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#include "test_precomp.hpp"

#ifdef HAVE_CUDA

//#define DUMP

//////////////////////////////////////////////////////
// BroxOpticalFlow

#define BROX_OPTICAL_FLOW_DUMP_FILE            "opticalflow/brox_optical_flow.bin"
#define BROX_OPTICAL_FLOW_DUMP_FILE_CC20       "opticalflow/brox_optical_flow_cc20.bin"

struct BroxOpticalFlow : testing::TestWithParam<cv::gpu::DeviceInfo>
{
    cv::gpu::DeviceInfo devInfo;

    virtual void SetUp()
    {
        devInfo = GetParam();

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

TEST_P(BroxOpticalFlow, Regression)
{
    try
    {
        cv::Mat frame0 = readImageType("opticalflow/frame0.png", CV_32FC1);
        ASSERT_FALSE(frame0.empty());

        cv::Mat frame1 = readImageType("opticalflow/frame1.png", CV_32FC1);
        ASSERT_FALSE(frame1.empty());

        cv::gpu::BroxOpticalFlow brox(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/,
                                      10 /*inner_iterations*/, 77 /*outer_iterations*/, 10 /*solver_iterations*/);

        cv::gpu::GpuMat u;
        cv::gpu::GpuMat v;
        brox(loadMat(frame0), loadMat(frame1), u, v);

    #ifndef DUMP
        std::string fname(cvtest::TS::ptr()->get_data_path());
        if (devInfo.majorVersion() >= 2)
            fname += BROX_OPTICAL_FLOW_DUMP_FILE_CC20;
        else
            fname += BROX_OPTICAL_FLOW_DUMP_FILE;

        std::ifstream f(fname.c_str(), std::ios_base::binary);

        int rows, cols;

        f.read((char*)&rows, sizeof(rows));
        f.read((char*)&cols, sizeof(cols));

        cv::Mat u_gold(rows, cols, CV_32FC1);

        for (int i = 0; i < u_gold.rows; ++i)
            f.read(u_gold.ptr<char>(i), u_gold.cols * sizeof(float));

        cv::Mat v_gold(rows, cols, CV_32FC1);

        for (int i = 0; i < v_gold.rows; ++i)
            f.read(v_gold.ptr<char>(i), v_gold.cols * sizeof(float));

        EXPECT_MAT_NEAR(u_gold, u, 0);
        EXPECT_MAT_NEAR(v_gold, v, 0);
    #else
        std::string fname(cvtest::TS::ptr()->get_data_path());
        if (devInfo.majorVersion() >= 2)
            fname += BROX_OPTICAL_FLOW_DUMP_FILE_CC20;
        else
            fname += BROX_OPTICAL_FLOW_DUMP_FILE;

        std::ofstream f(fname.c_str(), std::ios_base::binary);

        f.write((char*)&u.rows, sizeof(u.rows));
        f.write((char*)&u.cols, sizeof(u.cols));

        cv::Mat h_u(u);
        cv::Mat h_v(v);

        for (int i = 0; i < u.rows; ++i)
            f.write(h_u.ptr<char>(i), u.cols * sizeof(float));

        for (int i = 0; i < v.rows; ++i)
            f.write(h_v.ptr<char>(i), v.cols * sizeof(float));
    #endif
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

INSTANTIATE_TEST_CASE_P(GPU_Video, BroxOpticalFlow, ALL_DEVICES);

//////////////////////////////////////////////////////
// GoodFeaturesToTrack

IMPLEMENT_PARAM_CLASS(MinDistance, double)

PARAM_TEST_CASE(GoodFeaturesToTrack, cv::gpu::DeviceInfo, MinDistance)
{
    cv::gpu::DeviceInfo devInfo;
    double minDistance;

    virtual void SetUp()
    {
        devInfo = GET_PARAM(0);
        minDistance = GET_PARAM(1);

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

TEST_P(GoodFeaturesToTrack, Accuracy)
{
    try
    {
        cv::Mat image = readImage("opticalflow/frame0.png", cv::IMREAD_GRAYSCALE);
        ASSERT_FALSE(image.empty());

        int maxCorners = 1000;
        double qualityLevel = 0.01;

        cv::gpu::GoodFeaturesToTrackDetector_GPU detector(maxCorners, qualityLevel, minDistance);

        cv::gpu::GpuMat d_pts;
        detector(loadMat(image), d_pts);

        std::vector<cv::Point2f> pts(d_pts.cols);
        cv::Mat pts_mat(1, d_pts.cols, CV_32FC2, (void*)&pts[0]);
        d_pts.download(pts_mat);

        std::vector<cv::Point2f> pts_gold;
        cv::goodFeaturesToTrack(image, pts_gold, maxCorners, qualityLevel, minDistance);

        ASSERT_EQ(pts_gold.size(), pts.size());

        size_t mistmatch = 0;
        for (size_t i = 0; i < pts.size(); ++i)
        {
            cv::Point2i a = pts_gold[i];
            cv::Point2i b = pts[i];

            bool eq = std::abs(a.x - b.x) < 1 && std::abs(a.y - b.y) < 1;

            if (!eq)
                ++mistmatch;
        }

        double bad_ratio = static_cast<double>(mistmatch) / pts.size();

        ASSERT_LE(bad_ratio, 0.01);
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

TEST_P(GoodFeaturesToTrack, EmptyCorners)
{
    try
    {
        int maxCorners = 1000;
        double qualityLevel = 0.01;

        cv::gpu::GoodFeaturesToTrackDetector_GPU detector(maxCorners, qualityLevel, minDistance);

        cv::gpu::GpuMat src(100, 100, CV_8UC1, cv::Scalar::all(0));
        cv::gpu::GpuMat corners(1, maxCorners, CV_32FC2);

        detector(src, corners);

        ASSERT_TRUE( corners.empty() );
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

INSTANTIATE_TEST_CASE_P(GPU_Video, GoodFeaturesToTrack, testing::Combine(
    ALL_DEVICES,
    testing::Values(MinDistance(0.0), MinDistance(3.0))));

//////////////////////////////////////////////////////
// PyrLKOpticalFlow

IMPLEMENT_PARAM_CLASS(UseGray, bool)

PARAM_TEST_CASE(PyrLKOpticalFlow, cv::gpu::DeviceInfo, UseGray)
{
    cv::gpu::DeviceInfo devInfo;
    bool useGray;

    virtual void SetUp()
    {
        devInfo = GET_PARAM(0);
        useGray = GET_PARAM(1);

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

TEST_P(PyrLKOpticalFlow, Sparse)
{
    try
    {
        cv::Mat frame0 = readImage("opticalflow/frame0.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
        ASSERT_FALSE(frame0.empty());

        cv::Mat frame1 = readImage("opticalflow/frame1.png", useGray ? cv::IMREAD_GRAYSCALE : cv::IMREAD_COLOR);
        ASSERT_FALSE(frame1.empty());

        cv::Mat gray_frame;
        if (useGray)
            gray_frame = frame0;
        else
            cv::cvtColor(frame0, gray_frame, cv::COLOR_BGR2GRAY);

        std::vector<cv::Point2f> pts;
        cv::goodFeaturesToTrack(gray_frame, pts, 1000, 0.01, 0.0);

        cv::gpu::GpuMat d_pts;
        cv::Mat pts_mat(1, (int)pts.size(), CV_32FC2, (void*)&pts[0]);
        d_pts.upload(pts_mat);

        cv::gpu::PyrLKOpticalFlow pyrLK;

        cv::gpu::GpuMat d_nextPts;
        cv::gpu::GpuMat d_status;
        pyrLK.sparse(loadMat(frame0), loadMat(frame1), d_pts, d_nextPts, d_status);

        std::vector<cv::Point2f> nextPts(d_nextPts.cols);
        cv::Mat nextPts_mat(1, d_nextPts.cols, CV_32FC2, (void*)&nextPts[0]);
        d_nextPts.download(nextPts_mat);

        std::vector<unsigned char> status(d_status.cols);
        cv::Mat status_mat(1, d_status.cols, CV_8UC1, (void*)&status[0]);
        d_status.download(status_mat);

        std::vector<cv::Point2f> nextPts_gold;
        std::vector<unsigned char> status_gold;
        cv::calcOpticalFlowPyrLK(frame0, frame1, pts, nextPts_gold, status_gold, cv::noArray());

        ASSERT_EQ(nextPts_gold.size(), nextPts.size());
        ASSERT_EQ(status_gold.size(), status.size());

        size_t mistmatch = 0;
        for (size_t i = 0; i < nextPts.size(); ++i)
        {
            cv::Point2i a = nextPts[i];
            cv::Point2i b = nextPts_gold[i];

            if (status[i] != status_gold[i])
            {
                ++mistmatch;
                continue;
            }

            if (status[i])
            {
                bool eq = std::abs(a.x - b.x) <= 1 && std::abs(a.y - b.y) <= 1;

                if (!eq)
                    ++mistmatch;
            }
        }

        double bad_ratio = static_cast<double>(mistmatch) / nextPts.size();

        ASSERT_LE(bad_ratio, 0.01);
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

INSTANTIATE_TEST_CASE_P(GPU_Video, PyrLKOpticalFlow, testing::Combine(
    ALL_DEVICES,
    testing::Values(UseGray(true), UseGray(false))));

//////////////////////////////////////////////////////
// FarnebackOpticalFlow

IMPLEMENT_PARAM_CLASS(PyrScale, double)
IMPLEMENT_PARAM_CLASS(PolyN, int)
CV_FLAGS(FarnebackOptFlowFlags, 0, cv::OPTFLOW_FARNEBACK_GAUSSIAN)
IMPLEMENT_PARAM_CLASS(UseInitFlow, bool)

PARAM_TEST_CASE(FarnebackOpticalFlow, cv::gpu::DeviceInfo, PyrScale, PolyN, FarnebackOptFlowFlags, UseInitFlow)
{
    cv::gpu::DeviceInfo devInfo;
    double pyrScale;
    int polyN;
    int flags;
    bool useInitFlow;

    virtual void SetUp()
    {
        devInfo = GET_PARAM(0);
        pyrScale = GET_PARAM(1);
        polyN = GET_PARAM(2);
        flags = GET_PARAM(3);
        useInitFlow = GET_PARAM(4);

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

TEST_P(FarnebackOpticalFlow, Accuracy)
{
    try
    {
        cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
        ASSERT_FALSE(frame0.empty());

        cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
        ASSERT_FALSE(frame1.empty());

        double polySigma = polyN <= 5 ? 1.1 : 1.5;

        cv::gpu::FarnebackOpticalFlow calc;
        calc.pyrScale = pyrScale;
        calc.polyN = polyN;
        calc.polySigma = polySigma;
        calc.flags = flags;

        cv::gpu::GpuMat d_flowx, d_flowy;
        calc(loadMat(frame0), loadMat(frame1), d_flowx, d_flowy);

        cv::Mat flow;
        if (useInitFlow)
        {
            cv::Mat flowxy[] = {cv::Mat(d_flowx), cv::Mat(d_flowy)};
            cv::merge(flowxy, 2, flow);
        }

        if (useInitFlow)
        {
            calc.flags |= cv::OPTFLOW_USE_INITIAL_FLOW;
            calc(loadMat(frame0), loadMat(frame1), d_flowx, d_flowy);
        }

        cv::calcOpticalFlowFarneback(
            frame0, frame1, flow, calc.pyrScale, calc.numLevels, calc.winSize,
            calc.numIters,  calc.polyN, calc.polySigma, calc.flags);

        std::vector<cv::Mat> flowxy;
        cv::split(flow, flowxy);

        EXPECT_MAT_SIMILAR(flowxy[0], d_flowx, 0.1);
        EXPECT_MAT_SIMILAR(flowxy[1], d_flowy, 0.1);
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

INSTANTIATE_TEST_CASE_P(GPU_Video, FarnebackOpticalFlow, testing::Combine(
    ALL_DEVICES,
    testing::Values(PyrScale(0.3), PyrScale(0.5), PyrScale(0.8)),
    testing::Values(PolyN(5), PolyN(7)),
    testing::Values(FarnebackOptFlowFlags(0), FarnebackOptFlowFlags(cv::OPTFLOW_FARNEBACK_GAUSSIAN)),
    testing::Values(UseInitFlow(false), UseInitFlow(true))));

struct OpticalFlowNan : public BroxOpticalFlow {};

TEST_P(OpticalFlowNan, Regression)
{
    try
    {
        cv::Mat frame0 = readImageType("opticalflow/frame0.png", CV_32FC1);
        ASSERT_FALSE(frame0.empty());
        cv::Mat r_frame0, r_frame1;
        cv::resize(frame0, r_frame0, cv::Size(1380,1000));

        cv::Mat frame1 = readImageType("opticalflow/frame1.png", CV_32FC1);
        ASSERT_FALSE(frame1.empty());
        cv::resize(frame1, r_frame1, cv::Size(1380,1000));

        cv::gpu::BroxOpticalFlow brox(0.197f /*alpha*/, 50.0f /*gamma*/, 0.8f /*scale_factor*/,
                                      5 /*inner_iterations*/, 150 /*outer_iterations*/, 10 /*solver_iterations*/);

        cv::gpu::GpuMat u;
        cv::gpu::GpuMat v;
        brox(loadMat(r_frame0), loadMat(r_frame1), u, v);

        cv::Mat h_u, h_v;
        u.download(h_u);
        v.download(h_v);
        EXPECT_TRUE(cv::checkRange(h_u));
        EXPECT_TRUE(cv::checkRange(h_v));
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
};

INSTANTIATE_TEST_CASE_P(GPU_Video, OpticalFlowNan, ALL_DEVICES);

//////////////////////////////////////////////////////
// OpticalFlowDual_TVL1

PARAM_TEST_CASE(OpticalFlowDual_TVL1, cv::gpu::DeviceInfo, UseRoi)
{
};

TEST_P(OpticalFlowDual_TVL1, Accuracy)
{
    try
    {
        cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
        cv::gpu::setDevice(devInfo.deviceID());

        const bool useRoi = GET_PARAM(1);

        cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
        ASSERT_FALSE(frame0.empty());

        cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
        ASSERT_FALSE(frame1.empty());

        cv::gpu::OpticalFlowDual_TVL1_GPU d_alg;
        cv::gpu::GpuMat d_flowx = createMat(frame0.size(), CV_32FC1, useRoi);
        cv::gpu::GpuMat d_flowy = createMat(frame0.size(), CV_32FC1, useRoi);
        d_alg(loadMat(frame0, useRoi), loadMat(frame1, useRoi), d_flowx, d_flowy);

        cv::OpticalFlowDual_TVL1 alg;
        cv::Mat flow;
        alg(frame0, frame1, flow);
        cv::Mat gold[2];
        cv::split(flow, gold);

        EXPECT_MAT_SIMILAR(gold[0], d_flowx, 3e-3);
        EXPECT_MAT_SIMILAR(gold[1], d_flowy, 3e-3);
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

INSTANTIATE_TEST_CASE_P(GPU_Video, OpticalFlowDual_TVL1, testing::Combine(
    ALL_DEVICES,
    WHOLE_SUBMAT));

//////////////////////////////////////////////////////
// OpticalFlowBM

void calcOpticalFlowBM(const cv::Mat& prev, const cv::Mat& curr,
                       cv::Size bSize, cv::Size shiftSize, cv::Size maxRange, int usePrevious,
                       cv::Mat& velx, cv::Mat& vely)
{
    cv::Size sz((curr.cols - bSize.width + shiftSize.width)/shiftSize.width, (curr.rows - bSize.height + shiftSize.height)/shiftSize.height);

    velx.create(sz, CV_32FC1);
    vely.create(sz, CV_32FC1);

    CvMat cvprev = prev;
    CvMat cvcurr = curr;

    CvMat cvvelx = velx;
    CvMat cvvely = vely;

    cvCalcOpticalFlowBM(&cvprev, &cvcurr, bSize, shiftSize, maxRange, usePrevious, &cvvelx, &cvvely);
}

struct OpticalFlowBM : testing::TestWithParam<cv::gpu::DeviceInfo>
{
};

TEST_P(OpticalFlowBM, Accuracy)
{
    try
    {
        cv::gpu::DeviceInfo devInfo = GetParam();
        cv::gpu::setDevice(devInfo.deviceID());

        cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
        ASSERT_FALSE(frame0.empty());

        cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
        ASSERT_FALSE(frame1.empty());

        cv::Size block_size(16, 16);
        cv::Size shift_size(1, 1);
        cv::Size max_range(16, 16);

        cv::gpu::GpuMat d_velx, d_vely, buf;
        cv::gpu::calcOpticalFlowBM(loadMat(frame0), loadMat(frame1),
                                   block_size, shift_size, max_range, false,
                                   d_velx, d_vely, buf);

        cv::Mat velx, vely;
        calcOpticalFlowBM(frame0, frame1, block_size, shift_size, max_range, false, velx, vely);

        EXPECT_MAT_NEAR(velx, d_velx, 0);
        EXPECT_MAT_NEAR(vely, d_vely, 0);
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

INSTANTIATE_TEST_CASE_P(GPU_Video, OpticalFlowBM, ALL_DEVICES);

//////////////////////////////////////////////////////
// FastOpticalFlowBM

static void FastOpticalFlowBM_gold(const cv::Mat_<uchar>& I0, const cv::Mat_<uchar>& I1, cv::Mat_<float>& velx, cv::Mat_<float>& vely, int search_window, int block_window)
{
    velx.create(I0.size());
    vely.create(I0.size());

    int search_radius = search_window / 2;
    int block_radius = block_window / 2;

    for (int y = 0; y < I0.rows; ++y)
    {
        for (int x = 0; x < I0.cols; ++x)
        {
            int bestDist = std::numeric_limits<int>::max();
            int bestDx = 0;
            int bestDy = 0;

            for (int dy = -search_radius; dy <= search_radius; ++dy)
            {
                for (int dx = -search_radius; dx <= search_radius; ++dx)
                {
                    int dist = 0;

                    for (int by = -block_radius; by <= block_radius; ++by)
                    {
                        for (int bx = -block_radius; bx <= block_radius; ++bx)
                        {
                            int I0_val = I0(cv::borderInterpolate(y + by, I0.rows, cv::BORDER_DEFAULT), cv::borderInterpolate(x + bx, I0.cols, cv::BORDER_DEFAULT));
                            int I1_val = I1(cv::borderInterpolate(y + dy + by, I0.rows, cv::BORDER_DEFAULT), cv::borderInterpolate(x + dx + bx, I0.cols, cv::BORDER_DEFAULT));

                            dist += std::abs(I0_val - I1_val);
                        }
                    }

                    if (dist < bestDist)
                    {
                        bestDist = dist;
                        bestDx = dx;
                        bestDy = dy;
                    }
                }
            }

            velx(y, x) = (float) bestDx;
            vely(y, x) = (float) bestDy;
        }
    }
}

static double calc_rmse(const cv::Mat_<float>& flow1, const cv::Mat_<float>& flow2)
{
    double sum = 0.0;

    for (int y = 0; y < flow1.rows; ++y)
    {
        for (int x = 0; x < flow1.cols; ++x)
        {
            double diff = flow1(y, x) - flow2(y, x);
            sum += diff * diff;
        }
    }

    return std::sqrt(sum / flow1.size().area());
}

struct FastOpticalFlowBM : testing::TestWithParam<cv::gpu::DeviceInfo>
{
};

TEST_P(FastOpticalFlowBM, Accuracy)
{
    try
    {
        const double MAX_RMSE = 0.6;

        int search_window = 15;
        int block_window = 5;

        cv::gpu::DeviceInfo devInfo = GetParam();
        cv::gpu::setDevice(devInfo.deviceID());

        cv::Mat frame0 = readImage("opticalflow/rubberwhale1.png", cv::IMREAD_GRAYSCALE);
        ASSERT_FALSE(frame0.empty());

        cv::Mat frame1 = readImage("opticalflow/rubberwhale2.png", cv::IMREAD_GRAYSCALE);
        ASSERT_FALSE(frame1.empty());

        cv::Size smallSize(320, 240);
        cv::Mat frame0_small;
        cv::Mat frame1_small;

        cv::resize(frame0, frame0_small, smallSize);
        cv::resize(frame1, frame1_small, smallSize);

        cv::gpu::GpuMat d_flowx;
        cv::gpu::GpuMat d_flowy;
        cv::gpu::FastOpticalFlowBM fastBM;

        fastBM(loadMat(frame0_small), loadMat(frame1_small), d_flowx, d_flowy, search_window, block_window);

        cv::Mat_<float> flowx;
        cv::Mat_<float> flowy;
        FastOpticalFlowBM_gold(frame0_small, frame1_small, flowx, flowy, search_window, block_window);

        double err;

        err = calc_rmse(flowx, cv::Mat(d_flowx));
        EXPECT_LE(err, MAX_RMSE);

        err = calc_rmse(flowy, cv::Mat(d_flowy));
        EXPECT_LE(err, MAX_RMSE);
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

INSTANTIATE_TEST_CASE_P(GPU_Video, FastOpticalFlowBM, ALL_DEVICES);

//////////////////////////////////////////////////////
// FGDStatModel

namespace cv
{
    template<> void Ptr<CvBGStatModel>::delete_obj()
    {
        cvReleaseBGStatModel(&obj);
    }
}

PARAM_TEST_CASE(FGDStatModel, cv::gpu::DeviceInfo, std::string, Channels)
{
    cv::gpu::DeviceInfo devInfo;
    std::string inputFile;
    int out_cn;

    virtual void SetUp()
    {
        devInfo = GET_PARAM(0);
        cv::gpu::setDevice(devInfo.deviceID());

        inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1);

        out_cn = GET_PARAM(2);
    }
};

TEST_P(FGDStatModel, Update)
{
    try
    {
        cv::VideoCapture cap(inputFile);
        ASSERT_TRUE(cap.isOpened());

        cv::Mat frame;
        cap >> frame;
        ASSERT_FALSE(frame.empty());

        IplImage ipl_frame = frame;
        cv::Ptr<CvBGStatModel> model(cvCreateFGDStatModel(&ipl_frame));

        cv::gpu::GpuMat d_frame(frame);
        cv::gpu::FGDStatModel d_model(out_cn);
        d_model.create(d_frame);

        cv::Mat h_background;
        cv::Mat h_foreground;
        cv::Mat h_background3;

        cv::Mat backgroundDiff;
        cv::Mat foregroundDiff;

        for (int i = 0; i < 5; ++i)
        {
            cap >> frame;
            ASSERT_FALSE(frame.empty());

            ipl_frame = frame;
            int gold_count = cvUpdateBGStatModel(&ipl_frame, model);

            d_frame.upload(frame);

            int count = d_model.update(d_frame);

            ASSERT_EQ(gold_count, count);

            cv::Mat gold_background(model->background);
            cv::Mat gold_foreground(model->foreground);

            if (out_cn == 3)
                d_model.background.download(h_background3);
            else
            {
                d_model.background.download(h_background);
                cv::cvtColor(h_background, h_background3, cv::COLOR_BGRA2BGR);
            }
            d_model.foreground.download(h_foreground);

            ASSERT_MAT_NEAR(gold_background, h_background3, 1.0);
            ASSERT_MAT_NEAR(gold_foreground, h_foreground, 0.0);
        }
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

INSTANTIATE_TEST_CASE_P(GPU_Video, FGDStatModel, testing::Combine(
    ALL_DEVICES,
    testing::Values(std::string("768x576.avi")),
    testing::Values(Channels(3), Channels(4))));

//////////////////////////////////////////////////////
// MOG

IMPLEMENT_PARAM_CLASS(LearningRate, double)

PARAM_TEST_CASE(MOG, cv::gpu::DeviceInfo, std::string, UseGray, LearningRate, UseRoi)
{
    cv::gpu::DeviceInfo devInfo;
    std::string inputFile;
    bool useGray;
    double learningRate;
    bool useRoi;

    virtual void SetUp()
    {
        devInfo = GET_PARAM(0);
        cv::gpu::setDevice(devInfo.deviceID());

        inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1);

        useGray = GET_PARAM(2);

        learningRate = GET_PARAM(3);

        useRoi = GET_PARAM(4);
    }
};

TEST_P(MOG, Update)
{
    try
    {
        cv::VideoCapture cap(inputFile);
        ASSERT_TRUE(cap.isOpened());

        cv::Mat frame;
        cap >> frame;
        ASSERT_FALSE(frame.empty());

        cv::gpu::MOG_GPU mog;
        cv::gpu::GpuMat foreground = createMat(frame.size(), CV_8UC1, useRoi);

        cv::BackgroundSubtractorMOG mog_gold;
        cv::Mat foreground_gold;

        for (int i = 0; i < 10; ++i)
        {
            cap >> frame;
            ASSERT_FALSE(frame.empty());

            if (useGray)
            {
                cv::Mat temp;
                cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
                cv::swap(temp, frame);
            }

            mog(loadMat(frame, useRoi), foreground, (float)learningRate);

            mog_gold(frame, foreground_gold, learningRate);

            ASSERT_MAT_NEAR(foreground_gold, foreground, 0.0);
        }
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

INSTANTIATE_TEST_CASE_P(GPU_Video, MOG, testing::Combine(
    ALL_DEVICES,
    testing::Values(std::string("768x576.avi")),
    testing::Values(UseGray(true), UseGray(false)),
    testing::Values(LearningRate(0.0), LearningRate(0.01)),
    WHOLE_SUBMAT));

//////////////////////////////////////////////////////
// MOG2

PARAM_TEST_CASE(MOG2, cv::gpu::DeviceInfo, std::string, UseGray, UseRoi)
{
    cv::gpu::DeviceInfo devInfo;
    std::string inputFile;
    bool useGray;
    bool useRoi;

    virtual void SetUp()
    {
        devInfo = GET_PARAM(0);
        cv::gpu::setDevice(devInfo.deviceID());

        inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1);

        useGray = GET_PARAM(2);

        useRoi = GET_PARAM(3);
    }
};

TEST_P(MOG2, Update)
{
    try
    {
        cv::VideoCapture cap(inputFile);
        ASSERT_TRUE(cap.isOpened());

        cv::Mat frame;
        cap >> frame;
        ASSERT_FALSE(frame.empty());

        cv::gpu::MOG2_GPU mog2;
        cv::gpu::GpuMat foreground = createMat(frame.size(), CV_8UC1, useRoi);

        cv::BackgroundSubtractorMOG2 mog2_gold;
        cv::Mat foreground_gold;

        for (int i = 0; i < 10; ++i)
        {
            cap >> frame;
            ASSERT_FALSE(frame.empty());

            if (useGray)
            {
                cv::Mat temp;
                cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
                cv::swap(temp, frame);
            }

            mog2(loadMat(frame, useRoi), foreground);

            mog2_gold(frame, foreground_gold);

            double norm = cv::norm(foreground_gold, cv::Mat(foreground), cv::NORM_L1);

            norm /= foreground_gold.size().area();

            ASSERT_LE(norm, 0.09);
        }
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

TEST_P(MOG2, getBackgroundImage)
{
    if (useGray)
        return;

    try
    {
        cv::VideoCapture cap(inputFile);
        ASSERT_TRUE(cap.isOpened());

        cv::Mat frame;

        cv::gpu::MOG2_GPU mog2;
        cv::gpu::GpuMat foreground;

        cv::BackgroundSubtractorMOG2 mog2_gold;
        cv::Mat foreground_gold;

        for (int i = 0; i < 10; ++i)
        {
            cap >> frame;
            ASSERT_FALSE(frame.empty());

            mog2(loadMat(frame, useRoi), foreground);

            mog2_gold(frame, foreground_gold);
        }

        cv::gpu::GpuMat background = createMat(frame.size(), frame.type(), useRoi);
        mog2.getBackgroundImage(background);

        cv::Mat background_gold;
        mog2_gold.getBackgroundImage(background_gold);

        ASSERT_MAT_NEAR(background_gold, background, 0);
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

INSTANTIATE_TEST_CASE_P(GPU_Video, MOG2, testing::Combine(
    ALL_DEVICES,
    testing::Values(std::string("768x576.avi")),
    testing::Values(UseGray(true), UseGray(false)),
    WHOLE_SUBMAT));

//////////////////////////////////////////////////////
// VIBE

PARAM_TEST_CASE(VIBE, cv::gpu::DeviceInfo, cv::Size, MatType, UseRoi)
{
};

TEST_P(VIBE, Accuracy)
{
    try
    {
        const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
        cv::gpu::setDevice(devInfo.deviceID());
        const cv::Size size = GET_PARAM(1);
        const int type = GET_PARAM(2);
        const bool useRoi = GET_PARAM(3);

        const cv::Mat fullfg(size, CV_8UC1, cv::Scalar::all(255));

        cv::Mat frame = randomMat(size, type, 0.0, 100);
        cv::gpu::GpuMat d_frame = loadMat(frame, useRoi);

        cv::gpu::VIBE_GPU vibe;
        cv::gpu::GpuMat d_fgmask = createMat(size, CV_8UC1, useRoi);
        vibe.initialize(d_frame);

        for (int i = 0; i < 20; ++i)
            vibe(d_frame, d_fgmask);

        frame = randomMat(size, type, 160, 255);
        d_frame = loadMat(frame, useRoi);
        vibe(d_frame, d_fgmask);

        // now fgmask should be entirely foreground
        ASSERT_MAT_NEAR(fullfg, d_fgmask, 0);
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

INSTANTIATE_TEST_CASE_P(GPU_Video, VIBE, testing::Combine(
    ALL_DEVICES,
    DIFFERENT_SIZES,
    testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4)),
    WHOLE_SUBMAT));

//////////////////////////////////////////////////////
// GMG

PARAM_TEST_CASE(GMG, cv::gpu::DeviceInfo, cv::Size, MatDepth, Channels, UseRoi)
{
};

TEST_P(GMG, Accuracy)
{
    try
    {
        const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
        cv::gpu::setDevice(devInfo.deviceID());
        const cv::Size size = GET_PARAM(1);
        const int depth = GET_PARAM(2);
        const int channels = GET_PARAM(3);
        const bool useRoi = GET_PARAM(4);

        const int type = CV_MAKE_TYPE(depth, channels);

        const cv::Mat zeros(size, CV_8UC1, cv::Scalar::all(0));
        const cv::Mat fullfg(size, CV_8UC1, cv::Scalar::all(255));

        cv::Mat frame = randomMat(size, type, 0, 100);
        cv::gpu::GpuMat d_frame = loadMat(frame, useRoi);

        cv::gpu::GMG_GPU gmg;
        gmg.numInitializationFrames = 5;
        gmg.smoothingRadius = 0;
        gmg.initialize(d_frame.size(), 0, 255);

        cv::gpu::GpuMat d_fgmask = createMat(size, CV_8UC1, useRoi);

        for (int i = 0; i < gmg.numInitializationFrames; ++i)
        {
            gmg(d_frame, d_fgmask);

            // fgmask should be entirely background during training
            ASSERT_MAT_NEAR(zeros, d_fgmask, 0);
        }

        frame = randomMat(size, type, 160, 255);
        d_frame = loadMat(frame, useRoi);
        gmg(d_frame, d_fgmask);

        // now fgmask should be entirely foreground
        ASSERT_MAT_NEAR(fullfg, d_fgmask, 0);
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

INSTANTIATE_TEST_CASE_P(GPU_Video, GMG, testing::Combine(
    ALL_DEVICES,
    DIFFERENT_SIZES,
    testing::Values(MatType(CV_8U), MatType(CV_16U), MatType(CV_32F)),
    testing::Values(Channels(1), Channels(3), Channels(4)),
    WHOLE_SUBMAT));

#ifdef WIN32

//////////////////////////////////////////////////////
// VideoWriter

PARAM_TEST_CASE(VideoWriter, cv::gpu::DeviceInfo, std::string)
{
    cv::gpu::DeviceInfo devInfo;
    std::string inputFile;

    std::string outputFile;

    virtual void SetUp()
    {
        devInfo = GET_PARAM(0);
        inputFile = GET_PARAM(1);

        cv::gpu::setDevice(devInfo.deviceID());

        inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + inputFile;
        outputFile = cv::tempfile(".avi");
    }
};

TEST_P(VideoWriter, Regression)
{
    try
    {
        const double FPS = 25.0;

        cv::VideoCapture reader(inputFile);
        ASSERT_TRUE( reader.isOpened() );

        cv::gpu::VideoWriter_GPU d_writer;

        cv::Mat frame;
        cv::gpu::GpuMat d_frame;

        for (int i = 0; i < 10; ++i)
        {
            reader >> frame;
            ASSERT_FALSE(frame.empty());

            d_frame.upload(frame);

            if (!d_writer.isOpened())
                d_writer.open(outputFile, frame.size(), FPS);

            d_writer.write(d_frame);
        }

        reader.release();
        d_writer.close();

        reader.open(outputFile);
        ASSERT_TRUE( reader.isOpened() );

        for (int i = 0; i < 5; ++i)
        {
            reader >> frame;
            ASSERT_FALSE( frame.empty() );
        }
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

INSTANTIATE_TEST_CASE_P(GPU_Video, VideoWriter, testing::Combine(
    ALL_DEVICES,
    testing::Values(std::string("768x576.avi"), std::string("1920x1080.avi"))));

//////////////////////////////////////////////////////
// VideoReader

PARAM_TEST_CASE(VideoReader, cv::gpu::DeviceInfo, std::string)
{
    cv::gpu::DeviceInfo devInfo;
    std::string inputFile;

    virtual void SetUp()
    {
        devInfo = GET_PARAM(0);
        inputFile = GET_PARAM(1);

        cv::gpu::setDevice(devInfo.deviceID());

        inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + inputFile;
    }
};

TEST_P(VideoReader, Regression)
{
    try
    {
        cv::gpu::VideoReader_GPU reader(inputFile);
        ASSERT_TRUE( reader.isOpened() );

        cv::gpu::GpuMat frame;

        for (int i = 0; i < 10; ++i)
        {
            ASSERT_TRUE( reader.read(frame) );
            ASSERT_FALSE( frame.empty() );
        }

        reader.close();
        ASSERT_FALSE( reader.isOpened() );
    }
    catch (...)
    {
        cv::gpu::resetDevice();
        throw;
    }
}

INSTANTIATE_TEST_CASE_P(GPU_Video, VideoReader, testing::Combine(
    ALL_DEVICES,
    testing::Values(std::string("768x576.avi"), std::string("1920x1080.avi"))));

#endif // WIN32

#endif // HAVE_CUDA