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#include "precomp.hpp"
#include <opencv2/highgui/highgui.hpp>

#define MIN_SIZE 32

#define S(x) StreamAccessor::getStream(x)

// GPU resize() is fast, but it differs from the CPU analog. Disabling this flag
// leads to an inefficient code. It's for debug purposes only.
#define ENABLE_GPU_RESIZE 1

using namespace std;
using namespace cv;
using namespace cv::gpu;

#if !defined(HAVE_CUDA)

void cv::gpu::FarnebackOpticalFlow::operator ()(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }

#else

namespace cv { namespace gpu { namespace device { namespace optflow_farneback
{
    void setPolynomialExpansionConsts(
            int polyN, const float *g, const float *xg, const float *xxg,
            float ig11, float ig03, float ig33, float ig55);

    void polynomialExpansionGpu(const DevMem2Df &src, int polyN, DevMem2Df dst, cudaStream_t stream);

    void setUpdateMatricesConsts();

    void updateMatricesGpu(
            const DevMem2Df flowx, const DevMem2Df flowy, const DevMem2Df R0, const DevMem2Df R1,
            DevMem2Df M, cudaStream_t stream);

    void updateFlowGpu(
            const DevMem2Df M, DevMem2Df flowx, DevMem2Df flowy, cudaStream_t stream);

    /*void boxFilterGpu(const DevMem2Df src, int ksizeHalf, DevMem2Df dst, cudaStream_t stream);*/

    void boxFilter5Gpu(const DevMem2Df src, int ksizeHalf, DevMem2Df dst, cudaStream_t stream);

    void setGaussianBlurKernel(const float *gKer, int ksizeHalf);

    void gaussianBlurGpu(
            const DevMem2Df src, int ksizeHalf, DevMem2Df dst, int borderType, cudaStream_t stream);

    void gaussianBlur5Gpu(
            const DevMem2Df src, int ksizeHalf, DevMem2Df dst, int borderType, cudaStream_t stream);

}}}} // namespace cv { namespace gpu { namespace device { namespace optflow_farneback


void cv::gpu::FarnebackOpticalFlow::prepareGaussian(
        int n, double sigma, float *g, float *xg, float *xxg,
        double &ig11, double &ig03, double &ig33, double &ig55)
{
    double s = 0.;
    for (int x = -n; x <= n; x++)
    {
        g[x] = (float)std::exp(-x*x/(2*sigma*sigma));
        s += g[x];
    }

    s = 1./s;
    for (int x = -n; x <= n; x++)
    {
        g[x] = (float)(g[x]*s);
        xg[x] = (float)(x*g[x]);
        xxg[x] = (float)(x*x*g[x]);
    }

    Mat_<double> G(6, 6);
    G.setTo(0);

    for (int y = -n; y <= n; y++)
    {
        for (int x = -n; x <= n; x++)
        {
            G(0,0) += g[y]*g[x];
            G(1,1) += g[y]*g[x]*x*x;
            G(3,3) += g[y]*g[x]*x*x*x*x;
            G(5,5) += g[y]*g[x]*x*x*y*y;
        }
    }

    //G[0][0] = 1.;
    G(2,2) = G(0,3) = G(0,4) = G(3,0) = G(4,0) = G(1,1);
    G(4,4) = G(3,3);
    G(3,4) = G(4,3) = G(5,5);

    // invG:
    // [ x        e  e    ]
    // [    y             ]
    // [       y          ]
    // [ e        z       ]
    // [ e           z    ]
    // [                u ]
    Mat_<double> invG = G.inv(DECOMP_CHOLESKY);

    ig11 = invG(1,1);
    ig03 = invG(0,3);
    ig33 = invG(3,3);
    ig55 = invG(5,5);
}


void cv::gpu::FarnebackOpticalFlow::setPolynomialExpansionConsts(int n, double sigma)
{
    vector<float> buf(n*6 + 3);
    float* g = &buf[0] + n;
    float* xg = g + n*2 + 1;
    float* xxg = xg + n*2 + 1;

    if (sigma < FLT_EPSILON)
        sigma = n*0.3;

    double ig11, ig03, ig33, ig55;
    prepareGaussian(n, sigma, g, xg, xxg, ig11, ig03, ig33, ig55);

    device::optflow_farneback::setPolynomialExpansionConsts(n, g, xg, xxg, static_cast<float>(ig11), static_cast<float>(ig03), static_cast<float>(ig33), static_cast<float>(ig55));
}


void cv::gpu::FarnebackOpticalFlow::updateFlow_boxFilter(
        const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat &flowy,
        GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[])
{
    device::optflow_farneback::boxFilter5Gpu(M, blockSize/2, bufM, S(streams[0]));
    swap(M, bufM);

    for (int i = 1; i < 5; ++i)
        streams[i].waitForCompletion();
    device::optflow_farneback::updateFlowGpu(M, flowx, flowy, S(streams[0]));

    if (updateMatrices)
        device::optflow_farneback::updateMatricesGpu(flowx, flowy, R0, R1, M, S(streams[0]));
}


void cv::gpu::FarnebackOpticalFlow::updateFlow_gaussianBlur(
        const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat& flowy,
        GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[])
{
    device::optflow_farneback::gaussianBlur5Gpu(
                M, blockSize/2, bufM, BORDER_REPLICATE_GPU, S(streams[0]));
    swap(M, bufM);

    device::optflow_farneback::updateFlowGpu(M, flowx, flowy, S(streams[0]));

    if (updateMatrices)
        device::optflow_farneback::updateMatricesGpu(flowx, flowy, R0, R1, M, S(streams[0]));
}


void cv::gpu::FarnebackOpticalFlow::operator ()(
        const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s)
{
    CV_Assert(frame0.type() == CV_8U && frame1.type() == CV_8U);
    CV_Assert(frame0.size() == frame1.size());
    CV_Assert(polyN == 5 || polyN == 7);
    CV_Assert(!fastPyramids || std::abs(pyrScale - 0.5) < 1e-6);

    Stream streams[5];
    if (S(s))
        streams[0] = s;

    Size size = frame0.size();
    GpuMat prevFlowX, prevFlowY, curFlowX, curFlowY;

    flowx.create(size, CV_32F);
    flowy.create(size, CV_32F);
    GpuMat flowx0 = flowx;
    GpuMat flowy0 = flowy;

    // Crop unnecessary levels
    double scale = 1;
    int numLevelsCropped = 0;
    for (; numLevelsCropped < numLevels; numLevelsCropped++)
    {
        scale *= pyrScale;
        if (size.width*scale < MIN_SIZE || size.height*scale < MIN_SIZE)
            break;
    }

    streams[0].enqueueConvert(frame0, frames_[0], CV_32F);
    streams[1].enqueueConvert(frame1, frames_[1], CV_32F);

    if (fastPyramids)
    {
        // Build Gaussian pyramids using pyrDown()
        pyramid0_.resize(numLevelsCropped + 1);
        pyramid1_.resize(numLevelsCropped + 1);
        pyramid0_[0] = frames_[0];
        pyramid1_[0] = frames_[1];
        for (int i = 1; i <= numLevelsCropped; ++i)
        {
            pyrDown(pyramid0_[i - 1], pyramid0_[i], streams[0]);
            pyrDown(pyramid1_[i - 1], pyramid1_[i], streams[1]);
        }
    }

    setPolynomialExpansionConsts(polyN, polySigma);
    device::optflow_farneback::setUpdateMatricesConsts();

    for (int k = numLevelsCropped; k >= 0; k--)
    {
        streams[0].waitForCompletion();

        scale = 1;
        for (int i = 0; i < k; i++)
            scale *= pyrScale;

        double sigma = (1./scale - 1) * 0.5;
        int smoothSize = cvRound(sigma*5) | 1;
        smoothSize = std::max(smoothSize, 3);

        int width = cvRound(size.width*scale);
        int height = cvRound(size.height*scale);

        if (fastPyramids)
        {
            width = pyramid0_[k].cols;
            height = pyramid0_[k].rows;
        }

        if (k > 0)
        {
            curFlowX.create(height, width, CV_32F);
            curFlowY.create(height, width, CV_32F);
        }
        else
        {
            curFlowX = flowx0;
            curFlowY = flowy0;
        }

        if (!prevFlowX.data)
        {
            if (flags & OPTFLOW_USE_INITIAL_FLOW)
            {
#if ENABLE_GPU_RESIZE
                resize(flowx0, curFlowX, Size(width, height), 0, 0, INTER_LINEAR, streams[0]);
                resize(flowy0, curFlowY, Size(width, height), 0, 0, INTER_LINEAR, streams[1]);
                streams[0].enqueueConvert(curFlowX, curFlowX, curFlowX.depth(), scale);
                streams[1].enqueueConvert(curFlowY, curFlowY, curFlowY.depth(), scale);
#else
                Mat tmp1, tmp2;
                flowx0.download(tmp1);
                resize(tmp1, tmp2, Size(width, height), 0, 0, INTER_AREA);
                tmp2 *= scale;
                curFlowX.upload(tmp2);
                flowy0.download(tmp1);
                resize(tmp1, tmp2, Size(width, height), 0, 0, INTER_AREA);
                tmp2 *= scale;
                curFlowY.upload(tmp2);
#endif
            }
            else
            {
                streams[0].enqueueMemSet(curFlowX, 0);
                streams[1].enqueueMemSet(curFlowY, 0);
            }
        }
        else
        {
#if ENABLE_GPU_RESIZE
            resize(prevFlowX, curFlowX, Size(width, height), 0, 0, INTER_LINEAR, streams[0]);
            resize(prevFlowY, curFlowY, Size(width, height), 0, 0, INTER_LINEAR, streams[1]);
            streams[0].enqueueConvert(curFlowX, curFlowX, curFlowX.depth(), 1./pyrScale);
            streams[1].enqueueConvert(curFlowY, curFlowY, curFlowY.depth(), 1./pyrScale);
#else
            Mat tmp1, tmp2;
            prevFlowX.download(tmp1);
            resize(tmp1, tmp2, Size(width, height), 0, 0, INTER_LINEAR);
            tmp2 *= 1./pyrScale;
            curFlowX.upload(tmp2);
            prevFlowY.download(tmp1);
            resize(tmp1, tmp2, Size(width, height), 0, 0, INTER_LINEAR);
            tmp2 *= 1./pyrScale;
            curFlowY.upload(tmp2);
#endif
        }

        GpuMat M = allocMatFromBuf(5*height, width, CV_32F, M_);
        GpuMat bufM = allocMatFromBuf(5*height, width, CV_32F, bufM_);
        GpuMat R[2] =
        {
            allocMatFromBuf(5*height, width, CV_32F, R_[0]),
            allocMatFromBuf(5*height, width, CV_32F, R_[1])
        };

        if (fastPyramids)
        {
            device::optflow_farneback::polynomialExpansionGpu(pyramid0_[k], polyN, R[0], S(streams[0]));
            device::optflow_farneback::polynomialExpansionGpu(pyramid1_[k], polyN, R[1], S(streams[1]));
        }
        else
        {
            GpuMat blurredFrame[2] =
            {
                allocMatFromBuf(size.height, size.width, CV_32F, blurredFrame_[0]),
                allocMatFromBuf(size.height, size.width, CV_32F, blurredFrame_[1])
            };
            GpuMat pyrLevel[2] =
            {
                allocMatFromBuf(height, width, CV_32F, pyrLevel_[0]),
                allocMatFromBuf(height, width, CV_32F, pyrLevel_[1])
            };

            Mat g = getGaussianKernel(smoothSize, sigma, CV_32F);
            device::optflow_farneback::setGaussianBlurKernel(g.ptr<float>(smoothSize/2), smoothSize/2);           

            for (int i = 0; i < 2; i++)
            {
                device::optflow_farneback::gaussianBlurGpu(
                        frames_[i], smoothSize/2, blurredFrame[i], BORDER_REFLECT101_GPU, S(streams[i]));
#if ENABLE_GPU_RESIZE
                resize(blurredFrame[i], pyrLevel[i], Size(width, height), INTER_LINEAR, streams[i]);
#else
                Mat tmp1, tmp2;
                tmp[i].download(tmp1);
                resize(tmp1, tmp2, Size(width, height), INTER_LINEAR);
                I[i].upload(tmp2);
#endif
                device::optflow_farneback::polynomialExpansionGpu(pyrLevel[i], polyN, R[i], S(streams[i]));
            }
        }

        streams[1].waitForCompletion();
        device::optflow_farneback::updateMatricesGpu(curFlowX, curFlowY, R[0], R[1], M, S(streams[0]));

        if (flags & OPTFLOW_FARNEBACK_GAUSSIAN)
        {
            Mat g = getGaussianKernel(winSize, winSize/2*0.3f, CV_32F);
            device::optflow_farneback::setGaussianBlurKernel(g.ptr<float>(winSize/2), winSize/2);
        }
        for (int i = 0; i < numIters; i++)
        {
            if (flags & OPTFLOW_FARNEBACK_GAUSSIAN)
                updateFlow_gaussianBlur(R[0], R[1], curFlowX, curFlowY, M, bufM, winSize, i < numIters-1, streams);
            else
                updateFlow_boxFilter(R[0], R[1], curFlowX, curFlowY, M, bufM, winSize, i < numIters-1, streams);
        }

        prevFlowX = curFlowX;
        prevFlowY = curFlowY;
    }

    flowx = curFlowX;
    flowy = curFlowY;    

    if (!S(s))
        streams[0].waitForCompletion();
}

#endif