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

 org.opencv.imgproc.Imgproc

public class Imgproc extends java.lang.Object


Field Summary

Constructor Summary
Constructors Constructor Description Imgproc()

Method Summary
All Methods Static Methods Concrete Methods Deprecated Methods Modifier and Type Method Description 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 perelement product of two input images to the accumulator image.static void
accumulateProduct(Mat src1, Mat src2, Mat dst, Mat mask)
Adds the perelement 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, boolean closed)
Approximates a polygonal curve(s) with the specified precision.static double
arcLength(MatOfPoint2f curve, boolean closed)
Calculates a contour perimeter or a curve length.static void
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)
variant withoutmask
parameterstatic 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 upright bounding rectangle of a point set or nonzero pixels of grayscale image.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, boolean normalize)
Blurs an image using the box filter.static void
boxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor, boolean normalize, int borderType)
Blurs an image using the box filter.static void
boxPoints(RotatedRect box, Mat points)
Finds the four vertices of a rotated rect.static void
calcBackProject(java.util.List<Mat> images, MatOfInt channels, Mat hist, Mat dst, MatOfFloat ranges, double scale)
static void
calcHist(java.util.List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges)
this variant supports only uniform histograms.static void
calcHist(java.util.List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges, boolean accumulate)
this variant supports only uniform histograms.static void
Canny(Mat image, Mat edges, double threshold1, double threshold2)
Finds edges in an image using the Canny algorithm CITE: Canny86 .static void
Canny(Mat image, Mat edges, double threshold1, double threshold2, int apertureSize)
Finds edges in an image using the Canny algorithm CITE: Canny86 .static void
Canny(Mat image, Mat edges, double threshold1, double threshold2, int apertureSize, boolean L2gradient)
Finds edges in an image using the Canny algorithm CITE: Canny86 .static void
Canny(Mat dx, Mat dy, Mat edges, double threshold1, double threshold2)
\overload Finds edges in an image using the Canny algorithm with custom image gradient.static void
Canny(Mat dx, Mat dy, Mat edges, double threshold1, double threshold2, boolean L2gradient)
\overload Finds edges in an image using the Canny algorithm with custom image gradient.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 boolean
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 image with 4 or 8 way connectivity  returns N, the total number of labels [0, N1] where 0 represents the background label.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 image with 4 or 8 way connectivity  returns N, the total number of labels [0, N1] where 0 represents the background label.static double
contourArea(Mat contour)
Calculates a contour area.static double
contourArea(Mat contour, boolean oriented)
Calculates a contour area.static void
convertMaps(Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type)
Converts image transformation maps from one representation to another.static void
convertMaps(Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type, boolean nninterpolation)
Converts image transformation maps from one representation to another.static void
convexHull(MatOfPoint points, MatOfInt hull)
Finds the convex hull of a point set.static void
convexHull(MatOfPoint points, MatOfInt hull, boolean clockwise)
Finds the convex hull of a point set.static void
convexityDefects(MatOfPoint contour, MatOfInt convexhull, MatOfInt4 convexityDefects)
Finds the convexity defects of a contour.static void
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, 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, 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, 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 processesstatic void
demosaicing(Mat src, Mat dst, int code, int dstCn)
main function for all demosaicing processesstatic void
dilate(Mat src, Mat dst, Mat kernel)
Dilates an image by using a specific structuring element.static void
dilate(Mat src, Mat dst, Mat kernel, Point anchor)
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 perelement division of the first Fourier spectrum by the second Fourier spectrum.static void
divSpectrums(Mat a, Mat b, Mat c, int flags, boolean conjB)
Performs the perelement division of the first Fourier spectrum by the second Fourier spectrum.static void
drawContours(Mat image, java.util.List<MatOfPoint> contours, int contourIdx, Scalar color)
Draws contours outlines or filled contours.static void
drawContours(Mat image, java.util.List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness)
Draws contours outlines or filled contours.static void
drawContours(Mat image, java.util.List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType)
Draws contours outlines or filled contours.static void
drawContours(Mat image, java.util.List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy)
Draws contours outlines or filled contours.static void
drawContours(Mat image, java.util.List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy, int maxLevel)
Draws contours outlines or filled contours.static void
drawContours(Mat image, java.util.List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy, int maxLevel, Point offset)
Draws contours outlines or filled contours.static void
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, 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(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, 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, 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, java.util.List<MatOfPoint> pts, Scalar color)
Fills the area bounded by one or more polygons.static void
fillPoly(Mat img, java.util.List<MatOfPoint> pts, Scalar color, int lineType)
Fills the area bounded by one or more polygons.static void
fillPoly(Mat img, java.util.List<MatOfPoint> pts, Scalar color, int lineType, int shift)
Fills the area bounded by one or more polygons.static void
fillPoly(Mat img, java.util.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, 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, java.util.List<MatOfPoint> contours, Mat hierarchy, int mode, int method)
Finds contours in a binary image.static void
findContours(Mat image, java.util.List<MatOfPoint> contours, Mat hierarchy, int mode, int method, Point offset)
Finds contours in a binary image.static RotatedRect
fitEllipse(MatOfPoint2f points)
Fits an ellipse around a set of 2D points.static RotatedRect
fitEllipseAMS(Mat 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 void
fitLine(Mat points, Mat line, int distType, double param, double reps, double aeps)
Fits a line to a 2D or 3D point set.static int
floodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal)
Fills a connected component with the given color.static int
floodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect)
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, 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, boolean normalize)
Returns filter coefficients for computing spatial image derivatives.static void
getDerivKernels(Mat kx, Mat ky, int dx, int dy, int ksize, boolean normalize, int ktype)
Returns filter coefficients for computing spatial image derivatives.static double
getFontScaleFromHeight(int fontFace, int pixelHeight)
Calculates the fontspecific size to use to achieve a given height in pixels.static double
getFontScaleFromHeight(int fontFace, int pixelHeight, int thickness)
Calculates the fontspecific size to use to achieve a given height in pixels.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, Size patchSize, Point center, Mat patch)
Retrieves a pixel rectangle from an image with subpixel accuracy.static void
getRectSubPix(Mat image, Size patchSize, Point center, Mat patch, int patchType)
Retrieves a pixel rectangle from an image with subpixel accuracy.static Mat
getRotationMatrix2D(Point center, double angle, double scale)
Calculates an affine matrix of 2D rotation.static Mat
getStructuringElement(int shape, Size ksize)
Returns a structuring element of the specified size and shape for morphological operations.static Mat
getStructuringElement(int shape, Size ksize, Point anchor)
Returns a structuring element of the specified size and shape for morphological operations.static Size
getTextSize(java.lang.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, boolean useHarrisDetector)
Determines strong corners on an image.static void
goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, boolean useHarrisDetector, double k)
Determines strong corners on an image.static void
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, boolean useHarrisDetector)
static void
goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, boolean 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, boolean 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, boolean useHarrisDetector, double k)
Same as above, but returns also quality measure of the detected corners.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(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 polygonsstatic float
intersectConvexConvex(Mat p1, Mat p2, Mat p12, boolean handleNested)
Finds intersection of two convex polygonsstatic void
invertAffineTransform(Mat M, Mat iM)
Inverts an affine transformation.static boolean
isContourConvex(MatOfPoint contour)
Tests a contour convexity.static void
Laplacian(Mat src, Mat dst, int ddepth)
Calculates the Laplacian of an image.static void
Laplacian(Mat src, Mat dst, int ddepth, int ksize)
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, 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, Point center, double maxRadius, int flags)
Deprecated.This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags) Transform the source image using the following transformation (See REF: polar_remaps_reference_image "Polar remaps reference image c)"): \(\begin{array}{l} dst( \rho , \phi ) = src(x,y) \\ dst.size() \leftarrow src.size() \end{array}\) where \(\begin{array}{l} I = (dx,dy) = (x  center.x,y  center.y) \\ \rho = Kmag \cdot \texttt{magnitude} (I) ,\\ \phi = angle \cdot \texttt{angle} (I) \end{array}\) and \(\begin{array}{l} Kx = src.cols / maxRadius \\ Ky = src.rows / 2\Pi \end{array}\)static void
logPolar(Mat src, Mat dst, Point center, double M, int flags)
Deprecated.This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG); Transform the source image using the following transformation (See REF: polar_remaps_reference_image "Polar remaps reference image d)"): \(\begin{array}{l} dst( \rho , \phi ) = src(x,y) \\ dst.size() \leftarrow src.size() \end{array}\) where \(\begin{array}{l} I = (dx,dy) = (x  center.x,y  center.y) \\ \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\ \phi = Kangle \cdot \texttt{angle} (I) \\ \end{array}\) and \(\begin{array}{l} M = src.cols / log_e(maxRadius) \\ Kangle = src.rows / 2\Pi \\ \end{array}\) The function emulates the human "foveal" vision and can be used for fast scale and rotationinvariant template matching, for object tracking and so forth.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 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, boolean binaryImage)
Calculates all of the moments up to the third order of a polygon or rasterized shape.static void
morphologyEx(Mat src, Mat dst, int op, Mat kernel)
Performs advanced morphological transformations.static void
morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor)
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
pointPolygonTest(MatOfPoint2f contour, Point pt, boolean measureDist)
Performs a pointincontour test.static void
polylines(Mat img, java.util.List<MatOfPoint> pts, boolean isClosed, Scalar color)
Draws several polygonal curves.static void
polylines(Mat img, java.util.List<MatOfPoint> pts, boolean isClosed, Scalar color, int thickness)
Draws several polygonal curves.static void
polylines(Mat img, java.util.List<MatOfPoint> pts, boolean isClosed, Scalar color, int thickness, int lineType)
Draws several polygonal curves.static void
polylines(Mat img, java.util.List<MatOfPoint> pts, boolean 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, java.lang.String text, Point org, int fontFace, double fontScale, Scalar color)
Draws a text string.static void
putText(Mat img, java.lang.String text, Point org, int fontFace, double fontScale, Scalar color, int thickness)
Draws a text string.static void
putText(Mat img, java.lang.String text, Point org, int fontFace, double fontScale, Scalar color, int thickness, int lineType)
Draws a text string.static void
putText(Mat img, java.lang.String text, Point org, int fontFace, double fontScale, Scalar color, int thickness, int lineType, boolean 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, 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, TermCriteria termcrit)
Performs initial step of meanshift segmentation of an image.static void
pyrUp(Mat src, Mat dst)
Upsamples an image and then blurs it.static void
pyrUp(Mat src, Mat dst, Size dstsize)
Upsamples an image and then blurs it.static void
pyrUp(Mat src, Mat dst, Size dstsize, int borderType)
Upsamples an image and then blurs it.static void
rectangle(Mat img, Point pt1, Point pt2, Scalar color)
Draws a simple, thick, or filled upright rectangle.static void
rectangle(Mat img, Point pt1, Point pt2, Scalar color, int thickness)
Draws a simple, thick, or filled upright rectangle.static void
rectangle(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType)
Draws a simple, thick, or filled upright rectangle.static void
rectangle(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType, int shift)
Draws a simple, thick, or filled upright rectangle.static void
rectangle(Mat img, Rect rec, Scalar color)
userec
parameter as alternative specification of the drawn rectangle: `r.tl() and r.br()Point(1,1)` are opposite cornersstatic void
rectangle(Mat img, Rect rec, Scalar color, int thickness)
userec
parameter as alternative specification of the drawn rectangle: `r.tl() and r.br()Point(1,1)` are opposite cornersstatic void
rectangle(Mat img, Rect rec, Scalar color, int thickness, int lineType)
userec
parameter as alternative specification of the drawn rectangle: `r.tl() and r.br()Point(1,1)` are opposite cornersstatic void
rectangle(Mat img, Rect rec, Scalar color, int thickness, int lineType, int shift)
userec
parameter as alternative specification of the drawn rectangle: `r.tl() and r.br()Point(1,1)` are opposite cornersstatic 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, Scalar borderValue)
Applies a generic geometrical transformation to an image.static void
resize(Mat src, Mat dst, Size dsize)
Resizes an image.static void
resize(Mat src, Mat dst, Size dsize, double fx)
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(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, 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 Equivalent to calling:Sobel( src, dx, CV_16SC1, 1, 0, 3 ); Sobel( src, dy, CV_16SC1, 0, 1, 3 );
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 Equivalent to calling:Sobel( src, dx, CV_16SC1, 1, 0, 3 ); Sobel( src, dy, CV_16SC1, 0, 1, 3 );
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 Equivalent to calling:Sobel( src, dx, CV_16SC1, 1, 0, 3 ); Sobel( src, dy, CV_16SC1, 0, 1, 3 );
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, boolean 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, boolean normalize, int borderType)
Calculates the normalized sum of squares of the pixel values overlapping the filter.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 fixedlevel threshold to each array element.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, 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, Size dsize, Point center, double maxRadius, int flags)
Remaps an image to polar or semilogpolar coordinates space polar_remaps_reference_image ![Polar remaps reference](pics/polar_remap_doc.png) Transform the source image using the following transformation: \( dst(\rho , \phi ) = src(x,y) \) where \( \begin{array}{l} \vec{I} = (x  center.x, \;y  center.y) \\ \phi = Kangle \cdot \texttt{angle} (\vec{I}) \\ \rho = \left\{\begin{matrix} Klin \cdot \texttt{magnitude} (\vec{I}) & default \\ Klog \cdot log_e(\texttt{magnitude} (\vec{I})) & if \; semilog \\ \end{matrix}\right.static void
watershed(Mat image, Mat markers)
Performs a markerbased image segmentation using the watershed algorithm.



Field Detail

CV_GAUSSIAN_5x5
public static final int CV_GAUSSIAN_5x5
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 Constant Field Values

CV_SCHARR
public static final int CV_SCHARR
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CV_MAX_SOBEL_KSIZE
public static final int CV_MAX_SOBEL_KSIZE
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CV_RGBA2mRGBA
public static final int CV_RGBA2mRGBA
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CV_mRGBA2RGBA
public static final int CV_mRGBA2RGBA
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CV_WARP_FILL_OUTLIERS
public static final int CV_WARP_FILL_OUTLIERS
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CV_WARP_INVERSE_MAP
public static final int CV_WARP_INVERSE_MAP
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CV_CHAIN_CODE
public static final int CV_CHAIN_CODE
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CV_LINK_RUNS
public static final int CV_LINK_RUNS
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CV_POLY_APPROX_DP
public static final int CV_POLY_APPROX_DP
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CV_CONTOURS_MATCH_I1
public static final int CV_CONTOURS_MATCH_I1
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CV_CONTOURS_MATCH_I2
public static final int CV_CONTOURS_MATCH_I2
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CV_CONTOURS_MATCH_I3
public static final int CV_CONTOURS_MATCH_I3
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CV_CLOCKWISE
public static final int CV_CLOCKWISE
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CV_COUNTER_CLOCKWISE
public static final int CV_COUNTER_CLOCKWISE
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CV_COMP_CORREL
public static final int CV_COMP_CORREL
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CV_COMP_CHISQR
public static final int CV_COMP_CHISQR
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CV_COMP_INTERSECT
public static final int CV_COMP_INTERSECT
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CV_COMP_BHATTACHARYYA
public static final int CV_COMP_BHATTACHARYYA
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CV_COMP_HELLINGER
public static final int CV_COMP_HELLINGER
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CV_COMP_CHISQR_ALT
public static final int CV_COMP_CHISQR_ALT
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CV_COMP_KL_DIV
public static final int CV_COMP_KL_DIV
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CV_DIST_MASK_3
public static final int CV_DIST_MASK_3
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CV_DIST_MASK_5
public static final int CV_DIST_MASK_5
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CV_DIST_MASK_PRECISE
public static final int CV_DIST_MASK_PRECISE
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CV_DIST_LABEL_CCOMP
public static final int CV_DIST_LABEL_CCOMP
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CV_DIST_LABEL_PIXEL
public static final int CV_DIST_LABEL_PIXEL
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CV_DIST_USER
public static final int CV_DIST_USER
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CV_DIST_L1
public static final int CV_DIST_L1
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CV_DIST_L2
public static final int CV_DIST_L2
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CV_DIST_C
public static final int CV_DIST_C
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CV_DIST_L12
public static final int CV_DIST_L12
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CV_DIST_FAIR
public static final int CV_DIST_FAIR
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CV_DIST_WELSCH
public static final int CV_DIST_WELSCH
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CV_DIST_HUBER
public static final int CV_DIST_HUBER
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CV_CANNY_L2_GRADIENT
public static final int CV_CANNY_L2_GRADIENT
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CV_HOUGH_STANDARD
public static final int CV_HOUGH_STANDARD
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CV_HOUGH_PROBABILISTIC
public static final int CV_HOUGH_PROBABILISTIC
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CV_HOUGH_MULTI_SCALE
public static final int CV_HOUGH_MULTI_SCALE
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CV_HOUGH_GRADIENT
public static final int CV_HOUGH_GRADIENT
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CV_SHAPE_RECT
public static final int CV_SHAPE_RECT
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CV_SHAPE_CROSS
public static final int CV_SHAPE_CROSS
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CV_SHAPE_ELLIPSE
public static final int CV_SHAPE_ELLIPSE
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CV_SHAPE_CUSTOM
public static final int CV_SHAPE_CUSTOM
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CV_BLUR_NO_SCALE
public static final int CV_BLUR_NO_SCALE
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CV_BLUR
public static final int CV_BLUR
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CV_GAUSSIAN
public static final int CV_GAUSSIAN
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CV_MEDIAN
public static final int CV_MEDIAN
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CV_BILATERAL
public static final int CV_BILATERAL
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ADAPTIVE_THRESH_MEAN_C
public static final int ADAPTIVE_THRESH_MEAN_C
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ADAPTIVE_THRESH_GAUSSIAN_C
public static final int ADAPTIVE_THRESH_GAUSSIAN_C
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COLOR_BGR2BGRA
public static final int COLOR_BGR2BGRA
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COLOR_RGB2RGBA
public static final int COLOR_RGB2RGBA
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COLOR_BGRA2BGR
public static final int COLOR_BGRA2BGR
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COLOR_RGBA2RGB
public static final int COLOR_RGBA2RGB
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COLOR_BGR2RGBA
public static final int COLOR_BGR2RGBA
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COLOR_RGB2BGRA
public static final int COLOR_RGB2BGRA
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COLOR_RGBA2BGR
public static final int COLOR_RGBA2BGR
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COLOR_BGRA2RGB
public static final int COLOR_BGRA2RGB
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COLOR_BGR2RGB
public static final int COLOR_BGR2RGB
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COLOR_RGB2BGR
public static final int COLOR_RGB2BGR
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COLOR_BGRA2RGBA
public static final int COLOR_BGRA2RGBA
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COLOR_RGBA2BGRA
public static final int COLOR_RGBA2BGRA
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COLOR_BGR2GRAY
public static final int COLOR_BGR2GRAY
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COLOR_RGB2GRAY
public static final int COLOR_RGB2GRAY
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COLOR_GRAY2BGR
public static final int COLOR_GRAY2BGR
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COLOR_GRAY2RGB
public static final int COLOR_GRAY2RGB
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COLOR_GRAY2BGRA
public static final int COLOR_GRAY2BGRA
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COLOR_GRAY2RGBA
public static final int COLOR_GRAY2RGBA
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COLOR_BGRA2GRAY
public static final int COLOR_BGRA2GRAY
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COLOR_RGBA2GRAY
public static final int COLOR_RGBA2GRAY
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COLOR_BGR2BGR565
public static final int COLOR_BGR2BGR565
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COLOR_RGB2BGR565
public static final int COLOR_RGB2BGR565
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COLOR_BGR5652BGR
public static final int COLOR_BGR5652BGR
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COLOR_BGR5652RGB
public static final int COLOR_BGR5652RGB
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COLOR_BGRA2BGR565
public static final int COLOR_BGRA2BGR565
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COLOR_RGBA2BGR565
public static final int COLOR_RGBA2BGR565
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COLOR_BGR5652BGRA
public static final int COLOR_BGR5652BGRA
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COLOR_BGR5652RGBA
public static final int COLOR_BGR5652RGBA
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COLOR_GRAY2BGR565
public static final int COLOR_GRAY2BGR565
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COLOR_BGR5652GRAY
public static final int COLOR_BGR5652GRAY
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COLOR_BGR2BGR555
public static final int COLOR_BGR2BGR555
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COLOR_RGB2BGR555
public static final int COLOR_RGB2BGR555
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COLOR_BGR5552BGR
public static final int COLOR_BGR5552BGR
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COLOR_BGR5552RGB
public static final int COLOR_BGR5552RGB
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COLOR_BGRA2BGR555
public static final int COLOR_BGRA2BGR555
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COLOR_RGBA2BGR555
public static final int COLOR_RGBA2BGR555
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COLOR_BGR5552BGRA
public static final int COLOR_BGR5552BGRA
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COLOR_BGR5552RGBA
public static final int COLOR_BGR5552RGBA
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COLOR_GRAY2BGR555
public static final int COLOR_GRAY2BGR555
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 Constant Field Values

COLOR_BGR5552GRAY
public static final int COLOR_BGR5552GRAY
 See Also:
 Constant Field Values

COLOR_BGR2XYZ
public static final int COLOR_BGR2XYZ
 See Also:
 Constant Field Values

COLOR_RGB2XYZ
public static final int COLOR_RGB2XYZ
 See Also:
 Constant Field Values

COLOR_XYZ2BGR
public static final int COLOR_XYZ2BGR
 See Also:
 Constant Field Values

COLOR_XYZ2RGB
public static final int COLOR_XYZ2RGB
 See Also:
 Constant Field Values

COLOR_BGR2YCrCb
public static final int COLOR_BGR2YCrCb
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 Constant Field Values

COLOR_RGB2YCrCb
public static final int COLOR_RGB2YCrCb
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 Constant Field Values

COLOR_YCrCb2BGR
public static final int COLOR_YCrCb2BGR
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 Constant Field Values

COLOR_YCrCb2RGB
public static final int COLOR_YCrCb2RGB
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 Constant Field Values

COLOR_BGR2HSV
public static final int COLOR_BGR2HSV
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 Constant Field Values

COLOR_RGB2HSV
public static final int COLOR_RGB2HSV
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 Constant Field Values

COLOR_BGR2Lab
public static final int COLOR_BGR2Lab
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 Constant Field Values

COLOR_RGB2Lab
public static final int COLOR_RGB2Lab
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 Constant Field Values

COLOR_BGR2Luv
public static final int COLOR_BGR2Luv
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 Constant Field Values

COLOR_RGB2Luv
public static final int COLOR_RGB2Luv
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 Constant Field Values

COLOR_BGR2HLS
public static final int COLOR_BGR2HLS
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 Constant Field Values

COLOR_RGB2HLS
public static final int COLOR_RGB2HLS
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 Constant Field Values

COLOR_HSV2BGR
public static final int COLOR_HSV2BGR
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 Constant Field Values

COLOR_HSV2RGB
public static final int COLOR_HSV2RGB
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 Constant Field Values

COLOR_Lab2BGR
public static final int COLOR_Lab2BGR
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 Constant Field Values

COLOR_Lab2RGB
public static final int COLOR_Lab2RGB
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 Constant Field Values

COLOR_Luv2BGR
public static final int COLOR_Luv2BGR
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 Constant Field Values

COLOR_Luv2RGB
public static final int COLOR_Luv2RGB
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 Constant Field Values

COLOR_HLS2BGR
public static final int COLOR_HLS2BGR
 See Also:
 Constant Field Values

COLOR_HLS2RGB
public static final int COLOR_HLS2RGB
 See Also:
 Constant Field Values

COLOR_BGR2HSV_FULL
public static final int COLOR_BGR2HSV_FULL
 See Also:
 Constant Field Values

COLOR_RGB2HSV_FULL
public static final int COLOR_RGB2HSV_FULL
 See Also:
 Constant Field Values

COLOR_BGR2HLS_FULL
public static final int COLOR_BGR2HLS_FULL
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 Constant Field Values

COLOR_RGB2HLS_FULL
public static final int COLOR_RGB2HLS_FULL
 See Also:
 Constant Field Values

COLOR_HSV2BGR_FULL
public static final int COLOR_HSV2BGR_FULL
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 Constant Field Values

COLOR_HSV2RGB_FULL
public static final int COLOR_HSV2RGB_FULL
 See Also:
 Constant Field Values

COLOR_HLS2BGR_FULL
public static final int COLOR_HLS2BGR_FULL
 See Also:
 Constant Field Values

COLOR_HLS2RGB_FULL
public static final int COLOR_HLS2RGB_FULL
 See Also:
 Constant Field Values

COLOR_LBGR2Lab
public static final int COLOR_LBGR2Lab
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 Constant Field Values

COLOR_LRGB2Lab
public static final int COLOR_LRGB2Lab
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 Constant Field Values

COLOR_LBGR2Luv
public static final int COLOR_LBGR2Luv
 See Also:
 Constant Field Values

COLOR_LRGB2Luv
public static final int COLOR_LRGB2Luv
 See Also:
 Constant Field Values

COLOR_Lab2LBGR
public static final int COLOR_Lab2LBGR
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 Constant Field Values

COLOR_Lab2LRGB
public static final int COLOR_Lab2LRGB
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 Constant Field Values

COLOR_Luv2LBGR
public static final int COLOR_Luv2LBGR
 See Also:
 Constant Field Values

COLOR_Luv2LRGB
public static final int COLOR_Luv2LRGB
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 Constant Field Values

COLOR_BGR2YUV
public static final int COLOR_BGR2YUV
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 Constant Field Values

COLOR_RGB2YUV
public static final int COLOR_RGB2YUV
 See Also:
 Constant Field Values

COLOR_YUV2BGR
public static final int COLOR_YUV2BGR
 See Also:
 Constant Field Values

COLOR_YUV2RGB
public static final int COLOR_YUV2RGB
 See Also:
 Constant Field Values

COLOR_YUV2RGB_NV12
public static final int COLOR_YUV2RGB_NV12
 See Also:
 Constant Field Values

COLOR_YUV2BGR_NV12
public static final int COLOR_YUV2BGR_NV12
 See Also:
 Constant Field Values

COLOR_YUV2RGB_NV21
public static final int COLOR_YUV2RGB_NV21
 See Also:
 Constant Field Values

COLOR_YUV2BGR_NV21
public static final int COLOR_YUV2BGR_NV21
 See Also:
 Constant Field Values

COLOR_YUV420sp2RGB
public static final int COLOR_YUV420sp2RGB
 See Also:
 Constant Field Values

COLOR_YUV420sp2BGR
public static final int COLOR_YUV420sp2BGR
 See Also:
 Constant Field Values

COLOR_YUV2RGBA_NV12
public static final int COLOR_YUV2RGBA_NV12
 See Also:
 Constant Field Values

COLOR_YUV2BGRA_NV12
public static final int COLOR_YUV2BGRA_NV12
 See Also:
 Constant Field Values

COLOR_YUV2RGBA_NV21
public static final int COLOR_YUV2RGBA_NV21
 See Also:
 Constant Field Values

COLOR_YUV2BGRA_NV21
public static final int COLOR_YUV2BGRA_NV21
 See Also:
 Constant Field Values

COLOR_YUV420sp2RGBA
public static final int COLOR_YUV420sp2RGBA
 See Also:
 Constant Field Values

COLOR_YUV420sp2BGRA
public static final int COLOR_YUV420sp2BGRA
 See Also:
 Constant Field Values

COLOR_YUV2RGB_YV12
public static final int COLOR_YUV2RGB_YV12
 See Also:
 Constant Field Values

COLOR_YUV2BGR_YV12
public static final int COLOR_YUV2BGR_YV12
 See Also:
 Constant Field Values

COLOR_YUV2RGB_IYUV
public static final int COLOR_YUV2RGB_IYUV
 See Also:
 Constant Field Values

COLOR_YUV2BGR_IYUV
public static final int COLOR_YUV2BGR_IYUV
 See Also:
 Constant Field Values

COLOR_YUV2RGB_I420
public static final int COLOR_YUV2RGB_I420
 See Also:
 Constant Field Values

COLOR_YUV2BGR_I420
public static final int COLOR_YUV2BGR_I420
 See Also:
 Constant Field Values

COLOR_YUV420p2RGB
public static final int COLOR_YUV420p2RGB
 See Also:
 Constant Field Values

COLOR_YUV420p2BGR
public static final int COLOR_YUV420p2BGR
 See Also:
 Constant Field Values

COLOR_YUV2RGBA_YV12
public static final int COLOR_YUV2RGBA_YV12
 See Also:
 Constant Field Values

COLOR_YUV2BGRA_YV12
public static final int COLOR_YUV2BGRA_YV12
 See Also:
 Constant Field Values

COLOR_YUV2RGBA_IYUV
public static final int COLOR_YUV2RGBA_IYUV
 See Also:
 Constant Field Values

COLOR_YUV2BGRA_IYUV
public static final int COLOR_YUV2BGRA_IYUV
 See Also:
 Constant Field Values

COLOR_YUV2RGBA_I420
public static final int COLOR_YUV2RGBA_I420
 See Also:
 Constant Field Values

COLOR_YUV2BGRA_I420
public static final int COLOR_YUV2BGRA_I420
 See Also:
 Constant Field Values

COLOR_YUV420p2RGBA
public static final int COLOR_YUV420p2RGBA
 See Also:
 Constant Field Values

COLOR_YUV420p2BGRA
public static final int COLOR_YUV420p2BGRA
 See Also:
 Constant Field Values

COLOR_YUV2GRAY_420
public static final int COLOR_YUV2GRAY_420
 See Also:
 Constant Field Values

COLOR_YUV2GRAY_NV21
public static final int COLOR_YUV2GRAY_NV21
 See Also:
 Constant Field Values

COLOR_YUV2GRAY_NV12
public static final int COLOR_YUV2GRAY_NV12
 See Also:
 Constant Field Values

COLOR_YUV2GRAY_YV12
public static final int COLOR_YUV2GRAY_YV12
 See Also:
 Constant Field Values

COLOR_YUV2GRAY_IYUV
public static final int COLOR_YUV2GRAY_IYUV
 See Also:
 Constant Field Values

COLOR_YUV2GRAY_I420
public static final int COLOR_YUV2GRAY_I420
 See Also:
 Constant Field Values

COLOR_YUV420sp2GRAY
public static final int COLOR_YUV420sp2GRAY
 See Also:
 Constant Field Values

COLOR_YUV420p2GRAY
public static final int COLOR_YUV420p2GRAY
 See Also:
 Constant Field Values

COLOR_YUV2RGB_UYVY
public static final int COLOR_YUV2RGB_UYVY
 See Also:
 Constant Field Values

COLOR_YUV2BGR_UYVY
public static final int COLOR_YUV2BGR_UYVY
 See Also:
 Constant Field Values

COLOR_YUV2RGB_Y422
public static final int COLOR_YUV2RGB_Y422
 See Also:
 Constant Field Values

COLOR_YUV2BGR_Y422
public static final int COLOR_YUV2BGR_Y422
 See Also:
 Constant Field Values

COLOR_YUV2RGB_UYNV
public static final int COLOR_YUV2RGB_UYNV
 See Also:
 Constant Field Values

COLOR_YUV2BGR_UYNV
public static final int COLOR_YUV2BGR_UYNV
 See Also:
 Constant Field Values

COLOR_YUV2RGBA_UYVY
public static final int COLOR_YUV2RGBA_UYVY
 See Also:
 Constant Field Values

COLOR_YUV2BGRA_UYVY
public static final int COLOR_YUV2BGRA_UYVY
 See Also:
 Constant Field Values

COLOR_YUV2RGBA_Y422
public static final int COLOR_YUV2RGBA_Y422
 See Also:
 Constant Field Values

COLOR_YUV2BGRA_Y422
public static final int COLOR_YUV2BGRA_Y422
 See Also:
 Constant Field Values

COLOR_YUV2RGBA_UYNV
public static final int COLOR_YUV2RGBA_UYNV
 See Also:
 Constant Field Values

COLOR_YUV2BGRA_UYNV
public static final int COLOR_YUV2BGRA_UYNV
 See Also:
 Constant Field Values

COLOR_YUV2RGB_YUY2
public static final int COLOR_YUV2RGB_YUY2
 See Also:
 Constant Field Values

COLOR_YUV2BGR_YUY2
public static final int COLOR_YUV2BGR_YUY2
 See Also:
 Constant Field Values

COLOR_YUV2RGB_YVYU
public static final int COLOR_YUV2RGB_YVYU
 See Also:
 Constant Field Values

COLOR_YUV2BGR_YVYU
public static final int COLOR_YUV2BGR_YVYU
 See Also:
 Constant Field Values

COLOR_YUV2RGB_YUYV
public static final int COLOR_YUV2RGB_YUYV
 See Also:
 Constant Field Values

COLOR_YUV2BGR_YUYV
public static final int COLOR_YUV2BGR_YUYV
 See Also:
 Constant Field Values

COLOR_YUV2RGB_YUNV
public static final int COLOR_YUV2RGB_YUNV
 See Also:
 Constant Field Values

COLOR_YUV2BGR_YUNV
public static final int COLOR_YUV2BGR_YUNV
 See Also:
 Constant Field Values

COLOR_YUV2RGBA_YUY2
public static final int COLOR_YUV2RGBA_YUY2
 See Also:
 Constant Field Values

COLOR_YUV2BGRA_YUY2
public static final int COLOR_YUV2BGRA_YUY2
 See Also:
 Constant Field Values

COLOR_YUV2RGBA_YVYU
public static final int COLOR_YUV2RGBA_YVYU
 See Also:
 Constant Field Values

COLOR_YUV2BGRA_YVYU
public static final int COLOR_YUV2BGRA_YVYU
 See Also:
 Constant Field Values

COLOR_YUV2RGBA_YUYV
public static final int COLOR_YUV2RGBA_YUYV
 See Also:
 Constant Field Values

COLOR_YUV2BGRA_YUYV
public static final int COLOR_YUV2BGRA_YUYV
 See Also:
 Constant Field Values

COLOR_YUV2RGBA_YUNV
public static final int COLOR_YUV2RGBA_YUNV
 See Also:
 Constant Field Values

COLOR_YUV2BGRA_YUNV
public static final int COLOR_YUV2BGRA_YUNV
 See Also:
 Constant Field Values

COLOR_YUV2GRAY_UYVY
public static final int COLOR_YUV2GRAY_UYVY
 See Also:
 Constant Field Values

COLOR_YUV2GRAY_YUY2
public static final int COLOR_YUV2GRAY_YUY2
 See Also:
 Constant Field Values

COLOR_YUV2GRAY_Y422
public static final int COLOR_YUV2GRAY_Y422
 See Also:
 Constant Field Values

COLOR_YUV2GRAY_UYNV
public static final int COLOR_YUV2GRAY_UYNV
 See Also:
 Constant Field Values

COLOR_YUV2GRAY_YVYU
public static final int COLOR_YUV2GRAY_YVYU
 See Also:
 Constant Field Values

COLOR_YUV2GRAY_YUYV
public static final int COLOR_YUV2GRAY_YUYV
 See Also:
 Constant Field Values

COLOR_YUV2GRAY_YUNV
public static final int COLOR_YUV2GRAY_YUNV
 See Also:
 Constant Field Values

COLOR_RGBA2mRGBA
public static final int COLOR_RGBA2mRGBA
 See Also:
 Constant Field Values

COLOR_mRGBA2RGBA
public static final int COLOR_mRGBA2RGBA
 See Also:
 Constant Field Values

COLOR_RGB2YUV_I420
public static final int COLOR_RGB2YUV_I420
 See Also:
 Constant Field Values

COLOR_BGR2YUV_I420
public static final int COLOR_BGR2YUV_I420
 See Also:
 Constant Field Values

COLOR_RGB2YUV_IYUV
public static final int COLOR_RGB2YUV_IYUV
 See Also:
 Constant Field Values

COLOR_BGR2YUV_IYUV
public static final int COLOR_BGR2YUV_IYUV
 See Also:
 Constant Field Values

COLOR_RGBA2YUV_I420
public static final int COLOR_RGBA2YUV_I420
 See Also:
 Constant Field Values

COLOR_BGRA2YUV_I420
public static final int COLOR_BGRA2YUV_I420
 See Also:
 Constant Field Values

COLOR_RGBA2YUV_IYUV
public static final int COLOR_RGBA2YUV_IYUV
 See Also:
 Constant Field Values

COLOR_BGRA2YUV_IYUV
public static final int COLOR_BGRA2YUV_IYUV
 See Also:
 Constant Field Values

COLOR_RGB2YUV_YV12
public static final int COLOR_RGB2YUV_YV12
 See Also:
 Constant Field Values

COLOR_BGR2YUV_YV12
public static final int COLOR_BGR2YUV_YV12
 See Also:
 Constant Field Values

COLOR_RGBA2YUV_YV12
public static final int COLOR_RGBA2YUV_YV12
 See Also:
 Constant Field Values

COLOR_BGRA2YUV_YV12
public static final int COLOR_BGRA2YUV_YV12
 See Also:
 Constant Field Values

COLOR_BayerBG2BGR
public static final int COLOR_BayerBG2BGR
 See Also:
 Constant Field Values

COLOR_BayerGB2BGR
public static final int COLOR_BayerGB2BGR
 See Also:
 Constant Field Values

COLOR_BayerRG2BGR
public static final int COLOR_BayerRG2BGR
 See Also:
 Constant Field Values

COLOR_BayerGR2BGR
public static final int COLOR_BayerGR2BGR
 See Also:
 Constant Field Values

COLOR_BayerRGGB2BGR
public static final int COLOR_BayerRGGB2BGR
 See Also:
 Constant Field Values

COLOR_BayerGRBG2BGR
public static final int COLOR_BayerGRBG2BGR
 See Also:
 Constant Field Values

COLOR_BayerBGGR2BGR
public static final int COLOR_BayerBGGR2BGR
 See Also:
 Constant Field Values

COLOR_BayerGBRG2BGR
public static final int COLOR_BayerGBRG2BGR
 See Also:
 Constant Field Values

COLOR_BayerRGGB2RGB
public static final int COLOR_BayerRGGB2RGB
 See Also:
 Constant Field Values

COLOR_BayerGRBG2RGB
public static final int COLOR_BayerGRBG2RGB
 See Also:
 Constant Field Values

COLOR_BayerBGGR2RGB
public static final int COLOR_BayerBGGR2RGB
 See Also:
 Constant Field Values

COLOR_BayerGBRG2RGB
public static final int COLOR_BayerGBRG2RGB
 See Also:
 Constant Field Values

COLOR_BayerBG2RGB
public static final int COLOR_BayerBG2RGB
 See Also:
 Constant Field Values

COLOR_BayerGB2RGB
public static final int COLOR_BayerGB2RGB
 See Also:
 Constant Field Values

COLOR_BayerRG2RGB
public static final int COLOR_BayerRG2RGB
 See Also:
 Constant Field Values

COLOR_BayerGR2RGB
public static final int COLOR_BayerGR2RGB
 See Also:
 Constant Field Values

COLOR_BayerBG2GRAY
public static final int COLOR_BayerBG2GRAY
 See Also:
 Constant Field Values

COLOR_BayerGB2GRAY
public static final int COLOR_BayerGB2GRAY
 See Also:
 Constant Field Values

COLOR_BayerRG2GRAY
public static final int COLOR_BayerRG2GRAY
 See Also:
 Constant Field Values

COLOR_BayerGR2GRAY
public static final int COLOR_BayerGR2GRAY
 See Also:
 Constant Field Values

COLOR_BayerRGGB2GRAY
public static final int COLOR_BayerRGGB2GRAY
 See Also:
 Constant Field Values

COLOR_BayerGRBG2GRAY
public static final int COLOR_BayerGRBG2GRAY
 See Also:
 Constant Field Values

COLOR_BayerBGGR2GRAY
public static final int COLOR_BayerBGGR2GRAY
 See Also:
 Constant Field Values

COLOR_BayerGBRG2GRAY
public static final int COLOR_BayerGBRG2GRAY
 See Also:
 Constant Field Values

COLOR_BayerBG2BGR_VNG
public static final int COLOR_BayerBG2BGR_VNG
 See Also:
 Constant Field Values

COLOR_BayerGB2BGR_VNG
public static final int COLOR_BayerGB2BGR_VNG
 See Also:
 Constant Field Values

COLOR_BayerRG2BGR_VNG
public static final int COLOR_BayerRG2BGR_VNG
 See Also:
 Constant Field Values

COLOR_BayerGR2BGR_VNG
public static final int COLOR_BayerGR2BGR_VNG
 See Also:
 Constant Field Values

COLOR_BayerRGGB2BGR_VNG
public static final int COLOR_BayerRGGB2BGR_VNG
 See Also:
 Constant Field Values

COLOR_BayerGRBG2BGR_VNG
public static final int COLOR_BayerGRBG2BGR_VNG
 See Also:
 Constant Field Values

COLOR_BayerBGGR2BGR_VNG
public static final int COLOR_BayerBGGR2BGR_VNG
 See Also:
 Constant Field Values

COLOR_BayerGBRG2BGR_VNG
public static final int COLOR_BayerGBRG2BGR_VNG
 See Also:
 Constant Field Values

COLOR_BayerRGGB2RGB_VNG
public static final int COLOR_BayerRGGB2RGB_VNG
 See Also:
 Constant Field Values

COLOR_BayerGRBG2RGB_VNG
public static final int COLOR_BayerGRBG2RGB_VNG
 See Also:
 Constant Field Values

COLOR_BayerBGGR2RGB_VNG
public static final int COLOR_BayerBGGR2RGB_VNG
 See Also:
 Constant Field Values

COLOR_BayerGBRG2RGB_VNG
public static final int COLOR_BayerGBRG2RGB_VNG
 See Also:
 Constant Field Values

COLOR_BayerBG2RGB_VNG
public static final int COLOR_BayerBG2RGB_VNG
 See Also:
 Constant Field Values

COLOR_BayerGB2RGB_VNG
public static final int COLOR_BayerGB2RGB_VNG
 See Also:
 Constant Field Values

COLOR_BayerRG2RGB_VNG
public static final int COLOR_BayerRG2RGB_VNG
 See Also:
 Constant Field Values

COLOR_BayerGR2RGB_VNG
public static final int COLOR_BayerGR2RGB_VNG
 See Also:
 Constant Field Values

COLOR_BayerBG2BGR_EA
public static final int COLOR_BayerBG2BGR_EA
 See Also:
 Constant Field Values

COLOR_BayerGB2BGR_EA
public static final int COLOR_BayerGB2BGR_EA
 See Also:
 Constant Field Values

COLOR_BayerRG2BGR_EA
public static final int COLOR_BayerRG2BGR_EA
 See Also:
 Constant Field Values

COLOR_BayerGR2BGR_EA
public static final int COLOR_BayerGR2BGR_EA
 See Also:
 Constant Field Values

COLOR_BayerRGGB2BGR_EA
public static final int COLOR_BayerRGGB2BGR_EA
 See Also:
 Constant Field Values

COLOR_BayerGRBG2BGR_EA
public static final int COLOR_BayerGRBG2BGR_EA
 See Also:
 Constant Field Values

COLOR_BayerBGGR2BGR_EA
public static final int COLOR_BayerBGGR2BGR_EA
 See Also:
 Constant Field Values

COLOR_BayerGBRG2BGR_EA
public static final int COLOR_BayerGBRG2BGR_EA
 See Also:
 Constant Field Values

COLOR_BayerRGGB2RGB_EA
public static final int COLOR_BayerRGGB2RGB_EA
 See Also:
 Constant Field Values

COLOR_BayerGRBG2RGB_EA
public static final int COLOR_BayerGRBG2RGB_EA
 See Also:
 Constant Field Values

COLOR_BayerBGGR2RGB_EA
public static final int COLOR_BayerBGGR2RGB_EA
 See Also:
 Constant Field Values

COLOR_BayerGBRG2RGB_EA
public static final int COLOR_BayerGBRG2RGB_EA
 See Also:
 Constant Field Values

COLOR_BayerBG2RGB_EA
public static final int COLOR_BayerBG2RGB_EA
 See Also:
 Constant Field Values

COLOR_BayerGB2RGB_EA
public static final int COLOR_BayerGB2RGB_EA
 See Also:
 Constant Field Values

COLOR_BayerRG2RGB_EA
public static final int COLOR_BayerRG2RGB_EA
 See Also:
 Constant Field Values

COLOR_BayerGR2RGB_EA
public static final int COLOR_BayerGR2RGB_EA
 See Also:
 Constant Field Values

COLOR_BayerBG2BGRA
public static final int COLOR_BayerBG2BGRA
 See Also:
 Constant Field Values

COLOR_BayerGB2BGRA
public static final int COLOR_BayerGB2BGRA
 See Also:
 Constant Field Values

COLOR_BayerRG2BGRA
public static final int COLOR_BayerRG2BGRA
 See Also:
 Constant Field Values

COLOR_BayerGR2BGRA
public static final int COLOR_BayerGR2BGRA
 See Also:
 Constant Field Values

COLOR_BayerRGGB2BGRA
public static final int COLOR_BayerRGGB2BGRA
 See Also:
 Constant Field Values

COLOR_BayerGRBG2BGRA
public static final int COLOR_BayerGRBG2BGRA
 See Also:
 Constant Field Values

COLOR_BayerBGGR2BGRA
public static final int COLOR_BayerBGGR2BGRA
 See Also:
 Constant Field Values

COLOR_BayerGBRG2BGRA
public static final int COLOR_BayerGBRG2BGRA
 See Also:
 Constant Field Values

COLOR_BayerRGGB2RGBA
public static final int COLOR_BayerRGGB2RGBA
 See Also:
 Constant Field Values

COLOR_BayerGRBG2RGBA
public static final int COLOR_BayerGRBG2RGBA
 See Also:
 Constant Field Values

COLOR_BayerBGGR2RGBA
public static final int COLOR_BayerBGGR2RGBA
 See Also:
 Constant Field Values

COLOR_BayerGBRG2RGBA
public static final int COLOR_BayerGBRG2RGBA
 See Also:
 Constant Field Values

COLOR_BayerBG2RGBA
public static final int COLOR_BayerBG2RGBA
 See Also:
 Constant Field Values

COLOR_BayerGB2RGBA
public static final int COLOR_BayerGB2RGBA
 See Also:
 Constant Field Values

COLOR_BayerRG2RGBA
public static final int COLOR_BayerRG2RGBA
 See Also:
 Constant Field Values

COLOR_BayerGR2RGBA
public static final int COLOR_BayerGR2RGBA
 See Also:
 Constant Field Values

COLOR_COLORCVT_MAX
public static final int COLOR_COLORCVT_MAX
 See Also:
 Constant Field Values

COLORMAP_AUTUMN
public static final int COLORMAP_AUTUMN
 See Also:
 Constant Field Values

COLORMAP_BONE
public static final int COLORMAP_BONE
 See Also:
 Constant Field Values

COLORMAP_JET
public static final int COLORMAP_JET
 See Also:
 Constant Field Values

COLORMAP_WINTER
public static final int COLORMAP_WINTER
 See Also:
 Constant Field Values

COLORMAP_RAINBOW
public static final int COLORMAP_RAINBOW
 See Also:
 Constant Field Values

COLORMAP_OCEAN
public static final int COLORMAP_OCEAN
 See Also:
 Constant Field Values

COLORMAP_SUMMER
public static final int COLORMAP_SUMMER
 See Also:
 Constant Field Values

COLORMAP_SPRING
public static final int COLORMAP_SPRING
 See Also:
 Constant Field Values

COLORMAP_COOL
public static final int COLORMAP_COOL
 See Also:
 Constant Field Values

COLORMAP_HSV
public static final int COLORMAP_HSV
 See Also:
 Constant Field Values

COLORMAP_PINK
public static final int COLORMAP_PINK
 See Also:
 Constant Field Values

COLORMAP_HOT
public static final int COLORMAP_HOT
 See Also:
 Constant Field Values

COLORMAP_PARULA
public static final int COLORMAP_PARULA
 See Also:
 Constant Field Values

COLORMAP_MAGMA
public static final int COLORMAP_MAGMA
 See Also:
 Constant Field Values

COLORMAP_INFERNO
public static final int COLORMAP_INFERNO
 See Also:
 Constant Field Values

COLORMAP_PLASMA
public static final int COLORMAP_PLASMA
 See Also:
 Constant Field Values

COLORMAP_VIRIDIS
public static final int COLORMAP_VIRIDIS
 See Also:
 Constant Field Values

COLORMAP_CIVIDIS
public static final int COLORMAP_CIVIDIS
 See Also:
 Constant Field Values

COLORMAP_TWILIGHT
public static final int COLORMAP_TWILIGHT
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COLORMAP_TWILIGHT_SHIFTED
public static final int COLORMAP_TWILIGHT_SHIFTED
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COLORMAP_TURBO
public static final int COLORMAP_TURBO
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COLORMAP_DEEPGREEN
public static final int COLORMAP_DEEPGREEN
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CCL_DEFAULT
public static final int CCL_DEFAULT
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CCL_WU
public static final int CCL_WU
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CCL_GRANA
public static final int CCL_GRANA
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CCL_BOLELLI
public static final int CCL_BOLELLI
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CCL_SAUF
public static final int CCL_SAUF
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CCL_BBDT
public static final int CCL_BBDT
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CCL_SPAGHETTI
public static final int CCL_SPAGHETTI
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CC_STAT_LEFT
public static final int CC_STAT_LEFT
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CC_STAT_TOP
public static final int CC_STAT_TOP
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CC_STAT_WIDTH
public static final int CC_STAT_WIDTH
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CC_STAT_HEIGHT
public static final int CC_STAT_HEIGHT
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CC_STAT_AREA
public static final int CC_STAT_AREA
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CC_STAT_MAX
public static final int CC_STAT_MAX
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CHAIN_APPROX_NONE
public static final int CHAIN_APPROX_NONE
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CHAIN_APPROX_SIMPLE
public static final int CHAIN_APPROX_SIMPLE
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CHAIN_APPROX_TC89_L1
public static final int CHAIN_APPROX_TC89_L1
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CHAIN_APPROX_TC89_KCOS
public static final int CHAIN_APPROX_TC89_KCOS
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DIST_LABEL_CCOMP
public static final int DIST_LABEL_CCOMP
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DIST_LABEL_PIXEL
public static final int DIST_LABEL_PIXEL
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DIST_MASK_3
public static final int DIST_MASK_3
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DIST_MASK_5
public static final int DIST_MASK_5
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DIST_MASK_PRECISE
public static final int DIST_MASK_PRECISE
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DIST_USER
public static final int DIST_USER
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DIST_L1
public static final int DIST_L1
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DIST_L2
public static final int DIST_L2
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DIST_C
public static final int DIST_C
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 Constant Field Values

DIST_L12
public static final int DIST_L12
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DIST_FAIR
public static final int DIST_FAIR
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DIST_WELSCH
public static final int DIST_WELSCH
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DIST_HUBER
public static final int DIST_HUBER
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 Constant Field Values

FLOODFILL_FIXED_RANGE
public static final int FLOODFILL_FIXED_RANGE
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FLOODFILL_MASK_ONLY
public static final int FLOODFILL_MASK_ONLY
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GC_BGD
public static final int GC_BGD
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GC_FGD
public static final int GC_FGD
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GC_PR_BGD
public static final int GC_PR_BGD
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GC_PR_FGD
public static final int GC_PR_FGD
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 Constant Field Values

GC_INIT_WITH_RECT
public static final int GC_INIT_WITH_RECT
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GC_INIT_WITH_MASK
public static final int GC_INIT_WITH_MASK
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 Constant Field Values

GC_EVAL
public static final int GC_EVAL
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 Constant Field Values

GC_EVAL_FREEZE_MODEL
public static final int GC_EVAL_FREEZE_MODEL
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 Constant Field Values

FONT_HERSHEY_SIMPLEX
public static final int FONT_HERSHEY_SIMPLEX
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 Constant Field Values

FONT_HERSHEY_PLAIN
public static final int FONT_HERSHEY_PLAIN
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FONT_HERSHEY_DUPLEX
public static final int FONT_HERSHEY_DUPLEX
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FONT_HERSHEY_COMPLEX
public static final int FONT_HERSHEY_COMPLEX
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FONT_HERSHEY_TRIPLEX
public static final int FONT_HERSHEY_TRIPLEX
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FONT_HERSHEY_COMPLEX_SMALL
public static final int FONT_HERSHEY_COMPLEX_SMALL
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FONT_HERSHEY_SCRIPT_SIMPLEX
public static final int FONT_HERSHEY_SCRIPT_SIMPLEX
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FONT_HERSHEY_SCRIPT_COMPLEX
public static final int FONT_HERSHEY_SCRIPT_COMPLEX
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 Constant Field Values

FONT_ITALIC
public static final int FONT_ITALIC
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 Constant Field Values

HISTCMP_CORREL
public static final int HISTCMP_CORREL
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HISTCMP_CHISQR
public static final int HISTCMP_CHISQR
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HISTCMP_INTERSECT
public static final int HISTCMP_INTERSECT
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 Constant Field Values

HISTCMP_BHATTACHARYYA
public static final int HISTCMP_BHATTACHARYYA
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 Constant Field Values

HISTCMP_HELLINGER
public static final int HISTCMP_HELLINGER
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 Constant Field Values

HISTCMP_CHISQR_ALT
public static final int HISTCMP_CHISQR_ALT
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 Constant Field Values

HISTCMP_KL_DIV
public static final int HISTCMP_KL_DIV
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 Constant Field Values

HOUGH_STANDARD
public static final int HOUGH_STANDARD
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 Constant Field Values

HOUGH_PROBABILISTIC
public static final int HOUGH_PROBABILISTIC
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 Constant Field Values

HOUGH_MULTI_SCALE
public static final int HOUGH_MULTI_SCALE
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 Constant Field Values

HOUGH_GRADIENT
public static final int HOUGH_GRADIENT
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 Constant Field Values

HOUGH_GRADIENT_ALT
public static final int HOUGH_GRADIENT_ALT
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 Constant Field Values

INTER_NEAREST
public static final int INTER_NEAREST
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 Constant Field Values

INTER_LINEAR
public static final int INTER_LINEAR
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 Constant Field Values

INTER_CUBIC
public static final int INTER_CUBIC
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INTER_AREA
public static final int INTER_AREA
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 Constant Field Values

INTER_LANCZOS4
public static final int INTER_LANCZOS4
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 Constant Field Values

INTER_LINEAR_EXACT
public static final int INTER_LINEAR_EXACT
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 Constant Field Values

INTER_NEAREST_EXACT
public static final int INTER_NEAREST_EXACT
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 Constant Field Values

INTER_MAX
public static final int INTER_MAX
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 Constant Field Values

WARP_FILL_OUTLIERS
public static final int WARP_FILL_OUTLIERS
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 Constant Field Values

WARP_INVERSE_MAP
public static final int WARP_INVERSE_MAP
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 Constant Field Values

INTER_BITS
public static final int INTER_BITS
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 Constant Field Values

INTER_BITS2
public static final int INTER_BITS2
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 Constant Field Values

INTER_TAB_SIZE
public static final int INTER_TAB_SIZE
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 Constant Field Values

INTER_TAB_SIZE2
public static final int INTER_TAB_SIZE2
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 Constant Field Values

LSD_REFINE_NONE
public static final int LSD_REFINE_NONE
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 Constant Field Values

LSD_REFINE_STD
public static final int LSD_REFINE_STD
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 Constant Field Values

LSD_REFINE_ADV
public static final int LSD_REFINE_ADV
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 Constant Field Values

FILLED
public static final int FILLED
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 Constant Field Values

LINE_4
public static final int LINE_4
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LINE_8
public static final int LINE_8
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LINE_AA
public static final int LINE_AA
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 Constant Field Values

MARKER_CROSS
public static final int MARKER_CROSS
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 Constant Field Values

MARKER_TILTED_CROSS
public static final int MARKER_TILTED_CROSS
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MARKER_STAR
public static final int MARKER_STAR
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 Constant Field Values

MARKER_DIAMOND
public static final int MARKER_DIAMOND
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MARKER_SQUARE
public static final int MARKER_SQUARE
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MARKER_TRIANGLE_UP
public static final int MARKER_TRIANGLE_UP
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MARKER_TRIANGLE_DOWN
public static final int MARKER_TRIANGLE_DOWN
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 Constant Field Values

MORPH_RECT
public static final int MORPH_RECT
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 Constant Field Values

MORPH_CROSS
public static final int MORPH_CROSS
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MORPH_ELLIPSE
public static final int MORPH_ELLIPSE
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MORPH_ERODE
public static final int MORPH_ERODE
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MORPH_DILATE
public static final int MORPH_DILATE
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MORPH_OPEN
public static final int MORPH_OPEN
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MORPH_CLOSE
public static final int MORPH_CLOSE
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 Constant Field Values

MORPH_GRADIENT
public static final int MORPH_GRADIENT
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MORPH_TOPHAT
public static final int MORPH_TOPHAT
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MORPH_BLACKHAT
public static final int MORPH_BLACKHAT
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MORPH_HITMISS
public static final int MORPH_HITMISS
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INTERSECT_NONE
public static final int INTERSECT_NONE
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INTERSECT_PARTIAL
public static final int INTERSECT_PARTIAL
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INTERSECT_FULL
public static final int INTERSECT_FULL
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 Constant Field Values

RETR_EXTERNAL
public static final int RETR_EXTERNAL
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RETR_LIST
public static final int RETR_LIST
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RETR_CCOMP
public static final int RETR_CCOMP
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RETR_TREE
public static final int RETR_TREE
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RETR_FLOODFILL
public static final int RETR_FLOODFILL
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CONTOURS_MATCH_I1
public static final int CONTOURS_MATCH_I1
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CONTOURS_MATCH_I2
public static final int CONTOURS_MATCH_I2
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CONTOURS_MATCH_I3
public static final int CONTOURS_MATCH_I3
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FILTER_SCHARR
public static final int FILTER_SCHARR
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TM_SQDIFF
public static final int TM_SQDIFF
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TM_SQDIFF_NORMED
public static final int TM_SQDIFF_NORMED
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 Constant Field Values

TM_CCORR
public static final int TM_CCORR
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 Constant Field Values

TM_CCORR_NORMED
public static final int TM_CCORR_NORMED
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 Constant Field Values

TM_CCOEFF
public static final int TM_CCOEFF
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 Constant Field Values

TM_CCOEFF_NORMED
public static final int TM_CCOEFF_NORMED
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 Constant Field Values

THRESH_BINARY
public static final int THRESH_BINARY
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 Constant Field Values

THRESH_BINARY_INV
public static final int THRESH_BINARY_INV
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 Constant Field Values

THRESH_TRUNC
public static final int THRESH_TRUNC
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 Constant Field Values

THRESH_TOZERO
public static final int THRESH_TOZERO
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 Constant Field Values

THRESH_TOZERO_INV
public static final int THRESH_TOZERO_INV
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THRESH_MASK
public static final int THRESH_MASK
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THRESH_OTSU
public static final int THRESH_OTSU
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 Constant Field Values

THRESH_TRIANGLE
public static final int THRESH_TRIANGLE
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 Constant Field Values

WARP_POLAR_LINEAR
public static final int WARP_POLAR_LINEAR
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 Constant Field Values

WARP_POLAR_LOG
public static final int WARP_POLAR_LOG
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 Constant Field Values


Method Detail

createLineSegmentDetector
public static LineSegmentDetector createLineSegmentDetector(int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps, double density_th, int n_bins)
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 #LineSegmentDetectorModesscale
 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 pseudoordering of gradient modulus. Returns:
 automatically generated

createLineSegmentDetector
public 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. 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 #LineSegmentDetectorModesscale
 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. Returns:
 automatically generated

createLineSegmentDetector
public 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. 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 #LineSegmentDetectorModesscale
 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. Returns:
 automatically generated

createLineSegmentDetector
public 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. 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 #LineSegmentDetectorModesscale
 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. Returns:
 automatically generated

createLineSegmentDetector
public static LineSegmentDetector createLineSegmentDetector(int refine, double scale, double sigma_scale, double quant)
Creates a smart pointer to a LineSegmentDetector object and initializes it. The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want to edit those, as to tailor it for their own application. Parameters:
refine
 The way found lines will be refined, see #LineSegmentDetectorModesscale
 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. Returns:
 automatically generated

createLineSegmentDetector
public static LineSegmentDetector createLineSegmentDetector(int refine, double scale, double sigma_scale)
Creates a smart pointer to a LineSegmentDetector object and initializes it. The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want to edit those, as to tailor it for their own application. Parameters:
refine
 The way found lines will be refined, see #LineSegmentDetectorModesscale
 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. Returns:
 automatically generated

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

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

createLineSegmentDetector
public static LineSegmentDetector createLineSegmentDetector()
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. Returns:
 automatically generated

getGaussianKernel
public static Mat getGaussianKernel(int ksize, double sigma, int ktype)
Returns Gaussian filter coefficients. The function computes and returns the \(\texttt{ksize} \times 1\) matrix of Gaussian filter coefficients: \(G_i= \alpha *e^{(i( \texttt{ksize} 1)/2)^2/(2* \texttt{sigma}^2)},\) where \(i=0..\texttt{ksize}1\) and \(\alpha\) is the scale factor chosen so that \(\sum_i G_i=1\). Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. You may also use the higherlevel GaussianBlur. Parameters:
ksize
 Aperture size. It should be odd ( \(\texttt{ksize} \mod 2 = 1\) ) and positive.sigma
 Gaussian standard deviation. If it is nonpositive, it is computed from ksize assigma = 0.3*((ksize1)*0.5  1) + 0.8
.ktype
 Type of filter coefficients. It can be CV_32F or CV_64F . SEE: sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur Returns:
 automatically generated

getGaussianKernel
public static Mat getGaussianKernel(int ksize, double sigma)
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 higherlevel GaussianBlur. Parameters:
ksize
 Aperture size. It should be odd ( \(\texttt{ksize} \mod 2 = 1\) ) and positive.sigma
 Gaussian standard deviation. If it is nonpositive, it is computed from ksize assigma = 0.3*((ksize1)*0.5  1) + 0.8
. SEE: sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur Returns:
 automatically generated

getDerivKernels
public static void getDerivKernels(Mat kx, Mat ky, int dx, int dy, int ksize, boolean normalize, int ktype)
Returns filter coefficients for computing spatial image derivatives. The function computes and returns the filter coefficients for spatial image derivatives. Whenksize=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 \(=2^{ksize*2dxdy2}\). If you are going to filter floatingpoint images, you are likely to use the normalized kernels. But if you compute derivatives of an 8bit image, store the results in a 16bit 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
public static void getDerivKernels(Mat kx, Mat ky, int dx, int dy, int ksize, boolean normalize)
Returns filter coefficients for computing spatial image derivatives. The function computes and returns the filter coefficients for spatial image derivatives. Whenksize=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 \(=2^{ksize*2dxdy2}\). If you are going to filter floatingpoint images, you are likely to use the normalized kernels. But if you compute derivatives of an 8bit image, store the results in a 16bit image, and wish to preserve all the fractional bits, you may want to set normalize=false .

getDerivKernels
public static void getDerivKernels(Mat kx, Mat ky, int dx, int dy, int ksize)
Returns filter coefficients for computing spatial image derivatives. The function computes and returns the filter coefficients for spatial image derivatives. Whenksize=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. Theoretically, the coefficients should have the denominator \(=2^{ksize*2dxdy2}\). If you are going to filter floatingpoint images, you are likely to use the normalized kernels. But if you compute derivatives of an 8bit image, store the results in a 16bit image, and wish to preserve all the fractional bits, you may want to set normalize=false .

getGaborKernel
public static Mat getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma, double psi, int ktype)
Returns Gabor filter coefficients. For more details about gabor filter equations and parameters, see: [Gabor Filter](http://en.wikipedia.org/wiki/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 . Returns:
 automatically generated

getGaborKernel
public static Mat getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma, double psi)
Returns Gabor filter coefficients. For more details about gabor filter equations and parameters, see: [Gabor Filter](http://en.wikipedia.org/wiki/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. Returns:
 automatically generated

getGaborKernel
public static Mat getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma)
Returns Gabor filter coefficients. For more details about gabor filter equations and parameters, see: [Gabor Filter](http://en.wikipedia.org/wiki/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. Returns:
 automatically generated

getStructuringElement
public static Mat getStructuringElement(int shape, Size ksize, Point anchor)
Returns a structuring element of the specified size and shape for morphological operations. The function constructs and returns the structuring element that can be further passed to #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 #MorphShapesksize
 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 crossshaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted. Returns:
 automatically generated

getStructuringElement
public static Mat getStructuringElement(int shape, Size ksize)
Returns a structuring element of the specified size and shape for morphological operations. The function constructs and returns the structuring element that can be further passed to #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 #MorphShapesksize
 Size of the structuring element. anchor is at the center. Note that only the shape of a crossshaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted. Returns:
 automatically generated

medianBlur
public static void medianBlur(Mat src, Mat dst, int ksize)
Blurs an image using the median filter. The function smoothes an image using the median filter with the \(\texttt{ksize} \times \texttt{ksize}\) aperture. Each channel of a multichannel image is processed independently. Inplace operation is supported. Note: The median filter uses #BORDER_REPLICATE internally to cope with border pixels, see #BorderTypes Parameters:
src
 input 1, 3, or 4channel image; when ksize is 3 or 5, the image depth should be CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U.dst
 destination array of the same size and type as src.ksize
 aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ... SEE: bilateralFilter, blur, boxFilter, GaussianBlur

GaussianBlur
public static void GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY, int borderType)
Blurs an image using a Gaussian filter. The function convolves the source image with the specified Gaussian kernel. Inplace 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: sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur

GaussianBlur
public static void GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY)
Blurs an image using a Gaussian filter. The function convolves the source image with the specified Gaussian kernel. Inplace filtering is supported. Parameters:
src
 input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst
 output image of the same size and type as src.ksize
 Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma.sigmaX
 Gaussian kernel standard deviation in X direction.sigmaY
 Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see #getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY. SEE: sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur

GaussianBlur
public static void GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX)
Blurs an image using a Gaussian filter. The function convolves the source image with the specified Gaussian kernel. Inplace 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. equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see #getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY. SEE: sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur

bilateralFilter
public static void bilateralFilter(Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace, int borderType)
Applies the bilateral filter to an image. The function applies bilateral filtering to the input image, as described in http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is very slow compared to most filters. _Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (< 10), the filter will not have much effect, whereas if they are large (> 150), they will have a very strong effect, making the image look "cartoonish". _Filter size_: Large filters (d > 5) are very slow, so it is recommended to use d=5 for realtime applications, and perhaps d=9 for offline applications that need heavy noise filtering. This filter does not work inplace. Parameters:
src
 Source 8bit or floatingpoint, 1channel or 3channel 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 nonpositive, 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 semiequal 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
public static void bilateralFilter(Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace)
Applies the bilateral filter to an image. The function applies bilateral filtering to the input image, as described in http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is very slow compared to most filters. _Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (< 10), the filter will not have much effect, whereas if they are large (> 150), they will have a very strong effect, making the image look "cartoonish". _Filter size_: Large filters (d > 5) are very slow, so it is recommended to use d=5 for realtime applications, and perhaps d=9 for offline applications that need heavy noise filtering. This filter does not work inplace. Parameters:
src
 Source 8bit or floatingpoint, 1channel or 3channel 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 nonpositive, 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 semiequal 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.

boxFilter
public static void boxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor, boolean normalize, int borderType)
Blurs an image using the box filter. The function 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 variablesize 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. SEE: blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxFilter
public static void boxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor, boolean normalize)
Blurs an image using the box filter. The function 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 variablesize 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. SEE: blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxFilter
public static void boxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor)
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 variablesize 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. SEE: blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxFilter
public static void boxFilter(Mat src, Mat dst, int ddepth, Size ksize)
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 variablesize 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. center. SEE: blur, bilateralFilter, GaussianBlur, medianBlur, integral

sqrBoxFilter
public static void sqrBoxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor, boolean normalize, int borderType)
Calculates the normalized sum of squares of the pixel values overlapping the filter. For every pixel \( (x, y) \) in the source image, the function calculates the sum of squares of those neighboring pixel values which overlap the filter placed over the pixel \( (x, y) \). The unnormalized square box filter can be useful in computing local image statistics such as the local variance and standard deviation around the neighborhood of a pixel. Parameters:
src
 input imagedst
 output image of the same size and type as srcddepth
 the output image depth (1 to use src.depth())ksize
 kernel sizeanchor
 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. SEE: boxFilter

sqrBoxFilter
public static void sqrBoxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor, boolean normalize)
Calculates the normalized sum of squares of the pixel values overlapping the filter. For every pixel \( (x, y) \) in the source image, the function calculates the sum of squares of those neighboring pixel values which overlap the filter placed over the pixel \( (x, y) \). The unnormalized square box filter can be useful in computing local image statistics such as the local variance and standard deviation around the neighborhood of a pixel. Parameters:
src
 input imagedst
 output image of the same size and type as srcddepth
 the output image depth (1 to use src.depth())ksize
 kernel sizeanchor
 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. SEE: boxFilter

sqrBoxFilter
public 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. 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 imagedst
 output image of the same size and type as srcddepth
 the output image depth (1 to use src.depth())ksize
 kernel sizeanchor
 kernel anchor point. The default value of Point(1, 1) denotes that the anchor is at the kernel center. SEE: boxFilter

sqrBoxFilter
public static void sqrBoxFilter(Mat src, Mat dst, int ddepth, Size ksize)
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 imagedst
 output image of the same size and type as srcddepth
 the output image depth (1 to use src.depth())ksize
 kernel size center. SEE: boxFilter

blur
public static void blur(Mat src, Mat dst, Size ksize, Point anchor, int borderType)
Blurs an image using the normalized box filter. The function 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 callblur(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. SEE: boxFilter, bilateralFilter, GaussianBlur, medianBlur

blur
public static void blur(Mat src, Mat dst, Size ksize, Point anchor)
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 callblur(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. SEE: boxFilter, bilateralFilter, GaussianBlur, medianBlur

blur
public static void blur(Mat src, Mat dst, Size ksize)
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 callblur(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. center. SEE: boxFilter, bilateralFilter, GaussianBlur, medianBlur

stackBlur
public static void stackBlur(Mat src, Mat dst, Size ksize)
Blurs an image using the stackBlur. The function applies and stackBlur to an image. stackBlur can generate similar results as Gaussian blur, and the time consumption does not increase with the increase of kernel size. It creates a kind of moving stack of colors whilst scanning through the image. Thereby it just has to add one new block of color to the right side of the stack and remove the leftmost color. The remaining colors on the topmost layer of the stack are either added on or reduced by one, depending on if they are on the right or on the left side of the stack. The only supported borderType is BORDER_REPLICATE. Original paper was proposed by Mario Klingemann, which can be found http://underdestruction.com/2004/02/25/stackblur2004. 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
 stackblurring kernel size. The ksize.width and ksize.height can differ but they both must be positive and odd.

filter2D
public static void filter2D(Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta, int borderType)
Convolves an image with the kernel. The function applies an arbitrary linear filter to an image. Inplace 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 DFTbased algorithm in case of sufficiently large kernels (~11 x 11
or larger) and the direct algorithm for small kernels. Parameters:
src
 input image.dst
 output image of the same size and the same number of channels as src.ddepth
 desired depth of the destination image, see REF: filter_depths "combinations"kernel
 convolution kernel (or rather a correlation kernel), a singlechannel floating point matrix; if you want to apply different kernels to different channels, split the image into separate color planes using split and process them individually.anchor
 anchor of the kernel that indicates the relative position of a filtered point within the kernel; the anchor should lie within the kernel; default value (1,1) means that the anchor is at the kernel center.delta
 optional value added to the filtered pixels before storing them in dst.borderType
 pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. SEE: sepFilter2D, dft, matchTemplate

filter2D
public static void filter2D(Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta)
Convolves an image with the kernel. The function applies an arbitrary linear filter to an image. Inplace 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 DFTbased algorithm in case of sufficiently large kernels (~11 x 11
or larger) and the direct algorithm for small kernels. Parameters:
src
 input image.dst
 output image of the same size and the same number of channels as src.ddepth
 desired depth of the destination image, see REF: filter_depths "combinations"kernel
 convolution kernel (or rather a correlation kernel), a singlechannel floating point matrix; if you want to apply different kernels to different channels, split the image into separate color planes using split and process them individually.anchor
 anchor of the kernel that indicates the relative position of a filtered point within the kernel; the anchor should lie within the kernel; default value (1,1) means that the anchor is at the kernel center.delta
 optional value added to the filtered pixels before storing them in dst. SEE: sepFilter2D, dft, matchTemplate

filter2D
public static void filter2D(Mat src, Mat dst, int ddepth, Mat kernel, Point anchor)
Convolves an image with the kernel. The function applies an arbitrary linear filter to an image. Inplace 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 DFTbased algorithm in case of sufficiently large kernels (~11 x 11
or larger) and the direct algorithm for small kernels. Parameters:
src
 input image.dst
 output image of the same size and the same number of channels as src.ddepth
 desired depth of the destination image, see REF: filter_depths "combinations"kernel
 convolution kernel (or rather a correlation kernel), a singlechannel floating point matrix; if you want to apply different kernels to different channels, split the image into separate color planes using split and process them individually.anchor
 anchor of the kernel that indicates the relative position of a filtered point within the kernel; the anchor should lie within the kernel; default value (1,1) means that the anchor is at the kernel center. SEE: sepFilter2D, dft, matchTemplate

filter2D
public static void filter2D(Mat src, Mat dst, int ddepth, Mat kernel)
Convolves an image with the kernel. The function applies an arbitrary linear filter to an image. Inplace 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 DFTbased algorithm in case of sufficiently large kernels (~11 x 11
or larger) and the direct algorithm for small kernels. Parameters:
src
 input image.dst
 output image of the same size and the same number of channels as src.ddepth
 desired depth of the destination image, see REF: filter_depths "combinations"kernel
 convolution kernel (or rather a correlation kernel), a singlechannel floating point matrix; if you want to apply different kernels to different channels, split the image into separate color planes using split and process them individually. the kernel; the anchor should lie within the kernel; default value (1,1) means that the anchor is at the kernel center. SEE: sepFilter2D, dft, matchTemplate

sepFilter2D
public static void sepFilter2D(Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor, double delta, int borderType)
Applies a separable linear filter to an image. The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst . Parameters:
src
 Source image.dst
 Destination image of the same size and the same number of channels as src .ddepth
 Destination image depth, see REF: filter_depths "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. SEE: filter2D, Sobel, GaussianBlur, boxFilter, blur

sepFilter2D
public static void sepFilter2D(Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor, double delta)
Applies a separable linear filter to an image. The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst . Parameters:
src
 Source image.dst
 Destination image of the same size and the same number of channels as src .ddepth
 Destination image depth, see REF: filter_depths "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. SEE: filter2D, Sobel, GaussianBlur, boxFilter, blur

sepFilter2D
public static void sepFilter2D(Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor)
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 REF: filter_depths "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. SEE: filter2D, Sobel, GaussianBlur, boxFilter, blur

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

Sobel
public static void Sobel(Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale, double delta, int borderType)
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) 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 valueksize = #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 xderivative, or transposed for the yderivative. 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 REF: filter_depths "combinations"; in the case of 8bit 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. SEE: Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar

Sobel
public static void Sobel(Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale, double delta)
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) 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 valueksize = #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 xderivative, or transposed for the yderivative. 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 REF: filter_depths "combinations"; in the case of 8bit 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. SEE: Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar

Sobel
public 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. 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 valueksize = #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 xderivative, or transposed for the yderivative. 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 REF: filter_depths "combinations"; in the case of 8bit 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). SEE: Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar

Sobel
public 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. 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 valueksize = #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 xderivative, or transposed for the yderivative. 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 REF: filter_depths "combinations"; in the case of 8bit 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. applied (see #getDerivKernels for details). SEE: Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar

Sobel
public static void Sobel(Mat src, Mat dst, int ddepth, int dx, int dy)
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) 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 valueksize = #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 xderivative, or transposed for the yderivative. 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 REF: filter_depths "combinations"; in the case of 8bit input images it will result in truncated derivatives.dx
 order of the derivative x.dy
 order of the derivative y. applied (see #getDerivKernels for details). SEE: Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar

spatialGradient
public 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 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 firstorder derivative in x.dy
 output image with firstorder 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: Sobel

spatialGradient
public 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 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 firstorder derivative in x.dy
 output image with firstorder derivative in y.ksize
 size of Sobel kernel. It must be 3. Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported. SEE: Sobel

spatialGradient
public static void spatialGradient(Mat src, Mat dx, Mat dy)
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 firstorder derivative in x.dy
 output image with firstorder derivative in y. Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported. SEE: Sobel

Scharr
public static void Scharr(Mat src, Mat dst, int ddepth, int dx, int dy, double scale, double delta, int borderType)
Calculates the first x or y image derivative using Scharr operator. The function computes the first x or y spatial image derivative using the Scharr operator. The call \(\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\) 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 REF: filter_depths "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. SEE: cartToPolar

Scharr
public static void Scharr(Mat src, Mat dst, int ddepth, int dx, int dy, double scale, double delta)
Calculates the first x or y image derivative using Scharr operator. The function computes the first x or y spatial image derivative using the Scharr operator. The call \(\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\) 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 REF: filter_depths "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. SEE: cartToPolar

Scharr
public 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. 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 REF: filter_depths "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). SEE: cartToPolar

Scharr
public static void Scharr(Mat src, Mat dst, int ddepth, int dx, int dy)
Calculates the first x or y image derivative using Scharr operator. The function computes the first x or y spatial image derivative using the Scharr operator. The call \(\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\) 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 REF: filter_depths "combinations"dx
 order of the derivative x.dy
 order of the derivative y. applied (see #getDerivKernels for details). SEE: cartToPolar

Laplacian
public static void Laplacian(Mat src, Mat dst, int ddepth, int ksize, double scale, double delta, int borderType)
Calculates the Laplacian of an image. The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator: \(\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\) This is done whenksize > 1
. Whenksize == 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 REF: filter_depths "combinations".ksize
 Aperture size used to compute the secondderivative 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. SEE: Sobel, Scharr

Laplacian
public static void Laplacian(Mat src, Mat dst, int ddepth, int ksize, double scale, double delta)
Calculates the Laplacian of an image. The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator: \(\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\) This is done whenksize > 1
. Whenksize == 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 REF: filter_depths "combinations".ksize
 Aperture size used to compute the secondderivative filters. See #getDerivKernels for details. The size must be positive and odd.scale
 Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See #getDerivKernels for details.delta
 Optional delta value that is added to the results prior to storing them in dst . SEE: Sobel, Scharr

Laplacian
public static void Laplacian(Mat src, Mat dst, int ddepth, int ksize, double scale)
Calculates the Laplacian of an image. The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator: \(\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\) This is done whenksize > 1
. Whenksize == 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 REF: filter_depths "combinations".ksize
 Aperture size used to compute the secondderivative 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. SEE: Sobel, Scharr

Laplacian
public static void Laplacian(Mat src, Mat dst, int ddepth, int ksize)
Calculates the Laplacian of an image. The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator: \(\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\) This is done whenksize > 1
. Whenksize == 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 REF: filter_depths "combinations".ksize
 Aperture size used to compute the secondderivative filters. See #getDerivKernels for details. The size must be positive and odd. applied. See #getDerivKernels for details. SEE: Sobel, Scharr

Laplacian
public static void Laplacian(Mat src, Mat dst, int ddepth)
Calculates the Laplacian of an image. The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator: \(\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\) This is done whenksize > 1
. Whenksize == 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 REF: filter_depths "combinations". details. The size must be positive and odd. applied. See #getDerivKernels for details. SEE: Sobel, Scharr

Canny
public static void Canny(Mat image, Mat edges, double threshold1, double threshold2, int apertureSize, boolean L2gradient)
Finds edges in an image using the Canny algorithm CITE: Canny86 . The function finds edges in the input image and marks them in the output map edges using the Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The largest value is used to find initial segments of strong edges. See <http://en.wikipedia.org/wiki/Canny_edge_detector> Parameters:
image
 8bit input image.edges
 output edge map; single channels 8bit 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
public static void Canny(Mat image, Mat edges, double threshold1, double threshold2, int apertureSize)
Finds edges in an image using the Canny algorithm CITE: Canny86 . The function finds edges in the input image and marks them in the output map edges using the Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The largest value is used to find initial segments of strong edges. See <http://en.wikipedia.org/wiki/Canny_edge_detector> Parameters:
image
 8bit input image.edges
 output edge map; single channels 8bit 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. \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) 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
public static void Canny(Mat image, Mat edges, double threshold1, double threshold2)
Finds edges in an image using the Canny algorithm CITE: Canny86 . The function finds edges in the input image and marks them in the output map edges using the Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The largest value is used to find initial segments of strong edges. See <http://en.wikipedia.org/wiki/Canny_edge_detector> Parameters:
image
 8bit input image.edges
 output edge map; single channels 8bit image, which has the same size as image .threshold1
 first threshold for the hysteresis procedure.threshold2
 second threshold for the hysteresis procedure. \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) should be used to calculate the image gradient magnitude ( L2gradient=true ), or whether the default \(L_1\) norm \(=dI/dx+dI/dy\) is enough ( L2gradient=false ).

Canny
public static void Canny(Mat dx, Mat dy, Mat edges, double threshold1, double threshold2, boolean L2gradient)
\overload Finds edges in an image using the Canny algorithm with custom image gradient. Parameters:
dx
 16bit x derivative of input image (CV_16SC1 or CV_16SC3).dy
 16bit y derivative of input image (same type as dx).edges
 output edge map; single channels 8bit 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
public static void Canny(Mat dx, Mat dy, Mat edges, double threshold1, double threshold2)
\overload Finds edges in an image using the Canny algorithm with custom image gradient. Parameters:
dx
 16bit x derivative of input image (CV_16SC1 or CV_16SC3).dy
 16bit y derivative of input image (same type as dx).edges
 output edge map; single channels 8bit image, which has the same size as image .threshold1
 first threshold for the hysteresis procedure.threshold2
 second threshold for the hysteresis procedure. \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) should be used to calculate the image gradient magnitude ( L2gradient=true ), or whether the default \(L_1\) norm \(=dI/dx+dI/dy\) is enough ( L2gradient=false ).

cornerMinEigenVal
public static void cornerMinEigenVal(Mat src, Mat dst, int blockSize, int ksize, int borderType)
Calculates the minimal eigenvalue of gradient matrices for corner detection. The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is, \(\min(\lambda_1, \lambda_2)\) in terms of the formulae in the cornerEigenValsAndVecs description. Parameters:
src
 Input singlechannel 8bit or floatingpoint 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
public static void cornerMinEigenVal(Mat src, Mat dst, int blockSize, int ksize)
Calculates the minimal eigenvalue of gradient matrices for corner detection. The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is, \(\min(\lambda_1, \lambda_2)\) in terms of the formulae in the cornerEigenValsAndVecs description. Parameters:
src
 Input singlechannel 8bit or floatingpoint 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.

cornerMinEigenVal
public static void cornerMinEigenVal(Mat src, Mat dst, int blockSize)
Calculates the minimal eigenvalue of gradient matrices for corner detection. The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is, \(\min(\lambda_1, \lambda_2)\) in terms of the formulae in the cornerEigenValsAndVecs description. Parameters:
src
 Input singlechannel 8bit or floatingpoint 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 ).

cornerHarris
public static void cornerHarris(Mat src, Mat dst, int blockSize, int ksize, double k, int borderType)
Harris corner detector. The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and cornerEigenValsAndVecs , for each pixel \((x, y)\) it calculates a \(2\times2\) gradient covariance matrix \(M^{(x,y)}\) over a \(\texttt{blockSize} \times \texttt{blockSize}\) neighborhood. Then, it computes the following characteristic: \(\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)}  k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\) Corners in the image can be found as the local maxima of this response map. Parameters:
src
 Input singlechannel 8bit or floatingpoint 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
public static void cornerHarris(Mat src, Mat dst, int blockSize, int ksize, double k)
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 singlechannel 8bit or floatingpoint 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.

cornerEigenValsAndVecs
public static void cornerEigenValsAndVecs(Mat src, Mat dst, int blockSize, int ksize, int borderType)
Calculates eigenvalues and eigenvectors of image blocks for corner detection. For every pixel \(p\) , the function cornerEigenValsAndVecs considers a blockSize \(\times\) 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 nonsorted eigenvalues of \(M\)
 \(x_1, y_1\) are the eigenvectors corresponding to \(\lambda_1\)
 \(x_2, y_2\) are the eigenvectors corresponding to \(\lambda_2\)
 Parameters:
src
 Input singlechannel 8bit or floatingpoint 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: cornerMinEigenVal, cornerHarris, preCornerDetect

cornerEigenValsAndVecs
public static void cornerEigenValsAndVecs(Mat src, Mat dst, int blockSize, int ksize)
Calculates eigenvalues and eigenvectors of image blocks for corner detection. For every pixel \(p\) , the function cornerEigenValsAndVecs considers a blockSize \(\times\) 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 nonsorted eigenvalues of \(M\)
 \(x_1, y_1\) are the eigenvectors corresponding to \(\lambda_1\)
 \(x_2, y_2\) are the eigenvectors corresponding to \(\lambda_2\)
 Parameters:
src
 Input singlechannel 8bit or floatingpoint image.dst
 Image to store the results. It has the same size as src and the type CV_32FC(6) .blockSize
 Neighborhood size (see details below).ksize
 Aperture parameter for the Sobel operator. SEE: cornerMinEigenVal, cornerHarris, preCornerDetect

preCornerDetect
public static void preCornerDetect(Mat src, Mat dst, int ksize, int borderType)
Calculates a feature map for corner detection. The function calculates the complex spatial derivativebased 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 singlechannel 8bit of floatingpoint 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
public static void preCornerDetect(Mat src, Mat dst, int ksize)
Calculates a feature map for corner detection. The function calculates the complex spatial derivativebased 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 singlechannel 8bit of floatingpoint image.dst
 Output image that has the type CV_32F and the same size as src .ksize
 %Aperture size of the Sobel .

cornerSubPix
public static void cornerSubPix(Mat image, Mat corners, Size winSize, Size zeroZone, TermCriteria criteria)
Refines the corner locations. The function iterates to find the subpixel accurate location of corners or radial saddle points as described in CITE: forstner1987fast, and as shown on the figure below. ![image](pics/cornersubpix.png) Subpixel 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 singlechannel, 8bit 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.

goodFeaturesToTrack
public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, boolean useHarrisDetector, double k)
Determines strong corners on an image. The function finds the most prominent corners in the image or in the specified image region, as described in CITE: Shi94 Function calculates the corner quality measure at every source image pixel using the #cornerMinEigenVal or #cornerHarris .
 Function performs a nonmaximum 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.
 Parameters:
image
 Input 8bit or floatingpoint 32bit, singlechannel 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: cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

goodFeaturesToTrack
public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, boolean useHarrisDetector)
Determines strong corners on an image. The function finds the most prominent corners in the image or in the specified image region, as described in CITE: Shi94 Function calculates the corner quality measure at every source image pixel using the #cornerMinEigenVal or #cornerHarris .
 Function performs a nonmaximum 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.
 Parameters:
image
 Input 8bit or floatingpoint 32bit, singlechannel 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. SEE: cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

goodFeaturesToTrack
public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize)
Determines strong corners on an image. The function finds the most prominent corners in the image or in the specified image region, as described in CITE: Shi94 Function calculates the corner quality measure at every source image pixel using the #cornerMinEigenVal or #cornerHarris .
 Function performs a nonmaximum 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.
 Parameters:
image
 Input 8bit or floatingpoint 32bit, singlechannel 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 . or #cornerMinEigenVal. SEE: cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

goodFeaturesToTrack
public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask)
Determines strong corners on an image. The function finds the most prominent corners in the image or in the specified image region, as described in CITE: Shi94 Function calculates the corner quality measure at every source image pixel using the #cornerMinEigenVal or #cornerHarris .
 Function performs a nonmaximum 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.
 Parameters:
image
 Input 8bit or floatingpoint 32bit, singlechannel 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. pixel neighborhood. See cornerEigenValsAndVecs . or #cornerMinEigenVal. SEE: cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

goodFeaturesToTrack
public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance)
Determines strong corners on an image. The function finds the most prominent corners in the image or in the specified image region, as described in CITE: Shi94 Function calculates the corner quality measure at every source image pixel using the #cornerMinEigenVal or #cornerHarris .
 Function performs a nonmaximum 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.
 Parameters:
image
 Input 8bit or floatingpoint 32bit, singlechannel 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. CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. pixel neighborhood. See cornerEigenValsAndVecs . or #cornerMinEigenVal. SEE: cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

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

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

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

goodFeaturesToTrackWithQuality
public static void goodFeaturesToTrackWithQuality(Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize, boolean useHarrisDetector, double k)
Same as above, but returns also quality measure of the detected corners. Parameters:
image
 Input 8bit or floatingpoint 32bit, singlechannel 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
public static void goodFeaturesToTrackWithQuality(Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize, boolean useHarrisDetector)
Same as above, but returns also quality measure of the detected corners. Parameters:
image
 Input 8bit or floatingpoint 32bit, singlechannel 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.

goodFeaturesToTrackWithQuality
public 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. Parameters:
image
 Input 8bit or floatingpoint 32bit, singlechannel 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 . or #cornerMinEigenVal.

goodFeaturesToTrackWithQuality
public 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. Parameters:
image
 Input 8bit or floatingpoint 32bit, singlechannel 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 . See cornerEigenValsAndVecs . or #cornerMinEigenVal.

goodFeaturesToTrackWithQuality
public 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. Parameters:
image
 Input 8bit or floatingpoint 32bit, singlechannel 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. pixel neighborhood. See cornerEigenValsAndVecs . See cornerEigenValsAndVecs . or #cornerMinEigenVal.

HoughLines
public 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. The function implements the standard or standard multiscale Hough transform algorithm for line detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough transform. Parameters:
image
 8bit, singlechannel 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 \((0,0)\) (topleft 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 multiscale 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 multiscale Hough transform, it is a divisor for the distance resolution theta.min_theta
 For standard and multiscale Hough transform, minimum angle to check for lines. Must fall between 0 and max_theta.max_theta
 For standard and multiscale 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
public 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. The function implements the standard or standard multiscale Hough transform algorithm for line detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough transform. Parameters:
image
 8bit, singlechannel 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 \((0,0)\) (topleft 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 multiscale 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 multiscale Hough transform, it is a divisor for the distance resolution theta.min_theta
 For standard and multiscale Hough transform, minimum angle to check for lines. Must fall between 0 and max_theta. Must fall between min_theta and CV_PI. The actual maximum angle in the accumulator may be slightly less than max_theta, depending on the parameters min_theta and theta.

HoughLines
public static void HoughLines(Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn)
Finds lines in a binary image using the standard Hough transform. The function implements the standard or standard multiscale Hough transform algorithm for line detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough transform. Parameters:
image
 8bit, singlechannel 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 \((0,0)\) (topleft 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 multiscale 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 multiscale Hough transform, it is a divisor for the distance resolution theta. Must fall between 0 and max_theta. Must fall between min_theta and CV_PI. The actual maximum angle in the accumulator may be slightly less than max_theta, depending on the parameters min_theta and theta.

HoughLines
public 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. The function implements the standard or standard multiscale Hough transform algorithm for line detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough transform. Parameters:
image
 8bit, singlechannel 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 \((0,0)\) (topleft 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 multiscale Hough transform, it is a divisor for the distance resolution rho. The coarse accumulator distance resolution is rho and the accurate accumulator resolution is rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these parameters should be positive. Must fall between 0 and max_theta. Must fall between min_theta and CV_PI. The actual maximum angle in the accumulator may be slightly less than max_theta, depending on the parameters min_theta and theta.

HoughLines
public static void HoughLines(Mat image, Mat lines, double rho, double theta, int threshold)
Finds lines in a binary image using the standard Hough transform. The function implements the standard or standard multiscale Hough transform algorithm for line detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough transform. Parameters:
image
 8bit, singlechannel 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 \((0,0)\) (topleft 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 ( \(>\texttt{threshold}\) ). The coarse accumulator distance resolution is rho and the accurate accumulator resolution is rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these parameters should be positive. Must fall between 0 and max_theta. Must fall between min_theta and CV_PI. The actual maximum angle in the accumulator may be slightly less than max_theta, depending on the parameters min_theta and theta.

HoughLinesP
public static void HoughLinesP(Mat image, Mat lines, double rho, double theta, int threshold, double minLineLength, double maxLineGap)
Finds line segments in a binary image using the probabilistic Hough transform. The function implements the probabilistic Hough transform algorithm for line detection, described in CITE: Matas00 See the line detection example below: INCLUDE: snippets/imgproc_HoughLinesP.cpp This is a sample picture the function parameters have been tuned for: ![image](pics/building.jpg) And this is the output of the above program in case of the probabilistic Hough transform: ![image](pics/houghp.png) Parameters:
image
 8bit, singlechannel binary source image. The image may be modified by the function.lines
 Output vector of lines. Each line is represented by a 4element 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: LineSegmentDetector

HoughLinesP
public 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. The function implements the probabilistic Hough transform algorithm for line detection, described in CITE: Matas00 See the line detection example below: INCLUDE: snippets/imgproc_HoughLinesP.cpp This is a sample picture the function parameters have been tuned for: ![image](pics/building.jpg) And this is the output of the above program in case of the probabilistic Hough transform: ![image](pics/houghp.png) Parameters:
image
 8bit, singlechannel binary source image. The image may be modified by the function.lines
 Output vector of lines. Each line is represented by a 4element 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. SEE: LineSegmentDetector

HoughLinesP
public static void HoughLinesP(Mat image, Mat lines, double rho, double theta, int threshold)
Finds line segments in a binary image using the probabilistic Hough transform. The function implements the probabilistic Hough transform algorithm for line detection, described in CITE: Matas00 See the line detection example below: INCLUDE: snippets/imgproc_HoughLinesP.cpp This is a sample picture the function parameters have been tuned for: ![image](pics/building.jpg) And this is the output of the above program in case of the probabilistic Hough transform: ![image](pics/houghp.png) Parameters:
image
 8bit, singlechannel binary source image. The image may be modified by the function.lines
 Output vector of lines. Each line is represented by a 4element 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 ( \(>\texttt{threshold}\) ). SEE: LineSegmentDetector

HoughLinesPointSet
public static void HoughLinesPointSet(Mat point, Mat lines, int lines_max, int threshold, double min_rho, double max_rho, double rho_step, double min_theta, double max_theta, double theta_step)
Finds lines in a set of points using the standard Hough transform. The function finds lines in a set of points using a modification of the Hough transform. INCLUDE: snippets/imgproc_HoughLinesPointSet.cpp 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.

HoughCircles
public static void HoughCircles(Mat image, Mat circles, int method, double dp, double minDist, double param1, double param2, int minRadius, int maxRadius)
Finds circles in a grayscale image using the Hough transform. The function finds circles in a grayscale image using a modification of the Hough transform. Example: : INCLUDE: snippets/imgproc_HoughLinesCircles.cpp 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
 8bit, singlechannel, grayscale input image.circles
 Output vector of found circles. Each vector is encoded as 3 or 4 element floatingpoint 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 methodspecific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT, it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value shough normally be higher, such as 300 or normally exposed and contrasty images.param2
 Second methodspecific 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: fitEllipse, minEnclosingCircle

HoughCircles
public 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. The function finds circles in a grayscale image using a modification of the Hough transform. Example: : INCLUDE: snippets/imgproc_HoughLinesCircles.cpp 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
 8bit, singlechannel, grayscale input image.circles
 Output vector of found circles. Each vector is encoded as 3 or 4 element floatingpoint 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 methodspecific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT, it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value shough normally be higher, such as 300 or normally exposed and contrasty images.param2
 Second methodspecific 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. centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses. SEE: fitEllipse, minEnclosingCircle

HoughCircles
public 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. The function finds circles in a grayscale image using a modification of the Hough transform. Example: : INCLUDE: snippets/imgproc_HoughLinesCircles.cpp 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
 8bit, singlechannel, grayscale input image.circles
 Output vector of found circles. Each vector is encoded as 3 or 4 element floatingpoint 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 methodspecific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT, it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value shough normally be higher, such as 300 or normally exposed and contrasty images.param2
 Second methodspecific parameter. In case of #HOUGH_GRADIENT, it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses. SEE: fitEllipse, minEnclosingCircle

HoughCircles
public 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. The function finds circles in a grayscale image using a modification of the Hough transform. Example: : INCLUDE: snippets/imgproc_HoughLinesCircles.cpp 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
 8bit, singlechannel, grayscale input image.circles
 Output vector of found circles. Each vector is encoded as 3 or 4 element floatingpoint 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 methodspecific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT, it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value shough normally be higher, such as 300 or normally exposed and contrasty images. accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses. SEE: fitEllipse, minEnclosingCircle

HoughCircles
public static void HoughCircles(Mat image, Mat circles, int method, double dp, double minDist)
Finds circles in a grayscale image using the Hough transform. The function finds circles in a grayscale image using a modification of the Hough transform. Example: : INCLUDE: snippets/imgproc_HoughLinesCircles.cpp 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
 8bit, singlechannel, grayscale input image.circles
 Output vector of found circles. Each vector is encoded as 3 or 4 element floatingpoint 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. it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value shough normally be higher, such as 300 or normally exposed and contrasty images. accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses. SEE: fitEllipse, minEnclosingCircle

erode
public static void erode(Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue)
Erodes an image by using a specific structuring element. The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken: \(\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\) The function supports the inplace mode. Erosion can be applied several ( iterations ) times. In case of multichannel 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; ifelement=Mat()
, a3 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 SEE: dilate, morphologyEx, getStructuringElement

erode
public static void erode(Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType)
Erodes an image by using a specific structuring element. The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken: \(\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\) The function supports the inplace mode. Erosion can be applied several ( iterations ) times. In case of multichannel 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; ifelement=Mat()
, a3 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. SEE: dilate, morphologyEx, getStructuringElement

erode
public static void erode(Mat src, Mat dst, Mat kernel, Point anchor, int iterations)
Erodes an image by using a specific structuring element. The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken: \(\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\) The function supports the inplace mode. Erosion can be applied several ( iterations ) times. In case of multichannel 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; ifelement=Mat()
, a3 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. SEE: dilate, morphologyEx, getStructuringElement

erode
public static void erode(Mat src, Mat dst, Mat kernel, Point anchor)
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 inplace mode. Erosion can be applied several ( iterations ) times. In case of multichannel 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; ifelement=Mat()
, a3 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. SEE: dilate, morphologyEx, getStructuringElement

erode
public static void erode(Mat src, Mat dst, Mat kernel)
Erodes an image by using a specific structuring element. The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken: \(\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\) The function supports the inplace mode. Erosion can be applied several ( iterations ) times. In case of multichannel 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; ifelement=Mat()
, a3 x 3
rectangular structuring element is used. Kernel can be created using #getStructuringElement. anchor is at the element center. SEE: dilate, morphologyEx, getStructuringElement

dilate
public static void dilate(Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue)
Dilates an image by using a specific structuring element. The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken: \(\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\) The function supports the inplace mode. Dilation can be applied several ( iterations ) times. In case of multichannel 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 #getStructuringElementanchor
 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 SEE: erode, morphologyEx, getStructuringElement

dilate
public static void dilate(Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType)
Dilates an image by using a specific structuring element. The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken: \(\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\) The function supports the inplace mode. Dilation can be applied several ( iterations ) times. In case of multichannel 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 #getStructuringElementanchor
 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. SEE: erode, morphologyEx, getStructuringElement

dilate
public static void dilate(Mat src, Mat dst, Mat kernel, Point anchor, int iterations)
Dilates an image by using a specific structuring element. The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken: \(\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\) The function supports the inplace mode. Dilation can be applied several ( iterations ) times. In case of multichannel 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 #getStructuringElementanchor
 position of the anchor within the element; default value (1, 1) means that the anchor is at the element center.iterations
 number of times dilation is applied. SEE: erode, morphologyEx, getStructuringElement

dilate
public static void dilate(Mat src, Mat dst, Mat kernel, Point anchor)
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 inplace mode. Dilation can be applied several ( iterations ) times. In case of multichannel 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 #getStructuringElementanchor
 position of the anchor within the element; default value (1, 1) means that the anchor is at the element center. SEE: erode, morphologyEx, getStructuringElement

dilate
public static void dilate(Mat src, Mat dst, Mat kernel)
Dilates an image by using a specific structuring element. The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken: \(\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\) The function supports the inplace mode. Dilation can be applied several ( iterations ) times. In case of multichannel 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 is at the element center. SEE: erode, morphologyEx, getStructuringElement

morphologyEx
public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue)
Performs advanced morphological transformations. The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as basic operations. Any of the operations can be done inplace. In case of multichannel 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 #MorphTypeskernel
 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. SEE: 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
public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType)
Performs advanced morphological transformations. The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as basic operations. Any of the operations can be done inplace. In case of multichannel 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 #MorphTypeskernel
 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. meaning. SEE: 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
public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations)
Performs advanced morphological transformations. The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as basic operations. Any of the operations can be done inplace. In case of multichannel 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 #MorphTypeskernel
 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. meaning. SEE: 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
public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor)
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 inplace. In case of multichannel 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 #MorphTypeskernel
 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. meaning. SEE: 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
public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel)
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 inplace. In case of multichannel 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 #MorphTypeskernel
 Structuring element. It can be created using #getStructuringElement. kernel center. meaning. SEE: 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).

resize
public static void resize(Mat src, Mat dst, Size dsize, double fx, double fy, int interpolation)
Resizes an image. The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from thesrc
,dsize
,fx
, andfy
. If you want to resize src so that it fits the precreated 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 nonzero) 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 nonzero.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: warpAffine, warpPerspective, remap

resize
public static void resize(Mat src, Mat dst, Size dsize, double fx, double fy)
Resizes an image. The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from thesrc
,dsize
,fx
, andfy
. If you want to resize src so that it fits the precreated 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 nonzero) 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 nonzero.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 \(\texttt{(double)dsize.height/src.rows}\) SEE: warpAffine, warpPerspective, remap

resize
public static void resize(Mat src, Mat dst, Size dsize, double fx)
Resizes an image. The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from thesrc
,dsize
,fx
, andfy
. If you want to resize src so that it fits the precreated 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 nonzero) 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 nonzero.fx
 scale factor along the horizontal axis; when it equals 0, it is computed as \(\texttt{(double)dsize.width/src.cols}\) \(\texttt{(double)dsize.height/src.rows}\) SEE: warpAffine, warpPerspective, remap

resize
public static void resize(Mat src, Mat dst, Size dsize)
Resizes an image. The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from thesrc
,dsize
,fx
, andfy
. If you want to resize src so that it fits the precreated 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 nonzero) 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 nonzero. \(\texttt{(double)dsize.width/src.cols}\) \(\texttt{(double)dsize.height/src.rows}\) SEE: warpAffine, warpPerspective, remap

warpAffine
public static void warpAffine(Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue)
Applies an affine transformation to an image. The function warpAffine transforms the source image using the specified matrix: \(\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\) 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 inplace. 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: warpPerspective, resize, remap, getRectSubPix, transform

warpAffine
public static void warpAffine(Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode)
Applies an affine transformation to an image. The function warpAffine transforms the source image using the specified matrix: \(\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\) when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with #invertAffineTransform and then put in the formula above instead of M. The function cannot operate inplace. 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. SEE: warpPerspective, resize, remap, getRectSubPix, transform

warpAffine
public static void warpAffine(Mat src, Mat dst, Mat M, Size dsize, int flags)
Applies an affine transformation to an image. The function warpAffine transforms the source image using the specified matrix: \(\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\) 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 inplace. 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=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to the "outliers" in the source image are not modified by the function. SEE: warpPerspective, resize, remap, getRectSubPix, transform

warpAffine
public static void warpAffine(Mat src, Mat dst, Mat M, Size dsize)
Applies an affine transformation to an image. The function warpAffine transforms the source image using the specified matrix: \(\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\) 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 inplace. 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. flag #WARP_INVERSE_MAP that means that M is the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ). borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to the "outliers" in the source image are not modified by the function. SEE: warpPerspective, resize, remap, getRectSubPix, transform

warpPerspective
public static void warpPerspective(Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue)
Applies a perspective transformation to an image. The function warpPerspective transforms the source image using the specified matrix: \(\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\) 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 inplace. 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: warpAffine, resize, remap, getRectSubPix, perspectiveTransform

warpPerspective
public static void warpPerspective(Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode)
Applies a perspective transformation to an image. The function warpPerspective transforms the source image using the specified matrix: \(\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\) when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert and then put in the formula above instead of M. The function cannot operate inplace. 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). SEE: warpAffine, resize, remap, getRectSubPix, perspectiveTransform

warpPerspective
public static void warpPerspective(Mat src, Mat dst, Mat M, Size dsize, int flags)
Applies a perspective transformation to an image. The function warpPerspective transforms the source image using the specified matrix: \(\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\) 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 inplace. 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 ( \(\texttt{dst}\rightarrow\texttt{src}\) ). SEE: warpAffine, resize, remap, getRectSubPix, perspectiveTransform

warpPerspective
public static void warpPerspective(Mat src, Mat dst, Mat M, Size dsize)
Applies a perspective transformation to an image. The function warpPerspective transforms the source image using the specified matrix: \(\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\) 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 inplace. 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. optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ). SEE: warpAffine, resize, remap, getRectSubPix, perspectiveTransform

remap
public static void remap(Mat src, Mat dst, Mat map1, Mat map2, int interpolation, int borderMode, Scalar borderValue)
Applies a generic geometrical transformation to an image. The function remap transforms the source image using the specified map: \(\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\) where values of pixels with noninteger coordinates are computed using one of available interpolation methods. \(map_x\) and \(map_y\) can be encoded as separate floatingpoint maps in \(map_1\) and \(map_2\) respectively, or interleaved floatingpoint maps of \((x,y)\) in \(map_1\), or fixedpoint maps created by using #convertMaps. The reason you might want to convert from floating to fixedpoint 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 inplace. 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 fixedpoint 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 and #INTER_LINEAR_EXACT are not supported by this function.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. Note: Due to current implementation limitations the size of an input and output images should be less than 32767x32767.

remap
public static void remap(Mat src, Mat dst, Mat map1, Mat map2, int interpolation, int borderMode)
Applies a generic geometrical transformation to an image. The function remap transforms the source image using the specified map: \(\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\) where values of pixels with noninteger coordinates are computed using one of available interpolation methods. \(map_x\) and \(map_y\) can be encoded as separate floatingpoint maps in \(map_1\) and \(map_2\) respectively, or interleaved floatingpoint maps of \((x,y)\) in \(map_1\), or fixedpoint maps created by using #convertMaps. The reason you might want to convert from floating to fixedpoint 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 inplace. 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 fixedpoint 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 and #INTER_LINEAR_EXACT are not supported by this function.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. Note: Due to current implementation limitations the size of an input and output images should be less than 32767x32767.

remap
public static void remap(Mat src, Mat dst, Mat map1, Mat map2, int interpolation)
Applies a generic geometrical transformation to an image. The function remap transforms the source image using the specified map: \(\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\) where values of pixels with noninteger coordinates are computed using one of available interpolation methods. \(map_x\) and \(map_y\) can be encoded as separate floatingpoint maps in \(map_1\) and \(map_2\) respectively, or interleaved floatingpoint maps of \((x,y)\) in \(map_1\), or fixedpoint maps created by using #convertMaps. The reason you might want to convert from floating to fixedpoint 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 inplace. 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 fixedpoint 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 and #INTER_LINEAR_EXACT are not supported by this function. borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that corresponds to the "outliers" in the source image are not modified by the function. Note: Due to current implementation limitations the size of an input and output images should be less than 32767x32767.

convertMaps
public static void convertMaps(Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type, boolean nninterpolation)
Converts image transformation maps from one representation to another. The function converts a pair of maps for remap from one representation to another. The following options ( (map1.type(), map2.type()) \(\rightarrow\) (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 floatingpoint maps (see #remap) are converted to a more compact and much faster fixedpoint 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 2channel matrix.
 Reverse conversion. Obviously, the reconstructed floatingpoint 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 fixedpoint maps are used for the nearestneighbor or for a more complex interpolation. SEE: remap, undistort, initUndistortRectifyMap

convertMaps
public static void convertMaps(Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type)
Converts image transformation maps from one representation to another. The function converts a pair of maps for remap from one representation to another. The following options ( (map1.type(), map2.type()) \(\rightarrow\) (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 floatingpoint maps (see #remap) are converted to a more compact and much faster fixedpoint 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 2channel matrix.
 Reverse conversion. Obviously, the reconstructed floatingpoint 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 . nearestneighbor or for a more complex interpolation. SEE: remap, undistort, initUndistortRectifyMap

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

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

getPerspectiveTransform
public static Mat getPerspectiveTransform(Mat src, Mat dst, int solveMethod)
Calculates a perspective transform from four pairs of the corresponding points. The function calculates the \(3 \times 3\) matrix of a perspective transform so that: \(\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\) where \(dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\) 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: findHomography, warpPerspective, perspectiveTransform Returns:
 automatically generated

getPerspectiveTransform
public static Mat getPerspectiveTransform(Mat src, Mat dst)
Calculates a perspective transform from four pairs of the corresponding points. The function calculates the \(3 \times 3\) 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. SEE: findHomography, warpPerspective, perspectiveTransform Returns:
 automatically generated

getAffineTransform
public static Mat getAffineTransform(MatOfPoint2f src, MatOfPoint2f dst)

getRectSubPix
public static void getRectSubPix(Mat image, Size patchSize, Point center, Mat patch, int patchType)
Retrieves a pixel rectangle from an image with subpixel 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 noninteger coordinates are retrieved using bilinear interpolation. Every channel of multichannel 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: warpAffine, warpPerspective

getRectSubPix
public static void getRectSubPix(Mat image, Size patchSize, Point center, Mat patch)
Retrieves a pixel rectangle from an image with subpixel 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 noninteger coordinates are retrieved using bilinear interpolation. Every channel of multichannel 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 . SEE: warpAffine, warpPerspective

logPolar
@Deprecated public static void logPolar(Mat src, Mat dst, Point center, double M, int flags)
Deprecated.This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG); Transform the source image using the following transformation (See REF: polar_remaps_reference_image "Polar remaps reference image d)"): \(\begin{array}{l} dst( \rho , \phi ) = src(x,y) \\ dst.size() \leftarrow src.size() \end{array}\) where \(\begin{array}{l} I = (dx,dy) = (x  center.x,y  center.y) \\ \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\ \phi = Kangle \cdot \texttt{angle} (I) \\ \end{array}\) and \(\begin{array}{l} M = src.cols / log_e(maxRadius) \\ Kangle = src.rows / 2\Pi \\ \end{array}\) The function emulates the human "foveal" vision and can be used for fast scale and rotationinvariant template matching, for object tracking and so forth.Remaps an image to semilogpolar coordinates space. Parameters:
src
 Source imagedst
 Destination image. It will have same size and type as src.center
 The transformation center; where the output precision is maximalM
 Magnitude scale parameter. It determines the radius of the bounding circle to transform too.flags
 A combination of interpolation methods, see #InterpolationFlags Note: The function can not operate inplace.
 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.

linearPolar
@Deprecated public static void linearPolar(Mat src, Mat dst, Point center, double maxRadius, int flags)
Deprecated.This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags) Transform the source image using the following transformation (See REF: polar_remaps_reference_image "Polar remaps reference image c)"): \(\begin{array}{l} dst( \rho , \phi ) = src(x,y) \\ dst.size() \leftarrow src.size() \end{array}\) where \(\begin{array}{l} I = (dx,dy) = (x  center.x,y  center.y) \\ \rho = Kmag \cdot \texttt{magnitude} (I) ,\\ \phi = angle \cdot \texttt{angle} (I) \end{array}\) and \(\begin{array}{l} Kx = src.cols / maxRadius \\ Ky = src.rows / 2\Pi \end{array}\)Remaps an image to polar coordinates space. Parameters:
src
 Source imagedst
 Destination image. It will have same size and type as src.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, see #InterpolationFlags Note: The function can not operate inplace.
 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.

warpPolar
public static void warpPolar(Mat src, Mat dst, Size dsize, Point center, double maxRadius, int flags)
Remaps an image to polar or semilogpolar coordinates space polar_remaps_reference_image ![Polar remaps reference](pics/polar_remap_doc.png) Transform the source image using the following transformation: \( dst(\rho , \phi ) = src(x,y) \) where \( \begin{array}{l} \vec{I} = (x  center.x, \;y  center.y) \\ \phi = Kangle \cdot \texttt{angle} (\vec{I}) \\ \rho = \left\{\begin{matrix} Klin \cdot \texttt{magnitude} (\vec{I}) & default \\ Klog \cdot log_e(\texttt{magnitude} (\vec{I})) & if \; semilog \\ \end{matrix}\right. \end{array} \) and \( \begin{array}{l} Kangle = dsize.height / 2\Pi \\ Klin = dsize.width / maxRadius \\ Klog = dsize.width / log_e(maxRadius) \\ \end{array} \) \par Linear vs semilog mapping Polar mapping can be linear or semilog. Add one of #WarpPolarMode toflags
to specify the polar mapping mode. Linear is the default mode. The semilog mapping emulates the human "foveal" vision that permit very high acuity on the line of sight (central vision) in contrast to peripheral vision where acuity is minor. \par Option ondsize
:
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 byKx * 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 todsize
.
flags
\snippet polar_transforms.cpp InverseMap In addiction, to calculate the original coordinate from a polar mapped coordinate \((rho, phi)>(x, y)\): \snippet polar_transforms.cpp InverseCoordinate 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 inplace.
 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.

if both values in

integral3
public static void integral3(Mat src, Mat sum, Mat sqsum, Mat tilted, int sdepth, int sqdepth)
Calculates the integral of an image. The function calculates one or more integral images for the source image as follows: \(\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\) \(\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\) \(\texttt{tilted} (X,Y) = \sum _{y<Y,abs(xX+1) \leq Yy1} \texttt{image} (x,y)\) Using these integral images, you can calculate sum, mean, and standard deviation over a specific upright 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 multichannel images, sums for each channel are accumulated independently. As a practical example, the next figure shows the calculation of the integral of a straight rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the original image are shown, as well as the relative pixels in the integral images sum and tilted . ![integral calculation example](pics/integral.png) Parameters:
src
 input image as \(W \times H\), 8bit or floatingpoint (32f or 64f).sum
 integral image as \((W+1)\times (H+1)\) , 32bit integer or floatingpoint (32f or 64f).sqsum
 integral image for squared pixel values; it is \((W+1)\times (H+1)\), doubleprecision floatingpoint (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
public static void integral3(Mat src, Mat sum, Mat sqsum, Mat tilted, int sdepth)
Calculates the integral of an image. The function calculates one or more integral images for the source image as follows: \(\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\) \(\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\) \(\texttt{tilted} (X,Y) = \sum _{y<Y,abs(xX+1) \leq Yy1} \texttt{image} (x,y)\) Using these integral images, you can calculate sum, mean, and standard deviation over a specific upright 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 multichannel images, sums for each channel are accumulated independently. As a practical example, the next figure shows the calculation of the integral of a straight rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the original image are shown, as well as the relative pixels in the integral images sum and tilted . ![integral calculation example](pics/integral.png) Parameters:
src
 input image as \(W \times H\), 8bit or floatingpoint (32f or 64f).sum
 integral image as \((W+1)\times (H+1)\) , 32bit integer or floatingpoint (32f or 64f).sqsum
 integral image for squared pixel values; it is \((W+1)\times (H+1)\), doubleprecision floatingpoint (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.

integral3
public static void integral3(Mat src, Mat sum, Mat sqsum, Mat tilted)
Calculates the integral of an image. The function calculates one or more integral images for the source image as follows: \(\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\) \(\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\) \(\texttt{tilted} (X,Y) = \sum _{y<Y,abs(xX+1) \leq Yy1} \texttt{image} (x,y)\) Using these integral images, you can calculate sum, mean, and standard deviation over a specific upright 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 multichannel images, sums for each channel are accumulated independently. As a practical example, the next figure shows the calculation of the integral of a straight rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the original image are shown, as well as the relative pixels in the integral images sum and tilted . ![integral calculation example](pics/integral.png) Parameters:
src
 input image as \(W \times H\), 8bit or floatingpoint (32f or 64f).sum
 integral image as \((W+1)\times (H+1)\) , 32bit integer or floatingpoint (32f or 64f).sqsum
 integral image for squared pixel values; it is \((W+1)\times (H+1)\), doubleprecision floatingpoint (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. CV_64F.

accumulate
public static void accumulate(Mat src, Mat dst, Mat mask)
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 multichannel 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 foregroundbackground 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: accumulateSquare, accumulateProduct, accumulateWeighted

accumulate
public static void accumulate(Mat src, Mat dst)
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 multichannel 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 foregroundbackground 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. SEE: accumulateSquare, accumulateProduct, accumulateWeighted

accumulateSquare
public static void accumulateSquare(Mat src, Mat dst, Mat mask)
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 multichannel images. Each channel is processed independently. Parameters:
src
 Input image as 1 or 3channel, 8bit or 32bit floating point.dst
 %Accumulator image with the same number of channels as input image, 32bit or 64bit floatingpoint.mask
 Optional operation mask. SEE: accumulateSquare, accumulateProduct, accumulateWeighted

accumulateSquare
public static void accumulateSquare(Mat src, Mat dst)
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 multichannel images. Each channel is processed independently. Parameters:
src
 Input image as 1 or 3channel, 8bit or 32bit floating point.dst
 %Accumulator image with the same number of channels as input image, 32bit or 64bit floatingpoint. SEE: accumulateSquare, accumulateProduct, accumulateWeighted

accumulateProduct
public static void accumulateProduct(Mat src1, Mat src2, Mat dst, Mat mask)
Adds the perelement 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 multichannel images. Each channel is processed independently. Parameters:
src1
 First input image, 1 or 3channel, 8bit or 32bit 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, 32bit or 64bit floatingpoint.mask
 Optional operation mask. SEE: accumulate, accumulateSquare, accumulateWeighted

accumulateProduct
public static void accumulateProduct(Mat src1, Mat src2, Mat dst)
Adds the perelement 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 multichannel images. Each channel is processed independently. Parameters:
src1
 First input image, 1 or 3channel, 8bit or 32bit 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, 32bit or 64bit floatingpoint. SEE: accumulate, accumulateSquare, accumulateWeighted

accumulateWeighted
public static void accumulateWeighted(Mat src, Mat dst, double alpha, Mat mask)
Updates a running average. The function calculates the weighted sum of the input image src and the accumulator dst so that dst becomes a running average of a frame sequence: \(\texttt{dst} (x,y) \leftarrow (1 \texttt{alpha} ) \cdot \texttt{dst} (x,y) + \texttt{alpha} \cdot \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\) That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images). The function supports multichannel images. Each channel is processed independently. Parameters:
src
 Input image as 1 or 3channel, 8bit or 32bit floating point.dst
 %Accumulator image with the same number of channels as input image, 32bit or 64bit floatingpoint.alpha
 Weight of the input image.mask
 Optional operation mask. SEE: accumulate, accumulateSquare, accumulateProduct

accumulateWeighted
public static void accumulateWeighted(Mat src, Mat dst, double alpha)
Updates a running average. The function calculates the weighted sum of the input image src and the accumulator dst so that dst becomes a running average of a frame sequence: \(\texttt{dst} (x,y) \leftarrow (1 \texttt{alpha} ) \cdot \texttt{dst} (x,y) + \texttt{alpha} \cdot \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\) That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images). The function supports multichannel images. Each channel is processed independently. Parameters:
src
 Input image as 1 or 3channel, 8bit or 32bit floating point.dst
 %Accumulator image with the same number of channels as input image, 32bit or 64bit floatingpoint.alpha
 Weight of the input image. SEE: accumulate, accumulateSquare, accumulateProduct

phaseCorrelate
public static Point phaseCorrelate(Mat src1, Mat src2, Mat window, double[] response)
The function is used to detect translational shifts that occur between two images. The operation takes advantage of the Fourier shift theorem for detecting the translational shift in the frequency domain. It can be used for fast image registration as well as motion estimation. For more information please see <http://en.wikipedia.org/wiki/Phase_correlation> Calculates the crosspower spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize. The function performs the following equations: First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function>) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
 Next it computes the forward DFTs of each source array: \(\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\) where \(\mathcal{F}\) is the forward DFT.
 It then computes the crosspower spectrum of each frequency domain array: \(R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{\mathbf{G}_a \mathbf{G}_b^*}\)
 Next the crosscorrelation 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 subpixel accuracy. \((\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\)
 If nonzero, 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 (subpixel) between the two arrays. SEE: dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow

phaseCorrelate
public static Point phaseCorrelate(Mat src1, Mat src2, Mat window)
The function is used to detect translational shifts that occur between two images. The operation takes advantage of the Fourier shift theorem for detecting the translational shift in the frequency domain. It can be used for fast image registration as well as motion estimation. For more information please see <http://en.wikipedia.org/wiki/Phase_correlation> Calculates the crosspower spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize. The function performs the following equations: First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function>) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
 Next it computes the forward DFTs of each source array: \(\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\) where \(\mathcal{F}\) is the forward DFT.
 It then computes the crosspower spectrum of each frequency domain array: \(R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{\mathbf{G}_a \mathbf{G}_b^*}\)
 Next the crosscorrelation 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 subpixel accuracy. \((\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\)
 If nonzero, 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). Returns:
 detected phase shift (subpixel) between the two arrays. SEE: dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow

phaseCorrelate
public static Point phaseCorrelate(Mat src1, Mat src2)
The function is used to detect translational shifts that occur between two images. The operation takes advantage of the Fourier shift theorem for detecting the translational shift in the frequency domain. It can be used for fast image registration as well as motion estimation. For more information please see <http://en.wikipedia.org/wiki/Phase_correlation> Calculates the crosspower spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize. The function performs the following equations: First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function>) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
 Next it computes the forward DFTs of each source array: \(\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\) where \(\mathcal{F}\) is the forward DFT.
 It then computes the crosspower spectrum of each frequency domain array: \(R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{\mathbf{G}_a \mathbf{G}_b^*}\)
 Next the crosscorrelation 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 subpixel accuracy. \((\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\)
 If nonzero, 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) Returns:
 detected phase shift (subpixel) between the two arrays. SEE: dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow

createHanningWindow
public static void createHanningWindow(Mat dst, Size winSize, int type)
This function computes a Hanning window coefficients in two dimensions. See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function) for more information. An example is shown below:// 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 inwinSize
 The window size specifications (both width and height must be > 1)type
 Created array type

divSpectrums
public static void divSpectrums(Mat a, Mat b, Mat c, int flags, boolean conjB)
Performs the perelement division of the first Fourier spectrum by the second Fourier spectrum. The function cv::divSpectrums performs the perelement division of the first array by the second array. The arrays are CCSpacked 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 a0
as value.conjB
 optional flag that conjugates the second input array before the multiplication (true) or not (false).

divSpectrums
public static void divSpectrums(Mat a, Mat b, Mat c, int flags)
Performs the perelement division of the first Fourier spectrum by the second Fourier spectrum. The function cv::divSpectrums performs the perelement division of the first array by the second array. The arrays are CCSpacked 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 a0
as value. or not (false).

threshold
public static double threshold(Mat src, Mat dst, double thresh, double maxval, int type)
Applies a fixedlevel threshold to each array element. The function applies fixedlevel thresholding to a multiplechannel array. The function is typically used to get a bilevel (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 8bit singlechannel images. Parameters:
src
 input array (multiplechannel, 8bit or 32bit 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: adaptiveThreshold, findContours, compare, min, max

adaptiveThreshold
public static void adaptiveThreshold(Mat src, Mat dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C)
Applies an adaptive threshold to an array. The function transforms a grayscale image to a binary image according to the formulae: THRESH_BINARY \(dst(x,y) = \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).
 Parameters:
src
 Source 8bit singlechannel image.dst
 Destination image of the same size and the same type as src.maxValue
 Nonzero value assigned to the pixels for which the condition is satisfiedadaptiveMethod
 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: threshold, blur, GaussianBlur

pyrDown
public static void pyrDown(Mat src, Mat dst, Size dstsize, int borderType)
Blurs an image and downsamples it. By default, size of the output image is computed asSize((src.cols+1)/2, (src.rows+1)/2)
, but in any case, the following conditions should be satisfied: \(\begin{array}{l}  \texttt{dstsize.width} *2src.cols \leq 2 \\  \texttt{dstsize.height} *2src.rows \leq 2 \end{array}\) 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
public static void pyrDown(Mat src, Mat dst, Size dstsize)
Blurs an image and downsamples it. By default, size of the output image is computed asSize((src.cols+1)/2, (src.rows+1)/2)
, but in any case, the following conditions should be satisfied: \(\begin{array}{l}  \texttt{dstsize.width} *2src.cols \leq 2 \\  \texttt{dstsize.height} *2src.rows \leq 2 \end{array}\) 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.

pyrDown
public static void pyrDown(Mat src, Mat dst)
Blurs an image and downsamples it. By default, size of the output image is computed asSize((src.cols+1)/2, (src.rows+1)/2)
, but in any case, the following conditions should be satisfied: \(\begin{array}{l}  \texttt{dstsize.width} *2src.cols \leq 2 \\  \texttt{dstsize.height} *2src.rows \leq 2 \end{array}\) 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.

pyrUp
public static void pyrUp(Mat src, Mat dst, Size dstsize, int borderType)
Upsamples an image and then blurs it. By default, size of the output image is computed asSize(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
public static void pyrUp(Mat src, Mat dst, Size dstsize)
Upsamples an image and then blurs it. By default, size of the output image is computed asSize(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.

pyrUp
public static void pyrUp(Mat src, Mat dst)
Upsamples an image and then blurs it. By default, size of the output image is computed asSize(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 .

calcHist
public static void calcHist(java.util.List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges, boolean accumulate)
this variant supports only uniform histograms. ranges argument is either empty vector or a flattened vector of histSize.size()*2 elements (histSize.size() element pairs). The first and second elements of each pair specify the lower and upper boundaries. Parameters:
images
 automatically generatedchannels
 automatically generatedmask
 automatically generatedhist
 automatically generatedhistSize
 automatically generatedranges
 automatically generatedaccumulate
 automatically generated

calcHist
public static void calcHist(java.util.List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges)
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. Parameters:
images
 automatically generatedchannels
 automatically generatedmask
 automatically generatedhist
 automatically generatedhistSize
 automatically generatedranges
 automatically generated

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

compareHist
public static double compareHist(Mat H1, Mat H2, int method)
Compares two histograms. The function cv::compareHist compares two dense or two sparse histograms using the specified method. The function returns \(d(H_1, H_2)\) . While the function works well with 1, 2, 3dimensional dense histograms, it may not be suitable for highdimensional sparse histograms. In such histograms, because of aliasing and sampling problems, the coordinates of nonzero 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 Returns:
 automatically generated

equalizeHist
public static void equalizeHist(Mat src, Mat dst)
Equalizes the histogram of a grayscale image. The function equalizes the histogram of the input image using the following algorithm: Calculate the histogram \(H\) for src .
 Normalize the histogram so that the sum of histogram bins is 255.
 Compute the integral of the histogram: \(H'_i = \sum _{0 \le j < i} H(j)\)
 Transform the image using \(H'\) as a lookup table: \(\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\)
 Parameters:
src
 Source 8bit single channel image.dst
 Destination image of the same size and type as src .

createCLAHE
public static CLAHE createCLAHE(double clipLimit, Size tileGridSize)
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. Returns:
 automatically generated

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

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

EMD
public static float EMD(Mat signature1, Mat signature2, int distType, Mat cost, Mat flow)
Computes the "minimal work" distance between two weighted point configurations. The function computes the earth mover distance and/or a lower boundary of the distance between the two weighted point configurations. One of the applications described in CITE: RubnerSept98, CITE: Rubner2000 is multidimensional 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 lineartime 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 \(\texttt{size1}\times \texttt{dims}+1\) floatingpoint 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 userdefined cost matrix is used. The weights must be nonnegative and have at least one nonzero 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 nonnegative and have at least one nonzero value.distType
 Used metric. See #DistanceTypes.cost
 Userdefined \(\texttt{size1}\times \texttt{size2}\) cost matrix. Also, if a cost matrix is used, lower boundary lowerBound cannot be calculated because it needs a metric function. signatures that is a distance between mass centers. The lower boundary may not be calculated if the userdefined 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 . Returns:
 automatically generated

EMD
public static float EMD(Mat signature1, Mat signature2, int distType, Mat cost)
Computes the "minimal work" distance between two weighted point configurations. The function computes the earth mover distance and/or a lower boundary of the distance between the two weighted point configurations. One of the applications described in CITE: RubnerSept98, CITE: Rubner2000 is multidimensional 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 lineartime 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 \(\texttt{size1}\times \texttt{dims}+1\) floatingpoint 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 userdefined cost matrix is used. The weights must be nonnegative and have at least one nonzero 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 nonnegative and have at least one nonzero value.distType
 Used metric. See #DistanceTypes.cost
 Userdefined \(\texttt{size1}\times \texttt{size2}\) cost matrix. Also, if a cost matrix is used, lower boundary lowerBound cannot be calculated because it needs a metric function. signatures that is a distance between mass centers. The lower boundary may not be calculated if the userdefined 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. a flow from \(i\) th point of signature1 to \(j\) th point of signature2 . Returns:
 automatically generated

EMD
public static float EMD(Mat signature1, Mat signature2, int distType)
Computes the "minimal work" distance between two weighted point configurations. The function computes the earth mover distance and/or a lower boundary of the distance between the two weighted point configurations. One of the applications described in CITE: RubnerSept98, CITE: Rubner2000 is multidimensional 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 lineartime 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 \(\texttt{size1}\times \texttt{dims}+1\) floatingpoint 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 userdefined cost matrix is used. The weights must be nonnegative and have at least one nonzero 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 nonnegative and have at least one nonzero value.distType
 Used metric. See #DistanceTypes. is used, lower boundary lowerBound cannot be calculated because it needs a metric function. signatures that is a distance between mass centers. The lower boundary may not be calculated if the userdefined 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. a flow from \(i\) th point of signature1 to \(j\) th point of signature2 . Returns:
 automatically generated

watershed
public static void watershed(Mat image, Mat markers)
Performs a markerbased image segmentation using the watershed algorithm. The function implements one of the variants of watershed, nonparametric markerbased segmentation algorithm, described in CITE: Meyer92 . Before passing the image to the function, you have to roughly outline the desired regions in the image markers with positive (>0) indices. So, every region is represented as one or more connected components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary mask using #findContours and #drawContours (see the watershed.cpp demo). The markers are "seeds" of the future image regions. All the other pixels in markers , whose relation to the outlined regions is not known and should be defined by the algorithm, should be set to 0's. In the function output, each pixel in markers is set to a value of the "seed" components or to 1 at boundaries between the regions. Note: Any two neighbor connected components are not necessarily separated by a watershed boundary (1's pixels); for example, they can touch each other in the initial marker image passed to the function. Parameters:
image
 Input 8bit 3channel image.markers
 Input/output 32bit singlechannel image (map) of markers. It should have the same size as image . SEE: findContours

pyrMeanShiftFiltering
public static void pyrMeanShiftFiltering(Mat src, Mat dst, double sp, double sr, int maxLevel, TermCriteria termcrit)
Performs initial step of meanshift segmentation of an image. The function implements the filtering stage of meanshift segmentation, that is, the output of the function is the filtered "posterized" image with color gradients and finegrain texture flattened. At every pixel (X,Y) of the input image (or downsized input image, see below) the function executes meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint spacecolor 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 3component 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 lowerresolution 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 8bit, 3channel 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
public static void pyrMeanShiftFiltering(Mat src, Mat dst, double sp, double sr, int maxLevel)
Performs initial step of meanshift segmentation of an image. The function implements the filtering stage of meanshift segmentation, that is, the output of the function is the filtered "posterized" image with color gradients and finegrain texture flattened. At every pixel (X,Y) of the input image (or downsized input image, see below) the function executes meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint spacecolor 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 3component 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 lowerresolution 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 8bit, 3channel 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.

pyrMeanShiftFiltering
public static void pyrMeanShiftFiltering(Mat src, Mat dst, double sp, double sr)
Performs initial step of meanshift segmentation of an image. The function implements the filtering stage of meanshift segmentation, that is, the output of the function is the filtered "posterized" image with color gradients and finegrain texture flattened. At every pixel (X,Y) of the input image (or downsized input image, see below) the function executes meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint spacecolor 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 3component 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 lowerresolution 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 8bit, 3channel 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.

grabCut
public static void grabCut(Mat img, Mat mask, Rect rect, Mat bgdModel, Mat fgdModel, int iterCount, int mode)
Runs the GrabCut algorithm. The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut). Parameters:
img
 Input 8bit 3channel image.mask
 Input/output 8bit singlechannel 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
public static void grabCut(Mat img, Mat mask, Rect rect, Mat bgdModel, Mat fgdModel, int iterCount)
Runs the GrabCut algorithm. The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut). Parameters:
img
 Input 8bit 3channel image.mask
 Input/output 8bit singlechannel 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 .

distanceTransformWithLabels
public static void distanceTransformWithLabels(Mat src, Mat dst, Mat labels, int distanceType, int maskSize, int labelType)
Calculates the distance to the closest zero pixel for each pixel of the source image. The function cv::distanceTransform calculates the approximate or precise distance from every binary image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the algorithm described in CITE: Felzenszwalb04 . This algorithm is parallelized with the TBB library. In other cases, the algorithm CITE: Borgefors86 is used. This means that for a pixel the function finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, diagonal, or knight's move (the latest is available for a \(5\times 5\) mask). The overall distance is calculated as a sum of these basic distances. Since the distance function should be symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all the diagonal shifts must have the same cost (denoted asb
), and all knight's moves must have the same cost (denoted asc
). 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). Fora
,b
, andc
, 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
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
 8bit, singlechannel (binary) source image.dst
 Output image with calculated distances. It is a 8bit or 32bit floatingpoint, singlechannel 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 #DistanceTypesmaskSize
 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.

DIST_L1:

distanceTransformWithLabels
public static void distanceTransformWithLabels(Mat src, Mat dst, Mat labels, int distanceType, int maskSize)
Calculates the distance to the closest zero pixel for each pixel of the source image. The function cv::distanceTransform calculates the approximate or precise distance from every binary image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the algorithm described in CITE: Felzenszwalb04 . This algorithm is parallelized with the TBB library. In other cases, the algorithm CITE: Borgefors86 is used. This means that for a pixel the function finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, diagonal, or knight's move (the latest is available for a \(5\times 5\) mask). The overall distance is calculated as a sum of these basic distances. Since the distance function should be symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all the diagonal shifts must have the same cost (denoted asb
), and all knight's moves must have the same cost (denoted asc
). 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). Fora
,b
, andc
, 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
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
 8bit, singlechannel (binary) source image.dst
 Output image with calculated distances. It is a 8bit or 32bit floatingpoint, singlechannel 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 #DistanceTypesmaskSize
 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.

DIST_L1:

distanceTransform
public static void distanceTransform(Mat src, Mat dst, int distanceType, int maskSize, int dstType)
 Parameters:
src
 8bit, singlechannel (binary) source image.dst
 Output image with calculated distances. It is a 8bit or 32bit floatingpoint, singlechannel image of the same size as src .distanceType
 Type of distance, see #DistanceTypesmaskSize
 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
public static void distanceTransform(Mat src, Mat dst, int distanceType, int maskSize)
 Parameters:
src
 8bit, singlechannel (binary) source image.dst
 Output image with calculated distances. It is a 8bit or 32bit floatingpoint, singlechannel image of the same size as src .distanceType
 Type of distance, see #DistanceTypesmaskSize
 Size of the distance transform mask, see #DistanceTransformMasks. In case of the #DIST_L1 or #DIST_C distance type, the parameter is forced to 3 because a \(3\times 3\) mask gives the same result as \(5\times 5\) or any larger aperture. the first variant of the function and distanceType == #DIST_L1.

floodFill
public static int floodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect, Scalar loDiff, Scalar upDiff, int flags)
Fills a connected component with the given color. The 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 \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\)
 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.
 Parameters:
image
 Input/output 1 or 3channel, 8bit, or floatingpoint 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 singlechannel 8bit 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. Floodfilling cannot go across nonzero 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 (816) 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 bitwise 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: findContours Returns:
 automatically generated

floodFill
public 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. 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 \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\)
 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.
 Parameters:
image
 Input/output 1 or 3channel, 8bit, or floatingpoint 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 singlechannel 8bit 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. Floodfilling cannot go across nonzero 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. 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 (816) 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 bitwise 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: findContours Returns:
 automatically generated

floodFill
public static int floodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect, Scalar loDiff)
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 \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\)
 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.
 Parameters:
image
 Input/output 1 or 3channel, 8bit, or floatingpoint 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 singlechannel 8bit 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. Floodfilling cannot go across nonzero 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. 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. 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 (816) 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 bitwise 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: findContours Returns:
 automatically generated

floodFill
public static int floodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect)
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 \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\)
 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.
 Parameters:
image
 Input/output 1 or 3channel, 8bit, or floatingpoint 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 singlechannel 8bit 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. Floodfilling cannot go across nonzero 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. one of its neighbors belonging to the component, or a seed pixel being added to the component. one of its neighbors belonging to the component, or a seed pixel being added to the component.rect
 Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain. 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) will be considered. The next 8 bits (816) 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 bitwise 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: findContours Returns:
 automatically generated

floodFill
public static int floodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal)
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 \(\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\)
 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.
 Parameters:
image
 Input/output 1 or 3channel, 8bit, or floatingpoint 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 singlechannel 8bit 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. Floodfilling cannot go across nonzero 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. one of its neighbors belonging to the component, or a seed pixel being added to the component. one of its neighbors belonging to the component, or a seed pixel being added to the component. repainted domain. 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) will be considered. The next 8 bits (816) 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 bitwise 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: findContours Returns:
 automatically generated

blendLinear
public static void blendLinear(Mat src1, Mat src2, Mat weights1, Mat weights2, Mat dst)
variant withoutmask
parameter Parameters:
src1
 automatically generatedsrc2
 automatically generatedweights1
 automatically generatedweights2
 automatically generateddst
 automatically generated

cvtColor
public static void cvtColor(Mat src, Mat dst, int code, int dstCn)
Converts an image from one color space to another. The function converts an input image from one color space to another. In case of a transformation tofrom 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 (24bit) color image will be an 8bit 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
img *= 1./255; cvtColor(img, img, COLOR_BGR2Luv);
If you use #cvtColor with 8bit images, the conversion will have some information lost. For many applications, this will not be noticeable but it is recommended to use 32bit 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: 8bit unsigned, 16bit unsigned ( CV_16UC... ), or singleprecision floatingpoint.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: REF: imgproc_color_conversions

cvtColor
public static void cvtColor(Mat src, Mat dst, int code)
Converts an image from one color space to another. The function converts an input image from one color space to another. In case of a transformation tofrom 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 (24bit) color image will be an 8bit 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
img *= 1./255; cvtColor(img, img, COLOR_BGR2Luv);
If you use #cvtColor with 8bit images, the conversion will have some information lost. For many applications, this will not be noticeable but it is recommended to use 32bit 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: 8bit unsigned, 16bit unsigned ( CV_16UC... ), or singleprecision floatingpoint.dst
 output image of the same size and depth as src.code
 color space conversion code (see #ColorConversionCodes). channels is derived automatically from src and code. SEE: REF: imgproc_color_conversions

cvtColorTwoPlane
public 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. This function only supports YUV420 to RGB conversion as of now. #COLOR_YUV2BGR_NV12
 #COLOR_YUV2RGB_NV12
 #COLOR_YUV2BGRA_NV12
 #COLOR_YUV2RGBA_NV12
 #COLOR_YUV2BGR_NV21
 #COLOR_YUV2RGB_NV21
 #COLOR_YUV2BGRA_NV21
 #COLOR_YUV2RGBA_NV21
 Parameters:
src1
 automatically generatedsrc2
 automatically generateddst
 automatically generatedcode
 automatically generated

demosaicing
public static void demosaicing(Mat src, Mat dst, int code, int dstCn)
main function for all demosaicing processes Parameters:
src
 input image: 8bit unsigned or 16bit 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
 Demosaicing using Variable Number of Gradients.
 EdgeAware Demosaicing.
 Demosaicing with alpha channel

demosaicing
public static void demosaicing(Mat src, Mat dst, int code)
main function for all demosaicing processes Parameters:
src
 input image: 8bit unsigned or 16bit unsigned.dst
 output image of the same size and depth as src.code
 Color space conversion code (see the description below). channels is derived automatically from src and code. The function can do the following transformations: Demosaicing using bilinear interpolation
 Demosaicing using Variable Number of Gradients.
 EdgeAware Demosaicing.
 Demosaicing with alpha channel

moments
public static Moments moments(Mat array, boolean binaryImage)
Calculates all of the moments up to the third order of a polygon or rasterized shape. The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The results are returned in the structure cv::Moments. Parameters:
array
 Raster image (singlechannel, 8bit or floatingpoint 2D array) or an array ( \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f ).binaryImage
 If it is true, all nonzero 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: contourArea, arcLength

moments
public static Moments moments(Mat array)
Calculates all of the moments up to the third order of a polygon or rasterized shape. The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The results are returned in the structure cv::Moments. Parameters:
array
 Raster image (singlechannel, 8bit or floatingpoint 2D array) or an array ( \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f ). 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: contourArea, arcLength

matchTemplate
public static void matchTemplate(Mat image, Mat templ, Mat result, int method, Mat mask)
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: \(x' = 0...w1, y' = 0...h1\) 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 singlechannel image, which is easier to analyze. Parameters:
image
 Image where the search is running. It must be 8bit or 32bit floatingpoint.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 singlechannel 32bit floatingpoint. If image is \(W \times H\) and templ is \(w \times h\) , then result is \((Ww+1) \times (Hh+1)\) .method
 Parameter specifying the comparison method, see #TemplateMatchModesmask
 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
public static void matchTemplate(Mat image, Mat templ, Mat result, int method)
Compares a template against overlapped image regions. The function slides through image , compares the overlapped patches of size \(w \times h\) 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: \(x' = 0...w1, y' = 0...h1\) 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 singlechannel image, which is easier to analyze. Parameters:
image
 Image where the search is running. It must be 8bit or 32bit floatingpoint.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 singlechannel 32bit floatingpoint. If image is \(W \times H\) and templ is \(w \times h\) , then result is \((Ww+1) \times (Hh+1)\) .method
 Parameter specifying the comparison method, see #TemplateMatchModes of channels as template or only one channel, which is then used for all template and image channels. If the data type is #CV_8U, the mask is interpreted as a binary mask, meaning only elements where mask is nonzero are used and are kept unchanged independent of the actual mask value (weight equals 1). For data tpye #CV_32F, the mask values are used as weights. The exact formulas are documented in #TemplateMatchModes.

connectedComponentsWithAlgorithm
public static int connectedComponentsWithAlgorithm(Mat image, Mat labels, int connectivity, int ltype, int ccltype)
computes the connected components labeled image of boolean image image with 4 or 8 way connectivity  returns N, the total number of labels [0, N1] where 0 represents the background label. ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently Bolelli (Spaghetti) CITE: Bolelli2019, Grana (BBDT) CITE: Grana2010 and Wu's (SAUF) CITE: Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces a row major ordering of labels while Spaghetti and BBDT do not. This function uses parallel version of the algorithms if at least one allowed parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. Parameters:
image
 the 8bit singlechannel image to be labeledlabels
 destination labeled imageconnectivity
 8 or 4 for 8way or 4way connectivity respectivelyltype
 output image label type. Currently CV_32S and CV_16U are supported.ccltype
 connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes). Returns:
 automatically generated

connectedComponents
public static int connectedComponents(Mat image, Mat labels, int connectivity, int ltype)
 Parameters:
image
 the 8bit singlechannel image to be labeledlabels
 destination labeled imageconnectivity
 8 or 4 for 8way or 4way connectivity respectivelyltype
 output image label type. Currently CV_32S and CV_16U are supported. Returns:
 automatically generated

connectedComponents
public static int connectedComponents(Mat image, Mat labels, int connectivity)
 Parameters:
image
 the 8bit singlechannel image to be labeledlabels
 destination labeled imageconnectivity
 8 or 4 for 8way or 4way connectivity respectively Returns:
 automatically generated

connectedComponents
public static int connectedComponents(Mat image, Mat labels)
 Parameters:
image
 the 8bit singlechannel image to be labeledlabels
 destination labeled image Returns:
 automatically generated

connectedComponentsWithStatsWithAlgorithm
public 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 image with 4 or 8 way connectivity  returns N, the total number of labels [0, N1] where 0 represents the background label. ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently Bolelli (Spaghetti) CITE: Bolelli2019, Grana (BBDT) CITE: Grana2010 and Wu's (SAUF) CITE: Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces a row major ordering of labels while Spaghetti and BBDT do not. This function uses parallel version of the algorithms (statistics included) if at least one allowed parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. Parameters:
image
 the 8bit singlechannel image to be labeledlabels
 destination labeled imagestats
 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 8way or 4way connectivity respectivelyltype
 output image label type. Currently CV_32S and CV_16U are supported.ccltype
 connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes). Returns:
 automatically generated

connectedComponentsWithStats
public static int connectedComponentsWithStats(Mat image, Mat labels, Mat stats, Mat centroids, int connectivity, int ltype)
 Parameters:
image
 the 8bit singlechannel image to be labeledlabels
 destination labeled imagestats
 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 8way or 4way connectivity respectivelyltype
 output image label type. Currently CV_32S and CV_16U are supported. Returns:
 automatically generated

connectedComponentsWithStats
public static int connectedComponentsWithStats(Mat image, Mat labels, Mat stats, Mat centroids, int connectivity)
 Parameters:
image
 the 8bit singlechannel image to be labeledlabels
 destination labeled imagestats
 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 8way or 4way connectivity respectively Returns:
 automatically generated

connectedComponentsWithStats
public static int connectedComponentsWithStats(Mat image, Mat labels, Mat stats, Mat centroids)
 Parameters:
image
 the 8bit singlechannel image to be labeledlabels
 destination labeled imagestats
 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. Returns:
 automatically generated

findContours
public static void findContours(Mat image, java.util.List<MatOfPoint> contours, Mat hierarchy, int mode, int method, Point offset)
Finds contours in a binary image. The function retrieves contours from the binary image using the algorithm CITE: 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 8bit singlechannel image. Nonzero 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 32bit 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 ith contour contours[i], the elements hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0based 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. Note: In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of ith contour.mode
 Contour retrieval mode, see #RetrievalModesmethod
 Contour approximation method, see #ContourApproximationModesoffset
 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
public static void findContours(Mat image, java.util.List<MatOfPoint> contours, Mat hierarchy, int mode, int method)
Finds contours in a binary image. The function retrieves contours from the binary image using the algorithm CITE: 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 8bit singlechannel image. Nonzero 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 32bit 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 ith contour contours[i], the elements hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0based 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. Note: In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of ith contour.mode
 Contour retrieval mode, see #RetrievalModesmethod
 Contour approximation method, see #ContourApproximationModes contours are extracted from the image ROI and then they should be analyzed in the whole image context.

approxPolyDP
public static void approxPolyDP(MatOfPoint2f curve, MatOfPoint2f approxCurve, double epsilon, boolean closed)
Approximates a polygonal curve(s) with the specified precision. The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less vertices so that the distance between them is less or equal to the specified precision. It uses the DouglasPeucker algorithm <http://en.wikipedia.org/wiki/RamerDouglasPeucker_algorithm> Parameters:
curve
 Input vector of a 2D point stored in std::vector or MatapproxCurve
 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
public static double arcLength(MatOfPoint2f curve, boolean closed)
Calculates a contour perimeter or a curve length. The function computes a curve length or a closed contour perimeter. Parameters:
curve
 Input vector of 2D points, stored in std::vector or Mat.closed
 Flag indicating whether the curve is closed or not. Returns:
 automatically generated

boundingRect
public static Rect boundingRect(Mat array)
Calculates the upright bounding rectangle of a point set or nonzero pixels of grayscale image. The function calculates and returns the minimal upright bounding rectangle for the specified point set or nonzero pixels of grayscale image. Parameters:
array
 Input grayscale image or 2D point set, stored in std::vector or Mat. Returns:
 automatically generated

contourArea
public static double contourArea(Mat contour, boolean oriented)
Calculates a contour area. The function computes a contour area. Similarly to moments , the area is computed using the Green formula. Thus, the returned area and the number of nonzero 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 selfintersections. 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 counterclockwise). 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. Returns:
 automatically generated

contourArea
public static double contourArea(Mat contour)
Calculates a contour area. The function computes a contour area. Similarly to moments , the area is computed using the Green formula. Thus, the returned area and the number of nonzero 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 selfintersections. 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. depending on the contour orientation (clockwise or counterclockwise). 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. Returns:
 automatically generated

minAreaRect
public static RotatedRect minAreaRect(MatOfPoint2f points)
Finds a rotated rectangle of the minimum area enclosing the input 2D point set. The function calculates and returns the minimumarea 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 Returns:
 automatically generated

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

minEnclosingCircle
public static void minEnclosingCircle(MatOfPoint2f points, Point center, float[] radius)
Finds a circle of the minimum area enclosing a 2D point set. The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm. Parameters:
points
 Input vector of 2D points, stored in std::vector<> or Matcenter
 Output center of the circle.radius
 Output radius of the circle.

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

matchShapes
public static double matchShapes(Mat contour1, Mat contour2, int method, double parameter)
Compares two shapes. The function compares two shapes. All three implemented methods use the Hu invariants (see #HuMoments) Parameters:
contour1
 First contour or grayscale image.contour2
 Second contour or grayscale image.method
 Comparison method, see #ShapeMatchModesparameter
 Methodspecific parameter (not supported now). Returns:
 automatically generated

convexHull
public static void convexHull(MatOfPoint points, MatOfInt hull, boolean clockwise)
Finds the convex hull of a point set. The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm CITE: Sklansky82 that has *O(N logN)* complexity in the current implementation. 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 0based 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 counterclockwise. The assumed coordinate system has its X axis pointing to the right, and its Y axis pointing upwards. returns convex hull points. Otherwise, it returns indices of the convex hull points. When the output array is std::vector, the flag is ignored, and the output depends on the type of the vector: std::vector<int> implies returnPoints=false, std::vector<Point> implies returnPoints=true. Note:points
andhull
should be different arrays, inplace processing isn't supported. Check REF: tutorial_hull "the corresponding tutorial" for more details. useful links: https://www.learnopencv.com/convexhullusingopencvinpythonandc/

convexHull
public static void convexHull(MatOfPoint points, MatOfInt hull)
Finds the convex hull of a point set. The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm CITE: Sklansky82 that has *O(N logN)* complexity in the current implementation. 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 0based indices of the convex hull points in the original array (since the set of convex hull points is a subset of the original point set). In the second case, hull elements are the convex hull points themselves. Otherwise, it is oriented counterclockwise. The assumed coordinate system has its X axis pointing to the right, and its Y axis pointing upwards. returns convex hull points. Otherwise, it returns indices of the convex hull points. When the output array is std::vector, the flag is ignored, and the output depends on the type of the vector: std::vector<int> implies returnPoints=false, std::vector<Point> implies returnPoints=true. Note:points
andhull
should be different arrays, inplace processing isn't supported. Check REF: tutorial_hull "the corresponding tutorial" for more details. useful links: https://www.learnopencv.com/convexhullusingopencvinpythonandc/

convexityDefects
public static void convexityDefects(MatOfPoint contour, MatOfInt convexhull, MatOfInt4 convexityDefects)
Finds the convexity defects of a contour. The figure below displays convexity defects of a hand contour: ![image](pics/defects.png) 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 4element integer vector (a.k.a. #Vec4i): (start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0based indices in the original contour of the convexity defect beginning, end and the farthest point, and fixpt_depth is fixedpoint approximation (with 8 fractional bits) of the distance between the farthest contour point and the hull. That is, to get the floatingpoint value of the depth will be fixpt_depth/256.0.

isContourConvex
public static boolean isContourConvex(MatOfPoint contour)
Tests a contour convexity. The function tests whether the input contour is convex or not. The contour must be simple, that is, without selfintersections. Otherwise, the function output is undefined. Parameters:
contour
 Input vector of 2D points, stored in std::vector<> or Mat Returns:
 automatically generated

intersectConvexConvex
public static float intersectConvexConvex(Mat p1, Mat p2, Mat p12, boolean handleNested)
Finds intersection of two convex polygons Parameters:
p1
 First polygonp2
 Second polygonp12
 Output polygon describing the intersecting areahandleNested
 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
public static float intersectConvexConvex(Mat p1, Mat p2, Mat p12)
Finds intersection of two convex polygons Parameters:
p1
 First polygonp2
 Second polygonp12
 Output polygon describing the intersecting area When false, no intersection is found. If the polygons share a side or the vertex of one polygon lies on an edge of the other, they are not considered nested and an intersection will be found regardless of the value of handleNested. 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.

fitEllipse
public static RotatedRect fitEllipse(MatOfPoint2f points)
Fits an ellipse around a set of 2D points. The function calculates the ellipse that fits (in a leastsquares sense) a set of 2D points best of all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by CITE: Fitzgibbon95 is used. Developer should keep in mind that it is possible that the returned ellipse/rotatedRect data contains negative indices, due to the data points being close to the border of the containing Mat element. Parameters:
points
 Input 2D point set, stored in std::vector<> or Mat Returns:
 automatically generated

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

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

fitLine
public static void fitLine(Mat points, Mat line, int distType, double param, double reps, double aeps)
Fits a line to a 2D or 3D point set. The function fitLine fits a line to a 2D or 3D point set by minimizing \(\sum_i \rho(r_i)\) where \(r_i\) is a distance between the \(i^{th}\) point, the line and \(\rho(r)\) is a distance function, one of the following: DIST_L2 \(\rho (r) = r^2/2 \quad \text{(the simplest and the fastest leastsquares method)}\)
 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 (rC/2)}{otherwise} \quad \text{where} \quad C=1.345\)
 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 Mestimator, see #DistanceTypesparam
 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.

pointPolygonTest
public static double pointPolygonTest(MatOfPoint2f contour, Point pt, boolean measureDist)
Performs a pointincontour test. The function determines whether the point is inside a contour, outside, or lies on an edge (or coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) value, correspondingly. When measureDist=false , the return value is +1, 1, and 0, respectively. Otherwise, the return value is a signed distance between the point and the nearest contour edge. See below a sample output of the function where each image pixel is tested against the contour: ![sample output](pics/pointpolygon.png) 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. Returns:
 automatically generated

rotatedRectangleIntersection
public static int rotatedRectangleIntersection(RotatedRect rect1, RotatedRect rect2, Mat intersectingRegion)
Finds out if there is any intersection between two rotated rectangles. If there is then the vertices of the intersecting region are returned as well. Below are some examples of intersection configurations. The hatched pattern indicates the intersecting region and the red vertices are returned by the function. ![intersection examples](pics/intersection.png) Parameters:
rect1
 First rectanglerect2
 Second rectangleintersectingRegion
 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

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

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

applyColorMap
public static void applyColorMap(Mat src, Mat dst, int colormap)
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.dst
 The result is the colormapped source image. Note: Mat::create is called on dst.colormap
 The colormap to apply, see #ColormapTypes

applyColorMap
public static void applyColorMap(Mat src, Mat dst, Mat userColor)
Applies a user colormap on a given image. Parameters:
src
 The source image, grayscale or colored of type CV_8UC1 or CV_8UC3.dst
 The result is the colormapped source image. Note: Mat::create is called on dst.userColor
 The colormap to apply of type CV_8UC1 or CV_8UC3 and size 256

line
public static void line(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType, int shift)
Draws a line segment connecting two points. The function line draws the line segment between pt1 and pt2 points in the image. The line is clipped by the image boundaries. For nonantialiased lines with integer coordinates, the 8connected or 4connected 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
public static void line(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType)
Draws a line segment connecting two points. The function line draws the line segment between pt1 and pt2 points in the image. The line is clipped by the image boundaries. For nonantialiased lines with integer coordinates, the 8connected or 4connected 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.

line
public static void line(Mat img, Point pt1, Point pt2, Scalar color, int thickness)
Draws a line segment connecting two points. The function line draws the line segment between pt1 and pt2 points in the image. The line is clipped by the image boundaries. For nonantialiased lines with integer coordinates, the 8connected or 4connected 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.

line
public static void line(Mat img, Point pt1, Point pt2, Scalar color)
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 nonantialiased lines with integer coordinates, the 8connected or 4connected 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.

arrowedLine
public 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. 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 #LineTypesshift
 Number of fractional bits in the point coordinates.tipLength
 The length of the arrow tip in relation to the arrow length

arrowedLine
public 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. 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 #LineTypesshift
 Number of fractional bits in the point coordinates.

arrowedLine
public 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. 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

arrowedLine
public 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. 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.

arrowedLine
public static void arrowedLine(Mat img, Point pt1, Point pt2, Scalar color)
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.

rectangle
public static void rectangle(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType, int shift)
Draws a simple, thick, or filled upright 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 #LineTypesshift
 Number of fractional bits in the point coordinates.

rectangle
public static void rectangle(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType)
Draws a simple, thick, or filled upright 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

rectangle
public static void rectangle(Mat img, Point pt1, Point pt2, Scalar color, int thickness)
Draws a simple, thick, or filled upright 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.

rectangle
public static void rectangle(Mat img, Point pt1, Point pt2, Scalar color)
Draws a simple, thick, or filled upright 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). mean that the function has to draw a filled rectangle.

rectangle
public static void rectangle(Mat img, Rect rec, Scalar color, int thickness, int lineType, int shift)
userec
parameter as alternative specification of the drawn rectangle: `r.tl() and r.br()Point(1,1)` are opposite corners Parameters:
img
 automatically generatedrec
 automatically generatedcolor
 automatically generatedthickness
 automatically generatedlineType
 automatically generatedshift
 automatically generated

rectangle
public static void rectangle(Mat img, Rect rec, Scalar color, int thickness, int lineType)
userec
parameter as alternative specification of the drawn rectangle: `r.tl() and r.br()Point(1,1)` are opposite corners Parameters:
img
 automatically generatedrec
 automatically generatedcolor
 automatically generatedthickness
 automatically generatedlineType
 automatically generated

rectangle
public static void rectangle(Mat img, Rect rec, Scalar color, int thickness)
userec
parameter as alternative specification of the drawn rectangle: `r.tl() and r.br()Point(1,1)` are opposite corners Parameters:
img
 automatically generatedrec
 automatically generatedcolor
 automatically generatedthickness
 automatically generated

rectangle
public static void rectangle(

