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1107 lines
40 KiB
1107 lines
40 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// Intel License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000, Intel Corporation, all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of Intel Corporation may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "test_precomp.hpp" |
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#include "opencv2/highgui/highgui.hpp" |
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using namespace std; |
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using namespace cv; |
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const string FEATURES2D_DIR = "features2d"; |
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const string DETECTOR_DIR = FEATURES2D_DIR + "/feature_detectors"; |
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const string DESCRIPTOR_DIR = FEATURES2D_DIR + "/descriptor_extractors"; |
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const string IMAGE_FILENAME = "tsukuba.png"; |
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/****************************************************************************************\ |
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* Regression tests for feature detectors comparing keypoints. * |
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\****************************************************************************************/ |
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class CV_FeatureDetectorTest : public cvtest::BaseTest |
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{ |
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public: |
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CV_FeatureDetectorTest( const string& _name, const Ptr<FeatureDetector>& _fdetector ) : |
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name(_name), fdetector(_fdetector) {} |
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protected: |
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bool isSimilarKeypoints( const KeyPoint& p1, const KeyPoint& p2 ); |
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void compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints ); |
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void emptyDataTest(); |
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void regressionTest(); // TODO test of detect() with mask |
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virtual void run( int ); |
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string name; |
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Ptr<FeatureDetector> fdetector; |
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}; |
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void CV_FeatureDetectorTest::emptyDataTest() |
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{ |
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// One image. |
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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( cvtest::TS::LOG, "detect() on empty image must not generate exception (1).\n" ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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} |
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if( !keypoints.empty() ) |
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{ |
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ts->printf( cvtest::TS::LOG, "detect() on empty image must return empty keypoints vector (1).\n" ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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return; |
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} |
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// Several images. |
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vector<Mat> images; |
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vector<vector<KeyPoint> > keypointCollection; |
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try |
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{ |
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fdetector->detect( images, keypointCollection ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "detect() on empty image vector must not generate exception (2).\n" ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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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( cvtest::TS::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( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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return; |
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} |
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int progress = 0, progressCount = (int)(validKeypoints.size() * calcKeypoints.size()); |
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int badPointCount = 0, commonPointCount = max((int)validKeypoints.size(), (int)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, (int)(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 = (int)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( cvtest::TS::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( cvtest::TS::LOG, " - Bad accuracy!\n" ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
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return; |
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} |
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ts->printf( cvtest::TS::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 ); |
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if( image.empty() ) |
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{ |
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ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() ); |
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ts->set_failed_test_info( cvtest::TS::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( cvtest::TS::LOG, "Keypoints can not be read.\n" ); |
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ts->set_failed_test_info( cvtest::TS::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( cvtest::TS::LOG, "File %s can not be opened to write.\n", resFilename.c_str() ); |
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ts->set_failed_test_info( cvtest::TS::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( cvtest::TS::LOG, "Feature detector is empty.\n" ); |
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ts->set_failed_test_info( cvtest::TS::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( cvtest::TS::OK ); |
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} |
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/****************************************************************************************\ |
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* Regression tests for descriptor extractors. * |
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\****************************************************************************************/ |
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static void writeMatInBin( const Mat& mat, const string& filename ) |
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{ |
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FILE* f = fopen( filename.c_str(), "wb"); |
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if( f ) |
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{ |
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int type = mat.type(); |
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fwrite( (void*)&mat.rows, sizeof(int), 1, f ); |
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fwrite( (void*)&mat.cols, sizeof(int), 1, f ); |
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fwrite( (void*)&type, sizeof(int), 1, f ); |
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int dataSize = (int)(mat.step * mat.rows * mat.channels()); |
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fwrite( (void*)&dataSize, sizeof(int), 1, f ); |
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fwrite( (void*)mat.data, 1, dataSize, f ); |
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fclose(f); |
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} |
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} |
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static Mat readMatFromBin( const string& filename ) |
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{ |
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FILE* f = fopen( filename.c_str(), "rb" ); |
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if( f ) |
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{ |
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int rows, cols, type, dataSize; |
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fread( (void*)&rows, sizeof(int), 1, f ); |
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fread( (void*)&cols, sizeof(int), 1, f ); |
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fread( (void*)&type, sizeof(int), 1, f ); |
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fread( (void*)&dataSize, sizeof(int), 1, f ); |
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uchar* data = (uchar*)cvAlloc(dataSize); |
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fread( (void*)data, 1, dataSize, f ); |
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fclose(f); |
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return Mat( rows, cols, type, data ); |
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} |
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return Mat(); |
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} |
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template<class Distance> |
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class CV_DescriptorExtractorTest : public cvtest::BaseTest |
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{ |
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public: |
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typedef typename Distance::ValueType ValueType; |
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typedef typename Distance::ResultType DistanceType; |
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CV_DescriptorExtractorTest( const string _name, DistanceType _maxDist, const Ptr<DescriptorExtractor>& _dextractor, float _prevTime, |
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Distance d = Distance() ): |
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name(_name), maxDist(_maxDist), prevTime(_prevTime), dextractor(_dextractor), distance(d) {} |
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protected: |
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virtual void createDescriptorExtractor() {} |
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void compareDescriptors( const Mat& validDescriptors, const Mat& calcDescriptors ) |
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{ |
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if( validDescriptors.size != calcDescriptors.size || validDescriptors.type() != calcDescriptors.type() ) |
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{ |
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ts->printf(cvtest::TS::LOG, "Valid and computed descriptors matrices must have the same size and type.\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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return; |
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} |
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CV_Assert( DataType<ValueType>::type == validDescriptors.type() ); |
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int dimension = validDescriptors.cols; |
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DistanceType curMaxDist = std::numeric_limits<DistanceType>::min(); |
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for( int y = 0; y < validDescriptors.rows; y++ ) |
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{ |
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DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension ); |
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if( dist > curMaxDist ) |
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curMaxDist = dist; |
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} |
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stringstream ss; |
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ss << "Max distance between valid and computed descriptors " << curMaxDist; |
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if( curMaxDist < maxDist ) |
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ss << "." << endl; |
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else |
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{ |
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ss << ">" << maxDist << " - bad accuracy!"<< endl; |
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
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} |
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ts->printf(cvtest::TS::LOG, ss.str().c_str() ); |
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} |
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void emptyDataTest() |
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{ |
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assert( !dextractor.empty() ); |
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// One image. |
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Mat image; |
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vector<KeyPoint> keypoints; |
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Mat descriptors; |
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try |
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{ |
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dextractor->compute( image, keypoints, descriptors ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "compute() on empty image and empty keypoints must not generate exception (1).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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} |
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image.create( 50, 50, CV_8UC3 ); |
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try |
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{ |
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dextractor->compute( image, keypoints, descriptors ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "compute() on nonempty image and empty keypoints must not generate exception (1).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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} |
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// Several images. |
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vector<Mat> images; |
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vector<vector<KeyPoint> > keypointsCollection; |
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vector<Mat> descriptorsCollection; |
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try |
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{ |
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dextractor->compute( images, keypointsCollection, descriptorsCollection ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "compute() on empty images and empty keypoints collection must not generate exception (2).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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} |
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} |
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void regressionTest() |
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{ |
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assert( !dextractor.empty() ); |
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// Read the test image. |
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string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME; |
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Mat img = imread( imgFilename ); |
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if( img.empty() ) |
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{ |
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ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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return; |
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} |
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vector<KeyPoint> keypoints; |
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FileStorage fs( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::READ ); |
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if( fs.isOpened() ) |
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{ |
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read( fs.getFirstTopLevelNode(), keypoints ); |
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Mat calcDescriptors; |
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double t = (double)getTickCount(); |
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dextractor->compute( img, keypoints, calcDescriptors ); |
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t = getTickCount() - t; |
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ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms (previous time = %g ms).\n", t/((double)cvGetTickFrequency()*1000.)/calcDescriptors.rows, prevTime ); |
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if( calcDescriptors.rows != (int)keypoints.size() ) |
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{ |
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ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" ); |
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ts->printf( cvtest::TS::LOG, "Count of keypoints is %d.\n", (int)keypoints.size() ); |
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ts->printf( cvtest::TS::LOG, "Count of computed descriptors is %d.\n", calcDescriptors.rows ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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return; |
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} |
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if( calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType() ) |
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{ |
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ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" ); |
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ts->printf( cvtest::TS::LOG, "Expected size is %d.\n", dextractor->descriptorSize() ); |
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ts->printf( cvtest::TS::LOG, "Calculated size is %d.\n", calcDescriptors.cols ); |
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ts->printf( cvtest::TS::LOG, "Expected type is %d.\n", dextractor->descriptorType() ); |
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ts->printf( cvtest::TS::LOG, "Calculated type is %d.\n", calcDescriptors.type() ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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return; |
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} |
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// TODO read and write descriptor extractor parameters and check them |
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Mat validDescriptors = readDescriptors(); |
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if( !validDescriptors.empty() ) |
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compareDescriptors( validDescriptors, calcDescriptors ); |
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else |
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{ |
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if( !writeDescriptors( calcDescriptors ) ) |
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{ |
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ts->printf( cvtest::TS::LOG, "Descriptors can not be written.\n" ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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return; |
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} |
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} |
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} |
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else |
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{ |
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ts->printf( cvtest::TS::LOG, "Compute and write keypoints.\n" ); |
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fs.open( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::WRITE ); |
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if( fs.isOpened() ) |
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{ |
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SurfFeatureDetector fd; |
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fd.detect(img, keypoints); |
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write( fs, "keypoints", keypoints ); |
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} |
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else |
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{ |
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ts->printf(cvtest::TS::LOG, "File for writting keypoints can not be opened.\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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return; |
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} |
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} |
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} |
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void run(int) |
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{ |
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createDescriptorExtractor(); |
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if( dextractor.empty() ) |
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{ |
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ts->printf(cvtest::TS::LOG, "Descriptor extractor is empty.\n"); |
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ts->set_failed_test_info( cvtest::TS::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( cvtest::TS::OK ); |
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} |
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virtual Mat readDescriptors() |
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{ |
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Mat res = readMatFromBin( string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) ); |
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return res; |
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} |
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virtual bool writeDescriptors( Mat& descs ) |
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{ |
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writeMatInBin( descs, string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) ); |
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return true; |
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} |
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string name; |
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const DistanceType maxDist; |
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const float prevTime; |
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Ptr<DescriptorExtractor> dextractor; |
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Distance distance; |
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private: |
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CV_DescriptorExtractorTest& operator=(const CV_DescriptorExtractorTest&) { return *this; } |
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}; |
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template<typename T, typename Distance> |
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class CV_CalonderDescriptorExtractorTest : public CV_DescriptorExtractorTest<Distance> |
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{ |
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public: |
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CV_CalonderDescriptorExtractorTest( const char* testName, float _normDif, float _prevTime ) : |
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CV_DescriptorExtractorTest<Distance>( testName, _normDif, Ptr<DescriptorExtractor>(), _prevTime ) |
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{} |
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protected: |
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virtual void createDescriptorExtractor() |
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{ |
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CV_DescriptorExtractorTest<Distance>::dextractor = |
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new CalonderDescriptorExtractor<T>( string(CV_DescriptorExtractorTest<Distance>::ts->get_data_path()) + |
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FEATURES2D_DIR + "/calonder_classifier.rtc"); |
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} |
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}; |
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/****************************************************************************************\ |
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* Algorithmic tests for descriptor matchers * |
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\****************************************************************************************/ |
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class CV_DescriptorMatcherTest : public cvtest::BaseTest |
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{ |
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public: |
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CV_DescriptorMatcherTest( const string& _name, const Ptr<DescriptorMatcher>& _dmatcher, float _badPart ) : |
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badPart(_badPart), name(_name), dmatcher(_dmatcher) |
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{} |
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protected: |
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static const int dim = 500; |
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static const int queryDescCount = 300; // must be even number because we split train data in some cases in two |
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static const int countFactor = 4; // do not change it |
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const float badPart; |
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|
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virtual void run( int ); |
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void generateData( Mat& query, Mat& train ); |
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|
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void emptyDataTest(); |
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void matchTest( const Mat& query, const Mat& train ); |
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void knnMatchTest( const Mat& query, const Mat& train ); |
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void radiusMatchTest( const Mat& query, const Mat& train ); |
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|
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string name; |
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Ptr<DescriptorMatcher> dmatcher; |
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|
|
private: |
|
CV_DescriptorMatcherTest& operator=(const CV_DescriptorMatcherTest&) { return *this; } |
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}; |
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|
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void CV_DescriptorMatcherTest::emptyDataTest() |
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{ |
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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 ); |
|
} |
|
|
|
} |
|
|
|
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 decriptors 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 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 * |
|
\****************************************************************************************/ |
|
|
|
/* |
|
* Detectors |
|
*/ |
|
|
|
TEST( Features2d_Detector_FAST, regression ) |
|
{ |
|
CV_FeatureDetectorTest test( "detector-fast", FeatureDetector::create("FAST") ); |
|
test.safe_run(); |
|
} |
|
|
|
TEST( Features2d_Detector_GFTT, regression ) |
|
{ |
|
CV_FeatureDetectorTest test( "detector-gftt", FeatureDetector::create("GFTT") ); |
|
test.safe_run(); |
|
} |
|
|
|
TEST( Features2d_Detector_Harris, regression ) |
|
{ |
|
CV_FeatureDetectorTest test( "detector-harris", FeatureDetector::create("HARRIS") ); |
|
test.safe_run(); |
|
} |
|
|
|
TEST( Features2d_Detector_MSER, regression ) |
|
{ |
|
CV_FeatureDetectorTest test( "detector-mser", FeatureDetector::create("MSER") ); |
|
test.safe_run(); |
|
} |
|
|
|
TEST( Features2d_Detector_SIFT, regression ) |
|
{ |
|
CV_FeatureDetectorTest test( "detector-sift", FeatureDetector::create("SIFT") ); |
|
test.safe_run(); |
|
} |
|
|
|
TEST( Features2d_Detector_STAR, regression ) |
|
{ |
|
CV_FeatureDetectorTest test( "detector-star", FeatureDetector::create("STAR") ); |
|
test.safe_run(); |
|
} |
|
|
|
TEST( Features2d_Detector_SURF, regression ) |
|
{ |
|
CV_FeatureDetectorTest test( "detector-surf", FeatureDetector::create("SURF") ); |
|
test.safe_run(); |
|
} |
|
|
|
TEST( Features2d_Detector_ORB, regression ) |
|
{ |
|
CV_FeatureDetectorTest test( "detector-orb", FeatureDetector::create("ORB") ); |
|
test.safe_run(); |
|
} |
|
|
|
TEST( Features2d_Detector_GridFAST, regression ) |
|
{ |
|
CV_FeatureDetectorTest test( "detector-grid-fast", FeatureDetector::create("GridFAST") ); |
|
test.safe_run(); |
|
} |
|
|
|
TEST( Features2d_Detector_PyramidFAST, regression ) |
|
{ |
|
CV_FeatureDetectorTest test( "detector-pyramid-fast", FeatureDetector::create("PyramidFAST") ); |
|
test.safe_run(); |
|
} |
|
|
|
/* |
|
* Descriptors |
|
*/ |
|
TEST( Features2d_DescriptorExtractor_SIFT, regression ) |
|
{ |
|
CV_DescriptorExtractorTest<L2<float> > test( "descriptor-sift", 0.03f, |
|
DescriptorExtractor::create("SIFT"), 8.06652f ); |
|
test.safe_run(); |
|
} |
|
|
|
TEST( Features2d_DescriptorExtractor_SURF, regression ) |
|
{ |
|
CV_DescriptorExtractorTest<L2<float> > test( "descriptor-surf", 0.035f, |
|
DescriptorExtractor::create("SURF"), 0.147372f ); |
|
test.safe_run(); |
|
} |
|
|
|
TEST( Features2d_DescriptorExtractor_ORB, regression ) |
|
{ |
|
// TODO adjust the parameters below |
|
CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb", (CV_DescriptorExtractorTest<Hamming>::DistanceType)3.f, |
|
DescriptorExtractor::create("ORB"), 0.010f ); |
|
test.safe_run(); |
|
} |
|
|
|
TEST( Features2d_DescriptorExtractor_BRIEF, regression ) |
|
{ |
|
CV_DescriptorExtractorTest<Hamming> test( "descriptor-brief", 1, |
|
DescriptorExtractor::create("BRIEF"), 0.00527548f ); |
|
test.safe_run(); |
|
} |
|
|
|
/*TEST( Features2d_DescriptorExtractor_OpponentSIFT, regression ) |
|
{ |
|
CV_DescriptorExtractorTest<L2<float> > test( "descriptor-opponent-sift", 0.18f, |
|
DescriptorExtractor::create("OpponentSIFT"), 8.06652f ); |
|
test.safe_run(); |
|
}*/ |
|
|
|
TEST( Features2d_DescriptorExtractor_OpponentSURF, regression ) |
|
{ |
|
CV_DescriptorExtractorTest<L2<float> > test( "descriptor-opponent-surf", 0.18f, |
|
DescriptorExtractor::create("OpponentSURF"), 0.147372f ); |
|
test.safe_run(); |
|
} |
|
|
|
#if CV_SSE2 |
|
TEST( Features2d_DescriptorExtractor_Calonder_uchar, regression ) |
|
{ |
|
CV_CalonderDescriptorExtractorTest<uchar, L2<uchar> > test( "descriptor-calonder-uchar", |
|
std::numeric_limits<float>::epsilon() + 1, |
|
0.0132175f ); |
|
test.safe_run(); |
|
} |
|
|
|
TEST( Features2d_DescriptorExtractor_Calonder_float, regression ) |
|
{ |
|
CV_CalonderDescriptorExtractorTest<float, L2<float> > test( "descriptor-calonder-float", |
|
std::numeric_limits<float>::epsilon(), |
|
0.0221308f ); |
|
test.safe_run(); |
|
} |
|
#endif // CV_SSE2 |
|
|
|
/* |
|
* Matchers |
|
*/ |
|
TEST( Features2d_DescriptorMatcher_BruteForce, regression ) |
|
{ |
|
CV_DescriptorMatcherTest test( "descriptor-matcher-brute-force", new BruteForceMatcher<L2<float> >, 0.01f ); |
|
test.safe_run(); |
|
} |
|
|
|
TEST( Features2d_DescriptorMatcher_FlannBased, regression ) |
|
{ |
|
CV_DescriptorMatcherTest test( "descriptor-matcher-flann-based", new FlannBasedMatcher, 0.04f ); |
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test.safe_run(); |
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}
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|