混合高斯模型：opencv中MOG2的代码结构梳理

/*
头文件：OurGaussmix2.h
*/
#include "opencv2/core/core.hpp"
#include <list>
#include"cv.h"
using namespace cv;//InputArray 等的定义在cv里面
namespace ourGaussmix
{

class BackgroundSubtractor: public cv::Algorithm
{
public:
    virtual ~BackgroundSubtractor();
    virtual void operator()(InputArray _image, OutputArray _fgmask, double learningRate);
    virtual void getBackgroundImage(OutputArray backgroundImage,OutputArray backgroundImageslow) const;
};


class OurBackgroundSubtractorMOG2 : public BackgroundSubtractor
{
public:
    OurBackgroundSubtractorMOG2();
    OurBackgroundSubtractorMOG2(int history,  float varThreshold, bool bShadowDetection=true);
    virtual ~OurBackgroundSubtractorMOG2();
    virtual void operator()(InputArray _image, OutputArray _fgmask, double learningRate);
    virtual void getBackgroundImage(OutputArray backgroundImage,OutputArray backgroundImageslow) const;
    virtual void initialize(Size frameSize, int frameType);    
};
}

/*
cpp文件
*/
namespace ourGaussmix
{
    /****************************
    static const 类型的默认参数
    *****************************/

    struct GaussBGStatModel2Params 
    {
        //存储参数
    };

    struct GMM
    {
        float weight;
        float variance;
    };

    /***基类中的虚函数都设为空的***/
    BackgroundSubtractor::~BackgroundSubtractor() {}
    void BackgroundSubtractor::operator()(InputArray _image, OutputArray _fgmask, double learningRate)
    {
    }
    void BackgroundSubtractor::getBackgroundImage(OutputArray,OutputArray) const
    {
    }

    static CV_INLINE bool detectShadowGMM(const float* data, int nchannels, int nmodes,const GMM* gmm, const float* mean,
                                                                                            float Tb, float TB, float tau)
    {
        /*阴影检测函数内容*/
    }

    /*定义一个结构体用来执行OurBackgroundSubtractorMOG2*/
    struct MOG2Invoker
    {
        /*C++中结构体是一个特殊的类 下面的是构造函数*/
        MOG2Invoker(const Mat& _src, Mat& _dst,
                GMM* _gmm, float* _mean,
                uchar* _modesUsed,
                int _nmixtures, float _alphaT,
                float _Tb, float _TB, float _Tg,
                float _varInit, float _varMin, float _varMax,
                float _prune, float _tau, bool _detectShadows,
                uchar _shadowVal)
        {
            /*给结构体的参数赋值*/
        }


        void operator()(const BlockedRange& range) const
        {
            /*混合高斯模型参数的更新*/
        }

    };

    OurBackgroundSubtractorMOG2::OurBackgroundSubtractorMOG2()
    {
        /*默认构造函数赋值 全部赋予默认值*/
    }
    OurBackgroundSubtractorMOG2::OurBackgroundSubtractorMOG2(int _history,  float _varThreshold, bool _bShadowDetection)
    {
        /*构造函数赋值 部分自选 其他默认值*/
    }
    OurBackgroundSubtractorMOG2::~OurBackgroundSubtractorMOG2()
    {
    }

    void OurBackgroundSubtractorMOG2::initialize(Size _frameSize, int _frameType)
    {
        /*初始化函数 分配内存*/
        //bgmodel.create( 1, frameSize.height*frameSize.width*nmixtures*(2 + nchannels), CV_32F );
        //bgmodelUsedModes.create(frameSize,CV_8U);
        //bgmodelUsedModes = Scalar::all(0);
    }

    void OurBackgroundSubtractorMOG2::operator()(InputArray _image, OutputArray _fgmask, double learningRate)
    {
        /*判断是否初始化并调用initialize*/
   //parallel_for(BlockedRange(0, image.rows),
   //              MOG2Invoker(image, fgmask,
   //                          (GMM*)bgmodel.data,
   //                          (float*)(bgmodel.data + sizeof(GMM)*nmixtures*image.rows*image.cols),
   //                          bgmodelUsedModes.data, nmixtures, (float)learningRate,
   //                          (float)varThreshold,
   //                          backgroundRatio, varThresholdGen,
   //                          fVarInit, fVarMin, fVarMax, float(-learningRate*fCT), fTau,
   //                          bShadowDetection, nShadowDetection));
    }

    void OurBackgroundSubtractorMOG2::getBackgroundImage(OutputArray backgroundImage,OutputArray backgroundImageslow) const
    {

    }

}