Back_sub.py 文件源码

def subtract_back(self,frm):
        #dst=self.__back__-self.__foreground__
        temp=np.zeros((600,800),np.uint8)

        self.__foreground__=cv2.blur(self.__foreground__,(3,3))
        dst=cv2.absdiff(self.__back__,self.__foreground__)

        #dst=cv2.adaptiveThreshold(dst,255,cv.CV_THRESH_BINARY,cv.CV_ADAPTIVE_THRESH_GAUSSIAN_C,5,10)
        val,dst=cv2.threshold(dst,0,255,cv.CV_THRESH_BINARY+cv.CV_THRESH_OTSU)

        fg=cv2.erode(dst,None,iterations=1)
        bg=cv2.dilate(dst,None,iterations=4)

        _,bg=cv2.threshold(bg,1,128,1)

        mark=cv2.add(fg,bg)
        mark32=np.int32(mark)
        #dst.copy(temp)

        #seq=cv.FindContours(cv.fromarray(dst),self.mem,cv.CV_RETR_EXTERNAL,cv.CV_CHAIN_APPROX_SIMPLE)
        #cntr,h=cv2.findContours(dst,cv.CV_RETR_EXTERNAL,cv.CV_CHAIN_APPROX_SIMPLE)
        #print cntr,h
        #cv.DrawContours(cv.fromarray(temp),seq,(255,255,255),(255,255,255),1,cv.CV_FILLED)
        cv2.watershed(frm, mark32)
        self.final_mask=cv2.convertScaleAbs(mark32)
        #print temp

        #--outputs---
        #cv2.imshow("subtraction",fg)
        #cv2.imshow("thres",dst)
        #cv2.imshow("thres1",bg)
        #cv2.imshow("mark",mark)
        #cv2.imshow("final",self.final_mask)