Static Public Member Functions | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| static LineSegmentDetector | createLineSegmentDetector (int refine, double scale, double sigma_scale, double quant) |
| Creates a smart pointer to a LineSegmentDetector object and initializes it. More... | |
| static LineSegmentDetector | createLineSegmentDetector (int refine, double scale, double sigma_scale) |
| Creates a smart pointer to a LineSegmentDetector object and initializes it. More... | |
| static LineSegmentDetector | createLineSegmentDetector (int refine, double scale) |
| Creates a smart pointer to a LineSegmentDetector object and initializes it. More... | |
| static LineSegmentDetector | createLineSegmentDetector (int refine) |
| Creates a smart pointer to a LineSegmentDetector object and initializes it. More... | |
| static LineSegmentDetector | createLineSegmentDetector () |
| Creates a smart pointer to a LineSegmentDetector object and initializes it. More... | |
| static Mat | getGaussianKernel (int ksize, double sigma, int ktype) |
| Returns Gaussian filter coefficients. More... | |
| static Mat | getGaussianKernel (int ksize, double sigma) |
| Returns Gaussian filter coefficients. More... | |
| static void | getDerivKernels (Mat kx, Mat ky, int dx, int dy, int ksize, bool normalize, int ktype) |
| Returns filter coefficients for computing spatial image derivatives. More... | |
| static void | getDerivKernels (Mat kx, Mat ky, int dx, int dy, int ksize, bool normalize) |
| Returns filter coefficients for computing spatial image derivatives. More... | |
| static void | getDerivKernels (Mat kx, Mat ky, int dx, int dy, int ksize) |
| Returns filter coefficients for computing spatial image derivatives. More... | |
| static Mat | getGaborKernel (Size ksize, double sigma, double theta, double lambd, double gamma, double psi, int ktype) |
| Returns Gabor filter coefficients. More... | |
| static Mat | getGaborKernel (Size ksize, double sigma, double theta, double lambd, double gamma, double psi) |
| Returns Gabor filter coefficients. More... | |
| static Mat | getGaborKernel (Size ksize, double sigma, double theta, double lambd, double gamma) |
| Returns Gabor filter coefficients. More... | |
| static Mat | getStructuringElement (int shape, Size ksize, Point anchor) |
| Returns a structuring element of the specified size and shape for morphological operations. More... | |
| static Mat | getStructuringElement (int shape, Size ksize) |
| Returns a structuring element of the specified size and shape for morphological operations. More... | |
| static void | medianBlur (Mat src, Mat dst, int ksize) |
| Blurs an image using the median filter. More... | |
| static void | GaussianBlur (Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY, int borderType) |
| Blurs an image using a Gaussian filter. More... | |
| static void | GaussianBlur (Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY) |
| Blurs an image using a Gaussian filter. More... | |
| static void | GaussianBlur (Mat src, Mat dst, Size ksize, double sigmaX) |
| Blurs an image using a Gaussian filter. More... | |
| static void | bilateralFilter (Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace, int borderType) |
| Applies the bilateral filter to an image. More... | |
| static void | bilateralFilter (Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace) |
| Applies the bilateral filter to an image. More... | |
| static void | boxFilter (Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize, int borderType) |
| Blurs an image using the box filter. More... | |
| static void | boxFilter (Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize) |
| Blurs an image using the box filter. More... | |
| static void | boxFilter (Mat src, Mat dst, int ddepth, Size ksize, Point anchor) |
| Blurs an image using the box filter. More... | |
| static void | boxFilter (Mat src, Mat dst, int ddepth, Size ksize) |
| Blurs an image using the box filter. More... | |
| static void | sqrBoxFilter (Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize, int borderType) |
| Calculates the normalized sum of squares of the pixel values overlapping the filter. More... | |
| static void | sqrBoxFilter (Mat src, Mat dst, int ddepth, Size ksize, Point anchor, bool normalize) |
| Calculates the normalized sum of squares of the pixel values overlapping the filter. More... | |
| static void | sqrBoxFilter (Mat src, Mat dst, int ddepth, Size ksize, Point anchor) |
| Calculates the normalized sum of squares of the pixel values overlapping the filter. More... | |
| static void | sqrBoxFilter (Mat src, Mat dst, int ddepth, Size ksize) |
| Calculates the normalized sum of squares of the pixel values overlapping the filter. More... | |
| static void | blur (Mat src, Mat dst, Size ksize, Point anchor, int borderType) |
| Blurs an image using the normalized box filter. More... | |
| static void | blur (Mat src, Mat dst, Size ksize, Point anchor) |
| Blurs an image using the normalized box filter. More... | |
| static void | blur (Mat src, Mat dst, Size ksize) |
| Blurs an image using the normalized box filter. More... | |
| static void | stackBlur (Mat src, Mat dst, Size ksize) |
| Blurs an image using the stackBlur. More... | |
| static void | filter2D (Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta, int borderType) |
| Convolves an image with the kernel. More... | |
| static void | filter2D (Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta) |
| Convolves an image with the kernel. More... | |
| static void | filter2D (Mat src, Mat dst, int ddepth, Mat kernel, Point anchor) |
| Convolves an image with the kernel. More... | |
| static void | filter2D (Mat src, Mat dst, int ddepth, Mat kernel) |
| Convolves an image with the kernel. More... | |
| 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. More... | |
| 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. More... | |
| static void | sepFilter2D (Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor) |
| Applies a separable linear filter to an image. More... | |
| static void | sepFilter2D (Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY) |
| Applies a separable linear filter to an image. More... | |
| 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. More... | |
| 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. More... | |
| static void | Sobel (Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale) |
| Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. More... | |
| static void | Sobel (Mat src, Mat dst, int ddepth, int dx, int dy, int ksize) |
| Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. More... | |
| static void | Sobel (Mat src, Mat dst, int ddepth, int dx, int dy) |
| Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. More... | |
| static void | spatialGradient (Mat src, Mat dx, Mat dy, int ksize, int borderType) |
| Calculates the first order image derivative in both x and y using a Sobel operator. More... | |
| static void | spatialGradient (Mat src, Mat dx, Mat dy, int ksize) |
| Calculates the first order image derivative in both x and y using a Sobel operator. More... | |
| static void | spatialGradient (Mat src, Mat dx, Mat dy) |
| Calculates the first order image derivative in both x and y using a Sobel operator. More... | |
| 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. More... | |
| static void | Scharr (Mat src, Mat dst, int ddepth, int dx, int dy, double scale, double delta) |
| Calculates the first x- or y- image derivative using Scharr operator. More... | |
| static void | Scharr (Mat src, Mat dst, int ddepth, int dx, int dy, double scale) |
| Calculates the first x- or y- image derivative using Scharr operator. More... | |
| static void | Scharr (Mat src, Mat dst, int ddepth, int dx, int dy) |
| Calculates the first x- or y- image derivative using Scharr operator. More... | |
| static void | Laplacian (Mat src, Mat dst, int ddepth, int ksize, double scale, double delta, int borderType) |
| Calculates the Laplacian of an image. More... | |
| static void | Laplacian (Mat src, Mat dst, int ddepth, int ksize, double scale, double delta) |
| Calculates the Laplacian of an image. More... | |
| static void | Laplacian (Mat src, Mat dst, int ddepth, int ksize, double scale) |
| Calculates the Laplacian of an image. More... | |
| static void | Laplacian (Mat src, Mat dst, int ddepth, int ksize) |
| Calculates the Laplacian of an image. More... | |
| static void | Laplacian (Mat src, Mat dst, int ddepth) |
| Calculates the Laplacian of an image. More... | |
| static void | Canny (Mat image, Mat edges, double threshold1, double threshold2, int apertureSize, bool L2gradient) |
| Finds edges in an image using the Canny algorithm [Canny86] . More... | |
| static void | Canny (Mat image, Mat edges, double threshold1, double threshold2, int apertureSize) |
| Finds edges in an image using the Canny algorithm [Canny86] . More... | |
| static void | Canny (Mat image, Mat edges, double threshold1, double threshold2) |
| Finds edges in an image using the Canny algorithm [Canny86] . More... | |
| static void | Canny (Mat dx, Mat dy, Mat edges, double threshold1, double threshold2, bool L2gradient) |
| static void | Canny (Mat dx, Mat dy, Mat edges, double threshold1, double threshold2) |
| static void | cornerMinEigenVal (Mat src, Mat dst, int blockSize, int ksize, int borderType) |
| Calculates the minimal eigenvalue of gradient matrices for corner detection. More... | |
| static void | cornerMinEigenVal (Mat src, Mat dst, int blockSize, int ksize) |
| Calculates the minimal eigenvalue of gradient matrices for corner detection. More... | |
| static void | cornerMinEigenVal (Mat src, Mat dst, int blockSize) |
| Calculates the minimal eigenvalue of gradient matrices for corner detection. More... | |
| static void | cornerHarris (Mat src, Mat dst, int blockSize, int ksize, double k, int borderType) |
| Harris corner detector. More... | |
| static void | cornerHarris (Mat src, Mat dst, int blockSize, int ksize, double k) |
| Harris corner detector. More... | |
| static void | cornerEigenValsAndVecs (Mat src, Mat dst, int blockSize, int ksize, int borderType) |
| Calculates eigenvalues and eigenvectors of image blocks for corner detection. More... | |
| static void | cornerEigenValsAndVecs (Mat src, Mat dst, int blockSize, int ksize) |
| Calculates eigenvalues and eigenvectors of image blocks for corner detection. More... | |
| static void | preCornerDetect (Mat src, Mat dst, int ksize, int borderType) |
| Calculates a feature map for corner detection. More... | |
| static void | preCornerDetect (Mat src, Mat dst, int ksize) |
| Calculates a feature map for corner detection. More... | |
| static void | cornerSubPix (Mat image, Mat corners, Size winSize, Size zeroZone, TermCriteria criteria) |
| Refines the corner locations. More... | |
| static void | goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, bool useHarrisDetector, double k) |
| Determines strong corners on an image. More... | |
| static void | goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, bool useHarrisDetector) |
| Determines strong corners on an image. More... | |
| static void | goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize) |
| Determines strong corners on an image. More... | |
| static void | goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask) |
| Determines strong corners on an image. More... | |
| static void | goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance) |
| Determines strong corners on an image. More... | |
| static void | goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, bool useHarrisDetector, double k) |
| static void | goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, bool useHarrisDetector) |
| static void | goodFeaturesToTrack (Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize) |
| static void | goodFeaturesToTrackWithQuality (Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize, bool useHarrisDetector, double k) |
| Same as above, but returns also quality measure of the detected corners. More... | |
| static void | goodFeaturesToTrackWithQuality (Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize, bool useHarrisDetector) |
| Same as above, but returns also quality measure of the detected corners. More... | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| static void | HoughLines (Mat image, Mat lines, double rho, double theta, int threshold) |
| Finds lines in a binary image using the standard Hough transform. More... | |
| 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. More... | |
| 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. More... | |
| static void | HoughLinesP (Mat image, Mat lines, double rho, double theta, int threshold) |
| Finds line segments in a binary image using the probabilistic Hough transform. More... | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| static void | HoughCircles (Mat image, Mat circles, int method, double dp, double minDist) |
| Finds circles in a grayscale image using the Hough transform. More... | |
| 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. More... | |
| static void | erode (Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType) |
| Erodes an image by using a specific structuring element. More... | |
| static void | erode (Mat src, Mat dst, Mat kernel, Point anchor, int iterations) |
| Erodes an image by using a specific structuring element. More... | |
| static void | erode (Mat src, Mat dst, Mat kernel, Point anchor) |
| Erodes an image by using a specific structuring element. More... | |
| static void | erode (Mat src, Mat dst, Mat kernel) |
| Erodes an image by using a specific structuring element. More... | |
| 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. More... | |
| static void | dilate (Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType) |
| Dilates an image by using a specific structuring element. More... | |
| static void | dilate (Mat src, Mat dst, Mat kernel, Point anchor, int iterations) |
| Dilates an image by using a specific structuring element. More... | |
| static void | dilate (Mat src, Mat dst, Mat kernel, Point anchor) |
| Dilates an image by using a specific structuring element. More... | |
| static void | dilate (Mat src, Mat dst, Mat kernel) |
| Dilates an image by using a specific structuring element. More... | |
| static void | morphologyEx (Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue) |
| Performs advanced morphological transformations. More... | |
| static void | morphologyEx (Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType) |
| Performs advanced morphological transformations. More... | |
| static void | morphologyEx (Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations) |
| Performs advanced morphological transformations. More... | |
| static void | morphologyEx (Mat src, Mat dst, int op, Mat kernel, Point anchor) |
| Performs advanced morphological transformations. More... | |
| static void | morphologyEx (Mat src, Mat dst, int op, Mat kernel) |
| Performs advanced morphological transformations. More... | |
| static void | resize (Mat src, Mat dst, Size dsize, double fx, double fy, int interpolation) |
| Resizes an image. More... | |
| static void | resize (Mat src, Mat dst, Size dsize, double fx, double fy) |
| Resizes an image. More... | |
| static void | resize (Mat src, Mat dst, Size dsize, double fx) |
| Resizes an image. More... | |
| static void | resize (Mat src, Mat dst, Size dsize) |
| Resizes an image. More... | |
| static void | warpAffine (Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue) |
| Applies an affine transformation to an image. More... | |
| static void | warpAffine (Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode) |
| Applies an affine transformation to an image. More... | |
| static void | warpAffine (Mat src, Mat dst, Mat M, Size dsize, int flags) |
| Applies an affine transformation to an image. More... | |
| static void | warpAffine (Mat src, Mat dst, Mat M, Size dsize) |
| Applies an affine transformation to an image. More... | |
| static void | warpPerspective (Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue) |
| Applies a perspective transformation to an image. More... | |
| static void | warpPerspective (Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode) |
| Applies a perspective transformation to an image. More... | |
| static void | warpPerspective (Mat src, Mat dst, Mat M, Size dsize, int flags) |
| Applies a perspective transformation to an image. More... | |
| static void | warpPerspective (Mat src, Mat dst, Mat M, Size dsize) |
| Applies a perspective transformation to an image. More... | |
| 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. More... | |
| static void | remap (Mat src, Mat dst, Mat map1, Mat map2, int interpolation, int borderMode) |
| Applies a generic geometrical transformation to an image. More... | |
| static void | remap (Mat src, Mat dst, Mat map1, Mat map2, int interpolation) |
| Applies a generic geometrical transformation to an image. More... | |
| static void | convertMaps (Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type, bool nninterpolation) |
| Converts image transformation maps from one representation to another. More... | |
| static void | convertMaps (Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type) |
| Converts image transformation maps from one representation to another. More... | |
| static Mat | getRotationMatrix2D (Point center, double angle, double scale) |
| Calculates an affine matrix of 2D rotation. More... | |
| static void | invertAffineTransform (Mat M, Mat iM) |
| Inverts an affine transformation. More... | |
| static Mat | getPerspectiveTransform (Mat src, Mat dst, int solveMethod) |
| Calculates a perspective transform from four pairs of the corresponding points. More... | |
| static Mat | getPerspectiveTransform (Mat src, Mat dst) |
| Calculates a perspective transform from four pairs of the corresponding points. More... | |
| static Mat | getAffineTransform (MatOfPoint2f src, MatOfPoint2f dst) |
| static void | getRectSubPix (Mat image, Size patchSize, Point center, Mat patch, int patchType) |
| Retrieves a pixel rectangle from an image with sub-pixel accuracy. More... | |
| static void | getRectSubPix (Mat image, Size patchSize, Point center, Mat patch) |
| Retrieves a pixel rectangle from an image with sub-pixel accuracy. More... | |
| static void | logPolar (Mat src, Mat dst, Point center, double M, int flags) |
| Remaps an image to semilog-polar coordinates space. More... | |
| static void | linearPolar (Mat src, Mat dst, Point center, double maxRadius, int flags) |
| Remaps an image to polar coordinates space. More... | |
| 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. More... | |
| static void | integral3 (Mat src, Mat sum, Mat sqsum, Mat tilted, int sdepth, int sqdepth) |
| Calculates the integral of an image. More... | |
| static void | integral3 (Mat src, Mat sum, Mat sqsum, Mat tilted, int sdepth) |
| Calculates the integral of an image. More... | |
| static void | integral3 (Mat src, Mat sum, Mat sqsum, Mat tilted) |
| Calculates the integral of an image. More... | |
| static void | integral (Mat src, Mat sum, int sdepth) |
| static void | integral (Mat src, Mat sum) |
| static void | integral2 (Mat src, Mat sum, Mat sqsum, int sdepth, int sqdepth) |
| static void | integral2 (Mat src, Mat sum, Mat sqsum, int sdepth) |
| static void | integral2 (Mat src, Mat sum, Mat sqsum) |
| static void | accumulate (Mat src, Mat dst, Mat mask) |
| Adds an image to the accumulator image. More... | |
| static void | accumulate (Mat src, Mat dst) |
| Adds an image to the accumulator image. More... | |
| static void | accumulateSquare (Mat src, Mat dst, Mat mask) |
| Adds the square of a source image to the accumulator image. More... | |
| static void | accumulateSquare (Mat src, Mat dst) |
| Adds the square of a source image to the accumulator image. More... | |
| static void | accumulateProduct (Mat src1, Mat src2, Mat dst, Mat mask) |
| Adds the per-element product of two input images to the accumulator image. More... | |
| static void | accumulateProduct (Mat src1, Mat src2, Mat dst) |
| Adds the per-element product of two input images to the accumulator image. More... | |
| static void | accumulateWeighted (Mat src, Mat dst, double alpha, Mat mask) |
| Updates a running average. More... | |
| static void | accumulateWeighted (Mat src, Mat dst, double alpha) |
| Updates a running average. More... | |
| static Point | phaseCorrelate (Mat src1, Mat src2, Mat window, double[] response) |
| The function is used to detect translational shifts that occur between two images. More... | |
| static Point | phaseCorrelate (Mat src1, Mat src2, Mat window) |
| The function is used to detect translational shifts that occur between two images. More... | |
| static Point | phaseCorrelate (Mat src1, Mat src2) |
| The function is used to detect translational shifts that occur between two images. More... | |
| static void | createHanningWindow (Mat dst, Size winSize, int type) |
| This function computes a Hanning window coefficients in two dimensions. More... | |
| static void | divSpectrums (Mat a, Mat b, Mat c, int flags, bool conjB) |
| Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum. More... | |
| static void | divSpectrums (Mat a, Mat b, Mat c, int flags) |
| Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum. More... | |
| static double | threshold (Mat src, Mat dst, double thresh, double maxval, int type) |
| Applies a fixed-level threshold to each array element. More... | |
| static void | adaptiveThreshold (Mat src, Mat dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C) |
| Applies an adaptive threshold to an array. More... | |
| static void | pyrDown (Mat src, Mat dst, Size dstsize, int borderType) |
| Blurs an image and downsamples it. More... | |
| static void | pyrDown (Mat src, Mat dst, Size dstsize) |
| Blurs an image and downsamples it. More... | |
| static void | pyrDown (Mat src, Mat dst) |
| Blurs an image and downsamples it. More... | |
| static void | pyrUp (Mat src, Mat dst, Size dstsize, int borderType) |
| Upsamples an image and then blurs it. More... | |
| static void | pyrUp (Mat src, Mat dst, Size dstsize) |
| Upsamples an image and then blurs it. More... | |
| static void | pyrUp (Mat src, Mat dst) |
| Upsamples an image and then blurs it. More... | |
| static void | calcHist (List< Mat > images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges, bool accumulate) |
| static void | calcHist (List< Mat > images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges) |
| static void | calcBackProject (List< Mat > images, MatOfInt channels, Mat hist, Mat dst, MatOfFloat ranges, double scale) |
| static double | compareHist (Mat H1, Mat H2, int method) |
| Compares two histograms. More... | |
| static void | equalizeHist (Mat src, Mat dst) |
| Equalizes the histogram of a grayscale image. More... | |
| static CLAHE | createCLAHE (double clipLimit, Size tileGridSize) |
| Creates a smart pointer to a cv::CLAHE class and initializes it. More... | |
| static CLAHE | createCLAHE (double clipLimit) |
| Creates a smart pointer to a cv::CLAHE class and initializes it. More... | |
| static CLAHE | createCLAHE () |
| Creates a smart pointer to a cv::CLAHE class and initializes it. More... | |
| static float | EMD (Mat signature1, Mat signature2, int distType, Mat cost, Mat flow) |
| Computes the "minimal work" distance between two weighted point configurations. More... | |
| static float | EMD (Mat signature1, Mat signature2, int distType, Mat cost) |
| Computes the "minimal work" distance between two weighted point configurations. More... | |
| static float | EMD (Mat signature1, Mat signature2, int distType) |
| Computes the "minimal work" distance between two weighted point configurations. More... | |
| static void | watershed (Mat image, Mat markers) |
| Performs a marker-based image segmentation using the watershed algorithm. More... | |
| static void | pyrMeanShiftFiltering (Mat src, Mat dst, double sp, double sr, int maxLevel, TermCriteria termcrit) |
| Performs initial step of meanshift segmentation of an image. More... | |
| static void | pyrMeanShiftFiltering (Mat src, Mat dst, double sp, double sr, int maxLevel) |
| Performs initial step of meanshift segmentation of an image. More... | |
| static void | pyrMeanShiftFiltering (Mat src, Mat dst, double sp, double sr) |
| Performs initial step of meanshift segmentation of an image. More... | |
| static void | grabCut (Mat img, Mat mask, Rect rect, Mat bgdModel, Mat fgdModel, int iterCount, int mode) |
| Runs the GrabCut algorithm. More... | |
| static void | grabCut (Mat img, Mat mask, Rect rect, Mat bgdModel, Mat fgdModel, int iterCount) |
| Runs the GrabCut algorithm. More... | |
| 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. More... | |
| 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. More... | |
| static void | distanceTransform (Mat src, Mat dst, int distanceType, int maskSize, int dstType) |
| static void | distanceTransform (Mat src, Mat dst, int distanceType, int maskSize) |
| 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. More... | |
| 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. More... | |
| static int | floodFill (Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect, Scalar loDiff) |
| Fills a connected component with the given color. More... | |
| static int | floodFill (Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect) |
| Fills a connected component with the given color. More... | |
| static int | floodFill (Mat image, Mat mask, Point seedPoint, Scalar newVal) |
| Fills a connected component with the given color. More... | |
| static void | blendLinear (Mat src1, Mat src2, Mat weights1, Mat weights2, Mat dst) |
| static void | cvtColor (Mat src, Mat dst, int code, int dstCn) |
| Converts an image from one color space to another. More... | |
| static void | cvtColor (Mat src, Mat dst, int code) |
| Converts an image from one color space to another. More... | |
| static void | cvtColorTwoPlane (Mat src1, Mat src2, Mat dst, int code) |
| Converts an image from one color space to another where the source image is stored in two planes. More... | |
| static void | demosaicing (Mat src, Mat dst, int code, int dstCn) |
| main function for all demosaicing processes More... | |
| static void | demosaicing (Mat src, Mat dst, int code) |
| main function for all demosaicing processes More... | |
| static Moments | moments (Mat array, bool binaryImage) |
| Calculates all of the moments up to the third order of a polygon or rasterized shape. More... | |
| static Moments | moments (Mat array) |
| Calculates all of the moments up to the third order of a polygon or rasterized shape. More... | |
| static void | HuMoments (Moments m, Mat hu) |
| static void | matchTemplate (Mat image, Mat templ, Mat result, int method, Mat mask) |
| Compares a template against overlapped image regions. More... | |
| static void | matchTemplate (Mat image, Mat templ, Mat result, int method) |
| Compares a template against overlapped image regions. More... | |
| static int | connectedComponentsWithAlgorithm (Mat image, Mat labels, int connectivity, int ltype, int ccltype) |
| computes the connected components labeled image of boolean image More... | |
| static int | connectedComponents (Mat image, Mat labels, int connectivity, int ltype) |
| static int | connectedComponents (Mat image, Mat labels, int connectivity) |
| static int | connectedComponents (Mat image, Mat labels) |
| 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 More... | |
| static int | connectedComponentsWithStats (Mat image, Mat labels, Mat stats, Mat centroids, int connectivity, int ltype) |
| static int | connectedComponentsWithStats (Mat image, Mat labels, Mat stats, Mat centroids, int connectivity) |
| static int | connectedComponentsWithStats (Mat image, Mat labels, Mat stats, Mat centroids) |
| static void | findContours (Mat image, List< MatOfPoint > contours, Mat hierarchy, int mode, int method, Point offset) |
| Finds contours in a binary image. More... | |
| static void | findContours (Mat image, List< MatOfPoint > contours, Mat hierarchy, int mode, int method) |
| Finds contours in a binary image. More... | |
| static void | approxPolyDP (MatOfPoint2f curve, MatOfPoint2f approxCurve, double epsilon, bool closed) |
| Approximates a polygonal curve(s) with the specified precision. More... | |
| static double | arcLength (MatOfPoint2f curve, bool closed) |
| Calculates a contour perimeter or a curve length. More... | |
| static Rect | boundingRect (Mat array) |
| Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image. More... | |
| static double | contourArea (Mat contour, bool oriented) |
| Calculates a contour area. More... | |
| static double | contourArea (Mat contour) |
| Calculates a contour area. More... | |
| static RotatedRect | minAreaRect (MatOfPoint2f points) |
| Finds a rotated rectangle of the minimum area enclosing the input 2D point set. More... | |
| static void | boxPoints (RotatedRect box, Mat points) |
| Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle. More... | |
| static void | minEnclosingCircle (MatOfPoint2f points, Point center, float[] radius) |
| Finds a circle of the minimum area enclosing a 2D point set. More... | |
| static double | minEnclosingTriangle (Mat points, Mat triangle) |
| Finds a triangle of minimum area enclosing a 2D point set and returns its area. More... | |
| static double | matchShapes (Mat contour1, Mat contour2, int method, double parameter) |
| Compares two shapes. More... | |
| static void | convexHull (MatOfPoint points, MatOfInt hull, bool clockwise) |
| Finds the convex hull of a point set. More... | |
| static void | convexHull (MatOfPoint points, MatOfInt hull) |
| Finds the convex hull of a point set. More... | |
| static void | convexityDefects (MatOfPoint contour, MatOfInt convexhull, MatOfInt4 convexityDefects) |
| Finds the convexity defects of a contour. More... | |
| static bool | isContourConvex (MatOfPoint contour) |
| Tests a contour convexity. More... | |
| static float | intersectConvexConvex (Mat p1, Mat p2, Mat p12, bool handleNested) |
| Finds intersection of two convex polygons. More... | |
| static float | intersectConvexConvex (Mat p1, Mat p2, Mat p12) |
| Finds intersection of two convex polygons. More... | |
| static RotatedRect | fitEllipse (MatOfPoint2f points) |
| Fits an ellipse around a set of 2D points. More... | |
| static RotatedRect | fitEllipseAMS (Mat points) |
| Fits an ellipse around a set of 2D points. More... | |
| static RotatedRect | fitEllipseDirect (Mat points) |
| Fits an ellipse around a set of 2D points. More... | |
| 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. More... | |
| static double | pointPolygonTest (MatOfPoint2f contour, Point pt, bool measureDist) |
| Performs a point-in-contour test. More... | |
| static int | rotatedRectangleIntersection (RotatedRect rect1, RotatedRect rect2, Mat intersectingRegion) |
| Finds out if there is any intersection between two rotated rectangles. More... | |
| static GeneralizedHoughBallard | createGeneralizedHoughBallard () |
| Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it. More... | |
| static GeneralizedHoughGuil | createGeneralizedHoughGuil () |
| Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it. More... | |
| static void | applyColorMap (Mat src, Mat dst, int colormap) |
| Applies a GNU Octave/MATLAB equivalent colormap on a given image. More... | |
| static void | applyColorMap (Mat src, Mat dst, Mat userColor) |
| Applies a user colormap on a given image. More... | |
| static void | line (Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType, int shift) |
| Draws a line segment connecting two points. More... | |
| static void | line (Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType) |
| Draws a line segment connecting two points. More... | |
| static void | line (Mat img, Point pt1, Point pt2, Scalar color, int thickness) |
| Draws a line segment connecting two points. More... | |
| static void | line (Mat img, Point pt1, Point pt2, Scalar color) |
| Draws a line segment connecting two points. More... | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| static void | arrowedLine (Mat img, Point pt1, Point pt2, Scalar color, int thickness) |
| Draws an arrow segment pointing from the first point to the second one. More... | |
| static void | arrowedLine (Mat img, Point pt1, Point pt2, Scalar color) |
| Draws an arrow segment pointing from the first point to the second one. More... | |
| 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. More... | |
| static void | rectangle (Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType) |
| Draws a simple, thick, or filled up-right rectangle. More... | |
| static void | rectangle (Mat img, Point pt1, Point pt2, Scalar color, int thickness) |
| Draws a simple, thick, or filled up-right rectangle. More... | |
| static void | rectangle (Mat img, Point pt1, Point pt2, Scalar color) |
| Draws a simple, thick, or filled up-right rectangle. More... | |
| static void | rectangle (Mat img, Rect rec, Scalar color, int thickness, int lineType, int shift) |
| static void | rectangle (Mat img, Rect rec, Scalar color, int thickness, int lineType) |
| static void | rectangle (Mat img, Rect rec, Scalar color, int thickness) |
| static void | rectangle (Mat img, Rect rec, Scalar color) |
| static void | circle (Mat img, Point center, int radius, Scalar color, int thickness, int lineType, int shift) |
| Draws a circle. More... | |
| static void | circle (Mat img, Point center, int radius, Scalar color, int thickness, int lineType) |
| Draws a circle. More... | |
| static void | circle (Mat img, Point center, int radius, Scalar color, int thickness) |
| Draws a circle. More... | |
| static void | circle (Mat img, Point center, int radius, Scalar color) |
| Draws a circle. More... | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| static void | ellipse (Mat img, RotatedRect box, Scalar color, int thickness, int lineType) |
| static void | ellipse (Mat img, RotatedRect box, Scalar color, int thickness) |
| static void | ellipse (Mat img, RotatedRect box, Scalar color) |
| 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. More... | |
| 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. More... | |
| static void | drawMarker (Mat img, Point position, Scalar color, int markerType, int markerSize) |
| Draws a marker on a predefined position in an image. More... | |
| static void | drawMarker (Mat img, Point position, Scalar color, int markerType) |
| Draws a marker on a predefined position in an image. More... | |
| static void | drawMarker (Mat img, Point position, Scalar color) |
| Draws a marker on a predefined position in an image. More... | |
| static void | fillConvexPoly (Mat img, MatOfPoint points, Scalar color, int lineType, int shift) |
| Fills a convex polygon. More... | |
| static void | fillConvexPoly (Mat img, MatOfPoint points, Scalar color, int lineType) |
| Fills a convex polygon. More... | |
| static void | fillConvexPoly (Mat img, MatOfPoint points, Scalar color) |
| Fills a convex polygon. More... | |
| 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. More... | |
| static void | fillPoly (Mat img, List< MatOfPoint > pts, Scalar color, int lineType, int shift) |
| Fills the area bounded by one or more polygons. More... | |
| static void | fillPoly (Mat img, List< MatOfPoint > pts, Scalar color, int lineType) |
| Fills the area bounded by one or more polygons. More... | |
| static void | fillPoly (Mat img, List< MatOfPoint > pts, Scalar color) |
| Fills the area bounded by one or more polygons. More... | |
| static void | polylines (Mat img, List< MatOfPoint > pts, bool isClosed, Scalar color, int thickness, int lineType, int shift) |
| Draws several polygonal curves. More... | |
| static void | polylines (Mat img, List< MatOfPoint > pts, bool isClosed, Scalar color, int thickness, int lineType) |
| Draws several polygonal curves. More... | |
| static void | polylines (Mat img, List< MatOfPoint > pts, bool isClosed, Scalar color, int thickness) |
| Draws several polygonal curves. More... | |
| static void | polylines (Mat img, List< MatOfPoint > pts, bool isClosed, Scalar color) |
| Draws several polygonal curves. More... | |
| 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. More... | |
| 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. More... | |
| static void | drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy) |
| Draws contours outlines or filled contours. More... | |
| static void | drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, Scalar color, int thickness, int lineType) |
| Draws contours outlines or filled contours. More... | |
| static void | drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, Scalar color, int thickness) |
| Draws contours outlines or filled contours. More... | |
| static void | drawContours (Mat image, List< MatOfPoint > contours, int contourIdx, Scalar color) |
| Draws contours outlines or filled contours. More... | |
| static bool | clipLine (Rect imgRect, Point pt1, Point pt2) |
| static void | ellipse2Poly (Point center, Size axes, int angle, int arcStart, int arcEnd, int delta, MatOfPoint pts) |
| Approximates an elliptic arc with a polyline. More... | |
| static void | putText (Mat img, string text, Point org, int fontFace, double fontScale, Scalar color, int thickness, int lineType, bool bottomLeftOrigin) |
| Draws a text string. More... | |
| static void | putText (Mat img, string text, Point org, int fontFace, double fontScale, Scalar color, int thickness, int lineType) |
| Draws a text string. More... | |
| static void | putText (Mat img, string text, Point org, int fontFace, double fontScale, Scalar color, int thickness) |
| Draws a text string. More... | |
| static void | putText (Mat img, string text, Point org, int fontFace, double fontScale, Scalar color) |
| Draws a text string. More... | |
| static double | getFontScaleFromHeight (int fontFace, int pixelHeight, int thickness) |
| Calculates the font-specific size to use to achieve a given height in pixels. More... | |
| static double | getFontScaleFromHeight (int fontFace, int pixelHeight) |
| Calculates the font-specific size to use to achieve a given height in pixels. More... | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| 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. More... | |
| static Size | getTextSize (string text, int fontFace, double fontScale, int thickness, int[] baseLine) |
Member Function Documentation
◆ accumulate() [1/2]
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.
◆ accumulate() [2/2]
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.
◆ accumulateProduct() [1/2]
|
static |
Adds the per-element product of two input images to the accumulator image.
The function adds the product of two images or their selected regions to the accumulator dst :
\[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src1} (x,y) \cdot \texttt{src2} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\]
The function supports multi-channel images. Each channel is processed independently.
- Parameters
-
src1 First input image, 1- or 3-channel, 8-bit or 32-bit floating point. src2 Second input image of the same type and the same size as src1 . dst Accumulator image with the same number of channels as input images, 32-bit or 64-bit floating-point. mask Optional operation mask.
- See also
- accumulate, accumulateSquare, accumulateWeighted
◆ accumulateProduct() [2/2]
|
static |
Adds the per-element product of two input images to the accumulator image.
The function adds the product of two images or their selected regions to the accumulator dst :
\[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src1} (x,y) \cdot \texttt{src2} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\]
The function supports multi-channel images. Each channel is processed independently.
- Parameters
-
src1 First input image, 1- or 3-channel, 8-bit or 32-bit floating point. src2 Second input image of the same type and the same size as src1 . dst Accumulator image with the same number of channels as input images, 32-bit or 64-bit floating-point. mask Optional operation mask.
- See also
- accumulate, accumulateSquare, accumulateWeighted
◆ accumulateSquare() [1/2]
|
static |
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.
◆ accumulateSquare() [2/2]
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.
◆ accumulateWeighted() [1/2]
|
static |
Updates a running average.
The function calculates the weighted sum of the input image src and the accumulator dst so that dst becomes a running average of a frame sequence:
\[\texttt{dst} (x,y) \leftarrow (1- \texttt{alpha} ) \cdot \texttt{dst} (x,y) + \texttt{alpha} \cdot \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\]
That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images). The function supports multi-channel images. Each channel is processed independently.
- Parameters
-
src Input image as 1- or 3-channel, 8-bit or 32-bit floating point. dst Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point. alpha Weight of the input image. mask Optional operation mask.
- See also
- accumulate, accumulateSquare, accumulateProduct
◆ accumulateWeighted() [2/2]
|
static |
Updates a running average.
The function calculates the weighted sum of the input image src and the accumulator dst so that dst becomes a running average of a frame sequence:
\[\texttt{dst} (x,y) \leftarrow (1- \texttt{alpha} ) \cdot \texttt{dst} (x,y) + \texttt{alpha} \cdot \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\]
That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images). The function supports multi-channel images. Each channel is processed independently.
- Parameters
-
src Input image as 1- or 3-channel, 8-bit or 32-bit floating point. dst Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point. alpha Weight of the input image. mask Optional operation mask.
- See also
- accumulate, accumulateSquare, accumulateProduct
◆ adaptiveThreshold()
|
static |
Applies an adaptive threshold to an array.
The function transforms a grayscale image to a binary image according to the formulae:
- THRESH_BINARY
\[dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\]
- THRESH_BINARY_INV
\[dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\]
where \(T(x,y)\) is a threshold calculated individually for each pixel (see adaptiveMethod parameter).
The function can process the image in-place.
- Parameters
-
src Source 8-bit single-channel image. dst Destination image of the same size and the same type as src. maxValue Non-zero value assigned to the pixels for which the condition is satisfied adaptiveMethod Adaptive thresholding algorithm to use, see #AdaptiveThresholdTypes. The #BORDER_REPLICATE | #BORDER_ISOLATED is used to process boundaries. thresholdType Thresholding type that must be either THRESH_BINARY or THRESH_BINARY_INV, see #ThresholdTypes. blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on. C Constant subtracted from the mean or weighted mean (see the details below). Normally, it is positive but may be zero or negative as well.
- See also
- threshold, blur, GaussianBlur
◆ applyColorMap() [1/2]
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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() [2/2]
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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
◆ approxPolyDP()
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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 Mat approxCurve Result of the approximation. The type should match the type of the input curve. epsilon Parameter specifying the approximation accuracy. This is the maximum distance between the original curve and its approximation. closed If true, the approximated curve is closed (its first and last vertices are connected). Otherwise, it is not closed.
◆ arcLength()
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Calculates a contour perimeter or a curve length.
The function computes a curve length or a closed contour perimeter.
- Parameters
-
curve Input vector of 2D points, stored in std::vector or Mat. closed Flag indicating whether the curve is closed or not.
◆ arrowedLine() [1/5]
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Draws an arrow segment pointing from the first point to the second one.
The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also line.
- Parameters
-
img Image. pt1 The point the arrow starts from. pt2 The point the arrow points to. color Line color. thickness Line thickness. line_type Type of the line. See #LineTypes shift Number of fractional bits in the point coordinates. tipLength The length of the arrow tip in relation to the arrow length
◆ arrowedLine() [2/5]
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Draws an arrow segment pointing from the first point to the second one.
The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also line.
- Parameters
-
img Image. pt1 The point the arrow starts from. pt2 The point the arrow points to. color Line color. thickness Line thickness. line_type Type of the line. See #LineTypes shift Number of fractional bits in the point coordinates. tipLength The length of the arrow tip in relation to the arrow length
◆ arrowedLine() [3/5]
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Draws an arrow segment pointing from the first point to the second one.
The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also line.
- Parameters
-
img Image. pt1 The point the arrow starts from. pt2 The point the arrow points to. color Line color. thickness Line thickness. line_type Type of the line. See #LineTypes shift Number of fractional bits in the point coordinates. tipLength The length of the arrow tip in relation to the arrow length
◆ arrowedLine() [4/5]
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Draws an arrow segment pointing from the first point to the second one.
The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also line.
- Parameters
-
img Image. pt1 The point the arrow starts from. pt2 The point the arrow points to. color Line color. thickness Line thickness. line_type Type of the line. See #LineTypes shift Number of fractional bits in the point coordinates. tipLength The length of the arrow tip in relation to the arrow length
◆ arrowedLine() [5/5]
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Draws an arrow segment pointing from the first point to the second one.
The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also line.
- Parameters
-
img Image. pt1 The point the arrow starts from. pt2 The point the arrow points to. color Line color. thickness Line thickness. line_type Type of the line. See #LineTypes shift Number of fractional bits in the point coordinates. tipLength The length of the arrow tip in relation to the arrow length
◆ bilateralFilter() [1/2]
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Applies the bilateral filter to an image.
The function applies bilateral filtering to the input image, as described in http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is very slow compared to most filters.
Sigma values: For simplicity, you can set the 2 sigma values to be the same. If they are small (< 10), the filter will not have much effect, whereas if they are large (> 150), they will have a very strong effect, making the image look "cartoonish".
Filter size: Large filters (d > 5) are very slow, so it is recommended to use d=5 for real-time applications, and perhaps d=9 for offline applications that need heavy noise filtering.
This filter does not work inplace.
- Parameters
-
src Source 8-bit or floating-point, 1-channel or 3-channel image. dst Destination image of the same size and type as src . d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace. sigmaColor Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting in larger areas of semi-equal color. sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is proportional to sigmaSpace. borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
◆ bilateralFilter() [2/2]
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Applies the bilateral filter to an image.
The function applies bilateral filtering to the input image, as described in http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is very slow compared to most filters.
Sigma values: For simplicity, you can set the 2 sigma values to be the same. If they are small (< 10), the filter will not have much effect, whereas if they are large (> 150), they will have a very strong effect, making the image look "cartoonish".
Filter size: Large filters (d > 5) are very slow, so it is recommended to use d=5 for real-time applications, and perhaps d=9 for offline applications that need heavy noise filtering.
This filter does not work inplace.
- Parameters
-
src Source 8-bit or floating-point, 1-channel or 3-channel image. dst Destination image of the same size and type as src . d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace. sigmaColor Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting in larger areas of semi-equal color. sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is proportional to sigmaSpace. borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
◆ blendLinear()
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
variant without mask parameter
◆ blur() [1/3]
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Blurs an image using the normalized box filter.
The function smooths an image using the kernel:
\[\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\]
The call blur(src, dst, ksize, anchor, borderType) is equivalent to boxFilter(src, dst, src.type(), ksize, anchor, true, borderType).
- Parameters
-
src input image; it can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. ksize blurring kernel size. anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel center. borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- boxFilter, bilateralFilter, GaussianBlur, medianBlur
◆ blur() [2/3]
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Blurs an image using the normalized box filter.
The function smooths an image using the kernel:
\[\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\]
The call blur(src, dst, ksize, anchor, borderType) is equivalent to boxFilter(src, dst, src.type(), ksize, anchor, true, borderType).
- Parameters
-
src input image; it can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. ksize blurring kernel size. anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel center. borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- boxFilter, bilateralFilter, GaussianBlur, medianBlur
◆ blur() [3/3]
Blurs an image using the normalized box filter.
The function smooths an image using the kernel:
\[\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\]
The call blur(src, dst, ksize, anchor, borderType) is equivalent to boxFilter(src, dst, src.type(), ksize, anchor, true, borderType).
- Parameters
-
src input image; it can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. ksize blurring kernel size. anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel center. borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- boxFilter, bilateralFilter, GaussianBlur, medianBlur
◆ 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.
◆ boxFilter() [1/4]
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static |
Blurs an image using the box filter.
The function smooths an image using the kernel:
\[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\]
where
\[\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\]
Unnormalized box filter is useful for computing various integral characteristics over each pixel neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, and so on). If you need to compute pixel sums over variable-size windows, use integral.
- Parameters
-
src input image. dst output image of the same size and type as src. ddepth the output image depth (-1 to use src.depth()). ksize blurring kernel size. anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel center. normalize flag, specifying whether the kernel is normalized by its area or not. borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- blur, bilateralFilter, GaussianBlur, medianBlur, integral
◆ boxFilter() [2/4]
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static |
Blurs an image using the box filter.
The function smooths an image using the kernel:
\[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\]
where
\[\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\]
Unnormalized box filter is useful for computing various integral characteristics over each pixel neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, and so on). If you need to compute pixel sums over variable-size windows, use integral.
- Parameters
-
src input image. dst output image of the same size and type as src. ddepth the output image depth (-1 to use src.depth()). ksize blurring kernel size. anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel center. normalize flag, specifying whether the kernel is normalized by its area or not. borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- blur, bilateralFilter, GaussianBlur, medianBlur, integral
◆ boxFilter() [3/4]
|
static |
Blurs an image using the box filter.
The function smooths an image using the kernel:
\[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\]
where
\[\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\]
Unnormalized box filter is useful for computing various integral characteristics over each pixel neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, and so on). If you need to compute pixel sums over variable-size windows, use integral.
- Parameters
-
src input image. dst output image of the same size and type as src. ddepth the output image depth (-1 to use src.depth()). ksize blurring kernel size. anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel center. normalize flag, specifying whether the kernel is normalized by its area or not. borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- blur, bilateralFilter, GaussianBlur, medianBlur, integral
◆ boxFilter() [4/4]
|
static |
Blurs an image using the box filter.
The function smooths an image using the kernel:
\[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\]
where
\[\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\]
Unnormalized box filter is useful for computing various integral characteristics over each pixel neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, and so on). If you need to compute pixel sums over variable-size windows, use integral.
- Parameters
-
src input image. dst output image of the same size and type as src. ddepth the output image depth (-1 to use src.depth()). ksize blurring kernel size. anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel center. normalize flag, specifying whether the kernel is normalized by its area or not. borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- blur, bilateralFilter, GaussianBlur, medianBlur, integral
◆ boxPoints()
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Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.
The function finds the four vertices of a rotated rectangle. This function is useful to draw the rectangle. In C++, instead of using this function, you can directly use RotatedRect::points method. Please visit the tutorial on Creating Bounding rotated boxes and ellipses for contours for more information.
- Parameters
-
box The input rotated rectangle. It may be the output of minAreaRect. points The output array of four vertices of rectangles.
◆ calcBackProject()
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ calcHist() [1/2]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
this variant supports only uniform histograms.
ranges argument is either empty vector or a flattened vector of histSize.size()*2 elements (histSize.size() element pairs). The first and second elements of each pair specify the lower and upper boundaries.
◆ calcHist() [2/2]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
this variant supports only uniform histograms.
ranges argument is either empty vector or a flattened vector of histSize.size()*2 elements (histSize.size() element pairs). The first and second elements of each pair specify the lower and upper boundaries.
◆ Canny() [1/5]
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Finds edges in an image using the Canny algorithm [Canny86] .
The function finds edges in the input image and marks them in the output map edges using the Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The largest value is used to find initial segments of strong edges. See <http://en.wikipedia.org/wiki/Canny_edge_detector>
- 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() [2/5]
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static |
Finds edges in an image using the Canny algorithm [Canny86] .
The function finds edges in the input image and marks them in the output map edges using the Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The largest value is used to find initial segments of strong edges. See <http://en.wikipedia.org/wiki/Canny_edge_detector>
- 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() [3/5]
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Finds edges in an image using the Canny algorithm [Canny86] .
The function finds edges in the input image and marks them in the output map edges using the Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The largest value is used to find initial segments of strong edges. See <http://en.wikipedia.org/wiki/Canny_edge_detector>
- Parameters
-
image 8-bit input image. edges output edge map; single channels 8-bit image, which has the same size as image . threshold1 first threshold for the hysteresis procedure. threshold2 second threshold for the hysteresis procedure. apertureSize aperture size for the Sobel operator. L2gradient a flag, indicating whether a more accurate \(L_2\) norm \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) should be used to calculate the image gradient magnitude ( L2gradient=true ), or whether the default \(L_1\) norm \(=|dI/dx|+|dI/dy|\) is enough ( L2gradient=false ).
◆ Canny() [4/5]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
Finds edges in an image using the Canny algorithm with custom image gradient.
- Parameters
-
dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3). dy 16-bit y derivative of input image (same type as dx). edges output edge map; single channels 8-bit image, which has the same size as image . threshold1 first threshold for the hysteresis procedure. threshold2 second threshold for the hysteresis procedure. L2gradient a flag, indicating whether a more accurate \(L_2\) norm \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) should be used to calculate the image gradient magnitude ( L2gradient=true ), or whether the default \(L_1\) norm \(=|dI/dx|+|dI/dy|\) is enough ( L2gradient=false ).
◆ Canny() [5/5]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
Finds edges in an image using the Canny algorithm with custom image gradient.
- Parameters
-
dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3). dy 16-bit y derivative of input image (same type as dx). edges output edge map; single channels 8-bit image, which has the same size as image . threshold1 first threshold for the hysteresis procedure. threshold2 second threshold for the hysteresis procedure. L2gradient a flag, indicating whether a more accurate \(L_2\) norm \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) should be used to calculate the image gradient magnitude ( L2gradient=true ), or whether the default \(L_1\) norm \(=|dI/dx|+|dI/dy|\) is enough ( L2gradient=false ).
◆ circle() [1/4]
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Draws a circle.
The function cv::circle draws a simple or filled circle with a given center and radius.
- Parameters
-
img Image where the circle is drawn. center Center of the circle. radius Radius of the circle. color Circle color. thickness Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn. lineType Type of the circle boundary. See #LineTypes shift Number of fractional bits in the coordinates of the center and in the radius value.
◆ circle() [2/4]
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Draws a circle.
The function cv::circle draws a simple or filled circle with a given center and radius.
- Parameters
-
img Image where the circle is drawn. center Center of the circle. radius Radius of the circle. color Circle color. thickness Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn. lineType Type of the circle boundary. See #LineTypes shift Number of fractional bits in the coordinates of the center and in the radius value.
◆ circle() [3/4]
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Draws a circle.
The function cv::circle draws a simple or filled circle with a given center and radius.
- Parameters
-
img Image where the circle is drawn. center Center of the circle. radius Radius of the circle. color Circle color. thickness Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn. lineType Type of the circle boundary. See #LineTypes shift Number of fractional bits in the coordinates of the center and in the radius value.
◆ circle() [4/4]
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Draws a circle.
The function cv::circle draws a simple or filled circle with a given center and radius.
- Parameters
-
img Image where the circle is drawn. center Center of the circle. radius Radius of the circle. color Circle color. thickness Thickness of the circle outline, if positive. Negative values, like FILLED, mean that a filled circle is to be drawn. lineType Type of the circle boundary. See #LineTypes shift Number of fractional bits in the coordinates of the center and in the radius value.
◆ clipLine()
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
imgRect Image rectangle. pt1 First line point. pt2 Second line point.
◆ compareHist()
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Compares two histograms.
The function cv::compareHist compares two dense or two sparse histograms using the specified method.
The function returns \(d(H_1, H_2)\) .
While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms or more general sparse configurations of weighted points, consider using the EMD function.
- Parameters
-
H1 First compared histogram. H2 Second compared histogram of the same size as H1 . method Comparison method, see #HistCompMethods
◆ connectedComponents() [1/3]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
image the 8-bit single-channel image to be labeled labels destination labeled image connectivity 8 or 4 for 8-way or 4-way connectivity respectively ltype output image label type. Currently CV_32S and CV_16U are supported.
◆ connectedComponents() [2/3]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
image the 8-bit single-channel image to be labeled labels destination labeled image connectivity 8 or 4 for 8-way or 4-way connectivity respectively ltype output image label type. Currently CV_32S and CV_16U are supported.
◆ connectedComponents() [3/3]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
image the 8-bit single-channel image to be labeled labels destination labeled image connectivity 8 or 4 for 8-way or 4-way connectivity respectively ltype output image label type. Currently CV_32S and CV_16U are supported.
◆ connectedComponentsWithAlgorithm()
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computes the connected components labeled image of boolean image
image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 represents the background label. ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently Bolelli (Spaghetti) [Bolelli2019], Grana (BBDT) [Grana2010] and Wu's (SAUF) [Wu2009] algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces a row major ordering of labels while Spaghetti and BBDT do not. This function uses parallel version of the algorithms if at least one allowed parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.
- Parameters
-
image the 8-bit single-channel image to be labeled labels destination labeled image connectivity 8 or 4 for 8-way or 4-way connectivity respectively ltype output image label type. Currently CV_32S and CV_16U are supported. ccltype connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes).
◆ connectedComponentsWithStats() [1/3]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
image the 8-bit single-channel image to be labeled labels destination labeled image stats statistics output for each label, including the background label. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. centroids centroid output for each label, including the background label. Centroids are accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. connectivity 8 or 4 for 8-way or 4-way connectivity respectively ltype output image label type. Currently CV_32S and CV_16U are supported.
◆ connectedComponentsWithStats() [2/3]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
image the 8-bit single-channel image to be labeled labels destination labeled image stats statistics output for each label, including the background label. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. centroids centroid output for each label, including the background label. Centroids are accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. connectivity 8 or 4 for 8-way or 4-way connectivity respectively ltype output image label type. Currently CV_32S and CV_16U are supported.
◆ connectedComponentsWithStats() [3/3]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
image the 8-bit single-channel image to be labeled labels destination labeled image stats statistics output for each label, including the background label. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. centroids centroid output for each label, including the background label. Centroids are accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. connectivity 8 or 4 for 8-way or 4-way connectivity respectively ltype output image label type. Currently CV_32S and CV_16U are supported.
◆ connectedComponentsWithStatsWithAlgorithm()
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computes the connected components labeled image of boolean image and also produces a statistics output for each label
image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 represents the background label. ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently Bolelli (Spaghetti) [Bolelli2019], Grana (BBDT) [Grana2010] and Wu's (SAUF) [Wu2009] algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces a row major ordering of labels while Spaghetti and BBDT do not. This function uses parallel version of the algorithms (statistics included) if at least one allowed parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.
- Parameters
-
image the 8-bit single-channel image to be labeled labels destination labeled image stats statistics output for each label, including the background label. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. centroids centroid output for each label, including the background label. Centroids are accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. connectivity 8 or 4 for 8-way or 4-way connectivity respectively ltype output image label type. Currently CV_32S and CV_16U are supported. ccltype connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes).
◆ contourArea() [1/2]
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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:
- Parameters
-
contour Input vector of 2D points (contour vertices), stored in std::vector or Mat. oriented Oriented area flag. If it is true, the function returns a signed area value, depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can determine orientation of a contour by taking the sign of an area. By default, the parameter is false, which means that the absolute value is returned.
◆ contourArea() [2/2]
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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:
- Parameters
-
contour Input vector of 2D points (contour vertices), stored in std::vector or Mat. oriented Oriented area flag. If it is true, the function returns a signed area value, depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can determine orientation of a contour by taking the sign of an area. By default, the parameter is false, which means that the absolute value is returned.
◆ convertMaps() [1/2]
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Converts image transformation maps from one representation to another.
The function converts a pair of maps for remap from one representation to another. The following options ( (map1.type(), map2.type()) \(\rightarrow\) (dstmap1.type(), dstmap2.type()) ) are supported:
- \(\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). This is the most frequently used conversion operation, in which the original floating-point maps (see remap) are converted to a more compact and much faster fixed-point representation. The first output array contains the rounded coordinates and the second array (created only when nninterpolation=false ) contains indices in the interpolation tables.
- \(\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). The same as above but the original maps are stored in one 2-channel matrix.
- Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same as the originals.
- Parameters
-
map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 . map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), respectively. dstmap1 The first output map that has the type dstmap1type and the same size as src . dstmap2 The second output map. dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or CV_32FC2 . nninterpolation Flag indicating whether the fixed-point maps are used for the nearest-neighbor or for a more complex interpolation.
- See also
- remap, undistort, initUndistortRectifyMap
◆ convertMaps() [2/2]
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Converts image transformation maps from one representation to another.
The function converts a pair of maps for remap from one representation to another. The following options ( (map1.type(), map2.type()) \(\rightarrow\) (dstmap1.type(), dstmap2.type()) ) are supported:
- \(\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). This is the most frequently used conversion operation, in which the original floating-point maps (see remap) are converted to a more compact and much faster fixed-point representation. The first output array contains the rounded coordinates and the second array (created only when nninterpolation=false ) contains indices in the interpolation tables.
- \(\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). The same as above but the original maps are stored in one 2-channel matrix.
- Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same as the originals.
- Parameters
-
map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 . map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), respectively. dstmap1 The first output map that has the type dstmap1type and the same size as src . dstmap2 The second output map. dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or CV_32FC2 . nninterpolation Flag indicating whether the fixed-point maps are used for the nearest-neighbor or for a more complex interpolation.
- See also
- remap, undistort, initUndistortRectifyMap
◆ convexHull() [1/2]
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Finds the convex hull of a point set.
The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm [Sklansky82] that has O(N logN) complexity in the current implementation.
- Parameters
-
points Input 2D point set, stored in std::vector or Mat. hull Output convex hull. It is either an integer vector of indices or vector of points. In the first case, the hull elements are 0-based indices of the convex hull points in the original array (since the set of convex hull points is a subset of the original point set). In the second case, hull elements are the convex hull points themselves. clockwise Orientation flag. If it is true, the output convex hull is oriented clockwise. Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing to the right, and its Y axis pointing upwards. returnPoints Operation flag. In case of a matrix, when the flag is true, the function returns convex hull points. Otherwise, it returns indices of the convex hull points. When the output array is std::vector, the flag is ignored, and the output depends on the type of the vector: std::vector<int> implies returnPoints=false, std::vector<Point> implies returnPoints=true.
- Note
pointsandhullshould be different arrays, inplace processing isn't supported.
Check the corresponding tutorial for more details.
useful links:
https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/
◆ convexHull() [2/2]
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Finds the convex hull of a point set.
The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm [Sklansky82] that has O(N logN) complexity in the current implementation.
- Parameters
-
points Input 2D point set, stored in std::vector or Mat. hull Output convex hull. It is either an integer vector of indices or vector of points. In the first case, the hull elements are 0-based indices of the convex hull points in the original array (since the set of convex hull points is a subset of the original point set). In the second case, hull elements are the convex hull points themselves. clockwise Orientation flag. If it is true, the output convex hull is oriented clockwise. Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing to the right, and its Y axis pointing upwards. returnPoints Operation flag. In case of a matrix, when the flag is true, the function returns convex hull points. Otherwise, it returns indices of the convex hull points. When the output array is std::vector, the flag is ignored, and the output depends on the type of the vector: std::vector<int> implies returnPoints=false, std::vector<Point> implies returnPoints=true.
- Note
pointsandhullshould be different arrays, inplace processing isn't supported.
Check the corresponding tutorial for more details.
useful links:
https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/
◆ convexityDefects()
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Finds the convexity defects of a contour.
The figure below displays convexity defects of a hand contour:
- Parameters
-
contour Input contour. convexhull Convex hull obtained using convexHull that should contain indices of the contour points that make the hull. convexityDefects The output vector of convexity defects. In C++ and the new Python/Java interface each convexity defect is represented as 4-element integer vector (a.k.a. #Vec4i): (start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices in the original contour of the convexity defect beginning, end and the farthest point, and fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the farthest contour point and the hull. That is, to get the floating-point value of the depth will be fixpt_depth/256.0.
◆ cornerEigenValsAndVecs() [1/2]
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Calculates eigenvalues and eigenvectors of image blocks for corner detection.
For every pixel \(p\) , the function cornerEigenValsAndVecs considers a blockSize \(\times\) blockSize neighborhood \(S(p)\) . It calculates the covariation matrix of derivatives over the neighborhood as:
\[M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\]
where the derivatives are computed using the Sobel operator.
After that, it finds eigenvectors and eigenvalues of \(M\) and stores them in the destination image as \((\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\) where
- \(\lambda_1, \lambda_2\) are the non-sorted eigenvalues of \(M\)
- \(x_1, y_1\) are the eigenvectors corresponding to \(\lambda_1\)
- \(x_2, y_2\) are the eigenvectors corresponding to \(\lambda_2\)
The output of the function can be used for robust edge or corner detection.
- Parameters
-
src Input single-channel 8-bit or floating-point image. dst Image to store the results. It has the same size as src and the type CV_32FC(6) . blockSize Neighborhood size (see details below). ksize Aperture parameter for the Sobel operator. borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
- See also
- cornerMinEigenVal, cornerHarris, preCornerDetect
◆ cornerEigenValsAndVecs() [2/2]
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Calculates eigenvalues and eigenvectors of image blocks for corner detection.
For every pixel \(p\) , the function cornerEigenValsAndVecs considers a blockSize \(\times\) blockSize neighborhood \(S(p)\) . It calculates the covariation matrix of derivatives over the neighborhood as:
\[M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\]
where the derivatives are computed using the Sobel operator.
After that, it finds eigenvectors and eigenvalues of \(M\) and stores them in the destination image as \((\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\) where
- \(\lambda_1, \lambda_2\) are the non-sorted eigenvalues of \(M\)
- \(x_1, y_1\) are the eigenvectors corresponding to \(\lambda_1\)
- \(x_2, y_2\) are the eigenvectors corresponding to \(\lambda_2\)
The output of the function can be used for robust edge or corner detection.
- Parameters
-
src Input single-channel 8-bit or floating-point image. dst Image to store the results. It has the same size as src and the type CV_32FC(6) . blockSize Neighborhood size (see details below). ksize Aperture parameter for the Sobel operator. borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
- See also
- cornerMinEigenVal, cornerHarris, preCornerDetect
◆ cornerHarris() [1/2]
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Harris corner detector.
The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and cornerEigenValsAndVecs , for each pixel \((x, y)\) it calculates a \(2\times2\) gradient covariance matrix \(M^{(x,y)}\) over a \(\texttt{blockSize} \times \texttt{blockSize}\) neighborhood. Then, it computes the following characteristic:
\[\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\]
Corners in the image can be found as the local maxima of this response map.
- Parameters
-
src Input single-channel 8-bit or floating-point image. dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same size as src . blockSize Neighborhood size (see the details on cornerEigenValsAndVecs ). ksize Aperture parameter for the Sobel operator. k Harris detector free parameter. See the formula above. borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
◆ cornerHarris() [2/2]
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Harris corner detector.
The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and cornerEigenValsAndVecs , for each pixel \((x, y)\) it calculates a \(2\times2\) gradient covariance matrix \(M^{(x,y)}\) over a \(\texttt{blockSize} \times \texttt{blockSize}\) neighborhood. Then, it computes the following characteristic:
\[\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\]
Corners in the image can be found as the local maxima of this response map.
- Parameters
-
src Input single-channel 8-bit or floating-point image. dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same size as src . blockSize Neighborhood size (see the details on cornerEigenValsAndVecs ). ksize Aperture parameter for the Sobel operator. k Harris detector free parameter. See the formula above. borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
◆ cornerMinEigenVal() [1/3]
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Calculates the minimal eigenvalue of gradient matrices for corner detection.
The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is, \(\min(\lambda_1, \lambda_2)\) in terms of the formulae in the cornerEigenValsAndVecs description.
- Parameters
-
src Input single-channel 8-bit or floating-point image. dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as src . blockSize Neighborhood size (see the details on cornerEigenValsAndVecs ). ksize Aperture parameter for the Sobel operator. borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
◆ cornerMinEigenVal() [2/3]
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static |
Calculates the minimal eigenvalue of gradient matrices for corner detection.
The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is, \(\min(\lambda_1, \lambda_2)\) in terms of the formulae in the cornerEigenValsAndVecs description.
- Parameters
-
src Input single-channel 8-bit or floating-point image. dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as src . blockSize Neighborhood size (see the details on cornerEigenValsAndVecs ). ksize Aperture parameter for the Sobel operator. borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
◆ cornerMinEigenVal() [3/3]
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static |
Calculates the minimal eigenvalue of gradient matrices for corner detection.
The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is, \(\min(\lambda_1, \lambda_2)\) in terms of the formulae in the cornerEigenValsAndVecs description.
- Parameters
-
src Input single-channel 8-bit or floating-point image. dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as src . blockSize Neighborhood size (see the details on cornerEigenValsAndVecs ). ksize Aperture parameter for the Sobel operator. borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
◆ cornerSubPix()
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Refines the corner locations.
The function iterates to find the sub-pixel accurate location of corners or radial saddle points as described in [forstner1987fast], and as shown on the figure below.
Sub-pixel accurate corner locator is based on the observation that every vector from the center \(q\) to a point \(p\) located within a neighborhood of \(q\) is orthogonal to the image gradient at \(p\) subject to image and measurement noise. Consider the expression:
\[\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)\]
where \({DI_{p_i}}\) is an image gradient at one of the points \(p_i\) in a neighborhood of \(q\) . The value of \(q\) is to be found so that \(\epsilon_i\) is minimized. A system of equations may be set up with \(\epsilon_i\) set to zero:
\[\sum _i(DI_{p_i} \cdot {DI_{p_i}}^T) \cdot q - \sum _i(DI_{p_i} \cdot {DI_{p_i}}^T \cdot p_i)\]
where the gradients are summed within a neighborhood ("search window") of \(q\) . Calling the first gradient term \(G\) and the second gradient term \(b\) gives:
\[q = G^{-1} \cdot b\]
The algorithm sets the center of the neighborhood window at this new center \(q\) and then iterates until the center stays within a set threshold.
- Parameters
-
image Input single-channel, 8-bit or float image. corners Initial coordinates of the input corners and refined coordinates provided for output. winSize Half of the side length of the search window. For example, if winSize=Size(5,5) , then a \((5*2+1) \times (5*2+1) = 11 \times 11\) search window is used. zeroZone Half of the size of the dead region in the middle of the search zone over which the summation in the formula below is not done. It is used sometimes to avoid possible singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such a size. criteria Criteria for termination of the iterative process of corner refinement. That is, the process of corner position refinement stops either after criteria.maxCount iterations or when the corner position moves by less than criteria.epsilon on some iteration.
◆ createCLAHE() [1/3]
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static |
Creates a smart pointer to a cv::CLAHE class and initializes it.
- Parameters
-
clipLimit Threshold for contrast limiting. tileGridSize Size of grid for histogram equalization. Input image will be divided into equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column.
◆ createCLAHE() [2/3]
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static |
Creates a smart pointer to a cv::CLAHE class and initializes it.
- Parameters
-
clipLimit Threshold for contrast limiting. tileGridSize Size of grid for histogram equalization. Input image will be divided into equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column.
◆ createCLAHE() [3/3]
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static |
Creates a smart pointer to a cv::CLAHE class and initializes it.
- Parameters
-
clipLimit Threshold for contrast limiting. tileGridSize Size of grid for histogram equalization. Input image will be divided into equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column.
◆ createGeneralizedHoughBallard()
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static |
Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it.
◆ createGeneralizedHoughGuil()
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static |
Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it.
◆ createHanningWindow()
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static |
This function computes a Hanning window coefficients in two dimensions.
See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function) for more information.
An example is shown below:
- Parameters
-
dst Destination array to place Hann coefficients in winSize The window size specifications (both width and height must be > 1) type Created array type
◆ createLineSegmentDetector() [1/9]
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Creates a smart pointer to a LineSegmentDetector object and initializes it.
The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want to edit those, as to tailor it for their own application.
- Parameters
-
refine The way found lines will be refined, see #LineSegmentDetectorModes scale The scale of the image that will be used to find the lines. Range (0..1]. sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. quant Bound to the quantization error on the gradient norm. ang_th Gradient angle tolerance in degrees. log_eps Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen. density_th Minimal density of aligned region points in the enclosing rectangle. n_bins Number of bins in pseudo-ordering of gradient modulus.
◆ createLineSegmentDetector() [2/9]
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Creates a smart pointer to a LineSegmentDetector object and initializes it.
The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want to edit those, as to tailor it for their own application.
- Parameters
-
refine The way found lines will be refined, see #LineSegmentDetectorModes scale The scale of the image that will be used to find the lines. Range (0..1]. sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. quant Bound to the quantization error on the gradient norm. ang_th Gradient angle tolerance in degrees. log_eps Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen. density_th Minimal density of aligned region points in the enclosing rectangle. n_bins Number of bins in pseudo-ordering of gradient modulus.
◆ createLineSegmentDetector() [3/9]
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Creates a smart pointer to a LineSegmentDetector object and initializes it.
The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want to edit those, as to tailor it for their own application.
- Parameters
-
refine The way found lines will be refined, see #LineSegmentDetectorModes scale The scale of the image that will be used to find the lines. Range (0..1]. sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. quant Bound to the quantization error on the gradient norm. ang_th Gradient angle tolerance in degrees. log_eps Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen. density_th Minimal density of aligned region points in the enclosing rectangle. n_bins Number of bins in pseudo-ordering of gradient modulus.
◆ createLineSegmentDetector() [4/9]
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Creates a smart pointer to a LineSegmentDetector object and initializes it.
The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want to edit those, as to tailor it for their own application.
- Parameters
-
refine The way found lines will be refined, see #LineSegmentDetectorModes scale The scale of the image that will be used to find the lines. Range (0..1]. sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. quant Bound to the quantization error on the gradient norm. ang_th Gradient angle tolerance in degrees. log_eps Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen. density_th Minimal density of aligned region points in the enclosing rectangle. n_bins Number of bins in pseudo-ordering of gradient modulus.
◆ createLineSegmentDetector() [5/9]
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Creates a smart pointer to a LineSegmentDetector object and initializes it.
The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want to edit those, as to tailor it for their own application.
- Parameters
-
refine The way found lines will be refined, see #LineSegmentDetectorModes scale The scale of the image that will be used to find the lines. Range (0..1]. sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. quant Bound to the quantization error on the gradient norm. ang_th Gradient angle tolerance in degrees. log_eps Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen. density_th Minimal density of aligned region points in the enclosing rectangle. n_bins Number of bins in pseudo-ordering of gradient modulus.
◆ createLineSegmentDetector() [6/9]
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Creates a smart pointer to a LineSegmentDetector object and initializes it.
The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want to edit those, as to tailor it for their own application.
- Parameters
-
refine The way found lines will be refined, see #LineSegmentDetectorModes scale The scale of the image that will be used to find the lines. Range (0..1]. sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. quant Bound to the quantization error on the gradient norm. ang_th Gradient angle tolerance in degrees. log_eps Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen. density_th Minimal density of aligned region points in the enclosing rectangle. n_bins Number of bins in pseudo-ordering of gradient modulus.
◆ createLineSegmentDetector() [7/9]
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Creates a smart pointer to a LineSegmentDetector object and initializes it.
The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want to edit those, as to tailor it for their own application.
- Parameters
-
refine The way found lines will be refined, see #LineSegmentDetectorModes scale The scale of the image that will be used to find the lines. Range (0..1]. sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. quant Bound to the quantization error on the gradient norm. ang_th Gradient angle tolerance in degrees. log_eps Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen. density_th Minimal density of aligned region points in the enclosing rectangle. n_bins Number of bins in pseudo-ordering of gradient modulus.
◆ createLineSegmentDetector() [8/9]
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Creates a smart pointer to a LineSegmentDetector object and initializes it.
The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want to edit those, as to tailor it for their own application.
- Parameters
-
refine The way found lines will be refined, see #LineSegmentDetectorModes scale The scale of the image that will be used to find the lines. Range (0..1]. sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. quant Bound to the quantization error on the gradient norm. ang_th Gradient angle tolerance in degrees. log_eps Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen. density_th Minimal density of aligned region points in the enclosing rectangle. n_bins Number of bins in pseudo-ordering of gradient modulus.
◆ createLineSegmentDetector() [9/9]
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static |
Creates a smart pointer to a LineSegmentDetector object and initializes it.
The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want to edit those, as to tailor it for their own application.
- Parameters
-
refine The way found lines will be refined, see #LineSegmentDetectorModes scale The scale of the image that will be used to find the lines. Range (0..1]. sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. quant Bound to the quantization error on the gradient norm. ang_th Gradient angle tolerance in degrees. log_eps Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen. density_th Minimal density of aligned region points in the enclosing rectangle. n_bins Number of bins in pseudo-ordering of gradient modulus.
◆ cvtColor() [1/2]
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Converts an image from one color space to another.
The function converts an input image from one color space to another. In case of a transformation to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on.
The conventional ranges for R, G, and B channel values are:
- 0 to 255 for CV_8U images
- 0 to 65535 for CV_16U images
- 0 to 1 for CV_32F images
In case of linear transformations, the range does not matter. But in case of a non-linear transformation, an input RGB image should be normalized to the proper value range to get the correct results, for example, for RGB \(\rightarrow\) L*u*v* transformation. For example, if you have a 32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will have the 0..255 value range instead of 0..1 assumed by the function. So, before calling cvtColor , you need first to scale the image down:
If you use cvtColor with 8-bit images, the conversion will have some information lost. For many applications, this will not be noticeable but it is recommended to use 32-bit images in applications that need the full range of colors or that convert an image before an operation and then convert back.
If conversion adds the alpha channel, its value will set to the maximum of corresponding channel range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F.
- Parameters
-
src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision floating-point. dst output image of the same size and depth as src. code color space conversion code (see #ColorConversionCodes). dstCn number of channels in the destination image; if the parameter is 0, the number of the channels is derived automatically from src and code.
- See also
- imgproc_color_conversions
◆ cvtColor() [2/2]
Converts an image from one color space to another.
The function converts an input image from one color space to another. In case of a transformation to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on.
The conventional ranges for R, G, and B channel values are:
- 0 to 255 for CV_8U images
- 0 to 65535 for CV_16U images
- 0 to 1 for CV_32F images
In case of linear transformations, the range does not matter. But in case of a non-linear transformation, an input RGB image should be normalized to the proper value range to get the correct results, for example, for RGB \(\rightarrow\) L*u*v* transformation. For example, if you have a 32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will have the 0..255 value range instead of 0..1 assumed by the function. So, before calling cvtColor , you need first to scale the image down:
If you use cvtColor with 8-bit images, the conversion will have some information lost. For many applications, this will not be noticeable but it is recommended to use 32-bit images in applications that need the full range of colors or that convert an image before an operation and then convert back.
If conversion adds the alpha channel, its value will set to the maximum of corresponding channel range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F.
- Parameters
-
src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision floating-point. dst output image of the same size and depth as src. code color space conversion code (see #ColorConversionCodes). dstCn number of channels in the destination image; if the parameter is 0, the number of the channels is derived automatically from src and code.
- See also
- imgproc_color_conversions
◆ cvtColorTwoPlane()
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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:
◆ demosaicing() [1/2]
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main function for all demosaicing processes
- Parameters
-
src input image: 8-bit unsigned or 16-bit unsigned. dst output image of the same size and depth as src. code Color space conversion code (see the description below). dstCn number of channels in the destination image; if the parameter is 0, the number of the channels is derived automatically from src and code.
The function can do the following transformations:
Demosaicing using bilinear interpolation
COLOR_BayerBG2BGR , COLOR_BayerGB2BGR , COLOR_BayerRG2BGR , COLOR_BayerGR2BGR
COLOR_BayerBG2GRAY , COLOR_BayerGB2GRAY , COLOR_BayerRG2GRAY , COLOR_BayerGR2GRAY
Demosaicing using Variable Number of Gradients.
COLOR_BayerBG2BGR_VNG , COLOR_BayerGB2BGR_VNG , COLOR_BayerRG2BGR_VNG , COLOR_BayerGR2BGR_VNG
Edge-Aware Demosaicing.
COLOR_BayerBG2BGR_EA , COLOR_BayerGB2BGR_EA , COLOR_BayerRG2BGR_EA , COLOR_BayerGR2BGR_EA
Demosaicing with alpha channel
COLOR_BayerBG2BGRA , COLOR_BayerGB2BGRA , COLOR_BayerRG2BGRA , COLOR_BayerGR2BGRA
- See also
- cvtColor
◆ demosaicing() [2/2]
main function for all demosaicing processes
- Parameters
-
src input image: 8-bit unsigned or 16-bit unsigned. dst output image of the same size and depth as src. code Color space conversion code (see the description below). dstCn number of channels in the destination image; if the parameter is 0, the number of the channels is derived automatically from src and code.
The function can do the following transformations:
Demosaicing using bilinear interpolation
COLOR_BayerBG2BGR , COLOR_BayerGB2BGR , COLOR_BayerRG2BGR , COLOR_BayerGR2BGR
COLOR_BayerBG2GRAY , COLOR_BayerGB2GRAY , COLOR_BayerRG2GRAY , COLOR_BayerGR2GRAY
Demosaicing using Variable Number of Gradients.
COLOR_BayerBG2BGR_VNG , COLOR_BayerGB2BGR_VNG , COLOR_BayerRG2BGR_VNG , COLOR_BayerGR2BGR_VNG
Edge-Aware Demosaicing.
COLOR_BayerBG2BGR_EA , COLOR_BayerGB2BGR_EA , COLOR_BayerRG2BGR_EA , COLOR_BayerGR2BGR_EA
Demosaicing with alpha channel
COLOR_BayerBG2BGRA , COLOR_BayerGB2BGRA , COLOR_BayerRG2BGRA , COLOR_BayerGR2BGRA
- See also
- cvtColor
◆ dilate() [1/5]
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Dilates an image by using a specific structuring element.
The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken:
\[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]
The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.
- Parameters
-
src input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. kernel structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement anchor position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center. iterations number of times dilation is applied. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported. borderValue border value in case of a constant border
- See also
- erode, morphologyEx, getStructuringElement
◆ dilate() [2/5]
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Dilates an image by using a specific structuring element.
The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken:
\[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]
The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.
- Parameters
-
src input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. kernel structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement anchor position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center. iterations number of times dilation is applied. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported. borderValue border value in case of a constant border
- See also
- erode, morphologyEx, getStructuringElement
◆ dilate() [3/5]
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Dilates an image by using a specific structuring element.
The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken:
\[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]
The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.
- Parameters
-
src input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. kernel structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement anchor position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center. iterations number of times dilation is applied. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported. borderValue border value in case of a constant border
- See also
- erode, morphologyEx, getStructuringElement
◆ dilate() [4/5]
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static |
Dilates an image by using a specific structuring element.
The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken:
\[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]
The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.
- Parameters
-
src input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. kernel structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement anchor position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center. iterations number of times dilation is applied. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported. borderValue border value in case of a constant border
- See also
- erode, morphologyEx, getStructuringElement
◆ dilate() [5/5]
Dilates an image by using a specific structuring element.
The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken:
\[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]
The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.
- Parameters
-
src input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. kernel structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement anchor position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center. iterations number of times dilation is applied. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported. borderValue border value in case of a constant border
- See also
- erode, morphologyEx, getStructuringElement
◆ distanceTransform() [1/2]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
src 8-bit, single-channel (binary) source image. dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, single-channel image of the same size as src . distanceType Type of distance, see #DistanceTypes maskSize Size of the distance transform mask, see #DistanceTransformMasks. In case of the DIST_L1 or DIST_C distance type, the parameter is forced to 3 because a \(3\times 3\) mask gives the same result as \(5\times 5\) or any larger aperture. dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for the first variant of the function and distanceType == DIST_L1.
◆ distanceTransform() [2/2]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
src 8-bit, single-channel (binary) source image. dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, single-channel image of the same size as src . distanceType Type of distance, see #DistanceTypes maskSize Size of the distance transform mask, see #DistanceTransformMasks. In case of the DIST_L1 or DIST_C distance type, the parameter is forced to 3 because a \(3\times 3\) mask gives the same result as \(5\times 5\) or any larger aperture. dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for the first variant of the function and distanceType == DIST_L1.
◆ distanceTransformWithLabels() [1/2]
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Calculates the distance to the closest zero pixel for each pixel of the source image.
The function cv::distanceTransform calculates the approximate or precise distance from every binary image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero.
When maskSize == DIST_MASK_PRECISE and distanceType == DIST_L2 , the function runs the algorithm described in [Felzenszwalb04] . This algorithm is parallelized with the TBB library.
In other cases, the algorithm [Borgefors86] is used. This means that for a pixel the function finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, diagonal, or knight's move (the latest is available for a \(5\times 5\) mask). The overall distance is calculated as a sum of these basic distances. Since the distance function should be symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all the diagonal shifts must have the same cost (denoted as b), and all knight's moves must have the same cost (denoted as c). For the DIST_C and DIST_L1 types, the distance is calculated precisely, whereas for DIST_L2 (Euclidean distance) the distance can be calculated only with a relative error (a \(5\times 5\) mask gives more accurate results). For a,b, and c, OpenCV uses the values suggested in the original paper:
- DIST_L1:
a = 1, b = 2 - DIST_L2:
3 x 3:a=0.955, b=1.36935 x 5:a=1, b=1.4, c=2.1969
- DIST_C:
a = 1, b = 1
Typically, for a fast, coarse distance estimation DIST_L2, a \(3\times 3\) mask is used. For a more accurate distance estimation DIST_L2, a \(5\times 5\) mask or the precise algorithm is used. Note that both the precise and the approximate algorithms are linear on the number of pixels.
This variant of the function does not only compute the minimum distance for each pixel \((x, y)\) but also identifies the nearest connected component consisting of zero pixels (labelType==DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==DIST_LABEL_PIXEL). Index of the component/pixel is stored in labels(x, y). When labelType==DIST_LABEL_CCOMP, the function automatically finds connected components of zero pixels in the input image and marks them with distinct labels. When labelType==DIST_LABEL_PIXEL, the function scans through the input image and marks all the zero pixels with distinct labels.
In this mode, the complexity is still linear. That is, the function provides a very fast way to compute the Voronoi diagram for a binary image. Currently, the second variant can use only the approximate distance transform algorithm, i.e. maskSize=DIST_MASK_PRECISE is not supported yet.
- Parameters
-
src 8-bit, single-channel (binary) source image. dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, single-channel image of the same size as src. labels Output 2D array of labels (the discrete Voronoi diagram). It has the type CV_32SC1 and the same size as src. distanceType Type of distance, see #DistanceTypes maskSize Size of the distance transform mask, see #DistanceTransformMasks. DIST_MASK_PRECISE is not supported by this variant. In case of the DIST_L1 or DIST_C distance type, the parameter is forced to 3 because a \(3\times 3\) mask gives the same result as \(5\times 5\) or any larger aperture. labelType Type of the label array to build, see #DistanceTransformLabelTypes.
◆ distanceTransformWithLabels() [2/2]
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Calculates the distance to the closest zero pixel for each pixel of the source image.
The function cv::distanceTransform calculates the approximate or precise distance from every binary image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero.
When maskSize == DIST_MASK_PRECISE and distanceType == DIST_L2 , the function runs the algorithm described in [Felzenszwalb04] . This algorithm is parallelized with the TBB library.
In other cases, the algorithm [Borgefors86] is used. This means that for a pixel the function finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, diagonal, or knight's move (the latest is available for a \(5\times 5\) mask). The overall distance is calculated as a sum of these basic distances. Since the distance function should be symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all the diagonal shifts must have the same cost (denoted as b), and all knight's moves must have the same cost (denoted as c). For the DIST_C and DIST_L1 types, the distance is calculated precisely, whereas for DIST_L2 (Euclidean distance) the distance can be calculated only with a relative error (a \(5\times 5\) mask gives more accurate results). For a,b, and c, OpenCV uses the values suggested in the original paper:
- DIST_L1:
a = 1, b = 2 - DIST_L2:
3 x 3:a=0.955, b=1.36935 x 5:a=1, b=1.4, c=2.1969
- DIST_C:
a = 1, b = 1
Typically, for a fast, coarse distance estimation DIST_L2, a \(3\times 3\) mask is used. For a more accurate distance estimation DIST_L2, a \(5\times 5\) mask or the precise algorithm is used. Note that both the precise and the approximate algorithms are linear on the number of pixels.
This variant of the function does not only compute the minimum distance for each pixel \((x, y)\) but also identifies the nearest connected component consisting of zero pixels (labelType==DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==DIST_LABEL_PIXEL). Index of the component/pixel is stored in labels(x, y). When labelType==DIST_LABEL_CCOMP, the function automatically finds connected components of zero pixels in the input image and marks them with distinct labels. When labelType==DIST_LABEL_PIXEL, the function scans through the input image and marks all the zero pixels with distinct labels.
In this mode, the complexity is still linear. That is, the function provides a very fast way to compute the Voronoi diagram for a binary image. Currently, the second variant can use only the approximate distance transform algorithm, i.e. maskSize=DIST_MASK_PRECISE is not supported yet.
- Parameters
-
src 8-bit, single-channel (binary) source image. dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, single-channel image of the same size as src. labels Output 2D array of labels (the discrete Voronoi diagram). It has the type CV_32SC1 and the same size as src. distanceType Type of distance, see #DistanceTypes maskSize Size of the distance transform mask, see #DistanceTransformMasks. DIST_MASK_PRECISE is not supported by this variant. In case of the DIST_L1 or DIST_C distance type, the parameter is forced to 3 because a \(3\times 3\) mask gives the same result as \(5\times 5\) or any larger aperture. labelType Type of the label array to build, see #DistanceTransformLabelTypes.
◆ divSpectrums() [1/2]
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Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum.
The function cv::divSpectrums performs the per-element division of the first array by the second array. The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform.
- Parameters
-
a first input array. b second input array of the same size and type as src1 . c output array of the same size and type as src1 . flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a 0as value.conjB optional flag that conjugates the second input array before the multiplication (true) or not (false).
◆ divSpectrums() [2/2]
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Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum.
The function cv::divSpectrums performs the per-element division of the first array by the second array. The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform.
- Parameters
-
a first input array. b second input array of the same size and type as src1 . c output array of the same size and type as src1 . flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a 0as value.conjB optional flag that conjugates the second input array before the multiplication (true) or not (false).
◆ drawContours() [1/6]
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Draws contours outlines or filled contours.
The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area bounded by the contours if \(\texttt{thickness}<0\) . The example below shows how to retrieve connected components from the binary image and label them: :
- Parameters
-
image Destination image. contours All the input contours. Each contour is stored as a point vector. contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. color Color of the contours. thickness Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn. lineType Line connectivity. See #LineTypes hierarchy Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ). maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available. offset Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .
- 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() [2/6]
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Draws contours outlines or filled contours.
The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area bounded by the contours if \(\texttt{thickness}<0\) . The example below shows how to retrieve connected components from the binary image and label them: :
- Parameters
-
image Destination image. contours All the input contours. Each contour is stored as a point vector. contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. color Color of the contours. thickness Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn. lineType Line connectivity. See #LineTypes hierarchy Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ). maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available. offset Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .
- 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() [3/6]
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Draws contours outlines or filled contours.
The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area bounded by the contours if \(\texttt{thickness}<0\) . The example below shows how to retrieve connected components from the binary image and label them: :
- Parameters
-
image Destination image. contours All the input contours. Each contour is stored as a point vector. contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. color Color of the contours. thickness Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn. lineType Line connectivity. See #LineTypes hierarchy Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ). maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available. offset Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .
- 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() [4/6]
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Draws contours outlines or filled contours.
The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area bounded by the contours if \(\texttt{thickness}<0\) . The example below shows how to retrieve connected components from the binary image and label them: :
- Parameters
-
image Destination image. contours All the input contours. Each contour is stored as a point vector. contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. color Color of the contours. thickness Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn. lineType Line connectivity. See #LineTypes hierarchy Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ). maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available. offset Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .
- 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() [5/6]
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Draws contours outlines or filled contours.
The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area bounded by the contours if \(\texttt{thickness}<0\) . The example below shows how to retrieve connected components from the binary image and label them: :
- Parameters
-
image Destination image. contours All the input contours. Each contour is stored as a point vector. contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. color Color of the contours. thickness Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn. lineType Line connectivity. See #LineTypes hierarchy Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ). maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available. offset Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .
- 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() [6/6]
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Draws contours outlines or filled contours.
The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area bounded by the contours if \(\texttt{thickness}<0\) . The example below shows how to retrieve connected components from the binary image and label them: :
- Parameters
-
image Destination image. contours All the input contours. Each contour is stored as a point vector. contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. color Color of the contours. thickness Thickness of lines the contours are drawn with. If it is negative (for example, thickness=FILLED ), the contour interiors are drawn. lineType Line connectivity. See #LineTypes hierarchy Optional information about hierarchy. It is only needed if you want to draw only some of the contours (see maxLevel ). maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available. offset Optional contour shift parameter. Shift all the drawn contours by the specified \(\texttt{offset}=(dx,dy)\) .
- 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.
◆ drawMarker() [1/5]
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Draws a marker on a predefined position in an image.
The function cv::drawMarker draws a marker on a given position in the image. For the moment several marker types are supported, see #MarkerTypes for more information.
- Parameters
-
img Image. position The point where the crosshair is positioned. color Line color. markerType The specific type of marker you want to use, see #MarkerTypes thickness Line thickness. line_type Type of the line, See #LineTypes markerSize The length of the marker axis [default = 20 pixels]
◆ drawMarker() [2/5]
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Draws a marker on a predefined position in an image.
The function cv::drawMarker draws a marker on a given position in the image. For the moment several marker types are supported, see #MarkerTypes for more information.
- Parameters
-
img Image. position The point where the crosshair is positioned. color Line color. markerType The specific type of marker you want to use, see #MarkerTypes thickness Line thickness. line_type Type of the line, See #LineTypes markerSize The length of the marker axis [default = 20 pixels]
◆ drawMarker() [3/5]
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Draws a marker on a predefined position in an image.
The function cv::drawMarker draws a marker on a given position in the image. For the moment several marker types are supported, see #MarkerTypes for more information.
- Parameters
-
img Image. position The point where the crosshair is positioned. color Line color. markerType The specific type of marker you want to use, see #MarkerTypes thickness Line thickness. line_type Type of the line, See #LineTypes markerSize The length of the marker axis [default = 20 pixels]
◆ drawMarker() [4/5]
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Draws a marker on a predefined position in an image.
The function cv::drawMarker draws a marker on a given position in the image. For the moment several marker types are supported, see #MarkerTypes for more information.
- Parameters
-
img Image. position The point where the crosshair is positioned. color Line color. markerType The specific type of marker you want to use, see #MarkerTypes thickness Line thickness. line_type Type of the line, See #LineTypes markerSize The length of the marker axis [default = 20 pixels]
◆ drawMarker() [5/5]
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Draws a marker on a predefined position in an image.
The function cv::drawMarker draws a marker on a given position in the image. For the moment several marker types are supported, see #MarkerTypes for more information.
- Parameters
-
img Image. position The point where the crosshair is positioned. color Line color. markerType The specific type of marker you want to use, see #MarkerTypes thickness Line thickness. line_type Type of the line, See #LineTypes markerSize The length of the marker axis [default = 20 pixels]
◆ ellipse() [1/7]
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Draws a simple or thick elliptic arc or fills an ellipse sector.
The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic arc, or a filled ellipse sector. The drawing code uses general parametric form. A piecewise-linear curve is used to approximate the elliptic arc boundary. If you need more control of the ellipse rendering, you can retrieve the curve using ellipse2Poly and then render it with polylines or fill it with fillPoly. If you use the first variant of the function and want to draw the whole ellipse, not an arc, pass startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.
- Parameters
-
img Image. center Center of the ellipse. axes Half of the size of the ellipse main axes. angle Ellipse rotation angle in degrees. startAngle Starting angle of the elliptic arc in degrees. endAngle Ending angle of the elliptic arc in degrees. color Ellipse color. thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn. lineType Type of the ellipse boundary. See #LineTypes shift Number of fractional bits in the coordinates of the center and values of axes.
◆ ellipse() [2/7]
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Draws a simple or thick elliptic arc or fills an ellipse sector.
The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic arc, or a filled ellipse sector. The drawing code uses general parametric form. A piecewise-linear curve is used to approximate the elliptic arc boundary. If you need more control of the ellipse rendering, you can retrieve the curve using ellipse2Poly and then render it with polylines or fill it with fillPoly. If you use the first variant of the function and want to draw the whole ellipse, not an arc, pass startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.
- Parameters
-
img Image. center Center of the ellipse. axes Half of the size of the ellipse main axes. angle Ellipse rotation angle in degrees. startAngle Starting angle of the elliptic arc in degrees. endAngle Ending angle of the elliptic arc in degrees. color Ellipse color. thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn. lineType Type of the ellipse boundary. See #LineTypes shift Number of fractional bits in the coordinates of the center and values of axes.
◆ ellipse() [3/7]
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Draws a simple or thick elliptic arc or fills an ellipse sector.
The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic arc, or a filled ellipse sector. The drawing code uses general parametric form. A piecewise-linear curve is used to approximate the elliptic arc boundary. If you need more control of the ellipse rendering, you can retrieve the curve using ellipse2Poly and then render it with polylines or fill it with fillPoly. If you use the first variant of the function and want to draw the whole ellipse, not an arc, pass startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.
- Parameters
-
img Image. center Center of the ellipse. axes Half of the size of the ellipse main axes. angle Ellipse rotation angle in degrees. startAngle Starting angle of the elliptic arc in degrees. endAngle Ending angle of the elliptic arc in degrees. color Ellipse color. thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn. lineType Type of the ellipse boundary. See #LineTypes shift Number of fractional bits in the coordinates of the center and values of axes.
◆ ellipse() [4/7]
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static |
Draws a simple or thick elliptic arc or fills an ellipse sector.
The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic arc, or a filled ellipse sector. The drawing code uses general parametric form. A piecewise-linear curve is used to approximate the elliptic arc boundary. If you need more control of the ellipse rendering, you can retrieve the curve using ellipse2Poly and then render it with polylines or fill it with fillPoly. If you use the first variant of the function and want to draw the whole ellipse, not an arc, pass startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.
- Parameters
-
img Image. center Center of the ellipse. axes Half of the size of the ellipse main axes. angle Ellipse rotation angle in degrees. startAngle Starting angle of the elliptic arc in degrees. endAngle Ending angle of the elliptic arc in degrees. color Ellipse color. thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn. lineType Type of the ellipse boundary. See #LineTypes shift Number of fractional bits in the coordinates of the center and values of axes.
◆ ellipse() [5/7]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
img Image. box Alternative ellipse representation via RotatedRect. This means that the function draws an ellipse inscribed in the rotated rectangle. color Ellipse color. thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn. lineType Type of the ellipse boundary. See #LineTypes
◆ ellipse() [6/7]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
img Image. box Alternative ellipse representation via RotatedRect. This means that the function draws an ellipse inscribed in the rotated rectangle. color Ellipse color. thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn. lineType Type of the ellipse boundary. See #LineTypes
◆ ellipse() [7/7]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
img Image. box Alternative ellipse representation via RotatedRect. This means that the function draws an ellipse inscribed in the rotated rectangle. color Ellipse color. thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that a filled ellipse sector is to be drawn. lineType Type of the ellipse boundary. See #LineTypes
◆ ellipse2Poly()
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Approximates an elliptic arc with a polyline.
The function ellipse2Poly computes the vertices of a polyline that approximates the specified elliptic arc. It is used by ellipse. If arcStart is greater than arcEnd, they are swapped.
- Parameters
-
center Center of the arc. axes Half of the size of the ellipse main axes. See ellipse for details. angle Rotation angle of the ellipse in degrees. See ellipse for details. arcStart Starting angle of the elliptic arc in degrees. arcEnd Ending angle of the elliptic arc in degrees. delta Angle between the subsequent polyline vertices. It defines the approximation accuracy. pts Output vector of polyline vertices.
◆ EMD() [1/3]
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Computes the "minimal work" distance between two weighted point configurations.
The function computes the earth mover distance and/or a lower boundary of the distance between the two weighted point configurations. One of the applications described in [RubnerSept98], [Rubner2000] is multi-dimensional histogram comparison for image retrieval. EMD is a transportation problem that is solved using some modification of a simplex algorithm, thus the complexity is exponential in the worst case, though, on average it is much faster. In the case of a real metric the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used to determine roughly whether the two signatures are far enough so that they cannot relate to the same object.
- Parameters
-
signature1 First signature, a \(\texttt{size1}\times \texttt{dims}+1\) floating-point matrix. Each row stores the point weight followed by the point coordinates. The matrix is allowed to have a single column (weights only) if the user-defined cost matrix is used. The weights must be non-negative and have at least one non-zero value. signature2 Second signature of the same format as signature1 , though the number of rows may be different. The total weights may be different. In this case an extra "dummy" point is added to either signature1 or signature2. The weights must be non-negative and have at least one non-zero value. distType Used metric. See #DistanceTypes. cost User-defined \(\texttt{size1}\times \texttt{size2}\) cost matrix. Also, if a cost matrix is used, lower boundary lowerBound cannot be calculated because it needs a metric function. lowerBound Optional input/output parameter: lower boundary of a distance between the two signatures that is a distance between mass centers. The lower boundary may not be calculated if the user-defined cost matrix is used, the total weights of point configurations are not equal, or if the signatures consist of weights only (the signature matrices have a single column). You must** initialize *lowerBound . If the calculated distance between mass centers is greater or equal to *lowerBound (it means that the signatures are far enough), the function does not calculate EMD. In any case *lowerBound is set to the calculated distance between mass centers on return. Thus, if you want to calculate both distance between mass centers and EMD, *lowerBound should be set to 0. flow Resultant \(\texttt{size1} \times \texttt{size2}\) flow matrix: \(\texttt{flow}_{i,j}\) is a flow from \(i\) -th point of signature1 to \(j\) -th point of signature2 .
◆ EMD() [2/3]
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static |
Computes the "minimal work" distance between two weighted point configurations.
The function computes the earth mover distance and/or a lower boundary of the distance between the two weighted point configurations. One of the applications described in [RubnerSept98], [Rubner2000] is multi-dimensional histogram comparison for image retrieval. EMD is a transportation problem that is solved using some modification of a simplex algorithm, thus the complexity is exponential in the worst case, though, on average it is much faster. In the case of a real metric the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used to determine roughly whether the two signatures are far enough so that they cannot relate to the same object.
- Parameters
-
signature1 First signature, a \(\texttt{size1}\times \texttt{dims}+1\) floating-point matrix. Each row stores the point weight followed by the point coordinates. The matrix is allowed to have a single column (weights only) if the user-defined cost matrix is used. The weights must be non-negative and have at least one non-zero value. signature2 Second signature of the same format as signature1 , though the number of rows may be different. The total weights may be different. In this case an extra "dummy" point is added to either signature1 or signature2. The weights must be non-negative and have at least one non-zero value. distType Used metric. See #DistanceTypes. cost User-defined \(\texttt{size1}\times \texttt{size2}\) cost matrix. Also, if a cost matrix is used, lower boundary lowerBound cannot be calculated because it needs a metric function. lowerBound Optional input/output parameter: lower boundary of a distance between the two signatures that is a distance between mass centers. The lower boundary may not be calculated if the user-defined cost matrix is used, the total weights of point configurations are not equal, or if the signatures consist of weights only (the signature matrices have a single column). You must** initialize *lowerBound . If the calculated distance between mass centers is greater or equal to *lowerBound (it means that the signatures are far enough), the function does not calculate EMD. In any case *lowerBound is set to the calculated distance between mass centers on return. Thus, if you want to calculate both distance between mass centers and EMD, *lowerBound should be set to 0. flow Resultant \(\texttt{size1} \times \texttt{size2}\) flow matrix: \(\texttt{flow}_{i,j}\) is a flow from \(i\) -th point of signature1 to \(j\) -th point of signature2 .
◆ EMD() [3/3]
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static |
Computes the "minimal work" distance between two weighted point configurations.
The function computes the earth mover distance and/or a lower boundary of the distance between the two weighted point configurations. One of the applications described in [RubnerSept98], [Rubner2000] is multi-dimensional histogram comparison for image retrieval. EMD is a transportation problem that is solved using some modification of a simplex algorithm, thus the complexity is exponential in the worst case, though, on average it is much faster. In the case of a real metric the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used to determine roughly whether the two signatures are far enough so that they cannot relate to the same object.
- Parameters
-
signature1 First signature, a \(\texttt{size1}\times \texttt{dims}+1\) floating-point matrix. Each row stores the point weight followed by the point coordinates. The matrix is allowed to have a single column (weights only) if the user-defined cost matrix is used. The weights must be non-negative and have at least one non-zero value. signature2 Second signature of the same format as signature1 , though the number of rows may be different. The total weights may be different. In this case an extra "dummy" point is added to either signature1 or signature2. The weights must be non-negative and have at least one non-zero value. distType Used metric. See #DistanceTypes. cost User-defined \(\texttt{size1}\times \texttt{size2}\) cost matrix. Also, if a cost matrix is used, lower boundary lowerBound cannot be calculated because it needs a metric function. lowerBound Optional input/output parameter: lower boundary of a distance between the two signatures that is a distance between mass centers. The lower boundary may not be calculated if the user-defined cost matrix is used, the total weights of point configurations are not equal, or if the signatures consist of weights only (the signature matrices have a single column). You must** initialize *lowerBound . If the calculated distance between mass centers is greater or equal to *lowerBound (it means that the signatures are far enough), the function does not calculate EMD. In any case *lowerBound is set to the calculated distance between mass centers on return. Thus, if you want to calculate both distance between mass centers and EMD, *lowerBound should be set to 0. flow Resultant \(\texttt{size1} \times \texttt{size2}\) flow matrix: \(\texttt{flow}_{i,j}\) is a flow from \(i\) -th point of signature1 to \(j\) -th point of signature2 .
◆ equalizeHist()
Equalizes the histogram of a grayscale image.
The function equalizes the histogram of the input image using the following algorithm:
- Calculate the histogram \(H\) for src .
- Normalize the histogram so that the sum of histogram bins is 255.
- Compute the integral of the histogram:
\[H'_i = \sum _{0 \le j < i} H(j)\]
- Transform the image using \(H'\) as a look-up table: \(\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\)
The algorithm normalizes the brightness and increases the contrast of the image.
- Parameters
-
src Source 8-bit single channel image. dst Destination image of the same size and type as src .
◆ erode() [1/5]
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Erodes an image by using a specific structuring element.
The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken:
\[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]
The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.
- Parameters
-
src input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. kernel structuring element used for erosion; if element=Mat(), 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 also
- dilate, morphologyEx, getStructuringElement
◆ erode() [2/5]
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static |
Erodes an image by using a specific structuring element.
The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken:
\[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]
The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.
- Parameters
-
src input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. kernel structuring element used for erosion; if element=Mat(), 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 also
- dilate, morphologyEx, getStructuringElement
◆ erode() [3/5]
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static |
Erodes an image by using a specific structuring element.
The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken:
\[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]
The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.
- Parameters
-
src input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. kernel structuring element used for erosion; if element=Mat(), 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 also
- dilate, morphologyEx, getStructuringElement
◆ erode() [4/5]
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static |
Erodes an image by using a specific structuring element.
The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken:
\[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]
The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.
- Parameters
-
src input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. kernel structuring element used for erosion; if element=Mat(), 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 also
- dilate, morphologyEx, getStructuringElement
◆ erode() [5/5]
Erodes an image by using a specific structuring element.
The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken:
\[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]
The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.
- Parameters
-
src input image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. kernel structuring element used for erosion; if element=Mat(), 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 also
- dilate, morphologyEx, getStructuringElement
◆ fillConvexPoly() [1/3]
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Fills a convex polygon.
The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the function fillPoly . It can fill not only convex polygons but any monotonic polygon without self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) twice at the most (though, its top-most and/or the bottom edge could be horizontal).
- Parameters
-
img Image. points Polygon vertices. color Polygon color. lineType Type of the polygon boundaries. See #LineTypes shift Number of fractional bits in the vertex coordinates.
◆ fillConvexPoly() [2/3]
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Fills a convex polygon.
The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the function fillPoly . It can fill not only convex polygons but any monotonic polygon without self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) twice at the most (though, its top-most and/or the bottom edge could be horizontal).
- Parameters
-
img Image. points Polygon vertices. color Polygon color. lineType Type of the polygon boundaries. See #LineTypes shift Number of fractional bits in the vertex coordinates.
◆ fillConvexPoly() [3/3]
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Fills a convex polygon.
The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the function fillPoly . It can fill not only convex polygons but any monotonic polygon without self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) twice at the most (though, its top-most and/or the bottom edge could be horizontal).
- Parameters
-
img Image. points Polygon vertices. color Polygon color. lineType Type of the polygon boundaries. See #LineTypes shift Number of fractional bits in the vertex coordinates.
◆ fillPoly() [1/4]
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Fills the area bounded by one or more polygons.
The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill complex areas, for example, areas with holes, contours with self-intersections (some of their parts), and so forth.
- Parameters
-
img Image. pts Array of polygons where each polygon is represented as an array of points. color Polygon color. lineType Type of the polygon boundaries. See #LineTypes shift Number of fractional bits in the vertex coordinates. offset Optional offset of all points of the contours.
◆ fillPoly() [2/4]
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Fills the area bounded by one or more polygons.
The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill complex areas, for example, areas with holes, contours with self-intersections (some of their parts), and so forth.
- Parameters
-
img Image. pts Array of polygons where each polygon is represented as an array of points. color Polygon color. lineType Type of the polygon boundaries. See #LineTypes shift Number of fractional bits in the vertex coordinates. offset Optional offset of all points of the contours.
◆ fillPoly() [3/4]
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Fills the area bounded by one or more polygons.
The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill complex areas, for example, areas with holes, contours with self-intersections (some of their parts), and so forth.
- Parameters
-
img Image. pts Array of polygons where each polygon is represented as an array of points. color Polygon color. lineType Type of the polygon boundaries. See #LineTypes shift Number of fractional bits in the vertex coordinates. offset Optional offset of all points of the contours.
◆ fillPoly() [4/4]
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Fills the area bounded by one or more polygons.
The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill complex areas, for example, areas with holes, contours with self-intersections (some of their parts), and so forth.
- Parameters
-
img Image. pts Array of polygons where each polygon is represented as an array of points. color Polygon color. lineType Type of the polygon boundaries. See #LineTypes shift Number of fractional bits in the vertex coordinates. offset Optional offset of all points of the contours.
◆ filter2D() [1/4]
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Convolves an image with the kernel.
The function applies an arbitrary linear filter to an image. In-place operation is supported. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode.
The function does actually compute correlation, not the convolution:
\[\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\]
That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using #flip and set the new anchor to (kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1).
The function uses the DFT-based algorithm in case of sufficiently large kernels (~11 x 11 or larger) and the direct algorithm for small kernels.
- 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 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 also
- sepFilter2D, dft, matchTemplate
◆ filter2D() [2/4]
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Convolves an image with the kernel.
The function applies an arbitrary linear filter to an image. In-place operation is supported. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode.
The function does actually compute correlation, not the convolution:
\[\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\]
That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using #flip and set the new anchor to (kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1).
The function uses the DFT-based algorithm in case of sufficiently large kernels (~11 x 11 or larger) and the direct algorithm for small kernels.
- 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 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 also
- sepFilter2D, dft, matchTemplate
◆ filter2D() [3/4]
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static |
Convolves an image with the kernel.
The function applies an arbitrary linear filter to an image. In-place operation is supported. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode.
The function does actually compute correlation, not the convolution:
\[\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\]
That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using #flip and set the new anchor to (kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1).
The function uses the DFT-based algorithm in case of sufficiently large kernels (~11 x 11 or larger) and the direct algorithm for small kernels.
- 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 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 also
- sepFilter2D, dft, matchTemplate
◆ filter2D() [4/4]
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static |
Convolves an image with the kernel.
The function applies an arbitrary linear filter to an image. In-place operation is supported. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode.
The function does actually compute correlation, not the convolution:
\[\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\]
That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using #flip and set the new anchor to (kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1).
The function uses the DFT-based algorithm in case of sufficiently large kernels (~11 x 11 or larger) and the direct algorithm for small kernels.
- 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 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 also
- sepFilter2D, dft, matchTemplate
◆ findContours() [1/2]
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Finds contours in a binary image.
The function retrieves contours from the binary image using the algorithm [Suzuki85] . The contours are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the OpenCV sample directory.
- Note
- Since opencv 3.2 source image is not modified by this function.
- Parameters
-
image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, threshold , adaptiveThreshold, Canny, and others to create a binary image out of a grayscale or color one. If mode equals to RETR_CCOMP or RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). contours Detected contours. Each contour is stored as a vector of points (e.g. std::vector<std::vector<cv::Point> >). hierarchy Optional output vector (e.g. std::vector<cv::Vec4i>), containing information about the image topology. It has as many elements as the number of contours. For each i-th contour contours[i], the elements hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices in contours of the next and previous contours at the same hierarchical level, the first child contour and the parent contour, respectively. If for the contour i there are no next, previous, parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
- Note
- In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour.
- Parameters
-
mode Contour retrieval mode, see #RetrievalModes method Contour approximation method, see #ContourApproximationModes offset Optional offset by which every contour point is shifted. This is useful if the contours are extracted from the image ROI and then they should be analyzed in the whole image context.
◆ findContours() [2/2]
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Finds contours in a binary image.
The function retrieves contours from the binary image using the algorithm [Suzuki85] . The contours are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the OpenCV sample directory.
- Note
- Since opencv 3.2 source image is not modified by this function.
- Parameters
-
image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, threshold , adaptiveThreshold, Canny, and others to create a binary image out of a grayscale or color one. If mode equals to RETR_CCOMP or RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). contours Detected contours. Each contour is stored as a vector of points (e.g. std::vector<std::vector<cv::Point> >). hierarchy Optional output vector (e.g. std::vector<cv::Vec4i>), containing information about the image topology. It has as many elements as the number of contours. For each i-th contour contours[i], the elements hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices in contours of the next and previous contours at the same hierarchical level, the first child contour and the parent contour, respectively. If for the contour i there are no next, previous, parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
- Note
- In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour.
- Parameters
-
mode Contour retrieval mode, see #RetrievalModes method Contour approximation method, see #ContourApproximationModes offset Optional offset by which every contour point is shifted. This is useful if the contours are extracted from the image ROI and then they should be analyzed in the whole image context.
◆ fitEllipse()
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Fits an ellipse around a set of 2D points.
The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by [Fitzgibbon95] is used. Developer should keep in mind that it is possible that the returned ellipse/rotatedRect data contains negative indices, due to the data points being close to the border of the containing Mat element.
- Parameters
-
points Input 2D point set, stored in std::vector<> or Mat
◆ fitEllipseAMS()
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static |
Fits an ellipse around a set of 2D points.
The function calculates the ellipse that fits a set of 2D points. It returns the rotated rectangle in which the ellipse is inscribed. The Approximate Mean Square (AMS) proposed by [Taubin1991] is used.
For an ellipse, this basis set is \( \chi= \left(x^2, x y, y^2, x, y, 1\right) \), which is a set of six free coefficients \( A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \). However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \( (a,b) \), the position \( (x_0,y_0) \), and the orientation \( \theta \). This is because the basis set includes lines, quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. If the fit is found to be a parabolic or hyperbolic function then the standard fitEllipse method is used. The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves by imposing the condition that \( A^T ( D_x^T D_x + D_y^T D_y) A = 1 \) where the matrices \( Dx \) and \( Dy \) are the partial derivatives of the design matrix \( D \) with respect to x and y. The matrices are formed row by row applying the following to each of the points in the set:
\begin{align*} D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} & D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} & D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\} \end{align*}
The AMS method minimizes the cost function
\begin{equation*} \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } \end{equation*}
The minimum cost is found by solving the generalized eigenvalue problem.
\begin{equation*} D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A \end{equation*}
- Parameters
-
points Input 2D point set, stored in std::vector<> or Mat
◆ fitEllipseDirect()
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static |
Fits an ellipse around a set of 2D points.
The function calculates the ellipse that fits a set of 2D points. It returns the rotated rectangle in which the ellipse is inscribed. The Direct least square (Direct) method by [Fitzgibbon1999] is used.
For an ellipse, this basis set is \( \chi= \left(x^2, x y, y^2, x, y, 1\right) \), which is a set of six free coefficients \( A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \). However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \( (a,b) \), the position \( (x_0,y_0) \), and the orientation \( \theta \). This is because the basis set includes lines, quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. The Direct method confines the fit to ellipses by ensuring that \( 4 A_{xx} A_{yy}- A_{xy}^2 > 0 \). The condition imposed is that \( 4 A_{xx} A_{yy}- A_{xy}^2=1 \) which satisfies the inequality and as the coefficients can be arbitrarily scaled is not overly restrictive.
\begin{equation*} \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix} 0 & 0 & 2 & 0 & 0 & 0 \\ 0 & -1 & 0 & 0 & 0 & 0 \\ 2 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \end{matrix} \right) \end{equation*}
The minimum cost is found by solving the generalized eigenvalue problem.
\begin{equation*} D^T D A = \lambda \left( C\right) A \end{equation*}
The system produces only one positive eigenvalue \( \lambda\) which is chosen as the solution with its eigenvector \(\mathbf{u}\). These are used to find the coefficients
\begin{equation*} A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} \end{equation*}
The scaling factor guarantees that \(A^T C A =1\).
- Parameters
-
points Input 2D point set, stored in std::vector<> or Mat
◆ fitLine()
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static |
Fits a line to a 2D or 3D point set.
The function fitLine fits a line to a 2D or 3D point set by minimizing \(\sum_i \rho(r_i)\) where \(r_i\) is a distance between the \(i^{th}\) point, the line and \(\rho(r)\) is a distance function, one of the following:
- DIST_L2
\[\rho (r) = r^2/2 \quad \text{(the simplest and the fastest least-squares method)}\]
- DIST_L1
\[\rho (r) = r\]
- DIST_L12
\[\rho (r) = 2 \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\]
- DIST_FAIR
\[\rho \left (r \right ) = C^2 \cdot \left ( \frac{r}{C} - \log{\left(1 + \frac{r}{C}\right)} \right ) \quad \text{where} \quad C=1.3998\]
- DIST_WELSCH
\[\rho \left (r \right ) = \frac{C^2}{2} \cdot \left ( 1 - \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right ) \quad \text{where} \quad C=2.9846\]
- DIST_HUBER
\[\rho (r) = \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\]
The algorithm is based on the M-estimator ( <http://en.wikipedia.org/wiki/M-estimator> ) technique that iteratively fits the line using the weighted least-squares algorithm. After each iteration the weights \(w_i\) are adjusted to be inversely proportional to \(\rho(r_i)\) .
- Parameters
-
points Input vector of 2D or 3D points, stored in std::vector<> or Mat. line Output line parameters. In case of 2D fitting, it should be a vector of 4 elements (like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and (x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line and (x0, y0, z0) is a point on the line. distType Distance used by the M-estimator, see #DistanceTypes param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value is chosen. reps Sufficient accuracy for the radius (distance between the coordinate origin and the line). aeps Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps.
◆ floodFill() [1/5]
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static |
Fills a connected component with the given color.
The function cv::floodFill fills a connected component starting from the seed point with the specified color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The pixel at \((x,y)\) is considered to belong to the repainted domain if:
- in case of a grayscale image and floating range
\[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]
- in case of a grayscale image and fixed range
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]
- in case of a color image and floating range
\[\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\]
\[\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\]
and\[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]
- in case of a color image and fixed range
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\]
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\]
and\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]
where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:
- Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
- Color/brightness of the seed point in case of a fixed range.
Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.
- Parameters
-
image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below. mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller than image. If an empty Mat is passed it will be created automatically. Since this is both an input and output parameter, you must take responsibility of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags as described below. Additionally, the function fills the border of the mask with ones to simplify internal processing. It is therefore possible to use the same mask in multiple calls to the function to make sure the filled areas do not overlap. seedPoint Starting point. newVal New value of the repainted domain pixels. loDiff Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component. upDiff Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component. rect Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain. flags Operation flags. The first 8 bits contain a connectivity value. The default value of 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill the mask (the default value is 1). For example, 4 | ( 255 << 8 ) will consider 4 nearest neighbours and fill the mask with a value of 255. The following additional options occupy higher bits and therefore may be further combined with the connectivity and mask fill values using bit-wise or (|), see #FloodFillFlags.
- Note
- Since the mask is larger than the filled image, a pixel \((x, y)\) in image corresponds to the pixel \((x+1, y+1)\) in the mask .
- See also
- findContours
◆ floodFill() [2/5]
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static |
Fills a connected component with the given color.
The function cv::floodFill fills a connected component starting from the seed point with the specified color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The pixel at \((x,y)\) is considered to belong to the repainted domain if:
- in case of a grayscale image and floating range
\[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]
- in case of a grayscale image and fixed range
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]
- in case of a color image and floating range
\[\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\]
\[\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\]
and\[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]
- in case of a color image and fixed range
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\]
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\]
and\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]
where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:
- Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
- Color/brightness of the seed point in case of a fixed range.
Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.
- Parameters
-
image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below. mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller than image. If an empty Mat is passed it will be created automatically. Since this is both an input and output parameter, you must take responsibility of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags as described below. Additionally, the function fills the border of the mask with ones to simplify internal processing. It is therefore possible to use the same mask in multiple calls to the function to make sure the filled areas do not overlap. seedPoint Starting point. newVal New value of the repainted domain pixels. loDiff Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component. upDiff Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component. rect Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain. flags Operation flags. The first 8 bits contain a connectivity value. The default value of 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill the mask (the default value is 1). For example, 4 | ( 255 << 8 ) will consider 4 nearest neighbours and fill the mask with a value of 255. The following additional options occupy higher bits and therefore may be further combined with the connectivity and mask fill values using bit-wise or (|), see #FloodFillFlags.
- Note
- Since the mask is larger than the filled image, a pixel \((x, y)\) in image corresponds to the pixel \((x+1, y+1)\) in the mask .
- See also
- findContours
◆ floodFill() [3/5]
|
static |
Fills a connected component with the given color.
The function cv::floodFill fills a connected component starting from the seed point with the specified color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The pixel at \((x,y)\) is considered to belong to the repainted domain if:
- in case of a grayscale image and floating range
\[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]
- in case of a grayscale image and fixed range
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]
- in case of a color image and floating range
\[\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\]
\[\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\]
and\[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]
- in case of a color image and fixed range
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\]
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\]
and\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]
where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:
- Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
- Color/brightness of the seed point in case of a fixed range.
Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.
- Parameters
-
image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below. mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller than image. If an empty Mat is passed it will be created automatically. Since this is both an input and output parameter, you must take responsibility of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags as described below. Additionally, the function fills the border of the mask with ones to simplify internal processing. It is therefore possible to use the same mask in multiple calls to the function to make sure the filled areas do not overlap. seedPoint Starting point. newVal New value of the repainted domain pixels. loDiff Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component. upDiff Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component. rect Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain. flags Operation flags. The first 8 bits contain a connectivity value. The default value of 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill the mask (the default value is 1). For example, 4 | ( 255 << 8 ) will consider 4 nearest neighbours and fill the mask with a value of 255. The following additional options occupy higher bits and therefore may be further combined with the connectivity and mask fill values using bit-wise or (|), see #FloodFillFlags.
- Note
- Since the mask is larger than the filled image, a pixel \((x, y)\) in image corresponds to the pixel \((x+1, y+1)\) in the mask .
- See also
- findContours
◆ floodFill() [4/5]
|
static |
Fills a connected component with the given color.
The function cv::floodFill fills a connected component starting from the seed point with the specified color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The pixel at \((x,y)\) is considered to belong to the repainted domain if:
- in case of a grayscale image and floating range
\[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]
- in case of a grayscale image and fixed range
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]
- in case of a color image and floating range
\[\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\]
\[\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\]
and\[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]
- in case of a color image and fixed range
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\]
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\]
and\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]
where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:
- Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
- Color/brightness of the seed point in case of a fixed range.
Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.
- Parameters
-
image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below. mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller than image. If an empty Mat is passed it will be created automatically. Since this is both an input and output parameter, you must take responsibility of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags as described below. Additionally, the function fills the border of the mask with ones to simplify internal processing. It is therefore possible to use the same mask in multiple calls to the function to make sure the filled areas do not overlap. seedPoint Starting point. newVal New value of the repainted domain pixels. loDiff Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component. upDiff Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component. rect Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain. flags Operation flags. The first 8 bits contain a connectivity value. The default value of 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill the mask (the default value is 1). For example, 4 | ( 255 << 8 ) will consider 4 nearest neighbours and fill the mask with a value of 255. The following additional options occupy higher bits and therefore may be further combined with the connectivity and mask fill values using bit-wise or (|), see #FloodFillFlags.
- Note
- Since the mask is larger than the filled image, a pixel \((x, y)\) in image corresponds to the pixel \((x+1, y+1)\) in the mask .
- See also
- findContours
◆ floodFill() [5/5]
|
static |
Fills a connected component with the given color.
The function cv::floodFill fills a connected component starting from the seed point with the specified color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The pixel at \((x,y)\) is considered to belong to the repainted domain if:
- in case of a grayscale image and floating range
\[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]
- in case of a grayscale image and fixed range
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]
- in case of a color image and floating range
\[\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\]
\[\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\]
and\[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]
- in case of a color image and fixed range
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\]
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\]
and\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]
where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:
- Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
- Color/brightness of the seed point in case of a fixed range.
Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.
- Parameters
-
image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below. mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller than image. If an empty Mat is passed it will be created automatically. Since this is both an input and output parameter, you must take responsibility of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags as described below. Additionally, the function fills the border of the mask with ones to simplify internal processing. It is therefore possible to use the same mask in multiple calls to the function to make sure the filled areas do not overlap. seedPoint Starting point. newVal New value of the repainted domain pixels. loDiff Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component. upDiff Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component. rect Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain. flags Operation flags. The first 8 bits contain a connectivity value. The default value of 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill the mask (the default value is 1). For example, 4 | ( 255 << 8 ) will consider 4 nearest neighbours and fill the mask with a value of 255. The following additional options occupy higher bits and therefore may be further combined with the connectivity and mask fill values using bit-wise or (|), see #FloodFillFlags.
- Note
- Since the mask is larger than the filled image, a pixel \((x, y)\) in image corresponds to the pixel \((x+1, y+1)\) in the mask .
- See also
- findContours
◆ GaussianBlur() [1/3]
|
static |
Blurs an image using a Gaussian filter.
The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.
- Parameters
-
src input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma. sigmaX Gaussian kernel standard deviation in X direction. sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
◆ GaussianBlur() [2/3]
|
static |
Blurs an image using a Gaussian filter.
The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.
- Parameters
-
src input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma. sigmaX Gaussian kernel standard deviation in X direction. sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
◆ GaussianBlur() [3/3]
|
static |
Blurs an image using a Gaussian filter.
The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.
- Parameters
-
src input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst output image of the same size and type as src. ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma. sigmaX Gaussian kernel standard deviation in X direction. sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
◆ getAffineTransform()
|
static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ getDerivKernels() [1/3]
|
static |
Returns filter coefficients for computing spatial image derivatives.
The function computes and returns the filter coefficients for spatial image derivatives. When ksize=FILTER_SCHARR, the Scharr \(3 \times 3\) kernels are generated (see Scharr). Otherwise, Sobel kernels are generated (see Sobel). The filters are normally passed to sepFilter2D or to
- Parameters
-
kx Output matrix of row filter coefficients. It has the type ktype . ky Output matrix of column filter coefficients. It has the type ktype . dx Derivative order in respect of x. dy Derivative order in respect of y. ksize Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7. normalize Flag indicating whether to normalize (scale down) the filter coefficients or not. Theoretically, the coefficients should have the denominator \(=2^{ksize*2-dx-dy-2}\). If you are going to filter floating-point images, you are likely to use the normalized kernels. But if you compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve all the fractional bits, you may want to set normalize=false . ktype Type of filter coefficients. It can be CV_32f or CV_64F .
◆ getDerivKernels() [2/3]
|
static |
Returns filter coefficients for computing spatial image derivatives.
The function computes and returns the filter coefficients for spatial image derivatives. When ksize=FILTER_SCHARR, the Scharr \(3 \times 3\) kernels are generated (see Scharr). Otherwise, Sobel kernels are generated (see Sobel). The filters are normally passed to sepFilter2D or to
- Parameters
-
kx Output matrix of row filter coefficients. It has the type ktype . ky Output matrix of column filter coefficients. It has the type ktype . dx Derivative order in respect of x. dy Derivative order in respect of y. ksize Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7. normalize Flag indicating whether to normalize (scale down) the filter coefficients or not. Theoretically, the coefficients should have the denominator \(=2^{ksize*2-dx-dy-2}\). If you are going to filter floating-point images, you are likely to use the normalized kernels. But if you compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve all the fractional bits, you may want to set normalize=false . ktype Type of filter coefficients. It can be CV_32f or CV_64F .
◆ getDerivKernels() [3/3]
|
static |
Returns filter coefficients for computing spatial image derivatives.
The function computes and returns the filter coefficients for spatial image derivatives. When ksize=FILTER_SCHARR, the Scharr \(3 \times 3\) kernels are generated (see Scharr). Otherwise, Sobel kernels are generated (see Sobel). The filters are normally passed to sepFilter2D or to
- Parameters
-
kx Output matrix of row filter coefficients. It has the type ktype . ky Output matrix of column filter coefficients. It has the type ktype . dx Derivative order in respect of x. dy Derivative order in respect of y. ksize Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7. normalize Flag indicating whether to normalize (scale down) the filter coefficients or not. Theoretically, the coefficients should have the denominator \(=2^{ksize*2-dx-dy-2}\). If you are going to filter floating-point images, you are likely to use the normalized kernels. But if you compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve all the fractional bits, you may want to set normalize=false . ktype Type of filter coefficients. It can be CV_32f or CV_64F .
◆ getFontScaleFromHeight() [1/2]
|
static |
Calculates the font-specific size to use to achieve a given height in pixels.
- Parameters
-
fontFace Font to use, see cv::HersheyFonts. pixelHeight Pixel height to compute the fontScale for thickness Thickness of lines used to render the text.See putText for details.
- Returns
- The fontSize to use for cv::putText
- See also
- cv::putText
◆ getFontScaleFromHeight() [2/2]
|
static |
Calculates the font-specific size to use to achieve a given height in pixels.
- Parameters
-
fontFace Font to use, see cv::HersheyFonts. pixelHeight Pixel height to compute the fontScale for thickness Thickness of lines used to render the text.See putText for details.
- Returns
- The fontSize to use for cv::putText
- See also
- cv::putText
◆ getGaborKernel() [1/3]
|
static |
Returns Gabor filter coefficients.
For more details about gabor filter equations and parameters, see: Gabor Filter.
- Parameters
-
ksize Size of the filter returned. sigma Standard deviation of the gaussian envelope. theta Orientation of the normal to the parallel stripes of a Gabor function. lambd Wavelength of the sinusoidal factor. gamma Spatial aspect ratio. psi Phase offset. ktype Type of filter coefficients. It can be CV_32F or CV_64F .
◆ getGaborKernel() [2/3]
|
static |
Returns Gabor filter coefficients.
For more details about gabor filter equations and parameters, see: Gabor Filter.
- Parameters
-
ksize Size of the filter returned. sigma Standard deviation of the gaussian envelope. theta Orientation of the normal to the parallel stripes of a Gabor function. lambd Wavelength of the sinusoidal factor. gamma Spatial aspect ratio. psi Phase offset. ktype Type of filter coefficients. It can be CV_32F or CV_64F .
◆ getGaborKernel() [3/3]
|
static |
Returns Gabor filter coefficients.
For more details about gabor filter equations and parameters, see: Gabor Filter.
- Parameters
-
ksize Size of the filter returned. sigma Standard deviation of the gaussian envelope. theta Orientation of the normal to the parallel stripes of a Gabor function. lambd Wavelength of the sinusoidal factor. gamma Spatial aspect ratio. psi Phase offset. ktype Type of filter coefficients. It can be CV_32F or CV_64F .
◆ getGaussianKernel() [1/2]
|
static |
Returns Gaussian filter coefficients.
The function computes and returns the \(\texttt{ksize} \times 1\) matrix of Gaussian filter coefficients:
\[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\]
where \(i=0..\texttt{ksize}-1\) and \(\alpha\) is the scale factor chosen so that \(\sum_i G_i=1\).
Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. You may also use the higher-level GaussianBlur.
- Parameters
-
ksize Aperture size. It should be odd ( \(\texttt{ksize} \mod 2 = 1\) ) and positive. sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8.ktype Type of filter coefficients. It can be CV_32F or CV_64F .
◆ getGaussianKernel() [2/2]
|
static |
Returns Gaussian filter coefficients.
The function computes and returns the \(\texttt{ksize} \times 1\) matrix of Gaussian filter coefficients:
\[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\]
where \(i=0..\texttt{ksize}-1\) and \(\alpha\) is the scale factor chosen so that \(\sum_i G_i=1\).
Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. You may also use the higher-level GaussianBlur.
- Parameters
-
ksize Aperture size. It should be odd ( \(\texttt{ksize} \mod 2 = 1\) ) and positive. sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8.ktype Type of filter coefficients. It can be CV_32F or CV_64F .
◆ getPerspectiveTransform() [1/2]
|
static |
Calculates a perspective transform from four pairs of the corresponding points.
The function calculates the \(3 \times 3\) matrix of a perspective transform so that:
\[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\]
where
\[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\]
- Parameters
-
src Coordinates of quadrangle vertices in the source image. dst Coordinates of the corresponding quadrangle vertices in the destination image. solveMethod method passed to cv::solve (#DecompTypes)
- See also
- findHomography, warpPerspective, perspectiveTransform
◆ getPerspectiveTransform() [2/2]
Calculates a perspective transform from four pairs of the corresponding points.
The function calculates the \(3 \times 3\) matrix of a perspective transform so that:
\[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\]
where
\[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\]
- Parameters
-
src Coordinates of quadrangle vertices in the source image. dst Coordinates of the corresponding quadrangle vertices in the destination image. solveMethod method passed to cv::solve (#DecompTypes)
- See also
- findHomography, warpPerspective, perspectiveTransform
◆ getRectSubPix() [1/2]
|
static |
Retrieves a pixel rectangle from an image with sub-pixel accuracy.
The function getRectSubPix extracts pixels from src:
\[patch(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\]
where the values of the pixels at non-integer coordinates are retrieved using bilinear interpolation. Every channel of multi-channel images is processed independently. Also the image should be a single channel or three channel image. While the center of the rectangle must be inside the image, parts of the rectangle may be outside.
- Parameters
-
image Source image. patchSize Size of the extracted patch. center Floating point coordinates of the center of the extracted rectangle within the source image. The center must be inside the image. patch Extracted patch that has the size patchSize and the same number of channels as src . patchType Depth of the extracted pixels. By default, they have the same depth as src .
- See also
- warpAffine, warpPerspective
◆ getRectSubPix() [2/2]
|
static |
Retrieves a pixel rectangle from an image with sub-pixel accuracy.
The function getRectSubPix extracts pixels from src:
\[patch(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\]
where the values of the pixels at non-integer coordinates are retrieved using bilinear interpolation. Every channel of multi-channel images is processed independently. Also the image should be a single channel or three channel image. While the center of the rectangle must be inside the image, parts of the rectangle may be outside.
- Parameters
-
image Source image. patchSize Size of the extracted patch. center Floating point coordinates of the center of the extracted rectangle within the source image. The center must be inside the image. patch Extracted patch that has the size patchSize and the same number of channels as src . patchType Depth of the extracted pixels. By default, they have the same depth as src .
- See also
- warpAffine, warpPerspective
◆ getRotationMatrix2D()
|
static |
Calculates an affine matrix of 2D rotation.
The function calculates the following matrix:
\[\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\]
where
\[\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\]
The transformation maps the rotation center to itself. If this is not the target, adjust the shift.
- Parameters
-
center Center of the rotation in the source image. angle Rotation angle in degrees. Positive values mean counter-clockwise rotation (the coordinate origin is assumed to be the top-left corner). scale Isotropic scale factor.
- See also
- getAffineTransform, warpAffine, transform
◆ getStructuringElement() [1/2]
|
static |
Returns a structuring element of the specified size and shape for morphological operations.
The function constructs and returns the structuring element that can be further passed to erode, dilate or morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as the structuring element.
- Parameters
-
shape Element shape that could be one of #MorphShapes ksize Size of the structuring element. anchor Anchor position within the element. The default value \((-1, -1)\) means that the anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted.
◆ getStructuringElement() [2/2]
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Returns a structuring element of the specified size and shape for morphological operations.
The function constructs and returns the structuring element that can be further passed to erode, dilate or morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as the structuring element.
- Parameters
-
shape Element shape that could be one of #MorphShapes ksize Size of the structuring element. anchor Anchor position within the element. The default value \((-1, -1)\) means that the anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted.
◆ getTextSize()
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◆ goodFeaturesToTrack() [1/8]
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Determines strong corners on an image.
The function finds the most prominent corners in the image or in the specified image region, as described in [Shi94]
- Function calculates the corner quality measure at every source image pixel using the cornerMinEigenVal or cornerHarris .
- Function performs a non-maximum suppression (the local maximums in 3 x 3 neighborhood are retained).
- The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
- The remaining corners are sorted by the quality measure in the descending order.
- Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.
The function can be used to initialize a point-based tracker of an object.
- Note
- If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .
- Parameters
-
image Input 8-bit or floating-point 32-bit, single-channel image. corners Output vector of detected corners. maxCorners Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 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 also
- cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
◆ goodFeaturesToTrack() [2/8]
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static |
Determines strong corners on an image.
The function finds the most prominent corners in the image or in the specified image region, as described in [Shi94]
- Function calculates the corner quality measure at every source image pixel using the cornerMinEigenVal or cornerHarris .
- Function performs a non-maximum suppression (the local maximums in 3 x 3 neighborhood are retained).
- The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
- The remaining corners are sorted by the quality measure in the descending order.
- Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.
The function can be used to initialize a point-based tracker of an object.
- Note
- If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .
- Parameters
-
image Input 8-bit or floating-point 32-bit, single-channel image. corners Output vector of detected corners. maxCorners Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 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 also
- cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
◆ goodFeaturesToTrack() [3/8]
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static |
Determines strong corners on an image.
The function finds the most prominent corners in the image or in the specified image region, as described in [Shi94]
- Function calculates the corner quality measure at every source image pixel using the cornerMinEigenVal or cornerHarris .
- Function performs a non-maximum suppression (the local maximums in 3 x 3 neighborhood are retained).
- The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
- The remaining corners are sorted by the quality measure in the descending order.
- Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.
The function can be used to initialize a point-based tracker of an object.
- Note
- If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .
- Parameters
-
image Input 8-bit or floating-point 32-bit, single-channel image. corners Output vector of detected corners. maxCorners Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 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 also
- cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
◆ goodFeaturesToTrack() [4/8]
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static |
Determines strong corners on an image.
The function finds the most prominent corners in the image or in the specified image region, as described in [Shi94]
- Function calculates the corner quality measure at every source image pixel using the cornerMinEigenVal or cornerHarris .
- Function performs a non-maximum suppression (the local maximums in 3 x 3 neighborhood are retained).
- The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
- The remaining corners are sorted by the quality measure in the descending order.
- Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.
The function can be used to initialize a point-based tracker of an object.
- Note
- If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .
- Parameters
-
image Input 8-bit or floating-point 32-bit, single-channel image. corners Output vector of detected corners. maxCorners Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 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 also
- cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
◆ goodFeaturesToTrack() [5/8]
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static |
Determines strong corners on an image.
The function finds the most prominent corners in the image or in the specified image region, as described in [Shi94]
- Function calculates the corner quality measure at every source image pixel using the cornerMinEigenVal or cornerHarris .
- Function performs a non-maximum suppression (the local maximums in 3 x 3 neighborhood are retained).
- The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
- The remaining corners are sorted by the quality measure in the descending order.
- Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.
The function can be used to initialize a point-based tracker of an object.
- Note
- If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .
- Parameters
-
image Input 8-bit or floating-point 32-bit, single-channel image. corners Output vector of detected corners. maxCorners Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 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 also
- cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
◆ goodFeaturesToTrack() [6/8]
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◆ goodFeaturesToTrack() [7/8]
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static |
◆ goodFeaturesToTrack() [8/8]
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◆ goodFeaturesToTrackWithQuality() [1/5]
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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() [2/5]
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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() [3/5]
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static |
Same as above, but returns also quality measure of the detected corners.
- Parameters
-
image Input 8-bit or floating-point 32-bit, single-channel image. corners Output vector of detected corners. maxCorners Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 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() [4/5]
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static |
Same as above, but returns also quality measure of the detected corners.
- Parameters
-
image Input 8-bit or floating-point 32-bit, single-channel image. corners Output vector of detected corners. maxCorners Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 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() [5/5]
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static |
Same as above, but returns also quality measure of the detected corners.
- Parameters
-
image Input 8-bit or floating-point 32-bit, single-channel image. corners Output vector of detected corners. maxCorners Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 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.
◆ grabCut() [1/2]
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Runs the GrabCut algorithm.
The function implements the GrabCut image segmentation algorithm.
- Parameters
-
img Input 8-bit 3-channel image. mask Input/output 8-bit single-channel mask. The mask is initialized by the function when mode is set to GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses. rect ROI containing a segmented object. The pixels outside of the ROI are marked as "obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT . bgdModel Temporary array for the background model. Do not modify it while you are processing the same image. fgdModel Temporary arrays for the foreground model. Do not modify it while you are processing the same image. iterCount Number of iterations the algorithm should make before returning the result. Note that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or mode==GC_EVAL . mode Operation mode that could be one of the #GrabCutModes
◆ grabCut() [2/2]
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static |
Runs the GrabCut algorithm.
The function implements the GrabCut image segmentation algorithm.
- Parameters
-
img Input 8-bit 3-channel image. mask Input/output 8-bit single-channel mask. The mask is initialized by the function when mode is set to GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses. rect ROI containing a segmented object. The pixels outside of the ROI are marked as "obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT . bgdModel Temporary array for the background model. Do not modify it while you are processing the same image. fgdModel Temporary arrays for the foreground model. Do not modify it while you are processing the same image. iterCount Number of iterations the algorithm should make before returning the result. Note that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or mode==GC_EVAL . mode Operation mode that could be one of the #GrabCutModes
◆ HoughCircles() [1/5]
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Finds circles in a grayscale image using the Hough transform.
The function finds circles in a grayscale image using a modification of the Hough transform.
Example: :
- Note
- Usually the function detects the centers of circles well. However, it may fail to find correct radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if you know it. Or, in the case of HOUGH_GRADIENT method you may set maxRadius to a negative number to return centers only without radius search, and find the correct radius using an additional procedure.
It also helps to smooth image a bit unless it's already soft. For example, GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help.
- Parameters
-
image 8-bit, single-channel, grayscale input image. circles Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) . method Detection method, see #HoughModes. The available methods are HOUGH_GRADIENT and HOUGH_GRADIENT_ALT. dp Inverse ratio of the accumulator resolution to the image resolution. For example, if dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has half as big width and height. For HOUGH_GRADIENT_ALT the recommended value is dp=1.5, unless some small very circles need to be detected. minDist Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed. param1 First method-specific parameter. In case of HOUGH_GRADIENT and HOUGH_GRADIENT_ALT, it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that HOUGH_GRADIENT_ALT uses Scharr algorithm to compute image derivatives, so the threshold value should normally be higher, such as 300 or normally exposed and contrasty images. param2 Second method-specific parameter. In case of HOUGH_GRADIENT, it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first. In the case of HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. minRadius Minimum circle radius. maxRadius Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, HOUGH_GRADIENT returns centers without finding the radius. HOUGH_GRADIENT_ALT always computes circle radiuses.
- See also
- fitEllipse, minEnclosingCircle
◆ HoughCircles() [2/5]
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static |
Finds circles in a grayscale image using the Hough transform.
The function finds circles in a grayscale image using a modification of the Hough transform.
Example: :
- Note
- Usually the function detects the centers of circles well. However, it may fail to find correct radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if you know it. Or, in the case of HOUGH_GRADIENT method you may set maxRadius to a negative number to return centers only without radius search, and find the correct radius using an additional procedure.
It also helps to smooth image a bit unless it's already soft. For example, GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help.
- Parameters
-
image 8-bit, single-channel, grayscale input image. circles Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) . method Detection method, see #HoughModes. The available methods are HOUGH_GRADIENT and HOUGH_GRADIENT_ALT. dp Inverse ratio of the accumulator resolution to the image resolution. For example, if dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has half as big width and height. For HOUGH_GRADIENT_ALT the recommended value is dp=1.5, unless some small very circles need to be detected. minDist Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed. param1 First method-specific parameter. In case of HOUGH_GRADIENT and HOUGH_GRADIENT_ALT, it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that HOUGH_GRADIENT_ALT uses Scharr algorithm to compute image derivatives, so the threshold value should normally be higher, such as 300 or normally exposed and contrasty images. param2 Second method-specific parameter. In case of HOUGH_GRADIENT, it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first. In the case of HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. minRadius Minimum circle radius. maxRadius Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, HOUGH_GRADIENT returns centers without finding the radius. HOUGH_GRADIENT_ALT always computes circle radiuses.
- See also
- fitEllipse, minEnclosingCircle
◆ HoughCircles() [3/5]
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static |
Finds circles in a grayscale image using the Hough transform.
The function finds circles in a grayscale image using a modification of the Hough transform.
Example: :
- Note
- Usually the function detects the centers of circles well. However, it may fail to find correct radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if you know it. Or, in the case of HOUGH_GRADIENT method you may set maxRadius to a negative number to return centers only without radius search, and find the correct radius using an additional procedure.
It also helps to smooth image a bit unless it's already soft. For example, GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help.
- Parameters
-
image 8-bit, single-channel, grayscale input image. circles Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) . method Detection method, see #HoughModes. The available methods are HOUGH_GRADIENT and HOUGH_GRADIENT_ALT. dp Inverse ratio of the accumulator resolution to the image resolution. For example, if dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has half as big width and height. For HOUGH_GRADIENT_ALT the recommended value is dp=1.5, unless some small very circles need to be detected. minDist Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed. param1 First method-specific parameter. In case of HOUGH_GRADIENT and HOUGH_GRADIENT_ALT, it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that HOUGH_GRADIENT_ALT uses Scharr algorithm to compute image derivatives, so the threshold value should normally be higher, such as 300 or normally exposed and contrasty images. param2 Second method-specific parameter. In case of HOUGH_GRADIENT, it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first. In the case of HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. minRadius Minimum circle radius. maxRadius Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, HOUGH_GRADIENT returns centers without finding the radius. HOUGH_GRADIENT_ALT always computes circle radiuses.
- See also
- fitEllipse, minEnclosingCircle
◆ HoughCircles() [4/5]
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static |
Finds circles in a grayscale image using the Hough transform.
The function finds circles in a grayscale image using a modification of the Hough transform.
Example: :
- Note
- Usually the function detects the centers of circles well. However, it may fail to find correct radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if you know it. Or, in the case of HOUGH_GRADIENT method you may set maxRadius to a negative number to return centers only without radius search, and find the correct radius using an additional procedure.
It also helps to smooth image a bit unless it's already soft. For example, GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help.
- Parameters
-
image 8-bit, single-channel, grayscale input image. circles Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) . method Detection method, see #HoughModes. The available methods are HOUGH_GRADIENT and HOUGH_GRADIENT_ALT. dp Inverse ratio of the accumulator resolution to the image resolution. For example, if dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has half as big width and height. For HOUGH_GRADIENT_ALT the recommended value is dp=1.5, unless some small very circles need to be detected. minDist Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed. param1 First method-specific parameter. In case of HOUGH_GRADIENT and HOUGH_GRADIENT_ALT, it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that HOUGH_GRADIENT_ALT uses Scharr algorithm to compute image derivatives, so the threshold value should normally be higher, such as 300 or normally exposed and contrasty images. param2 Second method-specific parameter. In case of HOUGH_GRADIENT, it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first. In the case of HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. minRadius Minimum circle radius. maxRadius Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, HOUGH_GRADIENT returns centers without finding the radius. HOUGH_GRADIENT_ALT always computes circle radiuses.
- See also
- fitEllipse, minEnclosingCircle
◆ HoughCircles() [5/5]
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Finds circles in a grayscale image using the Hough transform.
The function finds circles in a grayscale image using a modification of the Hough transform.
Example: :
- Note
- Usually the function detects the centers of circles well. However, it may fail to find correct radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if you know it. Or, in the case of HOUGH_GRADIENT method you may set maxRadius to a negative number to return centers only without radius search, and find the correct radius using an additional procedure.
It also helps to smooth image a bit unless it's already soft. For example, GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help.
- Parameters
-
image 8-bit, single-channel, grayscale input image. circles Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) . method Detection method, see #HoughModes. The available methods are HOUGH_GRADIENT and HOUGH_GRADIENT_ALT. dp Inverse ratio of the accumulator resolution to the image resolution. For example, if dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has half as big width and height. For HOUGH_GRADIENT_ALT the recommended value is dp=1.5, unless some small very circles need to be detected. minDist Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed. param1 First method-specific parameter. In case of HOUGH_GRADIENT and HOUGH_GRADIENT_ALT, it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that HOUGH_GRADIENT_ALT uses Scharr algorithm to compute image derivatives, so the threshold value should normally be higher, such as 300 or normally exposed and contrasty images. param2 Second method-specific parameter. In case of HOUGH_GRADIENT, it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first. In the case of HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure. The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. minRadius Minimum circle radius. maxRadius Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, HOUGH_GRADIENT returns centers without finding the radius. HOUGH_GRADIENT_ALT always computes circle radiuses.
- See also
- fitEllipse, minEnclosingCircle
◆ HoughLines() [1/5]
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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() [2/5]
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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() [3/5]
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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() [4/5]
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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() [5/5]
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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.
◆ HoughLinesP() [1/3]
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Finds line segments in a binary image using the probabilistic Hough transform.
The function implements the probabilistic Hough transform algorithm for line detection, described in [Matas00]
See the line detection example below:
This is a sample picture the function parameters have been tuned for:
And this is the output of the above program in case of the probabilistic Hough transform:
- Parameters
-
image 8-bit, single-channel binary source image. The image may be modified by the function. lines Output vector of lines. Each line is represented by a 4-element vector \((x_1, y_1, x_2, y_2)\) , where \((x_1,y_1)\) and \((x_2, y_2)\) are the ending points of each detected line segment. rho Distance resolution of the accumulator in pixels. theta Angle resolution of the accumulator in radians. threshold Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ). minLineLength Minimum line length. Line segments shorter than that are rejected. maxLineGap Maximum allowed gap between points on the same line to link them.
- See also
- LineSegmentDetector
◆ HoughLinesP() [2/3]
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Finds line segments in a binary image using the probabilistic Hough transform.
The function implements the probabilistic Hough transform algorithm for line detection, described in [Matas00]
See the line detection example below:
This is a sample picture the function parameters have been tuned for:
And this is the output of the above program in case of the probabilistic Hough transform:
- Parameters
-
image 8-bit, single-channel binary source image. The image may be modified by the function. lines Output vector of lines. Each line is represented by a 4-element vector \((x_1, y_1, x_2, y_2)\) , where \((x_1,y_1)\) and \((x_2, y_2)\) are the ending points of each detected line segment. rho Distance resolution of the accumulator in pixels. theta Angle resolution of the accumulator in radians. threshold Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ). minLineLength Minimum line length. Line segments shorter than that are rejected. maxLineGap Maximum allowed gap between points on the same line to link them.
- See also
- LineSegmentDetector
◆ HoughLinesP() [3/3]
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Finds line segments in a binary image using the probabilistic Hough transform.
The function implements the probabilistic Hough transform algorithm for line detection, described in [Matas00]
See the line detection example below:
This is a sample picture the function parameters have been tuned for:
And this is the output of the above program in case of the probabilistic Hough transform:
- Parameters
-
image 8-bit, single-channel binary source image. The image may be modified by the function. lines Output vector of lines. Each line is represented by a 4-element vector \((x_1, y_1, x_2, y_2)\) , where \((x_1,y_1)\) and \((x_2, y_2)\) are the ending points of each detected line segment. rho Distance resolution of the accumulator in pixels. theta Angle resolution of the accumulator in radians. threshold Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ). minLineLength Minimum line length. Line segments shorter than that are rejected. maxLineGap Maximum allowed gap between points on the same line to link them.
- See also
- LineSegmentDetector
◆ HoughLinesPointSet()
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Finds lines in a set of points using the standard Hough transform.
The function finds lines in a set of points using a modification of the Hough transform.
- Parameters
-
point Input vector of points. Each vector must be encoded as a Point vector \((x,y)\). Type must be CV_32FC2 or CV_32SC2. lines Output vector of found lines. Each vector is encoded as a vector<Vec3d> \((votes, rho, theta)\). The larger the value of 'votes', the higher the reliability of the Hough line. lines_max Max count of Hough lines. threshold Accumulator threshold parameter. Only those lines are returned that get enough votes ( \(>\texttt{threshold}\) ). min_rho Minimum value for \(\rho\) for the accumulator (Note: \(\rho\) can be negative. The absolute value \(|\rho|\) is the distance of a line to the origin.). max_rho Maximum value for \(\rho\) for the accumulator. rho_step Distance resolution of the accumulator. min_theta Minimum angle value of the accumulator in radians. max_theta Upper bound for the angle value of the accumulator in radians. The actual maximum angle may be slightly less than max_theta, depending on the parameters min_theta and theta_step. theta_step Angle resolution of the accumulator in radians.
◆ HoughLinesWithAccumulator() [1/5]
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Finds lines in a binary image using the standard Hough transform and get accumulator.
- Note
- This function is for bindings use only. Use original function in C++ code
- See also
- HoughLines
◆ HoughLinesWithAccumulator() [2/5]
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Finds lines in a binary image using the standard Hough transform and get accumulator.
- Note
- This function is for bindings use only. Use original function in C++ code
- See also
- HoughLines
◆ HoughLinesWithAccumulator() [3/5]
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Finds lines in a binary image using the standard Hough transform and get accumulator.
- Note
- This function is for bindings use only. Use original function in C++ code
- See also
- HoughLines
◆ HoughLinesWithAccumulator() [4/5]
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Finds lines in a binary image using the standard Hough transform and get accumulator.
- Note
- This function is for bindings use only. Use original function in C++ code
- See also
- HoughLines
◆ HoughLinesWithAccumulator() [5/5]
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Finds lines in a binary image using the standard Hough transform and get accumulator.
- Note
- This function is for bindings use only. Use original function in C++ code
- See also
- HoughLines
◆ HuMoments()
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ integral() [1/2]
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ integral() [2/2]
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ integral2() [1/3]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ integral2() [2/3]
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This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ integral2() [3/3]
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ integral3() [1/3]
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Calculates the integral of an image.
The function calculates one or more integral images for the source image as follows:
\[\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\]
\[\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\]
\[\texttt{tilted} (X,Y) = \sum _{y<Y,abs(x-X+1) \leq Y-y-1} \texttt{image} (x,y)\]
Using these integral images, you can calculate sum, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example:
\[\sum _{x_1 \leq x < x_2, \, y_1 \leq y < y_2} \texttt{image} (x,y) = \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\]
It makes possible to do a fast blurring or fast block correlation with a variable window size, for example. In case of multi-channel images, sums for each channel are accumulated independently.
As a practical example, the next figure shows the calculation of the integral of a straight rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the original image are shown, as well as the relative pixels in the integral images sum and tilted .
- Parameters
-
src input image as \(W \times H\), 8-bit or floating-point (32f or 64f). sum integral image as \((W+1)\times (H+1)\) , 32-bit integer or floating-point (32f or 64f). sqsum integral image for squared pixel values; it is \((W+1)\times (H+1)\), double-precision floating-point (64f) array. tilted integral for the image rotated by 45 degrees; it is \((W+1)\times (H+1)\) array with the same data type as sum. sdepth desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or CV_64F. sqdepth desired depth of the integral image of squared pixel values, CV_32F or CV_64F.
◆ integral3() [2/3]
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Calculates the integral of an image.
The function calculates one or more integral images for the source image as follows:
\[\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\]
\[\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\]
\[\texttt{tilted} (X,Y) = \sum _{y<Y,abs(x-X+1) \leq Y-y-1} \texttt{image} (x,y)\]
Using these integral images, you can calculate sum, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example:
\[\sum _{x_1 \leq x < x_2, \, y_1 \leq y < y_2} \texttt{image} (x,y) = \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\]
It makes possible to do a fast blurring or fast block correlation with a variable window size, for example. In case of multi-channel images, sums for each channel are accumulated independently.
As a practical example, the next figure shows the calculation of the integral of a straight rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the original image are shown, as well as the relative pixels in the integral images sum and tilted .
- Parameters
-
src input image as \(W \times H\), 8-bit or floating-point (32f or 64f). sum integral image as \((W+1)\times (H+1)\) , 32-bit integer or floating-point (32f or 64f). sqsum integral image for squared pixel values; it is \((W+1)\times (H+1)\), double-precision floating-point (64f) array. tilted integral for the image rotated by 45 degrees; it is \((W+1)\times (H+1)\) array with the same data type as sum. sdepth desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or CV_64F. sqdepth desired depth of the integral image of squared pixel values, CV_32F or CV_64F.
◆ integral3() [3/3]
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Calculates the integral of an image.
The function calculates one or more integral images for the source image as follows:
\[\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\]
\[\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\]
\[\texttt{tilted} (X,Y) = \sum _{y<Y,abs(x-X+1) \leq Y-y-1} \texttt{image} (x,y)\]
Using these integral images, you can calculate sum, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example:
\[\sum _{x_1 \leq x < x_2, \, y_1 \leq y < y_2} \texttt{image} (x,y) = \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\]
It makes possible to do a fast blurring or fast block correlation with a variable window size, for example. In case of multi-channel images, sums for each channel are accumulated independently.
As a practical example, the next figure shows the calculation of the integral of a straight rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the original image are shown, as well as the relative pixels in the integral images sum and tilted .
- Parameters
-
src input image as \(W \times H\), 8-bit or floating-point (32f or 64f). sum integral image as \((W+1)\times (H+1)\) , 32-bit integer or floating-point (32f or 64f). sqsum integral image for squared pixel values; it is \((W+1)\times (H+1)\), double-precision floating-point (64f) array. tilted integral for the image rotated by 45 degrees; it is \((W+1)\times (H+1)\) array with the same data type as sum. sdepth desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or CV_64F. sqdepth desired depth of the integral image of squared pixel values, CV_32F or CV_64F.
◆ intersectConvexConvex() [1/2]
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Finds intersection of two convex polygons.
- Parameters
-
p1 First polygon p2 Second polygon p12 Output polygon describing the intersecting area handleNested When true, an intersection is found if one of the polygons is fully enclosed in the other. When false, no intersection is found. If the polygons share a side or the vertex of one polygon lies on an edge of the other, they are not considered nested and an intersection will be found regardless of the value of handleNested.
- Returns
- Absolute value of area of intersecting polygon
- Note
- intersectConvexConvex doesn't confirm that both polygons are convex and will return invalid results if they aren't.
◆ intersectConvexConvex() [2/2]
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Finds intersection of two convex polygons.
- Parameters
-
p1 First polygon p2 Second polygon p12 Output polygon describing the intersecting area handleNested When true, an intersection is found if one of the polygons is fully enclosed in the other. When false, no intersection is found. If the polygons share a side or the vertex of one polygon lies on an edge of the other, they are not considered nested and an intersection will be found regardless of the value of handleNested.
- Returns
- Absolute value of area of intersecting polygon
- Note
- intersectConvexConvex doesn't confirm that both polygons are convex and will return invalid results if they aren't.
◆ invertAffineTransform()
Inverts an affine transformation.
The function computes an inverse affine transformation represented by \(2 \times 3\) matrix M:
\[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{21} & a_{22} & b_2 \end{bmatrix}\]
The result is also a \(2 \times 3\) matrix of the same type as M.
- Parameters
-
M Original affine transformation. iM Output reverse affine transformation.
◆ isContourConvex()
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Tests a contour convexity.
The function tests whether the input contour is convex or not. The contour must be simple, that is, without self-intersections. Otherwise, the function output is undefined.
- Parameters
-
contour Input vector of 2D points, stored in std::vector<> or Mat
◆ Laplacian() [1/5]
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Calculates the Laplacian of an image.
The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator:
\[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\]
This is done when ksize > 1. When ksize == 1, the Laplacian is computed by filtering the image with the following \(3 \times 3\) aperture:
\[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\]
- Parameters
-
src Source image. dst Destination image of the same size and the same number of channels as src . ddepth Desired depth of the destination image, see combinations. ksize Aperture size used to compute the second-derivative filters. See getDerivKernels for details. The size must be positive and odd. scale Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See getDerivKernels for details. delta Optional delta value that is added to the results prior to storing them in dst . borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
◆ Laplacian() [2/5]
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Calculates the Laplacian of an image.
The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator:
\[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\]
This is done when ksize > 1. When ksize == 1, the Laplacian is computed by filtering the image with the following \(3 \times 3\) aperture:
\[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\]
- Parameters
-
src Source image. dst Destination image of the same size and the same number of channels as src . ddepth Desired depth of the destination image, see combinations. ksize Aperture size used to compute the second-derivative filters. See getDerivKernels for details. The size must be positive and odd. scale Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See getDerivKernels for details. delta Optional delta value that is added to the results prior to storing them in dst . borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
◆ Laplacian() [3/5]
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Calculates the Laplacian of an image.
The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator:
\[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\]
This is done when ksize > 1. When ksize == 1, the Laplacian is computed by filtering the image with the following \(3 \times 3\) aperture:
\[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\]
- Parameters
-
src Source image. dst Destination image of the same size and the same number of channels as src . ddepth Desired depth of the destination image, see combinations. ksize Aperture size used to compute the second-derivative filters. See getDerivKernels for details. The size must be positive and odd. scale Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See getDerivKernels for details. delta Optional delta value that is added to the results prior to storing them in dst . borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
◆ Laplacian() [4/5]
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Calculates the Laplacian of an image.
The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator:
\[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\]
This is done when ksize > 1. When ksize == 1, the Laplacian is computed by filtering the image with the following \(3 \times 3\) aperture:
\[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\]
- Parameters
-
src Source image. dst Destination image of the same size and the same number of channels as src . ddepth Desired depth of the destination image, see combinations. ksize Aperture size used to compute the second-derivative filters. See getDerivKernels for details. The size must be positive and odd. scale Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See getDerivKernels for details. delta Optional delta value that is added to the results prior to storing them in dst . borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
◆ Laplacian() [5/5]
Calculates the Laplacian of an image.
The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator:
\[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\]
This is done when ksize > 1. When ksize == 1, the Laplacian is computed by filtering the image with the following \(3 \times 3\) aperture:
\[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\]
- Parameters
-
src Source image. dst Destination image of the same size and the same number of channels as src . ddepth Desired depth of the destination image, see combinations. ksize Aperture size used to compute the second-derivative filters. See getDerivKernels for details. The size must be positive and odd. scale Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See getDerivKernels for details. delta Optional delta value that is added to the results prior to storing them in dst . borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
◆ line() [1/4]
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Draws a line segment connecting two points.
The function line draws the line segment between pt1 and pt2 points in the image. The line is clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased lines are drawn using Gaussian filtering.
- Parameters
-
img Image. pt1 First point of the line segment. pt2 Second point of the line segment. color Line color. thickness Line thickness. lineType Type of the line. See #LineTypes. shift Number of fractional bits in the point coordinates.
◆ line() [2/4]
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Draws a line segment connecting two points.
The function line draws the line segment between pt1 and pt2 points in the image. The line is clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased lines are drawn using Gaussian filtering.
- Parameters
-
img Image. pt1 First point of the line segment. pt2 Second point of the line segment. color Line color. thickness Line thickness. lineType Type of the line. See #LineTypes. shift Number of fractional bits in the point coordinates.
◆ line() [3/4]
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Draws a line segment connecting two points.
The function line draws the line segment between pt1 and pt2 points in the image. The line is clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased lines are drawn using Gaussian filtering.
- Parameters
-
img Image. pt1 First point of the line segment. pt2 Second point of the line segment. color Line color. thickness Line thickness. lineType Type of the line. See #LineTypes. shift Number of fractional bits in the point coordinates.
◆ line() [4/4]
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Draws a line segment connecting two points.
The function line draws the line segment between pt1 and pt2 points in the image. The line is clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased lines are drawn using Gaussian filtering.
- Parameters
-
img Image. pt1 First point of the line segment. pt2 Second point of the line segment. color Line color. thickness Line thickness. lineType Type of the line. See #LineTypes. shift Number of fractional bits in the point coordinates.
◆ linearPolar()
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Remaps an image to polar coordinates space.
- Deprecated:
- This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags)
◆ logPolar()
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Remaps an image to semilog-polar coordinates space.
- Deprecated:
- This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG);
◆ matchShapes()
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Compares two shapes.
The function compares two shapes. All three implemented methods use the Hu invariants (see HuMoments)
- Parameters
-
contour1 First contour or grayscale image. contour2 Second contour or grayscale image. method Comparison method, see #ShapeMatchModes parameter Method-specific parameter (not supported now).
◆ matchTemplate() [1/2]
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Compares a template against overlapped image regions.
The function slides through image , compares the overlapped patches of size \(w \times h\) against templ using the specified method and stores the comparison results in result . #TemplateMatchModes describes the formulae for the available comparison methods ( \(I\) denotes image, \(T\) template, \(R\) result, \(M\) the optional mask ). The summation is done over template and/or the image patch: \(x' = 0...w-1, y' = 0...h-1\)
After the function finishes the comparison, the best matches can be found as global minimums (when TM_SQDIFF was used) or maximums (when TM_CCORR or TM_CCOEFF was used) using the #minMaxLoc function. In case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels and separate mean values are used for each channel. That is, the function can take a color template and a color image. The result will still be a single-channel image, which is easier to analyze.
- Parameters
-
image Image where the search is running. It must be 8-bit or 32-bit floating-point. templ Searched template. It must be not greater than the source image and have the same data type. result Map of comparison results. It must be single-channel 32-bit floating-point. If image is \(W \times H\) and templ is \(w \times h\) , then result is \((W-w+1) \times (H-h+1)\) . method Parameter specifying the comparison method, see #TemplateMatchModes mask Optional mask. It must have the same size as templ. It must either have the same number of channels as template or only one channel, which is then used for all template and image channels. If the data type is #CV_8U, the mask is interpreted as a binary mask, meaning only elements where mask is nonzero are used and are kept unchanged independent of the actual mask value (weight equals 1). For data tpye #CV_32F, the mask values are used as weights. The exact formulas are documented in #TemplateMatchModes.
◆ matchTemplate() [2/2]
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Compares a template against overlapped image regions.
The function slides through image , compares the overlapped patches of size \(w \times h\) against templ using the specified method and stores the comparison results in result . #TemplateMatchModes describes the formulae for the available comparison methods ( \(I\) denotes image, \(T\) template, \(R\) result, \(M\) the optional mask ). The summation is done over template and/or the image patch: \(x' = 0...w-1, y' = 0...h-1\)
After the function finishes the comparison, the best matches can be found as global minimums (when TM_SQDIFF was used) or maximums (when TM_CCORR or TM_CCOEFF was used) using the #minMaxLoc function. In case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels and separate mean values are used for each channel. That is, the function can take a color template and a color image. The result will still be a single-channel image, which is easier to analyze.
- Parameters
-
image Image where the search is running. It must be 8-bit or 32-bit floating-point. templ Searched template. It must be not greater than the source image and have the same data type. result Map of comparison results. It must be single-channel 32-bit floating-point. If image is \(W \times H\) and templ is \(w \times h\) , then result is \((W-w+1) \times (H-h+1)\) . method Parameter specifying the comparison method, see #TemplateMatchModes mask Optional mask. It must have the same size as templ. It must either have the same number of channels as template or only one channel, which is then used for all template and image channels. If the data type is #CV_8U, the mask is interpreted as a binary mask, meaning only elements where mask is nonzero are used and are kept unchanged independent of the actual mask value (weight equals 1). For data tpye #CV_32F, the mask values are used as weights. The exact formulas are documented in #TemplateMatchModes.
◆ medianBlur()
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 also
- bilateralFilter, blur, boxFilter, GaussianBlur
◆ minAreaRect()
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Finds a rotated rectangle of the minimum area enclosing the input 2D point set.
The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a specified point set. Developer should keep in mind that the returned RotatedRect can contain negative indices when data is close to the containing Mat element boundary.
- Parameters
-
points Input vector of 2D points, stored in std::vector<> or Mat
◆ minEnclosingCircle()
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Finds a circle of the minimum area enclosing a 2D point set.
The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm.
- Parameters
-
points Input vector of 2D points, stored in std::vector<> or Mat center Output center of the circle. radius Output radius of the circle.
◆ minEnclosingTriangle()
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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 [ORourke86] and Klee and Laskowski's [KleeLaskowski85] papers. O'Rourke provides a \(\theta(n)\) algorithm for finding the minimal enclosing triangle of a 2D convex polygon with n vertices. Since the minEnclosingTriangle function takes a 2D point set as input an additional preprocessing step of computing the convex hull of the 2D point set is required. The complexity of the convexHull function is \(O(n log(n))\) which is higher than \(\theta(n)\). Thus the overall complexity of the function is \(O(n log(n))\).
- Parameters
-
points Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector<> or Mat triangle Output vector of three 2D points defining the vertices of the triangle. The depth of the OutputArray must be CV_32F.
◆ moments() [1/2]
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 also
- contourArea, arcLength
◆ moments() [2/2]
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 also
- contourArea, arcLength
◆ morphologyEx() [1/5]
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Performs advanced morphological transformations.
The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as basic operations.
Any of the operations can be done in-place. In case of multi-channel images, each channel is processed independently.
- Parameters
-
src Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst Destination image of the same size and type as source image. op Type of a morphological operation, see #MorphTypes kernel Structuring element. It can be created using getStructuringElement. anchor Anchor position with the kernel. Negative values mean that the anchor is at the kernel center. iterations Number of times erosion and dilation are applied. borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. borderValue Border value in case of a constant border. The default value has a special meaning.
- See also
- dilate, erode, getStructuringElement
- Note
- The number of iterations is the number of times erosion or dilatation operation will be applied. For instance, an opening operation (MORPH_OPEN) with two iterations is equivalent to apply successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).
◆ morphologyEx() [2/5]
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Performs advanced morphological transformations.
The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as basic operations.
Any of the operations can be done in-place. In case of multi-channel images, each channel is processed independently.
- Parameters
-
src Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst Destination image of the same size and type as source image. op Type of a morphological operation, see #MorphTypes kernel Structuring element. It can be created using getStructuringElement. anchor Anchor position with the kernel. Negative values mean that the anchor is at the kernel center. iterations Number of times erosion and dilation are applied. borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. borderValue Border value in case of a constant border. The default value has a special meaning.
- See also
- dilate, erode, getStructuringElement
- Note
- The number of iterations is the number of times erosion or dilatation operation will be applied. For instance, an opening operation (MORPH_OPEN) with two iterations is equivalent to apply successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).
◆ morphologyEx() [3/5]
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static |
Performs advanced morphological transformations.
The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as basic operations.
Any of the operations can be done in-place. In case of multi-channel images, each channel is processed independently.
- Parameters
-
src Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst Destination image of the same size and type as source image. op Type of a morphological operation, see #MorphTypes kernel Structuring element. It can be created using getStructuringElement. anchor Anchor position with the kernel. Negative values mean that the anchor is at the kernel center. iterations Number of times erosion and dilation are applied. borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. borderValue Border value in case of a constant border. The default value has a special meaning.
- See also
- dilate, erode, getStructuringElement
- Note
- The number of iterations is the number of times erosion or dilatation operation will be applied. For instance, an opening operation (MORPH_OPEN) with two iterations is equivalent to apply successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).
◆ morphologyEx() [4/5]
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static |
Performs advanced morphological transformations.
The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as basic operations.
Any of the operations can be done in-place. In case of multi-channel images, each channel is processed independently.
- Parameters
-
src Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst Destination image of the same size and type as source image. op Type of a morphological operation, see #MorphTypes kernel Structuring element. It can be created using getStructuringElement. anchor Anchor position with the kernel. Negative values mean that the anchor is at the kernel center. iterations Number of times erosion and dilation are applied. borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. borderValue Border value in case of a constant border. The default value has a special meaning.
- See also
- dilate, erode, getStructuringElement
- Note
- The number of iterations is the number of times erosion or dilatation operation will be applied. For instance, an opening operation (MORPH_OPEN) with two iterations is equivalent to apply successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).
◆ morphologyEx() [5/5]
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static |
Performs advanced morphological transformations.
The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as basic operations.
Any of the operations can be done in-place. In case of multi-channel images, each channel is processed independently.
- Parameters
-
src Source image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst Destination image of the same size and type as source image. op Type of a morphological operation, see #MorphTypes kernel Structuring element. It can be created using getStructuringElement. anchor Anchor position with the kernel. Negative values mean that the anchor is at the kernel center. iterations Number of times erosion and dilation are applied. borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. borderValue Border value in case of a constant border. The default value has a special meaning.
- See also
- dilate, erode, getStructuringElement
- Note
- The number of iterations is the number of times erosion or dilatation operation will be applied. For instance, an opening operation (MORPH_OPEN) with two iterations is equivalent to apply successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).
◆ phaseCorrelate() [1/3]
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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 also
- dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow
◆ phaseCorrelate() [2/3]
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static |
The function is used to detect translational shifts that occur between two images.
The operation takes advantage of the Fourier shift theorem for detecting the translational shift in the frequency domain. It can be used for fast image registration as well as motion estimation. For more information please see <http://en.wikipedia.org/wiki/Phase_correlation>
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 also
- dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow
◆ phaseCorrelate() [3/3]
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 also
- dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow
◆ pointPolygonTest()
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static |
Performs a point-in-contour test.
The function determines whether the point is inside a contour, outside, or lies on an edge (or coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. Otherwise, the return value is a signed distance between the point and the nearest contour edge.
See below a sample output of the function where each image pixel is tested against the contour:
- Parameters
-
contour Input contour. pt Point tested against the contour. measureDist If true, the function estimates the signed distance from the point to the nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.
◆ polylines() [1/4]
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Draws several polygonal curves.
- Parameters
-
img Image. pts Array of polygonal curves. isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed, the function draws a line from the last vertex of each curve to its first vertex. color Polyline color. thickness Thickness of the polyline edges. lineType Type of the line segments. See #LineTypes shift Number of fractional bits in the vertex coordinates.
The function cv::polylines draws one or more polygonal curves.
◆ polylines() [2/4]
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Draws several polygonal curves.
- Parameters
-
img Image. pts Array of polygonal curves. isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed, the function draws a line from the last vertex of each curve to its first vertex. color Polyline color. thickness Thickness of the polyline edges. lineType Type of the line segments. See #LineTypes shift Number of fractional bits in the vertex coordinates.
The function cv::polylines draws one or more polygonal curves.
◆ polylines() [3/4]
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Draws several polygonal curves.
- Parameters
-
img Image. pts Array of polygonal curves. isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed, the function draws a line from the last vertex of each curve to its first vertex. color Polyline color. thickness Thickness of the polyline edges. lineType Type of the line segments. See #LineTypes shift Number of fractional bits in the vertex coordinates.
The function cv::polylines draws one or more polygonal curves.
◆ polylines() [4/4]
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static |
Draws several polygonal curves.
- Parameters
-
img Image. pts Array of polygonal curves. isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed, the function draws a line from the last vertex of each curve to its first vertex. color Polyline color. thickness Thickness of the polyline edges. lineType Type of the line segments. See #LineTypes shift Number of fractional bits in the vertex coordinates.
The function cv::polylines draws one or more polygonal curves.
◆ preCornerDetect() [1/2]
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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:
- Parameters
-
src Source single-channel 8-bit of floating-point image. dst Output image that has the type CV_32F and the same size as src . ksize Aperture size of the Sobel . borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
◆ preCornerDetect() [2/2]
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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:
- Parameters
-
src Source single-channel 8-bit of floating-point image. dst Output image that has the type CV_32F and the same size as src . ksize Aperture size of the Sobel . borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
◆ putText() [1/4]
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Draws a text string.
The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered using the specified font are replaced by question marks. See getTextSize for a text rendering code example.
- Parameters
-
img Image. text Text string to be drawn. org Bottom-left corner of the text string in the image. fontFace Font type, see #HersheyFonts. fontScale Font scale factor that is multiplied by the font-specific base size. color Text color. thickness Thickness of the lines used to draw a text. lineType Line type. See #LineTypes bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.
◆ putText() [2/4]
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static |
Draws a text string.
The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered using the specified font are replaced by question marks. See getTextSize for a text rendering code example.
- Parameters
-
img Image. text Text string to be drawn. org Bottom-left corner of the text string in the image. fontFace Font type, see #HersheyFonts. fontScale Font scale factor that is multiplied by the font-specific base size. color Text color. thickness Thickness of the lines used to draw a text. lineType Line type. See #LineTypes bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.
◆ putText() [3/4]
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static |
Draws a text string.
The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered using the specified font are replaced by question marks. See getTextSize for a text rendering code example.
- Parameters
-
img Image. text Text string to be drawn. org Bottom-left corner of the text string in the image. fontFace Font type, see #HersheyFonts. fontScale Font scale factor that is multiplied by the font-specific base size. color Text color. thickness Thickness of the lines used to draw a text. lineType Line type. See #LineTypes bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.
◆ putText() [4/4]
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static |
Draws a text string.
The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered using the specified font are replaced by question marks. See getTextSize for a text rendering code example.
- Parameters
-
img Image. text Text string to be drawn. org Bottom-left corner of the text string in the image. fontFace Font type, see #HersheyFonts. fontScale Font scale factor that is multiplied by the font-specific base size. color Text color. thickness Thickness of the lines used to draw a text. lineType Line type. See #LineTypes bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner.
◆ pyrDown() [1/3]
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static |
Blurs an image and downsamples it.
By default, size of the output image is computed as Size((src.cols+1)/2, (src.rows+1)/2), but in any case, the following conditions should be satisfied:
\[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\]
The function performs the downsampling step of the Gaussian pyramid construction. First, it convolves the source image with the kernel:
\[\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\]
Then, it downsamples the image by rejecting even rows and columns.
- Parameters
-
src input image. dst output image; it has the specified size and the same type as src. dstsize size of the output image. borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)
◆ pyrDown() [2/3]
Blurs an image and downsamples it.
By default, size of the output image is computed as Size((src.cols+1)/2, (src.rows+1)/2), but in any case, the following conditions should be satisfied:
\[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\]
The function performs the downsampling step of the Gaussian pyramid construction. First, it convolves the source image with the kernel:
\[\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\]
Then, it downsamples the image by rejecting even rows and columns.
- Parameters
-
src input image. dst output image; it has the specified size and the same type as src. dstsize size of the output image. borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)
◆ pyrDown() [3/3]
Blurs an image and downsamples it.
By default, size of the output image is computed as Size((src.cols+1)/2, (src.rows+1)/2), but in any case, the following conditions should be satisfied:
\[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\]
The function performs the downsampling step of the Gaussian pyramid construction. First, it convolves the source image with the kernel:
\[\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\]
Then, it downsamples the image by rejecting even rows and columns.
- Parameters
-
src input image. dst output image; it has the specified size and the same type as src. dstsize size of the output image. borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)
◆ pyrMeanShiftFiltering() [1/3]
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static |
Performs initial step of meanshift segmentation of an image.
The function implements the filtering stage of meanshift segmentation, that is, the output of the function is the filtered "posterized" image with color gradients and fine-grain texture flattened. At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is considered:
\[(x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}\]
where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively (though, the algorithm does not depend on the color space used, so any 3-component color space can be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector (R',G',B') are found and they act as the neighborhood center on the next iteration:
\[(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\]
After the iterations over, the color components of the initial pixel (that is, the pixel from where the iterations started) are set to the final value (average color at the last iteration):
\[I(X,Y) <- (R*,G*,B*)\]
When maxLevel > 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is run on the smallest layer first. After that, the results are propagated to the larger layer and the iterations are run again only on those pixels where the layer colors differ by more than sr from the lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the results will be actually different from the ones obtained by running the meanshift procedure on the whole original image (i.e. when maxLevel==0).
- Parameters
-
src The source 8-bit, 3-channel image. dst The destination image of the same format and the same size as the source. sp The spatial window radius. sr The color window radius. maxLevel Maximum level of the pyramid for the segmentation. termcrit Termination criteria: when to stop meanshift iterations.
◆ pyrMeanShiftFiltering() [2/3]
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static |
Performs initial step of meanshift segmentation of an image.
The function implements the filtering stage of meanshift segmentation, that is, the output of the function is the filtered "posterized" image with color gradients and fine-grain texture flattened. At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is considered:
\[(x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}\]
where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively (though, the algorithm does not depend on the color space used, so any 3-component color space can be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector (R',G',B') are found and they act as the neighborhood center on the next iteration:
\[(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\]
After the iterations over, the color components of the initial pixel (that is, the pixel from where the iterations started) are set to the final value (average color at the last iteration):
\[I(X,Y) <- (R*,G*,B*)\]
When maxLevel > 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is run on the smallest layer first. After that, the results are propagated to the larger layer and the iterations are run again only on those pixels where the layer colors differ by more than sr from the lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the results will be actually different from the ones obtained by running the meanshift procedure on the whole original image (i.e. when maxLevel==0).
- Parameters
-
src The source 8-bit, 3-channel image. dst The destination image of the same format and the same size as the source. sp The spatial window radius. sr The color window radius. maxLevel Maximum level of the pyramid for the segmentation. termcrit Termination criteria: when to stop meanshift iterations.
◆ pyrMeanShiftFiltering() [3/3]
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static |
Performs initial step of meanshift segmentation of an image.
The function implements the filtering stage of meanshift segmentation, that is, the output of the function is the filtered "posterized" image with color gradients and fine-grain texture flattened. At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is considered:
\[(x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}\]
where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively (though, the algorithm does not depend on the color space used, so any 3-component color space can be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector (R',G',B') are found and they act as the neighborhood center on the next iteration:
\[(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\]
After the iterations over, the color components of the initial pixel (that is, the pixel from where the iterations started) are set to the final value (average color at the last iteration):
\[I(X,Y) <- (R*,G*,B*)\]
When maxLevel > 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is run on the smallest layer first. After that, the results are propagated to the larger layer and the iterations are run again only on those pixels where the layer colors differ by more than sr from the lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the results will be actually different from the ones obtained by running the meanshift procedure on the whole original image (i.e. when maxLevel==0).
- Parameters
-
src The source 8-bit, 3-channel image. dst The destination image of the same format and the same size as the source. sp The spatial window radius. sr The color window radius. maxLevel Maximum level of the pyramid for the segmentation. termcrit Termination criteria: when to stop meanshift iterations.
◆ pyrUp() [1/3]
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static |
Upsamples an image and then blurs it.
By default, size of the output image is computed as Size(src.cols\*2, (src.rows\*2), but in any case, the following conditions should be satisfied:
\[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\]
The function performs the upsampling step of the Gaussian pyramid construction, though it can actually be used to construct the Laplacian pyramid. First, it upsamples the source image by injecting even zero rows and columns and then convolves the result with the same kernel as in pyrDown multiplied by 4.
- Parameters
-
src input image. dst output image. It has the specified size and the same type as src . dstsize size of the output image. borderType Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported)
◆ pyrUp() [2/3]
Upsamples an image and then blurs it.
By default, size of the output image is computed as Size(src.cols\*2, (src.rows\*2), but in any case, the following conditions should be satisfied:
\[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\]
The function performs the upsampling step of the Gaussian pyramid construction, though it can actually be used to construct the Laplacian pyramid. First, it upsamples the source image by injecting even zero rows and columns and then convolves the result with the same kernel as in pyrDown multiplied by 4.
- Parameters
-
src input image. dst output image. It has the specified size and the same type as src . dstsize size of the output image. borderType Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported)
◆ pyrUp() [3/3]
Upsamples an image and then blurs it.
By default, size of the output image is computed as Size(src.cols\*2, (src.rows\*2), but in any case, the following conditions should be satisfied:
\[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\]
The function performs the upsampling step of the Gaussian pyramid construction, though it can actually be used to construct the Laplacian pyramid. First, it upsamples the source image by injecting even zero rows and columns and then convolves the result with the same kernel as in pyrDown multiplied by 4.
- Parameters
-
src input image. dst output image. It has the specified size and the same type as src . dstsize size of the output image. borderType Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported)
◆ rectangle() [1/8]
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static |
Draws a simple, thick, or filled up-right rectangle.
The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners are pt1 and pt2.
- Parameters
-
img Image. pt1 Vertex of the rectangle. pt2 Vertex of the rectangle opposite to pt1 . color Rectangle color or brightness (grayscale image). thickness Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle. lineType Type of the line. See #LineTypes shift Number of fractional bits in the point coordinates.
◆ rectangle() [2/8]
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static |
Draws a simple, thick, or filled up-right rectangle.
The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners are pt1 and pt2.
- Parameters
-
img Image. pt1 Vertex of the rectangle. pt2 Vertex of the rectangle opposite to pt1 . color Rectangle color or brightness (grayscale image). thickness Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle. lineType Type of the line. See #LineTypes shift Number of fractional bits in the point coordinates.
◆ rectangle() [3/8]
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static |
Draws a simple, thick, or filled up-right rectangle.
The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners are pt1 and pt2.
- Parameters
-
img Image. pt1 Vertex of the rectangle. pt2 Vertex of the rectangle opposite to pt1 . color Rectangle color or brightness (grayscale image). thickness Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle. lineType Type of the line. See #LineTypes shift Number of fractional bits in the point coordinates.
◆ rectangle() [4/8]
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static |
Draws a simple, thick, or filled up-right rectangle.
The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners are pt1 and pt2.
- Parameters
-
img Image. pt1 Vertex of the rectangle. pt2 Vertex of the rectangle opposite to pt1 . color Rectangle color or brightness (grayscale image). thickness Thickness of lines that make up the rectangle. Negative values, like FILLED, mean that the function has to draw a filled rectangle. lineType Type of the line. See #LineTypes shift Number of fractional bits in the point coordinates.
◆ rectangle() [5/8]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
use rec parameter as alternative specification of the drawn rectangle: r.tl() and r.br()-Point(1,1) are opposite corners
◆ rectangle() [6/8]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
use rec parameter as alternative specification of the drawn rectangle: r.tl() and r.br()-Point(1,1) are opposite corners
◆ rectangle() [7/8]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
use rec parameter as alternative specification of the drawn rectangle: r.tl() and r.br()-Point(1,1) are opposite corners
◆ rectangle() [8/8]
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static |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
use rec parameter as alternative specification of the drawn rectangle: r.tl() and r.br()-Point(1,1) are opposite corners
◆ remap() [1/3]
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static |
Applies a generic geometrical transformation to an image.
The function remap transforms the source image using the specified map:
\[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\]
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() [2/3]
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static |
Applies a generic geometrical transformation to an image.
The function remap transforms the source image using the specified map:
\[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\]
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() [3/3]
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static |
Applies a generic geometrical transformation to an image.
The function remap transforms the source image using the specified map:
\[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\]
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.
◆ resize() [1/4]
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static |
Resizes an image.
The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:
If you want to decimate the image by factor of 2 in each direction, you can call the function this way:
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 also
- warpAffine, warpPerspective, remap
◆ resize() [2/4]
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static |
Resizes an image.
The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:
If you want to decimate the image by factor of 2 in each direction, you can call the function this way:
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 also
- warpAffine, warpPerspective, remap
◆ resize() [3/4]
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static |
Resizes an image.
The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:
If you want to decimate the image by factor of 2 in each direction, you can call the function this way:
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 also
- warpAffine, warpPerspective, remap
◆ resize() [4/4]
Resizes an image.
The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:
If you want to decimate the image by factor of 2 in each direction, you can call the function this way:
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 also
- warpAffine, warpPerspective, remap
◆ rotatedRectangleIntersection()
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static |
Finds out if there is any intersection between two rotated rectangles.
If there is then the vertices of the intersecting region are returned as well.
Below are some examples of intersection configurations. The hatched pattern indicates the intersecting region and the red vertices are returned by the function.
- Parameters
-
rect1 First rectangle rect2 Second rectangle intersectingRegion The output array of the vertices of the intersecting region. It returns at most 8 vertices. Stored as std::vector<cv::Point2f> or cv::Mat as Mx1 of type CV_32FC2.
- Returns
- One of #RectanglesIntersectTypes
◆ Scharr() [1/4]
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static |
Calculates the first x- or y- image derivative using Scharr operator.
The function computes the first x- or y- spatial image derivative using the Scharr operator. The call
\[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\]
is equivalent to
\[\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER_SCHARR, scale, delta, borderType)} .\]
- Parameters
-
src input image. dst output image of the same size and the same number of channels as src. ddepth output image depth, see combinations dx order of the derivative x. dy order of the derivative y. scale optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details). delta optional delta value that is added to the results prior to storing them in dst. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- cartToPolar
◆ Scharr() [2/4]
|
static |
Calculates the first x- or y- image derivative using Scharr operator.
The function computes the first x- or y- spatial image derivative using the Scharr operator. The call
\[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\]
is equivalent to
\[\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER_SCHARR, scale, delta, borderType)} .\]
- Parameters
-
src input image. dst output image of the same size and the same number of channels as src. ddepth output image depth, see combinations dx order of the derivative x. dy order of the derivative y. scale optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details). delta optional delta value that is added to the results prior to storing them in dst. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- cartToPolar
◆ Scharr() [3/4]
|
static |
Calculates the first x- or y- image derivative using Scharr operator.
The function computes the first x- or y- spatial image derivative using the Scharr operator. The call
\[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\]
is equivalent to
\[\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER_SCHARR, scale, delta, borderType)} .\]
- Parameters
-
src input image. dst output image of the same size and the same number of channels as src. ddepth output image depth, see combinations dx order of the derivative x. dy order of the derivative y. scale optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details). delta optional delta value that is added to the results prior to storing them in dst. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- cartToPolar
◆ Scharr() [4/4]
|
static |
Calculates the first x- or y- image derivative using Scharr operator.
The function computes the first x- or y- spatial image derivative using the Scharr operator. The call
\[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\]
is equivalent to
\[\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER_SCHARR, scale, delta, borderType)} .\]
- Parameters
-
src input image. dst output image of the same size and the same number of channels as src. ddepth output image depth, see combinations dx order of the derivative x. dy order of the derivative y. scale optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details). delta optional delta value that is added to the results prior to storing them in dst. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- cartToPolar
◆ sepFilter2D() [1/4]
|
static |
Applies a separable linear filter to an image.
The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst .
- Parameters
-
src Source image. dst Destination image of the same size and the same number of channels as src . ddepth Destination image depth, see combinations kernelX Coefficients for filtering each row. kernelY Coefficients for filtering each column. anchor Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor is at the kernel center. delta Value added to the filtered results before storing them. borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- filter2D, Sobel, GaussianBlur, boxFilter, blur
◆ sepFilter2D() [2/4]
|
static |
Applies a separable linear filter to an image.
The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst .
- Parameters
-
src Source image. dst Destination image of the same size and the same number of channels as src . ddepth Destination image depth, see combinations kernelX Coefficients for filtering each row. kernelY Coefficients for filtering each column. anchor Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor is at the kernel center. delta Value added to the filtered results before storing them. borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- filter2D, Sobel, GaussianBlur, boxFilter, blur
◆ sepFilter2D() [3/4]
|
static |
Applies a separable linear filter to an image.
The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst .
- Parameters
-
src Source image. dst Destination image of the same size and the same number of channels as src . ddepth Destination image depth, see combinations kernelX Coefficients for filtering each row. kernelY Coefficients for filtering each column. anchor Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor is at the kernel center. delta Value added to the filtered results before storing them. borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- filter2D, Sobel, GaussianBlur, boxFilter, blur
◆ sepFilter2D() [4/4]
|
static |
Applies a separable linear filter to an image.
The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst .
- Parameters
-
src Source image. dst Destination image of the same size and the same number of channels as src . ddepth Destination image depth, see combinations kernelX Coefficients for filtering each row. kernelY Coefficients for filtering each column. anchor Anchor position within the kernel. The default value \((-1,-1)\) means that the anchor is at the kernel center. delta Value added to the filtered results before storing them. borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- filter2D, Sobel, GaussianBlur, boxFilter, blur
◆ Sobel() [1/5]
|
static |
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) separable kernel is used to calculate the derivative. When \(\texttt{ksize = 1}\), the \(3 \times 1\) or \(1 \times 3\) kernel is used (that is, no Gaussian smoothing is done). ksize = 1 can only be used for the first or the second x- or y- derivatives.
There is also the special value ksize = FILTER_SCHARR (-1) that corresponds to the \(3\times3\) Scharr filter that may give more accurate results than the \(3\times3\) Sobel. The Scharr aperture is
\[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\]
for the x-derivative, or transposed for the y-derivative.
The function calculates an image derivative by convolving the image with the appropriate kernel:
\[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\]
The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first case corresponds to a kernel of:
\[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\]
The second case corresponds to a kernel of:
\[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\]
- Parameters
-
src input image. dst output image of the same size and the same number of channels as src . ddepth output image depth, see combinations; in the case of 8-bit input images it will result in truncated derivatives. dx order of the derivative x. dy order of the derivative y. ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7. scale optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details). delta optional delta value that is added to the results prior to storing them in dst. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
◆ Sobel() [2/5]
|
static |
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) separable kernel is used to calculate the derivative. When \(\texttt{ksize = 1}\), the \(3 \times 1\) or \(1 \times 3\) kernel is used (that is, no Gaussian smoothing is done). ksize = 1 can only be used for the first or the second x- or y- derivatives.
There is also the special value ksize = FILTER_SCHARR (-1) that corresponds to the \(3\times3\) Scharr filter that may give more accurate results than the \(3\times3\) Sobel. The Scharr aperture is
\[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\]
for the x-derivative, or transposed for the y-derivative.
The function calculates an image derivative by convolving the image with the appropriate kernel:
\[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\]
The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first case corresponds to a kernel of:
\[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\]
The second case corresponds to a kernel of:
\[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\]
- Parameters
-
src input image. dst output image of the same size and the same number of channels as src . ddepth output image depth, see combinations; in the case of 8-bit input images it will result in truncated derivatives. dx order of the derivative x. dy order of the derivative y. ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7. scale optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details). delta optional delta value that is added to the results prior to storing them in dst. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
◆ Sobel() [3/5]
|
static |
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) separable kernel is used to calculate the derivative. When \(\texttt{ksize = 1}\), the \(3 \times 1\) or \(1 \times 3\) kernel is used (that is, no Gaussian smoothing is done). ksize = 1 can only be used for the first or the second x- or y- derivatives.
There is also the special value ksize = FILTER_SCHARR (-1) that corresponds to the \(3\times3\) Scharr filter that may give more accurate results than the \(3\times3\) Sobel. The Scharr aperture is
\[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\]
for the x-derivative, or transposed for the y-derivative.
The function calculates an image derivative by convolving the image with the appropriate kernel:
\[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\]
The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first case corresponds to a kernel of:
\[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\]
The second case corresponds to a kernel of:
\[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\]
- Parameters
-
src input image. dst output image of the same size and the same number of channels as src . ddepth output image depth, see combinations; in the case of 8-bit input images it will result in truncated derivatives. dx order of the derivative x. dy order of the derivative y. ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7. scale optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details). delta optional delta value that is added to the results prior to storing them in dst. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
◆ Sobel() [4/5]
|
static |
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) separable kernel is used to calculate the derivative. When \(\texttt{ksize = 1}\), the \(3 \times 1\) or \(1 \times 3\) kernel is used (that is, no Gaussian smoothing is done). ksize = 1 can only be used for the first or the second x- or y- derivatives.
There is also the special value ksize = FILTER_SCHARR (-1) that corresponds to the \(3\times3\) Scharr filter that may give more accurate results than the \(3\times3\) Sobel. The Scharr aperture is
\[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\]
for the x-derivative, or transposed for the y-derivative.
The function calculates an image derivative by convolving the image with the appropriate kernel:
\[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\]
The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first case corresponds to a kernel of:
\[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\]
The second case corresponds to a kernel of:
\[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\]
- Parameters
-
src input image. dst output image of the same size and the same number of channels as src . ddepth output image depth, see combinations; in the case of 8-bit input images it will result in truncated derivatives. dx order of the derivative x. dy order of the derivative y. ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7. scale optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details). delta optional delta value that is added to the results prior to storing them in dst. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
◆ Sobel() [5/5]
|
static |
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) separable kernel is used to calculate the derivative. When \(\texttt{ksize = 1}\), the \(3 \times 1\) or \(1 \times 3\) kernel is used (that is, no Gaussian smoothing is done). ksize = 1 can only be used for the first or the second x- or y- derivatives.
There is also the special value ksize = FILTER_SCHARR (-1) that corresponds to the \(3\times3\) Scharr filter that may give more accurate results than the \(3\times3\) Sobel. The Scharr aperture is
\[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\]
for the x-derivative, or transposed for the y-derivative.
The function calculates an image derivative by convolving the image with the appropriate kernel:
\[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\]
The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first case corresponds to a kernel of:
\[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\]
The second case corresponds to a kernel of:
\[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\]
- Parameters
-
src input image. dst output image of the same size and the same number of channels as src . ddepth output image depth, see combinations; in the case of 8-bit input images it will result in truncated derivatives. dx order of the derivative x. dy order of the derivative y. ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7. scale optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details). delta optional delta value that is added to the results prior to storing them in dst. borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
◆ spatialGradient() [1/3]
|
static |
Calculates the first order image derivative in both x and y using a Sobel operator.
Equivalent to calling:
- Parameters
-
src input image. dx output image with first-order derivative in x. dy output image with first-order derivative in y. ksize size of Sobel kernel. It must be 3. borderType pixel extrapolation method, see #BorderTypes. Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported.
- See also
- Sobel
◆ spatialGradient() [2/3]
|
static |
Calculates the first order image derivative in both x and y using a Sobel operator.
Equivalent to calling:
- Parameters
-
src input image. dx output image with first-order derivative in x. dy output image with first-order derivative in y. ksize size of Sobel kernel. It must be 3. borderType pixel extrapolation method, see #BorderTypes. Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported.
- See also
- Sobel
◆ spatialGradient() [3/3]
Calculates the first order image derivative in both x and y using a Sobel operator.
Equivalent to calling:
- Parameters
-
src input image. dx output image with first-order derivative in x. dy output image with first-order derivative in y. ksize size of Sobel kernel. It must be 3. borderType pixel extrapolation method, see #BorderTypes. Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported.
- See also
- Sobel
◆ sqrBoxFilter() [1/4]
|
static |
Calculates the normalized sum of squares of the pixel values overlapping the filter.
For every pixel \( (x, y) \) in the source image, the function calculates the sum of squares of those neighboring pixel values which overlap the filter placed over the pixel \( (x, y) \).
The unnormalized square box filter can be useful in computing local image statistics such as the local variance and standard deviation around the neighborhood of a pixel.
- Parameters
-
src input image dst output image of the same size and type as src ddepth the output image depth (-1 to use src.depth()) ksize kernel size anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center. normalize flag, specifying whether the kernel is to be normalized by it's area or not. borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- boxFilter
◆ sqrBoxFilter() [2/4]
|
static |
Calculates the normalized sum of squares of the pixel values overlapping the filter.
For every pixel \( (x, y) \) in the source image, the function calculates the sum of squares of those neighboring pixel values which overlap the filter placed over the pixel \( (x, y) \).
The unnormalized square box filter can be useful in computing local image statistics such as the local variance and standard deviation around the neighborhood of a pixel.
- Parameters
-
src input image dst output image of the same size and type as src ddepth the output image depth (-1 to use src.depth()) ksize kernel size anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center. normalize flag, specifying whether the kernel is to be normalized by it's area or not. borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- boxFilter
◆ sqrBoxFilter() [3/4]
|
static |
Calculates the normalized sum of squares of the pixel values overlapping the filter.
For every pixel \( (x, y) \) in the source image, the function calculates the sum of squares of those neighboring pixel values which overlap the filter placed over the pixel \( (x, y) \).
The unnormalized square box filter can be useful in computing local image statistics such as the local variance and standard deviation around the neighborhood of a pixel.
- Parameters
-
src input image dst output image of the same size and type as src ddepth the output image depth (-1 to use src.depth()) ksize kernel size anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center. normalize flag, specifying whether the kernel is to be normalized by it's area or not. borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- boxFilter
◆ sqrBoxFilter() [4/4]
|
static |
Calculates the normalized sum of squares of the pixel values overlapping the filter.
For every pixel \( (x, y) \) in the source image, the function calculates the sum of squares of those neighboring pixel values which overlap the filter placed over the pixel \( (x, y) \).
The unnormalized square box filter can be useful in computing local image statistics such as the local variance and standard deviation around the neighborhood of a pixel.
- Parameters
-
src input image dst output image of the same size and type as src ddepth the output image depth (-1 to use src.depth()) ksize kernel size anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center. normalize flag, specifying whether the kernel is to be normalized by it's area or not. borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
- See also
- boxFilter
◆ 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.
◆ threshold()
|
static |
Applies a fixed-level threshold to each array element.
The function applies fixed-level thresholding to a multiple-channel array. The function is typically used to get a bi-level (binary) image out of a grayscale image ( #compare could be also used for this purpose) or for removing a noise, that is, filtering out pixels with too small or too large values. There are several types of thresholding supported by the function. They are determined by type parameter.
Also, the special values THRESH_OTSU or THRESH_TRIANGLE may be combined with one of the above values. In these cases, the function determines the optimal threshold value using the Otsu's or Triangle algorithm and uses it instead of the specified thresh.
- Note
- Currently, the Otsu's and Triangle methods are implemented only for 8-bit single-channel images.
- Parameters
-
src input array (multiple-channel, 8-bit or 32-bit floating point). dst output array of the same size and type and the same number of channels as src. thresh threshold value. maxval maximum value to use with the THRESH_BINARY and THRESH_BINARY_INV thresholding types. type thresholding type (see #ThresholdTypes).
- Returns
- the computed threshold value if Otsu's or Triangle methods used.
- See also
- adaptiveThreshold, findContours, compare, min, max
◆ warpAffine() [1/4]
|
static |
Applies an affine transformation to an image.
The function warpAffine transforms the source image using the specified matrix:
\[\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\]
when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invertAffineTransform and then put in the formula above instead of M. The function cannot operate in-place.
- Parameters
-
src input image. dst output image that has the size dsize and the same type as src . M \(2\times 3\) transformation matrix. dsize size of the output image. flags combination of interpolation methods (see #InterpolationFlags) and the optional flag WARP_INVERSE_MAP that means that M is the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ). borderMode pixel extrapolation method (see #BorderTypes); when borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to the "outliers" in the source image are not modified by the function. borderValue value used in case of a constant border; by default, it is 0.
- See also
- warpPerspective, resize, remap, getRectSubPix, transform
◆ warpAffine() [2/4]
|
static |
Applies an affine transformation to an image.
The function warpAffine transforms the source image using the specified matrix:
\[\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\]
when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invertAffineTransform and then put in the formula above instead of M. The function cannot operate in-place.
- Parameters
-
src input image. dst output image that has the size dsize and the same type as src . M \(2\times 3\) transformation matrix. dsize size of the output image. flags combination of interpolation methods (see #InterpolationFlags) and the optional flag WARP_INVERSE_MAP that means that M is the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ). borderMode pixel extrapolation method (see #BorderTypes); when borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to the "outliers" in the source image are not modified by the function. borderValue value used in case of a constant border; by default, it is 0.
- See also
- warpPerspective, resize, remap, getRectSubPix, transform
◆ warpAffine() [3/4]
|
static |
Applies an affine transformation to an image.
The function warpAffine transforms the source image using the specified matrix:
\[\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\]
when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invertAffineTransform and then put in the formula above instead of M. The function cannot operate in-place.
- Parameters
-
src input image. dst output image that has the size dsize and the same type as src . M \(2\times 3\) transformation matrix. dsize size of the output image. flags combination of interpolation methods (see #InterpolationFlags) and the optional flag WARP_INVERSE_MAP that means that M is the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ). borderMode pixel extrapolation method (see #BorderTypes); when borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to the "outliers" in the source image are not modified by the function. borderValue value used in case of a constant border; by default, it is 0.
- See also
- warpPerspective, resize, remap, getRectSubPix, transform
◆ warpAffine() [4/4]
|
static |
Applies an affine transformation to an image.
The function warpAffine transforms the source image using the specified matrix:
\[\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\]
when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invertAffineTransform and then put in the formula above instead of M. The function cannot operate in-place.
- Parameters
-
src input image. dst output image that has the size dsize and the same type as src . M \(2\times 3\) transformation matrix. dsize size of the output image. flags combination of interpolation methods (see #InterpolationFlags) and the optional flag WARP_INVERSE_MAP that means that M is the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ). borderMode pixel extrapolation method (see #BorderTypes); when borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to the "outliers" in the source image are not modified by the function. borderValue value used in case of a constant border; by default, it is 0.
- See also
- warpPerspective, resize, remap, getRectSubPix, transform
◆ warpPerspective() [1/4]
|
static |
Applies a perspective transformation to an image.
The function warpPerspective transforms the source image using the specified matrix:
\[\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\]
when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert and then put in the formula above instead of M. The function cannot operate in-place.
- Parameters
-
src input image. dst output image that has the size dsize and the same type as src . M \(3\times 3\) transformation matrix. dsize size of the output image. flags combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ). borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE). borderValue value used in case of a constant border; by default, it equals 0.
- See also
- warpAffine, resize, remap, getRectSubPix, perspectiveTransform
◆ warpPerspective() [2/4]
|
static |
Applies a perspective transformation to an image.
The function warpPerspective transforms the source image using the specified matrix:
\[\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\]
when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert and then put in the formula above instead of M. The function cannot operate in-place.
- Parameters
-
src input image. dst output image that has the size dsize and the same type as src . M \(3\times 3\) transformation matrix. dsize size of the output image. flags combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ). borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE). borderValue value used in case of a constant border; by default, it equals 0.
- See also
- warpAffine, resize, remap, getRectSubPix, perspectiveTransform
◆ warpPerspective() [3/4]
|
static |
Applies a perspective transformation to an image.
The function warpPerspective transforms the source image using the specified matrix:
\[\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\]
when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert and then put in the formula above instead of M. The function cannot operate in-place.
- Parameters
-
src input image. dst output image that has the size dsize and the same type as src . M \(3\times 3\) transformation matrix. dsize size of the output image. flags combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ). borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE). borderValue value used in case of a constant border; by default, it equals 0.
- See also
- warpAffine, resize, remap, getRectSubPix, perspectiveTransform
◆ warpPerspective() [4/4]
|
static |
Applies a perspective transformation to an image.
The function warpPerspective transforms the source image using the specified matrix:
\[\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\]
when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert and then put in the formula above instead of M. The function cannot operate in-place.
- Parameters
-
src input image. dst output image that has the size dsize and the same type as src . M \(3\times 3\) transformation matrix. dsize size of the output image. flags combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ). borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE). borderValue value used in case of a constant border; by default, it equals 0.
- See also
- warpAffine, resize, remap, getRectSubPix, perspectiveTransform
◆ warpPolar()
|
static |
Remaps an image to polar or semilog-polar coordinates space.
Transform the source image using the following transformation:
\[ dst(\rho , \phi ) = src(x,y) \]
where
\[ \begin{array}{l} \vec{I} = (x - center.x, \;y - center.y) \\ \phi = Kangle \cdot \texttt{angle} (\vec{I}) \\ \rho = \left\{\begin{matrix} Klin \cdot \texttt{magnitude} (\vec{I}) & default \\ Klog \cdot log_e(\texttt{magnitude} (\vec{I})) & if \; semilog \\ \end{matrix}\right. \end{array} \]
and
\[ \begin{array}{l} Kangle = dsize.height / 2\Pi \\ Klin = dsize.width / maxRadius \\ Klog = dsize.width / log_e(maxRadius) \\ \end{array} \]
- Linear vs semilog mapping
Polar mapping can be linear or semi-log. Add one of #WarpPolarMode to flags to specify the polar mapping mode.
Linear is the default mode.
The semilog mapping emulates the human "foveal" vision that permit very high acuity on the line of sight (central vision) in contrast to peripheral vision where acuity is minor.
- Option on dsize:
- if both values in
dsize <=0(default), the destination image will have (almost) same area of source bounding circle:\[\begin{array}{l} dsize.area \leftarrow (maxRadius^2 \cdot \Pi) \\ dsize.width = \texttt{cvRound}(maxRadius) \\ dsize.height = \texttt{cvRound}(maxRadius \cdot \Pi) \\ \end{array}\]
- if only
dsize.height <= 0, the destination image area will be proportional to the bounding circle area but scaled 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.
- Reverse mapping
You can get reverse mapping adding WARP_INVERSE_MAP to flags
In addiction, to calculate the original coordinate from a polar mapped coordinate \((rho, phi)->(x, y)\):
- Parameters
-
src Source image. dst Destination image. It will have same type as src. dsize The destination image size (see description for valid options). center The transformation center. maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. flags A combination of interpolation methods, #InterpolationFlags + #WarpPolarMode. - Add WARP_POLAR_LINEAR to select linear polar mapping (default)
- Add WARP_POLAR_LOG to select semilog polar mapping
- Add WARP_INVERSE_MAP for reverse mapping.
- 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.
- This function uses remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767.
- See also
- cv::remap
◆ watershed()
Performs a marker-based image segmentation using the watershed algorithm.
The function implements one of the variants of watershed, non-parametric marker-based segmentation algorithm, described in [Meyer92] .
Before passing the image to the function, you have to roughly outline the desired regions in the image markers with positive (>0) indices. So, every region is represented as one or more connected components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary mask using findContours and drawContours (see the watershed.cpp demo). The markers are "seeds" of the future image regions. All the other pixels in markers , whose relation to the outlined regions is not known and should be defined by the algorithm, should be set to 0's. In the function output, each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the regions.
- Note
- Any two neighbor connected components are not necessarily separated by a watershed boundary (-1's pixels); for example, they can touch each other in the initial marker image passed to the function.
- Parameters
-
image Input 8-bit 3-channel image. markers Input/output 32-bit single-channel image (map) of markers. It should have the same size as image .
- See also
- findContours
Member Data Documentation
◆ ADAPTIVE_THRESH_GAUSSIAN_C
| const int OpenCVForUnity.ImgprocModule.Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C = 1 |
◆ ADAPTIVE_THRESH_MEAN_C
| const int OpenCVForUnity.ImgprocModule.Imgproc.ADAPTIVE_THRESH_MEAN_C = 0 |
◆ CC_STAT_AREA
| const int OpenCVForUnity.ImgprocModule.Imgproc.CC_STAT_AREA = 4 |
◆ CC_STAT_HEIGHT
| const int OpenCVForUnity.ImgprocModule.Imgproc.CC_STAT_HEIGHT = 3 |
◆ CC_STAT_LEFT
| const int OpenCVForUnity.ImgprocModule.Imgproc.CC_STAT_LEFT = 0 |
◆ CC_STAT_MAX
| const int OpenCVForUnity.ImgprocModule.Imgproc.CC_STAT_MAX = 5 |
◆ CC_STAT_TOP
| const int OpenCVForUnity.ImgprocModule.Imgproc.CC_STAT_TOP = 1 |
◆ CC_STAT_WIDTH
| const int OpenCVForUnity.ImgprocModule.Imgproc.CC_STAT_WIDTH = 2 |
◆ CCL_BBDT
| const int OpenCVForUnity.ImgprocModule.Imgproc.CCL_BBDT = 4 |
◆ CCL_BOLELLI
| const int OpenCVForUnity.ImgprocModule.Imgproc.CCL_BOLELLI = 2 |
◆ CCL_DEFAULT
| const int OpenCVForUnity.ImgprocModule.Imgproc.CCL_DEFAULT = -1 |
◆ CCL_GRANA
| const int OpenCVForUnity.ImgprocModule.Imgproc.CCL_GRANA = 1 |
◆ CCL_SAUF
| const int OpenCVForUnity.ImgprocModule.Imgproc.CCL_SAUF = 3 |
◆ CCL_SPAGHETTI
| const int OpenCVForUnity.ImgprocModule.Imgproc.CCL_SPAGHETTI = 5 |
◆ CCL_WU
| const int OpenCVForUnity.ImgprocModule.Imgproc.CCL_WU = 0 |
◆ CHAIN_APPROX_NONE
| const int OpenCVForUnity.ImgprocModule.Imgproc.CHAIN_APPROX_NONE = 1 |
◆ CHAIN_APPROX_SIMPLE
| const int OpenCVForUnity.ImgprocModule.Imgproc.CHAIN_APPROX_SIMPLE = 2 |
◆ CHAIN_APPROX_TC89_KCOS
| const int OpenCVForUnity.ImgprocModule.Imgproc.CHAIN_APPROX_TC89_KCOS = 4 |
◆ CHAIN_APPROX_TC89_L1
| const int OpenCVForUnity.ImgprocModule.Imgproc.CHAIN_APPROX_TC89_L1 = 3 |
◆ COLOR_BayerBG2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2BGR = 46 |
◆ COLOR_BayerBG2BGR_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2BGR_EA = 135 |
◆ COLOR_BayerBG2BGR_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2BGR_VNG = 62 |
◆ COLOR_BayerBG2BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2BGRA = 139 |
◆ COLOR_BayerBG2GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2GRAY = 86 |
◆ COLOR_BayerBG2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2RGB = COLOR_BayerRG2BGR |
◆ COLOR_BayerBG2RGB_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA |
◆ COLOR_BayerBG2RGB_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG |
◆ COLOR_BayerBG2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA |
◆ COLOR_BayerBGGR2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2BGR = COLOR_BayerRG2BGR |
◆ COLOR_BayerBGGR2BGR_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2BGR_EA = COLOR_BayerRG2BGR_EA |
◆ COLOR_BayerBGGR2BGR_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2BGR_VNG = COLOR_BayerRG2BGR_VNG |
◆ COLOR_BayerBGGR2BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2BGRA = COLOR_BayerRG2BGRA |
◆ COLOR_BayerBGGR2GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2GRAY = COLOR_BayerRG2GRAY |
◆ COLOR_BayerBGGR2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2RGB = COLOR_BayerRGGB2BGR |
◆ COLOR_BayerBGGR2RGB_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2RGB_EA = COLOR_BayerRGGB2BGR_EA |
◆ COLOR_BayerBGGR2RGB_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2RGB_VNG = COLOR_BayerRGGB2BGR_VNG |
◆ COLOR_BayerBGGR2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerBGGR2RGBA = COLOR_BayerRGGB2BGRA |
◆ COLOR_BayerGB2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2BGR = 47 |
◆ COLOR_BayerGB2BGR_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2BGR_EA = 136 |
◆ COLOR_BayerGB2BGR_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2BGR_VNG = 63 |
◆ COLOR_BayerGB2BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2BGRA = 140 |
◆ COLOR_BayerGB2GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2GRAY = 87 |
◆ COLOR_BayerGB2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2RGB = COLOR_BayerGR2BGR |
◆ COLOR_BayerGB2RGB_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA |
◆ COLOR_BayerGB2RGB_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG |
◆ COLOR_BayerGB2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA |
◆ COLOR_BayerGBRG2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2BGR = COLOR_BayerGR2BGR |
◆ COLOR_BayerGBRG2BGR_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2BGR_EA = COLOR_BayerGR2BGR_EA |
◆ COLOR_BayerGBRG2BGR_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2BGR_VNG = COLOR_BayerGR2BGR_VNG |
◆ COLOR_BayerGBRG2BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2BGRA = COLOR_BayerGR2BGRA |
◆ COLOR_BayerGBRG2GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2GRAY = COLOR_BayerGR2GRAY |
◆ COLOR_BayerGBRG2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2RGB = COLOR_BayerGRBG2BGR |
◆ COLOR_BayerGBRG2RGB_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2RGB_EA = COLOR_BayerGRBG2BGR_EA |
◆ COLOR_BayerGBRG2RGB_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2RGB_VNG = COLOR_BayerGRBG2BGR_VNG |
◆ COLOR_BayerGBRG2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGBRG2RGBA = COLOR_BayerGRBG2BGRA |
◆ COLOR_BayerGR2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2BGR = 49 |
◆ COLOR_BayerGR2BGR_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2BGR_EA = 138 |
◆ COLOR_BayerGR2BGR_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2BGR_VNG = 65 |
◆ COLOR_BayerGR2BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2BGRA = 142 |
◆ COLOR_BayerGR2GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2GRAY = 89 |
◆ COLOR_BayerGR2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2RGB = COLOR_BayerGB2BGR |
◆ COLOR_BayerGR2RGB_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA |
◆ COLOR_BayerGR2RGB_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG |
◆ COLOR_BayerGR2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA |
◆ COLOR_BayerGRBG2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2BGR = COLOR_BayerGB2BGR |
◆ COLOR_BayerGRBG2BGR_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2BGR_EA = COLOR_BayerGB2BGR_EA |
◆ COLOR_BayerGRBG2BGR_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2BGR_VNG = COLOR_BayerGB2BGR_VNG |
◆ COLOR_BayerGRBG2BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2BGRA = COLOR_BayerGB2BGRA |
◆ COLOR_BayerGRBG2GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2GRAY = COLOR_BayerGB2GRAY |
◆ COLOR_BayerGRBG2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2RGB = COLOR_BayerGBRG2BGR |
◆ COLOR_BayerGRBG2RGB_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2RGB_EA = COLOR_BayerGBRG2BGR_EA |
◆ COLOR_BayerGRBG2RGB_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2RGB_VNG = COLOR_BayerGBRG2BGR_VNG |
◆ COLOR_BayerGRBG2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerGRBG2RGBA = COLOR_BayerGBRG2BGRA |
◆ COLOR_BayerRG2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2BGR = 48 |
◆ COLOR_BayerRG2BGR_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2BGR_EA = 137 |
◆ COLOR_BayerRG2BGR_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2BGR_VNG = 64 |
◆ COLOR_BayerRG2BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2BGRA = 141 |
◆ COLOR_BayerRG2GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2GRAY = 88 |
◆ COLOR_BayerRG2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2RGB = COLOR_BayerBG2BGR |
◆ COLOR_BayerRG2RGB_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA |
◆ COLOR_BayerRG2RGB_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG |
◆ COLOR_BayerRG2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA |
◆ COLOR_BayerRGGB2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2BGR = COLOR_BayerBG2BGR |
◆ COLOR_BayerRGGB2BGR_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2BGR_EA = COLOR_BayerBG2BGR_EA |
◆ COLOR_BayerRGGB2BGR_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2BGR_VNG = COLOR_BayerBG2BGR_VNG |
◆ COLOR_BayerRGGB2BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2BGRA = COLOR_BayerBG2BGRA |
◆ COLOR_BayerRGGB2GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2GRAY = COLOR_BayerBG2GRAY |
◆ COLOR_BayerRGGB2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2RGB = COLOR_BayerBGGR2BGR |
◆ COLOR_BayerRGGB2RGB_EA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2RGB_EA = COLOR_BayerBGGR2BGR_EA |
◆ COLOR_BayerRGGB2RGB_VNG
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2RGB_VNG = COLOR_BayerBGGR2BGR_VNG |
◆ COLOR_BayerRGGB2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BayerRGGB2RGBA = COLOR_BayerBGGR2BGRA |
◆ COLOR_BGR2BGR555
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2BGR555 = 22 |
◆ COLOR_BGR2BGR565
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2BGR565 = 12 |
◆ COLOR_BGR2BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2BGRA = 0 |
◆ COLOR_BGR2GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2GRAY = 6 |
◆ COLOR_BGR2HLS
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2HLS = 52 |
◆ COLOR_BGR2HLS_FULL
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2HLS_FULL = 68 |
◆ COLOR_BGR2HSV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2HSV = 40 |
◆ COLOR_BGR2HSV_FULL
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2HSV_FULL = 66 |
◆ COLOR_BGR2Lab
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2Lab = 44 |
◆ COLOR_BGR2Luv
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2Luv = 50 |
◆ COLOR_BGR2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2RGB = 4 |
◆ COLOR_BGR2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2RGBA = 2 |
◆ COLOR_BGR2XYZ
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2XYZ = 32 |
◆ COLOR_BGR2YCrCb
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YCrCb = 36 |
◆ COLOR_BGR2YUV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV = 82 |
◆ COLOR_BGR2YUV_I420
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_I420 = 128 |
◆ COLOR_BGR2YUV_IYUV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420 |
◆ COLOR_BGR2YUV_UYNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_UYNV = COLOR_BGR2YUV_UYVY |
◆ COLOR_BGR2YUV_UYVY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_UYVY = 144 |
◆ COLOR_BGR2YUV_Y422
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_Y422 = COLOR_BGR2YUV_UYVY |
◆ COLOR_BGR2YUV_YUNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_YUNV = COLOR_BGR2YUV_YUY2 |
◆ COLOR_BGR2YUV_YUY2
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_YUY2 = 148 |
◆ COLOR_BGR2YUV_YUYV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_YUYV = COLOR_BGR2YUV_YUY2 |
◆ COLOR_BGR2YUV_YV12
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_YV12 = 132 |
◆ COLOR_BGR2YUV_YVYU
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR2YUV_YVYU = 150 |
◆ COLOR_BGR5552BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5552BGR = 24 |
◆ COLOR_BGR5552BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5552BGRA = 28 |
◆ COLOR_BGR5552GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5552GRAY = 31 |
◆ COLOR_BGR5552RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5552RGB = 25 |
◆ COLOR_BGR5552RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5552RGBA = 29 |
◆ COLOR_BGR5652BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5652BGR = 14 |
◆ COLOR_BGR5652BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5652BGRA = 18 |
◆ COLOR_BGR5652GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5652GRAY = 21 |
◆ COLOR_BGR5652RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5652RGB = 15 |
◆ COLOR_BGR5652RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGR5652RGBA = 19 |
◆ COLOR_BGRA2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2BGR = 1 |
◆ COLOR_BGRA2BGR555
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2BGR555 = 26 |
◆ COLOR_BGRA2BGR565
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2BGR565 = 16 |
◆ COLOR_BGRA2GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2GRAY = 10 |
◆ COLOR_BGRA2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2RGB = COLOR_RGBA2BGR |
◆ COLOR_BGRA2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2RGBA = 5 |
◆ COLOR_BGRA2YUV_I420
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_I420 = 130 |
◆ COLOR_BGRA2YUV_IYUV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420 |
◆ COLOR_BGRA2YUV_UYNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_UYNV = COLOR_BGRA2YUV_UYVY |
◆ COLOR_BGRA2YUV_UYVY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_UYVY = 146 |
◆ COLOR_BGRA2YUV_Y422
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_Y422 = COLOR_BGRA2YUV_UYVY |
◆ COLOR_BGRA2YUV_YUNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_YUNV = COLOR_BGRA2YUV_YUY2 |
◆ COLOR_BGRA2YUV_YUY2
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_YUY2 = 152 |
◆ COLOR_BGRA2YUV_YUYV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_YUYV = COLOR_BGRA2YUV_YUY2 |
◆ COLOR_BGRA2YUV_YV12
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_YV12 = 134 |
◆ COLOR_BGRA2YUV_YVYU
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_BGRA2YUV_YVYU = 154 |
◆ COLOR_COLORCVT_MAX
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_COLORCVT_MAX = 155 |
◆ COLOR_GRAY2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_GRAY2BGR = 8 |
◆ COLOR_GRAY2BGR555
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_GRAY2BGR555 = 30 |
◆ COLOR_GRAY2BGR565
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_GRAY2BGR565 = 20 |
◆ COLOR_GRAY2BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_GRAY2BGRA = 9 |
◆ COLOR_GRAY2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_GRAY2RGB = COLOR_GRAY2BGR |
◆ COLOR_GRAY2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_GRAY2RGBA = COLOR_GRAY2BGRA |
◆ COLOR_HLS2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HLS2BGR = 60 |
◆ COLOR_HLS2BGR_FULL
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HLS2BGR_FULL = 72 |
◆ COLOR_HLS2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HLS2RGB = 61 |
◆ COLOR_HLS2RGB_FULL
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HLS2RGB_FULL = 73 |
◆ COLOR_HSV2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HSV2BGR = 54 |
◆ COLOR_HSV2BGR_FULL
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HSV2BGR_FULL = 70 |
◆ COLOR_HSV2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HSV2RGB = 55 |
◆ COLOR_HSV2RGB_FULL
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_HSV2RGB_FULL = 71 |
◆ COLOR_Lab2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Lab2BGR = 56 |
◆ COLOR_Lab2LBGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Lab2LBGR = 78 |
◆ COLOR_Lab2LRGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Lab2LRGB = 79 |
◆ COLOR_Lab2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Lab2RGB = 57 |
◆ COLOR_LBGR2Lab
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_LBGR2Lab = 74 |
◆ COLOR_LBGR2Luv
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_LBGR2Luv = 76 |
◆ COLOR_LRGB2Lab
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_LRGB2Lab = 75 |
◆ COLOR_LRGB2Luv
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_LRGB2Luv = 77 |
◆ COLOR_Luv2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Luv2BGR = 58 |
◆ COLOR_Luv2LBGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Luv2LBGR = 80 |
◆ COLOR_Luv2LRGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Luv2LRGB = 81 |
◆ COLOR_Luv2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_Luv2RGB = 59 |
◆ COLOR_mRGBA2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_mRGBA2RGBA = 126 |
◆ COLOR_RGB2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2BGR = COLOR_BGR2RGB |
◆ COLOR_RGB2BGR555
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2BGR555 = 23 |
◆ COLOR_RGB2BGR565
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2BGR565 = 13 |
◆ COLOR_RGB2BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2BGRA = COLOR_BGR2RGBA |
◆ COLOR_RGB2GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2GRAY = 7 |
◆ COLOR_RGB2HLS
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2HLS = 53 |
◆ COLOR_RGB2HLS_FULL
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2HLS_FULL = 69 |
◆ COLOR_RGB2HSV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2HSV = 41 |
◆ COLOR_RGB2HSV_FULL
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2HSV_FULL = 67 |
◆ COLOR_RGB2Lab
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2Lab = 45 |
◆ COLOR_RGB2Luv
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2Luv = 51 |
◆ COLOR_RGB2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2RGBA = COLOR_BGR2BGRA |
◆ COLOR_RGB2XYZ
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2XYZ = 33 |
◆ COLOR_RGB2YCrCb
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YCrCb = 37 |
◆ COLOR_RGB2YUV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV = 83 |
◆ COLOR_RGB2YUV_I420
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_I420 = 127 |
◆ COLOR_RGB2YUV_IYUV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420 |
◆ COLOR_RGB2YUV_UYNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_UYNV = COLOR_RGB2YUV_UYVY |
◆ COLOR_RGB2YUV_UYVY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_UYVY = 143 |
◆ COLOR_RGB2YUV_Y422
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_Y422 = COLOR_RGB2YUV_UYVY |
◆ COLOR_RGB2YUV_YUNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_YUNV = COLOR_RGB2YUV_YUY2 |
◆ COLOR_RGB2YUV_YUY2
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_YUY2 = 147 |
◆ COLOR_RGB2YUV_YUYV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_YUYV = COLOR_RGB2YUV_YUY2 |
◆ COLOR_RGB2YUV_YV12
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_YV12 = 131 |
◆ COLOR_RGB2YUV_YVYU
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGB2YUV_YVYU = 149 |
◆ COLOR_RGBA2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2BGR = 3 |
◆ COLOR_RGBA2BGR555
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2BGR555 = 27 |
◆ COLOR_RGBA2BGR565
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2BGR565 = 17 |
◆ COLOR_RGBA2BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2BGRA = COLOR_BGRA2RGBA |
◆ COLOR_RGBA2GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2GRAY = 11 |
◆ COLOR_RGBA2mRGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2mRGBA = 125 |
◆ COLOR_RGBA2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2RGB = COLOR_BGRA2BGR |
◆ COLOR_RGBA2YUV_I420
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_I420 = 129 |
◆ COLOR_RGBA2YUV_IYUV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420 |
◆ COLOR_RGBA2YUV_UYNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_UYNV = COLOR_RGBA2YUV_UYVY |
◆ COLOR_RGBA2YUV_UYVY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_UYVY = 145 |
◆ COLOR_RGBA2YUV_Y422
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_Y422 = COLOR_RGBA2YUV_UYVY |
◆ COLOR_RGBA2YUV_YUNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_YUNV = COLOR_RGBA2YUV_YUY2 |
◆ COLOR_RGBA2YUV_YUY2
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_YUY2 = 151 |
◆ COLOR_RGBA2YUV_YUYV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_YUYV = COLOR_RGBA2YUV_YUY2 |
◆ COLOR_RGBA2YUV_YV12
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_YV12 = 133 |
◆ COLOR_RGBA2YUV_YVYU
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_RGBA2YUV_YVYU = 153 |
◆ COLOR_XYZ2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_XYZ2BGR = 34 |
◆ COLOR_XYZ2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_XYZ2RGB = 35 |
◆ COLOR_YCrCb2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YCrCb2BGR = 38 |
◆ COLOR_YCrCb2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YCrCb2RGB = 39 |
◆ COLOR_YUV2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR = 84 |
◆ COLOR_YUV2BGR_I420
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV |
◆ COLOR_YUV2BGR_IYUV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_IYUV = 101 |
◆ COLOR_YUV2BGR_NV12
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_NV12 = 91 |
◆ COLOR_YUV2BGR_NV21
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_NV21 = 93 |
◆ COLOR_YUV2BGR_UYNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY |
◆ COLOR_YUV2BGR_UYVY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_UYVY = 108 |
◆ COLOR_YUV2BGR_Y422
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY |
◆ COLOR_YUV2BGR_YUNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2 |
◆ COLOR_YUV2BGR_YUY2
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_YUY2 = 116 |
◆ COLOR_YUV2BGR_YUYV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2 |
◆ COLOR_YUV2BGR_YV12
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_YV12 = 99 |
◆ COLOR_YUV2BGR_YVYU
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGR_YVYU = 118 |
◆ COLOR_YUV2BGRA_I420
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV |
◆ COLOR_YUV2BGRA_IYUV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_IYUV = 105 |
◆ COLOR_YUV2BGRA_NV12
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_NV12 = 95 |
◆ COLOR_YUV2BGRA_NV21
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_NV21 = 97 |
◆ COLOR_YUV2BGRA_UYNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY |
◆ COLOR_YUV2BGRA_UYVY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_UYVY = 112 |
◆ COLOR_YUV2BGRA_Y422
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY |
◆ COLOR_YUV2BGRA_YUNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2 |
◆ COLOR_YUV2BGRA_YUY2
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_YUY2 = 120 |
◆ COLOR_YUV2BGRA_YUYV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2 |
◆ COLOR_YUV2BGRA_YV12
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_YV12 = 103 |
◆ COLOR_YUV2BGRA_YVYU
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2BGRA_YVYU = 122 |
◆ COLOR_YUV2GRAY_420
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_420 = 106 |
◆ COLOR_YUV2GRAY_I420
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420 |
◆ COLOR_YUV2GRAY_IYUV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420 |
◆ COLOR_YUV2GRAY_NV12
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420 |
◆ COLOR_YUV2GRAY_NV21
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420 |
◆ COLOR_YUV2GRAY_UYNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY |
◆ COLOR_YUV2GRAY_UYVY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_UYVY = 123 |
◆ COLOR_YUV2GRAY_Y422
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY |
◆ COLOR_YUV2GRAY_YUNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2 |
◆ COLOR_YUV2GRAY_YUY2
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_YUY2 = 124 |
◆ COLOR_YUV2GRAY_YUYV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2 |
◆ COLOR_YUV2GRAY_YV12
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420 |
◆ COLOR_YUV2GRAY_YVYU
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2 |
◆ COLOR_YUV2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB = 85 |
◆ COLOR_YUV2RGB_I420
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV |
◆ COLOR_YUV2RGB_IYUV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_IYUV = 100 |
◆ COLOR_YUV2RGB_NV12
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_NV12 = 90 |
◆ COLOR_YUV2RGB_NV21
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_NV21 = 92 |
◆ COLOR_YUV2RGB_UYNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY |
◆ COLOR_YUV2RGB_UYVY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_UYVY = 107 |
◆ COLOR_YUV2RGB_Y422
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY |
◆ COLOR_YUV2RGB_YUNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2 |
◆ COLOR_YUV2RGB_YUY2
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_YUY2 = 115 |
◆ COLOR_YUV2RGB_YUYV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2 |
◆ COLOR_YUV2RGB_YV12
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_YV12 = 98 |
◆ COLOR_YUV2RGB_YVYU
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGB_YVYU = 117 |
◆ COLOR_YUV2RGBA_I420
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV |
◆ COLOR_YUV2RGBA_IYUV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_IYUV = 104 |
◆ COLOR_YUV2RGBA_NV12
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_NV12 = 94 |
◆ COLOR_YUV2RGBA_NV21
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_NV21 = 96 |
◆ COLOR_YUV2RGBA_UYNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY |
◆ COLOR_YUV2RGBA_UYVY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_UYVY = 111 |
◆ COLOR_YUV2RGBA_Y422
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY |
◆ COLOR_YUV2RGBA_YUNV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2 |
◆ COLOR_YUV2RGBA_YUY2
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_YUY2 = 119 |
◆ COLOR_YUV2RGBA_YUYV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2 |
◆ COLOR_YUV2RGBA_YV12
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_YV12 = 102 |
◆ COLOR_YUV2RGBA_YVYU
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV2RGBA_YVYU = 121 |
◆ COLOR_YUV420p2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12 |
◆ COLOR_YUV420p2BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12 |
◆ COLOR_YUV420p2GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420 |
◆ COLOR_YUV420p2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12 |
◆ COLOR_YUV420p2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12 |
◆ COLOR_YUV420sp2BGR
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21 |
◆ COLOR_YUV420sp2BGRA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21 |
◆ COLOR_YUV420sp2GRAY
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420 |
◆ COLOR_YUV420sp2RGB
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21 |
◆ COLOR_YUV420sp2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21 |
◆ COLORMAP_AUTUMN
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_AUTUMN = 0 |
◆ COLORMAP_BONE
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_BONE = 1 |
◆ COLORMAP_CIVIDIS
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_CIVIDIS = 17 |
◆ COLORMAP_COOL
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_COOL = 8 |
◆ COLORMAP_DEEPGREEN
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_DEEPGREEN = 21 |
◆ COLORMAP_HOT
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_HOT = 11 |
◆ COLORMAP_HSV
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_HSV = 9 |
◆ COLORMAP_INFERNO
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_INFERNO = 14 |
◆ COLORMAP_JET
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_JET = 2 |
◆ COLORMAP_MAGMA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_MAGMA = 13 |
◆ COLORMAP_OCEAN
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_OCEAN = 5 |
◆ COLORMAP_PARULA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_PARULA = 12 |
◆ COLORMAP_PINK
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_PINK = 10 |
◆ COLORMAP_PLASMA
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_PLASMA = 15 |
◆ COLORMAP_RAINBOW
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_RAINBOW = 4 |
◆ COLORMAP_SPRING
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_SPRING = 7 |
◆ COLORMAP_SUMMER
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_SUMMER = 6 |
◆ COLORMAP_TURBO
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_TURBO = 20 |
◆ COLORMAP_TWILIGHT
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_TWILIGHT = 18 |
◆ COLORMAP_TWILIGHT_SHIFTED
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_TWILIGHT_SHIFTED = 19 |
◆ COLORMAP_VIRIDIS
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_VIRIDIS = 16 |
◆ COLORMAP_WINTER
| const int OpenCVForUnity.ImgprocModule.Imgproc.COLORMAP_WINTER = 3 |
◆ CONTOURS_MATCH_I1
| const int OpenCVForUnity.ImgprocModule.Imgproc.CONTOURS_MATCH_I1 = 1 |
◆ CONTOURS_MATCH_I2
| const int OpenCVForUnity.ImgprocModule.Imgproc.CONTOURS_MATCH_I2 = 2 |
◆ CONTOURS_MATCH_I3
| const int OpenCVForUnity.ImgprocModule.Imgproc.CONTOURS_MATCH_I3 = 3 |
◆ CV_BILATERAL
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_BILATERAL = 4 |
◆ CV_BLUR
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_BLUR = 1 |
◆ CV_BLUR_NO_SCALE
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_BLUR_NO_SCALE = 0 |
◆ CV_CANNY_L2_GRADIENT
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_CANNY_L2_GRADIENT = (1 << 31) |
◆ CV_CHAIN_CODE
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_CHAIN_CODE = 0 |
◆ CV_CLOCKWISE
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_CLOCKWISE = 1 |
◆ CV_COMP_BHATTACHARYYA
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COMP_BHATTACHARYYA = 3 |
◆ CV_COMP_CHISQR
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COMP_CHISQR = 1 |
◆ CV_COMP_CHISQR_ALT
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COMP_CHISQR_ALT = 4 |
◆ CV_COMP_CORREL
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COMP_CORREL = 0 |
◆ CV_COMP_HELLINGER
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COMP_HELLINGER = CV_COMP_BHATTACHARYYA |
◆ CV_COMP_INTERSECT
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COMP_INTERSECT = 2 |
◆ CV_COMP_KL_DIV
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COMP_KL_DIV = 5 |
◆ CV_CONTOURS_MATCH_I1
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_CONTOURS_MATCH_I1 = 1 |
◆ CV_CONTOURS_MATCH_I2
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_CONTOURS_MATCH_I2 = 2 |
◆ CV_CONTOURS_MATCH_I3
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_CONTOURS_MATCH_I3 = 3 |
◆ CV_COUNTER_CLOCKWISE
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_COUNTER_CLOCKWISE = 2 |
◆ CV_DIST_C
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_C = 3 |
◆ CV_DIST_FAIR
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_FAIR = 5 |
◆ CV_DIST_HUBER
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_HUBER = 7 |
◆ CV_DIST_L1
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_L1 = 1 |
◆ CV_DIST_L12
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_L12 = 4 |
◆ CV_DIST_L2
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_L2 = 2 |
◆ CV_DIST_LABEL_CCOMP
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_LABEL_CCOMP = 0 |
◆ CV_DIST_LABEL_PIXEL
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_LABEL_PIXEL = 1 |
◆ CV_DIST_MASK_3
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_MASK_3 = 3 |
◆ CV_DIST_MASK_5
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_MASK_5 = 5 |
◆ CV_DIST_MASK_PRECISE
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_MASK_PRECISE = 0 |
◆ CV_DIST_USER
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_USER = -1 |
◆ CV_DIST_WELSCH
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_DIST_WELSCH = 6 |
◆ CV_GAUSSIAN
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_GAUSSIAN = 2 |
◆ CV_GAUSSIAN_5x5
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_GAUSSIAN_5x5 = 7 |
◆ CV_HOUGH_GRADIENT
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_HOUGH_GRADIENT = 3 |
◆ CV_HOUGH_MULTI_SCALE
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_HOUGH_MULTI_SCALE = 2 |
◆ CV_HOUGH_PROBABILISTIC
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_HOUGH_PROBABILISTIC = 1 |
◆ CV_HOUGH_STANDARD
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_HOUGH_STANDARD = 0 |
◆ CV_LINK_RUNS
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_LINK_RUNS = 5 |
◆ CV_MAX_SOBEL_KSIZE
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_MAX_SOBEL_KSIZE = 7 |
◆ CV_MEDIAN
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_MEDIAN = 3 |
◆ CV_mRGBA2RGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_mRGBA2RGBA = 126 |
◆ CV_POLY_APPROX_DP
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_POLY_APPROX_DP = 0 |
◆ CV_RGBA2mRGBA
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_RGBA2mRGBA = 125 |
◆ CV_SCHARR
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_SCHARR = -1 |
◆ CV_SHAPE_CROSS
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_SHAPE_CROSS = 1 |
◆ CV_SHAPE_CUSTOM
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_SHAPE_CUSTOM = 100 |
◆ CV_SHAPE_ELLIPSE
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_SHAPE_ELLIPSE = 2 |
◆ CV_SHAPE_RECT
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_SHAPE_RECT = 0 |
◆ CV_WARP_FILL_OUTLIERS
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_WARP_FILL_OUTLIERS = 8 |
◆ CV_WARP_INVERSE_MAP
| const int OpenCVForUnity.ImgprocModule.Imgproc.CV_WARP_INVERSE_MAP = 16 |
◆ DIST_C
| const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_C = 3 |
◆ DIST_FAIR
| const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_FAIR = 5 |
◆ DIST_HUBER
| const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_HUBER = 7 |
◆ DIST_L1
| const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_L1 = 1 |
◆ DIST_L12
| const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_L12 = 4 |
◆ DIST_L2
| const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_L2 = 2 |
◆ DIST_LABEL_CCOMP
| const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_LABEL_CCOMP = 0 |
◆ DIST_LABEL_PIXEL
| const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_LABEL_PIXEL = 1 |
◆ DIST_MASK_3
| const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_MASK_3 = 3 |
◆ DIST_MASK_5
| const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_MASK_5 = 5 |
◆ DIST_MASK_PRECISE
| const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_MASK_PRECISE = 0 |
◆ DIST_USER
| const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_USER = -1 |
◆ DIST_WELSCH
| const int OpenCVForUnity.ImgprocModule.Imgproc.DIST_WELSCH = 6 |
◆ FILLED
| const int OpenCVForUnity.ImgprocModule.Imgproc.FILLED = -1 |
◆ FILTER_SCHARR
| const int OpenCVForUnity.ImgprocModule.Imgproc.FILTER_SCHARR = -1 |
◆ FLOODFILL_FIXED_RANGE
| const int OpenCVForUnity.ImgprocModule.Imgproc.FLOODFILL_FIXED_RANGE = 1 << 16 |
◆ FLOODFILL_MASK_ONLY
| const int OpenCVForUnity.ImgprocModule.Imgproc.FLOODFILL_MASK_ONLY = 1 << 17 |
◆ FONT_HERSHEY_COMPLEX
| const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_COMPLEX = 3 |
◆ FONT_HERSHEY_COMPLEX_SMALL
| const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_COMPLEX_SMALL = 5 |
◆ FONT_HERSHEY_DUPLEX
| const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_DUPLEX = 2 |
◆ FONT_HERSHEY_PLAIN
| const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_PLAIN = 1 |
◆ FONT_HERSHEY_SCRIPT_COMPLEX
| const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_SCRIPT_COMPLEX = 7 |
◆ FONT_HERSHEY_SCRIPT_SIMPLEX
| const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_SCRIPT_SIMPLEX = 6 |
◆ FONT_HERSHEY_SIMPLEX
| const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_SIMPLEX = 0 |
◆ FONT_HERSHEY_TRIPLEX
| const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_HERSHEY_TRIPLEX = 4 |
◆ FONT_ITALIC
| const int OpenCVForUnity.ImgprocModule.Imgproc.FONT_ITALIC = 16 |
◆ GC_BGD
| const int OpenCVForUnity.ImgprocModule.Imgproc.GC_BGD = 0 |
◆ GC_EVAL
| const int OpenCVForUnity.ImgprocModule.Imgproc.GC_EVAL = 2 |
◆ GC_EVAL_FREEZE_MODEL
| const int OpenCVForUnity.ImgprocModule.Imgproc.GC_EVAL_FREEZE_MODEL = 3 |
◆ GC_FGD
| const int OpenCVForUnity.ImgprocModule.Imgproc.GC_FGD = 1 |
◆ GC_INIT_WITH_MASK
| const int OpenCVForUnity.ImgprocModule.Imgproc.GC_INIT_WITH_MASK = 1 |
◆ GC_INIT_WITH_RECT
| const int OpenCVForUnity.ImgprocModule.Imgproc.GC_INIT_WITH_RECT = 0 |
◆ GC_PR_BGD
| const int OpenCVForUnity.ImgprocModule.Imgproc.GC_PR_BGD = 2 |
◆ GC_PR_FGD
| const int OpenCVForUnity.ImgprocModule.Imgproc.GC_PR_FGD = 3 |
◆ HISTCMP_BHATTACHARYYA
| const int OpenCVForUnity.ImgprocModule.Imgproc.HISTCMP_BHATTACHARYYA = 3 |
◆ HISTCMP_CHISQR
| const int OpenCVForUnity.ImgprocModule.Imgproc.HISTCMP_CHISQR = 1 |
◆ HISTCMP_CHISQR_ALT
| const int OpenCVForUnity.ImgprocModule.Imgproc.HISTCMP_CHISQR_ALT = 4 |
◆ HISTCMP_CORREL
| const int OpenCVForUnity.ImgprocModule.Imgproc.HISTCMP_CORREL = 0 |
◆ HISTCMP_HELLINGER
| const int OpenCVForUnity.ImgprocModule.Imgproc.HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA |
◆ HISTCMP_INTERSECT
| const int OpenCVForUnity.ImgprocModule.Imgproc.HISTCMP_INTERSECT = 2 |
◆ HISTCMP_KL_DIV
| const int OpenCVForUnity.ImgprocModule.Imgproc.HISTCMP_KL_DIV = 5 |
◆ HOUGH_GRADIENT
| const int OpenCVForUnity.ImgprocModule.Imgproc.HOUGH_GRADIENT = 3 |
◆ HOUGH_GRADIENT_ALT
| const int OpenCVForUnity.ImgprocModule.Imgproc.HOUGH_GRADIENT_ALT = 4 |
◆ HOUGH_MULTI_SCALE
| const int OpenCVForUnity.ImgprocModule.Imgproc.HOUGH_MULTI_SCALE = 2 |
◆ HOUGH_PROBABILISTIC
| const int OpenCVForUnity.ImgprocModule.Imgproc.HOUGH_PROBABILISTIC = 1 |
◆ HOUGH_STANDARD
| const int OpenCVForUnity.ImgprocModule.Imgproc.HOUGH_STANDARD = 0 |
◆ INTER_AREA
| const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_AREA = 3 |
◆ INTER_BITS
| const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_BITS = 5 |
◆ INTER_BITS2
| const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_BITS2 = INTER_BITS * 2 |
◆ INTER_CUBIC
| const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_CUBIC = 2 |
◆ INTER_LANCZOS4
| const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_LANCZOS4 = 4 |
◆ INTER_LINEAR
| const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_LINEAR = 1 |
◆ INTER_LINEAR_EXACT
| const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_LINEAR_EXACT = 5 |
◆ INTER_MAX
| const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_MAX = 7 |
◆ INTER_NEAREST
| const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_NEAREST = 0 |
◆ INTER_NEAREST_EXACT
| const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_NEAREST_EXACT = 6 |
◆ INTER_TAB_SIZE
| const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_TAB_SIZE = 1 << INTER_BITS |
◆ INTER_TAB_SIZE2
| const int OpenCVForUnity.ImgprocModule.Imgproc.INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE |
◆ INTERSECT_FULL
| const int OpenCVForUnity.ImgprocModule.Imgproc.INTERSECT_FULL = 2 |
◆ INTERSECT_NONE
| const int OpenCVForUnity.ImgprocModule.Imgproc.INTERSECT_NONE = 0 |
◆ INTERSECT_PARTIAL
| const int OpenCVForUnity.ImgprocModule.Imgproc.INTERSECT_PARTIAL = 1 |
◆ LINE_4
| const int OpenCVForUnity.ImgprocModule.Imgproc.LINE_4 = 4 |
◆ LINE_8
| const int OpenCVForUnity.ImgprocModule.Imgproc.LINE_8 = 8 |
◆ LINE_AA
| const int OpenCVForUnity.ImgprocModule.Imgproc.LINE_AA = 16 |
◆ LSD_REFINE_ADV
| const int OpenCVForUnity.ImgprocModule.Imgproc.LSD_REFINE_ADV = 2 |
◆ LSD_REFINE_NONE
| const int OpenCVForUnity.ImgprocModule.Imgproc.LSD_REFINE_NONE = 0 |
◆ LSD_REFINE_STD
| const int OpenCVForUnity.ImgprocModule.Imgproc.LSD_REFINE_STD = 1 |
◆ MARKER_CROSS
| const int OpenCVForUnity.ImgprocModule.Imgproc.MARKER_CROSS = 0 |
◆ MARKER_DIAMOND
| const int OpenCVForUnity.ImgprocModule.Imgproc.MARKER_DIAMOND = 3 |
◆ MARKER_SQUARE
| const int OpenCVForUnity.ImgprocModule.Imgproc.MARKER_SQUARE = 4 |
◆ MARKER_STAR
| const int OpenCVForUnity.ImgprocModule.Imgproc.MARKER_STAR = 2 |
◆ MARKER_TILTED_CROSS
| const int OpenCVForUnity.ImgprocModule.Imgproc.MARKER_TILTED_CROSS = 1 |
◆ MARKER_TRIANGLE_DOWN
| const int OpenCVForUnity.ImgprocModule.Imgproc.MARKER_TRIANGLE_DOWN = 6 |
◆ MARKER_TRIANGLE_UP
| const int OpenCVForUnity.ImgprocModule.Imgproc.MARKER_TRIANGLE_UP = 5 |
◆ MORPH_BLACKHAT
| const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_BLACKHAT = 6 |
◆ MORPH_CLOSE
| const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_CLOSE = 3 |
◆ MORPH_CROSS
| const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_CROSS = 1 |
◆ MORPH_DILATE
| const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_DILATE = 1 |
◆ MORPH_ELLIPSE
| const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_ELLIPSE = 2 |
◆ MORPH_ERODE
| const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_ERODE = 0 |
◆ MORPH_GRADIENT
| const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_GRADIENT = 4 |
◆ MORPH_HITMISS
| const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_HITMISS = 7 |
◆ MORPH_OPEN
| const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_OPEN = 2 |
◆ MORPH_RECT
| const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_RECT = 0 |
◆ MORPH_TOPHAT
| const int OpenCVForUnity.ImgprocModule.Imgproc.MORPH_TOPHAT = 5 |
◆ RETR_CCOMP
| const int OpenCVForUnity.ImgprocModule.Imgproc.RETR_CCOMP = 2 |
◆ RETR_EXTERNAL
| const int OpenCVForUnity.ImgprocModule.Imgproc.RETR_EXTERNAL = 0 |
◆ RETR_FLOODFILL
| const int OpenCVForUnity.ImgprocModule.Imgproc.RETR_FLOODFILL = 4 |
◆ RETR_LIST
| const int OpenCVForUnity.ImgprocModule.Imgproc.RETR_LIST = 1 |
◆ RETR_TREE
| const int OpenCVForUnity.ImgprocModule.Imgproc.RETR_TREE = 3 |
◆ THRESH_BINARY
| const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_BINARY = 0 |
◆ THRESH_BINARY_INV
| const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_BINARY_INV = 1 |
◆ THRESH_MASK
| const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_MASK = 7 |
◆ THRESH_OTSU
| const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_OTSU = 8 |
◆ THRESH_TOZERO
| const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_TOZERO = 3 |
◆ THRESH_TOZERO_INV
| const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_TOZERO_INV = 4 |
◆ THRESH_TRIANGLE
| const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_TRIANGLE = 16 |
◆ THRESH_TRUNC
| const int OpenCVForUnity.ImgprocModule.Imgproc.THRESH_TRUNC = 2 |
◆ TM_CCOEFF
| const int OpenCVForUnity.ImgprocModule.Imgproc.TM_CCOEFF = 4 |
◆ TM_CCOEFF_NORMED
| const int OpenCVForUnity.ImgprocModule.Imgproc.TM_CCOEFF_NORMED = 5 |
◆ TM_CCORR
| const int OpenCVForUnity.ImgprocModule.Imgproc.TM_CCORR = 2 |
◆ TM_CCORR_NORMED
| const int OpenCVForUnity.ImgprocModule.Imgproc.TM_CCORR_NORMED = 3 |
◆ TM_SQDIFF
| const int OpenCVForUnity.ImgprocModule.Imgproc.TM_SQDIFF = 0 |
◆ TM_SQDIFF_NORMED
| const int OpenCVForUnity.ImgprocModule.Imgproc.TM_SQDIFF_NORMED = 1 |
◆ WARP_FILL_OUTLIERS
| const int OpenCVForUnity.ImgprocModule.Imgproc.WARP_FILL_OUTLIERS = 8 |
◆ WARP_INVERSE_MAP
| const int OpenCVForUnity.ImgprocModule.Imgproc.WARP_INVERSE_MAP = 16 |
◆ WARP_POLAR_LINEAR
| const int OpenCVForUnity.ImgprocModule.Imgproc.WARP_POLAR_LINEAR = 0 |
◆ WARP_POLAR_LOG
| const int OpenCVForUnity.ImgprocModule.Imgproc.WARP_POLAR_LOG = 256 |
The documentation for this class was generated from the following file:
- OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/imgproc/Imgproc.cs
