opencv/modules/ocl/perf/perf_hough.cpp

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#include "perf_precomp.hpp"

#ifdef HAVE_OPENCL

using namespace cv;
using namespace perf;

//////////////////////////////////////////////////////////////////////
// HoughCircles

typedef std::tr1::tuple<cv::Size, float, float> Size_Dp_MinDist_t;
typedef perf::TestBaseWithParam<Size_Dp_MinDist_t> Size_Dp_MinDist;

PERF_TEST_P(Size_Dp_MinDist, OCL_HoughCircles,
            testing::Combine(
                testing::Values(perf::sz720p, perf::szSXGA, perf::sz1080p),
                testing::Values(1.0f, 2.0f, 4.0f),
                testing::Values(1.0f, 10.0f)))
{
    const cv::Size size = std::tr1::get<0>(GetParam());
    const float dp      = std::tr1::get<1>(GetParam());
    const float minDist = std::tr1::get<2>(GetParam());

    const int minRadius = 10;
    const int maxRadius = 30;
    const int cannyThreshold = 100;
    const int votesThreshold = 15;

    cv::RNG rng(123456789);

    cv::Mat src(size, CV_8UC1, cv::Scalar::all(0));

    const int numCircles = rng.uniform(50, 100);
    for (int i = 0; i < numCircles; ++i)
    {
        cv::Point center(rng.uniform(0, src.cols), rng.uniform(0, src.rows));
        const int radius = rng.uniform(minRadius, maxRadius + 1);

        cv::circle(src, center, radius, cv::Scalar::all(255), -1);
    }

    cv::ocl::oclMat ocl_src(src);
    cv::ocl::oclMat ocl_circles;

    declare.time(10.0).iterations(25);

    TEST_CYCLE()
    {
        cv::ocl::HoughCircles(ocl_src, ocl_circles, HOUGH_GRADIENT, dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
    }

    cv::Mat circles(ocl_circles);
    SANITY_CHECK(circles);
}

#endif // HAVE_OPENCL