org.opencv.xfeatures2d.PCTSignatures Maven / Gradle / Ivy
//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.xfeatures2d;
import java.util.ArrayList;
import java.util.List;
import org.opencv.core.Algorithm;
import org.opencv.core.Mat;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfInt;
import org.opencv.core.MatOfPoint2f;
import org.opencv.utils.Converters;
import org.opencv.xfeatures2d.PCTSignatures;
// C++: class PCTSignatures
/**
* Class implementing PCT (position-color-texture) signature extraction
* as described in CITE: 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.
* CITE: KrulisLS16
* CITE: BeecksUS10
*/
public class PCTSignatures extends Algorithm {
protected PCTSignatures(long addr) { super(addr); }
// internal usage only
public static PCTSignatures __fromPtr__(long addr) { return new PCTSignatures(addr); }
// C++: enum PointDistribution
public static final int
UNIFORM = 0,
REGULAR = 1,
NORMAL = 2;
// C++: enum SimilarityFunction
public static final int
MINUS = 0,
GAUSSIAN = 1,
HEURISTIC = 2;
// C++: enum DistanceFunction
public static final int
L0_25 = 0,
L0_5 = 1,
L1 = 2,
L2 = 3,
L2SQUARED = 4,
L5 = 5,
L_INFINITY = 6;
//
// C++: static Ptr_PCTSignatures cv::xfeatures2d::PCTSignatures::create(int initSampleCount = 2000, int initSeedCount = 400, int pointDistribution = 0)
//
/**
* Creates PCTSignatures algorithm using sample and seed count.
* It generates its own sets of sampling points and clusterization seed indexes.
* @param initSampleCount Number of points used for image sampling.
* @param initSeedCount Number of initial clusterization seeds.
* Must be lower or equal to initSampleCount
* @param pointDistribution Distribution of generated points. Default: UNIFORM.
* Available: UNIFORM, REGULAR, NORMAL.
* @return Created algorithm.
*/
public static PCTSignatures create(int initSampleCount, int initSeedCount, int pointDistribution) {
return PCTSignatures.__fromPtr__(create_0(initSampleCount, initSeedCount, pointDistribution));
}
/**
* Creates PCTSignatures algorithm using sample and seed count.
* It generates its own sets of sampling points and clusterization seed indexes.
* @param initSampleCount Number of points used for image sampling.
* @param initSeedCount Number of initial clusterization seeds.
* Must be lower or equal to initSampleCount
* Available: UNIFORM, REGULAR, NORMAL.
* @return Created algorithm.
*/
public static PCTSignatures create(int initSampleCount, int initSeedCount) {
return PCTSignatures.__fromPtr__(create_1(initSampleCount, initSeedCount));
}
/**
* Creates PCTSignatures algorithm using sample and seed count.
* It generates its own sets of sampling points and clusterization seed indexes.
* @param initSampleCount Number of points used for image sampling.
* Must be lower or equal to initSampleCount
* Available: UNIFORM, REGULAR, NORMAL.
* @return Created algorithm.
*/
public static PCTSignatures create(int initSampleCount) {
return PCTSignatures.__fromPtr__(create_2(initSampleCount));
}
/**
* Creates PCTSignatures algorithm using sample and seed count.
* It generates its own sets of sampling points and clusterization seed indexes.
* Must be lower or equal to initSampleCount
* Available: UNIFORM, REGULAR, NORMAL.
* @return Created algorithm.
*/
public static PCTSignatures create() {
return PCTSignatures.__fromPtr__(create_3());
}
//
// C++: static Ptr_PCTSignatures cv::xfeatures2d::PCTSignatures::create(vector_Point2f 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.
* @param initSamplingPoints Sampling points used in image sampling.
* @param initSeedCount Number of initial clusterization seeds.
* Must be lower or equal to initSamplingPoints.size().
* @return Created algorithm.
*/
public static PCTSignatures create(MatOfPoint2f initSamplingPoints, int initSeedCount) {
Mat initSamplingPoints_mat = initSamplingPoints;
return PCTSignatures.__fromPtr__(create_4(initSamplingPoints_mat.nativeObj, initSeedCount));
}
//
// C++: static Ptr_PCTSignatures cv::xfeatures2d::PCTSignatures::create(vector_Point2f initSamplingPoints, vector_int initClusterSeedIndexes)
//
/**
* Creates PCTSignatures algorithm using pre-generated sampling points
* and clusterization seeds indexes.
* @param initSamplingPoints Sampling points used in image sampling.
* @param initClusterSeedIndexes Indexes of initial clusterization seeds.
* Its size must be lower or equal to initSamplingPoints.size().
* @return Created algorithm.
*/
public static PCTSignatures create(MatOfPoint2f initSamplingPoints, MatOfInt initClusterSeedIndexes) {
Mat initSamplingPoints_mat = initSamplingPoints;
Mat initClusterSeedIndexes_mat = initClusterSeedIndexes;
return PCTSignatures.__fromPtr__(create_5(initSamplingPoints_mat.nativeObj, initClusterSeedIndexes_mat.nativeObj));
}
//
// C++: float cv::xfeatures2d::PCTSignatures::getDropThreshold()
//
/**
* Remove centroids in k-means whose weight is lesser or equal to given threshold.
* @return automatically generated
*/
public float getDropThreshold() {
return getDropThreshold_0(nativeObj);
}
//
// C++: float cv::xfeatures2d::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.
* @return automatically generated
*/
public float getJoiningDistance() {
return getJoiningDistance_0(nativeObj);
}
//
// C++: float cv::xfeatures2d::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)
* @return automatically generated
*/
public float getWeightA() {
return getWeightA_0(nativeObj);
}
//
// C++: float cv::xfeatures2d::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)
* @return automatically generated
*/
public float getWeightB() {
return getWeightB_0(nativeObj);
}
//
// C++: float cv::xfeatures2d::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)
* @return automatically generated
*/
public float getWeightContrast() {
return getWeightContrast_0(nativeObj);
}
//
// C++: float cv::xfeatures2d::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)
* @return automatically generated
*/
public float getWeightEntropy() {
return getWeightEntropy_0(nativeObj);
}
//
// C++: float cv::xfeatures2d::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)
* @return automatically generated
*/
public float getWeightL() {
return getWeightL_0(nativeObj);
}
//
// C++: float cv::xfeatures2d::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)
* @return automatically generated
*/
public float getWeightX() {
return getWeightX_0(nativeObj);
}
//
// C++: float cv::xfeatures2d::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)
* @return automatically generated
*/
public float getWeightY() {
return getWeightY_0(nativeObj);
}
//
// C++: int cv::xfeatures2d::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.
* @return automatically generated
*/
public int getClusterMinSize() {
return getClusterMinSize_0(nativeObj);
}
//
// C++: int cv::xfeatures2d::PCTSignatures::getDistanceFunction()
//
/**
* Distance function selector used for measuring distance between two points in k-means.
* @return automatically generated
*/
public int getDistanceFunction() {
return getDistanceFunction_0(nativeObj);
}
//
// C++: int cv::xfeatures2d::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.
* @return automatically generated
*/
public int getGrayscaleBits() {
return getGrayscaleBits_0(nativeObj);
}
//
// C++: int cv::xfeatures2d::PCTSignatures::getInitSeedCount()
//
/**
* Number of initial seeds (initial number of clusters) for the k-means algorithm.
* @return automatically generated
*/
public int getInitSeedCount() {
return getInitSeedCount_0(nativeObj);
}
//
// C++: int cv::xfeatures2d::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).
* @return automatically generated
*/
public int getIterationCount() {
return getIterationCount_0(nativeObj);
}
//
// C++: int cv::xfeatures2d::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.
* @return automatically generated
*/
public int getMaxClustersCount() {
return getMaxClustersCount_0(nativeObj);
}
//
// C++: int cv::xfeatures2d::PCTSignatures::getSampleCount()
//
/**
* Number of initial samples taken from the image.
* @return automatically generated
*/
public int getSampleCount() {
return getSampleCount_0(nativeObj);
}
//
// C++: int cv::xfeatures2d::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).
* @return automatically generated
*/
public int getWindowRadius() {
return getWindowRadius_0(nativeObj);
}
//
// C++: vector_Point2f cv::xfeatures2d::PCTSignatures::getSamplingPoints()
//
/**
* Initial samples taken from the image.
* These sampled features become the input for clustering.
* @return automatically generated
*/
public MatOfPoint2f getSamplingPoints() {
return MatOfPoint2f.fromNativeAddr(getSamplingPoints_0(nativeObj));
}
//
// C++: vector_int cv::xfeatures2d::PCTSignatures::getInitSeedIndexes()
//
/**
* Initial seeds (initial number of clusters) for the k-means algorithm.
* @return automatically generated
*/
public MatOfInt getInitSeedIndexes() {
return MatOfInt.fromNativeAddr(getInitSeedIndexes_0(nativeObj));
}
//
// C++: void cv::xfeatures2d::PCTSignatures::computeSignature(Mat image, Mat& signature)
//
/**
* Computes signature of given image.
* @param image Input image of CV_8U type.
* @param signature Output computed signature.
*/
public void computeSignature(Mat image, Mat signature) {
computeSignature_0(nativeObj, image.nativeObj, signature.nativeObj);
}
//
// C++: void cv::xfeatures2d::PCTSignatures::computeSignatures(vector_Mat images, vector_Mat signatures)
//
/**
* Computes signatures for multiple images in parallel.
* @param images Vector of input images of CV_8U type.
* @param signatures Vector of computed signatures.
*/
public void computeSignatures(List images, List signatures) {
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat signatures_mat = Converters.vector_Mat_to_Mat(signatures);
computeSignatures_0(nativeObj, images_mat.nativeObj, signatures_mat.nativeObj);
}
//
// C++: static void cv::xfeatures2d::PCTSignatures::drawSignature(Mat source, Mat signature, Mat& result, float radiusToShorterSideRatio = 1.0 / 8, int borderThickness = 1)
//
/**
* 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.
* @param source Source image.
* @param signature Image signature.
* @param result Output result.
* @param radiusToShorterSideRatio Determines maximal radius of signature in the output image.
* @param borderThickness Border thickness of the visualized signature.
*/
public static void drawSignature(Mat source, Mat signature, Mat result, float radiusToShorterSideRatio, int borderThickness) {
drawSignature_0(source.nativeObj, signature.nativeObj, result.nativeObj, radiusToShorterSideRatio, 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.
* @param source Source image.
* @param signature Image signature.
* @param result Output result.
* @param radiusToShorterSideRatio Determines maximal radius of signature in the output image.
*/
public static void drawSignature(Mat source, Mat signature, Mat result, float radiusToShorterSideRatio) {
drawSignature_1(source.nativeObj, signature.nativeObj, result.nativeObj, 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.
* @param source Source image.
* @param signature Image signature.
* @param result Output result.
*/
public static void drawSignature(Mat source, Mat signature, Mat result) {
drawSignature_2(source.nativeObj, signature.nativeObj, result.nativeObj);
}
//
// C++: static void cv::xfeatures2d::PCTSignatures::generateInitPoints(vector_Point2f initPoints, int count, int pointDistribution)
//
/**
* Generates initial sampling points according to selected point distribution.
* @param initPoints Output vector where the generated points will be saved.
* @param count Number of points to generate.
* @param pointDistribution Point distribution selector.
* Available: UNIFORM, REGULAR, NORMAL.
* Note: Generated coordinates are in range [0..1)
*/
public static void generateInitPoints(MatOfPoint2f initPoints, int count, int pointDistribution) {
Mat initPoints_mat = initPoints;
generateInitPoints_0(initPoints_mat.nativeObj, count, pointDistribution);
}
//
// C++: void cv::xfeatures2d::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.
* @param clusterMinSize automatically generated
*/
public void setClusterMinSize(int clusterMinSize) {
setClusterMinSize_0(nativeObj, clusterMinSize);
}
//
// C++: void cv::xfeatures2d::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.
* @param distanceFunction automatically generated
*/
public void setDistanceFunction(int distanceFunction) {
setDistanceFunction_0(nativeObj, distanceFunction);
}
//
// C++: void cv::xfeatures2d::PCTSignatures::setDropThreshold(float dropThreshold)
//
/**
* Remove centroids in k-means whose weight is lesser or equal to given threshold.
* @param dropThreshold automatically generated
*/
public void setDropThreshold(float dropThreshold) {
setDropThreshold_0(nativeObj, dropThreshold);
}
//
// C++: void cv::xfeatures2d::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.
* @param grayscaleBits automatically generated
*/
public void setGrayscaleBits(int grayscaleBits) {
setGrayscaleBits_0(nativeObj, grayscaleBits);
}
//
// C++: void cv::xfeatures2d::PCTSignatures::setInitSeedIndexes(vector_int initSeedIndexes)
//
/**
* Initial seed indexes for the k-means algorithm.
* @param initSeedIndexes automatically generated
*/
public void setInitSeedIndexes(MatOfInt initSeedIndexes) {
Mat initSeedIndexes_mat = initSeedIndexes;
setInitSeedIndexes_0(nativeObj, initSeedIndexes_mat.nativeObj);
}
//
// C++: void cv::xfeatures2d::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).
* @param iterationCount automatically generated
*/
public void setIterationCount(int iterationCount) {
setIterationCount_0(nativeObj, iterationCount);
}
//
// C++: void cv::xfeatures2d::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.
* @param joiningDistance automatically generated
*/
public void setJoiningDistance(float joiningDistance) {
setJoiningDistance_0(nativeObj, joiningDistance);
}
//
// C++: void cv::xfeatures2d::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.
* @param maxClustersCount automatically generated
*/
public void setMaxClustersCount(int maxClustersCount) {
setMaxClustersCount_0(nativeObj, maxClustersCount);
}
//
// C++: void cv::xfeatures2d::PCTSignatures::setSamplingPoints(vector_Point2f samplingPoints)
//
/**
* Sets sampling points used to sample the input image.
* @param samplingPoints Vector of sampling points in range [0..1)
* Note: Number of sampling points must be greater or equal to clusterization seed count.
*/
public void setSamplingPoints(MatOfPoint2f samplingPoints) {
Mat samplingPoints_mat = samplingPoints;
setSamplingPoints_0(nativeObj, samplingPoints_mat.nativeObj);
}
//
// C++: void cv::xfeatures2d::PCTSignatures::setTranslation(int idx, float value)
//
/**
* Translations of the individual axes of the feature space.
* @param idx ID of the translation
* @param value Value 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;
*/
public void setTranslation(int idx, float value) {
setTranslation_0(nativeObj, idx, value);
}
//
// C++: void cv::xfeatures2d::PCTSignatures::setTranslations(vector_float translations)
//
/**
* Translations of the individual axes of the feature space.
* @param translations Values 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;
*/
public void setTranslations(MatOfFloat translations) {
Mat translations_mat = translations;
setTranslations_0(nativeObj, translations_mat.nativeObj);
}
//
// C++: void cv::xfeatures2d::PCTSignatures::setWeight(int idx, float value)
//
/**
* Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
* @param idx ID of the weight
* @param value Value 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;
*/
public void setWeight(int idx, float value) {
setWeight_0(nativeObj, idx, value);
}
//
// C++: void cv::xfeatures2d::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)
* @param weight automatically generated
*/
public void setWeightA(float weight) {
setWeightA_0(nativeObj, weight);
}
//
// C++: void cv::xfeatures2d::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)
* @param weight automatically generated
*/
public void setWeightB(float weight) {
setWeightB_0(nativeObj, weight);
}
//
// C++: void cv::xfeatures2d::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)
* @param weight automatically generated
*/
public void setWeightContrast(float weight) {
setWeightContrast_0(nativeObj, weight);
}
//
// C++: void cv::xfeatures2d::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)
* @param weight automatically generated
*/
public void setWeightEntropy(float weight) {
setWeightEntropy_0(nativeObj, weight);
}
//
// C++: void cv::xfeatures2d::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)
* @param weight automatically generated
*/
public void setWeightL(float weight) {
setWeightL_0(nativeObj, weight);
}
//
// C++: void cv::xfeatures2d::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)
* @param weight automatically generated
*/
public void setWeightX(float weight) {
setWeightX_0(nativeObj, weight);
}
//
// C++: void cv::xfeatures2d::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)
* @param weight automatically generated
*/
public void setWeightY(float weight) {
setWeightY_0(nativeObj, weight);
}
//
// C++: void cv::xfeatures2d::PCTSignatures::setWeights(vector_float weights)
//
/**
* Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
* @param weights Values 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;
*/
public void setWeights(MatOfFloat weights) {
Mat weights_mat = weights;
setWeights_0(nativeObj, weights_mat.nativeObj);
}
//
// C++: void cv::xfeatures2d::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).
* @param radius automatically generated
*/
public void setWindowRadius(int radius) {
setWindowRadius_0(nativeObj, radius);
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: static Ptr_PCTSignatures cv::xfeatures2d::PCTSignatures::create(int initSampleCount = 2000, int initSeedCount = 400, int pointDistribution = 0)
private static native long create_0(int initSampleCount, int initSeedCount, int pointDistribution);
private static native long create_1(int initSampleCount, int initSeedCount);
private static native long create_2(int initSampleCount);
private static native long create_3();
// C++: static Ptr_PCTSignatures cv::xfeatures2d::PCTSignatures::create(vector_Point2f initSamplingPoints, int initSeedCount)
private static native long create_4(long initSamplingPoints_mat_nativeObj, int initSeedCount);
// C++: static Ptr_PCTSignatures cv::xfeatures2d::PCTSignatures::create(vector_Point2f initSamplingPoints, vector_int initClusterSeedIndexes)
private static native long create_5(long initSamplingPoints_mat_nativeObj, long initClusterSeedIndexes_mat_nativeObj);
// C++: float cv::xfeatures2d::PCTSignatures::getDropThreshold()
private static native float getDropThreshold_0(long nativeObj);
// C++: float cv::xfeatures2d::PCTSignatures::getJoiningDistance()
private static native float getJoiningDistance_0(long nativeObj);
// C++: float cv::xfeatures2d::PCTSignatures::getWeightA()
private static native float getWeightA_0(long nativeObj);
// C++: float cv::xfeatures2d::PCTSignatures::getWeightB()
private static native float getWeightB_0(long nativeObj);
// C++: float cv::xfeatures2d::PCTSignatures::getWeightContrast()
private static native float getWeightContrast_0(long nativeObj);
// C++: float cv::xfeatures2d::PCTSignatures::getWeightEntropy()
private static native float getWeightEntropy_0(long nativeObj);
// C++: float cv::xfeatures2d::PCTSignatures::getWeightL()
private static native float getWeightL_0(long nativeObj);
// C++: float cv::xfeatures2d::PCTSignatures::getWeightX()
private static native float getWeightX_0(long nativeObj);
// C++: float cv::xfeatures2d::PCTSignatures::getWeightY()
private static native float getWeightY_0(long nativeObj);
// C++: int cv::xfeatures2d::PCTSignatures::getClusterMinSize()
private static native int getClusterMinSize_0(long nativeObj);
// C++: int cv::xfeatures2d::PCTSignatures::getDistanceFunction()
private static native int getDistanceFunction_0(long nativeObj);
// C++: int cv::xfeatures2d::PCTSignatures::getGrayscaleBits()
private static native int getGrayscaleBits_0(long nativeObj);
// C++: int cv::xfeatures2d::PCTSignatures::getInitSeedCount()
private static native int getInitSeedCount_0(long nativeObj);
// C++: int cv::xfeatures2d::PCTSignatures::getIterationCount()
private static native int getIterationCount_0(long nativeObj);
// C++: int cv::xfeatures2d::PCTSignatures::getMaxClustersCount()
private static native int getMaxClustersCount_0(long nativeObj);
// C++: int cv::xfeatures2d::PCTSignatures::getSampleCount()
private static native int getSampleCount_0(long nativeObj);
// C++: int cv::xfeatures2d::PCTSignatures::getWindowRadius()
private static native int getWindowRadius_0(long nativeObj);
// C++: vector_Point2f cv::xfeatures2d::PCTSignatures::getSamplingPoints()
private static native long getSamplingPoints_0(long nativeObj);
// C++: vector_int cv::xfeatures2d::PCTSignatures::getInitSeedIndexes()
private static native long getInitSeedIndexes_0(long nativeObj);
// C++: void cv::xfeatures2d::PCTSignatures::computeSignature(Mat image, Mat& signature)
private static native void computeSignature_0(long nativeObj, long image_nativeObj, long signature_nativeObj);
// C++: void cv::xfeatures2d::PCTSignatures::computeSignatures(vector_Mat images, vector_Mat signatures)
private static native void computeSignatures_0(long nativeObj, long images_mat_nativeObj, long signatures_mat_nativeObj);
// C++: static void cv::xfeatures2d::PCTSignatures::drawSignature(Mat source, Mat signature, Mat& result, float radiusToShorterSideRatio = 1.0 / 8, int borderThickness = 1)
private static native void drawSignature_0(long source_nativeObj, long signature_nativeObj, long result_nativeObj, float radiusToShorterSideRatio, int borderThickness);
private static native void drawSignature_1(long source_nativeObj, long signature_nativeObj, long result_nativeObj, float radiusToShorterSideRatio);
private static native void drawSignature_2(long source_nativeObj, long signature_nativeObj, long result_nativeObj);
// C++: static void cv::xfeatures2d::PCTSignatures::generateInitPoints(vector_Point2f initPoints, int count, int pointDistribution)
private static native void generateInitPoints_0(long initPoints_mat_nativeObj, int count, int pointDistribution);
// C++: void cv::xfeatures2d::PCTSignatures::setClusterMinSize(int clusterMinSize)
private static native void setClusterMinSize_0(long nativeObj, int clusterMinSize);
// C++: void cv::xfeatures2d::PCTSignatures::setDistanceFunction(int distanceFunction)
private static native void setDistanceFunction_0(long nativeObj, int distanceFunction);
// C++: void cv::xfeatures2d::PCTSignatures::setDropThreshold(float dropThreshold)
private static native void setDropThreshold_0(long nativeObj, float dropThreshold);
// C++: void cv::xfeatures2d::PCTSignatures::setGrayscaleBits(int grayscaleBits)
private static native void setGrayscaleBits_0(long nativeObj, int grayscaleBits);
// C++: void cv::xfeatures2d::PCTSignatures::setInitSeedIndexes(vector_int initSeedIndexes)
private static native void setInitSeedIndexes_0(long nativeObj, long initSeedIndexes_mat_nativeObj);
// C++: void cv::xfeatures2d::PCTSignatures::setIterationCount(int iterationCount)
private static native void setIterationCount_0(long nativeObj, int iterationCount);
// C++: void cv::xfeatures2d::PCTSignatures::setJoiningDistance(float joiningDistance)
private static native void setJoiningDistance_0(long nativeObj, float joiningDistance);
// C++: void cv::xfeatures2d::PCTSignatures::setMaxClustersCount(int maxClustersCount)
private static native void setMaxClustersCount_0(long nativeObj, int maxClustersCount);
// C++: void cv::xfeatures2d::PCTSignatures::setSamplingPoints(vector_Point2f samplingPoints)
private static native void setSamplingPoints_0(long nativeObj, long samplingPoints_mat_nativeObj);
// C++: void cv::xfeatures2d::PCTSignatures::setTranslation(int idx, float value)
private static native void setTranslation_0(long nativeObj, int idx, float value);
// C++: void cv::xfeatures2d::PCTSignatures::setTranslations(vector_float translations)
private static native void setTranslations_0(long nativeObj, long translations_mat_nativeObj);
// C++: void cv::xfeatures2d::PCTSignatures::setWeight(int idx, float value)
private static native void setWeight_0(long nativeObj, int idx, float value);
// C++: void cv::xfeatures2d::PCTSignatures::setWeightA(float weight)
private static native void setWeightA_0(long nativeObj, float weight);
// C++: void cv::xfeatures2d::PCTSignatures::setWeightB(float weight)
private static native void setWeightB_0(long nativeObj, float weight);
// C++: void cv::xfeatures2d::PCTSignatures::setWeightContrast(float weight)
private static native void setWeightContrast_0(long nativeObj, float weight);
// C++: void cv::xfeatures2d::PCTSignatures::setWeightEntropy(float weight)
private static native void setWeightEntropy_0(long nativeObj, float weight);
// C++: void cv::xfeatures2d::PCTSignatures::setWeightL(float weight)
private static native void setWeightL_0(long nativeObj, float weight);
// C++: void cv::xfeatures2d::PCTSignatures::setWeightX(float weight)
private static native void setWeightX_0(long nativeObj, float weight);
// C++: void cv::xfeatures2d::PCTSignatures::setWeightY(float weight)
private static native void setWeightY_0(long nativeObj, float weight);
// C++: void cv::xfeatures2d::PCTSignatures::setWeights(vector_float weights)
private static native void setWeights_0(long nativeObj, long weights_mat_nativeObj);
// C++: void cv::xfeatures2d::PCTSignatures::setWindowRadius(int radius)
private static native void setWindowRadius_0(long nativeObj, int radius);
// native support for java finalize()
private static native void delete(long nativeObj);
}
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