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619 lines
22 KiB
C++
619 lines
22 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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namespace opencv_test { namespace {
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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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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 generateData( Mat& query, Mat& train );
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#if 0
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void emptyDataTest(); // FIXIT not used
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#endif
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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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#if 0
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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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#endif
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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 descriptors 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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|
{
|
|
int localBadCount = 0;
|
|
for( int k = 0; k < needMatchCount; k++ )
|
|
{
|
|
DMatch& match = matches[i][k];
|
|
{
|
|
if( i < queryDescCount/2 )
|
|
{
|
|
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor + k + shift) ||
|
|
(match.imgIdx != 0) )
|
|
localBadCount++;
|
|
}
|
|
else
|
|
{
|
|
if( (match.queryIdx != (int)i) || (match.trainIdx != ((int)i-queryDescCount/2)*countFactor + k + shift) ||
|
|
(match.imgIdx != 1) )
|
|
localBadCount++;
|
|
}
|
|
}
|
|
}
|
|
badCount += localBadCount > 0 ? 1 : 0;
|
|
}
|
|
}
|
|
if( (float)badCount > (float)queryDescCount*badPart )
|
|
{
|
|
//curRes = cvtest::TS::FAIL_INVALID_OUTPUT;
|
|
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test radiusMatch() function (2).\n",
|
|
(float)badCount/(float)queryDescCount );
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
|
}
|
|
}
|
|
}
|
|
|
|
void CV_DescriptorMatcherTest::run( int )
|
|
{
|
|
Mat query, train;
|
|
generateData( query, train );
|
|
|
|
matchTest( query, train );
|
|
|
|
knnMatchTest( query, train );
|
|
|
|
radiusMatchTest( query, train );
|
|
}
|
|
|
|
/****************************************************************************************\
|
|
* Tests registrations *
|
|
\****************************************************************************************/
|
|
|
|
TEST( Features2d_DescriptorMatcher_BruteForce, regression )
|
|
{
|
|
CV_DescriptorMatcherTest test( "descriptor-matcher-brute-force",
|
|
DescriptorMatcher::create("BruteForce"), 0.01f );
|
|
test.safe_run();
|
|
}
|
|
|
|
#ifdef HAVE_OPENCV_FLANN
|
|
TEST( Features2d_DescriptorMatcher_FlannBased, regression )
|
|
{
|
|
CV_DescriptorMatcherTest test( "descriptor-matcher-flann-based",
|
|
DescriptorMatcher::create("FlannBased"), 0.04f );
|
|
test.safe_run();
|
|
}
|
|
#endif
|
|
|
|
TEST( Features2d_DMatch, read_write )
|
|
{
|
|
FileStorage fs(".xml", FileStorage::WRITE + FileStorage::MEMORY);
|
|
vector<DMatch> matches;
|
|
matches.push_back(DMatch(1,2,3,4.5f));
|
|
fs << "Match" << matches;
|
|
String str = fs.releaseAndGetString();
|
|
ASSERT_NE( strstr(str.c_str(), "4.5"), (char*)0 );
|
|
}
|
|
|
|
#ifdef HAVE_OPENCV_FLANN
|
|
TEST( Features2d_FlannBasedMatcher, read_write )
|
|
{
|
|
static const char* ymlfile = "%YAML:1.0\n---\n"
|
|
"format: 3\n"
|
|
"indexParams:\n"
|
|
" -\n"
|
|
" name: algorithm\n"
|
|
" type: 9\n" // FLANN_INDEX_TYPE_ALGORITHM
|
|
" value: 6\n"// this line is changed!
|
|
" -\n"
|
|
" name: trees\n"
|
|
" type: 4\n"
|
|
" value: 4\n"
|
|
"searchParams:\n"
|
|
" -\n"
|
|
" name: checks\n"
|
|
" type: 4\n"
|
|
" value: 32\n"
|
|
" -\n"
|
|
" name: eps\n"
|
|
" type: 5\n"
|
|
" value: 4.\n"// this line is changed!
|
|
" -\n"
|
|
" name: sorted\n"
|
|
" type: 8\n" // FLANN_INDEX_TYPE_BOOL
|
|
" value: 1\n";
|
|
|
|
Ptr<DescriptorMatcher> matcher = FlannBasedMatcher::create();
|
|
FileStorage fs_in(ymlfile, FileStorage::READ + FileStorage::MEMORY);
|
|
matcher->read(fs_in.root());
|
|
FileStorage fs_out(".yml", FileStorage::WRITE + FileStorage::MEMORY);
|
|
matcher->write(fs_out);
|
|
std::string out = fs_out.releaseAndGetString();
|
|
|
|
EXPECT_EQ(ymlfile, out);
|
|
}
|
|
#endif
|
|
|
|
TEST(Features2d_DMatch, issue_11855)
|
|
{
|
|
Mat sources = (Mat_<uchar>(2, 3) << 1, 1, 0,
|
|
1, 1, 1);
|
|
Mat targets = (Mat_<uchar>(2, 3) << 1, 1, 1,
|
|
0, 0, 0);
|
|
|
|
Ptr<BFMatcher> bf = BFMatcher::create(NORM_HAMMING, true);
|
|
vector<vector<DMatch> > match;
|
|
bf->knnMatch(sources, targets, match, 1, noArray(), true);
|
|
|
|
ASSERT_EQ((size_t)1, match.size());
|
|
ASSERT_EQ((size_t)1, match[0].size());
|
|
EXPECT_EQ(1, match[0][0].queryIdx);
|
|
EXPECT_EQ(0, match[0][0].trainIdx);
|
|
EXPECT_EQ(0.0f, match[0][0].distance);
|
|
}
|
|
|
|
}} // namespace
|