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