KNN、MOG2和GMG

好像能判断物体移开，花屏效果不好,mouse也不能检测。

mog2:

  

   

    

     

    
    

     

      # coding:utf-8
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import cv2
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # 获取摄像头对象
     
    
   

    

     

    
    

     

      cap = cv2.VideoCapture(0)
     
    
   

    

     

    
    

     

      # 背景分割器对象
     
    
   

    

     

    
    

     

      mog = cv2.createBackgroundSubtractorMOG2()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      while True:
     
    
   

    

     

    
    

     

       ret, frame = cap.read()
     
    
   

    

     

    
    

     

       fgmask = mog.apply(frame)
     
    
   

    

     

    
    

     

       cv2.imshow("frame", fgmask)
     
    
   

    

     

    
    

     
 if cv2.waitKey(5) & 0xff == ord("q"):
     
    
   

    

     

    
    

     
 break
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      cap.release()
     
    
   

    

     

    
    

     

      cv2.destroyAllWindows()
     
    
  
 

knn:10ms以内，人是完整的，人走消失的挺快

  

   

    

     

    
    

     

      import time
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import cv2
     
    
   

    

     

    
    

     

      import numpy as np
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      camera = cv2.VideoCapture(0) # 参数0表示第一个摄像头
     
    
   

    

     

    
    

     

      bs = cv2.createBackgroundSubtractorKNN(detectShadows=True)
     
    
   

    

     

    
    

     

      es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
     
    
   

    

     

    
    

     

      while True:
     
    
   

    

     

    
    

     

       grabbed, frame_lwpCV = camera.read()
     
    
   

    

     

    
    

     

       start=time.time()
     
    
   

    

     

    
    

     

       frame_lwpCV = cv2.GaussianBlur(frame_lwpCV, (5, 5), 0)
     
    
   

    

     

    
    

     

       fgmask = bs.apply(frame_lwpCV) # 背景分割器，该函数计算了前景掩码
     
    
   

    

     

    
    

     
 # 二值化阈值处理，前景掩码含有前景的白色值以及阴影的灰色值，在阈值化图像中，将非纯白色（244~255）的所有像素都设为0，而不是255
     
    
   

    

     

    
    

     

       th = cv2.threshold(fgmask.copy(), 244, 255, cv2.THRESH_BINARY)[1]
     
    
   

    

     

    
    

     
 # 下面就跟基本运动检测中方法相同，识别目标，检测轮廓，在原始帧上绘制检测结果
     
    
   

    

     

    
    

     

       dilated = cv2.dilate(th, es, iterations=2) # 形态学膨胀
     
    
   

    

     

    
    

     

       image, contours, hierarchy = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 该函数计算一幅图像中目标的轮廓
     
    
   

    

     

    
    

     
 for c in contours:
     
    
   

    

     

    
    

     
 if cv2.contourArea(c) > 1600:
     
    
   

    

     

    
    

     

       (x, y, w, h) = cv2.boundingRect(c)
     
    
   

    

     

    
    

     

       cv2.rectangle(frame_lwpCV, (x, y), (x + w, y + h), (255, 255, 0), 2)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       print(time.time()-start,frame_lwpCV.shape)
     
    
   

    

     

    
    

     

       cv2.imshow('mog', fgmask)
     
    
   

    

     

    
    

     

       cv2.imshow('thresh', th)
     
    
   

    

     

    
    

     

       cv2.imshow('detection', frame_lwpCV)
     
    
   

    

     

    
    

     

       key = cv2.waitKey(1) & 0xFF
     
    
   

    

     

    
    

     
 # 按'q'健退出循环
     
    
   

    

     

    
    

     
 if key == ord('q'):
     
    
   

    

     

    
    

     
 break
     
    
   

    

     

    
    

     

      # When everything done, release the capture
     
    
   

    

     

    
    

     

      camera.release()
     
    
   

    

     

    
    

     

      cv2.destroyAllWindows()
     
    
  
 

花屏部分检测比较具体：不能屏蔽花屏

加了椭圆检测器：

  

   

    

     

    
    

     

      import time
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import cv2
     
    
   

    

     

    
    

     

      import numpy as np
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      '''
     
    
   

    

     

    
    

     

      retval = cv.createBackgroundSubtractorKNN([, history[, dist2Threshold[, detectShadows]]])
     
    
   

    

     

    
    

     

      Parameters
     
    
   

    

     

    
    

     

       history Length of the history.
     
    
   

    

     

    
    

     

       dist2Threshold Threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample. This parameter does not affect the background update.
     
    
   

    

     

    
    

     

       detectShadows If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.
     
    
   

    

     

    
    

     

      '''
     
    
   

    

     

    
    

     

      # KNN背景分割器
     
    
   

    

     

    
    

     

      knn = cv2.createBackgroundSubtractorKNN(detectShadows=True)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      camera = cv2.VideoCapture(0)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      def drawCnt(fn, cnt):
     
    
   

    

     

    
    

     
 if cv2.contourArea(cnt) > 1600:
     
    
   

    

     

    
    

     

       (x, y, w, h) = cv2.boundingRect(cnt)
     
    
   

    

     

    
    

     

       cv2.rectangle(fn, (x, y), (x + w, y + h), (255, 255, 0), 2)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      while True:
     
    
   

    

     

    
    

     

       ret, frame = camera.read()
     
    
   

    

     

    
    

     
 if not ret:
     
    
   

    

     

    
    

     
 break
     
    
   

    

     

    
    

     

       start=time.time()
     
    
   

    

     

    
    

     

       fgmask = knn.apply(frame)
     
    
   

    

     

    
    

     

       th = cv2.threshold(np.copy(fgmask), 244, 255, cv2.THRESH_BINARY)[1]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
     
    
   

    

     

    
    

     

       dilated = cv2.dilate(th, es, iterations=2)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       image, contours, hierarchy = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
     
    
   

    

     

    
    

     
 for c in contours:
     
    
   

    

     

    
    

     

       drawCnt(frame, c)
     
    
   

    

     

    
    

     

       print("time",time.time()-start,frame.shape)
     
    
   

    

     

    
    

     

       cv2.imshow("knn", fgmask)
     
    
   

    

     

    
    

     

       cv2.imshow("thresh", th)
     
    
   

    

     

    
    

     

       cv2.imshow("detection", frame)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 if cv2.waitKey(100) & 0xff == ord("q"):
     
    
   

    

     

    
    

     
 break
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      camera.release()
     
    
   

    

     

    
    

     

      cv2.destroyAllWindows()
     
    
  
 

gmg，初始化很慢：人进入会引起小的图，

  

   

    

     

    
    

     

      import time
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import numpy as np
     
    
   

    

     

    
    

     

      import cv2
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      '''
     
    
   

    

     

    
    

     

      retval = cv.bgsegm.createBackgroundSubtractorGMG( [, initializationFrames[, decisionThreshold]] )
     
    
   

    

     

    
    

     

      Parameters
     
    
   

    

     

    
    

     

       initializationFrames number of frames used to initialize the background models.
     
    
   

    

     

    
    

     

       decisionThreshold Threshold value, above which it is marked foreground, else background.
     
    
   

    

     

    
    

     

      '''
     
    
   

    

     

    
    

     

      # GMG背景分割器
     
    
   

    

     

    
    

     

      fgbg = cv2.bgsegm.createBackgroundSubtractorGMG()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      camera = cv2.VideoCapture(0)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      ret, frame = camera.read()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      while ret:
     
    
   

    

     

    
    

     

       start=time.time()
     
    
   

    

     

    
    

     

       fgmask = fgbg.apply(frame)
     
    
   

    

     

    
    

     

       th = cv2.threshold(np.copy(fgmask), 244, 255, cv2.THRESH_BINARY)[1]
     
    
   

    

     

    
    

     

       th = cv2.erode(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)), iterations=2)
     
    
   

    

     

    
    

     

       dilated = cv2.dilate(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 3)), iterations=2)
     
    
   

    

     

    
    

     

       image, contours, hier = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
     
    
   

    

     

    
    

     
 for c in contours:
     
    
   

    

     

    
    

     
 if cv2.contourArea(c) > 1000:
     
    
   

    

     

    
    

     

       (x, y, w, h) = cv2.boundingRect(c)
     
    
   

    

     

    
    

     

       cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 0), 2)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       print("time",time.time()-start)
     
    
   

    

     

    
    

     

       cv2.imshow("GMG", fgmask)
     
    
   

    

     

    
    

     

       cv2.imshow("thresh", th)
     
    
   

    

     

    
    

     

       cv2.imshow("diff", frame & cv2.cvtColor(fgmask, cv2.COLOR_GRAY2BGR))
     
    
   

    

     

    
    

     

       cv2.imshow("detection", frame)
     
    
   

    

     

    
    

     

       ret, frame = camera.read()  # 读取视频帧数据
     
    
   

    

     

    
    

     
 if cv2.waitKey(100) & 0xff == ord("q"):
     
    
   

    

     

    
    

     
 break
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      camera.release()
     
    
   

    

     

    
    

     

      cv2.destroyAllWindows()
     
    
  
 

文章来源: blog.csdn.net，作者：网奇，版权归原作者所有，如需转载，请联系作者。

原文链接：blog.csdn.net/jacke121/article/details/95661082