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opencv/modules/features2d/test/test_matchers_algorithmic.cpp
pemmanuelviel e6ec42d462
Merge pull request #17735 from pemmanuelviel:pev-fix-trees-descent 
* Fix trees parsing behavior in hierarchical_clustering_index:
Before, when maxCheck was reached in the first descent of a tree, time was still wasted parsing
the next trees till their best leaf, just to skip the points stored there.
Now we can choose either to keep this behavior, and so we skip parsing other trees after reaching
maxCheck, or we choose to do one descent in each tree, even if in one tree we reach maxCheck.

* Apply the same change to kdtree.
As each leaf contains only 1 point (unlike hierarchical_clustering), difference is visible if trees > maxCheck

* Add the new explore_all_trees parameters to miniflann

* Adapt the FlannBasedMatcher read_write test to the additional search parameter

* Adapt java tests to the additional parameter in SearchParams

* Fix the ABI dumps failure on SearchParams interface change

* Support of ctor calling another ctor of the class is only fully supported from C+11
2020-08-03 18:00:59 +00:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
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#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: explore_all_trees\n"
" type: 8\n"
" value: 0\n"
" -\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
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