Python+OpenCV实现动态物体识别

# -*- coding: utf-8 -*- 
""" 
Created on Wed Sep 27 15:47:54 2017 
 
@author: tina 
""" 
import cv2 
import numpy as np 
 
camera = cv2.VideoCapture(0) # 参数0表示第一个摄像头 
# 判断视频是否打开 
if (camera.isOpened()): 
  print('Open') 
else: 
  print('摄像头未打开') 
 
# 测试用,查看视频size 
size = (int(camera.get(cv2.CAP_PROP_FRAME_WIDTH)), 
    int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))) 
print('size:'+repr(size)) 
 
es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 4)) 
kernel = np.ones((5, 5), np.uint8) 
background = None 
 
while True: 
  # 读取视频流 
  grabbed, frame_lwpCV = camera.read() 
  # 对帧进行预处理，先转灰度图，再进行高斯滤波。 
  # 用高斯滤波进行模糊处理，进行处理的原因：每个输入的视频都会因自然震动、光照变化或者摄像头本身等原因而产生噪声。对噪声进行平滑是为了避免在运动和跟踪时将其检测出来。 
  gray_lwpCV = cv2.cvtColor(frame_lwpCV, cv2.COLOR_BGR2GRAY) 
  gray_lwpCV = cv2.GaussianBlur(gray_lwpCV, (21, 21), 0) 
 
  # 将第一帧设置为整个输入的背景 
  if background is None: 
    background = gray_lwpCV 
    continue 
  # 对于每个从背景之后读取的帧都会计算其与北京之间的差异，并得到一个差分图（different map）。 
  # 还需要应用阈值来得到一幅黑白图像，并通过下面代码来膨胀（dilate）图像，从而对孔（hole）和缺陷（imperfection）进行归一化处理 
  diff = cv2.absdiff(background, gray_lwpCV) 
  diff = cv2.threshold(diff, 148, 255, cv2.THRESH_BINARY)[1] # 二值化阈值处理 
  diff = cv2.dilate(diff, es, iterations=2) # 形态学膨胀 
  # 显示矩形框 
  image, contours, hierarchy = cv2.findContours(diff.copy(), 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) < 1500: # 对于矩形区域，只显示大于给定阈值的轮廓，所以一些微小的变化不会显示。对于光照不变和噪声低的摄像头可不设定轮廓最小尺寸的阈值 
      continue 
    (x, y, w, h) = cv2.boundingRect(c) # 该函数计算矩形的边界框 
    cv2.rectangle(frame_lwpCV, (x, y), (x+w, y+h), (0, 255, 0), 2) 
 
  cv2.imshow('contours', frame_lwpCV) 
  cv2.imshow('dis', diff) 
 
  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()