/*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. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., 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 the copyright holders 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*/ #ifndef __OPENCV_CUDA_TEST_UTILITY_HPP__ #define __OPENCV_CUDA_TEST_UTILITY_HPP__ #include #include "cvconfig.h" #include "opencv2/core.hpp" #include "opencv2/core/cuda.hpp" #include "opencv2/imgcodecs.hpp" #include "opencv2/highgui.hpp" #include "opencv2/imgproc.hpp" #include "opencv2/ts.hpp" namespace cvtest { ////////////////////////////////////////////////////////////////////// // random generators CV_EXPORTS int randomInt(int minVal, int maxVal); CV_EXPORTS double randomDouble(double minVal, double maxVal); CV_EXPORTS cv::Size randomSize(int minVal, int maxVal); CV_EXPORTS cv::Scalar randomScalar(double minVal, double maxVal); CV_EXPORTS cv::Mat randomMat(cv::Size size, int type, double minVal = 0.0, double maxVal = 255.0); ////////////////////////////////////////////////////////////////////// // GpuMat create CV_EXPORTS cv::cuda::GpuMat createMat(cv::Size size, int type, bool useRoi = false); CV_EXPORTS cv::cuda::GpuMat loadMat(const cv::Mat& m, bool useRoi = false); ////////////////////////////////////////////////////////////////////// // Image load //! read image from testdata folder CV_EXPORTS cv::Mat readImage(const std::string& fileName, int flags = cv::IMREAD_COLOR); //! read image from testdata folder and convert it to specified type CV_EXPORTS cv::Mat readImageType(const std::string& fname, int type); ////////////////////////////////////////////////////////////////////// // Gpu devices //! return true if device supports specified feature and gpu module was built with support the feature. CV_EXPORTS bool supportFeature(const cv::cuda::DeviceInfo& info, cv::cuda::FeatureSet feature); class CV_EXPORTS DeviceManager { public: static DeviceManager& instance(); void load(int i); void loadAll(); const std::vector& values() const { return devices_; } private: std::vector devices_; }; #define ALL_DEVICES testing::ValuesIn(cvtest::DeviceManager::instance().values()) ////////////////////////////////////////////////////////////////////// // Additional assertion CV_EXPORTS void minMaxLocGold(const cv::Mat& src, double* minVal_, double* maxVal_ = 0, cv::Point* minLoc_ = 0, cv::Point* maxLoc_ = 0, const cv::Mat& mask = cv::Mat()); CV_EXPORTS cv::Mat getMat(cv::InputArray arr); CV_EXPORTS testing::AssertionResult assertMatNear(const char* expr1, const char* expr2, const char* eps_expr, cv::InputArray m1, cv::InputArray m2, double eps); #define EXPECT_MAT_NEAR(m1, m2, eps) EXPECT_PRED_FORMAT3(cvtest::assertMatNear, m1, m2, eps) #define ASSERT_MAT_NEAR(m1, m2, eps) ASSERT_PRED_FORMAT3(cvtest::assertMatNear, m1, m2, eps) #define EXPECT_SCALAR_NEAR(s1, s2, eps) \ { \ EXPECT_NEAR(s1[0], s2[0], eps); \ EXPECT_NEAR(s1[1], s2[1], eps); \ EXPECT_NEAR(s1[2], s2[2], eps); \ EXPECT_NEAR(s1[3], s2[3], eps); \ } #define ASSERT_SCALAR_NEAR(s1, s2, eps) \ { \ ASSERT_NEAR(s1[0], s2[0], eps); \ ASSERT_NEAR(s1[1], s2[1], eps); \ ASSERT_NEAR(s1[2], s2[2], eps); \ ASSERT_NEAR(s1[3], s2[3], eps); \ } #define EXPECT_POINT2_NEAR(p1, p2, eps) \ { \ EXPECT_NEAR(p1.x, p2.x, eps); \ EXPECT_NEAR(p1.y, p2.y, eps); \ } #define ASSERT_POINT2_NEAR(p1, p2, eps) \ { \ ASSERT_NEAR(p1.x, p2.x, eps); \ ASSERT_NEAR(p1.y, p2.y, eps); \ } #define EXPECT_POINT3_NEAR(p1, p2, eps) \ { \ EXPECT_NEAR(p1.x, p2.x, eps); \ EXPECT_NEAR(p1.y, p2.y, eps); \ EXPECT_NEAR(p1.z, p2.z, eps); \ } #define ASSERT_POINT3_NEAR(p1, p2, eps) \ { \ ASSERT_NEAR(p1.x, p2.x, eps); \ ASSERT_NEAR(p1.y, p2.y, eps); \ ASSERT_NEAR(p1.z, p2.z, eps); \ } CV_EXPORTS double checkSimilarity(cv::InputArray m1, cv::InputArray m2); #define EXPECT_MAT_SIMILAR(mat1, mat2, eps) \ { \ ASSERT_EQ(mat1.type(), mat2.type()); \ ASSERT_EQ(mat1.size(), mat2.size()); \ EXPECT_LE(checkSimilarity(mat1, mat2), eps); \ } #define ASSERT_MAT_SIMILAR(mat1, mat2, eps) \ { \ ASSERT_EQ(mat1.type(), mat2.type()); \ ASSERT_EQ(mat1.size(), mat2.size()); \ ASSERT_LE(checkSimilarity(mat1, mat2), eps); \ } ////////////////////////////////////////////////////////////////////// // Helper structs for value-parameterized tests #define CUDA_TEST_P(test_case_name, test_name) \ class GTEST_TEST_CLASS_NAME_(test_case_name, test_name) \ : public test_case_name { \ public: \ GTEST_TEST_CLASS_NAME_(test_case_name, test_name)() {} \ virtual void TestBody(); \ private: \ void UnsafeTestBody(); \ static int AddToRegistry() { \ ::testing::UnitTest::GetInstance()->parameterized_test_registry(). \ GetTestCasePatternHolder(\ #test_case_name, __FILE__, __LINE__)->AddTestPattern(\ #test_case_name, \ #test_name, \ new ::testing::internal::TestMetaFactory< \ GTEST_TEST_CLASS_NAME_(test_case_name, test_name)>()); \ return 0; \ } \ static int gtest_registering_dummy_; \ GTEST_DISALLOW_COPY_AND_ASSIGN_(\ GTEST_TEST_CLASS_NAME_(test_case_name, test_name)); \ }; \ int GTEST_TEST_CLASS_NAME_(test_case_name, \ test_name)::gtest_registering_dummy_ = \ GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::AddToRegistry(); \ void GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::TestBody() \ { \ try \ { \ UnsafeTestBody(); \ } \ catch (...) \ { \ cv::cuda::resetDevice(); \ throw; \ } \ } \ void GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::UnsafeTestBody() #define PARAM_TEST_CASE(name, ...) struct name : testing::TestWithParam< std::tr1::tuple< __VA_ARGS__ > > #define GET_PARAM(k) std::tr1::get< k >(GetParam()) #define DIFFERENT_SIZES testing::Values(cv::Size(128, 128), cv::Size(113, 113)) // Depth using perf::MatDepth; #define ALL_DEPTH testing::Values(MatDepth(CV_8U), MatDepth(CV_8S), MatDepth(CV_16U), MatDepth(CV_16S), MatDepth(CV_32S), MatDepth(CV_32F), MatDepth(CV_64F)) #define DEPTH_PAIRS testing::Values(std::make_pair(MatDepth(CV_8U), MatDepth(CV_8U)), \ std::make_pair(MatDepth(CV_8U), MatDepth(CV_16U)), \ std::make_pair(MatDepth(CV_8U), MatDepth(CV_16S)), \ std::make_pair(MatDepth(CV_8U), MatDepth(CV_32S)), \ std::make_pair(MatDepth(CV_8U), MatDepth(CV_32F)), \ std::make_pair(MatDepth(CV_8U), MatDepth(CV_64F)), \ \ std::make_pair(MatDepth(CV_16U), MatDepth(CV_16U)), \ std::make_pair(MatDepth(CV_16U), MatDepth(CV_32S)), \ std::make_pair(MatDepth(CV_16U), MatDepth(CV_32F)), \ std::make_pair(MatDepth(CV_16U), MatDepth(CV_64F)), \ \ std::make_pair(MatDepth(CV_16S), MatDepth(CV_16S)), \ std::make_pair(MatDepth(CV_16S), MatDepth(CV_32S)), \ std::make_pair(MatDepth(CV_16S), MatDepth(CV_32F)), \ std::make_pair(MatDepth(CV_16S), MatDepth(CV_64F)), \ \ std::make_pair(MatDepth(CV_32S), MatDepth(CV_32S)), \ std::make_pair(MatDepth(CV_32S), MatDepth(CV_32F)), \ std::make_pair(MatDepth(CV_32S), MatDepth(CV_64F)), \ \ std::make_pair(MatDepth(CV_32F), MatDepth(CV_32F)), \ std::make_pair(MatDepth(CV_32F), MatDepth(CV_64F)), \ \ std::make_pair(MatDepth(CV_64F), MatDepth(CV_64F))) // Type using perf::MatType; //! return vector with types from specified range. CV_EXPORTS std::vector types(int depth_start, int depth_end, int cn_start, int cn_end); //! return vector with all types (depth: CV_8U-CV_64F, channels: 1-4). CV_EXPORTS const std::vector& all_types(); #define ALL_TYPES testing::ValuesIn(all_types()) #define TYPES(depth_start, depth_end, cn_start, cn_end) testing::ValuesIn(types(depth_start, depth_end, cn_start, cn_end)) // ROI class UseRoi { public: inline UseRoi(bool val = false) : val_(val) {} inline operator bool() const { return val_; } private: bool val_; }; CV_EXPORTS void PrintTo(const UseRoi& useRoi, std::ostream* os); #define WHOLE_SUBMAT testing::Values(UseRoi(false), UseRoi(true)) // Direct/Inverse class Inverse { public: inline Inverse(bool val = false) : val_(val) {} inline operator bool() const { return val_; } private: bool val_; }; CV_EXPORTS void PrintTo(const Inverse& useRoi, std::ostream* os); #define DIRECT_INVERSE testing::Values(Inverse(false), Inverse(true)) // Param class #define IMPLEMENT_PARAM_CLASS(name, type) \ class name \ { \ public: \ name ( type arg = type ()) : val_(arg) {} \ operator type () const {return val_;} \ private: \ type val_; \ }; \ inline void PrintTo( name param, std::ostream* os) \ { \ *os << #name << "(" << testing::PrintToString(static_cast< type >(param)) << ")"; \ } IMPLEMENT_PARAM_CLASS(Channels, int) #define ALL_CHANNELS testing::Values(Channels(1), Channels(2), Channels(3), Channels(4)) #define IMAGE_CHANNELS testing::Values(Channels(1), Channels(3), Channels(4)) // Flags and enums CV_ENUM(NormCode, NORM_INF, NORM_L1, NORM_L2, NORM_TYPE_MASK, NORM_RELATIVE, NORM_MINMAX) CV_ENUM(Interpolation, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_AREA) CV_ENUM(BorderType, BORDER_REFLECT101, BORDER_REPLICATE, BORDER_CONSTANT, BORDER_REFLECT, BORDER_WRAP) #define ALL_BORDER_TYPES testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_CONSTANT), BorderType(cv::BORDER_REFLECT), BorderType(cv::BORDER_WRAP)) CV_FLAGS(WarpFlags, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, WARP_INVERSE_MAP) ////////////////////////////////////////////////////////////////////// // Features2D CV_EXPORTS testing::AssertionResult assertKeyPointsEquals(const char* gold_expr, const char* actual_expr, std::vector& gold, std::vector& actual); #define ASSERT_KEYPOINTS_EQ(gold, actual) EXPECT_PRED_FORMAT2(assertKeyPointsEquals, gold, actual) CV_EXPORTS int getMatchedPointsCount(std::vector& gold, std::vector& actual); CV_EXPORTS int getMatchedPointsCount(const std::vector& keypoints1, const std::vector& keypoints2, const std::vector& matches); ////////////////////////////////////////////////////////////////////// // Other CV_EXPORTS void dumpImage(const std::string& fileName, const cv::Mat& image); CV_EXPORTS void showDiff(cv::InputArray gold, cv::InputArray actual, double eps); CV_EXPORTS void parseCudaDeviceOptions(int argc, char **argv); CV_EXPORTS void printCudaInfo(); } namespace cv { namespace cuda { CV_EXPORTS void PrintTo(const DeviceInfo& info, std::ostream* os); }} #ifdef HAVE_CUDA #define CV_CUDA_TEST_MAIN(resourcesubdir) \ CV_TEST_MAIN(resourcesubdir, cvtest::parseCudaDeviceOptions(argc, argv), cvtest::printCudaInfo(), cv::setUseOptimized(false)) #else // HAVE_CUDA #define CV_CUDA_TEST_MAIN(resourcesubdir) \ int main() \ { \ printf("OpenCV was built without CUDA support\n"); \ return 0; \ } #endif // HAVE_CUDA #endif // __OPENCV_CUDA_TEST_UTILITY_HPP__