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Open Source Computer Vision Library
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632 lines
24 KiB
632 lines
24 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 "opencv2/highgui/highgui.hpp" |
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using namespace std; |
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using namespace cv; |
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const string FEATURES2D_DIR = "features2d"; |
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const string IMAGE_FILENAME = "tsukuba.png"; |
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/****************************************************************************************\ |
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* Algorithmic tests for descriptor matchers * |
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\****************************************************************************************/ |
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class CV_DescriptorMatcherTest : public cvtest::BaseTest |
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{ |
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public: |
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CV_DescriptorMatcherTest( const string& _name, const Ptr<DescriptorMatcher>& _dmatcher, float _badPart ) : |
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badPart(_badPart), name(_name), dmatcher(_dmatcher) |
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{} |
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static void generateData( Mat& query, Mat& train ); |
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protected: |
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static const int dim = 500; |
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static const int queryDescCount = 300; // must be even number because we split train data in some cases in two |
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static const int countFactor = 4; // do not change it |
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const float badPart; |
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virtual void run( int ); |
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void emptyDataTest(); |
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void matchTest( const Mat& query, const Mat& train ); |
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void knnMatchTest( const Mat& query, const Mat& train ); |
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void radiusMatchTest( const Mat& query, const Mat& train ); |
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string name; |
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Ptr<DescriptorMatcher> dmatcher; |
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private: |
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CV_DescriptorMatcherTest& operator=(const CV_DescriptorMatcherTest&) { return *this; } |
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}; |
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void CV_DescriptorMatcherTest::emptyDataTest() |
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{ |
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assert( !dmatcher.empty() ); |
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Mat queryDescriptors, trainDescriptors, mask; |
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vector<Mat> trainDescriptorCollection, masks; |
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vector<DMatch> matches; |
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vector<vector<DMatch> > vmatches; |
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try |
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{ |
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dmatcher->match( queryDescriptors, trainDescriptors, matches, mask ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "match() on empty descriptors must not generate exception (1).\n" ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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try |
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{ |
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dmatcher->knnMatch( queryDescriptors, trainDescriptors, vmatches, 2, mask ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "knnMatch() on empty descriptors must not generate exception (1).\n" ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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try |
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{ |
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dmatcher->radiusMatch( queryDescriptors, trainDescriptors, vmatches, 10.f, mask ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "radiusMatch() on empty descriptors must not generate exception (1).\n" ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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try |
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{ |
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dmatcher->add( trainDescriptorCollection ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "add() on empty descriptors must not generate exception.\n" ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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try |
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{ |
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dmatcher->match( queryDescriptors, matches, masks ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "match() on empty descriptors must not generate exception (2).\n" ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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try |
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{ |
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dmatcher->knnMatch( queryDescriptors, vmatches, 2, masks ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "knnMatch() on empty descriptors must not generate exception (2).\n" ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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try |
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{ |
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dmatcher->radiusMatch( queryDescriptors, vmatches, 10.f, masks ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "radiusMatch() on empty descriptors must not generate exception (2).\n" ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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} |
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void CV_DescriptorMatcherTest::generateData( Mat& query, Mat& train ) |
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{ |
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RNG& rng = theRNG(); |
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// Generate query descriptors randomly. |
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// Descriptor vector elements are integer values. |
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Mat buf( queryDescCount, dim, CV_32SC1 ); |
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rng.fill( buf, RNG::UNIFORM, Scalar::all(0), Scalar(3) ); |
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buf.convertTo( query, CV_32FC1 ); |
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// Generate train decriptors as follows: |
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// copy each query descriptor to train set countFactor times |
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// and perturb some one element of the copied descriptors in |
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// in ascending order. General boundaries of the perturbation |
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// are (0.f, 1.f). |
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train.create( query.rows*countFactor, query.cols, CV_32FC1 ); |
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float step = 1.f / countFactor; |
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for( int qIdx = 0; qIdx < query.rows; qIdx++ ) |
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{ |
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Mat queryDescriptor = query.row(qIdx); |
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for( int c = 0; c < countFactor; c++ ) |
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{ |
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int tIdx = qIdx * countFactor + c; |
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Mat trainDescriptor = train.row(tIdx); |
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queryDescriptor.copyTo( trainDescriptor ); |
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int elem = rng(dim); |
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float diff = rng.uniform( step*c, step*(c+1) ); |
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trainDescriptor.at<float>(0, elem) += diff; |
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} |
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} |
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} |
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void CV_DescriptorMatcherTest::matchTest( const Mat& query, const Mat& train ) |
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{ |
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dmatcher->clear(); |
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// test const version of match() |
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{ |
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vector<DMatch> matches; |
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dmatcher->match( query, train, matches ); |
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if( (int)matches.size() != queryDescCount ) |
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{ |
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ts->printf(cvtest::TS::LOG, "Incorrect matches count while test match() function (1).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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else |
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{ |
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int badCount = 0; |
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for( size_t i = 0; i < matches.size(); i++ ) |
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{ |
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DMatch& match = matches[i]; |
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if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0) ) |
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badCount++; |
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} |
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if( (float)badCount > (float)queryDescCount*badPart ) |
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{ |
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ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test match() function (1).\n", |
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(float)badCount/(float)queryDescCount ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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} |
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} |
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// test const version of match() for the same query and test descriptors |
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{ |
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vector<DMatch> matches; |
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dmatcher->match( query, query, matches ); |
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if( (int)matches.size() != query.rows ) |
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{ |
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ts->printf(cvtest::TS::LOG, "Incorrect matches count while test match() function for the same query and test descriptors (1).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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else |
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{ |
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for( size_t i = 0; i < matches.size(); i++ ) |
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{ |
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DMatch& match = matches[i]; |
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//std::cout << match.distance << std::endl; |
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if( match.queryIdx != (int)i || match.trainIdx != (int)i || std::abs(match.distance) > FLT_EPSILON ) |
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{ |
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ts->printf( cvtest::TS::LOG, "Bad match (i=%d, queryIdx=%d, trainIdx=%d, distance=%f) while test match() function for the same query and test descriptors (1).\n", |
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i, match.queryIdx, match.trainIdx, match.distance ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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} |
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} |
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} |
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// test version of match() with add() |
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{ |
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vector<DMatch> matches; |
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// make add() twice to test such case |
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dmatcher->add( vector<Mat>(1,train.rowRange(0, train.rows/2)) ); |
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dmatcher->add( vector<Mat>(1,train.rowRange(train.rows/2, train.rows)) ); |
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// prepare masks (make first nearest match illegal) |
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vector<Mat> masks(2); |
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for(int mi = 0; mi < 2; mi++ ) |
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{ |
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masks[mi] = Mat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1)); |
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for( int di = 0; di < queryDescCount/2; di++ ) |
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masks[mi].col(di*countFactor).setTo(Scalar::all(0)); |
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} |
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dmatcher->match( query, matches, masks ); |
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if( (int)matches.size() != queryDescCount ) |
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{ |
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ts->printf(cvtest::TS::LOG, "Incorrect matches count while test match() function (2).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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else |
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{ |
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int badCount = 0; |
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for( size_t i = 0; i < matches.size(); i++ ) |
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{ |
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DMatch& match = matches[i]; |
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int shift = dmatcher->isMaskSupported() ? 1 : 0; |
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{ |
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if( i < queryDescCount/2 ) |
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{ |
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if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor + shift) || (match.imgIdx != 0) ) |
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badCount++; |
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} |
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else |
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{ |
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if( (match.queryIdx != (int)i) || (match.trainIdx != ((int)i-queryDescCount/2)*countFactor + shift) || (match.imgIdx != 1) ) |
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badCount++; |
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} |
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} |
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} |
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if( (float)badCount > (float)queryDescCount*badPart ) |
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{ |
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ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test match() function (2).\n", |
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(float)badCount/(float)queryDescCount ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
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} |
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} |
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} |
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} |
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void CV_DescriptorMatcherTest::knnMatchTest( const Mat& query, const Mat& train ) |
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{ |
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dmatcher->clear(); |
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// test const version of knnMatch() |
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{ |
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const int knn = 3; |
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vector<vector<DMatch> > matches; |
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dmatcher->knnMatch( query, train, matches, knn ); |
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if( (int)matches.size() != queryDescCount ) |
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{ |
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ts->printf(cvtest::TS::LOG, "Incorrect matches count while test knnMatch() function (1).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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else |
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{ |
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int badCount = 0; |
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for( size_t i = 0; i < matches.size(); i++ ) |
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{ |
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if( (int)matches[i].size() != knn ) |
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badCount++; |
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else |
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{ |
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int localBadCount = 0; |
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for( int k = 0; k < knn; k++ ) |
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{ |
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DMatch& match = matches[i][k]; |
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if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor+k) || (match.imgIdx != 0) ) |
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localBadCount++; |
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} |
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badCount += localBadCount > 0 ? 1 : 0; |
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} |
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} |
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if( (float)badCount > (float)queryDescCount*badPart ) |
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{ |
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ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test knnMatch() function (1).\n", |
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(float)badCount/(float)queryDescCount ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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} |
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} |
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// test version of knnMatch() with add() |
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{ |
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const int knn = 2; |
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vector<vector<DMatch> > matches; |
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// make add() twice to test such case |
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dmatcher->add( vector<Mat>(1,train.rowRange(0, train.rows/2)) ); |
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dmatcher->add( vector<Mat>(1,train.rowRange(train.rows/2, train.rows)) ); |
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// prepare masks (make first nearest match illegal) |
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vector<Mat> masks(2); |
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for(int mi = 0; mi < 2; mi++ ) |
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{ |
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masks[mi] = Mat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1)); |
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for( int di = 0; di < queryDescCount/2; di++ ) |
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masks[mi].col(di*countFactor).setTo(Scalar::all(0)); |
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} |
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dmatcher->knnMatch( query, matches, knn, masks ); |
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if( (int)matches.size() != queryDescCount ) |
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{ |
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ts->printf(cvtest::TS::LOG, "Incorrect matches count while test knnMatch() function (2).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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else |
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{ |
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int badCount = 0; |
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int shift = dmatcher->isMaskSupported() ? 1 : 0; |
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for( size_t i = 0; i < matches.size(); i++ ) |
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{ |
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if( (int)matches[i].size() != knn ) |
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badCount++; |
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else |
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{ |
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int localBadCount = 0; |
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for( int k = 0; k < knn; k++ ) |
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{ |
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DMatch& match = matches[i][k]; |
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{ |
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if( i < queryDescCount/2 ) |
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{ |
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if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor + k + shift) || |
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(match.imgIdx != 0) ) |
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localBadCount++; |
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} |
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else |
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{ |
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if( (match.queryIdx != (int)i) || (match.trainIdx != ((int)i-queryDescCount/2)*countFactor + k + shift) || |
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(match.imgIdx != 1) ) |
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localBadCount++; |
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} |
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} |
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} |
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badCount += localBadCount > 0 ? 1 : 0; |
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} |
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} |
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if( (float)badCount > (float)queryDescCount*badPart ) |
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{ |
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ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test knnMatch() function (2).\n", |
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(float)badCount/(float)queryDescCount ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
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} |
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} |
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} |
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} |
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void CV_DescriptorMatcherTest::radiusMatchTest( const Mat& query, const Mat& train ) |
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{ |
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dmatcher->clear(); |
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// test const version of match() |
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{ |
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const float radius = 1.f/countFactor; |
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vector<vector<DMatch> > matches; |
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dmatcher->radiusMatch( query, train, matches, radius ); |
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if( (int)matches.size() != queryDescCount ) |
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{ |
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ts->printf(cvtest::TS::LOG, "Incorrect matches count while test radiusMatch() function (1).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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else |
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{ |
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int badCount = 0; |
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for( size_t i = 0; i < matches.size(); i++ ) |
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{ |
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if( (int)matches[i].size() != 1 ) |
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badCount++; |
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else |
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{ |
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DMatch& match = matches[i][0]; |
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if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0) ) |
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badCount++; |
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} |
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} |
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if( (float)badCount > (float)queryDescCount*badPart ) |
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{ |
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ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test radiusMatch() function (1).\n", |
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(float)badCount/(float)queryDescCount ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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} |
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} |
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// test version of match() with add() |
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{ |
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int n = 3; |
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const float radius = 1.f/countFactor * n; |
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vector<vector<DMatch> > matches; |
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// make add() twice to test such case |
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dmatcher->add( vector<Mat>(1,train.rowRange(0, train.rows/2)) ); |
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dmatcher->add( vector<Mat>(1,train.rowRange(train.rows/2, train.rows)) ); |
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// prepare masks (make first nearest match illegal) |
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vector<Mat> masks(2); |
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for(int mi = 0; mi < 2; mi++ ) |
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{ |
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masks[mi] = Mat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1)); |
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for( int di = 0; di < queryDescCount/2; di++ ) |
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masks[mi].col(di*countFactor).setTo(Scalar::all(0)); |
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} |
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dmatcher->radiusMatch( query, matches, radius, masks ); |
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//int curRes = cvtest::TS::OK; |
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if( (int)matches.size() != queryDescCount ) |
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{ |
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ts->printf(cvtest::TS::LOG, "Incorrect matches count while test radiusMatch() function (1).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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int badCount = 0; |
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int shift = dmatcher->isMaskSupported() ? 1 : 0; |
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int needMatchCount = dmatcher->isMaskSupported() ? n-1 : n; |
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for( size_t i = 0; i < matches.size(); i++ ) |
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{ |
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if( (int)matches[i].size() != needMatchCount ) |
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badCount++; |
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else |
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{ |
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int localBadCount = 0; |
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for( int k = 0; k < needMatchCount; k++ ) |
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{ |
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DMatch& match = matches[i][k]; |
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{ |
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if( i < queryDescCount/2 ) |
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{ |
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if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor + k + shift) || |
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(match.imgIdx != 0) ) |
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localBadCount++; |
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} |
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else |
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{ |
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if( (match.queryIdx != (int)i) || (match.trainIdx != ((int)i-queryDescCount/2)*countFactor + k + shift) || |
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(match.imgIdx != 1) ) |
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localBadCount++; |
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} |
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} |
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} |
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badCount += localBadCount > 0 ? 1 : 0; |
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} |
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} |
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if( (float)badCount > (float)queryDescCount*badPart ) |
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{ |
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//curRes = cvtest::TS::FAIL_INVALID_OUTPUT; |
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ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test radiusMatch() function (2).\n", |
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(float)badCount/(float)queryDescCount ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
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} |
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} |
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} |
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void CV_DescriptorMatcherTest::run( int ) |
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{ |
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Mat query, train; |
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generateData( query, train ); |
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matchTest( query, train ); |
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knnMatchTest( query, train ); |
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radiusMatchTest( query, train ); |
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} |
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// bug #3172: test that knnMatch() can handle images with fewer than knn keypoints |
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class CV_DescriptorMatcherLowKeypointTest : public cvtest::BaseTest |
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{ |
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public: |
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CV_DescriptorMatcherLowKeypointTest( const string& _name, const Ptr<DescriptorMatcher>& _dmatcher ) : |
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name(_name), dmatcher(_dmatcher) |
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{} |
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protected: |
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virtual void run(int); |
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void knnMatchTest( const Mat& query, const Mat& train ); |
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|
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private: |
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string name; |
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Ptr<DescriptorMatcher> dmatcher; |
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}; |
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void CV_DescriptorMatcherLowKeypointTest::knnMatchTest( const Mat& query, const Mat& train ) |
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{ |
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const int knn = 6; |
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const int queryDescCount = query.rows; |
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vector<vector<DMatch> > matches; |
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|
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// three train images, the third one with only one keypoint |
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dmatcher->add( vector<Mat>(1,train.rowRange(0, train.rows/2)) ); |
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dmatcher->add( vector<Mat>(1,train.rowRange(train.rows/2, train.rows-1)) ); |
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dmatcher->add( vector<Mat>(1,train.rowRange(train.rows-1, train.rows)) ); |
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const int trainImgCount = (int)dmatcher->getTrainDescriptors().size(); |
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|
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dmatcher->knnMatch( query, matches, knn, std::vector<Mat>(), true ); |
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|
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if( matches.empty() ) |
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{ |
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ts->printf(cvtest::TS::LOG, "No matches while testing knnMatch() function (3).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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else |
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{ |
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int badImgIdxCount = 0, badQueryIdxCount = 0, badTrainIdxCount = 0; |
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for( size_t i = 0; i < matches.size(); i++ ) |
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{ |
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for( size_t j = 0; j < matches[i].size(); j++ ) |
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{ |
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const DMatch& match = matches[i][j]; |
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if( match.imgIdx < 0 || match.imgIdx >= trainImgCount ) |
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{ |
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++badImgIdxCount; |
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} |
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if( match.queryIdx < 0 || match.queryIdx >= queryDescCount ) |
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{ |
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++badQueryIdxCount; |
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} |
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if( match.trainIdx < 0 ) |
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{ |
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++badTrainIdxCount; |
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} |
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} |
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} |
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if( badImgIdxCount > 0 || badQueryIdxCount > 0 || badTrainIdxCount > 0 ) |
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{ |
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ts->printf( cvtest::TS::LOG, "%d/%d/%d - wrong image/query/train indices while testing knnMatch() function (3).\n", |
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badImgIdxCount, badQueryIdxCount, badTrainIdxCount ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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} |
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} |
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|
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void CV_DescriptorMatcherLowKeypointTest::run( int ) |
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{ |
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Mat query, train; |
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CV_DescriptorMatcherTest::generateData( query, train ); |
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|
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knnMatchTest( query, train ); |
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} |
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|
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/****************************************************************************************\ |
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* Tests registrations * |
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\****************************************************************************************/ |
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|
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TEST( Features2d_DescriptorMatcher_BruteForce, regression ) |
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{ |
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CV_DescriptorMatcherTest test( "descriptor-matcher-brute-force", Algorithm::create<DescriptorMatcher>("DescriptorMatcher.BFMatcher"), 0.01f ); |
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test.safe_run(); |
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} |
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|
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TEST( Features2d_DescriptorMatcher_FlannBased, regression ) |
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{ |
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CV_DescriptorMatcherTest test( "descriptor-matcher-flann-based", Algorithm::create<DescriptorMatcher>("DescriptorMatcher.FlannBasedMatcher"), 0.04f ); |
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test.safe_run(); |
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} |
|
|
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TEST( Features2d_DescriptorMatcher_LowKeypoint_BruteForce, regression ) |
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{ |
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CV_DescriptorMatcherLowKeypointTest test( "descriptor-matcher-low-keypoint-brute-force", Algorithm::create<DescriptorMatcher>("DescriptorMatcher.BFMatcher") ); |
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test.safe_run(); |
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} |
|
|
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TEST(Features2d_DescriptorMatcher_LowKeypoint_FlannBased, regression) |
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{ |
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CV_DescriptorMatcherLowKeypointTest test( "descriptor-matcher-low-keypoint-flann-based", Algorithm::create<DescriptorMatcher>("DescriptorMatcher.FlannBasedMatcher") ); |
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test.safe_run(); |
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}
|
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