python 检测视频区域是否有变化

#!/usr/bin/env python
# coding: utf-8
# @author: sSWans
# @file: main.py
# @time: 2018/1/11 15:54
 
import os
import random
from _datetime import datetime
 
import cv2
 
path = 'd:\\C理论\\ti'
 
 
# 遍历目录下的视频文件
def get_files(fpath):
    files_list = []
    for i in os.listdir(fpath):
        files_list.append(os.path.join(fpath, i))
    return files_list
 
 
# 视频处理
def process(file, fname):
    # camera = cv2.VideoCapture(0)  # 参数0表示第一个摄像头
    camera = cv2.VideoCapture(file)
    # 参数设置，监测矩形区域
    rectangleX = 750  # 矩形最左点x坐标
    rectangleXCols = 340  # 矩形x轴上的长度
    rectangleY = 320  # 矩形最上点y坐标
    rectangleYCols = 30  # 矩形y轴上的长度
    KeyFrame = 17  # 取关键帧的间隔数，根据视频的帧率设置，我的视频是16FPS
    counter = 1  # 取帧计数器
    pre_frame = None  # 总是取视频流前一帧做为背景相对下一帧进行比较
 
    # 判断视频是否打开
    if not camera.isOpened():
        print('视频文件打开失败！')
 
    width = int(camera.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
    print('视频尺寸（高，宽）:', height, width)
 
    if rectangleXCols == 0:
        rectangleXCols = width - rectangleX
    if rectangleYCols == 0:
        rectangleYCols = height - rectangleY
    start_time = datetime.now()
    print('{} 开始处理文件： {}'.format(start_time.strftime('%H:%M:%S'), fname))
    while True:
        grabbed, frame_lwpCV = camera.read()  # 读取视频流
        if grabbed:
            if counter % KeyFrame == 0:
                # if not grabbed:
                #     print('{} 完成处理文件： {} 。。。  '.format(datetime.now().strftime('%H:%M:%S'),fname))
                #     break
                gray_lwpCV = cv2.cvtColor(frame_lwpCV, cv2.COLOR_BGR2GRAY)  # 转灰度图
                gray_lwpCV = gray_lwpCV[rectangleY:rectangleY + rectangleYCols, rectangleX:rectangleX + rectangleXCols]
                lwpCV_box = cv2.rectangle(frame_lwpCV, (rectangleX, rectangleY),
                                          (rectangleX + rectangleXCols, rectangleY + rectangleYCols), (0, 255, 0),
                                          2)  # 用绿色矩形框显示监测区域
                # cv2.imshow('lwpCVWindow', frame_lwpCV)  # 显示视频播放窗口，开启消耗时间大概是3倍
                gray_lwpCV = cv2.GaussianBlur(gray_lwpCV, (21, 21), 0)
                if pre_frame is None:
                    pre_frame = gray_lwpCV
                else:
                    img_delta = cv2.absdiff(pre_frame, gray_lwpCV)
                    thresh = cv2.threshold(img_delta, 25, 255, cv2.THRESH_BINARY)[1]
                    thresh = cv2.dilate(thresh, None, iterations=2)
                    #image, contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
                    contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
                    for x in contours:
                        if cv2.contourArea(x) < 1000:  # 设置敏感度
                            continue
                        else:
                            cv2.imwrite(
                                'image/' + fname + '_' + datetime.now().strftime('%H%M%S') + '_' + str(
                                    random.randrange(0, 9999)) + '.jpg',
                                frame_lwpCV)
                            # print("监测到移动物体。。。  ", datetime.now().strftime('%H:%M:%S'))
                            break
                    pre_frame = gray_lwpCV
            counter += 1
            key = cv2.waitKey(1) & 0xFF
            if key == ord('q'):
                break
        else:
            end_time = datetime.now()
            print('{} 完成处理文件： {}  耗时：{}'.format(end_time.strftime('%H:%M:%S'), fname, end_time - start_time))
            break
    camera.release()
    # cv2.destroyAllWindows() #  与上面的imshow对应
 
 
for file in get_files(path):
    fname = file.split('\\')[-1].replace('.mp4', '')
    process(file, fname)