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opencv/modules/features2d/test/test_detectordescriptor_evaluation.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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#include "test_precomp.hpp"
#include <limits>
#include <cstdio>
#include <iostream>
#include <fstream>
using namespace std;
using namespace cv;
/****************************************************************************************\
* Functions to evaluate affine covariant detectors and descriptors. *
\****************************************************************************************/
static inline Point2f applyHomography( const Mat_<double>& H, const Point2f& pt )
{
double z = H(2,0)*pt.x + H(2,1)*pt.y + H(2,2);
if( z )
{
double w = 1./z;
return Point2f( (float)((H(0,0)*pt.x + H(0,1)*pt.y + H(0,2))*w),
(float)((H(1,0)*pt.x + H(1,1)*pt.y + H(1,2))*w) );
}
return Point2f( numeric_limits<float>::max(), numeric_limits<float>::max() );
}
static inline void linearizeHomographyAt( const Mat_<double>& H, const Point2f& pt, Mat_<double>& A )
{
A.create(2,2);
double p1 = H(0,0)*pt.x + H(0,1)*pt.y + H(0,2),
p2 = H(1,0)*pt.x + H(1,1)*pt.y + H(1,2),
p3 = H(2,0)*pt.x + H(2,1)*pt.y + H(2,2),
p3_2 = p3*p3;
if( p3 )
{
A(0,0) = H(0,0)/p3 - p1*H(2,0)/p3_2; // fxdx
A(0,1) = H(0,1)/p3 - p1*H(2,1)/p3_2; // fxdy
A(1,0) = H(1,0)/p3 - p2*H(2,0)/p3_2; // fydx
A(1,1) = H(1,1)/p3 - p2*H(2,1)/p3_2; // fydx
}
else
A.setTo(Scalar::all(numeric_limits<double>::max()));
}
static void calcKeyPointProjections( const vector<KeyPoint>& src, const Mat_<double>& H, vector<KeyPoint>& dst )
{
if( !src.empty() )
{
assert( !H.empty() && H.cols == 3 && H.rows == 3);
dst.resize(src.size());
vector<KeyPoint>::const_iterator srcIt = src.begin();
vector<KeyPoint>::iterator dstIt = dst.begin();
for( ; srcIt != src.end(); ++srcIt, ++dstIt )
{
Point2f dstPt = applyHomography(H, srcIt->pt);
float srcSize2 = srcIt->size * srcIt->size;
Mat_<double> M(2, 2);
M(0,0) = M(1,1) = 1./srcSize2;
M(1,0) = M(0,1) = 0;
Mat_<double> invM; invert(M, invM);
Mat_<double> Aff; linearizeHomographyAt(H, srcIt->pt, Aff);
Mat_<double> dstM; invert(Aff*invM*Aff.t(), dstM);
Mat_<double> eval; eigen( dstM, eval );
assert( eval(0,0) && eval(1,0) );
float dstSize = (float)pow(1./(eval(0,0)*eval(1,0)), 0.25);
// TODO: check angle projection
float srcAngleRad = (float)(srcIt->angle*CV_PI/180);
Point2f vec1(cos(srcAngleRad), sin(srcAngleRad)), vec2;
vec2.x = (float)(Aff(0,0)*vec1.x + Aff(0,1)*vec1.y);
vec2.y = (float)(Aff(1,0)*vec1.x + Aff(0,1)*vec1.y);
float dstAngleGrad = fastAtan2(vec2.y, vec2.x);
*dstIt = KeyPoint( dstPt, dstSize, dstAngleGrad, srcIt->response, srcIt->octave, srcIt->class_id );
}
}
}
static void filterKeyPointsByImageSize( vector<KeyPoint>& keypoints, const Size& imgSize )
{
if( !keypoints.empty() )
{
vector<KeyPoint> filtered;
filtered.reserve(keypoints.size());
Rect r(0, 0, imgSize.width, imgSize.height);
vector<KeyPoint>::const_iterator it = keypoints.begin();
for( int i = 0; it != keypoints.end(); ++it, i++ )
if( r.contains(it->pt) )
filtered.push_back(*it);
keypoints.assign(filtered.begin(), filtered.end());
}
}
/****************************************************************************************\
* Detectors evaluation *
\****************************************************************************************/
const int DATASETS_COUNT = 8;
const int TEST_CASE_COUNT = 5;
const string IMAGE_DATASETS_DIR = "detectors_descriptors_evaluation/images_datasets/";
const string DETECTORS_DIR = "detectors_descriptors_evaluation/detectors/";
const string DESCRIPTORS_DIR = "detectors_descriptors_evaluation/descriptors/";
const string KEYPOINTS_DIR = "detectors_descriptors_evaluation/keypoints_datasets/";
const string PARAMS_POSTFIX = "_params.xml";
const string RES_POSTFIX = "_res.xml";
const string REPEAT = "repeatability";
const string CORRESP_COUNT = "correspondence_count";
string DATASET_NAMES[DATASETS_COUNT] = { "bark", "bikes", "boat", "graf", "leuven", "trees", "ubc", "wall"};
string DEFAULT_PARAMS = "default";
string IS_ACTIVE_PARAMS = "isActiveParams";
string IS_SAVE_KEYPOINTS = "isSaveKeypoints";
class BaseQualityTest : public cvtest::BaseTest
{
public:
BaseQualityTest( const char* _algName ) : algName(_algName)
{
//TODO: change this
isWriteGraphicsData = true;
}
protected:
virtual string getRunParamsFilename() const = 0;
virtual string getResultsFilename() const = 0;
virtual string getPlotPath() const = 0;
virtual void validQualityClear( int datasetIdx ) = 0;
virtual void calcQualityClear( int datasetIdx ) = 0;
virtual void validQualityCreate( int datasetIdx ) = 0;
virtual bool isValidQualityEmpty( int datasetIdx ) const = 0;
virtual bool isCalcQualityEmpty( int datasetIdx ) const = 0;
void readAllDatasetsRunParams();
virtual void readDatasetRunParams( FileNode& fn, int datasetIdx ) = 0;
void writeAllDatasetsRunParams() const;
virtual void writeDatasetRunParams( FileStorage& fs, int datasetIdx ) const = 0;
void setDefaultAllDatasetsRunParams();
virtual void setDefaultDatasetRunParams( int datasetIdx ) = 0;
virtual void readDefaultRunParams( FileNode& /*fn*/ ) {}
virtual void writeDefaultRunParams( FileStorage& /*fs*/ ) const {}
virtual void readResults();
virtual void readResults( FileNode& fn, int datasetIdx, int caseIdx ) = 0;
void writeResults() const;
virtual void writeResults( FileStorage& fs, int datasetIdx, int caseIdx ) const = 0;
bool readDataset( const string& datasetName, vector<Mat>& Hs, vector<Mat>& imgs );
virtual void readAlgorithm( ) {};
virtual void processRunParamsFile () {};
virtual void runDatasetTest( const vector<Mat>& /*imgs*/, const vector<Mat>& /*Hs*/, int /*di*/, int& /*progress*/ ) {}
void run( int );
virtual void processResults( int datasetIdx );
virtual int processResults( int datasetIdx, int caseIdx ) = 0;
virtual void processResults();
virtual void writePlotData( int /*datasetIdx*/ ) const {}
virtual void writeAveragePlotData() const {};
string algName;
bool isWriteParams, isWriteResults, isWriteGraphicsData;
};
void BaseQualityTest::readAllDatasetsRunParams()
{
string filename = getRunParamsFilename();
FileStorage fs( filename, FileStorage::READ );
if( !fs.isOpened() )
{
isWriteParams = true;
setDefaultAllDatasetsRunParams();
ts->printf(cvtest::TS::LOG, "all runParams are default\n");
}
else
{
isWriteParams = false;
FileNode topfn = fs.getFirstTopLevelNode();
FileNode fn = topfn[DEFAULT_PARAMS];
readDefaultRunParams(fn);
for( int i = 0; i < DATASETS_COUNT; i++ )
{
FileNode fn = topfn[DATASET_NAMES[i]];
if( fn.empty() )
{
ts->printf( cvtest::TS::LOG, "%d-runParams is default\n", i);
setDefaultDatasetRunParams(i);
}
else
readDatasetRunParams(fn, i);
}
}
}
void BaseQualityTest::writeAllDatasetsRunParams() const
{
string filename = getRunParamsFilename();
FileStorage fs( filename, FileStorage::WRITE );
if( fs.isOpened() )
{
fs << "run_params" << "{"; // top file node
fs << DEFAULT_PARAMS << "{";
writeDefaultRunParams(fs);
fs << "}";
for( int i = 0; i < DATASETS_COUNT; i++ )
{
fs << DATASET_NAMES[i] << "{";
writeDatasetRunParams(fs, i);
fs << "}";
}
fs << "}";
}
else
ts->printf(cvtest::TS::LOG, "file %s for writing run params can not be opened\n", filename.c_str() );
}
void BaseQualityTest::setDefaultAllDatasetsRunParams()
{
for( int i = 0; i < DATASETS_COUNT; i++ )
setDefaultDatasetRunParams(i);
}
bool BaseQualityTest::readDataset( const string& datasetName, vector<Mat>& Hs, vector<Mat>& imgs )
{
Hs.resize( TEST_CASE_COUNT );
imgs.resize( TEST_CASE_COUNT+1 );
string dirname = string(ts->get_data_path()) + IMAGE_DATASETS_DIR + datasetName + "/";
for( int i = 0; i < (int)Hs.size(); i++ )
{
stringstream filename; filename << "H1to" << i+2 << "p.xml";
FileStorage fs( dirname + filename.str(), FileStorage::READ );
if( !fs.isOpened() )
return false;
fs.getFirstTopLevelNode() >> Hs[i];
}
for( int i = 0; i < (int)imgs.size(); i++ )
{
stringstream filename; filename << "img" << i+1 << ".png";
imgs[i] = imread( dirname + filename.str(), 0 );
if( imgs[i].empty() )
return false;
}
return true;
}
void BaseQualityTest::readResults()
{
string filename = getResultsFilename();
FileStorage fs( filename, FileStorage::READ );
if( fs.isOpened() )
{
isWriteResults = false;
FileNode topfn = fs.getFirstTopLevelNode();
for( int di = 0; di < DATASETS_COUNT; di++ )
{
FileNode datafn = topfn[DATASET_NAMES[di]];
if( datafn.empty() )
{
validQualityClear(di);
ts->printf( cvtest::TS::LOG, "results for %s dataset were not read\n",
DATASET_NAMES[di].c_str() );
}
else
{
validQualityCreate(di);
for( int ci = 0; ci < TEST_CASE_COUNT; ci++ )
{
stringstream ss; ss << "case" << ci;
FileNode casefn = datafn[ss.str()];
CV_Assert( !casefn.empty() );
readResults( casefn , di, ci );
}
}
}
}
else
isWriteResults = true;
}
void BaseQualityTest::writeResults() const
{
string filename = getResultsFilename();;
FileStorage fs( filename, FileStorage::WRITE );
if( fs.isOpened() )
{
fs << "results" << "{";
for( int di = 0; di < DATASETS_COUNT; di++ )
{
if( isCalcQualityEmpty(di) )
{
ts->printf(cvtest::TS::LOG, "results on %s dataset were not write because of empty\n",
DATASET_NAMES[di].c_str());
}
else
{
fs << DATASET_NAMES[di] << "{";
for( int ci = 0; ci < TEST_CASE_COUNT; ci++ )
{
stringstream ss; ss << "case" << ci;
fs << ss.str() << "{";
writeResults( fs, di, ci );
fs << "}"; //ss.str()
}
fs << "}"; //DATASET_NAMES[di]
}
}
fs << "}"; //results
}
else
ts->printf(cvtest::TS::LOG, "results were not written because file %s can not be opened\n", filename.c_str() );
}
void BaseQualityTest::processResults( int datasetIdx )
{
if( isWriteGraphicsData )
writePlotData( datasetIdx );
}
void BaseQualityTest::processResults()
{
if( isWriteParams )
writeAllDatasetsRunParams();
if( isWriteGraphicsData )
writeAveragePlotData();
int res = cvtest::TS::OK;
if( isWriteResults )
writeResults();
else
{
for( int di = 0; di < DATASETS_COUNT; di++ )
{
if( isValidQualityEmpty(di) || isCalcQualityEmpty(di) )
continue;
ts->printf(cvtest::TS::LOG, "\nDataset: %s\n", DATASET_NAMES[di].c_str() );
for( int ci = 0; ci < TEST_CASE_COUNT; ci++ )
{
ts->printf(cvtest::TS::LOG, "case%d\n", ci);
int currRes = processResults( di, ci );
res = currRes == cvtest::TS::OK ? res : currRes;
}
}
}
if( res != cvtest::TS::OK )
ts->printf(cvtest::TS::LOG, "BAD ACCURACY\n");
ts->set_failed_test_info( res );
}
void BaseQualityTest::run ( int )
{
readAlgorithm ();
processRunParamsFile ();
readResults();
int notReadDatasets = 0;
int progress = 0;
FileStorage runParamsFS( getRunParamsFilename(), FileStorage::READ );
isWriteParams = (! runParamsFS.isOpened());
FileNode topfn = runParamsFS.getFirstTopLevelNode();
FileNode defaultParams = topfn[DEFAULT_PARAMS];
readDefaultRunParams (defaultParams);
for(int di = 0; di < DATASETS_COUNT; di++ )
{
vector<Mat> imgs, Hs;
if( !readDataset( DATASET_NAMES[di], Hs, imgs ) )
{
calcQualityClear (di);
ts->printf( cvtest::TS::LOG, "images or homography matrices of dataset named %s can not be read\n",
DATASET_NAMES[di].c_str());
notReadDatasets++;
continue;
}
FileNode fn = topfn[DATASET_NAMES[di]];
readDatasetRunParams(fn, di);
runDatasetTest (imgs, Hs, di, progress);
processResults( di );
}
if( notReadDatasets == DATASETS_COUNT )
{
ts->printf(cvtest::TS::LOG, "All datasets were not be read\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
}
else
processResults();
runParamsFS.release();
}
class DetectorQualityTest : public BaseQualityTest
{
public:
DetectorQualityTest( const char* _detectorName ) :
BaseQualityTest( _detectorName )
{
validQuality.resize(DATASETS_COUNT);
calcQuality.resize(DATASETS_COUNT);
isSaveKeypoints.resize(DATASETS_COUNT);
isActiveParams.resize(DATASETS_COUNT);
isSaveKeypointsDefault = false;
isActiveParamsDefault = false;
}
protected:
using BaseQualityTest::readResults;
using BaseQualityTest::writeResults;
using BaseQualityTest::processResults;
virtual string getRunParamsFilename() const;
virtual string getResultsFilename() const;
virtual string getPlotPath() const;
virtual void validQualityClear( int datasetIdx );
virtual void calcQualityClear( int datasetIdx );
virtual void validQualityCreate( int datasetIdx );
virtual bool isValidQualityEmpty( int datasetIdx ) const;
virtual bool isCalcQualityEmpty( int datasetIdx ) const;
virtual void readResults( FileNode& fn, int datasetIdx, int caseIdx );
virtual void writeResults( FileStorage& fs, int datasetIdx, int caseIdx ) const;
virtual void readDatasetRunParams( FileNode& fn, int datasetIdx );
virtual void writeDatasetRunParams( FileStorage& fs, int datasetIdx ) const;
virtual void setDefaultDatasetRunParams( int datasetIdx );
virtual void readDefaultRunParams( FileNode &fn );
virtual void writeDefaultRunParams( FileStorage &fs ) const;
virtual void writePlotData( int di ) const;
virtual void writeAveragePlotData() const;
void openToWriteKeypointsFile( FileStorage& fs, int datasetIdx );
virtual void readAlgorithm( );
virtual void processRunParamsFile () {};
virtual void runDatasetTest( const vector<Mat> &imgs, const vector<Mat> &Hs, int di, int &progress );
virtual int processResults( int datasetIdx, int caseIdx );
Ptr<FeatureDetector> specificDetector;
Ptr<FeatureDetector> defaultDetector;
struct Quality
{
float repeatability;
int correspondenceCount;
};
vector<vector<Quality> > validQuality;
vector<vector<Quality> > calcQuality;
vector<bool> isSaveKeypoints;
vector<bool> isActiveParams;
bool isSaveKeypointsDefault;
bool isActiveParamsDefault;
};
string DetectorQualityTest::getRunParamsFilename() const
{
return string(ts->get_data_path()) + DETECTORS_DIR + algName + PARAMS_POSTFIX;
}
string DetectorQualityTest::getResultsFilename() const
{
return string(ts->get_data_path()) + DETECTORS_DIR + algName + RES_POSTFIX;
}
string DetectorQualityTest::getPlotPath() const
{
return string(ts->get_data_path()) + DETECTORS_DIR + "plots/";
}
void DetectorQualityTest::validQualityClear( int datasetIdx )
{
validQuality[datasetIdx].clear();
}
void DetectorQualityTest::calcQualityClear( int datasetIdx )
{
calcQuality[datasetIdx].clear();
}
void DetectorQualityTest::validQualityCreate( int datasetIdx )
{
validQuality[datasetIdx].resize(TEST_CASE_COUNT);
}
bool DetectorQualityTest::isValidQualityEmpty( int datasetIdx ) const
{
return validQuality[datasetIdx].empty();
}
bool DetectorQualityTest::isCalcQualityEmpty( int datasetIdx ) const
{
return calcQuality[datasetIdx].empty();
}
void DetectorQualityTest::readResults( FileNode& fn, int datasetIdx, int caseIdx )
{
validQuality[datasetIdx][caseIdx].repeatability = fn[REPEAT];
validQuality[datasetIdx][caseIdx].correspondenceCount = fn[CORRESP_COUNT];
}
void DetectorQualityTest::writeResults( FileStorage& fs, int datasetIdx, int caseIdx ) const
{
fs << REPEAT << calcQuality[datasetIdx][caseIdx].repeatability;
fs << CORRESP_COUNT << calcQuality[datasetIdx][caseIdx].correspondenceCount;
}
void DetectorQualityTest::readDefaultRunParams (FileNode &fn)
{
if (! fn.empty() )
{
isSaveKeypointsDefault = (int)fn[IS_SAVE_KEYPOINTS] != 0;
defaultDetector->read (fn);
}
}
void DetectorQualityTest::writeDefaultRunParams (FileStorage &fs) const
{
fs << IS_SAVE_KEYPOINTS << isSaveKeypointsDefault;
defaultDetector->write (fs);
}
void DetectorQualityTest::readDatasetRunParams( FileNode& fn, int datasetIdx )
{
isActiveParams[datasetIdx] = (int)fn[IS_ACTIVE_PARAMS] != 0;
if (isActiveParams[datasetIdx])
{
isSaveKeypoints[datasetIdx] = (int)fn[IS_SAVE_KEYPOINTS] != 0;
specificDetector->read (fn);
}
else
{
setDefaultDatasetRunParams(datasetIdx);
}
}
void DetectorQualityTest::writeDatasetRunParams( FileStorage& fs, int datasetIdx ) const
{
fs << IS_ACTIVE_PARAMS << isActiveParams[datasetIdx];
fs << IS_SAVE_KEYPOINTS << isSaveKeypoints[datasetIdx];
defaultDetector->write (fs);
}
void DetectorQualityTest::setDefaultDatasetRunParams( int datasetIdx )
{
isSaveKeypoints[datasetIdx] = isSaveKeypointsDefault;
isActiveParams[datasetIdx] = isActiveParamsDefault;
}
void DetectorQualityTest::writePlotData(int di ) const
{
int imgXVals[] = { 2, 3, 4, 5, 6 }; // if scale, blur or light changes
int viewpointXVals[] = { 20, 30, 40, 50, 60 }; // if viewpoint changes
int jpegXVals[] = { 60, 80, 90, 95, 98 }; // if jpeg compression
int* xVals = 0;
if( !DATASET_NAMES[di].compare("ubc") )
{
xVals = jpegXVals;
}
else if( !DATASET_NAMES[di].compare("graf") || !DATASET_NAMES[di].compare("wall") )
{
xVals = viewpointXVals;
}
else
xVals = imgXVals;
stringstream rFilename, cFilename;
rFilename << getPlotPath() << algName << "_" << DATASET_NAMES[di] << "_repeatability.csv";
cFilename << getPlotPath() << algName << "_" << DATASET_NAMES[di] << "_correspondenceCount.csv";
ofstream rfile(rFilename.str().c_str()), cfile(cFilename.str().c_str());
for( int ci = 0; ci < TEST_CASE_COUNT; ci++ )
{
rfile << xVals[ci] << ", " << calcQuality[di][ci].repeatability << endl;
cfile << xVals[ci] << ", " << calcQuality[di][ci].correspondenceCount << endl;
}
}
void DetectorQualityTest::writeAveragePlotData() const
{
stringstream rFilename, cFilename;
rFilename << getPlotPath() << algName << "_average_repeatability.csv";
cFilename << getPlotPath() << algName << "_average_correspondenceCount.csv";
ofstream rfile(rFilename.str().c_str()), cfile(cFilename.str().c_str());
float avRep = 0, avCorCount = 0;
for( int di = 0; di < DATASETS_COUNT; di++ )
{
for( int ci = 0; ci < TEST_CASE_COUNT; ci++ )
{
avRep += calcQuality[di][ci].repeatability;
avCorCount += calcQuality[di][ci].correspondenceCount;
}
}
avRep /= DATASETS_COUNT*TEST_CASE_COUNT;
avCorCount /= DATASETS_COUNT*TEST_CASE_COUNT;
rfile << algName << ", " << avRep << endl;
cfile << algName << ", " << cvRound(avCorCount) << endl;
}
void DetectorQualityTest::openToWriteKeypointsFile( FileStorage& fs, int datasetIdx )
{
string filename = string(ts->get_data_path()) + KEYPOINTS_DIR + algName + "_"+
DATASET_NAMES[datasetIdx] + ".xml.gz" ;
fs.open(filename, FileStorage::WRITE);
if( !fs.isOpened() )
ts->printf( cvtest::TS::LOG, "keypoints can not be written in file %s because this file can not be opened\n",
filename.c_str());
}
inline void writeKeypoints( FileStorage& fs, const vector<KeyPoint>& keypoints, int imgIdx )
{
if( fs.isOpened() )
{
stringstream imgName; imgName << "img" << imgIdx;
write( fs, imgName.str(), keypoints );
}
}
inline void readKeypoints( FileStorage& fs, vector<KeyPoint>& keypoints, int imgIdx )
{
assert( fs.isOpened() );
stringstream imgName; imgName << "img" << imgIdx;
read( fs[imgName.str()], keypoints);
}
void DetectorQualityTest::readAlgorithm ()
{
defaultDetector = FeatureDetector::create( algName );
specificDetector = FeatureDetector::create( algName );
if( defaultDetector == 0 )
{
ts->printf(cvtest::TS::LOG, "Algorithm can not be read\n");
ts->set_failed_test_info( cvtest::TS::FAIL_GENERIC);
}
}
void DetectorQualityTest::runDatasetTest (const vector<Mat> &imgs, const vector<Mat> &Hs, int di, int &progress)
{
Ptr<FeatureDetector> detector = isActiveParams[di] ? specificDetector : defaultDetector;
FileStorage keypontsFS;
if( isSaveKeypoints[di] )
openToWriteKeypointsFile( keypontsFS, di );
calcQuality[di].resize(TEST_CASE_COUNT);
vector<KeyPoint> keypoints1;
detector->detect( imgs[0], keypoints1 );
writeKeypoints( keypontsFS, keypoints1, 0);
int progressCount = DATASETS_COUNT*TEST_CASE_COUNT;
for( int ci = 0; ci < TEST_CASE_COUNT; ci++ )
{
progress = update_progress( progress, di*TEST_CASE_COUNT + ci, progressCount, 0 );
vector<KeyPoint> keypoints2;
float rep;
evaluateFeatureDetector( imgs[0], imgs[ci+1], Hs[ci], &keypoints1, &keypoints2,
rep, calcQuality[di][ci].correspondenceCount,
detector );
calcQuality[di][ci].repeatability = rep == -1 ? rep : 100.f*rep;
writeKeypoints( keypontsFS, keypoints2, ci+1);
}
}
void testLog( cvtest::TS* ts, bool isBadAccuracy )
{
if( isBadAccuracy )
ts->printf(cvtest::TS::LOG, " bad accuracy\n");
else
ts->printf(cvtest::TS::LOG, "\n");
}
int DetectorQualityTest::processResults( int datasetIdx, int caseIdx )
{
int res = cvtest::TS::OK;
bool isBadAccuracy;
Quality valid = validQuality[datasetIdx][caseIdx], calc = calcQuality[datasetIdx][caseIdx];
const int countEps = 1 + cvRound( 0.005f*(float)valid.correspondenceCount );
const float rltvEps = 0.5f;
ts->printf(cvtest::TS::LOG, "%s: calc=%f, valid=%f", REPEAT.c_str(), calc.repeatability, valid.repeatability );
isBadAccuracy = (valid.repeatability - calc.repeatability) > rltvEps;
testLog( ts, isBadAccuracy );
res = isBadAccuracy ? cvtest::TS::FAIL_BAD_ACCURACY : res;
ts->printf(cvtest::TS::LOG, "%s: calc=%d, valid=%d", CORRESP_COUNT.c_str(), calc.correspondenceCount, valid.correspondenceCount );
isBadAccuracy = (valid.correspondenceCount - calc.correspondenceCount) > countEps;
testLog( ts, isBadAccuracy );
res = isBadAccuracy ? cvtest::TS::FAIL_BAD_ACCURACY : res;
return res;
}
/****************************************************************************************\
* Descriptors evaluation *
\****************************************************************************************/
const string RECALL = "recall";
const string PRECISION = "precision";
const string KEYPOINTS_FILENAME = "keypointsFilename";
const string PROJECT_KEYPOINTS_FROM_1IMAGE = "projectKeypointsFrom1Image";
const string MATCH_FILTER = "matchFilter";
const string RUN_PARAMS_IS_IDENTICAL = "runParamsIsIdentical";
const string ONE_WAY_TRAIN_DIR = "detectors_descriptors_evaluation/one_way_train_images/";
const string ONE_WAY_IMAGES_LIST = "one_way_train_images.txt";
class DescriptorQualityTest : public BaseQualityTest
{
public:
enum{ NO_MATCH_FILTER = 0 };
DescriptorQualityTest( const char* _descriptorName, const char* _matcherName = 0 ) :
BaseQualityTest( _descriptorName )
{
validQuality.resize(DATASETS_COUNT);
calcQuality.resize(DATASETS_COUNT);
calcDatasetQuality.resize(DATASETS_COUNT);
commRunParams.resize(DATASETS_COUNT);
commRunParamsDefault.projectKeypointsFrom1Image = true;
commRunParamsDefault.matchFilter = NO_MATCH_FILTER;
commRunParamsDefault.isActiveParams = false;
if( _matcherName )
matcherName = _matcherName;
}
protected:
using BaseQualityTest::readResults;
using BaseQualityTest::writeResults;
using BaseQualityTest::processResults;
virtual string getRunParamsFilename() const;
virtual string getResultsFilename() const;
virtual string getPlotPath() const;
virtual void validQualityClear( int datasetIdx );
virtual void calcQualityClear( int datasetIdx );
virtual void validQualityCreate( int datasetIdx );
virtual bool isValidQualityEmpty( int datasetIdx ) const;
virtual bool isCalcQualityEmpty( int datasetIdx ) const;
virtual void readResults( FileNode& fn, int datasetIdx, int caseIdx );
virtual void writeResults( FileStorage& fs, int datasetIdx, int caseIdx ) const;
virtual void readDatasetRunParams( FileNode& fn, int datasetIdx ); //
virtual void writeDatasetRunParams( FileStorage& fs, int datasetIdx ) const;
virtual void setDefaultDatasetRunParams( int datasetIdx );
virtual void readDefaultRunParams( FileNode &fn );
virtual void writeDefaultRunParams( FileStorage &fs ) const;
virtual void readAlgorithm( );
virtual void processRunParamsFile () {};
virtual void runDatasetTest( const vector<Mat> &imgs, const vector<Mat> &Hs, int di, int &progress );
virtual int processResults( int datasetIdx, int caseIdx );
virtual void writePlotData( int di ) const;
void calculatePlotData( vector<vector<DMatch> > &allMatches, vector<vector<uchar> > &allCorrectMatchesMask, int di );
struct Quality
{
float recall;
float precision;
};
vector<vector<Quality> > validQuality;
vector<vector<Quality> > calcQuality;
vector<vector<Quality> > calcDatasetQuality;
struct CommonRunParams
{
string keypontsFilename;
bool projectKeypointsFrom1Image;
int matchFilter; // not used now
bool isActiveParams;
};
vector<CommonRunParams> commRunParams;
Ptr<GenericDescriptorMatch> specificDescMatcher;
Ptr<GenericDescriptorMatch> defaultDescMatcher;
CommonRunParams commRunParamsDefault;
string matcherName;
};
string DescriptorQualityTest::getRunParamsFilename() const
{
return string(ts->get_data_path()) + DESCRIPTORS_DIR + algName + PARAMS_POSTFIX;
}
string DescriptorQualityTest::getResultsFilename() const
{
return string(ts->get_data_path()) + DESCRIPTORS_DIR + algName + RES_POSTFIX;
}
string DescriptorQualityTest::getPlotPath() const
{
return string(ts->get_data_path()) + DESCRIPTORS_DIR + "plots/";
}
void DescriptorQualityTest::validQualityClear( int datasetIdx )
{
validQuality[datasetIdx].clear();
}
void DescriptorQualityTest::calcQualityClear( int datasetIdx )
{
calcQuality[datasetIdx].clear();
}
void DescriptorQualityTest::validQualityCreate( int datasetIdx )
{
validQuality[datasetIdx].resize(TEST_CASE_COUNT);
}
bool DescriptorQualityTest::isValidQualityEmpty( int datasetIdx ) const
{
return validQuality[datasetIdx].empty();
}
bool DescriptorQualityTest::isCalcQualityEmpty( int datasetIdx ) const
{
return calcQuality[datasetIdx].empty();
}
void DescriptorQualityTest::readResults( FileNode& fn, int datasetIdx, int caseIdx )
{
validQuality[datasetIdx][caseIdx].recall = fn[RECALL];
validQuality[datasetIdx][caseIdx].precision = fn[PRECISION];
}
void DescriptorQualityTest::writeResults( FileStorage& fs, int datasetIdx, int caseIdx ) const
{
fs << RECALL << calcQuality[datasetIdx][caseIdx].recall;
fs << PRECISION << calcQuality[datasetIdx][caseIdx].precision;
}
void DescriptorQualityTest::readDefaultRunParams (FileNode &fn)
{
if (! fn.empty() )
{
commRunParamsDefault.projectKeypointsFrom1Image = (int)fn[PROJECT_KEYPOINTS_FROM_1IMAGE] != 0;
commRunParamsDefault.matchFilter = (int)fn[MATCH_FILTER];
defaultDescMatcher->read (fn);
}
}
void DescriptorQualityTest::writeDefaultRunParams (FileStorage &fs) const
{
fs << PROJECT_KEYPOINTS_FROM_1IMAGE << commRunParamsDefault.projectKeypointsFrom1Image;
fs << MATCH_FILTER << commRunParamsDefault.matchFilter;
defaultDescMatcher->write (fs);
}
void DescriptorQualityTest::readDatasetRunParams( FileNode& fn, int datasetIdx )
{
commRunParams[datasetIdx].isActiveParams = (int)fn[IS_ACTIVE_PARAMS] != 0;
if (commRunParams[datasetIdx].isActiveParams)
{
commRunParams[datasetIdx].keypontsFilename = (string)fn[KEYPOINTS_FILENAME];
commRunParams[datasetIdx].projectKeypointsFrom1Image = (int)fn[PROJECT_KEYPOINTS_FROM_1IMAGE] != 0;
commRunParams[datasetIdx].matchFilter = (int)fn[MATCH_FILTER];
specificDescMatcher->read (fn);
}
else
{
setDefaultDatasetRunParams(datasetIdx);
}
}
void DescriptorQualityTest::writeDatasetRunParams( FileStorage& fs, int datasetIdx ) const
{
fs << IS_ACTIVE_PARAMS << commRunParams[datasetIdx].isActiveParams;
fs << KEYPOINTS_FILENAME << commRunParams[datasetIdx].keypontsFilename;
fs << PROJECT_KEYPOINTS_FROM_1IMAGE << commRunParams[datasetIdx].projectKeypointsFrom1Image;
fs << MATCH_FILTER << commRunParams[datasetIdx].matchFilter;
defaultDescMatcher->write (fs);
}
void DescriptorQualityTest::setDefaultDatasetRunParams( int datasetIdx )
{
commRunParams[datasetIdx] = commRunParamsDefault;
commRunParams[datasetIdx].keypontsFilename = "SURF_" + DATASET_NAMES[datasetIdx] + ".xml.gz";
}
void DescriptorQualityTest::writePlotData( int di ) const
{
stringstream filename;
filename << getPlotPath() << algName << "_" << DATASET_NAMES[di] << ".csv";
FILE *file = fopen (filename.str().c_str(), "w");
size_t size = calcDatasetQuality[di].size();
for (size_t i=0;i<size;i++)
{
fprintf( file, "%f, %f\n", 1 - calcDatasetQuality[di][i].precision, calcDatasetQuality[di][i].recall);
}
fclose( file );
}
void DescriptorQualityTest::readAlgorithm( )
{
defaultDescMatcher = GenericDescriptorMatcher::create( algName );
specificDescMatcher = GenericDescriptorMatcher::create( algName );
if( defaultDescMatcher == 0 )
{
Ptr<DescriptorExtractor> extractor = DescriptorExtractor::create( algName );
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create( matcherName );
defaultDescMatcher = new VectorDescriptorMatch( extractor, matcher );
specificDescMatcher = new VectorDescriptorMatch( extractor, matcher );
if( extractor == 0 || matcher == 0 )
{
ts->printf(cvtest::TS::LOG, "Algorithm can not be read\n");
ts->set_failed_test_info( cvtest::TS::FAIL_GENERIC);
}
}
}
void DescriptorQualityTest::calculatePlotData( vector<vector<DMatch> > &allMatches, vector<vector<uchar> > &allCorrectMatchesMask, int di )
{
vector<Point2f> recallPrecisionCurve;
computeRecallPrecisionCurve( allMatches, allCorrectMatchesMask, recallPrecisionCurve );
calcDatasetQuality[di].clear();
const float resultPrecision = 0.5;
bool isResultCalculated = false;
const double eps = 1e-2;
Quality initQuality;
initQuality.recall = 0;
initQuality.precision = 0;
calcDatasetQuality[di].push_back( initQuality );
for( size_t i=0;i<recallPrecisionCurve.size();i++ )
{
Quality quality;
quality.recall = recallPrecisionCurve[i].y;
quality.precision = 1 - recallPrecisionCurve[i].x;
Quality back = calcDatasetQuality[di].back();
if( fabs( quality.recall - back.recall ) < eps && fabs( quality.precision - back.precision ) < eps )
continue;
calcDatasetQuality[di].push_back( quality );
if( !isResultCalculated && quality.precision < resultPrecision )
{
for(int ci=0;ci<TEST_CASE_COUNT;ci++)
{
calcQuality[di][ci].recall = quality.recall;
calcQuality[di][ci].precision = quality.precision;
}
isResultCalculated = true;
}
}
}
void DescriptorQualityTest::runDatasetTest (const vector<Mat> &imgs, const vector<Mat> &Hs, int di, int &progress)
{
FileStorage keypontsFS( string(ts->get_data_path()) + KEYPOINTS_DIR + commRunParams[di].keypontsFilename,
FileStorage::READ );
if( !keypontsFS.isOpened())
{
calcQuality[di].clear();
ts->printf( cvtest::TS::LOG, "keypoints from file %s can not be read\n", commRunParams[di].keypontsFilename.c_str() );
return;
}
Ptr<GenericDescriptorMatcher> descMatch = commRunParams[di].isActiveParams ? specificDescMatcher : defaultDescMatcher;
calcQuality[di].resize(TEST_CASE_COUNT);
vector<KeyPoint> keypoints1;
readKeypoints( keypontsFS, keypoints1, 0);
int progressCount = DATASETS_COUNT*TEST_CASE_COUNT;
vector<vector<DMatch> > allMatches1to2;
vector<vector<uchar> > allCorrectMatchesMask;
for( int ci = 0; ci < TEST_CASE_COUNT; ci++ )
{
progress = update_progress( progress, di*TEST_CASE_COUNT + ci, progressCount, 0 );
vector<KeyPoint> keypoints2;
if( commRunParams[di].projectKeypointsFrom1Image )
{
// TODO need to test function calcKeyPointProjections
calcKeyPointProjections( keypoints1, Hs[ci], keypoints2 );
filterKeyPointsByImageSize( keypoints2, imgs[ci+1].size() );
}
else
readKeypoints( keypontsFS, keypoints2, ci+1 );
// TODO if( commRunParams[di].matchFilter )
vector<vector<DMatch> > matches1to2;
vector<vector<uchar> > correctMatchesMask;
vector<Point2f> recallPrecisionCurve; // not used because we need recallPrecisionCurve for
// all images in dataset
evaluateGenericDescriptorMatcher( imgs[0], imgs[ci+1], Hs[ci], keypoints1, keypoints2,
&matches1to2, &correctMatchesMask, recallPrecisionCurve,
descMatch );
allMatches1to2.insert( allMatches1to2.end(), matches1to2.begin(), matches1to2.end() );
allCorrectMatchesMask.insert( allCorrectMatchesMask.end(), correctMatchesMask.begin(), correctMatchesMask.end() );
}
calculatePlotData( allMatches1to2, allCorrectMatchesMask, di );
}
int DescriptorQualityTest::processResults( int datasetIdx, int caseIdx )
{
const float rltvEps = 0.001f;
int res = cvtest::TS::OK;
bool isBadAccuracy;
Quality valid = validQuality[datasetIdx][caseIdx], calc = calcQuality[datasetIdx][caseIdx];
ts->printf(cvtest::TS::LOG, "%s: calc=%f, valid=%f", RECALL.c_str(), calc.recall, valid.recall );
isBadAccuracy = (valid.recall - calc.recall) > rltvEps;
testLog( ts, isBadAccuracy );
res = isBadAccuracy ? cvtest::TS::FAIL_BAD_ACCURACY : res;
ts->printf(cvtest::TS::LOG, "%s: calc=%f, valid=%f", PRECISION.c_str(), calc.precision, valid.precision );
isBadAccuracy = (valid.precision - calc.precision) > rltvEps;
testLog( ts, isBadAccuracy );
res = isBadAccuracy ? cvtest::TS::FAIL_BAD_ACCURACY : res;
return res;
}
//--------------------------------- Calonder descriptor test --------------------------------------------
class CalonderDescriptorQualityTest : public DescriptorQualityTest
{
public:
CalonderDescriptorQualityTest() :
DescriptorQualityTest( "Calonder", "quality-descriptor-calonder") {}
virtual void readAlgorithm( )
{
string classifierFile = string(ts->get_data_path()) + "/features2d/calonder_classifier.rtc";
defaultDescMatcher = new VectorDescriptorMatch( new CalonderDescriptorExtractor<float>( classifierFile ),
new BruteForceMatcher<L2<float> > );
specificDescMatcher = defaultDescMatcher;
}
};
//--------------------------------- One Way descriptor test --------------------------------------------
class OneWayDescriptorQualityTest : public DescriptorQualityTest
{
public:
OneWayDescriptorQualityTest() :
DescriptorQualityTest("ONEWAY", "quality-descriptor-one-way")
{
}
protected:
virtual void processRunParamsFile ();
virtual void writeDatasetRunParams( FileStorage& fs, int datasetIdx ) const;
};
void OneWayDescriptorQualityTest::processRunParamsFile ()
{
string filename = getRunParamsFilename();
FileStorage fs = FileStorage (filename, FileStorage::READ);
FileNode fn = fs.getFirstTopLevelNode();
fn = fn[DEFAULT_PARAMS];
string pcaFilename = string(ts->get_data_path()) + (string)fn["pcaFilename"];
string trainPath = string(ts->get_data_path()) + (string)fn["trainPath"];
string trainImagesList = (string)fn["trainImagesList"];
int patch_width = fn["patchWidth"];
int patch_height = fn["patchHeight"];
Size patchSize = cvSize (patch_width, patch_height);
int poseCount = fn["poseCount"];
if (trainImagesList.length () == 0 )
return;
fs.release ();
readAllDatasetsRunParams();
OneWayDescriptorBase *base = new OneWayDescriptorBase(patchSize, poseCount, pcaFilename,
trainPath, trainImagesList);
OneWayDescriptorMatch *match = new OneWayDescriptorMatch ();
match->initialize( OneWayDescriptorMatch::Params (), base );
defaultDescMatcher = match;
writeAllDatasetsRunParams();
}
void OneWayDescriptorQualityTest::writeDatasetRunParams( FileStorage& fs, int datasetIdx ) const
{
fs << IS_ACTIVE_PARAMS << commRunParams[datasetIdx].isActiveParams;
fs << KEYPOINTS_FILENAME << commRunParams[datasetIdx].keypontsFilename;
fs << PROJECT_KEYPOINTS_FROM_1IMAGE << commRunParams[datasetIdx].projectKeypointsFrom1Image;
fs << MATCH_FILTER << commRunParams[datasetIdx].matchFilter;
}
// Detectors
//DetectorQualityTest fastDetectorQuality = DetectorQualityTest( "FAST", "quality-detector-fast" );
//DetectorQualityTest gfttDetectorQuality = DetectorQualityTest( "GFTT", "quality-detector-gftt" );
//DetectorQualityTest harrisDetectorQuality = DetectorQualityTest( "HARRIS", "quality-detector-harris" );
//DetectorQualityTest mserDetectorQuality = DetectorQualityTest( "MSER", "quality-detector-mser" );
//DetectorQualityTest starDetectorQuality = DetectorQualityTest( "STAR", "quality-detector-star" );
//DetectorQualityTest siftDetectorQuality = DetectorQualityTest( "SIFT", "quality-detector-sift" );
//DetectorQualityTest surfDetectorQuality = DetectorQualityTest( "SURF", "quality-detector-surf" );
// Descriptors
//DescriptorQualityTest siftDescriptorQuality = DescriptorQualityTest( "SIFT", "quality-descriptor-sift", "BruteForce" );
//DescriptorQualityTest surfDescriptorQuality = DescriptorQualityTest( "SURF", "quality-descriptor-surf", "BruteForce" );
//DescriptorQualityTest fernDescriptorQualityTest( "FERN", "quality-descriptor-fern");
//CalonderDescriptorQualityTest calonderDescriptorQualityTest;
// Don't run it because of bug in OneWayDescriptorBase many to many matching. TODO: fix this bug.
//OneWayDescriptorQualityTest oneWayDescriptorQuality;
// Don't run them (will validate and save results as "quality-descriptor-sift" and "quality-descriptor-surf" test data).
// TODO: differ result filenames.
//DescriptorQualityTest siftL1DescriptorQuality = DescriptorQualityTest( "SIFT", "quality-descriptor-sift-L1", "BruteForce-L1" );
//DescriptorQualityTest surfL1DescriptorQuality = DescriptorQualityTest( "SURF", "quality-descriptor-surf-L1", "BruteForce-L1" );
//DescriptorQualityTest oppSiftL1DescriptorQuality = DescriptorQualityTest( "SIFT", "quality-descriptor-opponent-sift-L1", "BruteForce-L1" );
//DescriptorQualityTest oppSurfL1DescriptorQuality = DescriptorQualityTest( "SURF", "quality-descriptor-opponent-surf-L1", "BruteForce-L1" );
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