org.opencv.ximgproc

## Class Ximgproc

- java.lang.Object
- - org.opencv.ximgproc.Ximgproc

-     public class Ximgproc
      extends Object

- - ### Field Summary

    Fields
    Modifier and Type | Field and Description
    --- | ---
    `static int` | `AM_FILTER`
    `static int` | `ARO_0_45`
    `static int` | `ARO_315_0`
    `static int` | `ARO_315_135`
    `static int` | `ARO_315_45`
    `static int` | `ARO_45_135`
    `static int` | `ARO_45_90`
    `static int` | `ARO_90_135`
    `static int` | `ARO_CTR_HOR`
    `static int` | `ARO_CTR_VER`
    `static int` | `BINARIZATION_NIBLACK`
    `static int` | `BINARIZATION_NICK`
    `static int` | `BINARIZATION_SAUVOLA`
    `static int` | `BINARIZATION_WOLF`
    `static int` | `DTF_IC`
    `static int` | `DTF_NC`
    `static int` | `DTF_RF`
    `static int` | `FHT_ADD`
    `static int` | `FHT_AVE`
    `static int` | `FHT_MAX`
    `static int` | `FHT_MIN`
    `static int` | `GUIDED_FILTER`
    `static int` | `HDO_DESKEW`
    `static int` | `HDO_RAW`
    `static int` | `MSLIC`
    `static int` | `SLIC`
    `static int` | `SLICO`
    `static int` | `THINNING_GUOHALL`
    `static int` | `THINNING_ZHANGSUEN`
    `static int` | `WMF_COS`
    `static int` | `WMF_EXP`
    `static int` | `WMF_IV1`
    `static int` | `WMF_IV2`
    `static int` | `WMF_JAC`
    `static int` | `WMF_OFF`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `Ximgproc()`

  - ### Method Summary

    All Methods Static Methods Concrete Methods
    Modifier and Type | Method and Description
    --- | ---
    `static void` | `amFilter(Mat joint, Mat src, Mat dst, double sigma_s, double sigma_r)` Simple one-line Adaptive Manifold Filter call.
    `static void` | `amFilter(Mat joint, Mat src, Mat dst, double sigma_s, double sigma_r, boolean adjust_outliers)` Simple one-line Adaptive Manifold Filter call.
    `static void` | `anisotropicDiffusion(Mat src, Mat dst, float alpha, float K, int niters)` Performs anisotropic diffusian on an image.
    `static void` | `bilateralTextureFilter(Mat src, Mat dst)` Applies the bilateral texture filter to an image.
    `static void` | `bilateralTextureFilter(Mat src, Mat dst, int fr)` Applies the bilateral texture filter to an image.
    `static void` | `bilateralTextureFilter(Mat src, Mat dst, int fr, int numIter)` Applies the bilateral texture filter to an image.
    `static void` | `bilateralTextureFilter(Mat src, Mat dst, int fr, int numIter, double sigmaAlpha)` Applies the bilateral texture filter to an image.
    `static void` | `bilateralTextureFilter(Mat src, Mat dst, int fr, int numIter, double sigmaAlpha, double sigmaAvg)` Applies the bilateral texture filter to an image.
    `static void` | `contourSampling(Mat src, Mat out, int nbElt)` Contour sampling .
    `static void` | `covarianceEstimation(Mat src, Mat dst, int windowRows, int windowCols)` Computes the estimated covariance matrix of an image using the sliding window forumlation.
    `static AdaptiveManifoldFilter` | `createAMFilter(double sigma_s, double sigma_r)` Factory method, create instance of AdaptiveManifoldFilter and produce some initialization routines.
    `static AdaptiveManifoldFilter` | `createAMFilter(double sigma_s, double sigma_r, boolean adjust_outliers)` Factory method, create instance of AdaptiveManifoldFilter and produce some initialization routines.
    `static ContourFitting` | `createContourFitting()` create ContourFitting algorithm object
    `static ContourFitting` | `createContourFitting(int ctr)` create ContourFitting algorithm object
    `static ContourFitting` | `createContourFitting(int ctr, int fd)` create ContourFitting algorithm object
    `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.
    `static DisparityWLSFilter` | `createDisparityWLSFilterGeneric(boolean use_confidence)` More generic factory method, create instance of DisparityWLSFilter and execute basic initialization routines.
    `static DTFilter` | `createDTFilter(Mat guide, double sigmaSpatial, double sigmaColor)` Factory method, create instance of DTFilter and produce initialization routines.
    `static DTFilter` | `createDTFilter(Mat guide, double sigmaSpatial, double sigmaColor, int mode)` Factory method, create instance of DTFilter and produce initialization routines.
    `static DTFilter` | `createDTFilter(Mat guide, double sigmaSpatial, double sigmaColor, int mode, int numIters)` Factory method, create instance of DTFilter and produce initialization routines.
    `static EdgeAwareInterpolator` | `createEdgeAwareInterpolator()` Factory method that creates an instance of the EdgeAwareInterpolator.
    `static EdgeBoxes` | `createEdgeBoxes()` Creates a Edgeboxes
    `static EdgeBoxes` | `createEdgeBoxes(float alpha)` Creates a Edgeboxes
    `static EdgeBoxes` | `createEdgeBoxes(float alpha, float beta)` Creates a Edgeboxes
    `static EdgeBoxes` | `createEdgeBoxes(float alpha, float beta, float eta)` Creates a Edgeboxes
    `static EdgeBoxes` | `createEdgeBoxes(float alpha, float beta, float eta, float minScore)` Creates a Edgeboxes
    `static EdgeBoxes` | `createEdgeBoxes(float alpha, float beta, float eta, float minScore, int maxBoxes)` Creates a Edgeboxes
    `static EdgeBoxes` | `createEdgeBoxes(float alpha, float beta, float eta, float minScore, int maxBoxes, float edgeMinMag)` Creates a Edgeboxes
    `static EdgeBoxes` | `createEdgeBoxes(float alpha, float beta, float eta, float minScore, int maxBoxes, float edgeMinMag, float edgeMergeThr)` Creates a Edgeboxes
    `static EdgeBoxes` | `createEdgeBoxes(float alpha, float beta, float eta, float minScore, int maxBoxes, float edgeMinMag, float edgeMergeThr, float clusterMinMag)` Creates a Edgeboxes
    `static EdgeBoxes` | `createEdgeBoxes(float alpha, float beta, float eta, float minScore, int maxBoxes, float edgeMinMag, float edgeMergeThr, float clusterMinMag, float maxAspectRatio)` Creates a Edgeboxes
    `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
    `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
    `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
    `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.
    `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.
    `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.
    `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.
    `static FastGlobalSmootherFilter` | `createFastGlobalSmootherFilter(Mat guide, double lambda, double sigma_color)` Factory method, create instance of FastGlobalSmootherFilter and execute the initialization routines.
    `static FastGlobalSmootherFilter` | `createFastGlobalSmootherFilter(Mat guide, double lambda, double sigma_color, double lambda_attenuation)` Factory method, create instance of FastGlobalSmootherFilter and execute the initialization routines.
    `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.
    `static FastLineDetector` | `createFastLineDetector()` Creates a smart pointer to a FastLineDetector object and initializes it segment farther than this will be regarded as an outlier hysteresis procedure in Canny() hysteresis procedure in Canny() operator in Canny() will be perfomred
    `static FastLineDetector` | `createFastLineDetector(int _length_threshold)` Creates a smart pointer to a FastLineDetector object and initializes it
    `static FastLineDetector` | `createFastLineDetector(int _length_threshold, float _distance_threshold)` Creates a smart pointer to a FastLineDetector object and initializes it
    `static FastLineDetector` | `createFastLineDetector(int _length_threshold, float _distance_threshold, double _canny_th1)` Creates a smart pointer to a FastLineDetector object and initializes it
    `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
    `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
    `static FastLineDetector` | `createFastLineDetector(int _length_threshold, float _distance_threshold, double _canny_th1, double _canny_th2, int _canny_aperture_size, boolean _do_merge)` Creates a smart pointer to a FastLineDetector object and initializes it
    `static GraphSegmentation` | `createGraphSegmentation()` Creates a graph based segmentor
    `static GraphSegmentation` | `createGraphSegmentation(double sigma)` Creates a graph based segmentor
    `static GraphSegmentation` | `createGraphSegmentation(double sigma, float k)` Creates a graph based segmentor
    `static GraphSegmentation` | `createGraphSegmentation(double sigma, float k, int min_size)` Creates a graph based segmentor
    `static GuidedFilter` | `createGuidedFilter(Mat guide, int radius, double eps)` Factory method, create instance of GuidedFilter and produce initialization routines.
    `static RFFeatureGetter` | `createRFFeatureGetter()`
    `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.
    `static SelectiveSearchSegmentation` | `createSelectiveSearchSegmentation()` Create a new SelectiveSearchSegmentation class.
    `static SelectiveSearchSegmentationStrategyColor` | `createSelectiveSearchSegmentationStrategyColor()` Create a new color-based strategy
    `static SelectiveSearchSegmentationStrategyFill` | `createSelectiveSearchSegmentationStrategyFill()` Create a new fill-based strategy
    `static SelectiveSearchSegmentationStrategyMultiple` | `createSelectiveSearchSegmentationStrategyMultiple()` Create a new multiple strategy
    `static SelectiveSearchSegmentationStrategyMultiple` | `createSelectiveSearchSegmentationStrategyMultiple(SelectiveSearchSegmentationStrategy s1)` Create a new multiple strategy and set one subtrategy
    `static SelectiveSearchSegmentationStrategyMultiple` | `createSelectiveSearchSegmentationStrategyMultiple(SelectiveSearchSegmentationStrategy s1, SelectiveSearchSegmentationStrategy s2)` Create a new multiple strategy and set two subtrategies, with equal weights
    `static SelectiveSearchSegmentationStrategyMultiple` | `createSelectiveSearchSegmentationStrategyMultiple(SelectiveSearchSegmentationStrategy s1, SelectiveSearchSegmentationStrategy s2, SelectiveSearchSegmentationStrategy s3)` Create a new multiple strategy and set three subtrategies, with equal weights
    `static SelectiveSearchSegmentationStrategyMultiple` | `createSelectiveSearchSegmentationStrategyMultiple(SelectiveSearchSegmentationStrategy s1, SelectiveSearchSegmentationStrategy s2, SelectiveSearchSegmentationStrategy s3, SelectiveSearchSegmentationStrategy s4)` Create a new multiple strategy and set four subtrategies, with equal weights
    `static SelectiveSearchSegmentationStrategySize` | `createSelectiveSearchSegmentationStrategySize()` Create a new size-based strategy
    `static SelectiveSearchSegmentationStrategyTexture` | `createSelectiveSearchSegmentationStrategyTexture()` Create a new size-based strategy
    `static StructuredEdgeDetection` | `createStructuredEdgeDetection(String model)`
    `static StructuredEdgeDetection` | `createStructuredEdgeDetection(String model, RFFeatureGetter howToGetFeatures)`
    `static SuperpixelLSC` | `createSuperpixelLSC(Mat image)` Class implementing the LSC (Linear Spectral Clustering) superpixels
    `static SuperpixelLSC` | `createSuperpixelLSC(Mat image, int region_size)` Class implementing the LSC (Linear Spectral Clustering) superpixels
    `static SuperpixelLSC` | `createSuperpixelLSC(Mat image, int region_size, float ratio)` Class implementing the LSC (Linear Spectral Clustering) superpixels
    `static SuperpixelSEEDS` | `createSuperpixelSEEDS(int image_width, int image_height, int image_channels, int num_superpixels, int num_levels)` Initializes a SuperpixelSEEDS object.
    `static SuperpixelSEEDS` | `createSuperpixelSEEDS(int image_width, int image_height, int image_channels, int num_superpixels, int num_levels, int prior)` Initializes a SuperpixelSEEDS object.
    `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.
    `static SuperpixelSEEDS` | `createSuperpixelSEEDS(int image_width, int image_height, int image_channels, int num_superpixels, int num_levels, int prior, int histogram_bins, boolean double_step)` Initializes a SuperpixelSEEDS object.
    `static SuperpixelSLIC` | `createSuperpixelSLIC(Mat image)` Initialize a SuperpixelSLIC object
    `static SuperpixelSLIC` | `createSuperpixelSLIC(Mat image, int algorithm)` Initialize a SuperpixelSLIC object
    `static SuperpixelSLIC` | `createSuperpixelSLIC(Mat image, int algorithm, int region_size)` Initialize a SuperpixelSLIC object
    `static SuperpixelSLIC` | `createSuperpixelSLIC(Mat image, int algorithm, int region_size, float ruler)` Initialize a SuperpixelSLIC object
    `static void` | `dtFilter(Mat guide, Mat src, Mat dst, double sigmaSpatial, double sigmaColor)` Simple one-line Domain Transform filter call.
    `static void` | `dtFilter(Mat guide, Mat src, Mat dst, double sigmaSpatial, double sigmaColor, int mode)` Simple one-line Domain Transform filter call.
    `static void` | `dtFilter(Mat guide, Mat src, Mat dst, double sigmaSpatial, double sigmaColor, int mode, int numIters)` Simple one-line Domain Transform filter call.
    `static void` | `fastBilateralSolverFilter(Mat guide, Mat src, Mat confidence, Mat dst)` Simple one-line Fast Bilateral Solver filter call.
    `static void` | `fastBilateralSolverFilter(Mat guide, Mat src, Mat confidence, Mat dst, double sigma_spatial)` Simple one-line Fast Bilateral Solver filter call.
    `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.
    `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.
    `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.
    `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.
    `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.
    `static void` | `fastGlobalSmootherFilter(Mat guide, Mat src, Mat dst, double lambda, double sigma_color)` Simple one-line Fast Global Smoother filter call.
    `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.
    `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.
    `static void` | `FastHoughTransform(Mat src, Mat dst, int dstMatDepth)` Calculates 2D Fast Hough transform of an image.
    `static void` | `FastHoughTransform(Mat src, Mat dst, int dstMatDepth, int angleRange)` Calculates 2D Fast Hough transform of an image.
    `static void` | `FastHoughTransform(Mat src, Mat dst, int dstMatDepth, int angleRange, int op)` Calculates 2D Fast Hough transform of an image.
    `static void` | `FastHoughTransform(Mat src, Mat dst, int dstMatDepth, int angleRange, int op, int makeSkew)` Calculates 2D Fast Hough transform of an image.
    `static void` | `fourierDescriptor(Mat src, Mat dst)` Fourier descriptors for planed closed curves For more details about this implementation, please see CITE: PersoonFu1977
    `static void` | `fourierDescriptor(Mat src, Mat dst, int nbElt)` Fourier descriptors for planed closed curves For more details about this implementation, please see CITE: PersoonFu1977
    `static void` | `fourierDescriptor(Mat src, Mat dst, int nbElt, int nbFD)` Fourier descriptors for planed closed curves For more details about this implementation, please see CITE: PersoonFu1977
    `static void` | `GradientDericheX(Mat op, Mat dst, double alpha, double omega)` Applies X Deriche filter to an image.
    `static void` | `GradientDericheY(Mat op, Mat dst, double alpha, double omega)` Applies Y Deriche filter to an image.
    `static void` | `guidedFilter(Mat guide, Mat src, Mat dst, int radius, double eps)` Simple one-line Guided Filter call.
    `static void` | `guidedFilter(Mat guide, Mat src, Mat dst, int radius, double eps, int dDepth)` Simple one-line Guided Filter call.
    `static void` | `jointBilateralFilter(Mat joint, Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace)` Applies the joint bilateral filter to an image.
    `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.
    `static void` | `l0Smooth(Mat src, Mat dst)` Global image smoothing via L0 gradient minimization.
    `static void` | `l0Smooth(Mat src, Mat dst, double lambda)` Global image smoothing via L0 gradient minimization.
    `static void` | `l0Smooth(Mat src, Mat dst, double lambda, double kappa)` Global image smoothing via L0 gradient minimization.
    `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.
    `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.
    `static void` | `PeiLinNormalization(Mat I, Mat T)`
    `static void` | `rollingGuidanceFilter(Mat src, Mat dst)` Applies the rolling guidance filter to an image.
    `static void` | `rollingGuidanceFilter(Mat src, Mat dst, int d)` Applies the rolling guidance filter to an image.
    `static void` | `rollingGuidanceFilter(Mat src, Mat dst, int d, double sigmaColor)` Applies the rolling guidance filter to an image.
    `static void` | `rollingGuidanceFilter(Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace)` Applies the rolling guidance filter to an image.
    `static void` | `rollingGuidanceFilter(Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace, int numOfIter)` Applies the rolling guidance filter to an image.
    `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.
    `static void` | `thinning(Mat src, Mat dst)` Applies a binary blob thinning operation, to achieve a skeletization of the input image.
    `static void` | `thinning(Mat src, Mat dst, int thinningType)` Applies a binary blob thinning operation, to achieve a skeletization of the input image.
    `static void` | `transformFD(Mat src, Mat t, Mat dst)` transform a contour
    `static void` | `transformFD(Mat src, Mat t, Mat dst, boolean fdContour)` transform a contour
    `static void` | `weightedMedianFilter(Mat joint, Mat src, Mat dst, int r)` Applies weighted median filter to an image.
    `static void` | `weightedMedianFilter(Mat joint, Mat src, Mat dst, int r, double sigma)` Applies weighted median filter to an image.
    `static void` | `weightedMedianFilter(Mat joint, Mat src, Mat dst, int r, double sigma, int weightType)` Applies weighted median filter to an image.
    `static void` | `weightedMedianFilter(Mat joint, Mat src, Mat dst, int r, double sigma, int weightType, Mat mask)` Applies weighted median filter to an image.

    - ### Methods inherited from class java.lang.Object

      `clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

- - ### Field Detail

    - #### WMF_EXP

          public static final int WMF_EXP

      See Also:
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    - #### WMF_IV1

          public static final int WMF_IV1

      See Also:
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    - #### WMF_IV2

          public static final int WMF_IV2

      See Also:
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    - #### WMF_COS

          public static final int WMF_COS

      See Also:
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    - #### WMF_JAC

          public static final int WMF_JAC

      See Also:
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    - #### WMF_OFF

          public static final int WMF_OFF

      See Also:
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    - #### SLIC

          public static final int SLIC

      See Also:
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    - #### SLICO

          public static final int SLICO

      See Also:
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    - #### MSLIC

          public static final int MSLIC

      See Also:
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    - #### DTF_NC

          public static final int DTF_NC

      See Also:
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    - #### DTF_IC

          public static final int DTF_IC

      See Also:
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    - #### DTF_RF

          public static final int DTF_RF

      See Also:
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    - #### GUIDED_FILTER

          public static final int GUIDED_FILTER

      See Also:
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    - #### AM_FILTER

          public static final int AM_FILTER

      See Also:
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    - #### FHT_MIN

          public static final int FHT_MIN

      See Also:
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    - #### FHT_MAX

          public static final int FHT_MAX

      See Also:
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    - #### FHT_ADD

          public static final int FHT_ADD

      See Also:
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    - #### FHT_AVE

          public static final int FHT_AVE

      See Also:
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    - #### BINARIZATION_NIBLACK

          public static final int BINARIZATION_NIBLACK

      See Also:
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    - #### BINARIZATION_SAUVOLA

          public static final int BINARIZATION_SAUVOLA

      See Also:
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    - #### BINARIZATION_WOLF

          public static final int BINARIZATION_WOLF

      See Also:
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    - #### BINARIZATION_NICK

          public static final int BINARIZATION_NICK

      See Also:
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    - #### HDO_RAW

          public static final int HDO_RAW

      See Also:
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    - #### HDO_DESKEW

          public static final int HDO_DESKEW

      See Also:
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    - #### ARO_0_45

          public static final int ARO_0_45

      See Also:
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    - #### ARO_45_90

          public static final int ARO_45_90

      See Also:
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    - #### ARO_90_135

          public static final int ARO_90_135

      See Also:
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    - #### ARO_315_0

          public static final int ARO_315_0

      See Also:
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    - #### ARO_315_45

          public static final int ARO_315_45

      See Also:
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    - #### ARO_45_135

          public static final int ARO_45_135

      See Also:
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    - #### ARO_315_135

          public static final int ARO_315_135

      See Also:
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    - #### ARO_CTR_HOR

          public static final int ARO_CTR_HOR

      See Also:
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    - #### ARO_CTR_VER

          public static final int ARO_CTR_VER

      See Also:
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    - #### THINNING_ZHANGSUEN

          public static final int THINNING_ZHANGSUEN

      See Also:
        Constant Field Values

    - #### THINNING_GUOHALL

          public static final int THINNING_GUOHALL

      See Also:
        Constant Field Values

  - ### Constructor Detail

    - #### Ximgproc

          public Ximgproc()

  - ### Method Detail

    - #### createAMFilter

          public static AdaptiveManifoldFilter createAMFilter(double sigma_s,
                                                              double sigma_r,
                                                              boolean adjust_outliers)

      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 CITE: 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.
      Returns:
        automatically generated

    - #### createAMFilter

          public static AdaptiveManifoldFilter createAMFilter(double sigma_s,
                                                              double sigma_r)

      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. original paper. For more details about Adaptive Manifold Filter parameters, see the original article CITE: 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.
      Returns:
        automatically generated

    - #### createContourFitting

          public static ContourFitting createContourFitting(int ctr,
                                                            int fd)

      create ContourFitting algorithm object
      Parameters:
        `ctr` - number of Fourier descriptors equal to number of contour points after resampling.
        `fd` - Contour defining second shape (Target).
      Returns:
        automatically generated

    - #### createContourFitting

          public static ContourFitting createContourFitting(int ctr)

      create ContourFitting algorithm object
      Parameters:
        `ctr` - number of Fourier descriptors equal to number of contour points after resampling.
      Returns:
        automatically generated

    - #### createContourFitting

          public static ContourFitting createContourFitting()

      create ContourFitting algorithm object
      Returns:
        automatically generated

    - #### createDTFilter

          public static DTFilter createDTFilter(Mat guide,
                                                double sigmaSpatial,
                                                double sigmaColor,
                                                int mode,
                                                int numIters)

      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 CITE: Gastal11 and [Domain Transform filter homepage](http://www.inf.ufrgs.br/~eslgastal/DomainTransform/).
      Returns:
        automatically generated

    - #### createDTFilter

          public static DTFilter createDTFilter(Mat guide,
                                                double sigmaSpatial,
                                                double sigmaColor,
                                                int mode)

      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. For more details about Domain Transform filter parameters, see the original article CITE: Gastal11 and [Domain Transform filter homepage](http://www.inf.ufrgs.br/~eslgastal/DomainTransform/).
      Returns:
        automatically generated

    - #### createDTFilter

          public static DTFilter createDTFilter(Mat guide,
                                                double sigmaSpatial,
                                                double sigmaColor)

      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. filtering 2D signals in the article. For more details about Domain Transform filter parameters, see the original article CITE: Gastal11 and [Domain Transform filter homepage](http://www.inf.ufrgs.br/~eslgastal/DomainTransform/).
      Returns:
        automatically generated

    - #### createDisparityWLSFilter

          public 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.
      Parameters:
        `matcher_left` - stereo matcher instance that will be used with the filter
      Returns:
        automatically generated

    - #### createDisparityWLSFilterGeneric

          public static DisparityWLSFilter createDisparityWLSFilterGeneric(boolean 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.
      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.
      Returns:
        automatically generated

    - #### createEdgeAwareInterpolator

          public static EdgeAwareInterpolator createEdgeAwareInterpolator()

      Factory method that creates an instance of the EdgeAwareInterpolator.
      Returns:
        automatically generated

    - #### createEdgeBoxes

          public 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
      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.
      Returns:
        automatically generated

    - #### createEdgeBoxes

          public 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
      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.
      Returns:
        automatically generated

    - #### createEdgeBoxes

          public 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
      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.
      Returns:
        automatically generated

    - #### createEdgeBoxes

          public static EdgeBoxes createEdgeBoxes(float alpha,
                                                  float beta,
                                                  float eta,
                                                  float minScore,
                                                  int maxBoxes,
                                                  float edgeMinMag,
                                                  float edgeMergeThr,
                                                  float clusterMinMag,
                                                  float maxAspectRatio)

      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.
      Returns:
        automatically generated

    - #### createEdgeBoxes

          public static EdgeBoxes createEdgeBoxes(float alpha,
                                                  float beta,
                                                  float eta,
                                                  float minScore,
                                                  int maxBoxes,
                                                  float edgeMinMag,
                                                  float edgeMergeThr,
                                                  float clusterMinMag)

      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.
      Returns:
        automatically generated

    - #### createEdgeBoxes

          public static EdgeBoxes createEdgeBoxes(float alpha,
                                                  float beta,
                                                  float eta,
                                                  float minScore,
                                                  int maxBoxes,
                                                  float edgeMinMag,
                                                  float edgeMergeThr)

      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.
      Returns:
        automatically generated

    - #### createEdgeBoxes

          public static EdgeBoxes createEdgeBoxes(float alpha,
                                                  float beta,
                                                  float eta,
                                                  float minScore,
                                                  int maxBoxes,
                                                  float edgeMinMag)

      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.
      Returns:
        automatically generated

    - #### createEdgeBoxes

          public static EdgeBoxes createEdgeBoxes(float alpha,
                                                  float beta,
                                                  float eta,
                                                  float minScore,
                                                  int maxBoxes)

      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.
      Returns:
        automatically generated

    - #### createEdgeBoxes

          public static EdgeBoxes createEdgeBoxes(float alpha,
                                                  float beta,
                                                  float eta,
                                                  float minScore)

      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.
      Returns:
        automatically generated

    - #### createEdgeBoxes

          public static EdgeBoxes createEdgeBoxes(float alpha,
                                                  float beta,
                                                  float eta)

      Creates a Edgeboxes
      Parameters:
        `alpha` - step size of sliding window search.
        `beta` - nms threshold for object proposals.
        `eta` - adaptation rate for nms threshold.
      Returns:
        automatically generated

    - #### createEdgeBoxes

          public static EdgeBoxes createEdgeBoxes(float alpha,
                                                  float beta)

      Creates a Edgeboxes
      Parameters:
        `alpha` - step size of sliding window search.
        `beta` - nms threshold for object proposals.
      Returns:
        automatically generated

    - #### createEdgeBoxes

          public static EdgeBoxes createEdgeBoxes(float alpha)

      Creates a Edgeboxes
      Parameters:
        `alpha` - step size of sliding window search.
      Returns:
        automatically generated

    - #### createEdgeBoxes

          public static EdgeBoxes createEdgeBoxes()

      Creates a Edgeboxes
      Returns:
        automatically generated

    - #### createFastBilateralSolverFilter

          public 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.
      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 CITE: BarronPoole2016.
      Returns:
        automatically generated

    - #### createFastBilateralSolverFilter

          public 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.
      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. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016.
      Returns:
        automatically generated

    - #### createFastBilateralSolverFilter

          public 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.
      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. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016.
      Returns:
        automatically generated

    - #### createFastBilateralSolverFilter

          public 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.
      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. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016.
      Returns:
        automatically generated

    - #### createFastGlobalSmootherFilter

          public 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.
      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 CITE: 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.
      Returns:
        automatically generated

    - #### createFastGlobalSmootherFilter

          public static FastGlobalSmootherFilter createFastGlobalSmootherFilter(Mat guide,
                                                                                double lambda,
                                                                                double sigma_color,
                                                                                double lambda_attenuation)

      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. For more details about Fast Global Smoother parameters, see the original paper CITE: 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.
      Returns:
        automatically generated

    - #### createFastGlobalSmootherFilter

          public static FastGlobalSmootherFilter createFastGlobalSmootherFilter(Mat guide,
                                                                                double lambda,
                                                                                double sigma_color)

      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. it should be 0.25. Setting it to 1.0 may lead to streaking artifacts. For more details about Fast Global Smoother parameters, see the original paper CITE: 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.
      Returns:
        automatically generated

    - #### createFastLineDetector

          public static FastLineDetector createFastLineDetector(int _length_threshold,
                                                                float _distance_threshold,
                                                                double _canny_th1,
                                                                double _canny_th2,
                                                                int _canny_aperture_size,
                                                                boolean _do_merge)

      Creates a smart pointer to a FastLineDetector object and initializes it
      Parameters:
        `_length_threshold` - 10 - Segment shorter than this will be discarded
        `_distance_threshold` - 1.41421356 - A point placed from a hypothesis line segment farther than this will be regarded as an outlier
        `_canny_th1` - 50 - First threshold for hysteresis procedure in Canny()
        `_canny_th2` - 50 - Second threshold for hysteresis procedure in Canny()
        `_canny_aperture_size` - 3 - Aperturesize for the sobel operator in Canny()
        `_do_merge` - false - If true, incremental merging of segments will be perfomred
      Returns:
        automatically generated

    - #### createFastLineDetector

          public 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
      Parameters:
        `_length_threshold` - 10 - Segment shorter than this will be discarded
        `_distance_threshold` - 1.41421356 - A point placed from a hypothesis line segment farther than this will be regarded as an outlier
        `_canny_th1` - 50 - First threshold for hysteresis procedure in Canny()
        `_canny_th2` - 50 - Second threshold for hysteresis procedure in Canny()
        `_canny_aperture_size` - 3 - Aperturesize for the sobel operator in Canny() will be perfomred
      Returns:
        automatically generated

    - #### createFastLineDetector

          public 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
      Parameters:
        `_length_threshold` - 10 - Segment shorter than this will be discarded
        `_distance_threshold` - 1.41421356 - A point placed from a hypothesis line segment farther than this will be regarded as an outlier
        `_canny_th1` - 50 - First threshold for hysteresis procedure in Canny()
        `_canny_th2` - 50 - Second threshold for hysteresis procedure in Canny() operator in Canny() will be perfomred
      Returns:
        automatically generated

    - #### createFastLineDetector

          public static FastLineDetector createFastLineDetector(int _length_threshold,
                                                                float _distance_threshold,
                                                                double _canny_th1)

      Creates a smart pointer to a FastLineDetector object and initializes it
      Parameters:
        `_length_threshold` - 10 - Segment shorter than this will be discarded
        `_distance_threshold` - 1.41421356 - A point placed from a hypothesis line segment farther than this will be regarded as an outlier
        `_canny_th1` - 50 - First threshold for hysteresis procedure in Canny() hysteresis procedure in Canny() operator in Canny() will be perfomred
      Returns:
        automatically generated

    - #### createFastLineDetector

          public static FastLineDetector createFastLineDetector(int _length_threshold,
                                                                float _distance_threshold)

      Creates a smart pointer to a FastLineDetector object and initializes it
      Parameters:
        `_length_threshold` - 10 - Segment shorter than this will be discarded
        `_distance_threshold` - 1.41421356 - A point placed from a hypothesis line segment farther than this will be regarded as an outlier hysteresis procedure in Canny() hysteresis procedure in Canny() operator in Canny() will be perfomred
      Returns:
        automatically generated

    - #### createFastLineDetector

          public static FastLineDetector createFastLineDetector(int _length_threshold)

      Creates a smart pointer to a FastLineDetector object and initializes it
      Parameters:
        `_length_threshold` - 10 - Segment shorter than this will be discarded segment farther than this will be regarded as an outlier hysteresis procedure in Canny() hysteresis procedure in Canny() operator in Canny() will be perfomred
      Returns:
        automatically generated

    - #### createFastLineDetector

          public static FastLineDetector createFastLineDetector()

      Creates a smart pointer to a FastLineDetector object and initializes it segment farther than this will be regarded as an outlier hysteresis procedure in Canny() hysteresis procedure in Canny() operator in Canny() will be perfomred
      Returns:
        automatically generated

    - #### createGraphSegmentation

          public static GraphSegmentation createGraphSegmentation(double sigma,
                                                                  float k,
                                                                  int min_size)

      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
      Returns:
        automatically generated

    - #### createGraphSegmentation

          public static GraphSegmentation createGraphSegmentation(double sigma,
                                                                  float k)

      Creates a graph based segmentor
      Parameters:
        `sigma` - The sigma parameter, used to smooth image
        `k` - The k parameter of the algorythm
      Returns:
        automatically generated

    - #### createGraphSegmentation

          public static GraphSegmentation createGraphSegmentation(double sigma)

      Creates a graph based segmentor
      Parameters:
        `sigma` - The sigma parameter, used to smooth image
      Returns:
        automatically generated

    - #### createGraphSegmentation

          public static GraphSegmentation createGraphSegmentation()

      Creates a graph based segmentor
      Returns:
        automatically generated

    - #### createGuidedFilter

          public static GuidedFilter createGuidedFilter(Mat guide,
                                                        int radius,
                                                        double eps)

      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 CITE: Kaiming10 .
      Returns:
        automatically generated

    - #### createRFFeatureGetter

          public static RFFeatureGetter createRFFeatureGetter()

    - #### createSelectiveSearchSegmentation

          public static SelectiveSearchSegmentation createSelectiveSearchSegmentation()

      Create a new SelectiveSearchSegmentation class.
      Returns:
        automatically generated

    - #### createSelectiveSearchSegmentationStrategyColor

          public static SelectiveSearchSegmentationStrategyColor createSelectiveSearchSegmentationStrategyColor()

      Create a new color-based strategy
      Returns:
        automatically generated

    - #### createSelectiveSearchSegmentationStrategyFill

          public static SelectiveSearchSegmentationStrategyFill createSelectiveSearchSegmentationStrategyFill()

      Create a new fill-based strategy
      Returns:
        automatically generated

    - #### createSelectiveSearchSegmentationStrategyMultiple

          public static SelectiveSearchSegmentationStrategyMultiple createSelectiveSearchSegmentationStrategyMultiple(SelectiveSearchSegmentationStrategy s1,
                                                                                                                      SelectiveSearchSegmentationStrategy s2,
                                                                                                                      SelectiveSearchSegmentationStrategy s3,
                                                                                                                      SelectiveSearchSegmentationStrategy s4)

      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
      Returns:
        automatically generated

    - #### createSelectiveSearchSegmentationStrategyMultiple

          public static SelectiveSearchSegmentationStrategyMultiple createSelectiveSearchSegmentationStrategyMultiple(SelectiveSearchSegmentationStrategy s1,
                                                                                                                      SelectiveSearchSegmentationStrategy s2,
                                                                                                                      SelectiveSearchSegmentationStrategy s3)

      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
      Returns:
        automatically generated

    - #### createSelectiveSearchSegmentationStrategyMultiple

          public static SelectiveSearchSegmentationStrategyMultiple createSelectiveSearchSegmentationStrategyMultiple(SelectiveSearchSegmentationStrategy s1,
                                                                                                                      SelectiveSearchSegmentationStrategy s2)

      Create a new multiple strategy and set two subtrategies, with equal weights
      Parameters:
        `s1` - The first strategy
        `s2` - The second strategy
      Returns:
        automatically generated

    - #### createSelectiveSearchSegmentationStrategyMultiple

          public static SelectiveSearchSegmentationStrategyMultiple createSelectiveSearchSegmentationStrategyMultiple(SelectiveSearchSegmentationStrategy s1)

      Create a new multiple strategy and set one subtrategy
      Parameters:
        `s1` - The first strategy
      Returns:
        automatically generated

    - #### createSelectiveSearchSegmentationStrategyMultiple

          public static SelectiveSearchSegmentationStrategyMultiple createSelectiveSearchSegmentationStrategyMultiple()

      Create a new multiple strategy
      Returns:
        automatically generated

    - #### createSelectiveSearchSegmentationStrategySize

          public static SelectiveSearchSegmentationStrategySize createSelectiveSearchSegmentationStrategySize()

      Create a new size-based strategy
      Returns:
        automatically generated

    - #### createSelectiveSearchSegmentationStrategyTexture

          public static SelectiveSearchSegmentationStrategyTexture createSelectiveSearchSegmentationStrategyTexture()

      Create a new size-based strategy
      Returns:
        automatically generated

    - #### createRightMatcher

          public 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.
      Parameters:
        `matcher_left` - main stereo matcher instance that will be used with the filter
      Returns:
        automatically generated

    - #### createStructuredEdgeDetection

          public static StructuredEdgeDetection createStructuredEdgeDetection(String model,
                                                                              RFFeatureGetter howToGetFeatures)

    - #### createStructuredEdgeDetection

          public static StructuredEdgeDetection createStructuredEdgeDetection(String model)

    - #### createSuperpixelLSC

          public static SuperpixelLSC createSuperpixelLSC(Mat image,
                                                          int region_size,
                                                          float ratio)

      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](pics/superpixels_lsc.png)
      Returns:
        automatically generated

    - #### createSuperpixelLSC

          public static SuperpixelLSC createSuperpixelLSC(Mat image,
                                                          int region_size)

      Class implementing the LSC (Linear Spectral Clustering) superpixels
      Parameters:
        `image` - Image to segment
        `region_size` - Chooses an average superpixel size measured in pixels 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](pics/superpixels_lsc.png)
      Returns:
        automatically generated

    - #### createSuperpixelLSC

          public static SuperpixelLSC createSuperpixelLSC(Mat image)

      Class implementing the LSC (Linear Spectral Clustering) superpixels
      Parameters:
        `image` - Image to segment 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](pics/superpixels_lsc.png)
      Returns:
        automatically generated

    - #### createSuperpixelSEEDS

          public static SuperpixelSEEDS createSuperpixelSEEDS(int image_width,
                                                              int image_height,
                                                              int image_channels,
                                                              int num_superpixels,
                                                              int num_levels,
                                                              int prior,
                                                              int histogram_bins,
                                                              boolean double_step)

      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](pics/superpixels_blocks.png)
      Returns:
        automatically generated

    - #### createSuperpixelSEEDS

          public 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.
      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. 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](pics/superpixels_blocks.png)
      Returns:
        automatically generated

    - #### createSuperpixelSEEDS

          public static SuperpixelSEEDS createSuperpixelSEEDS(int image_width,
                                                              int image_height,
                                                              int image_channels,
                                                              int num_superpixels,
                                                              int num_levels,
                                                              int prior)

      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]. 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](pics/superpixels_blocks.png)
      Returns:
        automatically generated

    - #### createSuperpixelSEEDS

          public static SuperpixelSEEDS createSuperpixelSEEDS(int image_width,
                                                              int image_height,
                                                              int image_channels,
                                                              int num_superpixels,
                                                              int num_levels)

      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. must be in the range [0, 5]. 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](pics/superpixels_blocks.png)
      Returns:
        automatically generated

    - #### createSuperpixelSLIC

          public static SuperpixelSLIC createSuperpixelSLIC(Mat image,
                                                            int algorithm,
                                                            int region_size,
                                                            float ruler)

      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](pics/superpixels_slic.png)
      Returns:
        automatically generated

    - #### createSuperpixelSLIC

          public static SuperpixelSLIC createSuperpixelSLIC(Mat image,
                                                            int algorithm,
                                                            int region_size)

      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 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](pics/superpixels_slic.png)
      Returns:
        automatically generated

    - #### createSuperpixelSLIC

          public static SuperpixelSLIC createSuperpixelSLIC(Mat image,
                                                            int algorithm)

      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. 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](pics/superpixels_slic.png)
      Returns:
        automatically generated

    - #### createSuperpixelSLIC

          public static SuperpixelSLIC createSuperpixelSLIC(Mat image)

      Initialize a SuperpixelSLIC object
      Parameters:
        `image` - Image to segment 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. 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](pics/superpixels_slic.png)
      Returns:
        automatically generated

    - #### FastHoughTransform

          public static void FastHoughTransform(Mat src,
                                                Mat dst,
                                                int dstMatDepth,
                                                int angleRange,
                                                int op,
                                                int makeSkew)

      Calculates 2D Fast Hough transform of an image. The function calculates the fast Hough transform for full, half or quarter range of angles.
      Parameters:
        `src` - automatically generated
        `dst` - automatically generated
        `dstMatDepth` - automatically generated
        `angleRange` - automatically generated
        `op` - automatically generated
        `makeSkew` - automatically generated

    - #### FastHoughTransform

          public static void FastHoughTransform(Mat src,
                                                Mat dst,
                                                int dstMatDepth,
                                                int angleRange,
                                                int op)

      Calculates 2D Fast Hough transform of an image. The function calculates the fast Hough transform for full, half or quarter range of angles.
      Parameters:
        `src` - automatically generated
        `dst` - automatically generated
        `dstMatDepth` - automatically generated
        `angleRange` - automatically generated
        `op` - automatically generated

    - #### FastHoughTransform

          public static void FastHoughTransform(Mat src,
                                                Mat dst,
                                                int dstMatDepth,
                                                int angleRange)

      Calculates 2D Fast Hough transform of an image. The function calculates the fast Hough transform for full, half or quarter range of angles.
      Parameters:
        `src` - automatically generated
        `dst` - automatically generated
        `dstMatDepth` - automatically generated
        `angleRange` - automatically generated

    - #### FastHoughTransform

          public static void FastHoughTransform(Mat src,
                                                Mat dst,
                                                int dstMatDepth)

      Calculates 2D Fast Hough transform of an image. The function calculates the fast Hough transform for full, half or quarter range of angles.
      Parameters:
        `src` - automatically generated
        `dst` - automatically generated
        `dstMatDepth` - automatically generated

    - #### GradientDericheX

          public static void GradientDericheX(Mat op,
                                              Mat dst,
                                              double alpha,
                                              double omega)

      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` - automatically generated
        `dst` - automatically generated
        `alpha` - automatically generated
        `omega` - automatically generated

    - #### GradientDericheY

          public static void GradientDericheY(Mat op,
                                              Mat dst,
                                              double alpha,
                                              double omega)

      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` - automatically generated
        `dst` - automatically generated
        `alpha` - automatically generated
        `omega` - automatically generated

    - #### PeiLinNormalization

          public static void PeiLinNormalization(Mat I,
                                                 Mat T)

    - #### amFilter

          public static void amFilter(Mat joint,
                                      Mat src,
                                      Mat dst,
                                      double sigma_s,
                                      double sigma_r,
                                      boolean adjust_outliers)

      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: bilateralFilter, dtFilter, guidedFilter

    - #### amFilter

          public static void amFilter(Mat joint,
                                      Mat src,
                                      Mat dst,
                                      double sigma_s,
                                      double sigma_r)

      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. 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: bilateralFilter, dtFilter, guidedFilter

    - #### anisotropicDiffusion

          public static void anisotropicDiffusion(Mat src,
                                                  Mat dst,
                                                  float alpha,
                                                  float K,
                                                  int niters)

      Performs anisotropic diffusian 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

          public 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 CITE: 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: rollingGuidanceFilter, bilateralFilter

    - #### bilateralTextureFilter

          public 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 CITE: 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. value is negative, it is automatically calculated as described in the paper. SEE: rollingGuidanceFilter, bilateralFilter

    - #### bilateralTextureFilter

          public 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 CITE: 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 a bigger value means sharper transition. When the value is negative, it is automatically calculated. value is negative, it is automatically calculated as described in the paper. SEE: rollingGuidanceFilter, bilateralFilter

    - #### bilateralTextureFilter

          public 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 CITE: 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 a bigger value means sharper transition. When the value is negative, it is automatically calculated. value is negative, it is automatically calculated as described in the paper. SEE: rollingGuidanceFilter, bilateralFilter

    - #### bilateralTextureFilter

          public 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 CITE: 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. a bigger value means sharper transition. When the value is negative, it is automatically calculated. value is negative, it is automatically calculated as described in the paper. SEE: rollingGuidanceFilter, bilateralFilter

    - #### contourSampling

          public static void contourSampling(Mat src,
                                             Mat out,
                                             int nbElt)

      Contour sampling .
      Parameters:
        `src` - automatically generated
        `out` - automatically generated
        `nbElt` - automatically generated

    - #### covarianceEstimation

          public static void covarianceEstimation(Mat src,
                                                  Mat dst,
                                                  int windowRows,
                                                  int windowCols)

      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.

    - #### dtFilter

          public 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.
      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: bilateralFilter, guidedFilter, amFilter

    - #### dtFilter

          public 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.
      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. SEE: bilateralFilter, guidedFilter, amFilter

    - #### dtFilter

          public 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.
      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. filtering 2D signals in the article. SEE: bilateralFilter, guidedFilter, amFilter

    - #### fastBilateralSolverFilter

          public 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.
      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 CITE: BarronPoole2016. Note: Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.

    - #### fastBilateralSolverFilter

          public 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.
      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. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016. Note: Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.

    - #### fastBilateralSolverFilter

          public 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.
      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. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016. Note: Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.

    - #### fastBilateralSolverFilter

          public 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.
      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. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016. Note: Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.

    - #### fastBilateralSolverFilter

          public 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.
      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. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016. Note: Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.

    - #### fastBilateralSolverFilter

          public 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.
      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. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016. Note: Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.

    - #### fastBilateralSolverFilter

          public 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.
      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. For more details about the Fast Bilateral Solver parameters, see the original paper CITE: BarronPoole2016. Note: Confidence images with CV_8U depth are expected to in [0, 255] and CV_32F in [0, 1] range.

    - #### fastGlobalSmootherFilter

          public 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.
      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

          public 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.
      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.

    - #### fastGlobalSmootherFilter

          public 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.
      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. it should be 0.25. Setting it to 1.0 may lead to streaking artifacts.

    - #### fourierDescriptor

          public static void fourierDescriptor(Mat src,
                                               Mat dst,
                                               int nbElt,
                                               int nbFD)

      Fourier descriptors for planed closed curves For more details about this implementation, please see CITE: PersoonFu1977
      Parameters:
        `src` - automatically generated
        `dst` - automatically generated
        `nbElt` - automatically generated
        `nbFD` - automatically generated

    - #### fourierDescriptor

          public static void fourierDescriptor(Mat src,
                                               Mat dst,
                                               int nbElt)

      Fourier descriptors for planed closed curves For more details about this implementation, please see CITE: PersoonFu1977
      Parameters:
        `src` - automatically generated
        `dst` - automatically generated
        `nbElt` - automatically generated

    - #### fourierDescriptor

          public static void fourierDescriptor(Mat src,
                                               Mat dst)

      Fourier descriptors for planed closed curves For more details about this implementation, please see CITE: PersoonFu1977
      Parameters:
        `src` - automatically generated
        `dst` - automatically generated

    - #### guidedFilter

          public static void guidedFilter(Mat guide,
                                          Mat src,
                                          Mat dst,
                                          int radius,
                                          double eps,
                                          int dDepth)

      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. SEE: bilateralFilter, dtFilter, amFilter

    - #### guidedFilter

          public static void guidedFilter(Mat guide,
                                          Mat src,
                                          Mat dst,
                                          int radius,
                                          double eps)

      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. SEE: bilateralFilter, dtFilter, amFilter

    - #### jointBilateralFilter

          public 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.
      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: bilateralFilter, amFilter

    - #### jointBilateralFilter

          public static void jointBilateralFilter(Mat joint,
                                                  Mat src,
                                                  Mat dst,
                                                  int d,
                                                  double sigmaColor,
                                                  double sigmaSpace)

      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 . Note: bilateralFilter and jointBilateralFilter use L1 norm to compute difference between colors. SEE: bilateralFilter, amFilter

    - #### l0Smooth

          public static void l0Smooth(Mat src,
                                      Mat dst,
                                      double lambda,
                                      double kappa)

      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 CITE: xu2011image.

    - #### l0Smooth

          public static void l0Smooth(Mat src,
                                      Mat dst,
                                      double lambda)

      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. For more details about L0 Smoother, see the original paper CITE: xu2011image.

    - #### l0Smooth

          public static void l0Smooth(Mat src,
                                      Mat dst)

      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. For more details about L0 Smoother, see the original paper CITE: xu2011image.

    - #### niBlackThreshold

          public 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. 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. SEE: threshold, adaptiveThreshold

    - #### niBlackThreshold

          public 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. 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. Other techniques can be specified, see cv::ximgproc::LocalBinarizationMethods. SEE: threshold, adaptiveThreshold

    - #### rollingGuidanceFilter

          public 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. For more details, please see CITE: 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: jointBilateralFilter, bilateralFilter, amFilter

    - #### rollingGuidanceFilter

          public static void rollingGuidanceFilter(Mat src,
                                                   Mat dst,
                                                   int d,
                                                   double sigmaColor,
                                                   double sigmaSpace,
                                                   int numOfIter)

      Applies the rolling guidance filter to an image. For more details, please see CITE: 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. Note: rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter. SEE: jointBilateralFilter, bilateralFilter, amFilter

    - #### rollingGuidanceFilter

          public static void rollingGuidanceFilter(Mat src,
                                                   Mat dst,
                                                   int d,
                                                   double sigmaColor,
                                                   double sigmaSpace)

      Applies the rolling guidance filter to an image. For more details, please see CITE: 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 . Note: rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter. SEE: jointBilateralFilter, bilateralFilter, amFilter

    - #### rollingGuidanceFilter

          public static void rollingGuidanceFilter(Mat src,
                                                   Mat dst,
                                                   int d,
                                                   double sigmaColor)

      Applies the rolling guidance filter to an image. For more details, please see CITE: 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. 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 . Note: rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter. SEE: jointBilateralFilter, bilateralFilter, amFilter

    - #### rollingGuidanceFilter

          public static void rollingGuidanceFilter(Mat src,
                                                   Mat dst,
                                                   int d)

      Applies the rolling guidance filter to an image. For more details, please see CITE: 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 . farther colors within the pixel neighborhood (see sigmaSpace ) will be mixed together, resulting in larger areas of semi-equal color. 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 . Note: rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter. SEE: jointBilateralFilter, bilateralFilter, amFilter

    - #### rollingGuidanceFilter

          public static void rollingGuidanceFilter(Mat src,
                                                   Mat dst)

      Applies the rolling guidance filter to an image. For more details, please see CITE: 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. it is computed from sigmaSpace . farther colors within the pixel neighborhood (see sigmaSpace ) will be mixed together, resulting in larger areas of semi-equal color. 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 . Note: rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter. SEE: jointBilateralFilter, bilateralFilter, amFilter

    - #### thinning

          public static void thinning(Mat src,
                                      Mat dst,
                                      int thinningType)

      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

          public static void thinning(Mat src,
                                      Mat dst)

      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.

    - #### transformFD

          public static void transformFD(Mat src,
                                         Mat t,
                                         Mat dst,
                                         boolean fdContour)

      transform a contour
      Parameters:
        `src` - automatically generated
        `t` - automatically generated
        `dst` - automatically generated
        `fdContour` - automatically generated

    - #### transformFD

          public static void transformFD(Mat src,
                                         Mat t,
                                         Mat dst)

      transform a contour
      Parameters:
        `src` - automatically generated
        `t` - automatically generated
        `dst` - automatically generated

    - #### weightedMedianFilter

          public static void weightedMedianFilter(Mat joint,
                                                  Mat src,
                                                  Mat dst,
                                                  int r,
                                                  double sigma,
                                                  int weightType,
                                                  Mat mask)

      Applies weighted median filter to an image. For more details about this implementation, please see CITE: zhang2014100+ the pixel will be ignored when maintaining the joint-histogram. This is useful for applications like optical flow occlusion handling. SEE: medianBlur, jointBilateralFilter
      Parameters:
        `joint` - automatically generated
        `src` - automatically generated
        `dst` - automatically generated
        `r` - automatically generated
        `sigma` - automatically generated
        `weightType` - automatically generated
        `mask` - automatically generated

    - #### weightedMedianFilter

          public static void weightedMedianFilter(Mat joint,
                                                  Mat src,
                                                  Mat dst,
                                                  int r,
                                                  double sigma,
                                                  int weightType)

      Applies weighted median filter to an image. For more details about this implementation, please see CITE: zhang2014100+ the pixel will be ignored when maintaining the joint-histogram. This is useful for applications like optical flow occlusion handling. SEE: medianBlur, jointBilateralFilter
      Parameters:
        `joint` - automatically generated
        `src` - automatically generated
        `dst` - automatically generated
        `r` - automatically generated
        `sigma` - automatically generated
        `weightType` - automatically generated

    - #### weightedMedianFilter

          public static void weightedMedianFilter(Mat joint,
                                                  Mat src,
                                                  Mat dst,
                                                  int r,
                                                  double sigma)

      Applies weighted median filter to an image. For more details about this implementation, please see CITE: zhang2014100+ the pixel will be ignored when maintaining the joint-histogram. This is useful for applications like optical flow occlusion handling. SEE: medianBlur, jointBilateralFilter
      Parameters:
        `joint` - automatically generated
        `src` - automatically generated
        `dst` - automatically generated
        `r` - automatically generated
        `sigma` - automatically generated

    - #### weightedMedianFilter

          public static void weightedMedianFilter(Mat joint,
                                                  Mat src,
                                                  Mat dst,
                                                  int r)

      Applies weighted median filter to an image. For more details about this implementation, please see CITE: zhang2014100+ the pixel will be ignored when maintaining the joint-histogram. This is useful for applications like optical flow occlusion handling. SEE: medianBlur, jointBilateralFilter
      Parameters:
        `joint` - automatically generated
        `src` - automatically generated
        `dst` - automatically generated
        `r` - automatically generated

Generated on Thu Jul 25 2019 21:39:44 UTC / OpenCV 3.4.7