opencv_ximgproc (JavaCPP Presets for OpenCV 3.1.0-1.2 API)

java.lang.Object
- org.bytedeco.javacpp.presets.opencv_ximgproc
- - org.bytedeco.javacpp.opencv_ximgproc

All Implemented Interfaces:: InfoMapper

public class opencv_ximgproc
extends opencv_ximgproc

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`static class`	`opencv_ximgproc.AdaptiveManifoldFilter` \brief Interface for Adaptive Manifold Filter realizations.
`static class`	`opencv_ximgproc.DisparityFilter` \addtogroup ximgproc_filters \{
`static class`	`opencv_ximgproc.DisparityWLSFilter` \brief Disparity map filter based on Weighted Least Squares filter (in form of Fast Global Smoother that is a lot faster than traditional Weighted Least Squares filter implementations) and optional use of left-right-consistency-based confidence to refine the results in half-occlusions and uniform areas.
`static class`	`opencv_ximgproc.DTFilter` \brief Interface for realizations of Domain Transform filter.
`static class`	`opencv_ximgproc.Edge` \}
`static class`	`opencv_ximgproc.EdgeAwareInterpolator` \brief Sparse match interpolation algorithm based on modified locally-weighted affine estimator from \cite Revaud2015 and Fast Global Smoother as post-processing filter.
`static class`	`opencv_ximgproc.FastGlobalSmootherFilter` \brief Interface for implementations of Fast Global Smoother filter.
`static class`	`opencv_ximgproc.GraphSegmentation` \addtogroup ximgproc_segmentation \{
`static class`	`opencv_ximgproc.GuidedFilter` \brief Interface for realizations of Guided Filter.
`static class`	`opencv_ximgproc.PointSetElement`
`static class`	`opencv_ximgproc.RFFeatureGetter` \addtogroup ximgproc_edge \{
`static class`	`opencv_ximgproc.SparseMatchInterpolator` \addtogroup ximgproc_filters \{
`static class`	`opencv_ximgproc.StructuredEdgeDetection` \brief Class implementing edge detection algorithm from \cite Dollar2013 :
`static class`	`opencv_ximgproc.SuperpixelLSC` \addtogroup ximgproc_superpixel \{
`static class`	`opencv_ximgproc.SuperpixelSEEDS` \addtogroup ximgproc_superpixel \{
`static class`	`opencv_ximgproc.SuperpixelSLIC` \addtogroup ximgproc_superpixel \{

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`AM_FILTER` enum cv::ximgproc::EdgeAwareFiltersList
`static int`	`ARO_0_45` enum cv::ximgproc::AngleRangeOption
`static int`	`ARO_315_0` enum cv::ximgproc::AngleRangeOption
`static int`	`ARO_315_135` enum cv::ximgproc::AngleRangeOption
`static int`	`ARO_315_45` enum cv::ximgproc::AngleRangeOption
`static int`	`ARO_45_135` enum cv::ximgproc::AngleRangeOption
`static int`	`ARO_45_90` enum cv::ximgproc::AngleRangeOption
`static int`	`ARO_90_135` enum cv::ximgproc::AngleRangeOption
`static int`	`ARO_CTR_HOR` enum cv::ximgproc::AngleRangeOption
`static int`	`ARO_CTR_VER` enum cv::ximgproc::AngleRangeOption
`static int`	`DTF_IC` enum cv::ximgproc::EdgeAwareFiltersList
`static int`	`DTF_NC` enum cv::ximgproc::EdgeAwareFiltersList
`static int`	`DTF_RF` enum cv::ximgproc::EdgeAwareFiltersList
`static int`	`FHT_ADD` enum cv::ximgproc::HoughOp
`static int`	`FHT_AVE` enum cv::ximgproc::HoughOp
`static int`	`FHT_MAX` enum cv::ximgproc::HoughOp
`static int`	`FHT_MIN` enum cv::ximgproc::HoughOp
`static int`	`GUIDED_FILTER` enum cv::ximgproc::EdgeAwareFiltersList
`static int`	`HDO_DESKEW` enum cv::ximgproc::HoughDeskewOption
`static int`	`HDO_RAW` enum cv::ximgproc::HoughDeskewOption
`static int`	`RO_IGNORE_BORDERS` enum cv::ximgproc::RulesOption
`static int`	`RO_STRICT` enum cv::ximgproc::RulesOption
`static int`	`SLIC` enum cv::ximgproc::SLIC
`static int`	`SLICO` enum cv::ximgproc::SLIC

Constructor Summary

Constructors
Constructor and Description

opencv_ximgproc()

Constructors
Constructor and Description
`opencv_ximgproc()`

Method Summary

Methods
Modifier and Type	Method and Description
`static void`	`amFilter(opencv_core.Mat joint, opencv_core.Mat src, opencv_core.Mat dst, double sigma_s, double sigma_r)`
`static void`	`amFilter(opencv_core.Mat joint, opencv_core.Mat src, opencv_core.Mat dst, double sigma_s, double sigma_r, boolean adjust_outliers)` \brief Simple one-line Adaptive Manifold Filter call.
`static double`	`computeBadPixelPercent(opencv_core.Mat GT, opencv_core.Mat src, opencv_core.Rect ROI)`
`static double`	`computeBadPixelPercent(opencv_core.Mat GT, opencv_core.Mat src, opencv_core.Rect ROI, int thresh)` \brief Function for computing the percent of "bad" pixels in the disparity map (pixels where error is higher than a specified threshold)
`static double`	`computeMSE(opencv_core.Mat GT, opencv_core.Mat src, opencv_core.Rect ROI)` \brief Function for computing mean square error for disparity maps
`static void`	`covarianceEstimation(opencv_core.Mat src, opencv_core.Mat dst, int windowRows, int windowCols)` \brief Computes the estimated covariance matrix of an image using the sliding window forumlation.
`static opencv_ximgproc.AdaptiveManifoldFilter`	`createAMFilter(double sigma_s, double sigma_r)`
`static opencv_ximgproc.AdaptiveManifoldFilter`	`createAMFilter(double sigma_s, double sigma_r, boolean adjust_outliers)` \brief Factory method, create instance of AdaptiveManifoldFilter and produce some initialization routines.
`static opencv_ximgproc.DisparityWLSFilter`	`createDisparityWLSFilter(opencv_calib3d.StereoMatcher matcher_left)` \brief Convenience factory method that creates an instance of DisparityWLSFilter and sets up all the relevant filter parameters automatically based on the matcher instance.
`static opencv_ximgproc.DisparityWLSFilter`	`createDisparityWLSFilterGeneric(boolean use_confidence)` \brief More generic factory method, create instance of DisparityWLSFilter and execute basic initialization routines.
`static opencv_ximgproc.DTFilter`	`createDTFilter(opencv_core.Mat guide, double sigmaSpatial, double sigmaColor)`
`static opencv_ximgproc.DTFilter`	`createDTFilter(opencv_core.Mat guide, double sigmaSpatial, double sigmaColor, int mode, int numIters)` \brief Factory method, create instance of DTFilter and produce initialization routines.
`static opencv_ximgproc.EdgeAwareInterpolator`	`createEdgeAwareInterpolator()` \brief Factory method that creates an instance of the EdgeAwareInterpolator.
`static opencv_ximgproc.FastGlobalSmootherFilter`	`createFastGlobalSmootherFilter(opencv_core.Mat guide, double lambda, double sigma_color)`
`static opencv_ximgproc.FastGlobalSmootherFilter`	`createFastGlobalSmootherFilter(opencv_core.Mat guide, double lambda, double sigma_color, double lambda_attenuation, int num_iter)` \brief Factory method, create instance of FastGlobalSmootherFilter and execute the initialization routines.
`static opencv_ximgproc.GraphSegmentation`	`createGraphSegmentation()`
`static opencv_ximgproc.GraphSegmentation`	`createGraphSegmentation(double sigma, float k, int min_size)` \brief Creates a graph based segmentor
`static opencv_ximgproc.GuidedFilter`	`createGuidedFilter(opencv_core.Mat guide, int radius, double eps)` \brief Factory method, create instance of GuidedFilter and produce initialization routines.
`static opencv_ximgproc.RFFeatureGetter`	`createRFFeatureGetter()`
`static opencv_calib3d.StereoMatcher`	`createRightMatcher(opencv_calib3d.StereoMatcher matcher_left)` \brief Convenience method to set up the matcher for computing the right-view disparity map that is required in case of filtering with confidence.
`static opencv_ximgproc.StructuredEdgeDetection`	`createStructuredEdgeDetection(BytePointer model)`
`static opencv_ximgproc.StructuredEdgeDetection`	`createStructuredEdgeDetection(BytePointer model, opencv_ximgproc.RFFeatureGetter howToGetFeatures)` The only constructor
`static opencv_ximgproc.StructuredEdgeDetection`	`createStructuredEdgeDetection(String model)`
`static opencv_ximgproc.StructuredEdgeDetection`	`createStructuredEdgeDetection(String model, opencv_ximgproc.RFFeatureGetter howToGetFeatures)`
`static opencv_ximgproc.SuperpixelLSC`	`createSuperpixelLSC(opencv_core.Mat image)`
`static opencv_ximgproc.SuperpixelLSC`	`createSuperpixelLSC(opencv_core.Mat image, int region_size, float ratio)` \brief Class implementing the LSC (Linear Spectral Clustering) superpixels
`static opencv_ximgproc.SuperpixelSEEDS`	`createSuperpixelSEEDS(int image_width, int image_height, int image_channels, int num_superpixels, int num_levels)`
`static opencv_ximgproc.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)` \brief Initializes a SuperpixelSEEDS object.
`static opencv_ximgproc.SuperpixelSLIC`	`createSuperpixelSLIC(opencv_core.Mat image)`
`static opencv_ximgproc.SuperpixelSLIC`	`createSuperpixelSLIC(opencv_core.Mat image, int algorithm, int region_size, float ruler)`
`static void`	`dtFilter(opencv_core.Mat guide, opencv_core.Mat src, opencv_core.Mat dst, double sigmaSpatial, double sigmaColor)`
`static void`	`dtFilter(opencv_core.Mat guide, opencv_core.Mat src, opencv_core.Mat dst, double sigmaSpatial, double sigmaColor, int mode, int numIters)` \brief Simple one-line Domain Transform filter call.
`static void`	`fastGlobalSmootherFilter(opencv_core.Mat guide, opencv_core.Mat src, opencv_core.Mat dst, double lambda, double sigma_color)`
`static void`	`fastGlobalSmootherFilter(opencv_core.Mat guide, opencv_core.Mat src, opencv_core.Mat dst, double lambda, double sigma_color, double lambda_attenuation, int num_iter)` \brief Simple one-line Fast Global Smoother filter call.
`static void`	`FastHoughTransform(opencv_core.Mat src, opencv_core.Mat dst, int dstMatDepth)`
`static void`	`FastHoughTransform(opencv_core.Mat src, opencv_core.Mat dst, int dstMatDepth, int angleRange, int op, int makeSkew)` \brief Calculates 2D Fast Hough transform of an image.
`static void`	`getDisparityVis(opencv_core.Mat src, opencv_core.Mat dst)`
`static void`	`getDisparityVis(opencv_core.Mat src, opencv_core.Mat dst, double scale)` \brief Function for creating a disparity map visualization (clamped CV_8U image)
`static void`	`guidedFilter(opencv_core.Mat guide, opencv_core.Mat src, opencv_core.Mat dst, int radius, double eps)`
`static void`	`guidedFilter(opencv_core.Mat guide, opencv_core.Mat src, opencv_core.Mat dst, int radius, double eps, int dDepth)` \brief Simple one-line Guided Filter call.
`static opencv_core.Scalar4i`	`HoughPoint2Line(opencv_core.Point houghPoint, opencv_core.Mat srcImgInfo)`
`static opencv_core.Scalar4i`	`HoughPoint2Line(opencv_core.Point houghPoint, opencv_core.Mat srcImgInfo, int angleRange, int makeSkew, int rules)` \brief Calculates coordinates of line segment corresponded by point in Hough space.
`static void`	`jointBilateralFilter(opencv_core.Mat joint, opencv_core.Mat src, opencv_core.Mat dst, int d, double sigmaColor, double sigmaSpace)`
`static void`	`jointBilateralFilter(opencv_core.Mat joint, opencv_core.Mat src, opencv_core.Mat dst, int d, double sigmaColor, double sigmaSpace, int borderType)` \brief Applies the joint bilateral filter to an image.
`static void`	`l0Smooth(opencv_core.Mat src, opencv_core.Mat dst)`
`static void`	`l0Smooth(opencv_core.Mat src, opencv_core.Mat dst, double lambda, double kappa)` \brief Global image smoothing via L0 gradient minimization.
`static void`	`niBlackThreshold(opencv_core.Mat _src, opencv_core.Mat _dst, double maxValue, int type, int blockSize, double delta)` \defgroup ximgproc Extended Image Processing \{ \defgroup ximgproc_edge Structured forests for fast edge detection
`static int`	`readGT(BytePointer src_path, opencv_core.Mat dst)` \brief Function for reading ground truth disparity maps.
`static int`	`readGT(String src_path, opencv_core.Mat dst)`
`static void`	`rollingGuidanceFilter(opencv_core.Mat src, opencv_core.Mat dst)`
`static void`	`rollingGuidanceFilter(opencv_core.Mat src, opencv_core.Mat dst, int d, double sigmaColor, double sigmaSpace, int numOfIter, int borderType)` \brief Applies the rolling guidance filter to an image.

Methods inherited from class org.bytedeco.javacpp.presets.opencv_ximgproc
map

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail
- DTF_NC
```
public static final int DTF_NC
```
  enum cv::ximgproc::EdgeAwareFiltersList
  
  See Also:
  Constant Field Values
- DTF_IC
```
public static final int DTF_IC
```
  enum cv::ximgproc::EdgeAwareFiltersList
  
  See Also:
  Constant Field Values
- DTF_RF
```
public static final int DTF_RF
```
  enum cv::ximgproc::EdgeAwareFiltersList
  
  See Also:
  Constant Field Values
- GUIDED_FILTER
```
public static final int GUIDED_FILTER
```
  enum cv::ximgproc::EdgeAwareFiltersList
  
  See Also:
  Constant Field Values
- AM_FILTER
```
public static final int AM_FILTER
```
  enum cv::ximgproc::EdgeAwareFiltersList
  
  See Also:
  Constant Field Values
- ARO_0_45
```
public static final int ARO_0_45
```
  enum cv::ximgproc::AngleRangeOption
  
  See Also:
  Constant Field Values
- ARO_45_90
```
public static final int ARO_45_90
```
  enum cv::ximgproc::AngleRangeOption
  
  See Also:
  Constant Field Values
- ARO_90_135
```
public static final int ARO_90_135
```
  enum cv::ximgproc::AngleRangeOption
  
  See Also:
  Constant Field Values
- ARO_315_0
```
public static final int ARO_315_0
```
  enum cv::ximgproc::AngleRangeOption
  
  See Also:
  Constant Field Values
- ARO_315_45
```
public static final int ARO_315_45
```
  enum cv::ximgproc::AngleRangeOption
  
  See Also:
  Constant Field Values
- ARO_45_135
```
public static final int ARO_45_135
```
  enum cv::ximgproc::AngleRangeOption
  
  See Also:
  Constant Field Values
- ARO_315_135
```
public static final int ARO_315_135
```
  enum cv::ximgproc::AngleRangeOption
  
  See Also:
  Constant Field Values
- ARO_CTR_HOR
```
public static final int ARO_CTR_HOR
```
  enum cv::ximgproc::AngleRangeOption
  
  See Also:
  Constant Field Values
- ARO_CTR_VER
```
public static final int ARO_CTR_VER
```
  enum cv::ximgproc::AngleRangeOption
  
  See Also:
  Constant Field Values
- FHT_MIN
```
public static final int FHT_MIN
```
  enum cv::ximgproc::HoughOp
  
  See Also:
  Constant Field Values
- FHT_MAX
```
public static final int FHT_MAX
```
  enum cv::ximgproc::HoughOp
  
  See Also:
  Constant Field Values
- FHT_ADD
```
public static final int FHT_ADD
```
  enum cv::ximgproc::HoughOp
  
  See Also:
  Constant Field Values
- FHT_AVE
```
public static final int FHT_AVE
```
  enum cv::ximgproc::HoughOp
  
  See Also:
  Constant Field Values
- HDO_RAW
```
public static final int HDO_RAW
```
  enum cv::ximgproc::HoughDeskewOption
  
  See Also:
  Constant Field Values
- HDO_DESKEW
```
public static final int HDO_DESKEW
```
  enum cv::ximgproc::HoughDeskewOption
  
  See Also:
  Constant Field Values
- RO_STRICT
```
public static final int RO_STRICT
```
  enum cv::ximgproc::RulesOption
  
  See Also:
  Constant Field Values
- RO_IGNORE_BORDERS
```
public static final int RO_IGNORE_BORDERS
```
  enum cv::ximgproc::RulesOption
  
  See Also:
  Constant Field Values
- SLIC
```
public static final int SLIC
```
  enum cv::ximgproc::SLIC
  
  See Also:
  Constant Field Values
- SLICO
```
public static final int SLICO
```
  enum cv::ximgproc::SLIC
  
  See Also:
  Constant Field Values

Constructor Detail
- opencv_ximgproc
```
public opencv_ximgproc()
```

Method Detail

niBlackThreshold

@Namespace(value="cv::ximgproc")
public static void niBlackThreshold(@ByVal
                                   opencv_core.Mat _src,
                                   @ByVal
                                   opencv_core.Mat _dst,
                                   double maxValue,
                                   int type,
                                   int blockSize,
                                   double delta)

\defgroup ximgproc Extended Image Processing \{ \defgroup ximgproc_edge Structured forests for fast edge detection

This module contains implementations of modern structured edge detection algorithms, i.e. algorithms which somehow takes into account pixel affinities in natural images.

\defgroup ximgproc_filters Filters

\defgroup ximgproc_superpixel Superpixels

\defgroup ximgproc_segmentation Image segmentation \}

createDTFilter
```
@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.DTFilter createDTFilter(@ByVal
                                                                     opencv_core.Mat guide,
                                                                     double sigmaSpatial,
                                                                     double sigmaColor,
                                                                     int mode,
                                                                     int numIters)
```
\brief 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 - \f${\sigma}_H\f$ parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter.
sigmaColor - \f${\sigma}_r\f$ 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/).

createDTFilter

@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.DTFilter createDTFilter(@ByVal
                                                                     opencv_core.Mat guide,
                                                                     double sigmaSpatial,
                                                                     double sigmaColor)

dtFilter
```
@Namespace(value="cv::ximgproc")
public static void dtFilter(@ByVal
                           opencv_core.Mat guide,
                           @ByVal
                           opencv_core.Mat src,
                           @ByVal
                           opencv_core.Mat dst,
                           double sigmaSpatial,
                           double sigmaColor,
                           int mode,
                           int numIters)
```
\brief 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 -
sigmaSpatial - \f${\sigma}_H\f$ parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter.
sigmaColor - \f${\sigma}_r\f$ 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. \sa bilateralFilter, guidedFilter, amFilter

dtFilter

@Namespace(value="cv::ximgproc")
public static void dtFilter(@ByVal
                           opencv_core.Mat guide,
                           @ByVal
                           opencv_core.Mat src,
                           @ByVal
                           opencv_core.Mat dst,
                           double sigmaSpatial,
                           double sigmaColor)

createGuidedFilter
```
@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.GuidedFilter createGuidedFilter(@ByVal
                                                                             opencv_core.Mat guide,
                                                                             int radius,
                                                                             double eps)
```
\brief 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. \f${eps}^2\f$ is similar to the sigma in the color space into bilateralFilter.
For more details about Guided Filter parameters, see the original article \cite Kaiming10 .

guidedFilter
```
@Namespace(value="cv::ximgproc")
public static void guidedFilter(@ByVal
                               opencv_core.Mat guide,
                               @ByVal
                               opencv_core.Mat src,
                               @ByVal
                               opencv_core.Mat dst,
                               int radius,
                               double eps,
                               int dDepth)
```
\brief 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. \f${eps}^2\f$ is similar to the sigma in the color space into bilateralFilter.
dDepth - optional depth of the output image.
\sa bilateralFilter, dtFilter, amFilter

guidedFilter

@Namespace(value="cv::ximgproc")
public static void guidedFilter(@ByVal
                               opencv_core.Mat guide,
                               @ByVal
                               opencv_core.Mat src,
                               @ByVal
                               opencv_core.Mat dst,
                               int radius,
                               double eps)

createAMFilter
```
@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.AdaptiveManifoldFilter createAMFilter(double sigma_s,
                                                                                   double sigma_r,
                                                                                   @Cast(value="bool")
                                                                                   boolean adjust_outliers)
```
\brief 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.

createAMFilter

@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.AdaptiveManifoldFilter createAMFilter(double sigma_s,
                                                                                   double sigma_r)

amFilter
```
@Namespace(value="cv::ximgproc")
public static void amFilter(@ByVal
                           opencv_core.Mat joint,
                           @ByVal
                           opencv_core.Mat src,
                           @ByVal
                           opencv_core.Mat dst,
                           double sigma_s,
                           double sigma_r,
                           @Cast(value="bool")
                           boolean adjust_outliers)
```
\brief 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. \sa bilateralFilter, dtFilter, guidedFilter

amFilter

@Namespace(value="cv::ximgproc")
public static void amFilter(@ByVal
                           opencv_core.Mat joint,
                           @ByVal
                           opencv_core.Mat src,
                           @ByVal
                           opencv_core.Mat dst,
                           double sigma_s,
                           double sigma_r)

jointBilateralFilter
```
@Namespace(value="cv::ximgproc")
public static void jointBilateralFilter(@ByVal
                                       opencv_core.Mat joint,
                                       @ByVal
                                       opencv_core.Mat src,
                                       @ByVal
                                       opencv_core.Mat dst,
                                       int d,
                                       double sigmaColor,
                                       double sigmaSpace,
                                       int borderType)
```
\brief 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.
\sa bilateralFilter, amFilter

jointBilateralFilter

@Namespace(value="cv::ximgproc")
public static void jointBilateralFilter(@ByVal
                                       opencv_core.Mat joint,
                                       @ByVal
                                       opencv_core.Mat src,
                                       @ByVal
                                       opencv_core.Mat dst,
                                       int d,
                                       double sigmaColor,
                                       double sigmaSpace)

rollingGuidanceFilter
```
@Namespace(value="cv::ximgproc")
public static void rollingGuidanceFilter(@ByVal
                                        opencv_core.Mat src,
                                        @ByVal
                                        opencv_core.Mat dst,
                                        int d,
                                        double sigmaColor,
                                        double sigmaSpace,
                                        int numOfIter,
                                        int borderType)
```
\brief Applies the rolling guidance filter to an image.

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.
\sa jointBilateralFilter, bilateralFilter, amFilter

rollingGuidanceFilter

@Namespace(value="cv::ximgproc")
public static void rollingGuidanceFilter(@ByVal
                                        opencv_core.Mat src,
                                        @ByVal
                                        opencv_core.Mat dst)

createFastGlobalSmootherFilter
```
@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.FastGlobalSmootherFilter createFastGlobalSmootherFilter(@ByVal
                                                                                                     opencv_core.Mat guide,
                                                                                                     double lambda,
                                                                                                     double sigma_color,
                                                                                                     double lambda_attenuation,
                                                                                                     int num_iter)
```
\brief 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.

createFastGlobalSmootherFilter

@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.FastGlobalSmootherFilter createFastGlobalSmootherFilter(@ByVal
                                                                                                     opencv_core.Mat guide,
                                                                                                     double lambda,
                                                                                                     double sigma_color)

fastGlobalSmootherFilter

@Namespace(value="cv::ximgproc")
public static void fastGlobalSmootherFilter(@ByVal
                                           opencv_core.Mat guide,
                                           @ByVal
                                           opencv_core.Mat src,
                                           @ByVal
                                           opencv_core.Mat dst,
                                           double lambda,
                                           double sigma_color,
                                           double lambda_attenuation,
                                           int num_iter)

\brief 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

@Namespace(value="cv::ximgproc")
public static void fastGlobalSmootherFilter(@ByVal
                                           opencv_core.Mat guide,
                                           @ByVal
                                           opencv_core.Mat src,
                                           @ByVal
                                           opencv_core.Mat dst,
                                           double lambda,
                                           double sigma_color)

l0Smooth
```
@Namespace(value="cv::ximgproc")
public static void l0Smooth(@ByVal
                           opencv_core.Mat src,
                           @ByVal
                           opencv_core.Mat dst,
                           double lambda,
                           double kappa)
```
\brief 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

@Namespace(value="cv::ximgproc")
public static void l0Smooth(@ByVal
                           opencv_core.Mat src,
                           @ByVal
                           opencv_core.Mat dst)

createDisparityWLSFilter

@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.DisparityWLSFilter createDisparityWLSFilter(@opencv_core.Ptr
                                                                                         opencv_calib3d.StereoMatcher matcher_left)

\brief 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

createRightMatcher

@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_calib3d.StereoMatcher createRightMatcher(@opencv_core.Ptr
                                                                             opencv_calib3d.StereoMatcher matcher_left)

\brief 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

createDisparityWLSFilterGeneric
```
@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.DisparityWLSFilter createDisparityWLSFilterGeneric(@Cast(value="bool")
                                                                                                boolean use_confidence)
```
\brief 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.

readGT
```
@Namespace(value="cv::ximgproc")
public static int readGT(@opencv_core.Str
                        BytePointer src_path,
                        @ByVal
                        opencv_core.Mat dst)
```
\brief 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
\result returns zero if successfully read the ground truth

readGT

@Namespace(value="cv::ximgproc")
public static int readGT(@opencv_core.Str
                        String src_path,
                        @ByVal
                        opencv_core.Mat dst)

computeMSE

@Namespace(value="cv::ximgproc")
public static double computeMSE(@ByVal
                               opencv_core.Mat GT,
                               @ByVal
                               opencv_core.Mat src,
                               @ByVal
                               opencv_core.Rect ROI)

\brief Function for computing mean square error for disparity maps

Parameters:: GT - ground truth disparity map; src - disparity map to evaluate; ROI - region of interest
\result returns mean square error between GT and src

computeBadPixelPercent

@Namespace(value="cv::ximgproc")
public static double computeBadPixelPercent(@ByVal
                                           opencv_core.Mat GT,
                                           @ByVal
                                           opencv_core.Mat src,
                                           @ByVal
                                           opencv_core.Rect ROI,
                                           int thresh)

\brief 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
\result returns mean square error between GT and src

computeBadPixelPercent

@Namespace(value="cv::ximgproc")
public static double computeBadPixelPercent(@ByVal
                                           opencv_core.Mat GT,
                                           @ByVal
                                           opencv_core.Mat src,
                                           @ByVal
                                           opencv_core.Rect ROI)

getDisparityVis

@Namespace(value="cv::ximgproc")
public static void getDisparityVis(@ByVal
                                  opencv_core.Mat src,
                                  @ByVal
                                  opencv_core.Mat dst,
                                  double scale)

\brief 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

@Namespace(value="cv::ximgproc")
public static void getDisparityVis(@ByVal
                                  opencv_core.Mat src,
                                  @ByVal
                                  opencv_core.Mat dst)

createEdgeAwareInterpolator

@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.EdgeAwareInterpolator createEdgeAwareInterpolator()

\brief Factory method that creates an instance of the EdgeAwareInterpolator.

createRFFeatureGetter

@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.RFFeatureGetter createRFFeatureGetter()

createStructuredEdgeDetection

@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.StructuredEdgeDetection createStructuredEdgeDetection(@opencv_core.Str
                                                                                                   BytePointer model,
                                                                                                   @Const@opencv_core.Ptr
                                                                                                   opencv_ximgproc.RFFeatureGetter howToGetFeatures)

The only constructor

Parameters:: model - : name of the file where the model is stored; howToGetFeatures - : optional object inheriting from RFFeatureGetter. You need it only if you would like to train your own forest, pass NULL otherwise

createStructuredEdgeDetection

@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.StructuredEdgeDetection createStructuredEdgeDetection(@opencv_core.Str
                                                                                                   BytePointer model)

createStructuredEdgeDetection

@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.StructuredEdgeDetection createStructuredEdgeDetection(@opencv_core.Str
                                                                                                   String model,
                                                                                                   @Const@opencv_core.Ptr
                                                                                                   opencv_ximgproc.RFFeatureGetter howToGetFeatures)

createStructuredEdgeDetection

@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.StructuredEdgeDetection createStructuredEdgeDetection(@opencv_core.Str
                                                                                                   String model)

createSuperpixelSEEDS
```
@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.SuperpixelSEEDS createSuperpixelSEEDS(int image_width,
                                                                                   int image_height,
                                                                                   int image_channels,
                                                                                   int num_superpixels,
                                                                                   int num_levels,
                                                                                   int prior,
                                                                                   int histogram_bins,
                                                                                   @Cast(value="bool")
                                                                                   boolean double_step)
```
\brief 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)

createSuperpixelSEEDS

@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.SuperpixelSEEDS createSuperpixelSEEDS(int image_width,
                                                                                   int image_height,
                                                                                   int image_channels,
                                                                                   int num_superpixels,
                                                                                   int num_levels)

createGraphSegmentation

@Namespace(value="cv::ximgproc::segmentation")
@opencv_core.Ptr
public static opencv_ximgproc.GraphSegmentation createGraphSegmentation(double sigma,
                                                                                       float k,
                                                                                       int min_size)

\brief Creates a graph based segmentor

Parameters:: sigma - The sigma parameter, used to smooth image; k - The k parameter of the algorythm; min_size - The minimum size of segments

createGraphSegmentation

@Namespace(value="cv::ximgproc::segmentation")
@opencv_core.Ptr
public static opencv_ximgproc.GraphSegmentation createGraphSegmentation()

FastHoughTransform

@Namespace(value="cv::ximgproc")
public static void FastHoughTransform(@ByVal
                                     opencv_core.Mat src,
                                     @ByVal
                                     opencv_core.Mat dst,
                                     int dstMatDepth,
                                     int angleRange,
                                     int op,
                                     int makeSkew)

\brief Calculates 2D Fast Hough transform of an image.

Parameters:: dst - The destination image, result of transformation.; src - The source (input) image.; dstMatDepth - The depth of destination image; op - The operation to be applied, see cv::HoughOp; angleRange - The part of Hough space to calculate, see cv::AngleRangeOption; makeSkew - Specifies to do or not to do image skewing, see cv::HoughDeskewOption The function calculates the fast Hough transform for full, half or quarter range of angles.

FastHoughTransform

@Namespace(value="cv::ximgproc")
public static void FastHoughTransform(@ByVal
                                     opencv_core.Mat src,
                                     @ByVal
                                     opencv_core.Mat dst,
                                     int dstMatDepth)

HoughPoint2Line
```
@Namespace(value="cv::ximgproc")
@ByVal
public static opencv_core.Scalar4i HoughPoint2Line(@Const@ByRef
                                                        opencv_core.Point houghPoint,
                                                        @ByVal
                                                        opencv_core.Mat srcImgInfo,
                                                        int angleRange,
                                                        int makeSkew,
                                                        int rules)
```
\brief Calculates coordinates of line segment corresponded by point in Hough space.

Parameters:
houghPoint - Point in Hough space.
srcImgInfo - The source (input) image of Hough transform.
angleRange - The part of Hough space where point is situated, see cv::AngleRangeOption
makeSkew - Specifies to do or not to do image skewing, see cv::HoughDeskewOption
rules - Specifies strictness of line segment calculating, see cv::RulesOption \retval [Vec4i] Coordinates of line segment corresponded by point in Hough space. \remarks If rules parameter set to RO_STRICT then returned line cut along the border of source image. \remarks If rules parameter set to RO_WEAK then in case of point, which belongs the incorrect part of Hough image, returned line will not intersect source image. The function calculates coordinates of line segment corresponded by point in Hough space.

HoughPoint2Line

@Namespace(value="cv::ximgproc")
@ByVal
public static opencv_core.Scalar4i HoughPoint2Line(@Const@ByRef
                                                        opencv_core.Point houghPoint,
                                                        @ByVal
                                                        opencv_core.Mat srcImgInfo)

covarianceEstimation
```
@Namespace(value="cv::ximgproc")
public static void covarianceEstimation(@ByVal
                                       opencv_core.Mat src,
                                       @ByVal
                                       opencv_core.Mat dst,
                                       int windowRows,
                                       int windowCols)
```
\brief 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.

createSuperpixelSLIC

@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.SuperpixelSLIC createSuperpixelSLIC(@ByVal
                                                                                 opencv_core.Mat image,
                                                                                 int algorithm,
                                                                                 int region_size,
                                                                                 float ruler)

createSuperpixelSLIC

@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.SuperpixelSLIC createSuperpixelSLIC(@ByVal
                                                                                 opencv_core.Mat image)

createSuperpixelLSC
```
@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.SuperpixelLSC createSuperpixelLSC(@ByVal
                                                                               opencv_core.Mat image,
                                                                               int region_size,
                                                                               float ratio)
```
\brief 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)

createSuperpixelLSC

@Namespace(value="cv::ximgproc")
@opencv_core.Ptr
public static opencv_ximgproc.SuperpixelLSC createSuperpixelLSC(@ByVal
                                                                               opencv_core.Mat image)

Class opencv_ximgproc

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class org.bytedeco.javacpp.presets.opencv_ximgproc

Methods inherited from class java.lang.Object

Field Detail

DTF_NC

DTF_IC

DTF_RF

GUIDED_FILTER

AM_FILTER

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

FHT_MIN

FHT_MAX

FHT_ADD

FHT_AVE

HDO_RAW

HDO_DESKEW

RO_STRICT

RO_IGNORE_BORDERS

SLIC

SLICO

Constructor Detail

opencv_ximgproc

Method Detail

niBlackThreshold

createDTFilter

createDTFilter

dtFilter

dtFilter

createGuidedFilter

guidedFilter

guidedFilter

createAMFilter

createAMFilter

amFilter

amFilter

jointBilateralFilter

jointBilateralFilter

rollingGuidanceFilter

rollingGuidanceFilter

createFastGlobalSmootherFilter

createFastGlobalSmootherFilter

fastGlobalSmootherFilter

fastGlobalSmootherFilter

l0Smooth

l0Smooth

createDisparityWLSFilter

createRightMatcher

createDisparityWLSFilterGeneric

readGT

readGT

computeMSE

computeBadPixelPercent

computeBadPixelPercent

getDisparityVis

getDisparityVis

createEdgeAwareInterpolator

createRFFeatureGetter

createStructuredEdgeDetection

createStructuredEdgeDetection

createStructuredEdgeDetection

createStructuredEdgeDetection

createSuperpixelSEEDS

createSuperpixelSEEDS

createGraphSegmentation

createGraphSegmentation

FastHoughTransform

FastHoughTransform

HoughPoint2Line