added to FeatureDetector test the check of detect() on empty data
This commit is contained in:
Maria Dimashova
parent
27690e3b6e
commit
43716f31b9
@ -60,112 +60,175 @@ public:
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CvTest( testName, "cv::FeatureDetector::detect"), fdetector(_fdetector) {}
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CvTest( testName, "cv::FeatureDetector::detect"), fdetector(_fdetector) {}
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protected:
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protected:
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virtual void run( int /*start_from*/ )
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bool isSimilarKeypoints( const KeyPoint& p1, const KeyPoint& p2 );
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{
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void compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints );
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const float maxPtDif = 1.f;
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const float maxSizeDif = 1.f;
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const float maxAngleDif = 2.f;
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const float maxResponseDif = 0.1f;
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string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
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void emptyDataTest();
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string resFilename = string(ts->get_data_path()) + DETECTOR_DIR + "/" + string(name) + ".xml.gz";
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void regressionTest(); // TODO test of detect() with mask
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if( fdetector.empty() )
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virtual void run( int );
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{
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ts->printf( CvTS::LOG, "Feature detector is empty" );
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ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
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return;
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}
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Mat image = imread( imgFilename, 0 );
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if( image.empty() )
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{
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ts->printf( CvTS::LOG, "image %s can not be read \n", imgFilename.c_str() );
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ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
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return;
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}
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FileStorage fs( resFilename, FileStorage::READ );
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vector<KeyPoint> calcKeypoints;
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fdetector->detect( image, calcKeypoints );
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if( fs.isOpened() ) // compare computed and valid keypoints
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{
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// TODO compare saved feature detector params with current ones
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vector<KeyPoint> validKeypoints;
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read( fs["keypoints"], validKeypoints );
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if( validKeypoints.empty() )
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{
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ts->printf( CvTS::LOG, "Keypoints can nod be read\n" );
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ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
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return;
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}
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int progress = 0, progressCount = validKeypoints.size() * calcKeypoints.size();
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int badPointCount = 0, commonPointCount = max(validKeypoints.size(), calcKeypoints.size());
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for( size_t v = 0; v < validKeypoints.size(); v++ )
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{
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int nearestIdx = -1;
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float minDist = std::numeric_limits<float>::max();
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for( size_t c = 0; c < calcKeypoints.size(); c++ )
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{
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progress = update_progress( progress, v*calcKeypoints.size() + c, progressCount, 0 );
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float curDist = (float)norm( calcKeypoints[c].pt - validKeypoints[v].pt );
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if( curDist < minDist )
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{
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minDist = curDist;
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nearestIdx = c;
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}
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}
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if( minDist > maxPtDif ||
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fabs(calcKeypoints[nearestIdx].size - validKeypoints[v].size) > maxSizeDif ||
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abs(calcKeypoints[nearestIdx].angle - validKeypoints[v].angle) > maxAngleDif ||
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abs(calcKeypoints[nearestIdx].response - validKeypoints[v].response) > maxResponseDif ||
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calcKeypoints[nearestIdx].octave != validKeypoints[v].octave
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// TODO !!!!!!!
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/*||
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calcKeypoints[nearestIdx].class_id != validKeypoints[v].class_id*/ )
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{
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badPointCount++;
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}
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}
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ts->printf( CvTS::LOG, "badPointCount = %d; validPointCount = %d; calcPointCount = %d\n",
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badPointCount, validKeypoints.size(), calcKeypoints.size() );
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if( badPointCount > 0.9 * commonPointCount )
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{
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ts->printf( CvTS::LOG, "Bad accuracy!\n" );
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ts->set_failed_test_info( CvTS::FAIL_BAD_ACCURACY );
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return;
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}
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}
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else // write
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{
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fs.open( resFilename, FileStorage::WRITE );
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if( !fs.isOpened() )
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{
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ts->printf( CvTS::LOG, "file %s can not be opened to write\n", resFilename.c_str() );
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ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
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return;
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}
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else
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{
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fs << "detector_params" << "{";
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fdetector->write( fs );
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fs << "}";
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write( fs, "keypoints", calcKeypoints );
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}
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}
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ts->set_failed_test_info( CvTS::OK );
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}
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Ptr<FeatureDetector> fdetector;
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Ptr<FeatureDetector> fdetector;
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};
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};
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void CV_FeatureDetectorTest::emptyDataTest()
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{
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Mat image;
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vector<KeyPoint> keypoints;
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try
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{
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fdetector->detect( image, keypoints );
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}
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catch(...)
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{
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ts->printf( CvTS::LOG, "emptyDataTest: Detect() on empty image must not generate exeption\n" );
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ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
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return;
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}
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if( !keypoints.empty() )
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{
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ts->printf( CvTS::LOG, "emptyDataTest: Detect() on empty image must return empty keypoints vector\n" );
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ts->set_failed_test_info( CvTS::FAIL_INVALID_OUTPUT );
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return;
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}
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}
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bool CV_FeatureDetectorTest::isSimilarKeypoints( const KeyPoint& p1, const KeyPoint& p2 )
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{
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const float maxPtDif = 1.f;
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const float maxSizeDif = 1.f;
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const float maxAngleDif = 2.f;
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const float maxResponseDif = 0.1f;
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float dist = (float)norm( p1.pt - p2.pt );
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return (dist < maxPtDif &&
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fabs(p1.size - p2.size) < maxSizeDif &&
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abs(p1.angle - p2.angle) < maxAngleDif &&
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abs(p1.response - p2.response) < maxResponseDif &&
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p1.octave == p2.octave &&
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p1.class_id == p2.class_id );
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}
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void CV_FeatureDetectorTest::compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints )
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{
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const float maxCountRatioDif = 0.01f;
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// Compare counts of validation and calculated keypoints.
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float countRatio = (float)validKeypoints.size() / (float)calcKeypoints.size();
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if( countRatio < 1 - maxCountRatioDif || countRatio > 1.f + maxCountRatioDif )
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{
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ts->printf( CvTS::LOG, "Bad keypoints count ratio (validCount = %d, calcCount = %d)!\n",
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validKeypoints.size(), calcKeypoints.size() );
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ts->set_failed_test_info( CvTS::FAIL_BAD_ACCURACY );
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return;
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}
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int progress = 0, progressCount = validKeypoints.size() * calcKeypoints.size();
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int badPointCount = 0, commonPointCount = max(validKeypoints.size(), calcKeypoints.size());
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for( size_t v = 0; v < validKeypoints.size(); v++ )
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{
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int nearestIdx = -1;
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float minDist = std::numeric_limits<float>::max();
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for( size_t c = 0; c < calcKeypoints.size(); c++ )
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{
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progress = update_progress( progress, v*calcKeypoints.size() + c, progressCount, 0 );
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float curDist = (float)norm( calcKeypoints[c].pt - validKeypoints[v].pt );
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if( curDist < minDist )
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{
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minDist = curDist;
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nearestIdx = c;
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}
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}
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assert( minDist >= 0 );
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if( !isSimilarKeypoints( validKeypoints[v], calcKeypoints[nearestIdx] ) )
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badPointCount++;
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}
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ts->printf( CvTS::LOG, "regressionTest: badPointCount = %d; validPointCount = %d; calcPointCount = %d\n",
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badPointCount, validKeypoints.size(), calcKeypoints.size() );
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if( badPointCount > 0.9 * commonPointCount )
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{
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ts->printf( CvTS::LOG, " - Bad accuracy!\n" );
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ts->set_failed_test_info( CvTS::FAIL_BAD_ACCURACY );
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return;
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}
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ts->printf( CvTS::LOG, " - OK\n" );
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}
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void CV_FeatureDetectorTest::regressionTest()
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{
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assert( !fdetector.empty() );
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string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
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string resFilename = string(ts->get_data_path()) + DETECTOR_DIR + "/" + string(name) + ".xml.gz";
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// Read the test image.
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Mat image = imread( imgFilename, 0 );
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if( image.empty() )
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{
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ts->printf( CvTS::LOG, "image %s can not be read \n", imgFilename.c_str() );
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ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
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return;
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}
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FileStorage fs( resFilename, FileStorage::READ );
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// Compute keypoints.
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vector<KeyPoint> calcKeypoints;
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fdetector->detect( image, calcKeypoints );
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if( fs.isOpened() ) // Compare computed and valid keypoints.
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{
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// TODO compare saved feature detector params with current ones
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// Read validation keypoints set.
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vector<KeyPoint> validKeypoints;
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read( fs["keypoints"], validKeypoints );
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if( validKeypoints.empty() )
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{
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ts->printf( CvTS::LOG, "Keypoints can nod be read\n" );
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ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
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return;
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}
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compareKeypointSets( validKeypoints, calcKeypoints );
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}
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else // Write detector parameters and computed keypoints as validation data.
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{
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fs.open( resFilename, FileStorage::WRITE );
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if( !fs.isOpened() )
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{
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ts->printf( CvTS::LOG, "file %s can not be opened to write\n", resFilename.c_str() );
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ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
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return;
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}
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else
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{
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fs << "detector_params" << "{";
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fdetector->write( fs );
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fs << "}";
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write( fs, "keypoints", calcKeypoints );
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}
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}
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}
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void CV_FeatureDetectorTest::run( int /*start_from*/ )
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{
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if( fdetector.empty() )
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{
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ts->printf( CvTS::LOG, "Feature detector is empty" );
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ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
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return;
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}
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emptyDataTest();
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regressionTest();
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ts->set_failed_test_info( CvTS::OK );
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}
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/****************************************************************************************\
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/****************************************************************************************\
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* Regression tests for descriptor extractors. *
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* Regression tests for descriptor extractors. *
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\****************************************************************************************/
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\****************************************************************************************/
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@ -707,6 +770,7 @@ void CV_DescriptorMatcherTest::run( int )
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/*
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/*
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* Detectors
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* Detectors
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* "detector-fast, detector-gftt, detector-harris, detector-mser, detector-sift, detector-star, detector-surf"
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*/
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*/
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CV_FeatureDetectorTest fastTest( "detector-fast", createFeatureDetector("FAST") );
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CV_FeatureDetectorTest fastTest( "detector-fast", createFeatureDetector("FAST") );
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CV_FeatureDetectorTest gfttTest( "detector-gftt", createFeatureDetector("GFTT") );
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CV_FeatureDetectorTest gfttTest( "detector-gftt", createFeatureDetector("GFTT") );
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