OpenCV for Unity  2.6.3
Enox Software / Please refer to OpenCV official document ( http://docs.opencv.org/4.10.0/index.html ) for the details of the argument of the method.
Public Member Functions | Static Public Member Functions | Public Attributes | Protected Member Functions | List of all members
OpenCVForUnity.Xfeatures2dModule.PCTSignatures Class Reference

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...

Inheritance diagram for OpenCVForUnity.Xfeatures2dModule.PCTSignatures:
OpenCVForUnity.CoreModule.Algorithm OpenCVForUnity.DisposableOpenCVObject OpenCVForUnity.DisposableObject

Public Member Functions

void computeSignature (Mat image, Mat signature)
 Computes signature of given image. More...
 
void computeSignatures (List< Mat > images, List< Mat > signatures)
 Computes signatures for multiple images in parallel. More...
 
int getSampleCount ()
 Number of initial samples taken from the image. More...
 
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. More...
 
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. More...
 
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). More...
 
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). More...
 
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) More...
 
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) More...
 
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) More...
 
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) More...
 
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) More...
 
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) More...
 
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) More...
 
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) More...
 
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) More...
 
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) More...
 
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) More...
 
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) More...
 
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) More...
 
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) More...
 
MatOfPoint2f getSamplingPoints ()
 Initial samples taken from the image. These sampled features become the input for clustering. More...
 
void setWeight (int idx, float value)
 Weights (multiplicative constants) that linearly stretch individual axes of the feature space. More...
 
void setWeights (MatOfFloat weights)
 Weights (multiplicative constants) that linearly stretch individual axes of the feature space. More...
 
void setTranslation (int idx, float value)
 Translations of the individual axes of the feature space. More...
 
void setTranslations (MatOfFloat translations)
 Translations of the individual axes of the feature space. More...
 
void setSamplingPoints (MatOfPoint2f samplingPoints)
 Sets sampling points used to sample the input image. More...
 
MatOfInt getInitSeedIndexes ()
 Initial seeds (initial number of clusters) for the k-means algorithm. More...
 
void setInitSeedIndexes (MatOfInt initSeedIndexes)
 Initial seed indexes for the k-means algorithm. More...
 
int getInitSeedCount ()
 Number of initial seeds (initial number of clusters) for the k-means algorithm. More...
 
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). More...
 
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). More...
 
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. More...
 
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. More...
 
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. More...
 
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. More...
 
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. More...
 
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. More...
 
float getDropThreshold ()
 Remove centroids in k-means whose weight is lesser or equal to given threshold. More...
 
void setDropThreshold (float dropThreshold)
 Remove centroids in k-means whose weight is lesser or equal to given threshold. More...
 
int getDistanceFunction ()
 Distance function selector used for measuring distance between two points in k-means. More...
 
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. More...
 
- Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
IntPtr getNativeObjAddr ()
 
virtual void clear ()
 Clears the algorithm state. More...
 
virtual bool empty ()
 Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...
 
void save (string filename)
 
virtual string getDefaultName ()
 
- Public Member Functions inherited from OpenCVForUnity.DisposableObject
void Dispose ()
 
void ThrowIfDisposed ()
 

Static Public Member Functions

static new PCTSignatures __fromPtr__ (IntPtr addr)
 
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. More...
 
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. More...
 
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. More...
 
static PCTSignatures create ()
 Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes. More...
 
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. More...
 
static PCTSignatures create (MatOfPoint2f initSamplingPoints, MatOfInt initClusterSeedIndexes)
 Creates PCTSignatures algorithm using pre-generated sampling points and clusterization seeds indexes. More...
 
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. More...
 
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. More...
 
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. More...
 
static void generateInitPoints (MatOfPoint2f initPoints, int count, int pointDistribution)
 Generates initial sampling points according to selected point distribution. More...
 
- 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)
 

Public Attributes

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 UNIFORM = 0
 
const int REGULAR = 1
 
const int NORMAL = 2
 
const int MINUS = 0
 
const int GAUSSIAN = 1
 
const int HEURISTIC = 2
 

Protected Member Functions

override void Dispose (bool disposing)
 
- Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject
 DisposableOpenCVObject ()
 
 DisposableOpenCVObject (IntPtr ptr)
 
 DisposableOpenCVObject (bool isEnabledDispose)
 
 DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)
 
- Protected Member Functions inherited from OpenCVForUnity.DisposableObject
 DisposableObject ()
 
 DisposableObject (bool isEnabledDispose)
 

Additional Inherited Members

- 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
imageInput image of CV_8U type.
signatureOutput computed signature.

◆ computeSignatures()

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

Computes signatures for multiple images in parallel.

Parameters
imagesVector of input images of CV_8U type.
signaturesVector of computed signatures.

◆ create() [1/6]

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
initSampleCountNumber of points used for image sampling.
initSeedCountNumber of initial clusterization seeds. Must be lower or equal to initSampleCount
pointDistributionDistribution of generated points. Default: UNIFORM. Available: UNIFORM, REGULAR, NORMAL.
Returns
Created algorithm.

◆ create() [2/6]

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
initSampleCountNumber of points used for image sampling.
initSeedCountNumber of initial clusterization seeds. Must be lower or equal to initSampleCount
pointDistributionDistribution of generated points. Default: UNIFORM. Available: UNIFORM, REGULAR, NORMAL.
Returns
Created algorithm.

◆ create() [3/6]

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
initSampleCountNumber of points used for image sampling.
initSeedCountNumber of initial clusterization seeds. Must be lower or equal to initSampleCount
pointDistributionDistribution of generated points. Default: UNIFORM. Available: UNIFORM, REGULAR, NORMAL.
Returns
Created algorithm.

◆ create() [4/6]

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
initSampleCountNumber of points used for image sampling.
initSeedCountNumber of initial clusterization seeds. Must be lower or equal to initSampleCount
pointDistributionDistribution 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
initSamplingPointsSampling points used in image sampling.
initSeedCountNumber 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
initSamplingPointsSampling points used in image sampling.
initClusterSeedIndexesIndexes 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

◆ drawSignature() [1/3]

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
sourceSource image.
signatureImage signature.
resultOutput result.
radiusToShorterSideRatioDetermines maximal radius of signature in the output image.
borderThicknessBorder 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
sourceSource image.
signatureImage signature.
resultOutput result.
radiusToShorterSideRatioDetermines maximal radius of signature in the output image.
borderThicknessBorder thickness of the visualized signature.

◆ drawSignature() [3/3]

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
sourceSource image.
signatureImage signature.
resultOutput result.
radiusToShorterSideRatioDetermines maximal radius of signature in the output image.
borderThicknessBorder 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
initPointsOutput vector where the generated points will be saved.
countNumber of points to generate.
pointDistributionPoint distribution selector. Available: UNIFORM, REGULAR, NORMAL.
Note
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
samplingPointsVector of sampling points in range [0..1)
Note
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
idxID of the translation
valueValue of the translation
Note
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
translationsValues of all translations.
Note
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
idxID of the weight
valueValue of the weight
Note
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
weightsValues of all weights.
Note
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

◆ HEURISTIC

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.HEURISTIC = 2

◆ L0_25

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L0_25 = 0

◆ L0_5

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L0_5 = 1

◆ L1

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L1 = 2

◆ L2

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L2 = 3

◆ L2SQUARED

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L2SQUARED = 4

◆ L5

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L5 = 5

◆ L_INFINITY

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.L_INFINITY = 6

◆ MINUS

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.MINUS = 0

◆ NORMAL

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.NORMAL = 2

◆ REGULAR

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.REGULAR = 1

◆ UNIFORM

const int OpenCVForUnity.Xfeatures2dModule.PCTSignatures.UNIFORM = 0

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