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Commit 0a2881ad authored by Maria Dimashova's avatar Maria Dimashova
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added to DetectorExtractor test the check of compute() on empty data. Added test for BRIEF.

parent 43716f31
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Showing with 137 additions and 42 deletions
modules/features2d/include/opencv2/features2d/features2d.hpp
View file @ 0a2881ad
......@@ -2109,8 +2109,7 @@ inline void BruteForceMatcher<Distance>::commonKnnMatchImpl( BruteForceMatcher<D
size_t imgCount = matcher.trainDescCollection.size();
vector<Mat> allDists( imgCount ); // distances between one query descriptor and all train descriptors
for( size_t i = 0; i < imgCount; i++ )
if( matcher.trainDescCollection[i].rows )
allDists[i] = Mat( 1, matcher.trainDescCollection[i].rows, DataType<DistanceType>::type );
allDists[i] = Mat( 1, matcher.trainDescCollection[i].rows, DataType<DistanceType>::type );
for( int qIdx = 0; qIdx < queryDescriptors.rows; qIdx++ )
{
......
modules/features2d/src/descriptors.cpp
View file @ 0a2881ad
......@@ -358,7 +358,7 @@ Ptr<DescriptorExtractor> createDescriptorExtractor(const string& descriptorExtra
}
else if (!descriptorExtractorType.compare("BRIEF"))
{
de = new BriefDescriptorExtractor(32);
de = new BriefDescriptorExtractor();
}
return de;
}
......
tests/cv/src/afeatures2d.cpp
View file @ 0a2881ad
......@@ -81,7 +81,7 @@ void CV_FeatureDetectorTest::emptyDataTest()
}
catch(...)
{
ts->printf( CvTS::LOG, "emptyDataTest: Detect() on empty image must not generate exeption\n" );
ts->printf( CvTS::LOG, "emptyDataTest: Detect() on empty image must not generate exception\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
return;
}
......@@ -267,25 +267,86 @@ static Mat readMatFromBin( const string& filename )
return Mat();
}
template<class Distance>
class CV_DescriptorExtractorTest : public CvTest
{
public:
CV_DescriptorExtractorTest( const char* testName, float _maxNormDif, const Ptr<DescriptorExtractor>& _dextractor, float _prevTime ) :
CvTest( testName, "cv::DescriptorExtractor::compute" ), maxNormDif(_maxNormDif), prevTime(_prevTime), dextractor(_dextractor) {}
typedef typename Distance::ValueType ValueType;
typedef typename Distance::ResultType DistanceType;
CV_DescriptorExtractorTest( const char* testName, DistanceType _maxDistDif, const Ptr<DescriptorExtractor>& _dextractor, float _prevTime,
Distance d = Distance() ):
CvTest( testName, "cv::DescriptorExtractor::compute" ),
maxDistDif(_maxDistDif), prevTime(_prevTime), dextractor(_dextractor), distance(d) {}
protected:
virtual void createDescriptorExtractor() {}
void run(int)
void compareDescriptors( const Mat& validDescriptors, const Mat& calcDescriptors )
{
createDescriptorExtractor();
if( dextractor.empty() )
if( validDescriptors.size != calcDescriptors.size || validDescriptors.type() != calcDescriptors.type() )
{
ts->printf(CvTS::LOG, "Descriptor extractor is empty\n");
ts->printf(CvTS::LOG, "Valid and computed descriptors matrices must have the same size and type\n");
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
return;
}
CV_Assert( DataType<ValueType>::type == validDescriptors.type() );
int dimension = validDescriptors.cols;
DistanceType maxDist = std::numeric_limits<DistanceType>::min();
for( int y = 0; y < validDescriptors.rows; y++ )
{
DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
if( dist > maxDistDif)
{
stringstream ss;
ss << "Discance between valid and computed " << y << "-descriptors > " << maxDistDif << endl;
ts->printf(CvTS::LOG, ss.str().c_str() );
ts->set_failed_test_info( CvTS::FAIL_BAD_ACCURACY );
return;
}
if( dist > maxDist )
maxDist = dist;
}
stringstream ss;
ss << "regressionTest: Max discance between valid and computed descriptors " << maxDist << endl;
ts->printf(CvTS::LOG, ss.str().c_str() );
}
void emptyDataTest()
{
assert( !dextractor.empty() );
Mat image;
vector<KeyPoint> keypoints;
Mat descriptors;
try
{
dextractor->compute( image, keypoints, descriptors );
}
catch(...)
{
ts->printf( CvTS::LOG, "emptyDataTest: compute() on empty image and empty keypoints must not generate exception\n");
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
}
image.create( 50, 50, CV_8UC3 );
try
{
dextractor->compute( image, keypoints, descriptors );
}
catch(...)
{
ts->printf( CvTS::LOG, "emptyDataTest: compute() on nonempty image and empty keypoints must not generate exception\n");
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
}
}
void regressionTest()
{
assert( !dextractor.empty() );
// Read the test image.
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
Mat img = imread( imgFilename, 0 );
if( img.empty() )
......@@ -298,7 +359,43 @@ protected:
vector<KeyPoint> keypoints;
FileStorage fs( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::READ );
if( fs.isOpened() )
{
read( fs.getFirstTopLevelNode(), keypoints );
Mat calcDescriptors;
double t = (double)getTickCount();
dextractor->compute( img, keypoints, calcDescriptors );
t = getTickCount() - t;
ts->printf(CvTS::LOG, "\nregressionTest: Average time of computiting one descriptor = %g ms (previous time = %g ms)\n", t/((double)cvGetTickFrequency()*1000.)/calcDescriptors.rows, prevTime );
if( calcDescriptors.rows != (int)keypoints.size() )
{
ts->printf( CvTS::LOG, "Count of computed descriptors and keypoints count must be equal\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
return;
}
if( calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType() )
{
ts->printf( CvTS::LOG, "Incorrect descriptor size or descriptor type\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
return;
}
// TODO read and write descriptor extractor parameters and check them
Mat validDescriptors = readDescriptors();
if( !validDescriptors.empty() )
compareDescriptors( validDescriptors, calcDescriptors );
else
{
if( !writeDescriptors( calcDescriptors ) )
{
ts->printf( CvTS::LOG, "Descriptors can not be written\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
return;
}
}
}
else
{
ts->printf( CvTS::LOG, "Compute and write keypoints\n" );
......@@ -316,31 +413,22 @@ protected:
return;
}
}
}
Mat calcDescriptors;
double t = (double)getTickCount();
dextractor->compute( img, keypoints, calcDescriptors );
t = getTickCount() - t;
ts->printf(CvTS::LOG, "\nAverage time of computiting one descriptor = %g ms (previous time = %g ms)\n", t/((double)cvGetTickFrequency()*1000.)/calcDescriptors.rows, prevTime );
// TODO read and write descriptor extractor parameters and check them
Mat validDescriptors = readDescriptors();
if( !validDescriptors.empty() )
{
double normValue = norm( calcDescriptors, validDescriptors, NORM_INF );
ts->printf( CvTS::LOG, "nofm (inf) BTW valid and calculated float descriptors = %f\n", normValue );
if( normValue > maxNormDif )
ts->set_failed_test_info( CvTS::FAIL_BAD_ACCURACY );
}
else
void run(int)
{
createDescriptorExtractor();
if( dextractor.empty() )
{
if( !writeDescriptors( calcDescriptors ) )
{
ts->printf( CvTS::LOG, "Descriptors can not be written\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
return;
}
ts->printf(CvTS::LOG, "Descriptor extractor is empty\n");
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
return;
}
emptyDataTest();
regressionTest();
ts->set_failed_test_info( CvTS::OK );
}
virtual Mat readDescriptors()
......@@ -355,23 +443,27 @@ protected:
return true;
}
const float maxNormDif;
const DistanceType maxDistDif;
const float prevTime;
Ptr<DescriptorExtractor> dextractor;
Distance distance;
};
template<typename T>
class CV_CalonderDescriptorExtractorTest : public CV_DescriptorExtractorTest
template<typename T, typename Distance>
class CV_CalonderDescriptorExtractorTest : public CV_DescriptorExtractorTest<Distance>
{
public:
CV_CalonderDescriptorExtractorTest( const char* testName, float _normDif, float _prevTime ) :
CV_DescriptorExtractorTest( testName, _normDif, Ptr<DescriptorExtractor>(), _prevTime )
CV_DescriptorExtractorTest<Distance>( testName, _normDif, Ptr<DescriptorExtractor>(), _prevTime )
{}
protected:
virtual void createDescriptorExtractor()
{
dextractor = new CalonderDescriptorExtractor<T>( string(ts->get_data_path()) + FEATURES2D_DIR + "/calonder_classifier.rtc");
CV_DescriptorExtractorTest<Distance>::dextractor =
new CalonderDescriptorExtractor<T>( string(CV_DescriptorExtractorTest<Distance>::ts->get_data_path()) +
FEATURES2D_DIR + "/calonder_classifier.rtc");
}
};
......@@ -782,21 +874,25 @@ CV_FeatureDetectorTest surfTest( "detector-surf", createFeatureDetector("SURF")
/*
* Descriptors
* "descriptor-sift, descriptor-surf, descriptor-calonder-uchar, descriptor-calonder-float, descriptor-brief"
*/
CV_DescriptorExtractorTest siftDescriptorTest( "descriptor-sift", 0.03f,
CV_DescriptorExtractorTest<L2<float> > siftDescriptorTest( "descriptor-sift", 0.03f,
createDescriptorExtractor("SIFT"), 8.06652f );
CV_DescriptorExtractorTest surfDescriptorTest( "descriptor-surf", 0.035f,
CV_DescriptorExtractorTest<L2<float> > surfDescriptorTest( "descriptor-surf", 0.035f,
createDescriptorExtractor("SURF"), 0.147372f );
CV_DescriptorExtractorTest<Hamming> briefDescriptorTest( "descriptor-brief", 1,
createDescriptorExtractor("BRIEF"), 0.00527548 );
//CV_DescriptorExtractorTest oppSiftDescriptorTest( "descriptor-opponent-sift", 0.008f,
// createDescriptorExtractor("OpponentSIFT"), 8.06652f );
//CV_DescriptorExtractorTest oppurfDescriptorTest( "descriptor-opponent-surf", 0.02f,
// createDescriptorExtractor("OpponentSURF"), 0.147372f );
#if CV_SSE2
CV_CalonderDescriptorExtractorTest<uchar> ucharCalonderTest( "descriptor-calonder-uchar",
CV_CalonderDescriptorExtractorTest<uchar, L2<uchar> > ucharCalonderTest( "descriptor-calonder-uchar",
std::numeric_limits<float>::epsilon() + 1,
0.0132175f );
CV_CalonderDescriptorExtractorTest<float> floatCalonderTest( "descriptor-calonder-float",
CV_CalonderDescriptorExtractorTest<float, L2<float> > floatCalonderTest( "descriptor-calonder-float",
std::numeric_limits<float>::epsilon(),
0.0221308f );
#endif // CV_SSE2
......
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