//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.imgproc;
import org.opencv.core.Mat;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfInt;
import org.opencv.core.MatOfInt4;
import org.opencv.core.MatOfPoint;
import org.opencv.core.MatOfPoint2f;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.RotatedRect;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.core.TermCriteria;
import org.opencv.utils.Converters;
import java.util.ArrayList;
import java.util.List;
// C++: class Imgproc
public class Imgproc {
private static final int
IPL_BORDER_CONSTANT = 0,
IPL_BORDER_REPLICATE = 1,
IPL_BORDER_REFLECT = 2,
IPL_BORDER_WRAP = 3,
IPL_BORDER_REFLECT_101 = 4,
IPL_BORDER_TRANSPARENT = 5,
CV_INTER_NN = 0,
CV_INTER_LINEAR = 1,
CV_INTER_CUBIC = 2,
CV_INTER_AREA = 3,
CV_INTER_LANCZOS4 = 4,
CV_MOP_ERODE = 0,
CV_MOP_DILATE = 1,
CV_MOP_OPEN = 2,
CV_MOP_CLOSE = 3,
CV_MOP_GRADIENT = 4,
CV_MOP_TOPHAT = 5,
CV_MOP_BLACKHAT = 6,
CV_RETR_EXTERNAL = 0,
CV_RETR_LIST = 1,
CV_RETR_CCOMP = 2,
CV_RETR_TREE = 3,
CV_RETR_FLOODFILL = 4,
CV_CHAIN_APPROX_NONE = 1,
CV_CHAIN_APPROX_SIMPLE = 2,
CV_CHAIN_APPROX_TC89_L1 = 3,
CV_CHAIN_APPROX_TC89_KCOS = 4,
CV_THRESH_BINARY = 0,
CV_THRESH_BINARY_INV = 1,
CV_THRESH_TRUNC = 2,
CV_THRESH_TOZERO = 3,
CV_THRESH_TOZERO_INV = 4,
CV_THRESH_MASK = 7,
CV_THRESH_OTSU = 8,
CV_THRESH_TRIANGLE = 16;
// C++: enum <unnamed>
public static final int
CV_GAUSSIAN_5x5 = 7,
CV_SCHARR = -1,
CV_MAX_SOBEL_KSIZE = 7,
CV_RGBA2mRGBA = 125,
CV_mRGBA2RGBA = 126,
CV_WARP_FILL_OUTLIERS = 8,
CV_WARP_INVERSE_MAP = 16,
CV_CHAIN_CODE = 0,
CV_LINK_RUNS = 5,
CV_POLY_APPROX_DP = 0,
CV_CONTOURS_MATCH_I1 = 1,
CV_CONTOURS_MATCH_I2 = 2,
CV_CONTOURS_MATCH_I3 = 3,
CV_CLOCKWISE = 1,
CV_COUNTER_CLOCKWISE = 2,
CV_COMP_CORREL = 0,
CV_COMP_CHISQR = 1,
CV_COMP_INTERSECT = 2,
CV_COMP_BHATTACHARYYA = 3,
CV_COMP_HELLINGER = CV_COMP_BHATTACHARYYA,
CV_COMP_CHISQR_ALT = 4,
CV_COMP_KL_DIV = 5,
CV_DIST_MASK_3 = 3,
CV_DIST_MASK_5 = 5,
CV_DIST_MASK_PRECISE = 0,
CV_DIST_LABEL_CCOMP = 0,
CV_DIST_LABEL_PIXEL = 1,
CV_DIST_USER = -1,
CV_DIST_L1 = 1,
CV_DIST_L2 = 2,
CV_DIST_C = 3,
CV_DIST_L12 = 4,
CV_DIST_FAIR = 5,
CV_DIST_WELSCH = 6,
CV_DIST_HUBER = 7,
CV_CANNY_L2_GRADIENT = (1 << 31),
CV_HOUGH_STANDARD = 0,
CV_HOUGH_PROBABILISTIC = 1,
CV_HOUGH_MULTI_SCALE = 2,
CV_HOUGH_GRADIENT = 3;
// C++: enum MorphShapes_c (MorphShapes_c)
public static final int
CV_SHAPE_RECT = 0,
CV_SHAPE_CROSS = 1,
CV_SHAPE_ELLIPSE = 2,
CV_SHAPE_CUSTOM = 100;
// C++: enum SmoothMethod_c (SmoothMethod_c)
public static final int
CV_BLUR_NO_SCALE = 0,
CV_BLUR = 1,
CV_GAUSSIAN = 2,
CV_MEDIAN = 3,
CV_BILATERAL = 4;
// C++: enum AdaptiveThresholdTypes (cv.AdaptiveThresholdTypes)
public static final int
ADAPTIVE_THRESH_MEAN_C = 0,
ADAPTIVE_THRESH_GAUSSIAN_C = 1;
// C++: enum ColorConversionCodes (cv.ColorConversionCodes)
public static final int
COLOR_BGR2BGRA = 0,
COLOR_RGB2RGBA = COLOR_BGR2BGRA,
COLOR_BGRA2BGR = 1,
COLOR_RGBA2RGB = COLOR_BGRA2BGR,
COLOR_BGR2RGBA = 2,
COLOR_RGB2BGRA = COLOR_BGR2RGBA,
COLOR_RGBA2BGR = 3,
COLOR_BGRA2RGB = COLOR_RGBA2BGR,
COLOR_BGR2RGB = 4,
COLOR_RGB2BGR = COLOR_BGR2RGB,
COLOR_BGRA2RGBA = 5,
COLOR_RGBA2BGRA = COLOR_BGRA2RGBA,
COLOR_BGR2GRAY = 6,
COLOR_RGB2GRAY = 7,
COLOR_GRAY2BGR = 8,
COLOR_GRAY2RGB = COLOR_GRAY2BGR,
COLOR_GRAY2BGRA = 9,
COLOR_GRAY2RGBA = COLOR_GRAY2BGRA,
COLOR_BGRA2GRAY = 10,
COLOR_RGBA2GRAY = 11,
COLOR_BGR2BGR565 = 12,
COLOR_RGB2BGR565 = 13,
COLOR_BGR5652BGR = 14,
COLOR_BGR5652RGB = 15,
COLOR_BGRA2BGR565 = 16,
COLOR_RGBA2BGR565 = 17,
COLOR_BGR5652BGRA = 18,
COLOR_BGR5652RGBA = 19,
COLOR_GRAY2BGR565 = 20,
COLOR_BGR5652GRAY = 21,
COLOR_BGR2BGR555 = 22,
COLOR_RGB2BGR555 = 23,
COLOR_BGR5552BGR = 24,
COLOR_BGR5552RGB = 25,
COLOR_BGRA2BGR555 = 26,
COLOR_RGBA2BGR555 = 27,
COLOR_BGR5552BGRA = 28,
COLOR_BGR5552RGBA = 29,
COLOR_GRAY2BGR555 = 30,
COLOR_BGR5552GRAY = 31,
COLOR_BGR2XYZ = 32,
COLOR_RGB2XYZ = 33,
COLOR_XYZ2BGR = 34,
COLOR_XYZ2RGB = 35,
COLOR_BGR2YCrCb = 36,
COLOR_RGB2YCrCb = 37,
COLOR_YCrCb2BGR = 38,
COLOR_YCrCb2RGB = 39,
COLOR_BGR2HSV = 40,
COLOR_RGB2HSV = 41,
COLOR_BGR2Lab = 44,
COLOR_RGB2Lab = 45,
COLOR_BGR2Luv = 50,
COLOR_RGB2Luv = 51,
COLOR_BGR2HLS = 52,
COLOR_RGB2HLS = 53,
COLOR_HSV2BGR = 54,
COLOR_HSV2RGB = 55,
COLOR_Lab2BGR = 56,
COLOR_Lab2RGB = 57,
COLOR_Luv2BGR = 58,
COLOR_Luv2RGB = 59,
COLOR_HLS2BGR = 60,
COLOR_HLS2RGB = 61,
COLOR_BGR2HSV_FULL = 66,
COLOR_RGB2HSV_FULL = 67,
COLOR_BGR2HLS_FULL = 68,
COLOR_RGB2HLS_FULL = 69,
COLOR_HSV2BGR_FULL = 70,
COLOR_HSV2RGB_FULL = 71,
COLOR_HLS2BGR_FULL = 72,
COLOR_HLS2RGB_FULL = 73,
COLOR_LBGR2Lab = 74,
COLOR_LRGB2Lab = 75,
COLOR_LBGR2Luv = 76,
COLOR_LRGB2Luv = 77,
COLOR_Lab2LBGR = 78,
COLOR_Lab2LRGB = 79,
COLOR_Luv2LBGR = 80,
COLOR_Luv2LRGB = 81,
COLOR_BGR2YUV = 82,
COLOR_RGB2YUV = 83,
COLOR_YUV2BGR = 84,
COLOR_YUV2RGB = 85,
COLOR_YUV2RGB_NV12 = 90,
COLOR_YUV2BGR_NV12 = 91,
COLOR_YUV2RGB_NV21 = 92,
COLOR_YUV2BGR_NV21 = 93,
COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21,
COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21,
COLOR_YUV2RGBA_NV12 = 94,
COLOR_YUV2BGRA_NV12 = 95,
COLOR_YUV2RGBA_NV21 = 96,
COLOR_YUV2BGRA_NV21 = 97,
COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21,
COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21,
COLOR_YUV2RGB_YV12 = 98,
COLOR_YUV2BGR_YV12 = 99,
COLOR_YUV2RGB_IYUV = 100,
COLOR_YUV2BGR_IYUV = 101,
COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV,
COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV,
COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12,
COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12,
COLOR_YUV2RGBA_YV12 = 102,
COLOR_YUV2BGRA_YV12 = 103,
COLOR_YUV2RGBA_IYUV = 104,
COLOR_YUV2BGRA_IYUV = 105,
COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV,
COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV,
COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12,
COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12,
COLOR_YUV2GRAY_420 = 106,
COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420,
COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420,
COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420,
COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420,
COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420,
COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420,
COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420,
COLOR_YUV2RGB_UYVY = 107,
COLOR_YUV2BGR_UYVY = 108,
COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY,
COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY,
COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY,
COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY,
COLOR_YUV2RGBA_UYVY = 111,
COLOR_YUV2BGRA_UYVY = 112,
COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY,
COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY,
COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY,
COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY,
COLOR_YUV2RGB_YUY2 = 115,
COLOR_YUV2BGR_YUY2 = 116,
COLOR_YUV2RGB_YVYU = 117,
COLOR_YUV2BGR_YVYU = 118,
COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2,
COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2,
COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2,
COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2,
COLOR_YUV2RGBA_YUY2 = 119,
COLOR_YUV2BGRA_YUY2 = 120,
COLOR_YUV2RGBA_YVYU = 121,
COLOR_YUV2BGRA_YVYU = 122,
COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2,
COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2,
COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2,
COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2,
COLOR_YUV2GRAY_UYVY = 123,
COLOR_YUV2GRAY_YUY2 = 124,
COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY,
COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY,
COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2,
COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2,
COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2,
COLOR_RGBA2mRGBA = 125,
COLOR_mRGBA2RGBA = 126,
COLOR_RGB2YUV_I420 = 127,
COLOR_BGR2YUV_I420 = 128,
COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420,
COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420,
COLOR_RGBA2YUV_I420 = 129,
COLOR_BGRA2YUV_I420 = 130,
COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420,
COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420,
COLOR_RGB2YUV_YV12 = 131,
COLOR_BGR2YUV_YV12 = 132,
COLOR_RGBA2YUV_YV12 = 133,
COLOR_BGRA2YUV_YV12 = 134,
COLOR_BayerBG2BGR = 46,
COLOR_BayerGB2BGR = 47,
COLOR_BayerRG2BGR = 48,
COLOR_BayerGR2BGR = 49,
COLOR_BayerRGGB2BGR = COLOR_BayerBG2BGR,
COLOR_BayerGRBG2BGR = COLOR_BayerGB2BGR,
COLOR_BayerBGGR2BGR = COLOR_BayerRG2BGR,
COLOR_BayerGBRG2BGR = COLOR_BayerGR2BGR,
COLOR_BayerRGGB2RGB = COLOR_BayerBGGR2BGR,
COLOR_BayerGRBG2RGB = COLOR_BayerGBRG2BGR,
COLOR_BayerBGGR2RGB = COLOR_BayerRGGB2BGR,
COLOR_BayerGBRG2RGB = COLOR_BayerGRBG2BGR,
COLOR_BayerBG2RGB = COLOR_BayerRG2BGR,
COLOR_BayerGB2RGB = COLOR_BayerGR2BGR,
COLOR_BayerRG2RGB = COLOR_BayerBG2BGR,
COLOR_BayerGR2RGB = COLOR_BayerGB2BGR,
COLOR_BayerBG2GRAY = 86,
COLOR_BayerGB2GRAY = 87,
COLOR_BayerRG2GRAY = 88,
COLOR_BayerGR2GRAY = 89,
COLOR_BayerRGGB2GRAY = COLOR_BayerBG2GRAY,
COLOR_BayerGRBG2GRAY = COLOR_BayerGB2GRAY,
COLOR_BayerBGGR2GRAY = COLOR_BayerRG2GRAY,
COLOR_BayerGBRG2GRAY = COLOR_BayerGR2GRAY,
COLOR_BayerBG2BGR_VNG = 62,
COLOR_BayerGB2BGR_VNG = 63,
COLOR_BayerRG2BGR_VNG = 64,
COLOR_BayerGR2BGR_VNG = 65,
COLOR_BayerRGGB2BGR_VNG = COLOR_BayerBG2BGR_VNG,
COLOR_BayerGRBG2BGR_VNG = COLOR_BayerGB2BGR_VNG,
COLOR_BayerBGGR2BGR_VNG = COLOR_BayerRG2BGR_VNG,
COLOR_BayerGBRG2BGR_VNG = COLOR_BayerGR2BGR_VNG,
COLOR_BayerRGGB2RGB_VNG = COLOR_BayerBGGR2BGR_VNG,
COLOR_BayerGRBG2RGB_VNG = COLOR_BayerGBRG2BGR_VNG,
COLOR_BayerBGGR2RGB_VNG = COLOR_BayerRGGB2BGR_VNG,
COLOR_BayerGBRG2RGB_VNG = COLOR_BayerGRBG2BGR_VNG,
COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG,
COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG,
COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG,
COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG,
COLOR_BayerBG2BGR_EA = 135,
COLOR_BayerGB2BGR_EA = 136,
COLOR_BayerRG2BGR_EA = 137,
COLOR_BayerGR2BGR_EA = 138,
COLOR_BayerRGGB2BGR_EA = COLOR_BayerBG2BGR_EA,
COLOR_BayerGRBG2BGR_EA = COLOR_BayerGB2BGR_EA,
COLOR_BayerBGGR2BGR_EA = COLOR_BayerRG2BGR_EA,
COLOR_BayerGBRG2BGR_EA = COLOR_BayerGR2BGR_EA,
COLOR_BayerRGGB2RGB_EA = COLOR_BayerBGGR2BGR_EA,
COLOR_BayerGRBG2RGB_EA = COLOR_BayerGBRG2BGR_EA,
COLOR_BayerBGGR2RGB_EA = COLOR_BayerRGGB2BGR_EA,
COLOR_BayerGBRG2RGB_EA = COLOR_BayerGRBG2BGR_EA,
COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA,
COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA,
COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA,
COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA,
COLOR_BayerBG2BGRA = 139,
COLOR_BayerGB2BGRA = 140,
COLOR_BayerRG2BGRA = 141,
COLOR_BayerGR2BGRA = 142,
COLOR_BayerRGGB2BGRA = COLOR_BayerBG2BGRA,
COLOR_BayerGRBG2BGRA = COLOR_BayerGB2BGRA,
COLOR_BayerBGGR2BGRA = COLOR_BayerRG2BGRA,
COLOR_BayerGBRG2BGRA = COLOR_BayerGR2BGRA,
COLOR_BayerRGGB2RGBA = COLOR_BayerBGGR2BGRA,
COLOR_BayerGRBG2RGBA = COLOR_BayerGBRG2BGRA,
COLOR_BayerBGGR2RGBA = COLOR_BayerRGGB2BGRA,
COLOR_BayerGBRG2RGBA = COLOR_BayerGRBG2BGRA,
COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA,
COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA,
COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA,
COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA,
COLOR_RGB2YUV_UYVY = 143,
COLOR_BGR2YUV_UYVY = 144,
COLOR_RGB2YUV_Y422 = COLOR_RGB2YUV_UYVY,
COLOR_BGR2YUV_Y422 = COLOR_BGR2YUV_UYVY,
COLOR_RGB2YUV_UYNV = COLOR_RGB2YUV_UYVY,
COLOR_BGR2YUV_UYNV = COLOR_BGR2YUV_UYVY,
COLOR_RGBA2YUV_UYVY = 145,
COLOR_BGRA2YUV_UYVY = 146,
COLOR_RGBA2YUV_Y422 = COLOR_RGBA2YUV_UYVY,
COLOR_BGRA2YUV_Y422 = COLOR_BGRA2YUV_UYVY,
COLOR_RGBA2YUV_UYNV = COLOR_RGBA2YUV_UYVY,
COLOR_BGRA2YUV_UYNV = COLOR_BGRA2YUV_UYVY,
COLOR_RGB2YUV_YUY2 = 147,
COLOR_BGR2YUV_YUY2 = 148,
COLOR_RGB2YUV_YVYU = 149,
COLOR_BGR2YUV_YVYU = 150,
COLOR_RGB2YUV_YUYV = COLOR_RGB2YUV_YUY2,
COLOR_BGR2YUV_YUYV = COLOR_BGR2YUV_YUY2,
COLOR_RGB2YUV_YUNV = COLOR_RGB2YUV_YUY2,
COLOR_BGR2YUV_YUNV = COLOR_BGR2YUV_YUY2,
COLOR_RGBA2YUV_YUY2 = 151,
COLOR_BGRA2YUV_YUY2 = 152,
COLOR_RGBA2YUV_YVYU = 153,
COLOR_BGRA2YUV_YVYU = 154,
COLOR_RGBA2YUV_YUYV = COLOR_RGBA2YUV_YUY2,
COLOR_BGRA2YUV_YUYV = COLOR_BGRA2YUV_YUY2,
COLOR_RGBA2YUV_YUNV = COLOR_RGBA2YUV_YUY2,
COLOR_BGRA2YUV_YUNV = COLOR_BGRA2YUV_YUY2,
COLOR_COLORCVT_MAX = 155;
// C++: enum ColormapTypes (cv.ColormapTypes)
public static final int
COLORMAP_AUTUMN = 0,
COLORMAP_BONE = 1,
COLORMAP_JET = 2,
COLORMAP_WINTER = 3,
COLORMAP_RAINBOW = 4,
COLORMAP_OCEAN = 5,
COLORMAP_SUMMER = 6,
COLORMAP_SPRING = 7,
COLORMAP_COOL = 8,
COLORMAP_HSV = 9,
COLORMAP_PINK = 10,
COLORMAP_HOT = 11,
COLORMAP_PARULA = 12,
COLORMAP_MAGMA = 13,
COLORMAP_INFERNO = 14,
COLORMAP_PLASMA = 15,
COLORMAP_VIRIDIS = 16,
COLORMAP_CIVIDIS = 17,
COLORMAP_TWILIGHT = 18,
COLORMAP_TWILIGHT_SHIFTED = 19,
COLORMAP_TURBO = 20,
COLORMAP_DEEPGREEN = 21;
// C++: enum ConnectedComponentsAlgorithmsTypes (cv.ConnectedComponentsAlgorithmsTypes)
public static final int
CCL_DEFAULT = -1,
CCL_WU = 0,
CCL_GRANA = 1,
CCL_BOLELLI = 2,
CCL_SAUF = 3,
CCL_BBDT = 4,
CCL_SPAGHETTI = 5;
// C++: enum ConnectedComponentsTypes (cv.ConnectedComponentsTypes)
public static final int
CC_STAT_LEFT = 0,
CC_STAT_TOP = 1,
CC_STAT_WIDTH = 2,
CC_STAT_HEIGHT = 3,
CC_STAT_AREA = 4,
CC_STAT_MAX = 5;
// C++: enum ContourApproximationModes (cv.ContourApproximationModes)
public static final int
CHAIN_APPROX_NONE = 1,
CHAIN_APPROX_SIMPLE = 2,
CHAIN_APPROX_TC89_L1 = 3,
CHAIN_APPROX_TC89_KCOS = 4;
// C++: enum DistanceTransformLabelTypes (cv.DistanceTransformLabelTypes)
public static final int
DIST_LABEL_CCOMP = 0,
DIST_LABEL_PIXEL = 1;
// C++: enum DistanceTransformMasks (cv.DistanceTransformMasks)
public static final int
DIST_MASK_3 = 3,
DIST_MASK_5 = 5,
DIST_MASK_PRECISE = 0;
// C++: enum DistanceTypes (cv.DistanceTypes)
public static final int
DIST_USER = -1,
DIST_L1 = 1,
DIST_L2 = 2,
DIST_C = 3,
DIST_L12 = 4,
DIST_FAIR = 5,
DIST_WELSCH = 6,
DIST_HUBER = 7;
// C++: enum FloodFillFlags (cv.FloodFillFlags)
public static final int
FLOODFILL_FIXED_RANGE = 1 << 16,
FLOODFILL_MASK_ONLY = 1 << 17;
// C++: enum GrabCutClasses (cv.GrabCutClasses)
public static final int
GC_BGD = 0,
GC_FGD = 1,
GC_PR_BGD = 2,
GC_PR_FGD = 3;
// C++: enum GrabCutModes (cv.GrabCutModes)
public static final int
GC_INIT_WITH_RECT = 0,
GC_INIT_WITH_MASK = 1,
GC_EVAL = 2,
GC_EVAL_FREEZE_MODEL = 3;
// C++: enum HersheyFonts (cv.HersheyFonts)
public static final int
FONT_HERSHEY_SIMPLEX = 0,
FONT_HERSHEY_PLAIN = 1,
FONT_HERSHEY_DUPLEX = 2,
FONT_HERSHEY_COMPLEX = 3,
FONT_HERSHEY_TRIPLEX = 4,
FONT_HERSHEY_COMPLEX_SMALL = 5,
FONT_HERSHEY_SCRIPT_SIMPLEX = 6,
FONT_HERSHEY_SCRIPT_COMPLEX = 7,
FONT_ITALIC = 16;
// C++: enum HistCompMethods (cv.HistCompMethods)
public static final int
HISTCMP_CORREL = 0,
HISTCMP_CHISQR = 1,
HISTCMP_INTERSECT = 2,
HISTCMP_BHATTACHARYYA = 3,
HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA,
HISTCMP_CHISQR_ALT = 4,
HISTCMP_KL_DIV = 5;
// C++: enum HoughModes (cv.HoughModes)
public static final int
HOUGH_STANDARD = 0,
HOUGH_PROBABILISTIC = 1,
HOUGH_MULTI_SCALE = 2,
HOUGH_GRADIENT = 3,
HOUGH_GRADIENT_ALT = 4;
// C++: enum InterpolationFlags (cv.InterpolationFlags)
public static final int
INTER_NEAREST = 0,
INTER_LINEAR = 1,
INTER_CUBIC = 2,
INTER_AREA = 3,
INTER_LANCZOS4 = 4,
INTER_LINEAR_EXACT = 5,
INTER_NEAREST_EXACT = 6,
INTER_MAX = 7,
WARP_FILL_OUTLIERS = 8,
WARP_INVERSE_MAP = 16;
// C++: enum InterpolationMasks (cv.InterpolationMasks)
public static final int
INTER_BITS = 5,
INTER_BITS2 = INTER_BITS * 2,
INTER_TAB_SIZE = 1 << INTER_BITS,
INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE;
// C++: enum LineSegmentDetectorModes (cv.LineSegmentDetectorModes)
public static final int
LSD_REFINE_NONE = 0,
LSD_REFINE_STD = 1,
LSD_REFINE_ADV = 2;
// C++: enum LineTypes (cv.LineTypes)
public static final int
FILLED = -1,
LINE_4 = 4,
LINE_8 = 8,
LINE_AA = 16;
// C++: enum MarkerTypes (cv.MarkerTypes)
public static final int
MARKER_CROSS = 0,
MARKER_TILTED_CROSS = 1,
MARKER_STAR = 2,
MARKER_DIAMOND = 3,
MARKER_SQUARE = 4,
MARKER_TRIANGLE_UP = 5,
MARKER_TRIANGLE_DOWN = 6;
// C++: enum MorphShapes (cv.MorphShapes)
public static final int
MORPH_RECT = 0,
MORPH_CROSS = 1,
MORPH_ELLIPSE = 2;
// C++: enum MorphTypes (cv.MorphTypes)
public static final int
MORPH_ERODE = 0,
MORPH_DILATE = 1,
MORPH_OPEN = 2,
MORPH_CLOSE = 3,
MORPH_GRADIENT = 4,
MORPH_TOPHAT = 5,
MORPH_BLACKHAT = 6,
MORPH_HITMISS = 7;
// C++: enum RectanglesIntersectTypes (cv.RectanglesIntersectTypes)
public static final int
INTERSECT_NONE = 0,
INTERSECT_PARTIAL = 1,
INTERSECT_FULL = 2;
// C++: enum RetrievalModes (cv.RetrievalModes)
public static final int
RETR_EXTERNAL = 0,
RETR_LIST = 1,
RETR_CCOMP = 2,
RETR_TREE = 3,
RETR_FLOODFILL = 4;
// C++: enum ShapeMatchModes (cv.ShapeMatchModes)
public static final int
CONTOURS_MATCH_I1 = 1,
CONTOURS_MATCH_I2 = 2,
CONTOURS_MATCH_I3 = 3;
// C++: enum SpecialFilter (cv.SpecialFilter)
public static final int
FILTER_SCHARR = -1;
// C++: enum TemplateMatchModes (cv.TemplateMatchModes)
public static final int
TM_SQDIFF = 0,
TM_SQDIFF_NORMED = 1,
TM_CCORR = 2,
TM_CCORR_NORMED = 3,
TM_CCOEFF = 4,
TM_CCOEFF_NORMED = 5;
// C++: enum ThresholdTypes (cv.ThresholdTypes)
public static final int
THRESH_BINARY = 0,
THRESH_BINARY_INV = 1,
THRESH_TRUNC = 2,
THRESH_TOZERO = 3,
THRESH_TOZERO_INV = 4,
THRESH_MASK = 7,
THRESH_OTSU = 8,
THRESH_TRIANGLE = 16;
// C++: enum WarpPolarMode (cv.WarpPolarMode)
public static final int
WARP_POLAR_LINEAR = 0,
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) > 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__(createLineSegmentDetector_0(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) > 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__(createLineSegmentDetector_1(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) > 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__(createLineSegmentDetector_2(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__(createLineSegmentDetector_3(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__(createLineSegmentDetector_4(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__(createLineSegmentDetector_5(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__(createLineSegmentDetector_6(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__(createLineSegmentDetector_7(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__(createLineSegmentDetector_8());
}
//
// 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(getGaussianKernel_0(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(getGaussianKernel_1(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, boolean normalize, int ktype) {
getDerivKernels_0(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, boolean normalize) {
getDerivKernels_1(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) {
getDerivKernels_2(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(getGaborKernel_0(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(getGaborKernel_1(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(getGaborKernel_2(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(getStructuringElement_0(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(getStructuringElement_1(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) {
medianBlur_0(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) {
GaussianBlur_0(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) {
GaussianBlur_1(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) {
GaussianBlur_2(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 (<
* 10), the filter will not have much effect, whereas if they are large (> 150), they will have a very
* strong effect, making the image look "cartoonish".
*
* _Filter size_: Large filters (d > 5) are very slow, so it is recommended to use d=5 for real-time
* applications, and perhaps d=9 for offline applications that need heavy noise filtering.
*
* This filter does not work inplace.
* @param src Source 8-bit or floating-point, 1-channel or 3-channel image.
* @param dst Destination image of the same size and type as src .
* @param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive,
* it is computed from sigmaSpace.
* @param sigmaColor Filter sigma in the color space. A larger value of the parameter means that
* farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting
* in larger areas of semi-equal color.
* @param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that
* farther pixels will influence each other as long as their colors are close enough (see sigmaColor
* ). When d>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is
* proportional to sigmaSpace.
* @param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
*/
public static void bilateralFilter(Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace, int borderType) {
bilateralFilter_0(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 (<
* 10), the filter will not have much effect, whereas if they are large (> 150), they will have a very
* strong effect, making the image look "cartoonish".
*
* _Filter size_: Large filters (d > 5) are very slow, so it is recommended to use d=5 for real-time
* applications, and perhaps d=9 for offline applications that need heavy noise filtering.
*
* This filter does not work inplace.
* @param src Source 8-bit or floating-point, 1-channel or 3-channel image.
* @param dst Destination image of the same size and type as src .
* @param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive,
* it is computed from sigmaSpace.
* @param sigmaColor Filter sigma in the color space. A larger value of the parameter means that
* farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting
* in larger areas of semi-equal color.
* @param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that
* farther pixels will influence each other as long as their colors are close enough (see sigmaColor
* ). When d>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is
* proportional to sigmaSpace.
*/
public static void bilateralFilter(Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace) {
bilateralFilter_1(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:
*
* \(\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\)
*
* where
*
* \(\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\)
*
* 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, boolean normalize, int borderType) {
boxFilter_0(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:
*
* \(\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\)
*
* where
*
* \(\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\)
*
* 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, boolean normalize) {
boxFilter_1(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:
*
* \(\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\)
*
* where
*
* \(\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\)
*
* 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) {
boxFilter_2(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:
*
* \(\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\)
*
* where
*
* \(\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\)
*
* 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) {
boxFilter_3(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, boolean normalize, int borderType) {
sqrBoxFilter_0(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, boolean normalize) {
sqrBoxFilter_1(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) {
sqrBoxFilter_2(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) {
sqrBoxFilter_3(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:
*
* \(\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\)
*
* 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) {
blur_0(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:
*
* \(\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\)
*
* 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) {
blur_1(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:
*
* \(\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\)
*
* 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) {
blur_2(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) {
stackBlur_0(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:
*
* \(\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\)
*
* 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) {
filter2D_0(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:
*
* \(\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\)
*
* 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) {
filter2D_1(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:
*
* \(\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\)
*
* 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) {
filter2D_2(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:
*
* \(\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\)
*
* 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) {
filter2D_3(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) {
sepFilter2D_0(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) {
sepFilter2D_1(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) {
sepFilter2D_2(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) {
sepFilter2D_3(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) {
Sobel_0(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) {
Sobel_1(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) {
Sobel_2(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) {
Sobel_3(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) {
Sobel_4(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) {
spatialGradient_0(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) {
spatialGradient_1(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) {
spatialGradient_2(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) {
Scharr_0(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) {
Scharr_1(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) {
Scharr_2(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) {
Scharr_3(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 > 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) {
Laplacian_0(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 > 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) {
Laplacian_1(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 > 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) {
Laplacian_2(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 > 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) {
Laplacian_3(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 > 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) {
Laplacian_4(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
* <http://en.wikipedia.org/wiki/Canny_edge_detector>
*
* @param image 8-bit input image.
* @param edges output edge map; single channels 8-bit image, which has the same size as image .
* @param threshold1 first threshold for the hysteresis procedure.
* @param threshold2 second threshold for the hysteresis procedure.
* @param apertureSize aperture size for the Sobel operator.
* @param L2gradient a flag, indicating whether a more accurate \(L_2\) 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, boolean L2gradient) {
Canny_0(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
* <http://en.wikipedia.org/wiki/Canny_edge_detector>
*
* @param image 8-bit input image.
* @param edges output edge map; single channels 8-bit image, which has the same size as image .
* @param threshold1 first threshold for the hysteresis procedure.
* @param threshold2 second threshold for the hysteresis procedure.
* @param apertureSize aperture size for the Sobel operator.
* \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) 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) {
Canny_1(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
* <http://en.wikipedia.org/wiki/Canny_edge_detector>
*
* @param image 8-bit input image.
* @param edges output edge map; single channels 8-bit image, which has the same size as image .
* @param threshold1 first threshold for the hysteresis procedure.
* @param threshold2 second threshold for the hysteresis procedure.
* \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) 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) {
Canny_2(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, boolean L2gradient) {
Canny_3(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) {
Canny_4(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) {
cornerMinEigenVal_0(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) {
cornerMinEigenVal_1(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) {
cornerMinEigenVal_2(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) {
cornerHarris_0(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) {
cornerHarris_1(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:
*
* \(M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\)
*
* 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) {
cornerEigenValsAndVecs_0(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:
*
* \(M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\)
*
* 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) {
cornerEigenValsAndVecs_1(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) {
preCornerDetect_0(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) {
preCornerDetect_1(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.
*
* 
*
* 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) {
cornerSubPix_0(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 > 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 <= 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, boolean useHarrisDetector, double k) {
Mat corners_mat = corners;
goodFeaturesToTrack_0(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 > 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 <= 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, boolean useHarrisDetector) {
Mat corners_mat = corners;
goodFeaturesToTrack_1(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 > 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 <= 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) {
Mat corners_mat = corners;
goodFeaturesToTrack_2(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 > 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 <= 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) {
Mat corners_mat = corners;
goodFeaturesToTrack_3(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 > 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 <= 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) {
Mat corners_mat = corners;
goodFeaturesToTrack_4(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, boolean useHarrisDetector, double k) {
Mat corners_mat = corners;
goodFeaturesToTrack_5(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, boolean useHarrisDetector) {
Mat corners_mat = corners;
goodFeaturesToTrack_6(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) {
Mat corners_mat = corners;
goodFeaturesToTrack_7(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 <= 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, boolean useHarrisDetector, double k) {
goodFeaturesToTrackWithQuality_0(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 <= 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, boolean useHarrisDetector) {
goodFeaturesToTrackWithQuality_1(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 <= 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) {
goodFeaturesToTrackWithQuality_2(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 <= 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) {
goodFeaturesToTrackWithQuality_3(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 <= 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) {
goodFeaturesToTrackWithQuality_4(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 <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough
* transform.
*
* @param image 8-bit, single-channel binary source image. The image may be modified by the function.
* @param lines Output vector of lines. Each line is represented by a 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 ( \(>\texttt{threshold}\) ).
* @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) {
HoughLines_0(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 <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough
* transform.
*
* @param image 8-bit, single-channel binary source image. The image may be modified by the function.
* @param lines Output vector of lines. Each line is represented by a 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 ( \(>\texttt{threshold}\) ).
* @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) {
HoughLines_1(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 <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough
* transform.
*
* @param image 8-bit, single-channel binary source image. The image may be modified by the function.
* @param lines Output vector of lines. Each line is represented by a 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 ( \(>\texttt{threshold}\) ).
* @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) {
HoughLines_2(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 <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough
* transform.
*
* @param image 8-bit, single-channel binary source image. The image may be modified by the function.
* @param lines Output vector of lines. Each line is represented by a 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 ( \(>\texttt{threshold}\) ).
* @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) {
HoughLines_3(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 <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough
* transform.
*
* @param image 8-bit, single-channel binary source image. The image may be modified by the function.
* @param lines Output vector of lines. Each line is represented by a 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 ( \(>\texttt{threshold}\) ).
* 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) {
HoughLines_4(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:
*
* 
*
* And this is the output of the above program in case of the probabilistic Hough transform:
*
* 
*
* @param image 8-bit, single-channel binary source image. The image may be modified by the function.
* @param lines Output vector of lines. Each line is represented by a 4-element vector
* \((x_1, y_1, x_2, y_2)\) , 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 ( \(>\texttt{threshold}\) ).
* @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) {
HoughLinesP_0(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:
*
* 
*
* And this is the output of the above program in case of the probabilistic Hough transform:
*
* 
*
* @param image 8-bit, single-channel binary source image. The image may be modified by the function.
* @param lines Output vector of lines. Each line is represented by a 4-element vector
* \((x_1, y_1, x_2, y_2)\) , 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 ( \(>\texttt{threshold}\) ).
* @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) {
HoughLinesP_1(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:
*
* 
*
* And this is the output of the above program in case of the probabilistic Hough transform:
*
* 
*
* @param image 8-bit, single-channel binary source image. The image may be modified by the function.
* @param lines Output vector of lines. Each line is represented by a 4-element vector
* \((x_1, y_1, x_2, y_2)\) , 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 ( \(>\texttt{threshold}\) ).
*
* SEE: LineSegmentDetector
*/
public static void HoughLinesP(Mat image, Mat lines, double rho, double theta, int threshold) {
HoughLinesP_2(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<Vec3d> \((votes, rho, theta)\).
* 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 ( \(>\texttt{threshold}\) ).
* @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) {
HoughLinesPointSet_0(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
* should 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 <= 0, uses the maximum image dimension. If < 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) {
HoughCircles_0(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
* should 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) {
HoughCircles_1(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
* should 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) {
HoughCircles_2(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
* should 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) {
HoughCircles_3(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
* should 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) {
HoughCircles_4(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) {
erode_0(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) {
erode_1(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) {
erode_2(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) {
erode_3(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) {
erode_4(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) {
dilate_0(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) {
dilate_1(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) {
dilate_2(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) {
dilate_3(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) {
dilate_4(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 -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).
*/
public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue) {
morphologyEx_0(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 -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).
*/
public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType) {
morphologyEx_1(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 -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).
*/
public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations) {
morphologyEx_2(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 -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).
*/
public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor) {
morphologyEx_3(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 -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).
*/
public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel) {
morphologyEx_4(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) {
resize_0(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) {
resize_1(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) {
resize_2(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) {
resize_3(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) {
warpAffine_0(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) {
warpAffine_1(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) {
warpAffine_2(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) {
warpAffine_3(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) {
warpPerspective_0(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) {
warpPerspective_1(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) {
warpPerspective_2(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) {
warpPerspective_3(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) {
remap_0(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) {
remap_1(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) {
remap_2(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, boolean nninterpolation) {
convertMaps_0(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) {
convertMaps_1(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:
*
* \(\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\)
*
* 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(getRotationMatrix2D_0(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:
*
* \(\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{21} & a_{22} & b_2 \end{bmatrix}\)
*
* 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) {
invertAffineTransform_0(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) {
return new Mat(getPerspectiveTransform_0(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) {
return new Mat(getPerspectiveTransform_1(src.nativeObj, dst.nativeObj));
}
//
// C++: Mat cv::getAffineTransform(vector_Point2f src, vector_Point2f dst)
//
public static Mat getAffineTransform(MatOfPoint2f src, MatOfPoint2f dst) {
Mat src_mat = src;
Mat dst_mat = dst;
return new Mat(getAffineTransform_0(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) {
getRectSubPix_0(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) {
getRectSubPix_1(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
*/
@Deprecated
public static void logPolar(Mat src, Mat dst, Point center, double M, int flags) {
logPolar_0(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
*/
@Deprecated
public static void linearPolar(Mat src, Mat dst, Point center, double maxRadius, int flags) {
linearPolar_0(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
* 
*
* Transform the source image using the following transformation:
* \(
* dst(\rho , \phi ) = src(x,y)
* \)
*
* where
* \(
* \begin{array}{l}
* \vec{I} = (x - center.x, \;y - center.y) \\
* \phi = Kangle \cdot \texttt{angle} (\vec{I}) \\
* \rho = \left\{\begin{matrix}
* Klin \cdot \texttt{magnitude} (\vec{I}) & default \\
* Klog \cdot log_e(\texttt{magnitude} (\vec{I})) & if \; semilog \\
* \end{matrix}\right.
* \end{array}
* \)
*
* 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 <=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 <= 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 > 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 \((rho, phi)->(x, y)\):
* \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) {
warpPolar_0(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:
*
* \(\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\)
*
* \(\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\)
*
* \(\texttt{tilted} (X,Y) = \sum _{y<Y,abs(x-X+1) \leq Y-y-1} \texttt{image} (x,y)\)
*
* 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:
*
* \(\sum _{x_1 \leq x < x_2, \, y_1 \leq y < y_2} \texttt{image} (x,y) = \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\)
*
* 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 .
*
* 
*
* @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) {
integral3_0(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:
*
* \(\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\)
*
* \(\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\)
*
* \(\texttt{tilted} (X,Y) = \sum _{y<Y,abs(x-X+1) \leq Y-y-1} \texttt{image} (x,y)\)
*
* 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:
*
* \(\sum _{x_1 \leq x < x_2, \, y_1 \leq y < y_2} \texttt{image} (x,y) = \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\)
*
* 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 .
*
* 
*
* @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) {
integral3_1(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:
*
* \(\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\)
*
* \(\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\)
*
* \(\texttt{tilted} (X,Y) = \sum _{y<Y,abs(x-X+1) \leq Y-y-1} \texttt{image} (x,y)\)
*
* 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:
*
* \(\sum _{x_1 \leq x < x_2, \, y_1 \leq y < y_2} \texttt{image} (x,y) = \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\)
*
* 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 .
*
* 
*
* @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) {
integral3_2(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) {
integral_0(src.nativeObj, sum.nativeObj, sdepth);
}
public static void integral(Mat src, Mat sum) {
integral_1(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) {
integral2_0(src.nativeObj, sum.nativeObj, sqsum.nativeObj, sdepth, sqdepth);
}
public static void integral2(Mat src, Mat sum, Mat sqsum, int sdepth) {
integral2_1(src.nativeObj, sum.nativeObj, sqsum.nativeObj, sdepth);
}
public static void integral2(Mat src, Mat sum, Mat sqsum) {
integral2_2(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) {
accumulate_0(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) {
accumulate_1(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) {
accumulateSquare_0(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) {
accumulateSquare_1(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) {
accumulateProduct_0(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) {
accumulateProduct_1(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) {
accumulateWeighted_0(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) {
accumulateWeighted_1(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 <http://en.wikipedia.org/wiki/Phase_correlation>
*
* Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed
* with getOptimalDFTSize.
*
* The function performs the following equations:
* <ul>
* <li>
* First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function>) to each
* image to remove possible edge effects. This window is cached until the array size changes to speed
* up processing time.
* </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) {
double[] response_out = new double[1];
Point retVal = new Point(phaseCorrelate_0(src1.nativeObj, src2.nativeObj, window.nativeObj, response_out));
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 <http://en.wikipedia.org/wiki/Phase_correlation>
*
* Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed
* with getOptimalDFTSize.
*
* The function performs the following equations:
* <ul>
* <li>
* First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function>) to each
* image to remove possible edge effects. This window is cached until the array size changes to speed
* up processing time.
* </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) {
return new Point(phaseCorrelate_1(src1.nativeObj, src2.nativeObj, window.nativeObj));
}
/**
* The function is used to detect translational shifts that occur between two images.
*
* The operation takes advantage of the Fourier shift theorem for detecting the translational shift in
* the frequency domain. It can be used for fast image registration as well as motion estimation. For
* more information please see <http://en.wikipedia.org/wiki/Phase_correlation>
*
* Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed
* with getOptimalDFTSize.
*
* The function performs the following equations:
* <ul>
* <li>
* First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function>) to each
* image to remove possible edge effects. This window is cached until the array size changes to speed
* up processing time.
* </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) {
return new Point(phaseCorrelate_2(src1.nativeObj, src2.nativeObj));
}
//
// 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 > 1)
* @param type Created array type
*/
public static void createHanningWindow(Mat dst, Size winSize, int type) {
createHanningWindow_0(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, boolean conjB) {
divSpectrums_0(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) {
divSpectrums_1(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) {
return threshold_0(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 \(src(x,y) > T(x,y)\)}{0}{otherwise}\)
* </li>
* <li>
* <b>THRESH_BINARY_INV</b>
* \(dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\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) {
adaptiveThreshold_0(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:
*
* \(\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\)
*
* 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) {
pyrDown_0(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:
*
* \(\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\)
*
* 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) {
pyrDown_1(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:
*
* \(\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\)
*
* 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) {
pyrDown_2(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) {
pyrUp_0(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) {
pyrUp_1(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) {
pyrUp_2(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, boolean accumulate) {
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat channels_mat = channels;
Mat histSize_mat = histSize;
Mat ranges_mat = ranges;
calcHist_0(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) {
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat channels_mat = channels;
Mat histSize_mat = histSize;
Mat ranges_mat = ranges;
calcHist_1(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) {
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat channels_mat = channels;
Mat ranges_mat = ranges;
calcBackProject_0(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) {
return compareHist_0(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:
* \(H'_i = \sum _{0 \le j < i} H(j)\)
* </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) {
equalizeHist_0(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__(createCLAHE_0(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__(createCLAHE_1(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__(createCLAHE_2());
}
//
// 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) {
return EMD_0(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) {
return EMD_1(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) {
return EMD_3(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 (>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) {
watershed_0(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):
*
* \(I(X,Y) <- (R*,G*,B*)\)
*
* When maxLevel > 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is
* run on the smallest layer first. After that, the results are propagated to the larger layer and the
* iterations are run again only on those pixels where the layer colors differ by more than sr from the
* lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the
* results will be actually different from the ones obtained by running the meanshift procedure on the
* whole original image (i.e. when maxLevel==0).
*
* @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) {
pyrMeanShiftFiltering_0(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):
*
* \(I(X,Y) <- (R*,G*,B*)\)
*
* When maxLevel > 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is
* run on the smallest layer first. After that, the results are propagated to the larger layer and the
* iterations are run again only on those pixels where the layer colors differ by more than sr from the
* lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the
* results will be actually different from the ones obtained by running the meanshift procedure on the
* whole original image (i.e. when maxLevel==0).
*
* @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) {
pyrMeanShiftFiltering_1(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):
*
* \(I(X,Y) <- (R*,G*,B*)\)
*
* When maxLevel > 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is
* run on the smallest layer first. After that, the results are propagated to the larger layer and the
* iterations are run again only on those pixels where the layer colors differ by more than sr from the
* lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the
* results will be actually different from the ones obtained by running the meanshift procedure on the
* whole original image (i.e. when maxLevel==0).
*
* @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) {
pyrMeanShiftFiltering_2(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) {
grabCut_0(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) {
grabCut_1(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) {
distanceTransformWithLabels_0(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) {
distanceTransformWithLabels_1(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) {
distanceTransform_0(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) {
distanceTransform_1(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 << 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) {
double[] rect_out = new double[4];
int retVal = floodFill_0(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 << 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) {
double[] rect_out = new double[4];
int retVal = floodFill_1(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 << 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) {
double[] rect_out = new double[4];
int retVal = floodFill_2(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 << 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) {
double[] rect_out = new double[4];
int retVal = floodFill_3(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 << 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) {
return floodFill_4(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) {
blendLinear_0(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) {
cvtColor_0(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) {
cvtColor_1(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.
*
* @param src1 8-bit image (#CV_8U) of the Y plane.
* @param src2 image containing interleaved U/V plane.
* @param dst output image.
* @param code Specifies the type of conversion. It can take any of the following values:
* <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>
*/
public static void cvtColorTwoPlane(Mat src1, Mat src2, Mat dst, int code) {
cvtColorTwoPlane_0(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) {
demosaicing_0(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) {
demosaicing_1(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, boolean binaryImage) {
return new Moments(moments_0(array.nativeObj, binaryImage));
}
/**
* Calculates all of the moments up to the third order of a polygon or rasterized shape.
*
* The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The
* results are returned in the structure 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) {
return new Moments(moments_1(array.nativeObj));
}
//
// C++: void cv::HuMoments(Moments m, Mat& hu)
//
public static void HuMoments(Moments m, Mat hu) {
HuMoments_0(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) {
matchTemplate_0(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) {
matchTemplate_1(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) {
return connectedComponentsWithAlgorithm_0(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) {
return connectedComponents_0(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) {
return connectedComponents_1(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) {
return connectedComponents_2(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) {
return connectedComponentsWithStatsWithAlgorithm_0(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) {
return connectedComponentsWithStats_0(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) {
return connectedComponentsWithStats_1(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) {
return connectedComponentsWithStats_2(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<std::vector<cv::Point> >).
* @param hierarchy Optional output vector (e.g. std::vector<cv::Vec4i>), containing information about the image topology. It has
* as many elements as the number of contours. For each i-th contour contours[i], the elements
* hierarchy[i][0] , 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) {
Mat contours_mat = new Mat();
findContours_0(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<std::vector<cv::Point> >).
* @param hierarchy Optional output vector (e.g. std::vector<cv::Vec4i>), containing information about the image topology. It has
* as many elements as the number of contours. For each i-th contour contours[i], the elements
* hierarchy[i][0] , 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) {
Mat contours_mat = new Mat();
findContours_1(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 <http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm>
*
* @param curve Input vector of a 2D point stored in std::vector or Mat
* @param approxCurve Result of the approximation. The type should match the type of the input curve.
* @param epsilon Parameter specifying the approximation accuracy. This is the maximum distance
* between the original curve and its approximation.
* @param closed If true, the approximated curve is closed (its first and last vertices are
* connected). Otherwise, it is not closed.
*/
public static void approxPolyDP(MatOfPoint2f curve, MatOfPoint2f approxCurve, double epsilon, boolean closed) {
Mat curve_mat = curve;
Mat approxCurve_mat = approxCurve;
approxPolyDP_0(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, boolean closed) {
Mat curve_mat = curve;
return arcLength_0(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) {
return new Rect(boundingRect_0(array.nativeObj));
}
//
// 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<Point> contour;
* contour.push_back(Point2f(0, 0));
* contour.push_back(Point2f(10, 0));
* contour.push_back(Point2f(10, 10));
* contour.push_back(Point2f(5, 4));
*
* double area0 = contourArea(contour);
* vector<Point> approx;
* approxPolyDP(contour, approx, 5, true);
* double area1 = contourArea(approx);
*
* cout << "area0 =" << area0 << endl <<
* "area1 =" << area1 << endl <<
* "approx poly vertices" << approx.size() << endl;
* </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, boolean oriented) {
return contourArea_0(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<Point> contour;
* contour.push_back(Point2f(0, 0));
* contour.push_back(Point2f(10, 0));
* contour.push_back(Point2f(10, 10));
* contour.push_back(Point2f(5, 4));
*
* double area0 = contourArea(contour);
* vector<Point> approx;
* approxPolyDP(contour, approx, 5, true);
* double area1 = contourArea(approx);
*
* cout << "area0 =" << area0 << endl <<
* "area1 =" << area1 << endl <<
* "approx poly vertices" << approx.size() << endl;
* </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) {
return contourArea_1(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<> or Mat
* @return automatically generated
*/
public static RotatedRect minAreaRect(MatOfPoint2f points) {
Mat points_mat = points;
return new RotatedRect(minAreaRect_0(points_mat.nativeObj));
}
//
// 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) {
boxPoints_0(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<> 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) {
Mat points_mat = points;
double[] center_out = new double[2];
double[] radius_out = new double[1];
minEnclosingCircle_0(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*.
*
* 
*
* 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<> 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) {
return minEnclosingTriangle_0(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) {
return matchShapes_0(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<int> implies returnPoints=false, std::vector<Point> 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, boolean clockwise) {
Mat points_mat = points;
Mat hull_mat = hull;
convexHull_0(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<int> implies returnPoints=false, std::vector<Point> 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) {
Mat points_mat = points;
Mat hull_mat = hull;
convexHull_2(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:
*
* 
*
* @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) {
Mat contour_mat = contour;
Mat convexhull_mat = convexhull;
Mat convexityDefects_mat = convexityDefects;
convexityDefects_0(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<> or Mat
* @return automatically generated
*/
public static boolean isContourConvex(MatOfPoint contour) {
Mat contour_mat = contour;
return isContourConvex_0(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, boolean handleNested) {
return intersectConvexConvex_0(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) {
return intersectConvexConvex_1(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<> or Mat
* @return automatically generated
*/
public static RotatedRect fitEllipse(MatOfPoint2f points) {
Mat points_mat = points;
return new RotatedRect(fitEllipse_0(points_mat.nativeObj));
}
//
// 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:
* \(align*}{
* D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} &
* D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} &
* D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\}
* \)
* 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<> or Mat
* @return automatically generated
*/
public static RotatedRect fitEllipseAMS(Mat points) {
return new RotatedRect(fitEllipseAMS_0(points.nativeObj));
}
//
// 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 \( 4 A_{xx} A_{yy}- A_{xy}^2 > 0 \).
* 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.
*
* \(equation*}{
* \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix}
* 0 & 0 & 2 & 0 & 0 & 0 \\
* 0 & -1 & 0 & 0 & 0 & 0 \\
* 2 & 0 & 0 & 0 & 0 & 0 \\
* 0 & 0 & 0 & 0 & 0 & 0 \\
* 0 & 0 & 0 & 0 & 0 & 0 \\
* 0 & 0 & 0 & 0 & 0 & 0
* \end{matrix} \right)
* \)
*
* 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<> or Mat
* @return automatically generated
*/
public static RotatedRect fitEllipseDirect(Mat points) {
return new RotatedRect(fitEllipseDirect_0(points.nativeObj));
}
//
// 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 \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\)
* </li>
* </ul>
*
* The algorithm is based on the M-estimator ( <http://en.wikipedia.org/wiki/M-estimator> ) technique
* that iteratively fits the line using the weighted least-squares algorithm. After each iteration the
* weights \(w_i\) are adjusted to be inversely proportional to \(\rho(r_i)\) .
*
* @param points Input vector of 2D or 3D points, stored in std::vector<> or Mat.
* @param line Output line parameters. In case of 2D fitting, it should be a vector of 4 elements
* (like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and
* (x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like
* Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line
* and (x0, y0, z0) is a point on the line.
* @param distType Distance used by the M-estimator, see #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) {
fitLine_0(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:
*
* 
*
* @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, boolean measureDist) {
Mat contour_mat = contour;
return pointPolygonTest_0(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.
*
* 
*
* @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<cv::Point2f> or cv::Mat as Mx1 of type CV_32FC2.
* @return One of #RectanglesIntersectTypes
*/
public static int rotatedRectangleIntersection(RotatedRect rect1, RotatedRect rect2, Mat intersectingRegion) {
return rotatedRectangleIntersection_0(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__(createGeneralizedHoughBallard_0());
}
//
// 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__(createGeneralizedHoughGuil_0());
}
//
// 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) {
applyColorMap_0(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) {
applyColorMap_1(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) {
line_0(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) {
line_1(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) {
line_2(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) {
line_3(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) {
arrowedLine_0(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) {
arrowedLine_1(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) {
arrowedLine_2(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) {
arrowedLine_3(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) {
arrowedLine_4(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) {
rectangle_0(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) {
rectangle_1(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) {
rectangle_2(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) {
rectangle_3(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) {
rectangle_4(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) {
rectangle_5(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) {
rectangle_6(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) {
rectangle_7(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) {
circle_0(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) {
circle_1(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) {
circle_2(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) {
circle_3(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.
*
* 
*
* @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) {
ellipse_0(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.
*
* 
*
* @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) {
ellipse_1(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.
*
* 
*
* @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) {
ellipse_2(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.
*
* 
*
* @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) {
ellipse_3(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) {
ellipse_4(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) {
ellipse_5(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) {
ellipse_6(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) {
drawMarker_0(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) {
drawMarker_1(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) {
drawMarker_2(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) {
drawMarker_3(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) {
drawMarker_4(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) {
Mat points_mat = points;
fillConvexPoly_0(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) {
Mat points_mat = points;
fillConvexPoly_1(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) {
Mat points_mat = points;
fillConvexPoly_2(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) {
List<Mat> pts_tmplm = new ArrayList<Mat>((pts != null) ? pts.size() : 0);
Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
fillPoly_0(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) {
List<Mat> pts_tmplm = new ArrayList<Mat>((pts != null) ? pts.size() : 0);
Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
fillPoly_1(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) {
List<Mat> pts_tmplm = new ArrayList<Mat>((pts != null) ? pts.size() : 0);
Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
fillPoly_2(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) {
List<Mat> pts_tmplm = new ArrayList<Mat>((pts != null) ? pts.size() : 0);
Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
fillPoly_3(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, boolean isClosed, Scalar color, int thickness, int lineType, int shift) {
List<Mat> pts_tmplm = new ArrayList<Mat>((pts != null) ? pts.size() : 0);
Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
polylines_0(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, boolean isClosed, Scalar color, int thickness, int lineType) {
List<Mat> pts_tmplm = new ArrayList<Mat>((pts != null) ? pts.size() : 0);
Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
polylines_1(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, boolean isClosed, Scalar color, int thickness) {
List<Mat> pts_tmplm = new ArrayList<Mat>((pts != null) ? pts.size() : 0);
Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
polylines_2(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, boolean isClosed, Scalar color) {
List<Mat> pts_tmplm = new ArrayList<Mat>((pts != null) ? pts.size() : 0);
Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
polylines_3(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 \(\texttt{thickness}<0\) . 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) {
List<Mat> contours_tmplm = new ArrayList<Mat>((contours != null) ? contours.size() : 0);
Mat contours_mat = Converters.vector_vector_Point_to_Mat(contours, contours_tmplm);
drawContours_0(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 \(\texttt{thickness}<0\) . 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) {
List<Mat> contours_tmplm = new ArrayList<Mat>((contours != null) ? contours.size() : 0);
Mat contours_mat = Converters.vector_vector_Point_to_Mat(contours, contours_tmplm);
drawContours_1(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 \(\texttt{thickness}<0\) . 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) {
List<Mat> contours_tmplm = new ArrayList<Mat>((contours != null) ? contours.size() : 0);
Mat contours_mat = Converters.vector_vector_Point_to_Mat(contours, contours_tmplm);
drawContours_2(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 \(\texttt{thickness}<0\) . 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) {
List<Mat> contours_tmplm = new ArrayList<Mat>((contours != null) ? contours.size() : 0);
Mat contours_mat = Converters.vector_vector_Point_to_Mat(contours, contours_tmplm);
drawContours_3(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 \(\texttt{thickness}<0\) . 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) {
List<Mat> contours_tmplm = new ArrayList<Mat>((contours != null) ? contours.size() : 0);
Mat contours_mat = Converters.vector_vector_Point_to_Mat(contours, contours_tmplm);
drawContours_4(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 \(\texttt{thickness}<0\) . 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) {
List<Mat> contours_tmplm = new ArrayList<Mat>((contours != null) ? contours.size() : 0);
Mat contours_mat = Converters.vector_vector_Point_to_Mat(contours, contours_tmplm);
drawContours_5(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 boolean clipLine(Rect imgRect, Point pt1, Point pt2) {
double[] pt1_out = new double[2];
double[] pt2_out = new double[2];
boolean retVal = clipLine_0(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) {
Mat pts_mat = pts;
ellipse2Poly_0(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, boolean bottomLeftOrigin) {
putText_0(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) {
putText_1(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) {
putText_2(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) {
putText_3(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 getFontScaleFromHeight_0(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 getFontScaleFromHeight_1(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) {
HoughLinesWithAccumulator_0(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) {
HoughLinesWithAccumulator_1(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) {
HoughLinesWithAccumulator_2(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) {
HoughLinesWithAccumulator_3(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) {
HoughLinesWithAccumulator_4(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 java.lang.IllegalArgumentException("'baseLine' must be 'int[1]' or 'null'.");
Size retVal = new Size(n_getTextSize(text, fontFace, fontScale, thickness, baseLine));
return retVal;
}
// 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)
private static native long createLineSegmentDetector_0(int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps, double density_th, int n_bins);
private static native long createLineSegmentDetector_1(int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps, double density_th);
private static native long createLineSegmentDetector_2(int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps);
private static native long createLineSegmentDetector_3(int refine, double scale, double sigma_scale, double quant, double ang_th);
private static native long createLineSegmentDetector_4(int refine, double scale, double sigma_scale, double quant);
private static native long createLineSegmentDetector_5(int refine, double scale, double sigma_scale);
private static native long createLineSegmentDetector_6(int refine, double scale);
private static native long createLineSegmentDetector_7(int refine);
private static native long createLineSegmentDetector_8();
// C++: Mat cv::getGaussianKernel(int ksize, double sigma, int ktype = CV_64F)
private static native long getGaussianKernel_0(int ksize, double sigma, int ktype);
private static native long getGaussianKernel_1(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)
private static native void getDerivKernels_0(long kx_nativeObj, long ky_nativeObj, int dx, int dy, int ksize, boolean normalize, int ktype);
private static native void getDerivKernels_1(long kx_nativeObj, long ky_nativeObj, int dx, int dy, int ksize, boolean normalize);
private static native void getDerivKernels_2(long kx_nativeObj, long 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)
private static native long getGaborKernel_0(double ksize_width, double ksize_height, double sigma, double theta, double lambd, double gamma, double psi, int ktype);
private static native long getGaborKernel_1(double ksize_width, double ksize_height, double sigma, double theta, double lambd, double gamma, double psi);
private static native long getGaborKernel_2(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))
private static native long getStructuringElement_0(int shape, double ksize_width, double ksize_height, double anchor_x, double anchor_y);
private static native long getStructuringElement_1(int shape, double ksize_width, double ksize_height);
// C++: void cv::medianBlur(Mat src, Mat& dst, int ksize)
private static native void medianBlur_0(long src_nativeObj, long dst_nativeObj, int ksize);
// C++: void cv::GaussianBlur(Mat src, Mat& dst, Size ksize, double sigmaX, double sigmaY = 0, int borderType = BORDER_DEFAULT)
private static native void GaussianBlur_0(long src_nativeObj, long dst_nativeObj, double ksize_width, double ksize_height, double sigmaX, double sigmaY, int borderType);
private static native void GaussianBlur_1(long src_nativeObj, long dst_nativeObj, double ksize_width, double ksize_height, double sigmaX, double sigmaY);
private static native void GaussianBlur_2(long src_nativeObj, long 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)
private static native void bilateralFilter_0(long src_nativeObj, long dst_nativeObj, int d, double sigmaColor, double sigmaSpace, int borderType);
private static native void bilateralFilter_1(long src_nativeObj, long 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)
private static native void boxFilter_0(long src_nativeObj, long dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y, boolean normalize, int borderType);
private static native void boxFilter_1(long src_nativeObj, long dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y, boolean normalize);
private static native void boxFilter_2(long src_nativeObj, long dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y);
private static native void boxFilter_3(long src_nativeObj, long 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)
private static native void sqrBoxFilter_0(long src_nativeObj, long dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y, boolean normalize, int borderType);
private static native void sqrBoxFilter_1(long src_nativeObj, long dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y, boolean normalize);
private static native void sqrBoxFilter_2(long src_nativeObj, long dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y);
private static native void sqrBoxFilter_3(long src_nativeObj, long 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)
private static native void blur_0(long src_nativeObj, long dst_nativeObj, double ksize_width, double ksize_height, double anchor_x, double anchor_y, int borderType);
private static native void blur_1(long src_nativeObj, long dst_nativeObj, double ksize_width, double ksize_height, double anchor_x, double anchor_y);
private static native void blur_2(long src_nativeObj, long dst_nativeObj, double ksize_width, double ksize_height);
// C++: void cv::stackBlur(Mat src, Mat& dst, Size ksize)
private static native void stackBlur_0(long src_nativeObj, long 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)
private static native void filter2D_0(long src_nativeObj, long dst_nativeObj, int ddepth, long kernel_nativeObj, double anchor_x, double anchor_y, double delta, int borderType);
private static native void filter2D_1(long src_nativeObj, long dst_nativeObj, int ddepth, long kernel_nativeObj, double anchor_x, double anchor_y, double delta);
private static native void filter2D_2(long src_nativeObj, long dst_nativeObj, int ddepth, long kernel_nativeObj, double anchor_x, double anchor_y);
private static native void filter2D_3(long src_nativeObj, long dst_nativeObj, int ddepth, long 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)
private static native void sepFilter2D_0(long src_nativeObj, long dst_nativeObj, int ddepth, long kernelX_nativeObj, long kernelY_nativeObj, double anchor_x, double anchor_y, double delta, int borderType);
private static native void sepFilter2D_1(long src_nativeObj, long dst_nativeObj, int ddepth, long kernelX_nativeObj, long kernelY_nativeObj, double anchor_x, double anchor_y, double delta);
private static native void sepFilter2D_2(long src_nativeObj, long dst_nativeObj, int ddepth, long kernelX_nativeObj, long kernelY_nativeObj, double anchor_x, double anchor_y);
private static native void sepFilter2D_3(long src_nativeObj, long dst_nativeObj, int ddepth, long kernelX_nativeObj, long 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)
private static native void Sobel_0(long src_nativeObj, long dst_nativeObj, int ddepth, int dx, int dy, int ksize, double scale, double delta, int borderType);
private static native void Sobel_1(long src_nativeObj, long dst_nativeObj, int ddepth, int dx, int dy, int ksize, double scale, double delta);
private static native void Sobel_2(long src_nativeObj, long dst_nativeObj, int ddepth, int dx, int dy, int ksize, double scale);
private static native void Sobel_3(long src_nativeObj, long dst_nativeObj, int ddepth, int dx, int dy, int ksize);
private static native void Sobel_4(long src_nativeObj, long 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)
private static native void spatialGradient_0(long src_nativeObj, long dx_nativeObj, long dy_nativeObj, int ksize, int borderType);
private static native void spatialGradient_1(long src_nativeObj, long dx_nativeObj, long dy_nativeObj, int ksize);
private static native void spatialGradient_2(long src_nativeObj, long dx_nativeObj, long 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)
private static native void Scharr_0(long src_nativeObj, long dst_nativeObj, int ddepth, int dx, int dy, double scale, double delta, int borderType);
private static native void Scharr_1(long src_nativeObj, long dst_nativeObj, int ddepth, int dx, int dy, double scale, double delta);
private static native void Scharr_2(long src_nativeObj, long dst_nativeObj, int ddepth, int dx, int dy, double scale);
private static native void Scharr_3(long src_nativeObj, long 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)
private static native void Laplacian_0(long src_nativeObj, long dst_nativeObj, int ddepth, int ksize, double scale, double delta, int borderType);
private static native void Laplacian_1(long src_nativeObj, long dst_nativeObj, int ddepth, int ksize, double scale, double delta);
private static native void Laplacian_2(long src_nativeObj, long dst_nativeObj, int ddepth, int ksize, double scale);
private static native void Laplacian_3(long src_nativeObj, long dst_nativeObj, int ddepth, int ksize);
private static native void Laplacian_4(long src_nativeObj, long dst_nativeObj, int ddepth);
// C++: void cv::Canny(Mat image, Mat& edges, double threshold1, double threshold2, int apertureSize = 3, bool L2gradient = false)
private static native void Canny_0(long image_nativeObj, long edges_nativeObj, double threshold1, double threshold2, int apertureSize, boolean L2gradient);
private static native void Canny_1(long image_nativeObj, long edges_nativeObj, double threshold1, double threshold2, int apertureSize);
private static native void Canny_2(long image_nativeObj, long edges_nativeObj, double threshold1, double threshold2);
// C++: void cv::Canny(Mat dx, Mat dy, Mat& edges, double threshold1, double threshold2, bool L2gradient = false)
private static native void Canny_3(long dx_nativeObj, long dy_nativeObj, long edges_nativeObj, double threshold1, double threshold2, boolean L2gradient);
private static native void Canny_4(long dx_nativeObj, long dy_nativeObj, long edges_nativeObj, double threshold1, double threshold2);
// C++: void cv::cornerMinEigenVal(Mat src, Mat& dst, int blockSize, int ksize = 3, int borderType = BORDER_DEFAULT)
private static native void cornerMinEigenVal_0(long src_nativeObj, long dst_nativeObj, int blockSize, int ksize, int borderType);
private static native void cornerMinEigenVal_1(long src_nativeObj, long dst_nativeObj, int blockSize, int ksize);
private static native void cornerMinEigenVal_2(long src_nativeObj, long dst_nativeObj, int blockSize);
// C++: void cv::cornerHarris(Mat src, Mat& dst, int blockSize, int ksize, double k, int borderType = BORDER_DEFAULT)
private static native void cornerHarris_0(long src_nativeObj, long dst_nativeObj, int blockSize, int ksize, double k, int borderType);
private static native void cornerHarris_1(long src_nativeObj, long dst_nativeObj, int blockSize, int ksize, double k);
// C++: void cv::cornerEigenValsAndVecs(Mat src, Mat& dst, int blockSize, int ksize, int borderType = BORDER_DEFAULT)
private static native void cornerEigenValsAndVecs_0(long src_nativeObj, long dst_nativeObj, int blockSize, int ksize, int borderType);
private static native void cornerEigenValsAndVecs_1(long src_nativeObj, long dst_nativeObj, int blockSize, int ksize);
// C++: void cv::preCornerDetect(Mat src, Mat& dst, int ksize, int borderType = BORDER_DEFAULT)
private static native void preCornerDetect_0(long src_nativeObj, long dst_nativeObj, int ksize, int borderType);
private static native void preCornerDetect_1(long src_nativeObj, long dst_nativeObj, int ksize);
// C++: void cv::cornerSubPix(Mat image, Mat& corners, Size winSize, Size zeroZone, TermCriteria criteria)
private static native void cornerSubPix_0(long image_nativeObj, long 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)
private static native void goodFeaturesToTrack_0(long image_nativeObj, long corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, long mask_nativeObj, int blockSize, boolean useHarrisDetector, double k);
private static native void goodFeaturesToTrack_1(long image_nativeObj, long corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, long mask_nativeObj, int blockSize, boolean useHarrisDetector);
private static native void goodFeaturesToTrack_2(long image_nativeObj, long corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, long mask_nativeObj, int blockSize);
private static native void goodFeaturesToTrack_3(long image_nativeObj, long corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, long mask_nativeObj);
private static native void goodFeaturesToTrack_4(long image_nativeObj, long 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)
private static native void goodFeaturesToTrack_5(long image_nativeObj, long corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, long mask_nativeObj, int blockSize, int gradientSize, boolean useHarrisDetector, double k);
private static native void goodFeaturesToTrack_6(long image_nativeObj, long corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, long mask_nativeObj, int blockSize, int gradientSize, boolean useHarrisDetector);
private static native void goodFeaturesToTrack_7(long image_nativeObj, long corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, long 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)
private static native void goodFeaturesToTrackWithQuality_0(long image_nativeObj, long corners_nativeObj, int maxCorners, double qualityLevel, double minDistance, long mask_nativeObj, long cornersQuality_nativeObj, int blockSize, int gradientSize, boolean useHarrisDetector, double k);
private static native void goodFeaturesToTrackWithQuality_1(long image_nativeObj, long corners_nativeObj, int maxCorners, double qualityLevel, double minDistance, long mask_nativeObj, long cornersQuality_nativeObj, int blockSize, int gradientSize, boolean useHarrisDetector);
private static native void goodFeaturesToTrackWithQuality_2(long image_nativeObj, long corners_nativeObj, int maxCorners, double qualityLevel, double minDistance, long mask_nativeObj, long cornersQuality_nativeObj, int blockSize, int gradientSize);
private static native void goodFeaturesToTrackWithQuality_3(long image_nativeObj, long corners_nativeObj, int maxCorners, double qualityLevel, double minDistance, long mask_nativeObj, long cornersQuality_nativeObj, int blockSize);
private static native void goodFeaturesToTrackWithQuality_4(long image_nativeObj, long corners_nativeObj, int maxCorners, double qualityLevel, double minDistance, long mask_nativeObj, long 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)
private static native void HoughLines_0(long image_nativeObj, long lines_nativeObj, double rho, double theta, int threshold, double srn, double stn, double min_theta, double max_theta);
private static native void HoughLines_1(long image_nativeObj, long lines_nativeObj, double rho, double theta, int threshold, double srn, double stn, double min_theta);
private static native void HoughLines_2(long image_nativeObj, long lines_nativeObj, double rho, double theta, int threshold, double srn, double stn);
private static native void HoughLines_3(long image_nativeObj, long lines_nativeObj, double rho, double theta, int threshold, double srn);
private static native void HoughLines_4(long image_nativeObj, long 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)
private static native void HoughLinesP_0(long image_nativeObj, long lines_nativeObj, double rho, double theta, int threshold, double minLineLength, double maxLineGap);
private static native void HoughLinesP_1(long image_nativeObj, long lines_nativeObj, double rho, double theta, int threshold, double minLineLength);
private static native void HoughLinesP_2(long image_nativeObj, long 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)
private static native void HoughLinesPointSet_0(long point_nativeObj, long 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)
private static native void HoughCircles_0(long image_nativeObj, long circles_nativeObj, int method, double dp, double minDist, double param1, double param2, int minRadius, int maxRadius);
private static native void HoughCircles_1(long image_nativeObj, long circles_nativeObj, int method, double dp, double minDist, double param1, double param2, int minRadius);
private static native void HoughCircles_2(long image_nativeObj, long circles_nativeObj, int method, double dp, double minDist, double param1, double param2);
private static native void HoughCircles_3(long image_nativeObj, long circles_nativeObj, int method, double dp, double minDist, double param1);
private static native void HoughCircles_4(long image_nativeObj, long 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())
private static native void erode_0(long src_nativeObj, long dst_nativeObj, long kernel_nativeObj, double anchor_x, double anchor_y, int iterations, int borderType, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
private static native void erode_1(long src_nativeObj, long dst_nativeObj, long kernel_nativeObj, double anchor_x, double anchor_y, int iterations, int borderType);
private static native void erode_2(long src_nativeObj, long dst_nativeObj, long kernel_nativeObj, double anchor_x, double anchor_y, int iterations);
private static native void erode_3(long src_nativeObj, long dst_nativeObj, long kernel_nativeObj, double anchor_x, double anchor_y);
private static native void erode_4(long src_nativeObj, long dst_nativeObj, long 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())
private static native void dilate_0(long src_nativeObj, long dst_nativeObj, long kernel_nativeObj, double anchor_x, double anchor_y, int iterations, int borderType, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
private static native void dilate_1(long src_nativeObj, long dst_nativeObj, long kernel_nativeObj, double anchor_x, double anchor_y, int iterations, int borderType);
private static native void dilate_2(long src_nativeObj, long dst_nativeObj, long kernel_nativeObj, double anchor_x, double anchor_y, int iterations);
private static native void dilate_3(long src_nativeObj, long dst_nativeObj, long kernel_nativeObj, double anchor_x, double anchor_y);
private static native void dilate_4(long src_nativeObj, long dst_nativeObj, long 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())
private static native void morphologyEx_0(long src_nativeObj, long dst_nativeObj, int op, long kernel_nativeObj, double anchor_x, double anchor_y, int iterations, int borderType, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
private static native void morphologyEx_1(long src_nativeObj, long dst_nativeObj, int op, long kernel_nativeObj, double anchor_x, double anchor_y, int iterations, int borderType);
private static native void morphologyEx_2(long src_nativeObj, long dst_nativeObj, int op, long kernel_nativeObj, double anchor_x, double anchor_y, int iterations);
private static native void morphologyEx_3(long src_nativeObj, long dst_nativeObj, int op, long kernel_nativeObj, double anchor_x, double anchor_y);
private static native void morphologyEx_4(long src_nativeObj, long dst_nativeObj, int op, long kernel_nativeObj);
// C++: void cv::resize(Mat src, Mat& dst, Size dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR)
private static native void resize_0(long src_nativeObj, long dst_nativeObj, double dsize_width, double dsize_height, double fx, double fy, int interpolation);
private static native void resize_1(long src_nativeObj, long dst_nativeObj, double dsize_width, double dsize_height, double fx, double fy);
private static native void resize_2(long src_nativeObj, long dst_nativeObj, double dsize_width, double dsize_height, double fx);
private static native void resize_3(long src_nativeObj, long 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())
private static native void warpAffine_0(long src_nativeObj, long dst_nativeObj, long M_nativeObj, double dsize_width, double dsize_height, int flags, int borderMode, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
private static native void warpAffine_1(long src_nativeObj, long dst_nativeObj, long M_nativeObj, double dsize_width, double dsize_height, int flags, int borderMode);
private static native void warpAffine_2(long src_nativeObj, long dst_nativeObj, long M_nativeObj, double dsize_width, double dsize_height, int flags);
private static native void warpAffine_3(long src_nativeObj, long dst_nativeObj, long 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())
private static native void warpPerspective_0(long src_nativeObj, long dst_nativeObj, long M_nativeObj, double dsize_width, double dsize_height, int flags, int borderMode, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
private static native void warpPerspective_1(long src_nativeObj, long dst_nativeObj, long M_nativeObj, double dsize_width, double dsize_height, int flags, int borderMode);
private static native void warpPerspective_2(long src_nativeObj, long dst_nativeObj, long M_nativeObj, double dsize_width, double dsize_height, int flags);
private static native void warpPerspective_3(long src_nativeObj, long dst_nativeObj, long 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())
private static native void remap_0(long src_nativeObj, long dst_nativeObj, long map1_nativeObj, long map2_nativeObj, int interpolation, int borderMode, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
private static native void remap_1(long src_nativeObj, long dst_nativeObj, long map1_nativeObj, long map2_nativeObj, int interpolation, int borderMode);
private static native void remap_2(long src_nativeObj, long dst_nativeObj, long map1_nativeObj, long map2_nativeObj, int interpolation);
// C++: void cv::convertMaps(Mat map1, Mat map2, Mat& dstmap1, Mat& dstmap2, int dstmap1type, bool nninterpolation = false)
private static native void convertMaps_0(long map1_nativeObj, long map2_nativeObj, long dstmap1_nativeObj, long dstmap2_nativeObj, int dstmap1type, boolean nninterpolation);
private static native void convertMaps_1(long map1_nativeObj, long map2_nativeObj, long dstmap1_nativeObj, long dstmap2_nativeObj, int dstmap1type);
// C++: Mat cv::getRotationMatrix2D(Point2f center, double angle, double scale)
private static native long getRotationMatrix2D_0(double center_x, double center_y, double angle, double scale);
// C++: void cv::invertAffineTransform(Mat M, Mat& iM)
private static native void invertAffineTransform_0(long M_nativeObj, long iM_nativeObj);
// C++: Mat cv::getPerspectiveTransform(Mat src, Mat dst, int solveMethod = DECOMP_LU)
private static native long getPerspectiveTransform_0(long src_nativeObj, long dst_nativeObj, int solveMethod);
private static native long getPerspectiveTransform_1(long src_nativeObj, long dst_nativeObj);
// C++: Mat cv::getAffineTransform(vector_Point2f src, vector_Point2f dst)
private static native long getAffineTransform_0(long src_mat_nativeObj, long dst_mat_nativeObj);
// C++: void cv::getRectSubPix(Mat image, Size patchSize, Point2f center, Mat& patch, int patchType = -1)
private static native void getRectSubPix_0(long image_nativeObj, double patchSize_width, double patchSize_height, double center_x, double center_y, long patch_nativeObj, int patchType);
private static native void getRectSubPix_1(long image_nativeObj, double patchSize_width, double patchSize_height, double center_x, double center_y, long patch_nativeObj);
// C++: void cv::logPolar(Mat src, Mat& dst, Point2f center, double M, int flags)
private static native void logPolar_0(long src_nativeObj, long 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)
private static native void linearPolar_0(long src_nativeObj, long 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)
private static native void warpPolar_0(long src_nativeObj, long 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)
private static native void integral3_0(long src_nativeObj, long sum_nativeObj, long sqsum_nativeObj, long tilted_nativeObj, int sdepth, int sqdepth);
private static native void integral3_1(long src_nativeObj, long sum_nativeObj, long sqsum_nativeObj, long tilted_nativeObj, int sdepth);
private static native void integral3_2(long src_nativeObj, long sum_nativeObj, long sqsum_nativeObj, long tilted_nativeObj);
// C++: void cv::integral(Mat src, Mat& sum, int sdepth = -1)
private static native void integral_0(long src_nativeObj, long sum_nativeObj, int sdepth);
private static native void integral_1(long src_nativeObj, long sum_nativeObj);
// C++: void cv::integral(Mat src, Mat& sum, Mat& sqsum, int sdepth = -1, int sqdepth = -1)
private static native void integral2_0(long src_nativeObj, long sum_nativeObj, long sqsum_nativeObj, int sdepth, int sqdepth);
private static native void integral2_1(long src_nativeObj, long sum_nativeObj, long sqsum_nativeObj, int sdepth);
private static native void integral2_2(long src_nativeObj, long sum_nativeObj, long sqsum_nativeObj);
// C++: void cv::accumulate(Mat src, Mat& dst, Mat mask = Mat())
private static native void accumulate_0(long src_nativeObj, long dst_nativeObj, long mask_nativeObj);
private static native void accumulate_1(long src_nativeObj, long dst_nativeObj);
// C++: void cv::accumulateSquare(Mat src, Mat& dst, Mat mask = Mat())
private static native void accumulateSquare_0(long src_nativeObj, long dst_nativeObj, long mask_nativeObj);
private static native void accumulateSquare_1(long src_nativeObj, long dst_nativeObj);
// C++: void cv::accumulateProduct(Mat src1, Mat src2, Mat& dst, Mat mask = Mat())
private static native void accumulateProduct_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, long mask_nativeObj);
private static native void accumulateProduct_1(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj);
// C++: void cv::accumulateWeighted(Mat src, Mat& dst, double alpha, Mat mask = Mat())
private static native void accumulateWeighted_0(long src_nativeObj, long dst_nativeObj, double alpha, long mask_nativeObj);
private static native void accumulateWeighted_1(long src_nativeObj, long dst_nativeObj, double alpha);
// C++: Point2d cv::phaseCorrelate(Mat src1, Mat src2, Mat window = Mat(), double* response = 0)
private static native double[] phaseCorrelate_0(long src1_nativeObj, long src2_nativeObj, long window_nativeObj, double[] response_out);
private static native double[] phaseCorrelate_1(long src1_nativeObj, long src2_nativeObj, long window_nativeObj);
private static native double[] phaseCorrelate_2(long src1_nativeObj, long src2_nativeObj);
// C++: void cv::createHanningWindow(Mat& dst, Size winSize, int type)
private static native void createHanningWindow_0(long 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)
private static native void divSpectrums_0(long a_nativeObj, long b_nativeObj, long c_nativeObj, int flags, boolean conjB);
private static native void divSpectrums_1(long a_nativeObj, long b_nativeObj, long c_nativeObj, int flags);
// C++: double cv::threshold(Mat src, Mat& dst, double thresh, double maxval, int type)
private static native double threshold_0(long src_nativeObj, long 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)
private static native void adaptiveThreshold_0(long src_nativeObj, long 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)
private static native void pyrDown_0(long src_nativeObj, long dst_nativeObj, double dstsize_width, double dstsize_height, int borderType);
private static native void pyrDown_1(long src_nativeObj, long dst_nativeObj, double dstsize_width, double dstsize_height);
private static native void pyrDown_2(long src_nativeObj, long dst_nativeObj);
// C++: void cv::pyrUp(Mat src, Mat& dst, Size dstsize = Size(), int borderType = BORDER_DEFAULT)
private static native void pyrUp_0(long src_nativeObj, long dst_nativeObj, double dstsize_width, double dstsize_height, int borderType);
private static native void pyrUp_1(long src_nativeObj, long dst_nativeObj, double dstsize_width, double dstsize_height);
private static native void pyrUp_2(long src_nativeObj, long 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)
private static native void calcHist_0(long images_mat_nativeObj, long channels_mat_nativeObj, long mask_nativeObj, long hist_nativeObj, long histSize_mat_nativeObj, long ranges_mat_nativeObj, boolean accumulate);
private static native void calcHist_1(long images_mat_nativeObj, long channels_mat_nativeObj, long mask_nativeObj, long hist_nativeObj, long histSize_mat_nativeObj, long ranges_mat_nativeObj);
// C++: void cv::calcBackProject(vector_Mat images, vector_int channels, Mat hist, Mat& dst, vector_float ranges, double scale)
private static native void calcBackProject_0(long images_mat_nativeObj, long channels_mat_nativeObj, long hist_nativeObj, long dst_nativeObj, long ranges_mat_nativeObj, double scale);
// C++: double cv::compareHist(Mat H1, Mat H2, int method)
private static native double compareHist_0(long H1_nativeObj, long H2_nativeObj, int method);
// C++: void cv::equalizeHist(Mat src, Mat& dst)
private static native void equalizeHist_0(long src_nativeObj, long dst_nativeObj);
// C++: Ptr_CLAHE cv::createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8))
private static native long createCLAHE_0(double clipLimit, double tileGridSize_width, double tileGridSize_height);
private static native long createCLAHE_1(double clipLimit);
private static native long createCLAHE_2();
// C++: float cv::wrapperEMD(Mat signature1, Mat signature2, int distType, Mat cost = Mat(), Ptr_float& lowerBound = Ptr<float>(), Mat& flow = Mat())
private static native float EMD_0(long signature1_nativeObj, long signature2_nativeObj, int distType, long cost_nativeObj, long flow_nativeObj);
private static native float EMD_1(long signature1_nativeObj, long signature2_nativeObj, int distType, long cost_nativeObj);
private static native float EMD_3(long signature1_nativeObj, long signature2_nativeObj, int distType);
// C++: void cv::watershed(Mat image, Mat& markers)
private static native void watershed_0(long image_nativeObj, long 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))
private static native void pyrMeanShiftFiltering_0(long src_nativeObj, long dst_nativeObj, double sp, double sr, int maxLevel, int termcrit_type, int termcrit_maxCount, double termcrit_epsilon);
private static native void pyrMeanShiftFiltering_1(long src_nativeObj, long dst_nativeObj, double sp, double sr, int maxLevel);
private static native void pyrMeanShiftFiltering_2(long src_nativeObj, long 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)
private static native void grabCut_0(long img_nativeObj, long mask_nativeObj, int rect_x, int rect_y, int rect_width, int rect_height, long bgdModel_nativeObj, long fgdModel_nativeObj, int iterCount, int mode);
private static native void grabCut_1(long img_nativeObj, long mask_nativeObj, int rect_x, int rect_y, int rect_width, int rect_height, long bgdModel_nativeObj, long fgdModel_nativeObj, int iterCount);
// C++: void cv::distanceTransform(Mat src, Mat& dst, Mat& labels, int distanceType, int maskSize, int labelType = DIST_LABEL_CCOMP)
private static native void distanceTransformWithLabels_0(long src_nativeObj, long dst_nativeObj, long labels_nativeObj, int distanceType, int maskSize, int labelType);
private static native void distanceTransformWithLabels_1(long src_nativeObj, long dst_nativeObj, long labels_nativeObj, int distanceType, int maskSize);
// C++: void cv::distanceTransform(Mat src, Mat& dst, int distanceType, int maskSize, int dstType = CV_32F)
private static native void distanceTransform_0(long src_nativeObj, long dst_nativeObj, int distanceType, int maskSize, int dstType);
private static native void distanceTransform_1(long src_nativeObj, long 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)
private static native int floodFill_0(long image_nativeObj, long 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);
private static native int floodFill_1(long image_nativeObj, long 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);
private static native int floodFill_2(long image_nativeObj, long 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);
private static native int floodFill_3(long image_nativeObj, long mask_nativeObj, double seedPoint_x, double seedPoint_y, double newVal_val0, double newVal_val1, double newVal_val2, double newVal_val3, double[] rect_out);
private static native int floodFill_4(long image_nativeObj, long 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)
private static native void blendLinear_0(long src1_nativeObj, long src2_nativeObj, long weights1_nativeObj, long weights2_nativeObj, long dst_nativeObj);
// C++: void cv::cvtColor(Mat src, Mat& dst, int code, int dstCn = 0)
private static native void cvtColor_0(long src_nativeObj, long dst_nativeObj, int code, int dstCn);
private static native void cvtColor_1(long src_nativeObj, long dst_nativeObj, int code);
// C++: void cv::cvtColorTwoPlane(Mat src1, Mat src2, Mat& dst, int code)
private static native void cvtColorTwoPlane_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, int code);
// C++: void cv::demosaicing(Mat src, Mat& dst, int code, int dstCn = 0)
private static native void demosaicing_0(long src_nativeObj, long dst_nativeObj, int code, int dstCn);
private static native void demosaicing_1(long src_nativeObj, long dst_nativeObj, int code);
// C++: Moments cv::moments(Mat array, bool binaryImage = false)
private static native double[] moments_0(long array_nativeObj, boolean binaryImage);
private static native double[] moments_1(long array_nativeObj);
// C++: void cv::HuMoments(Moments m, Mat& hu)
private static native void HuMoments_0(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, long hu_nativeObj);
// C++: void cv::matchTemplate(Mat image, Mat templ, Mat& result, int method, Mat mask = Mat())
private static native void matchTemplate_0(long image_nativeObj, long templ_nativeObj, long result_nativeObj, int method, long mask_nativeObj);
private static native void matchTemplate_1(long image_nativeObj, long templ_nativeObj, long result_nativeObj, int method);
// C++: int cv::connectedComponents(Mat image, Mat& labels, int connectivity, int ltype, int ccltype)
private static native int connectedComponentsWithAlgorithm_0(long image_nativeObj, long labels_nativeObj, int connectivity, int ltype, int ccltype);
// C++: int cv::connectedComponents(Mat image, Mat& labels, int connectivity = 8, int ltype = CV_32S)
private static native int connectedComponents_0(long image_nativeObj, long labels_nativeObj, int connectivity, int ltype);
private static native int connectedComponents_1(long image_nativeObj, long labels_nativeObj, int connectivity);
private static native int connectedComponents_2(long image_nativeObj, long labels_nativeObj);
// C++: int cv::connectedComponentsWithStats(Mat image, Mat& labels, Mat& stats, Mat& centroids, int connectivity, int ltype, int ccltype)
private static native int connectedComponentsWithStatsWithAlgorithm_0(long image_nativeObj, long labels_nativeObj, long stats_nativeObj, long 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)
private static native int connectedComponentsWithStats_0(long image_nativeObj, long labels_nativeObj, long stats_nativeObj, long centroids_nativeObj, int connectivity, int ltype);
private static native int connectedComponentsWithStats_1(long image_nativeObj, long labels_nativeObj, long stats_nativeObj, long centroids_nativeObj, int connectivity);
private static native int connectedComponentsWithStats_2(long image_nativeObj, long labels_nativeObj, long stats_nativeObj, long centroids_nativeObj);
// C++: void cv::findContours(Mat image, vector_vector_Point& contours, Mat& hierarchy, int mode, int method, Point offset = Point())
private static native void findContours_0(long image_nativeObj, long contours_mat_nativeObj, long hierarchy_nativeObj, int mode, int method, double offset_x, double offset_y);
private static native void findContours_1(long image_nativeObj, long contours_mat_nativeObj, long hierarchy_nativeObj, int mode, int method);
// C++: void cv::approxPolyDP(vector_Point2f curve, vector_Point2f& approxCurve, double epsilon, bool closed)
private static native void approxPolyDP_0(long curve_mat_nativeObj, long approxCurve_mat_nativeObj, double epsilon, boolean closed);
// C++: double cv::arcLength(vector_Point2f curve, bool closed)
private static native double arcLength_0(long curve_mat_nativeObj, boolean closed);
// C++: Rect cv::boundingRect(Mat array)
private static native double[] boundingRect_0(long array_nativeObj);
// C++: double cv::contourArea(Mat contour, bool oriented = false)
private static native double contourArea_0(long contour_nativeObj, boolean oriented);
private static native double contourArea_1(long contour_nativeObj);
// C++: RotatedRect cv::minAreaRect(vector_Point2f points)
private static native double[] minAreaRect_0(long points_mat_nativeObj);
// C++: void cv::boxPoints(RotatedRect box, Mat& points)
private static native void boxPoints_0(double box_center_x, double box_center_y, double box_size_width, double box_size_height, double box_angle, long points_nativeObj);
// C++: void cv::minEnclosingCircle(vector_Point2f points, Point2f& center, float& radius)
private static native void minEnclosingCircle_0(long points_mat_nativeObj, double[] center_out, double[] radius_out);
// C++: double cv::minEnclosingTriangle(Mat points, Mat& triangle)
private static native double minEnclosingTriangle_0(long points_nativeObj, long triangle_nativeObj);
// C++: double cv::matchShapes(Mat contour1, Mat contour2, int method, double parameter)
private static native double matchShapes_0(long contour1_nativeObj, long contour2_nativeObj, int method, double parameter);
// C++: void cv::convexHull(vector_Point points, vector_int& hull, bool clockwise = false, _hidden_ returnPoints = true)
private static native void convexHull_0(long points_mat_nativeObj, long hull_mat_nativeObj, boolean clockwise);
private static native void convexHull_2(long points_mat_nativeObj, long hull_mat_nativeObj);
// C++: void cv::convexityDefects(vector_Point contour, vector_int convexhull, vector_Vec4i& convexityDefects)
private static native void convexityDefects_0(long contour_mat_nativeObj, long convexhull_mat_nativeObj, long convexityDefects_mat_nativeObj);
// C++: bool cv::isContourConvex(vector_Point contour)
private static native boolean isContourConvex_0(long contour_mat_nativeObj);
// C++: float cv::intersectConvexConvex(Mat p1, Mat p2, Mat& p12, bool handleNested = true)
private static native float intersectConvexConvex_0(long p1_nativeObj, long p2_nativeObj, long p12_nativeObj, boolean handleNested);
private static native float intersectConvexConvex_1(long p1_nativeObj, long p2_nativeObj, long p12_nativeObj);
// C++: RotatedRect cv::fitEllipse(vector_Point2f points)
private static native double[] fitEllipse_0(long points_mat_nativeObj);
// C++: RotatedRect cv::fitEllipseAMS(Mat points)
private static native double[] fitEllipseAMS_0(long points_nativeObj);
// C++: RotatedRect cv::fitEllipseDirect(Mat points)
private static native double[] fitEllipseDirect_0(long points_nativeObj);
// C++: void cv::fitLine(Mat points, Mat& line, int distType, double param, double reps, double aeps)
private static native void fitLine_0(long points_nativeObj, long line_nativeObj, int distType, double param, double reps, double aeps);
// C++: double cv::pointPolygonTest(vector_Point2f contour, Point2f pt, bool measureDist)
private static native double pointPolygonTest_0(long contour_mat_nativeObj, double pt_x, double pt_y, boolean measureDist);
// C++: int cv::rotatedRectangleIntersection(RotatedRect rect1, RotatedRect rect2, Mat& intersectingRegion)
private static native int rotatedRectangleIntersection_0(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, long intersectingRegion_nativeObj);
// C++: Ptr_GeneralizedHoughBallard cv::createGeneralizedHoughBallard()
private static native long createGeneralizedHoughBallard_0();
// C++: Ptr_GeneralizedHoughGuil cv::createGeneralizedHoughGuil()
private static native long createGeneralizedHoughGuil_0();
// C++: void cv::applyColorMap(Mat src, Mat& dst, int colormap)
private static native void applyColorMap_0(long src_nativeObj, long dst_nativeObj, int colormap);
// C++: void cv::applyColorMap(Mat src, Mat& dst, Mat userColor)
private static native void applyColorMap_1(long src_nativeObj, long dst_nativeObj, long userColor_nativeObj);
// C++: void cv::line(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
private static native void line_0(long 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);
private static native void line_1(long 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);
private static native void line_2(long 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);
private static native void line_3(long 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)
private static native void arrowedLine_0(long 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);
private static native void arrowedLine_1(long 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);
private static native void arrowedLine_2(long 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);
private static native void arrowedLine_3(long 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);
private static native void arrowedLine_4(long 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)
private static native void rectangle_0(long 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);
private static native void rectangle_1(long 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);
private static native void rectangle_2(long 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);
private static native void rectangle_3(long 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)
private static native void rectangle_4(long 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);
private static native void rectangle_5(long 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);
private static native void rectangle_6(long 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);
private static native void rectangle_7(long 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)
private static native void circle_0(long 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);
private static native void circle_1(long 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);
private static native void circle_2(long img_nativeObj, double center_x, double center_y, int radius, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
private static native void circle_3(long 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)
private static native void ellipse_0(long 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);
private static native void ellipse_1(long 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);
private static native void ellipse_2(long 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);
private static native void ellipse_3(long 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)
private static native void ellipse_4(long 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);
private static native void ellipse_5(long 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);
private static native void ellipse_6(long 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)
private static native void drawMarker_0(long 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);
private static native void drawMarker_1(long 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);
private static native void drawMarker_2(long img_nativeObj, double position_x, double position_y, double color_val0, double color_val1, double color_val2, double color_val3, int markerType, int markerSize);
private static native void drawMarker_3(long img_nativeObj, double position_x, double position_y, double color_val0, double color_val1, double color_val2, double color_val3, int markerType);
private static native void drawMarker_4(long 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)
private static native void fillConvexPoly_0(long img_nativeObj, long points_mat_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3, int lineType, int shift);
private static native void fillConvexPoly_1(long img_nativeObj, long points_mat_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3, int lineType);
private static native void fillConvexPoly_2(long img_nativeObj, long 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())
private static native void fillPoly_0(long img_nativeObj, long 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);
private static native void fillPoly_1(long img_nativeObj, long pts_mat_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3, int lineType, int shift);
private static native void fillPoly_2(long img_nativeObj, long pts_mat_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3, int lineType);
private static native void fillPoly_3(long img_nativeObj, long 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)
private static native void polylines_0(long img_nativeObj, long pts_mat_nativeObj, boolean isClosed, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, int shift);
private static native void polylines_1(long img_nativeObj, long pts_mat_nativeObj, boolean isClosed, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType);
private static native void polylines_2(long img_nativeObj, long pts_mat_nativeObj, boolean isClosed, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
private static native void polylines_3(long img_nativeObj, long pts_mat_nativeObj, boolean 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())
private static native void drawContours_0(long image_nativeObj, long contours_mat_nativeObj, int contourIdx, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, long hierarchy_nativeObj, int maxLevel, double offset_x, double offset_y);
private static native void drawContours_1(long image_nativeObj, long contours_mat_nativeObj, int contourIdx, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, long hierarchy_nativeObj, int maxLevel);
private static native void drawContours_2(long image_nativeObj, long contours_mat_nativeObj, int contourIdx, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, long hierarchy_nativeObj);
private static native void drawContours_3(long image_nativeObj, long contours_mat_nativeObj, int contourIdx, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType);
private static native void drawContours_4(long image_nativeObj, long contours_mat_nativeObj, int contourIdx, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
private static native void drawContours_5(long image_nativeObj, long 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)
private static native boolean clipLine_0(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)
private static native void ellipse2Poly_0(double center_x, double center_y, double axes_width, double axes_height, int angle, int arcStart, int arcEnd, int delta, long 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)
private static native void putText_0(long 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, boolean bottomLeftOrigin);
private static native void putText_1(long 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);
private static native void putText_2(long 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);
private static native void putText_3(long 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)
private static native double getFontScaleFromHeight_0(int fontFace, int pixelHeight, int thickness);
private static native double getFontScaleFromHeight_1(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)
private static native void HoughLinesWithAccumulator_0(long image_nativeObj, long lines_nativeObj, double rho, double theta, int threshold, double srn, double stn, double min_theta, double max_theta);
private static native void HoughLinesWithAccumulator_1(long image_nativeObj, long lines_nativeObj, double rho, double theta, int threshold, double srn, double stn, double min_theta);
private static native void HoughLinesWithAccumulator_2(long image_nativeObj, long lines_nativeObj, double rho, double theta, int threshold, double srn, double stn);
private static native void HoughLinesWithAccumulator_3(long image_nativeObj, long lines_nativeObj, double rho, double theta, int threshold, double srn);
private static native void HoughLinesWithAccumulator_4(long image_nativeObj, long lines_nativeObj, double rho, double theta, int threshold);
private static native double[] n_getTextSize(String text, int fontFace, double fontScale, int thickness, int[] baseLine);
}
