Package org.opencv.imgproc
Class Imgproc
java.lang.Object
org.opencv.imgproc.Imgproc
-
Field Summary
FieldsModifier and TypeFieldDescriptionstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final intstatic final int -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionstatic voidaccumulate(Mat src, Mat dst) Adds an image to the accumulator image.static voidaccumulate(Mat src, Mat dst, Mat mask) Adds an image to the accumulator image.static voidaccumulateProduct(Mat src1, Mat src2, Mat dst) Adds the per-element product of two input images to the accumulator image.static voidaccumulateProduct(Mat src1, Mat src2, Mat dst, Mat mask) Adds the per-element product of two input images to the accumulator image.static voidaccumulateSquare(Mat src, Mat dst) Adds the square of a source image to the accumulator image.static voidaccumulateSquare(Mat src, Mat dst, Mat mask) Adds the square of a source image to the accumulator image.static voidaccumulateWeighted(Mat src, Mat dst, double alpha) Updates a running average.static voidaccumulateWeighted(Mat src, Mat dst, double alpha, Mat mask) Updates a running average.static voidadaptiveThreshold(Mat src, Mat dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C) Applies an adaptive threshold to an array.static voidapplyColorMap(Mat src, Mat dst, int colormap) Applies a GNU Octave/MATLAB equivalent colormap on a given image.static voidapplyColorMap(Mat src, Mat dst, Mat userColor) Applies a user colormap on a given image.static voidapproxPolyDP(MatOfPoint2f curve, MatOfPoint2f approxCurve, double epsilon, boolean closed) Approximates a polygonal curve(s) with the specified precision.static doublearcLength(MatOfPoint2f curve, boolean closed) Calculates a contour perimeter or a curve length.static voidarrowedLine(Mat img, Point pt1, Point pt2, Scalar color) Draws an arrow segment pointing from the first point to the second one.static voidarrowedLine(Mat img, Point pt1, Point pt2, Scalar color, int thickness) Draws an arrow segment pointing from the first point to the second one.static voidarrowedLine(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 voidarrowedLine(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 voidarrowedLine(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 voidbilateralFilter(Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace) Applies the bilateral filter to an image.static voidbilateralFilter(Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace, int borderType) Applies the bilateral filter to an image.static voidvariant withoutmaskparameterstatic voidBlurs an image using the normalized box filter.static voidBlurs an image using the normalized box filter.static voidBlurs an image using the normalized box filter.static RectboundingRect(Mat array) Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.static voidBlurs an image using the box filter.static voidBlurs an image using the box filter.static voidBlurs an image using the box filter.static voidboxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor, boolean normalize, int borderType) Blurs an image using the box filter.static voidboxPoints(RotatedRect box, Mat points) Finds the four vertices of a rotated rect.static voidcalcBackProject(List<Mat> images, MatOfInt channels, Mat hist, Mat dst, MatOfFloat ranges, double scale) static voidcalcHist(List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges) this variant supports only uniform histograms.static voidcalcHist(List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges, boolean accumulate) this variant supports only uniform histograms.static voidFinds edges in an image using the Canny algorithm CITE: Canny86 .static voidFinds edges in an image using the Canny algorithm CITE: Canny86 .static voidCanny(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\overload Finds edges in an image using the Canny algorithm with custom image gradient.static void\overload Finds edges in an image using the Canny algorithm with custom image gradient.static voidDraws a circle.static voidDraws a circle.static voidDraws a circle.static voidDraws a circle.static booleanstatic doublecompareHist(Mat H1, Mat H2, int method) Compares two histograms.static intconnectedComponents(Mat image, Mat labels) static intconnectedComponents(Mat image, Mat labels, int connectivity) static intconnectedComponents(Mat image, Mat labels, int connectivity, int ltype) static intconnectedComponentsWithAlgorithm(Mat image, Mat labels, int connectivity, int ltype, int ccltype) computes the connected components labeled image of boolean image image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 represents the background label. ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently Bolelli (Spaghetti) CITE: Bolelli2019, Grana (BBDT) CITE: Grana2010 and Wu's (SAUF) CITE: Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes for details.static intconnectedComponentsWithStats(Mat image, Mat labels, Mat stats, Mat centroids) static intconnectedComponentsWithStats(Mat image, Mat labels, Mat stats, Mat centroids, int connectivity) static intconnectedComponentsWithStats(Mat image, Mat labels, Mat stats, Mat centroids, int connectivity, int ltype) static intconnectedComponentsWithStatsWithAlgorithm(Mat image, Mat labels, Mat stats, Mat centroids, int connectivity, int ltype, int ccltype) computes the connected components labeled image of boolean image and also produces a statistics output for each label image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 represents the background label. ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently Bolelli (Spaghetti) CITE: Bolelli2019, Grana (BBDT) CITE: Grana2010 and Wu's (SAUF) CITE: Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes for details.static doublecontourArea(Mat contour) Calculates a contour area.static doublecontourArea(Mat contour, boolean oriented) Calculates a contour area.static voidconvertMaps(Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type) Converts image transformation maps from one representation to another.static voidconvertMaps(Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type, boolean nninterpolation) Converts image transformation maps from one representation to another.static voidconvexHull(MatOfPoint points, MatOfInt hull) Finds the convex hull of a point set.static voidconvexHull(MatOfPoint points, MatOfInt hull, boolean clockwise) Finds the convex hull of a point set.static voidconvexityDefects(MatOfPoint contour, MatOfInt convexhull, MatOfInt4 convexityDefects) Finds the convexity defects of a contour.static voidcornerEigenValsAndVecs(Mat src, Mat dst, int blockSize, int ksize) Calculates eigenvalues and eigenvectors of image blocks for corner detection.static voidcornerEigenValsAndVecs(Mat src, Mat dst, int blockSize, int ksize, int borderType) Calculates eigenvalues and eigenvectors of image blocks for corner detection.static voidcornerHarris(Mat src, Mat dst, int blockSize, int ksize, double k) Harris corner detector.static voidcornerHarris(Mat src, Mat dst, int blockSize, int ksize, double k, int borderType) Harris corner detector.static voidcornerMinEigenVal(Mat src, Mat dst, int blockSize) Calculates the minimal eigenvalue of gradient matrices for corner detection.static voidcornerMinEigenVal(Mat src, Mat dst, int blockSize, int ksize) Calculates the minimal eigenvalue of gradient matrices for corner detection.static voidcornerMinEigenVal(Mat src, Mat dst, int blockSize, int ksize, int borderType) Calculates the minimal eigenvalue of gradient matrices for corner detection.static voidcornerSubPix(Mat image, Mat corners, Size winSize, Size zeroZone, TermCriteria criteria) Refines the corner locations.static CLAHECreates a smart pointer to a cv::CLAHE class and initializes it.static CLAHEcreateCLAHE(double clipLimit) Creates a smart pointer to a cv::CLAHE class and initializes it.static CLAHEcreateCLAHE(double clipLimit, Size tileGridSize) Creates a smart pointer to a cv::CLAHE class and initializes it.static GeneralizedHoughBallardCreates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it.static GeneralizedHoughGuilCreates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it.static voidcreateHanningWindow(Mat dst, Size winSize, int type) This function computes a Hanning window coefficients in two dimensions.static LineSegmentDetectorCreates a smart pointer to a LineSegmentDetector object and initializes it.static LineSegmentDetectorcreateLineSegmentDetector(int refine) Creates a smart pointer to a LineSegmentDetector object and initializes it.static LineSegmentDetectorcreateLineSegmentDetector(int refine, double scale) Creates a smart pointer to a LineSegmentDetector object and initializes it.static LineSegmentDetectorcreateLineSegmentDetector(int refine, double scale, double sigma_scale) Creates a smart pointer to a LineSegmentDetector object and initializes it.static LineSegmentDetectorcreateLineSegmentDetector(int refine, double scale, double sigma_scale, double quant) Creates a smart pointer to a LineSegmentDetector object and initializes it.static LineSegmentDetectorcreateLineSegmentDetector(int refine, double scale, double sigma_scale, double quant, double ang_th) Creates a smart pointer to a LineSegmentDetector object and initializes it.static LineSegmentDetectorcreateLineSegmentDetector(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 LineSegmentDetectorcreateLineSegmentDetector(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 LineSegmentDetectorcreateLineSegmentDetector(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 voidConverts an image from one color space to another.static voidConverts an image from one color space to another.static voidcvtColorTwoPlane(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 voiddemosaicing(Mat src, Mat dst, int code) main function for all demosaicing processesstatic voiddemosaicing(Mat src, Mat dst, int code, int dstCn) main function for all demosaicing processesstatic voidDilates an image by using a specific structuring element.static voidDilates an image by using a specific structuring element.static voidDilates an image by using a specific structuring element.static voidDilates an image by using a specific structuring element.static voiddilate(Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue) Dilates an image by using a specific structuring element.static voiddistanceTransform(Mat src, Mat dst, int distanceType, int maskSize) static voiddistanceTransform(Mat src, Mat dst, int distanceType, int maskSize, int dstType) static voiddistanceTransformWithLabels(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 voiddistanceTransformWithLabels(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 voiddivSpectrums(Mat a, Mat b, Mat c, int flags) Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum.static voiddivSpectrums(Mat a, Mat b, Mat c, int flags, boolean conjB) Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum.static voiddrawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color) Draws contours outlines or filled contours.static voiddrawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness) Draws contours outlines or filled contours.static voiddrawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType) Draws contours outlines or filled contours.static voiddrawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy) Draws contours outlines or filled contours.static voiddrawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy, int maxLevel) Draws contours outlines or filled contours.static voiddrawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy, int maxLevel, Point offset) Draws contours outlines or filled contours.static voiddrawMarker(Mat img, Point position, Scalar color) Draws a marker on a predefined position in an image.static voiddrawMarker(Mat img, Point position, Scalar color, int markerType) Draws a marker on a predefined position in an image.static voiddrawMarker(Mat img, Point position, Scalar color, int markerType, int markerSize) Draws a marker on a predefined position in an image.static voiddrawMarker(Mat img, Point position, Scalar color, int markerType, int markerSize, int thickness) Draws a marker on a predefined position in an image.static voiddrawMarker(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 voidellipse(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 voidellipse(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 voidellipse(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 voidellipse(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 voidellipse(Mat img, RotatedRect box, Scalar color) static voidellipse(Mat img, RotatedRect box, Scalar color, int thickness) static voidellipse(Mat img, RotatedRect box, Scalar color, int thickness, int lineType) static voidellipse2Poly(Point center, Size axes, int angle, int arcStart, int arcEnd, int delta, MatOfPoint pts) Approximates an elliptic arc with a polyline.static floatComputes the "minimal work" distance between two weighted point configurations.static floatComputes the "minimal work" distance between two weighted point configurations.static floatComputes the "minimal work" distance between two weighted point configurations.static voidequalizeHist(Mat src, Mat dst) Equalizes the histogram of a grayscale image.static voidErodes an image by using a specific structuring element.static voidErodes an image by using a specific structuring element.static voidErodes an image by using a specific structuring element.static voidErodes an image by using a specific structuring element.static voiderode(Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue) Erodes an image by using a specific structuring element.static voidfillConvexPoly(Mat img, MatOfPoint points, Scalar color) Fills a convex polygon.static voidfillConvexPoly(Mat img, MatOfPoint points, Scalar color, int lineType) Fills a convex polygon.static voidfillConvexPoly(Mat img, MatOfPoint points, Scalar color, int lineType, int shift) Fills a convex polygon.static voidfillPoly(Mat img, List<MatOfPoint> pts, Scalar color) Fills the area bounded by one or more polygons.static voidfillPoly(Mat img, List<MatOfPoint> pts, Scalar color, int lineType) Fills the area bounded by one or more polygons.static voidfillPoly(Mat img, List<MatOfPoint> pts, Scalar color, int lineType, int shift) Fills the area bounded by one or more polygons.static voidFills the area bounded by one or more polygons.static voidConvolves an image with the kernel.static voidConvolves an image with the kernel.static voidConvolves an image with the kernel.static voidConvolves an image with the kernel.static voidfindContours(Mat image, List<MatOfPoint> contours, Mat hierarchy, int mode, int method) Finds contours in a binary image.static voidfindContours(Mat image, List<MatOfPoint> contours, Mat hierarchy, int mode, int method, Point offset) Finds contours in a binary image.static RotatedRectfitEllipse(MatOfPoint2f points) Fits an ellipse around a set of 2D points.static RotatedRectfitEllipseAMS(Mat points) Fits an ellipse around a set of 2D points.static RotatedRectfitEllipseDirect(Mat points) Fits an ellipse around a set of 2D points.static voidFits a line to a 2D or 3D point set.static intFills a connected component with the given color.static intFills a connected component with the given color.static intFills a connected component with the given color.static intfloodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect, Scalar loDiff, Scalar upDiff) Fills a connected component with the given color.static intfloodFill(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 voidGaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX) Blurs an image using a Gaussian filter.static voidGaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY) Blurs an image using a Gaussian filter.static voidGaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY, int borderType) Blurs an image using a Gaussian filter.static MatgetAffineTransform(MatOfPoint2f src, MatOfPoint2f dst) static voidgetDerivKernels(Mat kx, Mat ky, int dx, int dy, int ksize) Returns filter coefficients for computing spatial image derivatives.static voidgetDerivKernels(Mat kx, Mat ky, int dx, int dy, int ksize, boolean normalize) Returns filter coefficients for computing spatial image derivatives.static voidgetDerivKernels(Mat kx, Mat ky, int dx, int dy, int ksize, boolean normalize, int ktype) Returns filter coefficients for computing spatial image derivatives.static doublegetFontScaleFromHeight(int fontFace, int pixelHeight) Calculates the font-specific size to use to achieve a given height in pixels.static doublegetFontScaleFromHeight(int fontFace, int pixelHeight, int thickness) Calculates the font-specific size to use to achieve a given height in pixels.static MatgetGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma) Returns Gabor filter coefficients.static MatgetGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma, double psi) Returns Gabor filter coefficients.static MatgetGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma, double psi, int ktype) Returns Gabor filter coefficients.static MatgetGaussianKernel(int ksize, double sigma) Returns Gaussian filter coefficients.static MatgetGaussianKernel(int ksize, double sigma, int ktype) Returns Gaussian filter coefficients.static MatgetPerspectiveTransform(Mat src, Mat dst) Calculates a perspective transform from four pairs of the corresponding points.static MatgetPerspectiveTransform(Mat src, Mat dst, int solveMethod) Calculates a perspective transform from four pairs of the corresponding points.static voidgetRectSubPix(Mat image, Size patchSize, Point center, Mat patch) Retrieves a pixel rectangle from an image with sub-pixel accuracy.static voidgetRectSubPix(Mat image, Size patchSize, Point center, Mat patch, int patchType) Retrieves a pixel rectangle from an image with sub-pixel accuracy.static MatgetRotationMatrix2D(Point center, double angle, double scale) Calculates an affine matrix of 2D rotation.static MatgetStructuringElement(int shape, Size ksize) Returns a structuring element of the specified size and shape for morphological operations.static MatgetStructuringElement(int shape, Size ksize, Point anchor) Returns a structuring element of the specified size and shape for morphological operations.static SizegetTextSize(String text, int fontFace, double fontScale, int thickness, int[] baseLine) static voidgoodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance) Determines strong corners on an image.static voidgoodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask) Determines strong corners on an image.static voidgoodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize) Determines strong corners on an image.static voidgoodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, boolean useHarrisDetector) Determines strong corners on an image.static voidgoodFeaturesToTrack(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 voidgoodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize) static voidgoodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, boolean useHarrisDetector) static voidgoodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, boolean useHarrisDetector, double k) static voidgoodFeaturesToTrackWithQuality(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 voidgoodFeaturesToTrackWithQuality(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 voidgoodFeaturesToTrackWithQuality(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 voidgoodFeaturesToTrackWithQuality(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 voidgoodFeaturesToTrackWithQuality(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 voidRuns the GrabCut algorithm.static voidRuns the GrabCut algorithm.static voidHoughCircles(Mat image, Mat circles, int method, double dp, double minDist) Finds circles in a grayscale image using the Hough transform.static voidHoughCircles(Mat image, Mat circles, int method, double dp, double minDist, double param1) Finds circles in a grayscale image using the Hough transform.static voidHoughCircles(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 voidHoughCircles(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 voidHoughCircles(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 voidHoughLines(Mat image, Mat lines, double rho, double theta, int threshold) Finds lines in a binary image using the standard Hough transform.static voidHoughLines(Mat image, Mat lines, double rho, double theta, int threshold, double srn) Finds lines in a binary image using the standard Hough transform.static voidHoughLines(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 voidHoughLines(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 voidHoughLines(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 voidHoughLinesP(Mat image, Mat lines, double rho, double theta, int threshold) Finds line segments in a binary image using the probabilistic Hough transform.static voidHoughLinesP(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 voidHoughLinesP(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 voidHoughLinesPointSet(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 voidHoughLinesWithAccumulator(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 voidHoughLinesWithAccumulator(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 voidHoughLinesWithAccumulator(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 voidHoughLinesWithAccumulator(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 voidHoughLinesWithAccumulator(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 voidstatic voidstatic voidstatic voidstatic voidstatic voidstatic voidCalculates the integral of an image.static voidCalculates the integral of an image.static voidCalculates the integral of an image.static floatintersectConvexConvex(Mat p1, Mat p2, Mat p12) Finds intersection of two convex polygonsstatic floatintersectConvexConvex(Mat p1, Mat p2, Mat p12, boolean handleNested) Finds intersection of two convex polygonsstatic voidinvertAffineTransform(Mat M, Mat iM) Inverts an affine transformation.static booleanisContourConvex(MatOfPoint contour) Tests a contour convexity.static voidCalculates the Laplacian of an image.static voidCalculates the Laplacian of an image.static voidCalculates the Laplacian of an image.static voidCalculates the Laplacian of an image.static voidCalculates the Laplacian of an image.static voidDraws a line segment connecting two points.static voidDraws a line segment connecting two points.static voidDraws a line segment connecting two points.static voidDraws a line segment connecting two points.static voidlinearPolar(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 voidDeprecated.This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG); Transform the source image using the following transformation (See REF: polar_remaps_reference_image "Polar remaps reference image d)"): \(\begin{array}{l} dst( \rho , \phi ) = src(x,y) \\ dst.size() \leftarrow src.size() \end{array}\) where \(\begin{array}{l} I = (dx,dy) = (x - center.x,y - center.y) \\ \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\ \phi = Kangle \cdot \texttt{angle} (I) \\ \end{array}\) and \(\begin{array}{l} M = src.cols / log_e(maxRadius) \\ Kangle = src.rows / 2\Pi \\ \end{array}\) The function emulates the human "foveal" vision and can be used for fast scale and rotation-invariant template matching, for object tracking and so forth.static doublematchShapes(Mat contour1, Mat contour2, int method, double parameter) Compares two shapes.static voidmatchTemplate(Mat image, Mat templ, Mat result, int method) Compares a template against overlapped image regions.static voidmatchTemplate(Mat image, Mat templ, Mat result, int method, Mat mask) Compares a template against overlapped image regions.static voidmedianBlur(Mat src, Mat dst, int ksize) Blurs an image using the median filter.static RotatedRectminAreaRect(MatOfPoint2f points) Finds a rotated rectangle of the minimum area enclosing the input 2D point set.static voidminEnclosingCircle(MatOfPoint2f points, Point center, float[] radius) Finds a circle of the minimum area enclosing a 2D point set.static doubleminEnclosingTriangle(Mat points, Mat triangle) Finds a triangle of minimum area enclosing a 2D point set and returns its area.static MomentsCalculates all of the moments up to the third order of a polygon or rasterized shape.static MomentsCalculates all of the moments up to the third order of a polygon or rasterized shape.static voidmorphologyEx(Mat src, Mat dst, int op, Mat kernel) Performs advanced morphological transformations.static voidmorphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor) Performs advanced morphological transformations.static voidmorphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations) Performs advanced morphological transformations.static voidmorphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType) Performs advanced morphological transformations.static voidmorphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue) Performs advanced morphological transformations.static PointphaseCorrelate(Mat src1, Mat src2) The function is used to detect translational shifts that occur between two images.static PointphaseCorrelate(Mat src1, Mat src2, Mat window) The function is used to detect translational shifts that occur between two images.static PointphaseCorrelate(Mat src1, Mat src2, Mat window, double[] response) The function is used to detect translational shifts that occur between two images.static doublepointPolygonTest(MatOfPoint2f contour, Point pt, boolean measureDist) Performs a point-in-contour test.static voidpolylines(Mat img, List<MatOfPoint> pts, boolean isClosed, Scalar color) Draws several polygonal curves.static voidpolylines(Mat img, List<MatOfPoint> pts, boolean isClosed, Scalar color, int thickness) Draws several polygonal curves.static voidpolylines(Mat img, List<MatOfPoint> pts, boolean isClosed, Scalar color, int thickness, int lineType) Draws several polygonal curves.static voidpolylines(Mat img, List<MatOfPoint> pts, boolean isClosed, Scalar color, int thickness, int lineType, int shift) Draws several polygonal curves.static voidpreCornerDetect(Mat src, Mat dst, int ksize) Calculates a feature map for corner detection.static voidpreCornerDetect(Mat src, Mat dst, int ksize, int borderType) Calculates a feature map for corner detection.static voidDraws a text string.static voidputText(Mat img, String text, Point org, int fontFace, double fontScale, Scalar color, int thickness) Draws a text string.static voidputText(Mat img, String text, Point org, int fontFace, double fontScale, Scalar color, int thickness, int lineType) Draws a text string.static voidputText(Mat img, String text, Point org, int fontFace, double fontScale, Scalar color, int thickness, int lineType, boolean bottomLeftOrigin) Draws a text string.static voidBlurs an image and downsamples it.static voidBlurs an image and downsamples it.static voidBlurs an image and downsamples it.static voidpyrMeanShiftFiltering(Mat src, Mat dst, double sp, double sr) Performs initial step of meanshift segmentation of an image.static voidpyrMeanShiftFiltering(Mat src, Mat dst, double sp, double sr, int maxLevel) Performs initial step of meanshift segmentation of an image.static voidpyrMeanShiftFiltering(Mat src, Mat dst, double sp, double sr, int maxLevel, TermCriteria termcrit) Performs initial step of meanshift segmentation of an image.static voidUpsamples an image and then blurs it.static voidUpsamples an image and then blurs it.static voidUpsamples an image and then blurs it.static voidDraws a simple, thick, or filled up-right rectangle.static voidDraws a simple, thick, or filled up-right rectangle.static voidDraws a simple, thick, or filled up-right rectangle.static voidDraws a simple, thick, or filled up-right rectangle.static voiduserecparameter as alternative specification of the drawn rectangle: `r.tl() and r.br()-Point(1,1)` are opposite cornersstatic voiduserecparameter as alternative specification of the drawn rectangle: `r.tl() and r.br()-Point(1,1)` are opposite cornersstatic voiduserecparameter as alternative specification of the drawn rectangle: `r.tl() and r.br()-Point(1,1)` are opposite cornersstatic voiduserecparameter as alternative specification of the drawn rectangle: `r.tl() and r.br()-Point(1,1)` are opposite cornersstatic voidApplies a generic geometrical transformation to an image.static voidApplies a generic geometrical transformation to an image.static voidApplies a generic geometrical transformation to an image.static voidResizes an image.static voidResizes an image.static voidResizes an image.static voidResizes an image.static introtatedRectangleIntersection(RotatedRect rect1, RotatedRect rect2, Mat intersectingRegion) Finds out if there is any intersection between two rotated rectangles.static voidCalculates the first x- or y- image derivative using Scharr operator.static voidCalculates the first x- or y- image derivative using Scharr operator.static voidCalculates the first x- or y- image derivative using Scharr operator.static voidCalculates the first x- or y- image derivative using Scharr operator.static voidsepFilter2D(Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY) Applies a separable linear filter to an image.static voidApplies a separable linear filter to an image.static voidApplies a separable linear filter to an image.static voidsepFilter2D(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 voidCalculates the first, second, third, or mixed image derivatives using an extended Sobel operator.static voidCalculates the first, second, third, or mixed image derivatives using an extended Sobel operator.static voidCalculates the first, second, third, or mixed image derivatives using an extended Sobel operator.static voidCalculates the first, second, third, or mixed image derivatives using an extended Sobel operator.static voidSobel(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 voidspatialGradient(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 voidspatialGradient(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 voidspatialGradient(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 voidsqrBoxFilter(Mat src, Mat dst, int ddepth, Size ksize) Calculates the normalized sum of squares of the pixel values overlapping the filter.static voidsqrBoxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor) Calculates the normalized sum of squares of the pixel values overlapping the filter.static voidsqrBoxFilter(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 voidsqrBoxFilter(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 voidBlurs an image using the stackBlur.static doubleApplies a fixed-level threshold to each array element.static voidwarpAffine(Mat src, Mat dst, Mat M, Size dsize) Applies an affine transformation to an image.static voidwarpAffine(Mat src, Mat dst, Mat M, Size dsize, int flags) Applies an affine transformation to an image.static voidwarpAffine(Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode) Applies an affine transformation to an image.static voidApplies an affine transformation to an image.static voidwarpPerspective(Mat src, Mat dst, Mat M, Size dsize) Applies a perspective transformation to an image.static voidwarpPerspective(Mat src, Mat dst, Mat M, Size dsize, int flags) Applies a perspective transformation to an image.static voidwarpPerspective(Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode) Applies a perspective transformation to an image.static voidApplies a perspective transformation to an image.static voidRemaps an image to polar or semilog-polar coordinates space polar_remaps_reference_image !static voidPerforms a marker-based image segmentation using the watershed algorithm.
-
Field Details
-
CV_GAUSSIAN_5x5
public static final int CV_GAUSSIAN_5x5- See Also:
-
CV_SCHARR
public static final int CV_SCHARR- See Also:
-
CV_MAX_SOBEL_KSIZE
public static final int CV_MAX_SOBEL_KSIZE- See Also:
-
CV_RGBA2mRGBA
public static final int CV_RGBA2mRGBA- See Also:
-
CV_mRGBA2RGBA
public static final int CV_mRGBA2RGBA- See Also:
-
CV_WARP_FILL_OUTLIERS
public static final int CV_WARP_FILL_OUTLIERS- See Also:
-
CV_WARP_INVERSE_MAP
public static final int CV_WARP_INVERSE_MAP- See Also:
-
CV_CHAIN_CODE
public static final int CV_CHAIN_CODE- See Also:
-
CV_LINK_RUNS
public static final int CV_LINK_RUNS- See Also:
-
CV_POLY_APPROX_DP
public static final int CV_POLY_APPROX_DP- See Also:
-
CV_CONTOURS_MATCH_I1
public static final int CV_CONTOURS_MATCH_I1- See Also:
-
CV_CONTOURS_MATCH_I2
public static final int CV_CONTOURS_MATCH_I2- See Also:
-
CV_CONTOURS_MATCH_I3
public static final int CV_CONTOURS_MATCH_I3- See Also:
-
CV_CLOCKWISE
public static final int CV_CLOCKWISE- See Also:
-
CV_COUNTER_CLOCKWISE
public static final int CV_COUNTER_CLOCKWISE- See Also:
-
CV_COMP_CORREL
public static final int CV_COMP_CORREL- See Also:
-
CV_COMP_CHISQR
public static final int CV_COMP_CHISQR- See Also:
-
CV_COMP_INTERSECT
public static final int CV_COMP_INTERSECT- See Also:
-
CV_COMP_BHATTACHARYYA
public static final int CV_COMP_BHATTACHARYYA- See Also:
-
CV_COMP_HELLINGER
public static final int CV_COMP_HELLINGER- See Also:
-
CV_COMP_CHISQR_ALT
public static final int CV_COMP_CHISQR_ALT- See Also:
-
CV_COMP_KL_DIV
public static final int CV_COMP_KL_DIV- See Also:
-
CV_DIST_MASK_3
public static final int CV_DIST_MASK_3- See Also:
-
CV_DIST_MASK_5
public static final int CV_DIST_MASK_5- See Also:
-
CV_DIST_MASK_PRECISE
public static final int CV_DIST_MASK_PRECISE- See Also:
-
CV_DIST_LABEL_CCOMP
public static final int CV_DIST_LABEL_CCOMP- See Also:
-
CV_DIST_LABEL_PIXEL
public static final int CV_DIST_LABEL_PIXEL- See Also:
-
CV_DIST_USER
public static final int CV_DIST_USER- See Also:
-
CV_DIST_L1
public static final int CV_DIST_L1- See Also:
-
CV_DIST_L2
public static final int CV_DIST_L2- See Also:
-
CV_DIST_C
public static final int CV_DIST_C- See Also:
-
CV_DIST_L12
public static final int CV_DIST_L12- See Also:
-
CV_DIST_FAIR
public static final int CV_DIST_FAIR- See Also:
-
CV_DIST_WELSCH
public static final int CV_DIST_WELSCH- See Also:
-
CV_DIST_HUBER
public static final int CV_DIST_HUBER- See Also:
-
CV_CANNY_L2_GRADIENT
public static final int CV_CANNY_L2_GRADIENT- See Also:
-
CV_HOUGH_STANDARD
public static final int CV_HOUGH_STANDARD- See Also:
-
CV_HOUGH_PROBABILISTIC
public static final int CV_HOUGH_PROBABILISTIC- See Also:
-
CV_HOUGH_MULTI_SCALE
public static final int CV_HOUGH_MULTI_SCALE- See Also:
-
CV_HOUGH_GRADIENT
public static final int CV_HOUGH_GRADIENT- See Also:
-
CV_SHAPE_RECT
public static final int CV_SHAPE_RECT- See Also:
-
CV_SHAPE_CROSS
public static final int CV_SHAPE_CROSS- See Also:
-
CV_SHAPE_ELLIPSE
public static final int CV_SHAPE_ELLIPSE- See Also:
-
CV_SHAPE_CUSTOM
public static final int CV_SHAPE_CUSTOM- See Also:
-
CV_BLUR_NO_SCALE
public static final int CV_BLUR_NO_SCALE- See Also:
-
CV_BLUR
public static final int CV_BLUR- See Also:
-
CV_GAUSSIAN
public static final int CV_GAUSSIAN- See Also:
-
CV_MEDIAN
public static final int CV_MEDIAN- See Also:
-
CV_BILATERAL
public static final int CV_BILATERAL- See Also:
-
ADAPTIVE_THRESH_MEAN_C
public static final int ADAPTIVE_THRESH_MEAN_C- See Also:
-
ADAPTIVE_THRESH_GAUSSIAN_C
public static final int ADAPTIVE_THRESH_GAUSSIAN_C- See Also:
-
COLORMAP_AUTUMN
public static final int COLORMAP_AUTUMN- See Also:
-
COLORMAP_BONE
public static final int COLORMAP_BONE- See Also:
-
COLORMAP_JET
public static final int COLORMAP_JET- See Also:
-
COLORMAP_WINTER
public static final int COLORMAP_WINTER- See Also:
-
COLORMAP_RAINBOW
public static final int COLORMAP_RAINBOW- See Also:
-
COLORMAP_OCEAN
public static final int COLORMAP_OCEAN- See Also:
-
COLORMAP_SUMMER
public static final int COLORMAP_SUMMER- See Also:
-
COLORMAP_SPRING
public static final int COLORMAP_SPRING- See Also:
-
COLORMAP_COOL
public static final int COLORMAP_COOL- See Also:
-
COLORMAP_HSV
public static final int COLORMAP_HSV- See Also:
-
COLORMAP_PINK
public static final int COLORMAP_PINK- See Also:
-
COLORMAP_HOT
public static final int COLORMAP_HOT- See Also:
-
COLORMAP_PARULA
public static final int COLORMAP_PARULA- See Also:
-
COLORMAP_MAGMA
public static final int COLORMAP_MAGMA- See Also:
-
COLORMAP_INFERNO
public static final int COLORMAP_INFERNO- See Also:
-
COLORMAP_PLASMA
public static final int COLORMAP_PLASMA- See Also:
-
COLORMAP_VIRIDIS
public static final int COLORMAP_VIRIDIS- See Also:
-
COLORMAP_CIVIDIS
public static final int COLORMAP_CIVIDIS- See Also:
-
COLORMAP_TWILIGHT
public static final int COLORMAP_TWILIGHT- See Also:
-
COLORMAP_TWILIGHT_SHIFTED
public static final int COLORMAP_TWILIGHT_SHIFTED- See Also:
-
COLORMAP_TURBO
public static final int COLORMAP_TURBO- See Also:
-
COLORMAP_DEEPGREEN
public static final int COLORMAP_DEEPGREEN- See Also:
-
CCL_DEFAULT
public static final int CCL_DEFAULT- See Also:
-
CCL_WU
public static final int CCL_WU- See Also:
-
CCL_GRANA
public static final int CCL_GRANA- See Also:
-
CCL_BOLELLI
public static final int CCL_BOLELLI- See Also:
-
CCL_SAUF
public static final int CCL_SAUF- See Also:
-
CCL_BBDT
public static final int CCL_BBDT- See Also:
-
CCL_SPAGHETTI
public static final int CCL_SPAGHETTI- See Also:
-
COLOR_BGR2BGRA
public static final int COLOR_BGR2BGRA- See Also:
-
COLOR_RGB2RGBA
public static final int COLOR_RGB2RGBA- See Also:
-
COLOR_BGRA2BGR
public static final int COLOR_BGRA2BGR- See Also:
-
COLOR_RGBA2RGB
public static final int COLOR_RGBA2RGB- See Also:
-
COLOR_BGR2RGBA
public static final int COLOR_BGR2RGBA- See Also:
-
COLOR_RGB2BGRA
public static final int COLOR_RGB2BGRA- See Also:
-
COLOR_RGBA2BGR
public static final int COLOR_RGBA2BGR- See Also:
-
COLOR_BGRA2RGB
public static final int COLOR_BGRA2RGB- See Also:
-
COLOR_BGR2RGB
public static final int COLOR_BGR2RGB- See Also:
-
COLOR_RGB2BGR
public static final int COLOR_RGB2BGR- See Also:
-
COLOR_BGRA2RGBA
public static final int COLOR_BGRA2RGBA- See Also:
-
COLOR_RGBA2BGRA
public static final int COLOR_RGBA2BGRA- See Also:
-
COLOR_BGR2GRAY
public static final int COLOR_BGR2GRAY- See Also:
-
COLOR_RGB2GRAY
public static final int COLOR_RGB2GRAY- See Also:
-
COLOR_GRAY2BGR
public static final int COLOR_GRAY2BGR- See Also:
-
COLOR_GRAY2RGB
public static final int COLOR_GRAY2RGB- See Also:
-
COLOR_GRAY2BGRA
public static final int COLOR_GRAY2BGRA- See Also:
-
COLOR_GRAY2RGBA
public static final int COLOR_GRAY2RGBA- See Also:
-
COLOR_BGRA2GRAY
public static final int COLOR_BGRA2GRAY- See Also:
-
COLOR_RGBA2GRAY
public static final int COLOR_RGBA2GRAY- See Also:
-
COLOR_BGR2BGR565
public static final int COLOR_BGR2BGR565- See Also:
-
COLOR_RGB2BGR565
public static final int COLOR_RGB2BGR565- See Also:
-
COLOR_BGR5652BGR
public static final int COLOR_BGR5652BGR- See Also:
-
COLOR_BGR5652RGB
public static final int COLOR_BGR5652RGB- See Also:
-
COLOR_BGRA2BGR565
public static final int COLOR_BGRA2BGR565- See Also:
-
COLOR_RGBA2BGR565
public static final int COLOR_RGBA2BGR565- See Also:
-
COLOR_BGR5652BGRA
public static final int COLOR_BGR5652BGRA- See Also:
-
COLOR_BGR5652RGBA
public static final int COLOR_BGR5652RGBA- See Also:
-
COLOR_GRAY2BGR565
public static final int COLOR_GRAY2BGR565- See Also:
-
COLOR_BGR5652GRAY
public static final int COLOR_BGR5652GRAY- See Also:
-
COLOR_BGR2BGR555
public static final int COLOR_BGR2BGR555- See Also:
-
COLOR_RGB2BGR555
public static final int COLOR_RGB2BGR555- See Also:
-
COLOR_BGR5552BGR
public static final int COLOR_BGR5552BGR- See Also:
-
COLOR_BGR5552RGB
public static final int COLOR_BGR5552RGB- See Also:
-
COLOR_BGRA2BGR555
public static final int COLOR_BGRA2BGR555- See Also:
-
COLOR_RGBA2BGR555
public static final int COLOR_RGBA2BGR555- See Also:
-
COLOR_BGR5552BGRA
public static final int COLOR_BGR5552BGRA- See Also:
-
COLOR_BGR5552RGBA
public static final int COLOR_BGR5552RGBA- See Also:
-
COLOR_GRAY2BGR555
public static final int COLOR_GRAY2BGR555- See Also:
-
COLOR_BGR5552GRAY
public static final int COLOR_BGR5552GRAY- See Also:
-
COLOR_BGR2XYZ
public static final int COLOR_BGR2XYZ- See Also:
-
COLOR_RGB2XYZ
public static final int COLOR_RGB2XYZ- See Also:
-
COLOR_XYZ2BGR
public static final int COLOR_XYZ2BGR- See Also:
-
COLOR_XYZ2RGB
public static final int COLOR_XYZ2RGB- See Also:
-
COLOR_BGR2YCrCb
public static final int COLOR_BGR2YCrCb- See Also:
-
COLOR_RGB2YCrCb
public static final int COLOR_RGB2YCrCb- See Also:
-
COLOR_YCrCb2BGR
public static final int COLOR_YCrCb2BGR- See Also:
-
COLOR_YCrCb2RGB
public static final int COLOR_YCrCb2RGB- See Also:
-
COLOR_BGR2HSV
public static final int COLOR_BGR2HSV- See Also:
-
COLOR_RGB2HSV
public static final int COLOR_RGB2HSV- See Also:
-
COLOR_BGR2Lab
public static final int COLOR_BGR2Lab- See Also:
-
COLOR_RGB2Lab
public static final int COLOR_RGB2Lab- See Also:
-
COLOR_BGR2Luv
public static final int COLOR_BGR2Luv- See Also:
-
COLOR_RGB2Luv
public static final int COLOR_RGB2Luv- See Also:
-
COLOR_BGR2HLS
public static final int COLOR_BGR2HLS- See Also:
-
COLOR_RGB2HLS
public static final int COLOR_RGB2HLS- See Also:
-
COLOR_HSV2BGR
public static final int COLOR_HSV2BGR- See Also:
-
COLOR_HSV2RGB
public static final int COLOR_HSV2RGB- See Also:
-
COLOR_Lab2BGR
public static final int COLOR_Lab2BGR- See Also:
-
COLOR_Lab2RGB
public static final int COLOR_Lab2RGB- See Also:
-
COLOR_Luv2BGR
public static final int COLOR_Luv2BGR- See Also:
-
COLOR_Luv2RGB
public static final int COLOR_Luv2RGB- See Also:
-
COLOR_HLS2BGR
public static final int COLOR_HLS2BGR- See Also:
-
COLOR_HLS2RGB
public static final int COLOR_HLS2RGB- See Also:
-
COLOR_BGR2HSV_FULL
public static final int COLOR_BGR2HSV_FULL- See Also:
-
COLOR_RGB2HSV_FULL
public static final int COLOR_RGB2HSV_FULL- See Also:
-
COLOR_BGR2HLS_FULL
public static final int COLOR_BGR2HLS_FULL- See Also:
-
COLOR_RGB2HLS_FULL
public static final int COLOR_RGB2HLS_FULL- See Also:
-
COLOR_HSV2BGR_FULL
public static final int COLOR_HSV2BGR_FULL- See Also:
-
COLOR_HSV2RGB_FULL
public static final int COLOR_HSV2RGB_FULL- See Also:
-
COLOR_HLS2BGR_FULL
public static final int COLOR_HLS2BGR_FULL- See Also:
-
COLOR_HLS2RGB_FULL
public static final int COLOR_HLS2RGB_FULL- See Also:
-
COLOR_LBGR2Lab
public static final int COLOR_LBGR2Lab- See Also:
-
COLOR_LRGB2Lab
public static final int COLOR_LRGB2Lab- See Also:
-
COLOR_LBGR2Luv
public static final int COLOR_LBGR2Luv- See Also:
-
COLOR_LRGB2Luv
public static final int COLOR_LRGB2Luv- See Also:
-
COLOR_Lab2LBGR
public static final int COLOR_Lab2LBGR- See Also:
-
COLOR_Lab2LRGB
public static final int COLOR_Lab2LRGB- See Also:
-
COLOR_Luv2LBGR
public static final int COLOR_Luv2LBGR- See Also:
-
COLOR_Luv2LRGB
public static final int COLOR_Luv2LRGB- See Also:
-
COLOR_BGR2YUV
public static final int COLOR_BGR2YUV- See Also:
-
COLOR_RGB2YUV
public static final int COLOR_RGB2YUV- See Also:
-
COLOR_YUV2BGR
public static final int COLOR_YUV2BGR- See Also:
-
COLOR_YUV2RGB
public static final int COLOR_YUV2RGB- See Also:
-
COLOR_YUV2RGB_NV12
public static final int COLOR_YUV2RGB_NV12- See Also:
-
COLOR_YUV2BGR_NV12
public static final int COLOR_YUV2BGR_NV12- See Also:
-
COLOR_YUV2RGB_NV21
public static final int COLOR_YUV2RGB_NV21- See Also:
-
COLOR_YUV2BGR_NV21
public static final int COLOR_YUV2BGR_NV21- See Also:
-
COLOR_YUV420sp2RGB
public static final int COLOR_YUV420sp2RGB- See Also:
-
COLOR_YUV420sp2BGR
public static final int COLOR_YUV420sp2BGR- See Also:
-
COLOR_YUV2RGBA_NV12
public static final int COLOR_YUV2RGBA_NV12- See Also:
-
COLOR_YUV2BGRA_NV12
public static final int COLOR_YUV2BGRA_NV12- See Also:
-
COLOR_YUV2RGBA_NV21
public static final int COLOR_YUV2RGBA_NV21- See Also:
-
COLOR_YUV2BGRA_NV21
public static final int COLOR_YUV2BGRA_NV21- See Also:
-
COLOR_YUV420sp2RGBA
public static final int COLOR_YUV420sp2RGBA- See Also:
-
COLOR_YUV420sp2BGRA
public static final int COLOR_YUV420sp2BGRA- See Also:
-
COLOR_YUV2RGB_YV12
public static final int COLOR_YUV2RGB_YV12- See Also:
-
COLOR_YUV2BGR_YV12
public static final int COLOR_YUV2BGR_YV12- See Also:
-
COLOR_YUV2RGB_IYUV
public static final int COLOR_YUV2RGB_IYUV- See Also:
-
COLOR_YUV2BGR_IYUV
public static final int COLOR_YUV2BGR_IYUV- See Also:
-
COLOR_YUV2RGB_I420
public static final int COLOR_YUV2RGB_I420- See Also:
-
COLOR_YUV2BGR_I420
public static final int COLOR_YUV2BGR_I420- See Also:
-
COLOR_YUV420p2RGB
public static final int COLOR_YUV420p2RGB- See Also:
-
COLOR_YUV420p2BGR
public static final int COLOR_YUV420p2BGR- See Also:
-
COLOR_YUV2RGBA_YV12
public static final int COLOR_YUV2RGBA_YV12- See Also:
-
COLOR_YUV2BGRA_YV12
public static final int COLOR_YUV2BGRA_YV12- See Also:
-
COLOR_YUV2RGBA_IYUV
public static final int COLOR_YUV2RGBA_IYUV- See Also:
-
COLOR_YUV2BGRA_IYUV
public static final int COLOR_YUV2BGRA_IYUV- See Also:
-
COLOR_YUV2RGBA_I420
public static final int COLOR_YUV2RGBA_I420- See Also:
-
COLOR_YUV2BGRA_I420
public static final int COLOR_YUV2BGRA_I420- See Also:
-
COLOR_YUV420p2RGBA
public static final int COLOR_YUV420p2RGBA- See Also:
-
COLOR_YUV420p2BGRA
public static final int COLOR_YUV420p2BGRA- See Also:
-
COLOR_YUV2GRAY_420
public static final int COLOR_YUV2GRAY_420- See Also:
-
COLOR_YUV2GRAY_NV21
public static final int COLOR_YUV2GRAY_NV21- See Also:
-
COLOR_YUV2GRAY_NV12
public static final int COLOR_YUV2GRAY_NV12- See Also:
-
COLOR_YUV2GRAY_YV12
public static final int COLOR_YUV2GRAY_YV12- See Also:
-
COLOR_YUV2GRAY_IYUV
public static final int COLOR_YUV2GRAY_IYUV- See Also:
-
COLOR_YUV2GRAY_I420
public static final int COLOR_YUV2GRAY_I420- See Also:
-
COLOR_YUV420sp2GRAY
public static final int COLOR_YUV420sp2GRAY- See Also:
-
COLOR_YUV420p2GRAY
public static final int COLOR_YUV420p2GRAY- See Also:
-
COLOR_YUV2RGB_UYVY
public static final int COLOR_YUV2RGB_UYVY- See Also:
-
COLOR_YUV2BGR_UYVY
public static final int COLOR_YUV2BGR_UYVY- See Also:
-
COLOR_YUV2RGB_Y422
public static final int COLOR_YUV2RGB_Y422- See Also:
-
COLOR_YUV2BGR_Y422
public static final int COLOR_YUV2BGR_Y422- See Also:
-
COLOR_YUV2RGB_UYNV
public static final int COLOR_YUV2RGB_UYNV- See Also:
-
COLOR_YUV2BGR_UYNV
public static final int COLOR_YUV2BGR_UYNV- See Also:
-
COLOR_YUV2RGBA_UYVY
public static final int COLOR_YUV2RGBA_UYVY- See Also:
-
COLOR_YUV2BGRA_UYVY
public static final int COLOR_YUV2BGRA_UYVY- See Also:
-
COLOR_YUV2RGBA_Y422
public static final int COLOR_YUV2RGBA_Y422- See Also:
-
COLOR_YUV2BGRA_Y422
public static final int COLOR_YUV2BGRA_Y422- See Also:
-
COLOR_YUV2RGBA_UYNV
public static final int COLOR_YUV2RGBA_UYNV- See Also:
-
COLOR_YUV2BGRA_UYNV
public static final int COLOR_YUV2BGRA_UYNV- See Also:
-
COLOR_YUV2RGB_YUY2
public static final int COLOR_YUV2RGB_YUY2- See Also:
-
COLOR_YUV2BGR_YUY2
public static final int COLOR_YUV2BGR_YUY2- See Also:
-
COLOR_YUV2RGB_YVYU
public static final int COLOR_YUV2RGB_YVYU- See Also:
-
COLOR_YUV2BGR_YVYU
public static final int COLOR_YUV2BGR_YVYU- See Also:
-
COLOR_YUV2RGB_YUYV
public static final int COLOR_YUV2RGB_YUYV- See Also:
-
COLOR_YUV2BGR_YUYV
public static final int COLOR_YUV2BGR_YUYV- See Also:
-
COLOR_YUV2RGB_YUNV
public static final int COLOR_YUV2RGB_YUNV- See Also:
-
COLOR_YUV2BGR_YUNV
public static final int COLOR_YUV2BGR_YUNV- See Also:
-
COLOR_YUV2RGBA_YUY2
public static final int COLOR_YUV2RGBA_YUY2- See Also:
-
COLOR_YUV2BGRA_YUY2
public static final int COLOR_YUV2BGRA_YUY2- See Also:
-
COLOR_YUV2RGBA_YVYU
public static final int COLOR_YUV2RGBA_YVYU- See Also:
-
COLOR_YUV2BGRA_YVYU
public static final int COLOR_YUV2BGRA_YVYU- See Also:
-
COLOR_YUV2RGBA_YUYV
public static final int COLOR_YUV2RGBA_YUYV- See Also:
-
COLOR_YUV2BGRA_YUYV
public static final int COLOR_YUV2BGRA_YUYV- See Also:
-
COLOR_YUV2RGBA_YUNV
public static final int COLOR_YUV2RGBA_YUNV- See Also:
-
COLOR_YUV2BGRA_YUNV
public static final int COLOR_YUV2BGRA_YUNV- See Also:
-
COLOR_YUV2GRAY_UYVY
public static final int COLOR_YUV2GRAY_UYVY- See Also:
-
COLOR_YUV2GRAY_YUY2
public static final int COLOR_YUV2GRAY_YUY2- See Also:
-
COLOR_YUV2GRAY_Y422
public static final int COLOR_YUV2GRAY_Y422- See Also:
-
COLOR_YUV2GRAY_UYNV
public static final int COLOR_YUV2GRAY_UYNV- See Also:
-
COLOR_YUV2GRAY_YVYU
public static final int COLOR_YUV2GRAY_YVYU- See Also:
-
COLOR_YUV2GRAY_YUYV
public static final int COLOR_YUV2GRAY_YUYV- See Also:
-
COLOR_YUV2GRAY_YUNV
public static final int COLOR_YUV2GRAY_YUNV- See Also:
-
COLOR_RGBA2mRGBA
public static final int COLOR_RGBA2mRGBA- See Also:
-
COLOR_mRGBA2RGBA
public static final int COLOR_mRGBA2RGBA- See Also:
-
COLOR_RGB2YUV_I420
public static final int COLOR_RGB2YUV_I420- See Also:
-
COLOR_BGR2YUV_I420
public static final int COLOR_BGR2YUV_I420- See Also:
-
COLOR_RGB2YUV_IYUV
public static final int COLOR_RGB2YUV_IYUV- See Also:
-
COLOR_BGR2YUV_IYUV
public static final int COLOR_BGR2YUV_IYUV- See Also:
-
COLOR_RGBA2YUV_I420
public static final int COLOR_RGBA2YUV_I420- See Also:
-
COLOR_BGRA2YUV_I420
public static final int COLOR_BGRA2YUV_I420- See Also:
-
COLOR_RGBA2YUV_IYUV
public static final int COLOR_RGBA2YUV_IYUV- See Also:
-
COLOR_BGRA2YUV_IYUV
public static final int COLOR_BGRA2YUV_IYUV- See Also:
-
COLOR_RGB2YUV_YV12
public static final int COLOR_RGB2YUV_YV12- See Also:
-
COLOR_BGR2YUV_YV12
public static final int COLOR_BGR2YUV_YV12- See Also:
-
COLOR_RGBA2YUV_YV12
public static final int COLOR_RGBA2YUV_YV12- See Also:
-
COLOR_BGRA2YUV_YV12
public static final int COLOR_BGRA2YUV_YV12- See Also:
-
COLOR_BayerBG2BGR
public static final int COLOR_BayerBG2BGR- See Also:
-
COLOR_BayerGB2BGR
public static final int COLOR_BayerGB2BGR- See Also:
-
COLOR_BayerRG2BGR
public static final int COLOR_BayerRG2BGR- See Also:
-
COLOR_BayerGR2BGR
public static final int COLOR_BayerGR2BGR- See Also:
-
COLOR_BayerRGGB2BGR
public static final int COLOR_BayerRGGB2BGR- See Also:
-
COLOR_BayerGRBG2BGR
public static final int COLOR_BayerGRBG2BGR- See Also:
-
COLOR_BayerBGGR2BGR
public static final int COLOR_BayerBGGR2BGR- See Also:
-
COLOR_BayerGBRG2BGR
public static final int COLOR_BayerGBRG2BGR- See Also:
-
COLOR_BayerRGGB2RGB
public static final int COLOR_BayerRGGB2RGB- See Also:
-
COLOR_BayerGRBG2RGB
public static final int COLOR_BayerGRBG2RGB- See Also:
-
COLOR_BayerBGGR2RGB
public static final int COLOR_BayerBGGR2RGB- See Also:
-
COLOR_BayerGBRG2RGB
public static final int COLOR_BayerGBRG2RGB- See Also:
-
COLOR_BayerBG2RGB
public static final int COLOR_BayerBG2RGB- See Also:
-
COLOR_BayerGB2RGB
public static final int COLOR_BayerGB2RGB- See Also:
-
COLOR_BayerRG2RGB
public static final int COLOR_BayerRG2RGB- See Also:
-
COLOR_BayerGR2RGB
public static final int COLOR_BayerGR2RGB- See Also:
-
COLOR_BayerBG2GRAY
public static final int COLOR_BayerBG2GRAY- See Also:
-
COLOR_BayerGB2GRAY
public static final int COLOR_BayerGB2GRAY- See Also:
-
COLOR_BayerRG2GRAY
public static final int COLOR_BayerRG2GRAY- See Also:
-
COLOR_BayerGR2GRAY
public static final int COLOR_BayerGR2GRAY- See Also:
-
COLOR_BayerRGGB2GRAY
public static final int COLOR_BayerRGGB2GRAY- See Also:
-
COLOR_BayerGRBG2GRAY
public static final int COLOR_BayerGRBG2GRAY- See Also:
-
COLOR_BayerBGGR2GRAY
public static final int COLOR_BayerBGGR2GRAY- See Also:
-
COLOR_BayerGBRG2GRAY
public static final int COLOR_BayerGBRG2GRAY- See Also:
-
COLOR_BayerBG2BGR_VNG
public static final int COLOR_BayerBG2BGR_VNG- See Also:
-
COLOR_BayerGB2BGR_VNG
public static final int COLOR_BayerGB2BGR_VNG- See Also:
-
COLOR_BayerRG2BGR_VNG
public static final int COLOR_BayerRG2BGR_VNG- See Also:
-
COLOR_BayerGR2BGR_VNG
public static final int COLOR_BayerGR2BGR_VNG- See Also:
-
COLOR_BayerRGGB2BGR_VNG
public static final int COLOR_BayerRGGB2BGR_VNG- See Also:
-
COLOR_BayerGRBG2BGR_VNG
public static final int COLOR_BayerGRBG2BGR_VNG- See Also:
-
COLOR_BayerBGGR2BGR_VNG
public static final int COLOR_BayerBGGR2BGR_VNG- See Also:
-
COLOR_BayerGBRG2BGR_VNG
public static final int COLOR_BayerGBRG2BGR_VNG- See Also:
-
COLOR_BayerRGGB2RGB_VNG
public static final int COLOR_BayerRGGB2RGB_VNG- See Also:
-
COLOR_BayerGRBG2RGB_VNG
public static final int COLOR_BayerGRBG2RGB_VNG- See Also:
-
COLOR_BayerBGGR2RGB_VNG
public static final int COLOR_BayerBGGR2RGB_VNG- See Also:
-
COLOR_BayerGBRG2RGB_VNG
public static final int COLOR_BayerGBRG2RGB_VNG- See Also:
-
COLOR_BayerBG2RGB_VNG
public static final int COLOR_BayerBG2RGB_VNG- See Also:
-
COLOR_BayerGB2RGB_VNG
public static final int COLOR_BayerGB2RGB_VNG- See Also:
-
COLOR_BayerRG2RGB_VNG
public static final int COLOR_BayerRG2RGB_VNG- See Also:
-
COLOR_BayerGR2RGB_VNG
public static final int COLOR_BayerGR2RGB_VNG- See Also:
-
COLOR_BayerBG2BGR_EA
public static final int COLOR_BayerBG2BGR_EA- See Also:
-
COLOR_BayerGB2BGR_EA
public static final int COLOR_BayerGB2BGR_EA- See Also:
-
COLOR_BayerRG2BGR_EA
public static final int COLOR_BayerRG2BGR_EA- See Also:
-
COLOR_BayerGR2BGR_EA
public static final int COLOR_BayerGR2BGR_EA- See Also:
-
COLOR_BayerRGGB2BGR_EA
public static final int COLOR_BayerRGGB2BGR_EA- See Also:
-
COLOR_BayerGRBG2BGR_EA
public static final int COLOR_BayerGRBG2BGR_EA- See Also:
-
COLOR_BayerBGGR2BGR_EA
public static final int COLOR_BayerBGGR2BGR_EA- See Also:
-
COLOR_BayerGBRG2BGR_EA
public static final int COLOR_BayerGBRG2BGR_EA- See Also:
-
COLOR_BayerRGGB2RGB_EA
public static final int COLOR_BayerRGGB2RGB_EA- See Also:
-
COLOR_BayerGRBG2RGB_EA
public static final int COLOR_BayerGRBG2RGB_EA- See Also:
-
COLOR_BayerBGGR2RGB_EA
public static final int COLOR_BayerBGGR2RGB_EA- See Also:
-
COLOR_BayerGBRG2RGB_EA
public static final int COLOR_BayerGBRG2RGB_EA- See Also:
-
COLOR_BayerBG2RGB_EA
public static final int COLOR_BayerBG2RGB_EA- See Also:
-
COLOR_BayerGB2RGB_EA
public static final int COLOR_BayerGB2RGB_EA- See Also:
-
COLOR_BayerRG2RGB_EA
public static final int COLOR_BayerRG2RGB_EA- See Also:
-
COLOR_BayerGR2RGB_EA
public static final int COLOR_BayerGR2RGB_EA- See Also:
-
COLOR_BayerBG2BGRA
public static final int COLOR_BayerBG2BGRA- See Also:
-
COLOR_BayerGB2BGRA
public static final int COLOR_BayerGB2BGRA- See Also:
-
COLOR_BayerRG2BGRA
public static final int COLOR_BayerRG2BGRA- See Also:
-
COLOR_BayerGR2BGRA
public static final int COLOR_BayerGR2BGRA- See Also:
-
COLOR_BayerRGGB2BGRA
public static final int COLOR_BayerRGGB2BGRA- See Also:
-
COLOR_BayerGRBG2BGRA
public static final int COLOR_BayerGRBG2BGRA- See Also:
-
COLOR_BayerBGGR2BGRA
public static final int COLOR_BayerBGGR2BGRA- See Also:
-
COLOR_BayerGBRG2BGRA
public static final int COLOR_BayerGBRG2BGRA- See Also:
-
COLOR_BayerRGGB2RGBA
public static final int COLOR_BayerRGGB2RGBA- See Also:
-
COLOR_BayerGRBG2RGBA
public static final int COLOR_BayerGRBG2RGBA- See Also:
-
COLOR_BayerBGGR2RGBA
public static final int COLOR_BayerBGGR2RGBA- See Also:
-
COLOR_BayerGBRG2RGBA
public static final int COLOR_BayerGBRG2RGBA- See Also:
-
COLOR_BayerBG2RGBA
public static final int COLOR_BayerBG2RGBA- See Also:
-
COLOR_BayerGB2RGBA
public static final int COLOR_BayerGB2RGBA- See Also:
-
COLOR_BayerRG2RGBA
public static final int COLOR_BayerRG2RGBA- See Also:
-
COLOR_BayerGR2RGBA
public static final int COLOR_BayerGR2RGBA- See Also:
-
COLOR_RGB2YUV_UYVY
public static final int COLOR_RGB2YUV_UYVY- See Also:
-
COLOR_BGR2YUV_UYVY
public static final int COLOR_BGR2YUV_UYVY- See Also:
-
COLOR_RGB2YUV_Y422
public static final int COLOR_RGB2YUV_Y422- See Also:
-
COLOR_BGR2YUV_Y422
public static final int COLOR_BGR2YUV_Y422- See Also:
-
COLOR_RGB2YUV_UYNV
public static final int COLOR_RGB2YUV_UYNV- See Also:
-
COLOR_BGR2YUV_UYNV
public static final int COLOR_BGR2YUV_UYNV- See Also:
-
COLOR_RGBA2YUV_UYVY
public static final int COLOR_RGBA2YUV_UYVY- See Also:
-
COLOR_BGRA2YUV_UYVY
public static final int COLOR_BGRA2YUV_UYVY- See Also:
-
COLOR_RGBA2YUV_Y422
public static final int COLOR_RGBA2YUV_Y422- See Also:
-
COLOR_BGRA2YUV_Y422
public static final int COLOR_BGRA2YUV_Y422- See Also:
-
COLOR_RGBA2YUV_UYNV
public static final int COLOR_RGBA2YUV_UYNV- See Also:
-
COLOR_BGRA2YUV_UYNV
public static final int COLOR_BGRA2YUV_UYNV- See Also:
-
COLOR_RGB2YUV_YUY2
public static final int COLOR_RGB2YUV_YUY2- See Also:
-
COLOR_BGR2YUV_YUY2
public static final int COLOR_BGR2YUV_YUY2- See Also:
-
COLOR_RGB2YUV_YVYU
public static final int COLOR_RGB2YUV_YVYU- See Also:
-
COLOR_BGR2YUV_YVYU
public static final int COLOR_BGR2YUV_YVYU- See Also:
-
COLOR_RGB2YUV_YUYV
public static final int COLOR_RGB2YUV_YUYV- See Also:
-
COLOR_BGR2YUV_YUYV
public static final int COLOR_BGR2YUV_YUYV- See Also:
-
COLOR_RGB2YUV_YUNV
public static final int COLOR_RGB2YUV_YUNV- See Also:
-
COLOR_BGR2YUV_YUNV
public static final int COLOR_BGR2YUV_YUNV- See Also:
-
COLOR_RGBA2YUV_YUY2
public static final int COLOR_RGBA2YUV_YUY2- See Also:
-
COLOR_BGRA2YUV_YUY2
public static final int COLOR_BGRA2YUV_YUY2- See Also:
-
COLOR_RGBA2YUV_YVYU
public static final int COLOR_RGBA2YUV_YVYU- See Also:
-
COLOR_BGRA2YUV_YVYU
public static final int COLOR_BGRA2YUV_YVYU- See Also:
-
COLOR_RGBA2YUV_YUYV
public static final int COLOR_RGBA2YUV_YUYV- See Also:
-
COLOR_BGRA2YUV_YUYV
public static final int COLOR_BGRA2YUV_YUYV- See Also:
-
COLOR_RGBA2YUV_YUNV
public static final int COLOR_RGBA2YUV_YUNV- See Also:
-
COLOR_BGRA2YUV_YUNV
public static final int COLOR_BGRA2YUV_YUNV- See Also:
-
COLOR_COLORCVT_MAX
public static final int COLOR_COLORCVT_MAX- See Also:
-
CC_STAT_LEFT
public static final int CC_STAT_LEFT- See Also:
-
CC_STAT_TOP
public static final int CC_STAT_TOP- See Also:
-
CC_STAT_WIDTH
public static final int CC_STAT_WIDTH- See Also:
-
CC_STAT_HEIGHT
public static final int CC_STAT_HEIGHT- See Also:
-
CC_STAT_AREA
public static final int CC_STAT_AREA- See Also:
-
CC_STAT_MAX
public static final int CC_STAT_MAX- See Also:
-
CHAIN_APPROX_NONE
public static final int CHAIN_APPROX_NONE- See Also:
-
CHAIN_APPROX_SIMPLE
public static final int CHAIN_APPROX_SIMPLE- See Also:
-
CHAIN_APPROX_TC89_L1
public static final int CHAIN_APPROX_TC89_L1- See Also:
-
CHAIN_APPROX_TC89_KCOS
public static final int CHAIN_APPROX_TC89_KCOS- See Also:
-
DIST_LABEL_CCOMP
public static final int DIST_LABEL_CCOMP- See Also:
-
DIST_LABEL_PIXEL
public static final int DIST_LABEL_PIXEL- See Also:
-
DIST_MASK_3
public static final int DIST_MASK_3- See Also:
-
DIST_MASK_5
public static final int DIST_MASK_5- See Also:
-
DIST_MASK_PRECISE
public static final int DIST_MASK_PRECISE- See Also:
-
DIST_USER
public static final int DIST_USER- See Also:
-
DIST_L1
public static final int DIST_L1- See Also:
-
DIST_L2
public static final int DIST_L2- See Also:
-
DIST_C
public static final int DIST_C- See Also:
-
DIST_L12
public static final int DIST_L12- See Also:
-
DIST_FAIR
public static final int DIST_FAIR- See Also:
-
DIST_WELSCH
public static final int DIST_WELSCH- See Also:
-
DIST_HUBER
public static final int DIST_HUBER- See Also:
-
FLOODFILL_FIXED_RANGE
public static final int FLOODFILL_FIXED_RANGE- See Also:
-
FLOODFILL_MASK_ONLY
public static final int FLOODFILL_MASK_ONLY- See Also:
-
GC_BGD
public static final int GC_BGD- See Also:
-
GC_FGD
public static final int GC_FGD- See Also:
-
GC_PR_BGD
public static final int GC_PR_BGD- See Also:
-
GC_PR_FGD
public static final int GC_PR_FGD- See Also:
-
GC_INIT_WITH_RECT
public static final int GC_INIT_WITH_RECT- See Also:
-
GC_INIT_WITH_MASK
public static final int GC_INIT_WITH_MASK- See Also:
-
GC_EVAL
public static final int GC_EVAL- See Also:
-
GC_EVAL_FREEZE_MODEL
public static final int GC_EVAL_FREEZE_MODEL- See Also:
-
FONT_HERSHEY_SIMPLEX
public static final int FONT_HERSHEY_SIMPLEX- See Also:
-
FONT_HERSHEY_PLAIN
public static final int FONT_HERSHEY_PLAIN- See Also:
-
FONT_HERSHEY_DUPLEX
public static final int FONT_HERSHEY_DUPLEX- See Also:
-
FONT_HERSHEY_COMPLEX
public static final int FONT_HERSHEY_COMPLEX- See Also:
-
FONT_HERSHEY_TRIPLEX
public static final int FONT_HERSHEY_TRIPLEX- See Also:
-
FONT_HERSHEY_COMPLEX_SMALL
public static final int FONT_HERSHEY_COMPLEX_SMALL- See Also:
-
FONT_HERSHEY_SCRIPT_SIMPLEX
public static final int FONT_HERSHEY_SCRIPT_SIMPLEX- See Also:
-
FONT_HERSHEY_SCRIPT_COMPLEX
public static final int FONT_HERSHEY_SCRIPT_COMPLEX- See Also:
-
FONT_ITALIC
public static final int FONT_ITALIC- See Also:
-
HISTCMP_CORREL
public static final int HISTCMP_CORREL- See Also:
-
HISTCMP_CHISQR
public static final int HISTCMP_CHISQR- See Also:
-
HISTCMP_INTERSECT
public static final int HISTCMP_INTERSECT- See Also:
-
HISTCMP_BHATTACHARYYA
public static final int HISTCMP_BHATTACHARYYA- See Also:
-
HISTCMP_HELLINGER
public static final int HISTCMP_HELLINGER- See Also:
-
HISTCMP_CHISQR_ALT
public static final int HISTCMP_CHISQR_ALT- See Also:
-
HISTCMP_KL_DIV
public static final int HISTCMP_KL_DIV- See Also:
-
HOUGH_STANDARD
public static final int HOUGH_STANDARD- See Also:
-
HOUGH_PROBABILISTIC
public static final int HOUGH_PROBABILISTIC- See Also:
-
HOUGH_MULTI_SCALE
public static final int HOUGH_MULTI_SCALE- See Also:
-
HOUGH_GRADIENT
public static final int HOUGH_GRADIENT- See Also:
-
HOUGH_GRADIENT_ALT
public static final int HOUGH_GRADIENT_ALT- See Also:
-
INTER_NEAREST
public static final int INTER_NEAREST- See Also:
-
INTER_LINEAR
public static final int INTER_LINEAR- See Also:
-
INTER_CUBIC
public static final int INTER_CUBIC- See Also:
-
INTER_AREA
public static final int INTER_AREA- See Also:
-
INTER_LANCZOS4
public static final int INTER_LANCZOS4- See Also:
-
INTER_LINEAR_EXACT
public static final int INTER_LINEAR_EXACT- See Also:
-
INTER_NEAREST_EXACT
public static final int INTER_NEAREST_EXACT- See Also:
-
INTER_MAX
public static final int INTER_MAX- See Also:
-
WARP_FILL_OUTLIERS
public static final int WARP_FILL_OUTLIERS- See Also:
-
WARP_INVERSE_MAP
public static final int WARP_INVERSE_MAP- See Also:
-
LSD_REFINE_NONE
public static final int LSD_REFINE_NONE- See Also:
-
LSD_REFINE_STD
public static final int LSD_REFINE_STD- See Also:
-
LSD_REFINE_ADV
public static final int LSD_REFINE_ADV- See Also:
-
FILLED
public static final int FILLED- See Also:
-
LINE_4
public static final int LINE_4- See Also:
-
LINE_8
public static final int LINE_8- See Also:
-
LINE_AA
public static final int LINE_AA- See Also:
-
MARKER_CROSS
public static final int MARKER_CROSS- See Also:
-
MARKER_TILTED_CROSS
public static final int MARKER_TILTED_CROSS- See Also:
-
MARKER_STAR
public static final int MARKER_STAR- See Also:
-
MARKER_DIAMOND
public static final int MARKER_DIAMOND- See Also:
-
MARKER_SQUARE
public static final int MARKER_SQUARE- See Also:
-
MARKER_TRIANGLE_UP
public static final int MARKER_TRIANGLE_UP- See Also:
-
MARKER_TRIANGLE_DOWN
public static final int MARKER_TRIANGLE_DOWN- See Also:
-
INTER_BITS
public static final int INTER_BITS- See Also:
-
INTER_BITS2
public static final int INTER_BITS2- See Also:
-
INTER_TAB_SIZE
public static final int INTER_TAB_SIZE- See Also:
-
INTER_TAB_SIZE2
public static final int INTER_TAB_SIZE2- See Also:
-
MORPH_RECT
public static final int MORPH_RECT- See Also:
-
MORPH_CROSS
public static final int MORPH_CROSS- See Also:
-
MORPH_ELLIPSE
public static final int MORPH_ELLIPSE- See Also:
-
MORPH_ERODE
public static final int MORPH_ERODE- See Also:
-
MORPH_DILATE
public static final int MORPH_DILATE- See Also:
-
MORPH_OPEN
public static final int MORPH_OPEN- See Also:
-
MORPH_CLOSE
public static final int MORPH_CLOSE- See Also:
-
MORPH_GRADIENT
public static final int MORPH_GRADIENT- See Also:
-
MORPH_TOPHAT
public static final int MORPH_TOPHAT- See Also:
-
MORPH_BLACKHAT
public static final int MORPH_BLACKHAT- See Also:
-
MORPH_HITMISS
public static final int MORPH_HITMISS- See Also:
-
INTERSECT_NONE
public static final int INTERSECT_NONE- See Also:
-
INTERSECT_PARTIAL
public static final int INTERSECT_PARTIAL- See Also:
-
INTERSECT_FULL
public static final int INTERSECT_FULL- See Also:
-
RETR_EXTERNAL
public static final int RETR_EXTERNAL- See Also:
-
RETR_LIST
public static final int RETR_LIST- See Also:
-
RETR_CCOMP
public static final int RETR_CCOMP- See Also:
-
RETR_TREE
public static final int RETR_TREE- See Also:
-
RETR_FLOODFILL
public static final int RETR_FLOODFILL- See Also:
-
CONTOURS_MATCH_I1
public static final int CONTOURS_MATCH_I1- See Also:
-
CONTOURS_MATCH_I2
public static final int CONTOURS_MATCH_I2- See Also:
-
CONTOURS_MATCH_I3
public static final int CONTOURS_MATCH_I3- See Also:
-
FILTER_SCHARR
public static final int FILTER_SCHARR- See Also:
-
TM_SQDIFF
public static final int TM_SQDIFF- See Also:
-
TM_SQDIFF_NORMED
public static final int TM_SQDIFF_NORMED- See Also:
-
TM_CCORR
public static final int TM_CCORR- See Also:
-
TM_CCORR_NORMED
public static final int TM_CCORR_NORMED- See Also:
-
TM_CCOEFF
public static final int TM_CCOEFF- See Also:
-
TM_CCOEFF_NORMED
public static final int TM_CCOEFF_NORMED- See Also:
-
THRESH_BINARY
public static final int THRESH_BINARY- See Also:
-
THRESH_BINARY_INV
public static final int THRESH_BINARY_INV- See Also:
-
THRESH_TRUNC
public static final int THRESH_TRUNC- See Also:
-
THRESH_TOZERO
public static final int THRESH_TOZERO- See Also:
-
THRESH_TOZERO_INV
public static final int THRESH_TOZERO_INV- See Also:
-
THRESH_MASK
public static final int THRESH_MASK- See Also:
-
THRESH_OTSU
public static final int THRESH_OTSU- See Also:
-
THRESH_TRIANGLE
public static final int THRESH_TRIANGLE- See Also:
-
WARP_POLAR_LINEAR
public static final int WARP_POLAR_LINEAR- See Also:
-
WARP_POLAR_LOG
public static final int WARP_POLAR_LOG- See Also:
-
-
Constructor Details
-
Imgproc
public Imgproc()
-
-
Method Details
-
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 pseudo-ordering 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
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
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
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
Returns Gaussian filter coefficients. The function computes and returns the \(\texttt{ksize} \times 1\) matrix of Gaussian filter coefficients: \(G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\) where \(i=0..\texttt{ksize}-1\) and \(\alpha\) is the scale factor chosen so that \(\sum_i G_i=1\). Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. You may also use the higher-level GaussianBlur.- Parameters:
ksize- Aperture size. It should be odd ( \(\texttt{ksize} \mod 2 = 1\) ) and positive.sigma- Gaussian standard deviation. If it is non-positive, it is computed from ksize assigma = 0.3*((ksize-1)*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
Returns Gaussian filter coefficients. The function computes and returns the \(\texttt{ksize} \times 1\) matrix of Gaussian filter coefficients: \(G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\) where \(i=0..\texttt{ksize}-1\) and \(\alpha\) is the scale factor chosen so that \(\sum_i G_i=1\). Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. You may also use the higher-level GaussianBlur.- Parameters:
ksize- Aperture size. It should be odd ( \(\texttt{ksize} \mod 2 = 1\) ) and positive.sigma- Gaussian standard deviation. If it is non-positive, it is computed from ksize assigma = 0.3*((ksize-1)*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 \(=2^{ksize*2-dx-dy-2}\). If you are going to filter floating-point images, you are likely to use the normalized kernels. But if you compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve all the fractional bits, you may want to set normalize=false .ktype- Type of filter coefficients. It can be CV_32f or CV_64F .
-
getDerivKernels
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 \(=2^{ksize*2-dx-dy-2}\). If you are going to filter floating-point images, you are likely to use the normalized kernels. But if you compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve all the fractional bits, you may want to set normalize=false .
-
getDerivKernels
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 \(=2^{ksize*2-dx-dy-2}\). If you are going to filter floating-point images, you are likely to use the normalized kernels. But if you compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve all the fractional bits, you may want to set normalize=false .
-
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
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 cross-shaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted.- Returns:
- automatically generated
-
getStructuringElement
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 cross-shaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted.- Returns:
- automatically generated
-
medianBlur
Blurs an image using the median filter. The function smoothes an image using the median filter with the \(\texttt{ksize} \times \texttt{ksize}\) aperture. Each channel of a multi-channel image is processed independently. In-place operation is supported. Note: The median filter uses #BORDER_REPLICATE internally to cope with border pixels, see #BorderTypes- Parameters:
src- input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U.dst- destination array of the same size and type as src.ksize- aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ... 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. In-place filtering is supported.- Parameters:
src- input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- output image of the same size and type as src.ksize- Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma.sigmaX- Gaussian kernel standard deviation in X direction.sigmaY- Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see #getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY.borderType- pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. SEE: sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
-
GaussianBlur
Blurs an image using a Gaussian filter. The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.- Parameters:
src- input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- output image of the same size and type as src.ksize- Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma.sigmaX- Gaussian kernel standard deviation in X direction.sigmaY- Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see #getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY. SEE: sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
-
GaussianBlur
Blurs an image using a Gaussian filter. The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.- Parameters:
src- input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- output image of the same size and type as src.ksize- Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma.sigmaX- Gaussian kernel standard deviation in X direction. 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 real-time applications, and perhaps d=9 for offline applications that need heavy noise filtering. This filter does not work inplace.- Parameters:
src- Source 8-bit or floating-point, 1-channel or 3-channel image.dst- Destination image of the same size and type as src .d- Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace.sigmaColor- Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting in larger areas of semi-equal color.sigmaSpace- Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is proportional to sigmaSpace.borderType- border mode used to extrapolate pixels outside of the image, see #BorderTypes
-
bilateralFilter
Applies the bilateral filter to an image. The function applies bilateral filtering to the input image, as described in http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is very slow compared to most filters. _Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (< 10), the filter will not have much effect, whereas if they are large (> 150), they will have a very strong effect, making the image look "cartoonish". _Filter size_: Large filters (d > 5) are very slow, so it is recommended to use d=5 for real-time applications, and perhaps d=9 for offline applications that need heavy noise filtering. This filter does not work inplace.- Parameters:
src- Source 8-bit or floating-point, 1-channel or 3-channel image.dst- Destination image of the same size and type as src .d- Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace.sigmaColor- Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting in larger areas of semi-equal color.sigmaSpace- Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is proportional to sigmaSpace.
-
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 variable-size windows, use #integral.- Parameters:
src- input image.dst- output image of the same size and type as src.ddepth- the output image depth (-1 to use src.depth()).ksize- blurring kernel size.anchor- anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.normalize- flag, specifying whether the kernel is normalized by its area or not.borderType- border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported. 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 variable-size windows, use #integral.- Parameters:
src- input image.dst- output image of the same size and type as src.ddepth- the output image depth (-1 to use src.depth()).ksize- blurring kernel size.anchor- anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.normalize- flag, specifying whether the kernel is normalized by its area or not. SEE: blur, bilateralFilter, GaussianBlur, medianBlur, integral
-
boxFilter
Blurs an image using the box filter. The function smooths an image using the kernel: \(\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\) where \(\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\) Unnormalized box filter is useful for computing various integral characteristics over each pixel neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral.- Parameters:
src- input image.dst- output image of the same size and type as src.ddepth- the output image depth (-1 to use src.depth()).ksize- blurring kernel size.anchor- anchor point; default value Point(-1,-1) means that the anchor is at the kernel center. SEE: blur, bilateralFilter, GaussianBlur, medianBlur, integral
-
boxFilter
Blurs an image using the box filter. The function smooths an image using the kernel: \(\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\) where \(\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\) Unnormalized box filter is useful for computing various integral characteristics over each pixel neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral.- Parameters:
src- input image.dst- output image of the same size and type as src.ddepth- the output image depth (-1 to use src.depth()).ksize- blurring kernel size. 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
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
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
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
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
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
Blurs an image using the stackBlur. The function applies and stackBlur to an image. stackBlur can generate similar results as Gaussian blur, and the time consumption does not increase with the increase of kernel size. It creates a kind of moving stack of colors whilst scanning through the image. Thereby it just has to add one new block of color to the right side of the stack and remove the leftmost color. The remaining colors on the topmost layer of the stack are either added on or reduced by one, depending on if they are on the right or on the left side of the stack. The only supported borderType is BORDER_REPLICATE. Original paper was proposed by Mario Klingemann, which can be found http://underdestruction.com/2004/02/25/stackblur-2004.- Parameters:
src- input image. The number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S or CV_32F.dst- output image of the same size and type as src.ksize- stack-blurring kernel size. The ksize.width and ksize.height can differ but they both must be positive and odd.
-
filter2D
public static void filter2D(Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta, int borderType) Convolves an image with the kernel. The function applies an arbitrary linear filter to an image. In-place operation is supported. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode. The function does actually compute correlation, not the convolution: \(\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\) That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1)`. The function uses the DFT-based algorithm in case of sufficiently large kernels (~11 x 11or 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 single-channel floating point matrix; if you want to apply different kernels to different channels, split the image into separate color planes using split and process them individually.anchor- anchor of the kernel that indicates the relative position of a filtered point within the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor is at the kernel center.delta- optional value added to the filtered pixels before storing them in dst.borderType- pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. SEE: sepFilter2D, dft, matchTemplate
-
filter2D
Convolves an image with the kernel. The function applies an arbitrary linear filter to an image. In-place operation is supported. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode. The function does actually compute correlation, not the convolution: \(\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\) That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1)`. The function uses the DFT-based algorithm in case of sufficiently large kernels (~11 x 11or 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 single-channel floating point matrix; if you want to apply different kernels to different channels, split the image into separate color planes using split and process them individually.anchor- anchor of the kernel that indicates the relative position of a filtered point within the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor is at the kernel center.delta- optional value added to the filtered pixels before storing them in dst. SEE: sepFilter2D, dft, matchTemplate
-
filter2D
Convolves an image with the kernel. The function applies an arbitrary linear filter to an image. In-place operation is supported. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode. The function does actually compute correlation, not the convolution: \(\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\) That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1)`. The function uses the DFT-based algorithm in case of sufficiently large kernels (~11 x 11or 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 single-channel floating point matrix; if you want to apply different kernels to different channels, split the image into separate color planes using split and process them individually.anchor- anchor of the kernel that indicates the relative position of a filtered point within the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor is at the kernel center. SEE: sepFilter2D, dft, matchTemplate
-
filter2D
Convolves an image with the kernel. The function applies an arbitrary linear filter to an image. In-place operation is supported. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode. The function does actually compute correlation, not the convolution: \(\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\) That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1)`. The function uses the DFT-based algorithm in case of sufficiently large kernels (~11 x 11or 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 single-channel floating point matrix; if you want to apply different kernels to different channels, split the image into separate color planes using split and process them individually. the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor is at the kernel center. SEE: sepFilter2D, dft, matchTemplate
-
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
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 = 1can 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 x-derivative, or transposed for the y-derivative. The function calculates an image derivative by convolving the image with the appropriate kernel: \(\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\) The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first case corresponds to a kernel of: \(\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\) The second case corresponds to a kernel of: \(\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\)- Parameters:
src- input image.dst- output image of the same size and the same number of channels as src .ddepth- output image depth, see REF: filter_depths "combinations"; in the case of 8-bit input images it will result in truncated derivatives.dx- order of the derivative x.dy- order of the derivative y.ksize- size of the extended Sobel kernel; it must be 1, 3, 5, or 7.scale- optional scale factor for the computed derivative values; by default, no scaling is applied (see #getDerivKernels for details).delta- optional delta value that is added to the results prior to storing them in dst.borderType- pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. 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 = 1can 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 x-derivative, or transposed for the y-derivative. The function calculates an image derivative by convolving the image with the appropriate kernel: \(\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\) The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first case corresponds to a kernel of: \(\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\) The second case corresponds to a kernel of: \(\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\)- Parameters:
src- input image.dst- output image of the same size and the same number of channels as src .ddepth- output image depth, see REF: filter_depths "combinations"; in the case of 8-bit input images it will result in truncated derivatives.dx- order of the derivative x.dy- order of the derivative y.ksize- size of the extended Sobel kernel; it must be 1, 3, 5, or 7.scale- optional scale factor for the computed derivative values; by default, no scaling is applied (see #getDerivKernels for details).delta- optional delta value that is added to the results prior to storing them in dst. SEE: Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
-
Sobel
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 = 1can 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 x-derivative, or transposed for the y-derivative. The function calculates an image derivative by convolving the image with the appropriate kernel: \(\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\) The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first case corresponds to a kernel of: \(\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\) The second case corresponds to a kernel of: \(\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\)- Parameters:
src- input image.dst- output image of the same size and the same number of channels as src .ddepth- output image depth, see REF: filter_depths "combinations"; in the case of 8-bit input images it will result in truncated derivatives.dx- order of the derivative x.dy- order of the derivative y.ksize- size of the extended Sobel kernel; it must be 1, 3, 5, or 7.scale- optional scale factor for the computed derivative values; by default, no scaling is applied (see #getDerivKernels for details). SEE: Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
-
Sobel
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 = 1can 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 x-derivative, or transposed for the y-derivative. The function calculates an image derivative by convolving the image with the appropriate kernel: \(\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\) The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first case corresponds to a kernel of: \(\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\) The second case corresponds to a kernel of: \(\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\)- Parameters:
src- input image.dst- output image of the same size and the same number of channels as src .ddepth- output image depth, see REF: filter_depths "combinations"; in the case of 8-bit input images it will result in truncated derivatives.dx- order of the derivative x.dy- order of the derivative y.ksize- size of the extended Sobel kernel; it must be 1, 3, 5, or 7. applied (see #getDerivKernels for details). SEE: Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
-
Sobel
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 = 1can 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 x-derivative, or transposed for the y-derivative. The function calculates an image derivative by convolving the image with the appropriate kernel: \(\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\) The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first case corresponds to a kernel of: \(\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\) The second case corresponds to a kernel of: \(\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\)- Parameters:
src- input image.dst- output image of the same size and the same number of channels as src .ddepth- output image depth, see REF: filter_depths "combinations"; in the case of 8-bit input images it will result in truncated derivatives.dx- order of the derivative x.dy- order of the derivative y. applied (see #getDerivKernels for details). SEE: Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
-
spatialGradient
Calculates the first order image derivative in both x and y using a Sobel operator Equivalent to calling:Sobel( src, dx, CV_16SC1, 1, 0, 3 ); Sobel( src, dy, CV_16SC1, 0, 1, 3 );- Parameters:
src- input image.dx- output image with first-order derivative in x.dy- output image with first-order derivative in y.ksize- size of Sobel kernel. It must be 3.borderType- pixel extrapolation method, see #BorderTypes. Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported. SEE: Sobel
-
spatialGradient
Calculates the first order image derivative in both x and y using a Sobel operator Equivalent to calling:Sobel( src, dx, CV_16SC1, 1, 0, 3 ); Sobel( src, dy, CV_16SC1, 0, 1, 3 );- Parameters:
src- input image.dx- output image with first-order derivative in x.dy- output image with first-order derivative in y.ksize- size of Sobel kernel. It must be 3. Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported. SEE: Sobel
-
spatialGradient
Calculates the first order image derivative in both x and y using a Sobel operator Equivalent to calling:Sobel( src, dx, CV_16SC1, 1, 0, 3 ); Sobel( src, dy, CV_16SC1, 0, 1, 3 );- Parameters:
src- input image.dx- output image with first-order derivative in x.dy- output image with first-order derivative in y. 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
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
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
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 second-derivative filters. See #getDerivKernels for details. The size must be positive and odd.scale- Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See #getDerivKernels for details.delta- Optional delta value that is added to the results prior to storing them in dst .borderType- Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. SEE: Sobel, Scharr
-
Laplacian
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 second-derivative filters. See #getDerivKernels for details. The size must be positive and odd.scale- Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See #getDerivKernels for details.delta- Optional delta value that is added to the results prior to storing them in dst . SEE: Sobel, Scharr
-
Laplacian
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 second-derivative filters. See #getDerivKernels for details. The size must be positive and odd.scale- Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See #getDerivKernels for details. SEE: Sobel, Scharr
-
Laplacian
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 second-derivative filters. See #getDerivKernels for details. The size must be positive and odd. applied. See #getDerivKernels for details. SEE: Sobel, Scharr
-
Laplacian
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- 8-bit input image.edges- output edge map; single channels 8-bit image, which has the same size as image .threshold1- first threshold for the hysteresis procedure.threshold2- second threshold for the hysteresis procedure.apertureSize- aperture size for the Sobel operator.L2gradient- a flag, indicating whether a more accurate \(L_2\) norm \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) should be used to calculate the image gradient magnitude ( L2gradient=true ), or whether the default \(L_1\) norm \(=|dI/dx|+|dI/dy|\) is enough ( L2gradient=false ).
-
Canny
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- 8-bit input image.edges- output edge map; single channels 8-bit image, which has the same size as image .threshold1- first threshold for the hysteresis procedure.threshold2- second threshold for the hysteresis procedure.apertureSize- aperture size for the Sobel operator. \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) 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
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- 8-bit input image.edges- output edge map; single channels 8-bit image, which has the same size as image .threshold1- first threshold for the hysteresis procedure.threshold2- second threshold for the hysteresis procedure. \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) 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- 16-bit x derivative of input image (CV_16SC1 or CV_16SC3).dy- 16-bit y derivative of input image (same type as dx).edges- output edge map; single channels 8-bit image, which has the same size as image .threshold1- first threshold for the hysteresis procedure.threshold2- second threshold for the hysteresis procedure.L2gradient- a flag, indicating whether a more accurate \(L_2\) norm \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) should be used to calculate the image gradient magnitude ( L2gradient=true ), or whether the default \(L_1\) norm \(=|dI/dx|+|dI/dy|\) is enough ( L2gradient=false ).
-
Canny
\overload Finds edges in an image using the Canny algorithm with custom image gradient.- Parameters:
dx- 16-bit x derivative of input image (CV_16SC1 or CV_16SC3).dy- 16-bit y derivative of input image (same type as dx).edges- output edge map; single channels 8-bit image, which has the same size as image .threshold1- first threshold for the hysteresis procedure.threshold2- second threshold for the hysteresis procedure. \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) 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
Calculates the minimal eigenvalue of gradient matrices for corner detection. The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is, \(\min(\lambda_1, \lambda_2)\) in terms of the formulae in the cornerEigenValsAndVecs description.- Parameters:
src- Input single-channel 8-bit or floating-point image.dst- Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as src .blockSize- Neighborhood size (see the details on #cornerEigenValsAndVecs ).ksize- Aperture parameter for the Sobel operator.borderType- Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
-
cornerMinEigenVal
Calculates the minimal eigenvalue of gradient matrices for corner detection. The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is, \(\min(\lambda_1, \lambda_2)\) in terms of the formulae in the cornerEigenValsAndVecs description.- Parameters:
src- Input single-channel 8-bit or floating-point image.dst- Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as src .blockSize- Neighborhood size (see the details on #cornerEigenValsAndVecs ).ksize- Aperture parameter for the Sobel operator.
-
cornerMinEigenVal
Calculates the minimal eigenvalue of gradient matrices for corner detection. The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is, \(\min(\lambda_1, \lambda_2)\) in terms of the formulae in the cornerEigenValsAndVecs description.- Parameters:
src- Input single-channel 8-bit or floating-point image.dst- Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as src .blockSize- Neighborhood size (see the details on #cornerEigenValsAndVecs ).
-
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 single-channel 8-bit or floating-point image.dst- Image to store the Harris detector responses. It has the type CV_32FC1 and the same size as src .blockSize- Neighborhood size (see the details on #cornerEigenValsAndVecs ).ksize- Aperture parameter for the Sobel operator.k- Harris detector free parameter. See the formula above.borderType- Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
-
cornerHarris
Harris corner detector. The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and cornerEigenValsAndVecs , for each pixel \((x, y)\) it calculates a \(2\times2\) gradient covariance matrix \(M^{(x,y)}\) over a \(\texttt{blockSize} \times \texttt{blockSize}\) neighborhood. Then, it computes the following characteristic: \(\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\) Corners in the image can be found as the local maxima of this response map.- Parameters:
src- Input single-channel 8-bit or floating-point image.dst- Image to store the Harris detector responses. It has the type CV_32FC1 and the same size as src .blockSize- Neighborhood size (see the details on #cornerEigenValsAndVecs ).ksize- Aperture parameter for the Sobel operator.k- Harris detector free parameter. See the formula above.
-
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 non-sorted eigenvalues of \(M\)
- \(x_1, y_1\) are the eigenvectors corresponding to \(\lambda_1\)
- \(x_2, y_2\) are the eigenvectors corresponding to \(\lambda_2\)
- Parameters:
src- Input single-channel 8-bit or floating-point image.dst- Image to store the results. It has the same size as src and the type CV_32FC(6) .blockSize- Neighborhood size (see details below).ksize- Aperture parameter for the Sobel operator.borderType- Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported. SEE: cornerMinEigenVal, cornerHarris, preCornerDetect
-
cornerEigenValsAndVecs
Calculates eigenvalues and eigenvectors of image blocks for corner detection. For every pixel \(p\) , the function cornerEigenValsAndVecs considers a blockSize \(\times\) blockSize neighborhood \(S(p)\) . It calculates the covariation matrix of derivatives over the neighborhood as: \(M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\) where the derivatives are computed using the Sobel operator. After that, it finds eigenvectors and eigenvalues of \(M\) and stores them in the destination image as \((\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\) where- \(\lambda_1, \lambda_2\) are the non-sorted eigenvalues of \(M\)
- \(x_1, y_1\) are the eigenvectors corresponding to \(\lambda_1\)
- \(x_2, y_2\) are the eigenvectors corresponding to \(\lambda_2\)
- Parameters:
src- Input single-channel 8-bit or floating-point image.dst- Image to store the results. It has the same size as src and the type CV_32FC(6) .blockSize- Neighborhood size (see details below).ksize- Aperture parameter for the Sobel operator. SEE: cornerMinEigenVal, cornerHarris, preCornerDetect
-
preCornerDetect
Calculates a feature map for corner detection. The function calculates the complex spatial derivative-based function of the source image \(\texttt{dst} = (D_x \texttt{src} )^2 \cdot D_{yy} \texttt{src} + (D_y \texttt{src} )^2 \cdot D_{xx} \texttt{src} - 2 D_x \texttt{src} \cdot D_y \texttt{src} \cdot D_{xy} \texttt{src}\) where \(D_x\),\(D_y\) are the first image derivatives, \(D_{xx}\),\(D_{yy}\) are the second image derivatives, and \(D_{xy}\) is the mixed derivative. The corners can be found as local maximums of the functions, as shown below:Mat corners, dilated_corners; preCornerDetect(image, corners, 3); // dilation with 3x3 rectangular structuring element dilate(corners, dilated_corners, Mat(), 1); Mat corner_mask = corners == dilated_corners;- Parameters:
src- Source single-channel 8-bit of floating-point image.dst- Output image that has the type CV_32F and the same size as src .ksize- %Aperture size of the Sobel .borderType- Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
-
preCornerDetect
Calculates a feature map for corner detection. The function calculates the complex spatial derivative-based function of the source image \(\texttt{dst} = (D_x \texttt{src} )^2 \cdot D_{yy} \texttt{src} + (D_y \texttt{src} )^2 \cdot D_{xx} \texttt{src} - 2 D_x \texttt{src} \cdot D_y \texttt{src} \cdot D_{xy} \texttt{src}\) where \(D_x\),\(D_y\) are the first image derivatives, \(D_{xx}\),\(D_{yy}\) are the second image derivatives, and \(D_{xy}\) is the mixed derivative. The corners can be found as local maximums of the functions, as shown below:Mat corners, dilated_corners; preCornerDetect(image, corners, 3); // dilation with 3x3 rectangular structuring element dilate(corners, dilated_corners, Mat(), 1); Mat corner_mask = corners == dilated_corners;- Parameters:
src- Source single-channel 8-bit of floating-point image.dst- Output image that has the type CV_32F and the same size as src .ksize- %Aperture size of the Sobel .
-
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 sub-pixel accurate location of corners or radial saddle points as described in CITE: forstner1987fast, and as shown on the figure below.  Sub-pixel accurate corner locator is based on the observation that every vector from the center \(q\) to a point \(p\) located within a neighborhood of \(q\) is orthogonal to the image gradient at \(p\) subject to image and measurement noise. Consider the expression: \(\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)\) where \({DI_{p_i}}\) is an image gradient at one of the points \(p_i\) in a neighborhood of \(q\) . The value of \(q\) is to be found so that \(\epsilon_i\) is minimized. A system of equations may be set up with \(\epsilon_i\) set to zero: \(\sum _i(DI_{p_i} \cdot {DI_{p_i}}^T) \cdot q - \sum _i(DI_{p_i} \cdot {DI_{p_i}}^T \cdot p_i)\) where the gradients are summed within a neighborhood ("search window") of \(q\) . Calling the first gradient term \(G\) and the second gradient term \(b\) gives: \(q = G^{-1} \cdot b\) The algorithm sets the center of the neighborhood window at this new center \(q\) and then iterates until the center stays within a set threshold.- Parameters:
image- Input single-channel, 8-bit or float image.corners- Initial coordinates of the input corners and refined coordinates provided for output.winSize- Half of the side length of the search window. For example, if winSize=Size(5,5) , then a \((5*2+1) \times (5*2+1) = 11 \times 11\) search window is used.zeroZone- Half of the size of the dead region in the middle of the search zone over which the summation in the formula below is not done. It is used sometimes to avoid possible singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such a size.criteria- Criteria for termination of the iterative process of corner refinement. That is, the process of corner position refinement stops either after criteria.maxCount iterations or when the corner position moves by less than criteria.epsilon on some iteration.
-
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 non-maximum suppression (the local maximums in *3 x 3* neighborhood are retained).
- The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
- The remaining corners are sorted by the quality measure in the descending order.
- Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.
- Parameters:
image- Input 8-bit or floating-point 32-bit, single-channel image.corners- Output vector of detected corners.maxCorners- Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.maxCorners <= 0implies 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 non-maximum suppression (the local maximums in *3 x 3* neighborhood are retained).
- The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
- The remaining corners are sorted by the quality measure in the descending order.
- Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.
- Parameters:
image- Input 8-bit or floating-point 32-bit, single-channel image.corners- Output vector of detected corners.maxCorners- Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.maxCorners <= 0implies 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 non-maximum suppression (the local maximums in *3 x 3* neighborhood are retained).
- The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
- The remaining corners are sorted by the quality measure in the descending order.
- Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.
- Parameters:
image- Input 8-bit or floating-point 32-bit, single-channel image.corners- Output vector of detected corners.maxCorners- Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.maxCorners <= 0implies 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 non-maximum suppression (the local maximums in *3 x 3* neighborhood are retained).
- The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
- The remaining corners are sorted by the quality measure in the descending order.
- Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.
- Parameters:
image- Input 8-bit or floating-point 32-bit, single-channel image.corners- Output vector of detected corners.maxCorners- Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.maxCorners <= 0implies 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 non-maximum suppression (the local maximums in *3 x 3* neighborhood are retained).
- The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
- The remaining corners are sorted by the quality measure in the descending order.
- Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.
- Parameters:
image- Input 8-bit or floating-point 32-bit, single-channel image.corners- Output vector of detected corners.maxCorners- Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.maxCorners <= 0implies 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 8-bit or floating-point 32-bit, single-channel image.corners- Output vector of detected corners.maxCorners- Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.maxCorners <= 0implies 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 8-bit or floating-point 32-bit, single-channel image.corners- Output vector of detected corners.maxCorners- Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.maxCorners <= 0implies 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 8-bit or floating-point 32-bit, single-channel image.corners- Output vector of detected corners.maxCorners- Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.maxCorners <= 0implies 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 8-bit or floating-point 32-bit, single-channel image.corners- Output vector of detected corners.maxCorners- Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.maxCorners <= 0implies 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 8-bit or floating-point 32-bit, single-channel image.corners- Output vector of detected corners.maxCorners- Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.maxCorners <= 0implies 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 multi-scale Hough transform algorithm for line detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough transform.- Parameters:
image- 8-bit, single-channel binary source image. The image may be modified by the function.lines- Output vector of lines. Each line is represented by a 2 or 3 element vector \((\rho, \theta)\) or \((\rho, \theta, \textrm{votes})\), where \(\rho\) is the distance from the coordinate origin \((0,0)\) (top-left corner of the image), \(\theta\) is the line rotation angle in radians ( \(0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\) ), and \(\textrm{votes}\) is the value of accumulator.rho- Distance resolution of the accumulator in pixels.theta- Angle resolution of the accumulator in radians.threshold- %Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ).srn- For the multi-scale Hough transform, it is a divisor for the distance resolution rho. The coarse accumulator distance resolution is rho and the accurate accumulator resolution is rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these parameters should be positive.stn- For the multi-scale Hough transform, it is a divisor for the distance resolution theta.min_theta- For standard and multi-scale Hough transform, minimum angle to check for lines. Must fall between 0 and max_theta.max_theta- For standard and multi-scale Hough transform, an upper bound for the angle. Must fall between min_theta and CV_PI. The actual maximum angle in the accumulator may be slightly less than max_theta, depending on the parameters min_theta and theta.
-
HoughLines
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 multi-scale Hough transform algorithm for line detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough transform.- Parameters:
image- 8-bit, single-channel binary source image. The image may be modified by the function.lines- Output vector of lines. Each line is represented by a 2 or 3 element vector \((\rho, \theta)\) or \((\rho, \theta, \textrm{votes})\), where \(\rho\) is the distance from the coordinate origin \((0,0)\) (top-left corner of the image), \(\theta\) is the line rotation angle in radians ( \(0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\) ), and \(\textrm{votes}\) is the value of accumulator.rho- Distance resolution of the accumulator in pixels.theta- Angle resolution of the accumulator in radians.threshold- %Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ).srn- For the multi-scale Hough transform, it is a divisor for the distance resolution rho. The coarse accumulator distance resolution is rho and the accurate accumulator resolution is rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these parameters should be positive.stn- For the multi-scale Hough transform, it is a divisor for the distance resolution theta.min_theta- For standard and multi-scale Hough transform, minimum angle to check for lines. Must fall between 0 and max_theta. 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 multi-scale Hough transform algorithm for line detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough transform.- Parameters:
image- 8-bit, single-channel binary source image. The image may be modified by the function.lines- Output vector of lines. Each line is represented by a 2 or 3 element vector \((\rho, \theta)\) or \((\rho, \theta, \textrm{votes})\), where \(\rho\) is the distance from the coordinate origin \((0,0)\) (top-left corner of the image), \(\theta\) is the line rotation angle in radians ( \(0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\) ), and \(\textrm{votes}\) is the value of accumulator.rho- Distance resolution of the accumulator in pixels.theta- Angle resolution of the accumulator in radians.threshold- %Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ).srn- For the multi-scale Hough transform, it is a divisor for the distance resolution rho. The coarse accumulator distance resolution is rho and the accurate accumulator resolution is rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these parameters should be positive.stn- For the multi-scale Hough transform, it is a divisor for the distance resolution theta. 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 multi-scale Hough transform algorithm for line detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough transform.- Parameters:
image- 8-bit, single-channel binary source image. The image may be modified by the function.lines- Output vector of lines. Each line is represented by a 2 or 3 element vector \((\rho, \theta)\) or \((\rho, \theta, \textrm{votes})\), where \(\rho\) is the distance from the coordinate origin \((0,0)\) (top-left corner of the image), \(\theta\) is the line rotation angle in radians ( \(0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\) ), and \(\textrm{votes}\) is the value of accumulator.rho- Distance resolution of the accumulator in pixels.theta- Angle resolution of the accumulator in radians.threshold- %Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ).srn- For the multi-scale Hough transform, it is a divisor for the distance resolution rho. The coarse accumulator distance resolution is rho and the accurate accumulator resolution is rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these parameters should be positive. 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
Finds lines in a binary image using the standard Hough transform. The function implements the standard or standard multi-scale Hough transform algorithm for line detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough transform.- Parameters:
image- 8-bit, single-channel binary source image. The image may be modified by the function.lines- Output vector of lines. Each line is represented by a 2 or 3 element vector \((\rho, \theta)\) or \((\rho, \theta, \textrm{votes})\), where \(\rho\) is the distance from the coordinate origin \((0,0)\) (top-left corner of the image), \(\theta\) is the line rotation angle in radians ( \(0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\) ), and \(\textrm{votes}\) is the value of accumulator.rho- Distance resolution of the accumulator in pixels.theta- Angle resolution of the accumulator in radians.threshold- %Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ). 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:  And this is the output of the above program in case of the probabilistic Hough transform: - Parameters:
image- 8-bit, single-channel binary source image. The image may be modified by the function.lines- Output vector of lines. Each line is represented by a 4-element vector \((x_1, y_1, x_2, y_2)\) , where \((x_1,y_1)\) and \((x_2, y_2)\) are the ending points of each detected line segment.rho- Distance resolution of the accumulator in pixels.theta- Angle resolution of the accumulator in radians.threshold- %Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ).minLineLength- Minimum line length. Line segments shorter than that are rejected.maxLineGap- Maximum allowed gap between points on the same line to link them. SEE: 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:  And this is the output of the above program in case of the probabilistic Hough transform: - Parameters:
image- 8-bit, single-channel binary source image. The image may be modified by the function.lines- Output vector of lines. Each line is represented by a 4-element vector \((x_1, y_1, x_2, y_2)\) , where \((x_1,y_1)\) and \((x_2, y_2)\) are the ending points of each detected line segment.rho- Distance resolution of the accumulator in pixels.theta- Angle resolution of the accumulator in radians.threshold- %Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ).minLineLength- Minimum line length. Line segments shorter than that are rejected. SEE: LineSegmentDetector
-
HoughLinesP
Finds line segments in a binary image using the probabilistic Hough transform. The function implements the probabilistic Hough transform algorithm for line detection, described in CITE: Matas00 See the line detection example below: INCLUDE: snippets/imgproc_HoughLinesP.cpp This is a sample picture the function parameters have been tuned for:  And this is the output of the above program in case of the probabilistic Hough transform: - Parameters:
image- 8-bit, single-channel binary source image. The image may be modified by the function.lines- Output vector of lines. Each line is represented by a 4-element vector \((x_1, y_1, x_2, y_2)\) , where \((x_1,y_1)\) and \((x_2, y_2)\) are the ending points of each detected line segment.rho- Distance resolution of the accumulator in pixels.theta- Angle resolution of the accumulator in radians.threshold- %Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ). 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- 8-bit, single-channel, grayscale input image.circles- Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .method- Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT.dp- Inverse ratio of the accumulator resolution to the image resolution. For example, if dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5, unless some small very circles need to be detected.minDist- Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.param1- First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT, it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value should normally be higher, such as 300 or normally exposed and contrasty images.param2- Second method-specific parameter. In case of #HOUGH_GRADIENT, it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles.minRadius- Minimum circle radius.maxRadius- Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, #HOUGH_GRADIENT returns centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses. SEE: 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- 8-bit, single-channel, grayscale input image.circles- Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .method- Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT.dp- Inverse ratio of the accumulator resolution to the image resolution. For example, if dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5, unless some small very circles need to be detected.minDist- Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.param1- First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT, it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value should normally be higher, such as 300 or normally exposed and contrasty images.param2- Second method-specific parameter. In case of #HOUGH_GRADIENT, it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles.minRadius- Minimum circle radius. 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- 8-bit, single-channel, grayscale input image.circles- Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .method- Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT.dp- Inverse ratio of the accumulator resolution to the image resolution. For example, if dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5, unless some small very circles need to be detected.minDist- Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.param1- First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT, it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value should normally be higher, such as 300 or normally exposed and contrasty images.param2- Second method-specific parameter. In case of #HOUGH_GRADIENT, it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. 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- 8-bit, single-channel, grayscale input image.circles- Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .method- Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT.dp- Inverse ratio of the accumulator resolution to the image resolution. For example, if dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5, unless some small very circles need to be detected.minDist- Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.param1- First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT, it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value should normally be higher, such as 300 or normally exposed and contrasty images. 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
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- 8-bit, single-channel, grayscale input image.circles- Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .method- Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT.dp- Inverse ratio of the accumulator resolution to the image resolution. For example, if dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5, unless some small very circles need to be detected.minDist- Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed. it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value should normally be higher, such as 300 or normally exposed and contrasty images. accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses. SEE: fitEllipse, minEnclosingCircle
-
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 in-place mode. Erosion can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.- Parameters:
src- input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- output image of the same size and type as src.kernel- structuring element used for erosion; ifelement=Mat(), a3 x 3rectangular 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 in-place mode. Erosion can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.- Parameters:
src- input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- output image of the same size and type as src.kernel- structuring element used for erosion; ifelement=Mat(), a3 x 3rectangular 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
Erodes an image by using a specific structuring element. The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken: \(\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\) The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.- Parameters:
src- input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- output image of the same size and type as src.kernel- structuring element used for erosion; ifelement=Mat(), a3 x 3rectangular 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
Erodes an image by using a specific structuring element. The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken: \(\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\) The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.- Parameters:
src- input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- output image of the same size and type as src.kernel- structuring element used for erosion; ifelement=Mat(), a3 x 3rectangular 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
Erodes an image by using a specific structuring element. The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken: \(\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\) The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.- Parameters:
src- input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- output image of the same size and type as src.kernel- structuring element used for erosion; ifelement=Mat(), a3 x 3rectangular 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 in-place mode. Dilation can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.- Parameters:
src- input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- output image of the same size and type as src.kernel- structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using #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 in-place mode. Dilation can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.- Parameters:
src- input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- output image of the same size and type as src.kernel- structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using #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
Dilates an image by using a specific structuring element. The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken: \(\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\) The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.- Parameters:
src- input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- output image of the same size and type as src.kernel- structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using #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
Dilates an image by using a specific structuring element. The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken: \(\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\) The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.- Parameters:
src- input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- output image of the same size and type as src.kernel- structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using #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
Dilates an image by using a specific structuring element. The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken: \(\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\) The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.- Parameters:
src- input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- output image of the same size and type as src.kernel- structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using #getStructuringElement anchor 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 in-place. In case of multi-channel images, each channel is processed independently.- Parameters:
src- Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- Destination image of the same size and type as source image.op- Type of a morphological operation, see #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 in-place. In case of multi-channel images, each channel is processed independently.- Parameters:
src- Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- Destination image of the same size and type as source image.op- Type of a morphological operation, see #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
Performs advanced morphological transformations. The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as basic operations. Any of the operations can be done in-place. In case of multi-channel images, each channel is processed independently.- Parameters:
src- Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- Destination image of the same size and type as source image.op- Type of a morphological operation, see #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
Performs advanced morphological transformations. The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as basic operations. Any of the operations can be done in-place. In case of multi-channel images, each channel is processed independently.- Parameters:
src- Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- Destination image of the same size and type as source image.op- Type of a morphological operation, see #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
Performs advanced morphological transformations. The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as basic operations. Any of the operations can be done in-place. In case of multi-channel images, each channel is processed independently.- Parameters:
src- Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.dst- Destination image of the same size and type as source image.op- Type of a morphological operation, see #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
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 pre-created dst, you may call the function as follows:// explicitly specify dsize=dst.size(); fx and fy will be computed from that. resize(src, dst, dst.size(), 0, 0, interpolation);If you want to decimate the image by factor of 2 in each direction, you can call the function this way:// specify fx and fy and let the function compute the destination image size. resize(src, dst, Size(), 0.5, 0.5, interpolation);To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to enlarge an image, it will generally look best with #INTER_CUBIC (slow) or #INTER_LINEAR (faster but still looks OK).- Parameters:
src- input image.dst- output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.dsize- output image size; if it equals zero (Nonein Python), it is computed as: \(\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\) Either dsize or both fx and fy must be non-zero.fx- scale factor along the horizontal axis; when it equals 0, it is computed as \(\texttt{(double)dsize.width/src.cols}\)fy- scale factor along the vertical axis; when it equals 0, it is computed as \(\texttt{(double)dsize.height/src.rows}\)interpolation- interpolation method, see #InterpolationFlags SEE: warpAffine, warpPerspective, remap
-
resize
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 pre-created dst, you may call the function as follows:// explicitly specify dsize=dst.size(); fx and fy will be computed from that. resize(src, dst, dst.size(), 0, 0, interpolation);If you want to decimate the image by factor of 2 in each direction, you can call the function this way:// specify fx and fy and let the function compute the destination image size. resize(src, dst, Size(), 0.5, 0.5, interpolation);To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to enlarge an image, it will generally look best with #INTER_CUBIC (slow) or #INTER_LINEAR (faster but still looks OK).- Parameters:
src- input image.dst- output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.dsize- output image size; if it equals zero (Nonein Python), it is computed as: \(\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\) Either dsize or both fx and fy must be non-zero.fx- scale factor along the horizontal axis; when it equals 0, it is computed as \(\texttt{(double)dsize.width/src.cols}\)fy- scale factor along the vertical axis; when it equals 0, it is computed as \(\texttt{(double)dsize.height/src.rows}\) SEE: warpAffine, warpPerspective, remap
-
resize
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 pre-created dst, you may call the function as follows:// explicitly specify dsize=dst.size(); fx and fy will be computed from that. resize(src, dst, dst.size(), 0, 0, interpolation);If you want to decimate the image by factor of 2 in each direction, you can call the function this way:// specify fx and fy and let the function compute the destination image size. resize(src, dst, Size(), 0.5, 0.5, interpolation);To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to enlarge an image, it will generally look best with #INTER_CUBIC (slow) or #INTER_LINEAR (faster but still looks OK).- Parameters:
src- input image.dst- output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.dsize- output image size; if it equals zero (Nonein Python), it is computed as: \(\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\) Either dsize or both fx and fy must be non-zero.fx- scale factor along the horizontal axis; when it equals 0, it is computed as \(\texttt{(double)dsize.width/src.cols}\) \(\texttt{(double)dsize.height/src.rows}\) SEE: warpAffine, warpPerspective, remap
-
resize
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 pre-created dst, you may call the function as follows:// explicitly specify dsize=dst.size(); fx and fy will be computed from that. resize(src, dst, dst.size(), 0, 0, interpolation);If you want to decimate the image by factor of 2 in each direction, you can call the function this way:// specify fx and fy and let the function compute the destination image size. resize(src, dst, Size(), 0.5, 0.5, interpolation);To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to enlarge an image, it will generally look best with #INTER_CUBIC (slow) or #INTER_LINEAR (faster but still looks OK).- Parameters:
src- input image.dst- output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.dsize- output image size; if it equals zero (Nonein Python), it is computed as: \(\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\) Either dsize or both fx and fy must be non-zero. \(\texttt{(double)dsize.width/src.cols}\) \(\texttt{(double)dsize.height/src.rows}\) SEE: warpAffine, warpPerspective, remap
-
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 in-place.- Parameters:
src- input image.dst- output image that has the size dsize and the same type as src .M- \(2\times 3\) transformation matrix.dsize- size of the output image.flags- combination of interpolation methods (see #InterpolationFlags) and the optional flag #WARP_INVERSE_MAP that means that M is the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).borderMode- pixel extrapolation method (see #BorderTypes); when borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to the "outliers" in the source image are not modified by the function.borderValue- value used in case of a constant border; by default, it is 0. SEE: warpPerspective, resize, remap, getRectSubPix, transform
-
warpAffine
Applies an affine transformation to an image. The function warpAffine transforms the source image using the specified matrix: \(\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\) when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with #invertAffineTransform and then put in the formula above instead of M. The function cannot operate in-place.- Parameters:
src- input image.dst- output image that has the size dsize and the same type as src .M- \(2\times 3\) transformation matrix.dsize- size of the output image.flags- combination of interpolation methods (see #InterpolationFlags) and the optional flag #WARP_INVERSE_MAP that means that M is the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).borderMode- pixel extrapolation method (see #BorderTypes); when borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to the "outliers" in the source image are not modified by the function. SEE: warpPerspective, resize, remap, getRectSubPix, transform
-
warpAffine
Applies an affine transformation to an image. The function warpAffine transforms the source image using the specified matrix: \(\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\) when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with #invertAffineTransform and then put in the formula above instead of M. The function cannot operate in-place.- Parameters:
src- input image.dst- output image that has the size dsize and the same type as src .M- \(2\times 3\) transformation matrix.dsize- size of the output image.flags- combination of interpolation methods (see #InterpolationFlags) and the optional flag #WARP_INVERSE_MAP that means that M is the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ). borderMode=#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
Applies an affine transformation to an image. The function warpAffine transforms the source image using the specified matrix: \(\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\) when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with #invertAffineTransform and then put in the formula above instead of M. The function cannot operate in-place.- Parameters:
src- input image.dst- output image that has the size dsize and the same type as src .M- \(2\times 3\) transformation matrix.dsize- size of the output image. 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 in-place.- Parameters:
src- input image.dst- output image that has the size dsize and the same type as src .M- \(3\times 3\) transformation matrix.dsize- size of the output image.flags- combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).borderMode- pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).borderValue- value used in case of a constant border; by default, it equals 0. SEE: warpAffine, resize, remap, getRectSubPix, perspectiveTransform
-
warpPerspective
Applies a perspective transformation to an image. The function warpPerspective transforms the source image using the specified matrix: \(\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\) when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert and then put in the formula above instead of M. The function cannot operate in-place.- Parameters:
src- input image.dst- output image that has the size dsize and the same type as src .M- \(3\times 3\) transformation matrix.dsize- size of the output image.flags- combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).borderMode- pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE). SEE: warpAffine, resize, remap, getRectSubPix, perspectiveTransform
-
warpPerspective
Applies a perspective transformation to an image. The function warpPerspective transforms the source image using the specified matrix: \(\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\) when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert and then put in the formula above instead of M. The function cannot operate in-place.- Parameters:
src- input image.dst- output image that has the size dsize and the same type as src .M- \(3\times 3\) transformation matrix.dsize- size of the output image.flags- combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ). SEE: warpAffine, resize, remap, getRectSubPix, perspectiveTransform
-
warpPerspective
Applies a perspective transformation to an image. The function warpPerspective transforms the source image using the specified matrix: \(\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\) when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert and then put in the formula above instead of M. The function cannot operate in-place.- Parameters:
src- input image.dst- output image that has the size dsize and the same type as src .M- \(3\times 3\) transformation matrix.dsize- size of the output image. 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 non-integer coordinates are computed using one of available interpolation methods. \(map_x\) and \(map_y\) can be encoded as separate floating-point maps in \(map_1\) and \(map_2\) respectively, or interleaved floating-point maps of \((x,y)\) in \(map_1\), or fixed-point maps created by using #convertMaps. The reason you might want to convert from floating to fixed-point representations of a map is that they can yield much faster (\~2x) remapping operations. In the converted case, \(map_1\) contains pairs (cvFloor(x), cvFloor(y)) and \(map_2\) contains indices in a table of interpolation coefficients. This function cannot operate in-place.- Parameters:
src- Source image.dst- Destination image. It has the same size as map1 and the same type as src .map1- The first map of either (x,y) points or just x values having the type CV_16SC2 , CV_32FC1, or CV_32FC2. See #convertMaps for details on converting a floating point representation to fixed-point for speed.map2- The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map if map1 is (x,y) points), respectively.interpolation- Interpolation method (see #InterpolationFlags). The methods #INTER_AREA 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
Applies a generic geometrical transformation to an image. The function remap transforms the source image using the specified map: \(\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\) where values of pixels with non-integer coordinates are computed using one of available interpolation methods. \(map_x\) and \(map_y\) can be encoded as separate floating-point maps in \(map_1\) and \(map_2\) respectively, or interleaved floating-point maps of \((x,y)\) in \(map_1\), or fixed-point maps created by using #convertMaps. The reason you might want to convert from floating to fixed-point representations of a map is that they can yield much faster (\~2x) remapping operations. In the converted case, \(map_1\) contains pairs (cvFloor(x), cvFloor(y)) and \(map_2\) contains indices in a table of interpolation coefficients. This function cannot operate in-place.- Parameters:
src- Source image.dst- Destination image. It has the same size as map1 and the same type as src .map1- The first map of either (x,y) points or just x values having the type CV_16SC2 , CV_32FC1, or CV_32FC2. See #convertMaps for details on converting a floating point representation to fixed-point for speed.map2- The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map if map1 is (x,y) points), respectively.interpolation- Interpolation method (see #InterpolationFlags). The methods #INTER_AREA 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
Applies a generic geometrical transformation to an image. The function remap transforms the source image using the specified map: \(\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\) where values of pixels with non-integer coordinates are computed using one of available interpolation methods. \(map_x\) and \(map_y\) can be encoded as separate floating-point maps in \(map_1\) and \(map_2\) respectively, or interleaved floating-point maps of \((x,y)\) in \(map_1\), or fixed-point maps created by using #convertMaps. The reason you might want to convert from floating to fixed-point representations of a map is that they can yield much faster (\~2x) remapping operations. In the converted case, \(map_1\) contains pairs (cvFloor(x), cvFloor(y)) and \(map_2\) contains indices in a table of interpolation coefficients. This function cannot operate in-place.- Parameters:
src- Source image.dst- Destination image. It has the same size as map1 and the same type as src .map1- The first map of either (x,y) points or just x values having the type CV_16SC2 , CV_32FC1, or CV_32FC2. See #convertMaps for details on converting a floating point representation to fixed-point for speed.map2- The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map if map1 is (x,y) points), respectively.interpolation- Interpolation method (see #InterpolationFlags). The methods #INTER_AREA 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 floating-point maps (see #remap) are converted to a more compact and much faster fixed-point representation. The first output array contains the rounded coordinates and the second array (created only when nninterpolation=false ) contains indices in the interpolation tables.
- \(\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). The same as above but the original maps are stored in one 2-channel matrix.
- Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same as the originals.
- Parameters:
map1- The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 .map2- The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), respectively.dstmap1- The first output map that has the type dstmap1type and the same size as src .dstmap2- The second output map.dstmap1type- Type of the first output map that should be CV_16SC2, CV_32FC1, or CV_32FC2 .nninterpolation- Flag indicating whether the fixed-point maps are used for the nearest-neighbor or for a more complex interpolation. SEE: remap, undistort, initUndistortRectifyMap
-
convertMaps
Converts image transformation maps from one representation to another. The function converts a pair of maps for remap from one representation to another. The following options ( (map1.type(), map2.type()) \(\rightarrow\) (dstmap1.type(), dstmap2.type()) ) are supported:- \(\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). This is the most frequently used conversion operation, in which the original floating-point maps (see #remap) are converted to a more compact and much faster fixed-point representation. The first output array contains the rounded coordinates and the second array (created only when nninterpolation=false ) contains indices in the interpolation tables.
- \(\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). The same as above but the original maps are stored in one 2-channel matrix.
- Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same as the originals.
- Parameters:
map1- The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 .map2- The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), respectively.dstmap1- The first output map that has the type dstmap1type and the same size as src .dstmap2- The second output map.dstmap1type- Type of the first output map that should be CV_16SC2, CV_32FC1, or CV_32FC2 . nearest-neighbor or for a more complex interpolation. SEE: remap, undistort, initUndistortRectifyMap
-
getRotationMatrix2D
Calculates an affine matrix of 2D rotation. The function calculates the following matrix: \(\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\) where \(\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\) The transformation maps the rotation center to itself. If this is not the target, adjust the shift.- Parameters:
center- Center of the rotation in the source image.angle- Rotation angle in degrees. Positive values mean counter-clockwise rotation (the coordinate origin is assumed to be the top-left corner).scale- Isotropic scale factor. SEE: getAffineTransform, warpAffine, transform- Returns:
- automatically generated
-
invertAffineTransform
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
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
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
-
getRectSubPix
Retrieves a pixel rectangle from an image with sub-pixel accuracy. The function getRectSubPix extracts pixels from src: \(patch(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\) where the values of the pixels at non-integer coordinates are retrieved using bilinear interpolation. Every channel of multi-channel images is processed independently. Also the image should be a single channel or three channel image. While the center of the rectangle must be inside the image, parts of the rectangle may be outside.- Parameters:
image- Source image.patchSize- Size of the extracted patch.center- Floating point coordinates of the center of the extracted rectangle within the source image. The center must be inside the image.patch- Extracted patch that has the size patchSize and the same number of channels as src .patchType- Depth of the extracted pixels. By default, they have the same depth as src . SEE: warpAffine, warpPerspective
-
getRectSubPix
Retrieves a pixel rectangle from an image with sub-pixel accuracy. The function getRectSubPix extracts pixels from src: \(patch(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\) where the values of the pixels at non-integer coordinates are retrieved using bilinear interpolation. Every channel of multi-channel images is processed independently. Also the image should be a single channel or three channel image. While the center of the rectangle must be inside the image, parts of the rectangle may be outside.- Parameters:
image- Source image.patchSize- Size of the extracted patch.center- Floating point coordinates of the center of the extracted rectangle within the source image. The center must be inside the image.patch- Extracted patch that has the size patchSize and the same number of channels as src . SEE: warpAffine, warpPerspective
-
logPolar
Deprecated.This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG); Transform the source image using the following transformation (See REF: polar_remaps_reference_image "Polar remaps reference image d)"): \(\begin{array}{l} dst( \rho , \phi ) = src(x,y) \\ dst.size() \leftarrow src.size() \end{array}\) where \(\begin{array}{l} I = (dx,dy) = (x - center.x,y - center.y) \\ \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\ \phi = Kangle \cdot \texttt{angle} (I) \\ \end{array}\) and \(\begin{array}{l} M = src.cols / log_e(maxRadius) \\ Kangle = src.rows / 2\Pi \\ \end{array}\) The function emulates the human "foveal" vision and can be used for fast scale and rotation-invariant template matching, for object tracking and so forth.Remaps an image to semilog-polar 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 in-place.
- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
-
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 in-place.
- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
-
warpPolar
public static void warpPolar(Mat src, Mat dst, Size dsize, Point center, double maxRadius, int flags) Remaps an image to polar or semilog-polar coordinates space polar_remaps_reference_image  Transform the source image using the following transformation: \( dst(\rho , \phi ) = src(x,y) \) where \( \begin{array}{l} \vec{I} = (x - center.x, \;y - center.y) \\ \phi = Kangle \cdot \texttt{angle} (\vec{I}) \\ \rho = \left\{\begin{matrix} Klin \cdot \texttt{magnitude} (\vec{I}) & default \\ Klog \cdot log_e(\texttt{magnitude} (\vec{I})) & if \; semilog \\ \end{matrix}\right. \end{array} \) and \( \begin{array}{l} Kangle = dsize.height / 2\Pi \\ Klin = dsize.width / maxRadius \\ Klog = dsize.width / log_e(maxRadius) \\ \end{array} \) \par Linear vs semilog mapping Polar mapping can be linear or semi-log. Add one of #WarpPolarMode toflagsto 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 in-place.
- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
- This function uses #remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767.
- if both values in
-
integral3
Calculates the integral of an image. The function calculates one or more integral images for the source image as follows: \(\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\) \(\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\) \(\texttt{tilted} (X,Y) = \sum _{y<Y,abs(x-X+1) \leq Y-y-1} \texttt{image} (x,y)\) Using these integral images, you can calculate sum, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example: \(\sum _{x_1 \leq x < x_2, \, y_1 \leq y < y_2} \texttt{image} (x,y) = \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\) It makes possible to do a fast blurring or fast block correlation with a variable window size, for example. In case of multi-channel images, sums for each channel are accumulated independently. As a practical example, the next figure shows the calculation of the integral of a straight rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the original image are shown, as well as the relative pixels in the integral images sum and tilted . - Parameters:
src- input image as \(W \times H\), 8-bit or floating-point (32f or 64f).sum- integral image as \((W+1)\times (H+1)\) , 32-bit integer or floating-point (32f or 64f).sqsum- integral image for squared pixel values; it is \((W+1)\times (H+1)\), double-precision floating-point (64f) array.tilted- integral for the image rotated by 45 degrees; it is \((W+1)\times (H+1)\) array with the same data type as sum.sdepth- desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or CV_64F.sqdepth- desired depth of the integral image of squared pixel values, CV_32F or CV_64F.
-
integral3
Calculates the integral of an image. The function calculates one or more integral images for the source image as follows: \(\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\) \(\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\) \(\texttt{tilted} (X,Y) = \sum _{y<Y,abs(x-X+1) \leq Y-y-1} \texttt{image} (x,y)\) Using these integral images, you can calculate sum, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example: \(\sum _{x_1 \leq x < x_2, \, y_1 \leq y < y_2} \texttt{image} (x,y) = \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\) It makes possible to do a fast blurring or fast block correlation with a variable window size, for example. In case of multi-channel images, sums for each channel are accumulated independently. As a practical example, the next figure shows the calculation of the integral of a straight rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the original image are shown, as well as the relative pixels in the integral images sum and tilted . - Parameters:
src- input image as \(W \times H\), 8-bit or floating-point (32f or 64f).sum- integral image as \((W+1)\times (H+1)\) , 32-bit integer or floating-point (32f or 64f).sqsum- integral image for squared pixel values; it is \((W+1)\times (H+1)\), double-precision floating-point (64f) array.tilted- integral for the image rotated by 45 degrees; it is \((W+1)\times (H+1)\) array with the same data type as sum.sdepth- desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or CV_64F.
-
integral3
Calculates the integral of an image. The function calculates one or more integral images for the source image as follows: \(\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\) \(\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\) \(\texttt{tilted} (X,Y) = \sum _{y<Y,abs(x-X+1) \leq Y-y-1} \texttt{image} (x,y)\) Using these integral images, you can calculate sum, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example: \(\sum _{x_1 \leq x < x_2, \, y_1 \leq y < y_2} \texttt{image} (x,y) = \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\) It makes possible to do a fast blurring or fast block correlation with a variable window size, for example. In case of multi-channel images, sums for each channel are accumulated independently. As a practical example, the next figure shows the calculation of the integral of a straight rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the original image are shown, as well as the relative pixels in the integral images sum and tilted . - Parameters:
src- input image as \(W \times H\), 8-bit or floating-point (32f or 64f).sum- integral image as \((W+1)\times (H+1)\) , 32-bit integer or floating-point (32f or 64f).sqsum- integral image for squared pixel values; it is \((W+1)\times (H+1)\), double-precision floating-point (64f) array.tilted- integral for the image rotated by 45 degrees; it is \((W+1)\times (H+1)\) array with the same data type as sum. CV_64F.
-
integral
-
integral
-
integral2
-
integral2
-
integral2
-
accumulate
Adds an image to the accumulator image. The function adds src or some of its elements to dst : \(\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\) The function supports multi-channel images. Each channel is processed independently. The function cv::accumulate can be used, for example, to collect statistics of a scene background viewed by a still camera and for the further foreground-background segmentation.- Parameters:
src- Input image of type CV_8UC(n), CV_16UC(n), CV_32FC(n) or CV_64FC(n), where n is a positive integer.dst- %Accumulator image with the same number of channels as input image, and a depth of CV_32F or CV_64F.mask- Optional operation mask. SEE: accumulateSquare, accumulateProduct, accumulateWeighted
-
accumulate
Adds an image to the accumulator image. The function adds src or some of its elements to dst : \(\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\) The function supports multi-channel images. Each channel is processed independently. The function cv::accumulate can be used, for example, to collect statistics of a scene background viewed by a still camera and for the further foreground-background segmentation.- Parameters:
src- Input image of type CV_8UC(n), CV_16UC(n), CV_32FC(n) or CV_64FC(n), where n is a positive integer.dst- %Accumulator image with the same number of channels as input image, and a depth of CV_32F or CV_64F. SEE: accumulateSquare, accumulateProduct, accumulateWeighted
-
accumulateSquare
Adds the square of a source image to the accumulator image. The function adds the input image src or its selected region, raised to a power of 2, to the accumulator dst : \(\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y)^2 \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\) The function supports multi-channel images. Each channel is processed independently.- Parameters:
src- Input image as 1- or 3-channel, 8-bit or 32-bit floating point.dst- %Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point.mask- Optional operation mask. SEE: accumulateSquare, accumulateProduct, accumulateWeighted
-
accumulateSquare
Adds the square of a source image to the accumulator image. The function adds the input image src or its selected region, raised to a power of 2, to the accumulator dst : \(\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y)^2 \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\) The function supports multi-channel images. Each channel is processed independently.- Parameters:
src- Input image as 1- or 3-channel, 8-bit or 32-bit floating point.dst- %Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point. SEE: accumulateSquare, accumulateProduct, accumulateWeighted
-
accumulateProduct
Adds the per-element product of two input images to the accumulator image. The function adds the product of two images or their selected regions to the accumulator dst : \(\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src1} (x,y) \cdot \texttt{src2} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\) The function supports multi-channel images. Each channel is processed independently.- Parameters:
src1- First input image, 1- or 3-channel, 8-bit or 32-bit floating point.src2- Second input image of the same type and the same size as src1 .dst- %Accumulator image with the same number of channels as input images, 32-bit or 64-bit floating-point.mask- Optional operation mask. SEE: accumulate, accumulateSquare, accumulateWeighted
-
accumulateProduct
Adds the per-element product of two input images to the accumulator image. The function adds the product of two images or their selected regions to the accumulator dst : \(\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src1} (x,y) \cdot \texttt{src2} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\) The function supports multi-channel images. Each channel is processed independently.- Parameters:
src1- First input image, 1- or 3-channel, 8-bit or 32-bit floating point.src2- Second input image of the same type and the same size as src1 .dst- %Accumulator image with the same number of channels as input images, 32-bit or 64-bit floating-point. SEE: accumulate, accumulateSquare, accumulateWeighted
-
accumulateWeighted
Updates a running average. The function calculates the weighted sum of the input image src and the accumulator dst so that dst becomes a running average of a frame sequence: \(\texttt{dst} (x,y) \leftarrow (1- \texttt{alpha} ) \cdot \texttt{dst} (x,y) + \texttt{alpha} \cdot \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\) That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images). The function supports multi-channel images. Each channel is processed independently.- Parameters:
src- Input image as 1- or 3-channel, 8-bit or 32-bit floating point.dst- %Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point.alpha- Weight of the input image.mask- Optional operation mask. SEE: accumulate, accumulateSquare, accumulateProduct
-
accumulateWeighted
Updates a running average. The function calculates the weighted sum of the input image src and the accumulator dst so that dst becomes a running average of a frame sequence: \(\texttt{dst} (x,y) \leftarrow (1- \texttt{alpha} ) \cdot \texttt{dst} (x,y) + \texttt{alpha} \cdot \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\) That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images). The function supports multi-channel images. Each channel is processed independently.- Parameters:
src- Input image as 1- or 3-channel, 8-bit or 32-bit floating point.dst- %Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point.alpha- Weight of the input image. SEE: accumulate, accumulateSquare, accumulateProduct
-
phaseCorrelate
The function is used to detect translational shifts that occur between two images. The operation takes advantage of the Fourier shift theorem for detecting the translational shift in the frequency domain. It can be used for fast image registration as well as motion estimation. For more information please see <http://en.wikipedia.org/wiki/Phase_correlation> Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize. The function performs the following equations:- First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function>) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
- Next it computes the forward DFTs of each source array: \(\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\) where \(\mathcal{F}\) is the forward DFT.
- It then computes the cross-power spectrum of each frequency domain array: \(R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\)
- Next the cross-correlation is converted back into the time domain via the inverse DFT: \(r = \mathcal{F}^{-1}\{R\}\)
- Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy. \((\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\)
- If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single peak) and will be smaller when there are multiple peaks.
- Parameters:
src1- Source floating point array (CV_32FC1 or CV_64FC1)src2- Source floating point array (CV_32FC1 or CV_64FC1)window- Floating point array with windowing coefficients to reduce edge effects (optional).response- Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).- Returns:
- detected phase shift (sub-pixel) between the two arrays. SEE: dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow
-
phaseCorrelate
The function is used to detect translational shifts that occur between two images. The operation takes advantage of the Fourier shift theorem for detecting the translational shift in the frequency domain. It can be used for fast image registration as well as motion estimation. For more information please see <http://en.wikipedia.org/wiki/Phase_correlation> Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize. The function performs the following equations:- First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function>) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
- Next it computes the forward DFTs of each source array: \(\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\) where \(\mathcal{F}\) is the forward DFT.
- It then computes the cross-power spectrum of each frequency domain array: \(R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\)
- Next the cross-correlation is converted back into the time domain via the inverse DFT: \(r = \mathcal{F}^{-1}\{R\}\)
- Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy. \((\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\)
- If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single peak) and will be smaller when there are multiple peaks.
- Parameters:
src1- Source floating point array (CV_32FC1 or CV_64FC1)src2- Source floating point array (CV_32FC1 or CV_64FC1)window- Floating point array with windowing coefficients to reduce edge effects (optional).- Returns:
- detected phase shift (sub-pixel) between the two arrays. SEE: dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow
-
phaseCorrelate
The function is used to detect translational shifts that occur between two images. The operation takes advantage of the Fourier shift theorem for detecting the translational shift in the frequency domain. It can be used for fast image registration as well as motion estimation. For more information please see <http://en.wikipedia.org/wiki/Phase_correlation> Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize. The function performs the following equations:- First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function>) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
- Next it computes the forward DFTs of each source array: \(\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\) where \(\mathcal{F}\) is the forward DFT.
- It then computes the cross-power spectrum of each frequency domain array: \(R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\)
- Next the cross-correlation is converted back into the time domain via the inverse DFT: \(r = \mathcal{F}^{-1}\{R\}\)
- Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy. \((\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\)
- If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single peak) and will be smaller when there are multiple peaks.
- Parameters:
src1- Source floating point array (CV_32FC1 or CV_64FC1)src2- Source floating point array (CV_32FC1 or CV_64FC1)- Returns:
- detected phase shift (sub-pixel) between the two arrays. SEE: dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow
-
createHanningWindow
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
Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum. The function cv::divSpectrums performs the per-element division of the first array by the second array. The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform.- Parameters:
a- first input array.b- second input array of the same size and type as src1 .c- output array of the same size and type as src1 .flags- operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a0as value.conjB- optional flag that conjugates the second input array before the multiplication (true) or not (false).
-
divSpectrums
Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum. The function cv::divSpectrums performs the per-element division of the first array by the second array. The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform.- Parameters:
a- first input array.b- second input array of the same size and type as src1 .c- output array of the same size and type as src1 .flags- operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a0as value. or not (false).
-
threshold
Applies a fixed-level threshold to each array element. The function applies fixed-level thresholding to a multiple-channel array. The function is typically used to get a bi-level (binary) image out of a grayscale image ( #compare could be also used for this purpose) or for removing a noise, that is, filtering out pixels with too small or too large values. There are several types of thresholding supported by the function. They are determined by type parameter. Also, the special values #THRESH_OTSU or #THRESH_TRIANGLE may be combined with one of the above values. In these cases, the function determines the optimal threshold value using the Otsu's or Triangle algorithm and uses it instead of the specified thresh. Note: Currently, the Otsu's and Triangle methods are implemented only for 8-bit single-channel images.- Parameters:
src- input array (multiple-channel, 8-bit or 32-bit floating point).dst- output array of the same size and type and the same number of channels as src.thresh- threshold value.maxval- maximum value to use with the #THRESH_BINARY and #THRESH_BINARY_INV thresholding types.type- thresholding type (see #ThresholdTypes).- Returns:
- the computed threshold value if Otsu's or Triangle methods used. SEE: 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 8-bit single-channel image.dst- Destination image of the same size and the same type as src.maxValue- Non-zero value assigned to the pixels for which the condition is 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
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: \(\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\) The function performs the downsampling step of the Gaussian pyramid construction. First, it convolves the source image with the kernel: \(\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\) Then, it downsamples the image by rejecting even rows and columns.- Parameters:
src- input image.dst- output image; it has the specified size and the same type as src.dstsize- size of the output image.borderType- Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)
-
pyrDown
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: \(\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\) The function performs the downsampling step of the Gaussian pyramid construction. First, it convolves the source image with the kernel: \(\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\) Then, it downsamples the image by rejecting even rows and columns.- Parameters:
src- input image.dst- output image; it has the specified size and the same type as src.dstsize- size of the output image.
-
pyrDown
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: \(\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\) The function performs the downsampling step of the Gaussian pyramid construction. First, it convolves the source image with the kernel: \(\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\) Then, it downsamples the image by rejecting even rows and columns.- Parameters:
src- input image.dst- output image; it has the specified size and the same type as src.
-
pyrUp
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
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
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(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(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
-
compareHist
Compares two histograms. The function cv::compareHist compares two dense or two sparse histograms using the specified method. The function returns \(d(H_1, H_2)\) . While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms or more general sparse configurations of weighted points, consider using the #EMD function.- Parameters:
H1- First compared histogram.H2- Second compared histogram of the same size as H1 .method- Comparison method, see #HistCompMethods- Returns:
- automatically generated
-
equalizeHist
Equalizes the histogram of a grayscale image. The function equalizes the histogram of the input image using the following algorithm:- Calculate the histogram \(H\) for src .
- Normalize the histogram so that the sum of histogram bins is 255.
- Compute the integral of the histogram: \(H'_i = \sum _{0 \le j < i} H(j)\)
- Transform the image using \(H'\) as a look-up table: \(\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\)
- Parameters:
src- Source 8-bit single channel image.dst- Destination image of the same size and type as src .
-
createCLAHE
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
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
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
Computes the "minimal work" distance between two weighted point configurations. The function computes the earth mover distance and/or a lower boundary of the distance between the two weighted point configurations. One of the applications described in CITE: RubnerSept98, CITE: Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation problem that is solved using some modification of a simplex algorithm, thus the complexity is exponential in the worst case, though, on average it is much faster. In the case of a real metric the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used to determine roughly whether the two signatures are far enough so that they cannot relate to the same object.- Parameters:
signature1- First signature, a \(\texttt{size1}\times \texttt{dims}+1\) floating-point matrix. Each row stores the point weight followed by the point coordinates. The matrix is allowed to have a single column (weights only) if the user-defined cost matrix is used. The weights must be non-negative and have at least one non-zero value.signature2- Second signature of the same format as signature1 , though the number of rows may be different. The total weights may be different. In this case an extra "dummy" point is added to either signature1 or signature2. The weights must be non-negative and have at least one non-zero value.distType- Used metric. See #DistanceTypes.cost- User-defined \(\texttt{size1}\times \texttt{size2}\) cost matrix. Also, if a cost matrix is used, lower boundary lowerBound cannot be calculated because it needs a metric function. signatures that is a distance between mass centers. The lower boundary may not be calculated if the user-defined cost matrix is used, the total weights of point configurations are not equal, or if the signatures consist of weights only (the signature matrices have a single column). You must initialize \*lowerBound . If the calculated distance between mass centers is greater or equal to \*lowerBound (it means that the signatures are far enough), the function does not calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound should be set to 0.flow- Resultant \(\texttt{size1} \times \texttt{size2}\) flow matrix: \(\texttt{flow}_{i,j}\) is a flow from \(i\) -th point of signature1 to \(j\) -th point of signature2 .- Returns:
- automatically generated
-
EMD
Computes the "minimal work" distance between two weighted point configurations. The function computes the earth mover distance and/or a lower boundary of the distance between the two weighted point configurations. One of the applications described in CITE: RubnerSept98, CITE: Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation problem that is solved using some modification of a simplex algorithm, thus the complexity is exponential in the worst case, though, on average it is much faster. In the case of a real metric the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used to determine roughly whether the two signatures are far enough so that they cannot relate to the same object.- Parameters:
signature1- First signature, a \(\texttt{size1}\times \texttt{dims}+1\) floating-point matrix. Each row stores the point weight followed by the point coordinates. The matrix is allowed to have a single column (weights only) if the user-defined cost matrix is used. The weights must be non-negative and have at least one non-zero value.signature2- Second signature of the same format as signature1 , though the number of rows may be different. The total weights may be different. In this case an extra "dummy" point is added to either signature1 or signature2. The weights must be non-negative and have at least one non-zero value.distType- Used metric. See #DistanceTypes.cost- User-defined \(\texttt{size1}\times \texttt{size2}\) cost matrix. Also, if a cost matrix is used, lower boundary lowerBound cannot be calculated because it needs a metric function. signatures that is a distance between mass centers. The lower boundary may not be calculated if the user-defined cost matrix is used, the total weights of point configurations are not equal, or if the signatures consist of weights only (the signature matrices have a single column). You must initialize \*lowerBound . If the calculated distance between mass centers is greater or equal to \*lowerBound (it means that the signatures are far enough), the function does not calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound should be set to 0. a flow from \(i\) -th point of signature1 to \(j\) -th point of signature2 .- Returns:
- automatically generated
-
EMD
Computes the "minimal work" distance between two weighted point configurations. The function computes the earth mover distance and/or a lower boundary of the distance between the two weighted point configurations. One of the applications described in CITE: RubnerSept98, CITE: Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation problem that is solved using some modification of a simplex algorithm, thus the complexity is exponential in the worst case, though, on average it is much faster. In the case of a real metric the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used to determine roughly whether the two signatures are far enough so that they cannot relate to the same object.- Parameters:
signature1- First signature, a \(\texttt{size1}\times \texttt{dims}+1\) floating-point matrix. Each row stores the point weight followed by the point coordinates. The matrix is allowed to have a single column (weights only) if the user-defined cost matrix is used. The weights must be non-negative and have at least one non-zero value.signature2- Second signature of the same format as signature1 , though the number of rows may be different. The total weights may be different. In this case an extra "dummy" point is added to either signature1 or signature2. The weights must be non-negative and have at least one non-zero value.distType- Used metric. See #DistanceTypes. is used, lower boundary lowerBound cannot be calculated because it needs a metric function. signatures that is a distance between mass centers. The lower boundary may not be calculated if the user-defined cost matrix is used, the total weights of point configurations are not equal, or if the signatures consist of weights only (the signature matrices have a single column). You 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
Performs a marker-based image segmentation using the watershed algorithm. The function implements one of the variants of watershed, non-parametric marker-based segmentation algorithm, described in CITE: Meyer92 . Before passing the image to the function, you have to roughly outline the desired regions in the image markers with positive (>0) indices. So, every region is represented as one or more connected components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary mask using #findContours and #drawContours (see the watershed.cpp demo). The markers are "seeds" of the future image regions. All the other pixels in markers , whose relation to the outlined regions is not known and should be defined by the algorithm, should be set to 0's. In the function output, each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the regions. Note: Any two neighbor connected components are not necessarily separated by a watershed boundary (-1's pixels); for example, they can touch each other in the initial marker image passed to the function.- Parameters:
image- Input 8-bit 3-channel image.markers- Input/output 32-bit single-channel image (map) of markers. It should have the same size as image . SEE: 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 fine-grain texture flattened. At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is considered: \((x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}\) where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively (though, the algorithm does not depend on the color space used, so any 3-component color space can be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector (R',G',B') are found and they act as the neighborhood center on the next iteration: \((X,Y)~(X',Y'), (R,G,B)~(R',G',B').\) After the iterations over, the color components of the initial pixel (that is, the pixel from where the iterations started) are set to the final value (average color at the last iteration): \(I(X,Y) <- (R*,G*,B*)\) When maxLevel > 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is run on the smallest layer first. After that, the results are propagated to the larger layer and the iterations are run again only on those pixels where the layer colors differ by more than sr from the lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the results will be actually different from the ones obtained by running the meanshift procedure on the whole original image (i.e. when maxLevel==0).- Parameters:
src- The source 8-bit, 3-channel image.dst- The destination image of the same format and the same size as the source.sp- The spatial window radius.sr- The color window radius.maxLevel- Maximum level of the pyramid for the segmentation.termcrit- Termination criteria: when to stop meanshift iterations.
-
pyrMeanShiftFiltering
Performs initial step of meanshift segmentation of an image. The function implements the filtering stage of meanshift segmentation, that is, the output of the function is the filtered "posterized" image with color gradients and fine-grain texture flattened. At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is considered: \((x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}\) where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively (though, the algorithm does not depend on the color space used, so any 3-component color space can be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector (R',G',B') are found and they act as the neighborhood center on the next iteration: \((X,Y)~(X',Y'), (R,G,B)~(R',G',B').\) After the iterations over, the color components of the initial pixel (that is, the pixel from where the iterations started) are set to the final value (average color at the last iteration): \(I(X,Y) <- (R*,G*,B*)\) When maxLevel > 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is run on the smallest layer first. After that, the results are propagated to the larger layer and the iterations are run again only on those pixels where the layer colors differ by more than sr from the lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the results will be actually different from the ones obtained by running the meanshift procedure on the whole original image (i.e. when maxLevel==0).- Parameters:
src- The source 8-bit, 3-channel image.dst- The destination image of the same format and the same size as the source.sp- The spatial window radius.sr- The color window radius.maxLevel- Maximum level of the pyramid for the segmentation.
-
pyrMeanShiftFiltering
Performs initial step of meanshift segmentation of an image. The function implements the filtering stage of meanshift segmentation, that is, the output of the function is the filtered "posterized" image with color gradients and fine-grain texture flattened. At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is considered: \((x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}\) where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively (though, the algorithm does not depend on the color space used, so any 3-component color space can be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector (R',G',B') are found and they act as the neighborhood center on the next iteration: \((X,Y)~(X',Y'), (R,G,B)~(R',G',B').\) After the iterations over, the color components of the initial pixel (that is, the pixel from where the iterations started) are set to the final value (average color at the last iteration): \(I(X,Y) <- (R*,G*,B*)\) When maxLevel > 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is run on the smallest layer first. After that, the results are propagated to the larger layer and the iterations are run again only on those pixels where the layer colors differ by more than sr from the lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the results will be actually different from the ones obtained by running the meanshift procedure on the whole original image (i.e. when maxLevel==0).- Parameters:
src- The source 8-bit, 3-channel image.dst- The destination image of the same format and the same size as the source.sp- The spatial window radius.sr- The color window radius.
-
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 8-bit 3-channel image.mask- Input/output 8-bit single-channel mask. The mask is initialized by the function when mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses.rect- ROI containing a segmented object. The pixels outside of the ROI are marked as "obvious background". The parameter is only used when mode==#GC_INIT_WITH_RECT .bgdModel- Temporary array for the background model. Do not modify it while you are processing the same image.fgdModel- Temporary arrays for the foreground model. Do not modify it while you are processing the same image.iterCount- Number of iterations the algorithm should make before returning the result. Note that the result can be refined with further calls with mode==#GC_INIT_WITH_MASK or mode==GC_EVAL .mode- Operation mode that could be one of the #GrabCutModes
-
grabCut
Runs the GrabCut algorithm. The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut).- Parameters:
img- Input 8-bit 3-channel image.mask- Input/output 8-bit single-channel mask. The mask is initialized by the function when mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses.rect- ROI containing a segmented object. The pixels outside of the ROI are marked as "obvious background". The parameter is only used when mode==#GC_INIT_WITH_RECT .bgdModel- Temporary array for the background model. Do not modify it while you are processing the same image.fgdModel- Temporary arrays for the foreground model. Do not modify it while you are processing the same image.iterCount- Number of iterations the algorithm should make before returning the result. Note that the result can be refined with further calls with mode==#GC_INIT_WITH_MASK or mode==GC_EVAL .
-
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=1.36935 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- 8-bit, single-channel (binary) source image.dst- Output image with calculated distances. It is a 8-bit or 32-bit floating-point, single-channel image of the same size as src.labels- Output 2D array of labels (the discrete Voronoi diagram). It has the type CV_32SC1 and the same size as src.distanceType- Type of distance, see #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=1.36935 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- 8-bit, single-channel (binary) source image.dst- Output image with calculated distances. It is a 8-bit or 32-bit floating-point, single-channel image of the same size as src.labels- Output 2D array of labels (the discrete Voronoi diagram). It has the type CV_32SC1 and the same size as src.distanceType- Type of distance, see #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
- Parameters:
src- 8-bit, single-channel (binary) source image.dst- Output image with calculated distances. It is a 8-bit or 32-bit floating-point, single-channel image of the same size as src .distanceType- Type of distance, see #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
- Parameters:
src- 8-bit, single-channel (binary) source image.dst- Output image with calculated distances. It is a 8-bit or 32-bit floating-point, single-channel image of the same size as src .distanceType- Type of distance, see #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 3-channel, 8-bit, or floating-point image. It is modified by the function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.mask- Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller than image. If an empty Mat is passed it will be created automatically. Since this is both an input and output parameter, you must take responsibility of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags as described below. Additionally, the function fills the border of the mask with ones to simplify internal processing. It is therefore possible to use the same mask in multiple calls to the function to make sure the filled areas do not overlap.seedPoint- Starting point.newVal- New value of the repainted domain pixels.rect- Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.loDiff- Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.upDiff- Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.flags- Operation flags. The first 8 bits contain a connectivity value. The default value of 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill the mask (the default value is 1). For example, 4 | ( 255 << 8 ) will consider 4 nearest neighbours and fill the mask with a value of 255. The following additional options occupy higher bits and therefore may be further combined with the connectivity and mask fill values using bit-wise or (|), see #FloodFillFlags. Note: Since the mask is larger than the filled image, a pixel \((x, y)\) in image corresponds to the pixel \((x+1, y+1)\) in the mask . SEE: 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 3-channel, 8-bit, or floating-point image. It is modified by the function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.mask- Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller than image. If an empty Mat is passed it will be created automatically. Since this is both an input and output parameter, you must take responsibility of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags as described below. Additionally, the function fills the border of the mask with ones to simplify internal processing. It is therefore possible to use the same mask in multiple calls to the function to make sure the filled areas do not overlap.seedPoint- Starting point.newVal- New value of the repainted domain pixels.rect- Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain. 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill the mask (the default value is 1). For example, 4 | ( 255 << 8 ) will consider 4 nearest neighbours and fill the mask with a value of 255. The following additional options occupy higher bits and therefore may be further combined with the connectivity and mask fill values using bit-wise or (|), see #FloodFillFlags. 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: findContoursloDiff- 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.- 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 3-channel, 8-bit, or floating-point image. It is modified by the function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.mask- Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller than image. If an empty Mat is passed it will be created automatically. Since this is both an input and output parameter, you must take responsibility of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags as described below. Additionally, the function fills the border of the mask with ones to simplify internal processing. It is therefore possible to use the same mask in multiple calls to the function to make sure the filled areas do not overlap.seedPoint- Starting point.newVal- New value of the repainted domain pixels.rect- Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain. 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill the mask (the default value is 1). For example, 4 | ( 255 << 8 ) will consider 4 nearest neighbours and fill the mask with a value of 255. The following additional options occupy higher bits and therefore may be further combined with the connectivity and mask fill values using bit-wise or (|), see #FloodFillFlags. 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: findContoursloDiff- 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.- Returns:
- automatically generated
-
floodFill
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 3-channel, 8-bit, or floating-point image. It is modified by the function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.mask- Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller than image. If an empty Mat is passed it will be created automatically. Since this is both an input and output parameter, you must take responsibility of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags as described below. Additionally, the function fills the border of the mask with ones to simplify internal processing. It is therefore possible to use the same mask in multiple calls to the function to make sure the filled areas do not overlap.seedPoint- Starting point.newVal- New value of the repainted domain pixels. 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 (8-16) contain a value between 1 and 255 with which to fill the mask (the default value is 1). For example, 4 | ( 255 << 8 ) will consider 4 nearest neighbours and fill the mask with a value of 255. The following additional options occupy higher bits and therefore may be further combined with the connectivity and mask fill values using bit-wise or (|), see #FloodFillFlags. Note: Since the mask is larger than the filled image, a pixel \((x, y)\) in image corresponds to the pixel \((x+1, y+1)\) in the mask . SEE: findContours- Returns:
- automatically generated
-
floodFill
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 3-channel, 8-bit, or floating-point image. It is modified by the function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.mask- Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller than image. If an empty Mat is passed it will be created automatically. Since this is both an input and output parameter, you must take responsibility of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags as described below. Additionally, the function fills the border of the mask with ones to simplify internal processing. It is therefore possible to use the same mask in multiple calls to the function to make sure the filled areas do not overlap.seedPoint- Starting point.newVal- New value of the repainted domain pixels. one of its neighbors belonging to the component, or a seed pixel being added to the component. one of its neighbors belonging to the component, or a seed pixel being added to the component. repainted domain. 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill the mask (the default value is 1). For example, 4 | ( 255 << 8 ) will consider 4 nearest neighbours and fill the mask with a value of 255. The following additional options occupy higher bits and therefore may be further combined with the connectivity and mask fill values using bit-wise or (|), see #FloodFillFlags. 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
-
cvtColor
Converts an image from one color space to another. The function converts an input image from one color space to another. In case of a transformation to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on. The conventional ranges for R, G, and B channel values are:- 0 to 255 for CV_8U images
- 0 to 65535 for CV_16U images
- 0 to 1 for CV_32F images
img *= 1./255; cvtColor(img, img, COLOR_BGR2Luv);If you use #cvtColor with 8-bit images, the conversion will have some information lost. For many applications, this will not be noticeable but it is recommended to use 32-bit images in applications that need the full range of colors or that convert an image before an operation and then convert back. If conversion adds the alpha channel, its value will set to the maximum of corresponding channel range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F.- Parameters:
src- input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision floating-point.dst- output image of the same size and depth as src.code- color space conversion code (see #ColorConversionCodes).dstCn- number of channels in the destination image; if the parameter is 0, the number of the channels is derived automatically from src and code. SEE: REF: imgproc_color_conversions
-
cvtColor
Converts an image from one color space to another. The function converts an input image from one color space to another. In case of a transformation to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on. The conventional ranges for R, G, and B channel values are:- 0 to 255 for CV_8U images
- 0 to 65535 for CV_16U images
- 0 to 1 for CV_32F images
img *= 1./255; cvtColor(img, img, COLOR_BGR2Luv);If you use #cvtColor with 8-bit images, the conversion will have some information lost. For many applications, this will not be noticeable but it is recommended to use 32-bit images in applications that need the full range of colors or that convert an image before an operation and then convert back. If conversion adds the alpha channel, its value will set to the maximum of corresponding channel range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F.- Parameters:
src- input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision floating-point.dst- output image of the same size and depth as src.code- color space conversion code (see #ColorConversionCodes). channels is derived automatically from src and code. SEE: REF: imgproc_color_conversions
-
cvtColorTwoPlane
Converts an image from one color space to another where the source image is stored in two planes. This function only supports YUV420 to RGB conversion as of now.- Parameters:
src1- 8-bit image (#CV_8U) of the Y plane.src2- image containing interleaved U/V plane.dst- output image.code- Specifies the type of conversion. It can take any of the following values:- #COLOR_YUV2BGR_NV12
- #COLOR_YUV2RGB_NV12
- #COLOR_YUV2BGRA_NV12
- #COLOR_YUV2RGBA_NV12
- #COLOR_YUV2BGR_NV21
- #COLOR_YUV2RGB_NV21
- #COLOR_YUV2BGRA_NV21
- #COLOR_YUV2RGBA_NV21
-
demosaicing
main function for all demosaicing processes- Parameters:
src- input image: 8-bit unsigned or 16-bit unsigned.dst- output image of the same size and depth as src.code- Color space conversion code (see the description below).dstCn- number of channels in the destination image; if the parameter is 0, the number of the channels is derived automatically from src and code. The function can do the following transformations:- Demosaicing using bilinear interpolation
- Demosaicing using Variable Number of Gradients.
- Edge-Aware Demosaicing.
- Demosaicing with alpha channel
-
demosaicing
main function for all demosaicing processes- Parameters:
src- input image: 8-bit unsigned or 16-bit unsigned.dst- output image of the same size and depth as src.code- Color space conversion code (see the description below). 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.
- Edge-Aware Demosaicing.
- Demosaicing with alpha channel
-
moments
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 (single-channel, 8-bit or floating-point 2D array) or an array ( \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f ).binaryImage- If it is true, all non-zero image pixels are treated as 1's. The parameter is used for images only.- Returns:
- moments. Note: Only applicable to contour moments calculations from Python bindings: Note that the numpy type for the input array should be either np.int32 or np.float32. SEE: contourArea, arcLength
-
moments
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 (single-channel, 8-bit or floating-point 2D array) or an array ( \(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f ). used for images only.- 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
-
HuMoments
-
matchTemplate
Compares a template against overlapped image regions. The function slides through image , compares the overlapped patches of size \(w \times h\) against templ using the specified method and stores the comparison results in result . #TemplateMatchModes describes the formulae for the available comparison methods ( \(I\) denotes image, \(T\) template, \(R\) result, \(M\) the optional mask ). The summation is done over template and/or the image patch: \(x' = 0...w-1, y' = 0...h-1\) After the function finishes the comparison, the best matches can be found as global minimums (when #TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the #minMaxLoc function. In case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels and separate mean values are used for each channel. That is, the function can take a color template and a color image. The result will still be a single-channel image, which is easier to analyze.- Parameters:
image- Image where the search is running. It must be 8-bit or 32-bit floating-point.templ- Searched template. It must be not greater than the source image and have the same data type.result- Map of comparison results. It must be single-channel 32-bit floating-point. If image is \(W \times H\) and templ is \(w \times h\) , then result is \((W-w+1) \times (H-h+1)\) .method- Parameter specifying the comparison method, see #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
Compares a template against overlapped image regions. The function slides through image , compares the overlapped patches of size \(w \times h\) against templ using the specified method and stores the comparison results in result . #TemplateMatchModes describes the formulae for the available comparison methods ( \(I\) denotes image, \(T\) template, \(R\) result, \(M\) the optional mask ). The summation is done over template and/or the image patch: \(x' = 0...w-1, y' = 0...h-1\) After the function finishes the comparison, the best matches can be found as global minimums (when #TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the #minMaxLoc function. In case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels and separate mean values are used for each channel. That is, the function can take a color template and a color image. The result will still be a single-channel image, which is easier to analyze.- Parameters:
image- Image where the search is running. It must be 8-bit or 32-bit floating-point.templ- Searched template. It must be not greater than the source image and have the same data type.result- Map of comparison results. It must be single-channel 32-bit floating-point. If image is \(W \times H\) and templ is \(w \times h\) , then result is \((W-w+1) \times (H-h+1)\) .method- Parameter specifying the comparison method, see #TemplateMatchModes 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, N-1] where 0 represents the background label. ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently Bolelli (Spaghetti) CITE: Bolelli2019, Grana (BBDT) CITE: Grana2010 and Wu's (SAUF) CITE: Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces a row major ordering of labels while Spaghetti and BBDT do not. This function uses parallel version of the algorithms if at least one allowed parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.- Parameters:
image- the 8-bit single-channel image to be labeledlabels- destination labeled imageconnectivity- 8 or 4 for 8-way or 4-way 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
- Parameters:
image- the 8-bit single-channel image to be labeledlabels- destination labeled imageconnectivity- 8 or 4 for 8-way or 4-way connectivity respectivelyltype- output image label type. Currently CV_32S and CV_16U are supported.- Returns:
- automatically generated
-
connectedComponents
-
connectedComponents
-
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, N-1] where 0 represents the background label. ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently Bolelli (Spaghetti) CITE: Bolelli2019, Grana (BBDT) CITE: Grana2010 and Wu's (SAUF) CITE: Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces a row major ordering of labels while Spaghetti and BBDT do not. This function uses parallel version of the algorithms (statistics included) if at least one allowed parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.- Parameters:
image- the 8-bit single-channel 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 8-way or 4-way 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 8-bit single-channel 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 8-way or 4-way 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 8-bit single-channel 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 8-way or 4-way connectivity respectively- Returns:
- automatically generated
-
connectedComponentsWithStats
- Parameters:
image- the 8-bit single-channel 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, 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 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold , #adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one. If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).contours- Detected contours. Each contour is stored as a vector of points (e.g. std::vector<std::vector<cv::Point> >).hierarchy- Optional output vector (e.g. std::vector<cv::Vec4i>), containing information about the image topology. It has as many elements as the number of contours. For each i-th contour contours[i], the elements hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices in contours of the next and previous contours at the same hierarchical level, the first child contour and the parent contour, respectively. If for the contour i there are no next, previous, parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. Note: In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th 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, 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 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold , #adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one. If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).contours- Detected contours. Each contour is stored as a vector of points (e.g. std::vector<std::vector<cv::Point> >).hierarchy- Optional output vector (e.g. std::vector<cv::Vec4i>), containing information about the image topology. It has as many elements as the number of contours. For each i-th contour contours[i], the elements hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices in contours of the next and previous contours at the same hierarchical level, the first child contour and the parent contour, respectively. If for the contour i there are no next, previous, parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. Note: In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th 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 Douglas-Peucker algorithm <http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_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
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
Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image. The function calculates and returns the minimal up-right bounding rectangle for the specified point set or non-zero pixels of gray-scale image.- Parameters:
array- Input gray-scale image or 2D point set, stored in std::vector or Mat.- Returns:
- automatically generated
-
contourArea
Calculates a contour area. The function computes a contour area. Similarly to moments , the area is computed using the Green formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using #drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong results for contours with self-intersections. Example:vector<Point> contour; contour.push_back(Point2f(0, 0)); contour.push_back(Point2f(10, 0)); contour.push_back(Point2f(10, 10)); contour.push_back(Point2f(5, 4)); double area0 = contourArea(contour); vector<Point> approx; approxPolyDP(contour, approx, 5, true); double area1 = contourArea(approx); cout << "area0 =" << area0 << endl << "area1 =" << area1 << endl << "approx poly vertices" << approx.size() << endl;- Parameters:
contour- Input vector of 2D points (contour vertices), stored in std::vector or Mat.oriented- Oriented area flag. If it is true, the function returns a signed area value, depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can determine orientation of a contour by taking the sign of an area. By default, the parameter is false, which means that the absolute value is returned.- Returns:
- automatically generated
-
contourArea
Calculates a contour area. The function computes a contour area. Similarly to moments , the area is computed using the Green formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using #drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong results for contours with self-intersections. Example:vector<Point> contour; contour.push_back(Point2f(0, 0)); contour.push_back(Point2f(10, 0)); contour.push_back(Point2f(10, 10)); contour.push_back(Point2f(5, 4)); double area0 = contourArea(contour); vector<Point> approx; approxPolyDP(contour, approx, 5, true); double area1 = contourArea(approx); cout << "area0 =" << area0 << endl << "area1 =" << area1 << endl << "approx poly vertices" << approx.size() << endl;- Parameters:
contour- Input vector of 2D points (contour vertices), stored in std::vector or Mat. depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can determine orientation of a contour by taking the sign of an area. By default, the parameter is false, which means that the absolute value is returned.- Returns:
- automatically generated
-
minAreaRect
Finds a rotated rectangle of the minimum area enclosing the input 2D point set. The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a specified point set. Developer should keep in mind that the returned RotatedRect can contain negative indices when data is close to the containing Mat element boundary.- Parameters:
points- Input vector of 2D points, stored in std::vector<> or Mat- Returns:
- automatically generated
-
boxPoints
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 of REF: minAreaRect.points- The output array of four vertices of rectangles.
-
minEnclosingCircle
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
Finds a triangle of minimum area enclosing a 2D point set and returns its area. The function finds a triangle of minimum area enclosing the given set of 2D points and returns its area. The output for a given 2D point set is shown in the image below. 2D points are depicted in red* and the enclosing triangle in *yellow*.  The implementation of the algorithm is based on O'Rourke's CITE: ORourke86 and Klee and Laskowski's CITE: KleeLaskowski85 papers. O'Rourke provides a \(\theta(n)\) algorithm for finding the minimal enclosing triangle of a 2D convex polygon with n vertices. Since the #minEnclosingTriangle function takes a 2D point set as input an additional preprocessing step of computing the convex hull of the 2D point set is required. The complexity of the #convexHull function is \(O(n log(n))\) which is higher than \(\theta(n)\). Thus the overall complexity of the function is \(O(n log(n))\).- 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
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- Method-specific parameter (not supported now).- Returns:
- automatically generated
-
convexHull
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 0-based indices of the convex hull points in the original array (since the set of convex hull points is a subset of the original point set). In the second case, hull elements are the convex hull points themselves.clockwise- Orientation flag. If it is true, the output convex hull is oriented clockwise. Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing to the right, and its Y axis pointing upwards. 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:pointsandhullshould be different arrays, inplace processing isn't supported. Check REF: tutorial_hull "the corresponding tutorial" for more details. useful links: https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/
-
convexHull
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 0-based indices of the convex hull points in the original array (since the set of convex hull points is a subset of the original point set). In the second case, hull elements are the convex hull points themselves. Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing to the right, and its Y axis pointing upwards. returns convex hull points. Otherwise, it returns indices of the convex hull points. When the output array is std::vector, the flag is ignored, and the output depends on the type of the vector: std::vector<int> implies returnPoints=false, std::vector<Point> implies returnPoints=true. Note:pointsandhullshould be different arrays, inplace processing isn't supported. Check REF: tutorial_hull "the corresponding tutorial" for more details. useful links: https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/
-
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: - Parameters:
contour- Input contour.convexhull- Convex hull obtained using convexHull that should contain indices of the contour points that make the hull.convexityDefects- The output vector of convexity defects. In C++ and the new Python/Java interface each convexity defect is represented as 4-element integer vector (a.k.a. #Vec4i): (start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices in the original contour of the convexity defect beginning, end and the farthest point, and fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the farthest contour point and the hull. That is, to get the floating-point value of the depth will be fixpt_depth/256.0.
-
isContourConvex
Tests a contour convexity. The function tests whether the input contour is convex or not. The contour must be simple, that is, without self-intersections. Otherwise, the function output is undefined.- Parameters:
contour- Input vector of 2D points, stored in std::vector<> or Mat- Returns:
- automatically generated
-
intersectConvexConvex
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
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
Fits an ellipse around a set of 2D points. The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by CITE: Fitzgibbon95 is used. Developer should keep in mind that it is possible that the returned ellipse/rotatedRect data contains negative indices, due to the data points being close to the border of the containing Mat element.- Parameters:
points- Input 2D point set, stored in std::vector<> or Mat- Returns:
- automatically generated
-
fitEllipseAMS
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
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 \(\rho (r) = r^2/2 \quad \text{(the simplest and the fastest least-squares method)}\)
- DIST_L1 \(\rho (r) = r\)
- DIST_L12 \(\rho (r) = 2 \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\)
- DIST_FAIR \(\rho \left (r \right ) = C^2 \cdot \left ( \frac{r}{C} - \log{\left(1 + \frac{r}{C}\right)} \right ) \quad \text{where} \quad C=1.3998\)
- DIST_WELSCH \(\rho \left (r \right ) = \frac{C^2}{2} \cdot \left ( 1 - \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right ) \quad \text{where} \quad C=2.9846\)
- DIST_HUBER \(\rho (r) = \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\)
- Parameters:
points- Input vector of 2D or 3D points, stored in std::vector<> or Mat.line- Output line parameters. In case of 2D fitting, it should be a vector of 4 elements (like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and (x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line and (x0, y0, z0) is a point on the line.distType- Distance used by the M-estimator, see #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
Performs a point-in-contour test. The function determines whether the point is inside a contour, outside, or lies on an edge (or coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. Otherwise, the return value is a signed distance between the point and the nearest contour edge. See below a sample output of the function where each image pixel is tested against the contour: - Parameters:
contour- Input contour.pt- Point tested against the contour.measureDist- If true, the function estimates the signed distance from the point to the nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.- 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. - 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
Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it.- Returns:
- automatically generated
-
createGeneralizedHoughGuil
Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it.- Returns:
- automatically generated
-
applyColorMap
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
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 non-antialiased lines with integer coordinates, the 8-connected or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased lines are drawn using Gaussian filtering.- Parameters:
img- Image.pt1- First point of the line segment.pt2- Second point of the line segment.color- Line color.thickness- Line thickness.lineType- Type of the line. See #LineTypes.shift- Number of fractional bits in the point coordinates.
-
line
Draws a line segment connecting two points. The function line draws the line segment between pt1 and pt2 points in the image. The line is clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased lines are drawn using Gaussian filtering.- Parameters:
img- Image.pt1- First point of the line segment.pt2- Second point of the line segment.color- Line color.thickness- Line thickness.lineType- Type of the line. See #LineTypes.
-
line
Draws a line segment connecting two points. The function line draws the line segment between pt1 and pt2 points in the image. The line is clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased lines are drawn using Gaussian filtering.- Parameters:
img- Image.pt1- First point of the line segment.pt2- Second point of the line segment.color- Line color.thickness- Line thickness.
-
line
Draws a line segment connecting two points. The function line draws the line segment between pt1 and pt2 points in the image. The line is clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased lines are drawn using Gaussian filtering.- Parameters:
img- Image.pt1- First point of the line segment.pt2- Second point of the line segment.color- Line color.
-
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
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
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 up-right rectangle. The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners are pt1 and pt2.- Parameters:
img- Image.pt1- Vertex of the rectangle.pt2- Vertex of the rectangle opposite to pt1 .color- Rectangle color or brightness (grayscale image).thickness- Thickness of lines that make up the rectangle. Negative values, like #FILLED, mean that the function has to draw a filled rectangle.lineType- Type of the line. See #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 up-right rectangle. The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners are pt1 and pt2.- Parameters:
img- Image.pt1- Vertex of the rectangle.pt2- Vertex of the rectangle opposite to pt1 .color- Rectangle color or brightness (grayscale image).thickness- Thickness of lines that make up the rectangle. Negative values, like #FILLED, mean that the function has to draw a filled rectangle.lineType- Type of the line. See #LineTypes
-
rectangle
Draws a simple, thick, or filled up-right rectangle. The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners are pt1 and pt2.- Parameters:
img- Image.pt1- Vertex of the rectangle.pt2- Vertex of the rectangle opposite to pt1 .color- Rectangle color or brightness (grayscale image).thickness- Thickness of lines that make up the rectangle. Negative values, like #FILLED, mean that the function has to draw a filled rectangle.
-
rectangle
Draws a simple, thick, or filled up-right rectangle. The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners are pt1 and pt2.- Parameters:
img- Image.pt1- Vertex of the rectangle.pt2- Vertex of the rectangle opposite to pt1 .color- Rectangle color or brightness (grayscale image). 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) userecparameter 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
userecparameter 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
userecparameter 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
-
circle
public static void circle(Mat img, Point center, int radius, Scalar color, int thickness, int lineType, int shift) Draws a circle. The function cv::circle draws a simple or filled circle with a given center and radius.- Parameters:
img- Image where the circle is drawn.center- Center of the circle.radius- Radius of the circle.color- Circle color.thickness- Thickness of the circle outline, if positive. Negative values, like #FILLED, mean that a filled circle is to be drawn.lineType- Type of the circle boundary. See #LineTypesshift- Number of fractional bits in the coordinates of the center and in the radius value.
-
circle
public static void circle(Mat img, Point center, int radius, Scalar color, int thickness, int lineType) Draws a circle. The function cv::circle draws a simple or filled circle with a given center and radius.- Parameters:
img- Image where the circle is drawn.center- Center of the circle.radius- Radius of the circle.color- Circle color.thickness- Thickness of the circle outline, if positive. Negative values, like #FILLED, mean that a filled circle is to be drawn.lineType- Type of the circle boundary. See #LineTypes
-
circle
Draws a circle. The function cv::circle draws a simple or filled circle with a given center and radius.- Parameters:
img- Image where the circle is drawn.center- Center of the circle.radius- Radius of the circle.color- Circle color.thickness- Thickness of the circle outline, if positive. Negative values, like #FILLED, mean that a filled circle is to be drawn.
-
circle
Draws a circle. The function cv::circle draws a simple or filled circle with a given center and radius.- Parameters:
img- Image where the circle is drawn.center- Center of the circle.radius- Radius of the circle.color- Circle color. mean that a filled circle is to be drawn.
-
ellipse
public 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. The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic arc, or a filled ellipse sector. The drawing code uses general parametric form. A piecewise-linear curve is used to approximate the elliptic arc boundary. If you need more control of the ellipse rendering, you can retrieve the curve using #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first variant of the function and want to draw the whole ellipse, not an arc, passstartAngle=0andendAngle=360. IfstartAngleis greater thanendAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc. - Parameters:
img- Image.center- Center of the ellipse.axes- Half of the size of the ellipse main axes.angle- Ellipse rotation angle in degrees.startAngle- Starting angle of the elliptic arc in degrees.endAngle- Ending angle of the elliptic arc in degrees.color- Ellipse color.thickness- Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.lineType- Type of the ellipse boundary. See #LineTypesshift- Number of fractional bits in the coordinates of the center and values of axes.
-
ellipse
public 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. The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic arc, or a filled ellipse sector. The drawing code uses general parametric form. A piecewise-linear curve is used to approximate the elliptic arc boundary. If you need more control of the ellipse rendering, you can retrieve the curve using #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first variant of the function and want to draw the whole ellipse, not an arc, passstartAngle=0andendAngle=360. IfstartAngleis greater thanendAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc. - Parameters:
img- Image.center- Center of the ellipse.axes- Half of the size of the ellipse main axes.angle- Ellipse rotation angle in degrees.startAngle- Starting angle of the elliptic arc in degrees.endAngle- Ending angle of the elliptic arc in degrees.color- Ellipse color.thickness- Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.lineType- Type of the ellipse boundary. See #LineTypes
-
ellipse
public 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. The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic arc, or a filled ellipse sector. The drawing code uses general parametric form. A piecewise-linear curve is used to approximate the elliptic arc boundary. If you need more control of the ellipse rendering, you can retrieve the curve using #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first variant of the function and want to draw the whole ellipse, not an arc, passstartAngle=0andendAngle=360. IfstartAngleis greater thanendAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc. - Parameters:
img- Image.center- Center of the ellipse.axes- Half of the size of the ellipse main axes.angle- Ellipse rotation angle in degrees.startAngle- Starting angle of the elliptic arc in degrees.endAngle- Ending angle of the elliptic arc in degrees.color- Ellipse color.thickness- Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
-
ellipse
public 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. The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic arc, or a filled ellipse sector. The drawing code uses general parametric form. A piecewise-linear curve is used to approximate the elliptic arc boundary. If you need more control of the ellipse rendering, you can retrieve the curve using #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first variant of the function and want to draw the whole ellipse, not an arc, passstartAngle=0andendAngle=360. IfstartAngleis greater thanendAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc. - Parameters:
img- Image.center- Center of the ellipse.axes- Half of the size of the ellipse main axes.angle- Ellipse rotation angle in degrees.startAngle- Starting angle of the elliptic arc in degrees.endAngle- Ending angle of the elliptic arc in degrees.color- Ellipse color. a filled ellipse sector is to be drawn.
-
ellipse
- Parameters:
img- Image.box- Alternative ellipse representation via RotatedRect. This means that the function draws an ellipse inscribed in the rotated rectangle.color- Ellipse color.thickness- Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.lineType- Type of the ellipse boundary. See #LineTypes
-
ellipse
- Parameters:
img- Image.box- Alternative ellipse representation via RotatedRect. This means that the function draws an ellipse inscribed in the rotated rectangle.color- Ellipse color.thickness- Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn.
-
ellipse
- Parameters:
img- Image.box- Alternative ellipse representation via RotatedRect. This means that the function draws an ellipse inscribed in the rotated rectangle.color- Ellipse color. a filled ellipse sector is to be drawn.
-
drawMarker
public 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. The function cv::drawMarker draws a marker on a given position in the image. For the moment several marker types are supported, see #MarkerTypes for more information.- Parameters:
img- Image.position- The point where the crosshair is positioned.color- Line color.markerType- The specific type of marker you want to use, see #MarkerTypesmarkerSize- The length of the marker axis [default = 20 pixels]thickness- Line thickness.line_type- Type of the line, See #LineTypes
-
drawMarker
public 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. The function cv::drawMarker draws a marker on a given position in the image. For the moment several marker types are supported, see #MarkerTypes for more information.- Parameters:
img- Image.position- The point where the crosshair is positioned.color- Line color.markerType- The specific type of marker you want to use, see #MarkerTypesmarkerSize- The length of the marker axis [default = 20 pixels]thickness- Line thickness.
-
drawMarker
public static void drawMarker(Mat img, Point position, Scalar color, int markerType, int markerSize) Draws a marker on a predefined position in an image. The function cv::drawMarker draws a marker on a given position in the image. For the moment several marker types are supported, see #MarkerTypes for more information.- Parameters:
img- Image.position- The point where the crosshair is positioned.color- Line color.markerType- The specific type of marker you want to use, see #MarkerTypesmarkerSize- The length of the marker axis [default = 20 pixels]
-
drawMarker
Draws a marker on a predefined position in an image. The function cv::drawMarker draws a marker on a given position in the image. For the moment several marker types are supported, see #MarkerTypes for more information.- Parameters:
img- Image.position- The point where the crosshair is positioned.color- Line color.markerType- The specific type of marker you want to use, see #MarkerTypes
-
drawMarker
Draws a marker on a predefined position in an image. The function cv::drawMarker draws a marker on a given position in the image. For the moment several marker types are supported, see #MarkerTypes for more information.- Parameters:
img- Image.position- The point where the crosshair is positioned.color- Line color.
-
fillConvexPoly
public static void fillConvexPoly(Mat img, MatOfPoint points, Scalar color, int lineType, int shift) Fills a convex polygon. The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the function #fillPoly . It can fill not only convex polygons but any monotonic polygon without self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) twice at the most (though, its top-most and/or the bottom edge could be horizontal).- Parameters:
img- Image.points- Polygon vertices.color- Polygon color.lineType- Type of the polygon boundaries. See #LineTypesshift- Number of fractional bits in the vertex coordinates.
-
fillConvexPoly
Fills a convex polygon. The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the function #fillPoly . It can fill not only convex polygons but any monotonic polygon without self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) twice at the most (though, its top-most and/or the bottom edge could be horizontal).- Parameters:
img- Image.points- Polygon vertices.color- Polygon color.lineType- Type of the polygon boundaries. See #LineTypes
-
fillConvexPoly
Fills a convex polygon. The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the function #fillPoly . It can fill not only convex polygons but any monotonic polygon without self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) twice at the most (though, its top-most and/or the bottom edge could be horizontal).- Parameters:
img- Image.points- Polygon vertices.color- Polygon color.
-
fillPoly
public static void fillPoly(Mat img, List<MatOfPoint> pts, Scalar color, int lineType, int shift, Point offset) Fills the area bounded by one or more polygons. The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill complex areas, for example, areas with holes, contours with self-intersections (some of their parts), and so forth.- Parameters:
img- Image.pts- Array of polygons where each polygon is represented as an array of points.color- Polygon color.lineType- Type of the polygon boundaries. See #LineTypesshift- Number of fractional bits in the vertex coordinates.offset- Optional offset of all points of the contours.
-
fillPoly
Fills the area bounded by one or more polygons. The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill complex areas, for example, areas with holes, contours with self-intersections (some of their parts), and so forth.- Parameters:
img- Image.pts- Array of polygons where each polygon is represented as an array of points.color- Polygon color.lineType- Type of the polygon boundaries. See #LineTypesshift- Number of fractional bits in the vertex coordinates.
-
fillPoly
Fills the area bounded by one or more polygons. The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill complex areas, for example, areas with holes, contours with self-intersections (some of their parts), and so forth.- Parameters:
img- Image.pts- Array of polygons where each polygon is represented as an array of points.color- Polygon color.lineType- Type of the polygon boundaries. See #LineTypes
-
fillPoly
Fills the area bounded by one or more polygons. The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill complex areas, for example, areas with holes, contours with self-intersections (some of their parts), and so forth.- Parameters:
img- Image.pts- Array of polygons where each polygon is represented as an array of points.color- Polygon color.
-
polylines
public static void polylines(Mat img, List<MatOfPoint> pts, boolean isClosed, Scalar color, int thickness, int lineType, int shift) Draws several polygonal curves.- Parameters:
img- Image.pts- Array of polygonal curves.isClosed- Flag indicating whether the drawn polylines are closed or not. If they are closed, the function draws a line from the last vertex of each curve to its first vertex.color- Polyline color.thickness- Thickness of the polyline edges.lineType- Type of the line segments. See #LineTypesshift- Number of fractional bits in the vertex coordinates. The function cv::polylines draws one or more polygonal curves.
-
polylines
public static void polylines(Mat img, List<MatOfPoint> pts, boolean isClosed, Scalar color, int thickness, int lineType) Draws several polygonal curves.- Parameters:
img- Image.pts- Array of polygonal curves.isClosed- Flag indicating whether the drawn polylines are closed or not. If they are closed, the function draws a line from the last vertex of each curve to its first vertex.color- Polyline color.thickness- Thickness of the polyline edges.lineType- Type of the line segments. See #LineTypes The function cv::polylines draws one or more polygonal curves.
-
polylines
public static void polylines(Mat img, List<MatOfPoint> pts, boolean isClosed, Scalar color, int thickness) Draws several polygonal curves.- Parameters:
img- Image.pts- Array of polygonal curves.isClosed- Flag indicating whether the drawn polylines are closed or not. If they are closed, the function draws a line from the last vertex of each curve to its first vertex.color- Polyline color.thickness- Thickness of the polyline edges. The function cv::polylines draws one or more polygonal curves.
-
polylines
Draws several polygonal curves.- Parameters:
img- Image.pts- Array of polygonal curves.isClosed- Flag indicating whether the drawn polylines are closed or not. If they are closed, the function draws a line from the last vertex of each curve to its first vertex.color- Polyline color. The function cv::polylines draws one or more polygonal curves.
-
drawContours
public static void drawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy, int maxLevel, Point offset) Draws contours outlines or filled contours. The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area bounded by the contours if \(\texttt{thickness}<0\) . The example below shows how to retrieve connected components from the binary image and label them: : INCLUDE: snippets/imgproc_drawContours.cpp- Parameters:
image- Destination image.contours- All the input contours. Each contour is stored as a point vector.contourIdx- Parameter indicating a contour to draw. If it is negative, all the contours are drawn.color- Color of the contours.thickness- Thickness of lines the contours are drawn with. If it is negative (for example, thickness=#FILLED ), the contour interiors are drawn.lineType- Line connectivity. See #LineTypeshierarchy- Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).maxLevel- Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.offset- Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) . Note: When thickness=#FILLED, the function is designed to handle connected components with holes correctly even when no hierarchy data is provided. This is done by analyzing all the outlines together using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved contours. In order to solve this problem, you need to call #drawContours separately for each sub-group of contours, or iterate over the collection using contourIdx parameter.
-
drawContours
public static void drawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy, int maxLevel) Draws contours outlines or filled contours. The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area bounded by the contours if \(\texttt{thickness}<0\) . The example below shows how to retrieve connected components from the binary image and label them: : INCLUDE: snippets/imgproc_drawContours.cpp- Parameters:
image- Destination image.contours- All the input contours. Each contour is stored as a point vector.contourIdx- Parameter indicating a contour to draw. If it is negative, all the contours are drawn.color- Color of the contours.thickness- Thickness of lines the contours are drawn with. If it is negative (for example, thickness=#FILLED ), the contour interiors are drawn.lineType- Line connectivity. See #LineTypeshierarchy- Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ).maxLevel- Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available. \(\texttt{offset}=(dx,dy)\) . Note: When thickness=#FILLED, the function is designed to handle connected components with holes correctly even when no hierarchy data is provided. This is done by analyzing all the outlines together using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved contours. In order to solve this problem, you need to call #drawContours separately for each sub-group of contours, or iterate over the collection using contourIdx parameter.
-
drawContours
public static void drawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy) Draws contours outlines or filled contours. The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area bounded by the contours if \(\texttt{thickness}<0\) . The example below shows how to retrieve connected components from the binary image and label them: : INCLUDE: snippets/imgproc_drawContours.cpp- Parameters:
image- Destination image.contours- All the input contours. Each contour is stored as a point vector.contourIdx- Parameter indicating a contour to draw. If it is negative, all the contours are drawn.color- Color of the contours.thickness- Thickness of lines the contours are drawn with. If it is negative (for example, thickness=#FILLED ), the contour interiors are drawn.lineType- Line connectivity. See #LineTypeshierarchy- Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ). If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available. \(\texttt{offset}=(dx,dy)\) . Note: When thickness=#FILLED, the function is designed to handle connected components with holes correctly even when no hierarchy data is provided. This is done by analyzing all the outlines together using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved contours. In order to solve this problem, you need to call #drawContours separately for each sub-group of contours, or iterate over the collection using contourIdx parameter.
-
drawContours
public static void drawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType) Draws contours outlines or filled contours. The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area bounded by the contours if \(\texttt{thickness}<0\) . The example below shows how to retrieve connected components from the binary image and label them: : INCLUDE: snippets/imgproc_drawContours.cpp- Parameters:
image- Destination image.contours- All the input contours. Each contour is stored as a point vector.contourIdx- Parameter indicating a contour to draw. If it is negative, all the contours are drawn.color- Color of the contours.thickness- Thickness of lines the contours are drawn with. If it is negative (for example, thickness=#FILLED ), the contour interiors are drawn.lineType- Line connectivity. See #LineTypes some of the contours (see maxLevel ). If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available. \(\texttt{offset}=(dx,dy)\) . Note: When thickness=#FILLED, the function is designed to handle connected components with holes correctly even when no hierarchy data is provided. This is done by analyzing all the outlines together using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved contours. In order to solve this problem, you need to call #drawContours separately for each sub-group of contours, or iterate over the collection using contourIdx parameter.
-
drawContours
public static void drawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness) Draws contours outlines or filled contours. The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area bounded by the contours if \(\texttt{thickness}<0\) . The example below shows how to retrieve connected components from the binary image and label them: : INCLUDE: snippets/imgproc_drawContours.cpp- Parameters:
image- Destination image.contours- All the input contours. Each contour is stored as a point vector.contourIdx- Parameter indicating a contour to draw. If it is negative, all the contours are drawn.color- Color of the contours.thickness- Thickness of lines the contours are drawn with. If it is negative (for example, thickness=#FILLED ), the contour interiors are drawn. some of the contours (see maxLevel ). If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available. \(\texttt{offset}=(dx,dy)\) . Note: When thickness=#FILLED, the function is designed to handle connected components with holes correctly even when no hierarchy data is provided. This is done by analyzing all the outlines together using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved contours. In order to solve this problem, you need to call #drawContours separately for each sub-group of contours, or iterate over the collection using contourIdx parameter.
-
drawContours
Draws contours outlines or filled contours. The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area bounded by the contours if \(\texttt{thickness}<0\) . The example below shows how to retrieve connected components from the binary image and label them: : INCLUDE: snippets/imgproc_drawContours.cpp- Parameters:
image- Destination image.contours- All the input contours. Each contour is stored as a point vector.contourIdx- Parameter indicating a contour to draw. If it is negative, all the contours are drawn.color- Color of the contours. thickness=#FILLED ), the contour interiors are drawn. some of the contours (see maxLevel ). If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available. \(\texttt{offset}=(dx,dy)\) . Note: When thickness=#FILLED, the function is designed to handle connected components with holes correctly even when no hierarchy data is provided. This is done by analyzing all the outlines together using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved contours. In order to solve this problem, you need to call #drawContours separately for each sub-group of contours, or iterate over the collection using contourIdx parameter.
-
clipLine
-
ellipse2Poly
public static void ellipse2Poly(Point center, Size axes, int angle, int arcStart, int arcEnd, int delta, MatOfPoint pts) Approximates an elliptic arc with a polyline. The function ellipse2Poly computes the vertices of a polyline that approximates the specified elliptic arc. It is used by #ellipse. IfarcStartis greater thanarcEnd, they are swapped.- Parameters:
center- Center of the arc.axes- Half of the size of the ellipse main axes. See #ellipse for details.angle- Rotation angle of the ellipse in degrees. See #ellipse for details.arcStart- Starting angle of the elliptic arc in degrees.arcEnd- Ending angle of the elliptic arc in degrees.delta- Angle between the subsequent polyline vertices. It defines the approximation accuracy.pts- Output vector of polyline vertices.
-
putText
public static void putText(Mat img, String text, Point org, int fontFace, double fontScale, Scalar color, int thickness, int lineType, boolean bottomLeftOrigin) Draws a text string. The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered using the specified font are replaced by question marks. See #getTextSize for a text rendering code example.- Parameters:
img- Image.text- Text string to be drawn.org- Bottom-left corner of the text string in the image.fontFace- Font type, see #HersheyFonts.fontScale- Font scale factor that is multiplied by the font-specific base size.color- Text color.thickness- Thickness of the lines used to draw a text.lineType- Line type. See #LineTypesbottomLeftOrigin- When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.
-
putText
public static void putText(Mat img, String text, Point org, int fontFace, double fontScale, Scalar color, int thickness, int lineType) Draws a text string. The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered using the specified font are replaced by question marks. See #getTextSize for a text rendering code example.- Parameters:
img- Image.text- Text string to be drawn.org- Bottom-left corner of the text string in the image.fontFace- Font type, see #HersheyFonts.fontScale- Font scale factor that is multiplied by the font-specific base size.color- Text color.thickness- Thickness of the lines used to draw a text.lineType- Line type. See #LineTypes it is at the top-left corner.
-
putText
public static void putText(Mat img, String text, Point org, int fontFace, double fontScale, Scalar color, int thickness) Draws a text string. The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered using the specified font are replaced by question marks. See #getTextSize for a text rendering code example.- Parameters:
img- Image.text- Text string to be drawn.org- Bottom-left corner of the text string in the image.fontFace- Font type, see #HersheyFonts.fontScale- Font scale factor that is multiplied by the font-specific base size.color- Text color.thickness- Thickness of the lines used to draw a text. it is at the top-left corner.
-
putText
public static void putText(Mat img, String text, Point org, int fontFace, double fontScale, Scalar color) Draws a text string. The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered using the specified font are replaced by question marks. See #getTextSize for a text rendering code example.- Parameters:
img- Image.text- Text string to be drawn.org- Bottom-left corner of the text string in the image.fontFace- Font type, see #HersheyFonts.fontScale- Font scale factor that is multiplied by the font-specific base size.color- Text color. it is at the top-left corner.
-
getFontScaleFromHeight
public static double getFontScaleFromHeight(int fontFace, int pixelHeight, int thickness) Calculates the font-specific size to use to achieve a given height in pixels.- Parameters:
fontFace- Font to use, see cv::HersheyFonts.pixelHeight- Pixel height to compute the fontScale forthickness- Thickness of lines used to render the text.See putText for details.- Returns:
- The fontSize to use for cv::putText SEE: cv::putText
-
getFontScaleFromHeight
public static double getFontScaleFromHeight(int fontFace, int pixelHeight) Calculates the font-specific size to use to achieve a given height in pixels.- Parameters:
fontFace- Font to use, see cv::HersheyFonts.pixelHeight- Pixel height to compute the fontScale for- Returns:
- The fontSize to use for cv::putText SEE: cv::putText
-
HoughLinesWithAccumulator
public 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. Note: This function is for bindings use only. Use original function in C++ code SEE: HoughLines- Parameters:
image- automatically generatedlines- automatically generatedrho- automatically generatedtheta- automatically generatedthreshold- automatically generatedsrn- automatically generatedstn- automatically generatedmin_theta- automatically generatedmax_theta- automatically generated
-
HoughLinesWithAccumulator
public 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. Note: This function is for bindings use only. Use original function in C++ code SEE: HoughLines- Parameters:
image- automatically generatedlines- automatically generatedrho- automatically generatedtheta- automatically generatedthreshold- automatically generatedsrn- automatically generatedstn- automatically generatedmin_theta- automatically generated
-
HoughLinesWithAccumulator
public 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. Note: This function is for bindings use only. Use original function in C++ code SEE: HoughLines- Parameters:
image- automatically generatedlines- automatically generatedrho- automatically generatedtheta- automatically generatedthreshold- automatically generatedsrn- automatically generatedstn- automatically generated
-
HoughLinesWithAccumulator
public 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. Note: This function is for bindings use only. Use original function in C++ code SEE: HoughLines- Parameters:
image- automatically generatedlines- automatically generatedrho- automatically generatedtheta- automatically generatedthreshold- automatically generatedsrn- automatically generated
-
HoughLinesWithAccumulator
public 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. Note: This function is for bindings use only. Use original function in C++ code SEE: HoughLines- Parameters:
image- automatically generatedlines- automatically generatedrho- automatically generatedtheta- automatically generatedthreshold- automatically generated
-
getTextSize
-