运动检测ViBe算法python实现代码

class ViBe: 
 ''''' 
 classdocs 
 ''' 
 __defaultNbSamples = 20  #每个像素点的样本个数 
 __defaultReqMatches = 2  #min指数 
 __defaultRadius = 20;   #Sqthere半径 
 __defaultSubsamplingFactor = 16#子采样概率 
 __BG = 0      #背景像素 
 __FG = 255      #前景像素 
 __c_xoff=[-1,0,1,-1,1,-1,0,1,0] #x的邻居点 len=9 
 __c_yoff=[-1,0,1,-1,1,-1,0,1,0] #y的邻居点 len=9 
  
 __samples=[]    #保存每个像素点的样本值,len defaultNbSamples+1 
 __Height = 0 
 __Width = 0 
 
 def __init__(self, grayFrame): 
  ''''' 
  Constructor 
  ''' 
  self.__Height = grayFrame.shape[0] 
  self.__Width = grayFrame.shape[1] 
   
 
  for i in range(self.__defaultNbSamples+1): 
   self.__samples.insert(i,np.zeros((grayFrame.shape[0],grayFrame.shape[1]),dtype=grayFrame.dtype)); 
    
  self.__init_params(grayFrame) 
  
 def __init_params(self,grayFrame): 
  #记录随机生成的 行(r) 和 列(c) 
  rand=0 
  r=0 
  c=0 
 
  #对每个像素样本进行初始化 
  for y in range(self.__Height): 
   for x in range(self.__Width): 
    for k in range(self.__defaultNbSamples): 
     #随机获取像素样本值 
     rand=random.randint(0,8) 
     r=y+self.__c_yoff[rand] 
     if r<0: 
      r=0 
     if r>=self.__Height: 
      r=self.__Height-1 #行 
     c=x+self.__c_xoff[rand] 
     if c<0: 
      c=0 
     if c>=self.__Width: 
      c=self.__Width-1  #列 
     #存储像素样本值 
     self.__samples[k][y,x] = grayFrame[r,c] 
   self.__samples[self.__defaultNbSamples][y,x] = 0 
    
 def update(self,grayFrame,frameNo): 
  foreground = np.zeros((self.__Height,self.__Width),dtype=np.uint8) 
  for y in range(self.__Height): #Height 
   for x in range(self.__Width):  #Width 
    #用于判断一个点是否是背景点,index记录已比较的样本个数，count表示匹配的样本个数 
    count=0;index=0; 
    dist=0.0; 
    while (count<self.__defaultReqMatches) and (index<self.__defaultNbSamples): 
     dist= float(grayFrame[y,x]) - float(self.__samples[index][y,x]); 
     if dist<0: dist=-dist 
     if dist<self.__defaultRadius: count = count+1 
     index = index+1 
 
    if count>=self.__defaultReqMatches: 
     #判断为背景像素,只有背景点才能被用来传播和更新存储样本值 
     self.__samples[self.__defaultNbSamples][y,x]=0 
  
     foreground[y,x] = self.__BG 
  
     rand=random.randint(0,self.__defaultSubsamplingFactor) 
     if rand==0: 
      rand=random.randint(0,self.__defaultNbSamples) 
      self.__samples[rand][y,x]=grayFrame[y,x] 
     rand=random.randint(0,self.__defaultSubsamplingFactor) 
     if rand==0: 
      rand=random.randint(0,8) 
      yN=y+self.__c_yoff[rand] 
      if yN<0: yN=0 
      if yN>=self.__Height: yN=self.__Height-1 
      rand=random.randint(0,8) 
      xN=x+self.__c_xoff[rand] 
      if xN<0: xN=0 
      if xN>=self.__Width: xN=self.__Width-1 
      rand=random.randint(0,self.__defaultNbSamples) 
      self.__samples[rand][yN,xN]=grayFrame[y,x] 
    else: 
     #判断为前景像素 
     foreground[y,x] = self.__FG; 
     self.__samples[self.__defaultNbSamples][y,x] += 1 
     if self.__samples[self.__defaultNbSamples][y,x]>50: 
      rand=random.randint(0,self.__defaultNbSamples) 
      if rand==0: 
       rand=random.randint(0,self.__defaultNbSamples) 
       self.__samples[rand][y,x]=grayFrame[y,x] 
  return foreground