OpenCVForUnity.ImgprocModule.Imgproc Class Reference

Static Public Member Functions
static void	accumulate (Mat src, Mat dst)
	Adds an image to the accumulator image.
static void	accumulate (Mat src, Mat dst, Mat mask)
	Adds an image to the accumulator image.
static void	accumulateProduct (Mat src1, Mat src2, Mat dst)
	Adds the per-element product of two input images to the accumulator image.
static void	accumulateProduct (Mat src1, Mat src2, Mat dst, Mat mask)
	Adds the per-element product of two input images to the accumulator image.
static void	accumulateSquare (Mat src, Mat dst)
	Adds the square of a source image to the accumulator image.
static void	accumulateSquare (Mat src, Mat dst, Mat mask)
	Adds the square of a source image to the accumulator image.
static void	accumulateWeighted (Mat src, Mat dst, double alpha)
	Updates a running average.
static void	accumulateWeighted (Mat src, Mat dst, double alpha, Mat mask)
	Updates a running average.
static void	adaptiveThreshold (Mat src, Mat dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C)
	Applies an adaptive threshold to an array.
static void	applyColorMap (Mat src, Mat dst, int colormap)
	Applies a GNU Octave/MATLAB equivalent colormap on a given image.
static void	applyColorMap (Mat src, Mat dst, Mat userColor)
	Applies a user colormap on a given image.
static void	approxPolyDP (MatOfPoint2f curve, MatOfPoint2f approxCurve, double epsilon, bool closed)
	Approximates a polygonal curve(s) with the specified precision.
static double	arcLength (MatOfPoint2f curve, bool closed)
	Calculates a contour perimeter or a curve length.
static void	arrowedLine (Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color)
	Draws an arrow segment pointing from the first point to the second one.
static void	arrowedLine (Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness)
	Draws an arrow segment pointing from the first point to the second one.
static void	arrowedLine (Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness, int line_type)
	Draws an arrow segment pointing from the first point to the second one.
static void	arrowedLine (Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness, int line_type, int shift)
	Draws an arrow segment pointing from the first point to the second one.
static void	arrowedLine (Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness, int line_type, int shift, double tipLength)
	Draws an arrow segment pointing from the first point to the second one.
static void	arrowedLine (Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color)
	Draws an arrow segment pointing from the first point to the second one.
static void	arrowedLine (Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness)
	Draws an arrow segment pointing from the first point to the second one.
static void	arrowedLine (Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness, int line_type)
	Draws an arrow segment pointing from the first point to the second one.
static void	arrowedLine (Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness, int line_type, int shift)
	Draws an arrow segment pointing from the first point to the second one.
static void	arrowedLine (Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness, int line_type, int shift, double tipLength)
	Draws an arrow segment pointing from the first point to the second one.
static void	arrowedLine (Mat img, Point pt1, Point pt2, Scalar color)
	Draws an arrow segment pointing from the first point to the second one.
static void	arrowedLine (Mat img, Point pt1, Point pt2, Scalar color, int thickness)
	Draws an arrow segment pointing from the first point to the second one.
static void	arrowedLine (Mat img, Point pt1, Point pt2, Scalar color, int thickness, int line_type)
	Draws an arrow segment pointing from the first point to the second one.
static void	arrowedLine (Mat img, Point pt1, Point pt2, Scalar color, int thickness, int line_type, int shift)
	Draws an arrow segment pointing from the first point to the second one.
static void	arrowedLine (Mat img, Point pt1, Point pt2, Scalar color, int thickness, int line_type, int shift, double tipLength)
	Draws an arrow segment pointing from the first point to the second one.
static void	bilateralFilter (Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace)
	Applies the bilateral filter to an image.
static void	bilateralFilter (Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace, int borderType)
	Applies the bilateral filter to an image.
static void	blendLinear (Mat src1, Mat src2, Mat weights1, Mat weights2, Mat dst)
static void	blur (Mat src, Mat dst, in Vec2d ksize)
	Blurs an image using the normalized box filter.
static void	blur (Mat src, Mat dst, in Vec2d ksize, in Vec2d anchor)
	Blurs an image using the normalized box filter.
static void	blur (Mat src, Mat dst, in Vec2d ksize, in Vec2d anchor, int borderType)
	Blurs an image using the normalized box filter.
static void	blur (Mat src, Mat dst, in(double width, double height) ksize)
	Blurs an image using the normalized box filter.
static void	blur (Mat src, Mat dst, in(double width, double height) ksize, in(double x, double y) anchor)
	Blurs an image using the normalized box filter.
static void	blur (Mat src, Mat dst, in(double width, double height) ksize, in(double x, double y) anchor, int borderType)
	Blurs an image using the normalized box filter.
static void	blur (Mat src, Mat dst, Size ksize)
	Blurs an image using the normalized box filter.
static void	blur (Mat src, Mat dst, Size ksize, Point anchor)
	Blurs an image using the normalized box filter.
static void	blur (Mat src, Mat dst, Size ksize, Point anchor, int borderType)
	Blurs an image using the normalized box filter.
static Rect	boundingRect (Mat array)
	Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.
static int int int int height	boundingRectAsValueTuple (Mat array)
static Vec4i	boundingRectAsVec4i (Mat array)
	Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.
static void	boxFilter (Mat src, Mat dst, int ddepth, in Vec2d ksize)
	Blurs an image using the box filter.
static void	boxFilter (Mat src, Mat dst, int ddepth, in Vec2d ksize, in Vec2d anchor)
	Blurs an image using the box filter.
static void	boxFilter (Mat src, Mat dst, int ddepth, in Vec2d ksize, in Vec2d anchor, bool normalize)
	Blurs an image using the box filter.
static void	boxFilter (Mat src, Mat dst, int ddepth, in Vec2d ksize, in Vec2d anchor, bool normalize, int borderType)
	Blurs an image using the box filter.
static void	boxFilter (Mat src, Mat dst, int ddepth, in(double width, double height) ksize)
	Blurs an image using the box filter.
static void	boxFilter (Mat src, Mat dst, int ddepth, in(double width, double height) ksize, in(double x, double y) anchor)
	Blurs an image using the box filter.
static void	boxFilter (Mat src, Mat dst, int ddepth, in(double width, double height) ksize, in(double x, double y) anchor, bool normalize)
	Blurs an image using the box filter.
static void	boxFilter (Mat src, Mat dst, int ddepth, in(double width, double height) ksize, in(double x, double y) anchor, bool normalize, int borderType)
	Blurs an image using the box filter.
static void	boxFilter (Mat src, Mat dst, int ddepth, Size ksize)
	Blurs an image using the box filter.
static void	boxFilter (Mat src, Mat dst, int ddepth, Size ksize, Point anchor)
	Blurs an image using the box filter.
static void	boxFilter (Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize)
	Blurs an image using the box filter.
static void	boxFilter (Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize, int borderType)
	Blurs an image using the box filter.
static void	boxPoints (in Vec5d box, Mat points)
	Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.
static void	boxPoints (in(double x, double y, double width, double height, double angle) box, Mat points)
	Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.
static void	boxPoints (RotatedRect box, Mat points)
	Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.
static void	calcBackProject (List< Mat > images, MatOfInt channels, Mat hist, Mat dst, MatOfFloat ranges, double scale)
static void	calcHist (List< Mat > images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges)
static void	calcHist (List< Mat > images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges, bool accumulate)
static void	Canny (Mat dx, Mat dy, Mat edges, double threshold1, double threshold2)
static void	Canny (Mat dx, Mat dy, Mat edges, double threshold1, double threshold2, bool L2gradient)
static void	Canny (Mat image, Mat edges, double threshold1, double threshold2)
	Finds edges in an image using the Canny algorithm [Canny86] .
static void	Canny (Mat image, Mat edges, double threshold1, double threshold2, int apertureSize)
	Finds edges in an image using the Canny algorithm [Canny86] .
static void	Canny (Mat image, Mat edges, double threshold1, double threshold2, int apertureSize, bool L2gradient)
	Finds edges in an image using the Canny algorithm [Canny86] .
static void	circle (Mat img, in Vec2d center, int radius, in Vec4d color)
	Draws a circle.
static void	circle (Mat img, in Vec2d center, int radius, in Vec4d color, int thickness)
	Draws a circle.
static void	circle (Mat img, in Vec2d center, int radius, in Vec4d color, int thickness, int lineType)
	Draws a circle.
static void	circle (Mat img, in Vec2d center, int radius, in Vec4d color, int thickness, int lineType, int shift)
	Draws a circle.
static void	circle (Mat img, in(double x, double y) center, int radius, in(double v0, double v1, double v2, double v3) color)
	Draws a circle.
static void	circle (Mat img, in(double x, double y) center, int radius, in(double v0, double v1, double v2, double v3) color, int thickness)
	Draws a circle.
static void	circle (Mat img, in(double x, double y) center, int radius, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType)
	Draws a circle.
static void	circle (Mat img, in(double x, double y) center, int radius, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, int shift)
	Draws a circle.
static void	circle (Mat img, Point center, int radius, Scalar color)
	Draws a circle.
static void	circle (Mat img, Point center, int radius, Scalar color, int thickness)
	Draws a circle.
static void	circle (Mat img, Point center, int radius, Scalar color, int thickness, int lineType)
	Draws a circle.
static void	circle (Mat img, Point center, int radius, Scalar color, int thickness, int lineType, int shift)
	Draws a circle.
static bool	clipLine (in Vec4i imgRect, ref Vec2d pt1, ref Vec2d pt2)
static bool	clipLine (in(int x, int y, int width, int height) imgRect, ref(double x, double y) pt1, ref(double x, double y) pt2)
static bool	clipLine (Rect imgRect, Point pt1, Point pt2)
static double	compareHist (Mat H1, Mat H2, int method)
	Compares two histograms.
static int	connectedComponents (Mat image, Mat labels)
static int	connectedComponents (Mat image, Mat labels, int connectivity)
static int	connectedComponents (Mat image, Mat labels, int connectivity, int ltype)
static int	connectedComponentsWithAlgorithm (Mat image, Mat labels, int connectivity, int ltype, int ccltype)
	computes the connected components labeled image of boolean image
static int	connectedComponentsWithStats (Mat image, Mat labels, Mat stats, Mat centroids)
static int	connectedComponentsWithStats (Mat image, Mat labels, Mat stats, Mat centroids, int connectivity)
static int	connectedComponentsWithStats (Mat image, Mat labels, Mat stats, Mat centroids, int connectivity, int ltype)
static int	connectedComponentsWithStatsWithAlgorithm (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
static double	contourArea (Mat contour)
	Calculates a contour area.
static double	contourArea (Mat contour, bool oriented)
	Calculates a contour area.
static void	convertMaps (Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type)
	Converts image transformation maps from one representation to another.
static void	convertMaps (Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type, bool nninterpolation)
	Converts image transformation maps from one representation to another.
static void	convexHull (MatOfPoint points, MatOfInt hull)
	Finds the convex hull of a point set.
static void	convexHull (MatOfPoint points, MatOfInt hull, bool clockwise)
	Finds the convex hull of a point set.
static void	convexityDefects (MatOfPoint contour, MatOfInt convexhull, MatOfInt4 convexityDefects)
	Finds the convexity defects of a contour.
static void	cornerEigenValsAndVecs (Mat src, Mat dst, int blockSize, int ksize)
	Calculates eigenvalues and eigenvectors of image blocks for corner detection.
static void	cornerEigenValsAndVecs (Mat src, Mat dst, int blockSize, int ksize, int borderType)
	Calculates eigenvalues and eigenvectors of image blocks for corner detection.
static void	cornerHarris (Mat src, Mat dst, int blockSize, int ksize, double k)
	Harris corner detector.
static void	cornerHarris (Mat src, Mat dst, int blockSize, int ksize, double k, int borderType)
	Harris corner detector.
static void	cornerMinEigenVal (Mat src, Mat dst, int blockSize)
	Calculates the minimal eigenvalue of gradient matrices for corner detection.
static void	cornerMinEigenVal (Mat src, Mat dst, int blockSize, int ksize)
	Calculates the minimal eigenvalue of gradient matrices for corner detection.
static void	cornerMinEigenVal (Mat src, Mat dst, int blockSize, int ksize, int borderType)
	Calculates the minimal eigenvalue of gradient matrices for corner detection.
static void	cornerSubPix (Mat image, Mat corners, in Vec2d winSize, in Vec2d zeroZone, in Vec3d criteria)
	Refines the corner locations.
static void	cornerSubPix (Mat image, Mat corners, in(double width, double height) winSize, in(double width, double height) zeroZone, in(double type, double maxCount, double epsilon) criteria)
	Refines the corner locations.
static void	cornerSubPix (Mat image, Mat corners, Size winSize, Size zeroZone, TermCriteria criteria)
	Refines the corner locations.
static CLAHE	createCLAHE ()
	Creates a smart pointer to a cv::CLAHE class and initializes it.
static CLAHE	createCLAHE (double clipLimit)
	Creates a smart pointer to a cv::CLAHE class and initializes it.
static CLAHE	createCLAHE (double clipLimit, in Vec2d tileGridSize)
	Creates a smart pointer to a cv::CLAHE class and initializes it.
static CLAHE	createCLAHE (double clipLimit, in(double width, double height) tileGridSize)
	Creates a smart pointer to a cv::CLAHE class and initializes it.
static CLAHE	createCLAHE (double clipLimit, Size tileGridSize)
	Creates a smart pointer to a cv::CLAHE class and initializes it.
static GeneralizedHoughBallard	createGeneralizedHoughBallard ()
	Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it.
static GeneralizedHoughGuil	createGeneralizedHoughGuil ()
	Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it.
static void	createHanningWindow (Mat dst, in Vec2d winSize, int type)
	This function computes a Hanning window coefficients in two dimensions.
static void	createHanningWindow (Mat dst, in(double width, double height) winSize, int type)
	This function computes a Hanning window coefficients in two dimensions.
static void	createHanningWindow (Mat dst, Size winSize, int type)
	This function computes a Hanning window coefficients in two dimensions.
static LineSegmentDetector	createLineSegmentDetector ()
	Creates a smart pointer to a LineSegmentDetector object and initializes it.
static LineSegmentDetector	createLineSegmentDetector (int refine)
	Creates a smart pointer to a LineSegmentDetector object and initializes it.
static LineSegmentDetector	createLineSegmentDetector (int refine, double scale)
	Creates a smart pointer to a LineSegmentDetector object and initializes it.
static LineSegmentDetector	createLineSegmentDetector (int refine, double scale, double sigma_scale)
	Creates a smart pointer to a LineSegmentDetector object and initializes it.
static LineSegmentDetector	createLineSegmentDetector (int refine, double scale, double sigma_scale, double quant)
	Creates a smart pointer to a LineSegmentDetector object and initializes it.
static LineSegmentDetector	createLineSegmentDetector (int refine, double scale, double sigma_scale, double quant, double ang_th)
	Creates a smart pointer to a LineSegmentDetector object and initializes it.
static LineSegmentDetector	createLineSegmentDetector (int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps)
	Creates a smart pointer to a LineSegmentDetector object and initializes it.
static LineSegmentDetector	createLineSegmentDetector (int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps, double density_th)
	Creates a smart pointer to a LineSegmentDetector object and initializes it.
static LineSegmentDetector	createLineSegmentDetector (int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps, double density_th, int n_bins)
	Creates a smart pointer to a LineSegmentDetector object and initializes it.
static void	cvtColor (Mat src, Mat dst, int code)
	Converts an image from one color space to another.
static void	cvtColor (Mat src, Mat dst, int code, int dstCn)
	Converts an image from one color space to another.
static void	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.
static void	demosaicing (Mat src, Mat dst, int code)
	main function for all demosaicing processes
static void	demosaicing (Mat src, Mat dst, int code, int dstCn)
	main function for all demosaicing processes
static void	dilate (Mat src, Mat dst, Mat kernel)
	Dilates an image by using a specific structuring element.
static void	dilate (Mat src, Mat dst, Mat kernel, in Vec2d anchor)
	Dilates an image by using a specific structuring element.
static void	dilate (Mat src, Mat dst, Mat kernel, in Vec2d anchor, int iterations)
	Dilates an image by using a specific structuring element.
static void	dilate (Mat src, Mat dst, Mat kernel, in Vec2d anchor, int iterations, int borderType)
	Dilates an image by using a specific structuring element.
static void	dilate (Mat src, Mat dst, Mat kernel, in Vec2d anchor, int iterations, int borderType, in Vec4d borderValue)
	Dilates an image by using a specific structuring element.
static void	dilate (Mat src, Mat dst, Mat kernel, in(double x, double y) anchor)
	Dilates an image by using a specific structuring element.
static void	dilate (Mat src, Mat dst, Mat kernel, in(double x, double y) anchor, int iterations)
	Dilates an image by using a specific structuring element.
static void	dilate (Mat src, Mat dst, Mat kernel, in(double x, double y) anchor, int iterations, int borderType)
	Dilates an image by using a specific structuring element.
static void	dilate (Mat src, Mat dst, Mat kernel, in(double x, double y) anchor, int iterations, int borderType, in(double v0, double v1, double v2, double v3) borderValue)
	Dilates an image by using a specific structuring element.
static void	dilate (Mat src, Mat dst, Mat kernel, Point anchor)
	Dilates an image by using a specific structuring element.
static void	dilate (Mat src, Mat dst, Mat kernel, Point anchor, int iterations)
	Dilates an image by using a specific structuring element.
static void	dilate (Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType)
	Dilates an image by using a specific structuring element.
static void	dilate (Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue)
	Dilates an image by using a specific structuring element.
static void	distanceTransform (Mat src, Mat dst, int distanceType, int maskSize)
static void	distanceTransform (Mat src, Mat dst, int distanceType, int maskSize, int dstType)
static void	distanceTransformWithLabels (Mat src, Mat dst, Mat labels, int distanceType, int maskSize)
	Calculates the distance to the closest zero pixel for each pixel of the source image.
static void	distanceTransformWithLabels (Mat src, Mat dst, Mat labels, int distanceType, int maskSize, int labelType)
	Calculates the distance to the closest zero pixel for each pixel of the source image.
static void	divSpectrums (Mat a, Mat b, Mat c, int flags)
	Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum.
static void	divSpectrums (Mat a, Mat b, Mat c, int flags, bool conjB)
	Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, in Vec4d color)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, in Vec4d color, int thickness)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, in Vec4d color, int thickness, int lineType)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, in Vec4d color, int thickness, int lineType, Mat hierarchy)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, in Vec4d color, int thickness, int lineType, Mat hierarchy, int maxLevel)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, in Vec4d color, int thickness, int lineType, Mat hierarchy, int maxLevel, in Vec2d offset)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, in(double v0, double v1, double v2, double v3) color)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, in(double v0, double v1, double v2, double v3) color, int thickness)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, Mat hierarchy)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, Mat hierarchy, int maxLevel)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, Mat hierarchy, int maxLevel, in(double x, double y) offset)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, Scalar color)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, Scalar color, int thickness)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, Scalar color, int thickness, int lineType)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy, int maxLevel)
	Draws contours outlines or filled contours.
static void	drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy, int maxLevel, Point offset)
	Draws contours outlines or filled contours.
static void	drawMarker (Mat img, in Vec2d position, in Vec4d color)
	Draws a marker on a predefined position in an image.
static void	drawMarker (Mat img, in Vec2d position, in Vec4d color, int markerType)
	Draws a marker on a predefined position in an image.
static void	drawMarker (Mat img, in Vec2d position, in Vec4d color, int markerType, int markerSize)
	Draws a marker on a predefined position in an image.
static void	drawMarker (Mat img, in Vec2d position, in Vec4d color, int markerType, int markerSize, int thickness)
	Draws a marker on a predefined position in an image.
static void	drawMarker (Mat img, in Vec2d position, in Vec4d color, int markerType, int markerSize, int thickness, int line_type)
	Draws a marker on a predefined position in an image.
static void	drawMarker (Mat img, in(double x, double y) position, in(double v0, double v1, double v2, double v3) color)
	Draws a marker on a predefined position in an image.
static void	drawMarker (Mat img, in(double x, double y) position, in(double v0, double v1, double v2, double v3) color, int markerType)
	Draws a marker on a predefined position in an image.
static void	drawMarker (Mat img, in(double x, double y) position, in(double v0, double v1, double v2, double v3) color, int markerType, int markerSize)
	Draws a marker on a predefined position in an image.
static void	drawMarker (Mat img, in(double x, double y) position, in(double v0, double v1, double v2, double v3) color, int markerType, int markerSize, int thickness)
	Draws a marker on a predefined position in an image.
static void	drawMarker (Mat img, in(double x, double y) position, in(double v0, double v1, double v2, double v3) color, int markerType, int markerSize, int thickness, int line_type)
	Draws a marker on a predefined position in an image.
static void	drawMarker (Mat img, Point position, Scalar color)
	Draws a marker on a predefined position in an image.
static void	drawMarker (Mat img, Point position, Scalar color, int markerType)
	Draws a marker on a predefined position in an image.
static void	drawMarker (Mat img, Point position, Scalar color, int markerType, int markerSize)
	Draws a marker on a predefined position in an image.
static void	drawMarker (Mat img, Point position, Scalar color, int markerType, int markerSize, int thickness)
	Draws a marker on a predefined position in an image.
static void	drawMarker (Mat img, Point position, Scalar color, int markerType, int markerSize, int thickness, int line_type)
	Draws a marker on a predefined position in an image.
static void	ellipse (Mat img, in Vec2d center, in Vec2d axes, double angle, double startAngle, double endAngle, in Vec4d color)
	Draws a simple or thick elliptic arc or fills an ellipse sector.
static void	ellipse (Mat img, in Vec2d center, in Vec2d axes, double angle, double startAngle, double endAngle, in Vec4d color, int thickness)
	Draws a simple or thick elliptic arc or fills an ellipse sector.
static void	ellipse (Mat img, in Vec2d center, in Vec2d axes, double angle, double startAngle, double endAngle, in Vec4d color, int thickness, int lineType)
	Draws a simple or thick elliptic arc or fills an ellipse sector.
static void	ellipse (Mat img, in Vec2d center, in Vec2d axes, double angle, double startAngle, double endAngle, in Vec4d color, int thickness, int lineType, int shift)
	Draws a simple or thick elliptic arc or fills an ellipse sector.
static void	ellipse (Mat img, in Vec5d box, in Vec4d color)
static void	ellipse (Mat img, in Vec5d box, in Vec4d color, int thickness)
static void	ellipse (Mat img, in Vec5d box, in Vec4d color, int thickness, int lineType)
static void	ellipse (Mat img, in(double x, double y) center, in(double width, double height) axes, double angle, double startAngle, double endAngle, in(double v0, double v1, double v2, double v3) color)
	Draws a simple or thick elliptic arc or fills an ellipse sector.
static void	ellipse (Mat img, in(double x, double y) center, in(double width, double height) axes, double angle, double startAngle, double endAngle, in(double v0, double v1, double v2, double v3) color, int thickness)
	Draws a simple or thick elliptic arc or fills an ellipse sector.
static void	ellipse (Mat img, in(double x, double y) center, in(double width, double height) axes, double angle, double startAngle, double endAngle, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType)
	Draws a simple or thick elliptic arc or fills an ellipse sector.
static void	ellipse (Mat img, in(double x, double y) center, in(double width, double height) axes, double angle, double startAngle, double endAngle, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, int shift)
	Draws a simple or thick elliptic arc or fills an ellipse sector.
static void	ellipse (Mat img, in(double x, double y, double width, double height, double angle) box, in(double v0, double v1, double v2, double v3) color)
static void	ellipse (Mat img, in(double x, double y, double width, double height, double angle) box, in(double v0, double v1, double v2, double v3) color, int thickness)
static void	ellipse (Mat img, in(double x, double y, double width, double height, double angle) box, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType)
static void	ellipse (Mat img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color)
	Draws a simple or thick elliptic arc or fills an ellipse sector.
static void	ellipse (Mat img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness)
	Draws a simple or thick elliptic arc or fills an ellipse sector.
static void	ellipse (Mat img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness, int lineType)
	Draws a simple or thick elliptic arc or fills an ellipse sector.
static void	ellipse (Mat img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness, int lineType, int shift)
	Draws a simple or thick elliptic arc or fills an ellipse sector.
static void	ellipse (Mat img, RotatedRect box, Scalar color)
static void	ellipse (Mat img, RotatedRect box, Scalar color, int thickness)
static void	ellipse (Mat img, RotatedRect box, Scalar color, int thickness, int lineType)
static void	ellipse2Poly (in Vec2d center, in Vec2d axes, int angle, int arcStart, int arcEnd, int delta, MatOfPoint pts)
	Approximates an elliptic arc with a polyline.
static void	ellipse2Poly (in(double x, double y) center, in(double width, double height) axes, int angle, int arcStart, int arcEnd, int delta, MatOfPoint pts)
	Approximates an elliptic arc with a polyline.
static void	ellipse2Poly (Point center, Size axes, int angle, int arcStart, int arcEnd, int delta, MatOfPoint pts)
	Approximates an elliptic arc with a polyline.
static float	EMD (Mat signature1, Mat signature2, int distType)
	Computes the "minimal work" distance between two weighted point configurations.
static float	EMD (Mat signature1, Mat signature2, int distType, Mat cost)
	Computes the "minimal work" distance between two weighted point configurations.
static float	EMD (Mat signature1, Mat signature2, int distType, Mat cost, Mat flow)
	Computes the "minimal work" distance between two weighted point configurations.
static void	equalizeHist (Mat src, Mat dst)
	Equalizes the histogram of a grayscale image.
static void	erode (Mat src, Mat dst, Mat kernel)
	Erodes an image by using a specific structuring element.
static void	erode (Mat src, Mat dst, Mat kernel, in Vec2d anchor)
	Erodes an image by using a specific structuring element.
static void	erode (Mat src, Mat dst, Mat kernel, in Vec2d anchor, int iterations)
	Erodes an image by using a specific structuring element.
static void	erode (Mat src, Mat dst, Mat kernel, in Vec2d anchor, int iterations, int borderType)
	Erodes an image by using a specific structuring element.
static void	erode (Mat src, Mat dst, Mat kernel, in Vec2d anchor, int iterations, int borderType, in Vec4d borderValue)
	Erodes an image by using a specific structuring element.
static void	erode (Mat src, Mat dst, Mat kernel, in(double x, double y) anchor)
	Erodes an image by using a specific structuring element.
static void	erode (Mat src, Mat dst, Mat kernel, in(double x, double y) anchor, int iterations)
	Erodes an image by using a specific structuring element.
static void	erode (Mat src, Mat dst, Mat kernel, in(double x, double y) anchor, int iterations, int borderType)
	Erodes an image by using a specific structuring element.
static void	erode (Mat src, Mat dst, Mat kernel, in(double x, double y) anchor, int iterations, int borderType, in(double v0, double v1, double v2, double v3) borderValue)
	Erodes an image by using a specific structuring element.
static void	erode (Mat src, Mat dst, Mat kernel, Point anchor)
	Erodes an image by using a specific structuring element.
static void	erode (Mat src, Mat dst, Mat kernel, Point anchor, int iterations)
	Erodes an image by using a specific structuring element.
static void	erode (Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType)
	Erodes an image by using a specific structuring element.
static void	erode (Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue)
	Erodes an image by using a specific structuring element.
static void	fillConvexPoly (Mat img, MatOfPoint points, in Vec4d color)
	Fills a convex polygon.
static void	fillConvexPoly (Mat img, MatOfPoint points, in Vec4d color, int lineType)
	Fills a convex polygon.
static void	fillConvexPoly (Mat img, MatOfPoint points, in Vec4d color, int lineType, int shift)
	Fills a convex polygon.
static void	fillConvexPoly (Mat img, MatOfPoint points, in(double v0, double v1, double v2, double v3) color)
	Fills a convex polygon.
static void	fillConvexPoly (Mat img, MatOfPoint points, in(double v0, double v1, double v2, double v3) color, int lineType)
	Fills a convex polygon.
static void	fillConvexPoly (Mat img, MatOfPoint points, in(double v0, double v1, double v2, double v3) color, int lineType, int shift)
	Fills a convex polygon.
static void	fillConvexPoly (Mat img, MatOfPoint points, Scalar color)
	Fills a convex polygon.
static void	fillConvexPoly (Mat img, MatOfPoint points, Scalar color, int lineType)
	Fills a convex polygon.
static void	fillConvexPoly (Mat img, MatOfPoint points, Scalar color, int lineType, int shift)
	Fills a convex polygon.
static void	fillPoly (Mat img, List< MatOfPoint > pts, in Vec4d color)
	Fills the area bounded by one or more polygons.
static void	fillPoly (Mat img, List< MatOfPoint > pts, in Vec4d color, int lineType)
	Fills the area bounded by one or more polygons.
static void	fillPoly (Mat img, List< MatOfPoint > pts, in Vec4d color, int lineType, int shift)
	Fills the area bounded by one or more polygons.
static void	fillPoly (Mat img, List< MatOfPoint > pts, in Vec4d color, int lineType, int shift, in Vec2d offset)
	Fills the area bounded by one or more polygons.
static void	fillPoly (Mat img, List< MatOfPoint > pts, in(double v0, double v1, double v2, double v3) color)
	Fills the area bounded by one or more polygons.
static void	fillPoly (Mat img, List< MatOfPoint > pts, in(double v0, double v1, double v2, double v3) color, int lineType)
	Fills the area bounded by one or more polygons.
static void	fillPoly (Mat img, List< MatOfPoint > pts, in(double v0, double v1, double v2, double v3) color, int lineType, int shift)
	Fills the area bounded by one or more polygons.
static void	fillPoly (Mat img, List< MatOfPoint > pts, in(double v0, double v1, double v2, double v3) color, int lineType, int shift, in(double x, double y) offset)
	Fills the area bounded by one or more polygons.
static void	fillPoly (Mat img, List< MatOfPoint > pts, Scalar color)
	Fills the area bounded by one or more polygons.
static void	fillPoly (Mat img, List< MatOfPoint > pts, Scalar color, int lineType)
	Fills the area bounded by one or more polygons.
static void	fillPoly (Mat img, List< MatOfPoint > pts, Scalar color, int lineType, int shift)
	Fills the area bounded by one or more polygons.
static void	fillPoly (Mat img, List< MatOfPoint > pts, Scalar color, int lineType, int shift, Point offset)
	Fills the area bounded by one or more polygons.
static void	filter2D (Mat src, Mat dst, int ddepth, Mat kernel)
	Convolves an image with the kernel.
static void	filter2D (Mat src, Mat dst, int ddepth, Mat kernel, in Vec2d anchor)
	Convolves an image with the kernel.
static void	filter2D (Mat src, Mat dst, int ddepth, Mat kernel, in Vec2d anchor, double delta)
	Convolves an image with the kernel.
static void	filter2D (Mat src, Mat dst, int ddepth, Mat kernel, in Vec2d anchor, double delta, int borderType)
	Convolves an image with the kernel.
static void	filter2D (Mat src, Mat dst, int ddepth, Mat kernel, in(double x, double y) anchor)
	Convolves an image with the kernel.
static void	filter2D (Mat src, Mat dst, int ddepth, Mat kernel, in(double x, double y) anchor, double delta)
	Convolves an image with the kernel.
static void	filter2D (Mat src, Mat dst, int ddepth, Mat kernel, in(double x, double y) anchor, double delta, int borderType)
	Convolves an image with the kernel.
static void	filter2D (Mat src, Mat dst, int ddepth, Mat kernel, Point anchor)
	Convolves an image with the kernel.
static void	filter2D (Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta)
	Convolves an image with the kernel.
static void	filter2D (Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta, int borderType)
	Convolves an image with the kernel.
static void	findContours (Mat image, List< MatOfPoint > contours, Mat hierarchy, int mode, int method)
	Finds contours in a binary image.
static void	findContours (Mat image, List< MatOfPoint > contours, Mat hierarchy, int mode, int method, in Vec2d offset)
	Finds contours in a binary image.
static void	findContours (Mat image, List< MatOfPoint > contours, Mat hierarchy, int mode, int method, in(double x, double y) offset)
	Finds contours in a binary image.
static void	findContours (Mat image, List< MatOfPoint > contours, Mat hierarchy, int mode, int method, Point offset)
	Finds contours in a binary image.
static void	findContoursLinkRuns (Mat image, List< Mat > contours)
static void	findContoursLinkRuns (Mat image, List< Mat > contours, Mat hierarchy)
static RotatedRect	fitEllipse (MatOfPoint2f points)
	Fits an ellipse around a set of 2D points.
static RotatedRect	fitEllipseAMS (Mat points)
	Fits an ellipse around a set of 2D points.
static double double double double double angle	fitEllipseAMSAsValueTuple (Mat points)
static Vec5d	fitEllipseAMSAsVec5d (Mat points)
	Fits an ellipse around a set of 2D points.
static double double double double double angle	fitEllipseAsValueTuple (MatOfPoint2f points)
static Vec5d	fitEllipseAsVec5d (MatOfPoint2f points)
	Fits an ellipse around a set of 2D points.
static RotatedRect	fitEllipseDirect (Mat points)
	Fits an ellipse around a set of 2D points.
static double double double double double angle	fitEllipseDirectAsValueTuple (Mat points)
static Vec5d	fitEllipseDirectAsVec5d (Mat points)
	Fits an ellipse around a set of 2D points.
static void	fitLine (Mat points, Mat line, int distType, double param, double reps, double aeps)
	Fits a line to a 2D or 3D point set.
static int	floodFill (Mat image, Mat mask, in Vec2d seedPoint, in Vec4d newVal)
	Fills a connected component with the given color.
static int	floodFill (Mat image, Mat mask, in Vec2d seedPoint, in Vec4d newVal, out Vec4i rect)
	Fills a connected component with the given color.
static int	floodFill (Mat image, Mat mask, in Vec2d seedPoint, in Vec4d newVal, out Vec4i rect, in Vec4d loDiff)
	Fills a connected component with the given color.
static int	floodFill (Mat image, Mat mask, in Vec2d seedPoint, in Vec4d newVal, out Vec4i rect, in Vec4d loDiff, in Vec4d upDiff)
	Fills a connected component with the given color.
static int	floodFill (Mat image, Mat mask, in Vec2d seedPoint, in Vec4d newVal, out Vec4i rect, in Vec4d loDiff, in Vec4d upDiff, int flags)
	Fills a connected component with the given color.
static int	floodFill (Mat image, Mat mask, in(double x, double y) seedPoint, in(double v0, double v1, double v2, double v3) newVal)
	Fills a connected component with the given color.
static int	floodFill (Mat image, Mat mask, in(double x, double y) seedPoint, in(double v0, double v1, double v2, double v3) newVal, out(int x, int y, int width, int height) rect)
	Fills a connected component with the given color.
static int	floodFill (Mat image, Mat mask, in(double x, double y) seedPoint, in(double v0, double v1, double v2, double v3) newVal, out(int x, int y, int width, int height) rect, in(double v0, double v1, double v2, double v3) loDiff)
	Fills a connected component with the given color.
static int	floodFill (Mat image, Mat mask, in(double x, double y) seedPoint, in(double v0, double v1, double v2, double v3) newVal, out(int x, int y, int width, int height) rect, in(double v0, double v1, double v2, double v3) loDiff, in(double v0, double v1, double v2, double v3) upDiff)
	Fills a connected component with the given color.
static int	floodFill (Mat image, Mat mask, in(double x, double y) seedPoint, in(double v0, double v1, double v2, double v3) newVal, out(int x, int y, int width, int height) rect, in(double v0, double v1, double v2, double v3) loDiff, in(double v0, double v1, double v2, double v3) upDiff, int flags)
	Fills a connected component with the given color.
static int	floodFill (Mat image, Mat mask, Point seedPoint, Scalar newVal)
	Fills a connected component with the given color.
static int	floodFill (Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect)
	Fills a connected component with the given color.
static int	floodFill (Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect, Scalar loDiff)
	Fills a connected component with the given color.
static int	floodFill (Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect, Scalar loDiff, Scalar upDiff)
	Fills a connected component with the given color.
static int	floodFill (Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect, Scalar loDiff, Scalar upDiff, int flags)
	Fills a connected component with the given color.
static void	GaussianBlur (Mat src, Mat dst, in Vec2d ksize, double sigmaX)
	Blurs an image using a Gaussian filter.
static void	GaussianBlur (Mat src, Mat dst, in Vec2d ksize, double sigmaX, double sigmaY)
	Blurs an image using a Gaussian filter.
static void	GaussianBlur (Mat src, Mat dst, in Vec2d ksize, double sigmaX, double sigmaY, int borderType)
	Blurs an image using a Gaussian filter.
static void	GaussianBlur (Mat src, Mat dst, in(double width, double height) ksize, double sigmaX)
	Blurs an image using a Gaussian filter.
static void	GaussianBlur (Mat src, Mat dst, in(double width, double height) ksize, double sigmaX, double sigmaY)
	Blurs an image using a Gaussian filter.
static void	GaussianBlur (Mat src, Mat dst, in(double width, double height) ksize, double sigmaX, double sigmaY, int borderType)
	Blurs an image using a Gaussian filter.
static void	GaussianBlur (Mat src, Mat dst, Size ksize, double sigmaX)
	Blurs an image using a Gaussian filter.
static void	GaussianBlur (Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY)
	Blurs an image using a Gaussian filter.
static void	GaussianBlur (Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY, int borderType)
	Blurs an image using a Gaussian filter.
static Mat	getAffineTransform (MatOfPoint2f src, MatOfPoint2f dst)
static void	getDerivKernels (Mat kx, Mat ky, int dx, int dy, int ksize)
	Returns filter coefficients for computing spatial image derivatives.
static void	getDerivKernels (Mat kx, Mat ky, int dx, int dy, int ksize, bool normalize)
	Returns filter coefficients for computing spatial image derivatives.
static void	getDerivKernels (Mat kx, Mat ky, int dx, int dy, int ksize, bool normalize, int ktype)
	Returns filter coefficients for computing spatial image derivatives.
static double	getFontScaleFromHeight (int fontFace, int pixelHeight)
	Calculates the font-specific size to use to achieve a given height in pixels.
static double	getFontScaleFromHeight (int fontFace, int pixelHeight, int thickness)
	Calculates the font-specific size to use to achieve a given height in pixels.
static Mat	getGaborKernel (in Vec2d ksize, double sigma, double theta, double lambd, double gamma)
	Returns Gabor filter coefficients.
static Mat	getGaborKernel (in Vec2d ksize, double sigma, double theta, double lambd, double gamma, double psi)
	Returns Gabor filter coefficients.
static Mat	getGaborKernel (in Vec2d ksize, double sigma, double theta, double lambd, double gamma, double psi, int ktype)
	Returns Gabor filter coefficients.
static Mat	getGaborKernel (in(double width, double height) ksize, double sigma, double theta, double lambd, double gamma)
	Returns Gabor filter coefficients.
static Mat	getGaborKernel (in(double width, double height) ksize, double sigma, double theta, double lambd, double gamma, double psi)
	Returns Gabor filter coefficients.
static Mat	getGaborKernel (in(double width, double height) ksize, double sigma, double theta, double lambd, double gamma, double psi, int ktype)
	Returns Gabor filter coefficients.
static Mat	getGaborKernel (Size ksize, double sigma, double theta, double lambd, double gamma)
	Returns Gabor filter coefficients.
static Mat	getGaborKernel (Size ksize, double sigma, double theta, double lambd, double gamma, double psi)
	Returns Gabor filter coefficients.
static Mat	getGaborKernel (Size ksize, double sigma, double theta, double lambd, double gamma, double psi, int ktype)
	Returns Gabor filter coefficients.
static Mat	getGaussianKernel (int ksize, double sigma)
	Returns Gaussian filter coefficients.
static Mat	getGaussianKernel (int ksize, double sigma, int ktype)
	Returns Gaussian filter coefficients.
static Mat	getPerspectiveTransform (Mat src, Mat dst)
	Calculates a perspective transform from four pairs of the corresponding points.
static Mat	getPerspectiveTransform (Mat src, Mat dst, int solveMethod)
	Calculates a perspective transform from four pairs of the corresponding points.
static void	getRectSubPix (Mat image, in Vec2d patchSize, in Vec2d center, Mat patch)
	Retrieves a pixel rectangle from an image with sub-pixel accuracy.
static void	getRectSubPix (Mat image, in Vec2d patchSize, in Vec2d center, Mat patch, int patchType)
	Retrieves a pixel rectangle from an image with sub-pixel accuracy.
static void	getRectSubPix (Mat image, in(double width, double height) patchSize, in(double x, double y) center, Mat patch)
	Retrieves a pixel rectangle from an image with sub-pixel accuracy.
static void	getRectSubPix (Mat image, in(double width, double height) patchSize, in(double x, double y) center, Mat patch, int patchType)
	Retrieves a pixel rectangle from an image with sub-pixel accuracy.
static void	getRectSubPix (Mat image, Size patchSize, Point center, Mat patch)
	Retrieves a pixel rectangle from an image with sub-pixel accuracy.
static void	getRectSubPix (Mat image, Size patchSize, Point center, Mat patch, int patchType)
	Retrieves a pixel rectangle from an image with sub-pixel accuracy.
static Mat	getRotationMatrix2D (in Vec2d center, double angle, double scale)
	Calculates an affine matrix of 2D rotation.
static Mat	getRotationMatrix2D (in(double x, double y) center, double angle, double scale)
	Calculates an affine matrix of 2D rotation.
static Mat	getRotationMatrix2D (Point center, double angle, double scale)
	Calculates an affine matrix of 2D rotation.
static Mat	getStructuringElement (int shape, in Vec2d ksize)
	Returns a structuring element of the specified size and shape for morphological operations.
static Mat	getStructuringElement (int shape, in Vec2d ksize, in Vec2d anchor)
	Returns a structuring element of the specified size and shape for morphological operations.
static Mat	getStructuringElement (int shape, in(double width, double height) ksize)
	Returns a structuring element of the specified size and shape for morphological operations.
static Mat	getStructuringElement (int shape, in(double width, double height) ksize, in(double x, double y) anchor)
	Returns a structuring element of the specified size and shape for morphological operations.
static Mat	getStructuringElement (int shape, Size ksize)
	Returns a structuring element of the specified size and shape for morphological operations.
static Mat	getStructuringElement (int shape, Size ksize, Point anchor)
	Returns a structuring element of the specified size and shape for morphological operations.
static Size	getTextSize (string text, int fontFace, double fontScale, int thickness, int[] baseLine)
static double double height	getTextSizeAsValueTuple (string text, int fontFace, double fontScale, int thickness, int[] baseLine)
static Vec2d	getTextSizeAsVec2d (string text, int fontFace, double fontScale, int thickness, int[] baseLine)
static void	goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance)
	Determines strong corners on an image.
static void	goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask)
	Determines strong corners on an image.
static void	goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize)
	Determines strong corners on an image.
static void	goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, bool useHarrisDetector)
	Determines strong corners on an image.
static void	goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, bool useHarrisDetector, double k)
	Determines strong corners on an image.
static void	goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize)
static void	goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, bool useHarrisDetector)
static void	goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, bool useHarrisDetector, double k)
static void	goodFeaturesToTrackWithQuality (Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality)
	Same as above, but returns also quality measure of the detected corners.
static void	goodFeaturesToTrackWithQuality (Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize)
	Same as above, but returns also quality measure of the detected corners.
static void	goodFeaturesToTrackWithQuality (Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize)
	Same as above, but returns also quality measure of the detected corners.
static void	goodFeaturesToTrackWithQuality (Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize, bool useHarrisDetector)
	Same as above, but returns also quality measure of the detected corners.
static void	goodFeaturesToTrackWithQuality (Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize, bool useHarrisDetector, double k)
	Same as above, but returns also quality measure of the detected corners.
static void	grabCut (Mat img, Mat mask, in Vec4i rect, Mat bgdModel, Mat fgdModel, int iterCount)
	Runs the GrabCut algorithm.
static void	grabCut (Mat img, Mat mask, in Vec4i rect, Mat bgdModel, Mat fgdModel, int iterCount, int mode)
	Runs the GrabCut algorithm.
static void	grabCut (Mat img, Mat mask, in(int x, int y, int width, int height) rect, Mat bgdModel, Mat fgdModel, int iterCount)
	Runs the GrabCut algorithm.
static void	grabCut (Mat img, Mat mask, in(int x, int y, int width, int height) rect, Mat bgdModel, Mat fgdModel, int iterCount, int mode)
	Runs the GrabCut algorithm.
static void	grabCut (Mat img, Mat mask, Rect rect, Mat bgdModel, Mat fgdModel, int iterCount)
	Runs the GrabCut algorithm.
static void	grabCut (Mat img, Mat mask, Rect rect, Mat bgdModel, Mat fgdModel, int iterCount, int mode)
	Runs the GrabCut algorithm.
static void	HoughCircles (Mat image, Mat circles, int method, double dp, double minDist)
	Finds circles in a grayscale image using the Hough transform.
static void	HoughCircles (Mat image, Mat circles, int method, double dp, double minDist, double param1)
	Finds circles in a grayscale image using the Hough transform.
static void	HoughCircles (Mat image, Mat circles, int method, double dp, double minDist, double param1, double param2)
	Finds circles in a grayscale image using the Hough transform.
static void	HoughCircles (Mat image, Mat circles, int method, double dp, double minDist, double param1, double param2, int minRadius)
	Finds circles in a grayscale image using the Hough transform.
static void	HoughCircles (Mat image, Mat circles, int method, double dp, double minDist, double param1, double param2, int minRadius, int maxRadius)
	Finds circles in a grayscale image using the Hough transform.
static void	HoughLines (Mat image, Mat lines, double rho, double theta, int threshold)
	Finds lines in a binary image using the standard Hough transform.
static void	HoughLines (Mat image, Mat lines, double rho, double theta, int threshold, double srn)
	Finds lines in a binary image using the standard Hough transform.
static void	HoughLines (Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn)
	Finds lines in a binary image using the standard Hough transform.
static void	HoughLines (Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn, double min_theta)
	Finds lines in a binary image using the standard Hough transform.
static void	HoughLines (Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn, double min_theta, double max_theta)
	Finds lines in a binary image using the standard Hough transform.
static void	HoughLinesP (Mat image, Mat lines, double rho, double theta, int threshold)
	Finds line segments in a binary image using the probabilistic Hough transform.
static void	HoughLinesP (Mat image, Mat lines, double rho, double theta, int threshold, double minLineLength)
	Finds line segments in a binary image using the probabilistic Hough transform.
static void	HoughLinesP (Mat image, Mat lines, double rho, double theta, int threshold, double minLineLength, double maxLineGap)
	Finds line segments in a binary image using the probabilistic Hough transform.
static void	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.
static void	HoughLinesWithAccumulator (Mat image, Mat lines, double rho, double theta, int threshold)
	Finds lines in a binary image using the standard Hough transform and get accumulator.
static void	HoughLinesWithAccumulator (Mat image, Mat lines, double rho, double theta, int threshold, double srn)
	Finds lines in a binary image using the standard Hough transform and get accumulator.
static void	HoughLinesWithAccumulator (Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn)
	Finds lines in a binary image using the standard Hough transform and get accumulator.
static void	HoughLinesWithAccumulator (Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn, double min_theta)
	Finds lines in a binary image using the standard Hough transform and get accumulator.
static void	HoughLinesWithAccumulator (Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn, double min_theta, double max_theta)
	Finds lines in a binary image using the standard Hough transform and get accumulator.
static void	HuMoments (in Vec10d m, Mat hu)
static void	HuMoments (in(double m00, double m10, double m01, double m20, double m11, double m02, double m30, double m21, double m12, double m03) m, Mat hu)
static void	HuMoments (Moments m, Mat hu)
static void	integral (Mat src, Mat sum)
static void	integral (Mat src, Mat sum, int sdepth)
static void	integral2 (Mat src, Mat sum, Mat sqsum)
static void	integral2 (Mat src, Mat sum, Mat sqsum, int sdepth)
static void	integral2 (Mat src, Mat sum, Mat sqsum, int sdepth, int sqdepth)
static void	integral3 (Mat src, Mat sum, Mat sqsum, Mat tilted)
	Calculates the integral of an image.
static void	integral3 (Mat src, Mat sum, Mat sqsum, Mat tilted, int sdepth)
	Calculates the integral of an image.
static void	integral3 (Mat src, Mat sum, Mat sqsum, Mat tilted, int sdepth, int sqdepth)
	Calculates the integral of an image.
static float	intersectConvexConvex (Mat p1, Mat p2, Mat p12)
	Finds intersection of two convex polygons.
static float	intersectConvexConvex (Mat p1, Mat p2, Mat p12, bool handleNested)
	Finds intersection of two convex polygons.
static void	invertAffineTransform (Mat M, Mat iM)
	Inverts an affine transformation.
static bool	isContourConvex (MatOfPoint contour)
	Tests a contour convexity.
static void	Laplacian (Mat src, Mat dst, int ddepth)
	Calculates the Laplacian of an image.
static void	Laplacian (Mat src, Mat dst, int ddepth, int ksize)
	Calculates the Laplacian of an image.
static void	Laplacian (Mat src, Mat dst, int ddepth, int ksize, double scale)
	Calculates the Laplacian of an image.
static void	Laplacian (Mat src, Mat dst, int ddepth, int ksize, double scale, double delta)
	Calculates the Laplacian of an image.
static void	Laplacian (Mat src, Mat dst, int ddepth, int ksize, double scale, double delta, int borderType)
	Calculates the Laplacian of an image.
static void	line (Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color)
	Draws a line segment connecting two points.
static void	line (Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness)
	Draws a line segment connecting two points.
static void	line (Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness, int lineType)
	Draws a line segment connecting two points.
static void	line (Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness, int lineType, int shift)
	Draws a line segment connecting two points.
static void	line (Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color)
	Draws a line segment connecting two points.
static void	line (Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness)
	Draws a line segment connecting two points.
static void	line (Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType)
	Draws a line segment connecting two points.
static void	line (Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, int shift)
	Draws a line segment connecting two points.
static void	line (Mat img, Point pt1, Point pt2, Scalar color)
	Draws a line segment connecting two points.
static void	line (Mat img, Point pt1, Point pt2, Scalar color, int thickness)
	Draws a line segment connecting two points.
static void	line (Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType)
	Draws a line segment connecting two points.
static void	line (Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType, int shift)
	Draws a line segment connecting two points.
static void	linearPolar (Mat src, Mat dst, in Vec2d center, double maxRadius, int flags)
	Remaps an image to polar coordinates space.
static void	linearPolar (Mat src, Mat dst, in(double x, double y) center, double maxRadius, int flags)
	Remaps an image to polar coordinates space.
static void	linearPolar (Mat src, Mat dst, Point center, double maxRadius, int flags)
	Remaps an image to polar coordinates space.
static void	logPolar (Mat src, Mat dst, in Vec2d center, double M, int flags)
	Remaps an image to semilog-polar coordinates space.
static void	logPolar (Mat src, Mat dst, in(double x, double y) center, double M, int flags)
	Remaps an image to semilog-polar coordinates space.
static void	logPolar (Mat src, Mat dst, Point center, double M, int flags)
	Remaps an image to semilog-polar coordinates space.
static double	matchShapes (Mat contour1, Mat contour2, int method, double parameter)
	Compares two shapes.
static void	matchTemplate (Mat image, Mat templ, Mat result, int method)
	Compares a template against overlapped image regions.
static void	matchTemplate (Mat image, Mat templ, Mat result, int method, Mat mask)
	Compares a template against overlapped image regions.
static void	medianBlur (Mat src, Mat dst, int ksize)
	Blurs an image using the median filter.
static RotatedRect	minAreaRect (MatOfPoint2f points)
	Finds a rotated rectangle of the minimum area enclosing the input 2D point set.
static double double double double double angle	minAreaRectAsValueTuple (MatOfPoint2f points)
static Vec5d	minAreaRectAsVec5d (MatOfPoint2f points)
	Finds a rotated rectangle of the minimum area enclosing the input 2D point set.
static void	minEnclosingCircle (MatOfPoint2f points, out Vec2d center, float[] radius)
	Finds a circle of the minimum area enclosing a 2D point set.
static void	minEnclosingCircle (MatOfPoint2f points, out(double x, double y) center, float[] radius)
	Finds a circle of the minimum area enclosing a 2D point set.
static void	minEnclosingCircle (MatOfPoint2f points, Point center, float[] radius)
	Finds a circle of the minimum area enclosing a 2D point set.
static double	minEnclosingTriangle (Mat points, Mat triangle)
	Finds a triangle of minimum area enclosing a 2D point set and returns its area.
static Moments	moments (Mat array)
	Calculates all of the moments up to the third order of a polygon or rasterized shape.
static Moments	moments (Mat array, bool binaryImage)
	Calculates all of the moments up to the third order of a polygon or rasterized shape.
static double double double double double double double double double double m03	momentsAsValueTuple (Mat array)
static double double double double double double double double double double m03	momentsAsValueTuple (Mat array, bool binaryImage)
static Vec10d	momentsAsVec10d (Mat array)
	Calculates all of the moments up to the third order of a polygon or rasterized shape.
static Vec10d	momentsAsVec10d (Mat array, bool binaryImage)
	Calculates all of the moments up to the third order of a polygon or rasterized shape.
static void	morphologyEx (Mat src, Mat dst, int op, Mat kernel)
	Performs advanced morphological transformations.
static void	morphologyEx (Mat src, Mat dst, int op, Mat kernel, in Vec2d anchor)
	Performs advanced morphological transformations.
static void	morphologyEx (Mat src, Mat dst, int op, Mat kernel, in Vec2d anchor, int iterations)
	Performs advanced morphological transformations.
static void	morphologyEx (Mat src, Mat dst, int op, Mat kernel, in Vec2d anchor, int iterations, int borderType)
	Performs advanced morphological transformations.
static void	morphologyEx (Mat src, Mat dst, int op, Mat kernel, in Vec2d anchor, int iterations, int borderType, in Vec4d borderValue)
	Performs advanced morphological transformations.
static void	morphologyEx (Mat src, Mat dst, int op, Mat kernel, in(double x, double y) anchor)
	Performs advanced morphological transformations.
static void	morphologyEx (Mat src, Mat dst, int op, Mat kernel, in(double x, double y) anchor, int iterations)
	Performs advanced morphological transformations.
static void	morphologyEx (Mat src, Mat dst, int op, Mat kernel, in(double x, double y) anchor, int iterations, int borderType)
	Performs advanced morphological transformations.
static void	morphologyEx (Mat src, Mat dst, int op, Mat kernel, in(double x, double y) anchor, int iterations, int borderType, in(double v0, double v1, double v2, double v3) borderValue)
	Performs advanced morphological transformations.
static void	morphologyEx (Mat src, Mat dst, int op, Mat kernel, Point anchor)
	Performs advanced morphological transformations.
static void	morphologyEx (Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations)
	Performs advanced morphological transformations.
static void	morphologyEx (Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType)
	Performs advanced morphological transformations.
static void	morphologyEx (Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue)
	Performs advanced morphological transformations.
static Point	phaseCorrelate (Mat src1, Mat src2)
	The function is used to detect translational shifts that occur between two images.
static Point	phaseCorrelate (Mat src1, Mat src2, Mat window)
	The function is used to detect translational shifts that occur between two images.
static Point	phaseCorrelate (Mat src1, Mat src2, Mat window, double[] response)
	The function is used to detect translational shifts that occur between two images.
static double double y	phaseCorrelateAsValueTuple (Mat src1, Mat src2)
static double double y	phaseCorrelateAsValueTuple (Mat src1, Mat src2, Mat window)
static double double y	phaseCorrelateAsValueTuple (Mat src1, Mat src2, Mat window, double[] response)
static Vec2d	phaseCorrelateAsVec2d (Mat src1, Mat src2)
	The function is used to detect translational shifts that occur between two images.
static Vec2d	phaseCorrelateAsVec2d (Mat src1, Mat src2, Mat window)
	The function is used to detect translational shifts that occur between two images.
static Vec2d	phaseCorrelateAsVec2d (Mat src1, Mat src2, Mat window, double[] response)
	The function is used to detect translational shifts that occur between two images.
static double	pointPolygonTest (MatOfPoint2f contour, in Vec2d pt, bool measureDist)
	Performs a point-in-contour test.
static double	pointPolygonTest (MatOfPoint2f contour, in(double x, double y) pt, bool measureDist)
	Performs a point-in-contour test.
static double	pointPolygonTest (MatOfPoint2f contour, Point pt, bool measureDist)
	Performs a point-in-contour test.
static void	polylines (Mat img, List< MatOfPoint > pts, bool isClosed, in Vec4d color)
	Draws several polygonal curves.
static void	polylines (Mat img, List< MatOfPoint > pts, bool isClosed, in Vec4d color, int thickness)
	Draws several polygonal curves.
static void	polylines (Mat img, List< MatOfPoint > pts, bool isClosed, in Vec4d color, int thickness, int lineType)
	Draws several polygonal curves.
static void	polylines (Mat img, List< MatOfPoint > pts, bool isClosed, in Vec4d color, int thickness, int lineType, int shift)
	Draws several polygonal curves.
static void	polylines (Mat img, List< MatOfPoint > pts, bool isClosed, in(double v0, double v1, double v2, double v3) color)
	Draws several polygonal curves.
static void	polylines (Mat img, List< MatOfPoint > pts, bool isClosed, in(double v0, double v1, double v2, double v3) color, int thickness)
	Draws several polygonal curves.
static void	polylines (Mat img, List< MatOfPoint > pts, bool isClosed, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType)
	Draws several polygonal curves.
static void	polylines (Mat img, List< MatOfPoint > pts, bool isClosed, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, int shift)
	Draws several polygonal curves.
static void	polylines (Mat img, List< MatOfPoint > pts, bool isClosed, Scalar color)
	Draws several polygonal curves.
static void	polylines (Mat img, List< MatOfPoint > pts, bool isClosed, Scalar color, int thickness)
	Draws several polygonal curves.
static void	polylines (Mat img, List< MatOfPoint > pts, bool isClosed, Scalar color, int thickness, int lineType)
	Draws several polygonal curves.
static void	polylines (Mat img, List< MatOfPoint > pts, bool isClosed, Scalar color, int thickness, int lineType, int shift)
	Draws several polygonal curves.
static void	preCornerDetect (Mat src, Mat dst, int ksize)
	Calculates a feature map for corner detection.
static void	preCornerDetect (Mat src, Mat dst, int ksize, int borderType)
	Calculates a feature map for corner detection.
static void	putText (Mat img, string text, in Vec2d org, int fontFace, double fontScale, in Vec4d color)
	Draws a text string.
static void	putText (Mat img, string text, in Vec2d org, int fontFace, double fontScale, in Vec4d color, int thickness)
	Draws a text string.
static void	putText (Mat img, string text, in Vec2d org, int fontFace, double fontScale, in Vec4d color, int thickness, int lineType)
	Draws a text string.
static void	putText (Mat img, string text, in Vec2d org, int fontFace, double fontScale, in Vec4d color, int thickness, int lineType, bool bottomLeftOrigin)
	Draws a text string.
static void	putText (Mat img, string text, in(double x, double y) org, int fontFace, double fontScale, in(double v0, double v1, double v2, double v3) color)
	Draws a text string.
static void	putText (Mat img, string text, in(double x, double y) org, int fontFace, double fontScale, in(double v0, double v1, double v2, double v3) color, int thickness)
	Draws a text string.
static void	putText (Mat img, string text, in(double x, double y) org, int fontFace, double fontScale, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType)
	Draws a text string.
static void	putText (Mat img, string text, in(double x, double y) org, int fontFace, double fontScale, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, bool bottomLeftOrigin)
	Draws a text string.
static void	putText (Mat img, string text, Point org, int fontFace, double fontScale, Scalar color)
	Draws a text string.
static void	putText (Mat img, string text, Point org, int fontFace, double fontScale, Scalar color, int thickness)
	Draws a text string.
static void	putText (Mat img, string text, Point org, int fontFace, double fontScale, Scalar color, int thickness, int lineType)
	Draws a text string.
static void	putText (Mat img, string text, Point org, int fontFace, double fontScale, Scalar color, int thickness, int lineType, bool bottomLeftOrigin)
	Draws a text string.
static void	pyrDown (Mat src, Mat dst)
	Blurs an image and downsamples it.
static void	pyrDown (Mat src, Mat dst, in Vec2d dstsize)
	Blurs an image and downsamples it.
static void	pyrDown (Mat src, Mat dst, in Vec2d dstsize, int borderType)
	Blurs an image and downsamples it.
static void	pyrDown (Mat src, Mat dst, in(double width, double height) dstsize)
	Blurs an image and downsamples it.
static void	pyrDown (Mat src, Mat dst, in(double width, double height) dstsize, int borderType)
	Blurs an image and downsamples it.
static void	pyrDown (Mat src, Mat dst, Size dstsize)
	Blurs an image and downsamples it.
static void	pyrDown (Mat src, Mat dst, Size dstsize, int borderType)
	Blurs an image and downsamples it.
static void	pyrMeanShiftFiltering (Mat src, Mat dst, double sp, double sr)
	Performs initial step of meanshift segmentation of an image.
static void	pyrMeanShiftFiltering (Mat src, Mat dst, double sp, double sr, int maxLevel)
	Performs initial step of meanshift segmentation of an image.
static void	pyrMeanShiftFiltering (Mat src, Mat dst, double sp, double sr, int maxLevel, in Vec3d termcrit)
	Performs initial step of meanshift segmentation of an image.
static void	pyrMeanShiftFiltering (Mat src, Mat dst, double sp, double sr, int maxLevel, in(double type, double maxCount, double epsilon) termcrit)
	Performs initial step of meanshift segmentation of an image.
static void	pyrMeanShiftFiltering (Mat src, Mat dst, double sp, double sr, int maxLevel, TermCriteria termcrit)
	Performs initial step of meanshift segmentation of an image.
static void	pyrUp (Mat src, Mat dst)
	Upsamples an image and then blurs it.
static void	pyrUp (Mat src, Mat dst, in Vec2d dstsize)
	Upsamples an image and then blurs it.
static void	pyrUp (Mat src, Mat dst, in Vec2d dstsize, int borderType)
	Upsamples an image and then blurs it.
static void	pyrUp (Mat src, Mat dst, in(double width, double height) dstsize)
	Upsamples an image and then blurs it.
static void	pyrUp (Mat src, Mat dst, in(double width, double height) dstsize, int borderType)
	Upsamples an image and then blurs it.
static void	pyrUp (Mat src, Mat dst, Size dstsize)
	Upsamples an image and then blurs it.
static void	pyrUp (Mat src, Mat dst, Size dstsize, int borderType)
	Upsamples an image and then blurs it.
static void	rectangle (Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color)
	Draws a simple, thick, or filled up-right rectangle.
static void	rectangle (Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness)
	Draws a simple, thick, or filled up-right rectangle.
static void	rectangle (Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness, int lineType)
	Draws a simple, thick, or filled up-right rectangle.
static void	rectangle (Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness, int lineType, int shift)
	Draws a simple, thick, or filled up-right rectangle.
static void	rectangle (Mat img, in Vec4i rec, in Vec4d color)
static void	rectangle (Mat img, in Vec4i rec, in Vec4d color, int thickness)
static void	rectangle (Mat img, in Vec4i rec, in Vec4d color, int thickness, int lineType)
static void	rectangle (Mat img, in Vec4i rec, in Vec4d color, int thickness, int lineType, int shift)
static void	rectangle (Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color)
	Draws a simple, thick, or filled up-right rectangle.
static void	rectangle (Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness)
	Draws a simple, thick, or filled up-right rectangle.
static void	rectangle (Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType)
	Draws a simple, thick, or filled up-right rectangle.
static void	rectangle (Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, int shift)
	Draws a simple, thick, or filled up-right rectangle.
static void	rectangle (Mat img, in(int x, int y, int width, int height) rec, in(double v0, double v1, double v2, double v3) color)
static void	rectangle (Mat img, in(int x, int y, int width, int height) rec, in(double v0, double v1, double v2, double v3) color, int thickness)
static void	rectangle (Mat img, in(int x, int y, int width, int height) rec, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType)
static void	rectangle (Mat img, in(int x, int y, int width, int height) rec, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, int shift)
static void	rectangle (Mat img, Point pt1, Point pt2, Scalar color)
	Draws a simple, thick, or filled up-right rectangle.
static void	rectangle (Mat img, Point pt1, Point pt2, Scalar color, int thickness)
	Draws a simple, thick, or filled up-right rectangle.
static void	rectangle (Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType)
	Draws a simple, thick, or filled up-right rectangle.
static void	rectangle (Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType, int shift)
	Draws a simple, thick, or filled up-right rectangle.
static void	rectangle (Mat img, Rect rec, Scalar color)
static void	rectangle (Mat img, Rect rec, Scalar color, int thickness)
static void	rectangle (Mat img, Rect rec, Scalar color, int thickness, int lineType)
static void	rectangle (Mat img, Rect rec, Scalar color, int thickness, int lineType, int shift)
static void	remap (Mat src, Mat dst, Mat map1, Mat map2, int interpolation)
	Applies a generic geometrical transformation to an image.
static void	remap (Mat src, Mat dst, Mat map1, Mat map2, int interpolation, int borderMode)
	Applies a generic geometrical transformation to an image.
static void	remap (Mat src, Mat dst, Mat map1, Mat map2, int interpolation, int borderMode, in Vec4d borderValue)
	Applies a generic geometrical transformation to an image.
static void	remap (Mat src, Mat dst, Mat map1, Mat map2, int interpolation, int borderMode, in(double v0, double v1, double v2, double v3) borderValue)
	Applies a generic geometrical transformation to an image.
static void	remap (Mat src, Mat dst, Mat map1, Mat map2, int interpolation, int borderMode, Scalar borderValue)
	Applies a generic geometrical transformation to an image.
static void	resize (Mat src, Mat dst, in Vec2d dsize)
	Resizes an image.
static void	resize (Mat src, Mat dst, in Vec2d dsize, double fx)
	Resizes an image.
static void	resize (Mat src, Mat dst, in Vec2d dsize, double fx, double fy)
	Resizes an image.
static void	resize (Mat src, Mat dst, in Vec2d dsize, double fx, double fy, int interpolation)
	Resizes an image.
static void	resize (Mat src, Mat dst, in(double width, double height) dsize)
	Resizes an image.
static void	resize (Mat src, Mat dst, in(double width, double height) dsize, double fx)
	Resizes an image.
static void	resize (Mat src, Mat dst, in(double width, double height) dsize, double fx, double fy)
	Resizes an image.
static void	resize (Mat src, Mat dst, in(double width, double height) dsize, double fx, double fy, int interpolation)
	Resizes an image.
static void	resize (Mat src, Mat dst, Size dsize)
	Resizes an image.
static void	resize (Mat src, Mat dst, Size dsize, double fx)
	Resizes an image.
static void	resize (Mat src, Mat dst, Size dsize, double fx, double fy)
	Resizes an image.
static void	resize (Mat src, Mat dst, Size dsize, double fx, double fy, int interpolation)
	Resizes an image.
static int	rotatedRectangleIntersection (in Vec5d rect1, in Vec5d rect2, Mat intersectingRegion)
	Finds out if there is any intersection between two rotated rectangles.
static int	rotatedRectangleIntersection (in(double x, double y, double width, double height, double angle) rect1, in(double x, double y, double width, double height, double angle) rect2, Mat intersectingRegion)
	Finds out if there is any intersection between two rotated rectangles.
static int	rotatedRectangleIntersection (RotatedRect rect1, RotatedRect rect2, Mat intersectingRegion)
	Finds out if there is any intersection between two rotated rectangles.
static void	Scharr (Mat src, Mat dst, int ddepth, int dx, int dy)
	Calculates the first x- or y- image derivative using Scharr operator.
static void	Scharr (Mat src, Mat dst, int ddepth, int dx, int dy, double scale)
	Calculates the first x- or y- image derivative using Scharr operator.
static void	Scharr (Mat src, Mat dst, int ddepth, int dx, int dy, double scale, double delta)
	Calculates the first x- or y- image derivative using Scharr operator.
static void	Scharr (Mat src, Mat dst, int ddepth, int dx, int dy, double scale, double delta, int borderType)
	Calculates the first x- or y- image derivative using Scharr operator.
static void	sepFilter2D (Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY)
	Applies a separable linear filter to an image.
static void	sepFilter2D (Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, in Vec2d anchor)
	Applies a separable linear filter to an image.
static void	sepFilter2D (Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, in Vec2d anchor, double delta)
	Applies a separable linear filter to an image.
static void	sepFilter2D (Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, in Vec2d anchor, double delta, int borderType)
	Applies a separable linear filter to an image.
static void	sepFilter2D (Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, in(double x, double y) anchor)
	Applies a separable linear filter to an image.
static void	sepFilter2D (Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, in(double x, double y) anchor, double delta)
	Applies a separable linear filter to an image.
static void	sepFilter2D (Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, in(double x, double y) anchor, double delta, int borderType)
	Applies a separable linear filter to an image.
static void	sepFilter2D (Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor)
	Applies a separable linear filter to an image.
static void	sepFilter2D (Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor, double delta)
	Applies a separable linear filter to an image.
static void	sepFilter2D (Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor, double delta, int borderType)
	Applies a separable linear filter to an image.
static void	Sobel (Mat src, Mat dst, int ddepth, int dx, int dy)
	Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
static void	Sobel (Mat src, Mat dst, int ddepth, int dx, int dy, int ksize)
	Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
static void	Sobel (Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale)
	Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
static void	Sobel (Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale, double delta)
	Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
static void	Sobel (Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale, double delta, int borderType)
	Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
static void	spatialGradient (Mat src, Mat dx, Mat dy)
	Calculates the first order image derivative in both x and y using a Sobel operator.
static void	spatialGradient (Mat src, Mat dx, Mat dy, int ksize)
	Calculates the first order image derivative in both x and y using a Sobel operator.
static void	spatialGradient (Mat src, Mat dx, Mat dy, int ksize, int borderType)
	Calculates the first order image derivative in both x and y using a Sobel operator.
static void	sqrBoxFilter (Mat src, Mat dst, int ddepth, in Vec2d ksize)
	Calculates the normalized sum of squares of the pixel values overlapping the filter.
static void	sqrBoxFilter (Mat src, Mat dst, int ddepth, in Vec2d ksize, in Vec2d anchor)
	Calculates the normalized sum of squares of the pixel values overlapping the filter.
static void	sqrBoxFilter (Mat src, Mat dst, int ddepth, in Vec2d ksize, in Vec2d anchor, bool normalize)
	Calculates the normalized sum of squares of the pixel values overlapping the filter.
static void	sqrBoxFilter (Mat src, Mat dst, int ddepth, in Vec2d ksize, in Vec2d anchor, bool normalize, int borderType)
	Calculates the normalized sum of squares of the pixel values overlapping the filter.
static void	sqrBoxFilter (Mat src, Mat dst, int ddepth, in(double width, double height) ksize)
	Calculates the normalized sum of squares of the pixel values overlapping the filter.
static void	sqrBoxFilter (Mat src, Mat dst, int ddepth, in(double width, double height) ksize, in(double x, double y) anchor)
	Calculates the normalized sum of squares of the pixel values overlapping the filter.
static void	sqrBoxFilter (Mat src, Mat dst, int ddepth, in(double width, double height) ksize, in(double x, double y) anchor, bool normalize)
	Calculates the normalized sum of squares of the pixel values overlapping the filter.
static void	sqrBoxFilter (Mat src, Mat dst, int ddepth, in(double width, double height) ksize, in(double x, double y) anchor, bool normalize, int borderType)
	Calculates the normalized sum of squares of the pixel values overlapping the filter.
static void	sqrBoxFilter (Mat src, Mat dst, int ddepth, Size ksize)
	Calculates the normalized sum of squares of the pixel values overlapping the filter.
static void	sqrBoxFilter (Mat src, Mat dst, int ddepth, Size ksize, Point anchor)
	Calculates the normalized sum of squares of the pixel values overlapping the filter.
static void	sqrBoxFilter (Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize)
	Calculates the normalized sum of squares of the pixel values overlapping the filter.
static void	sqrBoxFilter (Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize, int borderType)
	Calculates the normalized sum of squares of the pixel values overlapping the filter.
static void	stackBlur (Mat src, Mat dst, in Vec2d ksize)
	Blurs an image using the stackBlur.
static void	stackBlur (Mat src, Mat dst, in(double width, double height) ksize)
	Blurs an image using the stackBlur.
static void	stackBlur (Mat src, Mat dst, Size ksize)
	Blurs an image using the stackBlur.
static double	threshold (Mat src, Mat dst, double thresh, double maxval, int type)
	Applies a fixed-level threshold to each array element.
static void	warpAffine (Mat src, Mat dst, Mat M, in Vec2d dsize)
	Applies an affine transformation to an image.
static void	warpAffine (Mat src, Mat dst, Mat M, in Vec2d dsize, int flags)
	Applies an affine transformation to an image.
static void	warpAffine (Mat src, Mat dst, Mat M, in Vec2d dsize, int flags, int borderMode)
	Applies an affine transformation to an image.
static void	warpAffine (Mat src, Mat dst, Mat M, in Vec2d dsize, int flags, int borderMode, in Vec4d borderValue)
	Applies an affine transformation to an image.
static void	warpAffine (Mat src, Mat dst, Mat M, in(double width, double height) dsize)
	Applies an affine transformation to an image.
static void	warpAffine (Mat src, Mat dst, Mat M, in(double width, double height) dsize, int flags)
	Applies an affine transformation to an image.
static void	warpAffine (Mat src, Mat dst, Mat M, in(double width, double height) dsize, int flags, int borderMode)
	Applies an affine transformation to an image.
static void	warpAffine (Mat src, Mat dst, Mat M, in(double width, double height) dsize, int flags, int borderMode, in(double v0, double v1, double v2, double v3) borderValue)
	Applies an affine transformation to an image.
static void	warpAffine (Mat src, Mat dst, Mat M, Size dsize)
	Applies an affine transformation to an image.
static void	warpAffine (Mat src, Mat dst, Mat M, Size dsize, int flags)
	Applies an affine transformation to an image.
static void	warpAffine (Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode)
	Applies an affine transformation to an image.
static void	warpAffine (Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue)
	Applies an affine transformation to an image.
static void	warpPerspective (Mat src, Mat dst, Mat M, in Vec2d dsize)
	Applies a perspective transformation to an image.
static void	warpPerspective (Mat src, Mat dst, Mat M, in Vec2d dsize, int flags)
	Applies a perspective transformation to an image.
static void	warpPerspective (Mat src, Mat dst, Mat M, in Vec2d dsize, int flags, int borderMode)
	Applies a perspective transformation to an image.
static void	warpPerspective (Mat src, Mat dst, Mat M, in Vec2d dsize, int flags, int borderMode, in Vec4d borderValue)
	Applies a perspective transformation to an image.
static void	warpPerspective (Mat src, Mat dst, Mat M, in(double width, double height) dsize)
	Applies a perspective transformation to an image.
static void	warpPerspective (Mat src, Mat dst, Mat M, in(double width, double height) dsize, int flags)
	Applies a perspective transformation to an image.
static void	warpPerspective (Mat src, Mat dst, Mat M, in(double width, double height) dsize, int flags, int borderMode)
	Applies a perspective transformation to an image.
static void	warpPerspective (Mat src, Mat dst, Mat M, in(double width, double height) dsize, int flags, int borderMode, in(double v0, double v1, double v2, double v3) borderValue)
	Applies a perspective transformation to an image.
static void	warpPerspective (Mat src, Mat dst, Mat M, Size dsize)
	Applies a perspective transformation to an image.
static void	warpPerspective (Mat src, Mat dst, Mat M, Size dsize, int flags)
	Applies a perspective transformation to an image.
static void	warpPerspective (Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode)
	Applies a perspective transformation to an image.
static void	warpPerspective (Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue)
	Applies a perspective transformation to an image.
static void	warpPolar (Mat src, Mat dst, in Vec2d dsize, in Vec2d center, double maxRadius, int flags)
	Remaps an image to polar or semilog-polar coordinates space.
static void	warpPolar (Mat src, Mat dst, in(double width, double height) dsize, in(double x, double y) center, double maxRadius, int flags)
	Remaps an image to polar or semilog-polar coordinates space.
static void	warpPolar (Mat src, Mat dst, Size dsize, Point center, double maxRadius, int flags)
	Remaps an image to polar or semilog-polar coordinates space.
static void	watershed (Mat image, Mat markers)
	Performs a marker-based image segmentation using the watershed algorithm.

Static Public Attributes
const int	ADAPTIVE_THRESH_GAUSSIAN_C = 1
const int	ADAPTIVE_THRESH_MEAN_C = 0
const int	CC_STAT_AREA = 4
const int	CC_STAT_HEIGHT = 3
const int	CC_STAT_LEFT = 0
const int	CC_STAT_MAX = 5
const int	CC_STAT_TOP = 1
const int	CC_STAT_WIDTH = 2
const int	CCL_BBDT = 4
const int	CCL_BOLELLI = 2
const int	CCL_DEFAULT = -1
const int	CCL_GRANA = 1
const int	CCL_SAUF = 3
const int	CCL_SPAGHETTI = 5
const int	CCL_WU = 0
const int	CHAIN_APPROX_NONE = 1
const int	CHAIN_APPROX_SIMPLE = 2
const int	CHAIN_APPROX_TC89_KCOS = 4
const int	CHAIN_APPROX_TC89_L1 = 3
const int	COLOR_BayerBG2BGR = 46
const int	COLOR_BayerBG2BGR_EA = 135
const int	COLOR_BayerBG2BGR_VNG = 62
const int	COLOR_BayerBG2BGRA = 139
const int	COLOR_BayerBG2GRAY = 86
const int	COLOR_BayerBG2RGB = COLOR_BayerRG2BGR
const int	COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA
const int	COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG
const int	COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA
const int	COLOR_BayerBGGR2BGR = COLOR_BayerRG2BGR
const int	COLOR_BayerBGGR2BGR_EA = COLOR_BayerRG2BGR_EA
const int	COLOR_BayerBGGR2BGR_VNG = COLOR_BayerRG2BGR_VNG
const int	COLOR_BayerBGGR2BGRA = COLOR_BayerRG2BGRA
const int	COLOR_BayerBGGR2GRAY = COLOR_BayerRG2GRAY
const int	COLOR_BayerBGGR2RGB = COLOR_BayerRGGB2BGR
const int	COLOR_BayerBGGR2RGB_EA = COLOR_BayerRGGB2BGR_EA
const int	COLOR_BayerBGGR2RGB_VNG = COLOR_BayerRGGB2BGR_VNG
const int	COLOR_BayerBGGR2RGBA = COLOR_BayerRGGB2BGRA
const int	COLOR_BayerGB2BGR = 47
const int	COLOR_BayerGB2BGR_EA = 136
const int	COLOR_BayerGB2BGR_VNG = 63
const int	COLOR_BayerGB2BGRA = 140
const int	COLOR_BayerGB2GRAY = 87
const int	COLOR_BayerGB2RGB = COLOR_BayerGR2BGR
const int	COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA
const int	COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG
const int	COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA
const int	COLOR_BayerGBRG2BGR = COLOR_BayerGR2BGR
const int	COLOR_BayerGBRG2BGR_EA = COLOR_BayerGR2BGR_EA
const int	COLOR_BayerGBRG2BGR_VNG = COLOR_BayerGR2BGR_VNG
const int	COLOR_BayerGBRG2BGRA = COLOR_BayerGR2BGRA
const int	COLOR_BayerGBRG2GRAY = COLOR_BayerGR2GRAY
const int	COLOR_BayerGBRG2RGB = COLOR_BayerGRBG2BGR
const int	COLOR_BayerGBRG2RGB_EA = COLOR_BayerGRBG2BGR_EA
const int	COLOR_BayerGBRG2RGB_VNG = COLOR_BayerGRBG2BGR_VNG
const int	COLOR_BayerGBRG2RGBA = COLOR_BayerGRBG2BGRA
const int	COLOR_BayerGR2BGR = 49
const int	COLOR_BayerGR2BGR_EA = 138
const int	COLOR_BayerGR2BGR_VNG = 65
const int	COLOR_BayerGR2BGRA = 142
const int	COLOR_BayerGR2GRAY = 89
const int	COLOR_BayerGR2RGB = COLOR_BayerGB2BGR
const int	COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA
const int	COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG
const int	COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA
const int	COLOR_BayerGRBG2BGR = COLOR_BayerGB2BGR
const int	COLOR_BayerGRBG2BGR_EA = COLOR_BayerGB2BGR_EA
const int	COLOR_BayerGRBG2BGR_VNG = COLOR_BayerGB2BGR_VNG
const int	COLOR_BayerGRBG2BGRA = COLOR_BayerGB2BGRA
const int	COLOR_BayerGRBG2GRAY = COLOR_BayerGB2GRAY
const int	COLOR_BayerGRBG2RGB = COLOR_BayerGBRG2BGR
const int	COLOR_BayerGRBG2RGB_EA = COLOR_BayerGBRG2BGR_EA
const int	COLOR_BayerGRBG2RGB_VNG = COLOR_BayerGBRG2BGR_VNG
const int	COLOR_BayerGRBG2RGBA = COLOR_BayerGBRG2BGRA
const int	COLOR_BayerRG2BGR = 48
const int	COLOR_BayerRG2BGR_EA = 137
const int	COLOR_BayerRG2BGR_VNG = 64
const int	COLOR_BayerRG2BGRA = 141
const int	COLOR_BayerRG2GRAY = 88
const int	COLOR_BayerRG2RGB = COLOR_BayerBG2BGR
const int	COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA
const int	COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG
const int	COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA
const int	COLOR_BayerRGGB2BGR = COLOR_BayerBG2BGR
const int	COLOR_BayerRGGB2BGR_EA = COLOR_BayerBG2BGR_EA
const int	COLOR_BayerRGGB2BGR_VNG = COLOR_BayerBG2BGR_VNG
const int	COLOR_BayerRGGB2BGRA = COLOR_BayerBG2BGRA
const int	COLOR_BayerRGGB2GRAY = COLOR_BayerBG2GRAY
const int	COLOR_BayerRGGB2RGB = COLOR_BayerBGGR2BGR
const int	COLOR_BayerRGGB2RGB_EA = COLOR_BayerBGGR2BGR_EA
const int	COLOR_BayerRGGB2RGB_VNG = COLOR_BayerBGGR2BGR_VNG
const int	COLOR_BayerRGGB2RGBA = COLOR_BayerBGGR2BGRA
const int	COLOR_BGR2BGR555 = 22
const int	COLOR_BGR2BGR565 = 12
const int	COLOR_BGR2BGRA = 0
const int	COLOR_BGR2GRAY = 6
const int	COLOR_BGR2HLS = 52
const int	COLOR_BGR2HLS_FULL = 68
const int	COLOR_BGR2HSV = 40
const int	COLOR_BGR2HSV_FULL = 66
const int	COLOR_BGR2Lab = 44
const int	COLOR_BGR2Luv = 50
const int	COLOR_BGR2RGB = 4
const int	COLOR_BGR2RGBA = 2
const int	COLOR_BGR2XYZ = 32
const int	COLOR_BGR2YCrCb = 36
const int	COLOR_BGR2YUV = 82
const int	COLOR_BGR2YUV_I420 = 128
const int	COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420
const int	COLOR_BGR2YUV_UYNV = COLOR_BGR2YUV_UYVY
const int	COLOR_BGR2YUV_UYVY = 144
const int	COLOR_BGR2YUV_Y422 = COLOR_BGR2YUV_UYVY
const int	COLOR_BGR2YUV_YUNV = COLOR_BGR2YUV_YUY2
const int	COLOR_BGR2YUV_YUY2 = 148
const int	COLOR_BGR2YUV_YUYV = COLOR_BGR2YUV_YUY2
const int	COLOR_BGR2YUV_YV12 = 132
const int	COLOR_BGR2YUV_YVYU = 150
const int	COLOR_BGR5552BGR = 24
const int	COLOR_BGR5552BGRA = 28
const int	COLOR_BGR5552GRAY = 31
const int	COLOR_BGR5552RGB = 25
const int	COLOR_BGR5552RGBA = 29
const int	COLOR_BGR5652BGR = 14
const int	COLOR_BGR5652BGRA = 18
const int	COLOR_BGR5652GRAY = 21
const int	COLOR_BGR5652RGB = 15
const int	COLOR_BGR5652RGBA = 19
const int	COLOR_BGRA2BGR = 1
const int	COLOR_BGRA2BGR555 = 26
const int	COLOR_BGRA2BGR565 = 16
const int	COLOR_BGRA2GRAY = 10
const int	COLOR_BGRA2RGB = COLOR_RGBA2BGR
const int	COLOR_BGRA2RGBA = 5
const int	COLOR_BGRA2YUV_I420 = 130
const int	COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420
const int	COLOR_BGRA2YUV_UYNV = COLOR_BGRA2YUV_UYVY
const int	COLOR_BGRA2YUV_UYVY = 146
const int	COLOR_BGRA2YUV_Y422 = COLOR_BGRA2YUV_UYVY
const int	COLOR_BGRA2YUV_YUNV = COLOR_BGRA2YUV_YUY2
const int	COLOR_BGRA2YUV_YUY2 = 152
const int	COLOR_BGRA2YUV_YUYV = COLOR_BGRA2YUV_YUY2
const int	COLOR_BGRA2YUV_YV12 = 134
const int	COLOR_BGRA2YUV_YVYU = 154
const int	COLOR_COLORCVT_MAX = 155
const int	COLOR_GRAY2BGR = 8
const int	COLOR_GRAY2BGR555 = 30
const int	COLOR_GRAY2BGR565 = 20
const int	COLOR_GRAY2BGRA = 9
const int	COLOR_GRAY2RGB = COLOR_GRAY2BGR
const int	COLOR_GRAY2RGBA = COLOR_GRAY2BGRA
const int	COLOR_HLS2BGR = 60
const int	COLOR_HLS2BGR_FULL = 72
const int	COLOR_HLS2RGB = 61
const int	COLOR_HLS2RGB_FULL = 73
const int	COLOR_HSV2BGR = 54
const int	COLOR_HSV2BGR_FULL = 70
const int	COLOR_HSV2RGB = 55
const int	COLOR_HSV2RGB_FULL = 71
const int	COLOR_Lab2BGR = 56
const int	COLOR_Lab2LBGR = 78
const int	COLOR_Lab2LRGB = 79
const int	COLOR_Lab2RGB = 57
const int	COLOR_LBGR2Lab = 74
const int	COLOR_LBGR2Luv = 76
const int	COLOR_LRGB2Lab = 75
const int	COLOR_LRGB2Luv = 77
const int	COLOR_Luv2BGR = 58
const int	COLOR_Luv2LBGR = 80
const int	COLOR_Luv2LRGB = 81
const int	COLOR_Luv2RGB = 59
const int	COLOR_mRGBA2RGBA = 126
const int	COLOR_RGB2BGR = COLOR_BGR2RGB
const int	COLOR_RGB2BGR555 = 23
const int	COLOR_RGB2BGR565 = 13
const int	COLOR_RGB2BGRA = COLOR_BGR2RGBA
const int	COLOR_RGB2GRAY = 7
const int	COLOR_RGB2HLS = 53
const int	COLOR_RGB2HLS_FULL = 69
const int	COLOR_RGB2HSV = 41
const int	COLOR_RGB2HSV_FULL = 67
const int	COLOR_RGB2Lab = 45
const int	COLOR_RGB2Luv = 51
const int	COLOR_RGB2RGBA = COLOR_BGR2BGRA
const int	COLOR_RGB2XYZ = 33
const int	COLOR_RGB2YCrCb = 37
const int	COLOR_RGB2YUV = 83
const int	COLOR_RGB2YUV_I420 = 127
const int	COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420
const int	COLOR_RGB2YUV_UYNV = COLOR_RGB2YUV_UYVY
const int	COLOR_RGB2YUV_UYVY = 143
const int	COLOR_RGB2YUV_Y422 = COLOR_RGB2YUV_UYVY
const int	COLOR_RGB2YUV_YUNV = COLOR_RGB2YUV_YUY2
const int	COLOR_RGB2YUV_YUY2 = 147
const int	COLOR_RGB2YUV_YUYV = COLOR_RGB2YUV_YUY2
const int	COLOR_RGB2YUV_YV12 = 131
const int	COLOR_RGB2YUV_YVYU = 149
const int	COLOR_RGBA2BGR = 3
const int	COLOR_RGBA2BGR555 = 27
const int	COLOR_RGBA2BGR565 = 17
const int	COLOR_RGBA2BGRA = COLOR_BGRA2RGBA
const int	COLOR_RGBA2GRAY = 11
const int	COLOR_RGBA2mRGBA = 125
const int	COLOR_RGBA2RGB = COLOR_BGRA2BGR
const int	COLOR_RGBA2YUV_I420 = 129
const int	COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420
const int	COLOR_RGBA2YUV_UYNV = COLOR_RGBA2YUV_UYVY
const int	COLOR_RGBA2YUV_UYVY = 145
const int	COLOR_RGBA2YUV_Y422 = COLOR_RGBA2YUV_UYVY
const int	COLOR_RGBA2YUV_YUNV = COLOR_RGBA2YUV_YUY2
const int	COLOR_RGBA2YUV_YUY2 = 151
const int	COLOR_RGBA2YUV_YUYV = COLOR_RGBA2YUV_YUY2
const int	COLOR_RGBA2YUV_YV12 = 133
const int	COLOR_RGBA2YUV_YVYU = 153
const int	COLOR_XYZ2BGR = 34
const int	COLOR_XYZ2RGB = 35
const int	COLOR_YCrCb2BGR = 38
const int	COLOR_YCrCb2RGB = 39
const int	COLOR_YUV2BGR = 84
const int	COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV
const int	COLOR_YUV2BGR_IYUV = 101
const int	COLOR_YUV2BGR_NV12 = 91
const int	COLOR_YUV2BGR_NV21 = 93
const int	COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY
const int	COLOR_YUV2BGR_UYVY = 108
const int	COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY
const int	COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2
const int	COLOR_YUV2BGR_YUY2 = 116
const int	COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2
const int	COLOR_YUV2BGR_YV12 = 99
const int	COLOR_YUV2BGR_YVYU = 118
const int	COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV
const int	COLOR_YUV2BGRA_IYUV = 105
const int	COLOR_YUV2BGRA_NV12 = 95
const int	COLOR_YUV2BGRA_NV21 = 97
const int	COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY
const int	COLOR_YUV2BGRA_UYVY = 112
const int	COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY
const int	COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2
const int	COLOR_YUV2BGRA_YUY2 = 120
const int	COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2
const int	COLOR_YUV2BGRA_YV12 = 103
const int	COLOR_YUV2BGRA_YVYU = 122
const int	COLOR_YUV2GRAY_420 = 106
const int	COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420
const int	COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420
const int	COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420
const int	COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420
const int	COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY
const int	COLOR_YUV2GRAY_UYVY = 123
const int	COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY
const int	COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2
const int	COLOR_YUV2GRAY_YUY2 = 124
const int	COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2
const int	COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420
const int	COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2
const int	COLOR_YUV2RGB = 85
const int	COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV
const int	COLOR_YUV2RGB_IYUV = 100
const int	COLOR_YUV2RGB_NV12 = 90
const int	COLOR_YUV2RGB_NV21 = 92
const int	COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY
const int	COLOR_YUV2RGB_UYVY = 107
const int	COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY
const int	COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2
const int	COLOR_YUV2RGB_YUY2 = 115
const int	COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2
const int	COLOR_YUV2RGB_YV12 = 98
const int	COLOR_YUV2RGB_YVYU = 117
const int	COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV
const int	COLOR_YUV2RGBA_IYUV = 104
const int	COLOR_YUV2RGBA_NV12 = 94
const int	COLOR_YUV2RGBA_NV21 = 96
const int	COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY
const int	COLOR_YUV2RGBA_UYVY = 111
const int	COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY
const int	COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2
const int	COLOR_YUV2RGBA_YUY2 = 119
const int	COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2
const int	COLOR_YUV2RGBA_YV12 = 102
const int	COLOR_YUV2RGBA_YVYU = 121
const int	COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12
const int	COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12
const int	COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420
const int	COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12
const int	COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12
const int	COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21
const int	COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21
const int	COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420
const int	COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21
const int	COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21
const int	COLORMAP_AUTUMN = 0
const int	COLORMAP_BONE = 1
const int	COLORMAP_CIVIDIS = 17
const int	COLORMAP_COOL = 8
const int	COLORMAP_DEEPGREEN = 21
const int	COLORMAP_HOT = 11
const int	COLORMAP_HSV = 9
const int	COLORMAP_INFERNO = 14
const int	COLORMAP_JET = 2
const int	COLORMAP_MAGMA = 13
const int	COLORMAP_OCEAN = 5
const int	COLORMAP_PARULA = 12
const int	COLORMAP_PINK = 10
const int	COLORMAP_PLASMA = 15
const int	COLORMAP_RAINBOW = 4
const int	COLORMAP_SPRING = 7
const int	COLORMAP_SUMMER = 6
const int	COLORMAP_TURBO = 20
const int	COLORMAP_TWILIGHT = 18
const int	COLORMAP_TWILIGHT_SHIFTED = 19
const int	COLORMAP_VIRIDIS = 16
const int	COLORMAP_WINTER = 3
const int	CONTOURS_MATCH_I1 = 1
const int	CONTOURS_MATCH_I2 = 2
const int	CONTOURS_MATCH_I3 = 3
const int	CV_BILATERAL = 4
const int	CV_BLUR = 1
const int	CV_BLUR_NO_SCALE = 0
const int	CV_CANNY_L2_GRADIENT = (1 << 31)
const int	CV_CHAIN_CODE = 0
const int	CV_CLOCKWISE = 1
const int	CV_COMP_BHATTACHARYYA = 3
const int	CV_COMP_CHISQR = 1
const int	CV_COMP_CHISQR_ALT = 4
const int	CV_COMP_CORREL = 0
const int	CV_COMP_HELLINGER = CV_COMP_BHATTACHARYYA
const int	CV_COMP_INTERSECT = 2
const int	CV_COMP_KL_DIV = 5
const int	CV_CONTOURS_MATCH_I1 = 1
const int	CV_CONTOURS_MATCH_I2 = 2
const int	CV_CONTOURS_MATCH_I3 = 3
const int	CV_COUNTER_CLOCKWISE = 2
const int	CV_DIST_C = 3
const int	CV_DIST_FAIR = 5
const int	CV_DIST_HUBER = 7
const int	CV_DIST_L1 = 1
const int	CV_DIST_L12 = 4
const int	CV_DIST_L2 = 2
const int	CV_DIST_LABEL_CCOMP = 0
const int	CV_DIST_LABEL_PIXEL = 1
const int	CV_DIST_MASK_3 = 3
const int	CV_DIST_MASK_5 = 5
const int	CV_DIST_MASK_PRECISE = 0
const int	CV_DIST_USER = -1
const int	CV_DIST_WELSCH = 6
const int	CV_GAUSSIAN = 2
const int	CV_GAUSSIAN_5x5 = 7
const int	CV_HOUGH_GRADIENT = 3
const int	CV_HOUGH_MULTI_SCALE = 2
const int	CV_HOUGH_PROBABILISTIC = 1
const int	CV_HOUGH_STANDARD = 0
const int	CV_LINK_RUNS = 5
const int	CV_MAX_SOBEL_KSIZE = 7
const int	CV_MEDIAN = 3
const int	CV_mRGBA2RGBA = 126
const int	CV_POLY_APPROX_DP = 0
const int	CV_RGBA2mRGBA = 125
const int	CV_SCHARR = -1
const int	CV_SHAPE_CROSS = 1
const int	CV_SHAPE_CUSTOM = 100
const int	CV_SHAPE_ELLIPSE = 2
const int	CV_SHAPE_RECT = 0
const int	CV_WARP_FILL_OUTLIERS = 8
const int	CV_WARP_INVERSE_MAP = 16
const int	CV_WARP_RELATIVE_MAP = 32
const int	DIST_C = 3
const int	DIST_FAIR = 5
const int	DIST_HUBER = 7
const int	DIST_L1 = 1
const int	DIST_L12 = 4
const int	DIST_L2 = 2
const int	DIST_LABEL_CCOMP = 0
const int	DIST_LABEL_PIXEL = 1
const int	DIST_MASK_3 = 3
const int	DIST_MASK_5 = 5
const int	DIST_MASK_PRECISE = 0
const int	DIST_USER = -1
const int	DIST_WELSCH = 6
const int	FILLED = -1
const int	FILTER_SCHARR = -1
const int	FLOODFILL_FIXED_RANGE = 1 << 16
const int	FLOODFILL_MASK_ONLY = 1 << 17
const int	FONT_HERSHEY_COMPLEX = 3
const int	FONT_HERSHEY_COMPLEX_SMALL = 5
const int	FONT_HERSHEY_DUPLEX = 2
const int	FONT_HERSHEY_PLAIN = 1
const int	FONT_HERSHEY_SCRIPT_COMPLEX = 7
const int	FONT_HERSHEY_SCRIPT_SIMPLEX = 6
const int	FONT_HERSHEY_SIMPLEX = 0
const int	FONT_HERSHEY_TRIPLEX = 4
const int	FONT_ITALIC = 16
const int	GC_BGD = 0
const int	GC_EVAL = 2
const int	GC_EVAL_FREEZE_MODEL = 3
const int	GC_FGD = 1
const int	GC_INIT_WITH_MASK = 1
const int	GC_INIT_WITH_RECT = 0
const int	GC_PR_BGD = 2
const int	GC_PR_FGD = 3
static double double double double	height
const int	HISTCMP_BHATTACHARYYA = 3
const int	HISTCMP_CHISQR = 1
const int	HISTCMP_CHISQR_ALT = 4
const int	HISTCMP_CORREL = 0
const int	HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA
const int	HISTCMP_INTERSECT = 2
const int	HISTCMP_KL_DIV = 5
const int	HOUGH_GRADIENT = 3
const int	HOUGH_GRADIENT_ALT = 4
const int	HOUGH_MULTI_SCALE = 2
const int	HOUGH_PROBABILISTIC = 1
const int	HOUGH_STANDARD = 0
const int	INTER_AREA = 3
const int	INTER_BITS = 5
const int	INTER_BITS2 = INTER_BITS * 2
const int	INTER_CUBIC = 2
const int	INTER_LANCZOS4 = 4
const int	INTER_LINEAR = 1
const int	INTER_LINEAR_EXACT = 5
const int	INTER_MAX = 7
const int	INTER_NEAREST = 0
const int	INTER_NEAREST_EXACT = 6
const int	INTER_TAB_SIZE = 1 << INTER_BITS
const int	INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE
const int	INTERSECT_FULL = 2
const int	INTERSECT_NONE = 0
const int	INTERSECT_PARTIAL = 1
const int	LINE_4 = 4
const int	LINE_8 = 8
const int	LINE_AA = 16
const int	LSD_REFINE_ADV = 2
const int	LSD_REFINE_NONE = 0
const int	LSD_REFINE_STD = 1
static double	m00
	Calculates all of the moments up to the third order of a polygon or rasterized shape.
static double double double	m01
static double double double double double double	m02
static double double	m10
static double double double double double	m11
static double double double double double double double double double	m12
static double double double double	m20
static double double double double double double double double	m21
static double double double double double double double	m30
const int	MARKER_CROSS = 0
const int	MARKER_DIAMOND = 3
const int	MARKER_SQUARE = 4
const int	MARKER_STAR = 2
const int	MARKER_TILTED_CROSS = 1
const int	MARKER_TRIANGLE_DOWN = 6
const int	MARKER_TRIANGLE_UP = 5
const int	MORPH_BLACKHAT = 6
const int	MORPH_CLOSE = 3
const int	MORPH_CROSS = 1
const int	MORPH_DILATE = 1
const int	MORPH_ELLIPSE = 2
const int	MORPH_ERODE = 0
const int	MORPH_GRADIENT = 4
const int	MORPH_HITMISS = 7
const int	MORPH_OPEN = 2
const int	MORPH_RECT = 0
const int	MORPH_TOPHAT = 5
const int	RETR_CCOMP = 2
const int	RETR_EXTERNAL = 0
const int	RETR_FLOODFILL = 4
const int	RETR_LIST = 1
const int	RETR_TREE = 3
const int	THRESH_BINARY = 0
const int	THRESH_BINARY_INV = 1
const int	THRESH_MASK = 7
const int	THRESH_OTSU = 8
const int	THRESH_TOZERO = 3
const int	THRESH_TOZERO_INV = 4
const int	THRESH_TRIANGLE = 16
const int	THRESH_TRUNC = 2
const int	TM_CCOEFF = 4
const int	TM_CCOEFF_NORMED = 5
const int	TM_CCORR = 2
const int	TM_CCORR_NORMED = 3
const int	TM_SQDIFF = 0
const int	TM_SQDIFF_NORMED = 1
const int	WARP_FILL_OUTLIERS = 8
const int	WARP_INVERSE_MAP = 16
const int	WARP_POLAR_LINEAR = 0
const int	WARP_POLAR_LOG = 256
const int	WARP_RELATIVE_MAP = 32
static int int int	width
static double double double	width
static double	width
static double	x
	The function is used to detect translational shifts that occur between two images.
static int	x
	Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.
static int int	y
static double double	y

Member Function Documentation

accumulate() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.accumulate ( Mat src, Mat dst )

static

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.

Parameters

src	Input image of type CV_8UC(n), CV_16UC(n), CV_32FC(n) or CV_64FC(n), where n is a positive integer.
dst	Accumulator image with the same number of channels as input image, and a depth of CV_32F or CV_64F.
mask	Optional operation mask.

See also

accumulateSquare, accumulateProduct, accumulateWeighted

accumulate() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.accumulate ( Mat src, Mat dst, Mat mask )

static

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.

Parameters

src	Input image of type CV_8UC(n), CV_16UC(n), CV_32FC(n) or CV_64FC(n), where n is a positive integer.
dst	Accumulator image with the same number of channels as input image, and a depth of CV_32F or CV_64F.
mask	Optional operation mask.

See also

accumulateSquare, accumulateProduct, accumulateWeighted

accumulateProduct() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.accumulateProduct ( Mat src1, Mat src2, Mat dst )

static

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.

Parameters

src1	First input image, 1- or 3-channel, 8-bit or 32-bit floating point.
src2	Second input image of the same type and the same size as src1 .
dst	Accumulator image with the same number of channels as input images, 32-bit or 64-bit floating-point.
mask	Optional operation mask.

See also

accumulate, accumulateSquare, accumulateWeighted

accumulateProduct() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.accumulateProduct ( Mat src1, Mat src2, Mat dst, Mat mask )

static

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.

Parameters

src1	First input image, 1- or 3-channel, 8-bit or 32-bit floating point.
src2	Second input image of the same type and the same size as src1 .
dst	Accumulator image with the same number of channels as input images, 32-bit or 64-bit floating-point.
mask	Optional operation mask.

See also

accumulate, accumulateSquare, accumulateWeighted

accumulateSquare() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.accumulateSquare ( Mat src, Mat dst )

static

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.

Parameters

src	Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
dst	Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point.
mask	Optional operation mask.

See also

accumulateSquare, accumulateProduct, accumulateWeighted

accumulateSquare() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.accumulateSquare ( Mat src, Mat dst, Mat mask )

static

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.

Parameters

src	Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
dst	Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point.
mask	Optional operation mask.

See also

accumulateSquare, accumulateProduct, accumulateWeighted

accumulateWeighted() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.accumulateWeighted ( Mat src, Mat dst, double alpha )

static

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.

Parameters

src	Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
dst	Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point.
alpha	Weight of the input image.
mask	Optional operation mask.

See also

accumulate, accumulateSquare, accumulateProduct

accumulateWeighted() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.accumulateWeighted ( Mat src, Mat dst, double alpha, Mat mask )

static

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.

Parameters

src	Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
dst	Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point.
alpha	Weight of the input image.
mask	Optional operation mask.

See also

accumulate, accumulateSquare, accumulateProduct

adaptiveThreshold()

static void OpenCVForUnity.ImgprocModule.Imgproc.adaptiveThreshold ( Mat src, Mat dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C )

static

Applies an adaptive threshold to an array.

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

• THRESH_BINARY

  \[dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\]

• THRESH_BINARY_INV

  \[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).

The function can process the image in-place.

Parameters

src	Source 8-bit single-channel image.
dst	Destination image of the same size and the same type as src.
maxValue	Non-zero value assigned to the pixels for which the condition is satisfied
adaptiveMethod	Adaptive thresholding algorithm to use, see #AdaptiveThresholdTypes. The #BORDER_REPLICATE | #BORDER_ISOLATED is used to process boundaries.
thresholdType	Thresholding type that must be either THRESH_BINARY or THRESH_BINARY_INV, see #ThresholdTypes.
blockSize	Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on.
C	Constant subtracted from the mean or weighted mean (see the details below). Normally, it is positive but may be zero or negative as well.

See also

threshold, blur, GaussianBlur

applyColorMap() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.applyColorMap ( Mat src, Mat dst, int colormap )

static

Applies a GNU Octave/MATLAB equivalent colormap on a given image.

Parameters

src	The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. If CV_8UC3, then the CV_8UC1 image is generated internally using cv::COLOR_BGR2GRAY.
dst	The result is the colormapped source image. Note: Mat.create is called on dst.
colormap	The colormap to apply, see #ColormapTypes

applyColorMap() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.applyColorMap ( Mat src, Mat dst, Mat userColor )

static

Applies a user colormap on a given image.

Parameters

src	The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. If CV_8UC3, then the CV_8UC1 image is generated internally using cv::COLOR_BGR2GRAY.
dst	The result is the colormapped source image of the same number of channels as userColor. Note: Mat.create is called on dst.
userColor	The colormap to apply of type CV_8UC1 or CV_8UC3 and size 256

approxPolyDP()

static void OpenCVForUnity.ImgprocModule.Imgproc.approxPolyDP ( MatOfPoint2f curve, MatOfPoint2f approxCurve, double epsilon, bool closed )

static

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&gt;

Parameters

curve	Input vector of a 2D point stored in std::vector or Mat
approxCurve	Result of the approximation. The type should match the type of the input curve.
epsilon	Parameter specifying the approximation accuracy. This is the maximum distance between the original curve and its approximation.
closed	If true, the approximated curve is closed (its first and last vertices are connected). Otherwise, it is not closed.

arcLength()

static double OpenCVForUnity.ImgprocModule.Imgproc.arcLength ( MatOfPoint2f curve, bool closed )

static

Calculates a contour perimeter or a curve length.

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

Parameters

curve	Input vector of 2D points, stored in std::vector or Mat.
closed	Flag indicating whether the curve is closed or not.

arrowedLine() [1/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.arrowedLine ( Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color )

static

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.

Parameters

img	Image.
pt1	The point the arrow starts from.
pt2	The point the arrow points to.
color	Line color.
thickness	Line thickness.
line_type	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.
tipLength	The length of the arrow tip in relation to the arrow length

arrowedLine() [2/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.arrowedLine ( Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness )

static

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.

Parameters

img	Image.
pt1	The point the arrow starts from.
pt2	The point the arrow points to.
color	Line color.
thickness	Line thickness.
line_type	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.
tipLength	The length of the arrow tip in relation to the arrow length

arrowedLine() [3/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.arrowedLine ( Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness, int line_type )

static

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.

Parameters

img	Image.
pt1	The point the arrow starts from.
pt2	The point the arrow points to.
color	Line color.
thickness	Line thickness.
line_type	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.
tipLength	The length of the arrow tip in relation to the arrow length

arrowedLine() [4/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.arrowedLine ( Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness, int line_type, int shift )

static

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.

Parameters

img	Image.
pt1	The point the arrow starts from.
pt2	The point the arrow points to.
color	Line color.
thickness	Line thickness.
line_type	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.
tipLength	The length of the arrow tip in relation to the arrow length

arrowedLine() [5/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.arrowedLine ( Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness, int line_type, int shift, double tipLength )

static

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.

Parameters

img	Image.
pt1	The point the arrow starts from.
pt2	The point the arrow points to.
color	Line color.
thickness	Line thickness.
line_type	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.
tipLength	The length of the arrow tip in relation to the arrow length

arrowedLine() [6/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.arrowedLine ( Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color )

static

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.

Parameters

img	Image.
pt1	The point the arrow starts from.
pt2	The point the arrow points to.
color	Line color.
thickness	Line thickness.
line_type	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.
tipLength	The length of the arrow tip in relation to the arrow length

arrowedLine() [7/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.arrowedLine ( Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness )

static

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.

Parameters

img	Image.
pt1	The point the arrow starts from.
pt2	The point the arrow points to.
color	Line color.
thickness	Line thickness.
line_type	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.
tipLength	The length of the arrow tip in relation to the arrow length

arrowedLine() [8/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.arrowedLine ( Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness, int line_type )

static

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.

Parameters

img	Image.
pt1	The point the arrow starts from.
pt2	The point the arrow points to.
color	Line color.
thickness	Line thickness.
line_type	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.
tipLength	The length of the arrow tip in relation to the arrow length

arrowedLine() [9/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.arrowedLine ( Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness, int line_type, int shift )

static

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.

Parameters

img	Image.
pt1	The point the arrow starts from.
pt2	The point the arrow points to.
color	Line color.
thickness	Line thickness.
line_type	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.
tipLength	The length of the arrow tip in relation to the arrow length

arrowedLine() [10/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.arrowedLine ( Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness, int line_type, int shift, double tipLength )

static

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.

Parameters

img	Image.
pt1	The point the arrow starts from.
pt2	The point the arrow points to.
color	Line color.
thickness	Line thickness.
line_type	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.
tipLength	The length of the arrow tip in relation to the arrow length

arrowedLine() [11/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.arrowedLine ( Mat img, Point pt1, Point pt2, Scalar color )

static

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.

Parameters

img	Image.
pt1	The point the arrow starts from.
pt2	The point the arrow points to.
color	Line color.
thickness	Line thickness.
line_type	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.
tipLength	The length of the arrow tip in relation to the arrow length

arrowedLine() [12/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.arrowedLine ( Mat img, Point pt1, Point pt2, Scalar color, int thickness )

static

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.

Parameters

img	Image.
pt1	The point the arrow starts from.
pt2	The point the arrow points to.
color	Line color.
thickness	Line thickness.
line_type	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.
tipLength	The length of the arrow tip in relation to the arrow length

arrowedLine() [13/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.arrowedLine ( Mat img, Point pt1, Point pt2, Scalar color, int thickness, int line_type )

static

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.

Parameters

img	Image.
pt1	The point the arrow starts from.
pt2	The point the arrow points to.
color	Line color.
thickness	Line thickness.
line_type	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.
tipLength	The length of the arrow tip in relation to the arrow length

arrowedLine() [14/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.arrowedLine ( Mat img, Point pt1, Point pt2, Scalar color, int thickness, int line_type, int shift )

static

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.

Parameters

img	Image.
pt1	The point the arrow starts from.
pt2	The point the arrow points to.
color	Line color.
thickness	Line thickness.
line_type	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.
tipLength	The length of the arrow tip in relation to the arrow length

arrowedLine() [15/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.arrowedLine ( Mat img, Point pt1, Point pt2, Scalar color, int thickness, int line_type, int shift, double tipLength )

static

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.

Parameters

img	Image.
pt1	The point the arrow starts from.
pt2	The point the arrow points to.
color	Line color.
thickness	Line thickness.
line_type	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.
tipLength	The length of the arrow tip in relation to the arrow length

bilateralFilter() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.bilateralFilter ( Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace )

static

Applies the bilateral filter to an image.

The function applies bilateral filtering to the input image, as described in http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is very slow compared to most filters.

Sigma values: For simplicity, you can set the 2 sigma values to be the same. If they are small (< 10), the filter will not have much effect, whereas if they are large (> 150), they will have a very strong effect, making the image look "cartoonish".

Filter size: Large filters (d > 5) are very slow, so it is recommended to use d=5 for real-time applications, and perhaps d=9 for offline applications that need heavy noise filtering.

This filter does not work inplace.

Parameters

src	Source 8-bit or floating-point, 1-channel or 3-channel image.
dst	Destination image of the same size and type as src .
d	Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace.
sigmaColor	Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting in larger areas of semi-equal color.
sigmaSpace	Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is proportional to sigmaSpace.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes

bilateralFilter() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.bilateralFilter ( Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace, int borderType )

static

Applies the bilateral filter to an image.

The function applies bilateral filtering to the input image, as described in http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is very slow compared to most filters.

Sigma values: For simplicity, you can set the 2 sigma values to be the same. If they are small (< 10), the filter will not have much effect, whereas if they are large (> 150), they will have a very strong effect, making the image look "cartoonish".

Filter size: Large filters (d > 5) are very slow, so it is recommended to use d=5 for real-time applications, and perhaps d=9 for offline applications that need heavy noise filtering.

This filter does not work inplace.

Parameters

src	Source 8-bit or floating-point, 1-channel or 3-channel image.
dst	Destination image of the same size and type as src .
d	Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace.
sigmaColor	Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting in larger areas of semi-equal color.
sigmaSpace	Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is proportional to sigmaSpace.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes

blendLinear()

static void OpenCVForUnity.ImgprocModule.Imgproc.blendLinear ( Mat src1, Mat src2, Mat weights1, Mat weights2, Mat dst )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

variant without mask parameter

blur() [1/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.blur ( Mat src, Mat dst, in Vec2d ksize )

static

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

Parameters

src	input image; it can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter, bilateralFilter, GaussianBlur, medianBlur

blur() [2/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.blur ( Mat src, Mat dst, in Vec2d ksize, in Vec2d anchor )

static

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

Parameters

src	input image; it can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter, bilateralFilter, GaussianBlur, medianBlur

blur() [3/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.blur ( Mat src, Mat dst, in Vec2d ksize, in Vec2d anchor, int borderType )

static

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

Parameters

src	input image; it can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter, bilateralFilter, GaussianBlur, medianBlur

blur() [4/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.blur ( Mat src, Mat dst, in(double width, double height) ksize )

static

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

Parameters

src	input image; it can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter, bilateralFilter, GaussianBlur, medianBlur

blur() [5/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.blur ( Mat src, Mat dst, in(double width, double height) ksize, in(double x, double y) anchor )

static

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

Parameters

src	input image; it can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter, bilateralFilter, GaussianBlur, medianBlur

blur() [6/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.blur ( Mat src, Mat dst, in(double width, double height) ksize, in(double x, double y) anchor, int borderType )

static

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

Parameters

src	input image; it can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter, bilateralFilter, GaussianBlur, medianBlur

blur() [7/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.blur ( Mat src, Mat dst, Size ksize )

static

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

Parameters

src	input image; it can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter, bilateralFilter, GaussianBlur, medianBlur

blur() [8/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.blur ( Mat src, Mat dst, Size ksize, Point anchor )

static

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

Parameters

src	input image; it can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter, bilateralFilter, GaussianBlur, medianBlur

blur() [9/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.blur ( Mat src, Mat dst, Size ksize, Point anchor, int borderType )

static

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

Parameters

src	input image; it can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter, bilateralFilter, GaussianBlur, medianBlur

boundingRect()

static Rect OpenCVForUnity.ImgprocModule.Imgproc.boundingRect ( Mat array )

static

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.

Parameters

array

Input gray-scale image or 2D point set, stored in std::vector or Mat.

boundingRectAsValueTuple()

static int int int int height OpenCVForUnity.ImgprocModule.Imgproc.boundingRectAsValueTuple ( Mat array )

static

boundingRectAsVec4i()

static Vec4i OpenCVForUnity.ImgprocModule.Imgproc.boundingRectAsVec4i ( Mat array )

static

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.

Parameters

array

Input gray-scale image or 2D point set, stored in std::vector or Mat.

boxFilter() [1/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.boxFilter ( Mat src, Mat dst, int ddepth, in Vec2d ksize )

static

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.

Parameters

src	input image.
dst	output image of the same size and type as src.
ddepth	the output image depth (-1 to use src.depth()).
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is normalized by its area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxFilter() [2/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.boxFilter ( Mat src, Mat dst, int ddepth, in Vec2d ksize, in Vec2d anchor )

static

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.

Parameters

src	input image.
dst	output image of the same size and type as src.
ddepth	the output image depth (-1 to use src.depth()).
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is normalized by its area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxFilter() [3/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.boxFilter ( Mat src, Mat dst, int ddepth, in Vec2d ksize, in Vec2d anchor, bool normalize )

static

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.

Parameters

src	input image.
dst	output image of the same size and type as src.
ddepth	the output image depth (-1 to use src.depth()).
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is normalized by its area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxFilter() [4/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.boxFilter ( Mat src, Mat dst, int ddepth, in Vec2d ksize, in Vec2d anchor, bool normalize, int borderType )

static

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.

Parameters

src	input image.
dst	output image of the same size and type as src.
ddepth	the output image depth (-1 to use src.depth()).
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is normalized by its area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxFilter() [5/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.boxFilter ( Mat src, Mat dst, int ddepth, in(double width, double height) ksize )

static

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.

Parameters

src	input image.
dst	output image of the same size and type as src.
ddepth	the output image depth (-1 to use src.depth()).
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is normalized by its area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxFilter() [6/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.boxFilter ( Mat src, Mat dst, int ddepth, in(double width, double height) ksize, in(double x, double y) anchor )

static

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.

Parameters

src	input image.
dst	output image of the same size and type as src.
ddepth	the output image depth (-1 to use src.depth()).
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is normalized by its area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxFilter() [7/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.boxFilter ( Mat src, Mat dst, int ddepth, in(double width, double height) ksize, in(double x, double y) anchor, bool normalize )

static

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.

Parameters

src	input image.
dst	output image of the same size and type as src.
ddepth	the output image depth (-1 to use src.depth()).
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is normalized by its area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxFilter() [8/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.boxFilter ( Mat src, Mat dst, int ddepth, in(double width, double height) ksize, in(double x, double y) anchor, bool normalize, int borderType )

static

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.

Parameters

src	input image.
dst	output image of the same size and type as src.
ddepth	the output image depth (-1 to use src.depth()).
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is normalized by its area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxFilter() [9/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.boxFilter ( Mat src, Mat dst, int ddepth, Size ksize )

static

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.

Parameters

src	input image.
dst	output image of the same size and type as src.
ddepth	the output image depth (-1 to use src.depth()).
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is normalized by its area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxFilter() [10/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.boxFilter ( Mat src, Mat dst, int ddepth, Size ksize, Point anchor )

static

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.

Parameters

src	input image.
dst	output image of the same size and type as src.
ddepth	the output image depth (-1 to use src.depth()).
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is normalized by its area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxFilter() [11/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.boxFilter ( Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize )

static

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.

Parameters

src	input image.
dst	output image of the same size and type as src.
ddepth	the output image depth (-1 to use src.depth()).
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is normalized by its area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxFilter() [12/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.boxFilter ( Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize, int borderType )

static

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.

Parameters

src	input image.
dst	output image of the same size and type as src.
ddepth	the output image depth (-1 to use src.depth()).
ksize	blurring kernel size.
anchor	anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is normalized by its area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxPoints() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.boxPoints ( in Vec5d box, Mat points )

static

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 tutorial on Creating Bounding rotated boxes and ellipses for contours for more information.

Parameters

box	The input rotated rectangle. It may be the output of minAreaRect.
points	The output array of four vertices of rectangles.

boxPoints() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.boxPoints ( in(double x, double y, double width, double height, double angle) box, Mat points )

static

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 tutorial on Creating Bounding rotated boxes and ellipses for contours for more information.

Parameters

box	The input rotated rectangle. It may be the output of minAreaRect.
points	The output array of four vertices of rectangles.

boxPoints() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.boxPoints ( RotatedRect box, Mat points )

static

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 tutorial on Creating Bounding rotated boxes and ellipses for contours for more information.

Parameters

box	The input rotated rectangle. It may be the output of minAreaRect.
points	The output array of four vertices of rectangles.

calcBackProject()

static void OpenCVForUnity.ImgprocModule.Imgproc.calcBackProject ( List< Mat > images, MatOfInt channels, Mat hist, Mat dst, MatOfFloat ranges, double scale )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

calcHist() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.calcHist ( List< Mat > images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

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.

calcHist() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.calcHist ( List< Mat > images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges, bool accumulate )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

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.

Canny() [1/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.Canny ( Mat dx, Mat dy, Mat edges, double threshold1, double threshold2 )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Finds edges in an image using the Canny algorithm with custom image gradient.

Parameters

dx	16-bit x derivative of input image (CV_16SC1 or CV_16SC3).
dy	16-bit y derivative of input image (same type as dx).
edges	output edge map; single channels 8-bit image, which has the same size as image .
threshold1	first threshold for the hysteresis procedure.
threshold2	second threshold for the hysteresis procedure.
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 ).

Canny() [2/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.Canny ( Mat dx, Mat dy, Mat edges, double threshold1, double threshold2, bool L2gradient )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Finds edges in an image using the Canny algorithm with custom image gradient.

Parameters

dx	16-bit x derivative of input image (CV_16SC1 or CV_16SC3).
dy	16-bit y derivative of input image (same type as dx).
edges	output edge map; single channels 8-bit image, which has the same size as image .
threshold1	first threshold for the hysteresis procedure.
threshold2	second threshold for the hysteresis procedure.
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 ).

Canny() [3/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.Canny ( Mat image, Mat edges, double threshold1, double threshold2 )

static

Finds edges in an image using the Canny algorithm [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&gt;

Parameters

image	8-bit input image.
edges	output edge map; single channels 8-bit image, which has the same size as image .
threshold1	first threshold for the hysteresis procedure.
threshold2	second threshold for the hysteresis procedure.
apertureSize	aperture size for the Sobel operator.
L2gradient	a flag, indicating whether a more accurate \(L_2\) norm \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) should be used to calculate the image gradient magnitude ( L2gradient=true ), or whether the default \(L_1\) norm \(=|dI/dx|+|dI/dy|\) is enough ( L2gradient=false ).

Canny() [4/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.Canny ( Mat image, Mat edges, double threshold1, double threshold2, int apertureSize )

static

Finds edges in an image using the Canny algorithm [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&gt;

Parameters

image	8-bit input image.
edges	output edge map; single channels 8-bit image, which has the same size as image .
threshold1	first threshold for the hysteresis procedure.
threshold2	second threshold for the hysteresis procedure.
apertureSize	aperture size for the Sobel operator.
L2gradient	a flag, indicating whether a more accurate \(L_2\) norm \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) should be used to calculate the image gradient magnitude ( L2gradient=true ), or whether the default \(L_1\) norm \(=|dI/dx|+|dI/dy|\) is enough ( L2gradient=false ).

Canny() [5/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.Canny ( Mat image, Mat edges, double threshold1, double threshold2, int apertureSize, bool L2gradient )

static

Finds edges in an image using the Canny algorithm [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&gt;

Parameters

image	8-bit input image.
edges	output edge map; single channels 8-bit image, which has the same size as image .
threshold1	first threshold for the hysteresis procedure.
threshold2	second threshold for the hysteresis procedure.
apertureSize	aperture size for the Sobel operator.
L2gradient	a flag, indicating whether a more accurate \(L_2\) norm \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) should be used to calculate the image gradient magnitude ( L2gradient=true ), or whether the default \(L_1\) norm \(=|dI/dx|+|dI/dy|\) is enough ( L2gradient=false ).

circle() [1/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.circle ( Mat img, in Vec2d center, int radius, in Vec4d color )

static

Draws a circle.

The function cv::circle draws a simple or filled circle with a given center and radius.

Parameters

img	Image where the circle is drawn.
center	Center of the circle.
radius	Radius of the circle.
color	Circle color.
thickness	Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn.
lineType	Type of the circle boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and in the radius value.

circle() [2/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.circle ( Mat img, in Vec2d center, int radius, in Vec4d color, int thickness )

static

Draws a circle.

The function cv::circle draws a simple or filled circle with a given center and radius.

Parameters

img	Image where the circle is drawn.
center	Center of the circle.
radius	Radius of the circle.
color	Circle color.
thickness	Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn.
lineType	Type of the circle boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and in the radius value.

circle() [3/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.circle ( Mat img, in Vec2d center, int radius, in Vec4d color, int thickness, int lineType )

static

Draws a circle.

The function cv::circle draws a simple or filled circle with a given center and radius.

Parameters

img	Image where the circle is drawn.
center	Center of the circle.
radius	Radius of the circle.
color	Circle color.
thickness	Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn.
lineType	Type of the circle boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and in the radius value.

circle() [4/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.circle ( Mat img, in Vec2d center, int radius, in Vec4d color, int thickness, int lineType, int shift )

static

Draws a circle.

The function cv::circle draws a simple or filled circle with a given center and radius.

Parameters

img	Image where the circle is drawn.
center	Center of the circle.
radius	Radius of the circle.
color	Circle color.
thickness	Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn.
lineType	Type of the circle boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and in the radius value.

circle() [5/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.circle ( Mat img, in(double x, double y) center, int radius, in(double v0, double v1, double v2, double v3) color )

static

Draws a circle.

The function cv::circle draws a simple or filled circle with a given center and radius.

Parameters

img	Image where the circle is drawn.
center	Center of the circle.
radius	Radius of the circle.
color	Circle color.
thickness	Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn.
lineType	Type of the circle boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and in the radius value.

circle() [6/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.circle ( Mat img, in(double x, double y) center, int radius, in(double v0, double v1, double v2, double v3) color, int thickness )

static

Draws a circle.

The function cv::circle draws a simple or filled circle with a given center and radius.

Parameters

img	Image where the circle is drawn.
center	Center of the circle.
radius	Radius of the circle.
color	Circle color.
thickness	Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn.
lineType	Type of the circle boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and in the radius value.

circle() [7/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.circle ( Mat img, in(double x, double y) center, int radius, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType )

static

Draws a circle.

The function cv::circle draws a simple or filled circle with a given center and radius.

Parameters

img	Image where the circle is drawn.
center	Center of the circle.
radius	Radius of the circle.
color	Circle color.
thickness	Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn.
lineType	Type of the circle boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and in the radius value.

circle() [8/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.circle ( Mat img, in(double x, double y) center, int radius, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, int shift )

static

Draws a circle.

The function cv::circle draws a simple or filled circle with a given center and radius.

Parameters

img	Image where the circle is drawn.
center	Center of the circle.
radius	Radius of the circle.
color	Circle color.
thickness	Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn.
lineType	Type of the circle boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and in the radius value.

circle() [9/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.circle ( Mat img, Point center, int radius, Scalar color )

static

Draws a circle.

The function cv::circle draws a simple or filled circle with a given center and radius.

Parameters

img	Image where the circle is drawn.
center	Center of the circle.
radius	Radius of the circle.
color	Circle color.
thickness	Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn.
lineType	Type of the circle boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and in the radius value.

circle() [10/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.circle ( Mat img, Point center, int radius, Scalar color, int thickness )

static

Draws a circle.

The function cv::circle draws a simple or filled circle with a given center and radius.

Parameters

img	Image where the circle is drawn.
center	Center of the circle.
radius	Radius of the circle.
color	Circle color.
thickness	Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn.
lineType	Type of the circle boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and in the radius value.

circle() [11/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.circle ( Mat img, Point center, int radius, Scalar color, int thickness, int lineType )

static

Draws a circle.

The function cv::circle draws a simple or filled circle with a given center and radius.

Parameters

img	Image where the circle is drawn.
center	Center of the circle.
radius	Radius of the circle.
color	Circle color.
thickness	Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn.
lineType	Type of the circle boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and in the radius value.

circle() [12/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.circle ( Mat img, Point center, int radius, Scalar color, int thickness, int lineType, int shift )

static

Draws a circle.

The function cv::circle draws a simple or filled circle with a given center and radius.

Parameters

img	Image where the circle is drawn.
center	Center of the circle.
radius	Radius of the circle.
color	Circle color.
thickness	Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn.
lineType	Type of the circle boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and in the radius value.

clipLine() [1/3]

static bool OpenCVForUnity.ImgprocModule.Imgproc.clipLine ( in Vec4i imgRect, ref Vec2d pt1, ref Vec2d pt2 )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

imgRect	Image rectangle.
pt1	First line point.
pt2	Second line point.

clipLine() [2/3]

static bool OpenCVForUnity.ImgprocModule.Imgproc.clipLine ( in(int x, int y, int width, int height) imgRect, ref(double x, double y) pt1, ref(double x, double y) pt2 )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

imgRect	Image rectangle.
pt1	First line point.
pt2	Second line point.

clipLine() [3/3]

static bool OpenCVForUnity.ImgprocModule.Imgproc.clipLine ( Rect imgRect, Point pt1, Point pt2 )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

imgRect	Image rectangle.
pt1	First line point.
pt2	Second line point.

compareHist()

static double OpenCVForUnity.ImgprocModule.Imgproc.compareHist ( Mat H1, Mat H2, int method )

static

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.

Parameters

H1	First compared histogram.
H2	Second compared histogram of the same size as H1 .
method	Comparison method, see #HistCompMethods

connectedComponents() [1/3]

static int OpenCVForUnity.ImgprocModule.Imgproc.connectedComponents ( Mat image, Mat labels )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

image	the 8-bit single-channel image to be labeled
labels	destination labeled image
connectivity	8 or 4 for 8-way or 4-way connectivity respectively
ltype	output image label type. Currently CV_32S and CV_16U are supported.

connectedComponents() [2/3]

static int OpenCVForUnity.ImgprocModule.Imgproc.connectedComponents ( Mat image, Mat labels, int connectivity )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

image	the 8-bit single-channel image to be labeled
labels	destination labeled image
connectivity	8 or 4 for 8-way or 4-way connectivity respectively
ltype	output image label type. Currently CV_32S and CV_16U are supported.

connectedComponents() [3/3]

static int OpenCVForUnity.ImgprocModule.Imgproc.connectedComponents ( Mat image, Mat labels, int connectivity, int ltype )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

image	the 8-bit single-channel image to be labeled
labels	destination labeled image
connectivity	8 or 4 for 8-way or 4-way connectivity respectively
ltype	output image label type. Currently CV_32S and CV_16U are supported.

connectedComponentsWithAlgorithm()

static int OpenCVForUnity.ImgprocModule.Imgproc.connectedComponentsWithAlgorithm ( Mat image, Mat labels, int connectivity, int ltype, int ccltype )

static

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) [Bolelli2019], Grana (BBDT) [Grana2010] and Wu's (SAUF) [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.

Parameters

image	the 8-bit single-channel image to be labeled
labels	destination labeled image
connectivity	8 or 4 for 8-way or 4-way connectivity respectively
ltype	output image label type. Currently CV_32S and CV_16U are supported.
ccltype	connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes).

connectedComponentsWithStats() [1/3]

static int OpenCVForUnity.ImgprocModule.Imgproc.connectedComponentsWithStats ( Mat image, Mat labels, Mat stats, Mat centroids )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

image	the 8-bit single-channel image to be labeled
labels	destination labeled image
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.
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.
connectivity	8 or 4 for 8-way or 4-way connectivity respectively
ltype	output image label type. Currently CV_32S and CV_16U are supported.

connectedComponentsWithStats() [2/3]

static int OpenCVForUnity.ImgprocModule.Imgproc.connectedComponentsWithStats ( Mat image, Mat labels, Mat stats, Mat centroids, int connectivity )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

image	the 8-bit single-channel image to be labeled
labels	destination labeled image
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.
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.
connectivity	8 or 4 for 8-way or 4-way connectivity respectively
ltype	output image label type. Currently CV_32S and CV_16U are supported.

connectedComponentsWithStats() [3/3]

static int OpenCVForUnity.ImgprocModule.Imgproc.connectedComponentsWithStats ( Mat image, Mat labels, Mat stats, Mat centroids, int connectivity, int ltype )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

image	the 8-bit single-channel image to be labeled
labels	destination labeled image
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.
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.
connectivity	8 or 4 for 8-way or 4-way connectivity respectively
ltype	output image label type. Currently CV_32S and CV_16U are supported.

connectedComponentsWithStatsWithAlgorithm()

static int OpenCVForUnity.ImgprocModule.Imgproc.connectedComponentsWithStatsWithAlgorithm ( Mat image, Mat labels, Mat stats, Mat centroids, int connectivity, int ltype, int ccltype )

static

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) [Bolelli2019], Grana (BBDT) [Grana2010] and Wu's (SAUF) [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.

Parameters

image	the 8-bit single-channel image to be labeled
labels	destination labeled image
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.
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.
connectivity	8 or 4 for 8-way or 4-way connectivity respectively
ltype	output image label type. Currently CV_32S and CV_16U are supported.
ccltype	connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes).

contourArea() [1/2]

static double OpenCVForUnity.ImgprocModule.Imgproc.contourArea ( Mat contour )

static

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:

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;

Parameters

contour	Input vector of 2D points (contour vertices), stored in std::vector or Mat.
oriented	Oriented area flag. If it is true, the function returns a signed area value, depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can determine orientation of a contour by taking the sign of an area. By default, the parameter is false, which means that the absolute value is returned.

contourArea() [2/2]

static double OpenCVForUnity.ImgprocModule.Imgproc.contourArea ( Mat contour, bool oriented )

static

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:

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;

Parameters

contour	Input vector of 2D points (contour vertices), stored in std::vector or Mat.
oriented	Oriented area flag. If it is true, the function returns a signed area value, depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can determine orientation of a contour by taking the sign of an area. By default, the parameter is false, which means that the absolute value is returned.

convertMaps() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.convertMaps ( Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type )

static

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:

• \(\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). 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.
• \(\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). The same as above but the original maps are stored in one 2-channel matrix.
• Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same as the originals.

Parameters

map1	The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 .
map2	The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), respectively.
dstmap1	The first output map that has the type dstmap1type and the same size as src .
dstmap2	The second output map.
dstmap1type	Type of the first output map that should be CV_16SC2, CV_32FC1, or CV_32FC2 .
nninterpolation	Flag indicating whether the fixed-point maps are used for the nearest-neighbor or for a more complex interpolation.

See also

remap, undistort, initUndistortRectifyMap

convertMaps() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.convertMaps ( Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type, bool nninterpolation )

static

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:

• \(\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). 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.
• \(\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). The same as above but the original maps are stored in one 2-channel matrix.
• Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same as the originals.

Parameters

map1	The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 .
map2	The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), respectively.
dstmap1	The first output map that has the type dstmap1type and the same size as src .
dstmap2	The second output map.
dstmap1type	Type of the first output map that should be CV_16SC2, CV_32FC1, or CV_32FC2 .
nninterpolation	Flag indicating whether the fixed-point maps are used for the nearest-neighbor or for a more complex interpolation.

See also

remap, undistort, initUndistortRectifyMap

convexHull() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.convexHull ( MatOfPoint points, MatOfInt hull )

static

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 [Sklansky82] that has O(N logN) complexity in the current implementation.

Parameters

points	Input 2D point set, stored in std::vector or Mat.
hull	Output convex hull. It is either an integer vector of indices or vector of points. In the first case, the hull elements are 0-based indices of the convex hull points in the original array (since the set of convex hull points is a subset of the original point set). In the second case, hull elements are the convex hull points themselves.
clockwise	Orientation flag. If it is true, the output convex hull is oriented clockwise. Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing to the right, and its Y axis pointing upwards.
returnPoints	Operation flag. In case of a matrix, when the flag is true, the function returns convex hull points. Otherwise, it returns indices of the convex hull points. When the output array is std::vector, the flag is ignored, and the output depends on the type of the vector: std::vector<int> implies returnPoints=false, std::vector<Point> implies returnPoints=true.

Note

points and hull should be different arrays, inplace processing isn't supported.

Check the corresponding tutorial for more details.

useful links:

https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/

convexHull() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.convexHull ( MatOfPoint points, MatOfInt hull, bool clockwise )

static

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 [Sklansky82] that has O(N logN) complexity in the current implementation.

Parameters

points	Input 2D point set, stored in std::vector or Mat.
hull	Output convex hull. It is either an integer vector of indices or vector of points. In the first case, the hull elements are 0-based indices of the convex hull points in the original array (since the set of convex hull points is a subset of the original point set). In the second case, hull elements are the convex hull points themselves.
clockwise	Orientation flag. If it is true, the output convex hull is oriented clockwise. Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing to the right, and its Y axis pointing upwards.
returnPoints	Operation flag. In case of a matrix, when the flag is true, the function returns convex hull points. Otherwise, it returns indices of the convex hull points. When the output array is std::vector, the flag is ignored, and the output depends on the type of the vector: std::vector<int> implies returnPoints=false, std::vector<Point> implies returnPoints=true.

Note

points and hull should be different arrays, inplace processing isn't supported.

Check the corresponding tutorial for more details.

useful links:

https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/

convexityDefects()

static void OpenCVForUnity.ImgprocModule.Imgproc.convexityDefects ( MatOfPoint contour, MatOfInt convexhull, MatOfInt4 convexityDefects )

static

Finds the convexity defects of a contour.

The figure below displays convexity defects of a hand contour:

Parameters

contour	Input contour.
convexhull	Convex hull obtained using convexHull that should contain indices of the contour points that make the hull.
convexityDefects	The output vector of convexity defects. In C++ and the new Python/Java interface each convexity defect is represented as 4-element integer vector (a.k.a. #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.

cornerEigenValsAndVecs() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.cornerEigenValsAndVecs ( Mat src, Mat dst, int blockSize, int ksize )

static

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

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

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

Parameters

src	Input single-channel 8-bit or floating-point image.
dst	Image to store the results. It has the same size as src and the type CV_32FC(6) .
blockSize	Neighborhood size (see details below).
ksize	Aperture parameter for the Sobel operator.
borderType	Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.

See also

cornerMinEigenVal, cornerHarris, preCornerDetect

cornerEigenValsAndVecs() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.cornerEigenValsAndVecs ( Mat src, Mat dst, int blockSize, int ksize, int borderType )

static

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

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

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

Parameters

src	Input single-channel 8-bit or floating-point image.
dst	Image to store the results. It has the same size as src and the type CV_32FC(6) .
blockSize	Neighborhood size (see details below).
ksize	Aperture parameter for the Sobel operator.
borderType	Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.

See also

cornerMinEigenVal, cornerHarris, preCornerDetect

cornerHarris() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.cornerHarris ( Mat src, Mat dst, int blockSize, int ksize, double k )

static

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.

Parameters

src	Input single-channel 8-bit or floating-point image.
dst	Image to store the Harris detector responses. It has the type CV_32FC1 and the same size as src .
blockSize	Neighborhood size (see the details on cornerEigenValsAndVecs ).
ksize	Aperture parameter for the Sobel operator.
k	Harris detector free parameter. See the formula above.
borderType	Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.

cornerHarris() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.cornerHarris ( Mat src, Mat dst, int blockSize, int ksize, double k, int borderType )

static

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.

Parameters

src	Input single-channel 8-bit or floating-point image.
dst	Image to store the Harris detector responses. It has the type CV_32FC1 and the same size as src .
blockSize	Neighborhood size (see the details on cornerEigenValsAndVecs ).
ksize	Aperture parameter for the Sobel operator.
k	Harris detector free parameter. See the formula above.
borderType	Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.

cornerMinEigenVal() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.cornerMinEigenVal ( Mat src, Mat dst, int blockSize )

static

Calculates the minimal eigenvalue of gradient matrices for corner detection.

The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is, \(\min(\lambda_1, \lambda_2)\) in terms of the formulae in the cornerEigenValsAndVecs description.

Parameters

src	Input single-channel 8-bit or floating-point image.
dst	Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as src .
blockSize	Neighborhood size (see the details on cornerEigenValsAndVecs ).
ksize	Aperture parameter for the Sobel operator.
borderType	Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.

cornerMinEigenVal() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.cornerMinEigenVal ( Mat src, Mat dst, int blockSize, int ksize )

static

Calculates the minimal eigenvalue of gradient matrices for corner detection.

The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is, \(\min(\lambda_1, \lambda_2)\) in terms of the formulae in the cornerEigenValsAndVecs description.

Parameters

src	Input single-channel 8-bit or floating-point image.
dst	Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as src .
blockSize	Neighborhood size (see the details on cornerEigenValsAndVecs ).
ksize	Aperture parameter for the Sobel operator.
borderType	Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.

cornerMinEigenVal() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.cornerMinEigenVal ( Mat src, Mat dst, int blockSize, int ksize, int borderType )

static

Calculates the minimal eigenvalue of gradient matrices for corner detection.

The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is, \(\min(\lambda_1, \lambda_2)\) in terms of the formulae in the cornerEigenValsAndVecs description.

Parameters

src	Input single-channel 8-bit or floating-point image.
dst	Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as src .
blockSize	Neighborhood size (see the details on cornerEigenValsAndVecs ).
ksize	Aperture parameter for the Sobel operator.
borderType	Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.

cornerSubPix() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.cornerSubPix ( Mat image, Mat corners, in Vec2d winSize, in Vec2d zeroZone, in Vec3d criteria )

static

Refines the corner locations.

The function iterates to find the sub-pixel accurate location of corners or radial saddle points as described in [forstner1987fast], and as shown on the figure below.

image

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.

Parameters

image	Input single-channel, 8-bit or float image.
corners	Initial coordinates of the input corners and refined coordinates provided for output.
winSize	Half of the side length of the search window. For example, if winSize=Size(5,5) , then a \((5*2+1) \times (5*2+1) = 11 \times 11\) search window is used.
zeroZone	Half of the size of the dead region in the middle of the search zone over which the summation in the formula below is not done. It is used sometimes to avoid possible singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such a size.
criteria	Criteria for termination of the iterative process of corner refinement. That is, the process of corner position refinement stops either after criteria.maxCount iterations or when the corner position moves by less than criteria.epsilon on some iteration.

cornerSubPix() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.cornerSubPix ( Mat image, Mat corners, in(double width, double height) winSize, in(double width, double height) zeroZone, in(double type, double maxCount, double epsilon) criteria )

static

Refines the corner locations.

The function iterates to find the sub-pixel accurate location of corners or radial saddle points as described in [forstner1987fast], and as shown on the figure below.

image

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.

Parameters

image	Input single-channel, 8-bit or float image.
corners	Initial coordinates of the input corners and refined coordinates provided for output.
winSize	Half of the side length of the search window. For example, if winSize=Size(5,5) , then a \((5*2+1) \times (5*2+1) = 11 \times 11\) search window is used.
zeroZone	Half of the size of the dead region in the middle of the search zone over which the summation in the formula below is not done. It is used sometimes to avoid possible singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such a size.
criteria	Criteria for termination of the iterative process of corner refinement. That is, the process of corner position refinement stops either after criteria.maxCount iterations or when the corner position moves by less than criteria.epsilon on some iteration.

cornerSubPix() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.cornerSubPix ( Mat image, Mat corners, Size winSize, Size zeroZone, TermCriteria criteria )

static

Refines the corner locations.

The function iterates to find the sub-pixel accurate location of corners or radial saddle points as described in [forstner1987fast], and as shown on the figure below.

image

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.

Parameters

image	Input single-channel, 8-bit or float image.
corners	Initial coordinates of the input corners and refined coordinates provided for output.
winSize	Half of the side length of the search window. For example, if winSize=Size(5,5) , then a \((5*2+1) \times (5*2+1) = 11 \times 11\) search window is used.
zeroZone	Half of the size of the dead region in the middle of the search zone over which the summation in the formula below is not done. It is used sometimes to avoid possible singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such a size.
criteria	Criteria for termination of the iterative process of corner refinement. That is, the process of corner position refinement stops either after criteria.maxCount iterations or when the corner position moves by less than criteria.epsilon on some iteration.

createCLAHE() [1/5]

static CLAHE OpenCVForUnity.ImgprocModule.Imgproc.createCLAHE ( )

static

Creates a smart pointer to a cv::CLAHE class and initializes it.

Parameters

clipLimit	Threshold for contrast limiting.
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.

createCLAHE() [2/5]

static CLAHE OpenCVForUnity.ImgprocModule.Imgproc.createCLAHE ( double clipLimit )

static

Creates a smart pointer to a cv::CLAHE class and initializes it.

Parameters

clipLimit	Threshold for contrast limiting.
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.

createCLAHE() [3/5]

static CLAHE OpenCVForUnity.ImgprocModule.Imgproc.createCLAHE ( double clipLimit, in Vec2d tileGridSize )

static

Creates a smart pointer to a cv::CLAHE class and initializes it.

Parameters

clipLimit	Threshold for contrast limiting.
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.

createCLAHE() [4/5]

static CLAHE OpenCVForUnity.ImgprocModule.Imgproc.createCLAHE ( double clipLimit, in(double width, double height) tileGridSize )

static

Creates a smart pointer to a cv::CLAHE class and initializes it.

Parameters

clipLimit	Threshold for contrast limiting.
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.

createCLAHE() [5/5]

static CLAHE OpenCVForUnity.ImgprocModule.Imgproc.createCLAHE ( double clipLimit, Size tileGridSize )

static

Creates a smart pointer to a cv::CLAHE class and initializes it.

Parameters

clipLimit	Threshold for contrast limiting.
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.

createGeneralizedHoughBallard()

static GeneralizedHoughBallard OpenCVForUnity.ImgprocModule.Imgproc.createGeneralizedHoughBallard ( )

static

Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it.

createGeneralizedHoughGuil()

static GeneralizedHoughGuil OpenCVForUnity.ImgprocModule.Imgproc.createGeneralizedHoughGuil ( )

static

Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it.

createHanningWindow() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.createHanningWindow ( Mat dst, in Vec2d winSize, int type )

static

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:

// create hanning window of size 100x100 and type CV_32F
Mat hann;
createHanningWindow(hann, Size(100, 100), CV_32F);

Parameters

dst	Destination array to place Hann coefficients in
winSize	The window size specifications (both width and height must be > 1)
type	Created array type

createHanningWindow() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.createHanningWindow ( Mat dst, in(double width, double height) winSize, int type )

static

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:

// create hanning window of size 100x100 and type CV_32F
Mat hann;
createHanningWindow(hann, Size(100, 100), CV_32F);

Parameters

dst	Destination array to place Hann coefficients in
winSize	The window size specifications (both width and height must be > 1)
type	Created array type

createHanningWindow() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.createHanningWindow ( Mat dst, Size winSize, int type )

static

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:

// create hanning window of size 100x100 and type CV_32F
Mat hann;
createHanningWindow(hann, Size(100, 100), CV_32F);

Parameters

dst	Destination array to place Hann coefficients in
winSize	The window size specifications (both width and height must be > 1)
type	Created array type

createLineSegmentDetector() [1/9]

static LineSegmentDetector OpenCVForUnity.ImgprocModule.Imgproc.createLineSegmentDetector ( )

static

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.

Parameters

refine	The way found lines will be refined, see #LineSegmentDetectorModes
scale	The scale of the image that will be used to find the lines. Range (0..1].
sigma_scale	Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
quant	Bound to the quantization error on the gradient norm.
ang_th	Gradient angle tolerance in degrees.
log_eps	Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen.
density_th	Minimal density of aligned region points in the enclosing rectangle.
n_bins	Number of bins in pseudo-ordering of gradient modulus.

createLineSegmentDetector() [2/9]

static LineSegmentDetector OpenCVForUnity.ImgprocModule.Imgproc.createLineSegmentDetector ( int refine )

static

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.

Parameters

refine	The way found lines will be refined, see #LineSegmentDetectorModes
scale	The scale of the image that will be used to find the lines. Range (0..1].
sigma_scale	Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
quant	Bound to the quantization error on the gradient norm.
ang_th	Gradient angle tolerance in degrees.
log_eps	Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen.
density_th	Minimal density of aligned region points in the enclosing rectangle.
n_bins	Number of bins in pseudo-ordering of gradient modulus.

createLineSegmentDetector() [3/9]

static LineSegmentDetector OpenCVForUnity.ImgprocModule.Imgproc.createLineSegmentDetector ( int refine, double scale )

static

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.

Parameters

refine	The way found lines will be refined, see #LineSegmentDetectorModes
scale	The scale of the image that will be used to find the lines. Range (0..1].
sigma_scale	Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
quant	Bound to the quantization error on the gradient norm.
ang_th	Gradient angle tolerance in degrees.
log_eps	Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen.
density_th	Minimal density of aligned region points in the enclosing rectangle.
n_bins	Number of bins in pseudo-ordering of gradient modulus.

createLineSegmentDetector() [4/9]

static LineSegmentDetector OpenCVForUnity.ImgprocModule.Imgproc.createLineSegmentDetector ( int refine, double scale, double sigma_scale )

static

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.

Parameters

refine	The way found lines will be refined, see #LineSegmentDetectorModes
scale	The scale of the image that will be used to find the lines. Range (0..1].
sigma_scale	Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
quant	Bound to the quantization error on the gradient norm.
ang_th	Gradient angle tolerance in degrees.
log_eps	Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen.
density_th	Minimal density of aligned region points in the enclosing rectangle.
n_bins	Number of bins in pseudo-ordering of gradient modulus.

createLineSegmentDetector() [5/9]

static LineSegmentDetector OpenCVForUnity.ImgprocModule.Imgproc.createLineSegmentDetector ( int refine, double scale, double sigma_scale, double quant )

static

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.

Parameters

refine	The way found lines will be refined, see #LineSegmentDetectorModes
scale	The scale of the image that will be used to find the lines. Range (0..1].
sigma_scale	Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
quant	Bound to the quantization error on the gradient norm.
ang_th	Gradient angle tolerance in degrees.
log_eps	Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen.
density_th	Minimal density of aligned region points in the enclosing rectangle.
n_bins	Number of bins in pseudo-ordering of gradient modulus.

createLineSegmentDetector() [6/9]

static LineSegmentDetector OpenCVForUnity.ImgprocModule.Imgproc.createLineSegmentDetector ( int refine, double scale, double sigma_scale, double quant, double ang_th )

static

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.

Parameters

refine	The way found lines will be refined, see #LineSegmentDetectorModes
scale	The scale of the image that will be used to find the lines. Range (0..1].
sigma_scale	Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
quant	Bound to the quantization error on the gradient norm.
ang_th	Gradient angle tolerance in degrees.
log_eps	Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen.
density_th	Minimal density of aligned region points in the enclosing rectangle.
n_bins	Number of bins in pseudo-ordering of gradient modulus.

createLineSegmentDetector() [7/9]

static LineSegmentDetector OpenCVForUnity.ImgprocModule.Imgproc.createLineSegmentDetector ( int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps )

static

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.

Parameters

refine	The way found lines will be refined, see #LineSegmentDetectorModes
scale	The scale of the image that will be used to find the lines. Range (0..1].
sigma_scale	Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
quant	Bound to the quantization error on the gradient norm.
ang_th	Gradient angle tolerance in degrees.
log_eps	Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen.
density_th	Minimal density of aligned region points in the enclosing rectangle.
n_bins	Number of bins in pseudo-ordering of gradient modulus.

createLineSegmentDetector() [8/9]

static LineSegmentDetector OpenCVForUnity.ImgprocModule.Imgproc.createLineSegmentDetector ( int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps, double density_th )

static

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.

Parameters

refine	The way found lines will be refined, see #LineSegmentDetectorModes
scale	The scale of the image that will be used to find the lines. Range (0..1].
sigma_scale	Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
quant	Bound to the quantization error on the gradient norm.
ang_th	Gradient angle tolerance in degrees.
log_eps	Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen.
density_th	Minimal density of aligned region points in the enclosing rectangle.
n_bins	Number of bins in pseudo-ordering of gradient modulus.

createLineSegmentDetector() [9/9]

static LineSegmentDetector OpenCVForUnity.ImgprocModule.Imgproc.createLineSegmentDetector ( int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps, double density_th, int n_bins )

static

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.

Parameters

refine	The way found lines will be refined, see #LineSegmentDetectorModes
scale	The scale of the image that will be used to find the lines. Range (0..1].
sigma_scale	Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
quant	Bound to the quantization error on the gradient norm.
ang_th	Gradient angle tolerance in degrees.
log_eps	Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen.
density_th	Minimal density of aligned region points in the enclosing rectangle.
n_bins	Number of bins in pseudo-ordering of gradient modulus.

cvtColor() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.cvtColor ( Mat src, Mat dst, int code )

static

Converts an image from one color space to another.

The function converts an input image from one color space to another. In case of a transformation to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on.

The conventional ranges for R, G, and B channel values are:

• 0 to 255 for CV_8U images
• 0 to 65535 for CV_16U images
• 0 to 1 for CV_32F images

In case of linear transformations, the range does not matter. But in case of a non-linear transformation, an input RGB image should be normalized to the proper value range to get the correct results, for example, for RGB \(\rightarrow\) 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:

img *= 1./255;
cvtColor(img, img, COLOR_BGR2Luv);

If you use cvtColor with 8-bit images, the conversion will have some information lost. For many applications, this will not be noticeable but it is recommended to use 32-bit images in applications that need the full range of colors or that convert an image before an operation and then convert back.

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.

Parameters

src	input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision floating-point.
dst	output image of the same size and depth as src.
code	color space conversion code (see #ColorConversionCodes).
dstCn	number of channels in the destination image; if the parameter is 0, the number of the channels is derived automatically from src and code.

See also

imgproc_color_conversions

cvtColor() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.cvtColor ( Mat src, Mat dst, int code, int dstCn )

static

Converts an image from one color space to another.

The function converts an input image from one color space to another. In case of a transformation to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on.

The conventional ranges for R, G, and B channel values are:

• 0 to 255 for CV_8U images
• 0 to 65535 for CV_16U images
• 0 to 1 for CV_32F images

In case of linear transformations, the range does not matter. But in case of a non-linear transformation, an input RGB image should be normalized to the proper value range to get the correct results, for example, for RGB \(\rightarrow\) 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:

img *= 1./255;
cvtColor(img, img, COLOR_BGR2Luv);

If you use cvtColor with 8-bit images, the conversion will have some information lost. For many applications, this will not be noticeable but it is recommended to use 32-bit images in applications that need the full range of colors or that convert an image before an operation and then convert back.

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.

Parameters

src	input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision floating-point.
dst	output image of the same size and depth as src.
code	color space conversion code (see #ColorConversionCodes).
dstCn	number of channels in the destination image; if the parameter is 0, the number of the channels is derived automatically from src and code.

See also

imgproc_color_conversions

cvtColorTwoPlane()

static void OpenCVForUnity.ImgprocModule.Imgproc.cvtColorTwoPlane ( Mat src1, Mat src2, Mat dst, int code )

static

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.

Parameters

src1	8-bit image (#CV_8U) of the Y plane.
src2	image containing interleaved U/V plane.
dst	output image.
code	Specifies the type of conversion. It can take any of the following values: COLOR_YUV2BGR_NV12 COLOR_YUV2RGB_NV12 COLOR_YUV2BGRA_NV12 COLOR_YUV2RGBA_NV12 COLOR_YUV2BGR_NV21 COLOR_YUV2RGB_NV21 COLOR_YUV2BGRA_NV21 COLOR_YUV2RGBA_NV21

demosaicing() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.demosaicing ( Mat src, Mat dst, int code )

static

main function for all demosaicing processes

Parameters

src	input image: 8-bit unsigned or 16-bit unsigned.
dst	output image of the same size and depth as src.
code	Color space conversion code (see the description below).
dstCn	number of channels in the destination image; if the parameter is 0, the number of the channels is derived automatically from src and code.

The function can do the following transformations:

• Demosaicing using bilinear interpolation

  COLOR_BayerBG2BGR , COLOR_BayerGB2BGR , COLOR_BayerRG2BGR , COLOR_BayerGR2BGR

  COLOR_BayerBG2GRAY , COLOR_BayerGB2GRAY , COLOR_BayerRG2GRAY , COLOR_BayerGR2GRAY

• Demosaicing using Variable Number of Gradients.

  COLOR_BayerBG2BGR_VNG , COLOR_BayerGB2BGR_VNG , COLOR_BayerRG2BGR_VNG , COLOR_BayerGR2BGR_VNG

• Edge-Aware Demosaicing.

  COLOR_BayerBG2BGR_EA , COLOR_BayerGB2BGR_EA , COLOR_BayerRG2BGR_EA , COLOR_BayerGR2BGR_EA

• Demosaicing with alpha channel

  COLOR_BayerBG2BGRA , COLOR_BayerGB2BGRA , COLOR_BayerRG2BGRA , COLOR_BayerGR2BGRA

See also

cvtColor

demosaicing() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.demosaicing ( Mat src, Mat dst, int code, int dstCn )

static

main function for all demosaicing processes

Parameters

src	input image: 8-bit unsigned or 16-bit unsigned.
dst	output image of the same size and depth as src.
code	Color space conversion code (see the description below).
dstCn	number of channels in the destination image; if the parameter is 0, the number of the channels is derived automatically from src and code.

The function can do the following transformations:

• Demosaicing using bilinear interpolation

  COLOR_BayerBG2BGR , COLOR_BayerGB2BGR , COLOR_BayerRG2BGR , COLOR_BayerGR2BGR

  COLOR_BayerBG2GRAY , COLOR_BayerGB2GRAY , COLOR_BayerRG2GRAY , COLOR_BayerGR2GRAY

• Demosaicing using Variable Number of Gradients.

  COLOR_BayerBG2BGR_VNG , COLOR_BayerGB2BGR_VNG , COLOR_BayerRG2BGR_VNG , COLOR_BayerGR2BGR_VNG

• Edge-Aware Demosaicing.

  COLOR_BayerBG2BGR_EA , COLOR_BayerGB2BGR_EA , COLOR_BayerRG2BGR_EA , COLOR_BayerGR2BGR_EA

• Demosaicing with alpha channel

  COLOR_BayerBG2BGRA , COLOR_BayerGB2BGRA , COLOR_BayerRG2BGRA , COLOR_BayerGR2BGRA

See also

cvtColor

dilate() [1/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.dilate ( Mat src, Mat dst, Mat kernel )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times dilation is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
borderValue	border value in case of a constant border

erode, morphologyEx, getStructuringElement

dilate() [2/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.dilate ( Mat src, Mat dst, Mat kernel, in Vec2d anchor )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times dilation is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
borderValue	border value in case of a constant border

erode, morphologyEx, getStructuringElement

dilate() [3/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.dilate ( Mat src, Mat dst, Mat kernel, in Vec2d anchor, int iterations )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times dilation is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
borderValue	border value in case of a constant border

erode, morphologyEx, getStructuringElement

dilate() [4/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.dilate ( Mat src, Mat dst, Mat kernel, in Vec2d anchor, int iterations, int borderType )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times dilation is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
borderValue	border value in case of a constant border

erode, morphologyEx, getStructuringElement

dilate() [5/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.dilate ( Mat src, Mat dst, Mat kernel, in Vec2d anchor, int iterations, int borderType, in Vec4d borderValue )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times dilation is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
borderValue	border value in case of a constant border

erode, morphologyEx, getStructuringElement

dilate() [6/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.dilate ( Mat src, Mat dst, Mat kernel, in(double x, double y) anchor )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times dilation is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
borderValue	border value in case of a constant border

erode, morphologyEx, getStructuringElement

dilate() [7/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.dilate ( Mat src, Mat dst, Mat kernel, in(double x, double y) anchor, int iterations )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times dilation is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
borderValue	border value in case of a constant border

erode, morphologyEx, getStructuringElement

dilate() [8/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.dilate ( Mat src, Mat dst, Mat kernel, in(double x, double y) anchor, int iterations, int borderType )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times dilation is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
borderValue	border value in case of a constant border

erode, morphologyEx, getStructuringElement

dilate() [9/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.dilate ( Mat src, Mat dst, Mat kernel, in(double x, double y) anchor, int iterations, int borderType, in(double v0, double v1, double v2, double v3) borderValue )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times dilation is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
borderValue	border value in case of a constant border

erode, morphologyEx, getStructuringElement

dilate() [10/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.dilate ( Mat src, Mat dst, Mat kernel, Point anchor )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times dilation is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
borderValue	border value in case of a constant border

erode, morphologyEx, getStructuringElement

dilate() [11/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.dilate ( Mat src, Mat dst, Mat kernel, Point anchor, int iterations )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times dilation is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
borderValue	border value in case of a constant border

erode, morphologyEx, getStructuringElement

dilate() [12/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.dilate ( Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times dilation is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
borderValue	border value in case of a constant border

erode, morphologyEx, getStructuringElement

dilate() [13/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.dilate ( Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times dilation is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
borderValue	border value in case of a constant border

erode, morphologyEx, getStructuringElement

distanceTransform() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.distanceTransform ( Mat src, Mat dst, int distanceType, int maskSize )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

src	8-bit, single-channel (binary) source image.
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 .
distanceType	Type of distance, see #DistanceTypes
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.
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.

distanceTransform() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.distanceTransform ( Mat src, Mat dst, int distanceType, int maskSize, int dstType )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

src	8-bit, single-channel (binary) source image.
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 .
distanceType	Type of distance, see #DistanceTypes
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.
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.

distanceTransformWithLabels() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.distanceTransformWithLabels ( Mat src, Mat dst, Mat labels, int distanceType, int maskSize )

static

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 [Felzenszwalb04] . This algorithm is parallelized with the TBB library.

In other cases, the algorithm [Borgefors86] is used. This means that for a pixel the function finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, diagonal, or knight's move (the latest is available for a \(5\times 5\) 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 b), and all knight's moves must have the same cost (denoted as 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 a,b, and c, OpenCV uses the values suggested in the original paper:

• DIST_L1: a = 1, b = 2
• DIST_L2:
  • 3 x 3: a=0.955, b=1.3693
  • 5 x 5: a=1, b=1.4, c=2.1969
• DIST_C: a = 1, b = 1

Typically, for a fast, coarse distance estimation DIST_L2, a \(3\times 3\) 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 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.

Parameters

src	8-bit, single-channel (binary) source image.
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.
labels	Output 2D array of labels (the discrete Voronoi diagram). It has the type CV_32SC1 and the same size as src.
distanceType	Type of distance, see #DistanceTypes
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.
labelType	Type of the label array to build, see #DistanceTransformLabelTypes.

distanceTransformWithLabels() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.distanceTransformWithLabels ( Mat src, Mat dst, Mat labels, int distanceType, int maskSize, int labelType )

static

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 [Felzenszwalb04] . This algorithm is parallelized with the TBB library.

In other cases, the algorithm [Borgefors86] is used. This means that for a pixel the function finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, diagonal, or knight's move (the latest is available for a \(5\times 5\) 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 b), and all knight's moves must have the same cost (denoted as 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 a,b, and c, OpenCV uses the values suggested in the original paper:

• DIST_L1: a = 1, b = 2
• DIST_L2:
  • 3 x 3: a=0.955, b=1.3693
  • 5 x 5: a=1, b=1.4, c=2.1969
• DIST_C: a = 1, b = 1

Typically, for a fast, coarse distance estimation DIST_L2, a \(3\times 3\) 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 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.

Parameters

src	8-bit, single-channel (binary) source image.
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.
labels	Output 2D array of labels (the discrete Voronoi diagram). It has the type CV_32SC1 and the same size as src.
distanceType	Type of distance, see #DistanceTypes
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.
labelType	Type of the label array to build, see #DistanceTransformLabelTypes.

divSpectrums() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.divSpectrums ( Mat a, Mat b, Mat c, int flags )

static

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.

Parameters

a	first input array.
b	second input array of the same size and type as src1 .
c	output array of the same size and type as src1 .
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 0 as value.
conjB	optional flag that conjugates the second input array before the multiplication (true) or not (false).

divSpectrums() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.divSpectrums ( Mat a, Mat b, Mat c, int flags, bool conjB )

static

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.

Parameters

a	first input array.
b	second input array of the same size and type as src1 .
c	output array of the same size and type as src1 .
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 0 as value.
conjB	optional flag that conjugates the second input array before the multiplication (true) or not (false).

drawContours() [1/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, in Vec4d color )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [2/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, in Vec4d color, int thickness )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [3/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, in Vec4d color, int thickness, int lineType )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [4/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, in Vec4d color, int thickness, int lineType, Mat hierarchy )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [5/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, in Vec4d color, int thickness, int lineType, Mat hierarchy, int maxLevel )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [6/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, in Vec4d color, int thickness, int lineType, Mat hierarchy, int maxLevel, in Vec2d offset )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [7/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, in(double v0, double v1, double v2, double v3) color )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [8/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, in(double v0, double v1, double v2, double v3) color, int thickness )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [9/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [10/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, Mat hierarchy )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [11/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, Mat hierarchy, int maxLevel )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [12/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, Mat hierarchy, int maxLevel, in(double x, double y) offset )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [13/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, Scalar color )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [14/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, Scalar color, int thickness )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [15/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, Scalar color, int thickness, int lineType )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [16/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [17/18]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawContours ( Mat image, List< MatOfPoint > contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy, int maxLevel )

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawContours() [18/18]

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

static

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: :

Parameters

image	Destination image.
contours	All the input contours. Each contour is stored as a point vector.
contourIdx	Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color	Color of the contours.
thickness	Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn.
lineType	Line connectivity. See #LineTypes
hierarchy	Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).
maxLevel	Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset	Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .

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.

drawMarker() [1/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawMarker ( Mat img, in Vec2d position, in Vec4d color )

static

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.

Parameters

img	Image.
position	The point where the crosshair is positioned.
color	Line color.
markerType	The specific type of marker you want to use, see #MarkerTypes
thickness	Line thickness.
line_type	Type of the line, See #LineTypes
markerSize	The length of the marker axis [default = 20 pixels]

drawMarker() [2/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawMarker ( Mat img, in Vec2d position, in Vec4d color, int markerType )

static

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.

Parameters

img	Image.
position	The point where the crosshair is positioned.
color	Line color.
markerType	The specific type of marker you want to use, see #MarkerTypes
thickness	Line thickness.
line_type	Type of the line, See #LineTypes
markerSize	The length of the marker axis [default = 20 pixels]

drawMarker() [3/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawMarker ( Mat img, in Vec2d position, in Vec4d color, int markerType, int markerSize )

static

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.

Parameters

img	Image.
position	The point where the crosshair is positioned.
color	Line color.
markerType	The specific type of marker you want to use, see #MarkerTypes
thickness	Line thickness.
line_type	Type of the line, See #LineTypes
markerSize	The length of the marker axis [default = 20 pixels]

drawMarker() [4/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawMarker ( Mat img, in Vec2d position, in Vec4d color, int markerType, int markerSize, int thickness )

static

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.

Parameters

img	Image.
position	The point where the crosshair is positioned.
color	Line color.
markerType	The specific type of marker you want to use, see #MarkerTypes
thickness	Line thickness.
line_type	Type of the line, See #LineTypes
markerSize	The length of the marker axis [default = 20 pixels]

drawMarker() [5/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawMarker ( Mat img, in Vec2d position, in Vec4d color, int markerType, int markerSize, int thickness, int line_type )

static

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.

Parameters

img	Image.
position	The point where the crosshair is positioned.
color	Line color.
markerType	The specific type of marker you want to use, see #MarkerTypes
thickness	Line thickness.
line_type	Type of the line, See #LineTypes
markerSize	The length of the marker axis [default = 20 pixels]

drawMarker() [6/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawMarker ( Mat img, in(double x, double y) position, in(double v0, double v1, double v2, double v3) color )

static

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.

Parameters

img	Image.
position	The point where the crosshair is positioned.
color	Line color.
markerType	The specific type of marker you want to use, see #MarkerTypes
thickness	Line thickness.
line_type	Type of the line, See #LineTypes
markerSize	The length of the marker axis [default = 20 pixels]

drawMarker() [7/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawMarker ( Mat img, in(double x, double y) position, in(double v0, double v1, double v2, double v3) color, int markerType )

static

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.

Parameters

img	Image.
position	The point where the crosshair is positioned.
color	Line color.
markerType	The specific type of marker you want to use, see #MarkerTypes
thickness	Line thickness.
line_type	Type of the line, See #LineTypes
markerSize	The length of the marker axis [default = 20 pixels]

drawMarker() [8/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawMarker ( Mat img, in(double x, double y) position, in(double v0, double v1, double v2, double v3) color, int markerType, int markerSize )

static

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.

Parameters

img	Image.
position	The point where the crosshair is positioned.
color	Line color.
markerType	The specific type of marker you want to use, see #MarkerTypes
thickness	Line thickness.
line_type	Type of the line, See #LineTypes
markerSize	The length of the marker axis [default = 20 pixels]

drawMarker() [9/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawMarker ( Mat img, in(double x, double y) position, in(double v0, double v1, double v2, double v3) color, int markerType, int markerSize, int thickness )

static

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.

Parameters

img	Image.
position	The point where the crosshair is positioned.
color	Line color.
markerType	The specific type of marker you want to use, see #MarkerTypes
thickness	Line thickness.
line_type	Type of the line, See #LineTypes
markerSize	The length of the marker axis [default = 20 pixels]

drawMarker() [10/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawMarker ( Mat img, in(double x, double y) position, in(double v0, double v1, double v2, double v3) color, int markerType, int markerSize, int thickness, int line_type )

static

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.

Parameters

img	Image.
position	The point where the crosshair is positioned.
color	Line color.
markerType	The specific type of marker you want to use, see #MarkerTypes
thickness	Line thickness.
line_type	Type of the line, See #LineTypes
markerSize	The length of the marker axis [default = 20 pixels]

drawMarker() [11/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawMarker ( Mat img, Point position, Scalar color )

static

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.

Parameters

img	Image.
position	The point where the crosshair is positioned.
color	Line color.
markerType	The specific type of marker you want to use, see #MarkerTypes
thickness	Line thickness.
line_type	Type of the line, See #LineTypes
markerSize	The length of the marker axis [default = 20 pixels]

drawMarker() [12/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawMarker ( Mat img, Point position, Scalar color, int markerType )

static

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.

Parameters

img	Image.
position	The point where the crosshair is positioned.
color	Line color.
markerType	The specific type of marker you want to use, see #MarkerTypes
thickness	Line thickness.
line_type	Type of the line, See #LineTypes
markerSize	The length of the marker axis [default = 20 pixels]

drawMarker() [13/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawMarker ( Mat img, Point position, Scalar color, int markerType, int markerSize )

static

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.

Parameters

img	Image.
position	The point where the crosshair is positioned.
color	Line color.
markerType	The specific type of marker you want to use, see #MarkerTypes
thickness	Line thickness.
line_type	Type of the line, See #LineTypes
markerSize	The length of the marker axis [default = 20 pixels]

drawMarker() [14/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawMarker ( Mat img, Point position, Scalar color, int markerType, int markerSize, int thickness )

static

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.

Parameters

img	Image.
position	The point where the crosshair is positioned.
color	Line color.
markerType	The specific type of marker you want to use, see #MarkerTypes
thickness	Line thickness.
line_type	Type of the line, See #LineTypes
markerSize	The length of the marker axis [default = 20 pixels]

drawMarker() [15/15]

static void OpenCVForUnity.ImgprocModule.Imgproc.drawMarker ( Mat img, Point position, Scalar color, int markerType, int markerSize, int thickness, int line_type )

static

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.

Parameters

img	Image.
position	The point where the crosshair is positioned.
color	Line color.
markerType	The specific type of marker you want to use, see #MarkerTypes
thickness	Line thickness.
line_type	Type of the line, See #LineTypes
markerSize	The length of the marker axis [default = 20 pixels]

ellipse() [1/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, in Vec2d center, in Vec2d axes, double angle, double startAngle, double endAngle, in Vec4d color )

static

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 startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.

Parameters

img	Image.
center	Center of the ellipse.
axes	Half of the size of the ellipse main axes.
angle	Ellipse rotation angle in degrees.
startAngle	Starting angle of the elliptic arc in degrees.
endAngle	Ending angle of the elliptic arc in degrees.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and values of axes.

ellipse() [2/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, in Vec2d center, in Vec2d axes, double angle, double startAngle, double endAngle, in Vec4d color, int thickness )

static

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 startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.

Parameters

img	Image.
center	Center of the ellipse.
axes	Half of the size of the ellipse main axes.
angle	Ellipse rotation angle in degrees.
startAngle	Starting angle of the elliptic arc in degrees.
endAngle	Ending angle of the elliptic arc in degrees.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and values of axes.

ellipse() [3/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, in Vec2d center, in Vec2d axes, double angle, double startAngle, double endAngle, in Vec4d color, int thickness, int lineType )

static

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 startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.

Parameters

img	Image.
center	Center of the ellipse.
axes	Half of the size of the ellipse main axes.
angle	Ellipse rotation angle in degrees.
startAngle	Starting angle of the elliptic arc in degrees.
endAngle	Ending angle of the elliptic arc in degrees.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and values of axes.

ellipse() [4/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, in Vec2d center, in Vec2d axes, double angle, double startAngle, double endAngle, in Vec4d color, int thickness, int lineType, int shift )

static

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 startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.

Parameters

img	Image.
center	Center of the ellipse.
axes	Half of the size of the ellipse main axes.
angle	Ellipse rotation angle in degrees.
startAngle	Starting angle of the elliptic arc in degrees.
endAngle	Ending angle of the elliptic arc in degrees.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and values of axes.

ellipse() [5/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, in Vec5d box, in Vec4d color )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

img	Image.
box	Alternative ellipse representation via RotatedRect. This means that the function draws an ellipse inscribed in the rotated rectangle.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes

ellipse() [6/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, in Vec5d box, in Vec4d color, int thickness )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

img	Image.
box	Alternative ellipse representation via RotatedRect. This means that the function draws an ellipse inscribed in the rotated rectangle.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes

ellipse() [7/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, in Vec5d box, in Vec4d color, int thickness, int lineType )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

img	Image.
box	Alternative ellipse representation via RotatedRect. This means that the function draws an ellipse inscribed in the rotated rectangle.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes

ellipse() [8/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, in(double x, double y) center, in(double width, double height) axes, double angle, double startAngle, double endAngle, in(double v0, double v1, double v2, double v3) color )

static

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 startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.

Parameters

img	Image.
center	Center of the ellipse.
axes	Half of the size of the ellipse main axes.
angle	Ellipse rotation angle in degrees.
startAngle	Starting angle of the elliptic arc in degrees.
endAngle	Ending angle of the elliptic arc in degrees.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and values of axes.

ellipse() [9/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, in(double x, double y) center, in(double width, double height) axes, double angle, double startAngle, double endAngle, in(double v0, double v1, double v2, double v3) color, int thickness )

static

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 startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.

Parameters

img	Image.
center	Center of the ellipse.
axes	Half of the size of the ellipse main axes.
angle	Ellipse rotation angle in degrees.
startAngle	Starting angle of the elliptic arc in degrees.
endAngle	Ending angle of the elliptic arc in degrees.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and values of axes.

ellipse() [10/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, in(double x, double y) center, in(double width, double height) axes, double angle, double startAngle, double endAngle, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType )

static

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 startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.

Parameters

img	Image.
center	Center of the ellipse.
axes	Half of the size of the ellipse main axes.
angle	Ellipse rotation angle in degrees.
startAngle	Starting angle of the elliptic arc in degrees.
endAngle	Ending angle of the elliptic arc in degrees.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and values of axes.

ellipse() [11/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, in(double x, double y) center, in(double width, double height) axes, double angle, double startAngle, double endAngle, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, int shift )

static

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 startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.

Parameters

img	Image.
center	Center of the ellipse.
axes	Half of the size of the ellipse main axes.
angle	Ellipse rotation angle in degrees.
startAngle	Starting angle of the elliptic arc in degrees.
endAngle	Ending angle of the elliptic arc in degrees.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and values of axes.

ellipse() [12/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, in(double x, double y, double width, double height, double angle) box, in(double v0, double v1, double v2, double v3) color )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

img	Image.
box	Alternative ellipse representation via RotatedRect. This means that the function draws an ellipse inscribed in the rotated rectangle.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes

ellipse() [13/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, in(double x, double y, double width, double height, double angle) box, in(double v0, double v1, double v2, double v3) color, int thickness )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

img	Image.
box	Alternative ellipse representation via RotatedRect. This means that the function draws an ellipse inscribed in the rotated rectangle.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes

ellipse() [14/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, in(double x, double y, double width, double height, double angle) box, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

img	Image.
box	Alternative ellipse representation via RotatedRect. This means that the function draws an ellipse inscribed in the rotated rectangle.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes

ellipse() [15/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color )

static

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 startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.

Parameters

img	Image.
center	Center of the ellipse.
axes	Half of the size of the ellipse main axes.
angle	Ellipse rotation angle in degrees.
startAngle	Starting angle of the elliptic arc in degrees.
endAngle	Ending angle of the elliptic arc in degrees.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and values of axes.

ellipse() [16/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness )

static

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 startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.

Parameters

img	Image.
center	Center of the ellipse.
axes	Half of the size of the ellipse main axes.
angle	Ellipse rotation angle in degrees.
startAngle	Starting angle of the elliptic arc in degrees.
endAngle	Ending angle of the elliptic arc in degrees.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and values of axes.

ellipse() [17/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness, int lineType )

static

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 startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.

Parameters

img	Image.
center	Center of the ellipse.
axes	Half of the size of the ellipse main axes.
angle	Ellipse rotation angle in degrees.
startAngle	Starting angle of the elliptic arc in degrees.
endAngle	Ending angle of the elliptic arc in degrees.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and values of axes.

ellipse() [18/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness, int lineType, int shift )

static

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 startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.

Parameters

img	Image.
center	Center of the ellipse.
axes	Half of the size of the ellipse main axes.
angle	Ellipse rotation angle in degrees.
startAngle	Starting angle of the elliptic arc in degrees.
endAngle	Ending angle of the elliptic arc in degrees.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes
shift	Number of fractional bits in the coordinates of the center and values of axes.

ellipse() [19/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, RotatedRect box, Scalar color )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

img	Image.
box	Alternative ellipse representation via RotatedRect. This means that the function draws an ellipse inscribed in the rotated rectangle.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes

ellipse() [20/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, RotatedRect box, Scalar color, int thickness )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

img	Image.
box	Alternative ellipse representation via RotatedRect. This means that the function draws an ellipse inscribed in the rotated rectangle.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes

ellipse() [21/21]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse ( Mat img, RotatedRect box, Scalar color, int thickness, int lineType )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

img	Image.
box	Alternative ellipse representation via RotatedRect. This means that the function draws an ellipse inscribed in the rotated rectangle.
color	Ellipse color.
thickness	Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
lineType	Type of the ellipse boundary. See #LineTypes

ellipse2Poly() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse2Poly ( in Vec2d center, in Vec2d axes, int angle, int arcStart, int arcEnd, int delta, MatOfPoint pts )

static

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 arcStart is greater than arcEnd, they are swapped.

Parameters

center	Center of the arc.
axes	Half of the size of the ellipse main axes. See ellipse for details.
angle	Rotation angle of the ellipse in degrees. See ellipse for details.
arcStart	Starting angle of the elliptic arc in degrees.
arcEnd	Ending angle of the elliptic arc in degrees.
delta	Angle between the subsequent polyline vertices. It defines the approximation accuracy.
pts	Output vector of polyline vertices.

ellipse2Poly() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse2Poly ( in(double x, double y) center, in(double width, double height) axes, int angle, int arcStart, int arcEnd, int delta, MatOfPoint pts )

static

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 arcStart is greater than arcEnd, they are swapped.

Parameters

center	Center of the arc.
axes	Half of the size of the ellipse main axes. See ellipse for details.
angle	Rotation angle of the ellipse in degrees. See ellipse for details.
arcStart	Starting angle of the elliptic arc in degrees.
arcEnd	Ending angle of the elliptic arc in degrees.
delta	Angle between the subsequent polyline vertices. It defines the approximation accuracy.
pts	Output vector of polyline vertices.

ellipse2Poly() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.ellipse2Poly ( Point center, Size axes, int angle, int arcStart, int arcEnd, int delta, MatOfPoint pts )

static

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 arcStart is greater than arcEnd, they are swapped.

Parameters

center	Center of the arc.
axes	Half of the size of the ellipse main axes. See ellipse for details.
angle	Rotation angle of the ellipse in degrees. See ellipse for details.
arcStart	Starting angle of the elliptic arc in degrees.
arcEnd	Ending angle of the elliptic arc in degrees.
delta	Angle between the subsequent polyline vertices. It defines the approximation accuracy.
pts	Output vector of polyline vertices.

EMD() [1/3]

static float OpenCVForUnity.ImgprocModule.Imgproc.EMD ( Mat signature1, Mat signature2, int distType )

static

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 [RubnerSept98], [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.

Parameters

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.
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.
distType	Used metric. See #DistanceTypes.
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.
lowerBound	Optional input/output parameter: lower boundary of a distance between the two signatures that is a distance between mass centers. The lower boundary may not be calculated if the user-defined cost matrix is used, the total weights of point configurations are not equal, or if the signatures consist of weights only (the signature matrices have a single column). You *must** initialize *lowerBound . If the calculated distance between mass centers is greater or equal to *lowerBound (it means that the signatures are far enough), the function does not calculate EMD. In any case *lowerBound is set to the calculated distance between mass centers on return. Thus, if you want to calculate both distance between mass centers and EMD, *lowerBound should be set to 0.
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 .

EMD() [2/3]

static float OpenCVForUnity.ImgprocModule.Imgproc.EMD ( Mat signature1, Mat signature2, int distType, Mat cost )

static

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 [RubnerSept98], [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.

Parameters

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.
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.
distType	Used metric. See #DistanceTypes.
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.
lowerBound	Optional input/output parameter: lower boundary of a distance between the two signatures that is a distance between mass centers. The lower boundary may not be calculated if the user-defined cost matrix is used, the total weights of point configurations are not equal, or if the signatures consist of weights only (the signature matrices have a single column). You *must** initialize *lowerBound . If the calculated distance between mass centers is greater or equal to *lowerBound (it means that the signatures are far enough), the function does not calculate EMD. In any case *lowerBound is set to the calculated distance between mass centers on return. Thus, if you want to calculate both distance between mass centers and EMD, *lowerBound should be set to 0.
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 .

EMD() [3/3]

static float OpenCVForUnity.ImgprocModule.Imgproc.EMD ( Mat signature1, Mat signature2, int distType, Mat cost, Mat flow )

static

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 [RubnerSept98], [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.

Parameters

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.
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.
distType	Used metric. See #DistanceTypes.
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.
lowerBound	Optional input/output parameter: lower boundary of a distance between the two signatures that is a distance between mass centers. The lower boundary may not be calculated if the user-defined cost matrix is used, the total weights of point configurations are not equal, or if the signatures consist of weights only (the signature matrices have a single column). You *must** initialize *lowerBound . If the calculated distance between mass centers is greater or equal to *lowerBound (it means that the signatures are far enough), the function does not calculate EMD. In any case *lowerBound is set to the calculated distance between mass centers on return. Thus, if you want to calculate both distance between mass centers and EMD, *lowerBound should be set to 0.
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 .

equalizeHist()

static void OpenCVForUnity.ImgprocModule.Imgproc.equalizeHist ( Mat src, Mat dst )

static

Equalizes the histogram of a grayscale image.

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

• Calculate the histogram \(H\) for src .
• Normalize the histogram so that the sum of histogram bins is 255.
• Compute the integral of the histogram:

  \[H'_i = \sum _{0 \le j < i} H(j)\]

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

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

Parameters

src	Source 8-bit single channel image.
dst	Destination image of the same size and type as src .

erode() [1/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.erode ( Mat src, Mat dst, Mat kernel )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for erosion; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement.
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times erosion is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	border value in case of a constant border

dilate, morphologyEx, getStructuringElement

erode() [2/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.erode ( Mat src, Mat dst, Mat kernel, in Vec2d anchor )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for erosion; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement.
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times erosion is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	border value in case of a constant border

dilate, morphologyEx, getStructuringElement

erode() [3/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.erode ( Mat src, Mat dst, Mat kernel, in Vec2d anchor, int iterations )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for erosion; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement.
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times erosion is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	border value in case of a constant border

dilate, morphologyEx, getStructuringElement

erode() [4/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.erode ( Mat src, Mat dst, Mat kernel, in Vec2d anchor, int iterations, int borderType )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for erosion; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement.
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times erosion is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	border value in case of a constant border

dilate, morphologyEx, getStructuringElement

erode() [5/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.erode ( Mat src, Mat dst, Mat kernel, in Vec2d anchor, int iterations, int borderType, in Vec4d borderValue )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for erosion; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement.
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times erosion is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	border value in case of a constant border

dilate, morphologyEx, getStructuringElement

erode() [6/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.erode ( Mat src, Mat dst, Mat kernel, in(double x, double y) anchor )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for erosion; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement.
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times erosion is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	border value in case of a constant border

dilate, morphologyEx, getStructuringElement

erode() [7/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.erode ( Mat src, Mat dst, Mat kernel, in(double x, double y) anchor, int iterations )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for erosion; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement.
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times erosion is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	border value in case of a constant border

dilate, morphologyEx, getStructuringElement

erode() [8/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.erode ( Mat src, Mat dst, Mat kernel, in(double x, double y) anchor, int iterations, int borderType )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for erosion; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement.
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times erosion is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	border value in case of a constant border

dilate, morphologyEx, getStructuringElement

erode() [9/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.erode ( Mat src, Mat dst, Mat kernel, in(double x, double y) anchor, int iterations, int borderType, in(double v0, double v1, double v2, double v3) borderValue )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for erosion; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement.
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times erosion is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	border value in case of a constant border

dilate, morphologyEx, getStructuringElement

erode() [10/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.erode ( Mat src, Mat dst, Mat kernel, Point anchor )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for erosion; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement.
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times erosion is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	border value in case of a constant border

dilate, morphologyEx, getStructuringElement

erode() [11/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.erode ( Mat src, Mat dst, Mat kernel, Point anchor, int iterations )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for erosion; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement.
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times erosion is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	border value in case of a constant border

dilate, morphologyEx, getStructuringElement

erode() [12/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.erode ( Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for erosion; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement.
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times erosion is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	border value in case of a constant border

dilate, morphologyEx, getStructuringElement

erode() [13/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.erode ( Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue )

static

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.

Parameters

src	input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
kernel	structuring element used for erosion; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement.
anchor	position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterations	number of times erosion is applied.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	border value in case of a constant border

dilate, morphologyEx, getStructuringElement

fillConvexPoly() [1/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillConvexPoly ( Mat img, MatOfPoint points, in Vec4d color )

static

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).

Parameters

img	Image.
points	Polygon vertices.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

fillConvexPoly() [2/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillConvexPoly ( Mat img, MatOfPoint points, in Vec4d color, int lineType )

static

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).

Parameters

img	Image.
points	Polygon vertices.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

fillConvexPoly() [3/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillConvexPoly ( Mat img, MatOfPoint points, in Vec4d color, int lineType, int shift )

static

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).

Parameters

img	Image.
points	Polygon vertices.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

fillConvexPoly() [4/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillConvexPoly ( Mat img, MatOfPoint points, in(double v0, double v1, double v2, double v3) color )

static

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).

Parameters

img	Image.
points	Polygon vertices.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

fillConvexPoly() [5/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillConvexPoly ( Mat img, MatOfPoint points, in(double v0, double v1, double v2, double v3) color, int lineType )

static

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).

Parameters

img	Image.
points	Polygon vertices.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

fillConvexPoly() [6/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillConvexPoly ( Mat img, MatOfPoint points, in(double v0, double v1, double v2, double v3) color, int lineType, int shift )

static

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).

Parameters

img	Image.
points	Polygon vertices.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

fillConvexPoly() [7/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillConvexPoly ( Mat img, MatOfPoint points, Scalar color )

static

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).

Parameters

img	Image.
points	Polygon vertices.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

fillConvexPoly() [8/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillConvexPoly ( Mat img, MatOfPoint points, Scalar color, int lineType )

static

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).

Parameters

img	Image.
points	Polygon vertices.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

fillConvexPoly() [9/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillConvexPoly ( Mat img, MatOfPoint points, Scalar color, int lineType, int shift )

static

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).

Parameters

img	Image.
points	Polygon vertices.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

fillPoly() [1/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillPoly ( Mat img, List< MatOfPoint > pts, in Vec4d color )

static

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.

Parameters

img	Image.
pts	Array of polygons where each polygon is represented as an array of points.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.
offset	Optional offset of all points of the contours.

fillPoly() [2/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillPoly ( Mat img, List< MatOfPoint > pts, in Vec4d color, int lineType )

static

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.

Parameters

img	Image.
pts	Array of polygons where each polygon is represented as an array of points.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.
offset	Optional offset of all points of the contours.

fillPoly() [3/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillPoly ( Mat img, List< MatOfPoint > pts, in Vec4d color, int lineType, int shift )

static

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.

Parameters

img	Image.
pts	Array of polygons where each polygon is represented as an array of points.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.
offset	Optional offset of all points of the contours.

fillPoly() [4/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillPoly ( Mat img, List< MatOfPoint > pts, in Vec4d color, int lineType, int shift, in Vec2d offset )

static

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.

Parameters

img	Image.
pts	Array of polygons where each polygon is represented as an array of points.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.
offset	Optional offset of all points of the contours.

fillPoly() [5/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillPoly ( Mat img, List< MatOfPoint > pts, in(double v0, double v1, double v2, double v3) color )

static

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.

Parameters

img	Image.
pts	Array of polygons where each polygon is represented as an array of points.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.
offset	Optional offset of all points of the contours.

fillPoly() [6/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillPoly ( Mat img, List< MatOfPoint > pts, in(double v0, double v1, double v2, double v3) color, int lineType )

static

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.

Parameters

img	Image.
pts	Array of polygons where each polygon is represented as an array of points.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.
offset	Optional offset of all points of the contours.

fillPoly() [7/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillPoly ( Mat img, List< MatOfPoint > pts, in(double v0, double v1, double v2, double v3) color, int lineType, int shift )

static

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.

Parameters

img	Image.
pts	Array of polygons where each polygon is represented as an array of points.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.
offset	Optional offset of all points of the contours.

fillPoly() [8/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillPoly ( Mat img, List< MatOfPoint > pts, in(double v0, double v1, double v2, double v3) color, int lineType, int shift, in(double x, double y) offset )

static

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.

Parameters

img	Image.
pts	Array of polygons where each polygon is represented as an array of points.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.
offset	Optional offset of all points of the contours.

fillPoly() [9/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillPoly ( Mat img, List< MatOfPoint > pts, Scalar color )

static

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.

Parameters

img	Image.
pts	Array of polygons where each polygon is represented as an array of points.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.
offset	Optional offset of all points of the contours.

fillPoly() [10/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillPoly ( Mat img, List< MatOfPoint > pts, Scalar color, int lineType )

static

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.

Parameters

img	Image.
pts	Array of polygons where each polygon is represented as an array of points.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.
offset	Optional offset of all points of the contours.

fillPoly() [11/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillPoly ( Mat img, List< MatOfPoint > pts, Scalar color, int lineType, int shift )

static

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.

Parameters

img	Image.
pts	Array of polygons where each polygon is represented as an array of points.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.
offset	Optional offset of all points of the contours.

fillPoly() [12/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.fillPoly ( Mat img, List< MatOfPoint > pts, Scalar color, int lineType, int shift, Point offset )

static

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.

Parameters

img	Image.
pts	Array of polygons where each polygon is represented as an array of points.
color	Polygon color.
lineType	Type of the polygon boundaries. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.
offset	Optional offset of all points of the contours.

filter2D() [1/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.filter2D ( Mat src, Mat dst, int ddepth, Mat kernel )

static

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 (~`11 x 11` or
         larger) and the direct algorithm for small kernels.
        </remarks>
        <param name="src">
        input image.
        </param>
        <param name="dst">
        output image of the same size and the same number of channels as src.
        </param>
        <param name="ddepth">
        desired depth of the destination image, see @ref filter_depths "combinations"
        </param>
        <param name="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>
        <param name="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>
        <param name="delta">
        optional value added to the filtered pixels before storing them in dst.
        </param>
        <param name="borderType">
        pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         @sa  sepFilter2D, dft, matchTemplate
        </param> 

filter2D() [2/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.filter2D ( Mat src, Mat dst, int ddepth, Mat kernel, in Vec2d anchor )

static

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 (~`11 x 11` or
         larger) and the direct algorithm for small kernels.
        </remarks>
        <param name="src">
        input image.
        </param>
        <param name="dst">
        output image of the same size and the same number of channels as src.
        </param>
        <param name="ddepth">
        desired depth of the destination image, see @ref filter_depths "combinations"
        </param>
        <param name="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>
        <param name="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>
        <param name="delta">
        optional value added to the filtered pixels before storing them in dst.
        </param>
        <param name="borderType">
        pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         @sa  sepFilter2D, dft, matchTemplate
        </param> 

filter2D() [3/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.filter2D ( Mat src, Mat dst, int ddepth, Mat kernel, in Vec2d anchor, double delta )

static

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 (~`11 x 11` or
         larger) and the direct algorithm for small kernels.
        </remarks>
        <param name="src">
        input image.
        </param>
        <param name="dst">
        output image of the same size and the same number of channels as src.
        </param>
        <param name="ddepth">
        desired depth of the destination image, see @ref filter_depths "combinations"
        </param>
        <param name="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>
        <param name="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>
        <param name="delta">
        optional value added to the filtered pixels before storing them in dst.
        </param>
        <param name="borderType">
        pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         @sa  sepFilter2D, dft, matchTemplate
        </param> 

filter2D() [4/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.filter2D ( Mat src, Mat dst, int ddepth, Mat kernel, in Vec2d anchor, double delta, int borderType )

static

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 (~`11 x 11` or
         larger) and the direct algorithm for small kernels.
        </remarks>
        <param name="src">
        input image.
        </param>
        <param name="dst">
        output image of the same size and the same number of channels as src.
        </param>
        <param name="ddepth">
        desired depth of the destination image, see @ref filter_depths "combinations"
        </param>
        <param name="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>
        <param name="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>
        <param name="delta">
        optional value added to the filtered pixels before storing them in dst.
        </param>
        <param name="borderType">
        pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         @sa  sepFilter2D, dft, matchTemplate
        </param> 

filter2D() [5/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.filter2D ( Mat src, Mat dst, int ddepth, Mat kernel, in(double x, double y) anchor )

static

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 (~`11 x 11` or
         larger) and the direct algorithm for small kernels.
        </remarks>
        <param name="src">
        input image.
        </param>
        <param name="dst">
        output image of the same size and the same number of channels as src.
        </param>
        <param name="ddepth">
        desired depth of the destination image, see @ref filter_depths "combinations"
        </param>
        <param name="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>
        <param name="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>
        <param name="delta">
        optional value added to the filtered pixels before storing them in dst.
        </param>
        <param name="borderType">
        pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         @sa  sepFilter2D, dft, matchTemplate
        </param> 

filter2D() [6/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.filter2D ( Mat src, Mat dst, int ddepth, Mat kernel, in(double x, double y) anchor, double delta )

static

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 (~`11 x 11` or
         larger) and the direct algorithm for small kernels.
        </remarks>
        <param name="src">
        input image.
        </param>
        <param name="dst">
        output image of the same size and the same number of channels as src.
        </param>
        <param name="ddepth">
        desired depth of the destination image, see @ref filter_depths "combinations"
        </param>
        <param name="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>
        <param name="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>
        <param name="delta">
        optional value added to the filtered pixels before storing them in dst.
        </param>
        <param name="borderType">
        pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         @sa  sepFilter2D, dft, matchTemplate
        </param> 

filter2D() [7/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.filter2D ( Mat src, Mat dst, int ddepth, Mat kernel, in(double x, double y) anchor, double delta, int borderType )

static

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 (~`11 x 11` or
         larger) and the direct algorithm for small kernels.
        </remarks>
        <param name="src">
        input image.
        </param>
        <param name="dst">
        output image of the same size and the same number of channels as src.
        </param>
        <param name="ddepth">
        desired depth of the destination image, see @ref filter_depths "combinations"
        </param>
        <param name="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>
        <param name="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>
        <param name="delta">
        optional value added to the filtered pixels before storing them in dst.
        </param>
        <param name="borderType">
        pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         @sa  sepFilter2D, dft, matchTemplate
        </param> 

filter2D() [8/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.filter2D ( Mat src, Mat dst, int ddepth, Mat kernel, Point anchor )

static

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 (~`11 x 11` or
         larger) and the direct algorithm for small kernels.
        </remarks>
        <param name="src">
        input image.
        </param>
        <param name="dst">
        output image of the same size and the same number of channels as src.
        </param>
        <param name="ddepth">
        desired depth of the destination image, see @ref filter_depths "combinations"
        </param>
        <param name="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>
        <param name="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>
        <param name="delta">
        optional value added to the filtered pixels before storing them in dst.
        </param>
        <param name="borderType">
        pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         @sa  sepFilter2D, dft, matchTemplate
        </param> 

filter2D() [9/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.filter2D ( Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta )

static

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 (~`11 x 11` or
         larger) and the direct algorithm for small kernels.
        </remarks>
        <param name="src">
        input image.
        </param>
        <param name="dst">
        output image of the same size and the same number of channels as src.
        </param>
        <param name="ddepth">
        desired depth of the destination image, see @ref filter_depths "combinations"
        </param>
        <param name="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>
        <param name="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>
        <param name="delta">
        optional value added to the filtered pixels before storing them in dst.
        </param>
        <param name="borderType">
        pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         @sa  sepFilter2D, dft, matchTemplate
        </param> 

filter2D() [10/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.filter2D ( Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta, int borderType )

static

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 (~`11 x 11` or
         larger) and the direct algorithm for small kernels.
        </remarks>
        <param name="src">
        input image.
        </param>
        <param name="dst">
        output image of the same size and the same number of channels as src.
        </param>
        <param name="ddepth">
        desired depth of the destination image, see @ref filter_depths "combinations"
        </param>
        <param name="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>
        <param name="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>
        <param name="delta">
        optional value added to the filtered pixels before storing them in dst.
        </param>
        <param name="borderType">
        pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
         @sa  sepFilter2D, dft, matchTemplate
        </param> 

findContours() [1/4]

static void OpenCVForUnity.ImgprocModule.Imgproc.findContours ( Mat image, List< MatOfPoint > contours, Mat hierarchy, int mode, int method )

static

Finds contours in a binary image.

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

Note

Since opencv 3.2 source image is not modified by this function.

Parameters

image	Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, threshold , adaptiveThreshold, Canny, and others to create a binary image out of a grayscale or color one. If mode equals to RETR_CCOMP or RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
contours	Detected contours. Each contour is stored as a vector of points (e.g. std::vector<std::vector<cv::Point> >).
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.

In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour.

Parameters

mode	Contour retrieval mode, see #RetrievalModes
method	Contour approximation method, see #ContourApproximationModes
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.

findContours() [2/4]

static void OpenCVForUnity.ImgprocModule.Imgproc.findContours ( Mat image, List< MatOfPoint > contours, Mat hierarchy, int mode, int method, in Vec2d offset )

static

Finds contours in a binary image.

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

Note

Since opencv 3.2 source image is not modified by this function.

Parameters

image	Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, threshold , adaptiveThreshold, Canny, and others to create a binary image out of a grayscale or color one. If mode equals to RETR_CCOMP or RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
contours	Detected contours. Each contour is stored as a vector of points (e.g. std::vector<std::vector<cv::Point> >).
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.

In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour.

Parameters

mode	Contour retrieval mode, see #RetrievalModes
method	Contour approximation method, see #ContourApproximationModes
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.

findContours() [3/4]

static void OpenCVForUnity.ImgprocModule.Imgproc.findContours ( Mat image, List< MatOfPoint > contours, Mat hierarchy, int mode, int method, in(double x, double y) offset )

static

Finds contours in a binary image.

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

Note

Since opencv 3.2 source image is not modified by this function.

Parameters

image	Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, threshold , adaptiveThreshold, Canny, and others to create a binary image out of a grayscale or color one. If mode equals to RETR_CCOMP or RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
contours	Detected contours. Each contour is stored as a vector of points (e.g. std::vector<std::vector<cv::Point> >).
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.

In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour.

Parameters

mode	Contour retrieval mode, see #RetrievalModes
method	Contour approximation method, see #ContourApproximationModes
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.

findContours() [4/4]

static void OpenCVForUnity.ImgprocModule.Imgproc.findContours ( Mat image, List< MatOfPoint > contours, Mat hierarchy, int mode, int method, Point offset )

static

Finds contours in a binary image.

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

Note

Since opencv 3.2 source image is not modified by this function.

Parameters

image	Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, threshold , adaptiveThreshold, Canny, and others to create a binary image out of a grayscale or color one. If mode equals to RETR_CCOMP or RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
contours	Detected contours. Each contour is stored as a vector of points (e.g. std::vector<std::vector<cv::Point> >).
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.

In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour.

Parameters

mode	Contour retrieval mode, see #RetrievalModes
method	Contour approximation method, see #ContourApproximationModes
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.

findContoursLinkRuns() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.findContoursLinkRuns ( Mat image, List< Mat > contours )

static

findContoursLinkRuns() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.findContoursLinkRuns ( Mat image, List< Mat > contours, Mat hierarchy )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

fitEllipse()

static RotatedRect OpenCVForUnity.ImgprocModule.Imgproc.fitEllipse ( MatOfPoint2f points )

static

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 [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.

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

fitEllipseAMS()

static RotatedRect OpenCVForUnity.ImgprocModule.Imgproc.fitEllipseAMS ( Mat points )

static

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 [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:

\begin{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\} \end{align*}

The AMS method minimizes the cost function

\begin{equation*} \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } \end{equation*}

The minimum cost is found by solving the generalized eigenvalue problem.

\begin{equation*} D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A \end{equation*}

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

fitEllipseAMSAsValueTuple()

static double double double double double angle OpenCVForUnity.ImgprocModule.Imgproc.fitEllipseAMSAsValueTuple ( Mat points )

static

fitEllipseAMSAsVec5d()

static Vec5d OpenCVForUnity.ImgprocModule.Imgproc.fitEllipseAMSAsVec5d ( Mat points )

static

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 [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:

\begin{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\} \end{align*}

The AMS method minimizes the cost function

\begin{equation*} \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } \end{equation*}

The minimum cost is found by solving the generalized eigenvalue problem.

\begin{equation*} D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A \end{equation*}

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

fitEllipseAsValueTuple()

static double double double double double angle OpenCVForUnity.ImgprocModule.Imgproc.fitEllipseAsValueTuple ( MatOfPoint2f points )

static

fitEllipseAsVec5d()

static Vec5d OpenCVForUnity.ImgprocModule.Imgproc.fitEllipseAsVec5d ( MatOfPoint2f points )

static

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 [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.

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

fitEllipseDirect()

static RotatedRect OpenCVForUnity.ImgprocModule.Imgproc.fitEllipseDirect ( Mat points )

static

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 [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.

\begin{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) \end{equation*}

The minimum cost is found by solving the generalized eigenvalue problem.

\begin{equation*} D^T D A = \lambda \left( C\right) A \end{equation*}

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

\begin{equation*} A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} \end{equation*}

The scaling factor guarantees that \(A^T C A =1\).

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

fitEllipseDirectAsValueTuple()

static double double double double double angle OpenCVForUnity.ImgprocModule.Imgproc.fitEllipseDirectAsValueTuple ( Mat points )

static

fitEllipseDirectAsVec5d()

static Vec5d OpenCVForUnity.ImgprocModule.Imgproc.fitEllipseDirectAsVec5d ( Mat points )

static

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 [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.

\begin{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) \end{equation*}

The minimum cost is found by solving the generalized eigenvalue problem.

\begin{equation*} D^T D A = \lambda \left( C\right) A \end{equation*}

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

\begin{equation*} A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} \end{equation*}

The scaling factor guarantees that \(A^T C A =1\).

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

fitLine()

static void OpenCVForUnity.ImgprocModule.Imgproc.fitLine ( Mat points, Mat line, int distType, double param, double reps, double aeps )

static

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:

• DIST_L2

  \[\rho (r) = r^2/2 \quad \text{(the simplest and the fastest least-squares method)}\]

• DIST_L1

  \[\rho (r) = r\]

• DIST_L12

  \[\rho (r) = 2 \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\]

• DIST_FAIR

  \[\rho \left (r \right ) = C^2 \cdot \left ( \frac{r}{C} - \log{\left(1 + \frac{r}{C}\right)} \right ) \quad \text{where} \quad C=1.3998\]

• DIST_WELSCH

  \[\rho \left (r \right ) = \frac{C^2}{2} \cdot \left ( 1 - \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right ) \quad \text{where} \quad C=2.9846\]

• DIST_HUBER

  \[\rho (r) = \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\]

The algorithm is based on the M-estimator ( <http://en.wikipedia.org/wiki/M-estimator&gt; ) technique that iteratively fits the line using the weighted least-squares algorithm. After each iteration the weights \(w_i\) are adjusted to be inversely proportional to \(\rho(r_i)\) .

Parameters

points	Input vector of 2D or 3D points, stored in std::vector<> or Mat.
line	Output line parameters. In case of 2D fitting, it should be a vector of 4 elements (like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and (x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line and (x0, y0, z0) is a point on the line.
distType	Distance used by the M-estimator, see #DistanceTypes
param	Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value is chosen.
reps	Sufficient accuracy for the radius (distance between the coordinate origin and the line).
aeps	Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps.

floodFill() [1/15]

static int OpenCVForUnity.ImgprocModule.Imgproc.floodFill ( Mat image, Mat mask, in Vec2d seedPoint, in Vec4d newVal )

static

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:

• in case of a grayscale image and floating range

  \[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]

• in case of a grayscale image and fixed range

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]

• 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

  \[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]

• 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

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	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.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
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.

Note

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

See also

findContours

floodFill() [2/15]

static int OpenCVForUnity.ImgprocModule.Imgproc.floodFill ( Mat image, Mat mask, in Vec2d seedPoint, in Vec4d newVal, out Vec4i rect )

static

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:

• in case of a grayscale image and floating range

  \[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]

• in case of a grayscale image and fixed range

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]

• 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

  \[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]

• 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

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	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.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
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.

Note

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

See also

findContours

floodFill() [3/15]

static int OpenCVForUnity.ImgprocModule.Imgproc.floodFill ( Mat image, Mat mask, in Vec2d seedPoint, in Vec4d newVal, out Vec4i rect, in Vec4d loDiff )

static

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:

• in case of a grayscale image and floating range

  \[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]

• in case of a grayscale image and fixed range

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]

• 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

  \[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]

• 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

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	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.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
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.

Note

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

See also

findContours

floodFill() [4/15]

static int OpenCVForUnity.ImgprocModule.Imgproc.floodFill ( Mat image, Mat mask, in Vec2d seedPoint, in Vec4d newVal, out Vec4i rect, in Vec4d loDiff, in Vec4d upDiff )

static

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:

• in case of a grayscale image and floating range

  \[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]

• in case of a grayscale image and fixed range

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]

• 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

  \[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]

• 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

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	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.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
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.

Note

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

See also

findContours

floodFill() [5/15]

static int OpenCVForUnity.ImgprocModule.Imgproc.floodFill ( Mat image, Mat mask, in Vec2d seedPoint, in Vec4d newVal, out Vec4i rect, in Vec4d loDiff, in Vec4d upDiff, int flags )

static

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:

• in case of a grayscale image and floating range

  \[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]

• in case of a grayscale image and fixed range

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]

• 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

  \[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]

• 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

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	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.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
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.

Note

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

See also

findContours

floodFill() [6/15]

static int OpenCVForUnity.ImgprocModule.Imgproc.floodFill ( Mat image, Mat mask, in(double x, double y) seedPoint, in(double v0, double v1, double v2, double v3) newVal )

static

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:

• in case of a grayscale image and floating range

  \[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]

• in case of a grayscale image and fixed range

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]

• 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

  \[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]

• 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

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	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.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
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.

Note

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

See also

findContours

floodFill() [7/15]

static int OpenCVForUnity.ImgprocModule.Imgproc.floodFill ( Mat image, Mat mask, in(double x, double y) seedPoint, in(double v0, double v1, double v2, double v3) newVal, out(int x, int y, int width, int height) rect )

static

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:

• in case of a grayscale image and floating range

  \[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]

• in case of a grayscale image and fixed range

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]

• 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

  \[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]

• 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

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	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.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
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.

Note

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

See also

findContours

floodFill() [8/15]

static int OpenCVForUnity.ImgprocModule.Imgproc.floodFill ( Mat image, Mat mask, in(double x, double y) seedPoint, in(double v0, double v1, double v2, double v3) newVal, out(int x, int y, int width, int height) rect, in(double v0, double v1, double v2, double v3) loDiff )

static

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:

• in case of a grayscale image and floating range

  \[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]

• in case of a grayscale image and fixed range

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]

• 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

  \[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]

• 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

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	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.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
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.

Note

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

See also

findContours

floodFill() [9/15]

static int OpenCVForUnity.ImgprocModule.Imgproc.floodFill ( Mat image, Mat mask, in(double x, double y) seedPoint, in(double v0, double v1, double v2, double v3) newVal, out(int x, int y, int width, int height) rect, in(double v0, double v1, double v2, double v3) loDiff, in(double v0, double v1, double v2, double v3) upDiff )

static

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:

• in case of a grayscale image and floating range

  \[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]

• in case of a grayscale image and fixed range

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]

• 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

  \[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]

• 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

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	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.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
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.

Note

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

See also

findContours

floodFill() [10/15]

static int OpenCVForUnity.ImgprocModule.Imgproc.floodFill ( Mat image, Mat mask, in(double x, double y) seedPoint, in(double v0, double v1, double v2, double v3) newVal, out(int x, int y, int width, int height) rect, in(double v0, double v1, double v2, double v3) loDiff, in(double v0, double v1, double v2, double v3) upDiff, int flags )

static

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:

• in case of a grayscale image and floating range

  \[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]

• in case of a grayscale image and fixed range

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]

• 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

  \[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]

• 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

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	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.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
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.

Note

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

See also

findContours

floodFill() [11/15]

static int OpenCVForUnity.ImgprocModule.Imgproc.floodFill ( Mat image, Mat mask, Point seedPoint, Scalar newVal )

static

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:

• in case of a grayscale image and floating range

  \[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]

• in case of a grayscale image and fixed range

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]

• 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

  \[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]

• 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

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	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.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
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.

Note

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

See also

findContours

floodFill() [12/15]

static int OpenCVForUnity.ImgprocModule.Imgproc.floodFill ( Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect )

static

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:

• in case of a grayscale image and floating range

  \[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]

• in case of a grayscale image and fixed range

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]

• 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

  \[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]

• 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

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	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.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
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.

Note

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

See also

findContours

floodFill() [13/15]

static int OpenCVForUnity.ImgprocModule.Imgproc.floodFill ( Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect, Scalar loDiff )

static

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:

• in case of a grayscale image and floating range

  \[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]

• in case of a grayscale image and fixed range

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]

• 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

  \[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]

• 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

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	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.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
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.

Note

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

See also

findContours

floodFill() [14/15]

static int OpenCVForUnity.ImgprocModule.Imgproc.floodFill ( Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect, Scalar loDiff, Scalar upDiff )

static

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:

• in case of a grayscale image and floating range

  \[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]

• in case of a grayscale image and fixed range

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]

• 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

  \[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]

• 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

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	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.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
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.

Note

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

See also

findContours

floodFill() [15/15]

static int OpenCVForUnity.ImgprocModule.Imgproc.floodFill ( Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect, Scalar loDiff, Scalar upDiff, int flags )

static

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:

• in case of a grayscale image and floating range

  \[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]

• in case of a grayscale image and fixed range

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]

• 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

  \[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]

• 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

  \[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	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.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
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.

Note

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

See also

findContours

GaussianBlur() [1/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.GaussianBlur ( Mat src, Mat dst, in Vec2d ksize, double sigmaX )

static

Blurs an image using a Gaussian filter.

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

Parameters

src	input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma.
sigmaX	Gaussian kernel standard deviation in X direction.
sigmaY	Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

See also

sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur

GaussianBlur() [2/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.GaussianBlur ( Mat src, Mat dst, in Vec2d ksize, double sigmaX, double sigmaY )

static

Blurs an image using a Gaussian filter.

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

Parameters

src	input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma.
sigmaX	Gaussian kernel standard deviation in X direction.
sigmaY	Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

See also

sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur

GaussianBlur() [3/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.GaussianBlur ( Mat src, Mat dst, in Vec2d ksize, double sigmaX, double sigmaY, int borderType )

static

Blurs an image using a Gaussian filter.

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

Parameters

src	input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma.
sigmaX	Gaussian kernel standard deviation in X direction.
sigmaY	Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

See also

sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur

GaussianBlur() [4/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.GaussianBlur ( Mat src, Mat dst, in(double width, double height) ksize, double sigmaX )

static

Blurs an image using a Gaussian filter.

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

Parameters

src	input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma.
sigmaX	Gaussian kernel standard deviation in X direction.
sigmaY	Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

See also

sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur

GaussianBlur() [5/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.GaussianBlur ( Mat src, Mat dst, in(double width, double height) ksize, double sigmaX, double sigmaY )

static

Blurs an image using a Gaussian filter.

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

Parameters

src	input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma.
sigmaX	Gaussian kernel standard deviation in X direction.
sigmaY	Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

See also

sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur

GaussianBlur() [6/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.GaussianBlur ( Mat src, Mat dst, in(double width, double height) ksize, double sigmaX, double sigmaY, int borderType )

static

Blurs an image using a Gaussian filter.

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

Parameters

src	input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma.
sigmaX	Gaussian kernel standard deviation in X direction.
sigmaY	Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

See also

sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur

GaussianBlur() [7/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.GaussianBlur ( Mat src, Mat dst, Size ksize, double sigmaX )

static

Blurs an image using a Gaussian filter.

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

Parameters

src	input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma.
sigmaX	Gaussian kernel standard deviation in X direction.
sigmaY	Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

See also

sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur

GaussianBlur() [8/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.GaussianBlur ( Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY )

static

Blurs an image using a Gaussian filter.

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

Parameters

src	input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma.
sigmaX	Gaussian kernel standard deviation in X direction.
sigmaY	Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

See also

sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur

GaussianBlur() [9/9]

static void OpenCVForUnity.ImgprocModule.Imgproc.GaussianBlur ( Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY, int borderType )

static

Blurs an image using a Gaussian filter.

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

Parameters

src	input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	output image of the same size and type as src.
ksize	Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma.
sigmaX	Gaussian kernel standard deviation in X direction.
sigmaY	Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

See also

sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur

getAffineTransform()

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getAffineTransform ( MatOfPoint2f src, MatOfPoint2f dst )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

getDerivKernels() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.getDerivKernels ( Mat kx, Mat ky, int dx, int dy, int ksize )

static

Returns filter coefficients for computing spatial image derivatives.

The function computes and returns the filter coefficients for spatial image derivatives. When 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

Parameters

kx	Output matrix of row filter coefficients. It has the type ktype .
ky	Output matrix of column filter coefficients. It has the type ktype .
dx	Derivative order in respect of x.
dy	Derivative order in respect of y.
ksize	Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7.
normalize	Flag indicating whether to normalize (scale down) the filter coefficients or not. Theoretically, the coefficients should have the denominator \(=2^{ksize*2-dx-dy-2}\). If you are going to filter floating-point images, you are likely to use the normalized kernels. But if you compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve all the fractional bits, you may want to set normalize=false .
ktype	Type of filter coefficients. It can be CV_32f or CV_64F .

getDerivKernels() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.getDerivKernels ( Mat kx, Mat ky, int dx, int dy, int ksize, bool normalize )

static

Returns filter coefficients for computing spatial image derivatives.

The function computes and returns the filter coefficients for spatial image derivatives. When 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

Parameters

kx	Output matrix of row filter coefficients. It has the type ktype .
ky	Output matrix of column filter coefficients. It has the type ktype .
dx	Derivative order in respect of x.
dy	Derivative order in respect of y.
ksize	Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7.
normalize	Flag indicating whether to normalize (scale down) the filter coefficients or not. Theoretically, the coefficients should have the denominator \(=2^{ksize*2-dx-dy-2}\). If you are going to filter floating-point images, you are likely to use the normalized kernels. But if you compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve all the fractional bits, you may want to set normalize=false .
ktype	Type of filter coefficients. It can be CV_32f or CV_64F .

getDerivKernels() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.getDerivKernels ( Mat kx, Mat ky, int dx, int dy, int ksize, bool normalize, int ktype )

static

Returns filter coefficients for computing spatial image derivatives.

The function computes and returns the filter coefficients for spatial image derivatives. When 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

Parameters

kx	Output matrix of row filter coefficients. It has the type ktype .
ky	Output matrix of column filter coefficients. It has the type ktype .
dx	Derivative order in respect of x.
dy	Derivative order in respect of y.
ksize	Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7.
normalize	Flag indicating whether to normalize (scale down) the filter coefficients or not. Theoretically, the coefficients should have the denominator \(=2^{ksize*2-dx-dy-2}\). If you are going to filter floating-point images, you are likely to use the normalized kernels. But if you compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve all the fractional bits, you may want to set normalize=false .
ktype	Type of filter coefficients. It can be CV_32f or CV_64F .

getFontScaleFromHeight() [1/2]

static double OpenCVForUnity.ImgprocModule.Imgproc.getFontScaleFromHeight ( int fontFace, int pixelHeight )

static

Calculates the font-specific size to use to achieve a given height in pixels.

Parameters

fontFace	Font to use, see cv::HersheyFonts.
pixelHeight	Pixel height to compute the fontScale for
thickness	Thickness of lines used to render the text.See putText for details.

Returns

The fontSize to use for cv::putText

See also

cv::putText

getFontScaleFromHeight() [2/2]

static double OpenCVForUnity.ImgprocModule.Imgproc.getFontScaleFromHeight ( int fontFace, int pixelHeight, int thickness )

static

Calculates the font-specific size to use to achieve a given height in pixels.

Parameters

fontFace	Font to use, see cv::HersheyFonts.
pixelHeight	Pixel height to compute the fontScale for
thickness	Thickness of lines used to render the text.See putText for details.

Returns

The fontSize to use for cv::putText

See also

cv::putText

getGaborKernel() [1/9]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getGaborKernel ( in Vec2d ksize, double sigma, double theta, double lambd, double gamma )

static

Returns Gabor filter coefficients.

For more details about gabor filter equations and parameters, see: Gabor Filter.

Parameters

ksize	Size of the filter returned.
sigma	Standard deviation of the gaussian envelope.
theta	Orientation of the normal to the parallel stripes of a Gabor function.
lambd	Wavelength of the sinusoidal factor.
gamma	Spatial aspect ratio.
psi	Phase offset.
ktype	Type of filter coefficients. It can be CV_32F or CV_64F .

getGaborKernel() [2/9]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getGaborKernel ( in Vec2d ksize, double sigma, double theta, double lambd, double gamma, double psi )

static

Returns Gabor filter coefficients.

For more details about gabor filter equations and parameters, see: Gabor Filter.

Parameters

ksize	Size of the filter returned.
sigma	Standard deviation of the gaussian envelope.
theta	Orientation of the normal to the parallel stripes of a Gabor function.
lambd	Wavelength of the sinusoidal factor.
gamma	Spatial aspect ratio.
psi	Phase offset.
ktype	Type of filter coefficients. It can be CV_32F or CV_64F .

getGaborKernel() [3/9]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getGaborKernel ( in Vec2d ksize, double sigma, double theta, double lambd, double gamma, double psi, int ktype )

static

Returns Gabor filter coefficients.

For more details about gabor filter equations and parameters, see: Gabor Filter.

Parameters

ksize	Size of the filter returned.
sigma	Standard deviation of the gaussian envelope.
theta	Orientation of the normal to the parallel stripes of a Gabor function.
lambd	Wavelength of the sinusoidal factor.
gamma	Spatial aspect ratio.
psi	Phase offset.
ktype	Type of filter coefficients. It can be CV_32F or CV_64F .

getGaborKernel() [4/9]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getGaborKernel ( in(double width, double height) ksize, double sigma, double theta, double lambd, double gamma )

static

Returns Gabor filter coefficients.

For more details about gabor filter equations and parameters, see: Gabor Filter.

Parameters

ksize	Size of the filter returned.
sigma	Standard deviation of the gaussian envelope.
theta	Orientation of the normal to the parallel stripes of a Gabor function.
lambd	Wavelength of the sinusoidal factor.
gamma	Spatial aspect ratio.
psi	Phase offset.
ktype	Type of filter coefficients. It can be CV_32F or CV_64F .

getGaborKernel() [5/9]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getGaborKernel ( in(double width, double height) ksize, double sigma, double theta, double lambd, double gamma, double psi )

static

Returns Gabor filter coefficients.

For more details about gabor filter equations and parameters, see: Gabor Filter.

Parameters

ksize	Size of the filter returned.
sigma	Standard deviation of the gaussian envelope.
theta	Orientation of the normal to the parallel stripes of a Gabor function.
lambd	Wavelength of the sinusoidal factor.
gamma	Spatial aspect ratio.
psi	Phase offset.
ktype	Type of filter coefficients. It can be CV_32F or CV_64F .

getGaborKernel() [6/9]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getGaborKernel ( in(double width, double height) ksize, double sigma, double theta, double lambd, double gamma, double psi, int ktype )

static

Returns Gabor filter coefficients.

For more details about gabor filter equations and parameters, see: Gabor Filter.

Parameters

ksize	Size of the filter returned.
sigma	Standard deviation of the gaussian envelope.
theta	Orientation of the normal to the parallel stripes of a Gabor function.
lambd	Wavelength of the sinusoidal factor.
gamma	Spatial aspect ratio.
psi	Phase offset.
ktype	Type of filter coefficients. It can be CV_32F or CV_64F .

getGaborKernel() [7/9]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getGaborKernel ( Size ksize, double sigma, double theta, double lambd, double gamma )

static

Returns Gabor filter coefficients.

For more details about gabor filter equations and parameters, see: Gabor Filter.

Parameters

ksize	Size of the filter returned.
sigma	Standard deviation of the gaussian envelope.
theta	Orientation of the normal to the parallel stripes of a Gabor function.
lambd	Wavelength of the sinusoidal factor.
gamma	Spatial aspect ratio.
psi	Phase offset.
ktype	Type of filter coefficients. It can be CV_32F or CV_64F .

getGaborKernel() [8/9]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getGaborKernel ( Size ksize, double sigma, double theta, double lambd, double gamma, double psi )

static

Returns Gabor filter coefficients.

For more details about gabor filter equations and parameters, see: Gabor Filter.

Parameters

ksize	Size of the filter returned.
sigma	Standard deviation of the gaussian envelope.
theta	Orientation of the normal to the parallel stripes of a Gabor function.
lambd	Wavelength of the sinusoidal factor.
gamma	Spatial aspect ratio.
psi	Phase offset.
ktype	Type of filter coefficients. It can be CV_32F or CV_64F .

getGaborKernel() [9/9]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getGaborKernel ( Size ksize, double sigma, double theta, double lambd, double gamma, double psi, int ktype )

static

Returns Gabor filter coefficients.

For more details about gabor filter equations and parameters, see: Gabor Filter.

Parameters

ksize	Size of the filter returned.
sigma	Standard deviation of the gaussian envelope.
theta	Orientation of the normal to the parallel stripes of a Gabor function.
lambd	Wavelength of the sinusoidal factor.
gamma	Spatial aspect ratio.
psi	Phase offset.
ktype	Type of filter coefficients. It can be CV_32F or CV_64F .

getGaussianKernel() [1/2]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getGaussianKernel ( int ksize, double sigma )

static

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.

Parameters

ksize	Aperture size. It should be odd ( \(\texttt{ksize} \mod 2 = 1\) ) and positive.
sigma	Gaussian standard deviation. If it is non-positive, it is computed from ksize as sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8.
ktype	Type of filter coefficients. It can be CV_32F or CV_64F .

sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur

getGaussianKernel() [2/2]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getGaussianKernel ( int ksize, double sigma, int ktype )

static

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.

Parameters

ksize	Aperture size. It should be odd ( \(\texttt{ksize} \mod 2 = 1\) ) and positive.
sigma	Gaussian standard deviation. If it is non-positive, it is computed from ksize as sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8.
ktype	Type of filter coefficients. It can be CV_32F or CV_64F .

sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur

getPerspectiveTransform() [1/2]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getPerspectiveTransform ( Mat src, Mat dst )

static

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

\[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\]

Parameters

src	Coordinates of quadrangle vertices in the source image.
dst	Coordinates of the corresponding quadrangle vertices in the destination image.
solveMethod	method passed to cv::solve (#DecompTypes)

See also

findHomography, warpPerspective, perspectiveTransform

getPerspectiveTransform() [2/2]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getPerspectiveTransform ( Mat src, Mat dst, int solveMethod )

static

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

\[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\]

Parameters

src	Coordinates of quadrangle vertices in the source image.
dst	Coordinates of the corresponding quadrangle vertices in the destination image.
solveMethod	method passed to cv::solve (#DecompTypes)

See also

findHomography, warpPerspective, perspectiveTransform

getRectSubPix() [1/6]

static void OpenCVForUnity.ImgprocModule.Imgproc.getRectSubPix ( Mat image, in Vec2d patchSize, in Vec2d center, Mat patch )

static

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.

Parameters

image	Source image.
patchSize	Size of the extracted patch.
center	Floating point coordinates of the center of the extracted rectangle within the source image. The center must be inside the image.
patch	Extracted patch that has the size patchSize and the same number of channels as src .
patchType	Depth of the extracted pixels. By default, they have the same depth as src .

See also

warpAffine, warpPerspective

getRectSubPix() [2/6]

static void OpenCVForUnity.ImgprocModule.Imgproc.getRectSubPix ( Mat image, in Vec2d patchSize, in Vec2d center, Mat patch, int patchType )

static

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.

Parameters

image	Source image.
patchSize	Size of the extracted patch.
center	Floating point coordinates of the center of the extracted rectangle within the source image. The center must be inside the image.
patch	Extracted patch that has the size patchSize and the same number of channels as src .
patchType	Depth of the extracted pixels. By default, they have the same depth as src .

See also

warpAffine, warpPerspective

getRectSubPix() [3/6]

static void OpenCVForUnity.ImgprocModule.Imgproc.getRectSubPix ( Mat image, in(double width, double height) patchSize, in(double x, double y) center, Mat patch )

static

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.

Parameters

image	Source image.
patchSize	Size of the extracted patch.
center	Floating point coordinates of the center of the extracted rectangle within the source image. The center must be inside the image.
patch	Extracted patch that has the size patchSize and the same number of channels as src .
patchType	Depth of the extracted pixels. By default, they have the same depth as src .

See also

warpAffine, warpPerspective

getRectSubPix() [4/6]

static void OpenCVForUnity.ImgprocModule.Imgproc.getRectSubPix ( Mat image, in(double width, double height) patchSize, in(double x, double y) center, Mat patch, int patchType )

static

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.

Parameters

image	Source image.
patchSize	Size of the extracted patch.
center	Floating point coordinates of the center of the extracted rectangle within the source image. The center must be inside the image.
patch	Extracted patch that has the size patchSize and the same number of channels as src .
patchType	Depth of the extracted pixels. By default, they have the same depth as src .

See also

warpAffine, warpPerspective

getRectSubPix() [5/6]

static void OpenCVForUnity.ImgprocModule.Imgproc.getRectSubPix ( Mat image, Size patchSize, Point center, Mat patch )

static

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.

Parameters

image	Source image.
patchSize	Size of the extracted patch.
center	Floating point coordinates of the center of the extracted rectangle within the source image. The center must be inside the image.
patch	Extracted patch that has the size patchSize and the same number of channels as src .
patchType	Depth of the extracted pixels. By default, they have the same depth as src .

See also

warpAffine, warpPerspective

getRectSubPix() [6/6]

static void OpenCVForUnity.ImgprocModule.Imgproc.getRectSubPix ( Mat image, Size patchSize, Point center, Mat patch, int patchType )

static

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.

Parameters

image	Source image.
patchSize	Size of the extracted patch.
center	Floating point coordinates of the center of the extracted rectangle within the source image. The center must be inside the image.
patch	Extracted patch that has the size patchSize and the same number of channels as src .
patchType	Depth of the extracted pixels. By default, they have the same depth as src .

See also

warpAffine, warpPerspective

getRotationMatrix2D() [1/3]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getRotationMatrix2D ( in Vec2d center, double angle, double scale )

static

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.

Parameters

center	Center of the rotation in the source image.
angle	Rotation angle in degrees. Positive values mean counter-clockwise rotation (the coordinate origin is assumed to be the top-left corner).
scale	Isotropic scale factor.

See also

getAffineTransform, warpAffine, transform

getRotationMatrix2D() [2/3]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getRotationMatrix2D ( in(double x, double y) center, double angle, double scale )

static

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.

Parameters

center	Center of the rotation in the source image.
angle	Rotation angle in degrees. Positive values mean counter-clockwise rotation (the coordinate origin is assumed to be the top-left corner).
scale	Isotropic scale factor.

See also

getAffineTransform, warpAffine, transform

getRotationMatrix2D() [3/3]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getRotationMatrix2D ( Point center, double angle, double scale )

static

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.

Parameters

center	Center of the rotation in the source image.
angle	Rotation angle in degrees. Positive values mean counter-clockwise rotation (the coordinate origin is assumed to be the top-left corner).
scale	Isotropic scale factor.

See also

getAffineTransform, warpAffine, transform

getStructuringElement() [1/6]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getStructuringElement ( int shape, in Vec2d ksize )

static

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.

Parameters

shape	Element shape that could be one of #MorphShapes
ksize	Size of the structuring element.
anchor	Anchor position within the element. The default value \((-1, -1)\) means that the anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted.

getStructuringElement() [2/6]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getStructuringElement ( int shape, in Vec2d ksize, in Vec2d anchor )

static

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.

Parameters

shape	Element shape that could be one of #MorphShapes
ksize	Size of the structuring element.
anchor	Anchor position within the element. The default value \((-1, -1)\) means that the anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted.

getStructuringElement() [3/6]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getStructuringElement ( int shape, in(double width, double height) ksize )

static

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.

Parameters

shape	Element shape that could be one of #MorphShapes
ksize	Size of the structuring element.
anchor	Anchor position within the element. The default value \((-1, -1)\) means that the anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted.

getStructuringElement() [4/6]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getStructuringElement ( int shape, in(double width, double height) ksize, in(double x, double y) anchor )

static

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.

Parameters

shape	Element shape that could be one of #MorphShapes
ksize	Size of the structuring element.
anchor	Anchor position within the element. The default value \((-1, -1)\) means that the anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted.

getStructuringElement() [5/6]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getStructuringElement ( int shape, Size ksize )

static

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.

Parameters

shape	Element shape that could be one of #MorphShapes
ksize	Size of the structuring element.
anchor	Anchor position within the element. The default value \((-1, -1)\) means that the anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted.

getStructuringElement() [6/6]

static Mat OpenCVForUnity.ImgprocModule.Imgproc.getStructuringElement ( int shape, Size ksize, Point anchor )

static

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.

Parameters

shape	Element shape that could be one of #MorphShapes
ksize	Size of the structuring element.
anchor	Anchor position within the element. The default value \((-1, -1)\) means that the anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted.

getTextSize()

static Size OpenCVForUnity.ImgprocModule.Imgproc.getTextSize ( string text, int fontFace, double fontScale, int thickness, int[] baseLine )

static

getTextSizeAsValueTuple()

static double double height OpenCVForUnity.ImgprocModule.Imgproc.getTextSizeAsValueTuple ( string text, int fontFace, double fontScale, int thickness, int[] baseLine )

static

getTextSizeAsVec2d()

static Vec2d OpenCVForUnity.ImgprocModule.Imgproc.getTextSizeAsVec2d ( string text, int fontFace, double fontScale, int thickness, int[] baseLine )

static

goodFeaturesToTrack() [1/8]

static void OpenCVForUnity.ImgprocModule.Imgproc.goodFeaturesToTrack ( Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance )

static

Determines strong corners on an image.

The function finds the most prominent corners in the image or in the specified image region, as described in [Shi94]

• Function calculates the corner quality measure at every source image pixel using the cornerMinEigenVal or cornerHarris .
• Function performs a non-maximum suppression (the local maximums in 3 x 3 neighborhood are retained).
• The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
• The remaining corners are sorted by the quality measure in the descending order.
• Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.

The function can be used to initialize a point-based tracker of an object.

Note

If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .

Parameters

image	Input 8-bit or floating-point 32-bit, single-channel image.
corners	Output vector of detected corners.
maxCorners	Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
qualityLevel	Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance	Minimum possible Euclidean distance between the returned corners.
mask	Optional region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
blockSize	Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
useHarrisDetector	Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.
k	Free parameter of the Harris detector.

See also

cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

goodFeaturesToTrack() [2/8]

static void OpenCVForUnity.ImgprocModule.Imgproc.goodFeaturesToTrack ( Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask )

static

Determines strong corners on an image.

The function finds the most prominent corners in the image or in the specified image region, as described in [Shi94]

• Function calculates the corner quality measure at every source image pixel using the cornerMinEigenVal or cornerHarris .
• Function performs a non-maximum suppression (the local maximums in 3 x 3 neighborhood are retained).
• The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
• The remaining corners are sorted by the quality measure in the descending order.
• Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.

The function can be used to initialize a point-based tracker of an object.

Note

If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .

Parameters

image	Input 8-bit or floating-point 32-bit, single-channel image.
corners	Output vector of detected corners.
maxCorners	Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
qualityLevel	Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance	Minimum possible Euclidean distance between the returned corners.
mask	Optional region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
blockSize	Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
useHarrisDetector	Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.
k	Free parameter of the Harris detector.

See also

cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

goodFeaturesToTrack() [3/8]

static void OpenCVForUnity.ImgprocModule.Imgproc.goodFeaturesToTrack ( Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize )

static

Determines strong corners on an image.

The function finds the most prominent corners in the image or in the specified image region, as described in [Shi94]

• Function calculates the corner quality measure at every source image pixel using the cornerMinEigenVal or cornerHarris .
• Function performs a non-maximum suppression (the local maximums in 3 x 3 neighborhood are retained).
• The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
• The remaining corners are sorted by the quality measure in the descending order.
• Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.

The function can be used to initialize a point-based tracker of an object.

Note

If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .

Parameters

image	Input 8-bit or floating-point 32-bit, single-channel image.
corners	Output vector of detected corners.
maxCorners	Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
qualityLevel	Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance	Minimum possible Euclidean distance between the returned corners.
mask	Optional region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
blockSize	Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
useHarrisDetector	Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.
k	Free parameter of the Harris detector.

See also

cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

goodFeaturesToTrack() [4/8]

static void OpenCVForUnity.ImgprocModule.Imgproc.goodFeaturesToTrack ( Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, bool useHarrisDetector )

static

Determines strong corners on an image.

The function finds the most prominent corners in the image or in the specified image region, as described in [Shi94]

• Function calculates the corner quality measure at every source image pixel using the cornerMinEigenVal or cornerHarris .
• Function performs a non-maximum suppression (the local maximums in 3 x 3 neighborhood are retained).
• The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
• The remaining corners are sorted by the quality measure in the descending order.
• Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.

The function can be used to initialize a point-based tracker of an object.

Note

If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .

Parameters

image	Input 8-bit or floating-point 32-bit, single-channel image.
corners	Output vector of detected corners.
maxCorners	Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
qualityLevel	Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance	Minimum possible Euclidean distance between the returned corners.
mask	Optional region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
blockSize	Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
useHarrisDetector	Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.
k	Free parameter of the Harris detector.

See also

cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

goodFeaturesToTrack() [5/8]

static void OpenCVForUnity.ImgprocModule.Imgproc.goodFeaturesToTrack ( Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, bool useHarrisDetector, double k )

static

Determines strong corners on an image.

The function finds the most prominent corners in the image or in the specified image region, as described in [Shi94]

• Function calculates the corner quality measure at every source image pixel using the cornerMinEigenVal or cornerHarris .
• Function performs a non-maximum suppression (the local maximums in 3 x 3 neighborhood are retained).
• The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
• The remaining corners are sorted by the quality measure in the descending order.
• Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.

The function can be used to initialize a point-based tracker of an object.

Note

If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .

Parameters

image	Input 8-bit or floating-point 32-bit, single-channel image.
corners	Output vector of detected corners.
maxCorners	Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
qualityLevel	Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance	Minimum possible Euclidean distance between the returned corners.
mask	Optional region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
blockSize	Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
useHarrisDetector	Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.
k	Free parameter of the Harris detector.

See also

cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

goodFeaturesToTrack() [6/8]

static void OpenCVForUnity.ImgprocModule.Imgproc.goodFeaturesToTrack ( Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize )

static

goodFeaturesToTrack() [7/8]

static void OpenCVForUnity.ImgprocModule.Imgproc.goodFeaturesToTrack ( Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, bool useHarrisDetector )

static

goodFeaturesToTrack() [8/8]

static void OpenCVForUnity.ImgprocModule.Imgproc.goodFeaturesToTrack ( Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, bool useHarrisDetector, double k )

static

goodFeaturesToTrackWithQuality() [1/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.goodFeaturesToTrackWithQuality ( Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality )

static

Same as above, but returns also quality measure of the detected corners.

Parameters

image	Input 8-bit or floating-point 32-bit, single-channel image.
corners	Output vector of detected corners.
maxCorners	Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
qualityLevel	Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance	Minimum possible Euclidean distance between the returned corners.
mask	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.
cornersQuality	Output vector of quality measure of the detected corners.
blockSize	Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
gradientSize	Aperture parameter for the Sobel operator used for derivatives computation. See cornerEigenValsAndVecs .
useHarrisDetector	Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.
k	Free parameter of the Harris detector.

goodFeaturesToTrackWithQuality() [2/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.goodFeaturesToTrackWithQuality ( Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize )

static

Same as above, but returns also quality measure of the detected corners.

Parameters

image	Input 8-bit or floating-point 32-bit, single-channel image.
corners	Output vector of detected corners.
maxCorners	Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
qualityLevel	Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance	Minimum possible Euclidean distance between the returned corners.
mask	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.
cornersQuality	Output vector of quality measure of the detected corners.
blockSize	Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
gradientSize	Aperture parameter for the Sobel operator used for derivatives computation. See cornerEigenValsAndVecs .
useHarrisDetector	Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.
k	Free parameter of the Harris detector.

goodFeaturesToTrackWithQuality() [3/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.goodFeaturesToTrackWithQuality ( Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize )

static

Same as above, but returns also quality measure of the detected corners.

Parameters

image	Input 8-bit or floating-point 32-bit, single-channel image.
corners	Output vector of detected corners.
maxCorners	Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
qualityLevel	Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance	Minimum possible Euclidean distance between the returned corners.
mask	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.
cornersQuality	Output vector of quality measure of the detected corners.
blockSize	Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
gradientSize	Aperture parameter for the Sobel operator used for derivatives computation. See cornerEigenValsAndVecs .
useHarrisDetector	Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.
k	Free parameter of the Harris detector.

goodFeaturesToTrackWithQuality() [4/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.goodFeaturesToTrackWithQuality ( Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize, bool useHarrisDetector )

static

Same as above, but returns also quality measure of the detected corners.

Parameters

image	Input 8-bit or floating-point 32-bit, single-channel image.
corners	Output vector of detected corners.
maxCorners	Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
qualityLevel	Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance	Minimum possible Euclidean distance between the returned corners.
mask	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.
cornersQuality	Output vector of quality measure of the detected corners.
blockSize	Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
gradientSize	Aperture parameter for the Sobel operator used for derivatives computation. See cornerEigenValsAndVecs .
useHarrisDetector	Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.
k	Free parameter of the Harris detector.

goodFeaturesToTrackWithQuality() [5/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.goodFeaturesToTrackWithQuality ( Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize, bool useHarrisDetector, double k )

static

Same as above, but returns also quality measure of the detected corners.

Parameters

image	Input 8-bit or floating-point 32-bit, single-channel image.
corners	Output vector of detected corners.
maxCorners	Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
qualityLevel	Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance	Minimum possible Euclidean distance between the returned corners.
mask	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.
cornersQuality	Output vector of quality measure of the detected corners.
blockSize	Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
gradientSize	Aperture parameter for the Sobel operator used for derivatives computation. See cornerEigenValsAndVecs .
useHarrisDetector	Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.
k	Free parameter of the Harris detector.

grabCut() [1/6]

static void OpenCVForUnity.ImgprocModule.Imgproc.grabCut ( Mat img, Mat mask, in Vec4i rect, Mat bgdModel, Mat fgdModel, int iterCount )

static

Runs the GrabCut algorithm.

The function implements the GrabCut image segmentation algorithm.

Parameters

img	Input 8-bit 3-channel image.
mask	Input/output 8-bit single-channel mask. The mask is initialized by the function when mode is set to GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses.
rect	ROI containing a segmented object. The pixels outside of the ROI are marked as "obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT .
bgdModel	Temporary array for the background model. Do not modify it while you are processing the same image.
fgdModel	Temporary arrays for the foreground model. Do not modify it while you are processing the same image.
iterCount	Number of iterations the algorithm should make before returning the result. Note that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or mode==GC_EVAL .
mode	Operation mode that could be one of the #GrabCutModes

grabCut() [2/6]

static void OpenCVForUnity.ImgprocModule.Imgproc.grabCut ( Mat img, Mat mask, in Vec4i rect, Mat bgdModel, Mat fgdModel, int iterCount, int mode )

static

Runs the GrabCut algorithm.

The function implements the GrabCut image segmentation algorithm.

Parameters

img	Input 8-bit 3-channel image.
mask	Input/output 8-bit single-channel mask. The mask is initialized by the function when mode is set to GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses.
rect	ROI containing a segmented object. The pixels outside of the ROI are marked as "obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT .
bgdModel	Temporary array for the background model. Do not modify it while you are processing the same image.
fgdModel	Temporary arrays for the foreground model. Do not modify it while you are processing the same image.
iterCount	Number of iterations the algorithm should make before returning the result. Note that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or mode==GC_EVAL .
mode	Operation mode that could be one of the #GrabCutModes

grabCut() [3/6]

static void OpenCVForUnity.ImgprocModule.Imgproc.grabCut ( Mat img, Mat mask, in(int x, int y, int width, int height) rect, Mat bgdModel, Mat fgdModel, int iterCount )

static

Runs the GrabCut algorithm.

The function implements the GrabCut image segmentation algorithm.

Parameters

img	Input 8-bit 3-channel image.
mask	Input/output 8-bit single-channel mask. The mask is initialized by the function when mode is set to GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses.
rect	ROI containing a segmented object. The pixels outside of the ROI are marked as "obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT .
bgdModel	Temporary array for the background model. Do not modify it while you are processing the same image.
fgdModel	Temporary arrays for the foreground model. Do not modify it while you are processing the same image.
iterCount	Number of iterations the algorithm should make before returning the result. Note that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or mode==GC_EVAL .
mode	Operation mode that could be one of the #GrabCutModes

grabCut() [4/6]

static void OpenCVForUnity.ImgprocModule.Imgproc.grabCut ( Mat img, Mat mask, in(int x, int y, int width, int height) rect, Mat bgdModel, Mat fgdModel, int iterCount, int mode )

static

Runs the GrabCut algorithm.

The function implements the GrabCut image segmentation algorithm.

Parameters

img	Input 8-bit 3-channel image.
mask	Input/output 8-bit single-channel mask. The mask is initialized by the function when mode is set to GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses.
rect	ROI containing a segmented object. The pixels outside of the ROI are marked as "obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT .
bgdModel	Temporary array for the background model. Do not modify it while you are processing the same image.
fgdModel	Temporary arrays for the foreground model. Do not modify it while you are processing the same image.
iterCount	Number of iterations the algorithm should make before returning the result. Note that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or mode==GC_EVAL .
mode	Operation mode that could be one of the #GrabCutModes

grabCut() [5/6]

static void OpenCVForUnity.ImgprocModule.Imgproc.grabCut ( Mat img, Mat mask, Rect rect, Mat bgdModel, Mat fgdModel, int iterCount )

static

Runs the GrabCut algorithm.

The function implements the GrabCut image segmentation algorithm.

Parameters

img	Input 8-bit 3-channel image.
mask	Input/output 8-bit single-channel mask. The mask is initialized by the function when mode is set to GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses.
rect	ROI containing a segmented object. The pixels outside of the ROI are marked as "obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT .
bgdModel	Temporary array for the background model. Do not modify it while you are processing the same image.
fgdModel	Temporary arrays for the foreground model. Do not modify it while you are processing the same image.
iterCount	Number of iterations the algorithm should make before returning the result. Note that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or mode==GC_EVAL .
mode	Operation mode that could be one of the #GrabCutModes

grabCut() [6/6]

static void OpenCVForUnity.ImgprocModule.Imgproc.grabCut ( Mat img, Mat mask, Rect rect, Mat bgdModel, Mat fgdModel, int iterCount, int mode )

static

Runs the GrabCut algorithm.

The function implements the GrabCut image segmentation algorithm.

Parameters

img	Input 8-bit 3-channel image.
mask	Input/output 8-bit single-channel mask. The mask is initialized by the function when mode is set to GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses.
rect	ROI containing a segmented object. The pixels outside of the ROI are marked as "obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT .
bgdModel	Temporary array for the background model. Do not modify it while you are processing the same image.
fgdModel	Temporary arrays for the foreground model. Do not modify it while you are processing the same image.
iterCount	Number of iterations the algorithm should make before returning the result. Note that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or mode==GC_EVAL .
mode	Operation mode that could be one of the #GrabCutModes

HoughCircles() [1/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughCircles ( Mat image, Mat circles, int method, double dp, double minDist )

static

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: :

Note

Usually the function detects the centers of circles well. However, it may fail to find correct radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if you know it. Or, 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.

Parameters

image	8-bit, single-channel, grayscale input image.
circles	Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .
method	Detection method, see #HoughModes. The available methods are HOUGH_GRADIENT and HOUGH_GRADIENT_ALT.
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.
minDist	Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.
param1	First method-specific parameter. In case of 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.
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.
minRadius	Minimum circle radius.
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 also

fitEllipse, minEnclosingCircle

HoughCircles() [2/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughCircles ( Mat image, Mat circles, int method, double dp, double minDist, double param1 )

static

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: :

Note

Usually the function detects the centers of circles well. However, it may fail to find correct radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if you know it. Or, 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.

Parameters

image	8-bit, single-channel, grayscale input image.
circles	Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .
method	Detection method, see #HoughModes. The available methods are HOUGH_GRADIENT and HOUGH_GRADIENT_ALT.
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.
minDist	Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.
param1	First method-specific parameter. In case of 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.
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.
minRadius	Minimum circle radius.
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 also

fitEllipse, minEnclosingCircle

HoughCircles() [3/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughCircles ( Mat image, Mat circles, int method, double dp, double minDist, double param1, double param2 )

static

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: :

Note

Usually the function detects the centers of circles well. However, it may fail to find correct radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if you know it. Or, 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.

Parameters

image	8-bit, single-channel, grayscale input image.
circles	Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .
method	Detection method, see #HoughModes. The available methods are HOUGH_GRADIENT and HOUGH_GRADIENT_ALT.
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.
minDist	Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.
param1	First method-specific parameter. In case of 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.
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.
minRadius	Minimum circle radius.
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 also

fitEllipse, minEnclosingCircle

HoughCircles() [4/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughCircles ( Mat image, Mat circles, int method, double dp, double minDist, double param1, double param2, int minRadius )

static

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: :

Note

Usually the function detects the centers of circles well. However, it may fail to find correct radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if you know it. Or, 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.

Parameters

image	8-bit, single-channel, grayscale input image.
circles	Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .
method	Detection method, see #HoughModes. The available methods are HOUGH_GRADIENT and HOUGH_GRADIENT_ALT.
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.
minDist	Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.
param1	First method-specific parameter. In case of 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.
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.
minRadius	Minimum circle radius.
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 also

fitEllipse, minEnclosingCircle

HoughCircles() [5/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughCircles ( Mat image, Mat circles, int method, double dp, double minDist, double param1, double param2, int minRadius, int maxRadius )

static

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: :

Note

Usually the function detects the centers of circles well. However, it may fail to find correct radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if you know it. Or, 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.

Parameters

image	8-bit, single-channel, grayscale input image.
circles	Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .
method	Detection method, see #HoughModes. The available methods are HOUGH_GRADIENT and HOUGH_GRADIENT_ALT.
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.
minDist	Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.
param1	First method-specific parameter. In case of 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.
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.
minRadius	Minimum circle radius.
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 also

fitEllipse, minEnclosingCircle

HoughLines() [1/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughLines ( Mat image, Mat lines, double rho, double theta, int threshold )

static

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&gt; for a good explanation of Hough transform.

Parameters

image	8-bit, single-channel binary source image. The image may be modified by the function.
lines	Output vector of lines. Each line is represented by a 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.
rho	Distance resolution of the accumulator in pixels.
theta	Angle resolution of the accumulator in radians.
threshold	Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ).
srn	For the multi-scale Hough transform, it is a divisor for the distance resolution rho. The coarse accumulator distance resolution is rho and the accurate accumulator resolution is rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these parameters should be positive.
stn	For the multi-scale Hough transform, it is a divisor for the distance resolution theta.
min_theta	For standard and multi-scale Hough transform, minimum angle to check for lines. Must fall between 0 and max_theta.
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.

HoughLines() [2/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughLines ( Mat image, Mat lines, double rho, double theta, int threshold, double srn )

static

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&gt; for a good explanation of Hough transform.

Parameters

image	8-bit, single-channel binary source image. The image may be modified by the function.
lines	Output vector of lines. Each line is represented by a 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.
rho	Distance resolution of the accumulator in pixels.
theta	Angle resolution of the accumulator in radians.
threshold	Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ).
srn	For the multi-scale Hough transform, it is a divisor for the distance resolution rho. The coarse accumulator distance resolution is rho and the accurate accumulator resolution is rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these parameters should be positive.
stn	For the multi-scale Hough transform, it is a divisor for the distance resolution theta.
min_theta	For standard and multi-scale Hough transform, minimum angle to check for lines. Must fall between 0 and max_theta.
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.

HoughLines() [3/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughLines ( Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn )

static

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&gt; for a good explanation of Hough transform.

Parameters

image	8-bit, single-channel binary source image. The image may be modified by the function.
lines	Output vector of lines. Each line is represented by a 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.
rho	Distance resolution of the accumulator in pixels.
theta	Angle resolution of the accumulator in radians.
threshold	Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ).
srn	For the multi-scale Hough transform, it is a divisor for the distance resolution rho. The coarse accumulator distance resolution is rho and the accurate accumulator resolution is rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these parameters should be positive.
stn	For the multi-scale Hough transform, it is a divisor for the distance resolution theta.
min_theta	For standard and multi-scale Hough transform, minimum angle to check for lines. Must fall between 0 and max_theta.
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.

HoughLines() [4/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughLines ( Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn, double min_theta )

static

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&gt; for a good explanation of Hough transform.

Parameters

image	8-bit, single-channel binary source image. The image may be modified by the function.
lines	Output vector of lines. Each line is represented by a 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.
rho	Distance resolution of the accumulator in pixels.
theta	Angle resolution of the accumulator in radians.
threshold	Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ).
srn	For the multi-scale Hough transform, it is a divisor for the distance resolution rho. The coarse accumulator distance resolution is rho and the accurate accumulator resolution is rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these parameters should be positive.
stn	For the multi-scale Hough transform, it is a divisor for the distance resolution theta.
min_theta	For standard and multi-scale Hough transform, minimum angle to check for lines. Must fall between 0 and max_theta.
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.

HoughLines() [5/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughLines ( Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn, double min_theta, double max_theta )

static

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&gt; for a good explanation of Hough transform.

Parameters

image	8-bit, single-channel binary source image. The image may be modified by the function.
lines	Output vector of lines. Each line is represented by a 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.
rho	Distance resolution of the accumulator in pixels.
theta	Angle resolution of the accumulator in radians.
threshold	Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ).
srn	For the multi-scale Hough transform, it is a divisor for the distance resolution rho. The coarse accumulator distance resolution is rho and the accurate accumulator resolution is rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these parameters should be positive.
stn	For the multi-scale Hough transform, it is a divisor for the distance resolution theta.
min_theta	For standard and multi-scale Hough transform, minimum angle to check for lines. Must fall between 0 and max_theta.
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.

HoughLinesP() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughLinesP ( Mat image, Mat lines, double rho, double theta, int threshold )

static

Finds line segments in a binary image using the probabilistic Hough transform.

The function implements the probabilistic Hough transform algorithm for line detection, described in [Matas00]

See the line detection example below:

This is a sample picture the function parameters have been tuned for:

image

And this is the output of the above program in case of the probabilistic Hough transform:

Parameters

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

See also

LineSegmentDetector

HoughLinesP() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughLinesP ( Mat image, Mat lines, double rho, double theta, int threshold, double minLineLength )

static

Finds line segments in a binary image using the probabilistic Hough transform.

The function implements the probabilistic Hough transform algorithm for line detection, described in [Matas00]

See the line detection example below:

This is a sample picture the function parameters have been tuned for:

image

And this is the output of the above program in case of the probabilistic Hough transform:

Parameters

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

See also

LineSegmentDetector

HoughLinesP() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughLinesP ( Mat image, Mat lines, double rho, double theta, int threshold, double minLineLength, double maxLineGap )

static

Finds line segments in a binary image using the probabilistic Hough transform.

The function implements the probabilistic Hough transform algorithm for line detection, described in [Matas00]

See the line detection example below:

This is a sample picture the function parameters have been tuned for:

image

And this is the output of the above program in case of the probabilistic Hough transform:

Parameters

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

See also

LineSegmentDetector

HoughLinesPointSet()

static void OpenCVForUnity.ImgprocModule.Imgproc.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 )

static

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.

Parameters

point	Input vector of points. Each vector must be encoded as a Point vector \((x,y)\). Type must be CV_32FC2 or CV_32SC2.
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.
lines_max	Max count of Hough lines.
threshold	Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ).
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.).
max_rho	Maximum value for \(\rho\) for the accumulator.
rho_step	Distance resolution of the accumulator.
min_theta	Minimum angle value of the accumulator in radians.
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.
theta_step	Angle resolution of the accumulator in radians.

HoughLinesWithAccumulator() [1/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughLinesWithAccumulator ( Mat image, Mat lines, double rho, double theta, int threshold )

static

Finds lines in a binary image using the standard Hough transform and get accumulator.

Note

This function is for bindings use only. Use original function in C++ code

See also

HoughLines

HoughLinesWithAccumulator() [2/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughLinesWithAccumulator ( Mat image, Mat lines, double rho, double theta, int threshold, double srn )

static

Finds lines in a binary image using the standard Hough transform and get accumulator.

Note

This function is for bindings use only. Use original function in C++ code

See also

HoughLines

HoughLinesWithAccumulator() [3/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughLinesWithAccumulator ( Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn )

static

Finds lines in a binary image using the standard Hough transform and get accumulator.

Note

This function is for bindings use only. Use original function in C++ code

See also

HoughLines

HoughLinesWithAccumulator() [4/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughLinesWithAccumulator ( Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn, double min_theta )

static

Finds lines in a binary image using the standard Hough transform and get accumulator.

Note

This function is for bindings use only. Use original function in C++ code

See also

HoughLines

HoughLinesWithAccumulator() [5/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.HoughLinesWithAccumulator ( Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn, double min_theta, double max_theta )

static

Finds lines in a binary image using the standard Hough transform and get accumulator.

Note

This function is for bindings use only. Use original function in C++ code

See also

HoughLines

HuMoments() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.HuMoments ( in Vec10d m, Mat hu )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

HuMoments() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.HuMoments ( in(double m00, double m10, double m01, double m20, double m11, double m02, double m30, double m21, double m12, double m03) m, Mat hu )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

HuMoments() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.HuMoments ( Moments m, Mat hu )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

integral() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.integral ( Mat src, Mat sum )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

integral() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.integral ( Mat src, Mat sum, int sdepth )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

integral2() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.integral2 ( Mat src, Mat sum, Mat sqsum )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

integral2() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.integral2 ( Mat src, Mat sum, Mat sqsum, int sdepth )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

integral2() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.integral2 ( Mat src, Mat sum, Mat sqsum, int sdepth, int sqdepth )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

integral3() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.integral3 ( Mat src, Mat sum, Mat sqsum, Mat tilted )

static

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 .

Parameters

src	input image as \(W \times H\), 8-bit or floating-point (32f or 64f).
sum	integral image as \((W+1)\times (H+1)\) , 32-bit integer or floating-point (32f or 64f).
sqsum	integral image for squared pixel values; it is \((W+1)\times (H+1)\), double-precision floating-point (64f) array.
tilted	integral for the image rotated by 45 degrees; it is \((W+1)\times (H+1)\) array with the same data type as sum.
sdepth	desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or CV_64F.
sqdepth	desired depth of the integral image of squared pixel values, CV_32F or CV_64F.

integral3() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.integral3 ( Mat src, Mat sum, Mat sqsum, Mat tilted, int sdepth )

static

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 .

Parameters

src	input image as \(W \times H\), 8-bit or floating-point (32f or 64f).
sum	integral image as \((W+1)\times (H+1)\) , 32-bit integer or floating-point (32f or 64f).
sqsum	integral image for squared pixel values; it is \((W+1)\times (H+1)\), double-precision floating-point (64f) array.
tilted	integral for the image rotated by 45 degrees; it is \((W+1)\times (H+1)\) array with the same data type as sum.
sdepth	desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or CV_64F.
sqdepth	desired depth of the integral image of squared pixel values, CV_32F or CV_64F.

integral3() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.integral3 ( Mat src, Mat sum, Mat sqsum, Mat tilted, int sdepth, int sqdepth )

static

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 .

Parameters

src	input image as \(W \times H\), 8-bit or floating-point (32f or 64f).
sum	integral image as \((W+1)\times (H+1)\) , 32-bit integer or floating-point (32f or 64f).
sqsum	integral image for squared pixel values; it is \((W+1)\times (H+1)\), double-precision floating-point (64f) array.
tilted	integral for the image rotated by 45 degrees; it is \((W+1)\times (H+1)\) array with the same data type as sum.
sdepth	desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or CV_64F.
sqdepth	desired depth of the integral image of squared pixel values, CV_32F or CV_64F.

intersectConvexConvex() [1/2]

static float OpenCVForUnity.ImgprocModule.Imgproc.intersectConvexConvex ( Mat p1, Mat p2, Mat p12 )

static

Finds intersection of two convex polygons.

Parameters

p1	First polygon
p2	Second polygon
p12	Output polygon describing the intersecting area
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.

Returns

Absolute value of area of intersecting polygon

Note

intersectConvexConvex doesn't confirm that both polygons are convex and will return invalid results if they aren't.

intersectConvexConvex() [2/2]

static float OpenCVForUnity.ImgprocModule.Imgproc.intersectConvexConvex ( Mat p1, Mat p2, Mat p12, bool handleNested )

static

Finds intersection of two convex polygons.

Parameters

p1	First polygon
p2	Second polygon
p12	Output polygon describing the intersecting area
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.

Returns

Absolute value of area of intersecting polygon

Note

intersectConvexConvex doesn't confirm that both polygons are convex and will return invalid results if they aren't.

invertAffineTransform()

static void OpenCVForUnity.ImgprocModule.Imgproc.invertAffineTransform ( Mat M, Mat iM )

static

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.

Parameters

M	Original affine transformation.
iM	Output reverse affine transformation.

isContourConvex()

static bool OpenCVForUnity.ImgprocModule.Imgproc.isContourConvex ( MatOfPoint contour )

static

Tests a contour convexity.

The function tests whether the input contour is convex or not. The contour must be simple, that is, without self-intersections. Otherwise, the function output is undefined.

Parameters

contour

Input vector of 2D points, stored in std::vector<> or Mat

Laplacian() [1/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.Laplacian ( Mat src, Mat dst, int ddepth )

static

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 ksize > 1. When ksize == 1, the Laplacian is computed by filtering the image with the following \(3 \times 3\) aperture:

\[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\]

Parameters

src	Source image.
dst	Destination image of the same size and the same number of channels as src .
ddepth	Desired depth of the destination image, see combinations.
ksize	Aperture size used to compute the second-derivative filters. See getDerivKernels for details. The size must be positive and odd.
scale	Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See getDerivKernels for details.
delta	Optional delta value that is added to the results prior to storing them in dst .
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Sobel, Scharr

Laplacian() [2/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.Laplacian ( Mat src, Mat dst, int ddepth, int ksize )

static

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 ksize > 1. When ksize == 1, the Laplacian is computed by filtering the image with the following \(3 \times 3\) aperture:

\[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\]

Parameters

src	Source image.
dst	Destination image of the same size and the same number of channels as src .
ddepth	Desired depth of the destination image, see combinations.
ksize	Aperture size used to compute the second-derivative filters. See getDerivKernels for details. The size must be positive and odd.
scale	Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See getDerivKernels for details.
delta	Optional delta value that is added to the results prior to storing them in dst .
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Sobel, Scharr

Laplacian() [3/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.Laplacian ( Mat src, Mat dst, int ddepth, int ksize, double scale )

static

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 ksize > 1. When ksize == 1, the Laplacian is computed by filtering the image with the following \(3 \times 3\) aperture:

\[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\]

Parameters

src	Source image.
dst	Destination image of the same size and the same number of channels as src .
ddepth	Desired depth of the destination image, see combinations.
ksize	Aperture size used to compute the second-derivative filters. See getDerivKernels for details. The size must be positive and odd.
scale	Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See getDerivKernels for details.
delta	Optional delta value that is added to the results prior to storing them in dst .
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Sobel, Scharr

Laplacian() [4/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.Laplacian ( Mat src, Mat dst, int ddepth, int ksize, double scale, double delta )

static

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 ksize > 1. When ksize == 1, the Laplacian is computed by filtering the image with the following \(3 \times 3\) aperture:

\[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\]

Parameters

src	Source image.
dst	Destination image of the same size and the same number of channels as src .
ddepth	Desired depth of the destination image, see combinations.
ksize	Aperture size used to compute the second-derivative filters. See getDerivKernels for details. The size must be positive and odd.
scale	Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See getDerivKernels for details.
delta	Optional delta value that is added to the results prior to storing them in dst .
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Sobel, Scharr

Laplacian() [5/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.Laplacian ( Mat src, Mat dst, int ddepth, int ksize, double scale, double delta, int borderType )

static

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 ksize > 1. When ksize == 1, the Laplacian is computed by filtering the image with the following \(3 \times 3\) aperture:

\[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\]

Parameters

src	Source image.
dst	Destination image of the same size and the same number of channels as src .
ddepth	Desired depth of the destination image, see combinations.
ksize	Aperture size used to compute the second-derivative filters. See getDerivKernels for details. The size must be positive and odd.
scale	Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See getDerivKernels for details.
delta	Optional delta value that is added to the results prior to storing them in dst .
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Sobel, Scharr

line() [1/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.line ( Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color )

static

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.

Parameters

img	Image.
pt1	First point of the line segment.
pt2	Second point of the line segment.
color	Line color.
thickness	Line thickness.
lineType	Type of the line. See #LineTypes.
shift	Number of fractional bits in the point coordinates.

line() [2/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.line ( Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness )

static

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.

Parameters

img	Image.
pt1	First point of the line segment.
pt2	Second point of the line segment.
color	Line color.
thickness	Line thickness.
lineType	Type of the line. See #LineTypes.
shift	Number of fractional bits in the point coordinates.

line() [3/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.line ( Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness, int lineType )

static

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.

Parameters

img	Image.
pt1	First point of the line segment.
pt2	Second point of the line segment.
color	Line color.
thickness	Line thickness.
lineType	Type of the line. See #LineTypes.
shift	Number of fractional bits in the point coordinates.

line() [4/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.line ( Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness, int lineType, int shift )

static

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.

Parameters

img	Image.
pt1	First point of the line segment.
pt2	Second point of the line segment.
color	Line color.
thickness	Line thickness.
lineType	Type of the line. See #LineTypes.
shift	Number of fractional bits in the point coordinates.

line() [5/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.line ( Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color )

static

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.

Parameters

img	Image.
pt1	First point of the line segment.
pt2	Second point of the line segment.
color	Line color.
thickness	Line thickness.
lineType	Type of the line. See #LineTypes.
shift	Number of fractional bits in the point coordinates.

line() [6/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.line ( Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness )

static

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.

Parameters

img	Image.
pt1	First point of the line segment.
pt2	Second point of the line segment.
color	Line color.
thickness	Line thickness.
lineType	Type of the line. See #LineTypes.
shift	Number of fractional bits in the point coordinates.

line() [7/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.line ( Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType )

static

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.

Parameters

img	Image.
pt1	First point of the line segment.
pt2	Second point of the line segment.
color	Line color.
thickness	Line thickness.
lineType	Type of the line. See #LineTypes.
shift	Number of fractional bits in the point coordinates.

line() [8/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.line ( Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, int shift )

static

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.

Parameters

img	Image.
pt1	First point of the line segment.
pt2	Second point of the line segment.
color	Line color.
thickness	Line thickness.
lineType	Type of the line. See #LineTypes.
shift	Number of fractional bits in the point coordinates.

line() [9/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.line ( Mat img, Point pt1, Point pt2, Scalar color )

static

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.

Parameters

img	Image.
pt1	First point of the line segment.
pt2	Second point of the line segment.
color	Line color.
thickness	Line thickness.
lineType	Type of the line. See #LineTypes.
shift	Number of fractional bits in the point coordinates.

line() [10/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.line ( Mat img, Point pt1, Point pt2, Scalar color, int thickness )

static

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.

Parameters

img	Image.
pt1	First point of the line segment.
pt2	Second point of the line segment.
color	Line color.
thickness	Line thickness.
lineType	Type of the line. See #LineTypes.
shift	Number of fractional bits in the point coordinates.

line() [11/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.line ( Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType )

static

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.

Parameters

img	Image.
pt1	First point of the line segment.
pt2	Second point of the line segment.
color	Line color.
thickness	Line thickness.
lineType	Type of the line. See #LineTypes.
shift	Number of fractional bits in the point coordinates.

line() [12/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.line ( Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType, int shift )

static

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.

Parameters

img	Image.
pt1	First point of the line segment.
pt2	Second point of the line segment.
color	Line color.
thickness	Line thickness.
lineType	Type of the line. See #LineTypes.
shift	Number of fractional bits in the point coordinates.

linearPolar() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.linearPolar ( Mat src, Mat dst, in Vec2d center, double maxRadius, int flags )

static

Remaps an image to polar coordinates space.

Deprecated

This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags)

linearPolar() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.linearPolar ( Mat src, Mat dst, in(double x, double y) center, double maxRadius, int flags )

static

Remaps an image to polar coordinates space.

Deprecated

This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags)

linearPolar() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.linearPolar ( Mat src, Mat dst, Point center, double maxRadius, int flags )

static

Remaps an image to polar coordinates space.

Deprecated

This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags)

logPolar() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.logPolar ( Mat src, Mat dst, in Vec2d center, double M, int flags )

static

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);

logPolar() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.logPolar ( Mat src, Mat dst, in(double x, double y) center, double M, int flags )

static

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);

logPolar() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.logPolar ( Mat src, Mat dst, Point center, double M, int flags )

static

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);

matchShapes()

static double OpenCVForUnity.ImgprocModule.Imgproc.matchShapes ( Mat contour1, Mat contour2, int method, double parameter )

static

Compares two shapes.

The function compares two shapes. All three implemented methods use the Hu invariants (see HuMoments)

Parameters

contour1	First contour or grayscale image.
contour2	Second contour or grayscale image.
method	Comparison method, see #ShapeMatchModes
parameter	Method-specific parameter (not supported now).

matchTemplate() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.matchTemplate ( Mat image, Mat templ, Mat result, int method )

static

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.

Parameters

image	Image where the search is running. It must be 8-bit or 32-bit floating-point.
templ	Searched template. It must be not greater than the source image and have the same data type.
result	Map of comparison results. It must be single-channel 32-bit floating-point. If image is \(W \times H\) and templ is \(w \times h\) , then result is \((W-w+1) \times (H-h+1)\) .
method	Parameter specifying the comparison method, see #TemplateMatchModes
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.

matchTemplate() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.matchTemplate ( Mat image, Mat templ, Mat result, int method, Mat mask )

static

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.

Parameters

image	Image where the search is running. It must be 8-bit or 32-bit floating-point.
templ	Searched template. It must be not greater than the source image and have the same data type.
result	Map of comparison results. It must be single-channel 32-bit floating-point. If image is \(W \times H\) and templ is \(w \times h\) , then result is \((W-w+1) \times (H-h+1)\) .
method	Parameter specifying the comparison method, see #TemplateMatchModes
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.

medianBlur()

static void OpenCVForUnity.ImgprocModule.Imgproc.medianBlur ( Mat src, Mat dst, int ksize )

static

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.

Note

The median filter uses #BORDER_REPLICATE internally to cope with border pixels, see #BorderTypes

Parameters

src	input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U.
dst	destination array of the same size and type as src.
ksize	aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ...

bilateralFilter, blur, boxFilter, GaussianBlur

minAreaRect()

static RotatedRect OpenCVForUnity.ImgprocModule.Imgproc.minAreaRect ( MatOfPoint2f points )

static

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.

Parameters

points

Input vector of 2D points, stored in std::vector<> or Mat

minAreaRectAsValueTuple()

static double double double double double angle OpenCVForUnity.ImgprocModule.Imgproc.minAreaRectAsValueTuple ( MatOfPoint2f points )

static

minAreaRectAsVec5d()

static Vec5d OpenCVForUnity.ImgprocModule.Imgproc.minAreaRectAsVec5d ( MatOfPoint2f points )

static

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.

Parameters

points

Input vector of 2D points, stored in std::vector<> or Mat

minEnclosingCircle() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.minEnclosingCircle ( MatOfPoint2f points, out Vec2d center, float[] radius )

static

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.

Parameters

points	Input vector of 2D points, stored in std::vector<> or Mat
center	Output center of the circle.
radius	Output radius of the circle.

minEnclosingCircle() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.minEnclosingCircle ( MatOfPoint2f points, out(double x, double y) center, float[] radius )

static

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.

Parameters

points	Input vector of 2D points, stored in std::vector<> or Mat
center	Output center of the circle.
radius	Output radius of the circle.

minEnclosingCircle() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.minEnclosingCircle ( MatOfPoint2f points, Point center, float[] radius )

static

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.

Parameters

points	Input vector of 2D points, stored in std::vector<> or Mat
center	Output center of the circle.
radius	Output radius of the circle.

minEnclosingTriangle()

static double OpenCVForUnity.ImgprocModule.Imgproc.minEnclosingTriangle ( Mat points, Mat triangle )

static

Finds a triangle of minimum area enclosing a 2D point set and returns its area.

The function finds a triangle of minimum area enclosing the given set of 2D points and returns its area. The output for a given 2D point set is shown in the image below. 2D points are depicted in red* and the enclosing triangle in yellow.

Sample output of the minimum enclosing triangle function

The implementation of the algorithm is based on O'Rourke's [ORourke86] and Klee and Laskowski's [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 \(O(n log(n))\).

Parameters

points	Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector<> or Mat
triangle	Output vector of three 2D points defining the vertices of the triangle. The depth of the OutputArray must be CV_32F.

moments() [1/2]

static Moments OpenCVForUnity.ImgprocModule.Imgproc.moments ( Mat array )

static

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.

Parameters

array	Single chanel raster image (CV_8U, CV_16U, CV_16S, CV_32F, CV_64F) or an array ( \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f).
binaryImage	If it is true, all non-zero image pixels are treated as 1's. The parameter is used for images only.

Returns

moments.

Note

Only applicable to contour moments calculations from Python bindings: Note that the numpy type for the input array should be either np.int32 or np.float32.

See also

contourArea, arcLength

moments() [2/2]

static Moments OpenCVForUnity.ImgprocModule.Imgproc.moments ( Mat array, bool binaryImage )

static

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.

Parameters

array	Single chanel raster image (CV_8U, CV_16U, CV_16S, CV_32F, CV_64F) or an array ( \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f).
binaryImage	If it is true, all non-zero image pixels are treated as 1's. The parameter is used for images only.

Returns

moments.

Note

Only applicable to contour moments calculations from Python bindings: Note that the numpy type for the input array should be either np.int32 or np.float32.

See also

contourArea, arcLength

momentsAsValueTuple() [1/2]

static double double double double double double double double double double m03 OpenCVForUnity.ImgprocModule.Imgproc.momentsAsValueTuple ( Mat array )

static

momentsAsValueTuple() [2/2]

static double double double double double double double double double double m03 OpenCVForUnity.ImgprocModule.Imgproc.momentsAsValueTuple ( Mat array, bool binaryImage )

static

momentsAsVec10d() [1/2]

static Vec10d OpenCVForUnity.ImgprocModule.Imgproc.momentsAsVec10d ( Mat array )

static

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.

Parameters

array	Single chanel raster image (CV_8U, CV_16U, CV_16S, CV_32F, CV_64F) or an array ( \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f).
binaryImage	If it is true, all non-zero image pixels are treated as 1's. The parameter is used for images only.

Returns

moments.

Note

Only applicable to contour moments calculations from Python bindings: Note that the numpy type for the input array should be either np.int32 or np.float32.

See also

contourArea, arcLength

momentsAsVec10d() [2/2]

static Vec10d OpenCVForUnity.ImgprocModule.Imgproc.momentsAsVec10d ( Mat array, bool binaryImage )

static

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.

Parameters

array	Single chanel raster image (CV_8U, CV_16U, CV_16S, CV_32F, CV_64F) or an array ( \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f).
binaryImage	If it is true, all non-zero image pixels are treated as 1's. The parameter is used for images only.

Returns

moments.

Note

Only applicable to contour moments calculations from Python bindings: Note that the numpy type for the input array should be either np.int32 or np.float32.

See also

contourArea, arcLength

morphologyEx() [1/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.morphologyEx ( Mat src, Mat dst, int op, Mat kernel )

static

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.

Parameters

src	Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	Destination image of the same size and type as source image.
op	Type of a morphological operation, see #MorphTypes
kernel	Structuring element. It can be created using getStructuringElement.
anchor	Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
iterations	Number of times erosion and dilation are applied.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	Border value in case of a constant border. The default value has a special meaning.

dilate, erode, getStructuringElement

Note

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).

morphologyEx() [2/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.morphologyEx ( Mat src, Mat dst, int op, Mat kernel, in Vec2d anchor )

static

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.

Parameters

src	Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	Destination image of the same size and type as source image.
op	Type of a morphological operation, see #MorphTypes
kernel	Structuring element. It can be created using getStructuringElement.
anchor	Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
iterations	Number of times erosion and dilation are applied.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	Border value in case of a constant border. The default value has a special meaning.

dilate, erode, getStructuringElement

Note

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).

morphologyEx() [3/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.morphologyEx ( Mat src, Mat dst, int op, Mat kernel, in Vec2d anchor, int iterations )

static

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.

Parameters

src	Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	Destination image of the same size and type as source image.
op	Type of a morphological operation, see #MorphTypes
kernel	Structuring element. It can be created using getStructuringElement.
anchor	Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
iterations	Number of times erosion and dilation are applied.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	Border value in case of a constant border. The default value has a special meaning.

dilate, erode, getStructuringElement

Note

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).

morphologyEx() [4/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.morphologyEx ( Mat src, Mat dst, int op, Mat kernel, in Vec2d anchor, int iterations, int borderType )

static

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.

Parameters

src	Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	Destination image of the same size and type as source image.
op	Type of a morphological operation, see #MorphTypes
kernel	Structuring element. It can be created using getStructuringElement.
anchor	Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
iterations	Number of times erosion and dilation are applied.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	Border value in case of a constant border. The default value has a special meaning.

dilate, erode, getStructuringElement

Note

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).

morphologyEx() [5/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.morphologyEx ( Mat src, Mat dst, int op, Mat kernel, in Vec2d anchor, int iterations, int borderType, in Vec4d borderValue )

static

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.

Parameters

src	Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	Destination image of the same size and type as source image.
op	Type of a morphological operation, see #MorphTypes
kernel	Structuring element. It can be created using getStructuringElement.
anchor	Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
iterations	Number of times erosion and dilation are applied.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	Border value in case of a constant border. The default value has a special meaning.

dilate, erode, getStructuringElement

Note

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).

morphologyEx() [6/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.morphologyEx ( Mat src, Mat dst, int op, Mat kernel, in(double x, double y) anchor )

static

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.

Parameters

src	Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	Destination image of the same size and type as source image.
op	Type of a morphological operation, see #MorphTypes
kernel	Structuring element. It can be created using getStructuringElement.
anchor	Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
iterations	Number of times erosion and dilation are applied.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	Border value in case of a constant border. The default value has a special meaning.

dilate, erode, getStructuringElement

Note

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).

morphologyEx() [7/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.morphologyEx ( Mat src, Mat dst, int op, Mat kernel, in(double x, double y) anchor, int iterations )

static

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.

Parameters

src	Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	Destination image of the same size and type as source image.
op	Type of a morphological operation, see #MorphTypes
kernel	Structuring element. It can be created using getStructuringElement.
anchor	Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
iterations	Number of times erosion and dilation are applied.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	Border value in case of a constant border. The default value has a special meaning.

dilate, erode, getStructuringElement

Note

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).

morphologyEx() [8/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.morphologyEx ( Mat src, Mat dst, int op, Mat kernel, in(double x, double y) anchor, int iterations, int borderType )

static

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.

Parameters

src	Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	Destination image of the same size and type as source image.
op	Type of a morphological operation, see #MorphTypes
kernel	Structuring element. It can be created using getStructuringElement.
anchor	Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
iterations	Number of times erosion and dilation are applied.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	Border value in case of a constant border. The default value has a special meaning.

dilate, erode, getStructuringElement

Note

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).

morphologyEx() [9/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.morphologyEx ( Mat src, Mat dst, int op, Mat kernel, in(double x, double y) anchor, int iterations, int borderType, in(double v0, double v1, double v2, double v3) borderValue )

static

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.

Parameters

src	Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	Destination image of the same size and type as source image.
op	Type of a morphological operation, see #MorphTypes
kernel	Structuring element. It can be created using getStructuringElement.
anchor	Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
iterations	Number of times erosion and dilation are applied.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	Border value in case of a constant border. The default value has a special meaning.

dilate, erode, getStructuringElement

Note

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).

morphologyEx() [10/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.morphologyEx ( Mat src, Mat dst, int op, Mat kernel, Point anchor )

static

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.

Parameters

src	Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	Destination image of the same size and type as source image.
op	Type of a morphological operation, see #MorphTypes
kernel	Structuring element. It can be created using getStructuringElement.
anchor	Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
iterations	Number of times erosion and dilation are applied.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	Border value in case of a constant border. The default value has a special meaning.

dilate, erode, getStructuringElement

Note

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).

morphologyEx() [11/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.morphologyEx ( Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations )

static

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.

Parameters

src	Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	Destination image of the same size and type as source image.
op	Type of a morphological operation, see #MorphTypes
kernel	Structuring element. It can be created using getStructuringElement.
anchor	Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
iterations	Number of times erosion and dilation are applied.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	Border value in case of a constant border. The default value has a special meaning.

dilate, erode, getStructuringElement

Note

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).

morphologyEx() [12/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.morphologyEx ( Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType )

static

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.

Parameters

src	Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	Destination image of the same size and type as source image.
op	Type of a morphological operation, see #MorphTypes
kernel	Structuring element. It can be created using getStructuringElement.
anchor	Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
iterations	Number of times erosion and dilation are applied.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	Border value in case of a constant border. The default value has a special meaning.

dilate, erode, getStructuringElement

Note

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).

morphologyEx() [13/13]

static void OpenCVForUnity.ImgprocModule.Imgproc.morphologyEx ( Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue )

static

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.

Parameters

src	Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst	Destination image of the same size and type as source image.
op	Type of a morphological operation, see #MorphTypes
kernel	Structuring element. It can be created using getStructuringElement.
anchor	Anchor position with the kernel. Negative values mean that the anchor is at the kernel center.
iterations	Number of times erosion and dilation are applied.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue	Border value in case of a constant border. The default value has a special meaning.

dilate, erode, getStructuringElement

Note

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).

phaseCorrelate() [1/3]

static Point OpenCVForUnity.ImgprocModule.Imgproc.phaseCorrelate ( Mat src1, Mat src2 )

static

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&gt;

Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize.

The function performs the following equations:

• First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function&gt;) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
• Next it computes the forward DFTs of each source array:

  \[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\]

  where \(\mathcal{F}\) is the forward DFT.
• It then computes the cross-power spectrum of each frequency domain array:

  \[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\]

• Next the cross-correlation is converted back into the time domain via the inverse DFT:

  \[r = \mathcal{F}^{-1}\{R\}\]

• Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy.

  \[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\]

• If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single peak) and will be smaller when there are multiple peaks.

Parameters

src1	Source floating point array (CV_32FC1 or CV_64FC1)
src2	Source floating point array (CV_32FC1 or CV_64FC1)
window	Floating point array with windowing coefficients to reduce edge effects (optional).
response	Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).

Returns

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

See also

dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow

phaseCorrelate() [2/3]

static Point OpenCVForUnity.ImgprocModule.Imgproc.phaseCorrelate ( Mat src1, Mat src2, Mat window )

static

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&gt;

Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize.

The function performs the following equations:

• First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function&gt;) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
• Next it computes the forward DFTs of each source array:

  \[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\]

  where \(\mathcal{F}\) is the forward DFT.
• It then computes the cross-power spectrum of each frequency domain array:

  \[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\]

• Next the cross-correlation is converted back into the time domain via the inverse DFT:

  \[r = \mathcal{F}^{-1}\{R\}\]

• Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy.

  \[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\]

• If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single peak) and will be smaller when there are multiple peaks.

Parameters

src1	Source floating point array (CV_32FC1 or CV_64FC1)
src2	Source floating point array (CV_32FC1 or CV_64FC1)
window	Floating point array with windowing coefficients to reduce edge effects (optional).
response	Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).

Returns

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

See also

dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow

phaseCorrelate() [3/3]

static Point OpenCVForUnity.ImgprocModule.Imgproc.phaseCorrelate ( Mat src1, Mat src2, Mat window, double[] response )

static

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&gt;

Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize.

The function performs the following equations:

• First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function&gt;) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
• Next it computes the forward DFTs of each source array:

  \[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\]

  where \(\mathcal{F}\) is the forward DFT.
• It then computes the cross-power spectrum of each frequency domain array:

  \[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\]

• Next the cross-correlation is converted back into the time domain via the inverse DFT:

  \[r = \mathcal{F}^{-1}\{R\}\]

• Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy.

  \[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\]

• If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single peak) and will be smaller when there are multiple peaks.

Parameters

src1	Source floating point array (CV_32FC1 or CV_64FC1)
src2	Source floating point array (CV_32FC1 or CV_64FC1)
window	Floating point array with windowing coefficients to reduce edge effects (optional).
response	Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).

Returns

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

See also

dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow

phaseCorrelateAsValueTuple() [1/3]

static double double y OpenCVForUnity.ImgprocModule.Imgproc.phaseCorrelateAsValueTuple ( Mat src1, Mat src2 )

static

phaseCorrelateAsValueTuple() [2/3]

static double double y OpenCVForUnity.ImgprocModule.Imgproc.phaseCorrelateAsValueTuple ( Mat src1, Mat src2, Mat window )

static

phaseCorrelateAsValueTuple() [3/3]

static double double y OpenCVForUnity.ImgprocModule.Imgproc.phaseCorrelateAsValueTuple ( Mat src1, Mat src2, Mat window, double[] response )

static

phaseCorrelateAsVec2d() [1/3]

static Vec2d OpenCVForUnity.ImgprocModule.Imgproc.phaseCorrelateAsVec2d ( Mat src1, Mat src2 )

static

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&gt;

Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize.

The function performs the following equations:

• First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function&gt;) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
• Next it computes the forward DFTs of each source array:

  \[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\]

  where \(\mathcal{F}\) is the forward DFT.
• It then computes the cross-power spectrum of each frequency domain array:

  \[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\]

• Next the cross-correlation is converted back into the time domain via the inverse DFT:

  \[r = \mathcal{F}^{-1}\{R\}\]

• Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy.

  \[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\]

• If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single peak) and will be smaller when there are multiple peaks.

Parameters

src1	Source floating point array (CV_32FC1 or CV_64FC1)
src2	Source floating point array (CV_32FC1 or CV_64FC1)
window	Floating point array with windowing coefficients to reduce edge effects (optional).
response	Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).

Returns

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

See also

dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow

phaseCorrelateAsVec2d() [2/3]

static Vec2d OpenCVForUnity.ImgprocModule.Imgproc.phaseCorrelateAsVec2d ( Mat src1, Mat src2, Mat window )

static

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&gt;

Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize.

The function performs the following equations:

• First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function&gt;) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
• Next it computes the forward DFTs of each source array:

  \[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\]

  where \(\mathcal{F}\) is the forward DFT.
• It then computes the cross-power spectrum of each frequency domain array:

  \[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\]

• Next the cross-correlation is converted back into the time domain via the inverse DFT:

  \[r = \mathcal{F}^{-1}\{R\}\]

• Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy.

  \[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\]

• If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single peak) and will be smaller when there are multiple peaks.

Parameters

src1	Source floating point array (CV_32FC1 or CV_64FC1)
src2	Source floating point array (CV_32FC1 or CV_64FC1)
window	Floating point array with windowing coefficients to reduce edge effects (optional).
response	Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).

Returns

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

See also

dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow

phaseCorrelateAsVec2d() [3/3]

static Vec2d OpenCVForUnity.ImgprocModule.Imgproc.phaseCorrelateAsVec2d ( Mat src1, Mat src2, Mat window, double[] response )

static

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&gt;

Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize.

The function performs the following equations:

• First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function&gt;) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
• Next it computes the forward DFTs of each source array:

  \[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\]

  where \(\mathcal{F}\) is the forward DFT.
• It then computes the cross-power spectrum of each frequency domain array:

  \[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\]

• Next the cross-correlation is converted back into the time domain via the inverse DFT:

  \[r = \mathcal{F}^{-1}\{R\}\]

• Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy.

  \[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\]

• If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single peak) and will be smaller when there are multiple peaks.

Parameters

src1	Source floating point array (CV_32FC1 or CV_64FC1)
src2	Source floating point array (CV_32FC1 or CV_64FC1)
window	Floating point array with windowing coefficients to reduce edge effects (optional).
response	Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).

Returns

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

See also

dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow

pointPolygonTest() [1/3]

static double OpenCVForUnity.ImgprocModule.Imgproc.pointPolygonTest ( MatOfPoint2f contour, in Vec2d pt, bool measureDist )

static

Performs a point-in-contour test.

The function determines whether the point is inside a contour, outside, or lies on an edge (or coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. Otherwise, the return value is a signed distance between the point and the nearest contour edge.

See below a sample output of the function where each image pixel is tested against the contour:

Parameters

contour	Input contour.
pt	Point tested against the contour.
measureDist	If true, the function estimates the signed distance from the point to the nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.

pointPolygonTest() [2/3]

static double OpenCVForUnity.ImgprocModule.Imgproc.pointPolygonTest ( MatOfPoint2f contour, in(double x, double y) pt, bool measureDist )

static

Performs a point-in-contour test.

The function determines whether the point is inside a contour, outside, or lies on an edge (or coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. Otherwise, the return value is a signed distance between the point and the nearest contour edge.

See below a sample output of the function where each image pixel is tested against the contour:

Parameters

contour	Input contour.
pt	Point tested against the contour.
measureDist	If true, the function estimates the signed distance from the point to the nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.

pointPolygonTest() [3/3]

static double OpenCVForUnity.ImgprocModule.Imgproc.pointPolygonTest ( MatOfPoint2f contour, Point pt, bool measureDist )

static

Performs a point-in-contour test.

The function determines whether the point is inside a contour, outside, or lies on an edge (or coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. Otherwise, the return value is a signed distance between the point and the nearest contour edge.

See below a sample output of the function where each image pixel is tested against the contour:

Parameters

contour	Input contour.
pt	Point tested against the contour.
measureDist	If true, the function estimates the signed distance from the point to the nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.

polylines() [1/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.polylines ( Mat img, List< MatOfPoint > pts, bool isClosed, in Vec4d color )

static

Draws several polygonal curves.

Parameters

img	Image.
pts	Array of polygonal curves.
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.
color	Polyline color.
thickness	Thickness of the polyline edges.
lineType	Type of the line segments. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

The function cv::polylines draws one or more polygonal curves.

polylines() [2/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.polylines ( Mat img, List< MatOfPoint > pts, bool isClosed, in Vec4d color, int thickness )

static

Draws several polygonal curves.

Parameters

img	Image.
pts	Array of polygonal curves.
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.
color	Polyline color.
thickness	Thickness of the polyline edges.
lineType	Type of the line segments. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

The function cv::polylines draws one or more polygonal curves.

polylines() [3/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.polylines ( Mat img, List< MatOfPoint > pts, bool isClosed, in Vec4d color, int thickness, int lineType )

static

Draws several polygonal curves.

Parameters

img	Image.
pts	Array of polygonal curves.
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.
color	Polyline color.
thickness	Thickness of the polyline edges.
lineType	Type of the line segments. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

The function cv::polylines draws one or more polygonal curves.

polylines() [4/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.polylines ( Mat img, List< MatOfPoint > pts, bool isClosed, in Vec4d color, int thickness, int lineType, int shift )

static

Draws several polygonal curves.

Parameters

img	Image.
pts	Array of polygonal curves.
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.
color	Polyline color.
thickness	Thickness of the polyline edges.
lineType	Type of the line segments. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

The function cv::polylines draws one or more polygonal curves.

polylines() [5/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.polylines ( Mat img, List< MatOfPoint > pts, bool isClosed, in(double v0, double v1, double v2, double v3) color )

static

Draws several polygonal curves.

Parameters

img	Image.
pts	Array of polygonal curves.
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.
color	Polyline color.
thickness	Thickness of the polyline edges.
lineType	Type of the line segments. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

The function cv::polylines draws one or more polygonal curves.

polylines() [6/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.polylines ( Mat img, List< MatOfPoint > pts, bool isClosed, in(double v0, double v1, double v2, double v3) color, int thickness )

static

Draws several polygonal curves.

Parameters

img	Image.
pts	Array of polygonal curves.
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.
color	Polyline color.
thickness	Thickness of the polyline edges.
lineType	Type of the line segments. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

The function cv::polylines draws one or more polygonal curves.

polylines() [7/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.polylines ( Mat img, List< MatOfPoint > pts, bool isClosed, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType )

static

Draws several polygonal curves.

Parameters

img	Image.
pts	Array of polygonal curves.
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.
color	Polyline color.
thickness	Thickness of the polyline edges.
lineType	Type of the line segments. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

The function cv::polylines draws one or more polygonal curves.

polylines() [8/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.polylines ( Mat img, List< MatOfPoint > pts, bool isClosed, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, int shift )

static

Draws several polygonal curves.

Parameters

img	Image.
pts	Array of polygonal curves.
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.
color	Polyline color.
thickness	Thickness of the polyline edges.
lineType	Type of the line segments. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

The function cv::polylines draws one or more polygonal curves.

polylines() [9/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.polylines ( Mat img, List< MatOfPoint > pts, bool isClosed, Scalar color )

static

Draws several polygonal curves.

Parameters

img	Image.
pts	Array of polygonal curves.
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.
color	Polyline color.
thickness	Thickness of the polyline edges.
lineType	Type of the line segments. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

The function cv::polylines draws one or more polygonal curves.

polylines() [10/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.polylines ( Mat img, List< MatOfPoint > pts, bool isClosed, Scalar color, int thickness )

static

Draws several polygonal curves.

Parameters

img	Image.
pts	Array of polygonal curves.
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.
color	Polyline color.
thickness	Thickness of the polyline edges.
lineType	Type of the line segments. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

The function cv::polylines draws one or more polygonal curves.

polylines() [11/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.polylines ( Mat img, List< MatOfPoint > pts, bool isClosed, Scalar color, int thickness, int lineType )

static

Draws several polygonal curves.

Parameters

img	Image.
pts	Array of polygonal curves.
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.
color	Polyline color.
thickness	Thickness of the polyline edges.
lineType	Type of the line segments. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

The function cv::polylines draws one or more polygonal curves.

polylines() [12/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.polylines ( Mat img, List< MatOfPoint > pts, bool isClosed, Scalar color, int thickness, int lineType, int shift )

static

Draws several polygonal curves.

Parameters

img	Image.
pts	Array of polygonal curves.
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.
color	Polyline color.
thickness	Thickness of the polyline edges.
lineType	Type of the line segments. See #LineTypes
shift	Number of fractional bits in the vertex coordinates.

The function cv::polylines draws one or more polygonal curves.

preCornerDetect() [1/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.preCornerDetect ( Mat src, Mat dst, int ksize )

static

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:

Mat corners, dilated_corners;
preCornerDetect(image, corners, 3);
// dilation with 3x3 rectangular structuring element
dilate(corners, dilated_corners, Mat(), 1);
Mat corner_mask = corners == dilated_corners;

Parameters

src	Source single-channel 8-bit of floating-point image.
dst	Output image that has the type CV_32F and the same size as src .
ksize	Aperture size of the Sobel .
borderType	Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.

preCornerDetect() [2/2]

static void OpenCVForUnity.ImgprocModule.Imgproc.preCornerDetect ( Mat src, Mat dst, int ksize, int borderType )

static

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:

Mat corners, dilated_corners;
preCornerDetect(image, corners, 3);
// dilation with 3x3 rectangular structuring element
dilate(corners, dilated_corners, Mat(), 1);
Mat corner_mask = corners == dilated_corners;

Parameters

src	Source single-channel 8-bit of floating-point image.
dst	Output image that has the type CV_32F and the same size as src .
ksize	Aperture size of the Sobel .
borderType	Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.

putText() [1/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.putText ( Mat img, string text, in Vec2d org, int fontFace, double fontScale, in Vec4d color )

static

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.

Parameters

img	Image.
text	Text string to be drawn.
org	Bottom-left corner of the text string in the image.
fontFace	Font type, see #HersheyFonts.
fontScale	Font scale factor that is multiplied by the font-specific base size.
color	Text color.
thickness	Thickness of the lines used to draw a text.
lineType	Line type. See #LineTypes
bottomLeftOrigin	When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.

putText() [2/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.putText ( Mat img, string text, in Vec2d org, int fontFace, double fontScale, in Vec4d color, int thickness )

static

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.

Parameters

img	Image.
text	Text string to be drawn.
org	Bottom-left corner of the text string in the image.
fontFace	Font type, see #HersheyFonts.
fontScale	Font scale factor that is multiplied by the font-specific base size.
color	Text color.
thickness	Thickness of the lines used to draw a text.
lineType	Line type. See #LineTypes
bottomLeftOrigin	When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.

putText() [3/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.putText ( Mat img, string text, in Vec2d org, int fontFace, double fontScale, in Vec4d color, int thickness, int lineType )

static

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.

Parameters

img	Image.
text	Text string to be drawn.
org	Bottom-left corner of the text string in the image.
fontFace	Font type, see #HersheyFonts.
fontScale	Font scale factor that is multiplied by the font-specific base size.
color	Text color.
thickness	Thickness of the lines used to draw a text.
lineType	Line type. See #LineTypes
bottomLeftOrigin	When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.

putText() [4/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.putText ( Mat img, string text, in Vec2d org, int fontFace, double fontScale, in Vec4d color, int thickness, int lineType, bool bottomLeftOrigin )

static

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.

Parameters

img	Image.
text	Text string to be drawn.
org	Bottom-left corner of the text string in the image.
fontFace	Font type, see #HersheyFonts.
fontScale	Font scale factor that is multiplied by the font-specific base size.
color	Text color.
thickness	Thickness of the lines used to draw a text.
lineType	Line type. See #LineTypes
bottomLeftOrigin	When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.

putText() [5/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.putText ( Mat img, string text, in(double x, double y) org, int fontFace, double fontScale, in(double v0, double v1, double v2, double v3) color )

static

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.

Parameters

img	Image.
text	Text string to be drawn.
org	Bottom-left corner of the text string in the image.
fontFace	Font type, see #HersheyFonts.
fontScale	Font scale factor that is multiplied by the font-specific base size.
color	Text color.
thickness	Thickness of the lines used to draw a text.
lineType	Line type. See #LineTypes
bottomLeftOrigin	When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.

putText() [6/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.putText ( Mat img, string text, in(double x, double y) org, int fontFace, double fontScale, in(double v0, double v1, double v2, double v3) color, int thickness )

static

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.

Parameters

img	Image.
text	Text string to be drawn.
org	Bottom-left corner of the text string in the image.
fontFace	Font type, see #HersheyFonts.
fontScale	Font scale factor that is multiplied by the font-specific base size.
color	Text color.
thickness	Thickness of the lines used to draw a text.
lineType	Line type. See #LineTypes
bottomLeftOrigin	When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.

putText() [7/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.putText ( Mat img, string text, in(double x, double y) org, int fontFace, double fontScale, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType )

static

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.

Parameters

img	Image.
text	Text string to be drawn.
org	Bottom-left corner of the text string in the image.
fontFace	Font type, see #HersheyFonts.
fontScale	Font scale factor that is multiplied by the font-specific base size.
color	Text color.
thickness	Thickness of the lines used to draw a text.
lineType	Line type. See #LineTypes
bottomLeftOrigin	When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.

putText() [8/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.putText ( Mat img, string text, in(double x, double y) org, int fontFace, double fontScale, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, bool bottomLeftOrigin )

static

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.

Parameters

img	Image.
text	Text string to be drawn.
org	Bottom-left corner of the text string in the image.
fontFace	Font type, see #HersheyFonts.
fontScale	Font scale factor that is multiplied by the font-specific base size.
color	Text color.
thickness	Thickness of the lines used to draw a text.
lineType	Line type. See #LineTypes
bottomLeftOrigin	When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.

putText() [9/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.putText ( Mat img, string text, Point org, int fontFace, double fontScale, Scalar color )

static

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.

Parameters

img	Image.
text	Text string to be drawn.
org	Bottom-left corner of the text string in the image.
fontFace	Font type, see #HersheyFonts.
fontScale	Font scale factor that is multiplied by the font-specific base size.
color	Text color.
thickness	Thickness of the lines used to draw a text.
lineType	Line type. See #LineTypes
bottomLeftOrigin	When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.

putText() [10/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.putText ( Mat img, string text, Point org, int fontFace, double fontScale, Scalar color, int thickness )

static

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.

Parameters

img	Image.
text	Text string to be drawn.
org	Bottom-left corner of the text string in the image.
fontFace	Font type, see #HersheyFonts.
fontScale	Font scale factor that is multiplied by the font-specific base size.
color	Text color.
thickness	Thickness of the lines used to draw a text.
lineType	Line type. See #LineTypes
bottomLeftOrigin	When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.

putText() [11/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.putText ( Mat img, string text, Point org, int fontFace, double fontScale, Scalar color, int thickness, int lineType )

static

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.

Parameters

img	Image.
text	Text string to be drawn.
org	Bottom-left corner of the text string in the image.
fontFace	Font type, see #HersheyFonts.
fontScale	Font scale factor that is multiplied by the font-specific base size.
color	Text color.
thickness	Thickness of the lines used to draw a text.
lineType	Line type. See #LineTypes
bottomLeftOrigin	When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.

putText() [12/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.putText ( Mat img, string text, Point org, int fontFace, double fontScale, Scalar color, int thickness, int lineType, bool bottomLeftOrigin )

static

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.

Parameters

img	Image.
text	Text string to be drawn.
org	Bottom-left corner of the text string in the image.
fontFace	Font type, see #HersheyFonts.
fontScale	Font scale factor that is multiplied by the font-specific base size.
color	Text color.
thickness	Thickness of the lines used to draw a text.
lineType	Line type. See #LineTypes
bottomLeftOrigin	When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.

pyrDown() [1/7]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrDown ( Mat src, Mat dst )

static

Blurs an image and downsamples it.

By default, size of the output image is computed as Size((src.cols+1)/2, (src.rows+1)/2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\]

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.

Parameters

src	input image.
dst	output image; it has the specified size and the same type as src.
dstsize	size of the output image.
borderType	Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)

pyrDown() [2/7]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrDown ( Mat src, Mat dst, in Vec2d dstsize )

static

Blurs an image and downsamples it.

By default, size of the output image is computed as Size((src.cols+1)/2, (src.rows+1)/2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\]

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.

Parameters

src	input image.
dst	output image; it has the specified size and the same type as src.
dstsize	size of the output image.
borderType	Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)

pyrDown() [3/7]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrDown ( Mat src, Mat dst, in Vec2d dstsize, int borderType )

static

Blurs an image and downsamples it.

By default, size of the output image is computed as Size((src.cols+1)/2, (src.rows+1)/2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\]

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.

Parameters

src	input image.
dst	output image; it has the specified size and the same type as src.
dstsize	size of the output image.
borderType	Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)

pyrDown() [4/7]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrDown ( Mat src, Mat dst, in(double width, double height) dstsize )

static

Blurs an image and downsamples it.

By default, size of the output image is computed as Size((src.cols+1)/2, (src.rows+1)/2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\]

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.

Parameters

src	input image.
dst	output image; it has the specified size and the same type as src.
dstsize	size of the output image.
borderType	Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)

pyrDown() [5/7]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrDown ( Mat src, Mat dst, in(double width, double height) dstsize, int borderType )

static

Blurs an image and downsamples it.

By default, size of the output image is computed as Size((src.cols+1)/2, (src.rows+1)/2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\]

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.

Parameters

src	input image.
dst	output image; it has the specified size and the same type as src.
dstsize	size of the output image.
borderType	Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)

pyrDown() [6/7]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrDown ( Mat src, Mat dst, Size dstsize )

static

Blurs an image and downsamples it.

By default, size of the output image is computed as Size((src.cols+1)/2, (src.rows+1)/2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\]

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.

Parameters

src	input image.
dst	output image; it has the specified size and the same type as src.
dstsize	size of the output image.
borderType	Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)

pyrDown() [7/7]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrDown ( Mat src, Mat dst, Size dstsize, int borderType )

static

Blurs an image and downsamples it.

By default, size of the output image is computed as Size((src.cols+1)/2, (src.rows+1)/2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\]

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.

Parameters

src	input image.
dst	output image; it has the specified size and the same type as src.
dstsize	size of the output image.
borderType	Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)

pyrMeanShiftFiltering() [1/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrMeanShiftFiltering ( Mat src, Mat dst, double sp, double sr )

static

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).

Parameters

src	The source 8-bit, 3-channel image.
dst	The destination image of the same format and the same size as the source.
sp	The spatial window radius.
sr	The color window radius.
maxLevel	Maximum level of the pyramid for the segmentation.
termcrit	Termination criteria: when to stop meanshift iterations.

pyrMeanShiftFiltering() [2/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrMeanShiftFiltering ( Mat src, Mat dst, double sp, double sr, int maxLevel )

static

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).

Parameters

src	The source 8-bit, 3-channel image.
dst	The destination image of the same format and the same size as the source.
sp	The spatial window radius.
sr	The color window radius.
maxLevel	Maximum level of the pyramid for the segmentation.
termcrit	Termination criteria: when to stop meanshift iterations.

pyrMeanShiftFiltering() [3/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrMeanShiftFiltering ( Mat src, Mat dst, double sp, double sr, int maxLevel, in Vec3d termcrit )

static

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).

Parameters

src	The source 8-bit, 3-channel image.
dst	The destination image of the same format and the same size as the source.
sp	The spatial window radius.
sr	The color window radius.
maxLevel	Maximum level of the pyramid for the segmentation.
termcrit	Termination criteria: when to stop meanshift iterations.

pyrMeanShiftFiltering() [4/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrMeanShiftFiltering ( Mat src, Mat dst, double sp, double sr, int maxLevel, in(double type, double maxCount, double epsilon) termcrit )

static

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).

Parameters

src	The source 8-bit, 3-channel image.
dst	The destination image of the same format and the same size as the source.
sp	The spatial window radius.
sr	The color window radius.
maxLevel	Maximum level of the pyramid for the segmentation.
termcrit	Termination criteria: when to stop meanshift iterations.

pyrMeanShiftFiltering() [5/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrMeanShiftFiltering ( Mat src, Mat dst, double sp, double sr, int maxLevel, TermCriteria termcrit )

static

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).

Parameters

src	The source 8-bit, 3-channel image.
dst	The destination image of the same format and the same size as the source.
sp	The spatial window radius.
sr	The color window radius.
maxLevel	Maximum level of the pyramid for the segmentation.
termcrit	Termination criteria: when to stop meanshift iterations.

pyrUp() [1/7]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrUp ( Mat src, Mat dst )

static

Upsamples an image and then blurs it.

By default, size of the output image is computed as Size(src.cols\*2, (src.rows\*2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\]

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.

Parameters

src	input image.
dst	output image. It has the specified size and the same type as src .
dstsize	size of the output image.
borderType	Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported)

pyrUp() [2/7]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrUp ( Mat src, Mat dst, in Vec2d dstsize )

static

Upsamples an image and then blurs it.

By default, size of the output image is computed as Size(src.cols\*2, (src.rows\*2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\]

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.

Parameters

src	input image.
dst	output image. It has the specified size and the same type as src .
dstsize	size of the output image.
borderType	Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported)

pyrUp() [3/7]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrUp ( Mat src, Mat dst, in Vec2d dstsize, int borderType )

static

Upsamples an image and then blurs it.

By default, size of the output image is computed as Size(src.cols\*2, (src.rows\*2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\]

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.

Parameters

src	input image.
dst	output image. It has the specified size and the same type as src .
dstsize	size of the output image.
borderType	Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported)

pyrUp() [4/7]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrUp ( Mat src, Mat dst, in(double width, double height) dstsize )

static

Upsamples an image and then blurs it.

By default, size of the output image is computed as Size(src.cols\*2, (src.rows\*2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\]

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.

Parameters

src	input image.
dst	output image. It has the specified size and the same type as src .
dstsize	size of the output image.
borderType	Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported)

pyrUp() [5/7]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrUp ( Mat src, Mat dst, in(double width, double height) dstsize, int borderType )

static

Upsamples an image and then blurs it.

By default, size of the output image is computed as Size(src.cols\*2, (src.rows\*2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\]

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.

Parameters

src	input image.
dst	output image. It has the specified size and the same type as src .
dstsize	size of the output image.
borderType	Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported)

pyrUp() [6/7]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrUp ( Mat src, Mat dst, Size dstsize )

static

Upsamples an image and then blurs it.

By default, size of the output image is computed as Size(src.cols\*2, (src.rows\*2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\]

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.

Parameters

src	input image.
dst	output image. It has the specified size and the same type as src .
dstsize	size of the output image.
borderType	Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported)

pyrUp() [7/7]

static void OpenCVForUnity.ImgprocModule.Imgproc.pyrUp ( Mat src, Mat dst, Size dstsize, int borderType )

static

Upsamples an image and then blurs it.

By default, size of the output image is computed as Size(src.cols\*2, (src.rows\*2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\]

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.

Parameters

src	input image.
dst	output image. It has the specified size and the same type as src .
dstsize	size of the output image.
borderType	Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported)

rectangle() [1/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color )

static

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.

Parameters

img	Image.
pt1	Vertex of the rectangle.
pt2	Vertex of the rectangle opposite to pt1 .
color	Rectangle color or brightness (grayscale image).
thickness	Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle.
lineType	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.

rectangle() [2/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness )

static

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.

Parameters

img	Image.
pt1	Vertex of the rectangle.
pt2	Vertex of the rectangle opposite to pt1 .
color	Rectangle color or brightness (grayscale image).
thickness	Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle.
lineType	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.

rectangle() [3/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness, int lineType )

static

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.

Parameters

img	Image.
pt1	Vertex of the rectangle.
pt2	Vertex of the rectangle opposite to pt1 .
color	Rectangle color or brightness (grayscale image).
thickness	Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle.
lineType	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.

rectangle() [4/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in Vec2d pt1, in Vec2d pt2, in Vec4d color, int thickness, int lineType, int shift )

static

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.

Parameters

img	Image.
pt1	Vertex of the rectangle.
pt2	Vertex of the rectangle opposite to pt1 .
color	Rectangle color or brightness (grayscale image).
thickness	Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle.
lineType	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.

rectangle() [5/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in Vec4i rec, in Vec4d color )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

use rec parameter as alternative specification of the drawn rectangle: r.tl() and / r.br()-Point(1,1) are opposite corners

rectangle() [6/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in Vec4i rec, in Vec4d color, int thickness )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

use rec parameter as alternative specification of the drawn rectangle: r.tl() and / r.br()-Point(1,1) are opposite corners

rectangle() [7/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in Vec4i rec, in Vec4d color, int thickness, int lineType )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

use rec parameter as alternative specification of the drawn rectangle: r.tl() and / r.br()-Point(1,1) are opposite corners

rectangle() [8/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in Vec4i rec, in Vec4d color, int thickness, int lineType, int shift )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

use rec parameter as alternative specification of the drawn rectangle: r.tl() and / r.br()-Point(1,1) are opposite corners

rectangle() [9/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color )

static

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.

Parameters

img	Image.
pt1	Vertex of the rectangle.
pt2	Vertex of the rectangle opposite to pt1 .
color	Rectangle color or brightness (grayscale image).
thickness	Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle.
lineType	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.

rectangle() [10/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness )

static

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.

Parameters

img	Image.
pt1	Vertex of the rectangle.
pt2	Vertex of the rectangle opposite to pt1 .
color	Rectangle color or brightness (grayscale image).
thickness	Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle.
lineType	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.

rectangle() [11/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType )

static

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.

Parameters

img	Image.
pt1	Vertex of the rectangle.
pt2	Vertex of the rectangle opposite to pt1 .
color	Rectangle color or brightness (grayscale image).
thickness	Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle.
lineType	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.

rectangle() [12/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in(double x, double y) pt1, in(double x, double y) pt2, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, int shift )

static

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.

Parameters

img	Image.
pt1	Vertex of the rectangle.
pt2	Vertex of the rectangle opposite to pt1 .
color	Rectangle color or brightness (grayscale image).
thickness	Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle.
lineType	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.

rectangle() [13/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in(int x, int y, int width, int height) rec, in(double v0, double v1, double v2, double v3) color )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

use rec parameter as alternative specification of the drawn rectangle: r.tl() and / r.br()-Point(1,1) are opposite corners

rectangle() [14/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in(int x, int y, int width, int height) rec, in(double v0, double v1, double v2, double v3) color, int thickness )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

use rec parameter as alternative specification of the drawn rectangle: r.tl() and / r.br()-Point(1,1) are opposite corners

rectangle() [15/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in(int x, int y, int width, int height) rec, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

use rec parameter as alternative specification of the drawn rectangle: r.tl() and / r.br()-Point(1,1) are opposite corners

rectangle() [16/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, in(int x, int y, int width, int height) rec, in(double v0, double v1, double v2, double v3) color, int thickness, int lineType, int shift )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

use rec parameter as alternative specification of the drawn rectangle: r.tl() and / r.br()-Point(1,1) are opposite corners

rectangle() [17/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, Point pt1, Point pt2, Scalar color )

static

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.

Parameters

img	Image.
pt1	Vertex of the rectangle.
pt2	Vertex of the rectangle opposite to pt1 .
color	Rectangle color or brightness (grayscale image).
thickness	Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle.
lineType	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.

rectangle() [18/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, Point pt1, Point pt2, Scalar color, int thickness )

static

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.

Parameters

img	Image.
pt1	Vertex of the rectangle.
pt2	Vertex of the rectangle opposite to pt1 .
color	Rectangle color or brightness (grayscale image).
thickness	Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle.
lineType	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.

rectangle() [19/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType )

static

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.

Parameters

img	Image.
pt1	Vertex of the rectangle.
pt2	Vertex of the rectangle opposite to pt1 .
color	Rectangle color or brightness (grayscale image).
thickness	Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle.
lineType	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.

rectangle() [20/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType, int shift )

static

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.

Parameters

img	Image.
pt1	Vertex of the rectangle.
pt2	Vertex of the rectangle opposite to pt1 .
color	Rectangle color or brightness (grayscale image).
thickness	Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle.
lineType	Type of the line. See #LineTypes
shift	Number of fractional bits in the point coordinates.

rectangle() [21/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, Rect rec, Scalar color )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

use rec parameter as alternative specification of the drawn rectangle: r.tl() and / r.br()-Point(1,1) are opposite corners

rectangle() [22/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, Rect rec, Scalar color, int thickness )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

use rec parameter as alternative specification of the drawn rectangle: r.tl() and / r.br()-Point(1,1) are opposite corners

rectangle() [23/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, Rect rec, Scalar color, int thickness, int lineType )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

use rec parameter as alternative specification of the drawn rectangle: r.tl() and / r.br()-Point(1,1) are opposite corners

rectangle() [24/24]

static void OpenCVForUnity.ImgprocModule.Imgproc.rectangle ( Mat img, Rect rec, Scalar color, int thickness, int lineType, int shift )

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

use rec parameter as alternative specification of the drawn rectangle: r.tl() and / r.br()-Point(1,1) are opposite corners

remap() [1/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.remap ( Mat src, Mat dst, Mat map1, Mat map2, int interpolation )

static

Applies a generic geometrical transformation to an image.

The function remap transforms the source image using the specified map:

\[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\]

\[\texttt{dst} (x,y) = \texttt{src} (x+map_x(x,y),y+map_y(x,y))\]

with WARP_RELATIVE_MAP

where values of pixels with non-integer coordinates are computed using one of available interpolation methods. \(map_x\) and \(map_y\) can be encoded as separate floating-point maps in \(map_1\) and \(map_2\) respectively, or interleaved floating-point maps of \((x,y)\) in \(map_1\), or fixed-point maps created by using convertMaps. The reason you might want to convert from floating to fixed-point representations of a map is that they can yield much faster (2x) remapping operations. In the converted case, \(map_1\) contains pairs (cvFloor(x), cvFloor(y)) and \(map_2\) contains indices in a table of interpolation coefficients.

This function cannot operate in-place.

Parameters

src	Source image.
dst	Destination image. It has the same size as map1 and the same type as src .
map1	The first map of either (x,y) points or just x values having the type CV_16SC2 , CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point representation to fixed-point for speed.
map2	The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map if map1 is (x,y) points), respectively.
interpolation	Interpolation method (see #InterpolationFlags). The methods INTER_AREA INTER_LINEAR_EXACT and INTER_NEAREST_EXACT are not supported by this function. The extra flag WARP_RELATIVE_MAP that can be ORed to the interpolation method (e.g. INTER_LINEAR | WARP_RELATIVE_MAP)
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.
borderValue	Value used in case of a constant border. By default, it is 0.

Due to current implementation limitations the size of an input and output images should be less than 32767x32767.

remap() [2/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.remap ( Mat src, Mat dst, Mat map1, Mat map2, int interpolation, int borderMode )

static

Applies a generic geometrical transformation to an image.

The function remap transforms the source image using the specified map:

\[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\]

\[\texttt{dst} (x,y) = \texttt{src} (x+map_x(x,y),y+map_y(x,y))\]

with WARP_RELATIVE_MAP

where values of pixels with non-integer coordinates are computed using one of available interpolation methods. \(map_x\) and \(map_y\) can be encoded as separate floating-point maps in \(map_1\) and \(map_2\) respectively, or interleaved floating-point maps of \((x,y)\) in \(map_1\), or fixed-point maps created by using convertMaps. The reason you might want to convert from floating to fixed-point representations of a map is that they can yield much faster (2x) remapping operations. In the converted case, \(map_1\) contains pairs (cvFloor(x), cvFloor(y)) and \(map_2\) contains indices in a table of interpolation coefficients.

This function cannot operate in-place.

Parameters

src	Source image.
dst	Destination image. It has the same size as map1 and the same type as src .
map1	The first map of either (x,y) points or just x values having the type CV_16SC2 , CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point representation to fixed-point for speed.
map2	The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map if map1 is (x,y) points), respectively.
interpolation	Interpolation method (see #InterpolationFlags). The methods INTER_AREA INTER_LINEAR_EXACT and INTER_NEAREST_EXACT are not supported by this function. The extra flag WARP_RELATIVE_MAP that can be ORed to the interpolation method (e.g. INTER_LINEAR | WARP_RELATIVE_MAP)
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.
borderValue	Value used in case of a constant border. By default, it is 0.

Due to current implementation limitations the size of an input and output images should be less than 32767x32767.

remap() [3/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.remap ( Mat src, Mat dst, Mat map1, Mat map2, int interpolation, int borderMode, in Vec4d borderValue )

static

Applies a generic geometrical transformation to an image.

The function remap transforms the source image using the specified map:

\[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\]

\[\texttt{dst} (x,y) = \texttt{src} (x+map_x(x,y),y+map_y(x,y))\]

with WARP_RELATIVE_MAP

where values of pixels with non-integer coordinates are computed using one of available interpolation methods. \(map_x\) and \(map_y\) can be encoded as separate floating-point maps in \(map_1\) and \(map_2\) respectively, or interleaved floating-point maps of \((x,y)\) in \(map_1\), or fixed-point maps created by using convertMaps. The reason you might want to convert from floating to fixed-point representations of a map is that they can yield much faster (2x) remapping operations. In the converted case, \(map_1\) contains pairs (cvFloor(x), cvFloor(y)) and \(map_2\) contains indices in a table of interpolation coefficients.

This function cannot operate in-place.

Parameters

src	Source image.
dst	Destination image. It has the same size as map1 and the same type as src .
map1	The first map of either (x,y) points or just x values having the type CV_16SC2 , CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point representation to fixed-point for speed.
map2	The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map if map1 is (x,y) points), respectively.
interpolation	Interpolation method (see #InterpolationFlags). The methods INTER_AREA INTER_LINEAR_EXACT and INTER_NEAREST_EXACT are not supported by this function. The extra flag WARP_RELATIVE_MAP that can be ORed to the interpolation method (e.g. INTER_LINEAR | WARP_RELATIVE_MAP)
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.
borderValue	Value used in case of a constant border. By default, it is 0.

Due to current implementation limitations the size of an input and output images should be less than 32767x32767.

remap() [4/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.remap ( Mat src, Mat dst, Mat map1, Mat map2, int interpolation, int borderMode, in(double v0, double v1, double v2, double v3) borderValue )

static

Applies a generic geometrical transformation to an image.

The function remap transforms the source image using the specified map:

\[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\]

\[\texttt{dst} (x,y) = \texttt{src} (x+map_x(x,y),y+map_y(x,y))\]

with WARP_RELATIVE_MAP

where values of pixels with non-integer coordinates are computed using one of available interpolation methods. \(map_x\) and \(map_y\) can be encoded as separate floating-point maps in \(map_1\) and \(map_2\) respectively, or interleaved floating-point maps of \((x,y)\) in \(map_1\), or fixed-point maps created by using convertMaps. The reason you might want to convert from floating to fixed-point representations of a map is that they can yield much faster (2x) remapping operations. In the converted case, \(map_1\) contains pairs (cvFloor(x), cvFloor(y)) and \(map_2\) contains indices in a table of interpolation coefficients.

This function cannot operate in-place.

Parameters

src	Source image.
dst	Destination image. It has the same size as map1 and the same type as src .
map1	The first map of either (x,y) points or just x values having the type CV_16SC2 , CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point representation to fixed-point for speed.
map2	The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map if map1 is (x,y) points), respectively.
interpolation	Interpolation method (see #InterpolationFlags). The methods INTER_AREA INTER_LINEAR_EXACT and INTER_NEAREST_EXACT are not supported by this function. The extra flag WARP_RELATIVE_MAP that can be ORed to the interpolation method (e.g. INTER_LINEAR | WARP_RELATIVE_MAP)
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.
borderValue	Value used in case of a constant border. By default, it is 0.

Due to current implementation limitations the size of an input and output images should be less than 32767x32767.

remap() [5/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.remap ( Mat src, Mat dst, Mat map1, Mat map2, int interpolation, int borderMode, Scalar borderValue )

static

Applies a generic geometrical transformation to an image.

The function remap transforms the source image using the specified map:

\[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\]

\[\texttt{dst} (x,y) = \texttt{src} (x+map_x(x,y),y+map_y(x,y))\]

with WARP_RELATIVE_MAP

where values of pixels with non-integer coordinates are computed using one of available interpolation methods. \(map_x\) and \(map_y\) can be encoded as separate floating-point maps in \(map_1\) and \(map_2\) respectively, or interleaved floating-point maps of \((x,y)\) in \(map_1\), or fixed-point maps created by using convertMaps. The reason you might want to convert from floating to fixed-point representations of a map is that they can yield much faster (2x) remapping operations. In the converted case, \(map_1\) contains pairs (cvFloor(x), cvFloor(y)) and \(map_2\) contains indices in a table of interpolation coefficients.

This function cannot operate in-place.

Parameters

src	Source image.
dst	Destination image. It has the same size as map1 and the same type as src .
map1	The first map of either (x,y) points or just x values having the type CV_16SC2 , CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point representation to fixed-point for speed.
map2	The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map if map1 is (x,y) points), respectively.
interpolation	Interpolation method (see #InterpolationFlags). The methods INTER_AREA INTER_LINEAR_EXACT and INTER_NEAREST_EXACT are not supported by this function. The extra flag WARP_RELATIVE_MAP that can be ORed to the interpolation method (e.g. INTER_LINEAR | WARP_RELATIVE_MAP)
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.
borderValue	Value used in case of a constant border. By default, it is 0.

Due to current implementation limitations the size of an input and output images should be less than 32767x32767.

resize() [1/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.resize ( Mat src, Mat dst, in Vec2d dsize )

static

Resizes an image.

The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:

// explicitly specify dsize=dst.size(); fx and fy will be computed from that.
resize(src, dst, dst.size(), 0, 0, interpolation);

If you want to decimate the image by factor of 2 in each direction, you can call the function this way:

// specify fx and fy and let the function compute the destination image size.
resize(src, dst, Size(), 0.5, 0.5, interpolation);

To shrink an image, it will generally look best with 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).

Parameters

src	input image.
dst	output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.
dsize	output image size; if it equals zero (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.
fx	scale factor along the horizontal axis; when it equals 0, it is computed as \[\texttt{(double)dsize.width/src.cols}\]
fy	scale factor along the vertical axis; when it equals 0, it is computed as \[\texttt{(double)dsize.height/src.rows}\]
interpolation	interpolation method, see #InterpolationFlags

See also

warpAffine, warpPerspective, remap

resize() [2/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.resize ( Mat src, Mat dst, in Vec2d dsize, double fx )

static

Resizes an image.

The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:

// explicitly specify dsize=dst.size(); fx and fy will be computed from that.
resize(src, dst, dst.size(), 0, 0, interpolation);

If you want to decimate the image by factor of 2 in each direction, you can call the function this way:

// specify fx and fy and let the function compute the destination image size.
resize(src, dst, Size(), 0.5, 0.5, interpolation);

To shrink an image, it will generally look best with 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).

Parameters

src	input image.
dst	output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.
dsize	output image size; if it equals zero (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.
fx	scale factor along the horizontal axis; when it equals 0, it is computed as \[\texttt{(double)dsize.width/src.cols}\]
fy	scale factor along the vertical axis; when it equals 0, it is computed as \[\texttt{(double)dsize.height/src.rows}\]
interpolation	interpolation method, see #InterpolationFlags

See also

warpAffine, warpPerspective, remap

resize() [3/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.resize ( Mat src, Mat dst, in Vec2d dsize, double fx, double fy )

static

Resizes an image.

The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:

// explicitly specify dsize=dst.size(); fx and fy will be computed from that.
resize(src, dst, dst.size(), 0, 0, interpolation);

If you want to decimate the image by factor of 2 in each direction, you can call the function this way:

// specify fx and fy and let the function compute the destination image size.
resize(src, dst, Size(), 0.5, 0.5, interpolation);

To shrink an image, it will generally look best with 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).

Parameters

src	input image.
dst	output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.
dsize	output image size; if it equals zero (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.
fx	scale factor along the horizontal axis; when it equals 0, it is computed as \[\texttt{(double)dsize.width/src.cols}\]
fy	scale factor along the vertical axis; when it equals 0, it is computed as \[\texttt{(double)dsize.height/src.rows}\]
interpolation	interpolation method, see #InterpolationFlags

See also

warpAffine, warpPerspective, remap

resize() [4/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.resize ( Mat src, Mat dst, in Vec2d dsize, double fx, double fy, int interpolation )

static

Resizes an image.

The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:

// explicitly specify dsize=dst.size(); fx and fy will be computed from that.
resize(src, dst, dst.size(), 0, 0, interpolation);

If you want to decimate the image by factor of 2 in each direction, you can call the function this way:

// specify fx and fy and let the function compute the destination image size.
resize(src, dst, Size(), 0.5, 0.5, interpolation);

To shrink an image, it will generally look best with 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).

Parameters

src	input image.
dst	output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.
dsize	output image size; if it equals zero (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.
fx	scale factor along the horizontal axis; when it equals 0, it is computed as \[\texttt{(double)dsize.width/src.cols}\]
fy	scale factor along the vertical axis; when it equals 0, it is computed as \[\texttt{(double)dsize.height/src.rows}\]
interpolation	interpolation method, see #InterpolationFlags

See also

warpAffine, warpPerspective, remap

resize() [5/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.resize ( Mat src, Mat dst, in(double width, double height) dsize )

static

Resizes an image.

The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:

// explicitly specify dsize=dst.size(); fx and fy will be computed from that.
resize(src, dst, dst.size(), 0, 0, interpolation);

If you want to decimate the image by factor of 2 in each direction, you can call the function this way:

// specify fx and fy and let the function compute the destination image size.
resize(src, dst, Size(), 0.5, 0.5, interpolation);

To shrink an image, it will generally look best with 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).

Parameters

src	input image.
dst	output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.
dsize	output image size; if it equals zero (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.
fx	scale factor along the horizontal axis; when it equals 0, it is computed as \[\texttt{(double)dsize.width/src.cols}\]
fy	scale factor along the vertical axis; when it equals 0, it is computed as \[\texttt{(double)dsize.height/src.rows}\]
interpolation	interpolation method, see #InterpolationFlags

See also

warpAffine, warpPerspective, remap

resize() [6/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.resize ( Mat src, Mat dst, in(double width, double height) dsize, double fx )

static

Resizes an image.

The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:

// explicitly specify dsize=dst.size(); fx and fy will be computed from that.
resize(src, dst, dst.size(), 0, 0, interpolation);

If you want to decimate the image by factor of 2 in each direction, you can call the function this way:

// specify fx and fy and let the function compute the destination image size.
resize(src, dst, Size(), 0.5, 0.5, interpolation);

To shrink an image, it will generally look best with 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).

Parameters

src	input image.
dst	output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.
dsize	output image size; if it equals zero (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.
fx	scale factor along the horizontal axis; when it equals 0, it is computed as \[\texttt{(double)dsize.width/src.cols}\]
fy	scale factor along the vertical axis; when it equals 0, it is computed as \[\texttt{(double)dsize.height/src.rows}\]
interpolation	interpolation method, see #InterpolationFlags

See also

warpAffine, warpPerspective, remap

resize() [7/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.resize ( Mat src, Mat dst, in(double width, double height) dsize, double fx, double fy )

static

Resizes an image.

The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:

// explicitly specify dsize=dst.size(); fx and fy will be computed from that.
resize(src, dst, dst.size(), 0, 0, interpolation);

If you want to decimate the image by factor of 2 in each direction, you can call the function this way:

// specify fx and fy and let the function compute the destination image size.
resize(src, dst, Size(), 0.5, 0.5, interpolation);

To shrink an image, it will generally look best with 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).

Parameters

src	input image.
dst	output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.
dsize	output image size; if it equals zero (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.
fx	scale factor along the horizontal axis; when it equals 0, it is computed as \[\texttt{(double)dsize.width/src.cols}\]
fy	scale factor along the vertical axis; when it equals 0, it is computed as \[\texttt{(double)dsize.height/src.rows}\]
interpolation	interpolation method, see #InterpolationFlags

See also

warpAffine, warpPerspective, remap

resize() [8/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.resize ( Mat src, Mat dst, in(double width, double height) dsize, double fx, double fy, int interpolation )

static

Resizes an image.

The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:

// explicitly specify dsize=dst.size(); fx and fy will be computed from that.
resize(src, dst, dst.size(), 0, 0, interpolation);

If you want to decimate the image by factor of 2 in each direction, you can call the function this way:

// specify fx and fy and let the function compute the destination image size.
resize(src, dst, Size(), 0.5, 0.5, interpolation);

To shrink an image, it will generally look best with 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).

Parameters

src	input image.
dst	output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.
dsize	output image size; if it equals zero (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.
fx	scale factor along the horizontal axis; when it equals 0, it is computed as \[\texttt{(double)dsize.width/src.cols}\]
fy	scale factor along the vertical axis; when it equals 0, it is computed as \[\texttt{(double)dsize.height/src.rows}\]
interpolation	interpolation method, see #InterpolationFlags

See also

warpAffine, warpPerspective, remap

resize() [9/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.resize ( Mat src, Mat dst, Size dsize )

static

Resizes an image.

The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:

// explicitly specify dsize=dst.size(); fx and fy will be computed from that.
resize(src, dst, dst.size(), 0, 0, interpolation);

If you want to decimate the image by factor of 2 in each direction, you can call the function this way:

// specify fx and fy and let the function compute the destination image size.
resize(src, dst, Size(), 0.5, 0.5, interpolation);

To shrink an image, it will generally look best with 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).

Parameters

src	input image.
dst	output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.
dsize	output image size; if it equals zero (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.
fx	scale factor along the horizontal axis; when it equals 0, it is computed as \[\texttt{(double)dsize.width/src.cols}\]
fy	scale factor along the vertical axis; when it equals 0, it is computed as \[\texttt{(double)dsize.height/src.rows}\]
interpolation	interpolation method, see #InterpolationFlags

See also

warpAffine, warpPerspective, remap

resize() [10/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.resize ( Mat src, Mat dst, Size dsize, double fx )

static

Resizes an image.

The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:

// explicitly specify dsize=dst.size(); fx and fy will be computed from that.
resize(src, dst, dst.size(), 0, 0, interpolation);

If you want to decimate the image by factor of 2 in each direction, you can call the function this way:

// specify fx and fy and let the function compute the destination image size.
resize(src, dst, Size(), 0.5, 0.5, interpolation);

To shrink an image, it will generally look best with 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).

Parameters

src	input image.
dst	output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.
dsize	output image size; if it equals zero (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.
fx	scale factor along the horizontal axis; when it equals 0, it is computed as \[\texttt{(double)dsize.width/src.cols}\]
fy	scale factor along the vertical axis; when it equals 0, it is computed as \[\texttt{(double)dsize.height/src.rows}\]
interpolation	interpolation method, see #InterpolationFlags

See also

warpAffine, warpPerspective, remap

resize() [11/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.resize ( Mat src, Mat dst, Size dsize, double fx, double fy )

static

Resizes an image.

The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:

// explicitly specify dsize=dst.size(); fx and fy will be computed from that.
resize(src, dst, dst.size(), 0, 0, interpolation);

If you want to decimate the image by factor of 2 in each direction, you can call the function this way:

// specify fx and fy and let the function compute the destination image size.
resize(src, dst, Size(), 0.5, 0.5, interpolation);

To shrink an image, it will generally look best with 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).

Parameters

src	input image.
dst	output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.
dsize	output image size; if it equals zero (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.
fx	scale factor along the horizontal axis; when it equals 0, it is computed as \[\texttt{(double)dsize.width/src.cols}\]
fy	scale factor along the vertical axis; when it equals 0, it is computed as \[\texttt{(double)dsize.height/src.rows}\]
interpolation	interpolation method, see #InterpolationFlags

See also

warpAffine, warpPerspective, remap

resize() [12/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.resize ( Mat src, Mat dst, Size dsize, double fx, double fy, int interpolation )

static

Resizes an image.

The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:

// explicitly specify dsize=dst.size(); fx and fy will be computed from that.
resize(src, dst, dst.size(), 0, 0, interpolation);

If you want to decimate the image by factor of 2 in each direction, you can call the function this way:

// specify fx and fy and let the function compute the destination image size.
resize(src, dst, Size(), 0.5, 0.5, interpolation);

To shrink an image, it will generally look best with 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).

Parameters

src	input image.
dst	output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.
dsize	output image size; if it equals zero (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.
fx	scale factor along the horizontal axis; when it equals 0, it is computed as \[\texttt{(double)dsize.width/src.cols}\]
fy	scale factor along the vertical axis; when it equals 0, it is computed as \[\texttt{(double)dsize.height/src.rows}\]
interpolation	interpolation method, see #InterpolationFlags

See also

warpAffine, warpPerspective, remap

rotatedRectangleIntersection() [1/3]

static int OpenCVForUnity.ImgprocModule.Imgproc.rotatedRectangleIntersection ( in Vec5d rect1, in Vec5d rect2, Mat intersectingRegion )

static

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.

Parameters

rect1	First rectangle
rect2	Second rectangle
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.

Returns

One of #RectanglesIntersectTypes

rotatedRectangleIntersection() [2/3]

static int OpenCVForUnity.ImgprocModule.Imgproc.rotatedRectangleIntersection ( in(double x, double y, double width, double height, double angle) rect1, in(double x, double y, double width, double height, double angle) rect2, Mat intersectingRegion )

static

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.

Parameters

rect1	First rectangle
rect2	Second rectangle
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.

Returns

One of #RectanglesIntersectTypes

rotatedRectangleIntersection() [3/3]

static int OpenCVForUnity.ImgprocModule.Imgproc.rotatedRectangleIntersection ( RotatedRect rect1, RotatedRect rect2, Mat intersectingRegion )

static

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.

Parameters

rect1	First rectangle
rect2	Second rectangle
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.

Returns

One of #RectanglesIntersectTypes

Scharr() [1/4]

static void OpenCVForUnity.ImgprocModule.Imgproc.Scharr ( Mat src, Mat dst, int ddepth, int dx, int dy )

static

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

\[\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER_SCHARR, scale, delta, borderType)} .\]

Parameters

src	input image.
dst	output image of the same size and the same number of channels as src.
ddepth	output image depth, see combinations
dx	order of the derivative x.
dy	order of the derivative y.
scale	optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details).
delta	optional delta value that is added to the results prior to storing them in dst.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

cartToPolar

Scharr() [2/4]

static void OpenCVForUnity.ImgprocModule.Imgproc.Scharr ( Mat src, Mat dst, int ddepth, int dx, int dy, double scale )

static

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

\[\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER_SCHARR, scale, delta, borderType)} .\]

Parameters

src	input image.
dst	output image of the same size and the same number of channels as src.
ddepth	output image depth, see combinations
dx	order of the derivative x.
dy	order of the derivative y.
scale	optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details).
delta	optional delta value that is added to the results prior to storing them in dst.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

cartToPolar

Scharr() [3/4]

static void OpenCVForUnity.ImgprocModule.Imgproc.Scharr ( Mat src, Mat dst, int ddepth, int dx, int dy, double scale, double delta )

static

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

\[\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER_SCHARR, scale, delta, borderType)} .\]

Parameters

src	input image.
dst	output image of the same size and the same number of channels as src.
ddepth	output image depth, see combinations
dx	order of the derivative x.
dy	order of the derivative y.
scale	optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details).
delta	optional delta value that is added to the results prior to storing them in dst.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

cartToPolar

Scharr() [4/4]

static void OpenCVForUnity.ImgprocModule.Imgproc.Scharr ( Mat src, Mat dst, int ddepth, int dx, int dy, double scale, double delta, int borderType )

static

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

\[\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER_SCHARR, scale, delta, borderType)} .\]

Parameters

src	input image.
dst	output image of the same size and the same number of channels as src.
ddepth	output image depth, see combinations
dx	order of the derivative x.
dy	order of the derivative y.
scale	optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details).
delta	optional delta value that is added to the results prior to storing them in dst.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

cartToPolar

sepFilter2D() [1/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.sepFilter2D ( Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY )

static

Applies a separable linear filter to an image.

The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst .

Parameters

src	Source image.
dst	Destination image of the same size and the same number of channels as src .
ddepth	Destination image depth, see combinations
kernelX	Coefficients for filtering each row.
kernelY	Coefficients for filtering each column.
anchor	Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor is at the kernel center.
delta	Value added to the filtered results before storing them.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

filter2D, Sobel, GaussianBlur, boxFilter, blur

sepFilter2D() [2/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.sepFilter2D ( Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, in Vec2d anchor )

static

Applies a separable linear filter to an image.

The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst .

Parameters

src	Source image.
dst	Destination image of the same size and the same number of channels as src .
ddepth	Destination image depth, see combinations
kernelX	Coefficients for filtering each row.
kernelY	Coefficients for filtering each column.
anchor	Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor is at the kernel center.
delta	Value added to the filtered results before storing them.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

filter2D, Sobel, GaussianBlur, boxFilter, blur

sepFilter2D() [3/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.sepFilter2D ( Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, in Vec2d anchor, double delta )

static

Applies a separable linear filter to an image.

The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst .

Parameters

src	Source image.
dst	Destination image of the same size and the same number of channels as src .
ddepth	Destination image depth, see combinations
kernelX	Coefficients for filtering each row.
kernelY	Coefficients for filtering each column.
anchor	Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor is at the kernel center.
delta	Value added to the filtered results before storing them.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

filter2D, Sobel, GaussianBlur, boxFilter, blur

sepFilter2D() [4/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.sepFilter2D ( Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, in Vec2d anchor, double delta, int borderType )

static

Applies a separable linear filter to an image.

The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst .

Parameters

src	Source image.
dst	Destination image of the same size and the same number of channels as src .
ddepth	Destination image depth, see combinations
kernelX	Coefficients for filtering each row.
kernelY	Coefficients for filtering each column.
anchor	Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor is at the kernel center.
delta	Value added to the filtered results before storing them.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

filter2D, Sobel, GaussianBlur, boxFilter, blur

sepFilter2D() [5/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.sepFilter2D ( Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, in(double x, double y) anchor )

static

Applies a separable linear filter to an image.

The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst .

Parameters

src	Source image.
dst	Destination image of the same size and the same number of channels as src .
ddepth	Destination image depth, see combinations
kernelX	Coefficients for filtering each row.
kernelY	Coefficients for filtering each column.
anchor	Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor is at the kernel center.
delta	Value added to the filtered results before storing them.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

filter2D, Sobel, GaussianBlur, boxFilter, blur

sepFilter2D() [6/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.sepFilter2D ( Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, in(double x, double y) anchor, double delta )

static

Applies a separable linear filter to an image.

The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst .

Parameters

src	Source image.
dst	Destination image of the same size and the same number of channels as src .
ddepth	Destination image depth, see combinations
kernelX	Coefficients for filtering each row.
kernelY	Coefficients for filtering each column.
anchor	Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor is at the kernel center.
delta	Value added to the filtered results before storing them.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

filter2D, Sobel, GaussianBlur, boxFilter, blur

sepFilter2D() [7/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.sepFilter2D ( Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, in(double x, double y) anchor, double delta, int borderType )

static

Applies a separable linear filter to an image.

The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst .

Parameters

src	Source image.
dst	Destination image of the same size and the same number of channels as src .
ddepth	Destination image depth, see combinations
kernelX	Coefficients for filtering each row.
kernelY	Coefficients for filtering each column.
anchor	Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor is at the kernel center.
delta	Value added to the filtered results before storing them.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

filter2D, Sobel, GaussianBlur, boxFilter, blur

sepFilter2D() [8/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.sepFilter2D ( Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor )

static

Applies a separable linear filter to an image.

The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst .

Parameters

src	Source image.
dst	Destination image of the same size and the same number of channels as src .
ddepth	Destination image depth, see combinations
kernelX	Coefficients for filtering each row.
kernelY	Coefficients for filtering each column.
anchor	Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor is at the kernel center.
delta	Value added to the filtered results before storing them.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

filter2D, Sobel, GaussianBlur, boxFilter, blur

sepFilter2D() [9/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.sepFilter2D ( Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor, double delta )

static

Applies a separable linear filter to an image.

The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst .

Parameters

src	Source image.
dst	Destination image of the same size and the same number of channels as src .
ddepth	Destination image depth, see combinations
kernelX	Coefficients for filtering each row.
kernelY	Coefficients for filtering each column.
anchor	Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor is at the kernel center.
delta	Value added to the filtered results before storing them.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

filter2D, Sobel, GaussianBlur, boxFilter, blur

sepFilter2D() [10/10]

static void OpenCVForUnity.ImgprocModule.Imgproc.sepFilter2D ( Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor, double delta, int borderType )

static

Applies a separable linear filter to an image.

The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst .

Parameters

src	Source image.
dst	Destination image of the same size and the same number of channels as src .
ddepth	Destination image depth, see combinations
kernelX	Coefficients for filtering each row.
kernelY	Coefficients for filtering each column.
anchor	Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor is at the kernel center.
delta	Value added to the filtered results before storing them.
borderType	Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

filter2D, Sobel, GaussianBlur, boxFilter, blur

Sobel() [1/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.Sobel ( Mat src, Mat dst, int ddepth, int dx, int dy )

static

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). ksize = 1 can only be used for the first or the second x- or y- derivatives.

There is also the special value 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:

\[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\]

Parameters

src	input image.
dst	output image of the same size and the same number of channels as src .
ddepth	output image depth, see combinations; in the case of 8-bit input images it will result in truncated derivatives.
dx	order of the derivative x.
dy	order of the derivative y.
ksize	size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
scale	optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details).
delta	optional delta value that is added to the results prior to storing them in dst.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar

Sobel() [2/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.Sobel ( Mat src, Mat dst, int ddepth, int dx, int dy, int ksize )

static

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). ksize = 1 can only be used for the first or the second x- or y- derivatives.

There is also the special value 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:

\[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\]

Parameters

src	input image.
dst	output image of the same size and the same number of channels as src .
ddepth	output image depth, see combinations; in the case of 8-bit input images it will result in truncated derivatives.
dx	order of the derivative x.
dy	order of the derivative y.
ksize	size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
scale	optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details).
delta	optional delta value that is added to the results prior to storing them in dst.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar

Sobel() [3/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.Sobel ( Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale )

static

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). ksize = 1 can only be used for the first or the second x- or y- derivatives.

There is also the special value 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:

\[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\]

Parameters

src	input image.
dst	output image of the same size and the same number of channels as src .
ddepth	output image depth, see combinations; in the case of 8-bit input images it will result in truncated derivatives.
dx	order of the derivative x.
dy	order of the derivative y.
ksize	size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
scale	optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details).
delta	optional delta value that is added to the results prior to storing them in dst.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar

Sobel() [4/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.Sobel ( Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale, double delta )

static

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). ksize = 1 can only be used for the first or the second x- or y- derivatives.

There is also the special value 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:

\[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\]

Parameters

src	input image.
dst	output image of the same size and the same number of channels as src .
ddepth	output image depth, see combinations; in the case of 8-bit input images it will result in truncated derivatives.
dx	order of the derivative x.
dy	order of the derivative y.
ksize	size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
scale	optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details).
delta	optional delta value that is added to the results prior to storing them in dst.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar

Sobel() [5/5]

static void OpenCVForUnity.ImgprocModule.Imgproc.Sobel ( Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale, double delta, int borderType )

static

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). ksize = 1 can only be used for the first or the second x- or y- derivatives.

There is also the special value 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:

\[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\]

Parameters

src	input image.
dst	output image of the same size and the same number of channels as src .
ddepth	output image depth, see combinations; in the case of 8-bit input images it will result in truncated derivatives.
dx	order of the derivative x.
dy	order of the derivative y.
ksize	size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
scale	optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details).
delta	optional delta value that is added to the results prior to storing them in dst.
borderType	pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar

spatialGradient() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.spatialGradient ( Mat src, Mat dx, Mat dy )

static

Calculates the first order image derivative in both x and y using a Sobel operator.

Equivalent to calling:

Sobel( src, dx, CV_16SC1, 1, 0, 3 );
Sobel( src, dy, CV_16SC1, 0, 1, 3 );

Parameters

src	input image.
dx	output image with first-order derivative in x.
dy	output image with first-order derivative in y.
ksize	size of Sobel kernel. It must be 3.
borderType	pixel extrapolation method, see #BorderTypes. Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported.

See also

Sobel

spatialGradient() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.spatialGradient ( Mat src, Mat dx, Mat dy, int ksize )

static

Calculates the first order image derivative in both x and y using a Sobel operator.

Equivalent to calling:

Sobel( src, dx, CV_16SC1, 1, 0, 3 );
Sobel( src, dy, CV_16SC1, 0, 1, 3 );

Parameters

src	input image.
dx	output image with first-order derivative in x.
dy	output image with first-order derivative in y.
ksize	size of Sobel kernel. It must be 3.
borderType	pixel extrapolation method, see #BorderTypes. Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported.

See also

Sobel

spatialGradient() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.spatialGradient ( Mat src, Mat dx, Mat dy, int ksize, int borderType )

static

Calculates the first order image derivative in both x and y using a Sobel operator.

Equivalent to calling:

Sobel( src, dx, CV_16SC1, 1, 0, 3 );
Sobel( src, dy, CV_16SC1, 0, 1, 3 );

Parameters

src	input image.
dx	output image with first-order derivative in x.
dy	output image with first-order derivative in y.
ksize	size of Sobel kernel. It must be 3.
borderType	pixel extrapolation method, see #BorderTypes. Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported.

See also

Sobel

sqrBoxFilter() [1/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.sqrBoxFilter ( Mat src, Mat dst, int ddepth, in Vec2d ksize )

static

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.

Parameters

src	input image
dst	output image of the same size and type as src
ddepth	the output image depth (-1 to use src.depth())
ksize	kernel size
anchor	kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is to be normalized by it's area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter

sqrBoxFilter() [2/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.sqrBoxFilter ( Mat src, Mat dst, int ddepth, in Vec2d ksize, in Vec2d anchor )

static

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.

Parameters

src	input image
dst	output image of the same size and type as src
ddepth	the output image depth (-1 to use src.depth())
ksize	kernel size
anchor	kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is to be normalized by it's area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter

sqrBoxFilter() [3/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.sqrBoxFilter ( Mat src, Mat dst, int ddepth, in Vec2d ksize, in Vec2d anchor, bool normalize )

static

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.

Parameters

src	input image
dst	output image of the same size and type as src
ddepth	the output image depth (-1 to use src.depth())
ksize	kernel size
anchor	kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is to be normalized by it's area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter

sqrBoxFilter() [4/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.sqrBoxFilter ( Mat src, Mat dst, int ddepth, in Vec2d ksize, in Vec2d anchor, bool normalize, int borderType )

static

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.

Parameters

src	input image
dst	output image of the same size and type as src
ddepth	the output image depth (-1 to use src.depth())
ksize	kernel size
anchor	kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is to be normalized by it's area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter

sqrBoxFilter() [5/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.sqrBoxFilter ( Mat src, Mat dst, int ddepth, in(double width, double height) ksize )

static

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.

Parameters

src	input image
dst	output image of the same size and type as src
ddepth	the output image depth (-1 to use src.depth())
ksize	kernel size
anchor	kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is to be normalized by it's area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter

sqrBoxFilter() [6/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.sqrBoxFilter ( Mat src, Mat dst, int ddepth, in(double width, double height) ksize, in(double x, double y) anchor )

static

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.

Parameters

src	input image
dst	output image of the same size and type as src
ddepth	the output image depth (-1 to use src.depth())
ksize	kernel size
anchor	kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is to be normalized by it's area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter

sqrBoxFilter() [7/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.sqrBoxFilter ( Mat src, Mat dst, int ddepth, in(double width, double height) ksize, in(double x, double y) anchor, bool normalize )

static

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.

Parameters

src	input image
dst	output image of the same size and type as src
ddepth	the output image depth (-1 to use src.depth())
ksize	kernel size
anchor	kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is to be normalized by it's area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter

sqrBoxFilter() [8/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.sqrBoxFilter ( Mat src, Mat dst, int ddepth, in(double width, double height) ksize, in(double x, double y) anchor, bool normalize, int borderType )

static

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.

Parameters

src	input image
dst	output image of the same size and type as src
ddepth	the output image depth (-1 to use src.depth())
ksize	kernel size
anchor	kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is to be normalized by it's area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter

sqrBoxFilter() [9/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.sqrBoxFilter ( Mat src, Mat dst, int ddepth, Size ksize )

static

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.

Parameters

src	input image
dst	output image of the same size and type as src
ddepth	the output image depth (-1 to use src.depth())
ksize	kernel size
anchor	kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is to be normalized by it's area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter

sqrBoxFilter() [10/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.sqrBoxFilter ( Mat src, Mat dst, int ddepth, Size ksize, Point anchor )

static

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.

Parameters

src	input image
dst	output image of the same size and type as src
ddepth	the output image depth (-1 to use src.depth())
ksize	kernel size
anchor	kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is to be normalized by it's area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter

sqrBoxFilter() [11/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.sqrBoxFilter ( Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize )

static

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.

Parameters

src	input image
dst	output image of the same size and type as src
ddepth	the output image depth (-1 to use src.depth())
ksize	kernel size
anchor	kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is to be normalized by it's area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter

sqrBoxFilter() [12/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.sqrBoxFilter ( Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize, int borderType )

static

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.

Parameters

src	input image
dst	output image of the same size and type as src
ddepth	the output image depth (-1 to use src.depth())
ksize	kernel size
anchor	kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center.
normalize	flag, specifying whether the kernel is to be normalized by it's area or not.
borderType	border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

boxFilter

stackBlur() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.stackBlur ( Mat src, Mat dst, in Vec2d ksize )

static

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.

Parameters

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.
dst	output image of the same size and type as src.
ksize	stack-blurring kernel size. The ksize.width and ksize.height can differ but they both must be positive and odd.

stackBlur() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.stackBlur ( Mat src, Mat dst, in(double width, double height) ksize )

static

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.

Parameters

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.
dst	output image of the same size and type as src.
ksize	stack-blurring kernel size. The ksize.width and ksize.height can differ but they both must be positive and odd.

stackBlur() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.stackBlur ( Mat src, Mat dst, Size ksize )

static

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.

Parameters

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.
dst	output image of the same size and type as src.
ksize	stack-blurring kernel size. The ksize.width and ksize.height can differ but they both must be positive and odd.

threshold()

static double OpenCVForUnity.ImgprocModule.Imgproc.threshold ( Mat src, Mat dst, double thresh, double maxval, int type )

static

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.

Note

Currently, the Otsu's and Triangle methods are implemented only for 8-bit single-channel images.

Parameters

src	input array (multiple-channel, 8-bit or 32-bit floating point).
dst	output array of the same size and type and the same number of channels as src.
thresh	threshold value.
maxval	maximum value to use with the THRESH_BINARY and THRESH_BINARY_INV thresholding types.
type	thresholding type (see #ThresholdTypes).

Returns

the computed threshold value if Otsu's or Triangle methods used.

See also

adaptiveThreshold, findContours, compare, min, max

warpAffine() [1/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpAffine ( Mat src, Mat dst, Mat M, in Vec2d dsize )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(2\times 3\) transformation matrix.
dsize	size of the output image.
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	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.
borderValue	value used in case of a constant border; by default, it is 0.

See also

warpPerspective, resize, remap, getRectSubPix, transform

warpAffine() [2/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpAffine ( Mat src, Mat dst, Mat M, in Vec2d dsize, int flags )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(2\times 3\) transformation matrix.
dsize	size of the output image.
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	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.
borderValue	value used in case of a constant border; by default, it is 0.

See also

warpPerspective, resize, remap, getRectSubPix, transform

warpAffine() [3/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpAffine ( Mat src, Mat dst, Mat M, in Vec2d dsize, int flags, int borderMode )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(2\times 3\) transformation matrix.
dsize	size of the output image.
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	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.
borderValue	value used in case of a constant border; by default, it is 0.

See also

warpPerspective, resize, remap, getRectSubPix, transform

warpAffine() [4/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpAffine ( Mat src, Mat dst, Mat M, in Vec2d dsize, int flags, int borderMode, in Vec4d borderValue )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(2\times 3\) transformation matrix.
dsize	size of the output image.
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	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.
borderValue	value used in case of a constant border; by default, it is 0.

See also

warpPerspective, resize, remap, getRectSubPix, transform

warpAffine() [5/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpAffine ( Mat src, Mat dst, Mat M, in(double width, double height) dsize )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(2\times 3\) transformation matrix.
dsize	size of the output image.
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	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.
borderValue	value used in case of a constant border; by default, it is 0.

See also

warpPerspective, resize, remap, getRectSubPix, transform

warpAffine() [6/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpAffine ( Mat src, Mat dst, Mat M, in(double width, double height) dsize, int flags )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(2\times 3\) transformation matrix.
dsize	size of the output image.
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	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.
borderValue	value used in case of a constant border; by default, it is 0.

See also

warpPerspective, resize, remap, getRectSubPix, transform

warpAffine() [7/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpAffine ( Mat src, Mat dst, Mat M, in(double width, double height) dsize, int flags, int borderMode )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(2\times 3\) transformation matrix.
dsize	size of the output image.
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	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.
borderValue	value used in case of a constant border; by default, it is 0.

See also

warpPerspective, resize, remap, getRectSubPix, transform

warpAffine() [8/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpAffine ( Mat src, Mat dst, Mat M, in(double width, double height) dsize, int flags, int borderMode, in(double v0, double v1, double v2, double v3) borderValue )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(2\times 3\) transformation matrix.
dsize	size of the output image.
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	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.
borderValue	value used in case of a constant border; by default, it is 0.

See also

warpPerspective, resize, remap, getRectSubPix, transform

warpAffine() [9/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpAffine ( Mat src, Mat dst, Mat M, Size dsize )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(2\times 3\) transformation matrix.
dsize	size of the output image.
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	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.
borderValue	value used in case of a constant border; by default, it is 0.

See also

warpPerspective, resize, remap, getRectSubPix, transform

warpAffine() [10/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpAffine ( Mat src, Mat dst, Mat M, Size dsize, int flags )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(2\times 3\) transformation matrix.
dsize	size of the output image.
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	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.
borderValue	value used in case of a constant border; by default, it is 0.

See also

warpPerspective, resize, remap, getRectSubPix, transform

warpAffine() [11/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpAffine ( Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(2\times 3\) transformation matrix.
dsize	size of the output image.
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	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.
borderValue	value used in case of a constant border; by default, it is 0.

See also

warpPerspective, resize, remap, getRectSubPix, transform

warpAffine() [12/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpAffine ( Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(2\times 3\) transformation matrix.
dsize	size of the output image.
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	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.
borderValue	value used in case of a constant border; by default, it is 0.

See also

warpPerspective, resize, remap, getRectSubPix, transform

warpPerspective() [1/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpPerspective ( Mat src, Mat dst, Mat M, in Vec2d dsize )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(3\times 3\) transformation matrix.
dsize	size of the output image.
flags	combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).
borderMode	pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
borderValue	value used in case of a constant border; by default, it equals 0.

See also

warpAffine, resize, remap, getRectSubPix, perspectiveTransform

warpPerspective() [2/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpPerspective ( Mat src, Mat dst, Mat M, in Vec2d dsize, int flags )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(3\times 3\) transformation matrix.
dsize	size of the output image.
flags	combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).
borderMode	pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
borderValue	value used in case of a constant border; by default, it equals 0.

See also

warpAffine, resize, remap, getRectSubPix, perspectiveTransform

warpPerspective() [3/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpPerspective ( Mat src, Mat dst, Mat M, in Vec2d dsize, int flags, int borderMode )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(3\times 3\) transformation matrix.
dsize	size of the output image.
flags	combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).
borderMode	pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
borderValue	value used in case of a constant border; by default, it equals 0.

See also

warpAffine, resize, remap, getRectSubPix, perspectiveTransform

warpPerspective() [4/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpPerspective ( Mat src, Mat dst, Mat M, in Vec2d dsize, int flags, int borderMode, in Vec4d borderValue )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(3\times 3\) transformation matrix.
dsize	size of the output image.
flags	combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).
borderMode	pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
borderValue	value used in case of a constant border; by default, it equals 0.

See also

warpAffine, resize, remap, getRectSubPix, perspectiveTransform

warpPerspective() [5/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpPerspective ( Mat src, Mat dst, Mat M, in(double width, double height) dsize )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(3\times 3\) transformation matrix.
dsize	size of the output image.
flags	combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).
borderMode	pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
borderValue	value used in case of a constant border; by default, it equals 0.

See also

warpAffine, resize, remap, getRectSubPix, perspectiveTransform

warpPerspective() [6/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpPerspective ( Mat src, Mat dst, Mat M, in(double width, double height) dsize, int flags )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(3\times 3\) transformation matrix.
dsize	size of the output image.
flags	combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).
borderMode	pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
borderValue	value used in case of a constant border; by default, it equals 0.

See also

warpAffine, resize, remap, getRectSubPix, perspectiveTransform

warpPerspective() [7/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpPerspective ( Mat src, Mat dst, Mat M, in(double width, double height) dsize, int flags, int borderMode )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(3\times 3\) transformation matrix.
dsize	size of the output image.
flags	combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).
borderMode	pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
borderValue	value used in case of a constant border; by default, it equals 0.

See also

warpAffine, resize, remap, getRectSubPix, perspectiveTransform

warpPerspective() [8/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpPerspective ( Mat src, Mat dst, Mat M, in(double width, double height) dsize, int flags, int borderMode, in(double v0, double v1, double v2, double v3) borderValue )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(3\times 3\) transformation matrix.
dsize	size of the output image.
flags	combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).
borderMode	pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
borderValue	value used in case of a constant border; by default, it equals 0.

See also

warpAffine, resize, remap, getRectSubPix, perspectiveTransform

warpPerspective() [9/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpPerspective ( Mat src, Mat dst, Mat M, Size dsize )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(3\times 3\) transformation matrix.
dsize	size of the output image.
flags	combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).
borderMode	pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
borderValue	value used in case of a constant border; by default, it equals 0.

See also

warpAffine, resize, remap, getRectSubPix, perspectiveTransform

warpPerspective() [10/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpPerspective ( Mat src, Mat dst, Mat M, Size dsize, int flags )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(3\times 3\) transformation matrix.
dsize	size of the output image.
flags	combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).
borderMode	pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
borderValue	value used in case of a constant border; by default, it equals 0.

See also

warpAffine, resize, remap, getRectSubPix, perspectiveTransform

warpPerspective() [11/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpPerspective ( Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(3\times 3\) transformation matrix.
dsize	size of the output image.
flags	combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).
borderMode	pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
borderValue	value used in case of a constant border; by default, it equals 0.

See also

warpAffine, resize, remap, getRectSubPix, perspectiveTransform

warpPerspective() [12/12]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpPerspective ( Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue )

static

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.

Parameters

src	input image.
dst	output image that has the size dsize and the same type as src .
M	\(3\times 3\) transformation matrix.
dsize	size of the output image.
flags	combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).
borderMode	pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
borderValue	value used in case of a constant border; by default, it equals 0.

See also

warpAffine, resize, remap, getRectSubPix, perspectiveTransform

warpPolar() [1/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpPolar ( Mat src, Mat dst, in Vec2d dsize, in Vec2d center, double maxRadius, int flags )

static

Remaps an image to polar or semilog-polar coordinates space.

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

\[ \begin{array}{l} Kangle = dsize.height / 2\Pi \\ Klin = dsize.width / maxRadius \\ Klog = dsize.width / log_e(maxRadius) \\ \end{array} \]

Linear vs semilog mapping

Polar mapping can be linear or semi-log. Add one of #WarpPolarMode to 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.

Option on dsize:

• if both values in dsize <=0 (default), the destination image will have (almost) same area of source bounding circle:

  \[\begin{array}{l} dsize.area \leftarrow (maxRadius^2 \cdot \Pi) \\ dsize.width = \texttt{cvRound}(maxRadius) \\ dsize.height = \texttt{cvRound}(maxRadius \cdot \Pi) \\ \end{array}\]

• if only dsize.height <= 0, the destination image area will be proportional to the bounding circle area but scaled by Kx * Kx:

  \[\begin{array}{l} dsize.height = \texttt{cvRound}(dsize.width \cdot \Pi) \\ \end{array} \]

• if both values in dsize > 0, the destination image will have the given size therefore the area of the bounding circle will be scaled to dsize.

Reverse mapping

You can get reverse mapping adding WARP_INVERSE_MAP to flags

In addiction, to calculate the original coordinate from a polar mapped coordinate \((rho, phi)->(x, y)\):

Parameters

src	Source image.
dst	Destination image. It will have same type as src.
dsize	The destination image size (see description for valid options).
center	The transformation center.
maxRadius	The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too.
flags	A combination of interpolation methods, #InterpolationFlags + #WarpPolarMode. Add WARP_POLAR_LINEAR to select linear polar mapping (default) Add WARP_POLAR_LOG to select semilog polar mapping Add WARP_INVERSE_MAP for reverse mapping.

• The function can not operate in-place.
• 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.
• This function uses remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767.

See also

cv::remap

warpPolar() [2/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpPolar ( Mat src, Mat dst, in(double width, double height) dsize, in(double x, double y) center, double maxRadius, int flags )

static

Remaps an image to polar or semilog-polar coordinates space.

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

\[ \begin{array}{l} Kangle = dsize.height / 2\Pi \\ Klin = dsize.width / maxRadius \\ Klog = dsize.width / log_e(maxRadius) \\ \end{array} \]

Linear vs semilog mapping

Polar mapping can be linear or semi-log. Add one of #WarpPolarMode to 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.

Option on dsize:

• if both values in dsize <=0 (default), the destination image will have (almost) same area of source bounding circle:

  \[\begin{array}{l} dsize.area \leftarrow (maxRadius^2 \cdot \Pi) \\ dsize.width = \texttt{cvRound}(maxRadius) \\ dsize.height = \texttt{cvRound}(maxRadius \cdot \Pi) \\ \end{array}\]

• if only dsize.height <= 0, the destination image area will be proportional to the bounding circle area but scaled by Kx * Kx:

  \[\begin{array}{l} dsize.height = \texttt{cvRound}(dsize.width \cdot \Pi) \\ \end{array} \]

• if both values in dsize > 0, the destination image will have the given size therefore the area of the bounding circle will be scaled to dsize.

Reverse mapping

You can get reverse mapping adding WARP_INVERSE_MAP to flags

In addiction, to calculate the original coordinate from a polar mapped coordinate \((rho, phi)->(x, y)\):

Parameters

src	Source image.
dst	Destination image. It will have same type as src.
dsize	The destination image size (see description for valid options).
center	The transformation center.
maxRadius	The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too.
flags	A combination of interpolation methods, #InterpolationFlags + #WarpPolarMode. Add WARP_POLAR_LINEAR to select linear polar mapping (default) Add WARP_POLAR_LOG to select semilog polar mapping Add WARP_INVERSE_MAP for reverse mapping.

• The function can not operate in-place.
• 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.
• This function uses remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767.

See also

cv::remap

warpPolar() [3/3]

static void OpenCVForUnity.ImgprocModule.Imgproc.warpPolar ( Mat src, Mat dst, Size dsize, Point center, double maxRadius, int flags )

static

Remaps an image to polar or semilog-polar coordinates space.

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

\[ \begin{array}{l} Kangle = dsize.height / 2\Pi \\ Klin = dsize.width / maxRadius \\ Klog = dsize.width / log_e(maxRadius) \\ \end{array} \]

Linear vs semilog mapping

Polar mapping can be linear or semi-log. Add one of #WarpPolarMode to 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.

Option on dsize:

• if both values in dsize <=0 (default), the destination image will have (almost) same area of source bounding circle:

  \[\begin{array}{l} dsize.area \leftarrow (maxRadius^2 \cdot \Pi) \\ dsize.width = \texttt{cvRound}(maxRadius) \\ dsize.height = \texttt{cvRound}(maxRadius \cdot \Pi) \\ \end{array}\]

• if only dsize.height <= 0, the destination image area will be proportional to the bounding circle area but scaled by Kx * Kx:

  \[\begin{array}{l} dsize.height = \texttt{cvRound}(dsize.width \cdot \Pi) \\ \end{array} \]

• if both values in dsize > 0, the destination image will have the given size therefore the area of the bounding circle will be scaled to dsize.

Reverse mapping

You can get reverse mapping adding WARP_INVERSE_MAP to flags

In addiction, to calculate the original coordinate from a polar mapped coordinate \((rho, phi)->(x, y)\):

Parameters

src	Source image.
dst	Destination image. It will have same type as src.
dsize	The destination image size (see description for valid options).
center	The transformation center.
maxRadius	The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too.
flags	A combination of interpolation methods, #InterpolationFlags + #WarpPolarMode. Add WARP_POLAR_LINEAR to select linear polar mapping (default) Add WARP_POLAR_LOG to select semilog polar mapping Add WARP_INVERSE_MAP for reverse mapping.

• The function can not operate in-place.
• 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.
• This function uses remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767.

See also

cv::remap

watershed()

static void OpenCVForUnity.ImgprocModule.Imgproc.watershed ( Mat image, Mat markers )

static

Performs a marker-based image segmentation using the watershed algorithm.

The function implements one of the variants of watershed, non-parametric marker-based segmentation algorithm, described in [Meyer92] .

Before passing the image to the function, you have to roughly outline the desired regions in the image markers with positive (>0) indices. So, every region is represented as one or more connected components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary mask using findContours and drawContours (see the watershed.cpp demo). The markers are "seeds" of the future image regions. All the other pixels in markers , whose relation to the outlined regions is not known and should be defined by the algorithm, should be set to 0's. In the function output, each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the regions.

Note

Any two neighbor connected components are not necessarily separated by a watershed boundary (-1's pixels); for example, they can touch each other in the initial marker image passed to the function.

Parameters

image	Input 8-bit 3-channel image.
markers	Input/output 32-bit single-channel image (map) of markers. It should have the same size as image .

See also

findContours

Member Data Documentation

ADAPTIVE_THRESH_GAUSSIAN_C

const int OpenCVForUnity.ImgprocModule.Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C = 1

static

ADAPTIVE_THRESH_MEAN_C

const int OpenCVForUnity.ImgprocModule.Imgproc.ADAPTIVE_THRESH_MEAN_C = 0

static

CC_STAT_AREA

const int OpenCVForUnity.ImgprocModule.Imgproc.CC_STAT_AREA = 4

static

CC_STAT_HEIGHT

const int OpenCVForUnity.ImgprocModule.Imgproc.CC_STAT_HEIGHT = 3

static

CC_STAT_LEFT

const int OpenCVForUnity.ImgprocModule.Imgproc.CC_STAT_LEFT = 0

static

CC_STAT_MAX

const int OpenCVForUnity.ImgprocModule.Imgproc.CC_STAT_MAX = 5

static

CC_STAT_TOP

const int OpenCVForUnity.ImgprocModule.Imgproc.CC_STAT_TOP = 1

static

CC_STAT_WIDTH

const int OpenCVForUnity.ImgprocModule.Imgproc.CC_STAT_WIDTH = 2

static

CCL_BBDT

const int OpenCVForUnity.ImgprocModule.Imgproc.CCL_BBDT = 4

static

CCL_BOLELLI

const int OpenCVForUnity.ImgprocModule.Imgproc.CCL_BOLELLI = 2

static

CCL_DEFAULT

const int OpenCVForUnity.ImgprocModule.Imgproc.CCL_DEFAULT = -1

static

CCL_GRANA

const int OpenCVForUnity.ImgprocModule.Imgproc.CCL_GRANA = 1

static

CCL_SAUF

const int OpenCVForUnity.ImgprocModule.Imgproc.CCL_SAUF = 3

static

CCL_SPAGHETTI

const int OpenCVForUnity.ImgprocModule.Imgproc.CCL_SPAGHETTI = 5

static

CCL_WU

const int OpenCVForUnity.ImgprocModule.Imgproc.CCL_WU = 0

static

CHAIN_APPROX_NONE

const int OpenCVForUnity.ImgprocModule.Imgproc.CHAIN_APPROX_NONE = 1

static

CHAIN_APPROX_SIMPLE

const int OpenCVForUnity.ImgprocModule.Imgproc.CHAIN_APPROX_SIMPLE = 2

static

CHAIN_APPROX_TC89_KCOS

const int OpenCVForUnity.ImgprocModule.Imgproc.CHAIN_APPROX_TC89_KCOS = 4

static

CHAIN_APPROX_TC89_L1

const int OpenCVForUnity.ImgprocModule.Imgproc.CHAIN_APPROX_TC89_L1 = 3

static

COLOR_BayerBG2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2BGR = 46

static

COLOR_BayerBG2BGR_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2BGR_EA = 135

static

COLOR_BayerBG2BGR_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2BGR_VNG = 62

static

COLOR_BayerBG2BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2BGRA = 139

static

COLOR_BayerBG2GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2GRAY = 86

static

COLOR_BayerBG2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2RGB = COLOR_BayerRG2BGR

static

COLOR_BayerBG2RGB_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA

static

COLOR_BayerBG2RGB_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG

static

COLOR_BayerBG2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA

static

COLOR_BayerBGGR2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2BGR = COLOR_BayerRG2BGR

static

COLOR_BayerBGGR2BGR_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2BGR_EA = COLOR_BayerRG2BGR_EA

static

COLOR_BayerBGGR2BGR_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2BGR_VNG = COLOR_BayerRG2BGR_VNG

static

COLOR_BayerBGGR2BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2BGRA = COLOR_BayerRG2BGRA

static

COLOR_BayerBGGR2GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2GRAY = COLOR_BayerRG2GRAY

static

COLOR_BayerBGGR2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2RGB = COLOR_BayerRGGB2BGR

static

COLOR_BayerBGGR2RGB_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2RGB_EA = COLOR_BayerRGGB2BGR_EA

static

COLOR_BayerBGGR2RGB_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2RGB_VNG = COLOR_BayerRGGB2BGR_VNG

static

COLOR_BayerBGGR2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2RGBA = COLOR_BayerRGGB2BGRA

static

COLOR_BayerGB2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2BGR = 47

static

COLOR_BayerGB2BGR_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2BGR_EA = 136

static

COLOR_BayerGB2BGR_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2BGR_VNG = 63

static

COLOR_BayerGB2BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2BGRA = 140

static

COLOR_BayerGB2GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2GRAY = 87

static

COLOR_BayerGB2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2RGB = COLOR_BayerGR2BGR

static

COLOR_BayerGB2RGB_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA

static

COLOR_BayerGB2RGB_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG

static

COLOR_BayerGB2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA

static

COLOR_BayerGBRG2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2BGR = COLOR_BayerGR2BGR

static

COLOR_BayerGBRG2BGR_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2BGR_EA = COLOR_BayerGR2BGR_EA

static

COLOR_BayerGBRG2BGR_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2BGR_VNG = COLOR_BayerGR2BGR_VNG

static

COLOR_BayerGBRG2BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2BGRA = COLOR_BayerGR2BGRA

static

COLOR_BayerGBRG2GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2GRAY = COLOR_BayerGR2GRAY

static

COLOR_BayerGBRG2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2RGB = COLOR_BayerGRBG2BGR

static

COLOR_BayerGBRG2RGB_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2RGB_EA = COLOR_BayerGRBG2BGR_EA

static

COLOR_BayerGBRG2RGB_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2RGB_VNG = COLOR_BayerGRBG2BGR_VNG

static

COLOR_BayerGBRG2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2RGBA = COLOR_BayerGRBG2BGRA

static

COLOR_BayerGR2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2BGR = 49

static

COLOR_BayerGR2BGR_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2BGR_EA = 138

static

COLOR_BayerGR2BGR_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2BGR_VNG = 65

static

COLOR_BayerGR2BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2BGRA = 142

static

COLOR_BayerGR2GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2GRAY = 89

static

COLOR_BayerGR2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2RGB = COLOR_BayerGB2BGR

static

COLOR_BayerGR2RGB_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA

static

COLOR_BayerGR2RGB_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG

static

COLOR_BayerGR2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA

static

COLOR_BayerGRBG2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2BGR = COLOR_BayerGB2BGR

static

COLOR_BayerGRBG2BGR_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2BGR_EA = COLOR_BayerGB2BGR_EA

static

COLOR_BayerGRBG2BGR_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2BGR_VNG = COLOR_BayerGB2BGR_VNG

static

COLOR_BayerGRBG2BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2BGRA = COLOR_BayerGB2BGRA

static

COLOR_BayerGRBG2GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2GRAY = COLOR_BayerGB2GRAY

static

COLOR_BayerGRBG2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2RGB = COLOR_BayerGBRG2BGR

static

COLOR_BayerGRBG2RGB_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2RGB_EA = COLOR_BayerGBRG2BGR_EA

static

COLOR_BayerGRBG2RGB_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2RGB_VNG = COLOR_BayerGBRG2BGR_VNG

static

COLOR_BayerGRBG2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2RGBA = COLOR_BayerGBRG2BGRA

static

COLOR_BayerRG2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2BGR = 48

static

COLOR_BayerRG2BGR_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2BGR_EA = 137

static

COLOR_BayerRG2BGR_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2BGR_VNG = 64

static

COLOR_BayerRG2BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2BGRA = 141

static

COLOR_BayerRG2GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2GRAY = 88

static

COLOR_BayerRG2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2RGB = COLOR_BayerBG2BGR

static

COLOR_BayerRG2RGB_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA

static

COLOR_BayerRG2RGB_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG

static

COLOR_BayerRG2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA

static

COLOR_BayerRGGB2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2BGR = COLOR_BayerBG2BGR

static

COLOR_BayerRGGB2BGR_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2BGR_EA = COLOR_BayerBG2BGR_EA

static

COLOR_BayerRGGB2BGR_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2BGR_VNG = COLOR_BayerBG2BGR_VNG

static

COLOR_BayerRGGB2BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2BGRA = COLOR_BayerBG2BGRA

static

COLOR_BayerRGGB2GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2GRAY = COLOR_BayerBG2GRAY

static

COLOR_BayerRGGB2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2RGB = COLOR_BayerBGGR2BGR

static

COLOR_BayerRGGB2RGB_EA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2RGB_EA = COLOR_BayerBGGR2BGR_EA

static

COLOR_BayerRGGB2RGB_VNG

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2RGB_VNG = COLOR_BayerBGGR2BGR_VNG

static

COLOR_BayerRGGB2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2RGBA = COLOR_BayerBGGR2BGRA

static

COLOR_BGR2BGR555

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2BGR555 = 22

static

COLOR_BGR2BGR565

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2BGR565 = 12

static

COLOR_BGR2BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2BGRA = 0

static

COLOR_BGR2GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2GRAY = 6

static

COLOR_BGR2HLS

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2HLS = 52

static

COLOR_BGR2HLS_FULL

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2HLS_FULL = 68

static

COLOR_BGR2HSV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2HSV = 40

static

COLOR_BGR2HSV_FULL

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2HSV_FULL = 66

static

COLOR_BGR2Lab

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2Lab = 44

static

COLOR_BGR2Luv

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2Luv = 50

static

COLOR_BGR2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2RGB = 4

static

COLOR_BGR2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2RGBA = 2

static

COLOR_BGR2XYZ

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2XYZ = 32

static

COLOR_BGR2YCrCb

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YCrCb = 36

static

COLOR_BGR2YUV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV = 82

static

COLOR_BGR2YUV_I420

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_I420 = 128

static

COLOR_BGR2YUV_IYUV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420

static

COLOR_BGR2YUV_UYNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_UYNV = COLOR_BGR2YUV_UYVY

static

COLOR_BGR2YUV_UYVY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_UYVY = 144

static

COLOR_BGR2YUV_Y422

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_Y422 = COLOR_BGR2YUV_UYVY

static

COLOR_BGR2YUV_YUNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_YUNV = COLOR_BGR2YUV_YUY2

static

COLOR_BGR2YUV_YUY2

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_YUY2 = 148

static

COLOR_BGR2YUV_YUYV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_YUYV = COLOR_BGR2YUV_YUY2

static

COLOR_BGR2YUV_YV12

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_YV12 = 132

static

COLOR_BGR2YUV_YVYU

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_YVYU = 150

static

COLOR_BGR5552BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5552BGR = 24

static

COLOR_BGR5552BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5552BGRA = 28

static

COLOR_BGR5552GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5552GRAY = 31

static

COLOR_BGR5552RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5552RGB = 25

static

COLOR_BGR5552RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5552RGBA = 29

static

COLOR_BGR5652BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5652BGR = 14

static

COLOR_BGR5652BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5652BGRA = 18

static

COLOR_BGR5652GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5652GRAY = 21

static

COLOR_BGR5652RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5652RGB = 15

static

COLOR_BGR5652RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5652RGBA = 19

static

COLOR_BGRA2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2BGR = 1

static

COLOR_BGRA2BGR555

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2BGR555 = 26

static

COLOR_BGRA2BGR565

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2BGR565 = 16

static

COLOR_BGRA2GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2GRAY = 10

static

COLOR_BGRA2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2RGB = COLOR_RGBA2BGR

static

COLOR_BGRA2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2RGBA = 5

static

COLOR_BGRA2YUV_I420

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_I420 = 130

static

COLOR_BGRA2YUV_IYUV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420

static

COLOR_BGRA2YUV_UYNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_UYNV = COLOR_BGRA2YUV_UYVY

static

COLOR_BGRA2YUV_UYVY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_UYVY = 146

static

COLOR_BGRA2YUV_Y422

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_Y422 = COLOR_BGRA2YUV_UYVY

static

COLOR_BGRA2YUV_YUNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_YUNV = COLOR_BGRA2YUV_YUY2

static

COLOR_BGRA2YUV_YUY2

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_YUY2 = 152

static

COLOR_BGRA2YUV_YUYV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_YUYV = COLOR_BGRA2YUV_YUY2

static

COLOR_BGRA2YUV_YV12

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_YV12 = 134

static

COLOR_BGRA2YUV_YVYU

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_YVYU = 154

static

COLOR_COLORCVT_MAX

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_COLORCVT_MAX = 155

static

COLOR_GRAY2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_GRAY2BGR = 8

static

COLOR_GRAY2BGR555

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_GRAY2BGR555 = 30

static

COLOR_GRAY2BGR565

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_GRAY2BGR565 = 20

static

COLOR_GRAY2BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_GRAY2BGRA = 9

static

COLOR_GRAY2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_GRAY2RGB = COLOR_GRAY2BGR

static

COLOR_GRAY2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_GRAY2RGBA = COLOR_GRAY2BGRA

static

COLOR_HLS2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HLS2BGR = 60

static

COLOR_HLS2BGR_FULL

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HLS2BGR_FULL = 72

static

COLOR_HLS2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HLS2RGB = 61

static

COLOR_HLS2RGB_FULL

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HLS2RGB_FULL = 73

static

COLOR_HSV2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HSV2BGR = 54

static

COLOR_HSV2BGR_FULL

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HSV2BGR_FULL = 70

static

COLOR_HSV2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HSV2RGB = 55

static

COLOR_HSV2RGB_FULL

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HSV2RGB_FULL = 71

static

COLOR_Lab2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Lab2BGR = 56

static

COLOR_Lab2LBGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Lab2LBGR = 78

static

COLOR_Lab2LRGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Lab2LRGB = 79

static

COLOR_Lab2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Lab2RGB = 57

static

COLOR_LBGR2Lab

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_LBGR2Lab = 74

static

COLOR_LBGR2Luv

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_LBGR2Luv = 76

static

COLOR_LRGB2Lab

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_LRGB2Lab = 75

static

COLOR_LRGB2Luv

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_LRGB2Luv = 77

static

COLOR_Luv2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Luv2BGR = 58

static

COLOR_Luv2LBGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Luv2LBGR = 80

static

COLOR_Luv2LRGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Luv2LRGB = 81

static

COLOR_Luv2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Luv2RGB = 59

static

COLOR_mRGBA2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_mRGBA2RGBA = 126

static

COLOR_RGB2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2BGR = COLOR_BGR2RGB

static

COLOR_RGB2BGR555

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2BGR555 = 23

static

COLOR_RGB2BGR565

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2BGR565 = 13

static

COLOR_RGB2BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2BGRA = COLOR_BGR2RGBA

static

COLOR_RGB2GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2GRAY = 7

static

COLOR_RGB2HLS

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2HLS = 53

static

COLOR_RGB2HLS_FULL

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2HLS_FULL = 69

static

COLOR_RGB2HSV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2HSV = 41

static

COLOR_RGB2HSV_FULL

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2HSV_FULL = 67

static

COLOR_RGB2Lab

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2Lab = 45

static

COLOR_RGB2Luv

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2Luv = 51

static

COLOR_RGB2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2RGBA = COLOR_BGR2BGRA

static

COLOR_RGB2XYZ

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2XYZ = 33

static

COLOR_RGB2YCrCb

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YCrCb = 37

static

COLOR_RGB2YUV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV = 83

static

COLOR_RGB2YUV_I420

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_I420 = 127

static

COLOR_RGB2YUV_IYUV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420

static

COLOR_RGB2YUV_UYNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_UYNV = COLOR_RGB2YUV_UYVY

static

COLOR_RGB2YUV_UYVY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_UYVY = 143

static

COLOR_RGB2YUV_Y422

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_Y422 = COLOR_RGB2YUV_UYVY

static

COLOR_RGB2YUV_YUNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_YUNV = COLOR_RGB2YUV_YUY2

static

COLOR_RGB2YUV_YUY2

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_YUY2 = 147

static

COLOR_RGB2YUV_YUYV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_YUYV = COLOR_RGB2YUV_YUY2

static

COLOR_RGB2YUV_YV12

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_YV12 = 131

static

COLOR_RGB2YUV_YVYU

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_YVYU = 149

static

COLOR_RGBA2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2BGR = 3

static

COLOR_RGBA2BGR555

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2BGR555 = 27

static

COLOR_RGBA2BGR565

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2BGR565 = 17

static

COLOR_RGBA2BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2BGRA = COLOR_BGRA2RGBA

static

COLOR_RGBA2GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2GRAY = 11

static

COLOR_RGBA2mRGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2mRGBA = 125

static

COLOR_RGBA2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2RGB = COLOR_BGRA2BGR

static

COLOR_RGBA2YUV_I420

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_I420 = 129

static

COLOR_RGBA2YUV_IYUV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420

static

COLOR_RGBA2YUV_UYNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_UYNV = COLOR_RGBA2YUV_UYVY

static

COLOR_RGBA2YUV_UYVY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_UYVY = 145

static

COLOR_RGBA2YUV_Y422

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_Y422 = COLOR_RGBA2YUV_UYVY

static

COLOR_RGBA2YUV_YUNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_YUNV = COLOR_RGBA2YUV_YUY2

static

COLOR_RGBA2YUV_YUY2

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_YUY2 = 151

static

COLOR_RGBA2YUV_YUYV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_YUYV = COLOR_RGBA2YUV_YUY2

static

COLOR_RGBA2YUV_YV12

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_YV12 = 133

static

COLOR_RGBA2YUV_YVYU

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_YVYU = 153

static

COLOR_XYZ2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_XYZ2BGR = 34

static

COLOR_XYZ2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_XYZ2RGB = 35

static

COLOR_YCrCb2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YCrCb2BGR = 38

static

COLOR_YCrCb2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YCrCb2RGB = 39

static

COLOR_YUV2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR = 84

static

COLOR_YUV2BGR_I420

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV

static

COLOR_YUV2BGR_IYUV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_IYUV = 101

static

COLOR_YUV2BGR_NV12

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_NV12 = 91

static

COLOR_YUV2BGR_NV21

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_NV21 = 93

static

COLOR_YUV2BGR_UYNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY

static

COLOR_YUV2BGR_UYVY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_UYVY = 108

static

COLOR_YUV2BGR_Y422

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY

static

COLOR_YUV2BGR_YUNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2

static

COLOR_YUV2BGR_YUY2

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_YUY2 = 116

static

COLOR_YUV2BGR_YUYV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2

static

COLOR_YUV2BGR_YV12

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_YV12 = 99

static

COLOR_YUV2BGR_YVYU

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_YVYU = 118

static

COLOR_YUV2BGRA_I420

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV

static

COLOR_YUV2BGRA_IYUV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_IYUV = 105

static

COLOR_YUV2BGRA_NV12

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_NV12 = 95

static

COLOR_YUV2BGRA_NV21

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_NV21 = 97

static

COLOR_YUV2BGRA_UYNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY

static

COLOR_YUV2BGRA_UYVY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_UYVY = 112

static

COLOR_YUV2BGRA_Y422

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY

static

COLOR_YUV2BGRA_YUNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2

static

COLOR_YUV2BGRA_YUY2

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_YUY2 = 120

static

COLOR_YUV2BGRA_YUYV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2

static

COLOR_YUV2BGRA_YV12

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_YV12 = 103

static

COLOR_YUV2BGRA_YVYU

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_YVYU = 122

static

COLOR_YUV2GRAY_420

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_420 = 106

static

COLOR_YUV2GRAY_I420

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420

static

COLOR_YUV2GRAY_IYUV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420

static

COLOR_YUV2GRAY_NV12

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420

static

COLOR_YUV2GRAY_NV21

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420

static

COLOR_YUV2GRAY_UYNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY

static

COLOR_YUV2GRAY_UYVY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_UYVY = 123

static

COLOR_YUV2GRAY_Y422

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY

static

COLOR_YUV2GRAY_YUNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2

static

COLOR_YUV2GRAY_YUY2

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_YUY2 = 124

static

COLOR_YUV2GRAY_YUYV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2

static

COLOR_YUV2GRAY_YV12

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420

static

COLOR_YUV2GRAY_YVYU

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2

static

COLOR_YUV2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB = 85

static

COLOR_YUV2RGB_I420

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV

static

COLOR_YUV2RGB_IYUV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_IYUV = 100

static

COLOR_YUV2RGB_NV12

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_NV12 = 90

static

COLOR_YUV2RGB_NV21

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_NV21 = 92

static

COLOR_YUV2RGB_UYNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY

static

COLOR_YUV2RGB_UYVY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_UYVY = 107

static

COLOR_YUV2RGB_Y422

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY

static

COLOR_YUV2RGB_YUNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2

static

COLOR_YUV2RGB_YUY2

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_YUY2 = 115

static

COLOR_YUV2RGB_YUYV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2

static

COLOR_YUV2RGB_YV12

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_YV12 = 98

static

COLOR_YUV2RGB_YVYU

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_YVYU = 117

static

COLOR_YUV2RGBA_I420

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV

static

COLOR_YUV2RGBA_IYUV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_IYUV = 104

static

COLOR_YUV2RGBA_NV12

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_NV12 = 94

static

COLOR_YUV2RGBA_NV21

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_NV21 = 96

static

COLOR_YUV2RGBA_UYNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY

static

COLOR_YUV2RGBA_UYVY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_UYVY = 111

static

COLOR_YUV2RGBA_Y422

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY

static

COLOR_YUV2RGBA_YUNV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2

static

COLOR_YUV2RGBA_YUY2

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_YUY2 = 119

static

COLOR_YUV2RGBA_YUYV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2

static

COLOR_YUV2RGBA_YV12

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_YV12 = 102

static

COLOR_YUV2RGBA_YVYU

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_YVYU = 121

static

COLOR_YUV420p2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12

static

COLOR_YUV420p2BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12

static

COLOR_YUV420p2GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420

static

COLOR_YUV420p2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12

static

COLOR_YUV420p2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12

static

COLOR_YUV420sp2BGR

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21

static

COLOR_YUV420sp2BGRA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21

static

COLOR_YUV420sp2GRAY

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420

static

COLOR_YUV420sp2RGB

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21

static

COLOR_YUV420sp2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21

static

COLORMAP_AUTUMN

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_AUTUMN = 0

static

COLORMAP_BONE

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_BONE = 1

static

COLORMAP_CIVIDIS

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_CIVIDIS = 17

static

COLORMAP_COOL

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_COOL = 8

static

COLORMAP_DEEPGREEN

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_DEEPGREEN = 21

static

COLORMAP_HOT

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_HOT = 11

static

COLORMAP_HSV

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_HSV = 9

static

COLORMAP_INFERNO

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_INFERNO = 14

static

COLORMAP_JET

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_JET = 2

static

COLORMAP_MAGMA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_MAGMA = 13

static

COLORMAP_OCEAN

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_OCEAN = 5

static

COLORMAP_PARULA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_PARULA = 12

static

COLORMAP_PINK

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_PINK = 10

static

COLORMAP_PLASMA

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_PLASMA = 15

static

COLORMAP_RAINBOW

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_RAINBOW = 4

static

COLORMAP_SPRING

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_SPRING = 7

static

COLORMAP_SUMMER

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_SUMMER = 6

static

COLORMAP_TURBO

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_TURBO = 20

static

COLORMAP_TWILIGHT

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_TWILIGHT = 18

static

COLORMAP_TWILIGHT_SHIFTED

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_TWILIGHT_SHIFTED = 19

static

COLORMAP_VIRIDIS

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_VIRIDIS = 16

static

COLORMAP_WINTER

const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_WINTER = 3

static

CONTOURS_MATCH_I1

const int OpenCVForUnity.ImgprocModule.Imgproc.CONTOURS_MATCH_I1 = 1

static

CONTOURS_MATCH_I2

const int OpenCVForUnity.ImgprocModule.Imgproc.CONTOURS_MATCH_I2 = 2

static

CONTOURS_MATCH_I3

const int OpenCVForUnity.ImgprocModule.Imgproc.CONTOURS_MATCH_I3 = 3

static

CV_BILATERAL

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_BILATERAL = 4

static

CV_BLUR

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_BLUR = 1

static

CV_BLUR_NO_SCALE

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_BLUR_NO_SCALE = 0

static

CV_CANNY_L2_GRADIENT

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_CANNY_L2_GRADIENT = (1 << 31)

static

CV_CHAIN_CODE

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_CHAIN_CODE = 0

static

CV_CLOCKWISE

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_CLOCKWISE = 1

static

CV_COMP_BHATTACHARYYA

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COMP_BHATTACHARYYA = 3

static

CV_COMP_CHISQR

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COMP_CHISQR = 1

static

CV_COMP_CHISQR_ALT

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COMP_CHISQR_ALT = 4

static

CV_COMP_CORREL

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COMP_CORREL = 0

static

CV_COMP_HELLINGER

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COMP_HELLINGER = CV_COMP_BHATTACHARYYA

static

CV_COMP_INTERSECT

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COMP_INTERSECT = 2

static

CV_COMP_KL_DIV

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COMP_KL_DIV = 5

static

CV_CONTOURS_MATCH_I1

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_CONTOURS_MATCH_I1 = 1

static

CV_CONTOURS_MATCH_I2

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_CONTOURS_MATCH_I2 = 2

static

CV_CONTOURS_MATCH_I3

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_CONTOURS_MATCH_I3 = 3

static

CV_COUNTER_CLOCKWISE

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COUNTER_CLOCKWISE = 2

static

CV_DIST_C

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_C = 3

static

CV_DIST_FAIR

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_FAIR = 5

static

CV_DIST_HUBER

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_HUBER = 7

static

CV_DIST_L1

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_L1 = 1

static

CV_DIST_L12

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_L12 = 4

static

CV_DIST_L2

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_L2 = 2

static

CV_DIST_LABEL_CCOMP

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_LABEL_CCOMP = 0

static

CV_DIST_LABEL_PIXEL

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_LABEL_PIXEL = 1

static

CV_DIST_MASK_3

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_MASK_3 = 3

static

CV_DIST_MASK_5

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_MASK_5 = 5

static

CV_DIST_MASK_PRECISE

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_MASK_PRECISE = 0

static

CV_DIST_USER

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_USER = -1

static

CV_DIST_WELSCH

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_WELSCH = 6

static

CV_GAUSSIAN

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_GAUSSIAN = 2

static

CV_GAUSSIAN_5x5

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_GAUSSIAN_5x5 = 7

static

CV_HOUGH_GRADIENT

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_HOUGH_GRADIENT = 3

static

CV_HOUGH_MULTI_SCALE

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_HOUGH_MULTI_SCALE = 2

static

CV_HOUGH_PROBABILISTIC

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_HOUGH_PROBABILISTIC = 1

static

CV_HOUGH_STANDARD

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_HOUGH_STANDARD = 0

static

CV_LINK_RUNS

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_LINK_RUNS = 5

static

CV_MAX_SOBEL_KSIZE

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_MAX_SOBEL_KSIZE = 7

static

CV_MEDIAN

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_MEDIAN = 3

static

CV_mRGBA2RGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_mRGBA2RGBA = 126

static

CV_POLY_APPROX_DP

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_POLY_APPROX_DP = 0

static

CV_RGBA2mRGBA

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_RGBA2mRGBA = 125

static

CV_SCHARR

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_SCHARR = -1

static

CV_SHAPE_CROSS

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_SHAPE_CROSS = 1

static

CV_SHAPE_CUSTOM

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_SHAPE_CUSTOM = 100

static

CV_SHAPE_ELLIPSE

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_SHAPE_ELLIPSE = 2

static

CV_SHAPE_RECT

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_SHAPE_RECT = 0

static

CV_WARP_FILL_OUTLIERS

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_WARP_FILL_OUTLIERS = 8

static

CV_WARP_INVERSE_MAP

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_WARP_INVERSE_MAP = 16

static

CV_WARP_RELATIVE_MAP

const int OpenCVForUnity.ImgprocModule.Imgproc.CV_WARP_RELATIVE_MAP = 32

static

DIST_C

const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_C = 3

static

DIST_FAIR

const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_FAIR = 5

static

DIST_HUBER

const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_HUBER = 7

static

DIST_L1

const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_L1 = 1

static

DIST_L12

const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_L12 = 4

static

DIST_L2

const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_L2 = 2

static

DIST_LABEL_CCOMP

const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_LABEL_CCOMP = 0

static

DIST_LABEL_PIXEL

const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_LABEL_PIXEL = 1

static

DIST_MASK_3

const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_MASK_3 = 3

static

DIST_MASK_5

const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_MASK_5 = 5

static

DIST_MASK_PRECISE

const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_MASK_PRECISE = 0

static

DIST_USER

const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_USER = -1

static

DIST_WELSCH

const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_WELSCH = 6

static

FILLED

const int OpenCVForUnity.ImgprocModule.Imgproc.FILLED = -1

static

FILTER_SCHARR

const int OpenCVForUnity.ImgprocModule.Imgproc.FILTER_SCHARR = -1

static

FLOODFILL_FIXED_RANGE

const int OpenCVForUnity.ImgprocModule.Imgproc.FLOODFILL_FIXED_RANGE = 1 << 16

static

FLOODFILL_MASK_ONLY

const int OpenCVForUnity.ImgprocModule.Imgproc.FLOODFILL_MASK_ONLY = 1 << 17

static

FONT_HERSHEY_COMPLEX

const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_COMPLEX = 3

static

FONT_HERSHEY_COMPLEX_SMALL

const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_COMPLEX_SMALL = 5

static

FONT_HERSHEY_DUPLEX

const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_DUPLEX = 2

static

FONT_HERSHEY_PLAIN

const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_PLAIN = 1

static

FONT_HERSHEY_SCRIPT_COMPLEX

const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_SCRIPT_COMPLEX = 7

static

FONT_HERSHEY_SCRIPT_SIMPLEX

const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_SCRIPT_SIMPLEX = 6

static

FONT_HERSHEY_SIMPLEX

const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_SIMPLEX = 0

static

FONT_HERSHEY_TRIPLEX

const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_TRIPLEX = 4

static

FONT_ITALIC

const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_ITALIC = 16

static

GC_BGD

const int OpenCVForUnity.ImgprocModule.Imgproc.GC_BGD = 0

static

GC_EVAL

const int OpenCVForUnity.ImgprocModule.Imgproc.GC_EVAL = 2

static

GC_EVAL_FREEZE_MODEL

const int OpenCVForUnity.ImgprocModule.Imgproc.GC_EVAL_FREEZE_MODEL = 3

static

GC_FGD

const int OpenCVForUnity.ImgprocModule.Imgproc.GC_FGD = 1

static

GC_INIT_WITH_MASK

const int OpenCVForUnity.ImgprocModule.Imgproc.GC_INIT_WITH_MASK = 1

static

GC_INIT_WITH_RECT

const int OpenCVForUnity.ImgprocModule.Imgproc.GC_INIT_WITH_RECT = 0

static

GC_PR_BGD

const int OpenCVForUnity.ImgprocModule.Imgproc.GC_PR_BGD = 2

static

GC_PR_FGD

const int OpenCVForUnity.ImgprocModule.Imgproc.GC_PR_FGD = 3

static

height

static double double double double OpenCVForUnity.ImgprocModule.Imgproc.height

static

HISTCMP_BHATTACHARYYA

const int OpenCVForUnity.ImgprocModule.Imgproc.HISTCMP_BHATTACHARYYA = 3

static

HISTCMP_CHISQR

const int OpenCVForUnity.ImgprocModule.Imgproc.HISTCMP_CHISQR = 1

static

HISTCMP_CHISQR_ALT

const int OpenCVForUnity.ImgprocModule.Imgproc.HISTCMP_CHISQR_ALT = 4

static

HISTCMP_CORREL

const int OpenCVForUnity.ImgprocModule.Imgproc.HISTCMP_CORREL = 0

static

HISTCMP_HELLINGER

const int OpenCVForUnity.ImgprocModule.Imgproc.HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA

static

HISTCMP_INTERSECT

const int OpenCVForUnity.ImgprocModule.Imgproc.HISTCMP_INTERSECT = 2

static

HISTCMP_KL_DIV

const int OpenCVForUnity.ImgprocModule.Imgproc.HISTCMP_KL_DIV = 5

static

HOUGH_GRADIENT

const int OpenCVForUnity.ImgprocModule.Imgproc.HOUGH_GRADIENT = 3

static

HOUGH_GRADIENT_ALT

const int OpenCVForUnity.ImgprocModule.Imgproc.HOUGH_GRADIENT_ALT = 4

static

HOUGH_MULTI_SCALE

const int OpenCVForUnity.ImgprocModule.Imgproc.HOUGH_MULTI_SCALE = 2

static

HOUGH_PROBABILISTIC

const int OpenCVForUnity.ImgprocModule.Imgproc.HOUGH_PROBABILISTIC = 1

static

HOUGH_STANDARD

const int OpenCVForUnity.ImgprocModule.Imgproc.HOUGH_STANDARD = 0

static

INTER_AREA

const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_AREA = 3

static

INTER_BITS

const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_BITS = 5

static

INTER_BITS2

const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_BITS2 = INTER_BITS * 2

static

INTER_CUBIC

const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_CUBIC = 2

static

INTER_LANCZOS4

const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_LANCZOS4 = 4

static

INTER_LINEAR

const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_LINEAR = 1

static

INTER_LINEAR_EXACT

const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_LINEAR_EXACT = 5

static

INTER_MAX

const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_MAX = 7

static

INTER_NEAREST

const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_NEAREST = 0

static

INTER_NEAREST_EXACT

const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_NEAREST_EXACT = 6

static

INTER_TAB_SIZE

const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_TAB_SIZE = 1 << INTER_BITS

static

INTER_TAB_SIZE2

const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE

static

INTERSECT_FULL

const int OpenCVForUnity.ImgprocModule.Imgproc.INTERSECT_FULL = 2

static

INTERSECT_NONE

const int OpenCVForUnity.ImgprocModule.Imgproc.INTERSECT_NONE = 0

static

INTERSECT_PARTIAL

const int OpenCVForUnity.ImgprocModule.Imgproc.INTERSECT_PARTIAL = 1

static

LINE_4

const int OpenCVForUnity.ImgprocModule.Imgproc.LINE_4 = 4

static

LINE_8

const int OpenCVForUnity.ImgprocModule.Imgproc.LINE_8 = 8

static

LINE_AA

const int OpenCVForUnity.ImgprocModule.Imgproc.LINE_AA = 16

static

LSD_REFINE_ADV

const int OpenCVForUnity.ImgprocModule.Imgproc.LSD_REFINE_ADV = 2

static

LSD_REFINE_NONE

const int OpenCVForUnity.ImgprocModule.Imgproc.LSD_REFINE_NONE = 0

static

LSD_REFINE_STD

const int OpenCVForUnity.ImgprocModule.Imgproc.LSD_REFINE_STD = 1

static

m00

static double OpenCVForUnity.ImgprocModule.Imgproc.m00

static

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.

Parameters

array	Single chanel raster image (CV_8U, CV_16U, CV_16S, CV_32F, CV_64F) or an array ( \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f).
binaryImage	If it is true, all non-zero image pixels are treated as 1's. The parameter is used for images only.

Returns

moments.

Note

Only applicable to contour moments calculations from Python bindings: Note that the numpy type for the input array should be either np.int32 or np.float32.

See also

contourArea, arcLength

m01

static double double double OpenCVForUnity.ImgprocModule.Imgproc.m01

static

m02

static double double double double double double OpenCVForUnity.ImgprocModule.Imgproc.m02

static

m10

static double double OpenCVForUnity.ImgprocModule.Imgproc.m10

static

m11

static double double double double double OpenCVForUnity.ImgprocModule.Imgproc.m11

static

m12

static double double double double double double double double double OpenCVForUnity.ImgprocModule.Imgproc.m12

static

m20

static double double double double OpenCVForUnity.ImgprocModule.Imgproc.m20

static

m21

static double double double double double double double double OpenCVForUnity.ImgprocModule.Imgproc.m21

static

m30

static double double double double double double double OpenCVForUnity.ImgprocModule.Imgproc.m30

static

MARKER_CROSS

const int OpenCVForUnity.ImgprocModule.Imgproc.MARKER_CROSS = 0

static

MARKER_DIAMOND

const int OpenCVForUnity.ImgprocModule.Imgproc.MARKER_DIAMOND = 3

static

MARKER_SQUARE

const int OpenCVForUnity.ImgprocModule.Imgproc.MARKER_SQUARE = 4

static

MARKER_STAR

const int OpenCVForUnity.ImgprocModule.Imgproc.MARKER_STAR = 2

static

MARKER_TILTED_CROSS

const int OpenCVForUnity.ImgprocModule.Imgproc.MARKER_TILTED_CROSS = 1

static

MARKER_TRIANGLE_DOWN

const int OpenCVForUnity.ImgprocModule.Imgproc.MARKER_TRIANGLE_DOWN = 6

static

MARKER_TRIANGLE_UP

const int OpenCVForUnity.ImgprocModule.Imgproc.MARKER_TRIANGLE_UP = 5

static

MORPH_BLACKHAT

const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_BLACKHAT = 6

static

MORPH_CLOSE

const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_CLOSE = 3

static

MORPH_CROSS

const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_CROSS = 1

static

MORPH_DILATE

const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_DILATE = 1

static

MORPH_ELLIPSE

const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_ELLIPSE = 2

static

MORPH_ERODE

const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_ERODE = 0

static

MORPH_GRADIENT

const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_GRADIENT = 4

static

MORPH_HITMISS

const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_HITMISS = 7

static

MORPH_OPEN

const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_OPEN = 2

static

MORPH_RECT

const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_RECT = 0

static

MORPH_TOPHAT

const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_TOPHAT = 5

static

RETR_CCOMP

const int OpenCVForUnity.ImgprocModule.Imgproc.RETR_CCOMP = 2

static

RETR_EXTERNAL

const int OpenCVForUnity.ImgprocModule.Imgproc.RETR_EXTERNAL = 0

static

RETR_FLOODFILL

const int OpenCVForUnity.ImgprocModule.Imgproc.RETR_FLOODFILL = 4

static

RETR_LIST

const int OpenCVForUnity.ImgprocModule.Imgproc.RETR_LIST = 1

static

RETR_TREE

const int OpenCVForUnity.ImgprocModule.Imgproc.RETR_TREE = 3

static

THRESH_BINARY

const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_BINARY = 0

static

THRESH_BINARY_INV

const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_BINARY_INV = 1

static

THRESH_MASK

const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_MASK = 7

static

THRESH_OTSU

const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_OTSU = 8

static

THRESH_TOZERO

const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_TOZERO = 3

static

THRESH_TOZERO_INV

const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_TOZERO_INV = 4

static

THRESH_TRIANGLE

const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_TRIANGLE = 16

static

THRESH_TRUNC

const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_TRUNC = 2

static

TM_CCOEFF

const int OpenCVForUnity.ImgprocModule.Imgproc.TM_CCOEFF = 4

static

TM_CCOEFF_NORMED

const int OpenCVForUnity.ImgprocModule.Imgproc.TM_CCOEFF_NORMED = 5

static

TM_CCORR

const int OpenCVForUnity.ImgprocModule.Imgproc.TM_CCORR = 2

static

TM_CCORR_NORMED

const int OpenCVForUnity.ImgprocModule.Imgproc.TM_CCORR_NORMED = 3

static

TM_SQDIFF

const int OpenCVForUnity.ImgprocModule.Imgproc.TM_SQDIFF = 0

static

TM_SQDIFF_NORMED

const int OpenCVForUnity.ImgprocModule.Imgproc.TM_SQDIFF_NORMED = 1

static

WARP_FILL_OUTLIERS

const int OpenCVForUnity.ImgprocModule.Imgproc.WARP_FILL_OUTLIERS = 8

static

WARP_INVERSE_MAP

const int OpenCVForUnity.ImgprocModule.Imgproc.WARP_INVERSE_MAP = 16

static

WARP_POLAR_LINEAR

const int OpenCVForUnity.ImgprocModule.Imgproc.WARP_POLAR_LINEAR = 0

static

WARP_POLAR_LOG

const int OpenCVForUnity.ImgprocModule.Imgproc.WARP_POLAR_LOG = 256

static

WARP_RELATIVE_MAP

const int OpenCVForUnity.ImgprocModule.Imgproc.WARP_RELATIVE_MAP = 32

static

width [1/3]

static double double double OpenCVForUnity.ImgprocModule.Imgproc.width

static

width [2/3]

double double double OpenCVForUnity.ImgprocModule.Imgproc.width

static

width [3/3]

double OpenCVForUnity.ImgprocModule.Imgproc.width

static

x [1/2]

static double OpenCVForUnity.ImgprocModule.Imgproc.x

static

The function is used to detect translational shifts that occur between two images.

Fits an ellipse around a set of 2D points.

Finds a rotated rectangle of the minimum area enclosing the input 2D point set.

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&gt;

Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize.

The function performs the following equations:

• First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function&gt;) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
• Next it computes the forward DFTs of each source array:

  \[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\]

  where \(\mathcal{F}\) is the forward DFT.
• It then computes the cross-power spectrum of each frequency domain array:

  \[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\]

• Next the cross-correlation is converted back into the time domain via the inverse DFT:

  \[r = \mathcal{F}^{-1}\{R\}\]

• Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy.

  \[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\]

• If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single peak) and will be smaller when there are multiple peaks.

Parameters

src1	Source floating point array (CV_32FC1 or CV_64FC1)
src2	Source floating point array (CV_32FC1 or CV_64FC1)
window	Floating point array with windowing coefficients to reduce edge effects (optional).
response	Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).

Returns

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

See also

dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow

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.

Parameters

points

Input vector of 2D points, stored in std::vector<> or Mat

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 [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.

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

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 [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:

\begin{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\} \end{align*}

The AMS method minimizes the cost function

\begin{equation*} \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } \end{equation*}

The minimum cost is found by solving the generalized eigenvalue problem.

\begin{equation*} D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A \end{equation*}

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

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 [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.

\begin{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) \end{equation*}

The minimum cost is found by solving the generalized eigenvalue problem.

\begin{equation*} D^T D A = \lambda \left( C\right) A \end{equation*}

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

\begin{equation*} A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} \end{equation*}

The scaling factor guarantees that \(A^T C A =1\).

Parameters

points

Input 2D point set, stored in std::vector<> or Mat

x [2/2]

int OpenCVForUnity.ImgprocModule.Imgproc.x

static

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.

Parameters

array

Input gray-scale image or 2D point set, stored in std::vector or Mat.

y [1/2]

static double double OpenCVForUnity.ImgprocModule.Imgproc.y

static

y [2/2]

double double OpenCVForUnity.ImgprocModule.Imgproc.y

static

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The documentation for this class was generated from the following files:

• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/imgproc/Imgproc.cs
• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/imgproc/Imgproc_Struct.cs
• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/imgproc/Imgproc_ValueTuple.cs