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opencv/modules/features2d/test/test_matchers_algorithmic.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
namespace opencv_test { namespace {
const string FEATURES2D_DIR = "features2d";
const string IMAGE_FILENAME = "tsukuba.png";
/****************************************************************************************\
* Algorithmic tests for descriptor matchers *
\****************************************************************************************/
class CV_DescriptorMatcherTest : public cvtest::BaseTest
{
public:
CV_DescriptorMatcherTest( const string& _name, const Ptr<DescriptorMatcher>& _dmatcher, float _badPart ) :
badPart(_badPart), name(_name), dmatcher(_dmatcher)
{}
protected:
static const int dim = 500;
static const int queryDescCount = 300; // must be even number because we split train data in some cases in two
static const int countFactor = 4; // do not change it
const float badPart;
virtual void run( int );
void generateData( Mat& query, Mat& train );
#if 0
void emptyDataTest(); // FIXIT not used
#endif
void matchTest( const Mat& query, const Mat& train );
void knnMatchTest( const Mat& query, const Mat& train );
void radiusMatchTest( const Mat& query, const Mat& train );
string name;
Ptr<DescriptorMatcher> dmatcher;
private:
CV_DescriptorMatcherTest& operator=(const CV_DescriptorMatcherTest&) { return *this; }
};
#if 0
void CV_DescriptorMatcherTest::emptyDataTest()
{
assert( !dmatcher.empty() );
Mat queryDescriptors, trainDescriptors, mask;
vector<Mat> trainDescriptorCollection, masks;
vector<DMatch> matches;
vector<vector<DMatch> > vmatches;
try
{
dmatcher->match( queryDescriptors, trainDescriptors, matches, mask );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "match() on empty descriptors must not generate exception (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->knnMatch( queryDescriptors, trainDescriptors, vmatches, 2, mask );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "knnMatch() on empty descriptors must not generate exception (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->radiusMatch( queryDescriptors, trainDescriptors, vmatches, 10.f, mask );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "radiusMatch() on empty descriptors must not generate exception (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->add( trainDescriptorCollection );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "add() on empty descriptors must not generate exception.\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->match( queryDescriptors, matches, masks );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "match() on empty descriptors must not generate exception (2).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->knnMatch( queryDescriptors, vmatches, 2, masks );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "knnMatch() on empty descriptors must not generate exception (2).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->radiusMatch( queryDescriptors, vmatches, 10.f, masks );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "radiusMatch() on empty descriptors must not generate exception (2).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
#endif
void CV_DescriptorMatcherTest::generateData( Mat& query, Mat& train )
{
RNG& rng = theRNG();
// Generate query descriptors randomly.
// Descriptor vector elements are integer values.
Mat buf( queryDescCount, dim, CV_32SC1 );
rng.fill( buf, RNG::UNIFORM, Scalar::all(0), Scalar(3) );
buf.convertTo( query, CV_32FC1 );
// Generate train descriptors as follows:
// copy each query descriptor to train set countFactor times
// and perturb some one element of the copied descriptors in
// in ascending order. General boundaries of the perturbation
// are (0.f, 1.f).
train.create( query.rows*countFactor, query.cols, CV_32FC1 );
float step = 1.f / countFactor;
for( int qIdx = 0; qIdx < query.rows; qIdx++ )
{
Mat queryDescriptor = query.row(qIdx);
for( int c = 0; c < countFactor; c++ )
{
int tIdx = qIdx * countFactor + c;
Mat trainDescriptor = train.row(tIdx);
queryDescriptor.copyTo( trainDescriptor );
int elem = rng(dim);
float diff = rng.uniform( step*c, step*(c+1) );
trainDescriptor.at<float>(0, elem) += diff;
}
}
}
void CV_DescriptorMatcherTest::matchTest( const Mat& query, const Mat& train )
{
dmatcher->clear();
// test const version of match()
{
vector<DMatch> matches;
dmatcher->match( query, train, matches );
if( (int)matches.size() != queryDescCount )
{
ts->printf(cvtest::TS::LOG, "Incorrect matches count while test match() 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++ )
{
DMatch& match = matches[i];
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0) )
badCount++;
}
if( (float)badCount > (float)queryDescCount*badPart )
{
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test match() function (1).\n",
(float)badCount/(float)queryDescCount );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
}
// test const version of match() for the same query and test descriptors
{
vector<DMatch> matches;
dmatcher->match( query, query, matches );
if( (int)matches.size() != query.rows )
{
ts->printf(cvtest::TS::LOG, "Incorrect matches count while test match() function for the same query and test descriptors (1).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
else
{
for( size_t i = 0; i < matches.size(); i++ )
{
DMatch& match = matches[i];
//std::cout << match.distance << std::endl;
if( match.queryIdx != (int)i || match.trainIdx != (int)i || std::abs(match.distance) > FLT_EPSILON )
{
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",
i, match.queryIdx, match.trainIdx, match.distance );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
}
}
// test version of match() with add()
{
vector<DMatch> matches;
// make add() twice to test such case
dmatcher->add( vector<Mat>(1,train.rowRange(0, train.rows/2)) );
dmatcher->add( vector<Mat>(1,train.rowRange(train.rows/2, train.rows)) );
// prepare masks (make first nearest match illegal)
vector<Mat> masks(2);
for(int mi = 0; mi < 2; mi++ )
{
masks[mi] = Mat(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->match( query, matches, masks );
if( (int)matches.size() != queryDescCount )
{
ts->printf(cvtest::TS::LOG, "Incorrect matches count while test match() function (2).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
for( size_t i = 0; i < matches.size(); i++ )
{
DMatch& match = matches[i];
int shift = dmatcher->isMaskSupported() ? 1 : 0;
{
if( i < queryDescCount/2 )
{
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor + shift) || (match.imgIdx != 0) )
badCount++;
}
else
{
if( (match.queryIdx != (int)i) || (match.trainIdx != ((int)i-queryDescCount/2)*countFactor + shift) || (match.imgIdx != 1) )
badCount++;
}
}
}
if( (float)badCount > (float)queryDescCount*badPart )
{
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test match() function (2).\n",
(float)badCount/(float)queryDescCount );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
}
}
}
void CV_DescriptorMatcherTest::knnMatchTest( const Mat& query, const Mat& train )
{
dmatcher->clear();
// test const version of knnMatch()
{
const int knn = 3;
vector<vector<DMatch> > matches;
dmatcher->knnMatch( query, train, matches, knn );
if( (int)matches.size() != queryDescCount )
{
ts->printf(cvtest::TS::LOG, "Incorrect matches count while test knnMatch() 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() != knn )
badCount++;
else
{
int localBadCount = 0;
for( int k = 0; k < knn; k++ )
{
DMatch& match = matches[i][k];
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor+k) || (match.imgIdx != 0) )
localBadCount++;
}
badCount += localBadCount > 0 ? 1 : 0;
}
}
if( (float)badCount > (float)queryDescCount*badPart )
{
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test knnMatch() function (1).\n",
(float)badCount/(float)queryDescCount );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
}
// test version of knnMatch() with add()
{
const int knn = 2;
vector<vector<DMatch> > matches;
// make add() twice to test such case
dmatcher->add( vector<Mat>(1,train.rowRange(0, train.rows/2)) );
dmatcher->add( vector<Mat>(1,train.rowRange(train.rows/2, train.rows)) );
// prepare masks (make first nearest match illegal)
vector<Mat> masks(2);
for(int mi = 0; mi < 2; mi++ )
{
masks[mi] = Mat(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->knnMatch( query, matches, knn, masks );
if( (int)matches.size() != queryDescCount )
{
ts->printf(cvtest::TS::LOG, "Incorrect matches count while test knnMatch() function (2).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
int shift = dmatcher->isMaskSupported() ? 1 : 0;
for( size_t i = 0; i < matches.size(); i++ )
{
if( (int)matches[i].size() != knn )
badCount++;
else
{
int localBadCount = 0;
for( int k = 0; k < knn; 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 )
{
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_DescriptorMatcherTest::radiusMatchTest( const Mat& query, const Mat& train )
{
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( (float)badCount > (float)queryDescCount*badPart )
{
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<Mat>(1,train.rowRange(0, train.rows/2)) );
dmatcher->add( vector<Mat>(1,train.rowRange(train.rows/2, train.rows)) );
// prepare masks (make first nearest match illegal)
vector<Mat> masks(2);
for(int mi = 0; mi < 2; mi++ )
{
masks[mi] = Mat(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( (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);
}
TEST(Features2d_DMatch, issue_17771)
{
Mat sources = (Mat_<uchar>(2, 3) << 1, 1, 0,
1, 1, 1);
Mat targets = (Mat_<uchar>(2, 3) << 1, 1, 1,
0, 0, 0);
UMat usources = sources.getUMat(ACCESS_READ);
UMat utargets = targets.getUMat(ACCESS_READ);
vector<vector<DMatch> > match;
Ptr<BFMatcher> ubf = BFMatcher::create(NORM_HAMMING);
Mat mask = (Mat_<uchar>(2, 2) << 1, 0, 0, 1);
EXPECT_NO_THROW(ubf->knnMatch(usources, utargets, match, 1, mask, true));
}
}} // namespace