详见:https://blog.csdn.net/qq_35624030/article/details/79833269
附上代码:
import cv2
import time
# 定义摄像头对象,其参数0表示第一个摄像头
camera = cv2.VideoCapture(0)
# 测试用,查看视频size
width = int(camera.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
size = width,height
#打印一下分辨率
print(repr(size))
#设置一下帧数和前背景
fps = 5
pre_frame = None
while (1):
start = time.time()
# 读取视频流
ret, frame = camera.read()
# 转灰度图
gray_pic = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if not ret:
print("打开摄像头失败")
break
end = time.time()
cv2.imshow("capture", frame)
# 运动检测部分,看看是不是5FPS
seconds = end - start
if seconds < 1.0 / fps:
time.sleep(1.0 / fps - seconds)
gray_pic = cv2.resize(gray_pic, (480, 480))
# 用高斯滤波进行模糊处理
gray_pic = cv2.GaussianBlur(gray_pic, (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_pic
else:
# absdiff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_pic)
# 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, 30, 255, cv2.THRESH_BINARY)[1]
# 用一下腐蚀与膨胀
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("有人员活动!!!")
# 保存图像
TI = time.strftime('%Y-%m-%d', time.localtime(time.time()))
cv2.imwrite("D:\\PYthon\\first_j\\" + "JC"+TI+ '.jpg', frame)
break
pre_frame = gray_pic
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()