1241 lines
44 KiB
C++
1241 lines
44 KiB
C++
/*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/calib3d/calib3d.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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#if defined(HAVE_OPENCV_OCL) && 0 // unblock this to see SURF_OCL tests failures
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static Ptr<Feature2D> getSURF()
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{
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ocl::PlatformsInfo p;
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if(ocl::getOpenCLPlatforms(p) > 0)
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return new ocl::SURF_OCL;
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else
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return new SURF;
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}
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#else
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static Ptr<Feature2D> getSURF()
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{
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return new SURF;
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}
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#endif
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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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size_t elements_read1 = fread( (void*)&rows, sizeof(int), 1, f );
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size_t elements_read2 = fread( (void*)&cols, sizeof(int), 1, f );
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size_t elements_read3 = fread( (void*)&type, sizeof(int), 1, f );
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size_t elements_read4 = fread( (void*)&dataSize, sizeof(int), 1, f );
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CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1);
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uchar* data = (uchar*)cvAlloc(dataSize);
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size_t elements_read = fread( (void*)data, 1, dataSize, f );
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CV_Assert(elements_read == (size_t)(dataSize));
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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,
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Distance d = Distance() ):
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name(_name), maxDist(_maxDist), 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->printf(cvtest::TS::LOG, "Valid size is (%d x %d) actual size is (%d x %d).\n", validDescriptors.rows, validDescriptors.cols, calcDescriptors.rows, calcDescriptors.cols);
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ts->printf(cvtest::TS::LOG, "Valid type is %d actual type is %d.\n", validDescriptors.type(), calcDescriptors.type());
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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.\n", t/((double)cvGetTickFrequency()*1000.)/calcDescriptors.rows );
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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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|
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void run(int)
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{
|
|
createDescriptorExtractor();
|
|
if( dextractor.empty() )
|
|
{
|
|
ts->printf(cvtest::TS::LOG, "Descriptor extractor is empty.\n");
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
|
return;
|
|
}
|
|
|
|
emptyDataTest();
|
|
regressionTest();
|
|
|
|
ts->set_failed_test_info( cvtest::TS::OK );
|
|
}
|
|
|
|
virtual Mat readDescriptors()
|
|
{
|
|
Mat res = readMatFromBin( string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
|
|
return res;
|
|
}
|
|
|
|
virtual bool writeDescriptors( Mat& descs )
|
|
{
|
|
writeMatInBin( descs, string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
|
|
return true;
|
|
}
|
|
|
|
string name;
|
|
const DistanceType maxDist;
|
|
Ptr<DescriptorExtractor> dextractor;
|
|
Distance distance;
|
|
|
|
private:
|
|
CV_DescriptorExtractorTest& operator=(const CV_DescriptorExtractorTest&) { return *this; }
|
|
};
|
|
|
|
/*template<typename T, typename Distance>
|
|
class CV_CalonderDescriptorExtractorTest : public CV_DescriptorExtractorTest<Distance>
|
|
{
|
|
public:
|
|
CV_CalonderDescriptorExtractorTest( const char* testName, float _normDif, float _prevTime ) :
|
|
CV_DescriptorExtractorTest<Distance>( testName, _normDif, Ptr<DescriptorExtractor>(), _prevTime )
|
|
{}
|
|
|
|
protected:
|
|
virtual void createDescriptorExtractor()
|
|
{
|
|
CV_DescriptorExtractorTest<Distance>::dextractor =
|
|
new CalonderDescriptorExtractor<T>( string(CV_DescriptorExtractorTest<Distance>::ts->get_data_path()) +
|
|
FEATURES2D_DIR + "/calonder_classifier.rtc");
|
|
}
|
|
};*/
|
|
|
|
/****************************************************************************************\
|
|
* Algorithmic tests for descriptor matchers *
|
|
\****************************************************************************************/
|
|
class CV_DescriptorMatcherTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
CV_DescriptorMatcherTest( const string& _name, const Ptr<DescriptorMatcher>& _dmatcher, float _badPart ) :
|
|
badPart(_badPart), name(_name), dmatcher(_dmatcher)
|
|
{}
|
|
protected:
|
|
static const int dim = 500;
|
|
static const int queryDescCount = 300; // must be even number because we split train data in some cases in two
|
|
static const int countFactor = 4; // do not change it
|
|
const float badPart;
|
|
|
|
virtual void run( int );
|
|
void generateData( Mat& query, Mat& train );
|
|
|
|
void emptyDataTest();
|
|
void matchTest( const Mat& query, const Mat& train );
|
|
void knnMatchTest( const Mat& query, const Mat& train );
|
|
void radiusMatchTest( const Mat& query, const Mat& train );
|
|
|
|
string name;
|
|
Ptr<DescriptorMatcher> dmatcher;
|
|
|
|
private:
|
|
CV_DescriptorMatcherTest& operator=(const CV_DescriptorMatcherTest&) { return *this; }
|
|
};
|
|
|
|
void CV_DescriptorMatcherTest::emptyDataTest()
|
|
{
|
|
assert( !dmatcher.empty() );
|
|
Mat queryDescriptors, trainDescriptors, mask;
|
|
vector<Mat> trainDescriptorCollection, masks;
|
|
vector<DMatch> matches;
|
|
vector<vector<DMatch> > vmatches;
|
|
|
|
try
|
|
{
|
|
dmatcher->match( queryDescriptors, trainDescriptors, matches, mask );
|
|
}
|
|
catch(...)
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "match() on empty descriptors must not generate exception (1).\n" );
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
|
}
|
|
|
|
try
|
|
{
|
|
dmatcher->knnMatch( queryDescriptors, trainDescriptors, vmatches, 2, mask );
|
|
}
|
|
catch(...)
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "knnMatch() on empty descriptors must not generate exception (1).\n" );
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
|
}
|
|
|
|
try
|
|
{
|
|
dmatcher->radiusMatch( queryDescriptors, trainDescriptors, vmatches, 10.f, mask );
|
|
}
|
|
catch(...)
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "radiusMatch() on empty descriptors must not generate exception (1).\n" );
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
|
}
|
|
|
|
try
|
|
{
|
|
dmatcher->add( trainDescriptorCollection );
|
|
}
|
|
catch(...)
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "add() on empty descriptors must not generate exception.\n" );
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
|
}
|
|
|
|
try
|
|
{
|
|
dmatcher->match( queryDescriptors, matches, masks );
|
|
}
|
|
catch(...)
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "match() on empty descriptors must not generate exception (2).\n" );
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
|
}
|
|
|
|
try
|
|
{
|
|
dmatcher->knnMatch( queryDescriptors, vmatches, 2, masks );
|
|
}
|
|
catch(...)
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "knnMatch() on empty descriptors must not generate exception (2).\n" );
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
|
}
|
|
|
|
try
|
|
{
|
|
dmatcher->radiusMatch( queryDescriptors, vmatches, 10.f, masks );
|
|
}
|
|
catch(...)
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "radiusMatch() on empty descriptors must not generate exception (2).\n" );
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
|
}
|
|
|
|
}
|
|
|
|
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 )
|
|
{
|
|
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 );
|
|
}
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/****************************************************************************************\
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* Tests registrations *
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\****************************************************************************************/
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/*
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* Detectors
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*/
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TEST( Features2d_Detector_SIFT, regression )
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{
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CV_FeatureDetectorTest test( "detector-sift", FeatureDetector::create("SIFT") );
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test.safe_run();
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}
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TEST( Features2d_Detector_SURF, regression )
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{
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CV_FeatureDetectorTest test( "detector-surf", Ptr<FeatureDetector>(getSURF()) );
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test.safe_run();
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}
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/*
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* Descriptors
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*/
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TEST( Features2d_DescriptorExtractor_SIFT, regression )
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{
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CV_DescriptorExtractorTest<L2<float> > test( "descriptor-sift", 0.03f,
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DescriptorExtractor::create("SIFT") );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_SURF, regression )
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{
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CV_DescriptorExtractorTest<L2<float> > test( "descriptor-surf", 0.05f,
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Ptr<DescriptorExtractor>(getSURF()) );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_OpponentSIFT, regression )
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{
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CV_DescriptorExtractorTest<L2<float> > test( "descriptor-opponent-sift", 0.18f,
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DescriptorExtractor::create("OpponentSIFT") );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_OpponentSURF, regression )
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{
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CV_DescriptorExtractorTest<L2<float> > test( "descriptor-opponent-surf", 0.3f,
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DescriptorExtractor::create("OpponentSURF") );
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test.safe_run();
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}
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/*#if CV_SSE2
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TEST( Features2d_DescriptorExtractor_Calonder_uchar, regression )
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{
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CV_CalonderDescriptorExtractorTest<uchar, L2<uchar> > test( "descriptor-calonder-uchar",
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std::numeric_limits<float>::epsilon() + 1,
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0.0132175f );
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test.safe_run();
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|
}
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TEST( Features2d_DescriptorExtractor_Calonder_float, regression )
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{
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CV_CalonderDescriptorExtractorTest<float, L2<float> > test( "descriptor-calonder-float",
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std::numeric_limits<float>::epsilon(),
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|
0.0221308f );
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test.safe_run();
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}
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#endif*/ // CV_SSE2
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|
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TEST(Features2d_BruteForceDescriptorMatcher_knnMatch, regression)
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|
{
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const int sz = 100;
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const int k = 3;
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|
|
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Ptr<DescriptorExtractor> ext = Ptr<DescriptorExtractor>(getSURF());
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ASSERT_TRUE(ext != NULL);
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Ptr<FeatureDetector> det = Ptr<FeatureDetector>(getSURF());
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|
//"%YAML:1.0\nhessianThreshold: 8000.\noctaves: 3\noctaveLayers: 4\nupright: 0\n"
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|
ASSERT_TRUE(det != NULL);
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|
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Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce");
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|
ASSERT_TRUE(matcher != NULL);
|
|
|
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Mat imgT(sz, sz, CV_8U, Scalar(255));
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line(imgT, Point(20, sz/2), Point(sz-21, sz/2), Scalar(100), 2);
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|
line(imgT, Point(sz/2, 20), Point(sz/2, sz-21), Scalar(100), 2);
|
|
vector<KeyPoint> kpT;
|
|
kpT.push_back( KeyPoint(50, 50, 16, 0, 20000, 1, -1) );
|
|
kpT.push_back( KeyPoint(42, 42, 16, 160, 10000, 1, -1) );
|
|
Mat descT;
|
|
ext->compute(imgT, kpT, descT);
|
|
|
|
Mat imgQ(sz, sz, CV_8U, Scalar(255));
|
|
line(imgQ, Point(30, sz/2), Point(sz-31, sz/2), Scalar(100), 3);
|
|
line(imgQ, Point(sz/2, 30), Point(sz/2, sz-31), Scalar(100), 3);
|
|
vector<KeyPoint> kpQ;
|
|
det->detect(imgQ, kpQ);
|
|
Mat descQ;
|
|
ext->compute(imgQ, kpQ, descQ);
|
|
|
|
vector<vector<DMatch> > matches;
|
|
|
|
matcher->knnMatch(descQ, descT, matches, k);
|
|
|
|
//cout << "\nBest " << k << " matches to " << descT.rows << " train desc-s." << endl;
|
|
ASSERT_EQ(descQ.rows, static_cast<int>(matches.size()));
|
|
for(size_t i = 0; i<matches.size(); i++)
|
|
{
|
|
//cout << "\nmatches[" << i << "].size()==" << matches[i].size() << endl;
|
|
ASSERT_GE(min(k, descT.rows), static_cast<int>(matches[i].size()));
|
|
for(size_t j = 0; j<matches[i].size(); j++)
|
|
{
|
|
//cout << "\t" << matches[i][j].queryIdx << " -> " << matches[i][j].trainIdx << endl;
|
|
ASSERT_EQ(matches[i][j].queryIdx, static_cast<int>(i));
|
|
}
|
|
}
|
|
}
|
|
|
|
/*TEST(Features2d_DescriptorExtractorParamTest, regression)
|
|
{
|
|
Ptr<DescriptorExtractor> s = DescriptorExtractor::create("SURF");
|
|
ASSERT_STREQ(s->paramHelp("extended").c_str(), "");
|
|
}
|
|
*/
|
|
|
|
class CV_DetectPlanarTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
CV_DetectPlanarTest(const string& _fname, int _min_ninliers) : fname(_fname), min_ninliers(_min_ninliers) {}
|
|
|
|
protected:
|
|
void run(int)
|
|
{
|
|
Ptr<Feature2D> f;
|
|
if(fname == "SURF")
|
|
f = getSURF();
|
|
else
|
|
f = Algorithm::create<Feature2D>("Feature2D." + fname);
|
|
if(f.empty())
|
|
return;
|
|
string path = string(ts->get_data_path()) + "detectors_descriptors_evaluation/planar/";
|
|
string imgname1 = path + "box.png";
|
|
string imgname2 = path + "box_in_scene.png";
|
|
Mat img1 = imread(imgname1, 0);
|
|
Mat img2 = imread(imgname2, 0);
|
|
if( img1.empty() || img2.empty() )
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "missing %s and/or %s\n", imgname1.c_str(), imgname2.c_str());
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
|
return;
|
|
}
|
|
vector<KeyPoint> kpt1, kpt2;
|
|
Mat d1, d2;
|
|
f->operator()(img1, Mat(), kpt1, d1);
|
|
f->operator()(img1, Mat(), kpt2, d2);
|
|
for( size_t i = 0; i < kpt1.size(); i++ )
|
|
CV_Assert(kpt1[i].response > 0 );
|
|
for( size_t i = 0; i < kpt2.size(); i++ )
|
|
CV_Assert(kpt2[i].response > 0 );
|
|
|
|
vector<DMatch> matches;
|
|
BFMatcher(NORM_L2, true).match(d1, d2, matches);
|
|
|
|
vector<Point2f> pt1, pt2;
|
|
for( size_t i = 0; i < matches.size(); i++ ) {
|
|
pt1.push_back(kpt1[matches[i].queryIdx].pt);
|
|
pt2.push_back(kpt2[matches[i].trainIdx].pt);
|
|
}
|
|
|
|
Mat inliers, H = findHomography(pt1, pt2, RANSAC, 10, inliers);
|
|
int ninliers = countNonZero(inliers);
|
|
|
|
if( ninliers < min_ninliers )
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "too little inliers (%d) vs expected %d\n", ninliers, min_ninliers);
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
|
return;
|
|
}
|
|
}
|
|
|
|
string fname;
|
|
int min_ninliers;
|
|
};
|
|
|
|
TEST(Features2d_SIFTHomographyTest, regression) { CV_DetectPlanarTest test("SIFT", 80); test.safe_run(); }
|
|
TEST(Features2d_SURFHomographyTest, regression) { CV_DetectPlanarTest test("SURF", 80); test.safe_run(); }
|
|
|
|
class FeatureDetectorUsingMaskTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
FeatureDetectorUsingMaskTest(const Ptr<FeatureDetector>& featureDetector) :
|
|
featureDetector_(featureDetector)
|
|
{
|
|
CV_Assert(!featureDetector_.empty());
|
|
}
|
|
|
|
protected:
|
|
|
|
void run(int)
|
|
{
|
|
const int nStepX = 2;
|
|
const int nStepY = 2;
|
|
|
|
const string imageFilename = string(ts->get_data_path()) + "/features2d/tsukuba.png";
|
|
|
|
Mat image = imread(imageFilename);
|
|
if(image.empty())
|
|
{
|
|
ts->printf(cvtest::TS::LOG, "Image %s can not be read.\n", imageFilename.c_str());
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
|
|
return;
|
|
}
|
|
|
|
Mat mask(image.size(), CV_8U);
|
|
|
|
const int stepX = image.size().width / nStepX;
|
|
const int stepY = image.size().height / nStepY;
|
|
|
|
vector<KeyPoint> keyPoints;
|
|
vector<Point2f> points;
|
|
for(int i=0; i<nStepX; ++i)
|
|
for(int j=0; j<nStepY; ++j)
|
|
{
|
|
|
|
mask.setTo(0);
|
|
Rect whiteArea(i * stepX, j * stepY, stepX, stepY);
|
|
mask(whiteArea).setTo(255);
|
|
|
|
featureDetector_->detect(image, keyPoints, mask);
|
|
KeyPoint::convert(keyPoints, points);
|
|
|
|
for(size_t k=0; k<points.size(); ++k)
|
|
{
|
|
if ( !whiteArea.contains(points[k]) )
|
|
{
|
|
ts->printf(cvtest::TS::LOG, "The feature point is outside of the mask.");
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
|
|
ts->set_failed_test_info( cvtest::TS::OK );
|
|
}
|
|
|
|
Ptr<FeatureDetector> featureDetector_;
|
|
};
|
|
|
|
TEST(Features2d_SIFT_using_mask, regression)
|
|
{
|
|
FeatureDetectorUsingMaskTest test(Algorithm::create<FeatureDetector>("Feature2D.SIFT"));
|
|
test.safe_run();
|
|
}
|
|
|
|
TEST(DISABLED_Features2d_SURF_using_mask, regression)
|
|
{
|
|
FeatureDetectorUsingMaskTest test(Algorithm::create<FeatureDetector>("Feature2D.SURF"));
|
|
test.safe_run();
|
|
}
|