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opencv/modules/features/test/test_matchers_algorithmic.cpp
T
Anand Mahesh 0fb620f0f0 Fix: BFMatcher isMaskSupported false on crossCheck 
isMaskSupported now returns false when the matcher is
created with crossCheck enabled, because the mask
path is unsupported in that mode. knnMatchImpl also
drops the mask so it cannot reach batchDistance and
trigger its mask.empty() assertion.

https://github.com/opencv/opencv/issues/22093
2026-07-13 08:56:07 +05:30

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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// For Open Source Computer Vision Library
//
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#include "test_precomp.hpp"
namespace opencv_test { namespace {
/****************************************************************************************\
* 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 1.2\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));
}
// Verify that cross-check BFMatcher gives identical results via Mat (CPU) and UMat (OCL or CPU).
// When OpenCL is active the UMat path exercises ocl_matchWithCrossCheck; when it is not,
// both paths fall through to the same CPU code — either way the results must match.
TEST(Features2d_BFMatcher_CrossCheck, ocl_matches_cpu)
{
RNG rng(42);
const int nQuery = 200;
const int nTrain = 400;
const int dim = 128;
// Float descriptors: the OCL dispatch in knnMatchImpl requires CV_32FC1
Mat queryMat(nQuery, dim, CV_32FC1);
Mat trainMat(nTrain, dim, CV_32FC1);
rng.fill(queryMat, RNG::UNIFORM, 0.f, 1.f);
rng.fill(trainMat, RNG::UNIFORM, 0.f, 1.f);
// CPU reference: Mat inputs always take the CPU path
Ptr<BFMatcher> cpuMatcher = BFMatcher::create(NORM_L2, true /*crossCheck*/);
vector<DMatch> cpuMatches;
cpuMatcher->match(queryMat, trainMat, cpuMatches);
// UMat path: activates OCL dispatch when OpenCL is available
UMat queryUMat = queryMat.getUMat(ACCESS_READ);
UMat trainUMat = trainMat.getUMat(ACCESS_READ);
Ptr<BFMatcher> oclMatcher = BFMatcher::create(NORM_L2, true /*crossCheck*/);
vector<DMatch> oclMatches;
oclMatcher->match(queryUMat, trainUMat, oclMatches);
// Both paths must return the same set of matches (order may differ)
ASSERT_EQ(cpuMatches.size(), oclMatches.size());
auto byQuery = [](const DMatch& a, const DMatch& b) { return a.queryIdx < b.queryIdx; };
sort(cpuMatches.begin(), cpuMatches.end(), byQuery);
sort(oclMatches.begin(), oclMatches.end(), byQuery);
for (size_t i = 0; i < cpuMatches.size(); ++i)
{
EXPECT_EQ(cpuMatches[i].queryIdx, oclMatches[i].queryIdx) << "at index " << i;
EXPECT_EQ(cpuMatches[i].trainIdx, oclMatches[i].trainIdx) << "at index " << i;
EXPECT_NEAR(cpuMatches[i].distance, oclMatches[i].distance, 1e-3f) << "at index " << i;
}
}
// Regression test for https://github.com/opencv/opencv/issues/22093
// A BFMatcher built with crossCheck enabled must report isMaskSupported()
// as false and must not assert/crash when a non-empty mask is supplied.
// Before the fix the mask reached batchDistance's CV_Assert(mask.empty())
// and aborted the process.
TEST(Features2d_BFMatcher_CrossCheck, issue_22093_mask)
{
const string imgPath = cvtest::findDataFile(
"cv/detectors_descriptors_evaluation/images_datasets/leuven/img1.png");
Mat img = imread(imgPath, IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
Ptr<ORB> orb = ORB::create();
vector<KeyPoint> keypoints;
Mat descriptors;
orb->detectAndCompute(img, noArray(), keypoints, descriptors);
ASSERT_FALSE(descriptors.empty());
Ptr<BFMatcher> matcher = BFMatcher::create(NORM_HAMMING, true /*crossCheck*/);
ASSERT_FALSE(matcher->isMaskSupported());
// Register the descriptors as the train set, then match query against
// train with a non-empty per-image mask. This is the exact call chain
// from the issue (match -> knnMatch -> knnMatchImpl -> batchDistance).
// Before the fix the mask reached batchDistance's CV_Assert(mask.empty())
// and aborted the process.
matcher->add(descriptors);
Mat mask = Mat::ones(descriptors.rows, descriptors.rows, CV_8UC1);
vector<Mat> masks(1, mask);
vector<DMatch> matches;
EXPECT_NO_THROW(matcher->match(descriptors, matches, masks));
ASSERT_FALSE(matches.empty());
}
// OCL coverage for https://github.com/opencv/opencv/issues/22093
// SIFT is used only to obtain float (CV_32FC1) descriptors, which are then
// wrapped as UMat so the OCL BFMatcher dispatch is eligible. With an empty
// mask the OCL cross-check kernel (ocl_matchWithCrossCheck) runs; with a
// non-empty mask the OCL dispatch refuses it and falls back to the CPU path,
// which (thanks to the fix) must not assert or crash.
TEST(Features2d_BFMatcher_CrossCheck, issue_22093_mask_ocl)
{
const string imgPath = cvtest::findDataFile(
"cv/detectors_descriptors_evaluation/images_datasets/leuven/img1.png");
Mat img = imread(imgPath, IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
Ptr<SIFT> sift = cv::SIFT::create();
vector<KeyPoint> keypoints;
Mat descriptors;
sift->detectAndCompute(img, noArray(), keypoints, descriptors);
ASSERT_EQ(descriptors.type(), CV_32FC1);
ASSERT_FALSE(descriptors.empty());
Ptr<BFMatcher> matcher = BFMatcher::create(NORM_L2, true /*crossCheck*/);
ASSERT_FALSE(matcher->isMaskSupported());
matcher->add(descriptors);
UMat query = descriptors.getUMat(ACCESS_READ);
// (1) explicit empty mask -> OCL cross-check kernel is eligible
vector<Mat> emptyMasks(1, Mat());
vector<DMatch> matchesEmpty;
EXPECT_NO_THROW(matcher->match(query, matchesEmpty, emptyMasks));
ASSERT_FALSE(matchesEmpty.empty());
// (2) non-empty mask -> OCL refused, CPU fallback must not crash
Mat mask = Mat::ones(descriptors.rows, descriptors.rows, CV_8UC1);
vector<Mat> masks(1, mask);
vector<DMatch> matchesMasked;
EXPECT_NO_THROW(matcher->match(query, matchesMasked, masks));
ASSERT_FALSE(matchesMasked.empty());
}
}} // namespace
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