root/modules/cudalegacy/src/gmg.cpp

DEFINITIONS

1. createBackgroundSubtractorGMG
2. getMaxFeatures
3. setMaxFeatures
4. getDefaultLearningRate
5. setDefaultLearningRate
6. getNumFrames
7. setNumFrames
8. getQuantizationLevels
9. setQuantizationLevels
10. getBackgroundPrior
11. setBackgroundPrior
12. getSmoothingRadius
13. setSmoothingRadius
14. getDecisionThreshold
15. setDecisionThreshold
16. getUpdateBackgroundModel
17. setUpdateBackgroundModel
18. getMinVal
19. setMinVal
20. getMaxVal
21. setMaxVal
22. apply
23. apply
24. getBackgroundImage
25. initialize
26. createBackgroundSubtractorGMG

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#include "precomp.hpp"

using namespace cv;
using namespace cv::cuda;

#if !defined HAVE_CUDA || defined(CUDA_DISABLER)

Ptr<cuda::BackgroundSubtractorGMG> cv::cuda::createBackgroundSubtractorGMG(int, double) { throw_no_cuda(); return Ptr<cuda::BackgroundSubtractorGMG>(); }

#else

namespace cv { namespace cuda { namespace device {
    namespace gmg
    {
        void loadConstants(int width, int height, float minVal, float maxVal, int quantizationLevels, float backgroundPrior,
                           float decisionThreshold, int maxFeatures, int numInitializationFrames);

        template <typename SrcT>
        void update_gpu(PtrStepSzb frame, PtrStepb fgmask, PtrStepSzi colors, PtrStepf weights, PtrStepi nfeatures,
                        int frameNum,  float learningRate, bool updateBackgroundModel, cudaStream_t stream);
    }
}}}

namespace
{
    class GMGImpl : public cuda::BackgroundSubtractorGMG
    {
    public:
        GMGImpl(int initializationFrames, double decisionThreshold);

        void apply(InputArray image, OutputArray fgmask, double learningRate=-1);
        void apply(InputArray image, OutputArray fgmask, double learningRate, Stream& stream);

        void getBackgroundImage(OutputArray backgroundImage) const;

        int getMaxFeatures() const { return maxFeatures_; }
        void setMaxFeatures(int maxFeatures) { maxFeatures_ = maxFeatures; }

        double getDefaultLearningRate() const { return learningRate_; }
        void setDefaultLearningRate(double lr) { learningRate_ = (float) lr; }

        int getNumFrames() const { return numInitializationFrames_; }
        void setNumFrames(int nframes) { numInitializationFrames_ = nframes; }

        int getQuantizationLevels() const { return quantizationLevels_; }
        void setQuantizationLevels(int nlevels) { quantizationLevels_ = nlevels; }

        double getBackgroundPrior() const { return backgroundPrior_; }
        void setBackgroundPrior(double bgprior) { backgroundPrior_ = (float) bgprior; }

        int getSmoothingRadius() const { return smoothingRadius_; }
        void setSmoothingRadius(int radius) { smoothingRadius_ = radius; }

        double getDecisionThreshold() const { return decisionThreshold_; }
        void setDecisionThreshold(double thresh) { decisionThreshold_ = (float) thresh; }

        bool getUpdateBackgroundModel() const { return updateBackgroundModel_; }
        void setUpdateBackgroundModel(bool update) { updateBackgroundModel_ = update; }

        double getMinVal() const { return minVal_; }
        void setMinVal(double val) { minVal_ = (float) val; }

        double getMaxVal() const { return maxVal_; }
        void setMaxVal(double val) { maxVal_ = (float) val; }

    private:
        void initialize(Size frameSize, float min, float max);

        //! Total number of distinct colors to maintain in histogram.
        int maxFeatures_;

        //! Set between 0.0 and 1.0, determines how quickly features are "forgotten" from histograms.
        float learningRate_;

        //! Number of frames of video to use to initialize histograms.
        int numInitializationFrames_;

        //! Number of discrete levels in each channel to be used in histograms.
        int quantizationLevels_;

        //! Prior probability that any given pixel is a background pixel. A sensitivity parameter.
        float backgroundPrior_;

        //! Smoothing radius, in pixels, for cleaning up FG image.
        int smoothingRadius_;

        //! Value above which pixel is determined to be FG.
        float decisionThreshold_;

        //! Perform background model update.
        bool updateBackgroundModel_;

        float minVal_, maxVal_;

        Size frameSize_;
        int frameNum_;

        GpuMat nfeatures_;
        GpuMat colors_;
        GpuMat weights_;

#if defined(HAVE_OPENCV_CUDAFILTERS) && defined(HAVE_OPENCV_CUDAARITHM)
        Ptr<cuda::Filter> boxFilter_;
        GpuMat buf_;
#endif
    };

    GMGImpl::GMGImpl(int initializationFrames, double decisionThreshold)
    {
        maxFeatures_ = 64;
        learningRate_ = 0.025f;
        numInitializationFrames_ = initializationFrames;
        quantizationLevels_ = 16;
        backgroundPrior_ = 0.8f;
        decisionThreshold_ = (float) decisionThreshold;
        smoothingRadius_ = 7;
        updateBackgroundModel_ = true;
        minVal_ = maxVal_ = 0;
    }

    void GMGImpl::apply(InputArray image, OutputArray fgmask, double learningRate)
    {
        apply(image, fgmask, learningRate, Stream::Null());
    }

    void GMGImpl::apply(InputArray _frame, OutputArray _fgmask, double newLearningRate, Stream& stream)
    {
        using namespace cv::cuda::device::gmg;

        typedef void (*func_t)(PtrStepSzb frame, PtrStepb fgmask, PtrStepSzi colors, PtrStepf weights, PtrStepi nfeatures,
                               int frameNum, float learningRate, bool updateBackgroundModel, cudaStream_t stream);
        static const func_t funcs[6][4] =
        {
            {update_gpu<uchar>, 0, update_gpu<uchar3>, update_gpu<uchar4>},
            {0,0,0,0},
            {update_gpu<ushort>, 0, update_gpu<ushort3>, update_gpu<ushort4>},
            {0,0,0,0},
            {0,0,0,0},
            {update_gpu<float>, 0, update_gpu<float3>, update_gpu<float4>}
        };

        GpuMat frame = _frame.getGpuMat();

        CV_Assert( frame.depth() == CV_8U || frame.depth() == CV_16U || frame.depth() == CV_32F );
        CV_Assert( frame.channels() == 1 || frame.channels() == 3 || frame.channels() == 4 );

        if (newLearningRate != -1.0)
        {
            CV_Assert( newLearningRate >= 0.0 && newLearningRate <= 1.0 );
            learningRate_ = (float) newLearningRate;
        }

        if (frame.size() != frameSize_)
        {
            double minVal = minVal_;
            double maxVal = maxVal_;

            if (minVal_ == 0 && maxVal_ == 0)
            {
                minVal = 0;
                maxVal = frame.depth() == CV_8U ? 255.0 : frame.depth() == CV_16U ? std::numeric_limits<ushort>::max() : 1.0;
            }

            initialize(frame.size(), (float) minVal, (float) maxVal);
        }

        _fgmask.create(frameSize_, CV_8UC1);
        GpuMat fgmask = _fgmask.getGpuMat();

        fgmask.setTo(Scalar::all(0), stream);

        funcs[frame.depth()][frame.channels() - 1](frame, fgmask, colors_, weights_, nfeatures_, frameNum_,
                                                   learningRate_, updateBackgroundModel_, StreamAccessor::getStream(stream));

#if defined(HAVE_OPENCV_CUDAFILTERS) && defined(HAVE_OPENCV_CUDAARITHM)
        // medianBlur
        if (smoothingRadius_ > 0)
        {
            boxFilter_->apply(fgmask, buf_, stream);
            const int minCount = (smoothingRadius_ * smoothingRadius_ + 1) / 2;
            const double thresh = 255.0 * minCount / (smoothingRadius_ * smoothingRadius_);
            cuda::threshold(buf_, fgmask, thresh, 255.0, THRESH_BINARY, stream);
        }
#endif

        // keep track of how many frames we have processed
        ++frameNum_;
    }

    void GMGImpl::getBackgroundImage(OutputArray backgroundImage) const
    {
        (void) backgroundImage;
        CV_Error(Error::StsNotImplemented, "Not implemented");
    }

    void GMGImpl::initialize(Size frameSize, float min, float max)
    {
        using namespace cv::cuda::device::gmg;

        CV_Assert( maxFeatures_ > 0 );
        CV_Assert( learningRate_ >= 0.0f && learningRate_ <= 1.0f);
        CV_Assert( numInitializationFrames_ >= 1);
        CV_Assert( quantizationLevels_ >= 1 && quantizationLevels_ <= 255);
        CV_Assert( backgroundPrior_ >= 0.0f && backgroundPrior_ <= 1.0f);

        minVal_ = min;
        maxVal_ = max;
        CV_Assert( minVal_ < maxVal_ );

        frameSize_ = frameSize;

        frameNum_ = 0;

        nfeatures_.create(frameSize_, CV_32SC1);
        colors_.create(maxFeatures_ * frameSize_.height, frameSize_.width, CV_32SC1);
        weights_.create(maxFeatures_ * frameSize_.height, frameSize_.width, CV_32FC1);

        nfeatures_.setTo(Scalar::all(0));

#if defined(HAVE_OPENCV_CUDAFILTERS) && defined(HAVE_OPENCV_CUDAARITHM)
        if (smoothingRadius_ > 0)
            boxFilter_ = cuda::createBoxFilter(CV_8UC1, -1, Size(smoothingRadius_, smoothingRadius_));
#endif

        loadConstants(frameSize_.width, frameSize_.height, minVal_, maxVal_,
                      quantizationLevels_, backgroundPrior_, decisionThreshold_, maxFeatures_, numInitializationFrames_);
    }
}

Ptr<cuda::BackgroundSubtractorGMG> cv::cuda::createBackgroundSubtractorGMG(int initializationFrames, double decisionThreshold)
{
    return makePtr<GMGImpl>(initializationFrames, decisionThreshold);
}

#endif