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236 lines
8.7 KiB
C++
236 lines
8.7 KiB
C++
/*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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#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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//! return true if device supports specified feature and gpu module was built with support the feature.
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//bool supportFeature(const cv::gpu::DeviceInfo& info, cv::gpu::FeatureSet feature);
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//! return all devices compatible with current gpu module build.
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//const std::vector<cv::ocl::DeviceInfo>& devices();
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//! return all devices compatible with current gpu module build which support specified feature.
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//std::vector<cv::ocl::DeviceInfo> devices(cv::gpu::FeatureSet feature);
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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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#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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#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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#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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CV_ENUM(CmpCode, cv::CMP_EQ, cv::CMP_GT, cv::CMP_GE, cv::CMP_LT, cv::CMP_LE, cv::CMP_NE)
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CV_ENUM(NormCode, cv::NORM_INF, cv::NORM_L1, cv::NORM_L2, cv::NORM_TYPE_MASK, cv::NORM_RELATIVE, cv::NORM_MINMAX)
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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(ReduceOp, CV_REDUCE_SUM, CV_REDUCE_AVG, CV_REDUCE_MAX, CV_REDUCE_MIN)
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CV_FLAGS(GemmFlags, cv::GEMM_1_T, cv::GEMM_2_T, cv::GEMM_3_T);
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CV_ENUM(MorphOp, cv::MORPH_OPEN, cv::MORPH_CLOSE, cv::MORPH_GRADIENT, cv::MORPH_TOPHAT, cv::MORPH_BLACKHAT)
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CV_ENUM(ThreshOp, cv::THRESH_BINARY, cv::THRESH_BINARY_INV, cv::THRESH_TRUNC, cv::THRESH_TOZERO, cv::THRESH_TOZERO_INV)
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CV_ENUM(Interpolation, cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_CUBIC)
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CV_ENUM(Border, cv::BORDER_REFLECT101, cv::BORDER_REPLICATE, cv::BORDER_CONSTANT, cv::BORDER_REFLECT, cv::BORDER_WRAP)
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CV_FLAGS(WarpFlags, cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_CUBIC, cv::WARP_INVERSE_MAP)
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CV_ENUM(TemplateMethod, cv::TM_SQDIFF, cv::TM_SQDIFF_NORMED, cv::TM_CCORR, cv::TM_CCORR_NORMED, cv::TM_CCOEFF, cv::TM_CCOEFF_NORMED)
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CV_FLAGS(DftFlags, cv::DFT_INVERSE, cv::DFT_SCALE, cv::DFT_ROWS, cv::DFT_COMPLEX_OUTPUT, cv::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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