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opencv/tests/cv/src/afeatures2d.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "cvtest.h"
#include "opencv2/core/core.hpp"
using namespace std;
using namespace cv;
const string FEATURES2D_DIR = "features2d";
const string DETECTOR_DIR = FEATURES2D_DIR + "/feature_detectors";
const string DESCRIPTOR_DIR = FEATURES2D_DIR + "/descriptor_extractors";
const string IMAGE_FILENAME = "tsukuba.png";
/****************************************************************************************\
* Regression tests for feature detectors comparing keypoints. *
\****************************************************************************************/
class CV_FeatureDetectorTest : public CvTest
{
public:
CV_FeatureDetectorTest( const char* testName, const Ptr<FeatureDetector>& _fdetector ) :
CvTest( testName, "cv::FeatureDetector::detect"), fdetector(_fdetector) {}
protected:
virtual void run( int /*start_from*/ )
{
const float maxPtDif = 1.f;
const float maxSizeDif = 1.f;
const float maxAngleDif = 2.f;
const float maxResponseDif = 0.1f;
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
string resFilename = string(ts->get_data_path()) + DETECTOR_DIR + "/" + string(name) + ".xml.gz";
if( fdetector.empty() )
{
ts->printf( CvTS::LOG, "Feature detector is empty" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
return;
}
Mat image = imread( imgFilename, 0 );
if( image.empty() )
{
ts->printf( CvTS::LOG, "image %s can not be read \n", imgFilename.c_str() );
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
return;
}
FileStorage fs( resFilename, FileStorage::READ );
vector<KeyPoint> calcKeypoints;
fdetector->detect( image, calcKeypoints );
if( fs.isOpened() ) // compare computed and valid keypoints
{
// TODO compare saved feature detector params with current ones
vector<KeyPoint> validKeypoints;
read( fs["keypoints"], validKeypoints );
if( validKeypoints.empty() )
{
ts->printf( CvTS::LOG, "Keypoints can nod be read\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
return;
}
int progress = 0, progressCount = validKeypoints.size() * calcKeypoints.size();
int badPointCount = 0, commonPointCount = max(validKeypoints.size(), calcKeypoints.size());
for( size_t v = 0; v < validKeypoints.size(); v++ )
{
int nearestIdx = -1;
float minDist = std::numeric_limits<float>::max();
for( size_t c = 0; c < calcKeypoints.size(); c++ )
{
progress = update_progress( progress, v*calcKeypoints.size() + c, progressCount, 0 );
float curDist = (float)norm( calcKeypoints[c].pt - validKeypoints[v].pt );
if( curDist < minDist )
{
minDist = curDist;
nearestIdx = c;
}
}
if( minDist > maxPtDif ||
fabs(calcKeypoints[nearestIdx].size - validKeypoints[v].size) > maxSizeDif ||
abs(calcKeypoints[nearestIdx].angle - validKeypoints[v].angle) > maxAngleDif ||
abs(calcKeypoints[nearestIdx].response - validKeypoints[v].response) > maxResponseDif ||
calcKeypoints[nearestIdx].octave != validKeypoints[v].octave
// TODO !!!!!!!
/*||
calcKeypoints[nearestIdx].class_id != validKeypoints[v].class_id*/ )
{
badPointCount++;
}
}
ts->printf( CvTS::LOG, "badPointCount = %d; validPointCount = %d; calcPointCount = %d\n",
badPointCount, validKeypoints.size(), calcKeypoints.size() );
if( badPointCount > 0.9 * commonPointCount )
{
ts->printf( CvTS::LOG, "Bad accuracy!\n" );
ts->set_failed_test_info( CvTS::FAIL_BAD_ACCURACY );
return;
}
}
else // write
{
fs.open( resFilename, FileStorage::WRITE );
if( !fs.isOpened() )
{
ts->printf( CvTS::LOG, "file %s can not be opened to write\n", resFilename.c_str() );
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
return;
}
else
{
fs << "detector_params" << "{";
fdetector->write( fs );
fs << "}";
write( fs, "keypoints", calcKeypoints );
}
}
ts->set_failed_test_info( CvTS::OK );
}
Ptr<FeatureDetector> fdetector;
};
/****************************************************************************************\
* Regression tests for descriptor extractors. *
\****************************************************************************************/
static void writeMatInBin( const Mat& mat, const string& filename )
{
FILE* f = fopen( filename.c_str(), "wb");
if( f )
{
int type = mat.type();
fwrite( (void*)&mat.rows, sizeof(int), 1, f );
fwrite( (void*)&mat.cols, sizeof(int), 1, f );
fwrite( (void*)&type, sizeof(int), 1, f );
fwrite( (void*)&mat.step, sizeof(int), 1, f );
fwrite( (void*)mat.data, 1, mat.step*mat.rows, f );
fclose(f);
}
}
static Mat readMatFromBin( const string& filename )
{
FILE* f = fopen( filename.c_str(), "rb" );
if( f )
{
int rows, cols, type, step;
fread( (void*)&rows, sizeof(int), 1, f );
fread( (void*)&cols, sizeof(int), 1, f );
fread( (void*)&type, sizeof(int), 1, f );
fread( (void*)&step, sizeof(int), 1, f );
uchar* data = (uchar*)cvAlloc(step*rows);
fread( (void*)data, 1, step*rows, f );
fclose(f);
return Mat( rows, cols, type, data );
}
return Mat();
}
class CV_DescriptorExtractorTest : public CvTest
{
public:
CV_DescriptorExtractorTest( const char* testName, float _maxNormDif, const Ptr<DescriptorExtractor>& _dextractor, float _prevTime ) :
CvTest( testName, "cv::DescriptorExtractor::compute" ), maxNormDif(_maxNormDif), prevTime(_prevTime), dextractor(_dextractor) {}
protected:
virtual void createDescriptorExtractor() {}
void run(int)
{
createDescriptorExtractor();
if( dextractor.empty() )
{
ts->printf(CvTS::LOG, "Descriptor extractor is empty\n");
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
return;
}
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
Mat img = imread( imgFilename, 0 );
if( img.empty() )
{
ts->printf( CvTS::LOG, "image %s can not be read\n", imgFilename.c_str() );
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
return;
}
vector<KeyPoint> keypoints;
FileStorage fs( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::READ );
if( fs.isOpened() )
read( fs.getFirstTopLevelNode(), keypoints );
else
{
ts->printf( CvTS::LOG, "Compute and write keypoints\n" );
fs.open( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::WRITE );
if( fs.isOpened() )
{
SurfFeatureDetector fd;
fd.detect(img, keypoints);
write( fs, "keypoints", keypoints );
}
else
{
ts->printf(CvTS::LOG, "File for writting keypoints can not be opened\n");
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
return;
}
}
Mat calcDescriptors;
double t = (double)getTickCount();
dextractor->compute( img, keypoints, calcDescriptors );
t = getTickCount() - t;
ts->printf(CvTS::LOG, "\nAverage time of computiting one descriptor = %g ms (previous time = %g ms)\n", t/((double)cvGetTickFrequency()*1000.)/calcDescriptors.rows, prevTime );
// TODO read and write descriptor extractor parameters and check them
Mat validDescriptors = readDescriptors();
if( !validDescriptors.empty() )
{
double normValue = norm( calcDescriptors, validDescriptors, NORM_INF );
ts->printf( CvTS::LOG, "nofm (inf) BTW valid and calculated float descriptors = %f\n", normValue );
if( normValue > maxNormDif )
ts->set_failed_test_info( CvTS::FAIL_BAD_ACCURACY );
}
else
{
if( !writeDescriptors( calcDescriptors ) )
{
ts->printf( CvTS::LOG, "Descriptors can not be written\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
return;
}
}
}
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;
}
const float maxNormDif;
const float prevTime;
Ptr<DescriptorExtractor> dextractor;
};
template<typename T>
class CV_CalonderDescriptorExtractorTest : public CV_DescriptorExtractorTest
{
public:
CV_CalonderDescriptorExtractorTest( const char* testName, float _normDif, float _prevTime ) :
CV_DescriptorExtractorTest( testName, _normDif, Ptr<DescriptorExtractor>(), _prevTime )
{}
virtual void createDescriptorExtractor()
{
dextractor = new CalonderDescriptorExtractor<T>( string(ts->get_data_path()) + FEATURES2D_DIR + "/calonder_classifier.rtc");
}
};
/****************************************************************************************\
* Algorithmic tests for descriptor matchers *
\****************************************************************************************/
class CV_DescriptorMatcherTest : public CvTest
{
public:
CV_DescriptorMatcherTest( const char* testName, const Ptr<DescriptorMatcher>& _dmatcher, float _badPart ) :
CvTest( testName, "cv::DescritorMatcher::[,knn,radius]match()"), badPart(_badPart), dmatcher(_dmatcher)
{ CV_Assert( queryDescCount % 2 == 0 ); // because we split train data in same cases in two
CV_Assert( countFactor == 4); }
protected:
static const int dim = 500;
static const int queryDescCount = 300;
static const int countFactor = 4;
const float badPart;
virtual void run( int );
void generateData( Mat& query, Mat& train );
int testMatch( const Mat& query, const Mat& train );
int testKnnMatch( const Mat& query, const Mat& train );
int testRadiusMatch( const Mat& query, const Mat& train );
Ptr<DescriptorMatcher> dmatcher;
};
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;
}
}
}
int CV_DescriptorMatcherTest::testMatch( const Mat& query, const Mat& train )
{
dmatcher->clear();
// test const version of match()
int res = CvTS::OK;
{
vector<DMatch> matches;
dmatcher->match( query, train, matches );
int curRes = CvTS::OK;
if( (int)matches.size() != queryDescCount )
{
curRes = CvTS::FAIL_INVALID_OUTPUT;
ts->printf(CvTS::LOG, "Incorrect matches count while test match() function (1)\n");
}
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 )
{
curRes = CvTS::FAIL_INVALID_OUTPUT;
ts->printf( CvTS::LOG, "%f - too large bad matches part while test match() function (1)\n",
(float)badCount/(float)queryDescCount );
}
}
res = curRes != CvTS::OK ? curRes : res;
}
// 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 );
int curRes = CvTS::OK;
if( (int)matches.size() != queryDescCount )
{
curRes = CvTS::FAIL_INVALID_OUTPUT;
ts->printf(CvTS::LOG, "Incorrect matches count while test match() function (2)\n");
}
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( CvTS::LOG, "%f - too large bad matches part while test match() function (2)\n",
(float)badCount/(float)queryDescCount );
}
}
res = curRes != CvTS::OK ? curRes : res;
}
return res;
}
int CV_DescriptorMatcherTest::testKnnMatch( const Mat& query, const Mat& train )
{
dmatcher->clear();
// test const version of knnMatch()
int res = CvTS::OK;
{
const int knn = 3;
vector<vector<DMatch> > matches;
dmatcher->knnMatch( query, train, matches, knn );
int curRes = CvTS::OK;
if( (int)matches.size() != queryDescCount )
{
curRes = CvTS::FAIL_INVALID_OUTPUT;
ts->printf(CvTS::LOG, "Incorrect matches count while test knnMatch() function (1)\n");
}
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 )
{
curRes = CvTS::FAIL_INVALID_OUTPUT;
ts->printf( CvTS::LOG, "%f - too large bad matches part while test knnMatch() function (1)\n",
(float)badCount/(float)queryDescCount );
}
}
res = curRes != CvTS::OK ? curRes : res;
}
// 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 );
int curRes = CvTS::OK;
if( (int)matches.size() != queryDescCount )
{
curRes = CvTS::FAIL_INVALID_OUTPUT;
ts->printf(CvTS::LOG, "Incorrect matches count while test knnMatch() function (2)\n");
}
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( CvTS::LOG, "%f - too large bad matches part while test knnMatch() function (2)\n",
(float)badCount/(float)queryDescCount );
}
}
res = curRes != CvTS::OK ? curRes : res;
}
return res;
}
int CV_DescriptorMatcherTest::testRadiusMatch( const Mat& query, const Mat& train )
{
dmatcher->clear();
// test const version of match()
int res = CvTS::OK;
{
const float radius = 1.f/countFactor;
vector<vector<DMatch> > matches;
dmatcher->radiusMatch( query, train, matches, radius );
int curRes = CvTS::OK;
if( (int)matches.size() != queryDescCount )
{
curRes = CvTS::FAIL_INVALID_OUTPUT;
ts->printf(CvTS::LOG, "Incorrect matches count while test radiusMatch() function (1)\n");
}
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 )
{
curRes = CvTS::FAIL_INVALID_OUTPUT;
ts->printf( CvTS::LOG, "%f - too large bad matches part while test radiusMatch() function (1)\n",
(float)badCount/(float)queryDescCount );
}
}
res = curRes != CvTS::OK ? curRes : res;
}
// 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 = CvTS::OK;
if( (int)matches.size() != queryDescCount )
{
curRes = CvTS::FAIL_INVALID_OUTPUT;
ts->printf(CvTS::LOG, "Incorrect matches count while test radiusMatch() function (1)\n");
}
res = curRes != CvTS::OK ? curRes : res;
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 = CvTS::FAIL_INVALID_OUTPUT;
ts->printf( CvTS::LOG, "%f - too large bad matches part while test radiusMatch() function (2)\n",
(float)badCount/(float)queryDescCount );
}
res = curRes != CvTS::OK ? curRes : res;
}
return res;
}
void CV_DescriptorMatcherTest::run( int )
{
Mat query, train;
generateData( query, train );
int res = CvTS::OK, curRes;
curRes = testMatch( query, train );
res = curRes != CvTS::OK ? curRes : res;
curRes = testKnnMatch( query, train );
res = curRes != CvTS::OK ? curRes : res;
curRes = testRadiusMatch( query, train );
res = curRes != CvTS::OK ? curRes : res;
ts->set_failed_test_info( res );
}
/****************************************************************************************\
* Tests registrations *
\****************************************************************************************/
/*
* Detectors
*/
CV_FeatureDetectorTest fastTest( "detector-fast", createFeatureDetector("FAST") );
CV_FeatureDetectorTest gfttTest( "detector-gftt", createFeatureDetector("GFTT") );
CV_FeatureDetectorTest harrisTest( "detector-harris", createFeatureDetector("HARRIS") );
CV_FeatureDetectorTest mserTest( "detector-mser", createFeatureDetector("MSER") );
CV_FeatureDetectorTest siftTest( "detector-sift", createFeatureDetector("SIFT") );
CV_FeatureDetectorTest starTest( "detector-star", createFeatureDetector("STAR") );
CV_FeatureDetectorTest surfTest( "detector-surf", createFeatureDetector("SURF") );
/*
* Descriptors
*/
CV_DescriptorExtractorTest siftDescriptorTest( "descriptor-sift", 0.03f,
createDescriptorExtractor("SIFT"), 8.06652f );
CV_DescriptorExtractorTest surfDescriptorTest( "descriptor-surf", 0.035f,
createDescriptorExtractor("SURF"), 0.147372f );
//CV_DescriptorExtractorTest oppSiftDescriptorTest( "descriptor-opponent-sift", 0.008f,
// createDescriptorExtractor("OpponentSIFT"), 8.06652f );
//CV_DescriptorExtractorTest oppurfDescriptorTest( "descriptor-opponent-surf", 0.02f,
// createDescriptorExtractor("OpponentSURF"), 0.147372f );
#if CV_SSE2
CV_CalonderDescriptorExtractorTest<uchar> ucharCalonderTest( "descriptor-calonder-uchar",
std::numeric_limits<float>::epsilon() + 1,
0.0132175f );
CV_CalonderDescriptorExtractorTest<float> floatCalonderTest( "descriptor-calonder-float",
std::numeric_limits<float>::epsilon(),
0.0221308f );
#endif // CV_SSE2
/*
* Matchers
*/
CV_DescriptorMatcherTest bruteForceMatcherTest( "descriptor-matcher-brute-force",
new BruteForceMatcher<L2<float> >, 0.01f );
CV_DescriptorMatcherTest flannBasedMatcherTest( "descriptor-matcher-flann-based",
new FlannBasedMatcher, 0.04f );