/*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. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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_TEST_UTILITY_HPP__ #define __OPENCV_TEST_UTILITY_HPP__ #define LOOP_TIMES 1 #define MWIDTH 256 #define MHEIGHT 256 //#define RANDOMROI int randomInt(int minVal, int maxVal); double randomDouble(double minVal, double maxVal); //std::string generateVarList(int first,...); std::string generateVarList(int &p1, int &p2); cv::Size randomSize(int minVal, int maxVal); cv::Scalar randomScalar(double minVal, double maxVal); cv::Mat randomMat(cv::Size size, int type, double minVal = 0.0, double maxVal = 255.0); void showDiff(cv::InputArray gold, cv::InputArray actual, double eps); //! return true if device supports specified feature and gpu module was built with support the feature. //bool supportFeature(const cv::gpu::DeviceInfo& info, cv::gpu::FeatureSet feature); //! return all devices compatible with current gpu module build. //const std::vector& devices(); //! return all devices compatible with current gpu module build which support specified feature. //std::vector devices(cv::gpu::FeatureSet feature); //! 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_NEAR(mat1, mat2, eps,s) \ { \ ASSERT_EQ(mat1.type(), mat2.type()); \ ASSERT_EQ(mat1.size(), mat2.size()); \ EXPECT_LE(checkNorm(cv::Mat(mat1), cv::Mat(mat2)), eps)< 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); CV_ENUM(CmpCode, cv::CMP_EQ, cv::CMP_GT, cv::CMP_GE, cv::CMP_LT, cv::CMP_LE, cv::CMP_NE) CV_ENUM(NormCode, cv::NORM_INF, cv::NORM_L1, cv::NORM_L2, cv::NORM_TYPE_MASK, cv::NORM_RELATIVE, cv::NORM_MINMAX) enum {FLIP_BOTH = 0, FLIP_X = 1, FLIP_Y = -1}; CV_ENUM(FlipCode, FLIP_BOTH, FLIP_X, FLIP_Y) CV_ENUM(ReduceOp, CV_REDUCE_SUM, CV_REDUCE_AVG, CV_REDUCE_MAX, CV_REDUCE_MIN) CV_FLAGS(GemmFlags, cv::GEMM_1_T, cv::GEMM_2_T, cv::GEMM_3_T); CV_ENUM(MorphOp, cv::MORPH_OPEN, cv::MORPH_CLOSE, cv::MORPH_GRADIENT, cv::MORPH_TOPHAT, cv::MORPH_BLACKHAT) CV_ENUM(ThreshOp, cv::THRESH_BINARY, cv::THRESH_BINARY_INV, cv::THRESH_TRUNC, cv::THRESH_TOZERO, cv::THRESH_TOZERO_INV) CV_ENUM(Interpolation, cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_CUBIC) CV_ENUM(Border, cv::BORDER_REFLECT101, cv::BORDER_REPLICATE, cv::BORDER_CONSTANT, cv::BORDER_REFLECT, cv::BORDER_WRAP) CV_FLAGS(WarpFlags, cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_CUBIC, cv::WARP_INVERSE_MAP) CV_ENUM(TemplateMethod, cv::TM_SQDIFF, cv::TM_SQDIFF_NORMED, cv::TM_CCORR, cv::TM_CCORR_NORMED, cv::TM_CCOEFF, cv::TM_CCOEFF_NORMED) CV_FLAGS(DftFlags, cv::DFT_INVERSE, cv::DFT_SCALE, cv::DFT_ROWS, cv::DFT_COMPLEX_OUTPUT, cv::DFT_REAL_OUTPUT) void run_perf_test(); #define PARAM_TEST_CASE(name, ...) struct name : testing::TestWithParam< std::tr1::tuple< __VA_ARGS__ > > #define GET_PARAM(k) std::tr1::get< k >(GetParam()) #define ALL_DEVICES testing::ValuesIn(devices()) #define DEVICES(feature) testing::ValuesIn(devices(feature)) #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 DIRECT_INVERSE testing::Values(Inverse(false), Inverse(true)) #ifndef ALL_DEPTH #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)) #endif #define REPEAT 1000 #define COUNT_U 0 // count the uploading execution time for ocl mat structures #define COUNT_D 0 // the following macro section tests the target function (kernel) performance // upload is the code snippet for converting cv::mat to cv::ocl::oclMat // downloading is the code snippet for converting cv::ocl::oclMat back to cv::mat // change COUNT_U and COUNT_D to take downloading and uploading time into account #define P_TEST_FULL( upload, kernel_call, download ) \ { \ std::cout<< "\n" #kernel_call "\n----------------------"; \ {upload;} \ R_TEST( kernel_call, 2 ); \ double t = (double)cvGetTickCount(); \ R_T( { \ if( COUNT_U ) {upload;} \ kernel_call; \ if( COUNT_D ) {download;} \ } ); \ t = (double)cvGetTickCount() - t; \ std::cout << "runtime is " << t/((double)cvGetTickFrequency()* 1000.) << "ms" << std::endl; \ } #define R_T2( test ) \ { \ std::cout<< "\n" #test "\n----------------------"; \ R_TEST( test, 15 ) \ clock_t st = clock(); \ R_T( test ) \ std::cout<< clock() - st << "ms\n"; \ } #define R_T( test ) \ R_TEST( test, REPEAT ) #define R_TEST( test, repeat ) \ try{ \ for( int i = 0; i < repeat; i ++ ) { test; } \ } catch( ... ) { std::cout << "||||| Exception catched! |||||\n"; return; } //////// Utility #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 #endif // __OPENCV_TEST_UTILITY_HPP__