981 lines
34 KiB
C++
981 lines
34 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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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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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#include "opencv2/imgproc/imgproc.hpp"
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#include "opencv2/highgui/highgui.hpp"
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#include "opencv2/features2d/features2d.hpp"
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#include "opencv2/legacy/legacy.hpp"
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#include <limits>
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#include <cstdio>
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#include <iostream>
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#include <fstream>
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using namespace std;
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using namespace cv;
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string data_path;
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/****************************************************************************************\
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* Functions to evaluate affine covariant detectors and descriptors. *
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\****************************************************************************************/
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static inline Point2f applyHomography( const Mat_<double>& H, const Point2f& pt )
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{
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double z = H(2,0)*pt.x + H(2,1)*pt.y + H(2,2);
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if( z )
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{
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double w = 1./z;
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return Point2f( (float)((H(0,0)*pt.x + H(0,1)*pt.y + H(0,2))*w),
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(float)((H(1,0)*pt.x + H(1,1)*pt.y + H(1,2))*w) );
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}
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return Point2f( numeric_limits<float>::max(), numeric_limits<float>::max() );
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}
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static inline void linearizeHomographyAt( const Mat_<double>& H, const Point2f& pt, Mat_<double>& A )
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{
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A.create(2,2);
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double p1 = H(0,0)*pt.x + H(0,1)*pt.y + H(0,2),
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p2 = H(1,0)*pt.x + H(1,1)*pt.y + H(1,2),
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p3 = H(2,0)*pt.x + H(2,1)*pt.y + H(2,2),
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p3_2 = p3*p3;
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if( p3 )
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{
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A(0,0) = H(0,0)/p3 - p1*H(2,0)/p3_2; // fxdx
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A(0,1) = H(0,1)/p3 - p1*H(2,1)/p3_2; // fxdy
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A(1,0) = H(1,0)/p3 - p2*H(2,0)/p3_2; // fydx
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A(1,1) = H(1,1)/p3 - p2*H(2,1)/p3_2; // fydx
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}
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else
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A.setTo(Scalar::all(numeric_limits<double>::max()));
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}
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static void calcKeyPointProjections( const vector<KeyPoint>& src, const Mat_<double>& H, vector<KeyPoint>& dst )
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{
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if( !src.empty() )
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{
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assert( !H.empty() && H.cols == 3 && H.rows == 3);
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dst.resize(src.size());
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vector<KeyPoint>::const_iterator srcIt = src.begin();
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vector<KeyPoint>::iterator dstIt = dst.begin();
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for( ; srcIt != src.end(); ++srcIt, ++dstIt )
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{
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Point2f dstPt = applyHomography(H, srcIt->pt);
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float srcSize2 = srcIt->size * srcIt->size;
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Mat_<double> M(2, 2);
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M(0,0) = M(1,1) = 1./srcSize2;
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M(1,0) = M(0,1) = 0;
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Mat_<double> invM; invert(M, invM);
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Mat_<double> Aff; linearizeHomographyAt(H, srcIt->pt, Aff);
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Mat_<double> dstM; invert(Aff*invM*Aff.t(), dstM);
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Mat_<double> eval; eigen( dstM, eval );
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assert( eval(0,0) && eval(1,0) );
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float dstSize = (float)pow(1./(eval(0,0)*eval(1,0)), 0.25);
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// TODO: check angle projection
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float srcAngleRad = (float)(srcIt->angle*CV_PI/180);
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Point2f vec1(cos(srcAngleRad), sin(srcAngleRad)), vec2;
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vec2.x = (float)(Aff(0,0)*vec1.x + Aff(0,1)*vec1.y);
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vec2.y = (float)(Aff(1,0)*vec1.x + Aff(0,1)*vec1.y);
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float dstAngleGrad = fastAtan2(vec2.y, vec2.x);
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*dstIt = KeyPoint( dstPt, dstSize, dstAngleGrad, srcIt->response, srcIt->octave, srcIt->class_id );
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}
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}
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}
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static void filterKeyPointsByImageSize( vector<KeyPoint>& keypoints, const Size& imgSize )
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{
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if( !keypoints.empty() )
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{
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vector<KeyPoint> filtered;
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filtered.reserve(keypoints.size());
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Rect r(0, 0, imgSize.width, imgSize.height);
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vector<KeyPoint>::const_iterator it = keypoints.begin();
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for( int i = 0; it != keypoints.end(); ++it, i++ )
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if( r.contains(it->pt) )
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filtered.push_back(*it);
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keypoints.assign(filtered.begin(), filtered.end());
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}
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}
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/****************************************************************************************\
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* Detectors evaluation *
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\****************************************************************************************/
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const int DATASETS_COUNT = 8;
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const int TEST_CASE_COUNT = 5;
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const string IMAGE_DATASETS_DIR = "detectors_descriptors_evaluation/images_datasets/";
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const string DETECTORS_DIR = "detectors_descriptors_evaluation/detectors/";
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const string DESCRIPTORS_DIR = "detectors_descriptors_evaluation/descriptors/";
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const string KEYPOINTS_DIR = "detectors_descriptors_evaluation/keypoints_datasets/";
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const string PARAMS_POSTFIX = "_params.xml";
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const string RES_POSTFIX = "_res.xml";
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const string REPEAT = "repeatability";
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const string CORRESP_COUNT = "correspondence_count";
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string DATASET_NAMES[DATASETS_COUNT] = { "bark", "bikes", "boat", "graf", "leuven", "trees", "ubc", "wall"};
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string DEFAULT_PARAMS = "default";
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string IS_ACTIVE_PARAMS = "isActiveParams";
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string IS_SAVE_KEYPOINTS = "isSaveKeypoints";
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class BaseQualityEvaluator
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{
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public:
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BaseQualityEvaluator( const char* _algName, const char* _testName ) : algName(_algName), testName(_testName)
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{
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//TODO: change this
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isWriteGraphicsData = true;
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}
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void run();
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virtual ~BaseQualityEvaluator(){}
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protected:
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virtual string getRunParamsFilename() const = 0;
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virtual string getResultsFilename() const = 0;
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virtual string getPlotPath() const = 0;
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virtual void calcQualityClear( int datasetIdx ) = 0;
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virtual bool isCalcQualityEmpty( int datasetIdx ) const = 0;
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void readAllDatasetsRunParams();
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virtual void readDatasetRunParams( FileNode& fn, int datasetIdx ) = 0;
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void writeAllDatasetsRunParams() const;
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virtual void writeDatasetRunParams( FileStorage& fs, int datasetIdx ) const = 0;
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void setDefaultAllDatasetsRunParams();
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virtual void setDefaultDatasetRunParams( int datasetIdx ) = 0;
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virtual void readDefaultRunParams( FileNode& /*fn*/ ) {}
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virtual void writeDefaultRunParams( FileStorage& /*fs*/ ) const {}
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bool readDataset( const string& datasetName, vector<Mat>& Hs, vector<Mat>& imgs );
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virtual void readAlgorithm() {}
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virtual void processRunParamsFile() {}
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virtual void runDatasetTest( const vector<Mat>& /*imgs*/, const vector<Mat>& /*Hs*/, int /*di*/, int& /*progress*/ ) {}
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virtual void processResults( int datasetIdx );
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virtual void processResults();
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virtual void writePlotData( int /*datasetIdx*/ ) const {}
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string algName, testName;
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bool isWriteParams, isWriteGraphicsData;
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};
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void BaseQualityEvaluator::readAllDatasetsRunParams()
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{
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string filename = getRunParamsFilename();
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FileStorage fs( filename, FileStorage::READ );
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if( !fs.isOpened() )
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{
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isWriteParams = true;
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setDefaultAllDatasetsRunParams();
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printf("All runParams are default.\n");
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}
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else
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{
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isWriteParams = false;
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FileNode topfn = fs.getFirstTopLevelNode();
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FileNode pfn = topfn[DEFAULT_PARAMS];
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readDefaultRunParams(pfn);
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for( int i = 0; i < DATASETS_COUNT; i++ )
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{
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FileNode fn = topfn[DATASET_NAMES[i]];
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if( fn.empty() )
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{
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printf( "%d-runParams is default.\n", i);
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setDefaultDatasetRunParams(i);
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}
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else
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readDatasetRunParams(fn, i);
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}
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}
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}
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void BaseQualityEvaluator::writeAllDatasetsRunParams() const
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{
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string filename = getRunParamsFilename();
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FileStorage fs( filename, FileStorage::WRITE );
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if( fs.isOpened() )
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{
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fs << "run_params" << "{"; // top file node
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fs << DEFAULT_PARAMS << "{";
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writeDefaultRunParams(fs);
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fs << "}";
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for( int i = 0; i < DATASETS_COUNT; i++ )
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{
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fs << DATASET_NAMES[i] << "{";
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writeDatasetRunParams(fs, i);
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fs << "}";
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}
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fs << "}";
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}
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else
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printf( "File %s for writing run params can not be opened.\n", filename.c_str() );
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}
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void BaseQualityEvaluator::setDefaultAllDatasetsRunParams()
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{
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for( int i = 0; i < DATASETS_COUNT; i++ )
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setDefaultDatasetRunParams(i);
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}
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bool BaseQualityEvaluator::readDataset( const string& datasetName, vector<Mat>& Hs, vector<Mat>& imgs )
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{
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Hs.resize( TEST_CASE_COUNT );
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imgs.resize( TEST_CASE_COUNT+1 );
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string dirname = data_path + IMAGE_DATASETS_DIR + datasetName + "/";
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for( int i = 0; i < (int)Hs.size(); i++ )
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{
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stringstream filename; filename << "H1to" << i+2 << "p.xml";
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FileStorage fs( dirname + filename.str(), FileStorage::READ );
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if( !fs.isOpened() )
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{
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cout << "filename " << dirname + filename.str() << endl;
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FileStorage fs2( dirname + filename.str(), FileStorage::READ );
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return false;
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}
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fs.getFirstTopLevelNode() >> Hs[i];
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}
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for( int i = 0; i < (int)imgs.size(); i++ )
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{
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stringstream filename; filename << "img" << i+1 << ".png";
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imgs[i] = imread( dirname + filename.str(), 0 );
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if( imgs[i].empty() )
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{
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cout << "filename " << filename.str() << endl;
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return false;
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}
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}
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return true;
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}
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void BaseQualityEvaluator::processResults( int datasetIdx )
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{
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if( isWriteGraphicsData )
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writePlotData( datasetIdx );
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}
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void BaseQualityEvaluator::processResults()
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{
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if( isWriteParams )
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writeAllDatasetsRunParams();
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}
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void BaseQualityEvaluator::run()
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{
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readAlgorithm ();
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processRunParamsFile ();
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int notReadDatasets = 0;
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int progress = 0;
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FileStorage runParamsFS( getRunParamsFilename(), FileStorage::READ );
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isWriteParams = (! runParamsFS.isOpened());
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FileNode topfn = runParamsFS.getFirstTopLevelNode();
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FileNode defaultParams = topfn[DEFAULT_PARAMS];
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readDefaultRunParams (defaultParams);
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cout << testName << endl;
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for(int di = 0; di < DATASETS_COUNT; di++ )
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{
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cout << "Dataset " << di << " [" << DATASET_NAMES[di] << "] " << flush;
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vector<Mat> imgs, Hs;
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if( !readDataset( DATASET_NAMES[di], Hs, imgs ) )
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{
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calcQualityClear (di);
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printf( "Images or homography matrices of dataset named %s can not be read\n",
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DATASET_NAMES[di].c_str());
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notReadDatasets++;
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continue;
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}
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FileNode fn = topfn[DATASET_NAMES[di]];
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readDatasetRunParams(fn, di);
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runDatasetTest (imgs, Hs, di, progress);
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processResults( di );
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cout << endl;
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}
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if( notReadDatasets == DATASETS_COUNT )
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{
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printf( "All datasets were not be read\n");
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exit(-1);
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}
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else
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processResults();
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runParamsFS.release();
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}
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class DetectorQualityEvaluator : public BaseQualityEvaluator
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{
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public:
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DetectorQualityEvaluator( const char* _detectorName, const char* _testName ) : BaseQualityEvaluator( _detectorName, _testName )
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{
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calcQuality.resize(DATASETS_COUNT);
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isSaveKeypoints.resize(DATASETS_COUNT);
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isActiveParams.resize(DATASETS_COUNT);
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isSaveKeypointsDefault = false;
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isActiveParamsDefault = false;
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}
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protected:
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virtual string getRunParamsFilename() const;
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virtual string getResultsFilename() const;
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virtual string getPlotPath() const;
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virtual void calcQualityClear( int datasetIdx );
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virtual bool isCalcQualityEmpty( int datasetIdx ) const;
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virtual void readDatasetRunParams( FileNode& fn, int datasetIdx );
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virtual void writeDatasetRunParams( FileStorage& fs, int datasetIdx ) const;
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virtual void setDefaultDatasetRunParams( int datasetIdx );
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virtual void readDefaultRunParams( FileNode &fn );
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virtual void writeDefaultRunParams( FileStorage &fs ) const;
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virtual void writePlotData( int di ) const;
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void openToWriteKeypointsFile( FileStorage& fs, int datasetIdx );
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virtual void readAlgorithm();
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virtual void processRunParamsFile() {}
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virtual void runDatasetTest( const vector<Mat> &imgs, const vector<Mat> &Hs, int di, int &progress );
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Ptr<FeatureDetector> specificDetector;
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Ptr<FeatureDetector> defaultDetector;
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struct Quality
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{
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float repeatability;
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int correspondenceCount;
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};
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vector<vector<Quality> > calcQuality;
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vector<bool> isSaveKeypoints;
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vector<bool> isActiveParams;
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bool isSaveKeypointsDefault;
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bool isActiveParamsDefault;
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};
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string DetectorQualityEvaluator::getRunParamsFilename() const
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{
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return data_path + DETECTORS_DIR + algName + PARAMS_POSTFIX;
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}
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string DetectorQualityEvaluator::getResultsFilename() const
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{
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return data_path + DETECTORS_DIR + algName + RES_POSTFIX;
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}
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string DetectorQualityEvaluator::getPlotPath() const
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{
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return data_path + DETECTORS_DIR + "plots/";
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}
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void DetectorQualityEvaluator::calcQualityClear( int datasetIdx )
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{
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calcQuality[datasetIdx].clear();
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}
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bool DetectorQualityEvaluator::isCalcQualityEmpty( int datasetIdx ) const
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{
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return calcQuality[datasetIdx].empty();
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}
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void DetectorQualityEvaluator::readDefaultRunParams (FileNode &fn)
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{
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if (! fn.empty() )
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{
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isSaveKeypointsDefault = (int)fn[IS_SAVE_KEYPOINTS] != 0;
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defaultDetector->read (fn);
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}
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}
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void DetectorQualityEvaluator::writeDefaultRunParams (FileStorage &fs) const
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{
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fs << IS_SAVE_KEYPOINTS << isSaveKeypointsDefault;
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defaultDetector->write (fs);
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}
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void DetectorQualityEvaluator::readDatasetRunParams( FileNode& fn, int datasetIdx )
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{
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isActiveParams[datasetIdx] = (int)fn[IS_ACTIVE_PARAMS] != 0;
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if (isActiveParams[datasetIdx])
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{
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isSaveKeypoints[datasetIdx] = (int)fn[IS_SAVE_KEYPOINTS] != 0;
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specificDetector->read (fn);
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}
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else
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{
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setDefaultDatasetRunParams(datasetIdx);
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}
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}
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void DetectorQualityEvaluator::writeDatasetRunParams( FileStorage& fs, int datasetIdx ) const
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{
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fs << IS_ACTIVE_PARAMS << isActiveParams[datasetIdx];
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fs << IS_SAVE_KEYPOINTS << isSaveKeypoints[datasetIdx];
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defaultDetector->write (fs);
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}
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void DetectorQualityEvaluator::setDefaultDatasetRunParams( int datasetIdx )
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{
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isSaveKeypoints[datasetIdx] = isSaveKeypointsDefault;
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isActiveParams[datasetIdx] = isActiveParamsDefault;
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}
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void DetectorQualityEvaluator::writePlotData(int di ) const
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{
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int imgXVals[] = { 2, 3, 4, 5, 6 }; // if scale, blur or light changes
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int viewpointXVals[] = { 20, 30, 40, 50, 60 }; // if viewpoint changes
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int jpegXVals[] = { 60, 80, 90, 95, 98 }; // if jpeg compression
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int* xVals = 0;
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if( !DATASET_NAMES[di].compare("ubc") )
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{
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xVals = jpegXVals;
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}
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else if( !DATASET_NAMES[di].compare("graf") || !DATASET_NAMES[di].compare("wall") )
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{
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xVals = viewpointXVals;
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}
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else
|
|
xVals = imgXVals;
|
|
|
|
stringstream rFilename, cFilename;
|
|
rFilename << getPlotPath() << algName << "_" << DATASET_NAMES[di] << "_repeatability.csv";
|
|
cFilename << getPlotPath() << algName << "_" << DATASET_NAMES[di] << "_correspondenceCount.csv";
|
|
ofstream rfile(rFilename.str().c_str()), cfile(cFilename.str().c_str());
|
|
for( int ci = 0; ci < TEST_CASE_COUNT; ci++ )
|
|
{
|
|
rfile << xVals[ci] << ", " << calcQuality[di][ci].repeatability << endl;
|
|
cfile << xVals[ci] << ", " << calcQuality[di][ci].correspondenceCount << endl;
|
|
}
|
|
}
|
|
|
|
void DetectorQualityEvaluator::openToWriteKeypointsFile( FileStorage& fs, int datasetIdx )
|
|
{
|
|
string filename = data_path + KEYPOINTS_DIR + algName + "_"+ DATASET_NAMES[datasetIdx] + ".xml.gz" ;
|
|
|
|
fs.open(filename, FileStorage::WRITE);
|
|
if( !fs.isOpened() )
|
|
printf( "keypoints can not be written in file %s because this file can not be opened\n", filename.c_str() );
|
|
}
|
|
|
|
inline void writeKeypoints( FileStorage& fs, const vector<KeyPoint>& keypoints, int imgIdx )
|
|
{
|
|
if( fs.isOpened() )
|
|
{
|
|
stringstream imgName; imgName << "img" << imgIdx;
|
|
write( fs, imgName.str(), keypoints );
|
|
}
|
|
}
|
|
|
|
inline void readKeypoints( FileStorage& fs, vector<KeyPoint>& keypoints, int imgIdx )
|
|
{
|
|
assert( fs.isOpened() );
|
|
stringstream imgName; imgName << "img" << imgIdx;
|
|
read( fs[imgName.str()], keypoints);
|
|
}
|
|
|
|
void DetectorQualityEvaluator::readAlgorithm ()
|
|
{
|
|
defaultDetector = FeatureDetector::create( algName );
|
|
specificDetector = FeatureDetector::create( algName );
|
|
if( defaultDetector.empty() )
|
|
{
|
|
printf( "Algorithm can not be read\n" );
|
|
exit(-1);
|
|
}
|
|
}
|
|
|
|
static int update_progress( const string& /*name*/, int progress, int test_case_idx, int count, double dt )
|
|
{
|
|
int width = 60 /*- (int)name.length()*/;
|
|
if( count > 0 )
|
|
{
|
|
int t = cvRound( ((double)test_case_idx * width)/count );
|
|
if( t > progress )
|
|
{
|
|
cout << "." << flush;
|
|
progress = t;
|
|
}
|
|
}
|
|
else if( cvRound(dt) > progress )
|
|
{
|
|
cout << "." << flush;
|
|
progress = cvRound(dt);
|
|
}
|
|
|
|
return progress;
|
|
}
|
|
|
|
void DetectorQualityEvaluator::runDatasetTest (const vector<Mat> &imgs, const vector<Mat> &Hs, int di, int &progress)
|
|
{
|
|
Ptr<FeatureDetector> detector = isActiveParams[di] ? specificDetector : defaultDetector;
|
|
FileStorage keypontsFS;
|
|
if( isSaveKeypoints[di] )
|
|
openToWriteKeypointsFile( keypontsFS, di );
|
|
|
|
calcQuality[di].resize(TEST_CASE_COUNT);
|
|
|
|
vector<KeyPoint> keypoints1;
|
|
detector->detect( imgs[0], keypoints1 );
|
|
writeKeypoints( keypontsFS, keypoints1, 0);
|
|
int progressCount = DATASETS_COUNT*TEST_CASE_COUNT;
|
|
|
|
for( int ci = 0; ci < TEST_CASE_COUNT; ci++ )
|
|
{
|
|
progress = update_progress( testName, progress, di*TEST_CASE_COUNT + ci + 1, progressCount, 0 );
|
|
vector<KeyPoint> keypoints2;
|
|
float rep;
|
|
evaluateFeatureDetector( imgs[0], imgs[ci+1], Hs[ci], &keypoints1, &keypoints2,
|
|
rep, calcQuality[di][ci].correspondenceCount,
|
|
detector );
|
|
calcQuality[di][ci].repeatability = rep == -1 ? rep : 100.f*rep;
|
|
writeKeypoints( keypontsFS, keypoints2, ci+1);
|
|
}
|
|
}
|
|
|
|
// static void testLog( bool isBadAccuracy )
|
|
// {
|
|
// if( isBadAccuracy )
|
|
// printf(" bad accuracy\n");
|
|
// else
|
|
// printf("\n");
|
|
// }
|
|
|
|
/****************************************************************************************\
|
|
* Descriptors evaluation *
|
|
\****************************************************************************************/
|
|
|
|
const string RECALL = "recall";
|
|
const string PRECISION = "precision";
|
|
|
|
const string KEYPOINTS_FILENAME = "keypointsFilename";
|
|
const string PROJECT_KEYPOINTS_FROM_1IMAGE = "projectKeypointsFrom1Image";
|
|
const string MATCH_FILTER = "matchFilter";
|
|
const string RUN_PARAMS_IS_IDENTICAL = "runParamsIsIdentical";
|
|
|
|
const string ONE_WAY_TRAIN_DIR = "detectors_descriptors_evaluation/one_way_train_images/";
|
|
const string ONE_WAY_IMAGES_LIST = "one_way_train_images.txt";
|
|
|
|
class DescriptorQualityEvaluator : public BaseQualityEvaluator
|
|
{
|
|
public:
|
|
enum{ NO_MATCH_FILTER = 0 };
|
|
DescriptorQualityEvaluator( const char* _descriptorName, const char* _testName, const char* _matcherName = 0 ) :
|
|
BaseQualityEvaluator( _descriptorName, _testName )
|
|
{
|
|
calcQuality.resize(DATASETS_COUNT);
|
|
calcDatasetQuality.resize(DATASETS_COUNT);
|
|
commRunParams.resize(DATASETS_COUNT);
|
|
|
|
commRunParamsDefault.projectKeypointsFrom1Image = true;
|
|
commRunParamsDefault.matchFilter = NO_MATCH_FILTER;
|
|
commRunParamsDefault.isActiveParams = false;
|
|
|
|
if( _matcherName )
|
|
matcherName = _matcherName;
|
|
}
|
|
|
|
protected:
|
|
virtual string getRunParamsFilename() const;
|
|
virtual string getResultsFilename() const;
|
|
virtual string getPlotPath() const;
|
|
|
|
virtual void calcQualityClear( int datasetIdx );
|
|
virtual bool isCalcQualityEmpty( int datasetIdx ) const;
|
|
|
|
virtual void readDatasetRunParams( FileNode& fn, int datasetIdx ); //
|
|
virtual void writeDatasetRunParams( FileStorage& fs, int datasetIdx ) const;
|
|
virtual void setDefaultDatasetRunParams( int datasetIdx );
|
|
virtual void readDefaultRunParams( FileNode &fn );
|
|
virtual void writeDefaultRunParams( FileStorage &fs ) const;
|
|
|
|
virtual void readAlgorithm();
|
|
virtual void processRunParamsFile() {}
|
|
virtual void runDatasetTest( const vector<Mat> &imgs, const vector<Mat> &Hs, int di, int &progress );
|
|
|
|
virtual void writePlotData( int di ) const;
|
|
void calculatePlotData( vector<vector<DMatch> > &allMatches, vector<vector<uchar> > &allCorrectMatchesMask, int di );
|
|
|
|
struct Quality
|
|
{
|
|
float recall;
|
|
float precision;
|
|
};
|
|
vector<vector<Quality> > calcQuality;
|
|
vector<vector<Quality> > calcDatasetQuality;
|
|
|
|
struct CommonRunParams
|
|
{
|
|
string keypontsFilename;
|
|
bool projectKeypointsFrom1Image;
|
|
int matchFilter; // not used now
|
|
bool isActiveParams;
|
|
};
|
|
vector<CommonRunParams> commRunParams;
|
|
|
|
Ptr<GenericDescriptorMatch> specificDescMatcher;
|
|
Ptr<GenericDescriptorMatch> defaultDescMatcher;
|
|
|
|
CommonRunParams commRunParamsDefault;
|
|
string matcherName;
|
|
};
|
|
|
|
string DescriptorQualityEvaluator::getRunParamsFilename() const
|
|
{
|
|
return data_path + DESCRIPTORS_DIR + algName + PARAMS_POSTFIX;
|
|
}
|
|
|
|
string DescriptorQualityEvaluator::getResultsFilename() const
|
|
{
|
|
return data_path + DESCRIPTORS_DIR + algName + RES_POSTFIX;
|
|
}
|
|
|
|
string DescriptorQualityEvaluator::getPlotPath() const
|
|
{
|
|
return data_path + DESCRIPTORS_DIR + "plots/";
|
|
}
|
|
|
|
void DescriptorQualityEvaluator::calcQualityClear( int datasetIdx )
|
|
{
|
|
calcQuality[datasetIdx].clear();
|
|
}
|
|
|
|
bool DescriptorQualityEvaluator::isCalcQualityEmpty( int datasetIdx ) const
|
|
{
|
|
return calcQuality[datasetIdx].empty();
|
|
}
|
|
|
|
void DescriptorQualityEvaluator::readDefaultRunParams (FileNode &fn)
|
|
{
|
|
if (! fn.empty() )
|
|
{
|
|
commRunParamsDefault.projectKeypointsFrom1Image = (int)fn[PROJECT_KEYPOINTS_FROM_1IMAGE] != 0;
|
|
commRunParamsDefault.matchFilter = (int)fn[MATCH_FILTER];
|
|
defaultDescMatcher->read (fn);
|
|
}
|
|
}
|
|
|
|
void DescriptorQualityEvaluator::writeDefaultRunParams (FileStorage &fs) const
|
|
{
|
|
fs << PROJECT_KEYPOINTS_FROM_1IMAGE << commRunParamsDefault.projectKeypointsFrom1Image;
|
|
fs << MATCH_FILTER << commRunParamsDefault.matchFilter;
|
|
defaultDescMatcher->write (fs);
|
|
}
|
|
|
|
void DescriptorQualityEvaluator::readDatasetRunParams( FileNode& fn, int datasetIdx )
|
|
{
|
|
commRunParams[datasetIdx].isActiveParams = (int)fn[IS_ACTIVE_PARAMS] != 0;
|
|
if (commRunParams[datasetIdx].isActiveParams)
|
|
{
|
|
commRunParams[datasetIdx].keypontsFilename = (string)fn[KEYPOINTS_FILENAME];
|
|
commRunParams[datasetIdx].projectKeypointsFrom1Image = (int)fn[PROJECT_KEYPOINTS_FROM_1IMAGE] != 0;
|
|
commRunParams[datasetIdx].matchFilter = (int)fn[MATCH_FILTER];
|
|
specificDescMatcher->read (fn);
|
|
}
|
|
else
|
|
{
|
|
setDefaultDatasetRunParams(datasetIdx);
|
|
}
|
|
}
|
|
|
|
void DescriptorQualityEvaluator::writeDatasetRunParams( FileStorage& fs, int datasetIdx ) const
|
|
{
|
|
fs << IS_ACTIVE_PARAMS << commRunParams[datasetIdx].isActiveParams;
|
|
fs << KEYPOINTS_FILENAME << commRunParams[datasetIdx].keypontsFilename;
|
|
fs << PROJECT_KEYPOINTS_FROM_1IMAGE << commRunParams[datasetIdx].projectKeypointsFrom1Image;
|
|
fs << MATCH_FILTER << commRunParams[datasetIdx].matchFilter;
|
|
|
|
defaultDescMatcher->write (fs);
|
|
}
|
|
|
|
void DescriptorQualityEvaluator::setDefaultDatasetRunParams( int datasetIdx )
|
|
{
|
|
commRunParams[datasetIdx] = commRunParamsDefault;
|
|
commRunParams[datasetIdx].keypontsFilename = "SURF_" + DATASET_NAMES[datasetIdx] + ".xml.gz";
|
|
}
|
|
|
|
void DescriptorQualityEvaluator::writePlotData( int di ) const
|
|
{
|
|
stringstream filename;
|
|
filename << getPlotPath() << algName << "_" << DATASET_NAMES[di] << ".csv";
|
|
FILE *file = fopen (filename.str().c_str(), "w");
|
|
size_t size = calcDatasetQuality[di].size();
|
|
for (size_t i=0;i<size;i++)
|
|
{
|
|
fprintf( file, "%f, %f\n", 1 - calcDatasetQuality[di][i].precision, calcDatasetQuality[di][i].recall);
|
|
}
|
|
fclose( file );
|
|
}
|
|
|
|
void DescriptorQualityEvaluator::readAlgorithm( )
|
|
{
|
|
defaultDescMatcher = GenericDescriptorMatcher::create( algName );
|
|
specificDescMatcher = GenericDescriptorMatcher::create( algName );
|
|
|
|
if( defaultDescMatcher.empty() )
|
|
{
|
|
Ptr<DescriptorExtractor> extractor = DescriptorExtractor::create( algName );
|
|
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create( matcherName );
|
|
defaultDescMatcher = new VectorDescriptorMatch( extractor, matcher );
|
|
specificDescMatcher = new VectorDescriptorMatch( extractor, matcher );
|
|
|
|
if( extractor.empty() || matcher.empty() )
|
|
{
|
|
printf("Algorithm can not be read\n");
|
|
exit(-1);
|
|
}
|
|
}
|
|
}
|
|
|
|
void DescriptorQualityEvaluator::calculatePlotData( vector<vector<DMatch> > &allMatches, vector<vector<uchar> > &allCorrectMatchesMask, int di )
|
|
{
|
|
vector<Point2f> recallPrecisionCurve;
|
|
computeRecallPrecisionCurve( allMatches, allCorrectMatchesMask, recallPrecisionCurve );
|
|
|
|
calcDatasetQuality[di].clear();
|
|
const float resultPrecision = 0.5;
|
|
bool isResultCalculated = false;
|
|
const double eps = 1e-2;
|
|
|
|
Quality initQuality;
|
|
initQuality.recall = 0;
|
|
initQuality.precision = 0;
|
|
calcDatasetQuality[di].push_back( initQuality );
|
|
|
|
for( size_t i=0;i<recallPrecisionCurve.size();i++ )
|
|
{
|
|
Quality quality;
|
|
quality.recall = recallPrecisionCurve[i].y;
|
|
quality.precision = 1 - recallPrecisionCurve[i].x;
|
|
Quality back = calcDatasetQuality[di].back();
|
|
|
|
if( fabs( quality.recall - back.recall ) < eps && fabs( quality.precision - back.precision ) < eps )
|
|
continue;
|
|
|
|
calcDatasetQuality[di].push_back( quality );
|
|
|
|
if( !isResultCalculated && quality.precision < resultPrecision )
|
|
{
|
|
for(int ci=0;ci<TEST_CASE_COUNT;ci++)
|
|
{
|
|
calcQuality[di][ci].recall = quality.recall;
|
|
calcQuality[di][ci].precision = quality.precision;
|
|
}
|
|
isResultCalculated = true;
|
|
}
|
|
}
|
|
}
|
|
|
|
void DescriptorQualityEvaluator::runDatasetTest (const vector<Mat> &imgs, const vector<Mat> &Hs, int di, int &progress)
|
|
{
|
|
FileStorage keypontsFS( data_path + KEYPOINTS_DIR + commRunParams[di].keypontsFilename, FileStorage::READ );
|
|
if( !keypontsFS.isOpened())
|
|
{
|
|
calcQuality[di].clear();
|
|
printf( "keypoints from file %s can not be read\n", commRunParams[di].keypontsFilename.c_str() );
|
|
return;
|
|
}
|
|
|
|
Ptr<GenericDescriptorMatcher> descMatch = commRunParams[di].isActiveParams ? specificDescMatcher : defaultDescMatcher;
|
|
calcQuality[di].resize(TEST_CASE_COUNT);
|
|
|
|
vector<KeyPoint> keypoints1;
|
|
readKeypoints( keypontsFS, keypoints1, 0);
|
|
|
|
int progressCount = DATASETS_COUNT*TEST_CASE_COUNT;
|
|
|
|
vector<vector<DMatch> > allMatches1to2;
|
|
vector<vector<uchar> > allCorrectMatchesMask;
|
|
for( int ci = 0; ci < TEST_CASE_COUNT; ci++ )
|
|
{
|
|
progress = update_progress( testName, progress, di*TEST_CASE_COUNT + ci + 1, progressCount, 0 );
|
|
|
|
vector<KeyPoint> keypoints2;
|
|
if( commRunParams[di].projectKeypointsFrom1Image )
|
|
{
|
|
// TODO need to test function calcKeyPointProjections
|
|
calcKeyPointProjections( keypoints1, Hs[ci], keypoints2 );
|
|
filterKeyPointsByImageSize( keypoints2, imgs[ci+1].size() );
|
|
}
|
|
else
|
|
readKeypoints( keypontsFS, keypoints2, ci+1 );
|
|
// TODO if( commRunParams[di].matchFilter )
|
|
|
|
vector<vector<DMatch> > matches1to2;
|
|
vector<vector<uchar> > correctMatchesMask;
|
|
vector<Point2f> recallPrecisionCurve; // not used because we need recallPrecisionCurve for
|
|
// all images in dataset
|
|
evaluateGenericDescriptorMatcher( imgs[0], imgs[ci+1], Hs[ci], keypoints1, keypoints2,
|
|
&matches1to2, &correctMatchesMask, recallPrecisionCurve,
|
|
descMatch );
|
|
allMatches1to2.insert( allMatches1to2.end(), matches1to2.begin(), matches1to2.end() );
|
|
allCorrectMatchesMask.insert( allCorrectMatchesMask.end(), correctMatchesMask.begin(), correctMatchesMask.end() );
|
|
}
|
|
|
|
calculatePlotData( allMatches1to2, allCorrectMatchesMask, di );
|
|
}
|
|
|
|
//--------------------------------- Calonder descriptor test --------------------------------------------
|
|
class CalonderDescriptorQualityEvaluator : public DescriptorQualityEvaluator
|
|
{
|
|
public:
|
|
CalonderDescriptorQualityEvaluator() :
|
|
DescriptorQualityEvaluator( "Calonder", "quality-descriptor-calonder") {}
|
|
virtual void readAlgorithm( )
|
|
{
|
|
string classifierFile = data_path + "/features2d/calonder_classifier.rtc";
|
|
defaultDescMatcher = new VectorDescriptorMatch( new CalonderDescriptorExtractor<float>( classifierFile ),
|
|
new BFMatcher(NORM_L2) );
|
|
specificDescMatcher = defaultDescMatcher;
|
|
}
|
|
};
|
|
|
|
//--------------------------------- One Way descriptor test --------------------------------------------
|
|
class OneWayDescriptorQualityTest : public DescriptorQualityEvaluator
|
|
{
|
|
public:
|
|
OneWayDescriptorQualityTest() :
|
|
DescriptorQualityEvaluator("ONEWAY", "quality-descriptor-one-way")
|
|
{
|
|
}
|
|
protected:
|
|
virtual void processRunParamsFile ();
|
|
virtual void writeDatasetRunParams( FileStorage& fs, int datasetIdx ) const;
|
|
};
|
|
|
|
void OneWayDescriptorQualityTest::processRunParamsFile ()
|
|
{
|
|
string filename = getRunParamsFilename();
|
|
FileStorage fs = FileStorage (filename, FileStorage::READ);
|
|
FileNode fn = fs.getFirstTopLevelNode();
|
|
fn = fn[DEFAULT_PARAMS];
|
|
|
|
string pcaFilename = data_path + (string)fn["pcaFilename"];
|
|
string trainPath = data_path + (string)fn["trainPath"];
|
|
string trainImagesList = (string)fn["trainImagesList"];
|
|
int patch_width = fn["patchWidth"];
|
|
int patch_height = fn["patchHeight"];
|
|
Size patchSize = cvSize (patch_width, patch_height);
|
|
int poseCount = fn["poseCount"];
|
|
|
|
if (trainImagesList.length () == 0 )
|
|
return;
|
|
|
|
fs.release ();
|
|
|
|
readAllDatasetsRunParams();
|
|
|
|
OneWayDescriptorBase *base = new OneWayDescriptorBase(patchSize, poseCount, pcaFilename,
|
|
trainPath, trainImagesList);
|
|
|
|
OneWayDescriptorMatch *match = new OneWayDescriptorMatch ();
|
|
match->initialize( OneWayDescriptorMatch::Params (), base );
|
|
defaultDescMatcher = match;
|
|
writeAllDatasetsRunParams();
|
|
}
|
|
|
|
void OneWayDescriptorQualityTest::writeDatasetRunParams( FileStorage& fs, int datasetIdx ) const
|
|
{
|
|
fs << IS_ACTIVE_PARAMS << commRunParams[datasetIdx].isActiveParams;
|
|
fs << KEYPOINTS_FILENAME << commRunParams[datasetIdx].keypontsFilename;
|
|
fs << PROJECT_KEYPOINTS_FROM_1IMAGE << commRunParams[datasetIdx].projectKeypointsFrom1Image;
|
|
fs << MATCH_FILTER << commRunParams[datasetIdx].matchFilter;
|
|
}
|
|
|
|
int main( int argc, char** argv )
|
|
{
|
|
if( argc != 2 )
|
|
{
|
|
cout << "Format: " << argv[0] << " testdata path (path to testdata/cv)" << endl;
|
|
return -1;
|
|
}
|
|
|
|
data_path = argv[1];
|
|
#ifdef WIN32
|
|
if( *data_path.rbegin() != '\\' )
|
|
data_path = data_path + "\\";
|
|
#else
|
|
if( *data_path.rbegin() != '/' )
|
|
data_path = data_path + "/";
|
|
#endif
|
|
|
|
Ptr<BaseQualityEvaluator> evals[] =
|
|
{
|
|
new DetectorQualityEvaluator( "FAST", "quality-detector-fast" ),
|
|
new DetectorQualityEvaluator( "GFTT", "quality-detector-gftt" ),
|
|
new DetectorQualityEvaluator( "HARRIS", "quality-detector-harris" ),
|
|
new DetectorQualityEvaluator( "MSER", "quality-detector-mser" ),
|
|
new DetectorQualityEvaluator( "STAR", "quality-detector-star" ),
|
|
new DetectorQualityEvaluator( "SIFT", "quality-detector-sift" ),
|
|
new DetectorQualityEvaluator( "SURF", "quality-detector-surf" ),
|
|
|
|
new DescriptorQualityEvaluator( "SIFT", "quality-descriptor-sift", "BruteForce" ),
|
|
new DescriptorQualityEvaluator( "SURF", "quality-descriptor-surf", "BruteForce" ),
|
|
new DescriptorQualityEvaluator( "FERN", "quality-descriptor-fern"),
|
|
new CalonderDescriptorQualityEvaluator()
|
|
};
|
|
|
|
for( size_t i = 0; i < sizeof(evals)/sizeof(evals[0]); i++ )
|
|
{
|
|
evals[i]->run();
|
|
cout << endl;
|
|
}
|
|
}
|