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// 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
#include "test_descriptors_regression.impl.hpp"
namespace opencv_test { namespace {
/****************************************************************************************\
* Tests registrations *
\****************************************************************************************/
TEST( Features2d_DescriptorExtractor_SIFT, regression )
{
CV_DescriptorExtractorTest<L1<float> > test( "descriptor-sift", 1.0f,
SIFT::create() );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_BRISK, regression )
{
CV_DescriptorExtractorTest<Hamming> test( "descriptor-brisk",
(CV_DescriptorExtractorTest<Hamming>::DistanceType)2.f,
BRISK::create() );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_ORB, regression )
{
// TODO adjust the parameters below
CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb",
#if CV_NEON
(CV_DescriptorExtractorTest<Hamming>::DistanceType)25.f,
#else
(CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
#endif
ORB::create() );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_KAZE, regression )
{
CV_DescriptorExtractorTest< L2<float> > test( "descriptor-kaze", 0.03f,
KAZE::create(),
L2<float>(), KAZE::create() );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_AKAZE, regression )
{
CV_DescriptorExtractorTest<Hamming> test( "descriptor-akaze",
(CV_DescriptorExtractorTest<Hamming>::DistanceType)(486*0.05f),
AKAZE::create(),
Hamming(), AKAZE::create());
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_AKAZE_DESCRIPTOR_KAZE, regression )
{
CV_DescriptorExtractorTest< L2<float> > test( "descriptor-akaze-with-kaze-desc", 0.03f,
AKAZE::create(AKAZE::DESCRIPTOR_KAZE),
L2<float>(), AKAZE::create(AKAZE::DESCRIPTOR_KAZE));
test.safe_run();
}
TEST( Features2d_DescriptorExtractor, batch_ORB )
{
string path = string(cvtest::TS::ptr()->get_data_path() + "detectors_descriptors_evaluation/images_datasets/graf");
vector<Mat> imgs, descriptors;
vector<vector<KeyPoint> > keypoints;
int i, n = 6;
Ptr<ORB> orb = ORB::create();
for( i = 0; i < n; i++ )
{
string imgname = format("%s/img%d.png", path.c_str(), i+1);
Mat img = imread(imgname, 0);
imgs.push_back(img);
}
orb->detect(imgs, keypoints);
orb->compute(imgs, keypoints, descriptors);
ASSERT_EQ((int)keypoints.size(), n);
ASSERT_EQ((int)descriptors.size(), n);
for( i = 0; i < n; i++ )
{
EXPECT_GT((int)keypoints[i].size(), 100);
EXPECT_GT(descriptors[i].rows, 100);
}
}
TEST( Features2d_DescriptorExtractor, batch_SIFT )
{
string path = string(cvtest::TS::ptr()->get_data_path() + "detectors_descriptors_evaluation/images_datasets/graf");
vector<Mat> imgs, descriptors;
vector<vector<KeyPoint> > keypoints;
int i, n = 6;
Ptr<SIFT> sift = SIFT::create();
for( i = 0; i < n; i++ )
{
string imgname = format("%s/img%d.png", path.c_str(), i+1);
Mat img = imread(imgname, 0);
imgs.push_back(img);
}
sift->detect(imgs, keypoints);
sift->compute(imgs, keypoints, descriptors);
ASSERT_EQ((int)keypoints.size(), n);
ASSERT_EQ((int)descriptors.size(), n);
for( i = 0; i < n; i++ )
{
EXPECT_GT((int)keypoints[i].size(), 100);
EXPECT_GT(descriptors[i].rows, 100);
}
}
class DescriptorImage : public TestWithParam<std::string>
{
protected:
virtual void SetUp() {
pattern = GetParam();
}
std::string pattern;
};
TEST_P(DescriptorImage, no_crash)
{
vector<String> fnames;
glob(cvtest::TS::ptr()->get_data_path() + pattern, fnames, false);
sort(fnames.begin(), fnames.end());
Ptr<AKAZE> akaze_mldb = AKAZE::create(AKAZE::DESCRIPTOR_MLDB);
Ptr<AKAZE> akaze_mldb_upright = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT);
Ptr<AKAZE> akaze_mldb_256 = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 256);
Ptr<AKAZE> akaze_mldb_upright_256 = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT, 256);
Ptr<AKAZE> akaze_kaze = AKAZE::create(AKAZE::DESCRIPTOR_KAZE);
Ptr<AKAZE> akaze_kaze_upright = AKAZE::create(AKAZE::DESCRIPTOR_KAZE_UPRIGHT);
Ptr<ORB> orb = ORB::create();
Ptr<KAZE> kaze = KAZE::create();
Ptr<BRISK> brisk = BRISK::create();
size_t n = fnames.size();
vector<KeyPoint> keypoints;
Mat descriptors;
orb->setMaxFeatures(5000);
for(size_t i = 0; i < n; i++ )
{
printf("%d. image: %s:\n", (int)i, fnames[i].c_str());
if( strstr(fnames[i].c_str(), "MP.png") != 0 )
{
printf("\tskip\n");
continue;
}
bool checkCount = strstr(fnames[i].c_str(), "templ.png") == 0;
Mat img = imread(fnames[i], -1);
printf("\t%dx%d\n", img.cols, img.rows);
#define TEST_DETECTOR(name, descriptor) \
keypoints.clear(); descriptors.release(); \
printf("\t" name "\n"); fflush(stdout); \
descriptor->detectAndCompute(img, noArray(), keypoints, descriptors); \
printf("\t\t\t(%d keypoints, descriptor size = %d)\n", (int)keypoints.size(), descriptors.cols); fflush(stdout); \
if (checkCount) \
{ \
EXPECT_GT((int)keypoints.size(), 0); \
} \
ASSERT_EQ(descriptors.rows, (int)keypoints.size());
TEST_DETECTOR("AKAZE:MLDB", akaze_mldb);
TEST_DETECTOR("AKAZE:MLDB_UPRIGHT", akaze_mldb_upright);
TEST_DETECTOR("AKAZE:MLDB_256", akaze_mldb_256);
TEST_DETECTOR("AKAZE:MLDB_UPRIGHT_256", akaze_mldb_upright_256);
TEST_DETECTOR("AKAZE:KAZE", akaze_kaze);
TEST_DETECTOR("AKAZE:KAZE_UPRIGHT", akaze_kaze_upright);
TEST_DETECTOR("KAZE", kaze);
TEST_DETECTOR("ORB", orb);
TEST_DETECTOR("BRISK", brisk);
}
}
INSTANTIATE_TEST_CASE_P(Features2d, DescriptorImage,
testing::Values(
"shared/lena.png",
"shared/box*.png",
"shared/fruits*.png",
"shared/airplane.png",
"shared/graffiti.png",
"shared/1_itseez-0001*.png",
"shared/pic*.png",
"shared/templ.png"
)
);
}} // namespace
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