opencv/modules/ocl/perf/perf_hog.cpp

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//    Peng Xiao, pengxiao@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;
extern std::string workdir;

#ifndef MWC_TEST_UTILITY
#define MWC_TEST_UTILITY

// 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)) << ")"; \
    }

#endif // IMPLEMENT_PARAM_CLASS
#endif // MWC_TEST_UTILITY

IMPLEMENT_PARAM_CLASS(WinSizw48, bool);

PARAM_TEST_CASE(HOG, WinSizw48, bool)
{
    bool is48;
    vector<float> detector;
    virtual void SetUp()
    {
        is48 = GET_PARAM(0);
        if(is48)
        {
            detector = cv::ocl::HOGDescriptor::getPeopleDetector48x96();
        }
        else
        {
            detector = cv::ocl::HOGDescriptor::getPeopleDetector64x128();
        }
    }
};

TEST_P(HOG, Performance)
{
    cv::Mat img = readImage(workdir + "lena.jpg", cv::IMREAD_GRAYSCALE);
    ASSERT_FALSE(img.empty());

    // define HOG related arguments
    float scale = 1.05;
    //int nlevels = 13;
    float gr_threshold = 8;
    float hit_threshold = 1.4;
    //bool hit_threshold_auto = true;

    int win_width = is48 ? 48 : 64;
    int win_stride_width = 8;
    int win_stride_height = 8;

    bool gamma_corr = true;

    Size win_size(win_width, win_width * 2); //(64, 128) or (48, 96)
    Size win_stride(win_stride_width, win_stride_height);

    cv::ocl::HOGDescriptor gpu_hog(win_size, Size(16, 16), Size(8, 8), Size(8, 8), 9,
                                   cv::ocl::HOGDescriptor::DEFAULT_WIN_SIGMA, 0.2, gamma_corr,
                                   cv::ocl::HOGDescriptor::DEFAULT_NLEVELS);

    gpu_hog.setSVMDetector(detector);

    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

        ocl::oclMat d_src(img);//upload

        t2 = (double)cvGetTickCount(); //kernel

        vector<Rect> found;
        gpu_hog.detectMultiScale(d_src, found, hit_threshold, win_stride,
                                 Size(0, 0), scale, gr_threshold);

        t2 = (double)cvGetTickCount() - t2;//kernel

        // no download time for HOG

        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;
}


INSTANTIATE_TEST_CASE_P(GPU_ObjDetect, HOG, testing::Combine(testing::Values(WinSizw48(false), WinSizw48(true)), testing::Values(false)));

#endif  //Have opencl