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split file of features2d tests

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Maria Dimashova 13 years ago
parent 76fdbeee8a
commit 507f546158
4 changed files with 720 additions and 581 deletions
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  1. 332
      modules/features2d/test/test_descriptors_regression.cpp
  2. 296
      modules/features2d/test/test_detectors_regression.cpp
  3. 581
      modules/features2d/test/test_matchers_algorithmic.cpp
  4. 92
      modules/features2d/test/test_orb.cpp

332
modules/features2d/test/test_descriptors_regression.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 "test_precomp.hpp"
#include "opencv2/highgui/highgui.hpp"
using namespace std;
using namespace cv;
const string FEATURES2D_DIR = "features2d";
const string IMAGE_FILENAME = "tsukuba.png";
const string DESCRIPTOR_DIR = FEATURES2D_DIR + "/descriptor_extractors";
/****************************************************************************************\
* 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 );
int dataSize = (int)(mat.step * mat.rows * mat.channels());
fwrite( (void*)&dataSize, sizeof(int), 1, f );
fwrite( (void*)mat.data, 1, dataSize, f );
fclose(f);
}
}
static Mat readMatFromBin( const string& filename )
{
FILE* f = fopen( filename.c_str(), "rb" );
if( f )
{
int rows, cols, type, dataSize;
size_t elements_read1 = fread( (void*)&rows, sizeof(int), 1, f );
size_t elements_read2 = fread( (void*)&cols, sizeof(int), 1, f );
size_t elements_read3 = fread( (void*)&type, sizeof(int), 1, f );
size_t elements_read4 = fread( (void*)&dataSize, sizeof(int), 1, f );
CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1);
uchar* data = (uchar*)cvAlloc(dataSize);
size_t elements_read = fread( (void*)data, 1, dataSize, f );
CV_Assert(elements_read == (size_t)(dataSize));
fclose(f);
return Mat( rows, cols, type, data );
}
return Mat();
}
template<class Distance>
class CV_DescriptorExtractorTest : public cvtest::BaseTest
{
public:
typedef typename Distance::ValueType ValueType;
typedef typename Distance::ResultType DistanceType;
CV_DescriptorExtractorTest( const string _name, DistanceType _maxDist, const Ptr<DescriptorExtractor>& _dextractor,
Distance d = Distance() ):
name(_name), maxDist(_maxDist), dextractor(_dextractor), distance(d) {}
protected:
virtual void createDescriptorExtractor() {}
void compareDescriptors( const Mat& validDescriptors, const Mat& calcDescriptors )
{
if( validDescriptors.size != calcDescriptors.size || validDescriptors.type() != calcDescriptors.type() )
{
ts->printf(cvtest::TS::LOG, "Valid and computed descriptors matrices must have the same size and type.\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
CV_Assert( DataType<ValueType>::type == validDescriptors.type() );
int dimension = validDescriptors.cols;
DistanceType curMaxDist = std::numeric_limits<DistanceType>::min();
for( int y = 0; y < validDescriptors.rows; y++ )
{
DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
if( dist > curMaxDist )
curMaxDist = dist;
}
stringstream ss;
ss << "Max distance between valid and computed descriptors " << curMaxDist;
if( curMaxDist < maxDist )
ss << "." << endl;
else
{
ss << ">" << maxDist << " - bad accuracy!"<< endl;
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
ts->printf(cvtest::TS::LOG, ss.str().c_str() );
}
void emptyDataTest()
{
assert( !dextractor.empty() );
// One image.
Mat image;
vector<KeyPoint> keypoints;
Mat descriptors;
try
{
dextractor->compute( image, keypoints, descriptors );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "compute() on empty image and empty keypoints must not generate exception (1).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
}
image.create( 50, 50, CV_8UC3 );
try
{
dextractor->compute( image, keypoints, descriptors );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "compute() on nonempty image and empty keypoints must not generate exception (1).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
}
// Several images.
vector<Mat> images;
vector<vector<KeyPoint> > keypointsCollection;
vector<Mat> descriptorsCollection;
try
{
dextractor->compute( images, keypointsCollection, descriptorsCollection );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "compute() on empty images and empty keypoints collection must not generate exception (2).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
}
}
void regressionTest()
{
assert( !dextractor.empty() );
// Read the test image.
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
Mat img = imread( imgFilename );
if( img.empty() )
{
ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() );
ts->set_failed_test_info( cvtest::TS::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 );
Mat calcDescriptors;
double t = (double)getTickCount();
dextractor->compute( img, keypoints, calcDescriptors );
t = getTickCount() - t;
ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms.\n", t/((double)cvGetTickFrequency()*1000.)/calcDescriptors.rows);
if( calcDescriptors.rows != (int)keypoints.size() )
{
ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" );
ts->printf( cvtest::TS::LOG, "Count of keypoints is %d.\n", (int)keypoints.size() );
ts->printf( cvtest::TS::LOG, "Count of computed descriptors is %d.\n", calcDescriptors.rows );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
if( calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType() )
{
ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" );
ts->printf( cvtest::TS::LOG, "Expected size is %d.\n", dextractor->descriptorSize() );
ts->printf( cvtest::TS::LOG, "Calculated size is %d.\n", calcDescriptors.cols );
ts->printf( cvtest::TS::LOG, "Expected type is %d.\n", dextractor->descriptorType() );
ts->printf( cvtest::TS::LOG, "Calculated type is %d.\n", calcDescriptors.type() );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
// TODO read and write descriptor extractor parameters and check them
Mat validDescriptors = readDescriptors();
if( !validDescriptors.empty() )
compareDescriptors( validDescriptors, calcDescriptors );
else
{
if( !writeDescriptors( calcDescriptors ) )
{
ts->printf( cvtest::TS::LOG, "Descriptors can not be written.\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
}
}
else
{
ts->printf( cvtest::TS::LOG, "Compute and write keypoints.\n" );
fs.open( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::WRITE );
if( fs.isOpened() )
{
ORB fd;
fd.detect(img, keypoints);
write( fs, "keypoints", keypoints );
}
else
{
ts->printf(cvtest::TS::LOG, "File for writting keypoints can not be opened.\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
}
}
void run(int)
{
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; }
};
/****************************************************************************************\
* Tests registrations *
\****************************************************************************************/
TEST( Features2d_DescriptorExtractor_ORB, regression )
{
// TODO adjust the parameters below
CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb", (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
DescriptorExtractor::create("ORB") );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_FREAK, regression )
{
// TODO adjust the parameters below
CV_DescriptorExtractorTest<Hamming> test( "descriptor-freak", (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
DescriptorExtractor::create("FREAK") );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_BRIEF, regression )
{
CV_DescriptorExtractorTest<Hamming> test( "descriptor-brief", 1,
DescriptorExtractor::create("BRIEF") );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_OpponentBRIEF, regression )
{
CV_DescriptorExtractorTest<Hamming> test( "descriptor-opponent-brief", 1,
DescriptorExtractor::create("OpponentBRIEF") );
test.safe_run();
}

296
modules/features2d/test/test_detectors_regression.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 "test_precomp.hpp"
#include "opencv2/highgui/highgui.hpp"
using namespace std;
using namespace cv;
const string FEATURES2D_DIR = "features2d";
const string IMAGE_FILENAME = "tsukuba.png";
const string DETECTOR_DIR = FEATURES2D_DIR + "/feature_detectors";
/****************************************************************************************\
* Regression tests for feature detectors comparing keypoints. *
\****************************************************************************************/
class CV_FeatureDetectorTest : public cvtest::BaseTest
{
public:
CV_FeatureDetectorTest( const string& _name, const Ptr<FeatureDetector>& _fdetector ) :
name(_name), fdetector(_fdetector) {}
protected:
bool isSimilarKeypoints( const KeyPoint& p1, const KeyPoint& p2 );
void compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints );
void emptyDataTest();
void regressionTest(); // TODO test of detect() with mask
virtual void run( int );
string name;
Ptr<FeatureDetector> fdetector;
};
void CV_FeatureDetectorTest::emptyDataTest()
{
// One image.
Mat image;
vector<KeyPoint> keypoints;
try
{
fdetector->detect( image, keypoints );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "detect() on empty image must not generate exception (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
if( !keypoints.empty() )
{
ts->printf( cvtest::TS::LOG, "detect() on empty image must return empty keypoints vector (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
// Several images.
vector<Mat> images;
vector<vector<KeyPoint> > keypointCollection;
try
{
fdetector->detect( images, keypointCollection );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "detect() on empty image vector must not generate exception (2).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
bool CV_FeatureDetectorTest::isSimilarKeypoints( const KeyPoint& p1, const KeyPoint& p2 )
{
const float maxPtDif = 1.f;
const float maxSizeDif = 1.f;
const float maxAngleDif = 2.f;
const float maxResponseDif = 0.1f;
float dist = (float)norm( p1.pt - p2.pt );
return (dist < maxPtDif &&
fabs(p1.size - p2.size) < maxSizeDif &&
abs(p1.angle - p2.angle) < maxAngleDif &&
abs(p1.response - p2.response) < maxResponseDif &&
p1.octave == p2.octave &&
p1.class_id == p2.class_id );
}
void CV_FeatureDetectorTest::compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints )
{
const float maxCountRatioDif = 0.01f;
// Compare counts of validation and calculated keypoints.
float countRatio = (float)validKeypoints.size() / (float)calcKeypoints.size();
if( countRatio < 1 - maxCountRatioDif || countRatio > 1.f + maxCountRatioDif )
{
ts->printf( cvtest::TS::LOG, "Bad keypoints count ratio (validCount = %d, calcCount = %d).\n",
validKeypoints.size(), calcKeypoints.size() );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
int progress = 0, progressCount = (int)(validKeypoints.size() * calcKeypoints.size());
int badPointCount = 0, commonPointCount = max((int)validKeypoints.size(), (int)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, (int)(v*calcKeypoints.size() + c), progressCount, 0 );
float curDist = (float)norm( calcKeypoints[c].pt - validKeypoints[v].pt );
if( curDist < minDist )
{
minDist = curDist;
nearestIdx = (int)c;
}
}
assert( minDist >= 0 );
if( !isSimilarKeypoints( validKeypoints[v], calcKeypoints[nearestIdx] ) )
badPointCount++;
}
ts->printf( cvtest::TS::LOG, "badPointCount = %d; validPointCount = %d; calcPointCount = %d\n",
badPointCount, validKeypoints.size(), calcKeypoints.size() );
if( badPointCount > 0.9 * commonPointCount )
{
ts->printf( cvtest::TS::LOG, " - Bad accuracy!\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
ts->printf( cvtest::TS::LOG, " - OK\n" );
}
void CV_FeatureDetectorTest::regressionTest()
{
assert( !fdetector.empty() );
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
string resFilename = string(ts->get_data_path()) + DETECTOR_DIR + "/" + string(name) + ".xml.gz";
// Read the test image.
Mat image = imread( imgFilename );
if( image.empty() )
{
ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
FileStorage fs( resFilename, FileStorage::READ );
// Compute keypoints.
vector<KeyPoint> calcKeypoints;
fdetector->detect( image, calcKeypoints );
if( fs.isOpened() ) // Compare computed and valid keypoints.
{
// TODO compare saved feature detector params with current ones
// Read validation keypoints set.
vector<KeyPoint> validKeypoints;
read( fs["keypoints"], validKeypoints );
if( validKeypoints.empty() )
{
ts->printf( cvtest::TS::LOG, "Keypoints can not be read.\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
compareKeypointSets( validKeypoints, calcKeypoints );
}
else // Write detector parameters and computed keypoints as validation data.
{
fs.open( resFilename, FileStorage::WRITE );
if( !fs.isOpened() )
{
ts->printf( cvtest::TS::LOG, "File %s can not be opened to write.\n", resFilename.c_str() );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
else
{
fs << "detector_params" << "{";
fdetector->write( fs );
fs << "}";
write( fs, "keypoints", calcKeypoints );
}
}
}
void CV_FeatureDetectorTest::run( int /*start_from*/ )
{
if( fdetector.empty() )
{
ts->printf( cvtest::TS::LOG, "Feature detector 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 );
}
/****************************************************************************************\
* Tests registrations *
\****************************************************************************************/
TEST( Features2d_Detector_FAST, regression )
{
CV_FeatureDetectorTest test( "detector-fast", FeatureDetector::create("FAST") );
test.safe_run();
}
TEST( Features2d_Detector_GFTT, regression )
{
CV_FeatureDetectorTest test( "detector-gftt", FeatureDetector::create("GFTT") );
test.safe_run();
}
TEST( Features2d_Detector_Harris, regression )
{
CV_FeatureDetectorTest test( "detector-harris", FeatureDetector::create("HARRIS") );
test.safe_run();
}
TEST( Features2d_Detector_MSER, DISABLED_regression )
{
CV_FeatureDetectorTest test( "detector-mser", FeatureDetector::create("MSER") );
test.safe_run();
}
TEST( Features2d_Detector_STAR, regression )
{
CV_FeatureDetectorTest test( "detector-star", FeatureDetector::create("STAR") );
test.safe_run();
}
TEST( Features2d_Detector_ORB, regression )
{
CV_FeatureDetectorTest test( "detector-orb", FeatureDetector::create("ORB") );
test.safe_run();
}
TEST( Features2d_Detector_GridFAST, regression )
{
CV_FeatureDetectorTest test( "detector-grid-fast", FeatureDetector::create("GridFAST") );
test.safe_run();
}
TEST( Features2d_Detector_PyramidFAST, regression )
{
CV_FeatureDetectorTest test( "detector-pyramid-fast", FeatureDetector::create("PyramidFAST") );
test.safe_run();
}

581
modules/features2d/test/test_features2d.cpp → modules/features2d/test/test_matchers_algorithmic.cpp
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@ -46,452 +46,8 @@ 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::BaseTest
{
public:
CV_FeatureDetectorTest( const string& _name, const Ptr<FeatureDetector>& _fdetector ) :
name(_name), fdetector(_fdetector) {}
protected:
bool isSimilarKeypoints( const KeyPoint& p1, const KeyPoint& p2 );
void compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints );
void emptyDataTest();
void regressionTest(); // TODO test of detect() with mask
virtual void run( int );
string name;
Ptr<FeatureDetector> fdetector;
};
void CV_FeatureDetectorTest::emptyDataTest()
{
// One image.
Mat image;
vector<KeyPoint> keypoints;
try
{
fdetector->detect( image, keypoints );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "detect() on empty image must not generate exception (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
if( !keypoints.empty() )
{
ts->printf( cvtest::TS::LOG, "detect() on empty image must return empty keypoints vector (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
// Several images.
vector<Mat> images;
vector<vector<KeyPoint> > keypointCollection;
try
{
fdetector->detect( images, keypointCollection );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "detect() on empty image vector must not generate exception (2).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
bool CV_FeatureDetectorTest::isSimilarKeypoints( const KeyPoint& p1, const KeyPoint& p2 )
{
const float maxPtDif = 1.f;
const float maxSizeDif = 1.f;
const float maxAngleDif = 2.f;
const float maxResponseDif = 0.1f;
float dist = (float)norm( p1.pt - p2.pt );
return (dist < maxPtDif &&
fabs(p1.size - p2.size) < maxSizeDif &&
abs(p1.angle - p2.angle) < maxAngleDif &&
abs(p1.response - p2.response) < maxResponseDif &&
p1.octave == p2.octave &&
p1.class_id == p2.class_id );
}
void CV_FeatureDetectorTest::compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints )
{
const float maxCountRatioDif = 0.01f;
// Compare counts of validation and calculated keypoints.
float countRatio = (float)validKeypoints.size() / (float)calcKeypoints.size();
if( countRatio < 1 - maxCountRatioDif || countRatio > 1.f + maxCountRatioDif )
{
ts->printf( cvtest::TS::LOG, "Bad keypoints count ratio (validCount = %d, calcCount = %d).\n",
validKeypoints.size(), calcKeypoints.size() );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
int progress = 0, progressCount = (int)(validKeypoints.size() * calcKeypoints.size());
int badPointCount = 0, commonPointCount = max((int)validKeypoints.size(), (int)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, (int)(v*calcKeypoints.size() + c), progressCount, 0 );
float curDist = (float)norm( calcKeypoints[c].pt - validKeypoints[v].pt );
if( curDist < minDist )
{
minDist = curDist;
nearestIdx = (int)c;
}
}
assert( minDist >= 0 );
if( !isSimilarKeypoints( validKeypoints[v], calcKeypoints[nearestIdx] ) )
badPointCount++;
}
ts->printf( cvtest::TS::LOG, "badPointCount = %d; validPointCount = %d; calcPointCount = %d\n",
badPointCount, validKeypoints.size(), calcKeypoints.size() );
if( badPointCount > 0.9 * commonPointCount )
{
ts->printf( cvtest::TS::LOG, " - Bad accuracy!\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
ts->printf( cvtest::TS::LOG, " - OK\n" );
}
void CV_FeatureDetectorTest::regressionTest()
{
assert( !fdetector.empty() );
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
string resFilename = string(ts->get_data_path()) + DETECTOR_DIR + "/" + string(name) + ".xml.gz";
// Read the test image.
Mat image = imread( imgFilename );
if( image.empty() )
{
ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
FileStorage fs( resFilename, FileStorage::READ );
// Compute keypoints.
vector<KeyPoint> calcKeypoints;
fdetector->detect( image, calcKeypoints );
if( fs.isOpened() ) // Compare computed and valid keypoints.
{
// TODO compare saved feature detector params with current ones
// Read validation keypoints set.
vector<KeyPoint> validKeypoints;
read( fs["keypoints"], validKeypoints );
if( validKeypoints.empty() )
{
ts->printf( cvtest::TS::LOG, "Keypoints can not be read.\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
compareKeypointSets( validKeypoints, calcKeypoints );
}
else // Write detector parameters and computed keypoints as validation data.
{
fs.open( resFilename, FileStorage::WRITE );
if( !fs.isOpened() )
{
ts->printf( cvtest::TS::LOG, "File %s can not be opened to write.\n", resFilename.c_str() );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
else
{
fs << "detector_params" << "{";
fdetector->write( fs );
fs << "}";
write( fs, "keypoints", calcKeypoints );
}
}
}
void CV_FeatureDetectorTest::run( int /*start_from*/ )
{
if( fdetector.empty() )
{
ts->printf( cvtest::TS::LOG, "Feature detector 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 );
}
/****************************************************************************************\
* 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 );
int dataSize = (int)(mat.step * mat.rows * mat.channels());
fwrite( (void*)&dataSize, sizeof(int), 1, f );
fwrite( (void*)mat.data, 1, dataSize, f );
fclose(f);
}
}
static Mat readMatFromBin( const string& filename )
{
FILE* f = fopen( filename.c_str(), "rb" );
if( f )
{
int rows, cols, type, dataSize;
size_t elements_read1 = fread( (void*)&rows, sizeof(int), 1, f );
size_t elements_read2 = fread( (void*)&cols, sizeof(int), 1, f );
size_t elements_read3 = fread( (void*)&type, sizeof(int), 1, f );
size_t elements_read4 = fread( (void*)&dataSize, sizeof(int), 1, f );
CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1);
uchar* data = (uchar*)cvAlloc(dataSize);
size_t elements_read = fread( (void*)data, 1, dataSize, f );
CV_Assert(elements_read == (size_t)(dataSize));
fclose(f);
return Mat( rows, cols, type, data );
}
return Mat();
}
template<class Distance>
class CV_DescriptorExtractorTest : public cvtest::BaseTest
{
public:
typedef typename Distance::ValueType ValueType;
typedef typename Distance::ResultType DistanceType;
CV_DescriptorExtractorTest( const string _name, DistanceType _maxDist, const Ptr<DescriptorExtractor>& _dextractor,
Distance d = Distance() ):
name(_name), maxDist(_maxDist), dextractor(_dextractor), distance(d) {}
protected:
virtual void createDescriptorExtractor() {}
void compareDescriptors( const Mat& validDescriptors, const Mat& calcDescriptors )
{
if( validDescriptors.size != calcDescriptors.size || validDescriptors.type() != calcDescriptors.type() )
{
ts->printf(cvtest::TS::LOG, "Valid and computed descriptors matrices must have the same size and type.\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
CV_Assert( DataType<ValueType>::type == validDescriptors.type() );
int dimension = validDescriptors.cols;
DistanceType curMaxDist = std::numeric_limits<DistanceType>::min();
for( int y = 0; y < validDescriptors.rows; y++ )
{
DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
if( dist > curMaxDist )
curMaxDist = dist;
}
stringstream ss;
ss << "Max distance between valid and computed descriptors " << curMaxDist;
if( curMaxDist < maxDist )
ss << "." << endl;
else
{
ss << ">" << maxDist << " - bad accuracy!"<< endl;
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
ts->printf(cvtest::TS::LOG, ss.str().c_str() );
}
void emptyDataTest()
{
assert( !dextractor.empty() );
// One image.
Mat image;
vector<KeyPoint> keypoints;
Mat descriptors;
try
{
dextractor->compute( image, keypoints, descriptors );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "compute() on empty image and empty keypoints must not generate exception (1).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
}
image.create( 50, 50, CV_8UC3 );
try
{
dextractor->compute( image, keypoints, descriptors );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "compute() on nonempty image and empty keypoints must not generate exception (1).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
}
// Several images.
vector<Mat> images;
vector<vector<KeyPoint> > keypointsCollection;
vector<Mat> descriptorsCollection;
try
{
dextractor->compute( images, keypointsCollection, descriptorsCollection );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "compute() on empty images and empty keypoints collection must not generate exception (2).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
}
}
void regressionTest()
{
assert( !dextractor.empty() );
// Read the test image.
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
Mat img = imread( imgFilename );
if( img.empty() )
{
ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() );
ts->set_failed_test_info( cvtest::TS::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 );
Mat calcDescriptors;
double t = (double)getTickCount();
dextractor->compute( img, keypoints, calcDescriptors );
t = getTickCount() - t;
ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms.\n", t/((double)cvGetTickFrequency()*1000.)/calcDescriptors.rows);
if( calcDescriptors.rows != (int)keypoints.size() )
{
ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" );
ts->printf( cvtest::TS::LOG, "Count of keypoints is %d.\n", (int)keypoints.size() );
ts->printf( cvtest::TS::LOG, "Count of computed descriptors is %d.\n", calcDescriptors.rows );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
if( calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType() )
{
ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" );
ts->printf( cvtest::TS::LOG, "Expected size is %d.\n", dextractor->descriptorSize() );
ts->printf( cvtest::TS::LOG, "Calculated size is %d.\n", calcDescriptors.cols );
ts->printf( cvtest::TS::LOG, "Expected type is %d.\n", dextractor->descriptorType() );
ts->printf( cvtest::TS::LOG, "Calculated type is %d.\n", calcDescriptors.type() );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
// TODO read and write descriptor extractor parameters and check them
Mat validDescriptors = readDescriptors();
if( !validDescriptors.empty() )
compareDescriptors( validDescriptors, calcDescriptors );
else
{
if( !writeDescriptors( calcDescriptors ) )
{
ts->printf( cvtest::TS::LOG, "Descriptors can not be written.\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
}
}
else
{
ts->printf( cvtest::TS::LOG, "Compute and write keypoints.\n" );
fs.open( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::WRITE );
if( fs.isOpened() )
{
ORB fd;
fd.detect(img, keypoints);
write( fs, "keypoints", keypoints );
}
else
{
ts->printf(cvtest::TS::LOG, "File for writting keypoints can not be opened.\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
}
}
void run(int)
{
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; }
};
/****************************************************************************************\
* Algorithmic tests for descriptor matchers *
\****************************************************************************************/
@ -974,95 +530,6 @@ void CV_DescriptorMatcherTest::run( int )
* Tests registrations *
\****************************************************************************************/
/*
* Detectors
*/
TEST( Features2d_Detector_FAST, regression )
{
CV_FeatureDetectorTest test( "detector-fast", FeatureDetector::create("FAST") );
test.safe_run();
}
TEST( Features2d_Detector_GFTT, regression )
{
CV_FeatureDetectorTest test( "detector-gftt", FeatureDetector::create("GFTT") );
test.safe_run();
}
TEST( Features2d_Detector_Harris, regression )
{
CV_FeatureDetectorTest test( "detector-harris", FeatureDetector::create("HARRIS") );
test.safe_run();
}
TEST( Features2d_Detector_MSER, DISABLED_regression )
{
CV_FeatureDetectorTest test( "detector-mser", FeatureDetector::create("MSER") );
test.safe_run();
}
TEST( Features2d_Detector_STAR, regression )
{
CV_FeatureDetectorTest test( "detector-star", FeatureDetector::create("STAR") );
test.safe_run();
}
TEST( Features2d_Detector_ORB, regression )
{
CV_FeatureDetectorTest test( "detector-orb", FeatureDetector::create("ORB") );
test.safe_run();
}
TEST( Features2d_Detector_GridFAST, regression )
{
CV_FeatureDetectorTest test( "detector-grid-fast", FeatureDetector::create("GridFAST") );
test.safe_run();
}
TEST( Features2d_Detector_PyramidFAST, regression )
{
CV_FeatureDetectorTest test( "detector-pyramid-fast", FeatureDetector::create("PyramidFAST") );
test.safe_run();
}
/*
* Descriptors
*/
TEST( Features2d_DescriptorExtractor_ORB, regression )
{
// TODO adjust the parameters below
CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb", (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
DescriptorExtractor::create("ORB") );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_FREAK, regression )
{
// TODO adjust the parameters below
CV_DescriptorExtractorTest<Hamming> test( "descriptor-freak", (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
DescriptorExtractor::create("FREAK") );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_BRIEF, regression )
{
CV_DescriptorExtractorTest<Hamming> test( "descriptor-brief", 1,
DescriptorExtractor::create("BRIEF") );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_OpponentBRIEF, regression )
{
CV_DescriptorExtractorTest<Hamming> test( "descriptor-opponent-brief", 1,
DescriptorExtractor::create("OpponentBRIEF") );
test.safe_run();
}
/*
* Matchers
*/
TEST( Features2d_DescriptorMatcher_BruteForce, regression )
{
CV_DescriptorMatcherTest test( "descriptor-matcher-brute-force", new BFMatcher(NORM_L2), 0.01f );
@ -1074,51 +541,3 @@ TEST( Features2d_DescriptorMatcher_FlannBased, regression )
CV_DescriptorMatcherTest test( "descriptor-matcher-flann-based", new FlannBasedMatcher, 0.04f );
test.safe_run();
}
TEST(Features2D_ORB, _1996)
{
cv::Ptr<cv::FeatureDetector> fd = cv::FeatureDetector::create("ORB");
cv::Ptr<cv::DescriptorExtractor> de = cv::DescriptorExtractor::create("ORB");
Mat image = cv::imread(string(cvtest::TS::ptr()->get_data_path()) + "shared/lena.jpg");
ASSERT_FALSE(image.empty());
Mat roi(image.size(), CV_8UC1, Scalar(0));
Point poly[] = {Point(100, 20), Point(300, 50), Point(400, 200), Point(10, 500)};
fillConvexPoly(roi, poly, int(sizeof(poly) / sizeof(poly[0])), Scalar(255));
std::vector<cv::KeyPoint> keypoints;
fd->detect(image, keypoints, roi);
cv::Mat descriptors;
de->compute(image, keypoints, descriptors);
//image.setTo(Scalar(255,255,255), roi);
int roiViolations = 0;
for(std::vector<cv::KeyPoint>::const_iterator kp = keypoints.begin(); kp != keypoints.end(); ++kp)
{
int x = cvRound(kp->pt.x);
int y = cvRound(kp->pt.y);
ASSERT_LE(0, x);
ASSERT_LE(0, y);
ASSERT_GT(image.cols, x);
ASSERT_GT(image.rows, y);
// if (!roi.at<uchar>(y,x))
// {
// roiViolations++;
// circle(image, kp->pt, 3, Scalar(0,0,255));
// }
}
// if(roiViolations)
// {
// imshow("img", image);
// waitKey();
// }
ASSERT_EQ(0, roiViolations);
}

92
modules/features2d/test/test_orb.cpp
Unescape View File

@ -0,0 +1,92 @@
/*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 "test_precomp.hpp"
#include "opencv2/highgui/highgui.hpp"
using namespace cv;
TEST(Features2D_ORB, _1996)
{
Ptr<FeatureDetector> fd = FeatureDetector::create("ORB");
Ptr<DescriptorExtractor> de = DescriptorExtractor::create("ORB");
Mat image = imread(string(cvtest::TS::ptr()->get_data_path()) + "shared/lena.jpg");
ASSERT_FALSE(image.empty());
Mat roi(image.size(), CV_8UC1, Scalar(0));
Point poly[] = {Point(100, 20), Point(300, 50), Point(400, 200), Point(10, 500)};
fillConvexPoly(roi, poly, int(sizeof(poly) / sizeof(poly[0])), Scalar(255));
std::vector<KeyPoint> keypoints;
fd->detect(image, keypoints, roi);
Mat descriptors;
de->compute(image, keypoints, descriptors);
//image.setTo(Scalar(255,255,255), roi);
int roiViolations = 0;
for(std::vector<KeyPoint>::const_iterator kp = keypoints.begin(); kp != keypoints.end(); ++kp)
{
int x = cvRound(kp->pt.x);
int y = cvRound(kp->pt.y);
ASSERT_LE(0, x);
ASSERT_LE(0, y);
ASSERT_GT(image.cols, x);
ASSERT_GT(image.rows, y);
// if (!roi.at<uchar>(y,x))
// {
// roiViolations++;
// circle(image, kp->pt, 3, Scalar(0,0,255));
// }
}
// if(roiViolations)
// {
// imshow("img", image);
// waitKey();
// }
ASSERT_EQ(0, roiViolations);
}
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