Python图像处理，cv2模块，OpenCV实现目标跟踪

 import cv2

    
  
    
 # 获取视频
    
 video = cv2.VideoCapture('007.mp4')
    
  
    
 # 生成椭圆结构元素
    
 es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 4))
    
 # 设置背景帧
    
 background = None
    
  
    
 while True:
    
     # 读取视频每一帧
    
     ret, frame = video.read()
    
  
    
     # 获取背景帧
    
     if background is None:
    
     # 将视频的第一帧图像转为灰度图
    
     background = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
     # 对灰度图进行高斯模糊,平滑图像
    
     background = cv2.GaussianBlur(background, (21, 21), 0)
    
     continue
    
  
    
     # 将视频的每一帧图像转为灰度图
    
     gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
  
    
     # 对灰度图进行高斯模糊,平滑图像
    
     gray_frame = cv2.GaussianBlur(gray_frame, (21, 21), 0)
    
  
    
     # 获取当前帧与背景帧之间的图像差异,得到差分图
    
     diff = cv2.absdiff(background, gray_frame)
    
  
    
     # 利用像素点值进行阈值分割,得到一副黑白图像
    
     diff = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1]
    
  
    
     # 膨胀图像,减少错误
    
     diff = cv2.dilate(diff, es, iterations=2)
    
  
    
     # 得到图像中的目标轮廓
    
     image, cnts, hierarchy = cv2.findContours(diff.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
  
    
     for c in cnts:
    
     if cv2.contourArea(c) < 1500:
    
         continue
    
     # 绘制目标矩形框
    
     (x, y, w, h) = cv2.boundingRect(c)
    
     cv2.rectangle(frame, (x+2, y+2), (x+w, y+h), (0, 255, 0), 2)
    
  
    
     # 显示检测视频
    
     cv2.namedWindow('contours', 0)
    
     cv2.resizeWindow('contours', 600, 400)
    
     cv2.imshow('contours', frame)
    
  
    
     # 显示差异视频
    
     cv2.namedWindow('diff', 0)
    
     cv2.resizeWindow('diff', 600, 400)
    
     cv2.imshow('diff', diff)
    
     if cv2.waitKey(1) & 0xff == ord('q'):
    
     break
    
  
    
 # 结束
    
 cv2.destroyAllWindows()
    
 video.release()
    
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 import cv2

    
  
    
 # 获取视频
    
 video = cv2.VideoCapture('traffic.flv')
    
 # KNN背景分割器,设置阴影检测
    
 bs = cv2.createBackgroundSubtractorKNN(detectShadows=True)
    
  
    
 while True:
    
     # 读取视频每一帧
    
     ret, frame = video.read()
    
     # 计算视频的前景掩码
    
     fgmask = bs.apply(frame)
    
     # 图像阈值化
    
     th = cv2.threshold(fgmask.copy(), 244, 255, cv2.THRESH_BINARY)[1]
    
     # 膨胀图像,减少错误
    
     dilated = cv2.dilate(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)), iterations=2)
    
  
    
     # 得到图像中的目标轮廓
    
     image, contours, hier = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
  
    
     for c in contours:
    
     if cv2.contourArea(c) > 1600:
    
         # 绘制目标矩形框
    
         (x, y, w, h) = cv2.boundingRect(c)
    
         cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 0), 2)
    
  
    
     # 显示差异视频
    
     cv2.imshow('mog', fgmask)
    
     # cv2.imshow('thresh', th)
    
     # 显示检测视频
    
     cv2.imshow('detection', frame)
    
     if cv2.waitKey(30) & 0xff == ord('q'):
    
     break
    
  
    
 video.release()
    
 cv2.destroyAllWindows()
    
  
    
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