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455 lines
20 KiB
455 lines
20 KiB
# 导入代码依赖
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import time
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import av
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import os
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import cv2
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import numpy as np
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import threading
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import importlib.util
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import datetime
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import queue
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from collections import deque
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from core.DBManager import mDBM,DBManager
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from myutils.MyLogger_logger import LogHandler
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from myutils.ConfigManager import myCongif
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from model.plugins.ModelBase import ModelBase
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import sys
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from PIL import Image
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class VideoCaptureWithFPS:
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'''视频捕获的封装类，是一个通道一个'''
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def __init__(self, source):
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self.source = source
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self.width = None
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self.height = None
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self.cap = cv2.VideoCapture(self.source)
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if self.cap.isOpened(): #若没有打开成功，在读取画面的时候，已有判断和处理 -- 这里也要检查下内存的释放情况
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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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#self.fps = fps # 线程保持最大帧率的刷新画面---过高的帧率会影响CPU性能，但过地的帧率会造成帧积压
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self.fps = self.cap.get(cv2.CAP_PROP_FPS) + 20 #只是个参考值，休眠时间要比帧率快点，由于read也需要耗时。
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#print(self.fps)
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self.running = True
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self.frame_queue = queue.Queue(maxsize=1)
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#self.frame = None
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#self.read_lock = threading.Lock()
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self.thread = threading.Thread(target=self.update)
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self.thread.start()
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def update(self):
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while self.running:
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ret, frame = self.cap.read()
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if not ret:
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#time.sleep(1.0 / self.fps)
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continue
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resized_frame = cv2.resize(frame, (int(self.width / 2), int(self.height / 2)))
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# with self.read_lock:
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# self.frame = resized_frame
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if not self.frame_queue.full():
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self.frame_queue.put(resized_frame)
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time.sleep(1.0 / self.fps) #按照视频源的帧率进行休眠
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#print("Frame updated at:",time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
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def read(self):
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# with self.read_lock:
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# frame = self.frame.copy() if self.frame is not None else None
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if not self.frame_queue.empty():
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return True, self.frame_queue.get()
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else:
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return False, None
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def release(self):
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self.running = False
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self.thread.join()
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self.cap.release()
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class ModelManager:
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def __init__(self):
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self.verify_list = {} #模型的主要数据
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self.bRun = True
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self.logger = LogHandler().get_logger("ModelManager")
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# 本地YOLOv5仓库路径
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self.yolov5_path = myCongif.get_data("yolov5_path")
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#self.buflen = myCongif.get_data("buffer_len")
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self.icout_max = myCongif.get_data("RESET_INTERVAL") #跟视频帧序用一个变量
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self.frame_rate = myCongif.get_data("frame_rate")
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self.frame_interval = 1.0 / int(myCongif.get_data("verify_rate"))
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#保存视频相关内容
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self.FPS = myCongif.get_data("verify_rate") # 视频帧率--是否能实现动态帧率
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self.fourcc = cv2.VideoWriter_fourcc(*'mp4v') # 使用 mp4 编码
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#基于模型运行环境进行相应初始化工作
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# self.model_platform = myCongif.get_data("model_platform")
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# if self.model_platform == "acl":
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# self._init_acl()
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def __del__(self):
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self.logger.debug("释放资源")
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def _open_view(self,url,itype): #打开摄像头 0--USB摄像头，1-RTSP,2-海康SDK
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if itype == 0:
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cap = VideoCaptureWithFPS(int(url))
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elif itype == 1:
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cap = VideoCaptureWithFPS(url)
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else:
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raise Exception("视频参数错误！")
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return cap
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def _import_model(self,model_name,model_path,threshold):
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'''
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根据路径，动态导入模块
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:param model_name: 模块名称
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:param model_path: 模块路径
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:param threshold: 置信阈值
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:return:
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'''
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if os.path.exists(model_path):
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module_spec = importlib.util.spec_from_file_location(model_name, model_path)
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if module_spec is None:
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self.logger.error(f"{model_path} 加载错误")
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return None
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module = importlib.util.module_from_spec(module_spec)
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module_spec.loader.exec_module(module)
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md = getattr(module, "Model")(model_path,threshold) #实例化类
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if not isinstance(md, ModelBase):
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self.logger.error("{} not zf_model".format(md))
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return None
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else:
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self.logger.error("{}文件不存在".format(model_path))
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return None
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return md
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def getschedule(self,c2m_id,myDBM):
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'''
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根据c2mID 查询该算法的布防时间
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:param c2m_id:
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:return: 以day为行，hour作为列的，布防标识值二维list
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'''
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strsql = f"select day,hour,status from schedule where channel2model_id ={c2m_id} order by hour asc,day asc;"
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datas = myDBM.do_select(strsql)
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onelist = []
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twolist = []
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threelist = []
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fourlist = []
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fivelist = []
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sixlist = []
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sevenlist = []
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if datas:
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for i in range(24):
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onelist.append(datas[i*7 + 0][2])
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twolist.append(datas[i*7 + 1][2])
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threelist.append(datas[i*7 + 2][2])
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fourlist.append(datas[i*7 + 3][2])
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fivelist.append(datas[i*7 + 4][2])
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sixlist.append(datas[i*7 + 5][2])
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sevenlist.append(datas[i*7 + 6][2])
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else:
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self.logger(f"没有数据？{c2m_id}")
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onelist = [1]*24
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twolist = [1]*24
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threelist = [1]*24
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fourlist = [1]*24
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fivelist = [1]*24
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sixlist = [1]*24
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sevenlist = [1]*24
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schedule_list = []
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schedule_list.append(onelist)
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schedule_list.append(twolist)
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schedule_list.append(threelist)
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schedule_list.append(fourlist)
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schedule_list.append(fivelist)
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schedule_list.append(sixlist)
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schedule_list.append(sevenlist)
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return schedule_list
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def verify(self,frame,myModle_list,myModle_data,channel_id,schedule_list,result_list,isdraw=1):
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'''验证执行主函数，实现遍历通道关联的模型，调用对应模型执行验证，模型文件遍历执行'''
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img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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#img = np.ascontiguousarray(img, dtype=np.float32) / 255.0 # 转换为内存连续存储的数组 --该函数可以待定下是不是所有模型都可以做
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# img = frame.to_ndarray(format="bgr24")
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#img = frame
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# 使用 模型 进行目标检测
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i_warn_count = 0 #报警标签
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isverify = False
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# for i in range(len(myModle_list)): # 遍历通道关联的算法进行检测，若不控制模型数量，有可能需要考虑多线程执行。
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# model = myModle_list[i]
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# data = myModle_data[i]
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# schedule = schedule_list[i]
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# result = result_list[i]
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# #验证检测计划，是否在布防时间内
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# now = datetime.datetime.now() # 获取当前日期和时间
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# weekday = now.weekday() # 获取星期几，星期一是0，星期天是6
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# hour = now.hour
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# result.pop(0) # 保障结果数组定长 --先把最早的结果推出数组
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# if schedule[weekday][hour] == 1: #不在计划则不进行验证，直接返回图片
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# # 调用模型，进行检测，model是动态加载的，具体的判断标准由模型内执行 ---- *********
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# isverify = True
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# detections, bwarn, warntext = model.verify(img, data,isdraw) #****************重要
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# # 对识别结果要部要进行处理
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# if bwarn: # 整个识别有产生报警
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# #根据模型设定的时间和占比判断是否
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# # 绘制报警文本
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# cv2.putText(img, 'Intruder detected!', (50, (i_warn_count + 1) * 50),
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# cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
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# i_warn_count += 1
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# result.append(1) #要验证数组修改，是地址修改吗？
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# else: #没有产生报警也需要记录，统一计算占比
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# result.append(0)
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# else:
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# result.append(0)
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if not isverify: #没做处理，直接返回的，需要控制下帧率，太快读取没有意义。
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time.sleep(1.0/self.frame_rate) #给个默认帧率，不超过30帧，---若经过模型计算，CPU下单模型也就12帧这样
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# 将检测结果图像转换为帧 -- 需要确认前面对img的处理都是累加的。
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#img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #numpy.ndarray
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if isinstance(img, np.ndarray):
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new_frame = av.VideoFrame.from_ndarray(img, format="rgb24") #AVFrame
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new_frame.pts = None # 添加此行确保有pts属性
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#self.logger.debug("img 是np.darry")
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else:
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#self.logger.error("img不是np.darry")
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new_frame = None
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#处理完的图片后返回-gbr模式 （new_frame 是 rgb)
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img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
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return new_frame,img
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def dowork_thread(self,channel_id):
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'''一个通道一个线程，关联的模型在一个线程检测,局部变量都是一个通道独有'''
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channel_data = self.verify_list[channel_id] #一通道一线程 [url,type,True,img_buffer,img,icount]
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view_buffer_deque = deque(maxlen=myCongif.get_data("buffer_len"))
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#查询关联的模型 --- 在循环运行前把基础数据都准备好
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myDBM = DBManager()
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myDBM.connect()
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strsql = (f"select t1.model_id,t1.check_area,t1.polygon ,t2.duration_time,t2.proportion,t2.model_path,t1.ID,"
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f"t2.model_name,t1.conf_threshold "
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f"from channel2model t1 left join model t2 on t1.model_id = t2.ID where t1.channel_id ={channel_id};")
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#print(strsql)
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myModels = myDBM.do_select(strsql)
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#加载模型 --- 是不是要做个限制，一个视频通道关联算法模块的上限 --- 关联多了一个线程执行耗时较多，造成帧率太低，或者再多线程并发 #？
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myModle_list = [] #存放模型对象List 一个模型一个
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myModle_data = [] #存放检测参数 一个模型一个
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schedule_list = [] #布防策略 -一个模型一个
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result_list = [] #检测结果记录 -一个模型一个
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proportion_list = []#占比设定 -一个模型一个
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warn_save_count = []#没个模型触发报警后，保存录像的最新帧序号 -一个模型一个
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threshold_list = [] #模型针对该通道的一个置信阈值， -个模型一个
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view_buffer = []
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ibuffer_count = 0
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last_img = None
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#获取视频通道的模型相关数据-list
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for model in myModels:
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#基于基类实例化模块类
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m = self._import_model("",model[5],model[8]) #动态加载模型处理文件py --需要验证模型文件是否能加载
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if m:
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myModle_list.append(m) #没有成功加载的模型原画输出
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myModle_data.append(model)
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#model[6] -- c2m_id --把通道对于模型的 0-周一，6-周日
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schedule_list.append(self.getschedule(model[6],myDBM))
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result = [0 for _ in range(model[3] * myCongif.get_data("verify_rate"))] #初始化时间*验证帧率数量的结果list
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result_list.append(result)
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proportion_list.append(model[4]) #判断是否报警的占比
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warn_save_count.append(0) #保存录像的最新帧初始化为0
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#开始拉取画面循环检测
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cap = None
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iread_count =0 #失败读取的次数
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last_frame_time = time.time() #初始化个读帧时间
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while channel_data[2]: #基于tag 作为运行标识。 线程里只是读，住线程更新，最多晚一轮，应该不用线程锁。需验证
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# 帧率控制帧率
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current_time = time.time()
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elapsed_time = current_time - last_frame_time
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if elapsed_time < self.frame_interval:
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time.sleep(self.frame_interval - elapsed_time) #若小于间隔时间则休眠
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#*********取画面*************
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if not cap: #第一次需要打开视频流
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try:
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cap = self._open_view(channel_data[0],channel_data[1]) #创建子线程读画面
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iread_count = 0
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except:
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self.logger.error("打开视频参数错误，终止线程!")
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return
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ret,frame = cap.read()
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if not ret:
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if iread_count > 30:
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self.logger.warning(f"通道-{channel_id}：view disconnected. Reconnecting...")
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cap.release()
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cap = None
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time.sleep(myCongif.get_data("cap_sleep_time"))
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else:
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iread_count += 1
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time.sleep(1.0/20) #20帧只是作为个默认参考值，一般验证帧率都比这个慢
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continue #没读到画面继续
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iread_count = 0 #重置下视频帧计算
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last_frame_time = time.time()
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#执行图片推理 -- 如何没有模型或不在工作时间，返回的是原画，要不要控制下帧率？ -- 在verify中做了sleep
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new_frame,img = self.verify(frame,myModle_list,myModle_data,channel_id,schedule_list,result_list)
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#分析图片放入内存中
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channel_data[3].append(img) # 缓冲区大小由maxlen控制 超上限后，删除最前的数据
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channel_data[5] += 1 #帧序列加一
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#print(self.verify_list[channel_id][5])
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if channel_data[5] > self.icout_max:
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channel_data = 0
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channel_data[4] = new_frame #一直更新最新帧**********
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#print(f"{channel_id}--Frame updated at:",time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
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#验证result_list -是否触发报警要求
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for i in range(len(result_list)):
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result = result_list[i]
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proportion = proportion_list[i]
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count_one = float(sum(result)) #1,0 把1累加的和就是1的数量
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ratio_of_ones = count_one / len(result)
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#self.logger.debug(result)
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if ratio_of_ones >= proportion: #触发报警
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model_name = myModle_data[i][7]
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w_s_count = warn_save_count[i]
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buffer_count = channel_data[5]
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self.save_warn(model_name,w_s_count,buffer_count,channel_data[3],cap.width,cap.height,
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channel_id,myDBM,self.FPS,self.fourcc)
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self.send_warn()
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#更新帧序列号
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warn_save_count[i] = buffer_count
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#结果记录要清空
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for i in range(len(result)):
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result[i] = 0
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# end_time = time.time() # 结束时间
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# print(f"Processing time: {end_time - start_time} seconds")
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# 本地显示---测试使用
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# cv2.imshow('Frame', img)
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# if cv2.waitKey(1) & 0xFF == ord('q'):
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# break
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#结束线程
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cap.release()
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#cv2.destroyAllWindows()
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def save_warn(self,model_name,w_s_count,buffer_count,buffer,width,height,channnel_id,myDBM,FPS,fourcc):
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'''
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保存报警信息
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:param model_name: 模型名称，如人员入侵
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:param w_s_count: 报警已存储的最新帧序列
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:param buffer_count: 当前视频缓冲区的最新帧序列
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:param buffer: 视频缓存区
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:param width: 视频画面的width
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:param height: 视频画面的height
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:param channnel_id: 视频通道ID
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:return: ret 数据库操作记录
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'''
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now = datetime.datetime.now() # 获取当前日期和时间
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current_time_str = now.strftime("%Y-%m-%d_%H-%M-%S")
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filename = f"{channnel_id}_{current_time_str}"
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#保存视频
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video_writer = cv2.VideoWriter(f"model/warn/{filename}.mp4", fourcc, FPS, (width, height))
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if not video_writer.isOpened():
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print(f"Failed to open video writer for model/warn/{filename}.mp4")
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return False
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ilen = len(buffer)
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istart = 0;
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iend = ilen
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if buffer_count < w_s_count or (buffer_count-w_s_count) > ilen: #buffer_count重置过
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#buffer区，都保存为视频
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istart = 0
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else:#只取差异的缓冲区大小
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istart = ilen - (buffer_count-w_s_count)
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for i in range(istart,iend):
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video_writer.write(buffer[i])
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video_writer.release()
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#保存图片
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ret = cv2.imwrite(f"model/warn/{filename}.png",buffer[-1])
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if not ret:
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print("保存图片失败")
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|
return False
|
|
#保存数据库
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strsql = (f"INSERT INTO warn (model_name ,video_path ,img_path ,creat_time,channel_id ) "
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|
f"Values ('{model_name}','model/warn/{filename}.mp4','model/warn/{filename}.png',"
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f"'{current_time_str}','{channnel_id}');")
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ret = myDBM.do_sql(strsql)
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|
return ret
|
|
|
|
def send_warn(self):
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|
'''发送报警信息'''
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|
pass
|
|
|
|
def save_frame_to_video(self):
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|
'''把缓冲区中的画面保存为录像'''
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|
pass
|
|
|
|
def start_work(self,channel_id=0):
|
|
'''算法模型是在后台根据画面实时分析的
|
|
1.布防开关需要触发通道关闭和开启
|
|
2.布防策略的调整也需要关闭和重启工作
|
|
'''
|
|
if channel_id ==0:
|
|
strsql = "select id,ulr,type from channel where is_work = 1;" #执行所有通道
|
|
else:
|
|
strsql = f"select id,ulr,type from channel where is_work = 1 and id = {channel_id};" #单通道启动检测线程
|
|
datas = mDBM.do_select(strsql)
|
|
for data in datas:
|
|
img_buffer = deque(maxlen=myCongif.get_data("buffer_len")) #创建个定长的视频buffer
|
|
img = None
|
|
icout = 0 #跟img_buffer对应，记录进入缓冲区的帧序列号
|
|
run_data = [data[1],data[2],True,img_buffer,img,icout]
|
|
self.verify_list[data[0]] = run_data #需要验证重复情况#?
|
|
th_chn = threading.Thread(target=self.dowork_thread, args=(data[0],)) #一个视频通道一个线程，线程句柄暂时部保留
|
|
th_chn.start()
|
|
|
|
def stop_work(self,channel_id=0):
|
|
'''停止工作线程，0-停止所有，非0停止对应通道ID的线程'''
|
|
if channel_id ==0: #所有线程停止
|
|
for data in self.verify_list:
|
|
data[2] = False
|
|
del data[3]
|
|
time.sleep(2)
|
|
self.verify_list.clear()
|
|
else:
|
|
data = self.verify_list[channel_id]
|
|
data[2] = False
|
|
del data[3]
|
|
time.sleep(1)
|
|
del self.verify_list[channel_id]
|
|
|
|
#print(f"Current working directory (ModelManager.py): {os.getcwd()}")
|
|
mMM = ModelManager()
|
|
def test1():
|
|
|
|
print(cv2.getBuildInformation())
|
|
source = 'rtsp://192.168.3.44/live1'
|
|
gstreamer_pipeline = (
|
|
f"rtspsrc location={source} protocols=udp latency=0 ! "
|
|
"rtph264depay ! h264parse ! avdec_h264 ! videoconvert ! appsink"
|
|
)
|
|
cap = cv2.VideoCapture(gstreamer_pipeline, cv2.CAP_GSTREAMER)
|
|
if not cap.isOpened():
|
|
print("Error: Unable to open the video source.")
|
|
return
|
|
else:
|
|
print("Successfully opened the video source.")
|
|
ret, frame = cap.read()
|
|
if ret:
|
|
cv2.imshow('Frame', frame)
|
|
cv2.waitKey(0)
|
|
cap.release()
|
|
cv2.destroyAllWindows()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
#mMM.start_work()
|
|
test1()
|
|
print("111")
|
|
# name = acl.get_soc_name()
|
|
# count, ret = acl.rt.get_device_count()
|
|
# print(name,count)
|
|
|
|
|
|
|
|
|
|
|