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523 lines
19 KiB
C++
523 lines
19 KiB
C++
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/*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 <algorithm>
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#include <iterator>
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using namespace cv;
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using namespace cv::gpu;
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using namespace std;
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class CV_GpuBruteForceMatcherTest : public cvtest::BaseTest
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{
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public:
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CV_GpuBruteForceMatcherTest()
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{
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}
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protected:
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virtual void run(int);
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void emptyDataTest();
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void dataTest(int dim);
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void generateData(GpuMat& query, GpuMat& train, int dim);
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void matchTest(const GpuMat& query, const GpuMat& train);
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void knnMatchTest(const GpuMat& query, const GpuMat& train);
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void radiusMatchTest(const GpuMat& query, const GpuMat& train);
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private:
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BruteForceMatcher_GPU< L2<float> > dmatcher;
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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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};
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void CV_GpuBruteForceMatcherTest::emptyDataTest()
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{
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GpuMat queryDescriptors, trainDescriptors, mask;
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vector<GpuMat> 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_GpuBruteForceMatcherTest::generateData( GpuMat& queryGPU, GpuMat& trainGPU, int dim )
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{
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Mat query, train;
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RNG& rng = ts->get_rng();
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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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queryGPU.upload(query);
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trainGPU.upload(train);
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}
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void CV_GpuBruteForceMatcherTest::matchTest( const GpuMat& query, const GpuMat& 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 (badCount > 0)
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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 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<GpuMat>(1,train.rowRange(0, train.rows/2)) );
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dmatcher.add( vector<GpuMat>(1,train.rowRange(train.rows/2, train.rows)) );
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// prepare masks (make first nearest match illegal)
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vector<GpuMat> masks(2);
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for(int mi = 0; mi < 2; mi++ )
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{
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masks[mi] = GpuMat(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 (badCount > 0)
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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_GpuBruteForceMatcherTest::knnMatchTest( const GpuMat& query, const GpuMat& 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 (badCount > 0)
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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<GpuMat>(1,train.rowRange(0, train.rows/2)) );
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dmatcher.add( vector<GpuMat>(1,train.rowRange(train.rows/2, train.rows)) );
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// prepare masks (make first nearest match illegal)
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vector<GpuMat> masks(2);
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for(int mi = 0; mi < 2; mi++ )
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{
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masks[mi] = GpuMat(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
|
||
|
|
{
|
||
|
|
if( (match.queryIdx != (int)i) || (match.trainIdx != ((int)i-queryDescCount/2)*countFactor + k + shift) ||
|
||
|
|
(match.imgIdx != 1) )
|
||
|
|
localBadCount++;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
badCount += localBadCount > 0 ? 1 : 0;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if (badCount > 0)
|
||
|
|
{
|
||
|
|
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test knnMatch() function (2).\n",
|
||
|
|
(float)badCount/(float)queryDescCount );
|
||
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
void CV_GpuBruteForceMatcherTest::radiusMatchTest( const GpuMat& query, const GpuMat& train )
|
||
|
|
{
|
||
|
|
bool atomics_ok = TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS);
|
||
|
|
if (!atomics_ok)
|
||
|
|
{
|
||
|
|
ts->printf(cvtest::TS::CONSOLE, "\nCode and device atomics support is required for radiusMatch (CC >= 1.1)");
|
||
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_GENERIC);
|
||
|
|
return;
|
||
|
|
}
|
||
|
|
|
||
|
|
dmatcher.clear();
|
||
|
|
// test const version of match()
|
||
|
|
{
|
||
|
|
const float radius = 1.f/countFactor;
|
||
|
|
vector< vector<DMatch> > matches;
|
||
|
|
dmatcher.radiusMatch( query, train, matches, radius );
|
||
|
|
|
||
|
|
if( (int)matches.size() != queryDescCount )
|
||
|
|
{
|
||
|
|
ts->printf(cvtest::TS::LOG, "Incorrect matches count while test radiusMatch() function (1).\n");
|
||
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
int badCount = 0;
|
||
|
|
for( size_t i = 0; i < matches.size(); i++ )
|
||
|
|
{
|
||
|
|
if( (int)matches[i].size() != 1 )
|
||
|
|
badCount++;
|
||
|
|
else
|
||
|
|
{
|
||
|
|
DMatch match = matches[i][0];
|
||
|
|
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0) )
|
||
|
|
badCount++;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if (badCount > 0)
|
||
|
|
{
|
||
|
|
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test radiusMatch() function (1).\n",
|
||
|
|
(float)badCount/(float)queryDescCount );
|
||
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
// test version of match() with add()
|
||
|
|
{
|
||
|
|
int n = 3;
|
||
|
|
const float radius = 1.f/countFactor * n;
|
||
|
|
vector< vector<DMatch> > matches;
|
||
|
|
// make add() twice to test such case
|
||
|
|
dmatcher.add( vector<GpuMat>(1,train.rowRange(0, train.rows/2)) );
|
||
|
|
dmatcher.add( vector<GpuMat>(1,train.rowRange(train.rows/2, train.rows)) );
|
||
|
|
// prepare masks (make first nearest match illegal)
|
||
|
|
vector<GpuMat> masks(2);
|
||
|
|
for(int mi = 0; mi < 2; mi++ )
|
||
|
|
{
|
||
|
|
masks[mi] = GpuMat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1));
|
||
|
|
for( int di = 0; di < queryDescCount/2; di++ )
|
||
|
|
masks[mi].col(di*countFactor).setTo(Scalar::all(0));
|
||
|
|
}
|
||
|
|
|
||
|
|
dmatcher.radiusMatch( query, matches, radius, masks );
|
||
|
|
|
||
|
|
int curRes = cvtest::TS::OK;
|
||
|
|
if( (int)matches.size() != queryDescCount )
|
||
|
|
{
|
||
|
|
ts->printf(cvtest::TS::LOG, "Incorrect matches count while test radiusMatch() function (1).\n");
|
||
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||
|
|
}
|
||
|
|
|
||
|
|
int badCount = 0;
|
||
|
|
int shift = dmatcher.isMaskSupported() ? 1 : 0;
|
||
|
|
int needMatchCount = dmatcher.isMaskSupported() ? n-1 : n;
|
||
|
|
for( size_t i = 0; i < matches.size(); i++ )
|
||
|
|
{
|
||
|
|
if( (int)matches[i].size() != needMatchCount )
|
||
|
|
badCount++;
|
||
|
|
else
|
||
|
|
{
|
||
|
|
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 (badCount > 0)
|
||
|
|
{
|
||
|
|
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_GpuBruteForceMatcherTest::dataTest(int dim)
|
||
|
|
{
|
||
|
|
GpuMat query, train;
|
||
|
|
generateData(query, train, dim);
|
||
|
|
|
||
|
|
matchTest(query, train);
|
||
|
|
knnMatchTest(query, train);
|
||
|
|
radiusMatchTest(query, train);
|
||
|
|
|
||
|
|
dmatcher.clear();
|
||
|
|
}
|
||
|
|
|
||
|
|
void CV_GpuBruteForceMatcherTest::run(int)
|
||
|
|
{
|
||
|
|
emptyDataTest();
|
||
|
|
|
||
|
|
dataTest(50);
|
||
|
|
dataTest(64);
|
||
|
|
dataTest(100);
|
||
|
|
dataTest(128);
|
||
|
|
dataTest(200);
|
||
|
|
dataTest(256);
|
||
|
|
dataTest(300);
|
||
|
|
}
|
||
|
|
|
||
|
|
TEST(BruteForceMatcher, accuracy) { CV_GpuBruteForceMatcherTest test; test.safe_run(); }
|