opencv：使用高斯混合模型（GMM）源码对视频进行背景差分法

非常感谢thefutureisour对opencv中c++版本的高斯混合模型的源代码完全注释 ，网上直接使用opencv源码编程的比较少，但是要想自己对高斯混合模型进行优化，或者要想在论文中对高斯混合模型有所创新，必须使用opencv源码来进行编程，而不仅仅是使用opencv的源码接口调用一下修改一下参数。自己废了些脑子提供给网友交流一把，

1、 my_background_segm.hpp

#include "opencv2/core/core.hpp"
#include 
#include "opencv2/video/background_segm.hpp"    //找到你自己安装包中该文件的位置
namespace cv
{
     class CV_EXPORTS_W my_BackgroundSubtractorMOG : public BackgroundSubtractor
     {
     public:
         //! the default constructor
         CV_WRAP my_BackgroundSubtractorMOG();
         //! the full constructor that takes the length of the history, the number of gaussian mixtures, the background ratio parameter and the noise strength
         CV_WRAP my_BackgroundSubtractorMOG(int history, int nmixtures, double backgroundRatio, double noiseSigma=0);
         //! the destructor
         virtual ~my_BackgroundSubtractorMOG();
         //! the update operator
         virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=0);
     
         //! re-initiaization method
         virtual void initialize(Size frameSize, int frameType);
     
        // virtual AlgorithmInfo* info() const;
     
     protected:
     	//public:
         Size frameSize;
         int frameType;
         Mat bgmodel;
         int nframes;
         int history;
         int nmixtures;
         double varThreshold;
         double backgroundRatio;
         double noiseSigma;
     };
}

2、my_background_segm.cpp

#include "my_background_segm.hpp"
using namespace cv;
static const int defaultNMixtures = 5;//默认混合模型个数
static const int defaultHistory = 200;//默认历史帧数
static const double defaultBackgroundRatio = 0.7;//默认背景门限
static const double defaultVarThreshold = 2.5*2.5;//默认方差门限
static const double defaultNoiseSigma = 30*0.5;//默认噪声方差
static const double defaultInitialWeight = 0.05;//默认初始权值
 //不带参数的构造函数，使用默认值  
my_BackgroundSubtractorMOG::my_BackgroundSubtractorMOG()
{
    frameSize = Size(0,0);
    frameType = 0;
    
    nframes = 0;
    nmixtures = defaultNMixtures;
    history = defaultHistory;
    varThreshold = defaultVarThreshold;
    backgroundRatio = defaultBackgroundRatio;
    noiseSigma = defaultNoiseSigma;
}
//带参数的构造函数，使用参数传进来的值    
my_BackgroundSubtractorMOG::my_BackgroundSubtractorMOG(int _history, int _nmixtures,
                                                 double _backgroundRatio,
                                                 double _noiseSigma)
{
    frameSize = Size(0,0);
    frameType = 0;
    
    nframes = 0;
    nmixtures = min(_nmixtures > 0 ? _nmixtures : defaultNMixtures, 8);//不能超过8个，否则就用默认的
    history = _history > 0 ? _history : defaultHistory;//不能小于0，否则就用默认的
    varThreshold = defaultVarThreshold;//门限使用默认的
    backgroundRatio = min(_backgroundRatio > 0 ? _backgroundRatio : 0.95, 1.);//背景门限必须大于0，小于1，否则使用0.95
    noiseSigma = _noiseSigma <= 0 ? defaultNoiseSigma : _noiseSigma;//噪声门限大于0
}
    
my_BackgroundSubtractorMOG::~my_BackgroundSubtractorMOG()
{
}


void my_BackgroundSubtractorMOG::initialize(Size _frameSize, int _frameType)
{
    frameSize = _frameSize;
    frameType = _frameType;
    nframes = 0;
    
    int nchannels = CV_MAT_CN(frameType);
    CV_Assert( CV_MAT_DEPTH(frameType) == CV_8U );
    
    // 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)
    bgmodel.create( 1, frameSize.height*frameSize.width*nmixtures*(2 + 2*nchannels), CV_32F );//初始化一个1行*XX列的矩阵
	//XX是这样计算的：图像的行*列*混合模型的个数*（1（优先级） + 1（权值） + 2（均值 + 方差） * 通道数）
    bgmodel = Scalar::all(0);//清零
}

//设为模版，就是考虑到了彩色图像与灰度图像两种情况    
template struct MixData
{
    float sortKey;
    float weight;
    VT mean;
    VT var;
};

    
static void process8uC1( const Mat& image, Mat& fgmask, double learningRate,
                         Mat& bgmodel, int nmixtures, double backgroundRatio,
                         double varThreshold, double noiseSigma )
{
    int x, y, k, k1, rows = image.rows, cols = image.cols;
    float alpha = (float)learningRate, T = (float)backgroundRatio, vT = (float)varThreshold;//学习速率、背景门限、方差门限
    int K = nmixtures;//混合模型个数
    MixData* mptr = (MixData*)bgmodel.data;
    
    const float w0 = (float)defaultInitialWeight;//初始权值
    const float sk0 = (float)(w0/(defaultNoiseSigma*2));//初始优先级
    const float var0 = (float)(defaultNoiseSigma*defaultNoiseSigma*4);//初始方差
    const float minVar = (float)(noiseSigma*noiseSigma);//最小方差
    
    for( y = 0; y < rows; y++ )
    {
        const uchar* src = image.ptr(y);
        uchar* dst = fgmask.ptr(y);
        
        if( alpha > 0 )//如果学习速率为0，则退化为背景相减
        {
            for( x = 0; x < cols; x++, mptr += K )
            {
                float wsum = 0;
                float pix = src[x];//每个像素
                int kHit = -1, kForeground = -1;//是否属于模型，是否属于前景
                
                for( k = 0; k < K; k++ )//每个高斯模型
                {
                    float w = mptr[k].weight;//当前模型的权值
                    wsum += w;//权值累加
                    if( w < FLT_EPSILON )
                        break;
                    float mu = mptr[k].mean;//当前模型的均值
                    float var = mptr[k].var;//当前模型的方差
                    float diff = pix - mu;//当前像素与模型均值之差
                    float d2 = diff*diff;//平方
                    if( d2 < vT*var )//是否小于方门限，即是否属于该模型
                    {
                        wsum -= w;//如果匹配，则把它减去，因为之后会更新它
                        float dw = alpha*(1.f - w);
                        mptr[k].weight = w + dw;//增加权值
						//注意源文章中涉及概率的部分多进行了简化，将概率变为1
                        mptr[k].mean = mu + alpha*diff;//修正均值		
                        var = max(var + alpha*(d2 - var), minVar);//开始时方差清零0，所以这里使用噪声方差作为默认方差，否则使用上一次方差
                        mptr[k].var = var;//修正方差
                        mptr[k].sortKey = w/sqrt(var);//重新计算优先级，貌似这里不对，应该使用更新后的mptr[k].weight而不是w
                        
                        for( k1 = k-1; k1 >= 0; k1-- )//从匹配的第k个模型开始向前比较，如果更新后的单高斯模型优先级超过他前面的那个，则交换顺序
                        {
                            if( mptr[k1].sortKey >= mptr[k1+1].sortKey )//如果优先级没有发生改变，则停止比较
                                break;
                            std::swap( mptr[k1], mptr[k1+1] );//交换它们的顺序，始终保证优先级最大的在前面
                        }
                        
                        kHit = k1+1;//记录属于哪个模型
                        break;
                    }
                }
                
                if( kHit < 0 ) // no appropriate gaussian mixture found at all, remove the weakest mixture and create a new one
								//当前像素不属于任何一个模型
                {
					//初始化一个新模型
                    kHit = k = min(k, K-1);//有两种情况，当最开始的初始化时，k并不是等于K-1的
                    wsum += w0 - mptr[k].weight;//从权值总和中减去原来的那个模型，并加上默认权值
                    mptr[k].weight = w0;//初始化权值
                    mptr[k].mean = pix;//初始化均值
                    mptr[k].var = var0;	//初始化方差
                    mptr[k].sortKey = sk0;//初始化权值
                }
                else
                    for( ; k < K; k++ )
                        wsum += mptr[k].weight;//求出剩下的总权值
                
                float wscale = 1.f/wsum;//归一化
                wsum = 0;
                for( k = 0; k < K; k++ )
                {
                    wsum += mptr[k].weight *= wscale;
                    mptr[k].sortKey *= wscale;//计算归一化权值
                    if( wsum > T && kForeground < 0 )
                        kForeground = k+1;//第几个模型之后就判为前景了
                }
                
                dst[x] = (uchar)(-(kHit >= kForeground));//判决：(ucahr)(-true) = 255;(uchar)(-(false)) = 0;
            }
        }
        else//如果学习速率小于等于0，则没有背景更新过程，其他过程类似
        {
            for( x = 0; x < cols; x++, mptr += K )
            {
                float pix = src[x];
                int kHit = -1, kForeground = -1;
                
                for( k = 0; k < K; k++ )
                {
                    if( mptr[k].weight < FLT_EPSILON )
                        break;
                    float mu = mptr[k].mean;
                    float var = mptr[k].var;
                    float diff = pix - mu;
                    float d2 = diff*diff;
                    if( d2 < vT*var )
                    {
                        kHit = k;
                        break;
                    }
                }
                
                if( kHit >= 0 )
                {
                    float wsum = 0;
                    for( k = 0; k < K; k++ )
                    {
                        wsum += mptr[k].weight;
                        if( wsum > T )
                        {
                            kForeground = k+1;
                            break;
                        }
                    }
                }
                
                dst[x] = (uchar)(kHit < 0 || kHit >= kForeground ? 255 : 0);
            }
        }
    }
}

    
static void process8uC3( const Mat& image, Mat& fgmask, double learningRate,
                         Mat& bgmodel, int nmixtures, double backgroundRatio,
                         double varThreshold, double noiseSigma )
{
    int x, y, k, k1, rows = image.rows, cols = image.cols;
    float alpha = (float)learningRate, T = (float)backgroundRatio, vT = (float)varThreshold;
    int K = nmixtures;
    
    const float w0 = (float)defaultInitialWeight;
    const float sk0 = (float)(w0/(defaultNoiseSigma*2*sqrt(3.)));
    const float var0 = (float)(defaultNoiseSigma*defaultNoiseSigma*4);
    const float minVar = (float)(noiseSigma*noiseSigma);
    MixData* mptr = (MixData*)bgmodel.data;
    
    for( y = 0; y < rows; y++ )
    {
        const uchar* src = image.ptr(y);
        uchar* dst = fgmask.ptr(y);
        
        if( alpha > 0 )
        {
            for( x = 0; x < cols; x++, mptr += K )
            {
                float wsum = 0;
                Vec3f pix(src[x*3], src[x*3+1], src[x*3+2]);
                int kHit = -1, kForeground = -1;
                
                for( k = 0; k < K; k++ )
                {
                    float w = mptr[k].weight;
                    wsum += w;
                    if( w < FLT_EPSILON )
                        break;
                    Vec3f mu = mptr[k].mean;
                    Vec3f var = mptr[k].var;
                    Vec3f diff = pix - mu;
                    float d2 = diff.dot(diff);
                    if( d2 < vT*(var[0] + var[1] + var[2]) )
                    {
                        wsum -= w;
                        float dw = alpha*(1.f - w);
                        mptr[k].weight = w + dw;
                        mptr[k].mean = mu + alpha*diff;
                        var = Vec3f(max(var[0] + alpha*(diff[0]*diff[0] - var[0]), minVar),
                                    max(var[1] + alpha*(diff[1]*diff[1] - var[1]), minVar),
                                    max(var[2] + alpha*(diff[2]*diff[2] - var[2]), minVar));
                        mptr[k].var = var;
                        mptr[k].sortKey = w/sqrt(var[0] + var[1] + var[2]);
                        
                        for( k1 = k-1; k1 >= 0; k1-- )
                        {
                            if( mptr[k1].sortKey >= mptr[k1+1].sortKey )
                                break;
                            std::swap( mptr[k1], mptr[k1+1] );
                        }
                        
                        kHit = k1+1;
                        break;
                    }
                }
                
                if( kHit < 0 ) // no appropriate gaussian mixture found at all, remove the weakest mixture and create a new one
                {
                    kHit = k = min(k, K-1);
                    wsum += w0 - mptr[k].weight;
                    mptr[k].weight = w0;
                    mptr[k].mean = pix;
                    mptr[k].var = Vec3f(var0, var0, var0);
                    mptr[k].sortKey = sk0;
                }
                else
                    for( ; k < K; k++ )
                        wsum += mptr[k].weight;
            
                float wscale = 1.f/wsum;
                wsum = 0;
                for( k = 0; k < K; k++ )
                {
                    wsum += mptr[k].weight *= wscale;
                    mptr[k].sortKey *= wscale;
                    if( wsum > T && kForeground < 0 )
                        kForeground = k+1;
                }
                
                dst[x] = (uchar)(-(kHit >= kForeground));
            }
        }
        else
        {
            for( x = 0; x < cols; x++, mptr += K )
            {
                Vec3f pix(src[x*3], src[x*3+1], src[x*3+2]);
                int kHit = -1, kForeground = -1;
                
                for( k = 0; k < K; k++ )
                {
                    if( mptr[k].weight < FLT_EPSILON )
                        break;
                    Vec3f mu = mptr[k].mean;
                    Vec3f var = mptr[k].var;
                    Vec3f diff = pix - mu;
                    float d2 = diff.dot(diff);
                    if( d2 < vT*(var[0] + var[1] + var[2]) )
                    {
                        kHit = k;
                        break;
                    }
                }
 
                if( kHit >= 0 )
                {
                    float wsum = 0;
                    for( k = 0; k < K; k++ )
                    {
                        wsum += mptr[k].weight;
                        if( wsum > T )
                        {
                            kForeground = k+1;
                            break;
                        }
                    }
                }
                
                dst[x] = (uchar)(kHit < 0 || kHit >= kForeground ? 255 : 0);
            }
        }
    }
}

void my_BackgroundSubtractorMOG::operator()(InputArray _image, OutputArray _fgmask, double learningRate)
{
    Mat image = _image.getMat();
    bool needToInitialize = nframes == 0 || learningRate >= 1 || image.size() != frameSize || image.type() != frameType;//是否需要初始化
    
    if( needToInitialize )
        initialize(image.size(), image.type());//初始化
    
    CV_Assert( image.depth() == CV_8U );
    _fgmask.create( image.size(), CV_8U );
    Mat fgmask = _fgmask.getMat();
    
    ++nframes;
    learningRate = learningRate >= 0 && nframes > 1 ? learningRate : 1./min( nframes, history );
    CV_Assert(learningRate >= 0);
    
    if( image.type() == CV_8UC1 )//处理灰度图像
        process8uC1( image, fgmask, learningRate, bgmodel, nmixtures, backgroundRatio, varThreshold, noiseSigma );
    else if( image.type() == CV_8UC3 )//处理彩色图像
        process8uC3( image, fgmask, learningRate, bgmodel, nmixtures, backgroundRatio, varThreshold, noiseSigma );
    else
        CV_Error( CV_StsUnsupportedFormat, "Only 1- and 3-channel 8-bit images are supported in my_BackgroundSubtractorMOG" );
}
    
//}

3、main.cpp

#include   
#include    
#include   
#include "my_background_segm.hpp"   //自己定义的头文件，默认的是直接调用opencv自带的GMM有关的函数，所以本文中重新定义一个不同的类
using namespace cv;
using namespace std;
int count_frame = 0;
int main()
{
  VideoCapture capture("3.avi");
  if( !capture.isOpened() )
  {
    cout<<"读取视频失败"<= frameToStop)
    {
      stop = true;
    }
    if ( c >= 0)
    {
      waitKey(0);
    }
    currentFrame++;

  }

  waitKey(0);
}

据说还用一种是使用cmake对opencv源码进行操作，我没哟使用过，还据说cmake挺简单的，如果有使用的网友可以提出更好的方法哈