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280 lines
10 KiB
280 lines
10 KiB
/*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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#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); |
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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_HaussTest::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_HaussTest::run(int /* */) |
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{ |
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mpegTest(); |
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displayMPEGResults(); |
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ts->set_failed_test_info(cvtest::TS::OK); |
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} |
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TEST(Hauss, regression) { CV_HaussTest test; test.safe_run(); }
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