297 lines
10 KiB
C++
297 lines
10 KiB
C++
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/*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 IMAGE_FILENAME = "tsukuba.png";
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const string DETECTOR_DIR = FEATURES2D_DIR + "/feature_detectors";
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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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* Tests registrations *
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\****************************************************************************************/
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TEST( Features2d_Detector_FAST, regression )
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{
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CV_FeatureDetectorTest test( "detector-fast", FeatureDetector::create("FAST") );
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test.safe_run();
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}
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TEST( Features2d_Detector_GFTT, regression )
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{
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CV_FeatureDetectorTest test( "detector-gftt", FeatureDetector::create("GFTT") );
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test.safe_run();
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}
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TEST( Features2d_Detector_Harris, regression )
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{
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CV_FeatureDetectorTest test( "detector-harris", FeatureDetector::create("HARRIS") );
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test.safe_run();
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}
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TEST( Features2d_Detector_MSER, DISABLED_regression )
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{
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CV_FeatureDetectorTest test( "detector-mser", FeatureDetector::create("MSER") );
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test.safe_run();
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}
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TEST( Features2d_Detector_STAR, regression )
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{
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CV_FeatureDetectorTest test( "detector-star", FeatureDetector::create("STAR") );
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test.safe_run();
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}
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TEST( Features2d_Detector_ORB, regression )
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{
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CV_FeatureDetectorTest test( "detector-orb", FeatureDetector::create("ORB") );
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test.safe_run();
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}
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TEST( Features2d_Detector_GridFAST, regression )
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{
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CV_FeatureDetectorTest test( "detector-grid-fast", FeatureDetector::create("GridFAST") );
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test.safe_run();
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}
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TEST( Features2d_Detector_PyramidFAST, regression )
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{
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CV_FeatureDetectorTest test( "detector-pyramid-fast", FeatureDetector::create("PyramidFAST") );
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test.safe_run();
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}
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