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// Targeted by JavaCPP version 1.5.1: DO NOT EDIT THIS FILE
package org.bytedeco.opencv.opencv_xfeatures2d;
import java.nio.*;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.annotation.*;
import static org.bytedeco.openblas.global.openblas_nolapack.*;
import static org.bytedeco.openblas.global.openblas.*;
import org.bytedeco.opencv.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_core.*;
import org.bytedeco.opencv.opencv_ml.*;
import static org.bytedeco.opencv.global.opencv_ml.*;
import org.bytedeco.opencv.opencv_imgproc.*;
import static org.bytedeco.opencv.global.opencv_imgproc.*;
import static org.bytedeco.opencv.global.opencv_imgcodecs.*;
import org.bytedeco.opencv.opencv_videoio.*;
import static org.bytedeco.opencv.global.opencv_videoio.*;
import org.bytedeco.opencv.opencv_highgui.*;
import static org.bytedeco.opencv.global.opencv_highgui.*;
import org.bytedeco.opencv.opencv_flann.*;
import static org.bytedeco.opencv.global.opencv_flann.*;
import org.bytedeco.opencv.opencv_features2d.*;
import static org.bytedeco.opencv.global.opencv_features2d.*;
import org.bytedeco.opencv.opencv_calib3d.*;
import static org.bytedeco.opencv.global.opencv_calib3d.*;
import org.bytedeco.opencv.opencv_shape.*;
import static org.bytedeco.opencv.global.opencv_shape.*;
import static org.bytedeco.opencv.global.opencv_xfeatures2d.*;
/*
* Position-Color-Texture signatures
*/
/**
* \brief 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
*/
@Namespace("cv::xfeatures2d") @Properties(inherit = org.bytedeco.opencv.presets.opencv_xfeatures2d.class)
public class PCTSignatures extends Algorithm {
static { Loader.load(); }
/** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
public PCTSignatures(Pointer p) { super(p); }
/**
* \brief Lp distance function selector.
*/
/** enum cv::xfeatures2d::PCTSignatures::DistanceFunction */
public static final int
L0_25 = 0, L0_5 = 1, L1 = 2, L2 = 3, L2SQUARED = 4, L5 = 5, L_INFINITY = 6;
/**
* \brief Point distributions supported by random point generator.
*/
/** enum cv::xfeatures2d::PCTSignatures::PointDistribution */
public static final int
/** Generate numbers uniformly. */
UNIFORM = 0,
/** Generate points in a regular grid. */
REGULAR = 1,
/** Generate points with normal (gaussian) distribution. */
NORMAL = 2;
/**
* \brief Similarity function selector.
* @see
* Christian Beecks, Merih Seran Uysal, Thomas Seidl.
* Signature quadratic form distance.
* In Proceedings of the ACM International Conference on Image and Video Retrieval, pages 438-445.
* ACM, 2010.
* \cite BeecksUS10
* \note For selected distance function:
{@code \[ d(c_i, c_j) \]}
and parameter:
{@code \[ \alpha \]}
*/
/** enum cv::xfeatures2d::PCTSignatures::SimilarityFunction */
public static final int
/**
{@code \[ -d(c_i, c_j) \]}
*/
MINUS = 0,
/**
{@code \[ e^{ -\alpha * d^2(c_i, c_j)} \]}
*/
GAUSSIAN = 1,
/**
{@code \[ \frac{1}{\alpha + d(c_i, c_j)} \]}
*/
HEURISTIC = 2;
/**
* \brief 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 native @Ptr PCTSignatures create(
int initSampleCount/*=2000*/,
int initSeedCount/*=400*/,
int pointDistribution/*=0*/);
public static native @Ptr PCTSignatures create();
/**
* \brief 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 native @Ptr PCTSignatures create(
@Const @ByRef Point2fVector initSamplingPoints,
int initSeedCount);
/**
* \brief 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 native @Ptr PCTSignatures create(
@Const @ByRef Point2fVector initSamplingPoints,
@StdVector IntPointer initClusterSeedIndexes);
public static native @Ptr PCTSignatures create(
@Const @ByRef Point2fVector initSamplingPoints,
@StdVector IntBuffer initClusterSeedIndexes);
public static native @Ptr PCTSignatures create(
@Const @ByRef Point2fVector initSamplingPoints,
@StdVector int[] initClusterSeedIndexes);
/**
* \brief Computes signature of given image.
* @param image Input image of CV_8U type.
* @param signature Output computed signature.
*/
public native void computeSignature(
@ByVal Mat image,
@ByVal Mat signature);
public native void computeSignature(
@ByVal UMat image,
@ByVal UMat signature);
public native void computeSignature(
@ByVal GpuMat image,
@ByVal GpuMat signature);
/**
* \brief Computes signatures for multiple images in parallel.
* @param images Vector of input images of CV_8U type.
* @param signatures Vector of computed signatures.
*/
public native void computeSignatures(
@Const @ByRef MatVector images,
@ByRef MatVector signatures);
/**
* \brief 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 native void drawSignature(
@ByVal Mat source,
@ByVal Mat signature,
@ByVal Mat result,
float radiusToShorterSideRatio/*=1.0 / 8*/,
int borderThickness/*=1*/);
public static native void drawSignature(
@ByVal Mat source,
@ByVal Mat signature,
@ByVal Mat result);
public static native void drawSignature(
@ByVal UMat source,
@ByVal UMat signature,
@ByVal UMat result,
float radiusToShorterSideRatio/*=1.0 / 8*/,
int borderThickness/*=1*/);
public static native void drawSignature(
@ByVal UMat source,
@ByVal UMat signature,
@ByVal UMat result);
public static native void drawSignature(
@ByVal GpuMat source,
@ByVal GpuMat signature,
@ByVal GpuMat result,
float radiusToShorterSideRatio/*=1.0 / 8*/,
int borderThickness/*=1*/);
public static native void drawSignature(
@ByVal GpuMat source,
@ByVal GpuMat signature,
@ByVal GpuMat result);
/**
* \brief 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 native void generateInitPoints(
@ByRef Point2fVector initPoints,
int count,
int pointDistribution);
/**** sampler ****/
/**
* \brief Number of initial samples taken from the image.
*/
public native int getSampleCount();
/**
* \brief 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.
*/
public native int getGrayscaleBits();
/**
* \brief 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.
*/
public native void setGrayscaleBits(int grayscaleBits);
/**
* \brief 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 native int getWindowRadius();
/**
* \brief 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 native void setWindowRadius(int radius);
/**
* \brief 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)
*/
public native float getWeightX();
/**
* \brief 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)
*/
public native void setWeightX(float weight);
/**
* \brief 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)
*/
public native float getWeightY();
/**
* \brief 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)
*/
public native void setWeightY(float weight);
/**
* \brief 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)
*/
public native float getWeightL();
/**
* \brief 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)
*/
public native void setWeightL(float weight);
/**
* \brief 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)
*/
public native float getWeightA();
/**
* \brief 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)
*/
public native void setWeightA(float weight);
/**
* \brief 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)
*/
public native float getWeightB();
/**
* \brief 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)
*/
public native void setWeightB(float weight);
/**
* \brief 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)
*/
public native float getWeightContrast();
/**
* \brief 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)
*/
public native void setWeightContrast(float weight);
/**
* \brief 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)
*/
public native float getWeightEntropy();
/**
* \brief 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)
*/
public native void setWeightEntropy(float weight);
/**
* \brief Initial samples taken from the image.
* These sampled features become the input for clustering.
*/
public native @ByVal Point2fVector getSamplingPoints();
/**
* \brief 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 native void setWeight(int idx, float value);
/**
* \brief 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 native void setWeights(@StdVector FloatPointer weights);
public native void setWeights(@StdVector FloatBuffer weights);
public native void setWeights(@StdVector float[] weights);
/**
* \brief 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 native void setTranslation(int idx, float value);
/**
* \brief 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 native void setTranslations(@StdVector FloatPointer translations);
public native void setTranslations(@StdVector FloatBuffer translations);
public native void setTranslations(@StdVector float[] translations);
/**
* \brief 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 native void setSamplingPoints(@ByVal Point2fVector samplingPoints);
/**** clusterizer ****/
/**
* \brief Initial seeds (initial number of clusters) for the k-means algorithm.
*/
public native @StdVector IntPointer getInitSeedIndexes();
/**
* \brief Initial seed indexes for the k-means algorithm.
*/
public native void setInitSeedIndexes(@StdVector IntPointer initSeedIndexes);
public native void setInitSeedIndexes(@StdVector IntBuffer initSeedIndexes);
public native void setInitSeedIndexes(@StdVector int[] initSeedIndexes);
/**
* \brief Number of initial seeds (initial number of clusters) for the k-means algorithm.
*/
public native int getInitSeedCount();
/**
* \brief 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).
*/
public native int getIterationCount();
/**
* \brief 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).
*/
public native void setIterationCount(int iterationCount);
/**
* \brief Maximal number of generated clusters. If the number is exceeded,
* the clusters are sorted by their weights and the smallest clusters are cropped.
*/
public native int getMaxClustersCount();
/**
* \brief Maximal number of generated clusters. If the number is exceeded,
* the clusters are sorted by their weights and the smallest clusters are cropped.
*/
public native void setMaxClustersCount(int maxClustersCount);
/**
* \brief 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.
*/
public native int getClusterMinSize();
/**
* \brief 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.
*/
public native void setClusterMinSize(int clusterMinSize);
/**
* \brief 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.
*/
public native float getJoiningDistance();
/**
* \brief 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.
*/
public native void setJoiningDistance(float joiningDistance);
/**
* \brief Remove centroids in k-means whose weight is lesser or equal to given threshold.
*/
public native float getDropThreshold();
/**
* \brief Remove centroids in k-means whose weight is lesser or equal to given threshold.
*/
public native void setDropThreshold(float dropThreshold);
/**
* \brief Distance function selector used for measuring distance between two points in k-means.
*/
public native int getDistanceFunction();
/**
* \brief Distance function selector used for measuring distance between two points in k-means.
* Available: L0_25, L0_5, L1, L2, L2SQUARED, L5, L_INFINITY.
*/
public native void setDistanceFunction(int distanceFunction);
}
