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()