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// 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_TEST_UTILITY_HPP__ #define __OPENCV_TEST_UTILITY_HPP__ #include "opencv2/core.hpp" extern int LOOP_TIMES; #define MWIDTH 256 #define MHEIGHT 256 #define MIN_VALUE 171 #define MAX_VALUE 357 namespace cvtest { //void showDiff(cv::InputArray gold, cv::InputArray actual, double eps); cv::ocl::oclMat createMat_ocl(cv::RNG& rng, Size size, int type, bool useRoi); cv::ocl::oclMat loadMat_ocl(cv::RNG& rng, const Mat& m, bool useRoi); // This function test if gpu_rst matches cpu_rst. // If the two vectors are not equal, it will return the difference in vector size // Else it will return (total diff of each cpu and gpu rects covered pixels)/(total cpu rects covered pixels) // The smaller, the better matched double checkRectSimilarity(cv::Size sz, std::vector& ob1, std::vector& ob2); //! read image from testdata folder. cv::Mat readImage(const std::string &fileName, int flags = cv::IMREAD_COLOR); cv::Mat readImageType(const std::string &fname, int type); double checkNorm(const cv::Mat &m); double checkNorm(const cv::Mat &m1, const cv::Mat &m2); double checkSimilarity(const cv::Mat &m1, const cv::Mat &m2); #define EXPECT_MAT_NORM(mat, eps) \ { \ EXPECT_LE(checkNorm(cv::Mat(mat)), eps) \ } #define EXPECT_MAT_NEAR(mat1, mat2, eps) \ { \ ASSERT_EQ(mat1.type(), mat2.type()); \ ASSERT_EQ(mat1.size(), mat2.size()); \ EXPECT_LE(checkNorm(cv::Mat(mat1), cv::Mat(mat2)), eps); \ } #define EXPECT_MAT_SIMILAR(mat1, mat2, eps) \ { \ ASSERT_EQ(mat1.type(), mat2.type()); \ ASSERT_EQ(mat1.size(), mat2.size()); \ EXPECT_LE(checkSimilarity(cv::Mat(mat1), cv::Mat(mat2)), eps); \ } using perf::MatDepth; using perf::MatType; //! return vector with types from specified range. 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). const std::vector &all_types(); class Inverse { public: inline Inverse(bool val = false) : val_(val) {} inline operator bool() const { return val_; } private: bool val_; }; void PrintTo(const Inverse &useRoi, std::ostream *os); #define OCL_RNG_SEED 123456 template struct TSTestWithParam : public ::testing::TestWithParam { cv::RNG rng; TSTestWithParam() { rng = cv::RNG(OCL_RNG_SEED); } int randomInt(int minVal, int maxVal) { return rng.uniform(minVal, maxVal); } double randomDouble(double minVal, double maxVal) { return rng.uniform(minVal, maxVal); } double randomDoubleLog(double minVal, double maxVal) { double logMin = log((double)minVal + 1); double logMax = log((double)maxVal + 1); double pow = rng.uniform(logMin, logMax); double v = exp(pow) - 1; CV_Assert(v >= minVal && (v < maxVal || (v == minVal && v == maxVal))); return v; } Size randomSize(int minVal, int maxVal) { #if 1 return cv::Size((int)randomDoubleLog(minVal, maxVal), (int)randomDoubleLog(minVal, maxVal)); #else return cv::Size(randomInt(minVal, maxVal), randomInt(minVal, maxVal)); #endif } Size randomSize(int minValX, int maxValX, int minValY, int maxValY) { #if 1 return cv::Size(randomDoubleLog(minValX, maxValX), randomDoubleLog(minValY, maxValY)); #else return cv::Size(randomInt(minVal, maxVal), randomInt(minVal, maxVal)); #endif } Scalar randomScalar(double minVal, double maxVal) { return Scalar(randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal)); } Mat randomMat(Size size, int type, double minVal, double maxVal, bool useRoi = false) { RNG dataRng(rng.next()); return cvtest::randomMat(dataRng, size, type, minVal, maxVal, useRoi); } struct Border { int top, bot, lef, rig; }; Border randomBorder(int minValue = 0, int maxValue = MAX_VALUE) { Border border = { (int)randomDoubleLog(minValue, maxValue), (int)randomDoubleLog(minValue, maxValue), (int)randomDoubleLog(minValue, maxValue), (int)randomDoubleLog(minValue, maxValue) }; return border; } void randomSubMat(Mat& whole, Mat& subMat, const Size& roiSize, const Border& border, int type, double minVal, double maxVal) { Size wholeSize = Size(roiSize.width + border.lef + border.rig, roiSize.height + border.top + border.bot); whole = randomMat(wholeSize, type, minVal, maxVal, false); subMat = whole(Rect(border.lef, border.top, roiSize.width, roiSize.height)); } void generateOclMat(cv::ocl::oclMat& whole, cv::ocl::oclMat& subMat, const Mat& wholeMat, const Size& roiSize, const Border& border) { whole = wholeMat; subMat = whole(Rect(border.lef, border.top, roiSize.width, roiSize.height)); } }; #define PARAM_TEST_CASE(name, ...) struct name : public TSTestWithParam< std::tr1::tuple< __VA_ARGS__ > > #define GET_PARAM(k) std::tr1::get< k >(GetParam()) #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)) #define DIFFERENT_SIZES testing::Values(cv::Size(128, 128), cv::Size(113, 113), cv::Size(1300, 1300)) #define IMAGE_CHANNELS testing::Values(Channels(1), Channels(3), Channels(4)) #ifndef IMPLEMENT_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) #endif // IMPLEMENT_PARAM_CLASS } // namespace cvtest enum {FLIP_BOTH = 0, FLIP_X = 1, FLIP_Y = -1}; CV_ENUM(FlipCode, FLIP_BOTH, FLIP_X, FLIP_Y) CV_ENUM(CmpCode, CMP_EQ, CMP_GT, CMP_GE, CMP_LT, CMP_LE, CMP_NE) CV_ENUM(NormCode, NORM_INF, NORM_L1, NORM_L2, NORM_TYPE_MASK, NORM_RELATIVE, NORM_MINMAX) CV_ENUM(ReduceOp, REDUCE_SUM, REDUCE_AVG, REDUCE_MAX, REDUCE_MIN) CV_ENUM(MorphOp, MORPH_OPEN, MORPH_CLOSE, MORPH_GRADIENT, MORPH_TOPHAT, MORPH_BLACKHAT) CV_ENUM(ThreshOp, THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV) CV_ENUM(Interpolation, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC) CV_ENUM(Border, BORDER_REFLECT101, BORDER_REPLICATE, BORDER_CONSTANT, BORDER_REFLECT, BORDER_WRAP) CV_ENUM(TemplateMethod, TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED) CV_FLAGS(GemmFlags, GEMM_1_T, GEMM_2_T, GEMM_3_T); CV_FLAGS(WarpFlags, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, WARP_INVERSE_MAP) CV_FLAGS(DftFlags, DFT_INVERSE, DFT_SCALE, DFT_ROWS, DFT_COMPLEX_OUTPUT, DFT_REAL_OUTPUT) # define OCL_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(); \ void OCLTestBody(); \ private: \ 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 \ { \ OCLTestBody(); \ } \ catch (const cv::Exception & ex) \ { \ if (ex.code == cv::Error::OpenCLDoubleNotSupported)\ std::cout << "Test skipped (selected device does not support double)" << std::endl; \ else if (ex.code == cv::Error::OpenCLNoAMDBlasFft) \ std::cout << "Test skipped (AMD Blas / Fft libraries are not available)" << std::endl; \ else \ throw; \ } \ } \ \ void GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::OCLTestBody() #endif // __OPENCV_TEST_UTILITY_HPP__