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Open Source Computer Vision Library
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230 lines
8.5 KiB
230 lines
8.5 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// Intel License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000, Intel Corporation, all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of Intel Corporation may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#ifndef __OPENCV_TEST_UTILITY_HPP__ |
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#define __OPENCV_TEST_UTILITY_HPP__ |
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#include "opencv2/core.hpp" |
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#define LOOP_TIMES 1 |
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#define MWIDTH 256 |
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#define MHEIGHT 256 |
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//#define RANDOMROI |
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int randomInt(int minVal, int maxVal); |
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double randomDouble(double minVal, double maxVal); |
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//std::string generateVarList(int first,...); |
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std::string generateVarList(int &p1, int &p2); |
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cv::Size randomSize(int minVal, int maxVal); |
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cv::Scalar randomScalar(double minVal, double maxVal); |
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cv::Mat randomMat(cv::Size size, int type, double minVal = 0.0, double maxVal = 255.0); |
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void showDiff(cv::InputArray gold, cv::InputArray actual, double eps); |
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// This function test if gpu_rst matches cpu_rst. |
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// If the two vectors are not equal, it will return the difference in vector size |
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// Else it will return (total diff of each cpu and gpu rects covered pixels)/(total cpu rects covered pixels) |
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// The smaller, the better matched |
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double checkRectSimilarity(cv::Size sz, std::vector<cv::Rect>& ob1, std::vector<cv::Rect>& ob2); |
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//! read image from testdata folder. |
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cv::Mat readImage(const std::string &fileName, int flags = cv::IMREAD_COLOR); |
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cv::Mat readImageType(const std::string &fname, int type); |
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double checkNorm(const cv::Mat &m); |
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double checkNorm(const cv::Mat &m1, const cv::Mat &m2); |
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double checkSimilarity(const cv::Mat &m1, const cv::Mat &m2); |
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//oclMat create |
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cv::ocl::oclMat createMat_ocl(cv::Size size, int type, bool useRoi = false); |
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cv::ocl::oclMat loadMat_ocl(const cv::Mat& m, bool useRoi = false); |
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#define EXPECT_MAT_NORM(mat, eps) \ |
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{ \ |
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EXPECT_LE(checkNorm(cv::Mat(mat)), eps) \ |
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} |
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#define EXPECT_MAT_NEAR(mat1, mat2, eps) \ |
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{ \ |
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ASSERT_EQ(mat1.type(), mat2.type()); \ |
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ASSERT_EQ(mat1.size(), mat2.size()); \ |
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EXPECT_LE(checkNorm(cv::Mat(mat1), cv::Mat(mat2)), eps); \ |
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} |
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/* |
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#define EXPECT_MAT_NEAR(mat1, mat2, eps,s) \ |
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{ \ |
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ASSERT_EQ(mat1.type(), mat2.type()); \ |
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ASSERT_EQ(mat1.size(), mat2.size()); \ |
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EXPECT_LE(checkNorm(cv::Mat(mat1), cv::Mat(mat2)), eps)<<s; \ |
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} |
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*/ |
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#define EXPECT_MAT_SIMILAR(mat1, mat2, eps) \ |
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{ \ |
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ASSERT_EQ(mat1.type(), mat2.type()); \ |
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ASSERT_EQ(mat1.size(), mat2.size()); \ |
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EXPECT_LE(checkSimilarity(cv::Mat(mat1), cv::Mat(mat2)), eps); \ |
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} |
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namespace cv |
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{ |
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namespace ocl |
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{ |
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// void PrintTo(const DeviceInfo& info, std::ostream* os); |
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} |
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} |
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using perf::MatDepth; |
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using perf::MatType; |
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//! return vector with types from specified range. |
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std::vector<MatType> types(int depth_start, int depth_end, int cn_start, int cn_end); |
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//! return vector with all types (depth: CV_8U-CV_64F, channels: 1-4). |
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const std::vector<MatType> &all_types(); |
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class Inverse |
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{ |
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public: |
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inline Inverse(bool val = false) : val_(val) {} |
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inline operator bool() const |
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{ |
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return val_; |
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} |
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private: |
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bool val_; |
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}; |
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void PrintTo(const Inverse &useRoi, std::ostream *os); |
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enum {FLIP_BOTH = 0, FLIP_X = 1, FLIP_Y = -1}; |
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CV_ENUM(FlipCode, FLIP_BOTH, FLIP_X, FLIP_Y) |
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CV_ENUM(CmpCode, CMP_EQ, CMP_GT, CMP_GE, CMP_LT, CMP_LE, CMP_NE) |
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CV_ENUM(NormCode, NORM_INF, NORM_L1, NORM_L2, NORM_TYPE_MASK, NORM_RELATIVE, NORM_MINMAX) |
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CV_ENUM(ReduceOp, REDUCE_SUM, REDUCE_AVG, REDUCE_MAX, REDUCE_MIN) |
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CV_ENUM(MorphOp, MORPH_OPEN, MORPH_CLOSE, MORPH_GRADIENT, MORPH_TOPHAT, MORPH_BLACKHAT) |
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CV_ENUM(ThreshOp, THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV) |
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CV_ENUM(Interpolation, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC) |
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CV_ENUM(Border, BORDER_REFLECT101, BORDER_REPLICATE, BORDER_CONSTANT, BORDER_REFLECT, BORDER_WRAP) |
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CV_ENUM(TemplateMethod, TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED) |
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CV_FLAGS(GemmFlags, GEMM_1_T, GEMM_2_T, GEMM_3_T); |
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CV_FLAGS(WarpFlags, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, WARP_INVERSE_MAP) |
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CV_FLAGS(DftFlags, DFT_INVERSE, DFT_SCALE, DFT_ROWS, DFT_COMPLEX_OUTPUT, DFT_REAL_OUTPUT) |
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void run_perf_test(); |
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#define PARAM_TEST_CASE(name, ...) struct name : testing::TestWithParam< std::tr1::tuple< __VA_ARGS__ > > |
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#define GET_PARAM(k) std::tr1::get< k >(GetParam()) |
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#define ALL_DEVICES testing::ValuesIn(devices()) |
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#define DEVICES(feature) testing::ValuesIn(devices(feature)) |
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#define ALL_TYPES testing::ValuesIn(all_types()) |
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#define TYPES(depth_start, depth_end, cn_start, cn_end) testing::ValuesIn(types(depth_start, depth_end, cn_start, cn_end)) |
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#define DIFFERENT_SIZES testing::Values(cv::Size(128, 128), cv::Size(113, 113), cv::Size(1300, 1300)) |
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#define DIRECT_INVERSE testing::Values(Inverse(false), Inverse(true)) |
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#ifndef ALL_DEPTH |
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#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)) |
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#endif |
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#define REPEAT 1000 |
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#define COUNT_U 0 // count the uploading execution time for ocl mat structures |
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#define COUNT_D 0 |
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// the following macro section tests the target function (kernel) performance |
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// upload is the code snippet for converting cv::mat to cv::ocl::oclMat |
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// downloading is the code snippet for converting cv::ocl::oclMat back to cv::mat |
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// change COUNT_U and COUNT_D to take downloading and uploading time into account |
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#define P_TEST_FULL( upload, kernel_call, download ) \ |
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{ \ |
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std::cout<< "\n" #kernel_call "\n----------------------"; \ |
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{upload;} \ |
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R_TEST( kernel_call, 2 ); \ |
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double t = (double)cvGetTickCount(); \ |
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R_T( { \ |
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if( COUNT_U ) {upload;} \ |
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kernel_call; \ |
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if( COUNT_D ) {download;} \ |
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} ); \ |
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t = (double)cvGetTickCount() - t; \ |
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std::cout << "runtime is " << t/((double)cvGetTickFrequency()* 1000.) << "ms" << std::endl; \ |
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} |
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#define R_T2( test ) \ |
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{ \ |
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std::cout<< "\n" #test "\n----------------------"; \ |
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R_TEST( test, 15 ) \ |
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clock_t st = clock(); \ |
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R_T( test ) \ |
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std::cout<< clock() - st << "ms\n"; \ |
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} |
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#define R_T( test ) \ |
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R_TEST( test, REPEAT ) |
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#define R_TEST( test, repeat ) \ |
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try{ \ |
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for( int i = 0; i < repeat; i ++ ) { test; } \ |
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} catch( ... ) { std::cout << "||||| Exception catched! |||||\n"; return; } |
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//////// Utility |
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#define IMAGE_CHANNELS testing::Values(Channels(1), Channels(3), Channels(4)) |
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#ifndef IMPLEMENT_PARAM_CLASS |
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#define IMPLEMENT_PARAM_CLASS(name, type) \ |
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class name \ |
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{ \ |
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public: \ |
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name ( type arg = type ()) : val_(arg) {} \ |
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operator type () const {return val_;} \ |
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private: \ |
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type val_; \ |
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}; \ |
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inline void PrintTo( name param, std::ostream* os) \ |
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{ \ |
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*os << #name << "(" << testing::PrintToString(static_cast< type >(param)) << ")"; \ |
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} |
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IMPLEMENT_PARAM_CLASS(Channels, int) |
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#endif // IMPLEMENT_PARAM_CLASS |
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#endif // __OPENCV_TEST_UTILITY_HPP__
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