model5.py-20170918

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本文介绍了一个使用Python实现的视频监控系统中的运动检测模块。通过对比当前帧与基准帧的差异来判断是否有物体移动，并对移动物体进行标记。系统首先初始化视频源，并逐帧处理，通过灰度化和高斯模糊等预处理步骤减少噪声，再利用阈值处理和轮廓检测技术识别运动区域。

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 18 13:30:05 2017

@author: vicky
"""

# # 导入必要的软件包
# import argparse
# import datetime
# import imutils
# import time
# import cv2
#
# # 创建参数解析器并解析参数
# ap = argparse.ArgumentParser()
# ap.add_argument("-v", "--video", help="path to the video file")
# ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")
# args = vars(ap.parse_args())
#
# # 如果video参数为None，那么我们从摄像头读取数据
# if args.get("/Users/vicky/Desktop/第五问视频/4p-c0.mp4", None) is None:
#     camera = cv2.VideoCapture(0)
#     time.sleep(0.25)
#
# # 否则我们读取一个视频文件
# else:
#     camera = cv2.VideoCapture(args["video"])
#
# # 初始化视频流的第一帧
# firstFrame = None
#
# # 遍历视频的每一帧
# while True:
#     # 获取当前帧并初始化occupied/unoccupied文本
#     (grabbed, frame) = camera.read()
#     text = "Unoccupied"
#
#     # 如果不能抓取到一帧，说明我们到了视频的结尾
#     if not grabbed:
#         break
#
#     # 调整该帧的大小，转换为灰阶图像并且对其进行高斯模糊
#     frame = imutils.resize(frame, width=500)
#     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#     gray = cv2.GaussianBlur(gray, (21, 21), 0)
#
#     # 如果第一帧是None，对其进行初始化
#     if firstFrame is None:
#         firstFrame = gray
#         continue
#
#     # 计算当前帧和第一帧的不同
#     frameDelta = cv2.absdiff(firstFrame, gray)
#     thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
#
#     # 扩展阀值图像填充孔洞，然后找到阀值图像上的轮廓
#     thresh = cv2.dilate(thresh, None, iterations=2)
#     (cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
#                                  cv2.CHAIN_APPROX_SIMPLE)
#
#     # 遍历轮廓
#     for c in cnts:
#         # if the contour is too small, ignore it
#         if cv2.contourArea(c) < args["min_area"]:
#             continue
#
#         # compute the bounding box for the contour, draw it on the frame,
#         # and update the text
#         # 计算轮廓的边界框，在当前帧中画出该框
#         (x, y, w, h) = cv2.boundingRect(c)
#         cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
#         text = "Occupied"
#
#     # draw the text and timestamp on the frame
#     # 在当前帧上写文字以及时间戳
#     cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
#                 cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
#     cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
#                 (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
#
#     #显示当前帧并记录用户是否按下按键
#     cv2.imshow("Security Feed", frame)
#     cv2.imshow("Thresh", thresh)
#     cv2.imshow("Frame Delta", frameDelta)
#     key = cv2.waitKey(1) & amp;
#     0xFF
#
#     # 如果q键被按下，跳出循环
#     if key == ord("q"):
#         break
#
# # 清理摄像机资源并关闭打开的窗口
# camera.release()
# cv2.destroyAllWindows()


import cv2
import numpy as np
 
camera = cv2.VideoCapture('/Users/vicky/Desktop/第五问视频/4p-c0.mp4',0)
width = int(camera.get(3))
height = int(camera.get(4))
 
firstFrame = None
 
while True:
  (grabbed, frame) = camera.read(0)
  gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
  gray = cv2.GaussianBlur(gray, (21, 21), 0)
 
  if firstFrame is None:
    firstFrame = gray
    continue
 
  frameDelta = cv2.absdiff(firstFrame, gray)
  thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
  # thresh = cv2.adaptiveThreshold(frameDelta,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
            # cv2.THRESH_BINARY,11,2)
  # thresh = cv2.adaptiveThreshold(frameDelta,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
  #           cv2.THRESH_BINARY,11,2)
  thresh = cv2.dilate(thresh, None, iterations=2)
  (_, cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
  for c in cnts:
     if cv2.contourArea(c) < 10000:
       continue
     (x, y, w, h) = cv2.boundingRect(c)
 
     cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

     cv2.imshow("frame", frame)
     firstFrame = gray.copy()

camera.release()
cv2.destroyAllWindows()