Class CvXImgProc

cv::ximgproc functions

Inheritance

System.Object

CvXImgProc

Inherited Members

System.Object.Equals(System.Object)

System.Object.Equals(System.Object, System.Object)

System.Object.GetHashCode()

System.Object.GetType()

System.Object.MemberwiseClone()

System.Object.ReferenceEquals(System.Object, System.Object)

System.Object.ToString()

Namespace: OpenCvSharp.XImgProc

Assembly: OpenCvSharp.dll

Syntax

public static class CvXImgProc

Methods

AMFilter(InputArray, InputArray, OutputArray, Double, Double, Boolean)

Simple one-line Adaptive Manifold Filter call.

Declaration

public static void AMFilter(InputArray joint, InputArray src, OutputArray dst, double sigmaS, double sigmaR, bool adjustOutliers = false)

Parameters

Type	Name	Description
InputArray	joint	joint (also called as guided) image or array of images with any numbers of channels.
InputArray	src	filtering image with any numbers of channels.
OutputArray	dst	output image.
System.Double	sigmaS	spatial standard deviation.
System.Double	sigmaR	color space standard deviation, it is similar to the sigma in the color space into bilateralFilter.
System.Boolean	adjustOutliers	optional, specify perform outliers adjust operation or not, (Eq. 9) in the original paper.

AnisotropicDiffusion(InputArray, OutputArray, Single, Single, Int32)

Performs anisotropic diffusian on an image. The function applies Perona-Malik anisotropic diffusion to an image.

Declaration

public static void AnisotropicDiffusion(InputArray src, OutputArray dst, float alpha, float k, int niters)

Parameters

Type	Name	Description
InputArray	src	Grayscale Source image.
OutputArray	dst	Destination image of the same size and the same number of channels as src.
System.Single	alpha	The amount of time to step forward by on each iteration (normally, it's between 0 and 1).
System.Single	k	sensitivity to the edges
System.Int32	niters	The number of iterations

BilateralTextureFilter(InputArray, OutputArray, Int32, Int32, Double, Double)

Applies the bilateral texture filter to an image. It performs structure-preserving texture filter. For more details about this filter see @cite Cho2014.

Declaration

public static void BilateralTextureFilter(InputArray src, OutputArray dst, int fr = 3, int numIter = 1, double sigmaAlpha = -1, double sigmaAvg = -1)

Parameters

Type	Name	Description
InputArray	src	Source image whose depth is 8-bit UINT or 32-bit FLOAT
OutputArray	dst	Destination image of the same size and type as src.
System.Int32	fr	Radius of kernel to be used for filtering. It should be positive integer
System.Int32	numIter	Number of iterations of algorithm, It should be positive integer
System.Double	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.
System.Double	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.

BrightEdges(Mat, Mat, Int32, Int32, Int32)

Declaration

public static void BrightEdges(Mat original, Mat edgeView, int contrast = 1, int shortRange = 3, int longRange = 9)

Parameters

Type	Name	Description
Mat	original
Mat	edgeView
System.Int32	contrast
System.Int32	shortRange
System.Int32	longRange

ColorMatchTemplate(InputArray, InputArray, OutputArray)

Compares a color template against overlapped color image regions.

Declaration

public static void ColorMatchTemplate(InputArray img, InputArray templ, OutputArray result)

Parameters

Type	Name	Description
InputArray	img	Image where the search is running. It must be 3 channels image
InputArray	templ	Searched template. It must be not greater than the source image and have 3 channels
OutputArray	result	Map of comparison results. It must be single-channel 64-bit floating-point

CovarianceEstimation(InputArray, OutputArray, Int32, Int32)

Computes the estimated covariance matrix of an image using the sliding window forumlation.

Declaration

public static void CovarianceEstimation(InputArray src, OutputArray dst, int windowRows, int windowCols)

Parameters

Type	Name	Description
InputArray	src	The source image. Input image must be of a complex type.
OutputArray	dst	The destination estimated covariance matrix. Output matrix will be size (windowRowswindowCols, windowRowswindowCols).
System.Int32	windowRows	The number of rows in the window.
System.Int32	windowCols	The number of cols in the window.

Remarks

The window size parameters control the accuracy of the estimation. The sliding window moves over the entire image from the top-left corner to the bottom right corner.Each location of the window represents a sample. If the window is the size of the image, then this gives the exact covariance matrix. For all other cases, the sizes of the window will impact the number of samples and the number of elements in the estimated covariance matrix.

CreateAMFilter(Double, Double, Boolean)

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

Declaration

public static AdaptiveManifoldFilter CreateAMFilter(double sigmaS, double sigmaR, bool adjustOutliers = false)

Parameters

Type	Name	Description
System.Double	sigmaS	spatial standard deviation.
System.Double	sigmaR	color space standard deviation, it is similar to the sigma in the color space into bilateralFilter.
System.Boolean	adjustOutliers	optional, specify perform outliers adjust operation or not, (Eq. 9) in the original paper.

Returns

Type	Description
AdaptiveManifoldFilter

CreateDTFilter(InputArray, Double, Double, EdgeAwareFiltersList, Int32)

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

Declaration

public static DTFilter CreateDTFilter(InputArray guide, double sigmaSpatial, double sigmaColor, EdgeAwareFiltersList mode = EdgeAwareFiltersList.DTF_NC, int numIters = 3)

Parameters

Type	Name	Description
InputArray	guide	guided image (used to build transformed distance, which describes edge structure of guided image).
System.Double	sigmaSpatial	sigma_H parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter.
System.Double	sigmaColor	sigma_r parameter in the original article, it's similar to the sigma in the color space into bilateralFilter.
EdgeAwareFiltersList	mode	one form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article.
System.Int32	numIters	optional number of iterations used for filtering, 3 is quite enough.

Returns

Type	Description
DTFilter

CreateEdgeBoxes(Single, Single, Single, Single, Int32, Single, Single, Single, Single, Single, Single, Single)

Creates a EdgeBoxes

Declaration

public static EdgeBoxes CreateEdgeBoxes(float alpha = 0.65F, float beta = 0.75F, float eta = 1F, float minScore = 0.01F, int maxBoxes = 10000, float edgeMinMag = 0.1F, float edgeMergeThr = 0.5F, float clusterMinMag = 0.5F, float maxAspectRatio = 3F, float minBoxArea = 1000F, float gamma = 2F, float kappa = 1.5F)

Parameters

Type	Name	Description
System.Single	alpha	step size of sliding window search.
System.Single	beta	nms threshold for object proposals.
System.Single	eta	adaptation rate for nms threshold.
System.Single	minScore	min score of boxes to detect.
System.Int32	maxBoxes	max number of boxes to detect.
System.Single	edgeMinMag	edge min magnitude. Increase to trade off accuracy for speed.
System.Single	edgeMergeThr	edge merge threshold. Increase to trade off accuracy for speed.
System.Single	clusterMinMag	cluster min magnitude. Increase to trade off accuracy for speed.
System.Single	maxAspectRatio	max aspect ratio of boxes.
System.Single	minBoxArea	minimum area of boxes.
System.Single	gamma	affinity sensitivity.
System.Single	kappa	scale sensitivity.

Returns

Type	Description
EdgeBoxes

CreateFastGlobalSmootherFilter(InputArray, Double, Double, Double, Int32)

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

Declaration

public static FastGlobalSmootherFilter CreateFastGlobalSmootherFilter(InputArray guide, double lambda, double sigmaColor, double lambdaAttenuation = 0.25, int numIter = 3)

Parameters

Type	Name	Description
InputArray	guide	image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
System.Double	lambda	parameter defining the amount of regularization
System.Double	sigmaColor	parameter, that is similar to color space sigma in bilateralFilter.
System.Double	lambdaAttenuation	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.
System.Int32	numIter	number of iterations used for filtering, 3 is usually enough.

Returns

Type	Description
FastGlobalSmootherFilter

CreateFastLineDetector(Int32, Single, Double, Double, Int32, Boolean)

Creates a smart pointer to a FastLineDetector object and initializes it

Declaration

public static FastLineDetector CreateFastLineDetector(int lengthThreshold = 10, float distanceThreshold = 1.41421354F, double cannyTh1 = 50, double cannyTh2 = 50, int cannyApertureSize = 3, bool doMerge = false)

Parameters

Type	Name	Description
System.Int32	lengthThreshold	Segment shorter than this will be discarded
System.Single	distanceThreshold	A point placed from a hypothesis line segment farther than this will be regarded as an outlier
System.Double	cannyTh1	First threshold for hysteresis procedure in Canny()
System.Double	cannyTh2	Second threshold for hysteresis procedure in Canny()
System.Int32	cannyApertureSize	Aperture size for the sobel operator in Canny()
System.Boolean	doMerge	If true, incremental merging of segments will be performed

Returns

Type	Description
FastLineDetector

CreateGuidedFilter(InputArray, Int32, Double)

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

Declaration

public static GuidedFilter CreateGuidedFilter(InputArray guide, int radius, double eps)

Parameters

Type	Name	Description
InputArray	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.
System.Int32	radius	radius of Guided Filter.
System.Double	eps	regularization term of Guided Filter. eps^2 is similar to the sigma in the color space into bilateralFilter.

Returns

Type	Description
GuidedFilter

CreateQuaternionImage(InputArray, OutputArray)

creates a quaternion image.

Declaration

public static void CreateQuaternionImage(InputArray img, OutputArray qimg)

Parameters

Type	Name	Description
InputArray	img	Source 8-bit, 32-bit or 64-bit image, with 3-channel image.
OutputArray	qimg	result CV_64FC4 a quaternion image( 4 chanels zero channel and B,G,R).

CreateRFFeatureGetter()

Creates a RFFeatureGetter

Declaration

public static RFFeatureGetter CreateRFFeatureGetter()

Returns

Type	Description
RFFeatureGetter

CreateStructuredEdgeDetection(String, RFFeatureGetter)

Creates a StructuredEdgeDetection

Declaration

public static StructuredEdgeDetection CreateStructuredEdgeDetection(string model, RFFeatureGetter howToGetFeatures = null)

Parameters

Type	Name	Description
System.String	model	name of the file where the model is stored
RFFeatureGetter	howToGetFeatures	optional object inheriting from RFFeatureGetter. You need it only if you would like to train your own forest, pass null otherwise

Returns

Type	Description
StructuredEdgeDetection

CreateSuperpixelLSC(InputArray, Int32, Single)

Class implementing the LSC (Linear Spectral Clustering) superpixels.

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 illustrated in the following picture. For enhanced 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.

Declaration

public static SuperpixelLSC CreateSuperpixelLSC(InputArray image, int regionSize = 10, float ratio = 0.075F)

Parameters

Type	Name	Description
InputArray	image	image Image to segment
System.Int32	regionSize	Chooses an average superpixel size measured in pixels
System.Single	ratio	Chooses the enforcement of superpixel compactness factor of superpixel

Returns

Type	Description
SuperpixelLSC

CreateSuperpixelSEEDS(Int32, Int32, Int32, Int32, Int32, Int32, Int32, Boolean)

Initializes a SuperpixelSEEDS object.

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.

Declaration

public static SuperpixelSEEDS CreateSuperpixelSEEDS(int imageWidth, int imageHeight, int imageChannels, int numSuperpixels, int numLevels, int prior = 2, int histogramBins = 5, bool doubleStep = false)

Parameters

Type	Name	Description
System.Int32	imageWidth	Image width.
System.Int32	imageHeight	Image height.
System.Int32	imageChannels	Number of channels of the image.
System.Int32	numSuperpixels	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.
System.Int32	numLevels	Number of block levels. The more levels, the more accurate is the segmentation, but needs more memory and CPU time.
System.Int32	prior	enable 3x3 shape smoothing term if >0. A larger value leads to smoother shapes. prior must be in the range[0, 5].
System.Int32	histogramBins	Number of histogram bins.
System.Boolean	doubleStep	If true, iterate each block level twice for higher accuracy.

Returns

Type	Description
SuperpixelSEEDS

DTFilter(InputArray, InputArray, OutputArray, Double, Double, EdgeAwareFiltersList, Int32)

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.

Declaration

public static void DTFilter(InputArray guide, InputArray src, OutputArray dst, double sigmaSpatial, double sigmaColor, EdgeAwareFiltersList mode = EdgeAwareFiltersList.DTF_NC, int numIters = 3)

Parameters

Type	Name	Description
InputArray	guide	guided image (also called as joint image) with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels.
InputArray	src	filtering image with unsigned 8-bit or floating-point 32-bit depth and up to 4 channels.
OutputArray	dst	destination image
System.Double	sigmaSpatial	sigma_H parameter in the original article, it's similar to the sigma in the coordinate space into bilateralFilter.
System.Double	sigmaColor	sigma_r parameter in the original article, it's similar to the sigma in the color space into bilateralFilter.
EdgeAwareFiltersList	mode	one form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article.
System.Int32	numIters	optional number of iterations used for filtering, 3 is quite enough.

EdgePreservingFilter(InputArray, OutputArray, Int32, Double)

Smoothes an image using the Edge-Preserving filter.

Declaration

public static void EdgePreservingFilter(InputArray src, OutputArray dst, int d, double threshold)

Parameters

Type	Name	Description
InputArray	src	Source 8-bit 3-channel image.
OutputArray	dst	Destination image of the same size and type as src.
System.Int32	d	Diameter of each pixel neighborhood that is used during filtering. Must be greater or equal 3.
System.Double	threshold	Threshold, which distinguishes between noise, outliers, and data.

FastBilateralSolverFilter(InputArray, InputArray, InputArray, OutputArray, Double, Double, Double, Double, Int32, Double)

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.

Declaration

public static void FastBilateralSolverFilter(InputArray guide, InputArray src, InputArray confidence, OutputArray dst, double sigmaSpatial = 8, double sigmaLuma = 8, double sigmaChroma = 8, double lambda = 128, int numIter = 25, double maxTol = 1E-05)

Parameters

Type	Name	Description
InputArray	guide	image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
InputArray	src	source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels.
InputArray	confidence	confidence image with unsigned 8-bit or floating-point 32-bit confidence and 1 channel.
OutputArray	dst	destination image.
System.Double	sigmaSpatial	parameter, that is similar to spatial space sigma (bandwidth) in bilateralFilter.
System.Double	sigmaLuma	parameter, that is similar to luma space sigma (bandwidth) in bilateralFilter.
System.Double	sigmaChroma	parameter, that is similar to chroma space sigma (bandwidth) in bilateralFilter.
System.Double	lambda	smoothness strength parameter for solver.
System.Int32	numIter	number of iterations used for solver, 25 is usually enough.
System.Double	maxTol	convergence tolerance used for solver.

FastGlobalSmootherFilter(InputArray, InputArray, OutputArray, Double, Double, Double, Int32)

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.

Declaration

public static void FastGlobalSmootherFilter(InputArray guide, InputArray src, OutputArray dst, double lambda, double sigmaColor, double lambdaAttenuation = 0.25, int numIter = 3)

Parameters

Type	Name	Description
InputArray	guide	image serving as guide for filtering. It should have 8-bit depth and either 1 or 3 channels.
InputArray	src	source image for filtering with unsigned 8-bit or signed 16-bit or floating-point 32-bit depth and up to 4 channels.
OutputArray	dst	destination image.
System.Double	lambda	parameter defining the amount of regularization
System.Double	sigmaColor	parameter, that is similar to color space sigma in bilateralFilter.
System.Double	lambdaAttenuation	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.
System.Int32	numIter	number of iterations used for filtering, 3 is usually enough.

FastHoughTransform(InputArray, OutputArray, MatType, AngleRangeOption, HoughOP, HoughDeskewOption)

Calculates 2D Fast Hough transform of an image.

Declaration

public static void FastHoughTransform(InputArray src, OutputArray dst, MatType dstMatDepth, AngleRangeOption angleRange = AngleRangeOption.ARO_315_135, HoughOP op = HoughOP.FHT_ADD, HoughDeskewOption makeSkew = HoughDeskewOption.DESKEW)

Parameters

Type	Name	Description
InputArray	src	The source (input) image.
OutputArray	dst	The destination image, result of transformation.
OpenCvSharp.MatType	dstMatDepth	The depth of destination image
AngleRangeOption	angleRange	The part of Hough space to calculate, see cv::AngleRangeOption
HoughOP	op	The operation to be applied, see cv::HoughOp
HoughDeskewOption	makeSkew	Specifies to do or not to do image skewing, see cv::HoughDeskewOption

GradientDericheX(InputArray, OutputArray, Double, Double)

Applies X Deriche filter to an image.

Declaration

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

Parameters

Type	Name	Description
InputArray	op	Source 8-bit or 16bit image, 1-channel or 3-channel image.
OutputArray	dst	result CV_32FC image with same number of channel than _op.
System.Double	alpha	double see paper
System.Double	omega	double see paper

GradientDericheY(InputArray, OutputArray, Double, Double)

Applies Y Deriche filter to an image.

Declaration

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

Parameters

Type	Name	Description
InputArray	op	Source 8-bit or 16bit image, 1-channel or 3-channel image.
OutputArray	dst	result CV_32FC image with same number of channel than _op.
System.Double	alpha	double see paper
System.Double	omega	double see paper

GradientPaillouX(InputArray, OutputArray, Double, Double)

Applies Paillou filter to an image.

Declaration

public static void GradientPaillouX(InputArray op, OutputArray dst, double alpha, double omega)

Parameters

Type	Name	Description
InputArray	op	Source CV_8U(S) or CV_16U(S), 1-channel or 3-channels image.
OutputArray	dst	Result CV_32F image with same number of channel than op.
System.Double	alpha	double see paper
System.Double	omega	double see paper

GradientPaillouY(InputArray, OutputArray, Double, Double)

Applies Paillou filter to an image.

Declaration

public static void GradientPaillouY(InputArray op, OutputArray dst, double alpha, double omega)

Parameters

Type	Name	Description
InputArray	op	Source CV_8U(S) or CV_16U(S), 1-channel or 3-channels image.
OutputArray	dst	Result CV_32F image with same number of channel than op.
System.Double	alpha	double see paper
System.Double	omega	double see paper

GuidedFilter(InputArray, InputArray, OutputArray, Int32, Double, Int32)

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.

Declaration

public static void GuidedFilter(InputArray guide, InputArray src, OutputArray dst, int radius, double eps, int dDepth = -1)

Parameters

Type	Name	Description
InputArray	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.
InputArray	src	filtering image with any numbers of channels.
OutputArray	dst	output image.
System.Int32	radius	radius of Guided Filter.
System.Double	eps	regularization term of Guided Filter. eps^2 is similar to the sigma in the color space into bilateralFilter.
System.Int32	dDepth	optional depth of the output image.

HoughPoint2Line(Point, InputArray, AngleRangeOption, HoughDeskewOption, RulesOption)

Calculates coordinates of line segment corresponded by point in Hough space.

Declaration

public static Vec4i HoughPoint2Line(Point houghPoint, InputArray srcImgInfo, AngleRangeOption angleRange = AngleRangeOption.ARO_315_135, HoughDeskewOption makeSkew = HoughDeskewOption.DESKEW, RulesOption rules = RulesOption.IGNORE_BORDERS)

Parameters

Type	Name	Description
OpenCvSharp.Point	houghPoint	Point in Hough space.
InputArray	srcImgInfo	The source (input) image of Hough transform.
AngleRangeOption	angleRange	The part of Hough space where point is situated, see cv::AngleRangeOption
HoughDeskewOption	makeSkew	Specifies to do or not to do image skewing, see cv::HoughDeskewOption
RulesOption	rules	Specifies strictness of line segment calculating, see cv::RulesOption

Returns

Type	Description
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. 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.

JointBilateralFilter(InputArray, InputArray, OutputArray, Int32, Double, Double, BorderTypes)

Applies the joint bilateral filter to an image.

Declaration

public static void JointBilateralFilter(InputArray joint, InputArray src, OutputArray dst, int d, double sigmaColor, double sigmaSpace, BorderTypes borderType = BorderTypes.Reflect101)

Parameters

Type	Name	Description
InputArray	joint	Joint 8-bit or floating-point, 1-channel or 3-channel image.
InputArray	src	Source 8-bit or floating-point, 1-channel or 3-channel image with the same depth as joint image.
OutputArray	dst	Destination image of the same size and type as src.
System.Int32	d	Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace.
System.Double	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.
System.Double	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.
BorderTypes	borderType

L0Smooth(InputArray, OutputArray, Double, Double)

Global image smoothing via L0 gradient minimization.

Declaration

public static void L0Smooth(InputArray src, OutputArray dst, double lambda = 0.02, double kappa = 2)

Parameters

Type	Name	Description
InputArray	src	source image for filtering with unsigned 8-bit or signed 16-bit or floating-point depth.
OutputArray	dst	destination image.
System.Double	lambda	parameter defining the smooth term weight.
System.Double	kappa	parameter defining the increasing factor of the weight of the gradient data term.

NiblackThreshold(InputArray, OutputArray, Double, ThresholdTypes, Int32, Double, LocalBinarizationMethods, Double)

Applies Niblack thresholding to input image.

Declaration

public static void NiblackThreshold(InputArray src, OutputArray dst, double maxValue, ThresholdTypes type, int blockSize, double k, LocalBinarizationMethods binarizationMethod = LocalBinarizationMethods.Niblack, double r = 128)

Parameters

Type	Name	Description
InputArray	src	Source 8-bit single-channel image.
OutputArray	dst	Destination image of the same size and the same type as src.
System.Double	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.
ThresholdTypes	type	Thresholding type, see cv::ThresholdTypes.
System.Int32	blockSize	Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on.
System.Double	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.
LocalBinarizationMethods	binarizationMethod	Binarization method to use. By default, Niblack's technique is used. Other techniques can be specified, see cv::ximgproc::LocalBinarizationMethods.
System.Double	r	The user-adjustable parameter used by Sauvola's technique. This is the dynamic range of standard deviation.

Remarks

The function transforms a grayscale image to a binary image according to the formulae:

• THRESH_BINARY \f[dst(x, y) = \fork{\texttt{maxValue } }{if (src(x, y) > T(x, y))}{0}{otherwise}\f]
• ** THRESH_BINARY_INV** \f[dst(x, y) = \fork{0}{if (src(x, y) > T(x, y))}{\texttt{maxValue}}{otherwise}\f] where \f$T(x, y)\f$ is a threshold calculated individually for each pixel. The threshold value \f$T(x, y)\f$ is the mean minus \f$ delta \f$ times standard deviation of \f$\texttt{blockSize} \times\texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$. The function can't process the image in-place.

PeiLinNormalization(InputArray)

Calculates an affine transformation that normalize given image using Pei&Lin Normalization.

Declaration

public static double[, ] PeiLinNormalization(InputArray i)

Parameters

Type	Name	Description
InputArray	i	Given transformed image.

Returns

Type	Description
System.Double[,]	Transformation matrix corresponding to inversed image transformation

PeiLinNormalization(InputArray, OutputArray)

Calculates an affine transformation that normalize given image using Pei&Lin Normalization.

Declaration

public static void PeiLinNormalization(InputArray i, OutputArray t)

Parameters

Type	Name	Description
InputArray	i	Given transformed image.
OutputArray	t	Inversed image transformation.

QConj(InputArray, OutputArray)

calculates conjugate of a quaternion image.

Declaration

public static void QConj(InputArray qimg, OutputArray qcimg)

Parameters

Type	Name	Description
InputArray	qimg	quaternion image.
OutputArray	qcimg	conjugate of qimg

QDft(InputArray, OutputArray, DftFlags, Boolean)

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

Declaration

public static void QDft(InputArray img, OutputArray qimg, DftFlags flags, bool sideLeft)

Parameters

Type	Name	Description
InputArray	img	quaternion image.
OutputArray	qimg	quaternion image in dual space.
DftFlags	flags	quaternion image in dual space. only DFT_INVERSE flags is supported
System.Boolean	sideLeft	true the hypercomplex exponential is to be multiplied on the left (false on the right ).

QMultiply(InputArray, InputArray, OutputArray)

Calculates the per-element quaternion product of two arrays

Declaration

public static void QMultiply(InputArray src1, InputArray src2, OutputArray dst)

Parameters

Type	Name	Description
InputArray	src1	quaternion image.
InputArray	src2	quaternion image.
OutputArray	dst	product dst(I)=src1(I) . src2(I)

QUnitary(InputArray, OutputArray)

divides each element by its modulus.

Declaration

public static void QUnitary(InputArray qimg, OutputArray qnimg)

Parameters

Type	Name	Description
InputArray	qimg	quaternion image.
OutputArray	qnimg	conjugate of qimg

RollingGuidanceFilter(InputArray, OutputArray, Int32, Double, Double, Int32, BorderTypes)

Applies the rolling guidance filter to an image.

Declaration

public static void RollingGuidanceFilter(InputArray src, OutputArray dst, int d = -1, double sigmaColor = 25, double sigmaSpace = 3, int numOfIter = 4, BorderTypes borderType = BorderTypes.Reflect101)

Parameters

Type	Name	Description
InputArray	src	8-bit or floating-point, 1-channel or 3-channel image.
OutputArray	dst	Destination image of the same size and type as src.
System.Int32	d	Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace.
System.Double	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.
System.Double	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.
System.Int32	numOfIter	Number of iterations of joint edge-preserving filtering applied on the source image.
BorderTypes	borderType

Thinning(InputArray, OutputArray, ThinningTypes)

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.

Declaration

public static void Thinning(InputArray src, OutputArray dst, ThinningTypes thinningType = ThinningTypes.ZHANGSUEN)

Parameters

Type	Name	Description
InputArray	src	Source 8-bit single-channel image, containing binary blobs, with blobs having 255 pixel values.
OutputArray	dst	Destination image of the same size and the same type as src. The function can work in-place.
ThinningTypes	thinningType	Value that defines which thinning algorithm should be used.

WeightedMedianFilter(InputArray, InputArray, OutputArray, Int32, Double, WMFWeightType, Mat)

Applies weighted median filter to an image.

Declaration

public static void WeightedMedianFilter(InputArray joint, InputArray src, OutputArray dst, int r, double sigma = 25.5, WMFWeightType weightType = WMFWeightType.EXP, Mat mask = null)

Parameters

Type	Name	Description
InputArray	joint	Joint 8-bit, 1-channel or 3-channel image.
InputArray	src	Source 8-bit or floating-point, 1-channel or 3-channel image.
OutputArray	dst	Destination image.
System.Int32	r	Radius of filtering kernel, should be a positive integer.
System.Double	sigma	Filter range standard deviation for the joint image.
WMFWeightType	weightType	The type of weight definition, see WMFWeightType
Mat	mask	A 0-1 mask that has the same size with I. This mask is used to ignore the effect of some pixels. If the pixel value on mask is 0, the pixel will be ignored when maintaining the joint-histogram.This is useful for applications like optical flow occlusion handling.

Remarks

For more details about this implementation, please see @cite zhang2014100+