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opencv/modules/nonfree/test/test_features2d.cpp

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lot's of changes; nonfree & photo modules added; SIFT & SURF -> nonfree module; Inpainting -> photo; refactored features2d (ORB is still failing tests), optimized brute-force matcher and made it non-template.
2012-03-15 22:36:01 +08:00
/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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// 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.
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//M*/
#include "test_precomp.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::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;
merged all the latest changes from 2.4 to trunk
2012-04-14 05:50:59 +08:00
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);
lot's of changes; nonfree & photo modules added; SIFT & SURF -> nonfree module; Inpainting -> photo; refactored features2d (ORB is still failing tests), optimized brute-force matcher and made it non-template.
2012-03-15 22:36:01 +08:00
uchar* data = (uchar*)cvAlloc(dataSize);
merged all the latest changes from 2.4 to trunk
2012-04-14 05:50:59 +08:00
size_t elements_read = fread( (void*)data, 1, dataSize, f );
CV_Assert(elements_read == (size_t)(dataSize));
lot's of changes; nonfree & photo modules added; SIFT & SURF -> nonfree module; Inpainting -> photo; refactored features2d (ORB is still failing tests), optimized brute-force matcher and made it non-template.
2012-03-15 22:36:01 +08:00
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, float _prevTime,
Distance d = Distance() ):
name(_name), maxDist(_maxDist), prevTime(_prevTime), 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 (previous time = %g ms).\n", t/((double)cvGetTickFrequency()*1000.)/calcDescriptors.rows, prevTime );
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() )
{
SurfFeatureDetector 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;
const float prevTime;
Ptr<DescriptorExtractor> dextractor;
Distance distance;
private:
CV_DescriptorExtractorTest& operator=(const CV_DescriptorExtractorTest&) { return *this; }
};
/*template<typename T, typename Distance>
class CV_CalonderDescriptorExtractorTest : public CV_DescriptorExtractorTest<Distance>
{
public:
CV_CalonderDescriptorExtractorTest( const char* testName, float _normDif, float _prevTime ) :
CV_DescriptorExtractorTest<Distance>( testName, _normDif, Ptr<DescriptorExtractor>(), _prevTime )
{}
protected:
virtual void createDescriptorExtractor()
{
CV_DescriptorExtractorTest<Distance>::dextractor =
new CalonderDescriptorExtractor<T>( string(CV_DescriptorExtractorTest<Distance>::ts->get_data_path()) +
FEATURES2D_DIR + "/calonder_classifier.rtc");
}
};*/
/****************************************************************************************\
* Algorithmic tests for descriptor matchers *
\****************************************************************************************/
class CV_DescriptorMatcherTest : public cvtest::BaseTest
{
public:
CV_DescriptorMatcherTest( const string& _name, const Ptr<DescriptorMatcher>& _dmatcher, float _badPart ) :
badPart(_badPart), name(_name), dmatcher(_dmatcher)
{}
protected:
static const int dim = 500;
static const int queryDescCount = 300; // must be even number because we split train data in some cases in two
static const int countFactor = 4; // do not change it
const float badPart;
virtual void run( int );
void generateData( Mat& query, Mat& train );
void emptyDataTest();
void matchTest( const Mat& query, const Mat& train );
void knnMatchTest( const Mat& query, const Mat& train );
void radiusMatchTest( const Mat& query, const Mat& train );
string name;
Ptr<DescriptorMatcher> dmatcher;
private:
CV_DescriptorMatcherTest& operator=(const CV_DescriptorMatcherTest&) { return *this; }
};
void CV_DescriptorMatcherTest::emptyDataTest()
{
assert( !dmatcher.empty() );
Mat queryDescriptors, trainDescriptors, mask;
vector<Mat> trainDescriptorCollection, masks;
vector<DMatch> matches;
vector<vector<DMatch> > vmatches;
try
{
dmatcher->match( queryDescriptors, trainDescriptors, matches, mask );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "match() on empty descriptors must not generate exception (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->knnMatch( queryDescriptors, trainDescriptors, vmatches, 2, mask );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "knnMatch() on empty descriptors must not generate exception (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->radiusMatch( queryDescriptors, trainDescriptors, vmatches, 10.f, mask );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "radiusMatch() on empty descriptors must not generate exception (1).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->add( trainDescriptorCollection );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "add() on empty descriptors must not generate exception.\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->match( queryDescriptors, matches, masks );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "match() on empty descriptors must not generate exception (2).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->knnMatch( queryDescriptors, vmatches, 2, masks );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "knnMatch() on empty descriptors must not generate exception (2).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
try
{
dmatcher->radiusMatch( queryDescriptors, vmatches, 10.f, masks );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "radiusMatch() on empty descriptors must not generate exception (2).\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
void CV_DescriptorMatcherTest::generateData( Mat& query, Mat& train )
{
RNG& rng = theRNG();
// Generate query descriptors randomly.
// Descriptor vector elements are integer values.
Mat buf( queryDescCount, dim, CV_32SC1 );
rng.fill( buf, RNG::UNIFORM, Scalar::all(0), Scalar(3) );
buf.convertTo( query, CV_32FC1 );
// Generate train decriptors as follows:
// copy each query descriptor to train set countFactor times
// and perturb some one element of the copied descriptors in
// in ascending order. General boundaries of the perturbation
// are (0.f, 1.f).
train.create( query.rows*countFactor, query.cols, CV_32FC1 );
float step = 1.f / countFactor;
for( int qIdx = 0; qIdx < query.rows; qIdx++ )
{
Mat queryDescriptor = query.row(qIdx);
for( int c = 0; c < countFactor; c++ )
{
int tIdx = qIdx * countFactor + c;
Mat trainDescriptor = train.row(tIdx);
queryDescriptor.copyTo( trainDescriptor );
int elem = rng(dim);
float diff = rng.uniform( step*c, step*(c+1) );
trainDescriptor.at<float>(0, elem) += diff;
}
}
}
void CV_DescriptorMatcherTest::matchTest( const Mat& query, const Mat& train )
{
dmatcher->clear();
// test const version of match()
{
vector<DMatch> matches;
dmatcher->match( query, train, matches );
if( (int)matches.size() != queryDescCount )
{
ts->printf(cvtest::TS::LOG, "Incorrect matches count while test match() function (1).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
for( size_t i = 0; i < matches.size(); i++ )
{
DMatch match = matches[i];
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0) )
badCount++;
}
if( (float)badCount > (float)queryDescCount*badPart )
{
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test match() function (1).\n",
(float)badCount/(float)queryDescCount );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
}
// test version of match() with add()
{
vector<DMatch> matches;
// make add() twice to test such case
dmatcher->add( vector<Mat>(1,train.rowRange(0, train.rows/2)) );
dmatcher->add( vector<Mat>(1,train.rowRange(train.rows/2, train.rows)) );
// prepare masks (make first nearest match illegal)
vector<Mat> masks(2);
for(int mi = 0; mi < 2; mi++ )
{
masks[mi] = Mat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1));
for( int di = 0; di < queryDescCount/2; di++ )
masks[mi].col(di*countFactor).setTo(Scalar::all(0));
}
dmatcher->match( query, matches, masks );
if( (int)matches.size() != queryDescCount )
{
ts->printf(cvtest::TS::LOG, "Incorrect matches count while test match() function (2).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
for( size_t i = 0; i < matches.size(); i++ )
{
DMatch match = matches[i];
int shift = dmatcher->isMaskSupported() ? 1 : 0;
{
if( i < queryDescCount/2 )
{
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor + shift) || (match.imgIdx != 0) )
badCount++;
}
else
{
if( (match.queryIdx != (int)i) || (match.trainIdx != ((int)i-queryDescCount/2)*countFactor + shift) || (match.imgIdx != 1) )
badCount++;
}
}
}
if( (float)badCount > (float)queryDescCount*badPart )
{
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test match() function (2).\n",
(float)badCount/(float)queryDescCount );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
}
}
}
void CV_DescriptorMatcherTest::knnMatchTest( const Mat& query, const Mat& train )
{
dmatcher->clear();
// test const version of knnMatch()
{
const int knn = 3;
vector<vector<DMatch> > matches;
dmatcher->knnMatch( query, train, matches, knn );
if( (int)matches.size() != queryDescCount )
{
ts->printf(cvtest::TS::LOG, "Incorrect matches count while test knnMatch() function (1).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
for( size_t i = 0; i < matches.size(); i++ )
{
if( (int)matches[i].size() != knn )
badCount++;
else
{
int localBadCount = 0;
for( int k = 0; k < knn; k++ )
{
DMatch match = matches[i][k];
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor+k) || (match.imgIdx != 0) )
localBadCount++;
}
badCount += localBadCount > 0 ? 1 : 0;
}
}
if( (float)badCount > (float)queryDescCount*badPart )
{
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test knnMatch() function (1).\n",
(float)badCount/(float)queryDescCount );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
}
// test version of knnMatch() with add()
{
const int knn = 2;
vector<vector<DMatch> > matches;
// make add() twice to test such case
dmatcher->add( vector<Mat>(1,train.rowRange(0, train.rows/2)) );
dmatcher->add( vector<Mat>(1,train.rowRange(train.rows/2, train.rows)) );
// prepare masks (make first nearest match illegal)
vector<Mat> masks(2);
for(int mi = 0; mi < 2; mi++ )
{
masks[mi] = Mat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1));
for( int di = 0; di < queryDescCount/2; di++ )
masks[mi].col(di*countFactor).setTo(Scalar::all(0));
}
dmatcher->knnMatch( query, matches, knn, masks );
if( (int)matches.size() != queryDescCount )
{
ts->printf(cvtest::TS::LOG, "Incorrect matches count while test knnMatch() function (2).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
int shift = dmatcher->isMaskSupported() ? 1 : 0;
for( size_t i = 0; i < matches.size(); i++ )
{
if( (int)matches[i].size() != knn )
badCount++;
else
{
int localBadCount = 0;
for( int k = 0; k < knn; k++ )
{
DMatch match = matches[i][k];
{
if( i < queryDescCount/2 )
{
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor + k + shift) ||
(match.imgIdx != 0) )
localBadCount++;
}
else
{
if( (match.queryIdx != (int)i) || (match.trainIdx != ((int)i-queryDescCount/2)*countFactor + k + shift) ||
(match.imgIdx != 1) )
localBadCount++;
}
}
}
badCount += localBadCount > 0 ? 1 : 0;
}
}
if( (float)badCount > (float)queryDescCount*badPart )
{
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test knnMatch() function (2).\n",
(float)badCount/(float)queryDescCount );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
}
}
}
void CV_DescriptorMatcherTest::radiusMatchTest( const Mat& query, const Mat& train )
{
dmatcher->clear();
// test const version of match()
{
const float radius = 1.f/countFactor;
vector<vector<DMatch> > matches;
dmatcher->radiusMatch( query, train, matches, radius );
if( (int)matches.size() != queryDescCount )
{
ts->printf(cvtest::TS::LOG, "Incorrect matches count while test radiusMatch() function (1).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
else
{
int badCount = 0;
for( size_t i = 0; i < matches.size(); i++ )
{
if( (int)matches[i].size() != 1 )
badCount++;
else
{
DMatch match = matches[i][0];
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0) )
badCount++;
}
}
if( (float)badCount > (float)queryDescCount*badPart )
{
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test radiusMatch() function (1).\n",
(float)badCount/(float)queryDescCount );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
}
// test version of match() with add()
{
int n = 3;
const float radius = 1.f/countFactor * n;
vector<vector<DMatch> > matches;
// make add() twice to test such case
dmatcher->add( vector<Mat>(1,train.rowRange(0, train.rows/2)) );
dmatcher->add( vector<Mat>(1,train.rowRange(train.rows/2, train.rows)) );
// prepare masks (make first nearest match illegal)
vector<Mat> masks(2);
for(int mi = 0; mi < 2; mi++ )
{
masks[mi] = Mat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1));
for( int di = 0; di < queryDescCount/2; di++ )
masks[mi].col(di*countFactor).setTo(Scalar::all(0));
}
dmatcher->radiusMatch( query, matches, radius, masks );
fixed many warnings from GCC 4.6.1
2012-03-17 17:22:31 +08:00
//int curRes = cvtest::TS::OK;
lot's of changes; nonfree & photo modules added; SIFT & SURF -> nonfree module; Inpainting -> photo; refactored features2d (ORB is still failing tests), optimized brute-force matcher and made it non-template.
2012-03-15 22:36:01 +08:00
if( (int)matches.size() != queryDescCount )
{
ts->printf(cvtest::TS::LOG, "Incorrect matches count while test radiusMatch() function (1).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
int badCount = 0;
int shift = dmatcher->isMaskSupported() ? 1 : 0;
int needMatchCount = dmatcher->isMaskSupported() ? n-1 : n;
for( size_t i = 0; i < matches.size(); i++ )
{
if( (int)matches[i].size() != needMatchCount )
badCount++;
else
{
int localBadCount = 0;
for( int k = 0; k < needMatchCount; k++ )
{
DMatch match = matches[i][k];
{
if( i < queryDescCount/2 )
{
if( (match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor + k + shift) ||
(match.imgIdx != 0) )
localBadCount++;
}
else
{
if( (match.queryIdx != (int)i) || (match.trainIdx != ((int)i-queryDescCount/2)*countFactor + k + shift) ||
(match.imgIdx != 1) )
localBadCount++;
}
}
}
badCount += localBadCount > 0 ? 1 : 0;
}
}
if( (float)badCount > (float)queryDescCount*badPart )
{
ts->printf( cvtest::TS::LOG, "%f - too large bad matches part while test radiusMatch() function (2).\n",
(float)badCount/(float)queryDescCount );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
}
}
void CV_DescriptorMatcherTest::run( int )
{
Mat query, train;
generateData( query, train );
matchTest( query, train );
knnMatchTest( query, train );
radiusMatchTest( query, train );
}
/****************************************************************************************\
* Tests registrations *
\****************************************************************************************/
/*
* Detectors
*/
TEST( Features2d_Detector_SIFT, regression )
{
CV_FeatureDetectorTest test( "detector-sift", FeatureDetector::create("SIFT") );
test.safe_run();
}
TEST( Features2d_Detector_SURF, regression )
{
CV_FeatureDetectorTest test( "detector-surf", FeatureDetector::create("SURF") );
test.safe_run();
}
/*
* Descriptors
*/
TEST( Features2d_DescriptorExtractor_SIFT, regression )
{
CV_DescriptorExtractorTest<L2<float> > test( "descriptor-sift", 0.03f,
DescriptorExtractor::create("SIFT"), 8.06652f );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_SURF, regression )
{
CV_DescriptorExtractorTest<L2<float> > test( "descriptor-surf", 0.035f,
DescriptorExtractor::create("SURF"), 0.147372f );
test.safe_run();
}
/*TEST( Features2d_DescriptorExtractor_OpponentSIFT, regression )
{
CV_DescriptorExtractorTest<L2<float> > test( "descriptor-opponent-sift", 0.18f,
DescriptorExtractor::create("OpponentSIFT"), 8.06652f );
test.safe_run();
}*/
TEST( Features2d_DescriptorExtractor_OpponentSURF, regression )
{
CV_DescriptorExtractorTest<L2<float> > test( "descriptor-opponent-surf", 0.18f,
DescriptorExtractor::create("OpponentSURF"), 0.147372f );
test.safe_run();
}
/*#if CV_SSE2
TEST( Features2d_DescriptorExtractor_Calonder_uchar, regression )
{
CV_CalonderDescriptorExtractorTest<uchar, L2<uchar> > test( "descriptor-calonder-uchar",
std::numeric_limits<float>::epsilon() + 1,
0.0132175f );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_Calonder_float, regression )
{
CV_CalonderDescriptorExtractorTest<float, L2<float> > test( "descriptor-calonder-float",
std::numeric_limits<float>::epsilon(),
0.0221308f );
test.safe_run();
}
#endif*/ // CV_SSE2
merged all the latest changes from 2.4 to trunk
2012-04-14 05:50:59 +08:00
TEST(Features2d_BruteForceDescriptorMatcher_knnMatch, regression)
{
const int sz = 100;
const int k = 3;
Ptr<DescriptorExtractor> ext = DescriptorExtractor::create("SURF");
ASSERT_TRUE(ext != NULL);
Ptr<FeatureDetector> det = FeatureDetector::create("SURF");
//"%YAML:1.0\nhessianThreshold: 8000.\noctaves: 3\noctaveLayers: 4\nupright: 0\n"
ASSERT_TRUE(det != NULL);
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce");
ASSERT_TRUE(matcher != NULL);
Mat imgT(sz, sz, CV_8U, Scalar(255));
line(imgT, Point(20, sz/2), Point(sz-21, sz/2), Scalar(100), 2);
line(imgT, Point(sz/2, 20), Point(sz/2, sz-21), Scalar(100), 2);
vector<KeyPoint> kpT;
kpT.push_back( KeyPoint(50, 50, 16, 0, 20000, 1, -1) );
kpT.push_back( KeyPoint(42, 42, 16, 160, 10000, 1, -1) );
Mat descT;
ext->compute(imgT, kpT, descT);
Mat imgQ(sz, sz, CV_8U, Scalar(255));
line(imgQ, Point(30, sz/2), Point(sz-31, sz/2), Scalar(100), 3);
line(imgQ, Point(sz/2, 30), Point(sz/2, sz-31), Scalar(100), 3);
vector<KeyPoint> kpQ;
det->detect(imgQ, kpQ);
Mat descQ;
ext->compute(imgQ, kpQ, descQ);
vector<vector<DMatch> > matches;
matcher->knnMatch(descQ, descT, matches, k);
//cout << "\nBest " << k << " matches to " << descT.rows << " train desc-s." << endl;
ASSERT_EQ(descQ.rows, matches.size());
for(size_t i = 0; i<matches.size(); i++)
{
//cout << "\nmatches[" << i << "].size()==" << matches[i].size() << endl;
ASSERT_GT(min(k, descT.rows), static_cast<int>(matches[i].size()));
for(size_t j = 0; j<matches[i].size(); j++)
{
//cout << "\t" << matches[i][j].queryIdx << " -> " << matches[i][j].trainIdx << endl;
ASSERT_EQ(matches[i][j].queryIdx, static_cast<int>(i));
}
}
}
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