added MOG_GPU and MOG2_GPU (Gaussian Mixture background subtraction)

modules/gpu/include/opencv2/gpu/gpu.hpp

@@ -1963,6 +1963,134 @@ private:
     std::auto_ptr<Impl> impl_;
 };
 
+/*!
+ Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm
+
+ The class implements the following algorithm:
+ "An improved adaptive background mixture model for real-time tracking with shadow detection"
+ P. KadewTraKuPong and R. Bowden,
+ Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
+ http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
+*/
+class CV_EXPORTS MOG_GPU
+{
+public:
+    //! the default constructor
+    MOG_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 = 0.0f, Stream& stream = Stream::Null());
+
+    int history;
+    float varThreshold;
+    float backgroundRatio;
+    float noiseSigma;
+
+private:
+    int nmixtures_;
+
+    Size frameSize_;
+    int nframes_;
+
+    GpuMat weight_;
+    GpuMat sortKey_;
+    GpuMat mean_;
+    GpuMat var_;
+};
+
+/*!
+ 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;
+
+    // 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
+};
+
 ////////////////////////////////// Video Encoding //////////////////////////////////
 
 // Works only under Windows