281 lines
10 KiB
C++
281 lines
10 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 "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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