# -*-coding:utf-8 -*-
import cv2
import time
# 保存截图
save_path = '/home/kali/Desktop/Cam/img/'
# 定义摄像头对象，其参数0表示第一个摄像头
camera = cv2.VideoCapture(0)
# 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))
# 帧率
fps = 5
# 总是取前一帧做为背景（不用考虑环境影响）
pre_frame = None
while(1):
start = time.time()
# 读取视频流
ret, frame = camera.read()
# 转灰度图
gray_lwpCV = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if not ret:
break
end = time.time()
cv2.imshow("capture", frame)
# 运动检测部分
seconds = end - start
if seconds < 1.0 / fps:
time.sleep(1.0 / fps - seconds)
gray_lwpCV = cv2.resize(gray_lwpCV, (500, 500))
# 用高斯滤波进行模糊处理
gray_lwpCV = cv2.GaussianBlur(gray_lwpCV, (21, 21), 0)
# 如果没有背景图像就将当前帧当作背景图片
if pre_frame is None:
pre_frame = gray_lwpCV
else:
#把两幅图的差的绝对值输出到另一幅图上面来
img_delta = cv2.absdiff(pre_frame, gray_lwpCV)
# threshold阈值函数(原图像应该是灰度图,对像素值进行分类的阈值,当像素值高于（有时是小于）阈值时应该被赋予的新的像素值,阈值方法)
thresh = cv2.threshold(img_delta, 25, 255, cv2.THRESH_BINARY)[1]
# 膨胀图像
thresh = cv2.dilate(thresh, None, iterations=2)
# findContours检测物体轮廓(寻找轮廓的图像,轮廓的检索模式,轮廓的近似办法)
contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
# 设置敏感度
# contourArea计算轮廓面积
if cv2.contourArea(c) < 1000:
continue
else:
print("出现目标物，请求核实")
cv2.imwrite(save_path + str(time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))) + '.jpg', frame)
#break
pre_frame = gray_lwpCV
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# release()释放摄像头
camera.release()
# destroyAllWindows()关闭所有图像窗口
cv2.destroyAllWindows()
