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use perf test replace performance sample

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This commit is contained in:
yao
2013-03-27 12:04:48 +08:00
parent 55c9a7c87d
commit 5539e85a11
26 changed files with 3791 additions and 14305 deletions
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278
modules/ocl/perf/perf_match_template.cpp
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@@ -42,191 +42,105 @@
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#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)
/////////// matchTemplate ////////////////////////
//void InitMatchTemplate()
//{
// Mat src; gen(src, 500, 500, CV_32F, 0, 1);
// Mat templ; gen(templ, 500, 500, CV_32F, 0, 1);
// ocl::oclMat d_src(src), d_templ(templ), d_dst;
// ocl::matchTemplate(d_src, d_templ, d_dst, CV_TM_CCORR);
//}
TEST(matchTemplate)
{
cv::Size size;
cv::Size templ_size;
int cn;
int method;
//vector<cv::ocl::Info> oclinfo;
//InitMatchTemplate();
virtual void SetUp()
Mat src, templ, dst;
int templ_size = 5;
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
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);
int all_type[] = {CV_32FC1, CV_32FC4};
std::string type_name[] = {"CV_32FC1", "CV_32FC4"};
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
{
for(templ_size = 5; templ_size <= 5; templ_size *= 5)
{
gen(src, size, size, all_type[j], 0, 1);
SUBTEST << src.cols << 'x' << src.rows << "; " << type_name[j] << "; templ " << templ_size << 'x' << templ_size << "; CCORR";
gen(templ, templ_size, templ_size, all_type[j], 0, 1);
matchTemplate(src, templ, dst, CV_TM_CCORR);
CPU_ON;
matchTemplate(src, templ, dst, CV_TM_CCORR);
CPU_OFF;
ocl::oclMat d_src(src), d_templ, d_dst;
d_templ.upload(templ);
WARMUP_ON;
ocl::matchTemplate(d_src, d_templ, d_dst, CV_TM_CCORR);
WARMUP_OFF;
GPU_ON;
ocl::matchTemplate(d_src, d_templ, d_dst, CV_TM_CCORR);
;
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
d_templ.upload(templ);
ocl::matchTemplate(d_src, d_templ, d_dst, CV_TM_CCORR);
d_dst.download(dst);
GPU_FULL_OFF;
}
}
int all_type_8U[] = {CV_8UC1};
std::string type_name_8U[] = {"CV_8UC1"};
for (size_t j = 0; j < sizeof(all_type_8U) / sizeof(int); j++)
{
for(templ_size = 5; templ_size <= 5; templ_size *= 5)
{
SUBTEST << src.cols << 'x' << src.rows << "; " << type_name_8U[j] << "; templ " << templ_size << 'x' << templ_size << "; CCORR_NORMED";
gen(src, size, size, all_type_8U[j], 0, 255);
gen(templ, templ_size, templ_size, all_type_8U[j], 0, 255);
matchTemplate(src, templ, dst, CV_TM_CCORR_NORMED);
CPU_ON;
matchTemplate(src, templ, dst, CV_TM_CCORR_NORMED);
CPU_OFF;
ocl::oclMat d_src(src);
ocl::oclMat d_templ(templ), d_dst;
WARMUP_ON;
ocl::matchTemplate(d_src, d_templ, d_dst, CV_TM_CCORR_NORMED);
WARMUP_OFF;
GPU_ON;
ocl::matchTemplate(d_src, d_templ, d_dst, CV_TM_CCORR_NORMED);
;
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
d_templ.upload(templ);
ocl::matchTemplate(d_src, d_templ, d_dst, CV_TM_CCORR_NORMED);
d_dst.download(dst);
GPU_FULL_OFF;
}
}
}
};
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
}