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

public class Ximgproc
extends java.lang.Object

Field Summary

Fields
Modifier and Type	Field	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 Description

Ximgproc()

Method Summary

All Methods Static Methods Concrete Methods
Modifier and Type	Method	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 diffusion 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 double`	`computeBadPixelPercent(Mat GT, Mat src, Rect ROI)`	Function for computing the percent of "bad" pixels in the disparity map (pixels where error is higher than a specified threshold)
`static double`	`computeBadPixelPercent(Mat GT, Mat src, Rect ROI, int thresh)`	Function for computing the percent of "bad" pixels in the disparity map (pixels where error is higher than a specified threshold)
`static double`	`computeMSE(Mat GT, Mat src, Rect ROI)`	Function for computing mean square error for disparity maps
`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 EdgeDrawing`	`createEdgeDrawing()`	Creates a smart pointer to a EdgeDrawing object and initializes it
`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 If zero, Canny() is not applied and the input image is taken as an edge image.
`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(java.lang.String model)`
`static StructuredEdgeDetection`	`createStructuredEdgeDetection(java.lang.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`	`getDisparityVis(Mat src, Mat dst)`	Function for creating a disparity map visualization (clamped CV_8U image)
`static void`	`getDisparityVis(Mat src, Mat dst, double scale)`	Function for creating a disparity map visualization (clamped CV_8U image)
`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 int`	`readGT(java.lang.String src_path, Mat dst)`	Function for reading ground truth disparity maps.
`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
  - ARO_0_45
```
public static final int ARO_0_45
```
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    Constant Field Values
  - ARO_45_90
```
public static final int ARO_45_90
```
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    Constant Field Values
  - ARO_90_135
```
public static final int ARO_90_135
```
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    Constant Field Values
  - ARO_315_0
```
public static final int ARO_315_0
```
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    Constant Field Values
  - ARO_315_45
```
public static final int ARO_315_45
```
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    Constant Field Values
  - ARO_45_135
```
public static final int ARO_45_135
```
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  - ARO_315_135
```
public static final int ARO_315_135
```
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    Constant Field Values
  - ARO_CTR_HOR
```
public static final int ARO_CTR_HOR
```
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  - ARO_CTR_VER
```
public static final int ARO_CTR_VER
```
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    Constant Field Values
  - DTF_NC
```
public static final int DTF_NC
```
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    Constant Field Values
  - DTF_IC
```
public static final int DTF_IC
```
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    Constant Field Values
  - DTF_RF
```
public static final int DTF_RF
```
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    Constant Field Values
  - GUIDED_FILTER
```
public static final int GUIDED_FILTER
```
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    Constant Field Values
  - AM_FILTER
```
public static final int AM_FILTER
```
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    Constant Field Values
  - HDO_RAW
```
public static final int HDO_RAW
```
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    Constant Field Values
  - HDO_DESKEW
```
public static final int HDO_DESKEW
```
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    Constant Field Values
  - FHT_MIN
```
public static final int FHT_MIN
```
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    Constant Field Values
  - FHT_MAX
```
public static final int FHT_MAX
```
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  - FHT_ADD
```
public static final int FHT_ADD
```
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  - FHT_AVE
```
public static final int FHT_AVE
```
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    Constant Field Values
  - BINARIZATION_NIBLACK
```
public static final int BINARIZATION_NIBLACK
```
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    Constant Field Values
  - BINARIZATION_SAUVOLA
```
public static final int BINARIZATION_SAUVOLA
```
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    Constant Field Values
  - BINARIZATION_WOLF
```
public static final int BINARIZATION_WOLF
```
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    Constant Field Values
  - BINARIZATION_NICK
```
public static final int BINARIZATION_NICK
```
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    Constant Field Values
  - SLIC
```
public static final int SLIC
```
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    Constant Field Values
  - SLICO
```
public static final int SLICO
```
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    Constant Field Values
  - MSLIC
```
public static final int MSLIC
```
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    Constant Field Values
  - THINNING_ZHANGSUEN
```
public static final int THINNING_ZHANGSUEN
```
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    Constant Field Values
  - THINNING_GUOHALL
```
public static final int THINNING_GUOHALL
```
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    Constant Field Values
  - WMF_EXP
```
public static final int WMF_EXP
```
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    Constant Field Values
  - WMF_IV1
```
public static final int WMF_IV1
```
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    Constant Field Values
  - WMF_IV2
```
public static final int WMF_IV2
```
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    Constant Field Values
  - WMF_COS
```
public static final int WMF_COS
```
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    Constant Field Values
  - WMF_JAC
```
public static final int WMF_JAC
```
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    Constant Field Values
  - WMF_OFF
```
public static final int WMF_OFF
```
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    Constant Field Values
- Constructor Detail
  - Ximgproc
```
public Ximgproc()
```
- Method Detail
  - 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
  - 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.
  - anisotropicDiffusion
```
public static void anisotropicDiffusion(Mat src,
                                        Mat dst,
                                        float alpha,
                                        float K,
                                        int niters)
```
    Performs anisotropic diffusion on an image. The function applies Perona-Malik anisotropic diffusion to an image. This is the solution to the partial differential equation: \({\frac {\partial I}{\partial t}}={\mathrm {div}}\left(c(x,y,t)\nabla I\right)=\nabla c\cdot \nabla I+c(x,y,t)\Delta I\) Suggested functions for c(x,y,t) are: \(c\left(\|\nabla I\|\right)=e^{{-\left(\|\nabla I\|/K\right)^{2}}}\) or \( c\left(\|\nabla I\|\right)={\frac {1}{1+\left({\frac {\|\nabla I\|}{K}}\right)^{2}}} \)
    
    Parameters:
    
    src - Source image with 3 channels.
    
    dst - Destination image of the same size and the same number of channels as src .
    
    alpha - The amount of time to step forward by on each iteration (normally, it's between 0 and 1).
    
    K - sensitivity to the edges
    
    niters - The number of iterations
  - PeiLinNormalization
```
public static void PeiLinNormalization(Mat I,
                                       Mat T)
```
  - createRFFeatureGetter
```
public static RFFeatureGetter createRFFeatureGetter()
```
  - createStructuredEdgeDetection
```
public static StructuredEdgeDetection createStructuredEdgeDetection(java.lang.String model,
                                                                    RFFeatureGetter howToGetFeatures)
```
  - createStructuredEdgeDetection
```
public static StructuredEdgeDetection createStructuredEdgeDetection(java.lang.String model)
```
  - 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
  - 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
  - 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
  - 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
  - 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 - Segment shorter than this will be discarded
    
    distance_threshold - A point placed from a hypothesis line segment farther than this will be regarded as an outlier
    
    canny_th1 - First threshold for hysteresis procedure in Canny()
    
    canny_th2 - Second threshold for hysteresis procedure in Canny()
    
    canny_aperture_size - Aperturesize for the sobel operator in Canny(). If zero, Canny() is not applied and the input image is taken as an edge image.
    
    do_merge - If true, incremental merging of segments will be performed
    
    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 - Segment shorter than this will be discarded
    
    distance_threshold - A point placed from a hypothesis line segment farther than this will be regarded as an outlier
    
    canny_th1 - First threshold for hysteresis procedure in Canny()
    
    canny_th2 - Second threshold for hysteresis procedure in Canny()
    
    canny_aperture_size - Aperturesize for the sobel operator in Canny(). If zero, Canny() is not applied and the input image is taken as an edge image.
    
    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 - Segment shorter than this will be discarded
    
    distance_threshold - A point placed from a hypothesis line segment farther than this will be regarded as an outlier
    
    canny_th1 - First threshold for hysteresis procedure in Canny()
    
    canny_th2 - Second threshold for hysteresis procedure in Canny() If zero, Canny() is not applied and the input image is taken as an edge image.
    
    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 - Segment shorter than this will be discarded
    
    distance_threshold - A point placed from a hypothesis line segment farther than this will be regarded as an outlier
    
    canny_th1 - First threshold for hysteresis procedure in Canny() If zero, Canny() is not applied and the input image is taken as an edge image.
    
    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 - Segment shorter than this will be discarded
    
    distance_threshold - A point placed from a hypothesis line segment farther than this will be regarded as an outlier If zero, Canny() is not applied and the input image is taken as an edge image.
    
    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 - Segment shorter than this will be discarded segment farther than this will be regarded as an outlier If zero, Canny() is not applied and the input image is taken as an edge image.
    
    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 If zero, Canny() is not applied and the input image is taken as an edge image.
    
    Returns:
    
    automatically generated
  - createEdgeDrawing
```
public static EdgeDrawing createEdgeDrawing()
```
    Creates a smart pointer to a EdgeDrawing object and initializes it
    
    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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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.
  - 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
  - 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.
  - 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.
  - 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
  - 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
  - createSelectiveSearchSegmentationStrategyColor
```
public static SelectiveSearchSegmentationStrategyColor createSelectiveSearchSegmentationStrategyColor()
```
    Create a new color-based 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
  - createSelectiveSearchSegmentationStrategyFill
```
public static SelectiveSearchSegmentationStrategyFill createSelectiveSearchSegmentationStrategyFill()
```
    Create a new fill-based strategy
    
    Returns:
    
    automatically generated
  - createSelectiveSearchSegmentationStrategyMultiple
```
public static SelectiveSearchSegmentationStrategyMultiple createSelectiveSearchSegmentationStrategyMultiple()
```
    Create a new multiple 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(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,
                                                                                                            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,
                                                                                                            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
  - createSelectiveSearchSegmentation
```
public static SelectiveSearchSegmentation createSelectiveSearchSegmentation()
```
    Create a new SelectiveSearchSegmentation class.
    
    Returns:
    
    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.
  - 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
  - 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
  - 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
  - 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
  - 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
  - contourSampling
```
public static void contourSampling(Mat src,
                                   Mat out,
                                   int nbElt)
```
    Contour sampling .
    
    Parameters:
    
    src - automatically generated
    
    out - automatically generated
    
    nbElt - 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
  - 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
  - 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
  - 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
  - readGT
```
public static int readGT(java.lang.String src_path,
                         Mat dst)
```
    Function for reading ground truth disparity maps. Supports basic Middlebury and MPI-Sintel formats. Note that the resulting disparity map is scaled by 16.
    
    Parameters:
    
    src_path - path to the image, containing ground-truth disparity map
    
    dst - output disparity map, CV_16S depth
    
    Returns:
    
    returns zero if successfully read the ground truth
  - computeMSE
```
public static double computeMSE(Mat GT,
                                Mat src,
                                Rect ROI)
```
    Function for computing mean square error for disparity maps
    
    Parameters:
    
    GT - ground truth disparity map
    
    src - disparity map to evaluate
    
    ROI - region of interest
    
    Returns:
    
    returns mean square error between GT and src
  - computeBadPixelPercent
```
public static double computeBadPixelPercent(Mat GT,
                                            Mat src,
                                            Rect ROI,
                                            int thresh)
```
    Function for computing the percent of "bad" pixels in the disparity map (pixels where error is higher than a specified threshold)
    
    Parameters:
    
    GT - ground truth disparity map
    
    src - disparity map to evaluate
    
    ROI - region of interest
    
    thresh - threshold used to determine "bad" pixels
    
    Returns:
    
    returns mean square error between GT and src
  - computeBadPixelPercent
```
public static double computeBadPixelPercent(Mat GT,
                                            Mat src,
                                            Rect ROI)
```
    Function for computing the percent of "bad" pixels in the disparity map (pixels where error is higher than a specified threshold)
    
    Parameters:
    
    GT - ground truth disparity map
    
    src - disparity map to evaluate
    
    ROI - region of interest
    
    Returns:
    
    returns mean square error between GT and src
  - getDisparityVis
```
public static void getDisparityVis(Mat src,
                                   Mat dst,
                                   double scale)
```
    Function for creating a disparity map visualization (clamped CV_8U image)
    
    Parameters:
    
    src - input disparity map (CV_16S depth)
    
    dst - output visualization
    
    scale - disparity map will be multiplied by this value for visualization
  - getDisparityVis
```
public static void getDisparityVis(Mat src,
                                   Mat dst)
```
    Function for creating a disparity map visualization (clamped CV_8U image)
    
    Parameters:
    
    src - input disparity map (CV_16S depth)
    
    dst - output visualization
  - createEdgeAwareInterpolator
```
public static EdgeAwareInterpolator createEdgeAwareInterpolator()
```
    Factory method that creates an instance of the EdgeAwareInterpolator.
    
    Returns:
    
    automatically generated

Class Ximgproc

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

ARO_0_45

ARO_45_90

ARO_90_135

ARO_315_0

ARO_315_45

ARO_45_135

ARO_315_135

ARO_CTR_HOR

ARO_CTR_VER

DTF_NC

DTF_IC

DTF_RF

GUIDED_FILTER

AM_FILTER

HDO_RAW

HDO_DESKEW

FHT_MIN

FHT_MAX

FHT_ADD

FHT_AVE

BINARIZATION_NIBLACK

BINARIZATION_SAUVOLA

BINARIZATION_WOLF

BINARIZATION_NICK

SLIC

SLICO

MSLIC

THINNING_ZHANGSUEN

THINNING_GUOHALL

WMF_EXP

WMF_IV1

WMF_IV2

WMF_COS

WMF_JAC

WMF_OFF

Constructor Detail

Ximgproc

Method Detail

niBlackThreshold

niBlackThreshold

thinning

thinning

anisotropicDiffusion

PeiLinNormalization

createRFFeatureGetter

createStructuredEdgeDetection

createStructuredEdgeDetection

weightedMedianFilter

weightedMedianFilter

weightedMedianFilter

weightedMedianFilter

createEdgeBoxes

createEdgeBoxes

createEdgeBoxes

createEdgeBoxes

createEdgeBoxes

createEdgeBoxes

createEdgeBoxes

createEdgeBoxes

createEdgeBoxes

createEdgeBoxes

createEdgeBoxes

createEdgeBoxes

createEdgeBoxes

createSuperpixelSEEDS

createSuperpixelSEEDS

createSuperpixelSEEDS

createSuperpixelSEEDS

createSuperpixelLSC

createSuperpixelLSC

createSuperpixelLSC

createFastLineDetector

createFastLineDetector

createFastLineDetector