Skip to content

O
opencv
Commit 40b1dc12 authored by Alexander Alekhin's avatar Alexander Alekhin
Browse files
Options

Merge pull request #12464 from alalek:fix_contrib_1754

parents 8eba3c1e b50c70bb
Hide whitespace changes
Inline Side-by-side
Showing with 918 additions and 922 deletions
modules/features2d/test/test_descriptors_invariance.cpp
View file @ 40b1dc12
......@@ -5,163 +5,13 @@
#include "test_precomp.hpp"
#include "test_invariance_utils.hpp"
namespace opencv_test { namespace {
#include "test_descriptors_invariance.impl.hpp"
#define SHOW_DEBUG_LOG 1
namespace opencv_test { namespace {
typedef tuple<std::string, Ptr<FeatureDetector>, Ptr<DescriptorExtractor>, float>
String_FeatureDetector_DescriptorExtractor_Float_t;
const static std::string IMAGE_TSUKUBA = "features2d/tsukuba.png";
const static std::string IMAGE_BIKES = "detectors_descriptors_evaluation/images_datasets/bikes/img1.png";
#define Value(...) Values(String_FeatureDetector_DescriptorExtractor_Float_t(__VA_ARGS__))
static
void rotateKeyPoints(const vector<KeyPoint>& src, const Mat& H, float angle, vector<KeyPoint>& dst)
{
// suppose that H is rotation given from rotateImage() and angle has value passed to rotateImage()
vector<Point2f> srcCenters, dstCenters;
KeyPoint::convert(src, srcCenters);
perspectiveTransform(srcCenters, dstCenters, H);
dst = src;
for(size_t i = 0; i < dst.size(); i++)
{
dst[i].pt = dstCenters[i];
float dstAngle = src[i].angle + angle;
if(dstAngle >= 360.f)
dstAngle -= 360.f;
dst[i].angle = dstAngle;
}
}
class DescriptorInvariance : public TestWithParam<String_FeatureDetector_DescriptorExtractor_Float_t>
{
protected:
virtual void SetUp() {
// Read test data
const std::string filename = cvtest::TS::ptr()->get_data_path() + get<0>(GetParam());
image0 = imread(filename);
ASSERT_FALSE(image0.empty()) << "couldn't read input image";
featureDetector = get<1>(GetParam());
descriptorExtractor = get<2>(GetParam());
minInliersRatio = get<3>(GetParam());
}
Ptr<FeatureDetector> featureDetector;
Ptr<DescriptorExtractor> descriptorExtractor;
float minInliersRatio;
Mat image0;
};
typedef DescriptorInvariance DescriptorScaleInvariance;
typedef DescriptorInvariance DescriptorRotationInvariance;
TEST_P(DescriptorRotationInvariance, rotation)
{
Mat image1, mask1;
const int borderSize = 16;
Mat mask0(image0.size(), CV_8UC1, Scalar(0));
mask0(Rect(borderSize, borderSize, mask0.cols - 2*borderSize, mask0.rows - 2*borderSize)).setTo(Scalar(255));
vector<KeyPoint> keypoints0;
Mat descriptors0;
featureDetector->detect(image0, keypoints0, mask0);
std::cout << "Keypoints: " << keypoints0.size() << std::endl;
EXPECT_GE(keypoints0.size(), 15u);
descriptorExtractor->compute(image0, keypoints0, descriptors0);
BFMatcher bfmatcher(descriptorExtractor->defaultNorm());
const float minIntersectRatio = 0.5f;
const int maxAngle = 360, angleStep = 15;
for(int angle = 0; angle < maxAngle; angle += angleStep)
{
Mat H = rotateImage(image0, mask0, static_cast<float>(angle), image1, mask1);
vector<KeyPoint> keypoints1;
rotateKeyPoints(keypoints0, H, static_cast<float>(angle), keypoints1);
Mat descriptors1;
descriptorExtractor->compute(image1, keypoints1, descriptors1);
vector<DMatch> descMatches;
bfmatcher.match(descriptors0, descriptors1, descMatches);
int descInliersCount = 0;
for(size_t m = 0; m < descMatches.size(); m++)
{
const KeyPoint& transformed_p0 = keypoints1[descMatches[m].queryIdx];
const KeyPoint& p1 = keypoints1[descMatches[m].trainIdx];
if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
p1.pt, 0.5f * p1.size) >= minIntersectRatio)
{
descInliersCount++;
}
}
float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
EXPECT_GE(descInliersRatio, minInliersRatio);
#if SHOW_DEBUG_LOG
std::cout
<< "angle = " << angle
<< ", inliers = " << descInliersCount
<< ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
<< std::endl;
#endif
}
}
TEST_P(DescriptorScaleInvariance, scale)
{
vector<KeyPoint> keypoints0;
featureDetector->detect(image0, keypoints0);
std::cout << "Keypoints: " << keypoints0.size() << std::endl;
EXPECT_GE(keypoints0.size(), 15u);
Mat descriptors0;
descriptorExtractor->compute(image0, keypoints0, descriptors0);
BFMatcher bfmatcher(descriptorExtractor->defaultNorm());
for(int scaleIdx = 1; scaleIdx <= 3; scaleIdx++)
{
float scale = 1.f + scaleIdx * 0.5f;
Mat image1;
resize(image0, image1, Size(), 1./scale, 1./scale, INTER_LINEAR_EXACT);
vector<KeyPoint> keypoints1;
scaleKeyPoints(keypoints0, keypoints1, 1.0f/scale);
Mat descriptors1;
descriptorExtractor->compute(image1, keypoints1, descriptors1);
vector<DMatch> descMatches;
bfmatcher.match(descriptors0, descriptors1, descMatches);
const float minIntersectRatio = 0.5f;
int descInliersCount = 0;
for(size_t m = 0; m < descMatches.size(); m++)
{
const KeyPoint& transformed_p0 = keypoints0[descMatches[m].queryIdx];
const KeyPoint& p1 = keypoints0[descMatches[m].trainIdx];
if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
p1.pt, 0.5f * p1.size) >= minIntersectRatio)
{
descInliersCount++;
}
}
float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
EXPECT_GE(descInliersRatio, minInliersRatio);
#if SHOW_DEBUG_LOG
std::cout
<< "scale = " << scale
<< ", inliers = " << descInliersCount
<< ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
<< std::endl;
#endif
}
}
#define Value(...) Values(make_tuple(__VA_ARGS__))
/*
* Descriptors's rotation invariance check
......
modules/features2d/test/test_descriptors_invariance.impl.hpp 0 → 100644
View file @ 40b1dc12
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
#include "test_invariance_utils.hpp"
namespace opencv_test { namespace {
#define SHOW_DEBUG_LOG 1
typedef tuple<std::string, Ptr<FeatureDetector>, Ptr<DescriptorExtractor>, float>
String_FeatureDetector_DescriptorExtractor_Float_t;
static
void rotateKeyPoints(const vector<KeyPoint>& src, const Mat& H, float angle, vector<KeyPoint>& dst)
{
// suppose that H is rotation given from rotateImage() and angle has value passed to rotateImage()
vector<Point2f> srcCenters, dstCenters;
KeyPoint::convert(src, srcCenters);
perspectiveTransform(srcCenters, dstCenters, H);
dst = src;
for(size_t i = 0; i < dst.size(); i++)
{
dst[i].pt = dstCenters[i];
float dstAngle = src[i].angle + angle;
if(dstAngle >= 360.f)
dstAngle -= 360.f;
dst[i].angle = dstAngle;
}
}
class DescriptorInvariance : public TestWithParam<String_FeatureDetector_DescriptorExtractor_Float_t>
{
protected:
virtual void SetUp() {
// Read test data
const std::string filename = cvtest::TS::ptr()->get_data_path() + get<0>(GetParam());
image0 = imread(filename);
ASSERT_FALSE(image0.empty()) << "couldn't read input image";
featureDetector = get<1>(GetParam());
descriptorExtractor = get<2>(GetParam());
minInliersRatio = get<3>(GetParam());
}
Ptr<FeatureDetector> featureDetector;
Ptr<DescriptorExtractor> descriptorExtractor;
float minInliersRatio;
Mat image0;
};
typedef DescriptorInvariance DescriptorScaleInvariance;
typedef DescriptorInvariance DescriptorRotationInvariance;
TEST_P(DescriptorRotationInvariance, rotation)
{
Mat image1, mask1;
const int borderSize = 16;
Mat mask0(image0.size(), CV_8UC1, Scalar(0));
mask0(Rect(borderSize, borderSize, mask0.cols - 2*borderSize, mask0.rows - 2*borderSize)).setTo(Scalar(255));
vector<KeyPoint> keypoints0;
Mat descriptors0;
featureDetector->detect(image0, keypoints0, mask0);
std::cout << "Keypoints: " << keypoints0.size() << std::endl;
EXPECT_GE(keypoints0.size(), 15u);
descriptorExtractor->compute(image0, keypoints0, descriptors0);
BFMatcher bfmatcher(descriptorExtractor->defaultNorm());
const float minIntersectRatio = 0.5f;
const int maxAngle = 360, angleStep = 15;
for(int angle = 0; angle < maxAngle; angle += angleStep)
{
Mat H = rotateImage(image0, mask0, static_cast<float>(angle), image1, mask1);
vector<KeyPoint> keypoints1;
rotateKeyPoints(keypoints0, H, static_cast<float>(angle), keypoints1);
Mat descriptors1;
descriptorExtractor->compute(image1, keypoints1, descriptors1);
vector<DMatch> descMatches;
bfmatcher.match(descriptors0, descriptors1, descMatches);
int descInliersCount = 0;
for(size_t m = 0; m < descMatches.size(); m++)
{
const KeyPoint& transformed_p0 = keypoints1[descMatches[m].queryIdx];
const KeyPoint& p1 = keypoints1[descMatches[m].trainIdx];
if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
p1.pt, 0.5f * p1.size) >= minIntersectRatio)
{
descInliersCount++;
}
}
float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
EXPECT_GE(descInliersRatio, minInliersRatio);
#if SHOW_DEBUG_LOG
std::cout
<< "angle = " << angle
<< ", inliers = " << descInliersCount
<< ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
<< std::endl;
#endif
}
}
TEST_P(DescriptorScaleInvariance, scale)
{
vector<KeyPoint> keypoints0;
featureDetector->detect(image0, keypoints0);
std::cout << "Keypoints: " << keypoints0.size() << std::endl;
EXPECT_GE(keypoints0.size(), 15u);
Mat descriptors0;
descriptorExtractor->compute(image0, keypoints0, descriptors0);
BFMatcher bfmatcher(descriptorExtractor->defaultNorm());
for(int scaleIdx = 1; scaleIdx <= 3; scaleIdx++)
{
float scale = 1.f + scaleIdx * 0.5f;
Mat image1;
resize(image0, image1, Size(), 1./scale, 1./scale, INTER_LINEAR_EXACT);
vector<KeyPoint> keypoints1;
scaleKeyPoints(keypoints0, keypoints1, 1.0f/scale);
Mat descriptors1;
descriptorExtractor->compute(image1, keypoints1, descriptors1);
vector<DMatch> descMatches;
bfmatcher.match(descriptors0, descriptors1, descMatches);
const float minIntersectRatio = 0.5f;
int descInliersCount = 0;
for(size_t m = 0; m < descMatches.size(); m++)
{
const KeyPoint& transformed_p0 = keypoints0[descMatches[m].queryIdx];
const KeyPoint& p1 = keypoints0[descMatches[m].trainIdx];
if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
p1.pt, 0.5f * p1.size) >= minIntersectRatio)
{
descInliersCount++;
}
}
float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
EXPECT_GE(descInliersRatio, minInliersRatio);
#if SHOW_DEBUG_LOG
std::cout
<< "scale = " << scale
<< ", inliers = " << descInliersCount
<< ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
<< std::endl;
#endif
}
}
#undef SHOW_DEBUG_LOG
}} // namespace
namespace std {
using namespace opencv_test;
static inline void PrintTo(const String_FeatureDetector_DescriptorExtractor_Float_t& v, std::ostream* os)
{
*os << "(\"" << get<0>(v)
<< "\", " << get<3>(v)
<< ")";
}
} // namespace
modules/features2d/test/test_descriptors_regression.cpp
View file @ 40b1dc12
/*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*/
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
#include "test_precomp.hpp"
namespace opencv_test { namespace {
const string FEATURES2D_DIR = "features2d";
const string IMAGE_FILENAME = "tsukuba.png";
const string DESCRIPTOR_DIR = FEATURES2D_DIR + "/descriptor_extractors";
}} // namespace
/****************************************************************************************\
* Regression tests for descriptor extractors. *
\****************************************************************************************/
static void writeMatInBin( const Mat& mat, const string& filename )
{
FILE* f = fopen( filename.c_str(), "wb");
if( f )
{
CV_Assert(4 == sizeof(int));
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);
fwrite( (void*)&dataSize, sizeof(int), 1, f );
fwrite( (void*)mat.ptr(), 1, dataSize, f );
fclose(f);
}
}
static Mat readMatFromBin( const string& filename )
{
FILE* f = fopen( filename.c_str(), "rb" );
if( f )
{
CV_Assert(4 == sizeof(int));
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);
int step = dataSize / rows / CV_ELEM_SIZE(type);
CV_Assert(step >= cols);
Mat returnMat = Mat(rows, step, type).colRange(0, cols);
size_t elements_read = fread( returnMat.ptr(), 1, dataSize, f );
CV_Assert(elements_read == (size_t)(dataSize));
fclose(f);
return returnMat;
}
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(), Ptr<FeatureDetector> _detector = Ptr<FeatureDetector>()):
name(_name), maxDist(_maxDist), dextractor(_dextractor), distance(d) , detector(_detector) {}
~CV_DescriptorExtractorTest()
{
}
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 = 0;
size_t exact_count = 0, failed_count = 0;
for( int y = 0; y < validDescriptors.rows; y++ )
{
DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
if (dist == 0)
exact_count++;
if( dist > curMaxDist )
{
if (dist > maxDist)
failed_count++;
curMaxDist = dist;
}
#if 0
if (dist > 0)
{
std::cout << "i=" << y << " fail_count=" << failed_count << " dist=" << dist << std::endl;
std::cout << "valid: " << validDescriptors.row(y) << std::endl;
std::cout << " calc: " << calcDescriptors.row(y) << std::endl;
}
#endif
}
float exact_percents = (100 * (float)exact_count / validDescriptors.rows);
float failed_percents = (100 * (float)failed_count / validDescriptors.rows);
std::stringstream ss;
ss << "Exact count (dist == 0): " << exact_count << " (" << (int)exact_percents << "%)" << std::endl
<< "Failed count (dist > " << maxDist << "): " << failed_count << " (" << (int)failed_percents << "%)" << std::endl
<< "Max distance between valid and computed descriptors (" << validDescriptors.size() << "): " << curMaxDist;
EXPECT_LE(failed_percents, 20.0f);
std::cout << ss.str() << std::endl;
}
void emptyDataTest()
{
assert( dextractor );
// 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 );
}
RNG rng;
image = cvtest::randomMat(rng, Size(50, 50), CV_8UC3, 0, 255, false);
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 );
// 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;
}
const std::string keypoints_filename = string(ts->get_data_path()) +
(detector.empty()
? (FEATURES2D_DIR + "/" + std::string("keypoints.xml.gz"))
: (DESCRIPTOR_DIR + "/" + name + "_keypoints.xml.gz"));
FileStorage fs(keypoints_filename, FileStorage::READ);
vector<KeyPoint> keypoints;
EXPECT_TRUE(fs.isOpened()) << "Keypoint testdata is missing. Re-computing and re-writing keypoints testdata...";
if (!fs.isOpened())
{
fs.open(keypoints_filename, FileStorage::WRITE);
ASSERT_TRUE(fs.isOpened()) << "File for writing keypoints can not be opened.";
if (detector.empty())
{
Ptr<ORB> fd = ORB::create();
fd->detect(img, keypoints);
}
else
{
detector->detect(img, keypoints);
}
write(fs, "keypoints", keypoints);
fs.release();
}
else
{
read(fs.getFirstTopLevelNode(), keypoints);
fs.release();
}
if(!detector.empty())
{
vector<KeyPoint> calcKeypoints;
detector->detect(img, calcKeypoints);
// TODO validate received keypoints
int diff = abs((int)calcKeypoints.size() - (int)keypoints.size());
if (diff > 0)
{
std::cout << "Keypoints difference: " << diff << std::endl;
EXPECT_LE(diff, (int)(keypoints.size() * 0.03f));
}
}
ASSERT_FALSE(keypoints.empty());
{
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)getTickFrequency()*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();
EXPECT_FALSE(validDescriptors.empty()) << "Descriptors testdata is missing. Re-writing descriptors testdata...";
if (!validDescriptors.empty())
{
compareDescriptors(validDescriptors, calcDescriptors);
}
else
{
ASSERT_TRUE(writeDescriptors(calcDescriptors)) << "Descriptors can not be written.";
}
}
}
void run(int)
{
createDescriptorExtractor();
if( !dextractor )
{
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;
Ptr<FeatureDetector> detector;
#include "test_descriptors_regression.impl.hpp"
private:
CV_DescriptorExtractorTest& operator=(const CV_DescriptorExtractorTest&) { return *this; }
};
namespace opencv_test { namespace {
/****************************************************************************************\
* Tests registrations *
......
modules/features2d/test/test_descriptors_regression.impl.hpp 0 → 100644
View file @ 40b1dc12
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
namespace opencv_test { namespace {
/****************************************************************************************\
* Regression tests for descriptor extractors. *
\****************************************************************************************/
static void writeMatInBin( const Mat& mat, const string& filename )
{
FILE* f = fopen( filename.c_str(), "wb");
if( f )
{
CV_Assert(4 == sizeof(int));
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);
fwrite( (void*)&dataSize, sizeof(int), 1, f );
fwrite( (void*)mat.ptr(), 1, dataSize, f );
fclose(f);
}
}
static Mat readMatFromBin( const string& filename )
{
FILE* f = fopen( filename.c_str(), "rb" );
if( f )
{
CV_Assert(4 == sizeof(int));
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);
int step = dataSize / rows / CV_ELEM_SIZE(type);
CV_Assert(step >= cols);
Mat returnMat = Mat(rows, step, type).colRange(0, cols);
size_t elements_read = fread( returnMat.ptr(), 1, dataSize, f );
CV_Assert(elements_read == (size_t)(dataSize));
fclose(f);
return returnMat;
}
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(), Ptr<FeatureDetector> _detector = Ptr<FeatureDetector>()):
name(_name), maxDist(_maxDist), dextractor(_dextractor), distance(d) , detector(_detector) {}
~CV_DescriptorExtractorTest()
{
}
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 = 0;
size_t exact_count = 0, failed_count = 0;
for( int y = 0; y < validDescriptors.rows; y++ )
{
DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
if (dist == 0)
exact_count++;
if( dist > curMaxDist )
{
if (dist > maxDist)
failed_count++;
curMaxDist = dist;
}
#if 0
if (dist > 0)
{
std::cout << "i=" << y << " fail_count=" << failed_count << " dist=" << dist << std::endl;
std::cout << "valid: " << validDescriptors.row(y) << std::endl;
std::cout << " calc: " << calcDescriptors.row(y) << std::endl;
}
#endif
}
float exact_percents = (100 * (float)exact_count / validDescriptors.rows);
float failed_percents = (100 * (float)failed_count / validDescriptors.rows);
std::stringstream ss;
ss << "Exact count (dist == 0): " << exact_count << " (" << (int)exact_percents << "%)" << std::endl
<< "Failed count (dist > " << maxDist << "): " << failed_count << " (" << (int)failed_percents << "%)" << std::endl
<< "Max distance between valid and computed descriptors (" << validDescriptors.size() << "): " << curMaxDist;
EXPECT_LE(failed_percents, 20.0f);
std::cout << ss.str() << std::endl;
}
void emptyDataTest()
{
assert( dextractor );
// 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 );
}
RNG rng;
image = cvtest::randomMat(rng, Size(50, 50), CV_8UC3, 0, 255, false);
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 );
// 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;
}
const std::string keypoints_filename = string(ts->get_data_path()) +
(detector.empty()
? (FEATURES2D_DIR + "/" + std::string("keypoints.xml.gz"))
: (DESCRIPTOR_DIR + "/" + name + "_keypoints.xml.gz"));
FileStorage fs(keypoints_filename, FileStorage::READ);
vector<KeyPoint> keypoints;
EXPECT_TRUE(fs.isOpened()) << "Keypoint testdata is missing. Re-computing and re-writing keypoints testdata...";
if (!fs.isOpened())
{
fs.open(keypoints_filename, FileStorage::WRITE);
ASSERT_TRUE(fs.isOpened()) << "File for writing keypoints can not be opened.";
if (detector.empty())
{
Ptr<ORB> fd = ORB::create();
fd->detect(img, keypoints);
}
else
{
detector->detect(img, keypoints);
}
write(fs, "keypoints", keypoints);
fs.release();
}
else
{
read(fs.getFirstTopLevelNode(), keypoints);
fs.release();
}
if(!detector.empty())
{
vector<KeyPoint> calcKeypoints;
detector->detect(img, calcKeypoints);
// TODO validate received keypoints
int diff = abs((int)calcKeypoints.size() - (int)keypoints.size());
if (diff > 0)
{
std::cout << "Keypoints difference: " << diff << std::endl;
EXPECT_LE(diff, (int)(keypoints.size() * 0.03f));
}
}
ASSERT_FALSE(keypoints.empty());
{
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)getTickFrequency()*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();
EXPECT_FALSE(validDescriptors.empty()) << "Descriptors testdata is missing. Re-writing descriptors testdata...";
if (!validDescriptors.empty())
{
compareDescriptors(validDescriptors, calcDescriptors);
}
else
{
ASSERT_TRUE(writeDescriptors(calcDescriptors)) << "Descriptors can not be written.";
}
}
}
void run(int)
{
createDescriptorExtractor();
if( !dextractor )
{
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;
Ptr<FeatureDetector> detector;
private:
CV_DescriptorExtractorTest& operator=(const CV_DescriptorExtractorTest&) { return *this; }
};
}} // namespace
modules/features2d/test/test_detectors_invariance.cpp
View file @ 40b1dc12
......@@ -5,216 +5,13 @@
#include "test_precomp.hpp"
#include "test_invariance_utils.hpp"
namespace opencv_test { namespace {
using namespace perf;
#include "test_detectors_invariance.impl.hpp"
#define SHOW_DEBUG_LOG 1
namespace opencv_test { namespace {
typedef tuple<std::string, Ptr<FeatureDetector>, float, float> String_FeatureDetector_Float_Float_t;
const static std::string IMAGE_TSUKUBA = "features2d/tsukuba.png";
const static std::string IMAGE_BIKES = "detectors_descriptors_evaluation/images_datasets/bikes/img1.png";
#define Value(...) Values(String_FeatureDetector_Float_Float_t(__VA_ARGS__))
static
void matchKeyPoints(const vector<KeyPoint>& keypoints0, const Mat& H,
const vector<KeyPoint>& keypoints1,
vector<DMatch>& matches)
{
vector<Point2f> points0;
KeyPoint::convert(keypoints0, points0);
Mat points0t;
if(H.empty())
points0t = Mat(points0);
else
perspectiveTransform(Mat(points0), points0t, H);
matches.clear();
vector<uchar> usedMask(keypoints1.size(), 0);
for(int i0 = 0; i0 < static_cast<int>(keypoints0.size()); i0++)
{
int nearestPointIndex = -1;
float maxIntersectRatio = 0.f;
const float r0 = 0.5f * keypoints0[i0].size;
for(size_t i1 = 0; i1 < keypoints1.size(); i1++)
{
if(nearestPointIndex >= 0 && usedMask[i1])
continue;
float r1 = 0.5f * keypoints1[i1].size;
float intersectRatio = calcIntersectRatio(points0t.at<Point2f>(i0), r0,
keypoints1[i1].pt, r1);
if(intersectRatio > maxIntersectRatio)
{
maxIntersectRatio = intersectRatio;
nearestPointIndex = static_cast<int>(i1);
}
}
matches.push_back(DMatch(i0, nearestPointIndex, maxIntersectRatio));
if(nearestPointIndex >= 0)
usedMask[nearestPointIndex] = 1;
}
}
class DetectorInvariance : public TestWithParam<String_FeatureDetector_Float_Float_t>
{
protected:
virtual void SetUp() {
// Read test data
const std::string filename = cvtest::TS::ptr()->get_data_path() + get<0>(GetParam());
image0 = imread(filename);
ASSERT_FALSE(image0.empty()) << "couldn't read input image";
featureDetector = get<1>(GetParam());
minKeyPointMatchesRatio = get<2>(GetParam());
minInliersRatio = get<3>(GetParam());
}
Ptr<FeatureDetector> featureDetector;
float minKeyPointMatchesRatio;
float minInliersRatio;
Mat image0;
};
typedef DetectorInvariance DetectorScaleInvariance;
typedef DetectorInvariance DetectorRotationInvariance;
TEST_P(DetectorRotationInvariance, rotation)
{
Mat image1, mask1;
const int borderSize = 16;
Mat mask0(image0.size(), CV_8UC1, Scalar(0));
mask0(Rect(borderSize, borderSize, mask0.cols - 2*borderSize, mask0.rows - 2*borderSize)).setTo(Scalar(255));
vector<KeyPoint> keypoints0;
featureDetector->detect(image0, keypoints0, mask0);
EXPECT_GE(keypoints0.size(), 15u);
const int maxAngle = 360, angleStep = 15;
for(int angle = 0; angle < maxAngle; angle += angleStep)
{
Mat H = rotateImage(image0, mask0, static_cast<float>(angle), image1, mask1);
vector<KeyPoint> keypoints1;
featureDetector->detect(image1, keypoints1, mask1);
vector<DMatch> matches;
matchKeyPoints(keypoints0, H, keypoints1, matches);
int angleInliersCount = 0;
const float minIntersectRatio = 0.5f;
int keyPointMatchesCount = 0;
for(size_t m = 0; m < matches.size(); m++)
{
if(matches[m].distance < minIntersectRatio)
continue;
keyPointMatchesCount++;
// Check does this inlier have consistent angles
const float maxAngleDiff = 15.f; // grad
float angle0 = keypoints0[matches[m].queryIdx].angle;
float angle1 = keypoints1[matches[m].trainIdx].angle;
ASSERT_FALSE(angle0 == -1 || angle1 == -1) << "Given FeatureDetector is not rotation invariant, it can not be tested here.";
ASSERT_GE(angle0, 0.f);
ASSERT_LT(angle0, 360.f);
ASSERT_GE(angle1, 0.f);
ASSERT_LT(angle1, 360.f);
float rotAngle0 = angle0 + angle;
if(rotAngle0 >= 360.f)
rotAngle0 -= 360.f;
float angleDiff = std::max(rotAngle0, angle1) - std::min(rotAngle0, angle1);
angleDiff = std::min(angleDiff, static_cast<float>(360.f - angleDiff));
ASSERT_GE(angleDiff, 0.f);
bool isAngleCorrect = angleDiff < maxAngleDiff;
if(isAngleCorrect)
angleInliersCount++;
}
float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints0.size();
EXPECT_GE(keyPointMatchesRatio, minKeyPointMatchesRatio) << "angle: " << angle;
if(keyPointMatchesCount)
{
float angleInliersRatio = static_cast<float>(angleInliersCount) / keyPointMatchesCount;
EXPECT_GE(angleInliersRatio, minInliersRatio) << "angle: " << angle;
}
#if SHOW_DEBUG_LOG
std::cout
<< "angle = " << angle
<< ", keypoints = " << keypoints1.size()
<< ", keyPointMatchesRatio = " << keyPointMatchesRatio
<< ", angleInliersRatio = " << (keyPointMatchesCount ? (static_cast<float>(angleInliersCount) / keyPointMatchesCount) : 0)
<< std::endl;
#endif
}
}
TEST_P(DetectorScaleInvariance, scale)
{
vector<KeyPoint> keypoints0;
featureDetector->detect(image0, keypoints0);
EXPECT_GE(keypoints0.size(), 15u);
for(int scaleIdx = 1; scaleIdx <= 3; scaleIdx++)
{
float scale = 1.f + scaleIdx * 0.5f;
Mat image1;
resize(image0, image1, Size(), 1./scale, 1./scale, INTER_LINEAR_EXACT);
vector<KeyPoint> keypoints1, osiKeypoints1; // osi - original size image
featureDetector->detect(image1, keypoints1);
EXPECT_GE(keypoints1.size(), 15u);
EXPECT_LE(keypoints1.size(), keypoints0.size()) << "Strange behavior of the detector. "
"It gives more points count in an image of the smaller size.";
scaleKeyPoints(keypoints1, osiKeypoints1, scale);
vector<DMatch> matches;
// image1 is query image (it's reduced image0)
// image0 is train image
matchKeyPoints(osiKeypoints1, Mat(), keypoints0, matches);
const float minIntersectRatio = 0.5f;
int keyPointMatchesCount = 0;
int scaleInliersCount = 0;
for(size_t m = 0; m < matches.size(); m++)
{
if(matches[m].distance < minIntersectRatio)
continue;
keyPointMatchesCount++;
// Check does this inlier have consistent sizes
const float maxSizeDiff = 0.8f;//0.9f; // grad
float size0 = keypoints0[matches[m].trainIdx].size;
float size1 = osiKeypoints1[matches[m].queryIdx].size;
ASSERT_GT(size0, 0);
ASSERT_GT(size1, 0);
if(std::min(size0, size1) > maxSizeDiff * std::max(size0, size1))
scaleInliersCount++;
}
float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints1.size();
EXPECT_GE(keyPointMatchesRatio, minKeyPointMatchesRatio);
if(keyPointMatchesCount)
{
float scaleInliersRatio = static_cast<float>(scaleInliersCount) / keyPointMatchesCount;
EXPECT_GE(scaleInliersRatio, minInliersRatio);
}
#if SHOW_DEBUG_LOG
std::cout
<< "scale = " << scale
<< ", keyPointMatchesRatio = " << keyPointMatchesRatio
<< ", scaleInliersRatio = " << (keyPointMatchesCount ? static_cast<float>(scaleInliersCount) / keyPointMatchesCount : 0)
<< std::endl;
#endif
}
}
#define Value(...) Values(make_tuple(__VA_ARGS__))
/*
* Detector's rotation invariance check
......
modules/features2d/test/test_detectors_invariance.impl.hpp 0 → 100644
View file @ 40b1dc12
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
#include "test_invariance_utils.hpp"
namespace opencv_test { namespace {
#define SHOW_DEBUG_LOG 1
typedef tuple<std::string, Ptr<FeatureDetector>, float, float> String_FeatureDetector_Float_Float_t;
static
void matchKeyPoints(const vector<KeyPoint>& keypoints0, const Mat& H,
const vector<KeyPoint>& keypoints1,
vector<DMatch>& matches)
{
vector<Point2f> points0;
KeyPoint::convert(keypoints0, points0);
Mat points0t;
if(H.empty())
points0t = Mat(points0);
else
perspectiveTransform(Mat(points0), points0t, H);
matches.clear();
vector<uchar> usedMask(keypoints1.size(), 0);
for(int i0 = 0; i0 < static_cast<int>(keypoints0.size()); i0++)
{
int nearestPointIndex = -1;
float maxIntersectRatio = 0.f;
const float r0 = 0.5f * keypoints0[i0].size;
for(size_t i1 = 0; i1 < keypoints1.size(); i1++)
{
if(nearestPointIndex >= 0 && usedMask[i1])
continue;
float r1 = 0.5f * keypoints1[i1].size;
float intersectRatio = calcIntersectRatio(points0t.at<Point2f>(i0), r0,
keypoints1[i1].pt, r1);
if(intersectRatio > maxIntersectRatio)
{
maxIntersectRatio = intersectRatio;
nearestPointIndex = static_cast<int>(i1);
}
}
matches.push_back(DMatch(i0, nearestPointIndex, maxIntersectRatio));
if(nearestPointIndex >= 0)
usedMask[nearestPointIndex] = 1;
}
}
class DetectorInvariance : public TestWithParam<String_FeatureDetector_Float_Float_t>
{
protected:
virtual void SetUp() {
// Read test data
const std::string filename = cvtest::TS::ptr()->get_data_path() + get<0>(GetParam());
image0 = imread(filename);
ASSERT_FALSE(image0.empty()) << "couldn't read input image";
featureDetector = get<1>(GetParam());
minKeyPointMatchesRatio = get<2>(GetParam());
minInliersRatio = get<3>(GetParam());
}
Ptr<FeatureDetector> featureDetector;
float minKeyPointMatchesRatio;
float minInliersRatio;
Mat image0;
};
typedef DetectorInvariance DetectorScaleInvariance;
typedef DetectorInvariance DetectorRotationInvariance;
TEST_P(DetectorRotationInvariance, rotation)
{
Mat image1, mask1;
const int borderSize = 16;
Mat mask0(image0.size(), CV_8UC1, Scalar(0));
mask0(Rect(borderSize, borderSize, mask0.cols - 2*borderSize, mask0.rows - 2*borderSize)).setTo(Scalar(255));
vector<KeyPoint> keypoints0;
featureDetector->detect(image0, keypoints0, mask0);
EXPECT_GE(keypoints0.size(), 15u);
const int maxAngle = 360, angleStep = 15;
for(int angle = 0; angle < maxAngle; angle += angleStep)
{
Mat H = rotateImage(image0, mask0, static_cast<float>(angle), image1, mask1);
vector<KeyPoint> keypoints1;
featureDetector->detect(image1, keypoints1, mask1);
vector<DMatch> matches;
matchKeyPoints(keypoints0, H, keypoints1, matches);
int angleInliersCount = 0;
const float minIntersectRatio = 0.5f;
int keyPointMatchesCount = 0;
for(size_t m = 0; m < matches.size(); m++)
{
if(matches[m].distance < minIntersectRatio)
continue;
keyPointMatchesCount++;
// Check does this inlier have consistent angles
const float maxAngleDiff = 15.f; // grad
float angle0 = keypoints0[matches[m].queryIdx].angle;
float angle1 = keypoints1[matches[m].trainIdx].angle;
ASSERT_FALSE(angle0 == -1 || angle1 == -1) << "Given FeatureDetector is not rotation invariant, it can not be tested here.";
ASSERT_GE(angle0, 0.f);
ASSERT_LT(angle0, 360.f);
ASSERT_GE(angle1, 0.f);
ASSERT_LT(angle1, 360.f);
float rotAngle0 = angle0 + angle;
if(rotAngle0 >= 360.f)
rotAngle0 -= 360.f;
float angleDiff = std::max(rotAngle0, angle1) - std::min(rotAngle0, angle1);
angleDiff = std::min(angleDiff, static_cast<float>(360.f - angleDiff));
ASSERT_GE(angleDiff, 0.f);
bool isAngleCorrect = angleDiff < maxAngleDiff;
if(isAngleCorrect)
angleInliersCount++;
}
float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints0.size();
EXPECT_GE(keyPointMatchesRatio, minKeyPointMatchesRatio) << "angle: " << angle;
if(keyPointMatchesCount)
{
float angleInliersRatio = static_cast<float>(angleInliersCount) / keyPointMatchesCount;
EXPECT_GE(angleInliersRatio, minInliersRatio) << "angle: " << angle;
}
#if SHOW_DEBUG_LOG
std::cout
<< "angle = " << angle
<< ", keypoints = " << keypoints1.size()
<< ", keyPointMatchesRatio = " << keyPointMatchesRatio
<< ", angleInliersRatio = " << (keyPointMatchesCount ? (static_cast<float>(angleInliersCount) / keyPointMatchesCount) : 0)
<< std::endl;
#endif
}
}
TEST_P(DetectorScaleInvariance, scale)
{
vector<KeyPoint> keypoints0;
featureDetector->detect(image0, keypoints0);
EXPECT_GE(keypoints0.size(), 15u);
for(int scaleIdx = 1; scaleIdx <= 3; scaleIdx++)
{
float scale = 1.f + scaleIdx * 0.5f;
Mat image1;
resize(image0, image1, Size(), 1./scale, 1./scale, INTER_LINEAR_EXACT);
vector<KeyPoint> keypoints1, osiKeypoints1; // osi - original size image
featureDetector->detect(image1, keypoints1);
EXPECT_GE(keypoints1.size(), 15u);
EXPECT_LE(keypoints1.size(), keypoints0.size()) << "Strange behavior of the detector. "
"It gives more points count in an image of the smaller size.";
scaleKeyPoints(keypoints1, osiKeypoints1, scale);
vector<DMatch> matches;
// image1 is query image (it's reduced image0)
// image0 is train image
matchKeyPoints(osiKeypoints1, Mat(), keypoints0, matches);
const float minIntersectRatio = 0.5f;
int keyPointMatchesCount = 0;
int scaleInliersCount = 0;
for(size_t m = 0; m < matches.size(); m++)
{
if(matches[m].distance < minIntersectRatio)
continue;
keyPointMatchesCount++;
// Check does this inlier have consistent sizes
const float maxSizeDiff = 0.8f;//0.9f; // grad
float size0 = keypoints0[matches[m].trainIdx].size;
float size1 = osiKeypoints1[matches[m].queryIdx].size;
ASSERT_GT(size0, 0);
ASSERT_GT(size1, 0);
if(std::min(size0, size1) > maxSizeDiff * std::max(size0, size1))
scaleInliersCount++;
}
float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints1.size();
EXPECT_GE(keyPointMatchesRatio, minKeyPointMatchesRatio);
if(keyPointMatchesCount)
{
float scaleInliersRatio = static_cast<float>(scaleInliersCount) / keyPointMatchesCount;
EXPECT_GE(scaleInliersRatio, minInliersRatio);
}
#if SHOW_DEBUG_LOG
std::cout
<< "scale = " << scale
<< ", keyPointMatchesRatio = " << keyPointMatchesRatio
<< ", scaleInliersRatio = " << (keyPointMatchesCount ? static_cast<float>(scaleInliersCount) / keyPointMatchesCount : 0)
<< std::endl;
#endif
}
}
#undef SHOW_DEBUG_LOG
}} // namespace
namespace std {
using namespace opencv_test;
static inline void PrintTo(const String_FeatureDetector_Float_Float_t& v, std::ostream* os)
{
*os << "(\"" << get<0>(v)
<< "\", " << get<2>(v)
<< ", " << get<3>(v)
<< ")";
}
} // namespace
modules/features2d/test/test_detectors_regression.cpp
View file @ 40b1dc12
/*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*/
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
#include "test_precomp.hpp"
namespace opencv_test { namespace {
const string FEATURES2D_DIR = "features2d";
const string IMAGE_FILENAME = "tsukuba.png";
const string DETECTOR_DIR = FEATURES2D_DIR + "/feature_detectors";
}} // namespace
/****************************************************************************************\
* 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)cv::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)cv::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 )
{
ts->printf( cvtest::TS::LOG, "Feature detector is empty.\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
emptyDataTest();
regressionTest();
#include "test_detectors_regression.impl.hpp"
ts->set_failed_test_info( cvtest::TS::OK );
}
namespace opencv_test { namespace {
/****************************************************************************************\
* Tests registrations *
......
modules/features2d/test/test_detectors_regression.impl.hpp 0 → 100644
View file @ 40b1dc12
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
namespace opencv_test { namespace {
/****************************************************************************************\
* 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)cv::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)cv::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 )
{
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 );
}
}} // namespace
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment