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#include "precomp.hpp"

#ifndef HAVE_CUDA

cv::gpu::MOG_GPU::MOG_GPU(int) { throw_nogpu(); }
void cv::gpu::MOG_GPU::initialize(cv::Size, int) { throw_nogpu(); }
void cv::gpu::MOG_GPU::operator()(const cv::gpu::GpuMat&, cv::gpu::GpuMat&, float, Stream&) { throw_nogpu(); }
void cv::gpu::MOG_GPU::getBackgroundImage(GpuMat&, Stream&) const { throw_nogpu(); }
void cv::gpu::MOG_GPU::release() {}

cv::gpu::MOG2_GPU::MOG2_GPU(int) { throw_nogpu(); }
void cv::gpu::MOG2_GPU::initialize(cv::Size, int) { throw_nogpu(); }
void cv::gpu::MOG2_GPU::operator()(const GpuMat&, GpuMat&, float, Stream&) { throw_nogpu(); }
void cv::gpu::MOG2_GPU::getBackgroundImage(GpuMat&, Stream&) const { throw_nogpu(); }
void cv::gpu::MOG2_GPU::release() {}

#else

namespace cv { namespace gpu { namespace device
{
    namespace mog
    {
        void mog_gpu(DevMem2Db frame, int cn, DevMem2Db fgmask, DevMem2Df weight, DevMem2Df sortKey, DevMem2Db mean, DevMem2Db var,
                     int nmixtures, float varThreshold, float learningRate, float backgroundRatio, float noiseSigma,
                     cudaStream_t stream);
        void getBackgroundImage_gpu(int cn, DevMem2Df weight, DevMem2Db mean, DevMem2Db dst, int nmixtures, float backgroundRatio, cudaStream_t stream);

        void loadConstants(int nmixtures, float Tb, float TB, float Tg, float varInit, float varMin, float varMax, float tau, unsigned char shadowVal);
        void mog2_gpu(DevMem2Db frame, int cn, DevMem2Db fgmask, DevMem2Db modesUsed, DevMem2Df weight, DevMem2Df variance, DevMem2Db mean, float alphaT, float prune, bool detectShadows, cudaStream_t stream);
        void getBackgroundImage2_gpu(int cn, DevMem2Db modesUsed, DevMem2Df weight, DevMem2Db mean, DevMem2Db dst, cudaStream_t stream);
    }
}}}

namespace mog
{
    const int defaultNMixtures = 5;
    const int defaultHistory = 200;
    const float defaultBackgroundRatio = 0.7f;
    const float defaultVarThreshold = 2.5f * 2.5f;
    const float defaultNoiseSigma = 30.0f * 0.5f;
    const float defaultInitialWeight = 0.05f;
}

cv::gpu::MOG_GPU::MOG_GPU(int nmixtures) :
    frameSize_(0, 0), frameType_(0), nframes_(0)
{
    nmixtures_ = std::min(nmixtures > 0 ? nmixtures : mog::defaultNMixtures, 8);
    history = mog::defaultHistory;
    varThreshold = mog::defaultVarThreshold;
    backgroundRatio = mog::defaultBackgroundRatio;
    noiseSigma = mog::defaultNoiseSigma;
}

void cv::gpu::MOG_GPU::initialize(cv::Size frameSize, int frameType)
{
    CV_Assert(frameType == CV_8UC1 || frameType == CV_8UC3 || frameType == CV_8UC4);

    frameSize_ = frameSize;
    frameType_ = frameType;

    int ch = CV_MAT_CN(frameType);
    int work_ch = ch;

    // for each gaussian mixture of each pixel bg model we store
    // the mixture sort key (w/sum_of_variances), the mixture weight (w),
    // the mean (nchannels values) and
    // the diagonal covariance matrix (another nchannels values)

    weight_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
    sortKey_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
    mean_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC(work_ch));
    var_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC(work_ch));

    weight_.setTo(cv::Scalar::all(0));
    sortKey_.setTo(cv::Scalar::all(0));
    mean_.setTo(cv::Scalar::all(0));
    var_.setTo(cv::Scalar::all(0));

    nframes_ = 0;
}

void cv::gpu::MOG_GPU::operator()(const cv::gpu::GpuMat& frame, cv::gpu::GpuMat& fgmask, float learningRate, Stream& stream)
{
    using namespace cv::gpu::device::mog;

    CV_Assert(frame.depth() == CV_8U);

    int ch = frame.channels();
    int work_ch = ch;

    if (nframes_ == 0 || learningRate >= 1.0 || frame.size() != frameSize_ || work_ch != mean_.channels())
        initialize(frame.size(), frame.type());

    fgmask.create(frameSize_, CV_8UC1);

    ++nframes_;
    learningRate = learningRate >= 0.0f && nframes_ > 1 ? learningRate : 1.0f / std::min(nframes_, history);
    CV_Assert(learningRate >= 0.0f);

    mog_gpu(frame, ch, fgmask, weight_, sortKey_, mean_, var_, nmixtures_,
            varThreshold, learningRate, backgroundRatio, noiseSigma,
            StreamAccessor::getStream(stream));
}

void cv::gpu::MOG_GPU::getBackgroundImage(GpuMat& backgroundImage, Stream& stream) const
{
    using namespace cv::gpu::device::mog;

    backgroundImage.create(frameSize_, frameType_);

    getBackgroundImage_gpu(backgroundImage.channels(), weight_, mean_, backgroundImage, nmixtures_, backgroundRatio, StreamAccessor::getStream(stream));
}

void cv::gpu::MOG_GPU::release()
{
    frameSize_ = Size(0, 0);
    frameType_ = 0;
    nframes_ = 0;

    weight_.release();
    sortKey_.release();
    mean_.release();
    var_.release();
}

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

namespace mog2
{
    // default parameters of gaussian background detection algorithm
    const int defaultHistory = 500; // Learning rate; alpha = 1/defaultHistory2
    const float defaultVarThreshold = 4.0f * 4.0f;
    const int defaultNMixtures = 5; // maximal number of Gaussians in mixture
    const float defaultBackgroundRatio = 0.9f; // threshold sum of weights for background test
    const float defaultVarThresholdGen = 3.0f * 3.0f;
    const float defaultVarInit = 15.0f; // initial variance for new components
    const float defaultVarMax = 5.0f * defaultVarInit;
    const float defaultVarMin = 4.0f;

    // additional parameters
    const float defaultfCT = 0.05f; // complexity reduction prior constant 0 - no reduction of number of components
    const unsigned char defaultnShadowDetection = 127; // value to use in the segmentation mask for shadows, set 0 not to do shadow detection
    const float defaultfTau = 0.5f; // Tau - shadow threshold, see the paper for explanation
}

cv::gpu::MOG2_GPU::MOG2_GPU(int nmixtures) :
    frameSize_(0, 0), frameType_(0), nframes_(0)
{
    nmixtures_ = nmixtures > 0 ? nmixtures : mog2::defaultNMixtures;

    history = mog2::defaultHistory;
    varThreshold = mog2::defaultVarThreshold;
    bShadowDetection = true;

    backgroundRatio = mog2::defaultBackgroundRatio;
    fVarInit = mog2::defaultVarInit;
    fVarMax  = mog2::defaultVarMax;
    fVarMin = mog2::defaultVarMin;

    varThresholdGen = mog2::defaultVarThresholdGen;
    fCT = mog2::defaultfCT;
    nShadowDetection =  mog2::defaultnShadowDetection;
    fTau = mog2::defaultfTau;
}

void cv::gpu::MOG2_GPU::initialize(cv::Size frameSize, int frameType)
{
    using namespace cv::gpu::device::mog;

    CV_Assert(frameType == CV_8UC1 || frameType == CV_8UC3 || frameType == CV_8UC4);

    frameSize_ = frameSize;
    frameType_ = frameType;
    nframes_ = 0;

    int ch = CV_MAT_CN(frameType);
    int work_ch = ch;

    // for each gaussian mixture of each pixel bg model we store ...
    // the mixture weight (w),
    // the mean (nchannels values) and
    // the covariance
    weight_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
    variance_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
    mean_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC(work_ch));

    //make the array for keeping track of the used modes per pixel - all zeros at start
    bgmodelUsedModes_.create(frameSize_, CV_8UC1);
    bgmodelUsedModes_.setTo(cv::Scalar::all(0));

    loadConstants(nmixtures_, varThreshold, backgroundRatio, varThresholdGen, fVarInit, fVarMin, fVarMax, fTau, nShadowDetection);
}

void cv::gpu::MOG2_GPU::operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate, Stream& stream)
{
    using namespace cv::gpu::device::mog;

    int ch = frame.channels();
    int work_ch = ch;

    if (nframes_ == 0 || learningRate >= 1.0f || frame.size() != frameSize_ || work_ch != mean_.channels())
        initialize(frame.size(), frame.type());

    fgmask.create(frameSize_, CV_8UC1);
    fgmask.setTo(cv::Scalar::all(0));

    ++nframes_;
    learningRate = learningRate >= 0.0f && nframes_ > 1 ? learningRate : 1.0f / std::min(2 * nframes_, history);
    CV_Assert(learningRate >= 0.0f);

    if (learningRate > 0.0f)
        mog2_gpu(frame, frame.channels(), fgmask, bgmodelUsedModes_, weight_, variance_, mean_, learningRate, -learningRate * fCT, bShadowDetection, StreamAccessor::getStream(stream));
}

void cv::gpu::MOG2_GPU::getBackgroundImage(GpuMat& backgroundImage, Stream& stream) const
{
    using namespace cv::gpu::device::mog;

    backgroundImage.create(frameSize_, frameType_);

    getBackgroundImage2_gpu(backgroundImage.channels(), bgmodelUsedModes_, weight_, mean_, backgroundImage, StreamAccessor::getStream(stream));
}

void cv::gpu::MOG2_GPU::release()
{
    frameSize_ = Size(0, 0);
    frameType_ = 0;
    nframes_ = 0;

    weight_.release();
    variance_.release();
    mean_.release();

    bgmodelUsedModes_.release();
}

#endif
