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