init - 初始化项目
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Lee Nony
211
modules/features2d/test/test_descriptors_regression.cpp
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211
modules/features2d/test/test_descriptors_regression.cpp
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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const string FEATURES2D_DIR = "features2d";
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const string IMAGE_FILENAME = "tsukuba.png";
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const string DESCRIPTOR_DIR = FEATURES2D_DIR + "/descriptor_extractors";
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}} // namespace
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#include "test_descriptors_regression.impl.hpp"
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namespace opencv_test { namespace {
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/****************************************************************************************\
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* Tests registrations *
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\****************************************************************************************/
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TEST( Features2d_DescriptorExtractor_SIFT, regression )
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{
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CV_DescriptorExtractorTest<L1<float> > test( "descriptor-sift", 1.0f,
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SIFT::create() );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_BRISK, regression )
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{
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CV_DescriptorExtractorTest<Hamming> test( "descriptor-brisk",
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(CV_DescriptorExtractorTest<Hamming>::DistanceType)2.f,
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BRISK::create() );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_ORB, regression )
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{
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// TODO adjust the parameters below
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CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb",
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#if CV_NEON
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(CV_DescriptorExtractorTest<Hamming>::DistanceType)25.f,
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#else
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(CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
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#endif
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ORB::create() );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_KAZE, regression )
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{
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CV_DescriptorExtractorTest< L2<float> > test( "descriptor-kaze", 0.03f,
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KAZE::create(),
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L2<float>(), KAZE::create() );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_AKAZE, regression )
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{
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CV_DescriptorExtractorTest<Hamming> test( "descriptor-akaze",
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(CV_DescriptorExtractorTest<Hamming>::DistanceType)(486*0.05f),
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AKAZE::create(),
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Hamming(), AKAZE::create());
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_AKAZE_DESCRIPTOR_KAZE, regression )
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{
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CV_DescriptorExtractorTest< L2<float> > test( "descriptor-akaze-with-kaze-desc", 0.03f,
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AKAZE::create(AKAZE::DESCRIPTOR_KAZE),
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L2<float>(), AKAZE::create(AKAZE::DESCRIPTOR_KAZE));
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor, batch_ORB )
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{
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string path = string(cvtest::TS::ptr()->get_data_path() + "detectors_descriptors_evaluation/images_datasets/graf");
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vector<Mat> imgs, descriptors;
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vector<vector<KeyPoint> > keypoints;
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int i, n = 6;
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Ptr<ORB> orb = ORB::create();
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for( i = 0; i < n; i++ )
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{
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string imgname = format("%s/img%d.png", path.c_str(), i+1);
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Mat img = imread(imgname, 0);
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imgs.push_back(img);
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}
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orb->detect(imgs, keypoints);
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orb->compute(imgs, keypoints, descriptors);
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ASSERT_EQ((int)keypoints.size(), n);
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ASSERT_EQ((int)descriptors.size(), n);
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for( i = 0; i < n; i++ )
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{
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EXPECT_GT((int)keypoints[i].size(), 100);
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EXPECT_GT(descriptors[i].rows, 100);
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}
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}
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TEST( Features2d_DescriptorExtractor, batch_SIFT )
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{
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string path = string(cvtest::TS::ptr()->get_data_path() + "detectors_descriptors_evaluation/images_datasets/graf");
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vector<Mat> imgs, descriptors;
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vector<vector<KeyPoint> > keypoints;
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int i, n = 6;
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Ptr<SIFT> sift = SIFT::create();
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for( i = 0; i < n; i++ )
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{
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string imgname = format("%s/img%d.png", path.c_str(), i+1);
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Mat img = imread(imgname, 0);
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imgs.push_back(img);
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}
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sift->detect(imgs, keypoints);
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sift->compute(imgs, keypoints, descriptors);
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ASSERT_EQ((int)keypoints.size(), n);
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ASSERT_EQ((int)descriptors.size(), n);
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for( i = 0; i < n; i++ )
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{
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EXPECT_GT((int)keypoints[i].size(), 100);
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EXPECT_GT(descriptors[i].rows, 100);
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}
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}
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class DescriptorImage : public TestWithParam<std::string>
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{
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protected:
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virtual void SetUp() {
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pattern = GetParam();
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}
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std::string pattern;
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};
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TEST_P(DescriptorImage, no_crash)
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{
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vector<String> fnames;
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glob(cvtest::TS::ptr()->get_data_path() + pattern, fnames, false);
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sort(fnames.begin(), fnames.end());
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Ptr<AKAZE> akaze_mldb = AKAZE::create(AKAZE::DESCRIPTOR_MLDB);
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Ptr<AKAZE> akaze_mldb_upright = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT);
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Ptr<AKAZE> akaze_mldb_256 = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 256);
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Ptr<AKAZE> akaze_mldb_upright_256 = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT, 256);
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Ptr<AKAZE> akaze_kaze = AKAZE::create(AKAZE::DESCRIPTOR_KAZE);
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Ptr<AKAZE> akaze_kaze_upright = AKAZE::create(AKAZE::DESCRIPTOR_KAZE_UPRIGHT);
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Ptr<ORB> orb = ORB::create();
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Ptr<KAZE> kaze = KAZE::create();
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Ptr<BRISK> brisk = BRISK::create();
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size_t n = fnames.size();
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vector<KeyPoint> keypoints;
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Mat descriptors;
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orb->setMaxFeatures(5000);
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for(size_t i = 0; i < n; i++ )
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{
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printf("%d. image: %s:\n", (int)i, fnames[i].c_str());
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if( strstr(fnames[i].c_str(), "MP.png") != 0 )
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{
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printf("\tskip\n");
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continue;
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}
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bool checkCount = strstr(fnames[i].c_str(), "templ.png") == 0;
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Mat img = imread(fnames[i], -1);
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printf("\t%dx%d\n", img.cols, img.rows);
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#define TEST_DETECTOR(name, descriptor) \
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keypoints.clear(); descriptors.release(); \
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printf("\t" name "\n"); fflush(stdout); \
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descriptor->detectAndCompute(img, noArray(), keypoints, descriptors); \
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printf("\t\t\t(%d keypoints, descriptor size = %d)\n", (int)keypoints.size(), descriptors.cols); fflush(stdout); \
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if (checkCount) \
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{ \
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EXPECT_GT((int)keypoints.size(), 0); \
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} \
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ASSERT_EQ(descriptors.rows, (int)keypoints.size());
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TEST_DETECTOR("AKAZE:MLDB", akaze_mldb);
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TEST_DETECTOR("AKAZE:MLDB_UPRIGHT", akaze_mldb_upright);
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TEST_DETECTOR("AKAZE:MLDB_256", akaze_mldb_256);
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TEST_DETECTOR("AKAZE:MLDB_UPRIGHT_256", akaze_mldb_upright_256);
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TEST_DETECTOR("AKAZE:KAZE", akaze_kaze);
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TEST_DETECTOR("AKAZE:KAZE_UPRIGHT", akaze_kaze_upright);
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TEST_DETECTOR("KAZE", kaze);
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TEST_DETECTOR("ORB", orb);
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TEST_DETECTOR("BRISK", brisk);
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}
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}
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INSTANTIATE_TEST_CASE_P(Features2d, DescriptorImage,
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testing::Values(
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"shared/lena.png",
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"shared/box*.png",
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"shared/fruits*.png",
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"shared/airplane.png",
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"shared/graffiti.png",
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"shared/1_itseez-0001*.png",
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"shared/pic*.png",
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"shared/templ.png"
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)
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);
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}} // namespace
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