that must be either #THRESH_BINARY or #THRESH_BINARY_INV,
         * see #ThresholdTypes.
         * param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the
         * pixel: 3, 5, 7, and so on.
         * param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it
         * is positive but may be zero or negative as well.
         *
         * SEE:  threshold, blur, GaussianBlur
         */
        public static void adaptiveThreshold(Mat src, Mat dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_adaptiveThreshold_10(src.nativeObj, dst.nativeObj, maxValue, adaptiveMethod, thresholdType, blockSize, C);


        }


        //
        // C++:  void cv::pyrDown(Mat src, Mat& dst, Size dstsize = Size(), int borderType = BORDER_DEFAULT)
        //

        /**
         * Blurs an image and downsamples it.
         *
         * By default, size of the output image is computed as {code Size((src.cols+1)/2, (src.rows+1)/2)}, but in
         * any case, the following conditions should be satisfied:
         *
         * \(\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\)
         *
         * The function performs the downsampling step of the Gaussian pyramid construction. First, it
         * convolves the source image with the kernel:
         *
         * \(\frac{1}{256} \begin{bmatrix} 1 &amp; 4 &amp; 6 &amp; 4 &amp; 1  \\ 4 &amp; 16 &amp; 24 &amp; 16 &amp; 4  \\ 6 &amp; 24 &amp; 36 &amp; 24 &amp; 6  \\ 4 &amp; 16 &amp; 24 &amp; 16 &amp; 4  \\ 1 &amp; 4 &amp; 6 &amp; 4 &amp; 1 \end{bmatrix}\)
         *
         * Then, it downsamples the image by rejecting even rows and columns.
         *
         * param src input image.
         * param dst output image; it has the specified size and the same type as src.
         * param dstsize size of the output image.
         * param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)
         */
        public static void pyrDown(Mat src, Mat dst, Size dstsize, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_pyrDown_10(src.nativeObj, dst.nativeObj, dstsize.width, dstsize.height, borderType);


        }

        /**
         * Blurs an image and downsamples it.
         *
         * By default, size of the output image is computed as {code Size((src.cols+1)/2, (src.rows+1)/2)}, but in
         * any case, the following conditions should be satisfied:
         *
         * \(\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\)
         *
         * The function performs the downsampling step of the Gaussian pyramid construction. First, it
         * convolves the source image with the kernel:
         *
         * \(\frac{1}{256} \begin{bmatrix} 1 &amp; 4 &amp; 6 &amp; 4 &amp; 1  \\ 4 &amp; 16 &amp; 24 &amp; 16 &amp; 4  \\ 6 &amp; 24 &amp; 36 &amp; 24 &amp; 6  \\ 4 &amp; 16 &amp; 24 &amp; 16 &amp; 4  \\ 1 &amp; 4 &amp; 6 &amp; 4 &amp; 1 \end{bmatrix}\)
         *
         * Then, it downsamples the image by rejecting even rows and columns.
         *
         * param src input image.
         * param dst output image; it has the specified size and the same type as src.
         * param dstsize size of the output image.
         */
        public static void pyrDown(Mat src, Mat dst, Size dstsize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_pyrDown_11(src.nativeObj, dst.nativeObj, dstsize.width, dstsize.height);


        }

        /**
         * Blurs an image and downsamples it.
         *
         * By default, size of the output image is computed as {code Size((src.cols+1)/2, (src.rows+1)/2)}, but in
         * any case, the following conditions should be satisfied:
         *
         * \(\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\)
         *
         * The function performs the downsampling step of the Gaussian pyramid construction. First, it
         * convolves the source image with the kernel:
         *
         * \(\frac{1}{256} \begin{bmatrix} 1 &amp; 4 &amp; 6 &amp; 4 &amp; 1  \\ 4 &amp; 16 &amp; 24 &amp; 16 &amp; 4  \\ 6 &amp; 24 &amp; 36 &amp; 24 &amp; 6  \\ 4 &amp; 16 &amp; 24 &amp; 16 &amp; 4  \\ 1 &amp; 4 &amp; 6 &amp; 4 &amp; 1 \end{bmatrix}\)
         *
         * Then, it downsamples the image by rejecting even rows and columns.
         *
         * param src input image.
         * param dst output image; it has the specified size and the same type as src.
         */
        public static void pyrDown(Mat src, Mat dst)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_pyrDown_12(src.nativeObj, dst.nativeObj);


        }


        //
        // C++:  void cv::pyrUp(Mat src, Mat& dst, Size dstsize = Size(), int borderType = BORDER_DEFAULT)
        //

        /**
         * Upsamples an image and then blurs it.
         *
         * By default, size of the output image is computed as {code Size(src.cols\*2, (src.rows\*2)}, but in any
         * case, the following conditions should be satisfied:
         *
         * \(\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq  ( \texttt{dstsize.width}   \mod  2)  \\ | \texttt{dstsize.height} -src.rows*2| \leq  ( \texttt{dstsize.height}   \mod  2) \end{array}\)
         *
         * The function performs the upsampling step of the Gaussian pyramid construction, though it can
         * actually be used to construct the Laplacian pyramid. First, it upsamples the source image by
         * injecting even zero rows and columns and then convolves the result with the same kernel as in
         * pyrDown multiplied by 4.
         *
         * param src input image.
         * param dst output image. It has the specified size and the same type as src .
         * param dstsize size of the output image.
         * param borderType Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported)
         */
        public static void pyrUp(Mat src, Mat dst, Size dstsize, int borderType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_pyrUp_10(src.nativeObj, dst.nativeObj, dstsize.width, dstsize.height, borderType);


        }

        /**
         * Upsamples an image and then blurs it.
         *
         * By default, size of the output image is computed as {code Size(src.cols\*2, (src.rows\*2)}, but in any
         * case, the following conditions should be satisfied:
         *
         * \(\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq  ( \texttt{dstsize.width}   \mod  2)  \\ | \texttt{dstsize.height} -src.rows*2| \leq  ( \texttt{dstsize.height}   \mod  2) \end{array}\)
         *
         * The function performs the upsampling step of the Gaussian pyramid construction, though it can
         * actually be used to construct the Laplacian pyramid. First, it upsamples the source image by
         * injecting even zero rows and columns and then convolves the result with the same kernel as in
         * pyrDown multiplied by 4.
         *
         * param src input image.
         * param dst output image. It has the specified size and the same type as src .
         * param dstsize size of the output image.
         */
        public static void pyrUp(Mat src, Mat dst, Size dstsize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_pyrUp_11(src.nativeObj, dst.nativeObj, dstsize.width, dstsize.height);


        }

        /**
         * Upsamples an image and then blurs it.
         *
         * By default, size of the output image is computed as {code Size(src.cols\*2, (src.rows\*2)}, but in any
         * case, the following conditions should be satisfied:
         *
         * \(\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq  ( \texttt{dstsize.width}   \mod  2)  \\ | \texttt{dstsize.height} -src.rows*2| \leq  ( \texttt{dstsize.height}   \mod  2) \end{array}\)
         *
         * The function performs the upsampling step of the Gaussian pyramid construction, though it can
         * actually be used to construct the Laplacian pyramid. First, it upsamples the source image by
         * injecting even zero rows and columns and then convolves the result with the same kernel as in
         * pyrDown multiplied by 4.
         *
         * param src input image.
         * param dst output image. It has the specified size and the same type as src .
         */
        public static void pyrUp(Mat src, Mat dst)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_pyrUp_12(src.nativeObj, dst.nativeObj);


        }


        //
        // C++:  void cv::calcHist(vector_Mat images, vector_int channels, Mat mask, Mat& hist, vector_int histSize, vector_float ranges, bool accumulate = false)
        //

        /**
         *
         *
         * this variant supports only uniform histograms.
         *
         * ranges argument is either empty vector or a flattened vector of histSize.size()*2 elements
         * (histSize.size() element pairs). The first and second elements of each pair specify the lower and
         * upper boundaries.
         * param images automatically generated
         * param channels automatically generated
         * param mask automatically generated
         * param hist automatically generated
         * param histSize automatically generated
         * param ranges automatically generated
         * param accumulate automatically generated
         */
        public static void calcHist(List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges, bool accumulate)
        {
            if (channels != null) channels.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            if (hist != null) hist.ThrowIfDisposed();
            if (histSize != null) histSize.ThrowIfDisposed();
            if (ranges != null) ranges.ThrowIfDisposed();
            Mat images_mat = Converters.vector_Mat_to_Mat(images);
            Mat channels_mat = channels;
            Mat histSize_mat = histSize;
            Mat ranges_mat = ranges;
            imgproc_Imgproc_calcHist_10(images_mat.nativeObj, channels_mat.nativeObj, mask.nativeObj, hist.nativeObj, histSize_mat.nativeObj, ranges_mat.nativeObj, accumulate);


        }

        /**
         *
         *
         * this variant supports only uniform histograms.
         *
         * ranges argument is either empty vector or a flattened vector of histSize.size()*2 elements
         * (histSize.size() element pairs). The first and second elements of each pair specify the lower and
         * upper boundaries.
         * param images automatically generated
         * param channels automatically generated
         * param mask automatically generated
         * param hist automatically generated
         * param histSize automatically generated
         * param ranges automatically generated
         */
        public static void calcHist(List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges)
        {
            if (channels != null) channels.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            if (hist != null) hist.ThrowIfDisposed();
            if (histSize != null) histSize.ThrowIfDisposed();
            if (ranges != null) ranges.ThrowIfDisposed();
            Mat images_mat = Converters.vector_Mat_to_Mat(images);
            Mat channels_mat = channels;
            Mat histSize_mat = histSize;
            Mat ranges_mat = ranges;
            imgproc_Imgproc_calcHist_11(images_mat.nativeObj, channels_mat.nativeObj, mask.nativeObj, hist.nativeObj, histSize_mat.nativeObj, ranges_mat.nativeObj);


        }


        //
        // C++:  void cv::calcBackProject(vector_Mat images, vector_int channels, Mat hist, Mat& dst, vector_float ranges, double scale)
        //

        public static void calcBackProject(List<Mat> images, MatOfInt channels, Mat hist, Mat dst, MatOfFloat ranges, double scale)
        {
            if (channels != null) channels.ThrowIfDisposed();
            if (hist != null) hist.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (ranges != null) ranges.ThrowIfDisposed();
            Mat images_mat = Converters.vector_Mat_to_Mat(images);
            Mat channels_mat = channels;
            Mat ranges_mat = ranges;
            imgproc_Imgproc_calcBackProject_10(images_mat.nativeObj, channels_mat.nativeObj, hist.nativeObj, dst.nativeObj, ranges_mat.nativeObj, scale);


        }


        //
        // C++:  double cv::compareHist(Mat H1, Mat H2, int method)
        //

        /**
         * Compares two histograms.
         *
         * The function cv::compareHist compares two dense or two sparse histograms using the specified method.
         *
         * 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.
         *
         * param H1 First compared histogram.
         * param H2 Second compared histogram of the same size as H1 .
         * param method Comparison method, see #HistCompMethods
         * return automatically generated
         */
        public static double compareHist(Mat H1, Mat H2, int method)
        {
            if (H1 != null) H1.ThrowIfDisposed();
            if (H2 != null) H2.ThrowIfDisposed();

            return imgproc_Imgproc_compareHist_10(H1.nativeObj, H2.nativeObj, method);


        }


        //
        // C++:  void cv::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:
         *
         * <ul>
         *   <li>
         *  Calculate the histogram \(H\) for src .
         *   </li>
         *   <li>
         *  Normalize the histogram so that the sum of histogram bins is 255.
         *   </li>
         *   <li>
         *  Compute the integral of the histogram:
         * \(H'_i =  \sum _{0  \le j &lt; i} H(j)\)
         *   </li>
         *   <li>
         *  Transform the image using \(H'\) as a look-up table: \(\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\)
         *   </li>
         * </ul>
         *
         * The algorithm normalizes the brightness and increases the contrast of the image.
         *
         * param src Source 8-bit single channel image.
         * param dst Destination image of the same size and type as src .
         */
        public static void equalizeHist(Mat src, Mat dst)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_equalizeHist_10(src.nativeObj, dst.nativeObj);


        }


        //
        // C++:  Ptr_CLAHE cv::createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8))
        //

        /**
         * Creates a smart pointer to a cv::CLAHE class and initializes it.
         *
         * param clipLimit Threshold for contrast limiting.
         * param tileGridSize Size of grid for histogram equalization. Input image will be divided into
         * equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column.
         * return automatically generated
         */
        public static CLAHE createCLAHE(double clipLimit, Size tileGridSize)
        {


            return CLAHE.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_createCLAHE_10(clipLimit, tileGridSize.width, tileGridSize.height)));


        }

        /**
         * Creates a smart pointer to a cv::CLAHE class and initializes it.
         *
         * param clipLimit Threshold for contrast limiting.
         * equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column.
         * return automatically generated
         */
        public static CLAHE createCLAHE(double clipLimit)
        {


            return CLAHE.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_createCLAHE_11(clipLimit)));


        }

        /**
         * Creates a smart pointer to a cv::CLAHE class and initializes it.
         *
         * equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column.
         * return automatically generated
         */
        public static CLAHE createCLAHE()
        {


            return CLAHE.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_createCLAHE_12()));


        }


        //
        // C++:  float cv::wrapperEMD(Mat signature1, Mat signature2, int distType, Mat cost = Mat(), Ptr_float& lowerBound = Ptr<float>(), Mat& flow = Mat())
        //

        /**
         * Computes the "minimal work" distance between two weighted point configurations.
         *
         * The function computes the earth mover distance and/or a lower boundary of the distance between the
         * two weighted point configurations. One of the applications described in CITE: RubnerSept98,
         * CITE: Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation
         * problem that is solved using some modification of a simplex algorithm, thus the complexity is
         * exponential in the worst case, though, on average it is much faster. In the case of a real metric
         * the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used
         * to determine roughly whether the two signatures are far enough so that they cannot relate to the
         * same object.
         *
         * param signature1 First signature, a \(\texttt{size1}\times \texttt{dims}+1\) floating-point matrix.
         * Each row stores the point weight followed by the point coordinates. The matrix is allowed to have
         * a single column (weights only) if the user-defined cost matrix is used. The weights must be
         * non-negative and have at least one non-zero value.
         * param signature2 Second signature of the same format as signature1 , though the number of rows
         * may be different. The total weights may be different. In this case an extra "dummy" point is added
         * to either signature1 or signature2. The weights must be non-negative and have at least one non-zero
         * value.
         * param distType Used metric. See #DistanceTypes.
         * param cost User-defined \(\texttt{size1}\times \texttt{size2}\) cost matrix. Also, if a cost matrix
         * is used, lower boundary lowerBound cannot be calculated because it needs a metric function.
         * signatures that is a distance between mass centers. The lower boundary may not be calculated if
         * the user-defined cost matrix is used, the total weights of point configurations are not equal, or
         * if the signatures consist of weights only (the signature matrices have a single column). You
         * <b>must</b> initialize \*lowerBound . If the calculated distance between mass centers is greater or
         * equal to \*lowerBound (it means that the signatures are far enough), the function does not
         * calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on
         * return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound
         * should be set to 0.
         * param flow Resultant \(\texttt{size1} \times \texttt{size2}\) flow matrix: \(\texttt{flow}_{i,j}\) is
         * a flow from \(i\) -th point of signature1 to \(j\) -th point of signature2 .
         * return automatically generated
         */
        public static float EMD(Mat signature1, Mat signature2, int distType, Mat cost, Mat flow)
        {
            if (signature1 != null) signature1.ThrowIfDisposed();
            if (signature2 != null) signature2.ThrowIfDisposed();
            if (cost != null) cost.ThrowIfDisposed();
            if (flow != null) flow.ThrowIfDisposed();

            return imgproc_Imgproc_EMD_10(signature1.nativeObj, signature2.nativeObj, distType, cost.nativeObj, flow.nativeObj);


        }

        /**
         * Computes the "minimal work" distance between two weighted point configurations.
         *
         * The function computes the earth mover distance and/or a lower boundary of the distance between the
         * two weighted point configurations. One of the applications described in CITE: RubnerSept98,
         * CITE: Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation
         * problem that is solved using some modification of a simplex algorithm, thus the complexity is
         * exponential in the worst case, though, on average it is much faster. In the case of a real metric
         * the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used
         * to determine roughly whether the two signatures are far enough so that they cannot relate to the
         * same object.
         *
         * param signature1 First signature, a \(\texttt{size1}\times \texttt{dims}+1\) floating-point matrix.
         * Each row stores the point weight followed by the point coordinates. The matrix is allowed to have
         * a single column (weights only) if the user-defined cost matrix is used. The weights must be
         * non-negative and have at least one non-zero value.
         * param signature2 Second signature of the same format as signature1 , though the number of rows
         * may be different. The total weights may be different. In this case an extra "dummy" point is added
         * to either signature1 or signature2. The weights must be non-negative and have at least one non-zero
         * value.
         * param distType Used metric. See #DistanceTypes.
         * param cost User-defined \(\texttt{size1}\times \texttt{size2}\) cost matrix. Also, if a cost matrix
         * is used, lower boundary lowerBound cannot be calculated because it needs a metric function.
         * signatures that is a distance between mass centers. The lower boundary may not be calculated if
         * the user-defined cost matrix is used, the total weights of point configurations are not equal, or
         * if the signatures consist of weights only (the signature matrices have a single column). You
         * <b>must</b> initialize \*lowerBound . If the calculated distance between mass centers is greater or
         * equal to \*lowerBound (it means that the signatures are far enough), the function does not
         * calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on
         * return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound
         * should be set to 0.
         * a flow from \(i\) -th point of signature1 to \(j\) -th point of signature2 .
         * return automatically generated
         */
        public static float EMD(Mat signature1, Mat signature2, int distType, Mat cost)
        {
            if (signature1 != null) signature1.ThrowIfDisposed();
            if (signature2 != null) signature2.ThrowIfDisposed();
            if (cost != null) cost.ThrowIfDisposed();

            return imgproc_Imgproc_EMD_11(signature1.nativeObj, signature2.nativeObj, distType, cost.nativeObj);


        }

        /**
         * Computes the "minimal work" distance between two weighted point configurations.
         *
         * The function computes the earth mover distance and/or a lower boundary of the distance between the
         * two weighted point configurations. One of the applications described in CITE: RubnerSept98,
         * CITE: Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation
         * problem that is solved using some modification of a simplex algorithm, thus the complexity is
         * exponential in the worst case, though, on average it is much faster. In the case of a real metric
         * the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used
         * to determine roughly whether the two signatures are far enough so that they cannot relate to the
         * same object.
         *
         * param signature1 First signature, a \(\texttt{size1}\times \texttt{dims}+1\) floating-point matrix.
         * Each row stores the point weight followed by the point coordinates. The matrix is allowed to have
         * a single column (weights only) if the user-defined cost matrix is used. The weights must be
         * non-negative and have at least one non-zero value.
         * param signature2 Second signature of the same format as signature1 , though the number of rows
         * may be different. The total weights may be different. In this case an extra "dummy" point is added
         * to either signature1 or signature2. The weights must be non-negative and have at least one non-zero
         * value.
         * param distType Used metric. See #DistanceTypes.
         * is used, lower boundary lowerBound cannot be calculated because it needs a metric function.
         * signatures that is a distance between mass centers. The lower boundary may not be calculated if
         * the user-defined cost matrix is used, the total weights of point configurations are not equal, or
         * if the signatures consist of weights only (the signature matrices have a single column). You
         * <b>must</b> initialize \*lowerBound . If the calculated distance between mass centers is greater or
         * equal to \*lowerBound (it means that the signatures are far enough), the function does not
         * calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on
         * return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound
         * should be set to 0.
         * a flow from \(i\) -th point of signature1 to \(j\) -th point of signature2 .
         * return automatically generated
         */
        public static float EMD(Mat signature1, Mat signature2, int distType)
        {
            if (signature1 != null) signature1.ThrowIfDisposed();
            if (signature2 != null) signature2.ThrowIfDisposed();

            return imgproc_Imgproc_EMD_13(signature1.nativeObj, signature2.nativeObj, distType);


        }


        //
        // C++:  void cv::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 CITE: Meyer92 .
         *
         * Before passing the image to the function, you have to roughly outline the desired regions in the
         * image markers with positive (&gt;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.
         *
         * <b>Note:</b> Any two neighbor connected components are not necessarily separated by a watershed boundary
         * (-1's pixels); for example, they can touch each other in the initial marker image passed to the
         * function.
         *
         * param image Input 8-bit 3-channel image.
         * param markers Input/output 32-bit single-channel image (map) of markers. It should have the same
         * size as image .
         *
         * SEE: findContours
         */
        public static void watershed(Mat image, Mat markers)
        {
            if (image != null) image.ThrowIfDisposed();
            if (markers != null) markers.ThrowIfDisposed();

            imgproc_Imgproc_watershed_10(image.nativeObj, markers.nativeObj);


        }


        //
        // C++:  void cv::pyrMeanShiftFiltering(Mat src, Mat& dst, double sp, double sr, int maxLevel = 1, TermCriteria termcrit = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1))
        //

        /**
         * 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:
         *
         * \((x,y): X- \texttt{sp} \le x  \le X+ \texttt{sp} , Y- \texttt{sp} \le y  \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)||   \le \texttt{sr}\)
         *
         * 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):
         *
         * \(I(X,Y) &lt;- (R*,G*,B*)\)
         *
         * When maxLevel &gt; 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is
         * run on the smallest layer first. After that, the results are propagated to the larger layer and the
         * iterations are run again only on those pixels where the layer colors differ by more than sr from the
         * lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the
         * results will be actually different from the ones obtained by running the meanshift procedure on the
         * whole original image (i.e. when maxLevel==0).
         *
         * param src The source 8-bit, 3-channel image.
         * param dst The destination image of the same format and the same size as the source.
         * param sp The spatial window radius.
         * param sr The color window radius.
         * param maxLevel Maximum level of the pyramid for the segmentation.
         * param termcrit Termination criteria: when to stop meanshift iterations.
         */
        public static void pyrMeanShiftFiltering(Mat src, Mat dst, double sp, double sr, int maxLevel, TermCriteria termcrit)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_pyrMeanShiftFiltering_10(src.nativeObj, dst.nativeObj, sp, sr, maxLevel, termcrit.type, termcrit.maxCount, termcrit.epsilon);


        }

        /**
         * 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:
         *
         * \((x,y): X- \texttt{sp} \le x  \le X+ \texttt{sp} , Y- \texttt{sp} \le y  \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)||   \le \texttt{sr}\)
         *
         * 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):
         *
         * \(I(X,Y) &lt;- (R*,G*,B*)\)
         *
         * When maxLevel &gt; 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is
         * run on the smallest layer first. After that, the results are propagated to the larger layer and the
         * iterations are run again only on those pixels where the layer colors differ by more than sr from the
         * lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the
         * results will be actually different from the ones obtained by running the meanshift procedure on the
         * whole original image (i.e. when maxLevel==0).
         *
         * param src The source 8-bit, 3-channel image.
         * param dst The destination image of the same format and the same size as the source.
         * param sp The spatial window radius.
         * param sr The color window radius.
         * param maxLevel Maximum level of the pyramid for the segmentation.
         */
        public static void pyrMeanShiftFiltering(Mat src, Mat dst, double sp, double sr, int maxLevel)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_pyrMeanShiftFiltering_11(src.nativeObj, dst.nativeObj, sp, sr, maxLevel);


        }

        /**
         * 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:
         *
         * \((x,y): X- \texttt{sp} \le x  \le X+ \texttt{sp} , Y- \texttt{sp} \le y  \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)||   \le \texttt{sr}\)
         *
         * 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):
         *
         * \(I(X,Y) &lt;- (R*,G*,B*)\)
         *
         * When maxLevel &gt; 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is
         * run on the smallest layer first. After that, the results are propagated to the larger layer and the
         * iterations are run again only on those pixels where the layer colors differ by more than sr from the
         * lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the
         * results will be actually different from the ones obtained by running the meanshift procedure on the
         * whole original image (i.e. when maxLevel==0).
         *
         * param src The source 8-bit, 3-channel image.
         * param dst The destination image of the same format and the same size as the source.
         * param sp The spatial window radius.
         * param sr The color window radius.
         */
        public static void pyrMeanShiftFiltering(Mat src, Mat dst, double sp, double sr)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_pyrMeanShiftFiltering_12(src.nativeObj, dst.nativeObj, sp, sr);


        }


        //
        // C++:  void cv::grabCut(Mat img, Mat& mask, Rect rect, Mat& bgdModel, Mat& fgdModel, int iterCount, int mode = GC_EVAL)
        //

        /**
         * Runs the GrabCut algorithm.
         *
         * The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut).
         *
         * param img Input 8-bit 3-channel image.
         * param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when
         * mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses.
         * param rect ROI containing a segmented object. The pixels outside of the ROI are marked as
         * "obvious background". The parameter is only used when mode==#GC_INIT_WITH_RECT .
         * param bgdModel Temporary array for the background model. Do not modify it while you are
         * processing the same image.
         * param fgdModel Temporary arrays for the foreground model. Do not modify it while you are
         * processing the same image.
         * param iterCount Number of iterations the algorithm should make before returning the result. Note
         * that the result can be refined with further calls with mode==#GC_INIT_WITH_MASK or
         * mode==GC_EVAL .
         * param mode Operation mode that could be one of the #GrabCutModes
         */
        public static void grabCut(Mat img, Mat mask, Rect rect, Mat bgdModel, Mat fgdModel, int iterCount, int mode)
        {
            if (img != null) img.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            if (bgdModel != null) bgdModel.ThrowIfDisposed();
            if (fgdModel != null) fgdModel.ThrowIfDisposed();

            imgproc_Imgproc_grabCut_10(img.nativeObj, mask.nativeObj, rect.x, rect.y, rect.width, rect.height, bgdModel.nativeObj, fgdModel.nativeObj, iterCount, mode);


        }

        /**
         * Runs the GrabCut algorithm.
         *
         * The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut).
         *
         * param img Input 8-bit 3-channel image.
         * param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when
         * mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses.
         * param rect ROI containing a segmented object. The pixels outside of the ROI are marked as
         * "obvious background". The parameter is only used when mode==#GC_INIT_WITH_RECT .
         * param bgdModel Temporary array for the background model. Do not modify it while you are
         * processing the same image.
         * param fgdModel Temporary arrays for the foreground model. Do not modify it while you are
         * processing the same image.
         * param iterCount Number of iterations the algorithm should make before returning the result. Note
         * that the result can be refined with further calls with mode==#GC_INIT_WITH_MASK or
         * mode==GC_EVAL .
         */
        public static void grabCut(Mat img, Mat mask, Rect rect, Mat bgdModel, Mat fgdModel, int iterCount)
        {
            if (img != null) img.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            if (bgdModel != null) bgdModel.ThrowIfDisposed();
            if (fgdModel != null) fgdModel.ThrowIfDisposed();

            imgproc_Imgproc_grabCut_11(img.nativeObj, mask.nativeObj, rect.x, rect.y, rect.width, rect.height, bgdModel.nativeObj, fgdModel.nativeObj, iterCount);


        }


        //
        // C++:  void cv::distanceTransform(Mat src, Mat& dst, Mat& labels, int distanceType, int maskSize, int labelType = DIST_LABEL_CCOMP)
        //

        /**
         * Calculates the distance to the closest zero pixel for each pixel of the source image.
         *
         * The function cv::distanceTransform calculates the approximate or precise distance from every binary
         * image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero.
         *
         * When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the
         * algorithm described in CITE: Felzenszwalb04 . This algorithm is parallelized with the TBB library.
         *
         * In other cases, the algorithm CITE: Borgefors86 is used. This means that for a pixel the function
         * finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical,
         * diagonal, or knight's move (the latest is available for a \(5\times 5\) 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 {code b}), and all knight's moves must have the
         * same cost (denoted as {code c}). For the #DIST_C and #DIST_L1 types, the distance is calculated
         * precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a
         * relative error (a \(5\times 5\) mask gives more accurate results). For {code a},{code b}, and {code c}, OpenCV
         * uses the values suggested in the original paper:
         * <ul>
         *   <li>
         *  DIST_L1: {code a = 1, b = 2}
         *   </li>
         *   <li>
         *  DIST_L2:
         *   <ul>
         *     <li>
         *      {code 3 x 3}: {code a=0.955, b=1.3693}
         *     </li>
         *     <li>
         *      {code 5 x 5}: {code a=1, b=1.4, c=2.1969}
         *     </li>
         *   </ul>
         *   <li>
         *  DIST_C: {code a = 1, b = 1}
         *   </li>
         * </ul>
         *
         * Typically, for a fast, coarse distance estimation #DIST_L2, a \(3\times 3\) mask is used. For a
         * more accurate distance estimation #DIST_L2, a \(5\times 5\) mask or the precise algorithm is used.
         * Note that both the precise and the approximate algorithms are linear on the number of pixels.
         *
         * This variant of the function does not only compute the minimum distance for each pixel \((x, y)\)
         * 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 {code 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_PIXEL, the function scans through the input image and
         * marks all the zero pixels with distinct labels.
         *
         * In this mode, the complexity is still linear. That is, the function provides a very fast way to
         * compute the Voronoi diagram for a binary image. Currently, the second variant can use only the
         * approximate distance transform algorithm, i.e. maskSize=#DIST_MASK_PRECISE is not supported
         * yet.
         *
         * param src 8-bit, single-channel (binary) source image.
         * param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
         * single-channel image of the same size as src.
         * param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type
         * CV_32SC1 and the same size as src.
         * param distanceType Type of distance, see #DistanceTypes
         * param maskSize Size of the distance transform mask, see #DistanceTransformMasks.
         * #DIST_MASK_PRECISE is not supported by this variant. In case of the #DIST_L1 or #DIST_C distance type,
         * the parameter is forced to 3 because a \(3\times 3\) mask gives the same result as \(5\times
         * 5\) or any larger aperture.
         * param labelType Type of the label array to build, see #DistanceTransformLabelTypes.
         */
        public static void distanceTransformWithLabels(Mat src, Mat dst, Mat labels, int distanceType, int maskSize, int labelType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (labels != null) labels.ThrowIfDisposed();

            imgproc_Imgproc_distanceTransformWithLabels_10(src.nativeObj, dst.nativeObj, labels.nativeObj, distanceType, maskSize, labelType);


        }

        /**
         * Calculates the distance to the closest zero pixel for each pixel of the source image.
         *
         * The function cv::distanceTransform calculates the approximate or precise distance from every binary
         * image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero.
         *
         * When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the
         * algorithm described in CITE: Felzenszwalb04 . This algorithm is parallelized with the TBB library.
         *
         * In other cases, the algorithm CITE: Borgefors86 is used. This means that for a pixel the function
         * finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical,
         * diagonal, or knight's move (the latest is available for a \(5\times 5\) 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 {code b}), and all knight's moves must have the
         * same cost (denoted as {code c}). For the #DIST_C and #DIST_L1 types, the distance is calculated
         * precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a
         * relative error (a \(5\times 5\) mask gives more accurate results). For {code a},{code b}, and {code c}, OpenCV
         * uses the values suggested in the original paper:
         * <ul>
         *   <li>
         *  DIST_L1: {code a = 1, b = 2}
         *   </li>
         *   <li>
         *  DIST_L2:
         *   <ul>
         *     <li>
         *      {code 3 x 3}: {code a=0.955, b=1.3693}
         *     </li>
         *     <li>
         *      {code 5 x 5}: {code a=1, b=1.4, c=2.1969}
         *     </li>
         *   </ul>
         *   <li>
         *  DIST_C: {code a = 1, b = 1}
         *   </li>
         * </ul>
         *
         * Typically, for a fast, coarse distance estimation #DIST_L2, a \(3\times 3\) mask is used. For a
         * more accurate distance estimation #DIST_L2, a \(5\times 5\) mask or the precise algorithm is used.
         * Note that both the precise and the approximate algorithms are linear on the number of pixels.
         *
         * This variant of the function does not only compute the minimum distance for each pixel \((x, y)\)
         * 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 {code 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_PIXEL, the function scans through the input image and
         * marks all the zero pixels with distinct labels.
         *
         * In this mode, the complexity is still linear. That is, the function provides a very fast way to
         * compute the Voronoi diagram for a binary image. Currently, the second variant can use only the
         * approximate distance transform algorithm, i.e. maskSize=#DIST_MASK_PRECISE is not supported
         * yet.
         *
         * param src 8-bit, single-channel (binary) source image.
         * param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
         * single-channel image of the same size as src.
         * param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type
         * CV_32SC1 and the same size as src.
         * param distanceType Type of distance, see #DistanceTypes
         * param maskSize Size of the distance transform mask, see #DistanceTransformMasks.
         * #DIST_MASK_PRECISE is not supported by this variant. In case of the #DIST_L1 or #DIST_C distance type,
         * the parameter is forced to 3 because a \(3\times 3\) mask gives the same result as \(5\times
         * 5\) or any larger aperture.
         */
        public static void distanceTransformWithLabels(Mat src, Mat dst, Mat labels, int distanceType, int maskSize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (labels != null) labels.ThrowIfDisposed();

            imgproc_Imgproc_distanceTransformWithLabels_11(src.nativeObj, dst.nativeObj, labels.nativeObj, distanceType, maskSize);


        }


        //
        // C++:  void cv::distanceTransform(Mat src, Mat& dst, int distanceType, int maskSize, int dstType = CV_32F)
        //

        /**
         *
         * param src 8-bit, single-channel (binary) source image.
         * param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
         * single-channel image of the same size as src .
         * param distanceType Type of distance, see #DistanceTypes
         * param maskSize Size of the distance transform mask, see #DistanceTransformMasks. In case of the
         * #DIST_L1 or #DIST_C distance type, the parameter is forced to 3 because a \(3\times 3\) mask gives
         * the same result as \(5\times 5\) or any larger aperture.
         * param dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for
         * the first variant of the function and distanceType == #DIST_L1.
         */
        public static void distanceTransform(Mat src, Mat dst, int distanceType, int maskSize, int dstType)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_distanceTransform_10(src.nativeObj, dst.nativeObj, distanceType, maskSize, dstType);


        }

        /**
         *
         * param src 8-bit, single-channel (binary) source image.
         * param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
         * single-channel image of the same size as src .
         * param distanceType Type of distance, see #DistanceTypes
         * param maskSize Size of the distance transform mask, see #DistanceTransformMasks. In case of the
         * #DIST_L1 or #DIST_C distance type, the parameter is forced to 3 because a \(3\times 3\) mask gives
         * the same result as \(5\times 5\) or any larger aperture.
         * the first variant of the function and distanceType == #DIST_L1.
         */
        public static void distanceTransform(Mat src, Mat dst, int distanceType, int maskSize)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_distanceTransform_11(src.nativeObj, dst.nativeObj, distanceType, maskSize);


        }


        //
        // C++:  int cv::floodFill(Mat& image, Mat& mask, Point seedPoint, Scalar newVal, Rect* rect = 0, Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), int flags = 4)
        //

        /**
         * Fills a connected component with the given color.
         *
         * The function cv::floodFill fills a connected component starting from the seed point with the specified
         * color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The
         * pixel at \((x,y)\) is considered to belong to the repainted domain if:
         *
         * <ul>
         *   <li>
         *  in case of a grayscale image and floating range
         * \(\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} (x',y')+ \texttt{upDiff}\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  in case of a grayscale image and fixed range
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  in case of a color image and floating range
         * \(\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\)
         * \(\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\)
         * and
         * \(\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  in case of a color image and fixed range
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\)
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\)
         * and
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\)
         *   </li>
         * </ul>
         *
         *
         * 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:
         * <ul>
         *   <li>
         *  Color/brightness of one of its neighbors that already belong to the connected component in case
         * of a floating range.
         *   </li>
         *   <li>
         *  Color/brightness of the seed point in case of a fixed range.
         *   </li>
         * </ul>
         *
         * Use these functions to either mark a connected component with the specified color in-place, or build
         * a mask and then extract the contour, or copy the region to another image, and so on.
         *
         * param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the
         * function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See
         * the details below.
         * param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels
         * taller than image. If an empty Mat is passed it will be created automatically. Since this is both an
         * input and output parameter, you must take responsibility of initializing it.
         * Flood-filling cannot go across non-zero pixels in the input mask. For example,
         * an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the
         * mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags
         * as described below. Additionally, the function fills the border of the mask with ones to simplify
         * internal processing. It is therefore possible to use the same mask in multiple calls to the function
         * to make sure the filled areas do not overlap.
         * param seedPoint Starting point.
         * param newVal New value of the repainted domain pixels.
         * param loDiff Maximal lower brightness/color difference between the currently observed pixel and
         * one of its neighbors belonging to the component, or a seed pixel being added to the component.
         * param upDiff Maximal upper brightness/color difference between the currently observed pixel and
         * one of its neighbors belonging to the component, or a seed pixel being added to the component.
         * param rect Optional output parameter set by the function to the minimum bounding rectangle of the
         * repainted domain.
         * param flags Operation flags. The first 8 bits contain a connectivity value. The default value of
         * 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A
         * connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner)
         * will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill
         * the mask (the default value is 1). For example, 4 | ( 255 &lt;&lt; 8 ) will consider 4 nearest
         * neighbours and fill the mask with a value of 255. The following additional options occupy higher
         * bits and therefore may be further combined with the connectivity and mask fill values using
         * bit-wise or (|), see #FloodFillFlags.
         *
         * <b>Note:</b> 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 .
         *
         * SEE: findContours
         * return automatically generated
         */
        public static int floodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect, Scalar loDiff, Scalar upDiff, int flags)
        {
            if (image != null) image.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            double[] rect_out = new double[4];
            int retVal = imgproc_Imgproc_floodFill_10(image.nativeObj, mask.nativeObj, seedPoint.x, seedPoint.y, newVal.val[0], newVal.val[1], newVal.val[2], newVal.val[3], rect_out, loDiff.val[0], loDiff.val[1], loDiff.val[2], loDiff.val[3], upDiff.val[0], upDiff.val[1], upDiff.val[2], upDiff.val[3], flags);
            if (rect != null) { rect.x = (int)rect_out[0]; rect.y = (int)rect_out[1]; rect.width = (int)rect_out[2]; rect.height = (int)rect_out[3]; }
            return retVal;
        }

        /**
         * Fills a connected component with the given color.
         *
         * The function cv::floodFill fills a connected component starting from the seed point with the specified
         * color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The
         * pixel at \((x,y)\) is considered to belong to the repainted domain if:
         *
         * <ul>
         *   <li>
         *  in case of a grayscale image and floating range
         * \(\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} (x',y')+ \texttt{upDiff}\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  in case of a grayscale image and fixed range
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  in case of a color image and floating range
         * \(\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\)
         * \(\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\)
         * and
         * \(\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  in case of a color image and fixed range
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\)
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\)
         * and
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\)
         *   </li>
         * </ul>
         *
         *
         * 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:
         * <ul>
         *   <li>
         *  Color/brightness of one of its neighbors that already belong to the connected component in case
         * of a floating range.
         *   </li>
         *   <li>
         *  Color/brightness of the seed point in case of a fixed range.
         *   </li>
         * </ul>
         *
         * Use these functions to either mark a connected component with the specified color in-place, or build
         * a mask and then extract the contour, or copy the region to another image, and so on.
         *
         * param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the
         * function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See
         * the details below.
         * param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels
         * taller than image. If an empty Mat is passed it will be created automatically. Since this is both an
         * input and output parameter, you must take responsibility of initializing it.
         * Flood-filling cannot go across non-zero pixels in the input mask. For example,
         * an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the
         * mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags
         * as described below. Additionally, the function fills the border of the mask with ones to simplify
         * internal processing. It is therefore possible to use the same mask in multiple calls to the function
         * to make sure the filled areas do not overlap.
         * param seedPoint Starting point.
         * param newVal New value of the repainted domain pixels.
         * param loDiff Maximal lower brightness/color difference between the currently observed pixel and
         * one of its neighbors belonging to the component, or a seed pixel being added to the component.
         * param upDiff Maximal upper brightness/color difference between the currently observed pixel and
         * one of its neighbors belonging to the component, or a seed pixel being added to the component.
         * param rect Optional output parameter set by the function to the minimum bounding rectangle of the
         * repainted domain.
         * 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A
         * connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner)
         * will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill
         * the mask (the default value is 1). For example, 4 | ( 255 &lt;&lt; 8 ) will consider 4 nearest
         * neighbours and fill the mask with a value of 255. The following additional options occupy higher
         * bits and therefore may be further combined with the connectivity and mask fill values using
         * bit-wise or (|), see #FloodFillFlags.
         *
         * <b>Note:</b> 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 .
         *
         * SEE: findContours
         * return automatically generated
         */
        public static int floodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect, Scalar loDiff, Scalar upDiff)
        {
            if (image != null) image.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            double[] rect_out = new double[4];
            int retVal = imgproc_Imgproc_floodFill_11(image.nativeObj, mask.nativeObj, seedPoint.x, seedPoint.y, newVal.val[0], newVal.val[1], newVal.val[2], newVal.val[3], rect_out, loDiff.val[0], loDiff.val[1], loDiff.val[2], loDiff.val[3], upDiff.val[0], upDiff.val[1], upDiff.val[2], upDiff.val[3]);
            if (rect != null) { rect.x = (int)rect_out[0]; rect.y = (int)rect_out[1]; rect.width = (int)rect_out[2]; rect.height = (int)rect_out[3]; }
            return retVal;
        }

        /**
         * Fills a connected component with the given color.
         *
         * The function cv::floodFill fills a connected component starting from the seed point with the specified
         * color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The
         * pixel at \((x,y)\) is considered to belong to the repainted domain if:
         *
         * <ul>
         *   <li>
         *  in case of a grayscale image and floating range
         * \(\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} (x',y')+ \texttt{upDiff}\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  in case of a grayscale image and fixed range
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  in case of a color image and floating range
         * \(\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\)
         * \(\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\)
         * and
         * \(\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  in case of a color image and fixed range
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\)
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\)
         * and
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\)
         *   </li>
         * </ul>
         *
         *
         * 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:
         * <ul>
         *   <li>
         *  Color/brightness of one of its neighbors that already belong to the connected component in case
         * of a floating range.
         *   </li>
         *   <li>
         *  Color/brightness of the seed point in case of a fixed range.
         *   </li>
         * </ul>
         *
         * Use these functions to either mark a connected component with the specified color in-place, or build
         * a mask and then extract the contour, or copy the region to another image, and so on.
         *
         * param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the
         * function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See
         * the details below.
         * param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels
         * taller than image. If an empty Mat is passed it will be created automatically. Since this is both an
         * input and output parameter, you must take responsibility of initializing it.
         * Flood-filling cannot go across non-zero pixels in the input mask. For example,
         * an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the
         * mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags
         * as described below. Additionally, the function fills the border of the mask with ones to simplify
         * internal processing. It is therefore possible to use the same mask in multiple calls to the function
         * to make sure the filled areas do not overlap.
         * param seedPoint Starting point.
         * param newVal New value of the repainted domain pixels.
         * param loDiff Maximal lower brightness/color difference between the currently observed pixel and
         * one of its neighbors belonging to the component, or a seed pixel being added to the component.
         * one of its neighbors belonging to the component, or a seed pixel being added to the component.
         * param rect Optional output parameter set by the function to the minimum bounding rectangle of the
         * repainted domain.
         * 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A
         * connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner)
         * will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill
         * the mask (the default value is 1). For example, 4 | ( 255 &lt;&lt; 8 ) will consider 4 nearest
         * neighbours and fill the mask with a value of 255. The following additional options occupy higher
         * bits and therefore may be further combined with the connectivity and mask fill values using
         * bit-wise or (|), see #FloodFillFlags.
         *
         * <b>Note:</b> 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 .
         *
         * SEE: findContours
         * return automatically generated
         */
        public static int floodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect, Scalar loDiff)
        {
            if (image != null) image.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            double[] rect_out = new double[4];
            int retVal = imgproc_Imgproc_floodFill_12(image.nativeObj, mask.nativeObj, seedPoint.x, seedPoint.y, newVal.val[0], newVal.val[1], newVal.val[2], newVal.val[3], rect_out, loDiff.val[0], loDiff.val[1], loDiff.val[2], loDiff.val[3]);
            if (rect != null) { rect.x = (int)rect_out[0]; rect.y = (int)rect_out[1]; rect.width = (int)rect_out[2]; rect.height = (int)rect_out[3]; }
            return retVal;
        }

        /**
         * Fills a connected component with the given color.
         *
         * The function cv::floodFill fills a connected component starting from the seed point with the specified
         * color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The
         * pixel at \((x,y)\) is considered to belong to the repainted domain if:
         *
         * <ul>
         *   <li>
         *  in case of a grayscale image and floating range
         * \(\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} (x',y')+ \texttt{upDiff}\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  in case of a grayscale image and fixed range
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  in case of a color image and floating range
         * \(\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\)
         * \(\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\)
         * and
         * \(\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  in case of a color image and fixed range
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\)
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\)
         * and
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\)
         *   </li>
         * </ul>
         *
         *
         * 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:
         * <ul>
         *   <li>
         *  Color/brightness of one of its neighbors that already belong to the connected component in case
         * of a floating range.
         *   </li>
         *   <li>
         *  Color/brightness of the seed point in case of a fixed range.
         *   </li>
         * </ul>
         *
         * Use these functions to either mark a connected component with the specified color in-place, or build
         * a mask and then extract the contour, or copy the region to another image, and so on.
         *
         * param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the
         * function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See
         * the details below.
         * param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels
         * taller than image. If an empty Mat is passed it will be created automatically. Since this is both an
         * input and output parameter, you must take responsibility of initializing it.
         * Flood-filling cannot go across non-zero pixels in the input mask. For example,
         * an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the
         * mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags
         * as described below. Additionally, the function fills the border of the mask with ones to simplify
         * internal processing. It is therefore possible to use the same mask in multiple calls to the function
         * to make sure the filled areas do not overlap.
         * param seedPoint Starting point.
         * param newVal New value of the repainted domain pixels.
         * one of its neighbors belonging to the component, or a seed pixel being added to the component.
         * one of its neighbors belonging to the component, or a seed pixel being added to the component.
         * param rect Optional output parameter set by the function to the minimum bounding rectangle of the
         * repainted domain.
         * 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A
         * connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner)
         * will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill
         * the mask (the default value is 1). For example, 4 | ( 255 &lt;&lt; 8 ) will consider 4 nearest
         * neighbours and fill the mask with a value of 255. The following additional options occupy higher
         * bits and therefore may be further combined with the connectivity and mask fill values using
         * bit-wise or (|), see #FloodFillFlags.
         *
         * <b>Note:</b> 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 .
         *
         * SEE: findContours
         * return automatically generated
         */
        public static int floodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect)
        {
            if (image != null) image.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();
            double[] rect_out = new double[4];
            int retVal = imgproc_Imgproc_floodFill_13(image.nativeObj, mask.nativeObj, seedPoint.x, seedPoint.y, newVal.val[0], newVal.val[1], newVal.val[2], newVal.val[3], rect_out);
            if (rect != null) { rect.x = (int)rect_out[0]; rect.y = (int)rect_out[1]; rect.width = (int)rect_out[2]; rect.height = (int)rect_out[3]; }
            return retVal;
        }

        /**
         * Fills a connected component with the given color.
         *
         * The function cv::floodFill fills a connected component starting from the seed point with the specified
         * color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The
         * pixel at \((x,y)\) is considered to belong to the repainted domain if:
         *
         * <ul>
         *   <li>
         *  in case of a grayscale image and floating range
         * \(\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} (x',y')+ \texttt{upDiff}\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  in case of a grayscale image and fixed range
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  in case of a color image and floating range
         * \(\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\)
         * \(\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\)
         * and
         * \(\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\)
         *   </li>
         * </ul>
         *
         *
         * <ul>
         *   <li>
         *  in case of a color image and fixed range
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\)
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\)
         * and
         * \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\)
         *   </li>
         * </ul>
         *
         *
         * 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:
         * <ul>
         *   <li>
         *  Color/brightness of one of its neighbors that already belong to the connected component in case
         * of a floating range.
         *   </li>
         *   <li>
         *  Color/brightness of the seed point in case of a fixed range.
         *   </li>
         * </ul>
         *
         * Use these functions to either mark a connected component with the specified color in-place, or build
         * a mask and then extract the contour, or copy the region to another image, and so on.
         *
         * param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the
         * function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See
         * the details below.
         * param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels
         * taller than image. If an empty Mat is passed it will be created automatically. Since this is both an
         * input and output parameter, you must take responsibility of initializing it.
         * Flood-filling cannot go across non-zero pixels in the input mask. For example,
         * an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the
         * mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags
         * as described below. Additionally, the function fills the border of the mask with ones to simplify
         * internal processing. It is therefore possible to use the same mask in multiple calls to the function
         * to make sure the filled areas do not overlap.
         * param seedPoint Starting point.
         * param newVal New value of the repainted domain pixels.
         * one of its neighbors belonging to the component, or a seed pixel being added to the component.
         * one of its neighbors belonging to the component, or a seed pixel being added to the component.
         * repainted domain.
         * 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A
         * connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner)
         * will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill
         * the mask (the default value is 1). For example, 4 | ( 255 &lt;&lt; 8 ) will consider 4 nearest
         * neighbours and fill the mask with a value of 255. The following additional options occupy higher
         * bits and therefore may be further combined with the connectivity and mask fill values using
         * bit-wise or (|), see #FloodFillFlags.
         *
         * <b>Note:</b> 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 .
         *
         * SEE: findContours
         * return automatically generated
         */
        public static int floodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal)
        {
            if (image != null) image.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();

            return imgproc_Imgproc_floodFill_14(image.nativeObj, mask.nativeObj, seedPoint.x, seedPoint.y, newVal.val[0], newVal.val[1], newVal.val[2], newVal.val[3]);


        }


        //
        // C++:  void cv::blendLinear(Mat src1, Mat src2, Mat weights1, Mat weights2, Mat& dst)
        //

        /**
         *
         *
         * variant without {code mask} parameter
         * param src1 automatically generated
         * param src2 automatically generated
         * param weights1 automatically generated
         * param weights2 automatically generated
         * param dst automatically generated
         */
        public static void blendLinear(Mat src1, Mat src2, Mat weights1, Mat weights2, Mat dst)
        {
            if (src1 != null) src1.ThrowIfDisposed();
            if (src2 != null) src2.ThrowIfDisposed();
            if (weights1 != null) weights1.ThrowIfDisposed();
            if (weights2 != null) weights2.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_blendLinear_10(src1.nativeObj, src2.nativeObj, weights1.nativeObj, weights2.nativeObj, dst.nativeObj);


        }


        //
        // C++:  void cv::cvtColor(Mat src, Mat& dst, int code, int dstCn = 0)
        //

        /**
         * 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:
         * <ul>
         *   <li>
         *    0 to 255 for CV_8U images
         *   </li>
         *   <li>
         *    0 to 65535 for CV_16U images
         *   </li>
         *   <li>
         *    0 to 1 for CV_32F images
         *   </li>
         * </ul>
         *
         * 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 \(\rightarrow\) L\*u\*v\* transformation. For example, if you have a
         * 32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will
         * have the 0..255 value range instead of 0..1 assumed by the function. So, before calling #cvtColor ,
         * you need first to scale the image down:
         * <code>
         *     img *= 1./255;
         *     cvtColor(img, img, COLOR_BGR2Luv);
         * </code>
         * If you use #cvtColor with 8-bit images, the conversion will have some information lost. For many
         * applications, this will not be noticeable but it is recommended to use 32-bit images in applications
         * that need the full range of colors or that convert an image before an operation and then convert
         * back.
         *
         * If conversion adds the alpha channel, its value will set to the maximum of corresponding channel
         * range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F.
         *
         * param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision
         * floating-point.
         * param dst output image of the same size and depth as src.
         * param code color space conversion code (see #ColorConversionCodes).
         * param dstCn number of channels in the destination image; if the parameter is 0, the number of the
         * channels is derived automatically from src and code.
         *
         * SEE: REF: imgproc_color_conversions
         */
        public static void cvtColor(Mat src, Mat dst, int code, int dstCn)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_cvtColor_10(src.nativeObj, dst.nativeObj, code, 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:
         * <ul>
         *   <li>
         *    0 to 255 for CV_8U images
         *   </li>
         *   <li>
         *    0 to 65535 for CV_16U images
         *   </li>
         *   <li>
         *    0 to 1 for CV_32F images
         *   </li>
         * </ul>
         *
         * 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 \(\rightarrow\) L\*u\*v\* transformation. For example, if you have a
         * 32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will
         * have the 0..255 value range instead of 0..1 assumed by the function. So, before calling #cvtColor ,
         * you need first to scale the image down:
         * <code>
         *     img *= 1./255;
         *     cvtColor(img, img, COLOR_BGR2Luv);
         * </code>
         * If you use #cvtColor with 8-bit images, the conversion will have some information lost. For many
         * applications, this will not be noticeable but it is recommended to use 32-bit images in applications
         * that need the full range of colors or that convert an image before an operation and then convert
         * back.
         *
         * If conversion adds the alpha channel, its value will set to the maximum of corresponding channel
         * range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F.
         *
         * param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision
         * floating-point.
         * param dst output image of the same size and depth as src.
         * param code color space conversion code (see #ColorConversionCodes).
         * channels is derived automatically from src and code.
         *
         * SEE: REF: imgproc_color_conversions
         */
        public static void cvtColor(Mat src, Mat dst, int code)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_cvtColor_11(src.nativeObj, dst.nativeObj, code);


        }


        //
        // C++:  void cv::cvtColorTwoPlane(Mat src1, Mat src2, Mat& dst, int code)
        //

        /**
         * Converts an image from one color space to another where the source image is
         * stored in two planes.
         *
         * This function only supports YUV420 to RGB conversion as of now.
         *
         * <ul>
         *   <li>
         *  #COLOR_YUV2BGR_NV12
         *   </li>
         *   <li>
         *  #COLOR_YUV2RGB_NV12
         *   </li>
         *   <li>
         *  #COLOR_YUV2BGRA_NV12
         *   </li>
         *   <li>
         *  #COLOR_YUV2RGBA_NV12
         *   </li>
         *   <li>
         *  #COLOR_YUV2BGR_NV21
         *   </li>
         *   <li>
         *  #COLOR_YUV2RGB_NV21
         *   </li>
         *   <li>
         *  #COLOR_YUV2BGRA_NV21
         *   </li>
         *   <li>
         *  #COLOR_YUV2RGBA_NV21
         *   </li>
         * </ul>
         * param src1 automatically generated
         * param src2 automatically generated
         * param dst automatically generated
         * param code automatically generated
         */
        public static void cvtColorTwoPlane(Mat src1, Mat src2, Mat dst, int code)
        {
            if (src1 != null) src1.ThrowIfDisposed();
            if (src2 != null) src2.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_cvtColorTwoPlane_10(src1.nativeObj, src2.nativeObj, dst.nativeObj, code);


        }


        //
        // C++:  void cv::demosaicing(Mat src, Mat& dst, int code, int dstCn = 0)
        //

        /**
         * main function for all demosaicing processes
         *
         * param src input image: 8-bit unsigned or 16-bit unsigned.
         * param dst output image of the same size and depth as src.
         * param code Color space conversion code (see the description below).
         * param dstCn number of channels in the destination image; if the parameter is 0, the number of the
         * channels is derived automatically from src and code.
         *
         * The function can do the following transformations:
         *
         * <ul>
         *   <li>
         *    Demosaicing using bilinear interpolation
         *   </li>
         * </ul>
         *
         *     #COLOR_BayerBG2BGR , #COLOR_BayerGB2BGR , #COLOR_BayerRG2BGR , #COLOR_BayerGR2BGR
         *
         *     #COLOR_BayerBG2GRAY , #COLOR_BayerGB2GRAY , #COLOR_BayerRG2GRAY , #COLOR_BayerGR2GRAY
         *
         * <ul>
         *   <li>
         *    Demosaicing using Variable Number of Gradients.
         *   </li>
         * </ul>
         *
         *     #COLOR_BayerBG2BGR_VNG , #COLOR_BayerGB2BGR_VNG , #COLOR_BayerRG2BGR_VNG , #COLOR_BayerGR2BGR_VNG
         *
         * <ul>
         *   <li>
         *    Edge-Aware Demosaicing.
         *   </li>
         * </ul>
         *
         *     #COLOR_BayerBG2BGR_EA , #COLOR_BayerGB2BGR_EA , #COLOR_BayerRG2BGR_EA , #COLOR_BayerGR2BGR_EA
         *
         * <ul>
         *   <li>
         *    Demosaicing with alpha channel
         *   </li>
         * </ul>
         *
         *     #COLOR_BayerBG2BGRA , #COLOR_BayerGB2BGRA , #COLOR_BayerRG2BGRA , #COLOR_BayerGR2BGRA
         *
         * SEE: cvtColor
         */
        public static void demosaicing(Mat src, Mat dst, int code, int dstCn)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_demosaicing_10(src.nativeObj, dst.nativeObj, code, dstCn);


        }

        /**
         * main function for all demosaicing processes
         *
         * param src input image: 8-bit unsigned or 16-bit unsigned.
         * param dst output image of the same size and depth as src.
         * param code Color space conversion code (see the description below).
         * channels is derived automatically from src and code.
         *
         * The function can do the following transformations:
         *
         * <ul>
         *   <li>
         *    Demosaicing using bilinear interpolation
         *   </li>
         * </ul>
         *
         *     #COLOR_BayerBG2BGR , #COLOR_BayerGB2BGR , #COLOR_BayerRG2BGR , #COLOR_BayerGR2BGR
         *
         *     #COLOR_BayerBG2GRAY , #COLOR_BayerGB2GRAY , #COLOR_BayerRG2GRAY , #COLOR_BayerGR2GRAY
         *
         * <ul>
         *   <li>
         *    Demosaicing using Variable Number of Gradients.
         *   </li>
         * </ul>
         *
         *     #COLOR_BayerBG2BGR_VNG , #COLOR_BayerGB2BGR_VNG , #COLOR_BayerRG2BGR_VNG , #COLOR_BayerGR2BGR_VNG
         *
         * <ul>
         *   <li>
         *    Edge-Aware Demosaicing.
         *   </li>
         * </ul>
         *
         *     #COLOR_BayerBG2BGR_EA , #COLOR_BayerGB2BGR_EA , #COLOR_BayerRG2BGR_EA , #COLOR_BayerGR2BGR_EA
         *
         * <ul>
         *   <li>
         *    Demosaicing with alpha channel
         *   </li>
         * </ul>
         *
         *     #COLOR_BayerBG2BGRA , #COLOR_BayerGB2BGRA , #COLOR_BayerRG2BGRA , #COLOR_BayerGR2BGRA
         *
         * SEE: cvtColor
         */
        public static void demosaicing(Mat src, Mat dst, int code)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_demosaicing_11(src.nativeObj, dst.nativeObj, code);


        }


        //
        // C++:  Moments cv::moments(Mat array, bool binaryImage = false)
        //

        /**
         * Calculates all of the moments up to the third order of a polygon or rasterized shape.
         *
         * The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The
         * results are returned in the structure cv::Moments.
         *
         * param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array (
         * \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f ).
         * param binaryImage If it is true, all non-zero image pixels are treated as 1's. The parameter is
         * used for images only.
         * return moments.
         *
         * <b>Note:</b> Only applicable to contour moments calculations from Python bindings: Note that the numpy
         * type for the input array should be either np.int32 or np.float32.
         *
         * SEE:  contourArea, arcLength
         */
        public static Moments moments(Mat array, bool binaryImage)
        {
            if (array != null) array.ThrowIfDisposed();

            double[] tmpArray = new double[10];
            imgproc_Imgproc_moments_10(array.nativeObj, binaryImage, tmpArray);
            Moments retVal = new Moments(tmpArray);

            return retVal;
        }

        /**
         * Calculates all of the moments up to the third order of a polygon or rasterized shape.
         *
         * The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The
         * results are returned in the structure cv::Moments.
         *
         * param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array (
         * \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f ).
         * used for images only.
         * return moments.
         *
         * <b>Note:</b> Only applicable to contour moments calculations from Python bindings: Note that the numpy
         * type for the input array should be either np.int32 or np.float32.
         *
         * SEE:  contourArea, arcLength
         */
        public static Moments moments(Mat array)
        {
            if (array != null) array.ThrowIfDisposed();

            double[] tmpArray = new double[10];
            imgproc_Imgproc_moments_11(array.nativeObj, tmpArray);
            Moments retVal = new Moments(tmpArray);

            return retVal;
        }


        //
        // C++:  void cv::HuMoments(Moments m, Mat& hu)
        //

        public static void HuMoments(Moments m, Mat hu)
        {
            if (hu != null) hu.ThrowIfDisposed();

            imgproc_Imgproc_HuMoments_10(m.m00, m.m10, m.m01, m.m20, m.m11, m.m02, m.m30, m.m21, m.m12, m.m03, hu.nativeObj);


        }


        //
        // C++:  void cv::matchTemplate(Mat image, Mat templ, Mat& result, int method, Mat mask = Mat())
        //

        /**
         * Compares a template against overlapped image regions.
         *
         * The function slides through image , compares the overlapped patches of size \(w \times h\) against
         * templ using the specified method and stores the comparison results in result . #TemplateMatchModes
         * describes the formulae for the available comparison methods ( \(I\) denotes image, \(T\)
         * template, \(R\) result, \(M\) the optional mask ). The summation is done over template and/or
         * the image patch: \(x' = 0...w-1, y' = 0...h-1\)
         *
         * After the function finishes the comparison, the best matches can be found as global minimums (when
         * #TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the
         * #minMaxLoc function. In case of a color image, template summation in the numerator and each sum in
         * the denominator is done over all of the channels and separate mean values are used for each channel.
         * That is, the function can take a color template and a color image. The result will still be a
         * single-channel image, which is easier to analyze.
         *
         * param image Image where the search is running. It must be 8-bit or 32-bit floating-point.
         * param templ Searched template. It must be not greater than the source image and have the same
         * data type.
         * param result Map of comparison results. It must be single-channel 32-bit floating-point. If image
         * is \(W \times H\) and templ is \(w \times h\) , then result is \((W-w+1) \times (H-h+1)\) .
         * param method Parameter specifying the comparison method, see #TemplateMatchModes
         * param mask Optional mask. It must have the same size as templ. It must either have the same number
         *             of channels as template or only one channel, which is then used for all template and
         *             image channels. If the data type is #CV_8U, the mask is interpreted as a binary mask,
         *             meaning only elements where mask is nonzero are used and are kept unchanged independent
         *             of the actual mask value (weight equals 1). For data tpye #CV_32F, the mask values are
         *             used as weights. The exact formulas are documented in #TemplateMatchModes.
         */
        public static void matchTemplate(Mat image, Mat templ, Mat result, int method, Mat mask)
        {
            if (image != null) image.ThrowIfDisposed();
            if (templ != null) templ.ThrowIfDisposed();
            if (result != null) result.ThrowIfDisposed();
            if (mask != null) mask.ThrowIfDisposed();

            imgproc_Imgproc_matchTemplate_10(image.nativeObj, templ.nativeObj, result.nativeObj, method, mask.nativeObj);


        }

        /**
         * Compares a template against overlapped image regions.
         *
         * The function slides through image , compares the overlapped patches of size \(w \times h\) against
         * templ using the specified method and stores the comparison results in result . #TemplateMatchModes
         * describes the formulae for the available comparison methods ( \(I\) denotes image, \(T\)
         * template, \(R\) result, \(M\) the optional mask ). The summation is done over template and/or
         * the image patch: \(x' = 0...w-1, y' = 0...h-1\)
         *
         * After the function finishes the comparison, the best matches can be found as global minimums (when
         * #TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the
         * #minMaxLoc function. In case of a color image, template summation in the numerator and each sum in
         * the denominator is done over all of the channels and separate mean values are used for each channel.
         * That is, the function can take a color template and a color image. The result will still be a
         * single-channel image, which is easier to analyze.
         *
         * param image Image where the search is running. It must be 8-bit or 32-bit floating-point.
         * param templ Searched template. It must be not greater than the source image and have the same
         * data type.
         * param result Map of comparison results. It must be single-channel 32-bit floating-point. If image
         * is \(W \times H\) and templ is \(w \times h\) , then result is \((W-w+1) \times (H-h+1)\) .
         * param method Parameter specifying the comparison method, see #TemplateMatchModes
         *             of channels as template or only one channel, which is then used for all template and
         *             image channels. If the data type is #CV_8U, the mask is interpreted as a binary mask,
         *             meaning only elements where mask is nonzero are used and are kept unchanged independent
         *             of the actual mask value (weight equals 1). For data tpye #CV_32F, the mask values are
         *             used as weights. The exact formulas are documented in #TemplateMatchModes.
         */
        public static void matchTemplate(Mat image, Mat templ, Mat result, int method)
        {
            if (image != null) image.ThrowIfDisposed();
            if (templ != null) templ.ThrowIfDisposed();
            if (result != null) result.ThrowIfDisposed();

            imgproc_Imgproc_matchTemplate_11(image.nativeObj, templ.nativeObj, result.nativeObj, method);


        }


        //
        // C++:  int cv::connectedComponents(Mat image, Mat& labels, int connectivity, int ltype, int ccltype)
        //

        /**
         * computes the connected components labeled image of boolean image
         *
         * image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
         * represents the background label. ltype specifies the output label image type, an important
         * consideration based on the total number of labels or alternatively the total number of pixels in
         * the source image. ccltype specifies the connected components labeling algorithm to use, currently
         * Bolelli (Spaghetti) CITE: Bolelli2019, Grana (BBDT) CITE: Grana2010 and Wu's (SAUF) CITE: Wu2009 algorithms
         * are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces
         * a row major ordering of labels while Spaghetti and BBDT do not.
         * This function uses parallel version of the algorithms if at least one allowed
         * parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.
         *
         * param image the 8-bit single-channel image to be labeled
         * param labels destination labeled image
         * param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
         * param ltype output image label type. Currently CV_32S and CV_16U are supported.
         * param ccltype connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes).
         * return automatically generated
         */
        public static int connectedComponentsWithAlgorithm(Mat image, Mat labels, int connectivity, int ltype, int ccltype)
        {
            if (image != null) image.ThrowIfDisposed();
            if (labels != null) labels.ThrowIfDisposed();

            return imgproc_Imgproc_connectedComponentsWithAlgorithm_10(image.nativeObj, labels.nativeObj, connectivity, ltype, ccltype);


        }


        //
        // C++:  int cv::connectedComponents(Mat image, Mat& labels, int connectivity = 8, int ltype = CV_32S)
        //

        /**
         *
         *
         * param image the 8-bit single-channel image to be labeled
         * param labels destination labeled image
         * param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
         * param ltype output image label type. Currently CV_32S and CV_16U are supported.
         * return automatically generated
         */
        public static int connectedComponents(Mat image, Mat labels, int connectivity, int ltype)
        {
            if (image != null) image.ThrowIfDisposed();
            if (labels != null) labels.ThrowIfDisposed();

            return imgproc_Imgproc_connectedComponents_10(image.nativeObj, labels.nativeObj, connectivity, ltype);


        }

        /**
         *
         *
         * param image the 8-bit single-channel image to be labeled
         * param labels destination labeled image
         * param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
         * return automatically generated
         */
        public static int connectedComponents(Mat image, Mat labels, int connectivity)
        {
            if (image != null) image.ThrowIfDisposed();
            if (labels != null) labels.ThrowIfDisposed();

            return imgproc_Imgproc_connectedComponents_11(image.nativeObj, labels.nativeObj, connectivity);


        }

        /**
         *
         *
         * param image the 8-bit single-channel image to be labeled
         * param labels destination labeled image
         * return automatically generated
         */
        public static int connectedComponents(Mat image, Mat labels)
        {
            if (image != null) image.ThrowIfDisposed();
            if (labels != null) labels.ThrowIfDisposed();

            return imgproc_Imgproc_connectedComponents_12(image.nativeObj, labels.nativeObj);


        }


        //
        // C++:  int cv::connectedComponentsWithStats(Mat image, Mat& labels, Mat& stats, Mat& centroids, int connectivity, int ltype, int ccltype)
        //

        /**
         * computes the connected components labeled image of boolean image and also produces a statistics output for each label
         *
         * image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
         * represents the background label. ltype specifies the output label image type, an important
         * consideration based on the total number of labels or alternatively the total number of pixels in
         * the source image. ccltype specifies the connected components labeling algorithm to use, currently
         * Bolelli (Spaghetti) CITE: Bolelli2019, Grana (BBDT) CITE: Grana2010 and Wu's (SAUF) CITE: Wu2009 algorithms
         * are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces
         * a row major ordering of labels while Spaghetti and BBDT do not.
         * This function uses parallel version of the algorithms (statistics included) if at least one allowed
         * parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.
         *
         * param image the 8-bit single-channel image to be labeled
         * param labels destination labeled image
         * param stats statistics output for each label, including the background label.
         * Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
         * #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S.
         * param centroids centroid output for each label, including the background label. Centroids are
         * accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
         * param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
         * param ltype output image label type. Currently CV_32S and CV_16U are supported.
         * param ccltype connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes).
         * return automatically generated
         */
        public static int connectedComponentsWithStatsWithAlgorithm(Mat image, Mat labels, Mat stats, Mat centroids, int connectivity, int ltype, int ccltype)
        {
            if (image != null) image.ThrowIfDisposed();
            if (labels != null) labels.ThrowIfDisposed();
            if (stats != null) stats.ThrowIfDisposed();
            if (centroids != null) centroids.ThrowIfDisposed();

            return imgproc_Imgproc_connectedComponentsWithStatsWithAlgorithm_10(image.nativeObj, labels.nativeObj, stats.nativeObj, centroids.nativeObj, connectivity, ltype, ccltype);


        }


        //
        // C++:  int cv::connectedComponentsWithStats(Mat image, Mat& labels, Mat& stats, Mat& centroids, int connectivity = 8, int ltype = CV_32S)
        //

        /**
         *
         * param image the 8-bit single-channel image to be labeled
         * param labels destination labeled image
         * param stats statistics output for each label, including the background label.
         * Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
         * #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S.
         * param centroids centroid output for each label, including the background label. Centroids are
         * accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
         * param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
         * param ltype output image label type. Currently CV_32S and CV_16U are supported.
         * return automatically generated
         */
        public static int connectedComponentsWithStats(Mat image, Mat labels, Mat stats, Mat centroids, int connectivity, int ltype)
        {
            if (image != null) image.ThrowIfDisposed();
            if (labels != null) labels.ThrowIfDisposed();
            if (stats != null) stats.ThrowIfDisposed();
            if (centroids != null) centroids.ThrowIfDisposed();

            return imgproc_Imgproc_connectedComponentsWithStats_10(image.nativeObj, labels.nativeObj, stats.nativeObj, centroids.nativeObj, connectivity, ltype);


        }

        /**
         *
         * param image the 8-bit single-channel image to be labeled
         * param labels destination labeled image
         * param stats statistics output for each label, including the background label.
         * Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
         * #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S.
         * param centroids centroid output for each label, including the background label. Centroids are
         * accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
         * param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
         * return automatically generated
         */
        public static int connectedComponentsWithStats(Mat image, Mat labels, Mat stats, Mat centroids, int connectivity)
        {
            if (image != null) image.ThrowIfDisposed();
            if (labels != null) labels.ThrowIfDisposed();
            if (stats != null) stats.ThrowIfDisposed();
            if (centroids != null) centroids.ThrowIfDisposed();

            return imgproc_Imgproc_connectedComponentsWithStats_11(image.nativeObj, labels.nativeObj, stats.nativeObj, centroids.nativeObj, connectivity);


        }

        /**
         *
         * param image the 8-bit single-channel image to be labeled
         * param labels destination labeled image
         * param stats statistics output for each label, including the background label.
         * Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
         * #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S.
         * param centroids centroid output for each label, including the background label. Centroids are
         * accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
         * return automatically generated
         */
        public static int connectedComponentsWithStats(Mat image, Mat labels, Mat stats, Mat centroids)
        {
            if (image != null) image.ThrowIfDisposed();
            if (labels != null) labels.ThrowIfDisposed();
            if (stats != null) stats.ThrowIfDisposed();
            if (centroids != null) centroids.ThrowIfDisposed();

            return imgproc_Imgproc_connectedComponentsWithStats_12(image.nativeObj, labels.nativeObj, stats.nativeObj, centroids.nativeObj);


        }


        //
        // C++:  void cv::findContours(Mat image, vector_vector_Point& contours, Mat& hierarchy, int mode, int method, Point offset = Point())
        //

        /**
         * Finds contours in a binary image.
         *
         * The function retrieves contours from the binary image using the algorithm CITE: Suzuki85 . The contours
         * are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the
         * OpenCV sample directory.
         * <b>Note:</b> Since opencv 3.2 source image is not modified by this function.
         *
         * param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero
         * pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold ,
         * #adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one.
         * If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
         * param contours Detected contours. Each contour is stored as a vector of points (e.g.
         * std::vector&lt;std::vector&lt;cv::Point&gt; &gt;).
         * param hierarchy Optional output vector (e.g. std::vector&lt;cv::Vec4i&gt;), containing information about the image topology. It has
         * as many elements as the number of contours. For each i-th contour contours[i], the elements
         * hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices
         * in contours of the next and previous contours at the same hierarchical level, the first child
         * contour and the parent contour, respectively. If for the contour i there are no next, previous,
         * parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
         * <b>Note:</b> In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour.
         * param mode Contour retrieval mode, see #RetrievalModes
         * param method Contour approximation method, see #ContourApproximationModes
         * param offset Optional offset by which every contour point is shifted. This is useful if the
         * contours are extracted from the image ROI and then they should be analyzed in the whole image
         * context.
         */
        public static void findContours(Mat image, List<MatOfPoint> contours, Mat hierarchy, int mode, int method, Point offset)
        {
            if (image != null) image.ThrowIfDisposed();
            if (hierarchy != null) hierarchy.ThrowIfDisposed();
            Mat contours_mat = new Mat();
            imgproc_Imgproc_findContours_10(image.nativeObj, contours_mat.nativeObj, hierarchy.nativeObj, mode, method, offset.x, offset.y);
            Converters.Mat_to_vector_vector_Point(contours_mat, contours);
            contours_mat.release();

        }

        /**
         * Finds contours in a binary image.
         *
         * The function retrieves contours from the binary image using the algorithm CITE: Suzuki85 . The contours
         * are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the
         * OpenCV sample directory.
         * <b>Note:</b> Since opencv 3.2 source image is not modified by this function.
         *
         * param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero
         * pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold ,
         * #adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one.
         * If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
         * param contours Detected contours. Each contour is stored as a vector of points (e.g.
         * std::vector&lt;std::vector&lt;cv::Point&gt; &gt;).
         * param hierarchy Optional output vector (e.g. std::vector&lt;cv::Vec4i&gt;), containing information about the image topology. It has
         * as many elements as the number of contours. For each i-th contour contours[i], the elements
         * hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices
         * in contours of the next and previous contours at the same hierarchical level, the first child
         * contour and the parent contour, respectively. If for the contour i there are no next, previous,
         * parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
         * <b>Note:</b> In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour.
         * param mode Contour retrieval mode, see #RetrievalModes
         * param method Contour approximation method, see #ContourApproximationModes
         * contours are extracted from the image ROI and then they should be analyzed in the whole image
         * context.
         */
        public static void findContours(Mat image, List<MatOfPoint> contours, Mat hierarchy, int mode, int method)
        {
            if (image != null) image.ThrowIfDisposed();
            if (hierarchy != null) hierarchy.ThrowIfDisposed();
            Mat contours_mat = new Mat();
            imgproc_Imgproc_findContours_11(image.nativeObj, contours_mat.nativeObj, hierarchy.nativeObj, mode, method);
            Converters.Mat_to_vector_vector_Point(contours_mat, contours);
            contours_mat.release();

        }


        //
        // C++:  void cv::approxPolyDP(vector_Point2f curve, vector_Point2f& approxCurve, double epsilon, bool closed)
        //

        /**
         * Approximates a polygonal curve(s) with the specified precision.
         *
         * The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less
         * vertices so that the distance between them is less or equal to the specified precision. It uses the
         * Douglas-Peucker algorithm &lt;http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm&gt;
         *
         * param curve Input vector of a 2D point stored in std::vector or Mat
         * param approxCurve Result of the approximation. The type should match the type of the input curve.
         * param epsilon Parameter specifying the approximation accuracy. This is the maximum distance
         * between the original curve and its approximation.
         * param closed If true, the approximated curve is closed (its first and last vertices are
         * connected). Otherwise, it is not closed.
         */
        public static void approxPolyDP(MatOfPoint2f curve, MatOfPoint2f approxCurve, double epsilon, bool closed)
        {
            if (curve != null) curve.ThrowIfDisposed();
            if (approxCurve != null) approxCurve.ThrowIfDisposed();
            Mat curve_mat = curve;
            Mat approxCurve_mat = approxCurve;
            imgproc_Imgproc_approxPolyDP_10(curve_mat.nativeObj, approxCurve_mat.nativeObj, epsilon, closed);


        }


        //
        // C++:  double cv::arcLength(vector_Point2f curve, bool closed)
        //

        /**
         * Calculates a contour perimeter or a curve length.
         *
         * The function computes a curve length or a closed contour perimeter.
         *
         * param curve Input vector of 2D points, stored in std::vector or Mat.
         * param closed Flag indicating whether the curve is closed or not.
         * return automatically generated
         */
        public static double arcLength(MatOfPoint2f curve, bool closed)
        {
            if (curve != null) curve.ThrowIfDisposed();
            Mat curve_mat = curve;
            return imgproc_Imgproc_arcLength_10(curve_mat.nativeObj, closed);


        }


        //
        // C++:  Rect cv::boundingRect(Mat array)
        //

        /**
         * Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.
         *
         * The function calculates and returns the minimal up-right bounding rectangle for the specified point set or
         * non-zero pixels of gray-scale image.
         *
         * param array Input gray-scale image or 2D point set, stored in std::vector or Mat.
         * return automatically generated
         */
        public static Rect boundingRect(Mat array)
        {
            if (array != null) array.ThrowIfDisposed();

            double[] tmpArray = new double[4];
            imgproc_Imgproc_boundingRect_10(array.nativeObj, tmpArray);
            Rect retVal = new Rect(tmpArray);

            return retVal;
        }


        //
        // C++:  double cv::contourArea(Mat contour, bool oriented = false)
        //

        /**
         * Calculates a contour area.
         *
         * The function computes a contour area. Similarly to moments , the area is computed using the Green
         * formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using
         * #drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong
         * results for contours with self-intersections.
         *
         * Example:
         * <code>
         *     vector&lt;Point&gt; 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&lt;Point&gt; approx;
         *     approxPolyDP(contour, approx, 5, true);
         *     double area1 = contourArea(approx);
         *
         *     cout &lt;&lt; "area0 =" &lt;&lt; area0 &lt;&lt; endl &lt;&lt;
         *             "area1 =" &lt;&lt; area1 &lt;&lt; endl &lt;&lt;
         *             "approx poly vertices" &lt;&lt; approx.size() &lt;&lt; endl;
         * </code>
         * param contour Input vector of 2D points (contour vertices), stored in std::vector or Mat.
         * param oriented Oriented area flag. If it is true, the function returns a signed area value,
         * depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can
         * determine orientation of a contour by taking the sign of an area. By default, the parameter is
         * false, which means that the absolute value is returned.
         * return automatically generated
         */
        public static double contourArea(Mat contour, bool oriented)
        {
            if (contour != null) contour.ThrowIfDisposed();

            return imgproc_Imgproc_contourArea_10(contour.nativeObj, 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:
         * <code>
         *     vector&lt;Point&gt; 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&lt;Point&gt; approx;
         *     approxPolyDP(contour, approx, 5, true);
         *     double area1 = contourArea(approx);
         *
         *     cout &lt;&lt; "area0 =" &lt;&lt; area0 &lt;&lt; endl &lt;&lt;
         *             "area1 =" &lt;&lt; area1 &lt;&lt; endl &lt;&lt;
         *             "approx poly vertices" &lt;&lt; approx.size() &lt;&lt; endl;
         * </code>
         * param contour Input vector of 2D points (contour vertices), stored in std::vector or Mat.
         * 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.
         * return automatically generated
         */
        public static double contourArea(Mat contour)
        {
            if (contour != null) contour.ThrowIfDisposed();

            return imgproc_Imgproc_contourArea_11(contour.nativeObj);


        }


        //
        // C++:  RotatedRect cv::minAreaRect(vector_Point2f 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. Developer should keep in mind that the returned RotatedRect can contain negative
         * indices when data is close to the containing Mat element boundary.
         *
         * param points Input vector of 2D points, stored in std::vector&lt;&gt; or Mat
         * return automatically generated
         */
        public static RotatedRect minAreaRect(MatOfPoint2f points)
        {
            if (points != null) points.ThrowIfDisposed();
            Mat points_mat = points;
            double[] tmpArray = new double[5];
            imgproc_Imgproc_minAreaRect_10(points_mat.nativeObj, tmpArray);
            RotatedRect retVal = new RotatedRect(tmpArray);

            return retVal;
        }


        //
        // C++:  void cv::boxPoints(RotatedRect box, Mat& points)
        //

        /**
         * Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.
         *
         * The function finds the four vertices of a rotated rectangle. This function is useful to draw the
         * rectangle. In C++, instead of using this function, you can directly use RotatedRect::points method. Please
         * visit the REF: tutorial_bounding_rotated_ellipses "tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information.
         *
         * param box The input rotated rectangle. It may be the output of REF: minAreaRect.
         * param points The output array of four vertices of rectangles.
         */
        public static void boxPoints(RotatedRect box, Mat points)
        {
            if (points != null) points.ThrowIfDisposed();

            imgproc_Imgproc_boxPoints_10(box.center.x, box.center.y, box.size.width, box.size.height, box.angle, points.nativeObj);


        }


        //
        // C++:  void cv::minEnclosingCircle(vector_Point2f points, Point2f& 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.
         *
         * param points Input vector of 2D points, stored in std::vector&lt;&gt; or Mat
         * param center Output center of the circle.
         * param radius Output radius of the circle.
         */
        public static void minEnclosingCircle(MatOfPoint2f points, Point center, float[] radius)
        {
            if (points != null) points.ThrowIfDisposed();
            Mat points_mat = points;
            double[] center_out = new double[2];
            double[] radius_out = new double[1];
            imgproc_Imgproc_minEnclosingCircle_10(points_mat.nativeObj, center_out, radius_out);
            if (center != null) { center.x = center_out[0]; center.y = center_out[1]; }
            if (radius != null) radius[0] = (float)radius_out[0];

        }


        //
        // C++:  double cv::minEnclosingTriangle(Mat points, Mat& triangle)
        //

        /**
         * Finds a triangle of minimum area enclosing a 2D point set and returns its area.
         *
         * The function finds a triangle of minimum area enclosing the given set of 2D points and returns its
         * area. The output for a given 2D point set is shown in the image below. 2D points are depicted in
         * red* and the enclosing triangle in *yellow*.
         *
         * ![Sample output of the minimum enclosing triangle function](pics/minenclosingtriangle.png)
         *
         * The implementation of the algorithm is based on O'Rourke's CITE: ORourke86 and Klee and Laskowski's
         * CITE: KleeLaskowski85 papers. O'Rourke provides a \(\theta(n)\) algorithm for finding the minimal
         * enclosing triangle of a 2D convex polygon with n vertices. Since the #minEnclosingTriangle function
         * takes a 2D point set as input an additional preprocessing step of computing the convex hull of the
         * 2D point set is required. The complexity of the #convexHull function is \(O(n log(n))\) which is higher
         * than \(\theta(n)\). Thus the overall complexity of the function is \(O(n log(n))\).
         *
         * param points Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector&lt;&gt; or Mat
         * param triangle Output vector of three 2D points defining the vertices of the triangle. The depth
         * of the OutputArray must be CV_32F.
         * return automatically generated
         */
        public static double minEnclosingTriangle(Mat points, Mat triangle)
        {
            if (points != null) points.ThrowIfDisposed();
            if (triangle != null) triangle.ThrowIfDisposed();

            return imgproc_Imgproc_minEnclosingTriangle_10(points.nativeObj, triangle.nativeObj);


        }


        //
        // C++:  double cv::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)
         *
         * param contour1 First contour or grayscale image.
         * param contour2 Second contour or grayscale image.
         * param method Comparison method, see #ShapeMatchModes
         * param parameter Method-specific parameter (not supported now).
         * return automatically generated
         */
        public static double matchShapes(Mat contour1, Mat contour2, int method, double parameter)
        {
            if (contour1 != null) contour1.ThrowIfDisposed();
            if (contour2 != null) contour2.ThrowIfDisposed();

            return imgproc_Imgproc_matchShapes_10(contour1.nativeObj, contour2.nativeObj, method, parameter);


        }


        //
        // C++:  void cv::convexHull(vector_Point points, vector_int& hull, bool clockwise = false,  _hidden_  returnPoints = true)
        //

        /**
         * Finds the convex hull of a point set.
         *
         * The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm CITE: Sklansky82
         * that has *O(N logN)* complexity in the current implementation.
         *
         * param points Input 2D point set, stored in std::vector or Mat.
         * param hull Output convex hull. It is either an integer vector of indices or vector of points. In
         * the first case, the hull elements are 0-based indices of the convex hull points in the original
         * array (since the set of convex hull points is a subset of the original point set). In the second
         * case, hull elements are the convex hull points themselves.
         * param clockwise Orientation flag. If it is true, the output convex hull is oriented clockwise.
         * Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing
         * to the right, and its Y axis pointing upwards.
         * returns convex hull points. Otherwise, it returns indices of the convex hull points. When the
         * output array is std::vector, the flag is ignored, and the output depends on the type of the
         * vector: std::vector&lt;int&gt; implies returnPoints=false, std::vector&lt;Point&gt; implies
         * returnPoints=true.
         *
         * <b>Note:</b> {code points} and {code hull} should be different arrays, inplace processing isn't supported.
         *
         * Check REF: tutorial_hull "the corresponding tutorial" for more details.
         *
         * useful links:
         *
         * https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/
         */
        public static void convexHull(MatOfPoint points, MatOfInt hull, bool clockwise)
        {
            if (points != null) points.ThrowIfDisposed();
            if (hull != null) hull.ThrowIfDisposed();
            Mat points_mat = points;
            Mat hull_mat = hull;
            imgproc_Imgproc_convexHull_10(points_mat.nativeObj, hull_mat.nativeObj, clockwise);


        }

        /**
         * Finds the convex hull of a point set.
         *
         * The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm CITE: Sklansky82
         * that has *O(N logN)* complexity in the current implementation.
         *
         * param points Input 2D point set, stored in std::vector or Mat.
         * param hull Output convex hull. It is either an integer vector of indices or vector of points. In
         * the first case, the hull elements are 0-based indices of the convex hull points in the original
         * array (since the set of convex hull points is a subset of the original point set). In the second
         * case, hull elements are the convex hull points themselves.
         * Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing
         * to the right, and its Y axis pointing upwards.
         * returns convex hull points. Otherwise, it returns indices of the convex hull points. When the
         * output array is std::vector, the flag is ignored, and the output depends on the type of the
         * vector: std::vector&lt;int&gt; implies returnPoints=false, std::vector&lt;Point&gt; implies
         * returnPoints=true.
         *
         * <b>Note:</b> {code points} and {code hull} should be different arrays, inplace processing isn't supported.
         *
         * Check REF: tutorial_hull "the corresponding tutorial" for more details.
         *
         * useful links:
         *
         * https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/
         */
        public static void convexHull(MatOfPoint points, MatOfInt hull)
        {
            if (points != null) points.ThrowIfDisposed();
            if (hull != null) hull.ThrowIfDisposed();
            Mat points_mat = points;
            Mat hull_mat = hull;
            imgproc_Imgproc_convexHull_12(points_mat.nativeObj, hull_mat.nativeObj);


        }


        //
        // C++:  void cv::convexityDefects(vector_Point contour, vector_int convexhull, vector_Vec4i& convexityDefects)
        //

        /**
         * Finds the convexity defects of a contour.
         *
         * The figure below displays convexity defects of a hand contour:
         *
         * ![image](pics/defects.png)
         *
         * param contour Input contour.
         * param convexhull Convex hull obtained using convexHull that should contain indices of the contour
         * points that make the hull.
         * param convexityDefects The output vector of convexity defects. In C++ and the new Python/Java
         * interface each convexity defect is represented as 4-element integer vector (a.k.a. #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.
         */
        public static void convexityDefects(MatOfPoint contour, MatOfInt convexhull, MatOfInt4 convexityDefects)
        {
            if (contour != null) contour.ThrowIfDisposed();
            if (convexhull != null) convexhull.ThrowIfDisposed();
            if (convexityDefects != null) convexityDefects.ThrowIfDisposed();
            Mat contour_mat = contour;
            Mat convexhull_mat = convexhull;
            Mat convexityDefects_mat = convexityDefects;
            imgproc_Imgproc_convexityDefects_10(contour_mat.nativeObj, convexhull_mat.nativeObj, convexityDefects_mat.nativeObj);


        }


        //
        // C++:  bool cv::isContourConvex(vector_Point 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.
         *
         * param contour Input vector of 2D points, stored in std::vector&lt;&gt; or Mat
         * return automatically generated
         */
        public static bool isContourConvex(MatOfPoint contour)
        {
            if (contour != null) contour.ThrowIfDisposed();
            Mat contour_mat = contour;
            return imgproc_Imgproc_isContourConvex_10(contour_mat.nativeObj);


        }


        //
        // C++:  float cv::intersectConvexConvex(Mat p1, Mat p2, Mat& p12, bool handleNested = true)
        //

        /**
         * Finds intersection of two convex polygons
         *
         * param p1 First polygon
         * param p2 Second polygon
         * param p12 Output polygon describing the intersecting area
         * param handleNested When true, an intersection is found if one of the polygons is fully enclosed in the other.
         * When false, no intersection is found. If the polygons share a side or the vertex of one polygon lies on an edge
         * of the other, they are not considered nested and an intersection will be found regardless of the value of handleNested.
         *
         * return Absolute value of area of intersecting polygon
         *
         * <b>Note:</b> intersectConvexConvex doesn't confirm that both polygons are convex and will return invalid results if they aren't.
         */
        public static float intersectConvexConvex(Mat p1, Mat p2, Mat p12, bool handleNested)
        {
            if (p1 != null) p1.ThrowIfDisposed();
            if (p2 != null) p2.ThrowIfDisposed();
            if (p12 != null) p12.ThrowIfDisposed();

            return imgproc_Imgproc_intersectConvexConvex_10(p1.nativeObj, p2.nativeObj, p12.nativeObj, handleNested);


        }

        /**
         * Finds intersection of two convex polygons
         *
         * param p1 First polygon
         * param p2 Second polygon
         * param p12 Output polygon describing the intersecting area
         * When false, no intersection is found. If the polygons share a side or the vertex of one polygon lies on an edge
         * of the other, they are not considered nested and an intersection will be found regardless of the value of handleNested.
         *
         * return Absolute value of area of intersecting polygon
         *
         * <b>Note:</b> intersectConvexConvex doesn't confirm that both polygons are convex and will return invalid results if they aren't.
         */
        public static float intersectConvexConvex(Mat p1, Mat p2, Mat p12)
        {
            if (p1 != null) p1.ThrowIfDisposed();
            if (p2 != null) p2.ThrowIfDisposed();
            if (p12 != null) p12.ThrowIfDisposed();

            return imgproc_Imgproc_intersectConvexConvex_11(p1.nativeObj, p2.nativeObj, p12.nativeObj);


        }


        //
        // C++:  RotatedRect cv::fitEllipse(vector_Point2f 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 first algorithm described by CITE: Fitzgibbon95
         * is used. Developer should keep in mind that it is possible that the returned
         * ellipse/rotatedRect data contains negative indices, due to the data points being close to the
         * border of the containing Mat element.
         *
         * param points Input 2D point set, stored in std::vector&lt;&gt; or Mat
         * return automatically generated
         */
        public static RotatedRect fitEllipse(MatOfPoint2f points)
        {
            if (points != null) points.ThrowIfDisposed();
            Mat points_mat = points;
            double[] tmpArray = new double[5];
            imgproc_Imgproc_fitEllipse_10(points_mat.nativeObj, tmpArray);
            RotatedRect retVal = new RotatedRect(tmpArray);

            return retVal;
        }


        //
        // C++:  RotatedRect cv::fitEllipseAMS(Mat points)
        //

        /**
         * Fits an ellipse around a set of 2D points.
         *
         *  The function calculates the ellipse that fits a set of 2D points.
         *  It returns the rotated rectangle in which the ellipse is inscribed.
         *  The Approximate Mean Square (AMS) proposed by CITE: Taubin1991 is used.
         *
         *  For an ellipse, this basis set is \( \chi= \left(x^2, x y, y^2, x, y, 1\right) \),
         *  which is a set of six free coefficients \( A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \).
         *  However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \( (a,b) \),
         *  the position \( (x_0,y_0) \), and the orientation \( \theta \). This is because the basis set includes lines,
         *  quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits.
         *  If the fit is found to be a parabolic or hyperbolic function then the standard #fitEllipse method is used.
         *  The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves
         *  by imposing the condition that \( A^T ( D_x^T D_x  +   D_y^T D_y) A = 1 \) where
         *  the matrices \( Dx \) and \( Dy \) are the partial derivatives of the design matrix \( D \) with
         *  respect to x and y. The matrices are formed row by row applying the following to
         *  each of the points in the set:
         *  \(align*}{
         *  D(i,:)&amp;=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} &amp;
         *  D_x(i,:)&amp;=\left\{2 x_i,y_i,0,1,0,0\right\} &amp;
         *  D_y(i,:)&amp;=\left\{0,x_i,2 y_i,0,1,0\right\}
         *  \)
         *  The AMS method minimizes the cost function
         *  \(equation*}{
         *  \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x +  D_y^T D_y) A^T }
         *  \)
         *
         *  The minimum cost is found by solving the generalized eigenvalue problem.
         *
         *  \(equation*}{
         *  D^T D A = \lambda  \left( D_x^T D_x +  D_y^T D_y\right) A
         *  \)
         *
         *  param points Input 2D point set, stored in std::vector&lt;&gt; or Mat
         * return automatically generated
         */
        public static RotatedRect fitEllipseAMS(Mat points)
        {
            if (points != null) points.ThrowIfDisposed();

            double[] tmpArray = new double[5];
            imgproc_Imgproc_fitEllipseAMS_10(points.nativeObj, tmpArray);
            RotatedRect retVal = new RotatedRect(tmpArray);

            return retVal;
        }


        //
        // C++:  RotatedRect cv::fitEllipseDirect(Mat points)
        //

        /**
         * Fits an ellipse around a set of 2D points.
         *
         *  The function calculates the ellipse that fits a set of 2D points.
         *  It returns the rotated rectangle in which the ellipse is inscribed.
         *  The Direct least square (Direct) method by CITE: Fitzgibbon1999 is used.
         *
         *  For an ellipse, this basis set is \( \chi= \left(x^2, x y, y^2, x, y, 1\right) \),
         *  which is a set of six free coefficients \( A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \).
         *  However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \( (a,b) \),
         *  the position \( (x_0,y_0) \), and the orientation \( \theta \). This is because the basis set includes lines,
         *  quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits.
         *  The Direct method confines the fit to ellipses by ensuring that \( 4 A_{xx} A_{yy}- A_{xy}^2 &gt; 0 \).
         *  The condition imposed is that \( 4 A_{xx} A_{yy}- A_{xy}^2=1 \) which satisfies the inequality
         *  and as the coefficients can be arbitrarily scaled is not overly restrictive.
         *
         *  \(equation*}{
         *  \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix}
         *  0 &amp; 0  &amp; 2  &amp; 0  &amp; 0  &amp;  0  \\
         *  0 &amp; -1  &amp; 0  &amp; 0  &amp; 0  &amp;  0 \\
         *  2 &amp; 0  &amp; 0  &amp; 0  &amp; 0  &amp;  0 \\
         *  0 &amp; 0  &amp; 0  &amp; 0  &amp; 0  &amp;  0 \\
         *  0 &amp; 0  &amp; 0  &amp; 0  &amp; 0  &amp;  0 \\
         *  0 &amp; 0  &amp; 0  &amp; 0  &amp; 0  &amp;  0
         *  \end{matrix} \right)
         *  \)
         *
         *  The minimum cost is found by solving the generalized eigenvalue problem.
         *
         *  \(equation*}{
         *  D^T D A = \lambda  \left( C\right) A
         *  \)
         *
         *  The system produces only one positive eigenvalue \( \lambda\) which is chosen as the solution
         *  with its eigenvector \(\mathbf{u}\). These are used to find the coefficients
         *
         *  \(equation*}{
         *  A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}}  \mathbf{u}
         *  \)
         *  The scaling factor guarantees that  \(A^T C A =1\).
         *
         *  param points Input 2D point set, stored in std::vector&lt;&gt; or Mat
         * return automatically generated
         */
        public static RotatedRect fitEllipseDirect(Mat points)
        {
            if (points != null) points.ThrowIfDisposed();

            double[] tmpArray = new double[5];
            imgproc_Imgproc_fitEllipseDirect_10(points.nativeObj, tmpArray);
            RotatedRect retVal = new RotatedRect(tmpArray);

            return retVal;
        }


        //
        // C++:  void cv::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:
         * <ul>
         *   <li>
         *   DIST_L2
         * \(\rho (r) = r^2/2  \quad \text{(the simplest and the fastest least-squares method)}\)
         *   </li>
         *   <li>
         *  DIST_L1
         * \(\rho (r) = r\)
         *   </li>
         *   <li>
         *  DIST_L12
         * \(\rho (r) = 2  \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\)
         *   </li>
         *   <li>
         *  DIST_FAIR
         * \(\rho \left (r \right ) = C^2  \cdot \left (  \frac{r}{C} -  \log{\left(1 + \frac{r}{C}\right)} \right )  \quad \text{where} \quad C=1.3998\)
         *   </li>
         *   <li>
         *  DIST_WELSCH
         * \(\rho \left (r \right ) =  \frac{C^2}{2} \cdot \left ( 1 -  \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right )  \quad \text{where} \quad C=2.9846\)
         *   </li>
         *   <li>
         *  DIST_HUBER
         * \(\rho (r) =  \fork{r^2/2}{if \(r &lt; C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\)
         *   </li>
         * </ul>
         *
         * The algorithm is based on the M-estimator ( &lt;http://en.wikipedia.org/wiki/M-estimator&gt; ) 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 \(\rho(r_i)\) .
         *
         * param points Input vector of 2D or 3D points, stored in std::vector&lt;&gt; or Mat.
         * param line Output line parameters. In case of 2D fitting, it should be a vector of 4 elements
         * (like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and
         * (x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like
         * Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line
         * and (x0, y0, z0) is a point on the line.
         * param distType Distance used by the M-estimator, see #DistanceTypes
         * param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value
         * is chosen.
         * param reps Sufficient accuracy for the radius (distance between the coordinate origin and the line).
         * param aeps Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps.
         */
        public static void fitLine(Mat points, Mat line, int distType, double param, double reps, double aeps)
        {
            if (points != null) points.ThrowIfDisposed();
            if (line != null) line.ThrowIfDisposed();

            imgproc_Imgproc_fitLine_10(points.nativeObj, line.nativeObj, distType, param, reps, aeps);


        }


        //
        // C++:  double cv::pointPolygonTest(vector_Point2f contour, Point2f pt, bool 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:
         *
         * ![sample output](pics/pointpolygon.png)
         *
         * param contour Input contour.
         * param pt Point tested against the contour.
         * param measureDist If true, the function estimates the signed distance from the point to the
         * nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.
         * return automatically generated
         */
        public static double pointPolygonTest(MatOfPoint2f contour, Point pt, bool measureDist)
        {
            if (contour != null) contour.ThrowIfDisposed();
            Mat contour_mat = contour;
            return imgproc_Imgproc_pointPolygonTest_10(contour_mat.nativeObj, pt.x, pt.y, measureDist);


        }


        //
        // C++:  int cv::rotatedRectangleIntersection(RotatedRect rect1, RotatedRect rect2, Mat& intersectingRegion)
        //

        /**
         * Finds out if there is any intersection between two rotated rectangles.
         *
         * If there is then the vertices of the intersecting region are returned as well.
         *
         * Below are some examples of intersection configurations. The hatched pattern indicates the
         * intersecting region and the red vertices are returned by the function.
         *
         * ![intersection examples](pics/intersection.png)
         *
         * param rect1 First rectangle
         * param rect2 Second rectangle
         * param intersectingRegion The output array of the vertices of the intersecting region. It returns
         * at most 8 vertices. Stored as std::vector&lt;cv::Point2f&gt; or cv::Mat as Mx1 of type CV_32FC2.
         * return One of #RectanglesIntersectTypes
         */
        public static int rotatedRectangleIntersection(RotatedRect rect1, RotatedRect rect2, Mat intersectingRegion)
        {
            if (intersectingRegion != null) intersectingRegion.ThrowIfDisposed();

            return imgproc_Imgproc_rotatedRectangleIntersection_10(rect1.center.x, rect1.center.y, rect1.size.width, rect1.size.height, rect1.angle, rect2.center.x, rect2.center.y, rect2.size.width, rect2.size.height, rect2.angle, intersectingRegion.nativeObj);


        }


        //
        // C++:  Ptr_GeneralizedHoughBallard cv::createGeneralizedHoughBallard()
        //

        /**
         * Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it.
         * return automatically generated
         */
        public static GeneralizedHoughBallard createGeneralizedHoughBallard()
        {


            return GeneralizedHoughBallard.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_createGeneralizedHoughBallard_10()));


        }


        //
        // C++:  Ptr_GeneralizedHoughGuil cv::createGeneralizedHoughGuil()
        //

        /**
         * Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it.
         * return automatically generated
         */
        public static GeneralizedHoughGuil createGeneralizedHoughGuil()
        {


            return GeneralizedHoughGuil.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(imgproc_Imgproc_createGeneralizedHoughGuil_10()));


        }


        //
        // C++:  void cv::applyColorMap(Mat src, Mat& dst, int colormap)
        //

        /**
         * Applies a GNU Octave/MATLAB equivalent colormap on a given image.
         *
         * param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3.
         * param dst The result is the colormapped source image. Note: Mat::create is called on dst.
         * param colormap The colormap to apply, see #ColormapTypes
         */
        public static void applyColorMap(Mat src, Mat dst, int colormap)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            imgproc_Imgproc_applyColorMap_10(src.nativeObj, dst.nativeObj, colormap);


        }


        //
        // C++:  void cv::applyColorMap(Mat src, Mat& dst, Mat userColor)
        //

        /**
         * Applies a user colormap on a given image.
         *
         * param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3.
         * param dst The result is the colormapped source image. Note: Mat::create is called on dst.
         * param userColor The colormap to apply of type CV_8UC1 or CV_8UC3 and size 256
         */
        public static void applyColorMap(Mat src, Mat dst, Mat userColor)
        {
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();
            if (userColor != null) userColor.ThrowIfDisposed();

            imgproc_Imgproc_applyColorMap_11(src.nativeObj, dst.nativeObj, userColor.nativeObj);


        }


        //
        // C++:  void cv::line(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
        //

        /**
         * Draws a line segment connecting two points.
         *
         * The function line draws the line segment between pt1 and pt2 points in the image. The line is
         * clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected
         * or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased
         * lines are drawn using Gaussian filtering.
         *
         * param img Image.
         * param pt1 First point of the line segment.
         * param pt2 Second point of the line segment.
         * param color Line color.
         * param thickness Line thickness.
         * param lineType Type of the line. See #LineTypes.
         * param shift Number of fractional bits in the point coordinates.
         */
        public static void line(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType, int shift)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_line_10(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, shift);


        }

        /**
         * Draws a line segment connecting two points.
         *
         * The function line draws the line segment between pt1 and pt2 points in the image. The line is
         * clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected
         * or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased
         * lines are drawn using Gaussian filtering.
         *
         * param img Image.
         * param pt1 First point of the line segment.
         * param pt2 Second point of the line segment.
         * param color Line color.
         * param thickness Line thickness.
         * param lineType Type of the line. See #LineTypes.
         */
        public static void line(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_line_11(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType);


        }

        /**
         * Draws a line segment connecting two points.
         *
         * The function line draws the line segment between pt1 and pt2 points in the image. The line is
         * clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected
         * or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased
         * lines are drawn using Gaussian filtering.
         *
         * param img Image.
         * param pt1 First point of the line segment.
         * param pt2 Second point of the line segment.
         * param color Line color.
         * param thickness Line thickness.
         */
        public static void line(Mat img, Point pt1, Point pt2, Scalar color, int thickness)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_line_12(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3], thickness);


        }

        /**
         * Draws a line segment connecting two points.
         *
         * The function line draws the line segment between pt1 and pt2 points in the image. The line is
         * clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected
         * or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased
         * lines are drawn using Gaussian filtering.
         *
         * param img Image.
         * param pt1 First point of the line segment.
         * param pt2 Second point of the line segment.
         * param color Line color.
         */
        public static void line(Mat img, Point pt1, Point pt2, Scalar color)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_line_13(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3]);


        }


        //
        // C++:  void cv::arrowedLine(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, int line_type = 8, int shift = 0, double tipLength = 0.1)
        //

        /**
         * Draws an arrow segment pointing from the first point to the second one.
         *
         * The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line.
         *
         * param img Image.
         * param pt1 The point the arrow starts from.
         * param pt2 The point the arrow points to.
         * param color Line color.
         * param thickness Line thickness.
         * param line_type Type of the line. See #LineTypes
         * param shift Number of fractional bits in the point coordinates.
         * param tipLength The length of the arrow tip in relation to the arrow length
         */
        public static void arrowedLine(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int line_type, int shift, double tipLength)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_arrowedLine_10(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3], thickness, line_type, shift, tipLength);


        }

        /**
         * Draws an arrow segment pointing from the first point to the second one.
         *
         * The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line.
         *
         * param img Image.
         * param pt1 The point the arrow starts from.
         * param pt2 The point the arrow points to.
         * param color Line color.
         * param thickness Line thickness.
         * param line_type Type of the line. See #LineTypes
         * param shift Number of fractional bits in the point coordinates.
         */
        public static void arrowedLine(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int line_type, int shift)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_arrowedLine_11(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3], thickness, line_type, shift);


        }

        /**
         * Draws an arrow segment pointing from the first point to the second one.
         *
         * The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line.
         *
         * param img Image.
         * param pt1 The point the arrow starts from.
         * param pt2 The point the arrow points to.
         * param color Line color.
         * param thickness Line thickness.
         * param line_type Type of the line. See #LineTypes
         */
        public static void arrowedLine(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int line_type)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_arrowedLine_12(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3], thickness, line_type);


        }

        /**
         * Draws an arrow segment pointing from the first point to the second one.
         *
         * The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line.
         *
         * param img Image.
         * param pt1 The point the arrow starts from.
         * param pt2 The point the arrow points to.
         * param color Line color.
         * param thickness Line thickness.
         */
        public static void arrowedLine(Mat img, Point pt1, Point pt2, Scalar color, int thickness)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_arrowedLine_13(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3], thickness);


        }

        /**
         * Draws an arrow segment pointing from the first point to the second one.
         *
         * The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line.
         *
         * param img Image.
         * param pt1 The point the arrow starts from.
         * param pt2 The point the arrow points to.
         * param color Line color.
         */
        public static void arrowedLine(Mat img, Point pt1, Point pt2, Scalar color)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_arrowedLine_14(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3]);


        }


        //
        // C++:  void cv::rectangle(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
        //

        /**
         * Draws a simple, thick, or filled up-right rectangle.
         *
         * The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners
         * are pt1 and pt2.
         *
         * param img Image.
         * param pt1 Vertex of the rectangle.
         * param pt2 Vertex of the rectangle opposite to pt1 .
         * param color Rectangle color or brightness (grayscale image).
         * param thickness Thickness of lines that make up the rectangle. Negative values, like #FILLED,
         * mean that the function has to draw a filled rectangle.
         * param lineType Type of the line. See #LineTypes
         * param shift Number of fractional bits in the point coordinates.
         */
        public static void rectangle(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType, int shift)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_rectangle_10(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, shift);


        }

        /**
         * Draws a simple, thick, or filled up-right rectangle.
         *
         * The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners
         * are pt1 and pt2.
         *
         * param img Image.
         * param pt1 Vertex of the rectangle.
         * param pt2 Vertex of the rectangle opposite to pt1 .
         * param color Rectangle color or brightness (grayscale image).
         * param thickness Thickness of lines that make up the rectangle. Negative values, like #FILLED,
         * mean that the function has to draw a filled rectangle.
         * param lineType Type of the line. See #LineTypes
         */
        public static void rectangle(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_rectangle_11(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType);


        }

        /**
         * Draws a simple, thick, or filled up-right rectangle.
         *
         * The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners
         * are pt1 and pt2.
         *
         * param img Image.
         * param pt1 Vertex of the rectangle.
         * param pt2 Vertex of the rectangle opposite to pt1 .
         * param color Rectangle color or brightness (grayscale image).
         * param thickness Thickness of lines that make up the rectangle. Negative values, like #FILLED,
         * mean that the function has to draw a filled rectangle.
         */
        public static void rectangle(Mat img, Point pt1, Point pt2, Scalar color, int thickness)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_rectangle_12(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3], thickness);


        }

        /**
         * Draws a simple, thick, or filled up-right rectangle.
         *
         * The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners
         * are pt1 and pt2.
         *
         * param img Image.
         * param pt1 Vertex of the rectangle.
         * param pt2 Vertex of the rectangle opposite to pt1 .
         * param color Rectangle color or brightness (grayscale image).
         * mean that the function has to draw a filled rectangle.
         */
        public static void rectangle(Mat img, Point pt1, Point pt2, Scalar color)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_rectangle_13(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3]);


        }


        //
        // C++:  void cv::rectangle(Mat& img, Rect rec, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
        //

        /**
         *
         *
         * use {code rec} parameter as alternative specification of the drawn rectangle: `r.tl() and
         * r.br()-Point(1,1)` are opposite corners
         * param img automatically generated
         * param rec automatically generated
         * param color automatically generated
         * param thickness automatically generated
         * param lineType automatically generated
         * param shift automatically generated
         */
        public static void rectangle(Mat img, Rect rec, Scalar color, int thickness, int lineType, int shift)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_rectangle_14(img.nativeObj, rec.x, rec.y, rec.width, rec.height, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, shift);


        }

        /**
         *
         *
         * use {code rec} parameter as alternative specification of the drawn rectangle: `r.tl() and
         * r.br()-Point(1,1)` are opposite corners
         * param img automatically generated
         * param rec automatically generated
         * param color automatically generated
         * param thickness automatically generated
         * param lineType automatically generated
         */
        public static void rectangle(Mat img, Rect rec, Scalar color, int thickness, int lineType)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_rectangle_15(img.nativeObj, rec.x, rec.y, rec.width, rec.height, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType);


        }

        /**
         *
         *
         * use {code rec} parameter as alternative specification of the drawn rectangle: `r.tl() and
         * r.br()-Point(1,1)` are opposite corners
         * param img automatically generated
         * param rec automatically generated
         * param color automatically generated
         * param thickness automatically generated
         */
        public static void rectangle(Mat img, Rect rec, Scalar color, int thickness)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_rectangle_16(img.nativeObj, rec.x, rec.y, rec.width, rec.height, color.val[0], color.val[1], color.val[2], color.val[3], thickness);


        }

        /**
         *
         *
         * use {code rec} parameter as alternative specification of the drawn rectangle: `r.tl() and
         * r.br()-Point(1,1)` are opposite corners
         * param img automatically generated
         * param rec automatically generated
         * param color automatically generated
         */
        public static void rectangle(Mat img, Rect rec, Scalar color)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_rectangle_17(img.nativeObj, rec.x, rec.y, rec.width, rec.height, color.val[0], color.val[1], color.val[2], color.val[3]);


        }


        //
        // C++:  void cv::circle(Mat& img, Point center, int radius, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
        //

        /**
         * Draws a circle.
         *
         * The function cv::circle draws a simple or filled circle with a given center and radius.
         * param img Image where the circle is drawn.
         * param center Center of the circle.
         * param radius Radius of the circle.
         * param color Circle color.
         * param thickness Thickness of the circle outline, if positive. Negative values, like #FILLED,
         * mean that a filled circle is to be drawn.
         * param lineType Type of the circle boundary. See #LineTypes
         * param shift Number of fractional bits in the coordinates of the center and in the radius value.
         */
        public static void circle(Mat img, Point center, int radius, Scalar color, int thickness, int lineType, int shift)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_circle_10(img.nativeObj, center.x, center.y, radius, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, shift);


        }

        /**
         * Draws a circle.
         *
         * The function cv::circle draws a simple or filled circle with a given center and radius.
         * param img Image where the circle is drawn.
         * param center Center of the circle.
         * param radius Radius of the circle.
         * param color Circle color.
         * param thickness Thickness of the circle outline, if positive. Negative values, like #FILLED,
         * mean that a filled circle is to be drawn.
         * param lineType Type of the circle boundary. See #LineTypes
         */
        public static void circle(Mat img, Point center, int radius, Scalar color, int thickness, int lineType)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_circle_11(img.nativeObj, center.x, center.y, radius, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType);


        }

        /**
         * Draws a circle.
         *
         * The function cv::circle draws a simple or filled circle with a given center and radius.
         * param img Image where the circle is drawn.
         * param center Center of the circle.
         * param radius Radius of the circle.
         * param color Circle color.
         * param thickness Thickness of the circle outline, if positive. Negative values, like #FILLED,
         * mean that a filled circle is to be drawn.
         */
        public static void circle(Mat img, Point center, int radius, Scalar color, int thickness)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_circle_12(img.nativeObj, center.x, center.y, radius, color.val[0], color.val[1], color.val[2], color.val[3], thickness);


        }

        /**
         * Draws a circle.
         *
         * The function cv::circle draws a simple or filled circle with a given center and radius.
         * param img Image where the circle is drawn.
         * param center Center of the circle.
         * param radius Radius of the circle.
         * param color Circle color.
         * mean that a filled circle is to be drawn.
         */
        public static void circle(Mat img, Point center, int radius, Scalar color)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_circle_13(img.nativeObj, center.x, center.y, radius, color.val[0], color.val[1], color.val[2], color.val[3]);


        }


        //
        // C++:  void cv::ellipse(Mat& img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
        //

        /**
         * Draws a simple or thick elliptic arc or fills an ellipse sector.
         *
         * The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic
         * arc, or a filled ellipse sector. The drawing code uses general parametric form.
         * A piecewise-linear curve is used to approximate the elliptic arc
         * boundary. If you need more control of the ellipse rendering, you can retrieve the curve using
         * #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first
         * variant of the function and want to draw the whole ellipse, not an arc, pass {code startAngle=0} and
         * {code endAngle=360}. If {code startAngle} is greater than {code endAngle}, they are swapped. The figure below explains
         * the meaning of the parameters to draw the blue arc.
         *
         * ![Parameters of Elliptic Arc](pics/ellipse.svg)
         *
         * param img Image.
         * param center Center of the ellipse.
         * param axes Half of the size of the ellipse main axes.
         * param angle Ellipse rotation angle in degrees.
         * param startAngle Starting angle of the elliptic arc in degrees.
         * param endAngle Ending angle of the elliptic arc in degrees.
         * param color Ellipse color.
         * param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
         * a filled ellipse sector is to be drawn.
         * param lineType Type of the ellipse boundary. See #LineTypes
         * param shift Number of fractional bits in the coordinates of the center and values of axes.
         */
        public static void ellipse(Mat img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness, int lineType, int shift)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_ellipse_10(img.nativeObj, center.x, center.y, axes.width, axes.height, angle, startAngle, endAngle, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, shift);


        }

        /**
         * Draws a simple or thick elliptic arc or fills an ellipse sector.
         *
         * The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic
         * arc, or a filled ellipse sector. The drawing code uses general parametric form.
         * A piecewise-linear curve is used to approximate the elliptic arc
         * boundary. If you need more control of the ellipse rendering, you can retrieve the curve using
         * #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first
         * variant of the function and want to draw the whole ellipse, not an arc, pass {code startAngle=0} and
         * {code endAngle=360}. If {code startAngle} is greater than {code endAngle}, they are swapped. The figure below explains
         * the meaning of the parameters to draw the blue arc.
         *
         * ![Parameters of Elliptic Arc](pics/ellipse.svg)
         *
         * param img Image.
         * param center Center of the ellipse.
         * param axes Half of the size of the ellipse main axes.
         * param angle Ellipse rotation angle in degrees.
         * param startAngle Starting angle of the elliptic arc in degrees.
         * param endAngle Ending angle of the elliptic arc in degrees.
         * param color Ellipse color.
         * param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
         * a filled ellipse sector is to be drawn.
         * param lineType Type of the ellipse boundary. See #LineTypes
         */
        public static void ellipse(Mat img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness, int lineType)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_ellipse_11(img.nativeObj, center.x, center.y, axes.width, axes.height, angle, startAngle, endAngle, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType);


        }

        /**
         * Draws a simple or thick elliptic arc or fills an ellipse sector.
         *
         * The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic
         * arc, or a filled ellipse sector. The drawing code uses general parametric form.
         * A piecewise-linear curve is used to approximate the elliptic arc
         * boundary. If you need more control of the ellipse rendering, you can retrieve the curve using
         * #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first
         * variant of the function and want to draw the whole ellipse, not an arc, pass {code startAngle=0} and
         * {code endAngle=360}. If {code startAngle} is greater than {code endAngle}, they are swapped. The figure below explains
         * the meaning of the parameters to draw the blue arc.
         *
         * ![Parameters of Elliptic Arc](pics/ellipse.svg)
         *
         * param img Image.
         * param center Center of the ellipse.
         * param axes Half of the size of the ellipse main axes.
         * param angle Ellipse rotation angle in degrees.
         * param startAngle Starting angle of the elliptic arc in degrees.
         * param endAngle Ending angle of the elliptic arc in degrees.
         * param color Ellipse color.
         * param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
         * a filled ellipse sector is to be drawn.
         */
        public static void ellipse(Mat img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_ellipse_12(img.nativeObj, center.x, center.y, axes.width, axes.height, angle, startAngle, endAngle, color.val[0], color.val[1], color.val[2], color.val[3], thickness);


        }

        /**
         * Draws a simple or thick elliptic arc or fills an ellipse sector.
         *
         * The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic
         * arc, or a filled ellipse sector. The drawing code uses general parametric form.
         * A piecewise-linear curve is used to approximate the elliptic arc
         * boundary. If you need more control of the ellipse rendering, you can retrieve the curve using
         * #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first
         * variant of the function and want to draw the whole ellipse, not an arc, pass {code startAngle=0} and
         * {code endAngle=360}. If {code startAngle} is greater than {code endAngle}, they are swapped. The figure below explains
         * the meaning of the parameters to draw the blue arc.
         *
         * ![Parameters of Elliptic Arc](pics/ellipse.svg)
         *
         * param img Image.
         * param center Center of the ellipse.
         * param axes Half of the size of the ellipse main axes.
         * param angle Ellipse rotation angle in degrees.
         * param startAngle Starting angle of the elliptic arc in degrees.
         * param endAngle Ending angle of the elliptic arc in degrees.
         * param color Ellipse color.
         * a filled ellipse sector is to be drawn.
         */
        public static void ellipse(Mat img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_ellipse_13(img.nativeObj, center.x, center.y, axes.width, axes.height, angle, startAngle, endAngle, color.val[0], color.val[1], color.val[2], color.val[3]);


        }


        //
        // C++:  void cv::ellipse(Mat& img, RotatedRect box, Scalar color, int thickness = 1, int lineType = LINE_8)
        //

        /**
         *
         * param img Image.
         * param box Alternative ellipse representation via RotatedRect. This means that the function draws
         * an ellipse inscribed in the rotated rectangle.
         * param color Ellipse color.
         * param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
         * a filled ellipse sector is to be drawn.
         * param lineType Type of the ellipse boundary. See #LineTypes
         */
        public static void ellipse(Mat img, RotatedRect box, Scalar color, int thickness, int lineType)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_ellipse_14(img.nativeObj, box.center.x, box.center.y, box.size.width, box.size.height, box.angle, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType);


        }

        /**
         *
         * param img Image.
         * param box Alternative ellipse representation via RotatedRect. This means that the function draws
         * an ellipse inscribed in the rotated rectangle.
         * param color Ellipse color.
         * param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
         * a filled ellipse sector is to be drawn.
         */
        public static void ellipse(Mat img, RotatedRect box, Scalar color, int thickness)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_ellipse_15(img.nativeObj, box.center.x, box.center.y, box.size.width, box.size.height, box.angle, color.val[0], color.val[1], color.val[2], color.val[3], thickness);


        }

        /**
         *
         * param img Image.
         * param box Alternative ellipse representation via RotatedRect. This means that the function draws
         * an ellipse inscribed in the rotated rectangle.
         * param color Ellipse color.
         * a filled ellipse sector is to be drawn.
         */
        public static void ellipse(Mat img, RotatedRect box, Scalar color)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_ellipse_16(img.nativeObj, box.center.x, box.center.y, box.size.width, box.size.height, box.angle, color.val[0], color.val[1], color.val[2], color.val[3]);


        }


        //
        // C++:  void cv::drawMarker(Mat& img, Point position, Scalar color, int markerType = MARKER_CROSS, int markerSize = 20, int thickness = 1, int line_type = 8)
        //

        /**
         * Draws a marker on a predefined position in an image.
         *
         * The function cv::drawMarker draws a marker on a given position in the image. For the moment several
         * marker types are supported, see #MarkerTypes for more information.
         *
         * param img Image.
         * param position The point where the crosshair is positioned.
         * param color Line color.
         * param markerType The specific type of marker you want to use, see #MarkerTypes
         * param thickness Line thickness.
         * param line_type Type of the line, See #LineTypes
         * param markerSize The length of the marker axis [default = 20 pixels]
         */
        public static void drawMarker(Mat img, Point position, Scalar color, int markerType, int markerSize, int thickness, int line_type)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_drawMarker_10(img.nativeObj, position.x, position.y, color.val[0], color.val[1], color.val[2], color.val[3], markerType, markerSize, thickness, line_type);


        }

        /**
         * Draws a marker on a predefined position in an image.
         *
         * The function cv::drawMarker draws a marker on a given position in the image. For the moment several
         * marker types are supported, see #MarkerTypes for more information.
         *
         * param img Image.
         * param position The point where the crosshair is positioned.
         * param color Line color.
         * param markerType The specific type of marker you want to use, see #MarkerTypes
         * param thickness Line thickness.
         * param markerSize The length of the marker axis [default = 20 pixels]
         */
        public static void drawMarker(Mat img, Point position, Scalar color, int markerType, int markerSize, int thickness)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_drawMarker_11(img.nativeObj, position.x, position.y, color.val[0], color.val[1], color.val[2], color.val[3], markerType, markerSize, thickness);


        }

        /**
         * Draws a marker on a predefined position in an image.
         *
         * The function cv::drawMarker draws a marker on a given position in the image. For the moment several
         * marker types are supported, see #MarkerTypes for more information.
         *
         * param img Image.
         * param position The point where the crosshair is positioned.
         * param color Line color.
         * param markerType The specific type of marker you want to use, see #MarkerTypes
         * param markerSize The length of the marker axis [default = 20 pixels]
         */
        public static void drawMarker(Mat img, Point position, Scalar color, int markerType, int markerSize)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_drawMarker_12(img.nativeObj, position.x, position.y, color.val[0], color.val[1], color.val[2], color.val[3], markerType, markerSize);


        }

        /**
         * Draws a marker on a predefined position in an image.
         *
         * The function cv::drawMarker draws a marker on a given position in the image. For the moment several
         * marker types are supported, see #MarkerTypes for more information.
         *
         * param img Image.
         * param position The point where the crosshair is positioned.
         * param color Line color.
         * param markerType The specific type of marker you want to use, see #MarkerTypes
         */
        public static void drawMarker(Mat img, Point position, Scalar color, int markerType)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_drawMarker_13(img.nativeObj, position.x, position.y, color.val[0], color.val[1], color.val[2], color.val[3], markerType);


        }

        /**
         * Draws a marker on a predefined position in an image.
         *
         * The function cv::drawMarker draws a marker on a given position in the image. For the moment several
         * marker types are supported, see #MarkerTypes for more information.
         *
         * param img Image.
         * param position The point where the crosshair is positioned.
         * param color Line color.
         */
        public static void drawMarker(Mat img, Point position, Scalar color)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_drawMarker_14(img.nativeObj, position.x, position.y, color.val[0], color.val[1], color.val[2], color.val[3]);


        }


        //
        // C++:  void cv::fillConvexPoly(Mat& img, vector_Point points, Scalar color, int lineType = LINE_8, int shift = 0)
        //

        /**
         * Fills a convex polygon.
         *
         * The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the
         * function #fillPoly . It can fill not only convex polygons but any monotonic polygon without
         * self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line)
         * twice at the most (though, its top-most and/or the bottom edge could be horizontal).
         *
         * param img Image.
         * param points Polygon vertices.
         * param color Polygon color.
         * param lineType Type of the polygon boundaries. See #LineTypes
         * param shift Number of fractional bits in the vertex coordinates.
         */
        public static void fillConvexPoly(Mat img, MatOfPoint points, Scalar color, int lineType, int shift)
        {
            if (img != null) img.ThrowIfDisposed();
            if (points != null) points.ThrowIfDisposed();
            Mat points_mat = points;
            imgproc_Imgproc_fillConvexPoly_10(img.nativeObj, points_mat.nativeObj, color.val[0], color.val[1], color.val[2], color.val[3], lineType, shift);


        }

        /**
         * Fills a convex polygon.
         *
         * The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the
         * function #fillPoly . It can fill not only convex polygons but any monotonic polygon without
         * self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line)
         * twice at the most (though, its top-most and/or the bottom edge could be horizontal).
         *
         * param img Image.
         * param points Polygon vertices.
         * param color Polygon color.
         * param lineType Type of the polygon boundaries. See #LineTypes
         */
        public static void fillConvexPoly(Mat img, MatOfPoint points, Scalar color, int lineType)
        {
            if (img != null) img.ThrowIfDisposed();
            if (points != null) points.ThrowIfDisposed();
            Mat points_mat = points;
            imgproc_Imgproc_fillConvexPoly_11(img.nativeObj, points_mat.nativeObj, color.val[0], color.val[1], color.val[2], color.val[3], lineType);


        }

        /**
         * Fills a convex polygon.
         *
         * The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the
         * function #fillPoly . It can fill not only convex polygons but any monotonic polygon without
         * self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line)
         * twice at the most (though, its top-most and/or the bottom edge could be horizontal).
         *
         * param img Image.
         * param points Polygon vertices.
         * param color Polygon color.
         */
        public static void fillConvexPoly(Mat img, MatOfPoint points, Scalar color)
        {
            if (img != null) img.ThrowIfDisposed();
            if (points != null) points.ThrowIfDisposed();
            Mat points_mat = points;
            imgproc_Imgproc_fillConvexPoly_12(img.nativeObj, points_mat.nativeObj, color.val[0], color.val[1], color.val[2], color.val[3]);


        }


        //
        // C++:  void cv::fillPoly(Mat& img, vector_vector_Point pts, Scalar color, int lineType = LINE_8, int shift = 0, Point offset = Point())
        //

        /**
         * Fills the area bounded by one or more polygons.
         *
         * The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill
         * complex areas, for example, areas with holes, contours with self-intersections (some of their
         * parts), and so forth.
         *
         * param img Image.
         * param pts Array of polygons where each polygon is represented as an array of points.
         * param color Polygon color.
         * param lineType Type of the polygon boundaries. See #LineTypes
         * param shift Number of fractional bits in the vertex coordinates.
         * param offset Optional offset of all points of the contours.
         */
        public static void fillPoly(Mat img, List<MatOfPoint> pts, Scalar color, int lineType, int shift, Point offset)
        {
            if (img != null) img.ThrowIfDisposed();
            List<Mat> pts_tmplm = new List<Mat>((pts != null) ? pts.Count : 0);
            Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
            imgproc_Imgproc_fillPoly_10(img.nativeObj, pts_mat.nativeObj, color.val[0], color.val[1], color.val[2], color.val[3], lineType, shift, offset.x, offset.y);


        }

        /**
         * Fills the area bounded by one or more polygons.
         *
         * The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill
         * complex areas, for example, areas with holes, contours with self-intersections (some of their
         * parts), and so forth.
         *
         * param img Image.
         * param pts Array of polygons where each polygon is represented as an array of points.
         * param color Polygon color.
         * param lineType Type of the polygon boundaries. See #LineTypes
         * param shift Number of fractional bits in the vertex coordinates.
         */
        public static void fillPoly(Mat img, List<MatOfPoint> pts, Scalar color, int lineType, int shift)
        {
            if (img != null) img.ThrowIfDisposed();
            List<Mat> pts_tmplm = new List<Mat>((pts != null) ? pts.Count : 0);
            Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
            imgproc_Imgproc_fillPoly_11(img.nativeObj, pts_mat.nativeObj, color.val[0], color.val[1], color.val[2], color.val[3], lineType, shift);


        }

        /**
         * Fills the area bounded by one or more polygons.
         *
         * The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill
         * complex areas, for example, areas with holes, contours with self-intersections (some of their
         * parts), and so forth.
         *
         * param img Image.
         * param pts Array of polygons where each polygon is represented as an array of points.
         * param color Polygon color.
         * param lineType Type of the polygon boundaries. See #LineTypes
         */
        public static void fillPoly(Mat img, List<MatOfPoint> pts, Scalar color, int lineType)
        {
            if (img != null) img.ThrowIfDisposed();
            List<Mat> pts_tmplm = new List<Mat>((pts != null) ? pts.Count : 0);
            Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
            imgproc_Imgproc_fillPoly_12(img.nativeObj, pts_mat.nativeObj, color.val[0], color.val[1], color.val[2], color.val[3], lineType);


        }

        /**
         * Fills the area bounded by one or more polygons.
         *
         * The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill
         * complex areas, for example, areas with holes, contours with self-intersections (some of their
         * parts), and so forth.
         *
         * param img Image.
         * param pts Array of polygons where each polygon is represented as an array of points.
         * param color Polygon color.
         */
        public static void fillPoly(Mat img, List<MatOfPoint> pts, Scalar color)
        {
            if (img != null) img.ThrowIfDisposed();
            List<Mat> pts_tmplm = new List<Mat>((pts != null) ? pts.Count : 0);
            Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
            imgproc_Imgproc_fillPoly_13(img.nativeObj, pts_mat.nativeObj, color.val[0], color.val[1], color.val[2], color.val[3]);


        }


        //
        // C++:  void cv::polylines(Mat& img, vector_vector_Point pts, bool isClosed, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
        //

        /**
         * Draws several polygonal curves.
         *
         * param img Image.
         * param pts Array of polygonal curves.
         * param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed,
         * the function draws a line from the last vertex of each curve to its first vertex.
         * param color Polyline color.
         * param thickness Thickness of the polyline edges.
         * param lineType Type of the line segments. See #LineTypes
         * param shift Number of fractional bits in the vertex coordinates.
         *
         * The function cv::polylines draws one or more polygonal curves.
         */
        public static void polylines(Mat img, List<MatOfPoint> pts, bool isClosed, Scalar color, int thickness, int lineType, int shift)
        {
            if (img != null) img.ThrowIfDisposed();
            List<Mat> pts_tmplm = new List<Mat>((pts != null) ? pts.Count : 0);
            Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
            imgproc_Imgproc_polylines_10(img.nativeObj, pts_mat.nativeObj, isClosed, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, shift);


        }

        /**
         * Draws several polygonal curves.
         *
         * param img Image.
         * param pts Array of polygonal curves.
         * param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed,
         * the function draws a line from the last vertex of each curve to its first vertex.
         * param color Polyline color.
         * param thickness Thickness of the polyline edges.
         * param lineType Type of the line segments. See #LineTypes
         *
         * The function cv::polylines draws one or more polygonal curves.
         */
        public static void polylines(Mat img, List<MatOfPoint> pts, bool isClosed, Scalar color, int thickness, int lineType)
        {
            if (img != null) img.ThrowIfDisposed();
            List<Mat> pts_tmplm = new List<Mat>((pts != null) ? pts.Count : 0);
            Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
            imgproc_Imgproc_polylines_11(img.nativeObj, pts_mat.nativeObj, isClosed, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType);


        }

        /**
         * Draws several polygonal curves.
         *
         * param img Image.
         * param pts Array of polygonal curves.
         * param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed,
         * the function draws a line from the last vertex of each curve to its first vertex.
         * param color Polyline color.
         * param thickness Thickness of the polyline edges.
         *
         * The function cv::polylines draws one or more polygonal curves.
         */
        public static void polylines(Mat img, List<MatOfPoint> pts, bool isClosed, Scalar color, int thickness)
        {
            if (img != null) img.ThrowIfDisposed();
            List<Mat> pts_tmplm = new List<Mat>((pts != null) ? pts.Count : 0);
            Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
            imgproc_Imgproc_polylines_12(img.nativeObj, pts_mat.nativeObj, isClosed, color.val[0], color.val[1], color.val[2], color.val[3], thickness);


        }

        /**
         * Draws several polygonal curves.
         *
         * param img Image.
         * param pts Array of polygonal curves.
         * param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed,
         * the function draws a line from the last vertex of each curve to its first vertex.
         * param color Polyline color.
         *
         * The function cv::polylines draws one or more polygonal curves.
         */
        public static void polylines(Mat img, List<MatOfPoint> pts, bool isClosed, Scalar color)
        {
            if (img != null) img.ThrowIfDisposed();
            List<Mat> pts_tmplm = new List<Mat>((pts != null) ? pts.Count : 0);
            Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
            imgproc_Imgproc_polylines_13(img.nativeObj, pts_mat.nativeObj, isClosed, color.val[0], color.val[1], color.val[2], color.val[3]);


        }


        //
        // C++:  void cv::drawContours(Mat& image, vector_vector_Point contours, int contourIdx, Scalar color, int thickness = 1, int lineType = LINE_8, Mat hierarchy = Mat(), int maxLevel = INT_MAX, Point offset = Point())
        //

        /**
         * Draws contours outlines or filled contours.
         *
         * The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area
         * bounded by the contours if \(\texttt{thickness}&lt;0\) . The example below shows how to retrieve
         * connected components from the binary image and label them: :
         * INCLUDE: snippets/imgproc_drawContours.cpp
         *
         * param image Destination image.
         * param contours All the input contours. Each contour is stored as a point vector.
         * param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
         * param color Color of the contours.
         * param thickness Thickness of lines the contours are drawn with. If it is negative (for example,
         * thickness=#FILLED ), the contour interiors are drawn.
         * param lineType Line connectivity. See #LineTypes
         * param hierarchy Optional information about hierarchy. It is only needed if you want to draw only
         * some of the contours (see maxLevel ).
         * param maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn.
         * If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function
         * draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This
         * parameter is only taken into account when there is hierarchy available.
         * param offset Optional contour shift parameter. Shift all the drawn contours by the specified
         * \(\texttt{offset}=(dx,dy)\) .
         * <b>Note:</b> When thickness=#FILLED, the function is designed to handle connected components with holes correctly
         * even when no hierarchy data is provided. This is done by analyzing all the outlines together
         * using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved
         * contours. In order to solve this problem, you need to call #drawContours separately for each sub-group
         * of contours, or iterate over the collection using contourIdx parameter.
         */
        public static void drawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy, int maxLevel, Point offset)
        {
            if (image != null) image.ThrowIfDisposed();
            if (hierarchy != null) hierarchy.ThrowIfDisposed();
            List<Mat> contours_tmplm = new List<Mat>((contours != null) ? contours.Count : 0);
            Mat contours_mat = Converters.vector_vector_Point_to_Mat(contours, contours_tmplm);
            imgproc_Imgproc_drawContours_10(image.nativeObj, contours_mat.nativeObj, contourIdx, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, hierarchy.nativeObj, maxLevel, offset.x, offset.y);


        }

        /**
         * Draws contours outlines or filled contours.
         *
         * The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area
         * bounded by the contours if \(\texttt{thickness}&lt;0\) . The example below shows how to retrieve
         * connected components from the binary image and label them: :
         * INCLUDE: snippets/imgproc_drawContours.cpp
         *
         * param image Destination image.
         * param contours All the input contours. Each contour is stored as a point vector.
         * param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
         * param color Color of the contours.
         * param thickness Thickness of lines the contours are drawn with. If it is negative (for example,
         * thickness=#FILLED ), the contour interiors are drawn.
         * param lineType Line connectivity. See #LineTypes
         * param hierarchy Optional information about hierarchy. It is only needed if you want to draw only
         * some of the contours (see maxLevel ).
         * param maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn.
         * If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function
         * draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This
         * parameter is only taken into account when there is hierarchy available.
         * \(\texttt{offset}=(dx,dy)\) .
         * <b>Note:</b> When thickness=#FILLED, the function is designed to handle connected components with holes correctly
         * even when no hierarchy data is provided. This is done by analyzing all the outlines together
         * using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved
         * contours. In order to solve this problem, you need to call #drawContours separately for each sub-group
         * of contours, or iterate over the collection using contourIdx parameter.
         */
        public static void drawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy, int maxLevel)
        {
            if (image != null) image.ThrowIfDisposed();
            if (hierarchy != null) hierarchy.ThrowIfDisposed();
            List<Mat> contours_tmplm = new List<Mat>((contours != null) ? contours.Count : 0);
            Mat contours_mat = Converters.vector_vector_Point_to_Mat(contours, contours_tmplm);
            imgproc_Imgproc_drawContours_11(image.nativeObj, contours_mat.nativeObj, contourIdx, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, hierarchy.nativeObj, maxLevel);


        }

        /**
         * Draws contours outlines or filled contours.
         *
         * The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area
         * bounded by the contours if \(\texttt{thickness}&lt;0\) . The example below shows how to retrieve
         * connected components from the binary image and label them: :
         * INCLUDE: snippets/imgproc_drawContours.cpp
         *
         * param image Destination image.
         * param contours All the input contours. Each contour is stored as a point vector.
         * param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
         * param color Color of the contours.
         * param thickness Thickness of lines the contours are drawn with. If it is negative (for example,
         * thickness=#FILLED ), the contour interiors are drawn.
         * param lineType Line connectivity. See #LineTypes
         * param hierarchy Optional information about hierarchy. It is only needed if you want to draw only
         * some of the contours (see maxLevel ).
         * 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.
         * \(\texttt{offset}=(dx,dy)\) .
         * <b>Note:</b> When thickness=#FILLED, the function is designed to handle connected components with holes correctly
         * even when no hierarchy data is provided. This is done by analyzing all the outlines together
         * using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved
         * contours. In order to solve this problem, you need to call #drawContours separately for each sub-group
         * of contours, or iterate over the collection using contourIdx parameter.
         */
        public static void drawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy)
        {
            if (image != null) image.ThrowIfDisposed();
            if (hierarchy != null) hierarchy.ThrowIfDisposed();
            List<Mat> contours_tmplm = new List<Mat>((contours != null) ? contours.Count : 0);
            Mat contours_mat = Converters.vector_vector_Point_to_Mat(contours, contours_tmplm);
            imgproc_Imgproc_drawContours_12(image.nativeObj, contours_mat.nativeObj, contourIdx, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, hierarchy.nativeObj);


        }

        /**
         * Draws contours outlines or filled contours.
         *
         * The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area
         * bounded by the contours if \(\texttt{thickness}&lt;0\) . The example below shows how to retrieve
         * connected components from the binary image and label them: :
         * INCLUDE: snippets/imgproc_drawContours.cpp
         *
         * param image Destination image.
         * param contours All the input contours. Each contour is stored as a point vector.
         * param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
         * param color Color of the contours.
         * param thickness Thickness of lines the contours are drawn with. If it is negative (for example,
         * thickness=#FILLED ), the contour interiors are drawn.
         * param lineType Line connectivity. See #LineTypes
         * some of the contours (see maxLevel ).
         * 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.
         * \(\texttt{offset}=(dx,dy)\) .
         * <b>Note:</b> When thickness=#FILLED, the function is designed to handle connected components with holes correctly
         * even when no hierarchy data is provided. This is done by analyzing all the outlines together
         * using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved
         * contours. In order to solve this problem, you need to call #drawContours separately for each sub-group
         * of contours, or iterate over the collection using contourIdx parameter.
         */
        public static void drawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType)
        {
            if (image != null) image.ThrowIfDisposed();
            List<Mat> contours_tmplm = new List<Mat>((contours != null) ? contours.Count : 0);
            Mat contours_mat = Converters.vector_vector_Point_to_Mat(contours, contours_tmplm);
            imgproc_Imgproc_drawContours_13(image.nativeObj, contours_mat.nativeObj, contourIdx, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType);


        }

        /**
         * Draws contours outlines or filled contours.
         *
         * The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area
         * bounded by the contours if \(\texttt{thickness}&lt;0\) . The example below shows how to retrieve
         * connected components from the binary image and label them: :
         * INCLUDE: snippets/imgproc_drawContours.cpp
         *
         * param image Destination image.
         * param contours All the input contours. Each contour is stored as a point vector.
         * param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
         * param color Color of the contours.
         * param thickness Thickness of lines the contours are drawn with. If it is negative (for example,
         * thickness=#FILLED ), the contour interiors are drawn.
         * some of the contours (see maxLevel ).
         * 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.
         * \(\texttt{offset}=(dx,dy)\) .
         * <b>Note:</b> When thickness=#FILLED, the function is designed to handle connected components with holes correctly
         * even when no hierarchy data is provided. This is done by analyzing all the outlines together
         * using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved
         * contours. In order to solve this problem, you need to call #drawContours separately for each sub-group
         * of contours, or iterate over the collection using contourIdx parameter.
         */
        public static void drawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness)
        {
            if (image != null) image.ThrowIfDisposed();
            List<Mat> contours_tmplm = new List<Mat>((contours != null) ? contours.Count : 0);
            Mat contours_mat = Converters.vector_vector_Point_to_Mat(contours, contours_tmplm);
            imgproc_Imgproc_drawContours_14(image.nativeObj, contours_mat.nativeObj, contourIdx, color.val[0], color.val[1], color.val[2], color.val[3], thickness);


        }

        /**
         * Draws contours outlines or filled contours.
         *
         * The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area
         * bounded by the contours if \(\texttt{thickness}&lt;0\) . The example below shows how to retrieve
         * connected components from the binary image and label them: :
         * INCLUDE: snippets/imgproc_drawContours.cpp
         *
         * param image Destination image.
         * param contours All the input contours. Each contour is stored as a point vector.
         * param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
         * param color Color of the contours.
         * thickness=#FILLED ), the contour interiors are drawn.
         * some of the contours (see maxLevel ).
         * 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.
         * \(\texttt{offset}=(dx,dy)\) .
         * <b>Note:</b> When thickness=#FILLED, the function is designed to handle connected components with holes correctly
         * even when no hierarchy data is provided. This is done by analyzing all the outlines together
         * using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved
         * contours. In order to solve this problem, you need to call #drawContours separately for each sub-group
         * of contours, or iterate over the collection using contourIdx parameter.
         */
        public static void drawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color)
        {
            if (image != null) image.ThrowIfDisposed();
            List<Mat> contours_tmplm = new List<Mat>((contours != null) ? contours.Count : 0);
            Mat contours_mat = Converters.vector_vector_Point_to_Mat(contours, contours_tmplm);
            imgproc_Imgproc_drawContours_15(image.nativeObj, contours_mat.nativeObj, contourIdx, color.val[0], color.val[1], color.val[2], color.val[3]);


        }


        //
        // C++:  bool cv::clipLine(Rect imgRect, Point& pt1, Point& pt2)
        //

        /**
         *
         * param imgRect Image rectangle.
         * param pt1 First line point.
         * param pt2 Second line point.
         * return automatically generated
         */
        public static bool clipLine(Rect imgRect, Point pt1, Point pt2)
        {

            double[] pt1_out = new double[2];
            double[] pt2_out = new double[2];
            bool retVal = imgproc_Imgproc_clipLine_10(imgRect.x, imgRect.y, imgRect.width, imgRect.height, pt1.x, pt1.y, pt1_out, pt2.x, pt2.y, pt2_out);
            if (pt1 != null) { pt1.x = pt1_out[0]; pt1.y = pt1_out[1]; }
            if (pt2 != null) { pt2.x = pt2_out[0]; pt2.y = pt2_out[1]; }
            return retVal;
        }


        //
        // C++:  void cv::ellipse2Poly(Point center, Size axes, int angle, int arcStart, int arcEnd, int delta, vector_Point& pts)
        //

        /**
         * Approximates an elliptic arc with a polyline.
         *
         * The function ellipse2Poly computes the vertices of a polyline that approximates the specified
         * elliptic arc. It is used by #ellipse. If {code arcStart} is greater than {code arcEnd}, they are swapped.
         *
         * param center Center of the arc.
         * param axes Half of the size of the ellipse main axes. See #ellipse for details.
         * param angle Rotation angle of the ellipse in degrees. See #ellipse for details.
         * param arcStart Starting angle of the elliptic arc in degrees.
         * param arcEnd Ending angle of the elliptic arc in degrees.
         * param delta Angle between the subsequent polyline vertices. It defines the approximation
         * accuracy.
         * param pts Output vector of polyline vertices.
         */
        public static void ellipse2Poly(Point center, Size axes, int angle, int arcStart, int arcEnd, int delta, MatOfPoint pts)
        {
            if (pts != null) pts.ThrowIfDisposed();
            Mat pts_mat = pts;
            imgproc_Imgproc_ellipse2Poly_10(center.x, center.y, axes.width, axes.height, angle, arcStart, arcEnd, delta, pts_mat.nativeObj);


        }


        //
        // C++:  void cv::putText(Mat& img, String text, Point org, int fontFace, double fontScale, Scalar color, int thickness = 1, int lineType = LINE_8, bool bottomLeftOrigin = false)
        //

        /**
         * Draws a text string.
         *
         * The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered
         * using the specified font are replaced by question marks. See #getTextSize for a text rendering code
         * example.
         *
         * param img Image.
         * param text Text string to be drawn.
         * param org Bottom-left corner of the text string in the image.
         * param fontFace Font type, see #HersheyFonts.
         * param fontScale Font scale factor that is multiplied by the font-specific base size.
         * param color Text color.
         * param thickness Thickness of the lines used to draw a text.
         * param lineType Line type. See #LineTypes
         * param bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise,
         * it is at the top-left corner.
         */
        public static void putText(Mat img, string text, Point org, int fontFace, double fontScale, Scalar color, int thickness, int lineType, bool bottomLeftOrigin)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_putText_10(img.nativeObj, text, org.x, org.y, fontFace, fontScale, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, bottomLeftOrigin);


        }

        /**
         * Draws a text string.
         *
         * The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered
         * using the specified font are replaced by question marks. See #getTextSize for a text rendering code
         * example.
         *
         * param img Image.
         * param text Text string to be drawn.
         * param org Bottom-left corner of the text string in the image.
         * param fontFace Font type, see #HersheyFonts.
         * param fontScale Font scale factor that is multiplied by the font-specific base size.
         * param color Text color.
         * param thickness Thickness of the lines used to draw a text.
         * param lineType Line type. See #LineTypes
         * it is at the top-left corner.
         */
        public static void putText(Mat img, string text, Point org, int fontFace, double fontScale, Scalar color, int thickness, int lineType)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_putText_11(img.nativeObj, text, org.x, org.y, fontFace, fontScale, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType);


        }

        /**
         * Draws a text string.
         *
         * The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered
         * using the specified font are replaced by question marks. See #getTextSize for a text rendering code
         * example.
         *
         * param img Image.
         * param text Text string to be drawn.
         * param org Bottom-left corner of the text string in the image.
         * param fontFace Font type, see #HersheyFonts.
         * param fontScale Font scale factor that is multiplied by the font-specific base size.
         * param color Text color.
         * param thickness Thickness of the lines used to draw a text.
         * it is at the top-left corner.
         */
        public static void putText(Mat img, string text, Point org, int fontFace, double fontScale, Scalar color, int thickness)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_putText_12(img.nativeObj, text, org.x, org.y, fontFace, fontScale, color.val[0], color.val[1], color.val[2], color.val[3], thickness);


        }

        /**
         * Draws a text string.
         *
         * The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered
         * using the specified font are replaced by question marks. See #getTextSize for a text rendering code
         * example.
         *
         * param img Image.
         * param text Text string to be drawn.
         * param org Bottom-left corner of the text string in the image.
         * param fontFace Font type, see #HersheyFonts.
         * param fontScale Font scale factor that is multiplied by the font-specific base size.
         * param color Text color.
         * it is at the top-left corner.
         */
        public static void putText(Mat img, string text, Point org, int fontFace, double fontScale, Scalar color)
        {
            if (img != null) img.ThrowIfDisposed();

            imgproc_Imgproc_putText_13(img.nativeObj, text, org.x, org.y, fontFace, fontScale, color.val[0], color.val[1], color.val[2], color.val[3]);


        }


        //
        // C++:  double cv::getFontScaleFromHeight(int fontFace, int pixelHeight, int thickness = 1)
        //

        /**
         * Calculates the font-specific size to use to achieve a given height in pixels.
         *
         * param fontFace Font to use, see cv::HersheyFonts.
         * param pixelHeight Pixel height to compute the fontScale for
         * param thickness Thickness of lines used to render the text.See putText for details.
         * return The fontSize to use for cv::putText
         *
         * SEE: cv::putText
         */
        public static double getFontScaleFromHeight(int fontFace, int pixelHeight, int thickness)
        {


            return imgproc_Imgproc_getFontScaleFromHeight_10(fontFace, pixelHeight, thickness);


        }

        /**
         * Calculates the font-specific size to use to achieve a given height in pixels.
         *
         * param fontFace Font to use, see cv::HersheyFonts.
         * param pixelHeight Pixel height to compute the fontScale for
         * return The fontSize to use for cv::putText
         *
         * SEE: cv::putText
         */
        public static double getFontScaleFromHeight(int fontFace, int pixelHeight)
        {


            return imgproc_Imgproc_getFontScaleFromHeight_11(fontFace, pixelHeight);


        }


        //
        // C++:  void cv::HoughLinesWithAccumulator(Mat image, Mat& lines, double rho, double theta, int threshold, double srn = 0, double stn = 0, double min_theta = 0, double max_theta = CV_PI)
        //

        /**
         * Finds lines in a binary image using the standard Hough transform and get accumulator.
         *
         * <b>Note:</b> This function is for bindings use only. Use original function in C++ code
         *
         * SEE: HoughLines
         * param image automatically generated
         * param lines automatically generated
         * param rho automatically generated
         * param theta automatically generated
         * param threshold automatically generated
         * param srn automatically generated
         * param stn automatically generated
         * param min_theta automatically generated
         * param max_theta automatically generated
         */
        public static void HoughLinesWithAccumulator(Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn, double min_theta, double max_theta)
        {
            if (image != null) image.ThrowIfDisposed();
            if (lines != null) lines.ThrowIfDisposed();

            imgproc_Imgproc_HoughLinesWithAccumulator_10(image.nativeObj, lines.nativeObj, rho, theta, threshold, srn, stn, min_theta, max_theta);


        }

        /**
         * Finds lines in a binary image using the standard Hough transform and get accumulator.
         *
         * <b>Note:</b> This function is for bindings use only. Use original function in C++ code
         *
         * SEE: HoughLines
         * param image automatically generated
         * param lines automatically generated
         * param rho automatically generated
         * param theta automatically generated
         * param threshold automatically generated
         * param srn automatically generated
         * param stn automatically generated
         * param min_theta automatically generated
         */
        public static void HoughLinesWithAccumulator(Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn, double min_theta)
        {
            if (image != null) image.ThrowIfDisposed();
            if (lines != null) lines.ThrowIfDisposed();

            imgproc_Imgproc_HoughLinesWithAccumulator_11(image.nativeObj, lines.nativeObj, rho, theta, threshold, srn, stn, min_theta);


        }

        /**
         * Finds lines in a binary image using the standard Hough transform and get accumulator.
         *
         * <b>Note:</b> This function is for bindings use only. Use original function in C++ code
         *
         * SEE: HoughLines
         * param image automatically generated
         * param lines automatically generated
         * param rho automatically generated
         * param theta automatically generated
         * param threshold automatically generated
         * param srn automatically generated
         * param stn automatically generated
         */
        public static void HoughLinesWithAccumulator(Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn)
        {
            if (image != null) image.ThrowIfDisposed();
            if (lines != null) lines.ThrowIfDisposed();

            imgproc_Imgproc_HoughLinesWithAccumulator_12(image.nativeObj, lines.nativeObj, rho, theta, threshold, srn, stn);


        }

        /**
         * Finds lines in a binary image using the standard Hough transform and get accumulator.
         *
         * <b>Note:</b> This function is for bindings use only. Use original function in C++ code
         *
         * SEE: HoughLines
         * param image automatically generated
         * param lines automatically generated
         * param rho automatically generated
         * param theta automatically generated
         * param threshold automatically generated
         * param srn automatically generated
         */
        public static void HoughLinesWithAccumulator(Mat image, Mat lines, double rho, double theta, int threshold, double srn)
        {
            if (image != null) image.ThrowIfDisposed();
            if (lines != null) lines.ThrowIfDisposed();

            imgproc_Imgproc_HoughLinesWithAccumulator_13(image.nativeObj, lines.nativeObj, rho, theta, threshold, srn);


        }

        /**
         * Finds lines in a binary image using the standard Hough transform and get accumulator.
         *
         * <b>Note:</b> This function is for bindings use only. Use original function in C++ code
         *
         * SEE: HoughLines
         * param image automatically generated
         * param lines automatically generated
         * param rho automatically generated
         * param theta automatically generated
         * param threshold automatically generated
         */
        public static void HoughLinesWithAccumulator(Mat image, Mat lines, double rho, double theta, int threshold)
        {
            if (image != null) image.ThrowIfDisposed();
            if (lines != null) lines.ThrowIfDisposed();

            imgproc_Imgproc_HoughLinesWithAccumulator_14(image.nativeObj, lines.nativeObj, rho, theta, threshold);


        }



        // C++: Size getTextSize(const String& text, int fontFace, double fontScale, int thickness, int* baseLine);
        //javadoc:getTextSize(text, fontFace, fontScale, thickness, baseLine)
        public static Size getTextSize(string text, int fontFace, double fontScale, int thickness, int[] baseLine)
        {
            if (baseLine != null && baseLine.Length != 1)
                throw new CvException("'baseLine' must be 'int[1]' or 'null'.");
            double[] tmpArray = new double[2];
            imgproc_Imgproc_n_1getTextSize(text, fontFace, fontScale, thickness, baseLine, tmpArray);
            Size retVal = new Size(tmpArray);
            return retVal;
        }


#if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR
        const string LIBNAME = "__Internal";
#else
        const string LIBNAME = "opencvforunity";
#endif



        // C++:  Ptr_LineSegmentDetector cv::createLineSegmentDetector(int refine = LSD_REFINE_STD, double scale = 0.8, double sigma_scale = 0.6, double quant = 2.0, double ang_th = 22.5, double log_eps = 0, double density_th = 0.7, int n_bins = 1024)
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_createLineSegmentDetector_10(int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps, double density_th, int n_bins);
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_createLineSegmentDetector_11(int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps, double density_th);
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_createLineSegmentDetector_12(int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps);
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_createLineSegmentDetector_13(int refine, double scale, double sigma_scale, double quant, double ang_th);
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_createLineSegmentDetector_14(int refine, double scale, double sigma_scale, double quant);
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_createLineSegmentDetector_15(int refine, double scale, double sigma_scale);
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_createLineSegmentDetector_16(int refine, double scale);
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_createLineSegmentDetector_17(int refine);
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_createLineSegmentDetector_18();

        // C++:  Mat cv::getGaussianKernel(int ksize, double sigma, int ktype = CV_64F)
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_getGaussianKernel_10(int ksize, double sigma, int ktype);
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_getGaussianKernel_11(int ksize, double sigma);

        // C++:  void cv::getDerivKernels(Mat& kx, Mat& ky, int dx, int dy, int ksize, bool normalize = false, int ktype = CV_32F)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_getDerivKernels_10(IntPtr kx_nativeObj, IntPtr ky_nativeObj, int dx, int dy, int ksize, [MarshalAs(UnmanagedType.U1)] bool normalize, int ktype);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_getDerivKernels_11(IntPtr kx_nativeObj, IntPtr ky_nativeObj, int dx, int dy, int ksize, [MarshalAs(UnmanagedType.U1)] bool normalize);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_getDerivKernels_12(IntPtr kx_nativeObj, IntPtr ky_nativeObj, int dx, int dy, int ksize);

        // C++:  Mat cv::getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma, double psi = CV_PI*0.5, int ktype = CV_64F)
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_getGaborKernel_10(double ksize_width, double ksize_height, double sigma, double theta, double lambd, double gamma, double psi, int ktype);
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_getGaborKernel_11(double ksize_width, double ksize_height, double sigma, double theta, double lambd, double gamma, double psi);
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_getGaborKernel_12(double ksize_width, double ksize_height, double sigma, double theta, double lambd, double gamma);

        // C++:  Mat cv::getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1))
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_getStructuringElement_10(int shape, double ksize_width, double ksize_height, double anchor_x, double anchor_y);
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_getStructuringElement_11(int shape, double ksize_width, double ksize_height);

        // C++:  void cv::medianBlur(Mat src, Mat& dst, int ksize)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_medianBlur_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ksize);

        // C++:  void cv::GaussianBlur(Mat src, Mat& dst, Size ksize, double sigmaX, double sigmaY = 0, int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_GaussianBlur_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, double ksize_width, double ksize_height, double sigmaX, double sigmaY, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_GaussianBlur_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, double ksize_width, double ksize_height, double sigmaX, double sigmaY);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_GaussianBlur_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, double ksize_width, double ksize_height, double sigmaX);

        // C++:  void cv::bilateralFilter(Mat src, Mat& dst, int d, double sigmaColor, double sigmaSpace, int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_bilateralFilter_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int d, double sigmaColor, double sigmaSpace, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_bilateralFilter_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int d, double sigmaColor, double sigmaSpace);

        // C++:  void cv::boxFilter(Mat src, Mat& dst, int ddepth, Size ksize, Point anchor = Point(-1,-1), bool normalize = true, int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_boxFilter_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y, [MarshalAs(UnmanagedType.U1)] bool normalize, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_boxFilter_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y, [MarshalAs(UnmanagedType.U1)] bool normalize);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_boxFilter_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_boxFilter_13(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, double ksize_width, double ksize_height);

        // C++:  void cv::sqrBoxFilter(Mat src, Mat& dst, int ddepth, Size ksize, Point anchor = Point(-1, -1), bool normalize = true, int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_sqrBoxFilter_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y, [MarshalAs(UnmanagedType.U1)] bool normalize, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_sqrBoxFilter_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y, [MarshalAs(UnmanagedType.U1)] bool normalize);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_sqrBoxFilter_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_sqrBoxFilter_13(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, double ksize_width, double ksize_height);

        // C++:  void cv::blur(Mat src, Mat& dst, Size ksize, Point anchor = Point(-1,-1), int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_blur_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, double ksize_width, double ksize_height, double anchor_x, double anchor_y, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_blur_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, double ksize_width, double ksize_height, double anchor_x, double anchor_y);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_blur_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, double ksize_width, double ksize_height);

        // C++:  void cv::stackBlur(Mat src, Mat& dst, Size ksize)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_stackBlur_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, double ksize_width, double ksize_height);

        // C++:  void cv::filter2D(Mat src, Mat& dst, int ddepth, Mat kernel, Point anchor = Point(-1,-1), double delta = 0, int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_filter2D_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, IntPtr kernel_nativeObj, double anchor_x, double anchor_y, double delta, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_filter2D_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, IntPtr kernel_nativeObj, double anchor_x, double anchor_y, double delta);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_filter2D_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, IntPtr kernel_nativeObj, double anchor_x, double anchor_y);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_filter2D_13(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, IntPtr kernel_nativeObj);

        // C++:  void cv::sepFilter2D(Mat src, Mat& dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor = Point(-1,-1), double delta = 0, int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_sepFilter2D_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, IntPtr kernelX_nativeObj, IntPtr kernelY_nativeObj, double anchor_x, double anchor_y, double delta, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_sepFilter2D_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, IntPtr kernelX_nativeObj, IntPtr kernelY_nativeObj, double anchor_x, double anchor_y, double delta);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_sepFilter2D_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, IntPtr kernelX_nativeObj, IntPtr kernelY_nativeObj, double anchor_x, double anchor_y);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_sepFilter2D_13(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, IntPtr kernelX_nativeObj, IntPtr kernelY_nativeObj);

        // C++:  void cv::Sobel(Mat src, Mat& dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Sobel_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, int dx, int dy, int ksize, double scale, double delta, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Sobel_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, int dx, int dy, int ksize, double scale, double delta);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Sobel_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, int dx, int dy, int ksize, double scale);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Sobel_13(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, int dx, int dy, int ksize);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Sobel_14(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, int dx, int dy);

        // C++:  void cv::spatialGradient(Mat src, Mat& dx, Mat& dy, int ksize = 3, int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_spatialGradient_10(IntPtr src_nativeObj, IntPtr dx_nativeObj, IntPtr dy_nativeObj, int ksize, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_spatialGradient_11(IntPtr src_nativeObj, IntPtr dx_nativeObj, IntPtr dy_nativeObj, int ksize);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_spatialGradient_12(IntPtr src_nativeObj, IntPtr dx_nativeObj, IntPtr dy_nativeObj);

        // C++:  void cv::Scharr(Mat src, Mat& dst, int ddepth, int dx, int dy, double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Scharr_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, int dx, int dy, double scale, double delta, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Scharr_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, int dx, int dy, double scale, double delta);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Scharr_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, int dx, int dy, double scale);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Scharr_13(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, int dx, int dy);

        // C++:  void cv::Laplacian(Mat src, Mat& dst, int ddepth, int ksize = 1, double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Laplacian_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, int ksize, double scale, double delta, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Laplacian_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, int ksize, double scale, double delta);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Laplacian_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, int ksize, double scale);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Laplacian_13(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth, int ksize);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Laplacian_14(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ddepth);

        // C++:  void cv::Canny(Mat image, Mat& edges, double threshold1, double threshold2, int apertureSize = 3, bool L2gradient = false)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Canny_10(IntPtr image_nativeObj, IntPtr edges_nativeObj, double threshold1, double threshold2, int apertureSize, [MarshalAs(UnmanagedType.U1)] bool L2gradient);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Canny_11(IntPtr image_nativeObj, IntPtr edges_nativeObj, double threshold1, double threshold2, int apertureSize);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Canny_12(IntPtr image_nativeObj, IntPtr edges_nativeObj, double threshold1, double threshold2);

        // C++:  void cv::Canny(Mat dx, Mat dy, Mat& edges, double threshold1, double threshold2, bool L2gradient = false)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Canny_13(IntPtr dx_nativeObj, IntPtr dy_nativeObj, IntPtr edges_nativeObj, double threshold1, double threshold2, [MarshalAs(UnmanagedType.U1)] bool L2gradient);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_Canny_14(IntPtr dx_nativeObj, IntPtr dy_nativeObj, IntPtr edges_nativeObj, double threshold1, double threshold2);

        // C++:  void cv::cornerMinEigenVal(Mat src, Mat& dst, int blockSize, int ksize = 3, int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_cornerMinEigenVal_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int blockSize, int ksize, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_cornerMinEigenVal_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int blockSize, int ksize);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_cornerMinEigenVal_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, int blockSize);

        // C++:  void cv::cornerHarris(Mat src, Mat& dst, int blockSize, int ksize, double k, int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_cornerHarris_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int blockSize, int ksize, double k, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_cornerHarris_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int blockSize, int ksize, double k);

        // C++:  void cv::cornerEigenValsAndVecs(Mat src, Mat& dst, int blockSize, int ksize, int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_cornerEigenValsAndVecs_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int blockSize, int ksize, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_cornerEigenValsAndVecs_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int blockSize, int ksize);

        // C++:  void cv::preCornerDetect(Mat src, Mat& dst, int ksize, int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_preCornerDetect_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ksize, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_preCornerDetect_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int ksize);

        // C++:  void cv::cornerSubPix(Mat image, Mat& corners, Size winSize, Size zeroZone, TermCriteria criteria)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_cornerSubPix_10(IntPtr image_nativeObj, IntPtr corners_nativeObj, double winSize_width, double winSize_height, double zeroZone_width, double zeroZone_height, int criteria_type, int criteria_maxCount, double criteria_epsilon);

        // C++:  void cv::goodFeaturesToTrack(Mat image, vector_Point& corners, int maxCorners, double qualityLevel, double minDistance, Mat mask = Mat(), int blockSize = 3, bool useHarrisDetector = false, double k = 0.04)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_goodFeaturesToTrack_10(IntPtr image_nativeObj, IntPtr corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, IntPtr mask_nativeObj, int blockSize, [MarshalAs(UnmanagedType.U1)] bool useHarrisDetector, double k);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_goodFeaturesToTrack_11(IntPtr image_nativeObj, IntPtr corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, IntPtr mask_nativeObj, int blockSize, [MarshalAs(UnmanagedType.U1)] bool useHarrisDetector);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_goodFeaturesToTrack_12(IntPtr image_nativeObj, IntPtr corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, IntPtr mask_nativeObj, int blockSize);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_goodFeaturesToTrack_13(IntPtr image_nativeObj, IntPtr corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, IntPtr mask_nativeObj);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_goodFeaturesToTrack_14(IntPtr image_nativeObj, IntPtr corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance);

        // C++:  void cv::goodFeaturesToTrack(Mat image, vector_Point& corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, bool useHarrisDetector = false, double k = 0.04)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_goodFeaturesToTrack_15(IntPtr image_nativeObj, IntPtr corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, IntPtr mask_nativeObj, int blockSize, int gradientSize, [MarshalAs(UnmanagedType.U1)] bool useHarrisDetector, double k);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_goodFeaturesToTrack_16(IntPtr image_nativeObj, IntPtr corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, IntPtr mask_nativeObj, int blockSize, int gradientSize, [MarshalAs(UnmanagedType.U1)] bool useHarrisDetector);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_goodFeaturesToTrack_17(IntPtr image_nativeObj, IntPtr corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, IntPtr mask_nativeObj, int blockSize, int gradientSize);

        // C++:  void cv::goodFeaturesToTrack(Mat image, Mat& corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat& cornersQuality, int blockSize = 3, int gradientSize = 3, bool useHarrisDetector = false, double k = 0.04)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_goodFeaturesToTrackWithQuality_10(IntPtr image_nativeObj, IntPtr corners_nativeObj, int maxCorners, double qualityLevel, double minDistance, IntPtr mask_nativeObj, IntPtr cornersQuality_nativeObj, int blockSize, int gradientSize, [MarshalAs(UnmanagedType.U1)] bool useHarrisDetector, double k);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_goodFeaturesToTrackWithQuality_11(IntPtr image_nativeObj, IntPtr corners_nativeObj, int maxCorners, double qualityLevel, double minDistance, IntPtr mask_nativeObj, IntPtr cornersQuality_nativeObj, int blockSize, int gradientSize, [MarshalAs(UnmanagedType.U1)] bool useHarrisDetector);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_goodFeaturesToTrackWithQuality_12(IntPtr image_nativeObj, IntPtr corners_nativeObj, int maxCorners, double qualityLevel, double minDistance, IntPtr mask_nativeObj, IntPtr cornersQuality_nativeObj, int blockSize, int gradientSize);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_goodFeaturesToTrackWithQuality_13(IntPtr image_nativeObj, IntPtr corners_nativeObj, int maxCorners, double qualityLevel, double minDistance, IntPtr mask_nativeObj, IntPtr cornersQuality_nativeObj, int blockSize);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_goodFeaturesToTrackWithQuality_14(IntPtr image_nativeObj, IntPtr corners_nativeObj, int maxCorners, double qualityLevel, double minDistance, IntPtr mask_nativeObj, IntPtr cornersQuality_nativeObj);

        // C++:  void cv::HoughLines(Mat image, Mat& lines, double rho, double theta, int threshold, double srn = 0, double stn = 0, double min_theta = 0, double max_theta = CV_PI)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughLines_10(IntPtr image_nativeObj, IntPtr lines_nativeObj, double rho, double theta, int threshold, double srn, double stn, double min_theta, double max_theta);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughLines_11(IntPtr image_nativeObj, IntPtr lines_nativeObj, double rho, double theta, int threshold, double srn, double stn, double min_theta);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughLines_12(IntPtr image_nativeObj, IntPtr lines_nativeObj, double rho, double theta, int threshold, double srn, double stn);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughLines_13(IntPtr image_nativeObj, IntPtr lines_nativeObj, double rho, double theta, int threshold, double srn);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughLines_14(IntPtr image_nativeObj, IntPtr lines_nativeObj, double rho, double theta, int threshold);

        // C++:  void cv::HoughLinesP(Mat image, Mat& lines, double rho, double theta, int threshold, double minLineLength = 0, double maxLineGap = 0)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughLinesP_10(IntPtr image_nativeObj, IntPtr lines_nativeObj, double rho, double theta, int threshold, double minLineLength, double maxLineGap);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughLinesP_11(IntPtr image_nativeObj, IntPtr lines_nativeObj, double rho, double theta, int threshold, double minLineLength);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughLinesP_12(IntPtr image_nativeObj, IntPtr lines_nativeObj, double rho, double theta, int threshold);

        // C++:  void cv::HoughLinesPointSet(Mat point, Mat& lines, int lines_max, int threshold, double min_rho, double max_rho, double rho_step, double min_theta, double max_theta, double theta_step)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughLinesPointSet_10(IntPtr point_nativeObj, IntPtr lines_nativeObj, int lines_max, int threshold, double min_rho, double max_rho, double rho_step, double min_theta, double max_theta, double theta_step);

        // C++:  void cv::HoughCircles(Mat image, Mat& circles, int method, double dp, double minDist, double param1 = 100, double param2 = 100, int minRadius = 0, int maxRadius = 0)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughCircles_10(IntPtr image_nativeObj, IntPtr circles_nativeObj, int method, double dp, double minDist, double param1, double param2, int minRadius, int maxRadius);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughCircles_11(IntPtr image_nativeObj, IntPtr circles_nativeObj, int method, double dp, double minDist, double param1, double param2, int minRadius);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughCircles_12(IntPtr image_nativeObj, IntPtr circles_nativeObj, int method, double dp, double minDist, double param1, double param2);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughCircles_13(IntPtr image_nativeObj, IntPtr circles_nativeObj, int method, double dp, double minDist, double param1);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughCircles_14(IntPtr image_nativeObj, IntPtr circles_nativeObj, int method, double dp, double minDist);

        // C++:  void cv::erode(Mat src, Mat& dst, Mat kernel, Point anchor = Point(-1,-1), int iterations = 1, int borderType = BORDER_CONSTANT, Scalar borderValue = morphologyDefaultBorderValue())
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_erode_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr kernel_nativeObj, double anchor_x, double anchor_y, int iterations, int borderType, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_erode_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr kernel_nativeObj, double anchor_x, double anchor_y, int iterations, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_erode_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr kernel_nativeObj, double anchor_x, double anchor_y, int iterations);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_erode_13(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr kernel_nativeObj, double anchor_x, double anchor_y);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_erode_14(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr kernel_nativeObj);

        // C++:  void cv::dilate(Mat src, Mat& dst, Mat kernel, Point anchor = Point(-1,-1), int iterations = 1, int borderType = BORDER_CONSTANT, Scalar borderValue = morphologyDefaultBorderValue())
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_dilate_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr kernel_nativeObj, double anchor_x, double anchor_y, int iterations, int borderType, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_dilate_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr kernel_nativeObj, double anchor_x, double anchor_y, int iterations, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_dilate_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr kernel_nativeObj, double anchor_x, double anchor_y, int iterations);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_dilate_13(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr kernel_nativeObj, double anchor_x, double anchor_y);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_dilate_14(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr kernel_nativeObj);

        // C++:  void cv::morphologyEx(Mat src, Mat& dst, int op, Mat kernel, Point anchor = Point(-1,-1), int iterations = 1, int borderType = BORDER_CONSTANT, Scalar borderValue = morphologyDefaultBorderValue())
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_morphologyEx_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int op, IntPtr kernel_nativeObj, double anchor_x, double anchor_y, int iterations, int borderType, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_morphologyEx_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int op, IntPtr kernel_nativeObj, double anchor_x, double anchor_y, int iterations, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_morphologyEx_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, int op, IntPtr kernel_nativeObj, double anchor_x, double anchor_y, int iterations);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_morphologyEx_13(IntPtr src_nativeObj, IntPtr dst_nativeObj, int op, IntPtr kernel_nativeObj, double anchor_x, double anchor_y);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_morphologyEx_14(IntPtr src_nativeObj, IntPtr dst_nativeObj, int op, IntPtr kernel_nativeObj);

        // C++:  void cv::resize(Mat src, Mat& dst, Size dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_resize_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, double dsize_width, double dsize_height, double fx, double fy, int interpolation);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_resize_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, double dsize_width, double dsize_height, double fx, double fy);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_resize_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, double dsize_width, double dsize_height, double fx);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_resize_13(IntPtr src_nativeObj, IntPtr dst_nativeObj, double dsize_width, double dsize_height);

        // C++:  void cv::warpAffine(Mat src, Mat& dst, Mat M, Size dsize, int flags = INTER_LINEAR, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar())
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_warpAffine_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr M_nativeObj, double dsize_width, double dsize_height, int flags, int borderMode, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_warpAffine_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr M_nativeObj, double dsize_width, double dsize_height, int flags, int borderMode);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_warpAffine_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr M_nativeObj, double dsize_width, double dsize_height, int flags);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_warpAffine_13(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr M_nativeObj, double dsize_width, double dsize_height);

        // C++:  void cv::warpPerspective(Mat src, Mat& dst, Mat M, Size dsize, int flags = INTER_LINEAR, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar())
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_warpPerspective_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr M_nativeObj, double dsize_width, double dsize_height, int flags, int borderMode, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_warpPerspective_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr M_nativeObj, double dsize_width, double dsize_height, int flags, int borderMode);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_warpPerspective_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr M_nativeObj, double dsize_width, double dsize_height, int flags);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_warpPerspective_13(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr M_nativeObj, double dsize_width, double dsize_height);

        // C++:  void cv::remap(Mat src, Mat& dst, Mat map1, Mat map2, int interpolation, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar())
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_remap_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr map1_nativeObj, IntPtr map2_nativeObj, int interpolation, int borderMode, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_remap_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr map1_nativeObj, IntPtr map2_nativeObj, int interpolation, int borderMode);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_remap_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr map1_nativeObj, IntPtr map2_nativeObj, int interpolation);

        // C++:  void cv::convertMaps(Mat map1, Mat map2, Mat& dstmap1, Mat& dstmap2, int dstmap1type, bool nninterpolation = false)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_convertMaps_10(IntPtr map1_nativeObj, IntPtr map2_nativeObj, IntPtr dstmap1_nativeObj, IntPtr dstmap2_nativeObj, int dstmap1type, [MarshalAs(UnmanagedType.U1)] bool nninterpolation);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_convertMaps_11(IntPtr map1_nativeObj, IntPtr map2_nativeObj, IntPtr dstmap1_nativeObj, IntPtr dstmap2_nativeObj, int dstmap1type);

        // C++:  Mat cv::getRotationMatrix2D(Point2f center, double angle, double scale)
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_getRotationMatrix2D_10(double center_x, double center_y, double angle, double scale);

        // C++:  void cv::invertAffineTransform(Mat M, Mat& iM)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_invertAffineTransform_10(IntPtr M_nativeObj, IntPtr iM_nativeObj);

        // C++:  Mat cv::getPerspectiveTransform(Mat src, Mat dst, int solveMethod = DECOMP_LU)
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_getPerspectiveTransform_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int solveMethod);
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_getPerspectiveTransform_11(IntPtr src_nativeObj, IntPtr dst_nativeObj);

        // C++:  Mat cv::getAffineTransform(vector_Point2f src, vector_Point2f dst)
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_getAffineTransform_10(IntPtr src_mat_nativeObj, IntPtr dst_mat_nativeObj);

        // C++:  void cv::getRectSubPix(Mat image, Size patchSize, Point2f center, Mat& patch, int patchType = -1)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_getRectSubPix_10(IntPtr image_nativeObj, double patchSize_width, double patchSize_height, double center_x, double center_y, IntPtr patch_nativeObj, int patchType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_getRectSubPix_11(IntPtr image_nativeObj, double patchSize_width, double patchSize_height, double center_x, double center_y, IntPtr patch_nativeObj);

        // C++:  void cv::logPolar(Mat src, Mat& dst, Point2f center, double M, int flags)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_logPolar_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, double center_x, double center_y, double M, int flags);

        // C++:  void cv::linearPolar(Mat src, Mat& dst, Point2f center, double maxRadius, int flags)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_linearPolar_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, double center_x, double center_y, double maxRadius, int flags);

        // C++:  void cv::warpPolar(Mat src, Mat& dst, Size dsize, Point2f center, double maxRadius, int flags)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_warpPolar_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, double dsize_width, double dsize_height, double center_x, double center_y, double maxRadius, int flags);

        // C++:  void cv::integral(Mat src, Mat& sum, Mat& sqsum, Mat& tilted, int sdepth = -1, int sqdepth = -1)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_integral3_10(IntPtr src_nativeObj, IntPtr sum_nativeObj, IntPtr sqsum_nativeObj, IntPtr tilted_nativeObj, int sdepth, int sqdepth);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_integral3_11(IntPtr src_nativeObj, IntPtr sum_nativeObj, IntPtr sqsum_nativeObj, IntPtr tilted_nativeObj, int sdepth);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_integral3_12(IntPtr src_nativeObj, IntPtr sum_nativeObj, IntPtr sqsum_nativeObj, IntPtr tilted_nativeObj);

        // C++:  void cv::integral(Mat src, Mat& sum, int sdepth = -1)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_integral_10(IntPtr src_nativeObj, IntPtr sum_nativeObj, int sdepth);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_integral_11(IntPtr src_nativeObj, IntPtr sum_nativeObj);

        // C++:  void cv::integral(Mat src, Mat& sum, Mat& sqsum, int sdepth = -1, int sqdepth = -1)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_integral2_10(IntPtr src_nativeObj, IntPtr sum_nativeObj, IntPtr sqsum_nativeObj, int sdepth, int sqdepth);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_integral2_11(IntPtr src_nativeObj, IntPtr sum_nativeObj, IntPtr sqsum_nativeObj, int sdepth);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_integral2_12(IntPtr src_nativeObj, IntPtr sum_nativeObj, IntPtr sqsum_nativeObj);

        // C++:  void cv::accumulate(Mat src, Mat& dst, Mat mask = Mat())
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_accumulate_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr mask_nativeObj);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_accumulate_11(IntPtr src_nativeObj, IntPtr dst_nativeObj);

        // C++:  void cv::accumulateSquare(Mat src, Mat& dst, Mat mask = Mat())
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_accumulateSquare_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr mask_nativeObj);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_accumulateSquare_11(IntPtr src_nativeObj, IntPtr dst_nativeObj);

        // C++:  void cv::accumulateProduct(Mat src1, Mat src2, Mat& dst, Mat mask = Mat())
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_accumulateProduct_10(IntPtr src1_nativeObj, IntPtr src2_nativeObj, IntPtr dst_nativeObj, IntPtr mask_nativeObj);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_accumulateProduct_11(IntPtr src1_nativeObj, IntPtr src2_nativeObj, IntPtr dst_nativeObj);

        // C++:  void cv::accumulateWeighted(Mat src, Mat& dst, double alpha, Mat mask = Mat())
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_accumulateWeighted_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, double alpha, IntPtr mask_nativeObj);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_accumulateWeighted_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, double alpha);

        // C++:  Point2d cv::phaseCorrelate(Mat src1, Mat src2, Mat window = Mat(), double* response = 0)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_phaseCorrelate_10(IntPtr src1_nativeObj, IntPtr src2_nativeObj, IntPtr window_nativeObj, double[] response_out, double[] retVal);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_phaseCorrelate_11(IntPtr src1_nativeObj, IntPtr src2_nativeObj, IntPtr window_nativeObj, double[] retVal);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_phaseCorrelate_12(IntPtr src1_nativeObj, IntPtr src2_nativeObj, double[] retVal);

        // C++:  void cv::createHanningWindow(Mat& dst, Size winSize, int type)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_createHanningWindow_10(IntPtr dst_nativeObj, double winSize_width, double winSize_height, int type);

        // C++:  void cv::divSpectrums(Mat a, Mat b, Mat& c, int flags, bool conjB = false)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_divSpectrums_10(IntPtr a_nativeObj, IntPtr b_nativeObj, IntPtr c_nativeObj, int flags, [MarshalAs(UnmanagedType.U1)] bool conjB);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_divSpectrums_11(IntPtr a_nativeObj, IntPtr b_nativeObj, IntPtr c_nativeObj, int flags);

        // C++:  double cv::threshold(Mat src, Mat& dst, double thresh, double maxval, int type)
        [DllImport(LIBNAME)]
        private static extern double imgproc_Imgproc_threshold_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, double thresh, double maxval, int type);

        // C++:  void cv::adaptiveThreshold(Mat src, Mat& dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_adaptiveThreshold_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C);

        // C++:  void cv::pyrDown(Mat src, Mat& dst, Size dstsize = Size(), int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_pyrDown_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, double dstsize_width, double dstsize_height, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_pyrDown_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, double dstsize_width, double dstsize_height);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_pyrDown_12(IntPtr src_nativeObj, IntPtr dst_nativeObj);

        // C++:  void cv::pyrUp(Mat src, Mat& dst, Size dstsize = Size(), int borderType = BORDER_DEFAULT)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_pyrUp_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, double dstsize_width, double dstsize_height, int borderType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_pyrUp_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, double dstsize_width, double dstsize_height);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_pyrUp_12(IntPtr src_nativeObj, IntPtr dst_nativeObj);

        // C++:  void cv::calcHist(vector_Mat images, vector_int channels, Mat mask, Mat& hist, vector_int histSize, vector_float ranges, bool accumulate = false)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_calcHist_10(IntPtr images_mat_nativeObj, IntPtr channels_mat_nativeObj, IntPtr mask_nativeObj, IntPtr hist_nativeObj, IntPtr histSize_mat_nativeObj, IntPtr ranges_mat_nativeObj, [MarshalAs(UnmanagedType.U1)] bool accumulate);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_calcHist_11(IntPtr images_mat_nativeObj, IntPtr channels_mat_nativeObj, IntPtr mask_nativeObj, IntPtr hist_nativeObj, IntPtr histSize_mat_nativeObj, IntPtr ranges_mat_nativeObj);

        // C++:  void cv::calcBackProject(vector_Mat images, vector_int channels, Mat hist, Mat& dst, vector_float ranges, double scale)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_calcBackProject_10(IntPtr images_mat_nativeObj, IntPtr channels_mat_nativeObj, IntPtr hist_nativeObj, IntPtr dst_nativeObj, IntPtr ranges_mat_nativeObj, double scale);

        // C++:  double cv::compareHist(Mat H1, Mat H2, int method)
        [DllImport(LIBNAME)]
        private static extern double imgproc_Imgproc_compareHist_10(IntPtr H1_nativeObj, IntPtr H2_nativeObj, int method);

        // C++:  void cv::equalizeHist(Mat src, Mat& dst)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_equalizeHist_10(IntPtr src_nativeObj, IntPtr dst_nativeObj);

        // C++:  Ptr_CLAHE cv::createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8))
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_createCLAHE_10(double clipLimit, double tileGridSize_width, double tileGridSize_height);
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_createCLAHE_11(double clipLimit);
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_createCLAHE_12();

        // C++:  float cv::wrapperEMD(Mat signature1, Mat signature2, int distType, Mat cost = Mat(), Ptr_float& lowerBound = Ptr<float>(), Mat& flow = Mat())
        [DllImport(LIBNAME)]
        private static extern float imgproc_Imgproc_EMD_10(IntPtr signature1_nativeObj, IntPtr signature2_nativeObj, int distType, IntPtr cost_nativeObj, IntPtr flow_nativeObj);
        [DllImport(LIBNAME)]
        private static extern float imgproc_Imgproc_EMD_11(IntPtr signature1_nativeObj, IntPtr signature2_nativeObj, int distType, IntPtr cost_nativeObj);
        [DllImport(LIBNAME)]
        private static extern float imgproc_Imgproc_EMD_13(IntPtr signature1_nativeObj, IntPtr signature2_nativeObj, int distType);

        // C++:  void cv::watershed(Mat image, Mat& markers)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_watershed_10(IntPtr image_nativeObj, IntPtr markers_nativeObj);

        // C++:  void cv::pyrMeanShiftFiltering(Mat src, Mat& dst, double sp, double sr, int maxLevel = 1, TermCriteria termcrit = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1))
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_pyrMeanShiftFiltering_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, double sp, double sr, int maxLevel, int termcrit_type, int termcrit_maxCount, double termcrit_epsilon);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_pyrMeanShiftFiltering_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, double sp, double sr, int maxLevel);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_pyrMeanShiftFiltering_12(IntPtr src_nativeObj, IntPtr dst_nativeObj, double sp, double sr);

        // C++:  void cv::grabCut(Mat img, Mat& mask, Rect rect, Mat& bgdModel, Mat& fgdModel, int iterCount, int mode = GC_EVAL)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_grabCut_10(IntPtr img_nativeObj, IntPtr mask_nativeObj, int rect_x, int rect_y, int rect_width, int rect_height, IntPtr bgdModel_nativeObj, IntPtr fgdModel_nativeObj, int iterCount, int mode);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_grabCut_11(IntPtr img_nativeObj, IntPtr mask_nativeObj, int rect_x, int rect_y, int rect_width, int rect_height, IntPtr bgdModel_nativeObj, IntPtr fgdModel_nativeObj, int iterCount);

        // C++:  void cv::distanceTransform(Mat src, Mat& dst, Mat& labels, int distanceType, int maskSize, int labelType = DIST_LABEL_CCOMP)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_distanceTransformWithLabels_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr labels_nativeObj, int distanceType, int maskSize, int labelType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_distanceTransformWithLabels_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr labels_nativeObj, int distanceType, int maskSize);

        // C++:  void cv::distanceTransform(Mat src, Mat& dst, int distanceType, int maskSize, int dstType = CV_32F)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_distanceTransform_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int distanceType, int maskSize, int dstType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_distanceTransform_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int distanceType, int maskSize);

        // C++:  int cv::floodFill(Mat& image, Mat& mask, Point seedPoint, Scalar newVal, Rect* rect = 0, Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), int flags = 4)
        [DllImport(LIBNAME)]
        private static extern int imgproc_Imgproc_floodFill_10(IntPtr image_nativeObj, IntPtr mask_nativeObj, double seedPoint_x, double seedPoint_y, double newVal_val0, double newVal_val1, double newVal_val2, double newVal_val3, double[] rect_out, double loDiff_val0, double loDiff_val1, double loDiff_val2, double loDiff_val3, double upDiff_val0, double upDiff_val1, double upDiff_val2, double upDiff_val3, int flags);
        [DllImport(LIBNAME)]
        private static extern int imgproc_Imgproc_floodFill_11(IntPtr image_nativeObj, IntPtr mask_nativeObj, double seedPoint_x, double seedPoint_y, double newVal_val0, double newVal_val1, double newVal_val2, double newVal_val3, double[] rect_out, double loDiff_val0, double loDiff_val1, double loDiff_val2, double loDiff_val3, double upDiff_val0, double upDiff_val1, double upDiff_val2, double upDiff_val3);
        [DllImport(LIBNAME)]
        private static extern int imgproc_Imgproc_floodFill_12(IntPtr image_nativeObj, IntPtr mask_nativeObj, double seedPoint_x, double seedPoint_y, double newVal_val0, double newVal_val1, double newVal_val2, double newVal_val3, double[] rect_out, double loDiff_val0, double loDiff_val1, double loDiff_val2, double loDiff_val3);
        [DllImport(LIBNAME)]
        private static extern int imgproc_Imgproc_floodFill_13(IntPtr image_nativeObj, IntPtr mask_nativeObj, double seedPoint_x, double seedPoint_y, double newVal_val0, double newVal_val1, double newVal_val2, double newVal_val3, double[] rect_out);
        [DllImport(LIBNAME)]
        private static extern int imgproc_Imgproc_floodFill_14(IntPtr image_nativeObj, IntPtr mask_nativeObj, double seedPoint_x, double seedPoint_y, double newVal_val0, double newVal_val1, double newVal_val2, double newVal_val3);

        // C++:  void cv::blendLinear(Mat src1, Mat src2, Mat weights1, Mat weights2, Mat& dst)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_blendLinear_10(IntPtr src1_nativeObj, IntPtr src2_nativeObj, IntPtr weights1_nativeObj, IntPtr weights2_nativeObj, IntPtr dst_nativeObj);

        // C++:  void cv::cvtColor(Mat src, Mat& dst, int code, int dstCn = 0)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_cvtColor_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int code, int dstCn);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_cvtColor_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int code);

        // C++:  void cv::cvtColorTwoPlane(Mat src1, Mat src2, Mat& dst, int code)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_cvtColorTwoPlane_10(IntPtr src1_nativeObj, IntPtr src2_nativeObj, IntPtr dst_nativeObj, int code);

        // C++:  void cv::demosaicing(Mat src, Mat& dst, int code, int dstCn = 0)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_demosaicing_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int code, int dstCn);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_demosaicing_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, int code);

        // C++:  Moments cv::moments(Mat array, bool binaryImage = false)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_moments_10(IntPtr array_nativeObj, [MarshalAs(UnmanagedType.U1)] bool binaryImage, double[] retVal);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_moments_11(IntPtr array_nativeObj, double[] retVal);

        // C++:  void cv::HuMoments(Moments m, Mat& hu)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HuMoments_10(double m_m00, double m_m10, double m_m01, double m_m20, double m_m11, double m_m02, double m_m30, double m_m21, double m_m12, double m_m03, IntPtr hu_nativeObj);

        // C++:  void cv::matchTemplate(Mat image, Mat templ, Mat& result, int method, Mat mask = Mat())
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_matchTemplate_10(IntPtr image_nativeObj, IntPtr templ_nativeObj, IntPtr result_nativeObj, int method, IntPtr mask_nativeObj);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_matchTemplate_11(IntPtr image_nativeObj, IntPtr templ_nativeObj, IntPtr result_nativeObj, int method);

        // C++:  int cv::connectedComponents(Mat image, Mat& labels, int connectivity, int ltype, int ccltype)
        [DllImport(LIBNAME)]
        private static extern int imgproc_Imgproc_connectedComponentsWithAlgorithm_10(IntPtr image_nativeObj, IntPtr labels_nativeObj, int connectivity, int ltype, int ccltype);

        // C++:  int cv::connectedComponents(Mat image, Mat& labels, int connectivity = 8, int ltype = CV_32S)
        [DllImport(LIBNAME)]
        private static extern int imgproc_Imgproc_connectedComponents_10(IntPtr image_nativeObj, IntPtr labels_nativeObj, int connectivity, int ltype);
        [DllImport(LIBNAME)]
        private static extern int imgproc_Imgproc_connectedComponents_11(IntPtr image_nativeObj, IntPtr labels_nativeObj, int connectivity);
        [DllImport(LIBNAME)]
        private static extern int imgproc_Imgproc_connectedComponents_12(IntPtr image_nativeObj, IntPtr labels_nativeObj);

        // C++:  int cv::connectedComponentsWithStats(Mat image, Mat& labels, Mat& stats, Mat& centroids, int connectivity, int ltype, int ccltype)
        [DllImport(LIBNAME)]
        private static extern int imgproc_Imgproc_connectedComponentsWithStatsWithAlgorithm_10(IntPtr image_nativeObj, IntPtr labels_nativeObj, IntPtr stats_nativeObj, IntPtr centroids_nativeObj, int connectivity, int ltype, int ccltype);

        // C++:  int cv::connectedComponentsWithStats(Mat image, Mat& labels, Mat& stats, Mat& centroids, int connectivity = 8, int ltype = CV_32S)
        [DllImport(LIBNAME)]
        private static extern int imgproc_Imgproc_connectedComponentsWithStats_10(IntPtr image_nativeObj, IntPtr labels_nativeObj, IntPtr stats_nativeObj, IntPtr centroids_nativeObj, int connectivity, int ltype);
        [DllImport(LIBNAME)]
        private static extern int imgproc_Imgproc_connectedComponentsWithStats_11(IntPtr image_nativeObj, IntPtr labels_nativeObj, IntPtr stats_nativeObj, IntPtr centroids_nativeObj, int connectivity);
        [DllImport(LIBNAME)]
        private static extern int imgproc_Imgproc_connectedComponentsWithStats_12(IntPtr image_nativeObj, IntPtr labels_nativeObj, IntPtr stats_nativeObj, IntPtr centroids_nativeObj);

        // C++:  void cv::findContours(Mat image, vector_vector_Point& contours, Mat& hierarchy, int mode, int method, Point offset = Point())
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_findContours_10(IntPtr image_nativeObj, IntPtr contours_mat_nativeObj, IntPtr hierarchy_nativeObj, int mode, int method, double offset_x, double offset_y);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_findContours_11(IntPtr image_nativeObj, IntPtr contours_mat_nativeObj, IntPtr hierarchy_nativeObj, int mode, int method);

        // C++:  void cv::approxPolyDP(vector_Point2f curve, vector_Point2f& approxCurve, double epsilon, bool closed)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_approxPolyDP_10(IntPtr curve_mat_nativeObj, IntPtr approxCurve_mat_nativeObj, double epsilon, [MarshalAs(UnmanagedType.U1)] bool closed);

        // C++:  double cv::arcLength(vector_Point2f curve, bool closed)
        [DllImport(LIBNAME)]
        private static extern double imgproc_Imgproc_arcLength_10(IntPtr curve_mat_nativeObj, [MarshalAs(UnmanagedType.U1)] bool closed);

        // C++:  Rect cv::boundingRect(Mat array)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_boundingRect_10(IntPtr array_nativeObj, double[] retVal);

        // C++:  double cv::contourArea(Mat contour, bool oriented = false)
        [DllImport(LIBNAME)]
        private static extern double imgproc_Imgproc_contourArea_10(IntPtr contour_nativeObj, [MarshalAs(UnmanagedType.U1)] bool oriented);
        [DllImport(LIBNAME)]
        private static extern double imgproc_Imgproc_contourArea_11(IntPtr contour_nativeObj);

        // C++:  RotatedRect cv::minAreaRect(vector_Point2f points)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_minAreaRect_10(IntPtr points_mat_nativeObj, double[] retVal);

        // C++:  void cv::boxPoints(RotatedRect box, Mat& points)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_boxPoints_10(double box_center_x, double box_center_y, double box_size_width, double box_size_height, double box_angle, IntPtr points_nativeObj);

        // C++:  void cv::minEnclosingCircle(vector_Point2f points, Point2f& center, float& radius)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_minEnclosingCircle_10(IntPtr points_mat_nativeObj, double[] center_out, double[] radius_out);

        // C++:  double cv::minEnclosingTriangle(Mat points, Mat& triangle)
        [DllImport(LIBNAME)]
        private static extern double imgproc_Imgproc_minEnclosingTriangle_10(IntPtr points_nativeObj, IntPtr triangle_nativeObj);

        // C++:  double cv::matchShapes(Mat contour1, Mat contour2, int method, double parameter)
        [DllImport(LIBNAME)]
        private static extern double imgproc_Imgproc_matchShapes_10(IntPtr contour1_nativeObj, IntPtr contour2_nativeObj, int method, double parameter);

        // C++:  void cv::convexHull(vector_Point points, vector_int& hull, bool clockwise = false,  _hidden_  returnPoints = true)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_convexHull_10(IntPtr points_mat_nativeObj, IntPtr hull_mat_nativeObj, [MarshalAs(UnmanagedType.U1)] bool clockwise);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_convexHull_12(IntPtr points_mat_nativeObj, IntPtr hull_mat_nativeObj);

        // C++:  void cv::convexityDefects(vector_Point contour, vector_int convexhull, vector_Vec4i& convexityDefects)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_convexityDefects_10(IntPtr contour_mat_nativeObj, IntPtr convexhull_mat_nativeObj, IntPtr convexityDefects_mat_nativeObj);

        // C++:  bool cv::isContourConvex(vector_Point contour)
        [DllImport(LIBNAME)]
        [return: MarshalAs(UnmanagedType.U1)]
        private static extern bool imgproc_Imgproc_isContourConvex_10(IntPtr contour_mat_nativeObj);

        // C++:  float cv::intersectConvexConvex(Mat p1, Mat p2, Mat& p12, bool handleNested = true)
        [DllImport(LIBNAME)]
        private static extern float imgproc_Imgproc_intersectConvexConvex_10(IntPtr p1_nativeObj, IntPtr p2_nativeObj, IntPtr p12_nativeObj, [MarshalAs(UnmanagedType.U1)] bool handleNested);
        [DllImport(LIBNAME)]
        private static extern float imgproc_Imgproc_intersectConvexConvex_11(IntPtr p1_nativeObj, IntPtr p2_nativeObj, IntPtr p12_nativeObj);

        // C++:  RotatedRect cv::fitEllipse(vector_Point2f points)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_fitEllipse_10(IntPtr points_mat_nativeObj, double[] retVal);

        // C++:  RotatedRect cv::fitEllipseAMS(Mat points)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_fitEllipseAMS_10(IntPtr points_nativeObj, double[] retVal);

        // C++:  RotatedRect cv::fitEllipseDirect(Mat points)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_fitEllipseDirect_10(IntPtr points_nativeObj, double[] retVal);

        // C++:  void cv::fitLine(Mat points, Mat& line, int distType, double param, double reps, double aeps)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_fitLine_10(IntPtr points_nativeObj, IntPtr line_nativeObj, int distType, double param, double reps, double aeps);

        // C++:  double cv::pointPolygonTest(vector_Point2f contour, Point2f pt, bool measureDist)
        [DllImport(LIBNAME)]
        private static extern double imgproc_Imgproc_pointPolygonTest_10(IntPtr contour_mat_nativeObj, double pt_x, double pt_y, [MarshalAs(UnmanagedType.U1)] bool measureDist);

        // C++:  int cv::rotatedRectangleIntersection(RotatedRect rect1, RotatedRect rect2, Mat& intersectingRegion)
        [DllImport(LIBNAME)]
        private static extern int imgproc_Imgproc_rotatedRectangleIntersection_10(double rect1_center_x, double rect1_center_y, double rect1_size_width, double rect1_size_height, double rect1_angle, double rect2_center_x, double rect2_center_y, double rect2_size_width, double rect2_size_height, double rect2_angle, IntPtr intersectingRegion_nativeObj);

        // C++:  Ptr_GeneralizedHoughBallard cv::createGeneralizedHoughBallard()
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_createGeneralizedHoughBallard_10();

        // C++:  Ptr_GeneralizedHoughGuil cv::createGeneralizedHoughGuil()
        [DllImport(LIBNAME)]
        private static extern IntPtr imgproc_Imgproc_createGeneralizedHoughGuil_10();

        // C++:  void cv::applyColorMap(Mat src, Mat& dst, int colormap)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_applyColorMap_10(IntPtr src_nativeObj, IntPtr dst_nativeObj, int colormap);

        // C++:  void cv::applyColorMap(Mat src, Mat& dst, Mat userColor)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_applyColorMap_11(IntPtr src_nativeObj, IntPtr dst_nativeObj, IntPtr userColor_nativeObj);

        // C++:  void cv::line(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_line_10(IntPtr img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, int shift);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_line_11(IntPtr img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_line_12(IntPtr img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_line_13(IntPtr img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3);

        // C++:  void cv::arrowedLine(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, int line_type = 8, int shift = 0, double tipLength = 0.1)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_arrowedLine_10(IntPtr img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int line_type, int shift, double tipLength);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_arrowedLine_11(IntPtr img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int line_type, int shift);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_arrowedLine_12(IntPtr img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int line_type);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_arrowedLine_13(IntPtr img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_arrowedLine_14(IntPtr img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3);

        // C++:  void cv::rectangle(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_rectangle_10(IntPtr img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, int shift);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_rectangle_11(IntPtr img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_rectangle_12(IntPtr img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_rectangle_13(IntPtr img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3);

        // C++:  void cv::rectangle(Mat& img, Rect rec, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_rectangle_14(IntPtr img_nativeObj, int rec_x, int rec_y, int rec_width, int rec_height, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, int shift);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_rectangle_15(IntPtr img_nativeObj, int rec_x, int rec_y, int rec_width, int rec_height, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_rectangle_16(IntPtr img_nativeObj, int rec_x, int rec_y, int rec_width, int rec_height, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_rectangle_17(IntPtr img_nativeObj, int rec_x, int rec_y, int rec_width, int rec_height, double color_val0, double color_val1, double color_val2, double color_val3);

        // C++:  void cv::circle(Mat& img, Point center, int radius, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_circle_10(IntPtr img_nativeObj, double center_x, double center_y, int radius, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, int shift);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_circle_11(IntPtr img_nativeObj, double center_x, double center_y, int radius, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_circle_12(IntPtr img_nativeObj, double center_x, double center_y, int radius, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_circle_13(IntPtr img_nativeObj, double center_x, double center_y, int radius, double color_val0, double color_val1, double color_val2, double color_val3);

        // C++:  void cv::ellipse(Mat& img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_ellipse_10(IntPtr img_nativeObj, double center_x, double center_y, double axes_width, double axes_height, double angle, double startAngle, double endAngle, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, int shift);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_ellipse_11(IntPtr img_nativeObj, double center_x, double center_y, double axes_width, double axes_height, double angle, double startAngle, double endAngle, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_ellipse_12(IntPtr img_nativeObj, double center_x, double center_y, double axes_width, double axes_height, double angle, double startAngle, double endAngle, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_ellipse_13(IntPtr img_nativeObj, double center_x, double center_y, double axes_width, double axes_height, double angle, double startAngle, double endAngle, double color_val0, double color_val1, double color_val2, double color_val3);

        // C++:  void cv::ellipse(Mat& img, RotatedRect box, Scalar color, int thickness = 1, int lineType = LINE_8)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_ellipse_14(IntPtr img_nativeObj, double box_center_x, double box_center_y, double box_size_width, double box_size_height, double box_angle, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_ellipse_15(IntPtr img_nativeObj, double box_center_x, double box_center_y, double box_size_width, double box_size_height, double box_angle, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_ellipse_16(IntPtr img_nativeObj, double box_center_x, double box_center_y, double box_size_width, double box_size_height, double box_angle, double color_val0, double color_val1, double color_val2, double color_val3);

        // C++:  void cv::drawMarker(Mat& img, Point position, Scalar color, int markerType = MARKER_CROSS, int markerSize = 20, int thickness = 1, int line_type = 8)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_drawMarker_10(IntPtr img_nativeObj, double position_x, double position_y, double color_val0, double color_val1, double color_val2, double color_val3, int markerType, int markerSize, int thickness, int line_type);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_drawMarker_11(IntPtr img_nativeObj, double position_x, double position_y, double color_val0, double color_val1, double color_val2, double color_val3, int markerType, int markerSize, int thickness);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_drawMarker_12(IntPtr img_nativeObj, double position_x, double position_y, double color_val0, double color_val1, double color_val2, double color_val3, int markerType, int markerSize);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_drawMarker_13(IntPtr img_nativeObj, double position_x, double position_y, double color_val0, double color_val1, double color_val2, double color_val3, int markerType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_drawMarker_14(IntPtr img_nativeObj, double position_x, double position_y, double color_val0, double color_val1, double color_val2, double color_val3);

        // C++:  void cv::fillConvexPoly(Mat& img, vector_Point points, Scalar color, int lineType = LINE_8, int shift = 0)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_fillConvexPoly_10(IntPtr img_nativeObj, IntPtr points_mat_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3, int lineType, int shift);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_fillConvexPoly_11(IntPtr img_nativeObj, IntPtr points_mat_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3, int lineType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_fillConvexPoly_12(IntPtr img_nativeObj, IntPtr points_mat_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3);

        // C++:  void cv::fillPoly(Mat& img, vector_vector_Point pts, Scalar color, int lineType = LINE_8, int shift = 0, Point offset = Point())
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_fillPoly_10(IntPtr img_nativeObj, IntPtr pts_mat_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3, int lineType, int shift, double offset_x, double offset_y);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_fillPoly_11(IntPtr img_nativeObj, IntPtr pts_mat_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3, int lineType, int shift);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_fillPoly_12(IntPtr img_nativeObj, IntPtr pts_mat_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3, int lineType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_fillPoly_13(IntPtr img_nativeObj, IntPtr pts_mat_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3);

        // C++:  void cv::polylines(Mat& img, vector_vector_Point pts, bool isClosed, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_polylines_10(IntPtr img_nativeObj, IntPtr pts_mat_nativeObj, [MarshalAs(UnmanagedType.U1)] bool isClosed, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, int shift);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_polylines_11(IntPtr img_nativeObj, IntPtr pts_mat_nativeObj, [MarshalAs(UnmanagedType.U1)] bool isClosed, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_polylines_12(IntPtr img_nativeObj, IntPtr pts_mat_nativeObj, [MarshalAs(UnmanagedType.U1)] bool isClosed, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_polylines_13(IntPtr img_nativeObj, IntPtr pts_mat_nativeObj, [MarshalAs(UnmanagedType.U1)] bool isClosed, double color_val0, double color_val1, double color_val2, double color_val3);

        // C++:  void cv::drawContours(Mat& image, vector_vector_Point contours, int contourIdx, Scalar color, int thickness = 1, int lineType = LINE_8, Mat hierarchy = Mat(), int maxLevel = INT_MAX, Point offset = Point())
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_drawContours_10(IntPtr image_nativeObj, IntPtr contours_mat_nativeObj, int contourIdx, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, IntPtr hierarchy_nativeObj, int maxLevel, double offset_x, double offset_y);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_drawContours_11(IntPtr image_nativeObj, IntPtr contours_mat_nativeObj, int contourIdx, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, IntPtr hierarchy_nativeObj, int maxLevel);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_drawContours_12(IntPtr image_nativeObj, IntPtr contours_mat_nativeObj, int contourIdx, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, IntPtr hierarchy_nativeObj);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_drawContours_13(IntPtr image_nativeObj, IntPtr contours_mat_nativeObj, int contourIdx, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_drawContours_14(IntPtr image_nativeObj, IntPtr contours_mat_nativeObj, int contourIdx, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_drawContours_15(IntPtr image_nativeObj, IntPtr contours_mat_nativeObj, int contourIdx, double color_val0, double color_val1, double color_val2, double color_val3);

        // C++:  bool cv::clipLine(Rect imgRect, Point& pt1, Point& pt2)
        [DllImport(LIBNAME)]
        [return: MarshalAs(UnmanagedType.U1)]
        private static extern bool imgproc_Imgproc_clipLine_10(int imgRect_x, int imgRect_y, int imgRect_width, int imgRect_height, double pt1_x, double pt1_y, double[] pt1_out, double pt2_x, double pt2_y, double[] pt2_out);

        // C++:  void cv::ellipse2Poly(Point center, Size axes, int angle, int arcStart, int arcEnd, int delta, vector_Point& pts)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_ellipse2Poly_10(double center_x, double center_y, double axes_width, double axes_height, int angle, int arcStart, int arcEnd, int delta, IntPtr pts_mat_nativeObj);

        // C++:  void cv::putText(Mat& img, String text, Point org, int fontFace, double fontScale, Scalar color, int thickness = 1, int lineType = LINE_8, bool bottomLeftOrigin = false)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_putText_10(IntPtr img_nativeObj, string text, double org_x, double org_y, int fontFace, double fontScale, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, [MarshalAs(UnmanagedType.U1)] bool bottomLeftOrigin);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_putText_11(IntPtr img_nativeObj, string text, double org_x, double org_y, int fontFace, double fontScale, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_putText_12(IntPtr img_nativeObj, string text, double org_x, double org_y, int fontFace, double fontScale, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_putText_13(IntPtr img_nativeObj, string text, double org_x, double org_y, int fontFace, double fontScale, double color_val0, double color_val1, double color_val2, double color_val3);

        // C++:  double cv::getFontScaleFromHeight(int fontFace, int pixelHeight, int thickness = 1)
        [DllImport(LIBNAME)]
        private static extern double imgproc_Imgproc_getFontScaleFromHeight_10(int fontFace, int pixelHeight, int thickness);
        [DllImport(LIBNAME)]
        private static extern double imgproc_Imgproc_getFontScaleFromHeight_11(int fontFace, int pixelHeight);

        // C++:  void cv::HoughLinesWithAccumulator(Mat image, Mat& lines, double rho, double theta, int threshold, double srn = 0, double stn = 0, double min_theta = 0, double max_theta = CV_PI)
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughLinesWithAccumulator_10(IntPtr image_nativeObj, IntPtr lines_nativeObj, double rho, double theta, int threshold, double srn, double stn, double min_theta, double max_theta);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughLinesWithAccumulator_11(IntPtr image_nativeObj, IntPtr lines_nativeObj, double rho, double theta, int threshold, double srn, double stn, double min_theta);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughLinesWithAccumulator_12(IntPtr image_nativeObj, IntPtr lines_nativeObj, double rho, double theta, int threshold, double srn, double stn);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughLinesWithAccumulator_13(IntPtr image_nativeObj, IntPtr lines_nativeObj, double rho, double theta, int threshold, double srn);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_HoughLinesWithAccumulator_14(IntPtr image_nativeObj, IntPtr lines_nativeObj, double rho, double theta, int threshold);
        [DllImport(LIBNAME)]
        private static extern void imgproc_Imgproc_n_1getTextSize(string text, int fontFace, double fontScale, int thickness, int[] baseLine, double[] vals);
    }
}