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import cv2
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import time
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import threading
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import platform
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import os
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import shutil
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# 背景介绍：
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# 1、采用USB摄像头后每台柜子的成本能降低200元，以每年300台柜子的比例来说，一年就能节约60000元（由于旧的USB摄像头容易松动，从而导致售后问题，只要每年因为摄像头的售后费用低于60000元，采用USB摄像头是可行的）
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# 2、旧的3.0系统发了很多的客户，主柜基本都是USB摄像头，考虑这批客户的USB摄像头都不能使用(大约数百个至1000个左右)，全部采用海康的摄像头后，一是增加了成本，二是大量的旧件无法使用，故需要考虑在5.0系统中集成USB摄像头
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#
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# 其他：
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# 1、5.0的方案中由于下位机采用了网络通讯，故交换机并不会受到摄像头方案切换的影响（不能带入方案成本计算中）
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# 2、5.0的方案中由于考虑了副柜的情况，故如果有副柜的主柜常常会配置扩展硬盘（常见是：1TB），用于视频录制，在摄像头方案的成本的时候只有主柜才会有扩展硬盘，硬盘选择与否与摄像头是网络摄像头还是USB摄像头无关
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# 3、如果有其他人问到USB摄像头和网络摄像头的方案技术选择问题，请转述上述的2个观点，如果有动机相关的问题，请转述“背景介绍”中的内容
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#
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# 目前的方案如下，共2种：
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# 1、RTSP -> SRS -> liveStream -> OpenCv -> Detect -> MP4 (RMS5.0初代，只支持网络摄像头)
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# + -> webrtc前端渲染
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#
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# 2、USB Camera -> VLC -> liveStream -> OpenCv -> Detect -> MP4 （支持USB摄像头和网络摄像头）
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# + -> 前端渲染
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#
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# 主柜采用黑色的USB摄像头，需要注意：
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# 1、当主柜的人脸USB摄像头也接入的时候，camera_path可能需要调整，或者通过udev绑定设备
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# 2、如果有其他的程序使用了大量的磁盘空间，可能会导致所有的视频文件被删除
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# 3、人脸和人体检测会导致CPU负载上升
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class Camera:
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def __init__(self,debug_flag=False,camera_path=0,folder_path="usb_camera",face_detect=False,body_detect=False):
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# 用于本地调试
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self.debug = debug_flag
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# 人脸检测和上半身检测开关
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self.face_detect=face_detect
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self.body_detect=body_detect
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# 获取操作系统类型
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self.sys_plat = platform.system().lower()
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# 初始化Linux下的视频文件夹
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self.video_folder_in_linux = "/data/"+folder_path
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self.init_linux_video_folder()
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# 初始化USB摄像头
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self.cap = cv2.VideoCapture(camera_path)
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# camera_worker状态控制
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self.worker_status = False
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# 获取摄像头的宽度和高度
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self.width = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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self.height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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# 视频编解码器和VideoWriter对象
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self.fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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self.out = None
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# 初始化前一帧、移动侦测状态和人脸检测器
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self.ret, frame1 = self.cap.read()
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self.prev_frame = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
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self.motion_detected = False
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self.motion_start_time = None
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# 人脸检测
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self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
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# 上半身检测
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self.upperbody_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_upperbody.xml')
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def start(self):
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self.worker_status=True
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p = threading.Thread(target=self.camera_worker)
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p.start()
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def camera_worker(self):
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while self.worker_status:
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ret, frame2 = self.cap.read()
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if not ret:
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break
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# 将当前帧转换为灰度图像
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curr_frame = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)
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# 计算当前帧和前一帧的差异
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frame_diff = cv2.absdiff(curr_frame, self.prev_frame)
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# 应用阈值处理
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_, thresh = cv2.threshold(frame_diff, 30, 255, cv2.THRESH_BINARY)
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# 找到轮廓
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contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 移动侦测并绘制矩形框显示移动区域（调试）
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for contour in contours:
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if cv2.contourArea(contour) > 1000:
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if self.debug:
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(x, y, w, h) = cv2.boundingRect(contour)
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cv2.rectangle(frame2, (x, y), (x+w, y+h), (0, 255, 0), 2)
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self.motion_detected = True
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self.motion_start_time = time.time()
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# 人脸和上半身检测(注意性能)
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faces=[]
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upperbodies=[]
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if self.face_detect:
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faces = self.face_cascade.detectMultiScale(curr_frame, 1.1, 4)
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if self.body_detect:
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upperbodies = self.upperbody_cascade.detectMultiScale(curr_frame, 1.1, 4)
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# 如果检测到移动、人脸、上半身，则开始录制视频
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if self.motion_detected or len(faces) > 0 or len(upperbodies) > 0:
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self.motion_start_time = time.time()
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if self.out is None:
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filename = self.new_video_filename()
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self.out = cv2.VideoWriter(filename, self.fourcc, 20.0, (self.width, self.height))
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self.out.write(frame2)
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else:
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if self.motion_start_time is not None and time.time() - self.motion_start_time > 3:
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if self.out is not None:
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self.out.release()
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self.out = None
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# 如果是Linux，如果磁盘空间超过阈值则删除目录下的文件
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if self.sys_plat == "linux":
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self.delete_oldest_mp4_files(self.video_folder_in_linux)
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if self.debug:
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# 显示结果
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cv2.imshow('Motion Detection', frame2)
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# 更新前一帧和移动侦测状态
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self.prev_frame = curr_frame
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self.motion_detected = False
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if self.debug:
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# 按下q键退出循环
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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def stop(self):
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self.worker_status=False
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# 释放资源
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# self.cap.release()
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if self.out is not None:
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self.out.release()
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if self.debug:
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cv2.destroyAllWindows()
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def new_video_filename(self):
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if self.sys_plat == "windows":
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timestamp = time.strftime("%Y%m%d%H%M%S")
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filename = "{}.mp4".format(timestamp)
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else:
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timestamp = time.strftime("%Y%m%d%H%M%S")
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filename = "{}.mp4".format(timestamp)
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filename = self.video_folder_in_linux+"/"+filename
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return filename
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def init_linux_video_folder(self):
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if self.sys_plat == "linux":
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self.create_directory(self.video_folder_in_linux)
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# 递归创建文件夹
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def create_directory(self,path):
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if not os.path.exists(path):
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parent_directory = os.path.dirname(path)
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if parent_directory != '':
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self.create_directory(parent_directory)
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os.mkdir(path)
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# 磁盘空间超过阈值的时候有且只删除最久的10个MP4文件
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def delete_oldest_mp4_files(self,folder_path):
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total, used, free = shutil.disk_usage(folder_path)
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if (used / total) > 0.7:
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mp4_files = []
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for root, dirs, filenames in os.walk(folder_path):
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for file in filenames:
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if file.endswith(".mp4"):
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mp4_files.append(os.path.join(root, file))
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mp4_files.sort(key=os.path.getmtime)
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for file in mp4_files[:10]:
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os.remove(file)
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class UsbCamera(Camera):
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pass
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class NetworkCamera(Camera):
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def __init__(self, debug_flag=False, camera_path=0, folder_path="usb_camera", face_detect=False, body_detect=False):
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super().__init__(debug_flag, camera_path, folder_path, face_detect, body_detect)
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def start_service():
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# 正常业务逻辑
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from conf import setting
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from models.hikvision import Hikvision
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current_terminal_id = setting.TERMINAL_ID
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camera_list = Hikvision.filter(terminal_id=current_terminal_id).all()
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usb_cameras = list(filter(lambda x:x.camera_type==0,camera_list))
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network_cameras = list(filter(lambda x:x.camera_type==1,camera_list))
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if usb_cameras:
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print("启动USB摄像头录制")
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UsbCamera().start()
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if network_cameras:
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for c in network_cameras:
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print(f"启动网络摄像头:{c.username}:{c.password}@{c.ip} , channel:{c.channel}")
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NetworkCamera(False,f"rtsp://{c.username}:{c.password}@{c.ip}:554/Streaming/Channels/102",c.channel).start()
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if __name__ == "__main__":
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Camera(True).start()
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