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784 lines
26 KiB
C++
784 lines
26 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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/*
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* Implementation of the paper Shape Matching and Object Recognition Using Shape Contexts
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* Belongie et al., 2002 by Juan Manuel Perez for GSoC 2013.
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*/
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#include "precomp.hpp"
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#include "opencv2/core.hpp"
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#include "scd_def.hpp"
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#include <limits>
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namespace cv
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{
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class ShapeContextDistanceExtractorImpl : public ShapeContextDistanceExtractor
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{
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public:
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/* Constructors */
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ShapeContextDistanceExtractorImpl(int _nAngularBins, int _nRadialBins, float _innerRadius, float _outerRadius, int _iterations,
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const Ptr<HistogramCostExtractor> &_comparer, const Ptr<ShapeTransformer> &_transformer)
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{
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nAngularBins=_nAngularBins;
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nRadialBins=_nRadialBins;
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innerRadius=_innerRadius;
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outerRadius=_outerRadius;
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rotationInvariant=false;
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comparer=_comparer;
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iterations=_iterations;
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transformer=_transformer;
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bendingEnergyWeight=0.3f;
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imageAppearanceWeight=0.0f;
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shapeContextWeight=1.0f;
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sigma=10.0f;
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name_ = "ShapeDistanceExtractor.SCD";
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}
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/* Destructor */
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~ShapeContextDistanceExtractorImpl()
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{
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}
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virtual AlgorithmInfo* info() const { return 0; }
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//! the main operator
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virtual float computeDistance(InputArray contour1, InputArray contour2);
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//! Setters/Getters
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virtual void setAngularBins(int _nAngularBins){CV_Assert(_nAngularBins>0); nAngularBins=_nAngularBins;}
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virtual int getAngularBins() const {return nAngularBins;}
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virtual void setRadialBins(int _nRadialBins){CV_Assert(_nRadialBins>0); nRadialBins=_nRadialBins;}
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virtual int getRadialBins() const {return nRadialBins;}
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virtual void setInnerRadius(float _innerRadius) {CV_Assert(_innerRadius>0); innerRadius=_innerRadius;}
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virtual float getInnerRadius() const {return innerRadius;}
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virtual void setOuterRadius(float _outerRadius) {CV_Assert(_outerRadius>0); outerRadius=_outerRadius;}
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virtual float getOuterRadius() const {return outerRadius;}
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virtual void setRotationInvariant(bool _rotationInvariant) {rotationInvariant=_rotationInvariant;}
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virtual bool getRotationInvariant() const {return rotationInvariant;}
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virtual void setCostExtractor(Ptr<HistogramCostExtractor> _comparer) { comparer = _comparer; }
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virtual Ptr<HistogramCostExtractor> getCostExtractor() const { return comparer; }
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virtual void setShapeContextWeight(float _shapeContextWeight) {shapeContextWeight=_shapeContextWeight;}
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virtual float getShapeContextWeight() const {return shapeContextWeight;}
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virtual void setImageAppearanceWeight(float _imageAppearanceWeight) {imageAppearanceWeight=_imageAppearanceWeight;}
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virtual float getImageAppearanceWeight() const {return imageAppearanceWeight;}
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virtual void setBendingEnergyWeight(float _bendingEnergyWeight) {bendingEnergyWeight=_bendingEnergyWeight;}
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virtual float getBendingEnergyWeight() const {return bendingEnergyWeight;}
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virtual void setStdDev(float _sigma) {sigma=_sigma;}
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virtual float getStdDev() const {return sigma;}
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virtual void setImages(InputArray _image1, InputArray _image2)
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{
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Mat image1_=_image1.getMat(), image2_=_image2.getMat();
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CV_Assert((image1_.depth()==0) && (image2_.depth()==0));
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image1=image1_;
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image2=image2_;
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}
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virtual void getImages(OutputArray _image1, OutputArray _image2) const
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{
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CV_Assert((!image1.empty()) && (!image2.empty()));
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_image1.create(image1.size(), image1.type());
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_image2.create(image2.size(), image2.type());
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_image1.getMat()=image1;
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_image2.getMat()=image2;
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}
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virtual void setIterations(int _iterations) {CV_Assert(_iterations>0); iterations=_iterations;}
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virtual int getIterations() const {return iterations;}
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virtual void setTransformAlgorithm(Ptr<ShapeTransformer> _transformer) {transformer=_transformer;}
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virtual Ptr<ShapeTransformer> getTransformAlgorithm() const {return transformer;}
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//! write/read
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virtual void write(FileStorage& fs) const
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{
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fs << "name" << name_
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<< "nRads" << nRadialBins
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<< "nAngs" << nAngularBins
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<< "iters" << iterations
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<< "img_1" << image1
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<< "img_2" << image2
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<< "beWei" << bendingEnergyWeight
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<< "scWei" << shapeContextWeight
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<< "iaWei" << imageAppearanceWeight
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<< "costF" << costFlag
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<< "rotIn" << rotationInvariant
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<< "sigma" << sigma;
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}
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virtual void read(const FileNode& fn)
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{
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CV_Assert( (String)fn["name"] == name_ );
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nRadialBins = (int)fn["nRads"];
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nAngularBins = (int)fn["nAngs"];
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iterations = (int)fn["iters"];
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bendingEnergyWeight = (float)fn["beWei"];
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shapeContextWeight = (float)fn["scWei"];
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imageAppearanceWeight = (float)fn["iaWei"];
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costFlag = (int)fn["costF"];
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sigma = (float)fn["sigma"];
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}
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protected:
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int nAngularBins;
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int nRadialBins;
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float innerRadius;
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float outerRadius;
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bool rotationInvariant;
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int costFlag;
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int iterations;
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Ptr<ShapeTransformer> transformer;
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Ptr<HistogramCostExtractor> comparer;
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Mat image1;
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Mat image2;
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float bendingEnergyWeight;
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float imageAppearanceWeight;
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float shapeContextWeight;
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float sigma;
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String name_;
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};
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float ShapeContextDistanceExtractorImpl::computeDistance(InputArray contour1, InputArray contour2)
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{
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// Checking //
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Mat sset1=contour1.getMat(), sset2=contour2.getMat(), set1, set2;
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if (set1.type() != CV_32F)
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sset1.convertTo(set1, CV_32F);
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else
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sset1.copyTo(set1);
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if (set2.type() != CV_32F)
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sset2.convertTo(set2, CV_32F);
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else
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sset2.copyTo(set2);
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CV_Assert((set1.channels()==2) && (set1.cols>0));
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CV_Assert((set2.channels()==2) && (set2.cols>0));
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if (imageAppearanceWeight!=0)
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{
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CV_Assert((!image1.empty()) && (!image2.empty()));
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}
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// Initializing Extractor, Descriptor structures and Matcher //
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SCD set1SCE(nAngularBins, nRadialBins, innerRadius, outerRadius, rotationInvariant);
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Mat set1SCD;
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SCD set2SCE(nAngularBins, nRadialBins, innerRadius, outerRadius, rotationInvariant);
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Mat set2SCD;
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SCDMatcher matcher;
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std::vector<DMatch> matches;
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// Distance components (The output is a linear combination of these 3) //
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float sDistance=0, bEnergy=0, iAppearance=0;
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float beta;
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// Initializing some variables //
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std::vector<int> inliers1, inliers2;
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Ptr<ThinPlateSplineShapeTransformer> transDown = transformer.dynamicCast<ThinPlateSplineShapeTransformer>();
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Mat warpedImage;
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int ii, jj, pt;
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for (ii=0; ii<iterations; ii++)
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{
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// Extract SCD descriptor in the set1 //
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set1SCE.extractSCD(set1, set1SCD, inliers1);
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// Extract SCD descriptor of the set2 (TARGET) //
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set2SCE.extractSCD(set2, set2SCD, inliers2, set1SCE.getMeanDistance());
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// regularization parameter with annealing rate annRate //
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beta=set1SCE.getMeanDistance();
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beta *= beta;
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// match //
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matcher.matchDescriptors(set1SCD, set2SCD, matches, comparer, inliers1, inliers2);
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// apply TPS transform //
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if ( !transDown.empty() )
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transDown->setRegularizationParameter(beta);
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transformer->estimateTransformation(set1, set2, matches);
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bEnergy += transformer->applyTransformation(set1, set1);
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// Image appearance //
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if (imageAppearanceWeight!=0)
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{
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// Have to accumulate the transformation along all the iterations
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if (ii==0)
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{
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if ( !transDown.empty() )
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{
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image2.copyTo(warpedImage);
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}
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else
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{
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image1.copyTo(warpedImage);
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}
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}
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transformer->warpImage(warpedImage, warpedImage);
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}
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}
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Mat gaussWindow, diffIm;
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if (imageAppearanceWeight!=0)
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{
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// compute appearance cost
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if ( !transDown.empty() )
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{
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resize(warpedImage, warpedImage, image1.size());
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Mat temp=(warpedImage-image1);
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multiply(temp, temp, diffIm);
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}
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else
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{
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resize(warpedImage, warpedImage, image2.size());
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Mat temp=(warpedImage-image2);
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multiply(temp, temp, diffIm);
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}
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gaussWindow = Mat::zeros(warpedImage.rows, warpedImage.cols, CV_32F);
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for (pt=0; pt<sset1.cols; pt++)
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{
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Point2f p = sset1.at<Point2f>(0,pt);
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for (ii=0; ii<diffIm.rows; ii++)
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{
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for (jj=0; jj<diffIm.cols; jj++)
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{
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float val = float(std::exp( -float( (p.x-jj)*(p.x-jj) + (p.y-ii)*(p.y-ii) )/(2*sigma*sigma) ) / (sigma*sigma*2*CV_PI));
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gaussWindow.at<float>(ii,jj) += val;
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}
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}
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}
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Mat appIm(diffIm.rows, diffIm.cols, CV_32F);
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for (ii=0; ii<diffIm.rows; ii++)
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{
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for (jj=0; jj<diffIm.cols; jj++)
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{
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float elema=float( diffIm.at<uchar>(ii,jj) )/255;
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float elemb=gaussWindow.at<float>(ii,jj);
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appIm.at<float>(ii,jj) = elema*elemb;
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}
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}
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iAppearance = float(cv::sum(appIm)[0]/sset1.cols);
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}
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sDistance = matcher.getMatchingCost();
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return (sDistance*shapeContextWeight+bEnergy*bendingEnergyWeight+iAppearance*imageAppearanceWeight);
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}
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Ptr <ShapeContextDistanceExtractor> createShapeContextDistanceExtractor(int nAngularBins, int nRadialBins, float innerRadius, float outerRadius, int iterations,
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const Ptr<HistogramCostExtractor> &comparer, const Ptr<ShapeTransformer> &transformer)
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{
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return Ptr <ShapeContextDistanceExtractor> ( new ShapeContextDistanceExtractorImpl(nAngularBins, nRadialBins, innerRadius,
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outerRadius, iterations, comparer, transformer) );
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}
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//! SCD
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void SCD::extractSCD(cv::Mat &contour, cv::Mat &descriptors, const std::vector<int> &queryInliers, const float _meanDistance)
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{
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cv::Mat contourMat = contour;
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cv::Mat disMatrix = cv::Mat::zeros(contourMat.cols, contourMat.cols, CV_32F);
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cv::Mat angleMatrix = cv::Mat::zeros(contourMat.cols, contourMat.cols, CV_32F);
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std::vector<double> logspaces, angspaces;
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logarithmicSpaces(logspaces);
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angularSpaces(angspaces);
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buildNormalizedDistanceMatrix(contourMat, disMatrix, queryInliers, _meanDistance);
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buildAngleMatrix(contourMat, angleMatrix);
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// Now, build the descriptor matrix (each row is a point) //
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descriptors = cv::Mat::zeros(contourMat.cols, descriptorSize(), CV_32F);
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for (int ptidx=0; ptidx<contourMat.cols; ptidx++)
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{
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for (int cmp=0; cmp<contourMat.cols; cmp++)
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{
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if (ptidx==cmp) continue;
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if ((int)queryInliers.size()>0)
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{
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if (queryInliers[ptidx]==0 || queryInliers[cmp]==0) continue; //avoid outliers
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}
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int angidx=-1, radidx=-1;
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for (int i=0; i<nRadialBins; i++)
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{
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if (disMatrix.at<float>(ptidx, cmp)<logspaces[i])
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{
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radidx=i;
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break;
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}
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}
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for (int i=0; i<nAngularBins; i++)
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{
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if (angleMatrix.at<float>(ptidx, cmp)<angspaces[i])
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{
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angidx=i;
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break;
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}
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}
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if (angidx!=-1 && radidx!=-1)
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{
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int idx = angidx+radidx*nAngularBins;
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descriptors.at<float>(ptidx, idx)++;
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}
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}
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}
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}
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void SCD::logarithmicSpaces(std::vector<double> &vecSpaces) const
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{
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double logmin=log10(innerRadius);
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double logmax=log10(outerRadius);
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double delta=(logmax-logmin)/(nRadialBins-1);
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double accdelta=0;
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for (int i=0; i<nRadialBins; i++)
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{
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double val = std::pow(10,logmin+accdelta);
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vecSpaces.push_back(val);
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accdelta += delta;
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}
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}
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void SCD::angularSpaces(std::vector<double> &vecSpaces) const
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{
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double delta=2*CV_PI/nAngularBins;
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double val=0;
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for (int i=0; i<nAngularBins; i++)
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{
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val += delta;
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vecSpaces.push_back(val);
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}
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}
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void SCD::buildNormalizedDistanceMatrix(cv::Mat &contour, cv::Mat &disMatrix, const std::vector<int> &queryInliers, const float _meanDistance)
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{
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cv::Mat contourMat = contour;
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cv::Mat mask(disMatrix.rows, disMatrix.cols, CV_8U);
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for (int i=0; i<contourMat.cols; i++)
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{
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for (int j=0; j<contourMat.cols; j++)
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{
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disMatrix.at<float>(i,j) = (float)norm( cv::Mat(contourMat.at<cv::Point2f>(0,i)-contourMat.at<cv::Point2f>(0,j)), cv::NORM_L2 );
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if (_meanDistance<0)
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{
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if (queryInliers.size()>0)
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{
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mask.at<char>(i,j)=char(queryInliers[j] && queryInliers[i]);
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}
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else
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{
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mask.at<char>(i,j)=1;
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}
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}
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}
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}
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if (_meanDistance<0)
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{
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meanDistance=(float)mean(disMatrix, mask)[0];
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}
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else
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{
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meanDistance=_meanDistance;
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}
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disMatrix/=meanDistance+FLT_EPSILON;
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}
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void SCD::buildAngleMatrix(cv::Mat &contour, cv::Mat &angleMatrix) const
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{
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cv::Mat contourMat = contour;
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// if descriptor is rotationInvariant compute massCenter //
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cv::Point2f massCenter(0,0);
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if (rotationInvariant)
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{
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for (int i=0; i<contourMat.cols; i++)
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{
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massCenter+=contourMat.at<cv::Point2f>(0,i);
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}
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massCenter.x=massCenter.x/(float)contourMat.cols;
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massCenter.y=massCenter.y/(float)contourMat.cols;
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}
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for (int i=0; i<contourMat.cols; i++)
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{
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for (int j=0; j<contourMat.cols; j++)
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{
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if (i==j)
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{
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angleMatrix.at<float>(i,j)=0.0;
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}
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else
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{
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cv::Point2f dif = contourMat.at<cv::Point2f>(0,i) - contourMat.at<cv::Point2f>(0,j);
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angleMatrix.at<float>(i,j) = std::atan2(dif.y, dif.x);
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if (rotationInvariant)
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{
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cv::Point2f refPt = contourMat.at<cv::Point2f>(0,i) - massCenter;
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float refAngle = atan2(refPt.y, refPt.x);
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angleMatrix.at<float>(i,j) -= refAngle;
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}
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angleMatrix.at<float>(i,j) = float(fmod(double(angleMatrix.at<float>(i,j)+(double)FLT_EPSILON),2*CV_PI)+CV_PI);
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}
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}
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}
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}
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//! SCDMatcher
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void SCDMatcher::matchDescriptors(cv::Mat &descriptors1, cv::Mat &descriptors2, std::vector<cv::DMatch> &matches,
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cv::Ptr<cv::HistogramCostExtractor> &comparer, std::vector<int> &inliers1, std::vector<int> &inliers2)
|
|
{
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|
matches.clear();
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|
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// Build the cost Matrix between descriptors //
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|
cv::Mat costMat;
|
|
buildCostMatrix(descriptors1, descriptors2, costMat, comparer);
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|
|
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// Solve the matching problem using the hungarian method //
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|
hungarian(costMat, matches, inliers1, inliers2, descriptors1.rows, descriptors2.rows);
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}
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|
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|
void SCDMatcher::buildCostMatrix(const cv::Mat &descriptors1, const cv::Mat &descriptors2,
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cv::Mat &costMatrix, cv::Ptr<cv::HistogramCostExtractor> &comparer) const
|
|
{
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comparer->buildCostMatrix(descriptors1, descriptors2, costMatrix);
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}
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|
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void SCDMatcher::hungarian(cv::Mat &costMatrix, std::vector<cv::DMatch> &outMatches, std::vector<int> &inliers1,
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std::vector<int> &inliers2, int sizeScd1, int sizeScd2)
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|
{
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|
std::vector<int> free(costMatrix.rows, 0), collist(costMatrix.rows, 0);
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std::vector<int> matches(costMatrix.rows, 0), colsol(costMatrix.rows), rowsol(costMatrix.rows);
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std::vector<float> d(costMatrix.rows), pred(costMatrix.rows), v(costMatrix.rows);
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|
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const float LOWV = 1e-10f;
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bool unassignedfound;
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int i=0, imin=0, numfree=0, prvnumfree=0, f=0, i0=0, k=0, freerow=0;
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int j=0, j1=0, j2=0, endofpath=0, last=0, low=0, up=0;
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float min=0, h=0, umin=0, usubmin=0, v2=0;
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|
|
|
// COLUMN REDUCTION //
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|
for (j = costMatrix.rows-1; j >= 0; j--)
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|
{
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|
// find minimum cost over rows.
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|
min = costMatrix.at<float>(0,j);
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|
imin = 0;
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|
for (i = 1; i < costMatrix.rows; i++)
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if (costMatrix.at<float>(i,j) < min)
|
|
{
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|
min = costMatrix.at<float>(i,j);
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|
imin = i;
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|
}
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|
v[j] = min;
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|
|
|
if (++matches[imin] == 1)
|
|
{
|
|
rowsol[imin] = j;
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|
colsol[j] = imin;
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|
}
|
|
else
|
|
{
|
|
colsol[j]=-1;
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|
}
|
|
}
|
|
|
|
// REDUCTION TRANSFER //
|
|
for (i=0; i<costMatrix.rows; i++)
|
|
{
|
|
if (matches[i] == 0)
|
|
{
|
|
free[numfree++] = i;
|
|
}
|
|
else
|
|
{
|
|
if (matches[i] == 1)
|
|
{
|
|
j1=rowsol[i];
|
|
min=std::numeric_limits<float>::max();
|
|
for (j=0; j<costMatrix.rows; j++)
|
|
{
|
|
if (j!=j1)
|
|
{
|
|
if (costMatrix.at<float>(i,j)-v[j] < min)
|
|
{
|
|
min=costMatrix.at<float>(i,j)-v[j];
|
|
}
|
|
}
|
|
}
|
|
v[j1] = v[j1]-min;
|
|
}
|
|
}
|
|
}
|
|
// AUGMENTING ROW REDUCTION //
|
|
int loopcnt = 0;
|
|
do
|
|
{
|
|
loopcnt++;
|
|
k=0;
|
|
prvnumfree=numfree;
|
|
numfree=0;
|
|
while (k < prvnumfree)
|
|
{
|
|
i=free[k];
|
|
k++;
|
|
umin = costMatrix.at<float>(i,0)-v[0];
|
|
j1=0;
|
|
usubmin = std::numeric_limits<float>::max();
|
|
for (j=1; j<costMatrix.rows; j++)
|
|
{
|
|
h = costMatrix.at<float>(i,j)-v[j];
|
|
if (h < usubmin)
|
|
{
|
|
if (h >= umin)
|
|
{
|
|
usubmin = h;
|
|
j2 = j;
|
|
}
|
|
else
|
|
{
|
|
usubmin = umin;
|
|
umin = h;
|
|
j2 = j1;
|
|
j1 = j;
|
|
}
|
|
}
|
|
}
|
|
i0 = colsol[j1];
|
|
|
|
if (fabs(umin-usubmin) > LOWV) //if( umin < usubmin )
|
|
{
|
|
v[j1] = v[j1] - (usubmin - umin);
|
|
}
|
|
else // minimum and subminimum equal.
|
|
{
|
|
if (i0 >= 0) // minimum column j1 is assigned.
|
|
{
|
|
j1 = j2;
|
|
i0 = colsol[j2];
|
|
}
|
|
}
|
|
// (re-)assign i to j1, possibly de-assigning an i0.
|
|
rowsol[i]=j1;
|
|
colsol[j1]=i;
|
|
|
|
if (i0 >= 0)
|
|
{
|
|
//if( umin < usubmin )
|
|
if (fabs(umin-usubmin) > LOWV)
|
|
{
|
|
free[--k] = i0;
|
|
}
|
|
else
|
|
{
|
|
free[numfree++] = i0;
|
|
}
|
|
}
|
|
}
|
|
}while (loopcnt<2); // repeat once.
|
|
|
|
// AUGMENT SOLUTION for each free row //
|
|
for (f = 0; f<numfree; f++)
|
|
{
|
|
freerow = free[f]; // start row of augmenting path.
|
|
// Dijkstra shortest path algorithm.
|
|
// runs until unassigned column added to shortest path tree.
|
|
for (j = 0; j < costMatrix.rows; j++)
|
|
{
|
|
d[j] = costMatrix.at<float>(freerow,j) - v[j];
|
|
pred[j] = float(freerow);
|
|
collist[j] = j; // init column list.
|
|
}
|
|
|
|
low=0; // columns in 0..low-1 are ready, now none.
|
|
up=0; // columns in low..up-1 are to be scanned for current minimum, now none.
|
|
unassignedfound = false;
|
|
do
|
|
{
|
|
if (up == low)
|
|
{
|
|
last=low-1;
|
|
min = d[collist[up++]];
|
|
for (k = up; k < costMatrix.rows; k++)
|
|
{
|
|
j = collist[k];
|
|
h = d[j];
|
|
if (h <= min)
|
|
{
|
|
if (h < min) // new minimum.
|
|
{
|
|
up = low; // restart list at index low.
|
|
min = h;
|
|
}
|
|
collist[k] = collist[up];
|
|
collist[up++] = j;
|
|
}
|
|
}
|
|
for (k=low; k<up; k++)
|
|
{
|
|
if (colsol[collist[k]] < 0)
|
|
{
|
|
endofpath = collist[k];
|
|
unassignedfound = true;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (!unassignedfound)
|
|
{
|
|
// update 'distances' between freerow and all unscanned columns, via next scanned column.
|
|
j1 = collist[low];
|
|
low++;
|
|
i = colsol[j1];
|
|
h = costMatrix.at<float>(i,j1)-v[j1]-min;
|
|
|
|
for (k = up; k < costMatrix.rows; k++)
|
|
{
|
|
j = collist[k];
|
|
v2 = costMatrix.at<float>(i,j) - v[j] - h;
|
|
if (v2 < d[j])
|
|
{
|
|
pred[j] = float(i);
|
|
if (v2 == min)
|
|
{
|
|
if (colsol[j] < 0)
|
|
{
|
|
// if unassigned, shortest augmenting path is complete.
|
|
endofpath = j;
|
|
unassignedfound = true;
|
|
break;
|
|
}
|
|
else
|
|
{
|
|
collist[k] = collist[up];
|
|
collist[up++] = j;
|
|
}
|
|
}
|
|
d[j] = v2;
|
|
}
|
|
}
|
|
}
|
|
}while (!unassignedfound);
|
|
|
|
// update column prices.
|
|
for (k = 0; k <= last; k++)
|
|
{
|
|
j1 = collist[k];
|
|
v[j1] = v[j1] + d[j1] - min;
|
|
}
|
|
|
|
// reset row and column assignments along the alternating path.
|
|
do
|
|
{
|
|
i = int(pred[endofpath]);
|
|
colsol[endofpath] = i;
|
|
j1 = endofpath;
|
|
endofpath = rowsol[i];
|
|
rowsol[i] = j1;
|
|
}while (i != freerow);
|
|
}
|
|
|
|
// calculate symmetric shape context cost
|
|
cv::Mat trueCostMatrix(costMatrix, cv::Rect(0,0,sizeScd1, sizeScd2));
|
|
float leftcost = 0;
|
|
for (int nrow=0; nrow<trueCostMatrix.rows; nrow++)
|
|
{
|
|
double minval;
|
|
minMaxIdx(trueCostMatrix.row(nrow), &minval);
|
|
leftcost+=float(minval);
|
|
}
|
|
leftcost /= trueCostMatrix.rows;
|
|
|
|
float rightcost = 0;
|
|
for (int ncol=0; ncol<trueCostMatrix.cols; ncol++)
|
|
{
|
|
double minval;
|
|
minMaxIdx(trueCostMatrix.col(ncol), &minval);
|
|
rightcost+=float(minval);
|
|
}
|
|
rightcost /= trueCostMatrix.cols;
|
|
|
|
minMatchCost = std::max(leftcost,rightcost);
|
|
|
|
// Save in a DMatch vector
|
|
for (i=0;i<costMatrix.cols;i++)
|
|
{
|
|
cv::DMatch singleMatch(colsol[i],i,costMatrix.at<float>(colsol[i],i));//queryIdx,trainIdx,distance
|
|
outMatches.push_back(singleMatch);
|
|
}
|
|
|
|
// Update inliers
|
|
inliers1.reserve(sizeScd1);
|
|
for (size_t kc = 0; kc<inliers1.size(); kc++)
|
|
{
|
|
if (rowsol[kc]<sizeScd1) // if a real match
|
|
inliers1[kc]=1;
|
|
else
|
|
inliers1[kc]=0;
|
|
}
|
|
inliers2.reserve(sizeScd2);
|
|
for (size_t kc = 0; kc<inliers2.size(); kc++)
|
|
{
|
|
if (colsol[kc]<sizeScd2) // if a real match
|
|
inliers2[kc]=1;
|
|
else
|
|
inliers2[kc]=0;
|
|
}
|
|
}
|
|
|
|
}
|