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#include "test_precomp.hpp"

#ifdef HAVE_CUDA

using namespace cvtest;

///////////////////////////////////////////////////////////////////
// Gold implementation

namespace
{
    template <typename T, template <typename> class Interpolator>
    void resizeImpl(const cv::Mat& src, cv::Mat& dst, double fx, double fy)
    {
        const int cn = src.channels();

        cv::Size dsize(cv::saturate_cast<int>(src.cols * fx), cv::saturate_cast<int>(src.rows * fy));

        dst.create(dsize, src.type());

        float ifx = static_cast<float>(1.0 / fx);
        float ify = static_cast<float>(1.0 / fy);

        for (int y = 0; y < dsize.height; ++y)
        {
            for (int x = 0; x < dsize.width; ++x)
            {
                for (int c = 0; c < cn; ++c)
                    dst.at<T>(y, x * cn + c) = Interpolator<T>::getValue(src, y * ify, x * ifx, c, cv::BORDER_REPLICATE);
            }
        }
    }

    void resizeGold(const cv::Mat& src, cv::Mat& dst, double fx, double fy, int interpolation)
    {
        typedef void (*func_t)(const cv::Mat& src, cv::Mat& dst, double fx, double fy);

        static const func_t nearest_funcs[] =
        {
            resizeImpl<unsigned char, NearestInterpolator>,
            resizeImpl<signed char, NearestInterpolator>,
            resizeImpl<unsigned short, NearestInterpolator>,
            resizeImpl<short, NearestInterpolator>,
            resizeImpl<int, NearestInterpolator>,
            resizeImpl<float, NearestInterpolator>
        };


        static const func_t linear_funcs[] =
        {
            resizeImpl<unsigned char, LinearInterpolator>,
            resizeImpl<signed char, LinearInterpolator>,
            resizeImpl<unsigned short, LinearInterpolator>,
            resizeImpl<short, LinearInterpolator>,
            resizeImpl<int, LinearInterpolator>,
            resizeImpl<float, LinearInterpolator>
        };

        static const func_t cubic_funcs[] =
        {
            resizeImpl<unsigned char, CubicInterpolator>,
            resizeImpl<signed char, CubicInterpolator>,
            resizeImpl<unsigned short, CubicInterpolator>,
            resizeImpl<short, CubicInterpolator>,
            resizeImpl<int, CubicInterpolator>,
            resizeImpl<float, CubicInterpolator>
        };

        static const func_t* funcs[] = {nearest_funcs, linear_funcs, cubic_funcs};

        funcs[interpolation][src.depth()](src, dst, fx, fy);
    }
}

///////////////////////////////////////////////////////////////////
// Test

PARAM_TEST_CASE(Resize, cv::cuda::DeviceInfo, cv::Size, MatType, double, Interpolation, UseRoi)
{
    cv::cuda::DeviceInfo devInfo;
    cv::Size size;
    double coeff;
    int interpolation;
    int type;
    bool useRoi;

    virtual void SetUp()
    {
        devInfo = GET_PARAM(0);
        size = GET_PARAM(1);
        type = GET_PARAM(2);
        coeff = GET_PARAM(3);
        interpolation = GET_PARAM(4);
        useRoi = GET_PARAM(5);

        cv::cuda::setDevice(devInfo.deviceID());
    }
};

CUDA_TEST_P(Resize, Accuracy)
{
    cv::Mat src = randomMat(size, type);

    cv::cuda::GpuMat dst = createMat(cv::Size(cv::saturate_cast<int>(src.cols * coeff), cv::saturate_cast<int>(src.rows * coeff)), type, useRoi);
    cv::cuda::resize(loadMat(src, useRoi), dst, cv::Size(), coeff, coeff, interpolation);

    cv::Mat dst_gold;
    resizeGold(src, dst_gold, coeff, coeff, interpolation);

    EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-2 : 1.0);
}

INSTANTIATE_TEST_CASE_P(CUDA_Warping, Resize, testing::Combine(
    ALL_DEVICES,
    DIFFERENT_SIZES,
    testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
    testing::Values(0.3, 0.5, 1.5, 2.0),
    testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
    WHOLE_SUBMAT));

/////////////////

PARAM_TEST_CASE(ResizeSameAsHost, cv::cuda::DeviceInfo, cv::Size, MatType, double, Interpolation, UseRoi)
{
    cv::cuda::DeviceInfo devInfo;
    cv::Size size;
    double coeff;
    int interpolation;
    int type;
    bool useRoi;

    virtual void SetUp()
    {
        devInfo = GET_PARAM(0);
        size = GET_PARAM(1);
        type = GET_PARAM(2);
        coeff = GET_PARAM(3);
        interpolation = GET_PARAM(4);
        useRoi = GET_PARAM(5);

        cv::cuda::setDevice(devInfo.deviceID());
    }
};

// downscaling only: used for classifiers
CUDA_TEST_P(ResizeSameAsHost, Accuracy)
{
    cv::Mat src = randomMat(size, type);

    cv::cuda::GpuMat dst = createMat(cv::Size(cv::saturate_cast<int>(src.cols * coeff), cv::saturate_cast<int>(src.rows * coeff)), type, useRoi);
    cv::cuda::resize(loadMat(src, useRoi), dst, cv::Size(), coeff, coeff, interpolation);

    cv::Mat dst_gold;
    cv::resize(src, dst_gold, cv::Size(), coeff, coeff, interpolation);

    EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-2 : 1.0);
}

INSTANTIATE_TEST_CASE_P(CUDA_Warping, ResizeSameAsHost, testing::Combine(
    ALL_DEVICES,
    DIFFERENT_SIZES,
    testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
    testing::Values(0.3, 0.5),
    testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_AREA)),
    WHOLE_SUBMAT));

#endif // HAVE_CUDA