OpenCV  4.0.0
Open Source Computer Vision
Classes | Enumerations | Functions

Classes

class  cv::ximgproc::AdaptiveManifoldFilter
 Interface for Adaptive Manifold Filter realizations. More...
 
class  cv::ximgproc::DisparityFilter
 Main interface for all disparity map filters. More...
 
class  cv::ximgproc::DisparityWLSFilter
 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. More...
 
class  cv::ximgproc::DTFilter
 Interface for realizations of Domain Transform filter. More...
 
class  cv::ximgproc::EdgeAwareInterpolator
 Sparse match interpolation algorithm based on modified locally-weighted affine estimator from [164] and Fast Global Smoother as post-processing filter. More...
 
class  cv::ximgproc::FastBilateralSolverFilter
 Interface for implementations of Fast Bilateral Solver. More...
 
class  cv::ximgproc::FastGlobalSmootherFilter
 Interface for implementations of Fast Global Smoother filter. More...
 
class  cv::ximgproc::GuidedFilter
 Interface for realizations of Guided Filter. More...
 
class  cv::ximgproc::RidgeDetectionFilter
 Applies Ridge Detection Filter to an input image. Implements Ridge detection similar to the one in Mathematica using the eigen values from the Hessian Matrix of the input image using Sobel Derivatives. Additional refinement can be done using Skeletonization and Binarization. Adapted from [52] and [134]. More...
 
class  cv::ximgproc::SparseMatchInterpolator
 Main interface for all filters, that take sparse matches as an input and produce a dense per-pixel matching (optical flow) as an output. More...
 

Enumerations

enum  cv::ximgproc::EdgeAwareFiltersList {
  cv::ximgproc::DTF_NC,
  cv::ximgproc::DTF_IC,
  cv::ximgproc::DTF_RF,
  cv::ximgproc::GUIDED_FILTER,
  cv::ximgproc::AM_FILTER
}
 

Functions

void cv::ximgproc::amFilter (InputArray joint, InputArray src, OutputArray dst, double sigma_s, double sigma_r, bool adjust_outliers=false)
 Simple one-line Adaptive Manifold Filter call. More...
 
void cv::ximgproc::bilateralTextureFilter (InputArray src, OutputArray dst, int fr=3, int numIter=1, double sigmaAlpha=-1., double sigmaAvg=-1.)
 Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see [38]. More...
 
void cv::ximgproc::colorMatchTemplate (InputArray img, InputArray templ, OutputArray result)
 Compares a color template against overlapped color image regions. More...
 
double cv::ximgproc::computeBadPixelPercent (InputArray GT, InputArray src, Rect ROI, int thresh=24)
 Function for computing the percent of "bad" pixels in the disparity map (pixels where error is higher than a specified threshold) More...
 
double cv::ximgproc::computeMSE (InputArray GT, InputArray src, Rect ROI)
 Function for computing mean square error for disparity maps. More...
 
Ptr< AdaptiveManifoldFiltercv::ximgproc::createAMFilter (double sigma_s, double sigma_r, bool adjust_outliers=false)
 Factory method, create instance of AdaptiveManifoldFilter and produce some initialization routines. More...
 
Ptr< DisparityWLSFiltercv::ximgproc::createDisparityWLSFilter (Ptr< StereoMatcher > matcher_left)
 Convenience factory method that creates an instance of DisparityWLSFilter and sets up all the relevant filter parameters automatically based on the matcher instance. Currently supports only StereoBM and StereoSGBM. More...
 
Ptr< DisparityWLSFiltercv::ximgproc::createDisparityWLSFilterGeneric (bool use_confidence)
 More generic factory method, create instance of DisparityWLSFilter and execute basic initialization routines. When using this method you will need to set-up the ROI, matchers and other parameters by yourself. More...
 
Ptr< DTFiltercv::ximgproc::createDTFilter (InputArray guide, double sigmaSpatial, double sigmaColor, int mode=DTF_NC, int numIters=3)
 Factory method, create instance of DTFilter and produce initialization routines. More...
 
Ptr< EdgeAwareInterpolatorcv::ximgproc::createEdgeAwareInterpolator ()
 Factory method that creates an instance of the EdgeAwareInterpolator. More...
 
Ptr< FastBilateralSolverFiltercv::ximgproc::createFastBilateralSolverFilter (InputArray guide, double sigma_spatial, double sigma_luma, double sigma_chroma, double lambda=128.0, int num_iter=25, double max_tol=1e-5)
 Factory method, create instance of FastBilateralSolverFilter and execute the initialization routines. More...
 
Ptr< FastGlobalSmootherFiltercv::ximgproc::createFastGlobalSmootherFilter (InputArray guide, double lambda, double sigma_color, double lambda_attenuation=0.25, int num_iter=3)
 Factory method, create instance of FastGlobalSmootherFilter and execute the initialization routines. More...
 
Ptr< GuidedFiltercv::ximgproc::createGuidedFilter (InputArray guide, int radius, double eps)
 Factory method, create instance of GuidedFilter and produce initialization routines. More...
 
void cv::ximgproc::createQuaternionImage (InputArray img, OutputArray qimg)
 creates a quaternion image. More...
 
Ptr< StereoMatchercv::ximgproc::createRightMatcher (Ptr< StereoMatcher > matcher_left)
 Convenience method to set up the matcher for computing the right-view disparity map that is required in case of filtering with confidence. More...
 
void cv::ximgproc::dtFilter (InputArray guide, InputArray src, OutputArray dst, double sigmaSpatial, double sigmaColor, int mode=DTF_NC, int numIters=3)
 Simple one-line Domain Transform filter call. If you have multiple images to filter with the same guided image then use DTFilter interface to avoid extra computations on initialization stage. More...
 
void cv::ximgproc::fastBilateralSolverFilter (InputArray guide, InputArray src, InputArray confidence, OutputArray dst, double sigma_spatial=8, double sigma_luma=8, double sigma_chroma=8, double lambda=128.0, int num_iter=25, double max_tol=1e-5)
 Simple one-line Fast Bilateral Solver filter call. If you have multiple images to filter with the same guide then use FastBilateralSolverFilter interface to avoid extra computations. More...
 
void cv::ximgproc::fastGlobalSmootherFilter (InputArray guide, InputArray src, OutputArray dst, double lambda, double sigma_color, double lambda_attenuation=0.25, int num_iter=3)
 Simple one-line Fast Global Smoother filter call. If you have multiple images to filter with the same guide then use FastGlobalSmootherFilter interface to avoid extra computations. More...
 
void cv::ximgproc::getDisparityVis (InputArray src, OutputArray dst, double scale=1.0)
 Function for creating a disparity map visualization (clamped CV_8U image) More...
 
void cv::ximgproc::GradientDericheX (InputArray op, OutputArray dst, double alpha, double omega)
 Applies X Deriche filter to an image. More...
 
void cv::ximgproc::GradientDericheY (InputArray op, OutputArray dst, double alpha, double omega)
 Applies Y Deriche filter to an image. More...
 
void cv::ximgproc::GradientPaillouX (InputArray op, OutputArray _dst, double alpha, double omega)
 
void cv::ximgproc::GradientPaillouY (InputArray op, OutputArray _dst, double alpha, double omega)
 Applies Paillou filter to an image. More...
 
void cv::ximgproc::guidedFilter (InputArray guide, InputArray src, OutputArray dst, int radius, double eps, int dDepth=-1)
 Simple one-line Guided Filter call. More...
 
void cv::ximgproc::jointBilateralFilter (InputArray joint, InputArray src, OutputArray dst, int d, double sigmaColor, double sigmaSpace, int borderType=BORDER_DEFAULT)
 Applies the joint bilateral filter to an image. More...
 
void cv::ximgproc::l0Smooth (InputArray src, OutputArray dst, double lambda=0.02, double kappa=2.0)
 Global image smoothing via L0 gradient minimization. More...
 
void cv::ximgproc::qconj (InputArray qimg, OutputArray qcimg)
 calculates conjugate of a quaternion image. More...
 
void cv::ximgproc::qdft (InputArray img, OutputArray qimg, int flags, bool sideLeft)
 Performs a forward or inverse Discrete quaternion Fourier transform of a 2D quaternion array. More...
 
void cv::ximgproc::qmultiply (InputArray src1, InputArray src2, OutputArray dst)
 Calculates the per-element quaternion product of two arrays. More...
 
void cv::ximgproc::qunitary (InputArray qimg, OutputArray qnimg)
 divides each element by its modulus. More...
 
int cv::ximgproc::readGT (String src_path, OutputArray dst)
 Function for reading ground truth disparity maps. Supports basic Middlebury and MPI-Sintel formats. Note that the resulting disparity map is scaled by 16. More...
 
void cv::ximgproc::rollingGuidanceFilter (InputArray src, OutputArray dst, int d=-1, double sigmaColor=25, double sigmaSpace=3, int numOfIter=4, int borderType=BORDER_DEFAULT)
 Applies the rolling guidance filter to an image. More...
 

Detailed Description

Enumeration Type Documentation

§ EdgeAwareFiltersList

Enumerator
DTF_NC 
Python: cv.ximgproc.DTF_NC
DTF_IC 
Python: cv.ximgproc.DTF_IC
DTF_RF 
Python: cv.ximgproc.DTF_RF
GUIDED_FILTER 
Python: cv.ximgproc.GUIDED_FILTER
AM_FILTER 
Python: cv.ximgproc.AM_FILTER

Function Documentation

§ amFilter()

void cv::ximgproc::amFilter ( InputArray  joint,
InputArray  src,
OutputArray  dst,
double  sigma_s,
double  sigma_r,
bool  adjust_outliers = false 
)
Python:
dst=cv.ximgproc.amFilter(joint, src, sigma_s, sigma_r[, dst[, adjust_outliers]])

Simple one-line Adaptive Manifold Filter call.

Parameters
jointjoint (also called as guided) image or array of images with any numbers of channels.
srcfiltering image with any numbers of channels.
dstoutput image.
sigma_sspatial standard deviation.
sigma_rcolor space standard deviation, it is similar to the sigma in the color space into bilateralFilter.
adjust_outliersoptional, specify perform outliers adjust operation or not, (Eq. 9) in the original paper.
Note
Joint images with CV_8U and CV_16U depth converted to images with CV_32F depth and [0; 1] color range before processing. Hence color space sigma sigma_r must be in [0; 1] range, unlike same sigmas in bilateralFilter and dtFilter functions.
See also
bilateralFilter, dtFilter, guidedFilter

§ bilateralTextureFilter()

void cv::ximgproc::bilateralTextureFilter ( InputArray  src,
OutputArray  dst,
int  fr = 3,
int  numIter = 1,
double  sigmaAlpha = -1.,
double  sigmaAvg = -1. 
)
Python:
dst=cv.ximgproc.bilateralTextureFilter(src[, dst[, fr[, numIter[, sigmaAlpha[, sigmaAvg]]]]])

Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see [38].

Parameters
srcSource image whose depth is 8-bit UINT or 32-bit FLOAT
dstDestination image of the same size and type as src.
frRadius of kernel to be used for filtering. It should be positive integer
numIterNumber of iterations of algorithm, It should be positive integer
sigmaAlphaControls 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.
sigmaAvgRange blur parameter for texture blurring. Larger value makes result to be more blurred. When the value is negative, it is automatically calculated as described in the paper.
See also
rollingGuidanceFilter, bilateralFilter

§ colorMatchTemplate()

void cv::ximgproc::colorMatchTemplate ( InputArray  img,
InputArray  templ,
OutputArray  result 
)
Python:
result=cv.ximgproc.colorMatchTemplate(img, templ[, result])

Compares a color template against overlapped color image regions.

Parameters
imgImage where the search is running. It must be 3 channels image
templSearched template. It must be not greater than the source image and have 3 channels
resultMap of comparison results. It must be single-channel 64-bit floating-point

§ computeBadPixelPercent()

double cv::ximgproc::computeBadPixelPercent ( InputArray  GT,
InputArray  src,
Rect  ROI,
int  thresh = 24 
)

Function for computing the percent of "bad" pixels in the disparity map (pixels where error is higher than a specified threshold)

Parameters
GTground truth disparity map
srcdisparity map to evaluate
ROIregion of interest
threshthreshold used to determine "bad" pixels
Returns
returns mean square error between GT and src

§ computeMSE()

double cv::ximgproc::computeMSE ( InputArray  GT,
InputArray  src,
Rect  ROI 
)

Function for computing mean square error for disparity maps.

Parameters
GTground truth disparity map
srcdisparity map to evaluate
ROIregion of interest
Returns
returns mean square error between GT and src

§ createAMFilter()

Ptr<AdaptiveManifoldFilter> cv::ximgproc::createAMFilter ( double  sigma_s,
double  sigma_r,
bool  adjust_outliers = false 
)
Python:
retval=cv.ximgproc.createAMFilter(sigma_s, sigma_r[, adjust_outliers])

Factory method, create instance of AdaptiveManifoldFilter and produce some initialization routines.

Parameters
sigma_sspatial standard deviation.
sigma_rcolor space standard deviation, it is similar to the sigma in the color space into bilateralFilter.
adjust_outliersoptional, 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 [72] .

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.

§ createDisparityWLSFilter()

Ptr<DisparityWLSFilter> cv::ximgproc::createDisparityWLSFilter ( Ptr< StereoMatcher matcher_left)
Python:
retval=cv.ximgproc.createDisparityWLSFilter(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_leftstereo matcher instance that will be used with the filter

§ createDisparityWLSFilterGeneric()

Ptr<DisparityWLSFilter> cv::ximgproc::createDisparityWLSFilterGeneric ( bool  use_confidence)
Python:
retval=cv.ximgproc.createDisparityWLSFilterGeneric(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_confidencefiltering with confidence requires two disparity maps (for the left and right views) and is approximately two times slower. However, quality is typically significantly better.

§ createDTFilter()

Ptr<DTFilter> cv::ximgproc::createDTFilter ( InputArray  guide,
double  sigmaSpatial,
double  sigmaColor,
int  mode = DTF_NC,
int  numIters = 3 
)
Python:
retval=cv.ximgproc.createDTFilter(guide, sigmaSpatial, sigmaColor[, mode[, numIters]])

Factory method, create instance of DTFilter and produce initialization routines.

Parameters
guideguided 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.
modeone form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article.
numItersoptional number of iterations used for filtering, 3 is quite enough.

For more details about Domain Transform filter parameters, see the original article [71] and Domain Transform filter homepage.

§ createEdgeAwareInterpolator()

Ptr<EdgeAwareInterpolator> cv::ximgproc::createEdgeAwareInterpolator ( )
Python:
retval=cv.ximgproc.createEdgeAwareInterpolator()

Factory method that creates an instance of the EdgeAwareInterpolator.

§ createFastBilateralSolverFilter()

Ptr<FastBilateralSolverFilter> cv::ximgproc::createFastBilateralSolverFilter ( InputArray  guide,
double  sigma_spatial,
double  sigma_luma,
double  sigma_chroma,
double  lambda = 128.0,
int  num_iter = 25,
double  max_tol = 1e-5 
)
Python:
retval=cv.ximgproc.createFastBilateralSolverFilter(guide, sigma_spatial, sigma_luma, sigma_chroma[, lambda[, num_iter[, max_tol]]])

Factory method, create instance of FastBilateralSolverFilter and execute the initialization routines.

Parameters
guideimage serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
sigma_spatialparameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter.
sigma_lumaparameter, that is similar to luma space sigma (bandwidth) in bilateralFilter.
sigma_chromaparameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter.
lambdasmoothness strength parameter for solver.
num_iternumber of iterations used for solver, 25 is usually enough.
max_tolconvergence tolerance used for solver.

For more details about the Fast Bilateral Solver parameters, see the original paper [10].

§ createFastGlobalSmootherFilter()

Ptr<FastGlobalSmootherFilter> cv::ximgproc::createFastGlobalSmootherFilter ( InputArray  guide,
double  lambda,
double  sigma_color,
double  lambda_attenuation = 0.25,
int  num_iter = 3 
)
Python:
retval=cv.ximgproc.createFastGlobalSmootherFilter(guide, lambda, sigma_color[, lambda_attenuation[, num_iter]])

Factory method, create instance of FastGlobalSmootherFilter and execute the initialization routines.

Parameters
guideimage serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
lambdaparameter defining the amount of regularization
sigma_colorparameter, that is similar to color space sigma in bilateralFilter.
lambda_attenuationinternal 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_iternumber of iterations used for filtering, 3 is usually enough.

For more details about Fast Global Smoother parameters, see the original paper [141]. 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.

§ createGuidedFilter()

Ptr<GuidedFilter> cv::ximgproc::createGuidedFilter ( InputArray  guide,
int  radius,
double  eps 
)
Python:
retval=cv.ximgproc.createGuidedFilter(guide, radius, eps)

Factory method, create instance of GuidedFilter and produce initialization routines.

Parameters
guideguided 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.
radiusradius of Guided Filter.
epsregularization 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 [84] .

§ createQuaternionImage()

void cv::ximgproc::createQuaternionImage ( InputArray  img,
OutputArray  qimg 
)
Python:
qimg=cv.ximgproc.createQuaternionImage(img[, qimg])

creates a quaternion image.

Parameters
imgSource 8-bit, 32-bit or 64-bit image, with 3-channel image.
qimgresult CV_64FC4 a quaternion image( 4 chanels zero channel and B,G,R).

§ createRightMatcher()

Ptr<StereoMatcher> cv::ximgproc::createRightMatcher ( Ptr< StereoMatcher matcher_left)
Python:
retval=cv.ximgproc.createRightMatcher(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_leftmain stereo matcher instance that will be used with the filter

§ dtFilter()

void cv::ximgproc::dtFilter ( InputArray  guide,
InputArray  src,
OutputArray  dst,
double  sigmaSpatial,
double  sigmaColor,
int  mode = DTF_NC,
int  numIters = 3 
)
Python:
dst=cv.ximgproc.dtFilter(guide, src, sigmaSpatial, sigmaColor[, dst[, mode[, 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
guideguided image (also called as joint image) with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels.
srcfiltering image with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels.
dst
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.
modeone form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article.
numItersoptional number of iterations used for filtering, 3 is quite enough.
See also
bilateralFilter, guidedFilter, amFilter

§ fastBilateralSolverFilter()

void cv::ximgproc::fastBilateralSolverFilter ( InputArray  guide,
InputArray  src,
InputArray  confidence,
OutputArray  dst,
double  sigma_spatial = 8,
double  sigma_luma = 8,
double  sigma_chroma = 8,
double  lambda = 128.0,
int  num_iter = 25,
double  max_tol = 1e-5 
)
Python:
dst=cv.ximgproc.fastBilateralSolverFilter(guide, src, confidence[, dst[, sigma_spatial[, sigma_luma[, sigma_chroma[, lambda[, num_iter[, 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
guideimage serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
srcsource image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels.
confidenceconfidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel.
dstdestination image.
sigma_spatialparameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter.
sigma_lumaparameter, that is similar to luma space sigma (bandwidth) in bilateralFilter.
sigma_chromaparameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter.
lambdasmoothness strength parameter for solver.
num_iternumber of iterations used for solver, 25 is usually enough.
max_tolconvergence tolerance used for solver.

For more details about the Fast Bilateral Solver parameters, see the original paper [10].

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

§ fastGlobalSmootherFilter()

void cv::ximgproc::fastGlobalSmootherFilter ( InputArray  guide,
InputArray  src,
OutputArray  dst,
double  lambda,
double  sigma_color,
double  lambda_attenuation = 0.25,
int  num_iter = 3 
)
Python:
dst=cv.ximgproc.fastGlobalSmootherFilter(guide, src, lambda, sigma_color[, dst[, lambda_attenuation[, 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
guideimage serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
srcsource image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels.
dstdestination image.
lambdaparameter defining the amount of regularization
sigma_colorparameter, that is similar to color space sigma in bilateralFilter.
lambda_attenuationinternal 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_iternumber of iterations used for filtering, 3 is usually enough.

§ getDisparityVis()

void cv::ximgproc::getDisparityVis ( InputArray  src,
OutputArray  dst,
double  scale = 1.0 
)

Function for creating a disparity map visualization (clamped CV_8U image)

Parameters
srcinput disparity map (CV_16S depth)
dstoutput visualization
scaledisparity map will be multiplied by this value for visualization

§ GradientDericheX()

void cv::ximgproc::GradientDericheX ( InputArray  op,
OutputArray  dst,
double  alpha,
double  omega 
)
Python:
dst=cv.ximgproc.GradientDericheX(op, alpha, omega[, dst])

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
opSource 8-bit or 16bit image, 1-channel or 3-channel image.
dstresult CV_32FC image with same number of channel than _op.
alphadouble see paper
omegadouble see paper

§ GradientDericheY()

void cv::ximgproc::GradientDericheY ( InputArray  op,
OutputArray  dst,
double  alpha,
double  omega 
)
Python:
dst=cv.ximgproc.GradientDericheY(op, alpha, omega[, dst])

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
opSource 8-bit or 16bit image, 1-channel or 3-channel image.
dstresult CV_32FC image with same number of channel than _op.
alphadouble see paper
omegadouble see paper

§ GradientPaillouX()

void cv::ximgproc::GradientPaillouX ( InputArray  op,
OutputArray  _dst,
double  alpha,
double  omega 
)

§ GradientPaillouY()

void cv::ximgproc::GradientPaillouY ( InputArray  op,
OutputArray  _dst,
double  alpha,
double  omega 
)

Applies Paillou filter to an image.

For more details about this implementation, please see [153]

Parameters
opSource CV_8U(S) or CV_16U(S), 1-channel or 3-channels image.
_dstresult CV_32F image with same number of channel than op.
omegadouble see paper
alphadouble see paper
See also
GradientPaillouX, GradientPaillouY

§ guidedFilter()

void cv::ximgproc::guidedFilter ( InputArray  guide,
InputArray  src,
OutputArray  dst,
int  radius,
double  eps,
int  dDepth = -1 
)
Python:
dst=cv.ximgproc.guidedFilter(guide, src, radius, eps[, dst[, 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
guideguided 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.
srcfiltering image with any numbers of channels.
dstoutput image.
radiusradius of Guided Filter.
epsregularization term of Guided Filter. \({eps}^2\) is similar to the sigma in the color space into bilateralFilter.
dDepthoptional depth of the output image.
See also
bilateralFilter, dtFilter, amFilter

§ jointBilateralFilter()

void cv::ximgproc::jointBilateralFilter ( InputArray  joint,
InputArray  src,
OutputArray  dst,
int  d,
double  sigmaColor,
double  sigmaSpace,
int  borderType = BORDER_DEFAULT 
)
Python:
dst=cv.ximgproc.jointBilateralFilter(joint, src, d, sigmaColor, sigmaSpace[, dst[, borderType]])

Applies the joint bilateral filter to an image.

Parameters
jointJoint 8-bit or floating-point, 1-channel or 3-channel image.
srcSource 8-bit or floating-point, 1-channel or 3-channel image with the same depth as joint image.
dstDestination image of the same size and type as src .
dDiameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace .
sigmaColorFilter 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.
sigmaSpaceFilter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0 , it specifies the neighborhood size regardless of sigmaSpace . Otherwise, d is proportional to sigmaSpace .
borderType
Note
bilateralFilter and jointBilateralFilter use L1 norm to compute difference between colors.
See also
bilateralFilter, amFilter

§ l0Smooth()

void cv::ximgproc::l0Smooth ( InputArray  src,
OutputArray  dst,
double  lambda = 0.02,
double  kappa = 2.0 
)
Python:
dst=cv.ximgproc.l0Smooth(src[, dst[, lambda[, kappa]]])

Global image smoothing via L0 gradient minimization.

Parameters
srcsource image for filtering with unsigned 8-bit or signed 16-bit or floating-point depth.
dstdestination image.
lambdaparameter defining the smooth term weight.
kappaparameter defining the increasing factor of the weight of the gradient data term.

For more details about L0 Smoother, see the original paper [223].

§ qconj()

void cv::ximgproc::qconj ( InputArray  qimg,
OutputArray  qcimg 
)
Python:
qcimg=cv.ximgproc.qconj(qimg[, qcimg])

calculates conjugate of a quaternion image.

Parameters
qimgquaternion image.
qcimgconjugate of qimg

§ qdft()

void cv::ximgproc::qdft ( InputArray  img,
OutputArray  qimg,
int  flags,
bool  sideLeft 
)
Python:
qimg=cv.ximgproc.qdft(img, flags, sideLeft[, qimg])

Performs a forward or inverse Discrete quaternion Fourier transform of a 2D quaternion array.

Parameters
imgquaternion image.
qimgquaternion image in dual space.
flagsquaternion image in dual space. only DFT_INVERSE flags is supported
sideLefttrue the hypercomplex exponential is to be multiplied on the left (false on the right ).

§ qmultiply()

void cv::ximgproc::qmultiply ( InputArray  src1,
InputArray  src2,
OutputArray  dst 
)
Python:
dst=cv.ximgproc.qmultiply(src1, src2[, dst])

Calculates the per-element quaternion product of two arrays.

Parameters
src1quaternion image.
src2quaternion image.
dstproduct dst(I)=src1(I) . src2(I)

§ qunitary()

void cv::ximgproc::qunitary ( InputArray  qimg,
OutputArray  qnimg 
)
Python:
qnimg=cv.ximgproc.qunitary(qimg[, qnimg])

divides each element by its modulus.

Parameters
qimgquaternion image.
qnimgconjugate of qimg

§ readGT()

int cv::ximgproc::readGT ( String  src_path,
OutputArray  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_pathpath to the image, containing ground-truth disparity map
dstoutput disparity map, CV_16S depth
Returns
returns zero if successfully read the ground truth

§ rollingGuidanceFilter()

void cv::ximgproc::rollingGuidanceFilter ( InputArray  src,
OutputArray  dst,
int  d = -1,
double  sigmaColor = 25,
double  sigmaSpace = 3,
int  numOfIter = 4,
int  borderType = BORDER_DEFAULT 
)
Python:
dst=cv.ximgproc.rollingGuidanceFilter(src[, dst[, d[, sigmaColor[, sigmaSpace[, numOfIter[, borderType]]]]]])

Applies the rolling guidance filter to an image.

For more details, please see [230]

Parameters
srcSource 8-bit or floating-point, 1-channel or 3-channel image.
dstDestination image of the same size and type as src.
dDiameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace .
sigmaColorFilter 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.
sigmaSpaceFilter 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 .
numOfIterNumber of iterations of joint edge-preserving filtering applied on the source image.
borderType
Note
rollingGuidanceFilter uses jointBilateralFilter as the edge-preserving filter.
See also
jointBilateralFilter, bilateralFilter, amFilter