688 lines
22 KiB
Objective-C
688 lines
22 KiB
Objective-C
//
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// This file is auto-generated. Please don't modify it!
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//
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#pragma once
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#ifdef __cplusplus
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//#import "opencv.hpp"
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#import "opencv2/xfeatures2d.hpp"
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#else
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#define CV_EXPORTS
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#endif
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#import <Foundation/Foundation.h>
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#import "Algorithm.h"
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@class FloatVector;
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@class IntVector;
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@class Mat;
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@class Point2f;
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// C++: enum DistanceFunction (cv.xfeatures2d.PCTSignatures.DistanceFunction)
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typedef NS_ENUM(int, DistanceFunction) {
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PCTSignatures_L0_25 NS_SWIFT_NAME(L0_25) = 0,
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PCTSignatures_L0_5 NS_SWIFT_NAME(L0_5) = 1,
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PCTSignatures_L1 NS_SWIFT_NAME(L1) = 2,
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PCTSignatures_L2 NS_SWIFT_NAME(L2) = 3,
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PCTSignatures_L2SQUARED NS_SWIFT_NAME(L2SQUARED) = 4,
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PCTSignatures_L5 NS_SWIFT_NAME(L5) = 5,
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PCTSignatures_L_INFINITY NS_SWIFT_NAME(L_INFINITY) = 6
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};
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// C++: enum PointDistribution (cv.xfeatures2d.PCTSignatures.PointDistribution)
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typedef NS_ENUM(int, PointDistribution) {
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PCTSignatures_UNIFORM NS_SWIFT_NAME(UNIFORM) = 0,
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PCTSignatures_REGULAR NS_SWIFT_NAME(REGULAR) = 1,
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PCTSignatures_NORMAL NS_SWIFT_NAME(NORMAL) = 2
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};
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// C++: enum SimilarityFunction (cv.xfeatures2d.PCTSignatures.SimilarityFunction)
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typedef NS_ENUM(int, SimilarityFunction) {
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PCTSignatures_MINUS NS_SWIFT_NAME(MINUS) = 0,
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PCTSignatures_GAUSSIAN NS_SWIFT_NAME(GAUSSIAN) = 1,
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PCTSignatures_HEURISTIC NS_SWIFT_NAME(HEURISTIC) = 2
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};
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NS_ASSUME_NONNULL_BEGIN
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// C++: class PCTSignatures
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/**
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* Class implementing PCT (position-color-texture) signature extraction
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* as described in CITE: KrulisLS16.
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* The algorithm is divided to a feature sampler and a clusterizer.
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* Feature sampler produces samples at given set of coordinates.
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* Clusterizer then produces clusters of these samples using k-means algorithm.
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* Resulting set of clusters is the signature of the input image.
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*
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* A signature is an array of SIGNATURE_DIMENSION-dimensional points.
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* Used dimensions are:
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* weight, x, y position; lab color, contrast, entropy.
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* CITE: KrulisLS16
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* CITE: BeecksUS10
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*
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* Member of `Xfeatures2d`
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*/
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CV_EXPORTS @interface PCTSignatures : Algorithm
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#ifdef __cplusplus
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@property(readonly)cv::Ptr<cv::xfeatures2d::PCTSignatures> nativePtrPCTSignatures;
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#endif
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#ifdef __cplusplus
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- (instancetype)initWithNativePtr:(cv::Ptr<cv::xfeatures2d::PCTSignatures>)nativePtr;
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+ (instancetype)fromNative:(cv::Ptr<cv::xfeatures2d::PCTSignatures>)nativePtr;
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#endif
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#pragma mark - Methods
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//
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// static Ptr_PCTSignatures cv::xfeatures2d::PCTSignatures::create(int initSampleCount = 2000, int initSeedCount = 400, int pointDistribution = 0)
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//
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/**
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* Creates PCTSignatures algorithm using sample and seed count.
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* It generates its own sets of sampling points and clusterization seed indexes.
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* @param initSampleCount Number of points used for image sampling.
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* @param initSeedCount Number of initial clusterization seeds.
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* Must be lower or equal to initSampleCount
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* @param pointDistribution Distribution of generated points. Default: UNIFORM.
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* Available: UNIFORM, REGULAR, NORMAL.
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* @return Created algorithm.
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*/
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+ (PCTSignatures*)create:(int)initSampleCount initSeedCount:(int)initSeedCount pointDistribution:(int)pointDistribution NS_SWIFT_NAME(create(initSampleCount:initSeedCount:pointDistribution:));
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/**
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* Creates PCTSignatures algorithm using sample and seed count.
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* It generates its own sets of sampling points and clusterization seed indexes.
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* @param initSampleCount Number of points used for image sampling.
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* @param initSeedCount Number of initial clusterization seeds.
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* Must be lower or equal to initSampleCount
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* Available: UNIFORM, REGULAR, NORMAL.
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* @return Created algorithm.
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*/
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+ (PCTSignatures*)create:(int)initSampleCount initSeedCount:(int)initSeedCount NS_SWIFT_NAME(create(initSampleCount:initSeedCount:));
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/**
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* Creates PCTSignatures algorithm using sample and seed count.
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* It generates its own sets of sampling points and clusterization seed indexes.
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* @param initSampleCount Number of points used for image sampling.
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* Must be lower or equal to initSampleCount
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* Available: UNIFORM, REGULAR, NORMAL.
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* @return Created algorithm.
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*/
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+ (PCTSignatures*)create:(int)initSampleCount NS_SWIFT_NAME(create(initSampleCount:));
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/**
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* Creates PCTSignatures algorithm using sample and seed count.
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* It generates its own sets of sampling points and clusterization seed indexes.
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* Must be lower or equal to initSampleCount
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* Available: UNIFORM, REGULAR, NORMAL.
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* @return Created algorithm.
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*/
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+ (PCTSignatures*)create NS_SWIFT_NAME(create());
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//
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// static Ptr_PCTSignatures cv::xfeatures2d::PCTSignatures::create(vector_Point2f initSamplingPoints, int initSeedCount)
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//
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/**
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* Creates PCTSignatures algorithm using pre-generated sampling points
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* and number of clusterization seeds. It uses the provided
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* sampling points and generates its own clusterization seed indexes.
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* @param initSamplingPoints Sampling points used in image sampling.
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* @param initSeedCount Number of initial clusterization seeds.
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* Must be lower or equal to initSamplingPoints.size().
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* @return Created algorithm.
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*/
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+ (PCTSignatures*)create2:(NSArray<Point2f*>*)initSamplingPoints initSeedCount:(int)initSeedCount NS_SWIFT_NAME(create(initSamplingPoints:initSeedCount:));
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//
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// static Ptr_PCTSignatures cv::xfeatures2d::PCTSignatures::create(vector_Point2f initSamplingPoints, vector_int initClusterSeedIndexes)
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//
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/**
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* Creates PCTSignatures algorithm using pre-generated sampling points
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* and clusterization seeds indexes.
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* @param initSamplingPoints Sampling points used in image sampling.
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* @param initClusterSeedIndexes Indexes of initial clusterization seeds.
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* Its size must be lower or equal to initSamplingPoints.size().
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* @return Created algorithm.
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*/
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+ (PCTSignatures*)create:(NSArray<Point2f*>*)initSamplingPoints initClusterSeedIndexes:(IntVector*)initClusterSeedIndexes NS_SWIFT_NAME(create(initSamplingPoints:initClusterSeedIndexes:));
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//
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// void cv::xfeatures2d::PCTSignatures::computeSignature(Mat image, Mat& signature)
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//
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/**
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* Computes signature of given image.
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* @param image Input image of CV_8U type.
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* @param signature Output computed signature.
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*/
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- (void)computeSignature:(Mat*)image signature:(Mat*)signature NS_SWIFT_NAME(computeSignature(image:signature:));
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//
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// void cv::xfeatures2d::PCTSignatures::computeSignatures(vector_Mat images, vector_Mat signatures)
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//
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/**
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* Computes signatures for multiple images in parallel.
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* @param images Vector of input images of CV_8U type.
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* @param signatures Vector of computed signatures.
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*/
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- (void)computeSignatures:(NSArray<Mat*>*)images signatures:(NSArray<Mat*>*)signatures NS_SWIFT_NAME(computeSignatures(images:signatures:));
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//
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// static void cv::xfeatures2d::PCTSignatures::drawSignature(Mat source, Mat signature, Mat& result, float radiusToShorterSideRatio = 1.0 / 8, int borderThickness = 1)
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//
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/**
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* Draws signature in the source image and outputs the result.
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* Signatures are visualized as a circle
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* with radius based on signature weight
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* and color based on signature color.
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* Contrast and entropy are not visualized.
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* @param source Source image.
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* @param signature Image signature.
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* @param result Output result.
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* @param radiusToShorterSideRatio Determines maximal radius of signature in the output image.
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* @param borderThickness Border thickness of the visualized signature.
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*/
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+ (void)drawSignature:(Mat*)source signature:(Mat*)signature result:(Mat*)result radiusToShorterSideRatio:(float)radiusToShorterSideRatio borderThickness:(int)borderThickness NS_SWIFT_NAME(drawSignature(source:signature:result:radiusToShorterSideRatio:borderThickness:));
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/**
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* Draws signature in the source image and outputs the result.
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* Signatures are visualized as a circle
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* with radius based on signature weight
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* and color based on signature color.
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* Contrast and entropy are not visualized.
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* @param source Source image.
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* @param signature Image signature.
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* @param result Output result.
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* @param radiusToShorterSideRatio Determines maximal radius of signature in the output image.
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*/
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+ (void)drawSignature:(Mat*)source signature:(Mat*)signature result:(Mat*)result radiusToShorterSideRatio:(float)radiusToShorterSideRatio NS_SWIFT_NAME(drawSignature(source:signature:result:radiusToShorterSideRatio:));
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/**
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* Draws signature in the source image and outputs the result.
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* Signatures are visualized as a circle
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* with radius based on signature weight
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* and color based on signature color.
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* Contrast and entropy are not visualized.
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* @param source Source image.
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* @param signature Image signature.
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* @param result Output result.
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*/
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+ (void)drawSignature:(Mat*)source signature:(Mat*)signature result:(Mat*)result NS_SWIFT_NAME(drawSignature(source:signature:result:));
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//
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// static void cv::xfeatures2d::PCTSignatures::generateInitPoints(vector_Point2f initPoints, int count, int pointDistribution)
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//
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/**
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* Generates initial sampling points according to selected point distribution.
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* @param initPoints Output vector where the generated points will be saved.
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* @param count Number of points to generate.
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* @param pointDistribution Point distribution selector.
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* Available: UNIFORM, REGULAR, NORMAL.
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* NOTE: Generated coordinates are in range [0..1)
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*/
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+ (void)generateInitPoints:(NSArray<Point2f*>*)initPoints count:(int)count pointDistribution:(int)pointDistribution NS_SWIFT_NAME(generateInitPoints(initPoints:count:pointDistribution:));
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//
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// int cv::xfeatures2d::PCTSignatures::getSampleCount()
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//
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/**
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* Number of initial samples taken from the image.
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*/
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- (int)getSampleCount NS_SWIFT_NAME(getSampleCount());
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//
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// int cv::xfeatures2d::PCTSignatures::getGrayscaleBits()
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//
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/**
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* Color resolution of the greyscale bitmap represented in allocated bits
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* (i.e., value 4 means that 16 shades of grey are used).
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* The greyscale bitmap is used for computing contrast and entropy values.
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*/
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- (int)getGrayscaleBits NS_SWIFT_NAME(getGrayscaleBits());
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//
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// void cv::xfeatures2d::PCTSignatures::setGrayscaleBits(int grayscaleBits)
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//
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/**
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* Color resolution of the greyscale bitmap represented in allocated bits
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* (i.e., value 4 means that 16 shades of grey are used).
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* The greyscale bitmap is used for computing contrast and entropy values.
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*/
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- (void)setGrayscaleBits:(int)grayscaleBits NS_SWIFT_NAME(setGrayscaleBits(grayscaleBits:));
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//
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// int cv::xfeatures2d::PCTSignatures::getWindowRadius()
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//
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/**
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* Size of the texture sampling window used to compute contrast and entropy
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* (center of the window is always in the pixel selected by x,y coordinates
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* of the corresponding feature sample).
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*/
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- (int)getWindowRadius NS_SWIFT_NAME(getWindowRadius());
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//
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// void cv::xfeatures2d::PCTSignatures::setWindowRadius(int radius)
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//
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/**
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* Size of the texture sampling window used to compute contrast and entropy
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* (center of the window is always in the pixel selected by x,y coordinates
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* of the corresponding feature sample).
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*/
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- (void)setWindowRadius:(int)radius NS_SWIFT_NAME(setWindowRadius(radius:));
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//
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// float cv::xfeatures2d::PCTSignatures::getWeightX()
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space
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* (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
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*/
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- (float)getWeightX NS_SWIFT_NAME(getWeightX());
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//
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// void cv::xfeatures2d::PCTSignatures::setWeightX(float weight)
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space
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* (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
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*/
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- (void)setWeightX:(float)weight NS_SWIFT_NAME(setWeightX(weight:));
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//
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// float cv::xfeatures2d::PCTSignatures::getWeightY()
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space
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* (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
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*/
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- (float)getWeightY NS_SWIFT_NAME(getWeightY());
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//
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// void cv::xfeatures2d::PCTSignatures::setWeightY(float weight)
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space
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* (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
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*/
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- (void)setWeightY:(float)weight NS_SWIFT_NAME(setWeightY(weight:));
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//
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// float cv::xfeatures2d::PCTSignatures::getWeightL()
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space
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* (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
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*/
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- (float)getWeightL NS_SWIFT_NAME(getWeightL());
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//
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// void cv::xfeatures2d::PCTSignatures::setWeightL(float weight)
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space
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* (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
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*/
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- (void)setWeightL:(float)weight NS_SWIFT_NAME(setWeightL(weight:));
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//
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// float cv::xfeatures2d::PCTSignatures::getWeightA()
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space
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* (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
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*/
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- (float)getWeightA NS_SWIFT_NAME(getWeightA());
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//
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// void cv::xfeatures2d::PCTSignatures::setWeightA(float weight)
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space
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* (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
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*/
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- (void)setWeightA:(float)weight NS_SWIFT_NAME(setWeightA(weight:));
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//
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// float cv::xfeatures2d::PCTSignatures::getWeightB()
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space
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* (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
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*/
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- (float)getWeightB NS_SWIFT_NAME(getWeightB());
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//
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// void cv::xfeatures2d::PCTSignatures::setWeightB(float weight)
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space
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* (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
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*/
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- (void)setWeightB:(float)weight NS_SWIFT_NAME(setWeightB(weight:));
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//
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// float cv::xfeatures2d::PCTSignatures::getWeightContrast()
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space
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* (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
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*/
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- (float)getWeightContrast NS_SWIFT_NAME(getWeightContrast());
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//
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// void cv::xfeatures2d::PCTSignatures::setWeightContrast(float weight)
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space
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* (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
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*/
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- (void)setWeightContrast:(float)weight NS_SWIFT_NAME(setWeightContrast(weight:));
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//
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// float cv::xfeatures2d::PCTSignatures::getWeightEntropy()
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space
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* (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
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*/
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- (float)getWeightEntropy NS_SWIFT_NAME(getWeightEntropy());
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//
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// void cv::xfeatures2d::PCTSignatures::setWeightEntropy(float weight)
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space
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* (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
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*/
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- (void)setWeightEntropy:(float)weight NS_SWIFT_NAME(setWeightEntropy(weight:));
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//
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// vector_Point2f cv::xfeatures2d::PCTSignatures::getSamplingPoints()
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//
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/**
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* Initial samples taken from the image.
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* These sampled features become the input for clustering.
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*/
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- (NSArray<Point2f*>*)getSamplingPoints NS_SWIFT_NAME(getSamplingPoints());
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//
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// void cv::xfeatures2d::PCTSignatures::setWeight(int idx, float value)
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
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* @param idx ID of the weight
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* @param value Value of the weight
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* NOTE:
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* WEIGHT_IDX = 0;
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* X_IDX = 1;
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* Y_IDX = 2;
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* L_IDX = 3;
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* A_IDX = 4;
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* B_IDX = 5;
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* CONTRAST_IDX = 6;
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* ENTROPY_IDX = 7;
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*/
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- (void)setWeight:(int)idx value:(float)value NS_SWIFT_NAME(setWeight(idx:value:));
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//
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// void cv::xfeatures2d::PCTSignatures::setWeights(vector_float weights)
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//
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/**
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* Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
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* @param weights Values of all weights.
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* NOTE:
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* WEIGHT_IDX = 0;
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* X_IDX = 1;
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* Y_IDX = 2;
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* L_IDX = 3;
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* A_IDX = 4;
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* B_IDX = 5;
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* CONTRAST_IDX = 6;
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* ENTROPY_IDX = 7;
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*/
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- (void)setWeights:(FloatVector*)weights NS_SWIFT_NAME(setWeights(weights:));
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//
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// void cv::xfeatures2d::PCTSignatures::setTranslation(int idx, float value)
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//
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/**
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|
* Translations of the individual axes of the feature space.
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* @param idx ID of the translation
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* @param value Value of the translation
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* NOTE:
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* WEIGHT_IDX = 0;
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|
* X_IDX = 1;
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* Y_IDX = 2;
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* L_IDX = 3;
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* A_IDX = 4;
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* B_IDX = 5;
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* CONTRAST_IDX = 6;
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* ENTROPY_IDX = 7;
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*/
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- (void)setTranslation:(int)idx value:(float)value NS_SWIFT_NAME(setTranslation(idx:value:));
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//
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// void cv::xfeatures2d::PCTSignatures::setTranslations(vector_float translations)
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//
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|
/**
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|
* Translations of the individual axes of the feature space.
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|
* @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;
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|
* ENTROPY_IDX = 7;
|
|
*/
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- (void)setTranslations:(FloatVector*)translations NS_SWIFT_NAME(setTranslations(translations:));
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|
|
|
|
//
|
|
// 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.
|
|
*/
|
|
- (void)setSamplingPoints:(NSArray<Point2f*>*)samplingPoints NS_SWIFT_NAME(setSamplingPoints(samplingPoints:));
|
|
|
|
|
|
//
|
|
// vector_int cv::xfeatures2d::PCTSignatures::getInitSeedIndexes()
|
|
//
|
|
/**
|
|
* Initial seeds (initial number of clusters) for the k-means algorithm.
|
|
*/
|
|
- (IntVector*)getInitSeedIndexes NS_SWIFT_NAME(getInitSeedIndexes());
|
|
|
|
|
|
//
|
|
// void cv::xfeatures2d::PCTSignatures::setInitSeedIndexes(vector_int initSeedIndexes)
|
|
//
|
|
/**
|
|
* Initial seed indexes for the k-means algorithm.
|
|
*/
|
|
- (void)setInitSeedIndexes:(IntVector*)initSeedIndexes NS_SWIFT_NAME(setInitSeedIndexes(initSeedIndexes:));
|
|
|
|
|
|
//
|
|
// int cv::xfeatures2d::PCTSignatures::getInitSeedCount()
|
|
//
|
|
/**
|
|
* Number of initial seeds (initial number of clusters) for the k-means algorithm.
|
|
*/
|
|
- (int)getInitSeedCount NS_SWIFT_NAME(getInitSeedCount());
|
|
|
|
|
|
//
|
|
// 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).
|
|
*/
|
|
- (int)getIterationCount NS_SWIFT_NAME(getIterationCount());
|
|
|
|
|
|
//
|
|
// 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).
|
|
*/
|
|
- (void)setIterationCount:(int)iterationCount NS_SWIFT_NAME(setIterationCount(iterationCount:));
|
|
|
|
|
|
//
|
|
// 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.
|
|
*/
|
|
- (int)getMaxClustersCount NS_SWIFT_NAME(getMaxClustersCount());
|
|
|
|
|
|
//
|
|
// 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.
|
|
*/
|
|
- (void)setMaxClustersCount:(int)maxClustersCount NS_SWIFT_NAME(setMaxClustersCount(maxClustersCount:));
|
|
|
|
|
|
//
|
|
// 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.
|
|
*/
|
|
- (int)getClusterMinSize NS_SWIFT_NAME(getClusterMinSize());
|
|
|
|
|
|
//
|
|
// 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.
|
|
*/
|
|
- (void)setClusterMinSize:(int)clusterMinSize NS_SWIFT_NAME(setClusterMinSize(clusterMinSize:));
|
|
|
|
|
|
//
|
|
// 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.
|
|
*/
|
|
- (float)getJoiningDistance NS_SWIFT_NAME(getJoiningDistance());
|
|
|
|
|
|
//
|
|
// 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.
|
|
*/
|
|
- (void)setJoiningDistance:(float)joiningDistance NS_SWIFT_NAME(setJoiningDistance(joiningDistance:));
|
|
|
|
|
|
//
|
|
// float cv::xfeatures2d::PCTSignatures::getDropThreshold()
|
|
//
|
|
/**
|
|
* Remove centroids in k-means whose weight is lesser or equal to given threshold.
|
|
*/
|
|
- (float)getDropThreshold NS_SWIFT_NAME(getDropThreshold());
|
|
|
|
|
|
//
|
|
// void cv::xfeatures2d::PCTSignatures::setDropThreshold(float dropThreshold)
|
|
//
|
|
/**
|
|
* Remove centroids in k-means whose weight is lesser or equal to given threshold.
|
|
*/
|
|
- (void)setDropThreshold:(float)dropThreshold NS_SWIFT_NAME(setDropThreshold(dropThreshold:));
|
|
|
|
|
|
//
|
|
// int cv::xfeatures2d::PCTSignatures::getDistanceFunction()
|
|
//
|
|
/**
|
|
* Distance function selector used for measuring distance between two points in k-means.
|
|
*/
|
|
- (int)getDistanceFunction NS_SWIFT_NAME(getDistanceFunction());
|
|
|
|
|
|
//
|
|
// 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.
|
|
*/
|
|
- (void)setDistanceFunction:(int)distanceFunction NS_SWIFT_NAME(setDistanceFunction(distanceFunction:));
|
|
|
|
|
|
|
|
@end
|
|
|
|
NS_ASSUME_NONNULL_END
|
|
|
|
|