gpubgsegm.hpp

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#ifndef __OPENCV_GPUBGSEGM_HPP__
#define __OPENCV_GPUBGSEGM_HPP__

#ifndef __cplusplus
#  error gpubgsegm.hpp header must be compiled as C++
#endif

#include "opencv2/core/gpu.hpp"
#include "opencv2/video/background_segm.hpp"

#include <memory>
#include "opencv2/gpufilters.hpp"

namespace cv { namespace gpu {

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

class CV_EXPORTS BackgroundSubtractorMOG : public cv::BackgroundSubtractorMOG
{
public:
    using cv::BackgroundSubtractorMOG::apply;
    using cv::BackgroundSubtractorMOG::getBackgroundImage;

    virtual void apply(InputArray image, OutputArray fgmask, double learningRate, Stream& stream) = 0;

    virtual void getBackgroundImage(OutputArray backgroundImage, Stream& stream) const = 0;
};

CV_EXPORTS Ptr<gpu::BackgroundSubtractorMOG>
    createBackgroundSubtractorMOG(int history = 200, int nmixtures = 5,
                                  double backgroundRatio = 0.7, double noiseSigma = 0);






// Foreground Object Detection from Videos Containing Complex Background.
// Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian.
// ACM MM2003 9p
class CV_EXPORTS FGDStatModel
{
public:
    struct CV_EXPORTS Params
    {
        int Lc;  // Quantized levels per 'color' component. Power of two, typically 32, 64 or 128.
        int N1c; // Number of color vectors used to model normal background color variation at a given pixel.
        int N2c; // Number of color vectors retained at given pixel.  Must be > N1c, typically ~ 5/3 of N1c.
        // Used to allow the first N1c vectors to adapt over time to changing background.

        int Lcc;  // Quantized levels per 'color co-occurrence' component.  Power of two, typically 16, 32 or 64.
        int N1cc; // Number of color co-occurrence vectors used to model normal background color variation at a given pixel.
        int N2cc; // Number of color co-occurrence vectors retained at given pixel.  Must be > N1cc, typically ~ 5/3 of N1cc.
        // Used to allow the first N1cc vectors to adapt over time to changing background.

        bool is_obj_without_holes; // If TRUE we ignore holes within foreground blobs. Defaults to TRUE.
        int perform_morphing;     // Number of erode-dilate-erode foreground-blob cleanup iterations.
        // These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1.

        float alpha1; // How quickly we forget old background pixel values seen. Typically set to 0.1.
        float alpha2; // "Controls speed of feature learning". Depends on T. Typical value circa 0.005.
        float alpha3; // Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1.

        float delta;   // Affects color and color co-occurrence quantization, typically set to 2.
        float T;       // A percentage value which determines when new features can be recognized as new background. (Typically 0.9).
        float minArea; // Discard foreground blobs whose bounding box is smaller than this threshold.

        // default Params
        Params();
    };

    // out_cn - channels count in output result (can be 3 or 4)
    // 4-channels require more memory, but a bit faster
    explicit FGDStatModel(int out_cn = 3);
    explicit FGDStatModel(const cv::gpu::GpuMat& firstFrame, const Params& params = Params(), int out_cn = 3);

    ~FGDStatModel();

    void create(const cv::gpu::GpuMat& firstFrame, const Params& params = Params());
    void release();

    int update(const cv::gpu::GpuMat& curFrame);

    //8UC3 or 8UC4 reference background image
    cv::gpu::GpuMat background;

    //8UC1 foreground image
    cv::gpu::GpuMat foreground;

    std::vector< std::vector<cv::Point> > foreground_regions;

private:
    FGDStatModel(const FGDStatModel&);
    FGDStatModel& operator=(const FGDStatModel&);

    class Impl;
    std::auto_ptr<Impl> impl_;
};

/*!
 The class implements the following algorithm:
 "Improved adaptive Gausian mixture model for background subtraction"
 Z.Zivkovic
 International Conference Pattern Recognition, UK, August, 2004.
 http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
*/
class CV_EXPORTS MOG2_GPU
{
public:
    //! the default constructor
    MOG2_GPU(int nmixtures = -1);

    //! re-initiaization method
    void initialize(Size frameSize, int frameType);

    //! the update operator
    void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());

    //! computes a background image which are the mean of all background gaussians
    void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const;

    //! releases all inner buffers
    void release();

    // parameters
    // you should call initialize after parameters changes

    int history;

    //! here it is the maximum allowed number of mixture components.
    //! Actual number is determined dynamically per pixel
    float varThreshold;
    // threshold on the squared Mahalanobis distance to decide if it is well described
    // by the background model or not. Related to Cthr from the paper.
    // This does not influence the update of the background. A typical value could be 4 sigma
    // and that is varThreshold=4*4=16; Corresponds to Tb in the paper.

    /////////////////////////
    // less important parameters - things you might change but be carefull
    ////////////////////////

    float backgroundRatio;
    // corresponds to fTB=1-cf from the paper
    // TB - threshold when the component becomes significant enough to be included into
    // the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
    // For alpha=0.001 it means that the mode should exist for approximately 105 frames before
    // it is considered foreground
    // float noiseSigma;
    float varThresholdGen;

    //correspondts to Tg - threshold on the squared Mahalan. dist. to decide
    //when a sample is close to the existing components. If it is not close
    //to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
    //Smaller Tg leads to more generated components and higher Tg might make
    //lead to small number of components but they can grow too large
    float fVarInit;
    float fVarMin;
    float fVarMax;

    //initial variance  for the newly generated components.
    //It will will influence the speed of adaptation. A good guess should be made.
    //A simple way is to estimate the typical standard deviation from the images.
    //I used here 10 as a reasonable value
    // min and max can be used to further control the variance
    float fCT; //CT - complexity reduction prior
    //this is related to the number of samples needed to accept that a component
    //actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
    //the standard Stauffer&Grimson algorithm (maybe not exact but very similar)

    //shadow detection parameters
    bool bShadowDetection; //default 1 - do shadow detection
    unsigned char nShadowDetection; //do shadow detection - insert this value as the detection result - 127 default value
    float fTau;
    // Tau - shadow threshold. The shadow is detected if the pixel is darker
    //version of the background. Tau is a threshold on how much darker the shadow can be.
    //Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
    //See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.

private:
    int nmixtures_;

    Size frameSize_;
    int frameType_;
    int nframes_;

    GpuMat weight_;
    GpuMat variance_;
    GpuMat mean_;

    GpuMat bgmodelUsedModes_; //keep track of number of modes per pixel
};

/**
 * Background Subtractor module. Takes a series of images and returns a sequence of mask (8UC1)
 * images of the same size, where 255 indicates Foreground and 0 represents Background.
 * This class implements an algorithm described in "Visual Tracking of Human Visitors under
 * Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere,
 * A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012.
 */
class CV_EXPORTS GMG_GPU
{
public:
    GMG_GPU();

    /**
     * Validate parameters and set up data structures for appropriate frame size.
     * @param frameSize Input frame size
     * @param min       Minimum value taken on by pixels in image sequence. Usually 0
     * @param max       Maximum value taken on by pixels in image sequence. e.g. 1.0 or 255
     */
    void initialize(Size frameSize, float min = 0.0f, float max = 255.0f);

    /**
     * Performs single-frame background subtraction and builds up a statistical background image
     * model.
     * @param frame        Input frame
     * @param fgmask       Output mask image representing foreground and background pixels
     * @param stream       Stream for the asynchronous version
     */
    void operator ()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());

    //! Releases all inner buffers
    void release();

    //! 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;

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

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

    //! Perform background model update.
    bool updateBackgroundModel;

private:
    float maxVal_, minVal_;

    Size frameSize_;

    int frameNum_;

    GpuMat nfeatures_;
    GpuMat colors_;
    GpuMat weights_;

    Ptr<gpu::Filter> boxFilter_;
    GpuMat buf_;
};

}} // namespace cv { namespace gpu {

#endif /* __OPENCV_GPUBGSEGM_HPP__ */