opencv/modules/ocl/perf/perf_match_template.cpp

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//    Fangfang Bai, fangfang@multicorewareinc.com
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#include "precomp.hpp"
#include <iomanip>
#ifdef HAVE_OPENCL
using namespace cv;
using namespace cv::ocl;
using namespace cvtest;
using namespace testing;
using namespace std;

#ifndef MWC_TEST_UTILITY
#define MWC_TEST_UTILITY
//////// Utility
#ifndef DIFFERENT_SIZES
#else
#undef DIFFERENT_SIZES
#endif
#define DIFFERENT_SIZES testing::Values(cv::Size(256, 256), cv::Size(3000, 3000))

// Param class
#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 // MWC_TEST_UTILITY

////////////////////////////////////////////////////////////////////////////////
// MatchTemplate
#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))

IMPLEMENT_PARAM_CLASS(TemplateSize, cv::Size);

const char* TEMPLATE_METHOD_NAMES[6] = {"TM_SQDIFF", "TM_SQDIFF_NORMED", "TM_CCORR", "TM_CCORR_NORMED", "TM_CCOEFF", "TM_CCOEFF_NORMED"};

PARAM_TEST_CASE(MatchTemplate, cv::Size, TemplateSize, Channels, TemplateMethod)
{
	cv::Size size;
	cv::Size templ_size;
	int cn;
	int method;
	//vector<cv::ocl::Info> oclinfo;

	virtual void SetUp()
	{
		size = GET_PARAM(0);
		templ_size = GET_PARAM(1);
		cn = GET_PARAM(2);
		method = GET_PARAM(3);
		//int devnums = getDevice(oclinfo);
		//CV_Assert(devnums > 0);
	}
};
struct MatchTemplate8U : MatchTemplate {};

TEST_P(MatchTemplate8U, Performance)
{
	std::cout << "Method: " << TEMPLATE_METHOD_NAMES[method] << std::endl;
	std::cout << "Image Size: (" << size.width << ", " << size.height << ")"<< std::endl;
	std::cout << "Template Size: (" << templ_size.width << ", " << templ_size.height << ")"<< std::endl;
	std::cout << "Channels: " << cn << std::endl;

	cv::Mat image = randomMat(size, CV_MAKETYPE(CV_8U, cn));
	cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_8U, cn));
	cv::Mat dst_gold;
	cv::ocl::oclMat dst;




	
	double totalgputick=0;
	double totalgputick_kernel=0;

	double t1=0;
	double t2=0;
	for(int j = 0; j < LOOP_TIMES+1; j ++)
	{

		t1 = (double)cvGetTickCount();//gpu start1

        cv::ocl::oclMat ocl_image = cv::ocl::oclMat(image);//upload
		cv::ocl::oclMat ocl_templ = cv::ocl::oclMat(templ);//upload

		t2=(double)cvGetTickCount();//kernel
		cv::ocl::matchTemplate(ocl_image, ocl_templ, dst, method);
		t2 = (double)cvGetTickCount() - t2;//kernel

		cv::Mat cpu_dst;
		dst.download (cpu_dst);//download

		t1 = (double)cvGetTickCount() - t1;//gpu end1

		if(j == 0)
			continue;

		totalgputick=t1+totalgputick;	
		totalgputick_kernel=t2+totalgputick_kernel;	

	}

	cout << "average gpu runtime is  " << totalgputick/((double)cvGetTickFrequency()* LOOP_TIMES *1000.) << "ms" << endl;
	cout << "average gpu runtime without data transfer is  " << totalgputick_kernel/((double)cvGetTickFrequency()* LOOP_TIMES *1000.) << "ms" << endl;


}


struct MatchTemplate32F : MatchTemplate {};
TEST_P(MatchTemplate32F, Performance)
{
	std::cout << "Method: " << TEMPLATE_METHOD_NAMES[method] << std::endl;
	std::cout << "Image Size: (" << size.width << ", " << size.height << ")"<< std::endl;
	std::cout << "Template Size: (" << templ_size.width << ", " << templ_size.height << ")"<< std::endl;
	std::cout << "Channels: " << cn << std::endl;
	cv::Mat image = randomMat(size, CV_MAKETYPE(CV_32F, cn));
	cv::Mat templ = randomMat(templ_size, CV_MAKETYPE(CV_32F, cn));

	cv::Mat dst_gold;
	cv::ocl::oclMat dst;




	double totalgputick=0;
	double totalgputick_kernel=0;

	double t1=0;
	double t2=0;
	for(int j = 0; j < LOOP_TIMES; j ++)
	{

		t1 = (double)cvGetTickCount();//gpu start1

        cv::ocl::oclMat ocl_image = cv::ocl::oclMat(image);//upload
		cv::ocl::oclMat ocl_templ = cv::ocl::oclMat(templ);//upload

		t2=(double)cvGetTickCount();//kernel
		cv::ocl::matchTemplate(ocl_image, ocl_templ, dst, method);
		t2 = (double)cvGetTickCount() - t2;//kernel

		cv::Mat cpu_dst;
		dst.download (cpu_dst);//download

		t1 = (double)cvGetTickCount() - t1;//gpu end1		

		totalgputick=t1+totalgputick;
	
		totalgputick_kernel=t2+totalgputick_kernel;	

	}

   cout << "average gpu runtime is  " << totalgputick/((double)cvGetTickFrequency()* LOOP_TIMES *1000.) << "ms" << endl;
   cout << "average gpu runtime without data transfer is  " << totalgputick_kernel/((double)cvGetTickFrequency()* LOOP_TIMES *1000.) << "ms" << endl;



}


INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate8U, 
	testing::Combine(
    testing::Values(cv::Size(1280, 1024), cv::Size(MWIDTH, MHEIGHT),cv::Size(1800, 1500)),
    testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16))/*, TemplateSize(cv::Size(30, 30))*/),
    testing::Values(Channels(1), Channels(4)/*, Channels(3)*/),
	ALL_TEMPLATE_METHODS
	)
);

INSTANTIATE_TEST_CASE_P(GPU_ImgProc, MatchTemplate32F, testing::Combine(
    testing::Values(cv::Size(1280, 1024), cv::Size(MWIDTH, MHEIGHT),cv::Size(1800, 1500)),
    testing::Values(TemplateSize(cv::Size(5, 5)), TemplateSize(cv::Size(16, 16))/*, TemplateSize(cv::Size(30, 30))*/),
    testing::Values(Channels(1), Channels(4) /*, Channels(3)*/),
    testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR))));

#endif //HAVE_OPENCL