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118 lines
3.7 KiB
118 lines
3.7 KiB
// 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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#ifndef OPENCV_TEST_PRECOMP_HPP |
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#define OPENCV_TEST_PRECOMP_HPP |
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#include <chrono> |
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#include <opencv2/core.hpp> |
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#include <opencv2/ts.hpp> |
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#include <opencv2/ts/ocl_test.hpp> // OCL_ON, OCL_OFF |
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#include <opencv2/imgcodecs.hpp> |
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#include <opencv2/quality.hpp> |
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#include <opencv2/quality/quality_utils.hpp> |
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namespace opencv_test |
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{ |
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namespace quality_test |
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{ |
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const cv::String |
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dataDir = "cv/optflow/" |
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, testfile1a = dataDir + "rock_1.bmp" |
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, testfile1b = dataDir + "rock_2.bmp" |
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, testfile2a = dataDir + "RubberWhale1.png" |
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, testfile2b = dataDir + "RubberWhale2.png" |
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; |
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const cv::Scalar |
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MSE_EXPECTED_1 = { 2136.0525 } // matlab: immse('rock_1.bmp', 'rock_2.bmp') == 2.136052552083333e+03 |
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, MSE_EXPECTED_2 = { 92.8235, 109.4104, 121.4 } // matlab: immse('rubberwhale1.png', 'rubberwhale2.png') == {92.8235, 109.4104, 121.4} |
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; |
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inline cv::Mat get_testfile(const cv::String& path, int flags = IMREAD_UNCHANGED ) |
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{ |
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auto full_path = TS::ptr()->get_data_path() + path; |
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auto result = cv::imread( full_path, flags ); |
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if (result.empty()) |
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CV_Error(cv::Error::StsObjectNotFound, "Cannot find file: " + full_path ); |
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return result; |
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} |
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inline cv::Mat get_testfile_1a() { return get_testfile(testfile1a, IMREAD_GRAYSCALE); } |
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inline cv::Mat get_testfile_1b() { return get_testfile(testfile1b, IMREAD_GRAYSCALE); } |
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inline cv::Mat get_testfile_2a() { return get_testfile(testfile2a); } |
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inline cv::Mat get_testfile_2b() { return get_testfile(testfile2b); } |
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inline std::vector<cv::Mat> get_testfile_1a2a() { return { get_testfile_1a(), get_testfile_2a() }; } |
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inline std::vector<cv::Mat> get_testfile_1b2b() { return { get_testfile_1b(), get_testfile_2b() }; } |
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const double QUALITY_ERR_TOLERANCE = .001 // allowed margin of error |
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; |
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inline void quality_expect_near( const cv::Scalar& a, const cv::Scalar& b, double err_tolerance = QUALITY_ERR_TOLERANCE) |
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{ |
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for (int i = 0; i < a.rows; ++i) |
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{ |
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if (std::isinf(a(i))) |
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EXPECT_EQ(a(i), b(i)); |
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else |
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EXPECT_NEAR(a(i), b(i), err_tolerance); |
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} |
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} |
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// execute quality test for a pair of images |
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template <typename TMat> |
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inline void quality_test(cv::Ptr<quality::QualityBase> ptr, const TMat& cmp, const Scalar& expected, const std::size_t quality_maps_expected = 1, const bool empty_expected = false ) |
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{ |
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std::vector<cv::Mat> qMats = {}; |
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ptr->getQualityMaps(qMats); |
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EXPECT_TRUE( qMats.empty()); |
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quality_expect_near( expected, ptr->compute(cmp)); |
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if (empty_expected) |
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EXPECT_TRUE(ptr->empty()); |
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else |
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EXPECT_FALSE(ptr->empty()); |
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ptr->getQualityMaps(qMats); |
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EXPECT_EQ( qMats.size(), quality_maps_expected); |
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for (auto& qm : qMats) |
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{ |
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EXPECT_GT(qm.rows, 0); |
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EXPECT_GT(qm.cols, 0); |
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} |
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ptr->clear(); |
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EXPECT_TRUE(ptr->empty()); |
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} |
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/* A/B test benchmarking for development purposes */ |
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/* |
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template <typename Fn> |
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inline void quality_performance_test( const char* name, Fn&& op ) |
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{ |
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const auto exec_test = [&]() |
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{ |
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const int NRUNS = 100; |
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const auto start_t = std::chrono::high_resolution_clock::now(); |
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for (int i = 0; i < NRUNS; ++i) |
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op(); |
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const auto end_t = std::chrono::high_resolution_clock::now(); |
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std::cout << name << " performance (OCL=" << cv::ocl::useOpenCL() << "): " << (double)(std::chrono::duration_cast<std::chrono::milliseconds>(end_t - start_t).count()) / (double)NRUNS << "ms\n"; |
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}; |
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// only run tests in NDEBUG mode |
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#ifdef NDEBUG |
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OCL_OFF(exec_test()); |
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OCL_ON(exec_test()); |
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#endif |
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} |
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*/ |
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} |
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} |
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#endif |