Static Public Member Functions | |
| static void | niBlackThreshold (Mat _src, Mat _dst, double maxValue, int type, int blockSize, double k, int binarizationMethod, double r) |
| Performs thresholding on input images using Niblack's technique or some of the popular variations it inspired. More... | |
| static void | niBlackThreshold (Mat _src, Mat _dst, double maxValue, int type, int blockSize, double k, int binarizationMethod) |
| Performs thresholding on input images using Niblack's technique or some of the popular variations it inspired. More... | |
| static void | niBlackThreshold (Mat _src, Mat _dst, double maxValue, int type, int blockSize, double k) |
| Performs thresholding on input images using Niblack's technique or some of the popular variations it inspired. More... | |
| static void | thinning (Mat src, Mat dst, int thinningType) |
| Applies a binary blob thinning operation, to achieve a skeletization of the input image. More... | |
| static void | thinning (Mat src, Mat dst) |
| Applies a binary blob thinning operation, to achieve a skeletization of the input image. More... | |
| static void | anisotropicDiffusion (Mat src, Mat dst, float alpha, float K, int niters) |
| Performs anisotropic diffusion on an image. More... | |
| static void | createQuaternionImage (Mat img, Mat qimg) |
| creates a quaternion image. More... | |
| static void | qconj (Mat qimg, Mat qcimg) |
| calculates conjugate of a quaternion image. More... | |
| static void | qunitary (Mat qimg, Mat qnimg) |
| divides each element by its modulus. More... | |
| static void | qmultiply (Mat src1, Mat src2, Mat dst) |
| Calculates the per-element quaternion product of two arrays. More... | |
| static void | qdft (Mat img, Mat qimg, int flags, bool sideLeft) |
| Performs a forward or inverse Discrete quaternion Fourier transform of a 2D quaternion array. More... | |
| static void | colorMatchTemplate (Mat img, Mat templ, Mat result) |
| Compares a color template against overlapped color image regions. More... | |
| static void | GradientDericheY (Mat op, Mat dst, double alpha, double omega) |
| Applies Y Deriche filter to an image. More... | |
| static void | GradientDericheX (Mat op, Mat dst, double alpha, double omega) |
| Applies X Deriche filter to an image. More... | |
| static DisparityWLSFilter | createDisparityWLSFilter (StereoMatcher matcher_left) |
| Convenience factory method that creates an instance of DisparityWLSFilter and sets up all the relevant filter parameters automatically based on the matcher instance. Currently supports only StereoBM and StereoSGBM. More... | |
| static StereoMatcher | createRightMatcher (StereoMatcher matcher_left) |
| Convenience method to set up the matcher for computing the right-view disparity map that is required in case of filtering with confidence. More... | |
| static DisparityWLSFilter | createDisparityWLSFilterGeneric (bool use_confidence) |
| More generic factory method, create instance of DisparityWLSFilter and execute basic initialization routines. When using this method you will need to set-up the ROI, matchers and other parameters by yourself. More... | |
| static int | readGT (string src_path, Mat dst) |
| Function for reading ground truth disparity maps. Supports basic Middlebury and MPI-Sintel formats. Note that the resulting disparity map is scaled by 16. More... | |
| static double | computeMSE (Mat GT, Mat src, Rect ROI) |
| Function for computing mean square error for disparity maps. More... | |
| static double | computeBadPixelPercent (Mat GT, Mat src, Rect ROI, int thresh) |
| Function for computing the percent of "bad" pixels in the disparity map (pixels where error is higher than a specified threshold) More... | |
| static double | computeBadPixelPercent (Mat GT, Mat src, Rect ROI) |
| Function for computing the percent of "bad" pixels in the disparity map (pixels where error is higher than a specified threshold) More... | |
| static void | getDisparityVis (Mat src, Mat dst, double scale) |
| Function for creating a disparity map visualization (clamped CV_8U image) More... | |
| static void | getDisparityVis (Mat src, Mat dst) |
| Function for creating a disparity map visualization (clamped CV_8U image) More... | |
| static EdgeBoxes | createEdgeBoxes (float alpha, float beta, float eta, float minScore, int maxBoxes, float edgeMinMag, float edgeMergeThr, float clusterMinMag, float maxAspectRatio, float minBoxArea, float gamma, float kappa) |
| Creates a Edgeboxes. More... | |
| static EdgeBoxes | createEdgeBoxes (float alpha, float beta, float eta, float minScore, int maxBoxes, float edgeMinMag, float edgeMergeThr, float clusterMinMag, float maxAspectRatio, float minBoxArea, float gamma) |
| Creates a Edgeboxes. More... | |
| static EdgeBoxes | createEdgeBoxes (float alpha, float beta, float eta, float minScore, int maxBoxes, float edgeMinMag, float edgeMergeThr, float clusterMinMag, float maxAspectRatio, float minBoxArea) |
| Creates a Edgeboxes. More... | |
| static EdgeBoxes | createEdgeBoxes (float alpha, float beta, float eta, float minScore, int maxBoxes, float edgeMinMag, float edgeMergeThr, float clusterMinMag, float maxAspectRatio) |
| Creates a Edgeboxes. More... | |
| static EdgeBoxes | createEdgeBoxes (float alpha, float beta, float eta, float minScore, int maxBoxes, float edgeMinMag, float edgeMergeThr, float clusterMinMag) |
| Creates a Edgeboxes. More... | |
| static EdgeBoxes | createEdgeBoxes (float alpha, float beta, float eta, float minScore, int maxBoxes, float edgeMinMag, float edgeMergeThr) |
| Creates a Edgeboxes. More... | |
| static EdgeBoxes | createEdgeBoxes (float alpha, float beta, float eta, float minScore, int maxBoxes, float edgeMinMag) |
| Creates a Edgeboxes. More... | |
| static EdgeBoxes | createEdgeBoxes (float alpha, float beta, float eta, float minScore, int maxBoxes) |
| Creates a Edgeboxes. More... | |
| static EdgeBoxes | createEdgeBoxes (float alpha, float beta, float eta, float minScore) |
| Creates a Edgeboxes. More... | |
| static EdgeBoxes | createEdgeBoxes (float alpha, float beta, float eta) |
| Creates a Edgeboxes. More... | |
| static EdgeBoxes | createEdgeBoxes (float alpha, float beta) |
| Creates a Edgeboxes. More... | |
| static EdgeBoxes | createEdgeBoxes (float alpha) |
| Creates a Edgeboxes. More... | |
| static EdgeBoxes | createEdgeBoxes () |
| Creates a Edgeboxes. More... | |
| static void | edgePreservingFilter (Mat src, Mat dst, int d, double threshold) |
| Smoothes an image using the Edge-Preserving filter. More... | |
| static EdgeDrawing | createEdgeDrawing () |
| Creates a smart pointer to a EdgeDrawing object and initializes it. More... | |
| static DTFilter | createDTFilter (Mat guide, double sigmaSpatial, double sigmaColor, int mode, int numIters) |
| Factory method, create instance of DTFilter and produce initialization routines. More... | |
| static DTFilter | createDTFilter (Mat guide, double sigmaSpatial, double sigmaColor, int mode) |
| Factory method, create instance of DTFilter and produce initialization routines. More... | |
| static DTFilter | createDTFilter (Mat guide, double sigmaSpatial, double sigmaColor) |
| Factory method, create instance of DTFilter and produce initialization routines. More... | |
| static void | dtFilter (Mat guide, Mat src, Mat dst, double sigmaSpatial, double sigmaColor, int mode, int numIters) |
| Simple one-line Domain Transform filter call. If you have multiple images to filter with the same guided image then use DTFilter interface to avoid extra computations on initialization stage. More... | |
| static void | dtFilter (Mat guide, Mat src, Mat dst, double sigmaSpatial, double sigmaColor, int mode) |
| Simple one-line Domain Transform filter call. If you have multiple images to filter with the same guided image then use DTFilter interface to avoid extra computations on initialization stage. More... | |
| static void | dtFilter (Mat guide, Mat src, Mat dst, double sigmaSpatial, double sigmaColor) |
| Simple one-line Domain Transform filter call. If you have multiple images to filter with the same guided image then use DTFilter interface to avoid extra computations on initialization stage. More... | |
| static GuidedFilter | createGuidedFilter (Mat guide, int radius, double eps) |
| Factory method, create instance of GuidedFilter and produce initialization routines. More... | |
| static void | guidedFilter (Mat guide, Mat src, Mat dst, int radius, double eps, int dDepth) |
| Simple one-line Guided Filter call. More... | |
| static void | guidedFilter (Mat guide, Mat src, Mat dst, int radius, double eps) |
| Simple one-line Guided Filter call. More... | |
| static AdaptiveManifoldFilter | createAMFilter (double sigma_s, double sigma_r, bool adjust_outliers) |
| Factory method, create instance of AdaptiveManifoldFilter and produce some initialization routines. More... | |
| static AdaptiveManifoldFilter | createAMFilter (double sigma_s, double sigma_r) |
| Factory method, create instance of AdaptiveManifoldFilter and produce some initialization routines. More... | |
| static void | amFilter (Mat joint, Mat src, Mat dst, double sigma_s, double sigma_r, bool adjust_outliers) |
| Simple one-line Adaptive Manifold Filter call. More... | |
| static void | amFilter (Mat joint, Mat src, Mat dst, double sigma_s, double sigma_r) |
| Simple one-line Adaptive Manifold Filter call. More... | |
| static void | jointBilateralFilter (Mat joint, Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace, int borderType) |
| Applies the joint bilateral filter to an image. More... | |
| static void | jointBilateralFilter (Mat joint, Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace) |
| Applies the joint bilateral filter to an image. More... | |
| static void | bilateralTextureFilter (Mat src, Mat dst, int fr, int numIter, double sigmaAlpha, double sigmaAvg) |
| Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see [Cho2014]. More... | |
| static void | bilateralTextureFilter (Mat src, Mat dst, int fr, int numIter, double sigmaAlpha) |
| Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see [Cho2014]. More... | |
| static void | bilateralTextureFilter (Mat src, Mat dst, int fr, int numIter) |
| Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see [Cho2014]. More... | |
| static void | bilateralTextureFilter (Mat src, Mat dst, int fr) |
| Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see [Cho2014]. More... | |
| static void | bilateralTextureFilter (Mat src, Mat dst) |
| Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see [Cho2014]. More... | |
| static void | rollingGuidanceFilter (Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace, int numOfIter, int borderType) |
| Applies the rolling guidance filter to an image. More... | |
| static void | rollingGuidanceFilter (Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace, int numOfIter) |
| Applies the rolling guidance filter to an image. More... | |
| static void | rollingGuidanceFilter (Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace) |
| Applies the rolling guidance filter to an image. More... | |
| static void | rollingGuidanceFilter (Mat src, Mat dst, int d, double sigmaColor) |
| Applies the rolling guidance filter to an image. More... | |
| static void | rollingGuidanceFilter (Mat src, Mat dst, int d) |
| Applies the rolling guidance filter to an image. More... | |
| static void | rollingGuidanceFilter (Mat src, Mat dst) |
| Applies the rolling guidance filter to an image. More... | |
| static FastBilateralSolverFilter | createFastBilateralSolverFilter (Mat guide, double sigma_spatial, double sigma_luma, double sigma_chroma, double lambda, int num_iter, double max_tol) |
| Factory method, create instance of FastBilateralSolverFilter and execute the initialization routines. More... | |
| static FastBilateralSolverFilter | createFastBilateralSolverFilter (Mat guide, double sigma_spatial, double sigma_luma, double sigma_chroma, double lambda, int num_iter) |
| Factory method, create instance of FastBilateralSolverFilter and execute the initialization routines. More... | |
| static FastBilateralSolverFilter | createFastBilateralSolverFilter (Mat guide, double sigma_spatial, double sigma_luma, double sigma_chroma, double lambda) |
| Factory method, create instance of FastBilateralSolverFilter and execute the initialization routines. More... | |
| static FastBilateralSolverFilter | createFastBilateralSolverFilter (Mat guide, double sigma_spatial, double sigma_luma, double sigma_chroma) |
| Factory method, create instance of FastBilateralSolverFilter and execute the initialization routines. More... | |
| static void | fastBilateralSolverFilter (Mat guide, Mat src, Mat confidence, Mat dst, double sigma_spatial, double sigma_luma, double sigma_chroma, double lambda, int num_iter, double max_tol) |
| Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations. More... | |
| static void | fastBilateralSolverFilter (Mat guide, Mat src, Mat confidence, Mat dst, double sigma_spatial, double sigma_luma, double sigma_chroma, double lambda, int num_iter) |
| Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations. More... | |
| static void | fastBilateralSolverFilter (Mat guide, Mat src, Mat confidence, Mat dst, double sigma_spatial, double sigma_luma, double sigma_chroma, double lambda) |
| Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations. More... | |
| static void | fastBilateralSolverFilter (Mat guide, Mat src, Mat confidence, Mat dst, double sigma_spatial, double sigma_luma, double sigma_chroma) |
| Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations. More... | |
| static void | fastBilateralSolverFilter (Mat guide, Mat src, Mat confidence, Mat dst, double sigma_spatial, double sigma_luma) |
| Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations. More... | |
| static void | fastBilateralSolverFilter (Mat guide, Mat src, Mat confidence, Mat dst, double sigma_spatial) |
| Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations. More... | |
| static void | fastBilateralSolverFilter (Mat guide, Mat src, Mat confidence, Mat dst) |
| Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations. More... | |
| static FastGlobalSmootherFilter | createFastGlobalSmootherFilter (Mat guide, double lambda, double sigma_color, double lambda_attenuation, int num_iter) |
| Factory method, create instance of FastGlobalSmootherFilter and execute the initialization routines. More... | |
| static FastGlobalSmootherFilter | createFastGlobalSmootherFilter (Mat guide, double lambda, double sigma_color, double lambda_attenuation) |
| Factory method, create instance of FastGlobalSmootherFilter and execute the initialization routines. More... | |
| static FastGlobalSmootherFilter | createFastGlobalSmootherFilter (Mat guide, double lambda, double sigma_color) |
| Factory method, create instance of FastGlobalSmootherFilter and execute the initialization routines. More... | |
| static void | fastGlobalSmootherFilter (Mat guide, Mat src, Mat dst, double lambda, double sigma_color, double lambda_attenuation, int num_iter) |
| Simple one-line Fast Global Smoother filter call. If you have multiple images to filter with the same guide then use FastGlobalSmootherFilter interface to avoid extra computations. More... | |
| static void | fastGlobalSmootherFilter (Mat guide, Mat src, Mat dst, double lambda, double sigma_color, double lambda_attenuation) |
| Simple one-line Fast Global Smoother filter call. If you have multiple images to filter with the same guide then use FastGlobalSmootherFilter interface to avoid extra computations. More... | |
| static void | fastGlobalSmootherFilter (Mat guide, Mat src, Mat dst, double lambda, double sigma_color) |
| Simple one-line Fast Global Smoother filter call. If you have multiple images to filter with the same guide then use FastGlobalSmootherFilter interface to avoid extra computations. More... | |
| static void | l0Smooth (Mat src, Mat dst, double lambda, double kappa) |
| Global image smoothing via L0 gradient minimization. More... | |
| static void | l0Smooth (Mat src, Mat dst, double lambda) |
| Global image smoothing via L0 gradient minimization. More... | |
| static void | l0Smooth (Mat src, Mat dst) |
| Global image smoothing via L0 gradient minimization. More... | |
| static void | covarianceEstimation (Mat src, Mat dst, int windowRows, int windowCols) |
| Computes the estimated covariance matrix of an image using the sliding window forumlation. More... | |
| static void | FastHoughTransform (Mat src, Mat dst, int dstMatDepth, int angleRange, int op, int makeSkew) |
| Calculates 2D Fast Hough transform of an image. More... | |
| static void | FastHoughTransform (Mat src, Mat dst, int dstMatDepth, int angleRange, int op) |
| Calculates 2D Fast Hough transform of an image. More... | |
| static void | FastHoughTransform (Mat src, Mat dst, int dstMatDepth, int angleRange) |
| Calculates 2D Fast Hough transform of an image. More... | |
| static void | FastHoughTransform (Mat src, Mat dst, int dstMatDepth) |
| Calculates 2D Fast Hough transform of an image. More... | |
| static FastLineDetector | createFastLineDetector (int length_threshold, float distance_threshold, double canny_th1, double canny_th2, int canny_aperture_size, bool do_merge) |
| Creates a smart pointer to a FastLineDetector object and initializes it. More... | |
| static FastLineDetector | createFastLineDetector (int length_threshold, float distance_threshold, double canny_th1, double canny_th2, int canny_aperture_size) |
| Creates a smart pointer to a FastLineDetector object and initializes it. More... | |
| static FastLineDetector | createFastLineDetector (int length_threshold, float distance_threshold, double canny_th1, double canny_th2) |
| Creates a smart pointer to a FastLineDetector object and initializes it. More... | |
| static FastLineDetector | createFastLineDetector (int length_threshold, float distance_threshold, double canny_th1) |
| Creates a smart pointer to a FastLineDetector object and initializes it. More... | |
| static FastLineDetector | createFastLineDetector (int length_threshold, float distance_threshold) |
| Creates a smart pointer to a FastLineDetector object and initializes it. More... | |
| static FastLineDetector | createFastLineDetector (int length_threshold) |
| Creates a smart pointer to a FastLineDetector object and initializes it. More... | |
| static FastLineDetector | createFastLineDetector () |
| Creates a smart pointer to a FastLineDetector object and initializes it. More... | |
| static void | findEllipses (Mat image, Mat ellipses, float scoreThreshold, float reliabilityThreshold, float centerDistanceThreshold) |
| Finds ellipses fastly in an image using projective invariant pruning. More... | |
| static void | findEllipses (Mat image, Mat ellipses, float scoreThreshold, float reliabilityThreshold) |
| Finds ellipses fastly in an image using projective invariant pruning. More... | |
| static void | findEllipses (Mat image, Mat ellipses, float scoreThreshold) |
| Finds ellipses fastly in an image using projective invariant pruning. More... | |
| static void | findEllipses (Mat image, Mat ellipses) |
| Finds ellipses fastly in an image using projective invariant pruning. More... | |
| static void | fourierDescriptor (Mat src, Mat dst, int nbElt, int nbFD) |
| Fourier descriptors for planed closed curves. More... | |
| static void | fourierDescriptor (Mat src, Mat dst, int nbElt) |
| Fourier descriptors for planed closed curves. More... | |
| static void | fourierDescriptor (Mat src, Mat dst) |
| Fourier descriptors for planed closed curves. More... | |
| static void | transformFD (Mat src, Mat t, Mat dst, bool fdContour) |
| transform a contour More... | |
| static void | transformFD (Mat src, Mat t, Mat dst) |
| transform a contour More... | |
| static void | contourSampling (Mat src, Mat _out, int nbElt) |
| Contour sampling . More... | |
| static ContourFitting | createContourFitting (int ctr, int fd) |
| create ContourFitting algorithm object More... | |
| static ContourFitting | createContourFitting (int ctr) |
| create ContourFitting algorithm object More... | |
| static ContourFitting | createContourFitting () |
| create ContourFitting algorithm object More... | |
| static SuperpixelLSC | createSuperpixelLSC (Mat image, int region_size, float ratio) |
| Class implementing the LSC (Linear Spectral Clustering) superpixels. More... | |
| static SuperpixelLSC | createSuperpixelLSC (Mat image, int region_size) |
| Class implementing the LSC (Linear Spectral Clustering) superpixels. More... | |
| static SuperpixelLSC | createSuperpixelLSC (Mat image) |
| Class implementing the LSC (Linear Spectral Clustering) superpixels. More... | |
| static void | PeiLinNormalization (Mat I, Mat T) |
| static void | RadonTransform (Mat src, Mat dst, double theta, double start_angle, double end_angle, bool crop, bool norm) |
| Calculate Radon Transform of an image. More... | |
| static void | RadonTransform (Mat src, Mat dst, double theta, double start_angle, double end_angle, bool crop) |
| Calculate Radon Transform of an image. More... | |
| static void | RadonTransform (Mat src, Mat dst, double theta, double start_angle, double end_angle) |
| Calculate Radon Transform of an image. More... | |
| static void | RadonTransform (Mat src, Mat dst, double theta, double start_angle) |
| Calculate Radon Transform of an image. More... | |
| static void | RadonTransform (Mat src, Mat dst, double theta) |
| Calculate Radon Transform of an image. More... | |
| static void | RadonTransform (Mat src, Mat dst) |
| Calculate Radon Transform of an image. More... | |
| static ScanSegment | createScanSegment (int image_width, int image_height, int num_superpixels, int slices, bool merge_small) |
| Initializes a ScanSegment object. More... | |
| static ScanSegment | createScanSegment (int image_width, int image_height, int num_superpixels, int slices) |
| Initializes a ScanSegment object. More... | |
| static ScanSegment | createScanSegment (int image_width, int image_height, int num_superpixels) |
| Initializes a ScanSegment object. More... | |
| static SuperpixelSEEDS | createSuperpixelSEEDS (int image_width, int image_height, int image_channels, int num_superpixels, int num_levels, int prior, int histogram_bins, bool double_step) |
| Initializes a SuperpixelSEEDS object. More... | |
| static SuperpixelSEEDS | createSuperpixelSEEDS (int image_width, int image_height, int image_channels, int num_superpixels, int num_levels, int prior, int histogram_bins) |
| Initializes a SuperpixelSEEDS object. More... | |
| static SuperpixelSEEDS | createSuperpixelSEEDS (int image_width, int image_height, int image_channels, int num_superpixels, int num_levels, int prior) |
| Initializes a SuperpixelSEEDS object. More... | |
| static SuperpixelSEEDS | createSuperpixelSEEDS (int image_width, int image_height, int image_channels, int num_superpixels, int num_levels) |
| Initializes a SuperpixelSEEDS object. More... | |
| static GraphSegmentation | createGraphSegmentation (double sigma, float k, int min_size) |
| Creates a graph based segmentor. More... | |
| static GraphSegmentation | createGraphSegmentation (double sigma, float k) |
| Creates a graph based segmentor. More... | |
| static GraphSegmentation | createGraphSegmentation (double sigma) |
| Creates a graph based segmentor. More... | |
| static GraphSegmentation | createGraphSegmentation () |
| Creates a graph based segmentor. More... | |
| static SelectiveSearchSegmentationStrategyColor | createSelectiveSearchSegmentationStrategyColor () |
| Create a new color-based strategy. More... | |
| static SelectiveSearchSegmentationStrategySize | createSelectiveSearchSegmentationStrategySize () |
| Create a new size-based strategy. More... | |
| static SelectiveSearchSegmentationStrategyTexture | createSelectiveSearchSegmentationStrategyTexture () |
| Create a new size-based strategy. More... | |
| static SelectiveSearchSegmentationStrategyFill | createSelectiveSearchSegmentationStrategyFill () |
| Create a new fill-based strategy. More... | |
| static SelectiveSearchSegmentationStrategyMultiple | createSelectiveSearchSegmentationStrategyMultiple () |
| Create a new multiple strategy. More... | |
| static SelectiveSearchSegmentationStrategyMultiple | createSelectiveSearchSegmentationStrategyMultiple (SelectiveSearchSegmentationStrategy s1) |
| Create a new multiple strategy and set one subtrategy. More... | |
| static SelectiveSearchSegmentationStrategyMultiple | createSelectiveSearchSegmentationStrategyMultiple (SelectiveSearchSegmentationStrategy s1, SelectiveSearchSegmentationStrategy s2) |
| Create a new multiple strategy and set two subtrategies, with equal weights. More... | |
| static SelectiveSearchSegmentationStrategyMultiple | createSelectiveSearchSegmentationStrategyMultiple (SelectiveSearchSegmentationStrategy s1, SelectiveSearchSegmentationStrategy s2, SelectiveSearchSegmentationStrategy s3) |
| Create a new multiple strategy and set three subtrategies, with equal weights. More... | |
| static SelectiveSearchSegmentationStrategyMultiple | createSelectiveSearchSegmentationStrategyMultiple (SelectiveSearchSegmentationStrategy s1, SelectiveSearchSegmentationStrategy s2, SelectiveSearchSegmentationStrategy s3, SelectiveSearchSegmentationStrategy s4) |
| Create a new multiple strategy and set four subtrategies, with equal weights. More... | |
| static SelectiveSearchSegmentation | createSelectiveSearchSegmentation () |
| Create a new SelectiveSearchSegmentation class. More... | |
| static SuperpixelSLIC | createSuperpixelSLIC (Mat image, int algorithm, int region_size, float ruler) |
| Initialize a SuperpixelSLIC object. More... | |
| static SuperpixelSLIC | createSuperpixelSLIC (Mat image, int algorithm, int region_size) |
| Initialize a SuperpixelSLIC object. More... | |
| static SuperpixelSLIC | createSuperpixelSLIC (Mat image, int algorithm) |
| Initialize a SuperpixelSLIC object. More... | |
| static SuperpixelSLIC | createSuperpixelSLIC (Mat image) |
| Initialize a SuperpixelSLIC object. More... | |
| static EdgeAwareInterpolator | createEdgeAwareInterpolator () |
| Factory method that creates an instance of the EdgeAwareInterpolator. More... | |
| static RICInterpolator | createRICInterpolator () |
| Factory method that creates an instance of the RICInterpolator. More... | |
| static RFFeatureGetter | createRFFeatureGetter () |
| static StructuredEdgeDetection | createStructuredEdgeDetection (string model, RFFeatureGetter howToGetFeatures) |
| static StructuredEdgeDetection | createStructuredEdgeDetection (string model) |
| static void | weightedMedianFilter (Mat joint, Mat src, Mat dst, int r, double sigma, int weightType, Mat mask) |
| Applies weighted median filter to an image. More... | |
| static void | weightedMedianFilter (Mat joint, Mat src, Mat dst, int r, double sigma, int weightType) |
| Applies weighted median filter to an image. More... | |
| static void | weightedMedianFilter (Mat joint, Mat src, Mat dst, int r, double sigma) |
| Applies weighted median filter to an image. More... | |
| static void | weightedMedianFilter (Mat joint, Mat src, Mat dst, int r) |
| Applies weighted median filter to an image. More... | |
Public Attributes | |
| const int | ARO_0_45 = 0 |
| const int | ARO_45_90 = 1 |
| const int | ARO_90_135 = 2 |
| const int | ARO_315_0 = 3 |
| const int | ARO_315_45 = 4 |
| const int | ARO_45_135 = 5 |
| const int | ARO_315_135 = 6 |
| const int | ARO_CTR_HOR = 7 |
| const int | ARO_CTR_VER = 8 |
| const int | DTF_NC = 0 |
| const int | DTF_IC = 1 |
| const int | DTF_RF = 2 |
| const int | GUIDED_FILTER = 3 |
| const int | AM_FILTER = 4 |
| const int | HDO_RAW = 0 |
| const int | HDO_DESKEW = 1 |
| const int | FHT_MIN = 0 |
| const int | FHT_MAX = 1 |
| const int | FHT_ADD = 2 |
| const int | FHT_AVE = 3 |
| const int | BINARIZATION_NIBLACK = 0 |
| const int | BINARIZATION_SAUVOLA = 1 |
| const int | BINARIZATION_WOLF = 2 |
| const int | BINARIZATION_NICK = 3 |
| const int | SLIC = 100 |
| const int | SLICO = 101 |
| const int | MSLIC = 102 |
| const int | THINNING_ZHANGSUEN = 0 |
| const int | THINNING_GUOHALL = 1 |
| const int | WMF_EXP = 1 |
| const int | WMF_IV1 = 1 << 1 |
| const int | WMF_IV2 = 1 << 2 |
| const int | WMF_COS = 1 << 3 |
| const int | WMF_JAC = 1 << 4 |
| const int | WMF_OFF = 1 << 5 |
Member Function Documentation
◆ amFilter() [1/2]
|
static |
Simple one-line Adaptive Manifold Filter call.
- Parameters
-
joint joint (also called as guided) image or array of images with any numbers of channels. src filtering image with any numbers of channels. dst output image. sigma_s spatial standard deviation. sigma_r color space standard deviation, it is similar to the sigma in the color space into bilateralFilter. adjust_outliers optional, specify perform outliers adjust operation or not, (Eq. 9) in the original paper.
- Note
- Joint images with CV_8U and CV_16U depth converted to images with CV_32F depth and [0; 1] color range before processing. Hence color space sigma sigma_r must be in [0; 1] range, unlike same sigmas in bilateralFilter and dtFilter functions.
- See also
- bilateralFilter, dtFilter, guidedFilter
◆ amFilter() [2/2]
|
static |
Simple one-line Adaptive Manifold Filter call.
- Parameters
-
joint joint (also called as guided) image or array of images with any numbers of channels. src filtering image with any numbers of channels. dst output image. sigma_s spatial standard deviation. sigma_r color space standard deviation, it is similar to the sigma in the color space into bilateralFilter. adjust_outliers optional, specify perform outliers adjust operation or not, (Eq. 9) in the original paper.
- Note
- Joint images with CV_8U and CV_16U depth converted to images with CV_32F depth and [0; 1] color range before processing. Hence color space sigma sigma_r must be in [0; 1] range, unlike same sigmas in bilateralFilter and dtFilter functions.
- See also
- bilateralFilter, dtFilter, guidedFilter
◆ anisotropicDiffusion()
|
static |
Performs anisotropic diffusion on an image.
The function applies Perona-Malik anisotropic diffusion to an image. This is the solution to the partial differential equation:
\[{\frac {\partial I}{\partial t}}={\mathrm {div}}\left(c(x,y,t)\nabla I\right)=\nabla c\cdot \nabla I+c(x,y,t)\Delta I\]
Suggested functions for c(x,y,t) are:
\[c\left(\|\nabla I\|\right)=e^{{-\left(\|\nabla I\|/K\right)^{2}}}\]
or
\[ c\left(\|\nabla I\|\right)={\frac {1}{1+\left({\frac {\|\nabla I\|}{K}}\right)^{2}}} \]
- Parameters
-
src Source image with 3 channels. dst Destination image of the same size and the same number of channels as src . alpha The amount of time to step forward by on each iteration (normally, it's between 0 and 1). K sensitivity to the edges niters The number of iterations
◆ bilateralTextureFilter() [1/5]
|
static |
Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see [Cho2014].
- Parameters
-
src Source image whose depth is 8-bit UINT or 32-bit FLOAT dst Destination image of the same size and type as src. fr Radius of kernel to be used for filtering. It should be positive integer numIter Number of iterations of algorithm, It should be positive integer sigmaAlpha Controls the sharpness of the weight transition from edges to smooth/texture regions, where a bigger value means sharper transition. When the value is negative, it is automatically calculated. sigmaAvg Range blur parameter for texture blurring. Larger value makes result to be more blurred. When the value is negative, it is automatically calculated as described in the paper.
- See also
- rollingGuidanceFilter, bilateralFilter
◆ bilateralTextureFilter() [2/5]
|
static |
Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see [Cho2014].
- Parameters
-
src Source image whose depth is 8-bit UINT or 32-bit FLOAT dst Destination image of the same size and type as src. fr Radius of kernel to be used for filtering. It should be positive integer numIter Number of iterations of algorithm, It should be positive integer sigmaAlpha Controls the sharpness of the weight transition from edges to smooth/texture regions, where a bigger value means sharper transition. When the value is negative, it is automatically calculated. sigmaAvg Range blur parameter for texture blurring. Larger value makes result to be more blurred. When the value is negative, it is automatically calculated as described in the paper.
- See also
- rollingGuidanceFilter, bilateralFilter
◆ bilateralTextureFilter() [3/5]
|
static |
Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see [Cho2014].
- Parameters
-
src Source image whose depth is 8-bit UINT or 32-bit FLOAT dst Destination image of the same size and type as src. fr Radius of kernel to be used for filtering. It should be positive integer numIter Number of iterations of algorithm, It should be positive integer sigmaAlpha Controls the sharpness of the weight transition from edges to smooth/texture regions, where a bigger value means sharper transition. When the value is negative, it is automatically calculated. sigmaAvg Range blur parameter for texture blurring. Larger value makes result to be more blurred. When the value is negative, it is automatically calculated as described in the paper.
- See also
- rollingGuidanceFilter, bilateralFilter
◆ bilateralTextureFilter() [4/5]
|
static |
Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see [Cho2014].
- Parameters
-
src Source image whose depth is 8-bit UINT or 32-bit FLOAT dst Destination image of the same size and type as src. fr Radius of kernel to be used for filtering. It should be positive integer numIter Number of iterations of algorithm, It should be positive integer sigmaAlpha Controls the sharpness of the weight transition from edges to smooth/texture regions, where a bigger value means sharper transition. When the value is negative, it is automatically calculated. sigmaAvg Range blur parameter for texture blurring. Larger value makes result to be more blurred. When the value is negative, it is automatically calculated as described in the paper.
- See also
- rollingGuidanceFilter, bilateralFilter
◆ bilateralTextureFilter() [5/5]
|
static |
Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see [Cho2014].
- Parameters
-
src Source image whose depth is 8-bit UINT or 32-bit FLOAT dst Destination image of the same size and type as src. fr Radius of kernel to be used for filtering. It should be positive integer numIter Number of iterations of algorithm, It should be positive integer sigmaAlpha Controls the sharpness of the weight transition from edges to smooth/texture regions, where a bigger value means sharper transition. When the value is negative, it is automatically calculated. sigmaAvg Range blur parameter for texture blurring. Larger value makes result to be more blurred. When the value is negative, it is automatically calculated as described in the paper.
- See also
- rollingGuidanceFilter, bilateralFilter
◆ colorMatchTemplate()
|
static |
Compares a color template against overlapped color image regions.
- Parameters
-
img Image where the search is running. It must be 3 channels image templ Searched template. It must be not greater than the source image and have 3 channels result Map of comparison results. It must be single-channel 64-bit floating-point
◆ computeBadPixelPercent() [1/2]
|
static |
Function for computing the percent of "bad" pixels in the disparity map (pixels where error is higher than a specified threshold)
- Parameters
-
GT ground truth disparity map src disparity map to evaluate ROI region of interest thresh threshold used to determine "bad" pixels
- Returns
- returns mean square error between GT and src
◆ computeBadPixelPercent() [2/2]
|
static |
Function for computing the percent of "bad" pixels in the disparity map (pixels where error is higher than a specified threshold)
- Parameters
-
GT ground truth disparity map src disparity map to evaluate ROI region of interest thresh threshold used to determine "bad" pixels
- Returns
- returns mean square error between GT and src
◆ computeMSE()
|
static |
Function for computing mean square error for disparity maps.
- Parameters
-
GT ground truth disparity map src disparity map to evaluate ROI region of interest
- Returns
- returns mean square error between GT and src
◆ contourSampling()
|
static |
Contour sampling .
- Parameters
-
src contour type vector<Point> , vector<Point2f> or vector<Point2d> out Mat of type CV_64FC2 and nbElt rows nbElt number of points in out contour
◆ covarianceEstimation()
|
static |
Computes the estimated covariance matrix of an image using the sliding window forumlation.
- Parameters
-
src The source image. Input image must be of a complex type. dst The destination estimated covariance matrix. Output matrix will be size (windowRows*windowCols, windowRows*windowCols). windowRows The number of rows in the window. windowCols The number of cols in the window. The window size parameters control the accuracy of the estimation. The sliding window moves over the entire image from the top-left corner to the bottom right corner. Each location of the window represents a sample. If the window is the size of the image, then this gives the exact covariance matrix. For all other cases, the sizes of the window will impact the number of samples and the number of elements in the estimated covariance matrix.
◆ createAMFilter() [1/2]
|
static |
Factory method, create instance of AdaptiveManifoldFilter and produce some initialization routines.
- Parameters
-
sigma_s spatial standard deviation. sigma_r color space standard deviation, it is similar to the sigma in the color space into bilateralFilter. adjust_outliers optional, specify perform outliers adjust operation or not, (Eq. 9) in the original paper.
For more details about Adaptive Manifold Filter parameters, see the original article [Gastal12] .
- Note
- Joint images with CV_8U and CV_16U depth converted to images with CV_32F depth and [0; 1] color range before processing. Hence color space sigma sigma_r must be in [0; 1] range, unlike same sigmas in bilateralFilter and dtFilter functions.
◆ createAMFilter() [2/2]
|
static |
Factory method, create instance of AdaptiveManifoldFilter and produce some initialization routines.
- Parameters
-
sigma_s spatial standard deviation. sigma_r color space standard deviation, it is similar to the sigma in the color space into bilateralFilter. adjust_outliers optional, specify perform outliers adjust operation or not, (Eq. 9) in the original paper.
For more details about Adaptive Manifold Filter parameters, see the original article [Gastal12] .
- Note
- Joint images with CV_8U and CV_16U depth converted to images with CV_32F depth and [0; 1] color range before processing. Hence color space sigma sigma_r must be in [0; 1] range, unlike same sigmas in bilateralFilter and dtFilter functions.
◆ createContourFitting() [1/3]
|
static |
create ContourFitting algorithm object
- Parameters
-
ctr number of Fourier descriptors equal to number of contour points after resampling. fd Contour defining second shape (Target).
◆ createContourFitting() [2/3]
|
static |
create ContourFitting algorithm object
- Parameters
-
ctr number of Fourier descriptors equal to number of contour points after resampling. fd Contour defining second shape (Target).
◆ createContourFitting() [3/3]
|
static |
create ContourFitting algorithm object
- Parameters
-
ctr number of Fourier descriptors equal to number of contour points after resampling. fd Contour defining second shape (Target).
◆ createDisparityWLSFilter()
|
static |
Convenience factory method that creates an instance of DisparityWLSFilter and sets up all the relevant filter parameters automatically based on the matcher instance. Currently supports only StereoBM and StereoSGBM.
- Parameters
-
matcher_left stereo matcher instance that will be used with the filter
◆ createDisparityWLSFilterGeneric()
|
static |
More generic factory method, create instance of DisparityWLSFilter and execute basic initialization routines. When using this method you will need to set-up the ROI, matchers and other parameters by yourself.
- Parameters
-
use_confidence filtering with confidence requires two disparity maps (for the left and right views) and is approximately two times slower. However, quality is typically significantly better.
◆ createDTFilter() [1/3]
|
static |
Factory method, create instance of DTFilter and produce initialization routines.
- Parameters
-
guide guided image (used to build transformed distance, which describes edge structure of guided image). sigmaSpatial \({\sigma}_H\) parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter. sigmaColor \({\sigma}_r\) parameter in the original article, it's similar to the sigma in the color space into bilateralFilter. mode one form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article. numIters optional number of iterations used for filtering, 3 is quite enough.
For more details about Domain Transform filter parameters, see the original article [Gastal11] and Domain Transform filter homepage.
◆ createDTFilter() [2/3]
|
static |
Factory method, create instance of DTFilter and produce initialization routines.
- Parameters
-
guide guided image (used to build transformed distance, which describes edge structure of guided image). sigmaSpatial \({\sigma}_H\) parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter. sigmaColor \({\sigma}_r\) parameter in the original article, it's similar to the sigma in the color space into bilateralFilter. mode one form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article. numIters optional number of iterations used for filtering, 3 is quite enough.
For more details about Domain Transform filter parameters, see the original article [Gastal11] and Domain Transform filter homepage.
◆ createDTFilter() [3/3]
|
static |
Factory method, create instance of DTFilter and produce initialization routines.
- Parameters
-
guide guided image (used to build transformed distance, which describes edge structure of guided image). sigmaSpatial \({\sigma}_H\) parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter. sigmaColor \({\sigma}_r\) parameter in the original article, it's similar to the sigma in the color space into bilateralFilter. mode one form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article. numIters optional number of iterations used for filtering, 3 is quite enough.
For more details about Domain Transform filter parameters, see the original article [Gastal11] and Domain Transform filter homepage.
◆ createEdgeAwareInterpolator()
|
static |
Factory method that creates an instance of the EdgeAwareInterpolator.
◆ createEdgeBoxes() [1/13]
|
static |
Creates a Edgeboxes.
- Parameters
-
alpha step size of sliding window search. beta nms threshold for object proposals. eta adaptation rate for nms threshold. minScore min score of boxes to detect. maxBoxes max number of boxes to detect. edgeMinMag edge min magnitude. Increase to trade off accuracy for speed. edgeMergeThr edge merge threshold. Increase to trade off accuracy for speed. clusterMinMag cluster min magnitude. Increase to trade off accuracy for speed. maxAspectRatio max aspect ratio of boxes. minBoxArea minimum area of boxes. gamma affinity sensitivity. kappa scale sensitivity.
◆ createEdgeBoxes() [2/13]
|
static |
Creates a Edgeboxes.
- Parameters
-
alpha step size of sliding window search. beta nms threshold for object proposals. eta adaptation rate for nms threshold. minScore min score of boxes to detect. maxBoxes max number of boxes to detect. edgeMinMag edge min magnitude. Increase to trade off accuracy for speed. edgeMergeThr edge merge threshold. Increase to trade off accuracy for speed. clusterMinMag cluster min magnitude. Increase to trade off accuracy for speed. maxAspectRatio max aspect ratio of boxes. minBoxArea minimum area of boxes. gamma affinity sensitivity. kappa scale sensitivity.
◆ createEdgeBoxes() [3/13]
|
static |
Creates a Edgeboxes.
- Parameters
-
alpha step size of sliding window search. beta nms threshold for object proposals. eta adaptation rate for nms threshold. minScore min score of boxes to detect. maxBoxes max number of boxes to detect. edgeMinMag edge min magnitude. Increase to trade off accuracy for speed. edgeMergeThr edge merge threshold. Increase to trade off accuracy for speed. clusterMinMag cluster min magnitude. Increase to trade off accuracy for speed. maxAspectRatio max aspect ratio of boxes. minBoxArea minimum area of boxes. gamma affinity sensitivity. kappa scale sensitivity.
◆ createEdgeBoxes() [4/13]
|
static |
Creates a Edgeboxes.
- Parameters
-
alpha step size of sliding window search. beta nms threshold for object proposals. eta adaptation rate for nms threshold. minScore min score of boxes to detect. maxBoxes max number of boxes to detect. edgeMinMag edge min magnitude. Increase to trade off accuracy for speed. edgeMergeThr edge merge threshold. Increase to trade off accuracy for speed. clusterMinMag cluster min magnitude. Increase to trade off accuracy for speed. maxAspectRatio max aspect ratio of boxes. minBoxArea minimum area of boxes. gamma affinity sensitivity. kappa scale sensitivity.
◆ createEdgeBoxes() [5/13]
|
static |
Creates a Edgeboxes.
- Parameters
-
alpha step size of sliding window search. beta nms threshold for object proposals. eta adaptation rate for nms threshold. minScore min score of boxes to detect. maxBoxes max number of boxes to detect. edgeMinMag edge min magnitude. Increase to trade off accuracy for speed. edgeMergeThr edge merge threshold. Increase to trade off accuracy for speed. clusterMinMag cluster min magnitude. Increase to trade off accuracy for speed. maxAspectRatio max aspect ratio of boxes. minBoxArea minimum area of boxes. gamma affinity sensitivity. kappa scale sensitivity.
◆ createEdgeBoxes() [6/13]
|
static |
Creates a Edgeboxes.
- Parameters
-
alpha step size of sliding window search. beta nms threshold for object proposals. eta adaptation rate for nms threshold. minScore min score of boxes to detect. maxBoxes max number of boxes to detect. edgeMinMag edge min magnitude. Increase to trade off accuracy for speed. edgeMergeThr edge merge threshold. Increase to trade off accuracy for speed. clusterMinMag cluster min magnitude. Increase to trade off accuracy for speed. maxAspectRatio max aspect ratio of boxes. minBoxArea minimum area of boxes. gamma affinity sensitivity. kappa scale sensitivity.
◆ createEdgeBoxes() [7/13]
|
static |
Creates a Edgeboxes.
- Parameters
-
alpha step size of sliding window search. beta nms threshold for object proposals. eta adaptation rate for nms threshold. minScore min score of boxes to detect. maxBoxes max number of boxes to detect. edgeMinMag edge min magnitude. Increase to trade off accuracy for speed. edgeMergeThr edge merge threshold. Increase to trade off accuracy for speed. clusterMinMag cluster min magnitude. Increase to trade off accuracy for speed. maxAspectRatio max aspect ratio of boxes. minBoxArea minimum area of boxes. gamma affinity sensitivity. kappa scale sensitivity.
◆ createEdgeBoxes() [8/13]
|
static |
Creates a Edgeboxes.
- Parameters
-
alpha step size of sliding window search. beta nms threshold for object proposals. eta adaptation rate for nms threshold. minScore min score of boxes to detect. maxBoxes max number of boxes to detect. edgeMinMag edge min magnitude. Increase to trade off accuracy for speed. edgeMergeThr edge merge threshold. Increase to trade off accuracy for speed. clusterMinMag cluster min magnitude. Increase to trade off accuracy for speed. maxAspectRatio max aspect ratio of boxes. minBoxArea minimum area of boxes. gamma affinity sensitivity. kappa scale sensitivity.
◆ createEdgeBoxes() [9/13]
|
static |
Creates a Edgeboxes.
- Parameters
-
alpha step size of sliding window search. beta nms threshold for object proposals. eta adaptation rate for nms threshold. minScore min score of boxes to detect. maxBoxes max number of boxes to detect. edgeMinMag edge min magnitude. Increase to trade off accuracy for speed. edgeMergeThr edge merge threshold. Increase to trade off accuracy for speed. clusterMinMag cluster min magnitude. Increase to trade off accuracy for speed. maxAspectRatio max aspect ratio of boxes. minBoxArea minimum area of boxes. gamma affinity sensitivity. kappa scale sensitivity.
◆ createEdgeBoxes() [10/13]
|
static |
Creates a Edgeboxes.
- Parameters
-
alpha step size of sliding window search. beta nms threshold for object proposals. eta adaptation rate for nms threshold. minScore min score of boxes to detect. maxBoxes max number of boxes to detect. edgeMinMag edge min magnitude. Increase to trade off accuracy for speed. edgeMergeThr edge merge threshold. Increase to trade off accuracy for speed. clusterMinMag cluster min magnitude. Increase to trade off accuracy for speed. maxAspectRatio max aspect ratio of boxes. minBoxArea minimum area of boxes. gamma affinity sensitivity. kappa scale sensitivity.
◆ createEdgeBoxes() [11/13]
|
static |
Creates a Edgeboxes.
- Parameters
-
alpha step size of sliding window search. beta nms threshold for object proposals. eta adaptation rate for nms threshold. minScore min score of boxes to detect. maxBoxes max number of boxes to detect. edgeMinMag edge min magnitude. Increase to trade off accuracy for speed. edgeMergeThr edge merge threshold. Increase to trade off accuracy for speed. clusterMinMag cluster min magnitude. Increase to trade off accuracy for speed. maxAspectRatio max aspect ratio of boxes. minBoxArea minimum area of boxes. gamma affinity sensitivity. kappa scale sensitivity.
◆ createEdgeBoxes() [12/13]
|
static |
Creates a Edgeboxes.
- Parameters
-
alpha step size of sliding window search. beta nms threshold for object proposals. eta adaptation rate for nms threshold. minScore min score of boxes to detect. maxBoxes max number of boxes to detect. edgeMinMag edge min magnitude. Increase to trade off accuracy for speed. edgeMergeThr edge merge threshold. Increase to trade off accuracy for speed. clusterMinMag cluster min magnitude. Increase to trade off accuracy for speed. maxAspectRatio max aspect ratio of boxes. minBoxArea minimum area of boxes. gamma affinity sensitivity. kappa scale sensitivity.
◆ createEdgeBoxes() [13/13]
|
static |
Creates a Edgeboxes.
- Parameters
-
alpha step size of sliding window search. beta nms threshold for object proposals. eta adaptation rate for nms threshold. minScore min score of boxes to detect. maxBoxes max number of boxes to detect. edgeMinMag edge min magnitude. Increase to trade off accuracy for speed. edgeMergeThr edge merge threshold. Increase to trade off accuracy for speed. clusterMinMag cluster min magnitude. Increase to trade off accuracy for speed. maxAspectRatio max aspect ratio of boxes. minBoxArea minimum area of boxes. gamma affinity sensitivity. kappa scale sensitivity.
◆ createEdgeDrawing()
|
static |
Creates a smart pointer to a EdgeDrawing object and initializes it.
◆ createFastBilateralSolverFilter() [1/4]
|
static |
Factory method, create instance of FastBilateralSolverFilter and execute the initialization routines.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. sigma_spatial parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter. sigma_luma parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter. sigma_chroma parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter. lambda smoothness strength parameter for solver. num_iter number of iterations used for solver, 25 is usually enough. max_tol convergence tolerance used for solver.
For more details about the Fast Bilateral Solver parameters, see the original paper [BarronPoole2016].
◆ createFastBilateralSolverFilter() [2/4]
|
static |
Factory method, create instance of FastBilateralSolverFilter and execute the initialization routines.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. sigma_spatial parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter. sigma_luma parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter. sigma_chroma parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter. lambda smoothness strength parameter for solver. num_iter number of iterations used for solver, 25 is usually enough. max_tol convergence tolerance used for solver.
For more details about the Fast Bilateral Solver parameters, see the original paper [BarronPoole2016].
◆ createFastBilateralSolverFilter() [3/4]
|
static |
Factory method, create instance of FastBilateralSolverFilter and execute the initialization routines.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. sigma_spatial parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter. sigma_luma parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter. sigma_chroma parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter. lambda smoothness strength parameter for solver. num_iter number of iterations used for solver, 25 is usually enough. max_tol convergence tolerance used for solver.
For more details about the Fast Bilateral Solver parameters, see the original paper [BarronPoole2016].
◆ createFastBilateralSolverFilter() [4/4]
|
static |
Factory method, create instance of FastBilateralSolverFilter and execute the initialization routines.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. sigma_spatial parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter. sigma_luma parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter. sigma_chroma parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter. lambda smoothness strength parameter for solver. num_iter number of iterations used for solver, 25 is usually enough. max_tol convergence tolerance used for solver.
For more details about the Fast Bilateral Solver parameters, see the original paper [BarronPoole2016].
◆ createFastGlobalSmootherFilter() [1/3]
|
static |
Factory method, create instance of FastGlobalSmootherFilter and execute the initialization routines.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. lambda parameter defining the amount of regularization sigma_color parameter, that is similar to color space sigma in bilateralFilter. lambda_attenuation internal parameter, defining how much lambda decreases after each iteration. Normally, it should be 0.25. Setting it to 1.0 may lead to streaking artifacts. num_iter number of iterations used for filtering, 3 is usually enough.
For more details about Fast Global Smoother parameters, see the original paper [Min2014]. However, please note that there are several differences. Lambda attenuation described in the paper is implemented a bit differently so do not expect the results to be identical to those from the paper; sigma_color values from the paper should be multiplied by 255.0 to achieve the same effect. Also, in case of image filtering where source and guide image are the same, authors propose to dynamically update the guide image after each iteration. To maximize the performance this feature was not implemented here.
◆ createFastGlobalSmootherFilter() [2/3]
|
static |
Factory method, create instance of FastGlobalSmootherFilter and execute the initialization routines.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. lambda parameter defining the amount of regularization sigma_color parameter, that is similar to color space sigma in bilateralFilter. lambda_attenuation internal parameter, defining how much lambda decreases after each iteration. Normally, it should be 0.25. Setting it to 1.0 may lead to streaking artifacts. num_iter number of iterations used for filtering, 3 is usually enough.
For more details about Fast Global Smoother parameters, see the original paper [Min2014]. However, please note that there are several differences. Lambda attenuation described in the paper is implemented a bit differently so do not expect the results to be identical to those from the paper; sigma_color values from the paper should be multiplied by 255.0 to achieve the same effect. Also, in case of image filtering where source and guide image are the same, authors propose to dynamically update the guide image after each iteration. To maximize the performance this feature was not implemented here.
◆ createFastGlobalSmootherFilter() [3/3]
|
static |
Factory method, create instance of FastGlobalSmootherFilter and execute the initialization routines.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. lambda parameter defining the amount of regularization sigma_color parameter, that is similar to color space sigma in bilateralFilter. lambda_attenuation internal parameter, defining how much lambda decreases after each iteration. Normally, it should be 0.25. Setting it to 1.0 may lead to streaking artifacts. num_iter number of iterations used for filtering, 3 is usually enough.
For more details about Fast Global Smoother parameters, see the original paper [Min2014]. However, please note that there are several differences. Lambda attenuation described in the paper is implemented a bit differently so do not expect the results to be identical to those from the paper; sigma_color values from the paper should be multiplied by 255.0 to achieve the same effect. Also, in case of image filtering where source and guide image are the same, authors propose to dynamically update the guide image after each iteration. To maximize the performance this feature was not implemented here.
◆ createFastLineDetector() [1/7]
|
static |
Creates a smart pointer to a FastLineDetector object and initializes it.
- Parameters
-
length_threshold Segment shorter than this will be discarded distance_threshold A point placed from a hypothesis line segment farther than this will be regarded as an outlier canny_th1 First threshold for hysteresis procedure in Canny() canny_th2 Second threshold for hysteresis procedure in Canny() canny_aperture_size Aperturesize for the sobel operator in Canny(). If zero, Canny() is not applied and the input image is taken as an edge image. do_merge If true, incremental merging of segments will be performed
◆ createFastLineDetector() [2/7]
|
static |
Creates a smart pointer to a FastLineDetector object and initializes it.
- Parameters
-
length_threshold Segment shorter than this will be discarded distance_threshold A point placed from a hypothesis line segment farther than this will be regarded as an outlier canny_th1 First threshold for hysteresis procedure in Canny() canny_th2 Second threshold for hysteresis procedure in Canny() canny_aperture_size Aperturesize for the sobel operator in Canny(). If zero, Canny() is not applied and the input image is taken as an edge image. do_merge If true, incremental merging of segments will be performed
◆ createFastLineDetector() [3/7]
|
static |
Creates a smart pointer to a FastLineDetector object and initializes it.
- Parameters
-
length_threshold Segment shorter than this will be discarded distance_threshold A point placed from a hypothesis line segment farther than this will be regarded as an outlier canny_th1 First threshold for hysteresis procedure in Canny() canny_th2 Second threshold for hysteresis procedure in Canny() canny_aperture_size Aperturesize for the sobel operator in Canny(). If zero, Canny() is not applied and the input image is taken as an edge image. do_merge If true, incremental merging of segments will be performed
◆ createFastLineDetector() [4/7]
|
static |
Creates a smart pointer to a FastLineDetector object and initializes it.
- Parameters
-
length_threshold Segment shorter than this will be discarded distance_threshold A point placed from a hypothesis line segment farther than this will be regarded as an outlier canny_th1 First threshold for hysteresis procedure in Canny() canny_th2 Second threshold for hysteresis procedure in Canny() canny_aperture_size Aperturesize for the sobel operator in Canny(). If zero, Canny() is not applied and the input image is taken as an edge image. do_merge If true, incremental merging of segments will be performed
◆ createFastLineDetector() [5/7]
|
static |
Creates a smart pointer to a FastLineDetector object and initializes it.
- Parameters
-
length_threshold Segment shorter than this will be discarded distance_threshold A point placed from a hypothesis line segment farther than this will be regarded as an outlier canny_th1 First threshold for hysteresis procedure in Canny() canny_th2 Second threshold for hysteresis procedure in Canny() canny_aperture_size Aperturesize for the sobel operator in Canny(). If zero, Canny() is not applied and the input image is taken as an edge image. do_merge If true, incremental merging of segments will be performed
◆ createFastLineDetector() [6/7]
|
static |
Creates a smart pointer to a FastLineDetector object and initializes it.
- Parameters
-
length_threshold Segment shorter than this will be discarded distance_threshold A point placed from a hypothesis line segment farther than this will be regarded as an outlier canny_th1 First threshold for hysteresis procedure in Canny() canny_th2 Second threshold for hysteresis procedure in Canny() canny_aperture_size Aperturesize for the sobel operator in Canny(). If zero, Canny() is not applied and the input image is taken as an edge image. do_merge If true, incremental merging of segments will be performed
◆ createFastLineDetector() [7/7]
|
static |
Creates a smart pointer to a FastLineDetector object and initializes it.
- Parameters
-
length_threshold Segment shorter than this will be discarded distance_threshold A point placed from a hypothesis line segment farther than this will be regarded as an outlier canny_th1 First threshold for hysteresis procedure in Canny() canny_th2 Second threshold for hysteresis procedure in Canny() canny_aperture_size Aperturesize for the sobel operator in Canny(). If zero, Canny() is not applied and the input image is taken as an edge image. do_merge If true, incremental merging of segments will be performed
◆ createGraphSegmentation() [1/4]
|
static |
Creates a graph based segmentor.
- Parameters
-
sigma The sigma parameter, used to smooth image k The k parameter of the algorythm min_size The minimum size of segments
◆ createGraphSegmentation() [2/4]
|
static |
Creates a graph based segmentor.
- Parameters
-
sigma The sigma parameter, used to smooth image k The k parameter of the algorythm min_size The minimum size of segments
◆ createGraphSegmentation() [3/4]
|
static |
Creates a graph based segmentor.
- Parameters
-
sigma The sigma parameter, used to smooth image k The k parameter of the algorythm min_size The minimum size of segments
◆ createGraphSegmentation() [4/4]
|
static |
Creates a graph based segmentor.
- Parameters
-
sigma The sigma parameter, used to smooth image k The k parameter of the algorythm min_size The minimum size of segments
◆ createGuidedFilter()
|
static |
Factory method, create instance of GuidedFilter and produce initialization routines.
- Parameters
-
guide guided image (or array of images) with up to 3 channels, if it have more then 3 channels then only first 3 channels will be used. radius radius of Guided Filter. eps regularization term of Guided Filter. \({eps}^2\) is similar to the sigma in the color space into bilateralFilter.
For more details about Guided Filter parameters, see the original article [Kaiming10] .
◆ createQuaternionImage()
|
static |
creates a quaternion image.
- Parameters
-
img Source 8-bit, 32-bit or 64-bit image, with 3-channel image. qimg result CV_64FC4 a quaternion image( 4 chanels zero channel and B,G,R).
◆ createRFFeatureGetter()
|
static |
◆ createRICInterpolator()
|
static |
Factory method that creates an instance of the RICInterpolator.
◆ createRightMatcher()
|
static |
Convenience method to set up the matcher for computing the right-view disparity map that is required in case of filtering with confidence.
- Parameters
-
matcher_left main stereo matcher instance that will be used with the filter
◆ createScanSegment() [1/3]
|
static |
Initializes a ScanSegment object.
The function initializes a ScanSegment object for the input image. It stores the parameters of the image: image_width and image_height. It also sets the parameters of the F-DBSCAN superpixel algorithm, which are: num_superpixels, threads, and merge_small.
- Parameters
-
image_width Image width. image_height Image height. num_superpixels Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size). Use getNumberOfSuperpixels() to get the actual number. slices Number of processing threads for parallelisation. Setting -1 uses the maximum number of threads. In practice, four threads is enough for smaller images and eight threads for larger ones. merge_small merge small segments to give the desired number of superpixels. Processing is much faster without merging, but many small segments will be left in the image.
◆ createScanSegment() [2/3]
|
static |
Initializes a ScanSegment object.
The function initializes a ScanSegment object for the input image. It stores the parameters of the image: image_width and image_height. It also sets the parameters of the F-DBSCAN superpixel algorithm, which are: num_superpixels, threads, and merge_small.
- Parameters
-
image_width Image width. image_height Image height. num_superpixels Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size). Use getNumberOfSuperpixels() to get the actual number. slices Number of processing threads for parallelisation. Setting -1 uses the maximum number of threads. In practice, four threads is enough for smaller images and eight threads for larger ones. merge_small merge small segments to give the desired number of superpixels. Processing is much faster without merging, but many small segments will be left in the image.
◆ createScanSegment() [3/3]
|
static |
Initializes a ScanSegment object.
The function initializes a ScanSegment object for the input image. It stores the parameters of the image: image_width and image_height. It also sets the parameters of the F-DBSCAN superpixel algorithm, which are: num_superpixels, threads, and merge_small.
- Parameters
-
image_width Image width. image_height Image height. num_superpixels Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size). Use getNumberOfSuperpixels() to get the actual number. slices Number of processing threads for parallelisation. Setting -1 uses the maximum number of threads. In practice, four threads is enough for smaller images and eight threads for larger ones. merge_small merge small segments to give the desired number of superpixels. Processing is much faster without merging, but many small segments will be left in the image.
◆ createSelectiveSearchSegmentation()
|
static |
Create a new SelectiveSearchSegmentation class.
◆ createSelectiveSearchSegmentationStrategyColor()
|
static |
Create a new color-based strategy.
◆ createSelectiveSearchSegmentationStrategyFill()
|
static |
Create a new fill-based strategy.
◆ createSelectiveSearchSegmentationStrategyMultiple() [1/5]
|
static |
Create a new multiple strategy.
◆ createSelectiveSearchSegmentationStrategyMultiple() [2/5]
|
static |
Create a new multiple strategy and set one subtrategy.
- Parameters
-
s1 The first strategy
◆ createSelectiveSearchSegmentationStrategyMultiple() [3/5]
|
static |
Create a new multiple strategy and set two subtrategies, with equal weights.
- Parameters
-
s1 The first strategy s2 The second strategy
◆ createSelectiveSearchSegmentationStrategyMultiple() [4/5]
|
static |
Create a new multiple strategy and set three subtrategies, with equal weights.
- Parameters
-
s1 The first strategy s2 The second strategy s3 The third strategy
◆ createSelectiveSearchSegmentationStrategyMultiple() [5/5]
|
static |
Create a new multiple strategy and set four subtrategies, with equal weights.
- Parameters
-
s1 The first strategy s2 The second strategy s3 The third strategy s4 The forth strategy
◆ createSelectiveSearchSegmentationStrategySize()
|
static |
Create a new size-based strategy.
◆ createSelectiveSearchSegmentationStrategyTexture()
|
static |
Create a new size-based strategy.
◆ createStructuredEdgeDetection() [1/2]
|
static |
◆ createStructuredEdgeDetection() [2/2]
|
static |
◆ createSuperpixelLSC() [1/3]
|
static |
Class implementing the LSC (Linear Spectral Clustering) superpixels.
- Parameters
-
image Image to segment region_size Chooses an average superpixel size measured in pixels ratio Chooses the enforcement of superpixel compactness factor of superpixel
The function initializes a SuperpixelLSC object for the input image. It sets the parameters of superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future computing iterations over the given image. An example of LSC is ilustrated in the following picture. For enanched results it is recommended for color images to preprocess image with little gaussian blur with a small 3 x 3 kernel and additional conversion into CieLAB color space.
image
◆ createSuperpixelLSC() [2/3]
|
static |
Class implementing the LSC (Linear Spectral Clustering) superpixels.
- Parameters
-
image Image to segment region_size Chooses an average superpixel size measured in pixels ratio Chooses the enforcement of superpixel compactness factor of superpixel
The function initializes a SuperpixelLSC object for the input image. It sets the parameters of superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future computing iterations over the given image. An example of LSC is ilustrated in the following picture. For enanched results it is recommended for color images to preprocess image with little gaussian blur with a small 3 x 3 kernel and additional conversion into CieLAB color space.
image
◆ createSuperpixelLSC() [3/3]
|
static |
Class implementing the LSC (Linear Spectral Clustering) superpixels.
- Parameters
-
image Image to segment region_size Chooses an average superpixel size measured in pixels ratio Chooses the enforcement of superpixel compactness factor of superpixel
The function initializes a SuperpixelLSC object for the input image. It sets the parameters of superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future computing iterations over the given image. An example of LSC is ilustrated in the following picture. For enanched results it is recommended for color images to preprocess image with little gaussian blur with a small 3 x 3 kernel and additional conversion into CieLAB color space.
image
◆ createSuperpixelSEEDS() [1/4]
|
static |
Initializes a SuperpixelSEEDS object.
- Parameters
-
image_width Image width. image_height Image height. image_channels Number of channels of the image. num_superpixels Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size and num_levels). Use getNumberOfSuperpixels() to get the actual number. num_levels Number of block levels. The more levels, the more accurate is the segmentation, but needs more memory and CPU time. prior enable 3x3 shape smoothing term if >0. A larger value leads to smoother shapes. prior must be in the range [0, 5]. histogram_bins Number of histogram bins. double_step If true, iterate each block level twice for higher accuracy.
The function initializes a SuperpixelSEEDS object for the input image. It stores the parameters of the image: image_width, image_height and image_channels. It also sets the parameters of the SEEDS superpixel algorithm, which are: num_superpixels, num_levels, use_prior, histogram_bins and double_step.
The number of levels in num_levels defines the amount of block levels that the algorithm use in the optimization. The initialization is a grid, in which the superpixels are equally distributed through the width and the height of the image. The larger blocks correspond to the superpixel size, and the levels with smaller blocks are formed by dividing the larger blocks into 2 x 2 blocks of pixels, recursively until the smaller block level. An example of initialization of 4 block levels is illustrated in the following figure.
image
◆ createSuperpixelSEEDS() [2/4]
|
static |
Initializes a SuperpixelSEEDS object.
- Parameters
-
image_width Image width. image_height Image height. image_channels Number of channels of the image. num_superpixels Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size and num_levels). Use getNumberOfSuperpixels() to get the actual number. num_levels Number of block levels. The more levels, the more accurate is the segmentation, but needs more memory and CPU time. prior enable 3x3 shape smoothing term if >0. A larger value leads to smoother shapes. prior must be in the range [0, 5]. histogram_bins Number of histogram bins. double_step If true, iterate each block level twice for higher accuracy.
The function initializes a SuperpixelSEEDS object for the input image. It stores the parameters of the image: image_width, image_height and image_channels. It also sets the parameters of the SEEDS superpixel algorithm, which are: num_superpixels, num_levels, use_prior, histogram_bins and double_step.
The number of levels in num_levels defines the amount of block levels that the algorithm use in the optimization. The initialization is a grid, in which the superpixels are equally distributed through the width and the height of the image. The larger blocks correspond to the superpixel size, and the levels with smaller blocks are formed by dividing the larger blocks into 2 x 2 blocks of pixels, recursively until the smaller block level. An example of initialization of 4 block levels is illustrated in the following figure.
image
◆ createSuperpixelSEEDS() [3/4]
|
static |
Initializes a SuperpixelSEEDS object.
- Parameters
-
image_width Image width. image_height Image height. image_channels Number of channels of the image. num_superpixels Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size and num_levels). Use getNumberOfSuperpixels() to get the actual number. num_levels Number of block levels. The more levels, the more accurate is the segmentation, but needs more memory and CPU time. prior enable 3x3 shape smoothing term if >0. A larger value leads to smoother shapes. prior must be in the range [0, 5]. histogram_bins Number of histogram bins. double_step If true, iterate each block level twice for higher accuracy.
The function initializes a SuperpixelSEEDS object for the input image. It stores the parameters of the image: image_width, image_height and image_channels. It also sets the parameters of the SEEDS superpixel algorithm, which are: num_superpixels, num_levels, use_prior, histogram_bins and double_step.
The number of levels in num_levels defines the amount of block levels that the algorithm use in the optimization. The initialization is a grid, in which the superpixels are equally distributed through the width and the height of the image. The larger blocks correspond to the superpixel size, and the levels with smaller blocks are formed by dividing the larger blocks into 2 x 2 blocks of pixels, recursively until the smaller block level. An example of initialization of 4 block levels is illustrated in the following figure.
image
◆ createSuperpixelSEEDS() [4/4]
|
static |
Initializes a SuperpixelSEEDS object.
- Parameters
-
image_width Image width. image_height Image height. image_channels Number of channels of the image. num_superpixels Desired number of superpixels. Note that the actual number may be smaller due to restrictions (depending on the image size and num_levels). Use getNumberOfSuperpixels() to get the actual number. num_levels Number of block levels. The more levels, the more accurate is the segmentation, but needs more memory and CPU time. prior enable 3x3 shape smoothing term if >0. A larger value leads to smoother shapes. prior must be in the range [0, 5]. histogram_bins Number of histogram bins. double_step If true, iterate each block level twice for higher accuracy.
The function initializes a SuperpixelSEEDS object for the input image. It stores the parameters of the image: image_width, image_height and image_channels. It also sets the parameters of the SEEDS superpixel algorithm, which are: num_superpixels, num_levels, use_prior, histogram_bins and double_step.
The number of levels in num_levels defines the amount of block levels that the algorithm use in the optimization. The initialization is a grid, in which the superpixels are equally distributed through the width and the height of the image. The larger blocks correspond to the superpixel size, and the levels with smaller blocks are formed by dividing the larger blocks into 2 x 2 blocks of pixels, recursively until the smaller block level. An example of initialization of 4 block levels is illustrated in the following figure.
image
◆ createSuperpixelSLIC() [1/4]
|
static |
Initialize a SuperpixelSLIC object.
- Parameters
-
image Image to segment algorithm Chooses the algorithm variant to use: SLIC segments image using a desired region_size, and in addition SLICO will optimize using adaptive compactness factor, while MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels. region_size Chooses an average superpixel size measured in pixels ruler Chooses the enforcement of superpixel smoothness factor of superpixel
The function initializes a SuperpixelSLIC object for the input image. It sets the parameters of choosed superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future computing iterations over the given image. For enanched results it is recommended for color images to preprocess image with little gaussian blur using a small 3 x 3 kernel and additional conversion into CieLAB color space. An example of SLIC versus SLICO and MSLIC is ilustrated in the following picture.
image
◆ createSuperpixelSLIC() [2/4]
|
static |
Initialize a SuperpixelSLIC object.
- Parameters
-
image Image to segment algorithm Chooses the algorithm variant to use: SLIC segments image using a desired region_size, and in addition SLICO will optimize using adaptive compactness factor, while MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels. region_size Chooses an average superpixel size measured in pixels ruler Chooses the enforcement of superpixel smoothness factor of superpixel
The function initializes a SuperpixelSLIC object for the input image. It sets the parameters of choosed superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future computing iterations over the given image. For enanched results it is recommended for color images to preprocess image with little gaussian blur using a small 3 x 3 kernel and additional conversion into CieLAB color space. An example of SLIC versus SLICO and MSLIC is ilustrated in the following picture.
image
◆ createSuperpixelSLIC() [3/4]
|
static |
Initialize a SuperpixelSLIC object.
- Parameters
-
image Image to segment algorithm Chooses the algorithm variant to use: SLIC segments image using a desired region_size, and in addition SLICO will optimize using adaptive compactness factor, while MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels. region_size Chooses an average superpixel size measured in pixels ruler Chooses the enforcement of superpixel smoothness factor of superpixel
The function initializes a SuperpixelSLIC object for the input image. It sets the parameters of choosed superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future computing iterations over the given image. For enanched results it is recommended for color images to preprocess image with little gaussian blur using a small 3 x 3 kernel and additional conversion into CieLAB color space. An example of SLIC versus SLICO and MSLIC is ilustrated in the following picture.
image
◆ createSuperpixelSLIC() [4/4]
|
static |
Initialize a SuperpixelSLIC object.
- Parameters
-
image Image to segment algorithm Chooses the algorithm variant to use: SLIC segments image using a desired region_size, and in addition SLICO will optimize using adaptive compactness factor, while MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels. region_size Chooses an average superpixel size measured in pixels ruler Chooses the enforcement of superpixel smoothness factor of superpixel
The function initializes a SuperpixelSLIC object for the input image. It sets the parameters of choosed superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future computing iterations over the given image. For enanched results it is recommended for color images to preprocess image with little gaussian blur using a small 3 x 3 kernel and additional conversion into CieLAB color space. An example of SLIC versus SLICO and MSLIC is ilustrated in the following picture.
image
◆ dtFilter() [1/3]
|
static |
Simple one-line Domain Transform filter call. If you have multiple images to filter with the same guided image then use DTFilter interface to avoid extra computations on initialization stage.
- Parameters
-
guide guided image (also called as joint image) with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels. src filtering image with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels. dst destination image sigmaSpatial \({\sigma}_H\) parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter. sigmaColor \({\sigma}_r\) parameter in the original article, it's similar to the sigma in the color space into bilateralFilter. mode one form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article. numIters optional number of iterations used for filtering, 3 is quite enough.
- See also
- bilateralFilter, guidedFilter, amFilter
◆ dtFilter() [2/3]
|
static |
Simple one-line Domain Transform filter call. If you have multiple images to filter with the same guided image then use DTFilter interface to avoid extra computations on initialization stage.
- Parameters
-
guide guided image (also called as joint image) with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels. src filtering image with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels. dst destination image sigmaSpatial \({\sigma}_H\) parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter. sigmaColor \({\sigma}_r\) parameter in the original article, it's similar to the sigma in the color space into bilateralFilter. mode one form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article. numIters optional number of iterations used for filtering, 3 is quite enough.
- See also
- bilateralFilter, guidedFilter, amFilter
◆ dtFilter() [3/3]
|
static |
Simple one-line Domain Transform filter call. If you have multiple images to filter with the same guided image then use DTFilter interface to avoid extra computations on initialization stage.
- Parameters
-
guide guided image (also called as joint image) with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels. src filtering image with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels. dst destination image sigmaSpatial \({\sigma}_H\) parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter. sigmaColor \({\sigma}_r\) parameter in the original article, it's similar to the sigma in the color space into bilateralFilter. mode one form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article. numIters optional number of iterations used for filtering, 3 is quite enough.
- See also
- bilateralFilter, guidedFilter, amFilter
◆ edgePreservingFilter()
|
static |
Smoothes an image using the Edge-Preserving filter.
The function smoothes Gaussian noise as well as salt & pepper noise. For more details about this implementation, please see [ReiWoe18] Reich, S. and Wörgötter, F. and Dellen, B. (2018). A Real-Time Edge-Preserving Denoising Filter. Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP): Visapp, 85-94, 4. DOI: 10.5220/0006509000850094.
- Parameters
-
src Source 8-bit 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. Must be greater or equal 3. threshold Threshold, which distinguishes between noise, outliers, and data.
◆ fastBilateralSolverFilter() [1/7]
|
static |
Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. src source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels. confidence confidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel. dst destination image. sigma_spatial parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter. sigma_luma parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter. sigma_chroma parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter. lambda smoothness strength parameter for solver. num_iter number of iterations used for solver, 25 is usually enough. max_tol convergence tolerance used for solver.
For more details about the Fast Bilateral Solver parameters, see the original paper [BarronPoole2016].
- Note
- Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.
◆ fastBilateralSolverFilter() [2/7]
|
static |
Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. src source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels. confidence confidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel. dst destination image. sigma_spatial parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter. sigma_luma parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter. sigma_chroma parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter. lambda smoothness strength parameter for solver. num_iter number of iterations used for solver, 25 is usually enough. max_tol convergence tolerance used for solver.
For more details about the Fast Bilateral Solver parameters, see the original paper [BarronPoole2016].
- Note
- Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.
◆ fastBilateralSolverFilter() [3/7]
|
static |
Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. src source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels. confidence confidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel. dst destination image. sigma_spatial parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter. sigma_luma parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter. sigma_chroma parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter. lambda smoothness strength parameter for solver. num_iter number of iterations used for solver, 25 is usually enough. max_tol convergence tolerance used for solver.
For more details about the Fast Bilateral Solver parameters, see the original paper [BarronPoole2016].
- Note
- Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.
◆ fastBilateralSolverFilter() [4/7]
|
static |
Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. src source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels. confidence confidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel. dst destination image. sigma_spatial parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter. sigma_luma parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter. sigma_chroma parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter. lambda smoothness strength parameter for solver. num_iter number of iterations used for solver, 25 is usually enough. max_tol convergence tolerance used for solver.
For more details about the Fast Bilateral Solver parameters, see the original paper [BarronPoole2016].
- Note
- Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.
◆ fastBilateralSolverFilter() [5/7]
|
static |
Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. src source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels. confidence confidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel. dst destination image. sigma_spatial parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter. sigma_luma parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter. sigma_chroma parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter. lambda smoothness strength parameter for solver. num_iter number of iterations used for solver, 25 is usually enough. max_tol convergence tolerance used for solver.
For more details about the Fast Bilateral Solver parameters, see the original paper [BarronPoole2016].
- Note
- Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.
◆ fastBilateralSolverFilter() [6/7]
|
static |
Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. src source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels. confidence confidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel. dst destination image. sigma_spatial parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter. sigma_luma parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter. sigma_chroma parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter. lambda smoothness strength parameter for solver. num_iter number of iterations used for solver, 25 is usually enough. max_tol convergence tolerance used for solver.
For more details about the Fast Bilateral Solver parameters, see the original paper [BarronPoole2016].
- Note
- Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.
◆ fastBilateralSolverFilter() [7/7]
|
static |
Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. src source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels. confidence confidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel. dst destination image. sigma_spatial parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter. sigma_luma parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter. sigma_chroma parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter. lambda smoothness strength parameter for solver. num_iter number of iterations used for solver, 25 is usually enough. max_tol convergence tolerance used for solver.
For more details about the Fast Bilateral Solver parameters, see the original paper [BarronPoole2016].
- Note
- Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.
◆ fastGlobalSmootherFilter() [1/3]
|
static |
Simple one-line Fast Global Smoother filter call. If you have multiple images to filter with the same guide then use FastGlobalSmootherFilter interface to avoid extra computations.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. src source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels. dst destination image. lambda parameter defining the amount of regularization sigma_color parameter, that is similar to color space sigma in bilateralFilter. lambda_attenuation internal parameter, defining how much lambda decreases after each iteration. Normally, it should be 0.25. Setting it to 1.0 may lead to streaking artifacts. num_iter number of iterations used for filtering, 3 is usually enough.
◆ fastGlobalSmootherFilter() [2/3]
|
static |
Simple one-line Fast Global Smoother filter call. If you have multiple images to filter with the same guide then use FastGlobalSmootherFilter interface to avoid extra computations.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. src source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels. dst destination image. lambda parameter defining the amount of regularization sigma_color parameter, that is similar to color space sigma in bilateralFilter. lambda_attenuation internal parameter, defining how much lambda decreases after each iteration. Normally, it should be 0.25. Setting it to 1.0 may lead to streaking artifacts. num_iter number of iterations used for filtering, 3 is usually enough.
◆ fastGlobalSmootherFilter() [3/3]
|
static |
Simple one-line Fast Global Smoother filter call. If you have multiple images to filter with the same guide then use FastGlobalSmootherFilter interface to avoid extra computations.
- Parameters
-
guide image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels. src source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels. dst destination image. lambda parameter defining the amount of regularization sigma_color parameter, that is similar to color space sigma in bilateralFilter. lambda_attenuation internal parameter, defining how much lambda decreases after each iteration. Normally, it should be 0.25. Setting it to 1.0 may lead to streaking artifacts. num_iter number of iterations used for filtering, 3 is usually enough.
◆ FastHoughTransform() [1/4]
|
static |
Calculates 2D Fast Hough transform of an image.
- Parameters
-
dst The destination image, result of transformation. src The source (input) image. dstMatDepth The depth of destination image op The operation to be applied, see cv::HoughOp angleRange The part of Hough space to calculate, see cv::AngleRangeOption makeSkew Specifies to do or not to do image skewing, see cv::HoughDeskewOption
The function calculates the fast Hough transform for full, half or quarter range of angles.
◆ FastHoughTransform() [2/4]
|
static |
Calculates 2D Fast Hough transform of an image.
- Parameters
-
dst The destination image, result of transformation. src The source (input) image. dstMatDepth The depth of destination image op The operation to be applied, see cv::HoughOp angleRange The part of Hough space to calculate, see cv::AngleRangeOption makeSkew Specifies to do or not to do image skewing, see cv::HoughDeskewOption
The function calculates the fast Hough transform for full, half or quarter range of angles.
◆ FastHoughTransform() [3/4]
|
static |
Calculates 2D Fast Hough transform of an image.
- Parameters
-
dst The destination image, result of transformation. src The source (input) image. dstMatDepth The depth of destination image op The operation to be applied, see cv::HoughOp angleRange The part of Hough space to calculate, see cv::AngleRangeOption makeSkew Specifies to do or not to do image skewing, see cv::HoughDeskewOption
The function calculates the fast Hough transform for full, half or quarter range of angles.
◆ FastHoughTransform() [4/4]
|
static |
Calculates 2D Fast Hough transform of an image.
- Parameters
-
dst The destination image, result of transformation. src The source (input) image. dstMatDepth The depth of destination image op The operation to be applied, see cv::HoughOp angleRange The part of Hough space to calculate, see cv::AngleRangeOption makeSkew Specifies to do or not to do image skewing, see cv::HoughDeskewOption
The function calculates the fast Hough transform for full, half or quarter range of angles.
◆ findEllipses() [1/4]
|
static |
Finds ellipses fastly in an image using projective invariant pruning.
The function detects ellipses in images using projective invariant pruning. For more details about this implementation, please see [jia2017fast] Jia, Qi et al, (2017). A Fast Ellipse Detector using Projective Invariant Pruning. IEEE Transactions on Image Processing.
- Parameters
-
image input image, could be gray or color. ellipses output vector of found ellipses. each vector is encoded as five float $x, y, a, b, radius, score$. scoreThreshold float, the threshold of ellipse score. reliabilityThreshold float, the threshold of reliability. centerDistanceThreshold float, the threshold of center distance.
◆ findEllipses() [2/4]
|
static |
Finds ellipses fastly in an image using projective invariant pruning.
The function detects ellipses in images using projective invariant pruning. For more details about this implementation, please see [jia2017fast] Jia, Qi et al, (2017). A Fast Ellipse Detector using Projective Invariant Pruning. IEEE Transactions on Image Processing.
- Parameters
-
image input image, could be gray or color. ellipses output vector of found ellipses. each vector is encoded as five float $x, y, a, b, radius, score$. scoreThreshold float, the threshold of ellipse score. reliabilityThreshold float, the threshold of reliability. centerDistanceThreshold float, the threshold of center distance.
◆ findEllipses() [3/4]
|
static |
Finds ellipses fastly in an image using projective invariant pruning.
The function detects ellipses in images using projective invariant pruning. For more details about this implementation, please see [jia2017fast] Jia, Qi et al, (2017). A Fast Ellipse Detector using Projective Invariant Pruning. IEEE Transactions on Image Processing.
- Parameters
-
image input image, could be gray or color. ellipses output vector of found ellipses. each vector is encoded as five float $x, y, a, b, radius, score$. scoreThreshold float, the threshold of ellipse score. reliabilityThreshold float, the threshold of reliability. centerDistanceThreshold float, the threshold of center distance.
◆ findEllipses() [4/4]
Finds ellipses fastly in an image using projective invariant pruning.
The function detects ellipses in images using projective invariant pruning. For more details about this implementation, please see [jia2017fast] Jia, Qi et al, (2017). A Fast Ellipse Detector using Projective Invariant Pruning. IEEE Transactions on Image Processing.
- Parameters
-
image input image, could be gray or color. ellipses output vector of found ellipses. each vector is encoded as five float $x, y, a, b, radius, score$. scoreThreshold float, the threshold of ellipse score. reliabilityThreshold float, the threshold of reliability. centerDistanceThreshold float, the threshold of center distance.
◆ fourierDescriptor() [1/3]
|
static |
Fourier descriptors for planed closed curves.
For more details about this implementation, please see [PersoonFu1977]
- Parameters
-
src contour type vector<Point> , vector<Point2f> or vector<Point2d> dst Mat of type CV_64FC2 and nbElt rows A VERIFIER nbElt number of rows in dst or getOptimalDFTSize rows if nbElt=-1 nbFD number of FD return in dst dst = [FD(1...nbFD/2) FD(nbFD/2-nbElt+1...:nbElt)]
◆ fourierDescriptor() [2/3]
|
static |
Fourier descriptors for planed closed curves.
For more details about this implementation, please see [PersoonFu1977]
- Parameters
-
src contour type vector<Point> , vector<Point2f> or vector<Point2d> dst Mat of type CV_64FC2 and nbElt rows A VERIFIER nbElt number of rows in dst or getOptimalDFTSize rows if nbElt=-1 nbFD number of FD return in dst dst = [FD(1...nbFD/2) FD(nbFD/2-nbElt+1...:nbElt)]
◆ fourierDescriptor() [3/3]
Fourier descriptors for planed closed curves.
For more details about this implementation, please see [PersoonFu1977]
- Parameters
-
src contour type vector<Point> , vector<Point2f> or vector<Point2d> dst Mat of type CV_64FC2 and nbElt rows A VERIFIER nbElt number of rows in dst or getOptimalDFTSize rows if nbElt=-1 nbFD number of FD return in dst dst = [FD(1...nbFD/2) FD(nbFD/2-nbElt+1...:nbElt)]
◆ getDisparityVis() [1/2]
|
static |
Function for creating a disparity map visualization (clamped CV_8U image)
- Parameters
-
src input disparity map (CV_16S depth) dst output visualization scale disparity map will be multiplied by this value for visualization
◆ getDisparityVis() [2/2]
Function for creating a disparity map visualization (clamped CV_8U image)
- Parameters
-
src input disparity map (CV_16S depth) dst output visualization scale disparity map will be multiplied by this value for visualization
◆ GradientDericheX()
|
static |
Applies X Deriche filter to an image.
For more details about this implementation, please see http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.5736&rep=rep1&type=pdf
- Parameters
-
op Source 8-bit or 16bit image, 1-channel or 3-channel image. dst result CV_32FC image with same number of channel than _op. alpha double see paper omega double see paper
◆ GradientDericheY()
|
static |
Applies Y Deriche filter to an image.
For more details about this implementation, please see http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.5736&rep=rep1&type=pdf
- Parameters
-
op Source 8-bit or 16bit image, 1-channel or 3-channel image. dst result CV_32FC image with same number of channel than _op. alpha double see paper omega double see paper
◆ guidedFilter() [1/2]
|
static |
Simple one-line Guided Filter call.
If you have multiple images to filter with the same guided image then use GuidedFilter interface to avoid extra computations on initialization stage.
- Parameters
-
guide guided image (or array of images) with up to 3 channels, if it have more then 3 channels then only first 3 channels will be used. src filtering image with any numbers of channels. dst output image. radius radius of Guided Filter. eps regularization term of Guided Filter. \({eps}^2\) is similar to the sigma in the color space into bilateralFilter. dDepth optional depth of the output image.
◆ guidedFilter() [2/2]
|
static |
Simple one-line Guided Filter call.
If you have multiple images to filter with the same guided image then use GuidedFilter interface to avoid extra computations on initialization stage.
- Parameters
-
guide guided image (or array of images) with up to 3 channels, if it have more then 3 channels then only first 3 channels will be used. src filtering image with any numbers of channels. dst output image. radius radius of Guided Filter. eps regularization term of Guided Filter. \({eps}^2\) is similar to the sigma in the color space into bilateralFilter. dDepth optional depth of the output image.
◆ jointBilateralFilter() [1/2]
|
static |
Applies the joint bilateral filter to an image.
- Parameters
-
joint Joint 8-bit or floating-point, 1-channel or 3-channel image. src Source 8-bit or floating-point, 1-channel or 3-channel image with the same depth as joint 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
- Note
- bilateralFilter and jointBilateralFilter use L1 norm to compute difference between colors.
- See also
- bilateralFilter, amFilter
◆ jointBilateralFilter() [2/2]
|
static |
Applies the joint bilateral filter to an image.
- Parameters
-
joint Joint 8-bit or floating-point, 1-channel or 3-channel image. src Source 8-bit or floating-point, 1-channel or 3-channel image with the same depth as joint 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
- Note
- bilateralFilter and jointBilateralFilter use L1 norm to compute difference between colors.
- See also
- bilateralFilter, amFilter
◆ l0Smooth() [1/3]
|
static |
Global image smoothing via L0 gradient minimization.
- Parameters
-
src source image for filtering with unsigned 8-bit or signed 16-bit or floating-point depth. dst destination image. lambda parameter defining the smooth term weight. kappa parameter defining the increasing factor of the weight of the gradient data term.
For more details about L0 Smoother, see the original paper [xu2011image].
◆ l0Smooth() [2/3]
|
static |
Global image smoothing via L0 gradient minimization.
- Parameters
-
src source image for filtering with unsigned 8-bit or signed 16-bit or floating-point depth. dst destination image. lambda parameter defining the smooth term weight. kappa parameter defining the increasing factor of the weight of the gradient data term.
For more details about L0 Smoother, see the original paper [xu2011image].
◆ l0Smooth() [3/3]
Global image smoothing via L0 gradient minimization.
- Parameters
-
src source image for filtering with unsigned 8-bit or signed 16-bit or floating-point depth. dst destination image. lambda parameter defining the smooth term weight. kappa parameter defining the increasing factor of the weight of the gradient data term.
For more details about L0 Smoother, see the original paper [xu2011image].
◆ niBlackThreshold() [1/3]
|
static |
Performs thresholding on input images using Niblack's technique or some of the popular variations it inspired.
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.
The threshold value \(T(x, y)\) is determined based on the binarization method chosen. For classic Niblack, it is the mean minus \( k \) times standard deviation of \(\texttt{blockSize} \times\texttt{blockSize}\) neighborhood of \((x, y)\).
The function can't 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, used with the THRESH_BINARY and THRESH_BINARY_INV thresholding types. type Thresholding type, see cv::ThresholdTypes. blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on. k The user-adjustable parameter used by Niblack and inspired techniques. For Niblack, this is normally a value between 0 and 1 that is multiplied with the standard deviation and subtracted from the mean. binarizationMethod Binarization method to use. By default, Niblack's technique is used. Other techniques can be specified, see cv::ximgproc::LocalBinarizationMethods. r The user-adjustable parameter used by Sauvola's technique. This is the dynamic range of standard deviation.
- See also
- threshold, adaptiveThreshold
◆ niBlackThreshold() [2/3]
|
static |
Performs thresholding on input images using Niblack's technique or some of the popular variations it inspired.
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.
The threshold value \(T(x, y)\) is determined based on the binarization method chosen. For classic Niblack, it is the mean minus \( k \) times standard deviation of \(\texttt{blockSize} \times\texttt{blockSize}\) neighborhood of \((x, y)\).
The function can't 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, used with the THRESH_BINARY and THRESH_BINARY_INV thresholding types. type Thresholding type, see cv::ThresholdTypes. blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on. k The user-adjustable parameter used by Niblack and inspired techniques. For Niblack, this is normally a value between 0 and 1 that is multiplied with the standard deviation and subtracted from the mean. binarizationMethod Binarization method to use. By default, Niblack's technique is used. Other techniques can be specified, see cv::ximgproc::LocalBinarizationMethods. r The user-adjustable parameter used by Sauvola's technique. This is the dynamic range of standard deviation.
- See also
- threshold, adaptiveThreshold
◆ niBlackThreshold() [3/3]
|
static |
Performs thresholding on input images using Niblack's technique or some of the popular variations it inspired.
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.
The threshold value \(T(x, y)\) is determined based on the binarization method chosen. For classic Niblack, it is the mean minus \( k \) times standard deviation of \(\texttt{blockSize} \times\texttt{blockSize}\) neighborhood of \((x, y)\).
The function can't 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, used with the THRESH_BINARY and THRESH_BINARY_INV thresholding types. type Thresholding type, see cv::ThresholdTypes. blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on. k The user-adjustable parameter used by Niblack and inspired techniques. For Niblack, this is normally a value between 0 and 1 that is multiplied with the standard deviation and subtracted from the mean. binarizationMethod Binarization method to use. By default, Niblack's technique is used. Other techniques can be specified, see cv::ximgproc::LocalBinarizationMethods. r The user-adjustable parameter used by Sauvola's technique. This is the dynamic range of standard deviation.
- See also
- threshold, adaptiveThreshold
◆ PeiLinNormalization()
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
◆ qconj()
calculates conjugate of a quaternion image.
- Parameters
-
qimg quaternion image. qcimg conjugate of qimg
◆ qdft()
|
static |
Performs a forward or inverse Discrete quaternion Fourier transform of a 2D quaternion array.
- Parameters
-
img quaternion image. qimg quaternion image in dual space. flags quaternion image in dual space. only DFT_INVERSE flags is supported sideLeft true the hypercomplex exponential is to be multiplied on the left (false on the right ).
◆ qmultiply()
Calculates the per-element quaternion product of two arrays.
- Parameters
-
src1 quaternion image. src2 quaternion image. dst product dst(I)=src1(I) . src2(I)
◆ qunitary()
divides each element by its modulus.
- Parameters
-
qimg quaternion image. qnimg conjugate of qimg
◆ RadonTransform() [1/6]
|
static |
Calculate Radon Transform of an image.
- Parameters
-
src The source (input) image. dst The destination image, result of transformation. theta Angle resolution of the transform in degrees. start_angle Start angle of the transform in degrees. end_angle End angle of the transform in degrees. crop Crop the source image into a circle. norm Normalize the output Mat to grayscale and convert type to CV_8U
This function calculates the Radon Transform of a given image in any range. See https://engineering.purdue.edu/~malcolm/pct/CTI_Ch03.pdf for detail. If the input type is CV_8U, the output will be CV_32S. If the input type is CV_32F or CV_64F, the output will be CV_64F The output size will be num_of_integral x src_diagonal_length. If crop is selected, the input image will be crop into square then circle, and output size will be num_of_integral x min_edge.
◆ RadonTransform() [2/6]
|
static |
Calculate Radon Transform of an image.
- Parameters
-
src The source (input) image. dst The destination image, result of transformation. theta Angle resolution of the transform in degrees. start_angle Start angle of the transform in degrees. end_angle End angle of the transform in degrees. crop Crop the source image into a circle. norm Normalize the output Mat to grayscale and convert type to CV_8U
This function calculates the Radon Transform of a given image in any range. See https://engineering.purdue.edu/~malcolm/pct/CTI_Ch03.pdf for detail. If the input type is CV_8U, the output will be CV_32S. If the input type is CV_32F or CV_64F, the output will be CV_64F The output size will be num_of_integral x src_diagonal_length. If crop is selected, the input image will be crop into square then circle, and output size will be num_of_integral x min_edge.
◆ RadonTransform() [3/6]
|
static |
Calculate Radon Transform of an image.
- Parameters
-
src The source (input) image. dst The destination image, result of transformation. theta Angle resolution of the transform in degrees. start_angle Start angle of the transform in degrees. end_angle End angle of the transform in degrees. crop Crop the source image into a circle. norm Normalize the output Mat to grayscale and convert type to CV_8U
This function calculates the Radon Transform of a given image in any range. See https://engineering.purdue.edu/~malcolm/pct/CTI_Ch03.pdf for detail. If the input type is CV_8U, the output will be CV_32S. If the input type is CV_32F or CV_64F, the output will be CV_64F The output size will be num_of_integral x src_diagonal_length. If crop is selected, the input image will be crop into square then circle, and output size will be num_of_integral x min_edge.
◆ RadonTransform() [4/6]
|
static |
Calculate Radon Transform of an image.
- Parameters
-
src The source (input) image. dst The destination image, result of transformation. theta Angle resolution of the transform in degrees. start_angle Start angle of the transform in degrees. end_angle End angle of the transform in degrees. crop Crop the source image into a circle. norm Normalize the output Mat to grayscale and convert type to CV_8U
This function calculates the Radon Transform of a given image in any range. See https://engineering.purdue.edu/~malcolm/pct/CTI_Ch03.pdf for detail. If the input type is CV_8U, the output will be CV_32S. If the input type is CV_32F or CV_64F, the output will be CV_64F The output size will be num_of_integral x src_diagonal_length. If crop is selected, the input image will be crop into square then circle, and output size will be num_of_integral x min_edge.
◆ RadonTransform() [5/6]
|
static |
Calculate Radon Transform of an image.
- Parameters
-
src The source (input) image. dst The destination image, result of transformation. theta Angle resolution of the transform in degrees. start_angle Start angle of the transform in degrees. end_angle End angle of the transform in degrees. crop Crop the source image into a circle. norm Normalize the output Mat to grayscale and convert type to CV_8U
This function calculates the Radon Transform of a given image in any range. See https://engineering.purdue.edu/~malcolm/pct/CTI_Ch03.pdf for detail. If the input type is CV_8U, the output will be CV_32S. If the input type is CV_32F or CV_64F, the output will be CV_64F The output size will be num_of_integral x src_diagonal_length. If crop is selected, the input image will be crop into square then circle, and output size will be num_of_integral x min_edge.
◆ RadonTransform() [6/6]
Calculate Radon Transform of an image.
- Parameters
-
src The source (input) image. dst The destination image, result of transformation. theta Angle resolution of the transform in degrees. start_angle Start angle of the transform in degrees. end_angle End angle of the transform in degrees. crop Crop the source image into a circle. norm Normalize the output Mat to grayscale and convert type to CV_8U
This function calculates the Radon Transform of a given image in any range. See https://engineering.purdue.edu/~malcolm/pct/CTI_Ch03.pdf for detail. If the input type is CV_8U, the output will be CV_32S. If the input type is CV_32F or CV_64F, the output will be CV_64F The output size will be num_of_integral x src_diagonal_length. If crop is selected, the input image will be crop into square then circle, and output size will be num_of_integral x min_edge.
◆ readGT()
|
static |
Function for reading ground truth disparity maps. Supports basic Middlebury and MPI-Sintel formats. Note that the resulting disparity map is scaled by 16.
- Parameters
-
src_path path to the image, containing ground-truth disparity map dst output disparity map, CV_16S depth
- Returns
- returns zero if successfully read the ground truth
◆ rollingGuidanceFilter() [1/6]
|
static |
Applies the rolling guidance filter to an image.
For more details, please see [zhang2014rolling]
- 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 . numOfIter Number of iterations of joint edge-preserving filtering applied on the source image. borderType
- Note
- rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter.
- See also
- jointBilateralFilter, bilateralFilter, amFilter
◆ rollingGuidanceFilter() [2/6]
|
static |
Applies the rolling guidance filter to an image.
For more details, please see [zhang2014rolling]
- 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 . numOfIter Number of iterations of joint edge-preserving filtering applied on the source image. borderType
- Note
- rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter.
- See also
- jointBilateralFilter, bilateralFilter, amFilter
◆ rollingGuidanceFilter() [3/6]
|
static |
Applies the rolling guidance filter to an image.
For more details, please see [zhang2014rolling]
- 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 . numOfIter Number of iterations of joint edge-preserving filtering applied on the source image. borderType
- Note
- rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter.
- See also
- jointBilateralFilter, bilateralFilter, amFilter
◆ rollingGuidanceFilter() [4/6]
|
static |
Applies the rolling guidance filter to an image.
For more details, please see [zhang2014rolling]
- 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 . numOfIter Number of iterations of joint edge-preserving filtering applied on the source image. borderType
- Note
- rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter.
- See also
- jointBilateralFilter, bilateralFilter, amFilter
◆ rollingGuidanceFilter() [5/6]
|
static |
Applies the rolling guidance filter to an image.
For more details, please see [zhang2014rolling]
- 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 . numOfIter Number of iterations of joint edge-preserving filtering applied on the source image. borderType
- Note
- rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter.
- See also
- jointBilateralFilter, bilateralFilter, amFilter
◆ rollingGuidanceFilter() [6/6]
|
static |
Applies the rolling guidance filter to an image.
For more details, please see [zhang2014rolling]
- 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 . numOfIter Number of iterations of joint edge-preserving filtering applied on the source image. borderType
- Note
- rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter.
- See also
- jointBilateralFilter, bilateralFilter, amFilter
◆ thinning() [1/2]
|
static |
Applies a binary blob thinning operation, to achieve a skeletization of the input image.
The function transforms a binary blob image into a skeletized form using the technique of Zhang-Suen.
- Parameters
-
src Source 8-bit single-channel image, containing binary blobs, with blobs having 255 pixel values. dst Destination image of the same size and the same type as src. The function can work in-place. thinningType Value that defines which thinning algorithm should be used. See cv::ximgproc::ThinningTypes
◆ thinning() [2/2]
Applies a binary blob thinning operation, to achieve a skeletization of the input image.
The function transforms a binary blob image into a skeletized form using the technique of Zhang-Suen.
- Parameters
-
src Source 8-bit single-channel image, containing binary blobs, with blobs having 255 pixel values. dst Destination image of the same size and the same type as src. The function can work in-place. thinningType Value that defines which thinning algorithm should be used. See cv::ximgproc::ThinningTypes
◆ transformFD() [1/2]
|
static |
transform a contour
- Parameters
-
src contour or Fourier Descriptors if fd is true t transform Mat given by estimateTransformation dst Mat of type CV_64FC2 and nbElt rows fdContour true src are Fourier Descriptors. fdContour false src is a contour
◆ transformFD() [2/2]
transform a contour
- Parameters
-
src contour or Fourier Descriptors if fd is true t transform Mat given by estimateTransformation dst Mat of type CV_64FC2 and nbElt rows fdContour true src are Fourier Descriptors. fdContour false src is a contour
◆ weightedMedianFilter() [1/4]
|
static |
Applies weighted median filter to an image.
For more details about this implementation, please see [zhang2014100+]
- Parameters
-
joint Joint 8-bit, 1-channel or 3-channel image. src Source 8-bit or floating-point, 1-channel or 3-channel image. dst Destination image. r Radius of filtering kernel, should be a positive integer. sigma Filter range standard deviation for the joint image. weightType weightType The type of weight definition, see WMFWeightType mask A 0-1 mask that has the same size with I. This mask is used to ignore the effect of some pixels. If the pixel value on mask is 0, the pixel will be ignored when maintaining the joint-histogram. This is useful for applications like optical flow occlusion handling.
- See also
- medianBlur, jointBilateralFilter
◆ weightedMedianFilter() [2/4]
|
static |
Applies weighted median filter to an image.
For more details about this implementation, please see [zhang2014100+]
- Parameters
-
joint Joint 8-bit, 1-channel or 3-channel image. src Source 8-bit or floating-point, 1-channel or 3-channel image. dst Destination image. r Radius of filtering kernel, should be a positive integer. sigma Filter range standard deviation for the joint image. weightType weightType The type of weight definition, see WMFWeightType mask A 0-1 mask that has the same size with I. This mask is used to ignore the effect of some pixels. If the pixel value on mask is 0, the pixel will be ignored when maintaining the joint-histogram. This is useful for applications like optical flow occlusion handling.
- See also
- medianBlur, jointBilateralFilter
◆ weightedMedianFilter() [3/4]
|
static |
Applies weighted median filter to an image.
For more details about this implementation, please see [zhang2014100+]
- Parameters
-
joint Joint 8-bit, 1-channel or 3-channel image. src Source 8-bit or floating-point, 1-channel or 3-channel image. dst Destination image. r Radius of filtering kernel, should be a positive integer. sigma Filter range standard deviation for the joint image. weightType weightType The type of weight definition, see WMFWeightType mask A 0-1 mask that has the same size with I. This mask is used to ignore the effect of some pixels. If the pixel value on mask is 0, the pixel will be ignored when maintaining the joint-histogram. This is useful for applications like optical flow occlusion handling.
- See also
- medianBlur, jointBilateralFilter
◆ weightedMedianFilter() [4/4]
|
static |
Applies weighted median filter to an image.
For more details about this implementation, please see [zhang2014100+]
- Parameters
-
joint Joint 8-bit, 1-channel or 3-channel image. src Source 8-bit or floating-point, 1-channel or 3-channel image. dst Destination image. r Radius of filtering kernel, should be a positive integer. sigma Filter range standard deviation for the joint image. weightType weightType The type of weight definition, see WMFWeightType mask A 0-1 mask that has the same size with I. This mask is used to ignore the effect of some pixels. If the pixel value on mask is 0, the pixel will be ignored when maintaining the joint-histogram. This is useful for applications like optical flow occlusion handling.
- See also
- medianBlur, jointBilateralFilter
Member Data Documentation
◆ AM_FILTER
| const int OpenCVForUnity.XimgprocModule.Ximgproc.AM_FILTER = 4 |
◆ ARO_0_45
| const int OpenCVForUnity.XimgprocModule.Ximgproc.ARO_0_45 = 0 |
◆ ARO_315_0
| const int OpenCVForUnity.XimgprocModule.Ximgproc.ARO_315_0 = 3 |
◆ ARO_315_135
| const int OpenCVForUnity.XimgprocModule.Ximgproc.ARO_315_135 = 6 |
◆ ARO_315_45
| const int OpenCVForUnity.XimgprocModule.Ximgproc.ARO_315_45 = 4 |
◆ ARO_45_135
| const int OpenCVForUnity.XimgprocModule.Ximgproc.ARO_45_135 = 5 |
◆ ARO_45_90
| const int OpenCVForUnity.XimgprocModule.Ximgproc.ARO_45_90 = 1 |
◆ ARO_90_135
| const int OpenCVForUnity.XimgprocModule.Ximgproc.ARO_90_135 = 2 |
◆ ARO_CTR_HOR
| const int OpenCVForUnity.XimgprocModule.Ximgproc.ARO_CTR_HOR = 7 |
◆ ARO_CTR_VER
| const int OpenCVForUnity.XimgprocModule.Ximgproc.ARO_CTR_VER = 8 |
◆ BINARIZATION_NIBLACK
| const int OpenCVForUnity.XimgprocModule.Ximgproc.BINARIZATION_NIBLACK = 0 |
◆ BINARIZATION_NICK
| const int OpenCVForUnity.XimgprocModule.Ximgproc.BINARIZATION_NICK = 3 |
◆ BINARIZATION_SAUVOLA
| const int OpenCVForUnity.XimgprocModule.Ximgproc.BINARIZATION_SAUVOLA = 1 |
◆ BINARIZATION_WOLF
| const int OpenCVForUnity.XimgprocModule.Ximgproc.BINARIZATION_WOLF = 2 |
◆ DTF_IC
| const int OpenCVForUnity.XimgprocModule.Ximgproc.DTF_IC = 1 |
◆ DTF_NC
| const int OpenCVForUnity.XimgprocModule.Ximgproc.DTF_NC = 0 |
◆ DTF_RF
| const int OpenCVForUnity.XimgprocModule.Ximgproc.DTF_RF = 2 |
◆ FHT_ADD
| const int OpenCVForUnity.XimgprocModule.Ximgproc.FHT_ADD = 2 |
◆ FHT_AVE
| const int OpenCVForUnity.XimgprocModule.Ximgproc.FHT_AVE = 3 |
◆ FHT_MAX
| const int OpenCVForUnity.XimgprocModule.Ximgproc.FHT_MAX = 1 |
◆ FHT_MIN
| const int OpenCVForUnity.XimgprocModule.Ximgproc.FHT_MIN = 0 |
◆ GUIDED_FILTER
| const int OpenCVForUnity.XimgprocModule.Ximgproc.GUIDED_FILTER = 3 |
◆ HDO_DESKEW
| const int OpenCVForUnity.XimgprocModule.Ximgproc.HDO_DESKEW = 1 |
◆ HDO_RAW
| const int OpenCVForUnity.XimgprocModule.Ximgproc.HDO_RAW = 0 |
◆ MSLIC
| const int OpenCVForUnity.XimgprocModule.Ximgproc.MSLIC = 102 |
◆ SLIC
| const int OpenCVForUnity.XimgprocModule.Ximgproc.SLIC = 100 |
◆ SLICO
| const int OpenCVForUnity.XimgprocModule.Ximgproc.SLICO = 101 |
◆ THINNING_GUOHALL
| const int OpenCVForUnity.XimgprocModule.Ximgproc.THINNING_GUOHALL = 1 |
◆ THINNING_ZHANGSUEN
| const int OpenCVForUnity.XimgprocModule.Ximgproc.THINNING_ZHANGSUEN = 0 |
◆ WMF_COS
| const int OpenCVForUnity.XimgprocModule.Ximgproc.WMF_COS = 1 << 3 |
◆ WMF_EXP
| const int OpenCVForUnity.XimgprocModule.Ximgproc.WMF_EXP = 1 |
◆ WMF_IV1
| const int OpenCVForUnity.XimgprocModule.Ximgproc.WMF_IV1 = 1 << 1 |
◆ WMF_IV2
| const int OpenCVForUnity.XimgprocModule.Ximgproc.WMF_IV2 = 1 << 2 |
◆ WMF_JAC
| const int OpenCVForUnity.XimgprocModule.Ximgproc.WMF_JAC = 1 << 4 |
◆ WMF_OFF
| const int OpenCVForUnity.XimgprocModule.Ximgproc.WMF_OFF = 1 << 5 |
The documentation for this class was generated from the following file:
- OpenCVForUnity/Assets/OpenCVForUnity/org/opencv_contrib/ximgproc/Ximgproc.cs
