302 lines
11 KiB
C++
302 lines
11 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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using namespace cv;
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using namespace std;
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template <typename T, typename compute>
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class ShapeBaseTest : public cvtest::BaseTest
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{
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public:
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typedef Point_<T> PointType;
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ShapeBaseTest(int _NSN, int _NP, float _CURRENT_MAX_ACCUR)
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: NSN(_NSN), NP(_NP), CURRENT_MAX_ACCUR(_CURRENT_MAX_ACCUR)
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{
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// generate file list
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vector<string> shapeNames;
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shapeNames.push_back("apple"); //ok
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shapeNames.push_back("children"); // ok
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shapeNames.push_back("device7"); // ok
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shapeNames.push_back("Heart"); // ok
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shapeNames.push_back("teddy"); // ok
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for (vector<string>::const_iterator i = shapeNames.begin(); i != shapeNames.end(); ++i)
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{
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for (int j = 0; j < NSN; ++j)
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{
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stringstream filename;
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filename << cvtest::TS::ptr()->get_data_path()
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<< "shape/mpeg_test/" << *i << "-" << j + 1 << ".png";
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filenames.push_back(filename.str());
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}
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}
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// distance matrix
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const int totalCount = (int)filenames.size();
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distanceMat = Mat::zeros(totalCount, totalCount, CV_32F);
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}
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protected:
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void run(int)
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{
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mpegTest();
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displayMPEGResults();
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}
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vector<PointType> convertContourType(const Mat& currentQuery) const
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{
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vector<vector<Point> > _contoursQuery;
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findContours(currentQuery, _contoursQuery, RETR_LIST, CHAIN_APPROX_NONE);
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vector <PointType> contoursQuery;
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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(PointType((T)_contoursQuery[border][p].x,
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(T)_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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for (int add=(int)contoursQuery.size()-1; add<NP; 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=NP;
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vector<PointType> 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 mpegTest()
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{
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// query contours (normal v flipped, h flipped) and testing contour
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vector<PointType> contoursQuery1, contoursQuery2, contoursQuery3, contoursTesting;
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// reading query and computing its properties
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for (vector<string>::const_iterator a = filenames.begin(); a != filenames.end(); ++a)
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{
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// read current image
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int aIndex = (int)(a - filenames.begin());
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Mat currentQuery = imread(*a, IMREAD_GRAYSCALE);
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Mat flippedHQuery, flippedVQuery;
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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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contoursQuery1=convertContourType(currentQuery);
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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 (vector<string>::const_iterator b = filenames.begin(); b != filenames.end(); ++b)
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{
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int bIndex = (int)(b - filenames.begin());
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float distance = 0;
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// skip self-comparisson
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if (a != b)
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{
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// read testing image
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Mat currentTest = imread(*b, IMREAD_GRAYSCALE);
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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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distance = cmp(contoursQuery1, contoursQuery2,
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contoursQuery3, contoursTesting);
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}
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distanceMat.at<float>(aIndex, bIndex) = distance;
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}
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}
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}
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void displayMPEGResults()
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{
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const int FIRST_MANY=2*NSN;
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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 << "Test result: " << 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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protected:
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int NSN;
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int NP;
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float CURRENT_MAX_ACCUR;
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vector<string> filenames;
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Mat distanceMat;
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compute cmp;
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};
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//------------------------------------------------------------------------
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// Test Shape_SCD.regression
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//------------------------------------------------------------------------
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class computeShapeDistance_Chi
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{
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Ptr <ShapeContextDistanceExtractor> mysc;
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public:
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computeShapeDistance_Chi()
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{
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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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mysc = createShapeContextDistanceExtractor(angularBins, radialBins, minRad, maxRad);
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mysc->setIterations(1);
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mysc->setCostExtractor(createChiHistogramCostExtractor(30,0.15f));
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mysc->setTransformAlgorithm( createThinPlateSplineShapeTransformer() );
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}
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float operator()(vector <Point2f>& query1, vector <Point2f>& query2,
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vector <Point2f>& query3, vector <Point2f>& testq)
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{
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return std::min(mysc->computeDistance(query1, testq),
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std::min(mysc->computeDistance(query2, testq),
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mysc->computeDistance(query3, testq)));
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}
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};
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TEST(Shape_SCD, regression)
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{
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const int NSN_val=5;//10;//20; //number of shapes per class
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const int NP_val=120; //number of points simplifying the contour
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const float CURRENT_MAX_ACCUR_val=95; //99% and 100% reached in several tests, 95 is fixed as minimum boundary
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ShapeBaseTest<float, computeShapeDistance_Chi> test(NSN_val, NP_val, CURRENT_MAX_ACCUR_val);
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test.safe_run();
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}
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//------------------------------------------------------------------------
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// Test ShapeEMD_SCD.regression
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//------------------------------------------------------------------------
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class computeShapeDistance_EMD
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{
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Ptr <ShapeContextDistanceExtractor> mysc;
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public:
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computeShapeDistance_EMD()
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{
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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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mysc = createShapeContextDistanceExtractor(angularBins, radialBins, minRad, maxRad);
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mysc->setIterations(1);
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mysc->setCostExtractor( createEMDL1HistogramCostExtractor() );
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mysc->setTransformAlgorithm( createThinPlateSplineShapeTransformer() );
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}
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float operator()(vector <Point2f>& query1, vector <Point2f>& query2,
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vector <Point2f>& query3, vector <Point2f>& testq)
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{
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return std::min(mysc->computeDistance(query1, testq),
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std::min(mysc->computeDistance(query2, testq),
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mysc->computeDistance(query3, testq)));
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}
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};
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TEST(ShapeEMD_SCD, regression)
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{
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const int NSN_val=5;//10;//20; //number of shapes per class
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const int NP_val=100; //number of points simplifying the contour
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const float CURRENT_MAX_ACCUR_val=95; //98% and 99% reached in several tests, 95 is fixed as minimum boundary
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ShapeBaseTest<float, computeShapeDistance_EMD> test(NSN_val, NP_val, CURRENT_MAX_ACCUR_val);
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test.safe_run();
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}
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//------------------------------------------------------------------------
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// Test Hauss.regression
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//------------------------------------------------------------------------
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class computeShapeDistance_Haussdorf
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{
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Ptr <HausdorffDistanceExtractor> haus;
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public:
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computeShapeDistance_Haussdorf()
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{
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haus = createHausdorffDistanceExtractor();
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}
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float operator()(vector<Point> &query1, vector<Point> &query2,
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vector<Point> &query3, vector<Point> &testq)
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{
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return std::min(haus->computeDistance(query1,testq),
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std::min(haus->computeDistance(query2,testq),
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haus->computeDistance(query3,testq)));
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}
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};
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TEST(Hauss, regression)
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{
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const int NSN_val=5;//10;//20; //number of shapes per class
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const int NP_val = 180; //number of points simplifying the contour
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const float CURRENT_MAX_ACCUR_val=85; //90% and 91% reached in several tests, 85 is fixed as minimum boundary
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ShapeBaseTest<int, computeShapeDistance_Haussdorf> test(NSN_val, NP_val, CURRENT_MAX_ACCUR_val);
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test.safe_run();
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}
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