#!/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 = 'c:\python37'
# 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 = 80 # 矩形最左点x坐标
rectangleXCols = 0 # 矩形x轴上的长度
rectangleY = 340 # 矩形最上点y坐标
rectangleYCols = 100 # 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)
#cnts=cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
#print(1,cnts)
#image, contours, hierarchy = cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
image, contours= cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
print(contours)
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', '')
fname="aa.mp4"
#file=fname
#process(file, fname)
camera = cv2.VideoCapture(file)
# 参数设置，监测矩形区域
rectangleX = 80 # 矩形最左点x坐标
rectangleXCols = 0 # 矩形x轴上的长度
rectangleY = 340 # 矩形最上点y坐标
rectangleYCols = 100 # 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)
#cnts=cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
#print(1,cnts)
#image, contours, hierarchy = cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
image, contours= cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
print(contours)
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()
