mirror of https://github.com/opencv/opencv.git
- common operations moved to separate class - debug console messages removed - test results are stored in memory instead of filepull/3651/head
Maksim Shabunin
10 years ago
parent
6e565ab4a4
commit
10639c9526
5 changed files with 216 additions and 726 deletions
@ -1,263 +0,0 @@ |
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/*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 "test_precomp.hpp" |
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using namespace cv; |
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using namespace std; |
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const int angularBins=12; |
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const int radialBins=4; |
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const float minRad=0.2f; |
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const float maxRad=2; |
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const int NSN=5;//10;//20; //number of shapes per class
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const int NP=100; //number of points sympliying the contour
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const float CURRENT_MAX_ACCUR=95; //98% and 99% reached in several tests, 95 is fixed as minimum boundary
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class CV_ShapeEMDTest : public cvtest::BaseTest |
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{ |
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public: |
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CV_ShapeEMDTest(); |
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~CV_ShapeEMDTest(); |
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protected: |
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void run(int); |
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private: |
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void mpegTest(); |
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void listShapeNames(vector<string> &listHeaders); |
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vector<Point2f> convertContourType(const Mat &, int n=0 ); |
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float computeShapeDistance(vector <Point2f>& queryNormal, |
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vector <Point2f>& queryFlipped1, |
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vector <Point2f>& queryFlipped2, |
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vector<Point2f>& testq); |
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void displayMPEGResults(); |
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}; |
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CV_ShapeEMDTest::CV_ShapeEMDTest() |
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{ |
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} |
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CV_ShapeEMDTest::~CV_ShapeEMDTest() |
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{ |
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} |
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vector <Point2f> CV_ShapeEMDTest::convertContourType(const Mat& currentQuery, int n) |
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{ |
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vector<vector<Point> > _contoursQuery; |
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vector <Point2f> contoursQuery; |
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findContours(currentQuery, _contoursQuery, RETR_LIST, CHAIN_APPROX_NONE); |
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for (size_t border=0; border<_contoursQuery.size(); border++) |
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{ |
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for (size_t p=0; p<_contoursQuery[border].size(); p++) |
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{ |
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contoursQuery.push_back(Point2f((float)_contoursQuery[border][p].x, |
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(float)_contoursQuery[border][p].y)); |
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} |
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} |
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// In case actual number of points is less than n
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int dum=0; |
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for (int add=(int)contoursQuery.size()-1; add<n; add++) |
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{ |
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contoursQuery.push_back(contoursQuery[dum++]); //adding dummy values
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} |
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// Uniformly sampling
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random_shuffle(contoursQuery.begin(), contoursQuery.end()); |
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int nStart=n; |
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vector<Point2f> cont; |
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for (int i=0; i<nStart; i++) |
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{ |
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cont.push_back(contoursQuery[i]); |
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} |
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return cont; |
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} |
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void CV_ShapeEMDTest::listShapeNames( vector<string> &listHeaders) |
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{ |
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listHeaders.push_back("apple"); //ok
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listHeaders.push_back("children"); // ok
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listHeaders.push_back("device7"); // ok
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listHeaders.push_back("Heart"); // ok
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listHeaders.push_back("teddy"); // ok
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} |
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float CV_ShapeEMDTest::computeShapeDistance(vector <Point2f>& query1, vector <Point2f>& query2, |
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vector <Point2f>& query3, vector <Point2f>& testq) |
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{ |
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//waitKey(0);
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Ptr <ShapeContextDistanceExtractor> mysc = createShapeContextDistanceExtractor(angularBins, radialBins, minRad, maxRad); |
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//Ptr <HistogramCostExtractor> cost = createNormHistogramCostExtractor(cv::DIST_L1);
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//Ptr <HistogramCostExtractor> cost = createChiHistogramCostExtractor(30,0.15);
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//Ptr <HistogramCostExtractor> cost = createEMDHistogramCostExtractor();
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// Ptr <HistogramCostExtractor> cost = createEMDL1HistogramCostExtractor();
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mysc->setIterations(1); //(3)
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mysc->setCostExtractor( createEMDL1HistogramCostExtractor() ); |
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//mysc->setTransformAlgorithm(createAffineTransformer(true));
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mysc->setTransformAlgorithm( createThinPlateSplineShapeTransformer() ); |
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//mysc->setImageAppearanceWeight(1.6);
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//mysc->setImageAppearanceWeight(0.0);
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//mysc->setImages(im1,imtest);
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return ( std::min( mysc->computeDistance(query1, testq), |
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std::min(mysc->computeDistance(query2, testq), mysc->computeDistance(query3, testq) ))); |
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} |
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void CV_ShapeEMDTest::mpegTest() |
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{ |
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string baseTestFolder="shape/mpeg_test/"; |
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string path = cvtest::TS::ptr()->get_data_path() + baseTestFolder; |
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vector<string> namesHeaders; |
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listShapeNames(namesHeaders); |
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// distance matrix //
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Mat distanceMat=Mat::zeros(NSN*(int)namesHeaders.size(), NSN*(int)namesHeaders.size(), CV_32F); |
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// query contours (normal v flipped, h flipped) and testing contour //
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vector<Point2f> contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting; |
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// reading query and computing its properties //
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int counter=0; |
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const int loops=NSN*(int)namesHeaders.size()*NSN*(int)namesHeaders.size(); |
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for (size_t n=0; n<namesHeaders.size(); n++) |
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{ |
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for (int i=1; i<=NSN; i++) |
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{ |
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// read current image //
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stringstream thepathandname; |
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thepathandname<<path+namesHeaders[n]<<"-"<<i<<".png"; |
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Mat currentQuery, flippedHQuery, flippedVQuery; |
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currentQuery=imread(thepathandname.str(), IMREAD_GRAYSCALE); |
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flip(currentQuery, flippedHQuery, 0); |
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flip(currentQuery, flippedVQuery, 1); |
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// compute border of the query and its flipped versions //
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vector<Point2f> origContour; |
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contoursQuery1=convertContourType(currentQuery, NP); |
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origContour=contoursQuery1; |
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contoursQuery2=convertContourType(flippedHQuery, NP); |
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contoursQuery3=convertContourType(flippedVQuery, NP); |
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// compare with all the rest of the images: testing //
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for (size_t nt=0; nt<namesHeaders.size(); nt++) |
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{ |
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for (int it=1; it<=NSN; it++) |
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{ |
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// skip self-comparisson //
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counter++; |
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if (nt==n && it==i) |
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{ |
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distanceMat.at<float>(NSN*(int)n+i-1, |
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NSN*(int)nt+it-1)=0; |
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continue; |
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} |
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// read testing image //
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stringstream thetestpathandname; |
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thetestpathandname<<path+namesHeaders[nt]<<"-"<<it<<".png"; |
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Mat currentTest; |
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currentTest=imread(thetestpathandname.str().c_str(), 0); |
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// compute border of the testing //
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contoursTesting=convertContourType(currentTest, NP); |
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// compute shape distance //
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std::cout<<std::endl<<"Progress: "<<counter<<"/"<<loops<<": "<<100*double(counter)/loops<<"% *******"<<std::endl; |
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std::cout<<"Computing shape distance between "<<namesHeaders[n]<<i<< |
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" and "<<namesHeaders[nt]<<it<<": "; |
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distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)= |
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computeShapeDistance(contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting); |
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std::cout<<distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)<<std::endl; |
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} |
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} |
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} |
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} |
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// save distance matrix //
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FileStorage fs(cvtest::TS::ptr()->get_data_path() + baseTestFolder + "distanceMatrixMPEGTest.yml", FileStorage::WRITE); |
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fs << "distanceMat" << distanceMat; |
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} |
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const int FIRST_MANY=2*NSN; |
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void CV_ShapeEMDTest::displayMPEGResults() |
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{ |
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string baseTestFolder="shape/mpeg_test/"; |
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Mat distanceMat; |
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FileStorage fs(cvtest::TS::ptr()->get_data_path() + baseTestFolder + "distanceMatrixMPEGTest.yml", FileStorage::READ); |
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vector<string> namesHeaders; |
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listShapeNames(namesHeaders); |
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// Read generated MAT //
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fs["distanceMat"]>>distanceMat; |
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int corrects=0; |
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int divi=0; |
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for (int row=0; row<distanceMat.rows; row++) |
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{ |
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if (row%NSN==0) //another group
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{ |
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divi+=NSN; |
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} |
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for (int col=divi-NSN; col<divi; col++) |
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{ |
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int nsmall=0; |
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for (int i=0; i<distanceMat.cols; i++) |
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{ |
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if (distanceMat.at<float>(row,col)>distanceMat.at<float>(row,i)) |
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{ |
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nsmall++; |
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} |
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} |
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if (nsmall<=FIRST_MANY) |
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{ |
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corrects++; |
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} |
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} |
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} |
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float porc = 100*float(corrects)/(NSN*distanceMat.rows); |
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std::cout<<"%="<<porc<<std::endl; |
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if (porc >= CURRENT_MAX_ACCUR) |
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ts->set_failed_test_info(cvtest::TS::OK); |
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else |
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); |
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} |
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void CV_ShapeEMDTest::run( int /*start_from*/ ) |
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{ |
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mpegTest(); |
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displayMPEGResults(); |
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} |
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TEST(ShapeEMD_SCD, regression) { CV_ShapeEMDTest test; test.safe_run(); } |
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@ -1,280 +0,0 @@ |
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// 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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// 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:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// 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
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// 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 "test_precomp.hpp" |
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#include <stdlib.h> |
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using namespace cv; |
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using namespace std; |
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const int NSN=5;//10;//20; //number of shapes per class
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const float CURRENT_MAX_ACCUR=85; //90% and 91% reached in several tests, 85 is fixed as minimum boundary
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class CV_HaussTest : public cvtest::BaseTest |
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{ |
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public: |
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CV_HaussTest(); |
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~CV_HaussTest(); |
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protected: |
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void run(int); |
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private: |
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float computeShapeDistance(vector<Point> &query1, vector<Point> &query2, |
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vector<Point> &query3, vector<Point> &testq); |
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vector <Point> convertContourType(const Mat& currentQuery, int n=180); |
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vector<Point2f> normalizeContour(const vector <Point>& contour); |
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void listShapeNames( vector<string> &listHeaders); |
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void mpegTest(); |
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void displayMPEGResults(); |
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}; |
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CV_HaussTest::CV_HaussTest() |
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{ |
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} |
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CV_HaussTest::~CV_HaussTest() |
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{ |
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} |
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vector<Point2f> CV_HaussTest::normalizeContour(const vector<Point> &contour) |
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{ |
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vector<Point2f> output(contour.size()); |
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Mat disMat((int)contour.size(),(int)contour.size(),CV_32F); |
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Point2f meanpt(0,0); |
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float meanVal=1; |
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for (int ii=0, end1 = (int)contour.size(); ii<end1; ii++) |
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{ |
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for (int jj=0, end2 = (int)contour.size(); end2; jj++) |
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{ |
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if (ii==jj) disMat.at<float>(ii,jj)=0; |
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else |
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{ |
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disMat.at<float>(ii,jj)= |
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float(fabs(double(contour[ii].x*contour[jj].x)))+float(fabs(double(contour[ii].y*contour[jj].y))); |
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} |
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} |
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meanpt.x+=contour[ii].x; |
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meanpt.y+=contour[ii].y; |
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} |
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meanpt.x/=contour.size(); |
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meanpt.y/=contour.size(); |
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meanVal=float(cv::mean(disMat)[0]); |
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for (size_t ii=0; ii<contour.size(); ii++) |
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{ |
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output[ii].x = (contour[ii].x-meanpt.x)/meanVal; |
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output[ii].y = (contour[ii].y-meanpt.y)/meanVal; |
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} |
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return output; |
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} |
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void CV_HaussTest::listShapeNames( vector<string> &listHeaders) |
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{ |
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listHeaders.push_back("apple"); //ok
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listHeaders.push_back("children"); // ok
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listHeaders.push_back("device7"); // ok
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listHeaders.push_back("Heart"); // ok
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listHeaders.push_back("teddy"); // ok
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} |
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vector <Point> CV_HaussTest::convertContourType(const Mat& currentQuery, int n) |
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{ |
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vector<vector<Point> > _contoursQuery; |
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vector <Point> contoursQuery; |
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findContours(currentQuery, _contoursQuery, RETR_LIST, CHAIN_APPROX_NONE); |
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for (size_t border=0; border<_contoursQuery.size(); border++) |
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{ |
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for (size_t p=0; p<_contoursQuery[border].size(); p++) |
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{ |
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contoursQuery.push_back(_contoursQuery[border][p]); |
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} |
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} |
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// In case actual number of points is less than n
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for (int add=(int)contoursQuery.size()-1; add<n; add++) |
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{ |
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contoursQuery.push_back(contoursQuery[contoursQuery.size()-add+1]); //adding dummy values
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} |
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// Uniformly sampling
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random_shuffle(contoursQuery.begin(), contoursQuery.end()); |
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int nStart=n; |
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vector<Point> cont; |
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for (int i=0; i<nStart; i++) |
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{ |
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cont.push_back(contoursQuery[i]); |
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} |
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return cont; |
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} |
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float CV_HaussTest::computeShapeDistance(vector <Point>& query1, vector <Point>& query2, |
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vector <Point>& query3, vector <Point>& testq) |
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{ |
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Ptr <HausdorffDistanceExtractor> haus = createHausdorffDistanceExtractor(); |
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return std::min(haus->computeDistance(query1,testq), std::min(haus->computeDistance(query2,testq), |
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haus->computeDistance(query3,testq))); |
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} |
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void CV_HaussTest::mpegTest() |
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{ |
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string baseTestFolder="shape/mpeg_test/"; |
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string path = cvtest::TS::ptr()->get_data_path() + baseTestFolder; |
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vector<string> namesHeaders; |
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listShapeNames(namesHeaders); |
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// distance matrix //
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Mat distanceMat=Mat::zeros(NSN*(int)namesHeaders.size(), NSN*(int)namesHeaders.size(), CV_32F); |
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// query contours (normal v flipped, h flipped) and testing contour //
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vector<Point> contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting; |
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// reading query and computing its properties //
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int counter=0; |
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const int loops=NSN*(int)namesHeaders.size()*NSN*(int)namesHeaders.size(); |
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for (size_t n=0; n<namesHeaders.size(); n++) |
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{ |
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for (int i=1; i<=NSN; i++) |
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{ |
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// read current image //
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stringstream thepathandname; |
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thepathandname<<path+namesHeaders[n]<<"-"<<i<<".png"; |
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Mat currentQuery, flippedHQuery, flippedVQuery; |
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currentQuery=imread(thepathandname.str(), IMREAD_GRAYSCALE); |
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flip(currentQuery, flippedHQuery, 0); |
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flip(currentQuery, flippedVQuery, 1); |
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// compute border of the query and its flipped versions //
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vector<Point> origContour; |
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contoursQuery1=convertContourType(currentQuery); |
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origContour=contoursQuery1; |
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contoursQuery2=convertContourType(flippedHQuery); |
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contoursQuery3=convertContourType(flippedVQuery); |
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// compare with all the rest of the images: testing //
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for (size_t nt=0; nt<namesHeaders.size(); nt++) |
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{ |
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for (int it=1; it<=NSN; it++) |
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{ |
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/* skip self-comparisson */ |
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counter++; |
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if (nt==n && it==i) |
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{ |
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distanceMat.at<float>(NSN*(int)n+i-1, |
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NSN*(int)nt+it-1)=0; |
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continue; |
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} |
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// read testing image //
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stringstream thetestpathandname; |
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thetestpathandname<<path+namesHeaders[nt]<<"-"<<it<<".png"; |
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Mat currentTest; |
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currentTest=imread(thetestpathandname.str().c_str(), 0); |
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// compute border of the testing //
|
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contoursTesting=convertContourType(currentTest); |
||||
|
||||
// compute shape distance //
|
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std::cout<<std::endl<<"Progress: "<<counter<<"/"<<loops<<": "<<100*double(counter)/loops<<"% *******"<<std::endl; |
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std::cout<<"Computing shape distance between "<<namesHeaders[n]<<i<< |
||||
" and "<<namesHeaders[nt]<<it<<": "; |
||||
distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)= |
||||
computeShapeDistance(contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting); |
||||
std::cout<<distanceMat.at<float>(NSN*(int)n+i-1, NSN*(int)nt+it-1)<<std::endl; |
||||
} |
||||
} |
||||
} |
||||
} |
||||
// save distance matrix //
|
||||
FileStorage fs(cvtest::TS::ptr()->get_data_path() + baseTestFolder + "distanceMatrixMPEGTest.yml", FileStorage::WRITE); |
||||
fs << "distanceMat" << distanceMat; |
||||
} |
||||
|
||||
const int FIRST_MANY=2*NSN; |
||||
void CV_HaussTest::displayMPEGResults() |
||||
{ |
||||
string baseTestFolder="shape/mpeg_test/"; |
||||
Mat distanceMat; |
||||
FileStorage fs(cvtest::TS::ptr()->get_data_path() + baseTestFolder + "distanceMatrixMPEGTest.yml", FileStorage::READ); |
||||
vector<string> namesHeaders; |
||||
listShapeNames(namesHeaders); |
||||
|
||||
// Read generated MAT //
|
||||
fs["distanceMat"]>>distanceMat; |
||||
|
||||
int corrects=0; |
||||
int divi=0; |
||||
for (int row=0; row<distanceMat.rows; row++) |
||||
{ |
||||
if (row%NSN==0) //another group
|
||||
{ |
||||
divi+=NSN; |
||||
} |
||||
for (int col=divi-NSN; col<divi; col++) |
||||
{ |
||||
int nsmall=0; |
||||
for (int i=0; i<distanceMat.cols; i++) |
||||
{ |
||||
if (distanceMat.at<float>(row,col)>distanceMat.at<float>(row,i)) |
||||
{ |
||||
nsmall++; |
||||
} |
||||
} |
||||
if (nsmall<=FIRST_MANY) |
||||
{ |
||||
corrects++; |
||||
} |
||||
} |
||||
} |
||||
float porc = 100*float(corrects)/(NSN*distanceMat.rows); |
||||
std::cout<<"%="<<porc<<std::endl; |
||||
if (porc >= CURRENT_MAX_ACCUR) |
||||
ts->set_failed_test_info(cvtest::TS::OK); |
||||
else |
||||
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); |
||||
|
||||
} |
||||
|
||||
|
||||
void CV_HaussTest::run(int /* */) |
||||
{ |
||||
mpegTest(); |
||||
displayMPEGResults(); |
||||
ts->set_failed_test_info(cvtest::TS::OK); |
||||
} |
||||
|
||||
TEST(Hauss, regression) { CV_HaussTest test; test.safe_run(); } |
||||
@ -1 +0,0 @@ |
||||
#include "test_precomp.hpp" |
||||
Loading…
Reference in new issue