232 lines
8.3 KiB
C++
232 lines
8.3 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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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
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// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// @Authors
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// Fangfang Bai, fangfang@multicorewareinc.com
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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 oclMaterials provided with the distribution.
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//
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// * The name of the copyright holders 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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#include "precomp.hpp"
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#include <iomanip>
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#ifdef HAVE_OPENCL
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using namespace cv;
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using namespace cv::ocl;
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using namespace cvtest;
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using namespace testing;
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using namespace std;
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#ifndef MWC_TEST_UTILITY
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#define MWC_TEST_UTILITY
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//////// Utility
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#ifndef DIFFERENT_SIZES
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#else
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#undef DIFFERENT_SIZES
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#endif
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#define DIFFERENT_SIZES testing::Values(cv::Size(256, 256), cv::Size(3000, 3000))
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// Param class
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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 // MWC_TEST_UTILITY
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////////////////////////////////////////////////////////////////////////////////
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// MatchTemplate
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#define ALL_TEMPLATE_METHODS testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR), TemplateMethod(cv::TM_CCOEFF), TemplateMethod(cv::TM_SQDIFF_NORMED), TemplateMethod(cv::TM_CCORR_NORMED), TemplateMethod(cv::TM_CCOEFF_NORMED))
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IMPLEMENT_PARAM_CLASS(TemplateSize, cv::Size);
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const char *TEMPLATE_METHOD_NAMES[6] = {"TM_SQDIFF", "TM_SQDIFF_NORMED", "TM_CCORR", "TM_CCORR_NORMED", "TM_CCOEFF", "TM_CCOEFF_NORMED"};
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PARAM_TEST_CASE(MatchTemplate, cv::Size, TemplateSize, Channels, TemplateMethod)
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{
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cv::Size size;
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cv::Size templ_size;
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int cn;
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int method;
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//vector<cv::ocl::Info> oclinfo;
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virtual void SetUp()
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{
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size = GET_PARAM(0);
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templ_size = GET_PARAM(1);
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cn = GET_PARAM(2);
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method = GET_PARAM(3);
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//int devnums = getDevice(oclinfo);
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//CV_Assert(devnums > 0);
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}
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};
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struct MatchTemplate8U : MatchTemplate {};
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TEST_P(MatchTemplate8U, Performance)
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{
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std::cout << "Method: " << TEMPLATE_METHOD_NAMES[method] << std::endl;
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std::cout << "Image Size: (" << size.width << ", " << size.height << ")" << std::endl;
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std::cout << "Template Size: (" << templ_size.width << ", " << templ_size.height << ")" << std::endl;
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std::cout << "Channels: " << cn << std::endl;
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cv::Mat image = randomMat(size, CV_MAKETYPE(CV_8U, cn));
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cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_8U, cn));
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cv::Mat dst_gold;
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cv::ocl::oclMat dst;
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double totalgputick = 0;
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double totalgputick_kernel = 0;
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double t1 = 0;
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double t2 = 0;
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for(int j = 0; j < LOOP_TIMES + 1; j ++)
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{
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t1 = (double)cvGetTickCount();//gpu start1
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cv::ocl::oclMat ocl_image = cv::ocl::oclMat(image);//upload
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cv::ocl::oclMat ocl_templ = cv::ocl::oclMat(templ);//upload
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t2 = (double)cvGetTickCount(); //kernel
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cv::ocl::matchTemplate(ocl_image, ocl_templ, dst, method);
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t2 = (double)cvGetTickCount() - t2;//kernel
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cv::Mat cpu_dst;
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dst.download (cpu_dst);//download
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t1 = (double)cvGetTickCount() - t1;//gpu end1
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if(j == 0)
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continue;
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totalgputick = t1 + totalgputick;
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totalgputick_kernel = t2 + totalgputick_kernel;
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}
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cout << "average gpu runtime is " << totalgputick / ((double)cvGetTickFrequency()* LOOP_TIMES * 1000.) << "ms" << endl;
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cout << "average gpu runtime without data transfer is " << totalgputick_kernel / ((double)cvGetTickFrequency()* LOOP_TIMES * 1000.) << "ms" << endl;
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}
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struct MatchTemplate32F : MatchTemplate {};
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TEST_P(MatchTemplate32F, Performance)
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{
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std::cout << "Method: " << TEMPLATE_METHOD_NAMES[method] << std::endl;
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std::cout << "Image Size: (" << size.width << ", " << size.height << ")" << std::endl;
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std::cout << "Template Size: (" << templ_size.width << ", " << templ_size.height << ")" << std::endl;
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std::cout << "Channels: " << cn << std::endl;
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cv::Mat image = randomMat(size, CV_MAKETYPE(CV_32F, cn));
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cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_32F, cn));
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cv::Mat dst_gold;
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cv::ocl::oclMat dst;
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double totalgputick = 0;
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double totalgputick_kernel = 0;
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double t1 = 0;
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double t2 = 0;
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for(int j = 0; j < LOOP_TIMES; j ++)
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{
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t1 = (double)cvGetTickCount();//gpu start1
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cv::ocl::oclMat ocl_image = cv::ocl::oclMat(image);//upload
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cv::ocl::oclMat ocl_templ = cv::ocl::oclMat(templ);//upload
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t2 = (double)cvGetTickCount(); //kernel
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cv::ocl::matchTemplate(ocl_image, ocl_templ, dst, method);
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t2 = (double)cvGetTickCount() - t2;//kernel
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cv::Mat cpu_dst;
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dst.download (cpu_dst);//download
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t1 = (double)cvGetTickCount() - t1;//gpu end1
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totalgputick = t1 + totalgputick;
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totalgputick_kernel = t2 + totalgputick_kernel;
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}
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cout << "average gpu runtime is " << totalgputick / ((double)cvGetTickFrequency()* LOOP_TIMES * 1000.) << "ms" << endl;
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cout << "average gpu runtime without data transfer is " << totalgputick_kernel / ((double)cvGetTickFrequency()* LOOP_TIMES * 1000.) << "ms" << endl;
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}
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate8U,
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testing::Combine(
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testing::Values(cv::Size(1280, 1024), cv::Size(MWIDTH, MHEIGHT), cv::Size(1800, 1500)),
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testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16))/*, TemplateSize(cv::Size(30, 30))*/),
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testing::Values(Channels(1), Channels(4)/*, Channels(3)*/),
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ALL_TEMPLATE_METHODS
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)
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);
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INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate32F, testing::Combine(
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testing::Values(cv::Size(1280, 1024), cv::Size(MWIDTH, MHEIGHT), cv::Size(1800, 1500)),
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testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16))/*, TemplateSize(cv::Size(30, 30))*/),
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testing::Values(Channels(1), Channels(4) /*, Channels(3)*/),
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testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR))));
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#endif //HAVE_OPENCL
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