Class implementing PCT (position-color-texture) signature extraction as described in [KrulisLS16]. The algorithm is divided to a feature sampler and a clusterizer. Feature sampler produces samples at given set of coordinates. Clusterizer then produces clusters of these samples using k-means algorithm. Resulting set of clusters is the signature of the input image. More...

Public Member Functions
void	computeSignature (Mat image, Mat signature)
	Computes signature of given image.

void	computeSignatures (List< Mat > images, List< Mat > signatures)
	Computes signatures for multiple images in parallel.

int	getClusterMinSize ()
	This parameter multiplied by the index of iteration gives lower limit for cluster size. Clusters containing fewer points than specified by the limit have their centroid dismissed and points are reassigned.

int	getDistanceFunction ()
	Distance function selector used for measuring distance between two points in k-means.

float	getDropThreshold ()
	Remove centroids in k-means whose weight is lesser or equal to given threshold.

int	getGrayscaleBits ()
	Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used). The greyscale bitmap is used for computing contrast and entropy values.

int	getInitSeedCount ()
	Number of initial seeds (initial number of clusters) for the k-means algorithm.

MatOfInt	getInitSeedIndexes ()
	Initial seeds (initial number of clusters) for the k-means algorithm.

int	getIterationCount ()
	Number of iterations of the k-means clustering. We use fixed number of iterations, since the modified clustering is pruning clusters (not iteratively refining k clusters).

float	getJoiningDistance ()
	Threshold euclidean distance between two centroids. If two cluster centers are closer than this distance, one of the centroid is dismissed and points are reassigned.

int	getMaxClustersCount ()
	Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.

int	getSampleCount ()
	Number of initial samples taken from the image.

MatOfPoint2f	getSamplingPoints ()
	Initial samples taken from the image. These sampled features become the input for clustering.

float	getWeightA ()
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

float	getWeightB ()
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

float	getWeightContrast ()
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

float	getWeightEntropy ()
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

float	getWeightL ()
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

float	getWeightX ()
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

float	getWeightY ()
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

int	getWindowRadius ()
	Size of the texture sampling window used to compute contrast and entropy (center of the window is always in the pixel selected by x,y coordinates of the corresponding feature sample).

void	setClusterMinSize (int clusterMinSize)
	This parameter multiplied by the index of iteration gives lower limit for cluster size. Clusters containing fewer points than specified by the limit have their centroid dismissed and points are reassigned.

void	setDistanceFunction (int distanceFunction)
	Distance function selector used for measuring distance between two points in k-means. Available: L0_25, L0_5, L1, L2, L2SQUARED, L5, L_INFINITY.

void	setDropThreshold (float dropThreshold)
	Remove centroids in k-means whose weight is lesser or equal to given threshold.

void	setGrayscaleBits (int grayscaleBits)
	Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used). The greyscale bitmap is used for computing contrast and entropy values.

void	setInitSeedIndexes (MatOfInt initSeedIndexes)
	Initial seed indexes for the k-means algorithm.

void	setIterationCount (int iterationCount)
	Number of iterations of the k-means clustering. We use fixed number of iterations, since the modified clustering is pruning clusters (not iteratively refining k clusters).

void	setJoiningDistance (float joiningDistance)
	Threshold euclidean distance between two centroids. If two cluster centers are closer than this distance, one of the centroid is dismissed and points are reassigned.

void	setMaxClustersCount (int maxClustersCount)
	Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.

void	setSamplingPoints (MatOfPoint2f samplingPoints)
	Sets sampling points used to sample the input image.

void	setTranslation (int idx, float value)
	Translations of the individual axes of the feature space.

void	setTranslations (MatOfFloat translations)
	Translations of the individual axes of the feature space.

void	setWeight (int idx, float value)
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space.

void	setWeightA (float weight)
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

void	setWeightB (float weight)
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

void	setWeightContrast (float weight)
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

void	setWeightEntropy (float weight)
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

void	setWeightL (float weight)
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

void	setWeights (MatOfFloat weights)
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space.

void	setWeightX (float weight)
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

void	setWeightY (float weight)
	Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

void	setWindowRadius (int radius)
	Size of the texture sampling window used to compute contrast and entropy (center of the window is always in the pixel selected by x,y coordinates of the corresponding feature sample).

Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
virtual void	clear ()
	Clears the algorithm state.

virtual bool	empty ()
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.

virtual string	getDefaultName ()

IntPtr	getNativeObjAddr ()

void	save (string filename)

Public Member Functions inherited from OpenCVForUnity.DisposableObject
void	Dispose ()

void	ThrowIfDisposed ()

Static Public Member Functions
static new PCTSignatures	__fromPtr__ (IntPtr addr)

static PCTSignatures	create ()
	Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.

static PCTSignatures	create (int initSampleCount)
	Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.

static PCTSignatures	create (int initSampleCount, int initSeedCount)
	Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.

static PCTSignatures	create (int initSampleCount, int initSeedCount, int pointDistribution)
	Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.

static PCTSignatures	create (MatOfPoint2f initSamplingPoints, int initSeedCount)
	Creates PCTSignatures algorithm using pre-generated sampling points and number of clusterization seeds. It uses the provided sampling points and generates its own clusterization seed indexes.

static PCTSignatures	create (MatOfPoint2f initSamplingPoints, MatOfInt initClusterSeedIndexes)
	Creates PCTSignatures algorithm using pre-generated sampling points and clusterization seeds indexes.

static void	drawSignature (Mat source, Mat signature, Mat result)
	Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized.

static void	drawSignature (Mat source, Mat signature, Mat result, float radiusToShorterSideRatio)
	Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized.

static void	drawSignature (Mat source, Mat signature, Mat result, float radiusToShorterSideRatio, int borderThickness)
	Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized.

static void	generateInitPoints (MatOfPoint2f initPoints, int count, int pointDistribution)
	Generates initial sampling points according to selected point distribution.

Static Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
static Algorithm	__fromPtr__ (IntPtr addr)

Static Public Member Functions inherited from OpenCVForUnity.DisposableObject
static IntPtr	ThrowIfNullIntPtr (IntPtr ptr)

Static Public Attributes
const int	GAUSSIAN = 1

const int	HEURISTIC = 2

const int	L0_25 = 0

const int	L0_5 = 1

const int	L1 = 2

const int	L2 = 3

const int	L2SQUARED = 4

const int	L5 = 5

const int	L_INFINITY = 6

const int	MINUS = 0

const int	NORMAL = 2

const int	REGULAR = 1

const int	UNIFORM = 0

Protected Member Functions
override void	Dispose (bool disposing)

Protected Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject
	DisposableOpenCVObject ()

	DisposableOpenCVObject (bool isEnabledDispose)

	DisposableOpenCVObject (IntPtr ptr)

	DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)

Protected Member Functions inherited from OpenCVForUnity.DisposableObject
	DisposableObject ()

	DisposableObject (bool isEnabledDispose)

Additional Inherited Members
Package Functions inherited from OpenCVForUnity.CoreModule.Algorithm
Package Attributes inherited from OpenCVForUnity.DisposableOpenCVObject
Properties inherited from OpenCVForUnity.DisposableObject
bool	IsDisposed `[get, protected set]`

bool	IsEnabledDispose `[get, set]`

Detailed Description

Class implementing PCT (position-color-texture) signature extraction as described in [KrulisLS16]. The algorithm is divided to a feature sampler and a clusterizer. Feature sampler produces samples at given set of coordinates. Clusterizer then produces clusters of these samples using k-means algorithm. Resulting set of clusters is the signature of the input image.

A signature is an array of SIGNATURE_DIMENSION-dimensional points. Used dimensions are: weight, x, y position; lab color, contrast, entropy. [KrulisLS16] [BeecksUS10]

Member Function Documentation

◆ fromPtr()

static new PCTSignatures OpenCVForUnity.Xfeatures2dModule.PCTSignatures.__fromPtr__ ( IntPtr addr )

static

◆ computeSignature()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.computeSignature	(	Mat	image,
		Mat	signature )

Computes signature of given image.

Parameters

image	Input image of CV_8U type.
signature	Output computed signature.

◆ computeSignatures()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.computeSignatures	(	List< Mat >	images,
		List< Mat >	signatures )

Computes signatures for multiple images in parallel.

Parameters

images	Vector of input images of CV_8U type.
signatures	Vector of computed signatures.

◆ create() [1/6]

static PCTSignatures OpenCVForUnity.Xfeatures2dModule.PCTSignatures.create ( )

static

Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.

Parameters

initSampleCount	Number of points used for image sampling.
initSeedCount	Number of initial clusterization seeds. Must be lower or equal to initSampleCount
pointDistribution	Distribution of generated points. Default: UNIFORM. Available: UNIFORM, REGULAR, NORMAL.

Returns: Created algorithm.

◆ create() [2/6]

static PCTSignatures OpenCVForUnity.Xfeatures2dModule.PCTSignatures.create ( int initSampleCount )

static

Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.

Parameters

initSampleCount	Number of points used for image sampling.
initSeedCount	Number of initial clusterization seeds. Must be lower or equal to initSampleCount
pointDistribution	Distribution of generated points. Default: UNIFORM. Available: UNIFORM, REGULAR, NORMAL.

Returns: Created algorithm.

◆ create() [3/6]

static PCTSignatures OpenCVForUnity.Xfeatures2dModule.PCTSignatures.create	(	int	initSampleCount,
		int	initSeedCount )

static

Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.

Parameters

initSampleCount	Number of points used for image sampling.
initSeedCount	Number of initial clusterization seeds. Must be lower or equal to initSampleCount
pointDistribution	Distribution of generated points. Default: UNIFORM. Available: UNIFORM, REGULAR, NORMAL.

Returns: Created algorithm.

◆ create() [4/6]

static PCTSignatures OpenCVForUnity.Xfeatures2dModule.PCTSignatures.create	(	int	initSampleCount,
		int	initSeedCount,
		int	pointDistribution )

static

Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.

Parameters

initSampleCount	Number of points used for image sampling.
initSeedCount	Number of initial clusterization seeds. Must be lower or equal to initSampleCount
pointDistribution	Distribution of generated points. Default: UNIFORM. Available: UNIFORM, REGULAR, NORMAL.

Returns: Created algorithm.

◆ create() [5/6]

static PCTSignatures OpenCVForUnity.Xfeatures2dModule.PCTSignatures.create	(	MatOfPoint2f	initSamplingPoints,
		int	initSeedCount )

static

Creates PCTSignatures algorithm using pre-generated sampling points and number of clusterization seeds. It uses the provided sampling points and generates its own clusterization seed indexes.

Parameters

initSamplingPoints	Sampling points used in image sampling.
initSeedCount	Number of initial clusterization seeds. Must be lower or equal to initSamplingPoints.size().

Returns: Created algorithm.

◆ create() [6/6]

static PCTSignatures OpenCVForUnity.Xfeatures2dModule.PCTSignatures.create	(	MatOfPoint2f	initSamplingPoints,
		MatOfInt	initClusterSeedIndexes )

static

Creates PCTSignatures algorithm using pre-generated sampling points and clusterization seeds indexes.

Parameters

initSamplingPoints	Sampling points used in image sampling.
initClusterSeedIndexes	Indexes of initial clusterization seeds. Its size must be lower or equal to initSamplingPoints.size().

Returns: Created algorithm.

◆ Dispose()

override void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.Dispose ( bool disposing )

protectedvirtual

Reimplemented from OpenCVForUnity.CoreModule.Algorithm.

◆ drawSignature() [1/3]

static void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.drawSignature	(	Mat	source,
		Mat	signature,
		Mat	result )

static

Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized.

Parameters

source	Source image.
signature	Image signature.
result	Output result.
radiusToShorterSideRatio	Determines maximal radius of signature in the output image.
borderThickness	Border thickness of the visualized signature.

◆ drawSignature() [2/3]

static void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.drawSignature	(	Mat	source,
		Mat	signature,
		Mat	result,
		float	radiusToShorterSideRatio )

static

Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized.

Parameters

source	Source image.
signature	Image signature.
result	Output result.
radiusToShorterSideRatio	Determines maximal radius of signature in the output image.
borderThickness	Border thickness of the visualized signature.

◆ drawSignature() [3/3]

static void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.drawSignature	(	Mat	source,
		Mat	signature,
		Mat	result,
		float	radiusToShorterSideRatio,
		int	borderThickness )

static

Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized.

Parameters

source	Source image.
signature	Image signature.
result	Output result.
radiusToShorterSideRatio	Determines maximal radius of signature in the output image.
borderThickness	Border thickness of the visualized signature.

◆ generateInitPoints()

static void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.generateInitPoints	(	MatOfPoint2f	initPoints,
		int	count,
		int	pointDistribution )

static

Generates initial sampling points according to selected point distribution.

Parameters

initPoints	Output vector where the generated points will be saved.
count	Number of points to generate.
pointDistribution	Point distribution selector. Available: UNIFORM, REGULAR, NORMAL.

Generated coordinates are in range [0..1)

◆ getClusterMinSize()

int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getClusterMinSize ( )

This parameter multiplied by the index of iteration gives lower limit for cluster size. Clusters containing fewer points than specified by the limit have their centroid dismissed and points are reassigned.

◆ getDistanceFunction()

int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getDistanceFunction ( )

Distance function selector used for measuring distance between two points in k-means.

◆ getDropThreshold()

float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getDropThreshold ( )

Remove centroids in k-means whose weight is lesser or equal to given threshold.

◆ getGrayscaleBits()

int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getGrayscaleBits ( )

Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used). The greyscale bitmap is used for computing contrast and entropy values.

◆ getInitSeedCount()

int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getInitSeedCount ( )

Number of initial seeds (initial number of clusters) for the k-means algorithm.

◆ getInitSeedIndexes()

MatOfInt OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getInitSeedIndexes ( )

Initial seeds (initial number of clusters) for the k-means algorithm.

◆ getIterationCount()

int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getIterationCount ( )

Number of iterations of the k-means clustering. We use fixed number of iterations, since the modified clustering is pruning clusters (not iteratively refining k clusters).

◆ getJoiningDistance()

float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getJoiningDistance ( )

Threshold euclidean distance between two centroids. If two cluster centers are closer than this distance, one of the centroid is dismissed and points are reassigned.

◆ getMaxClustersCount()

int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getMaxClustersCount ( )

Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.

◆ getSampleCount()

int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getSampleCount ( )

Number of initial samples taken from the image.

◆ getSamplingPoints()

MatOfPoint2f OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getSamplingPoints ( )

Initial samples taken from the image. These sampled features become the input for clustering.

◆ getWeightA()

float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getWeightA ( )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

◆ getWeightB()

float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getWeightB ( )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

◆ getWeightContrast()

float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getWeightContrast ( )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

◆ getWeightEntropy()

float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getWeightEntropy ( )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

◆ getWeightL()

float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getWeightL ( )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

◆ getWeightX()

float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getWeightX ( )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

◆ getWeightY()

float OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getWeightY ( )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

◆ getWindowRadius()

int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.getWindowRadius ( )

Size of the texture sampling window used to compute contrast and entropy (center of the window is always in the pixel selected by x,y coordinates of the corresponding feature sample).

◆ setClusterMinSize()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setClusterMinSize ( int clusterMinSize )

This parameter multiplied by the index of iteration gives lower limit for cluster size. Clusters containing fewer points than specified by the limit have their centroid dismissed and points are reassigned.

◆ setDistanceFunction()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setDistanceFunction ( int distanceFunction )

Distance function selector used for measuring distance between two points in k-means. Available: L0_25, L0_5, L1, L2, L2SQUARED, L5, L_INFINITY.

◆ setDropThreshold()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setDropThreshold ( float dropThreshold )

Remove centroids in k-means whose weight is lesser or equal to given threshold.

◆ setGrayscaleBits()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setGrayscaleBits ( int grayscaleBits )

Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used). The greyscale bitmap is used for computing contrast and entropy values.

◆ setInitSeedIndexes()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setInitSeedIndexes ( MatOfInt initSeedIndexes )

Initial seed indexes for the k-means algorithm.

◆ setIterationCount()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setIterationCount ( int iterationCount )

Number of iterations of the k-means clustering. We use fixed number of iterations, since the modified clustering is pruning clusters (not iteratively refining k clusters).

◆ setJoiningDistance()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setJoiningDistance ( float joiningDistance )

Threshold euclidean distance between two centroids. If two cluster centers are closer than this distance, one of the centroid is dismissed and points are reassigned.

◆ setMaxClustersCount()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setMaxClustersCount ( int maxClustersCount )

Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.

◆ setSamplingPoints()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setSamplingPoints ( MatOfPoint2f samplingPoints )

Sets sampling points used to sample the input image.

Parameters

samplingPoints Vector of sampling points in range [0..1)

Number of sampling points must be greater or equal to clusterization seed count.

◆ setTranslation()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setTranslation	(	int	idx,
		float	value )

Translations of the individual axes of the feature space.

Parameters

idx	ID of the translation
value	Value of the translation

WEIGHT_IDX = 0; X_IDX = 1; Y_IDX = 2; L_IDX = 3; A_IDX = 4; B_IDX = 5; CONTRAST_IDX = 6; ENTROPY_IDX = 7;

◆ setTranslations()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setTranslations ( MatOfFloat translations )

Translations of the individual axes of the feature space.

Parameters

translations Values of all translations.

WEIGHT_IDX = 0; X_IDX = 1; Y_IDX = 2; L_IDX = 3; A_IDX = 4; B_IDX = 5; CONTRAST_IDX = 6; ENTROPY_IDX = 7;

◆ setWeight()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setWeight	(	int	idx,
		float	value )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space.

Parameters

idx	ID of the weight
value	Value of the weight

WEIGHT_IDX = 0; X_IDX = 1; Y_IDX = 2; L_IDX = 3; A_IDX = 4; B_IDX = 5; CONTRAST_IDX = 6; ENTROPY_IDX = 7;

◆ setWeightA()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setWeightA ( float weight )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

◆ setWeightB()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setWeightB ( float weight )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

◆ setWeightContrast()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setWeightContrast ( float weight )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

◆ setWeightEntropy()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setWeightEntropy ( float weight )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

◆ setWeightL()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setWeightL ( float weight )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

◆ setWeights()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setWeights ( MatOfFloat weights )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space.

Parameters

weights Values of all weights.

WEIGHT_IDX = 0; X_IDX = 1; Y_IDX = 2; L_IDX = 3; A_IDX = 4; B_IDX = 5; CONTRAST_IDX = 6; ENTROPY_IDX = 7;

◆ setWeightX()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setWeightX ( float weight )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

◆ setWeightY()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setWeightY ( float weight )

Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)

◆ setWindowRadius()

void OpenCVForUnity.Xfeatures2dModule.PCTSignatures.setWindowRadius ( int radius )

Size of the texture sampling window used to compute contrast and entropy (center of the window is always in the pixel selected by x,y coordinates of the corresponding feature sample).

Member Data Documentation

◆ GAUSSIAN

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.GAUSSIAN = 1

static

◆ HEURISTIC

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.HEURISTIC = 2

static

◆ L0_25

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L0_25 = 0

static

◆ L0_5

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L0_5 = 1

static

◆ L1

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L1 = 2

static

◆ L2

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L2 = 3

static

◆ L2SQUARED

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L2SQUARED = 4

static

◆ L5

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L5 = 5

static

◆ L_INFINITY

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L_INFINITY = 6

static

◆ MINUS

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.MINUS = 0

static

◆ NORMAL

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.NORMAL = 2

static

◆ REGULAR

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.REGULAR = 1

static

◆ UNIFORM

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.UNIFORM = 0

static

The documentation for this class was generated from the following file:

OpenCVForUnity/Assets/OpenCVForUnity/org/opencv_contrib/xfeatures2d/PCTSignatures.cs

Public Member Functions

Static Public Member Functions

Static Public Attributes

Protected Member Functions

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ __fromPtr__()

◆ computeSignature()

◆ computeSignatures()

◆ create() [1/6]

◆ create() [2/6]

◆ create() [3/6]

◆ create() [4/6]

◆ create() [5/6]

◆ create() [6/6]

◆ Dispose()

◆ drawSignature() [1/3]

◆ drawSignature() [2/3]

◆ drawSignature() [3/3]

◆ generateInitPoints()

◆ getClusterMinSize()

◆ getDistanceFunction()

◆ getDropThreshold()

◆ getGrayscaleBits()

◆ getInitSeedCount()

◆ getInitSeedIndexes()

◆ getIterationCount()

◆ getJoiningDistance()

◆ getMaxClustersCount()

◆ getSampleCount()

◆ getSamplingPoints()

◆ getWeightA()

◆ getWeightB()

◆ getWeightContrast()

◆ getWeightEntropy()

◆ getWeightL()

◆ getWeightX()

◆ getWeightY()

◆ getWindowRadius()

◆ setClusterMinSize()

◆ setDistanceFunction()

◆ setDropThreshold()

◆ setGrayscaleBits()

◆ setInitSeedIndexes()

◆ setIterationCount()

◆ setJoiningDistance()

◆ setMaxClustersCount()

◆ setSamplingPoints()

◆ setTranslation()

◆ setTranslations()

◆ setWeight()

◆ setWeightA()

◆ setWeightB()

◆ setWeightContrast()

◆ setWeightEntropy()

◆ setWeightL()

◆ setWeights()

◆ setWeightX()

◆ setWeightY()

◆ setWindowRadius()

Member Data Documentation

◆ GAUSSIAN

◆ HEURISTIC

◆ L0_25

◆ L0_5

◆ L1

◆ L2

◆ L2SQUARED

◆ L5

◆ L_INFINITY

◆ MINUS

◆ NORMAL

◆ REGULAR

◆ UNIFORM

◆ fromPtr()