16337
6116f0b982
根本原因： 1. pipeline.py 中调用 register_algorithm 时未传入 roi_polygon，导致算法内 roi_polygon 为空 2. is_point_in_roi 函数在 roi_polygon 为空或点数 <3 时错误返回 True，使系统误判“有人在岗” 3. 因此即使 ROI 内无人，算法也永远不会进入离岗倒计时 修复措施： - 在注册算法时正确传递 ROI 多边形坐标 - 修正 is_point_in_roi：当 ROI 无效时应返回 False（无人） - 确保无检测框时仍能触发状态机超时逻辑
373 lines
12 KiB
Python
373 lines
12 KiB
Python
import asyncio
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import json
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import os
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import threading
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import time
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from collections import deque
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from datetime import datetime
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from typing import Any, Dict, List, Optional, Tuple
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import cv2
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import numpy as np
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from config import get_config
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from db.crud import (
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create_alarm,
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update_camera_status,
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)
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from db.models import init_db, get_session_factory
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from inference.engine import YOLOEngine
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from inference.roi.cache_manager import get_roi_cache
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from inference.roi.roi_filter import ROIFilter
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from inference.rules.algorithms import AlgorithmManager
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from inference.stream import StreamManager
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class InferencePipeline:
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def __init__(self):
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self.config = get_config()
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self.db_initialized = False
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self.yolo_engine = YOLOEngine(use_trt=True)
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self.stream_manager = StreamManager(
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buffer_size=self.config.stream.buffer_size,
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reconnect_delay=self.config.stream.reconnect_delay,
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)
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self.roi_filter = ROIFilter()
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self.roi_cache = get_roi_cache()
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self.algo_manager = AlgorithmManager(working_hours=[
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{
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"start": [wh.start[0], wh.start[1]],
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"end": [wh.end[0], wh.end[1]],
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}
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for wh in self.config.working_hours
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])
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self.camera_threads: Dict[int, threading.Thread] = {}
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self.camera_stop_events: Dict[int, threading.Event] = {}
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self.camera_latest_frames: Dict[int, Any] = {}
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self.camera_frame_times: Dict[int, datetime] = {}
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self.camera_process_counts: Dict[int, int] = {}
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self.event_queue: deque = deque(maxlen=self.config.inference.event_queue_maxlen)
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self.running = False
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def _init_database(self):
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if not self.db_initialized:
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init_db()
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self.db_initialized = True
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self.roi_cache.initialize(get_session_factory(), refresh_interval=10.0)
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self.roi_cache.start_background_refresh()
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def _get_db_session(self):
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from db.models import get_session_factory
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SessionLocal = get_session_factory()
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return SessionLocal()
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def _load_cameras(self):
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db = self._get_db_session()
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try:
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from db.crud import get_all_cameras
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cameras = get_all_cameras(db, enabled_only=True)
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for camera in cameras:
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self.add_camera(camera)
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finally:
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db.close()
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def add_camera(self, camera) -> bool:
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camera_id = camera.id
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if camera_id in self.camera_threads:
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return False
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self.camera_stop_events[camera_id] = threading.Event()
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self.camera_process_counts[camera_id] = 0
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self.stream_manager.add_stream(
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str(camera_id),
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camera.rtsp_url,
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self.config.stream.buffer_size,
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)
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thread = threading.Thread(
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target=self._camera_inference_loop,
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args=(camera,),
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daemon=True,
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)
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thread.start()
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self.camera_threads[camera_id] = thread
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self._update_camera_status(camera_id, is_running=True)
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return True
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def remove_camera(self, camera_id: int):
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if camera_id not in self.camera_threads:
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return
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self.camera_stop_events[camera_id].set()
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self.camera_threads[camera_id].join(timeout=10.0)
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del self.camera_threads[camera_id]
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del self.camera_stop_events[camera_id]
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self.stream_manager.remove_stream(str(camera_id))
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self.roi_filter.clear_cache(camera_id)
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self.algo_manager.remove_roi(str(camera_id))
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if camera_id in self.camera_latest_frames:
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del self.camera_latest_frames[camera_id]
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if camera_id in self.camera_frame_times:
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del self.camera_frame_times[camera_id]
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if camera_id in self.camera_process_counts:
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del self.camera_process_counts[camera_id]
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self._update_camera_status(camera_id, is_running=False)
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def _update_camera_status(
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self,
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camera_id: int,
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is_running: Optional[bool] = None,
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fps: Optional[float] = None,
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error_message: Optional[str] = None,
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):
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try:
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db = self._get_db_session()
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update_camera_status(
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db,
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camera_id,
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is_running=is_running,
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fps=fps,
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error_message=error_message,
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)
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except Exception as e:
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print(f"[{camera_id}] 更新状态失败: {e}")
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finally:
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db.close()
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def _camera_inference_loop(self, camera):
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camera_id = camera.id
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stop_event = self.camera_stop_events[camera_id]
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while not stop_event.is_set():
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ret, frame = self.stream_manager.read(str(camera_id))
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if not ret or frame is None:
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time.sleep(0.1)
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continue
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self.camera_latest_frames[camera_id] = frame
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self.camera_frame_times[camera_id] = datetime.now()
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self.camera_process_counts[camera_id] += 1
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if self.camera_process_counts[camera_id] % camera.process_every_n_frames != 0:
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continue
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try:
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self._process_frame(camera_id, frame, camera)
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except Exception as e:
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print(f"[{camera_id}] 处理帧失败: {e}")
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print(f"[{camera_id}] 推理线程已停止")
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def _process_frame(self, camera_id: int, frame: np.ndarray, camera):
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from ultralytics.engine.results import Results
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roi_configs = self.roi_cache.get_rois(camera_id)
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if roi_configs:
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self.roi_filter.update_cache(camera_id, roi_configs)
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for roi_config in roi_configs:
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roi_id = roi_config["roi_id"]
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rule_type = roi_config["rule"]
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self.algo_manager.register_algorithm(
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roi_id,
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rule_type,
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{
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"threshold_sec": roi_config.get("threshold_sec", 300),
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"confirm_sec": roi_config.get("confirm_sec", 10),
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"return_sec": roi_config.get("return_sec", 30),
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},
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)
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results = self.yolo_engine(frame, verbose=False, classes=[0])
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if not results:
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return
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result = results[0]
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detections = []
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if hasattr(result, "boxes") and result.boxes is not None:
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boxes = result.boxes.xyxy.cpu().numpy()
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confs = result.boxes.conf.cpu().numpy()
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for i, box in enumerate(boxes):
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detections.append({
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"bbox": box.tolist(),
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"conf": float(confs[i]),
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"cls": 0,
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})
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if roi_configs:
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filtered_detections = self.roi_filter.filter_detections(
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detections, roi_configs
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)
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else:
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filtered_detections = detections
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roi_detections: Dict[str, List[Dict]] = {}
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for detection in filtered_detections:
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matched_rois = detection.get("matched_rois", [])
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for roi_conf in matched_rois:
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roi_id = roi_conf["roi_id"]
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if roi_id not in roi_detections:
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roi_detections[roi_id] = []
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roi_detections[roi_id].append(detection)
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for roi_config in roi_configs:
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roi_id = roi_config["roi_id"]
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rule_type = roi_config["rule"]
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roi_dets = roi_detections.get(roi_id, [])
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alerts = self.algo_manager.process(
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roi_id,
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str(camera_id),
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rule_type,
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roi_dets,
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datetime.now(),
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)
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for alert in alerts:
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self._handle_alert(camera_id, alert, frame, roi_config)
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def _handle_alert(
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self,
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camera_id: int,
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alert: Dict[str, Any],
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frame: np.ndarray,
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roi_config: Dict[str, Any],
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):
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try:
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snapshot_path = None
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bbox = alert.get("bbox", [])
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if bbox and len(bbox) >= 4:
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x1, y1, x2, y2 = [int(v) for v in bbox]
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x1, y1 = max(0, x1), max(0, y1)
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x2, y2 = min(frame.shape[1], x2), min(frame.shape[0], y2)
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snapshot_dir = self.config.alert.snapshot_path
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os.makedirs(snapshot_dir, exist_ok=True)
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"cam_{camera_id}_{roi_config['roi_id']}_{alert['alert_type']}_{timestamp}.jpg"
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snapshot_path = os.path.join(snapshot_dir, filename)
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cv2.imwrite(snapshot_path, frame, [cv2.IMWRITE_JPEG_QUALITY, self.config.alert.image_quality])
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db = self._get_db_session()
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try:
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alarm = create_alarm(
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db,
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camera_id=camera_id,
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event_type=alert["alert_type"],
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confidence=alert.get("confidence", alert.get("conf", 0.0)),
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snapshot_path=snapshot_path,
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roi_id=roi_config["roi_id"],
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)
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alert["alarm_id"] = alarm.id
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finally:
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db.close()
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self.event_queue.append({
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"camera_id": camera_id,
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"roi_id": roi_config["roi_id"],
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"event_type": alert["alert_type"],
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"confidence": alert.get("confidence", alert.get("conf", 0.0)),
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"message": alert.get("message", ""),
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"snapshot_path": snapshot_path,
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"timestamp": datetime.now().isoformat(),
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"llm_checked": False,
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})
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print(f"[{camera_id}] 🚨 告警: {alert['alert_type']} - {alert.get('message', '')}")
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except Exception as e:
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print(f"[{camera_id}] 处理告警失败: {e}")
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def get_latest_frame(self, camera_id: int) -> Optional[np.ndarray]:
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return self.camera_latest_frames.get(camera_id)
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def get_camera_fps(self, camera_id: int) -> float:
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stream = self.stream_manager.get_stream(str(camera_id))
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if stream:
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return stream.fps
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return 0.0
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def get_event_queue(self) -> List[Dict[str, Any]]:
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return list(self.event_queue)
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def start(self):
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if self.running:
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return
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self._init_database()
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self._load_cameras()
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self.running = True
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def stop(self):
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if not self.running:
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return
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self.running = False
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for camera_id in list(self.camera_threads.keys()):
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self.remove_camera(camera_id)
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self.stream_manager.stop_all()
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self.algo_manager.reset_all()
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print("推理pipeline已停止")
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def get_status(self) -> Dict[str, Any]:
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result = {
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"running": self.running,
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"camera_count": len(self.camera_threads),
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"cameras": {},
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"event_queue_size": len(self.event_queue),
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}
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for cid in self.camera_threads:
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frame_time = self.camera_frame_times.get(cid)
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result["cameras"][str(cid)] = {
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"is_running": self.camera_stop_events[cid] is not None and not self.camera_stop_events[cid].is_set(),
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"fps": self.get_camera_fps(cid),
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"last_check_time": frame_time.isoformat() if frame_time else None,
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}
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return result
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_pipeline: Optional[InferencePipeline] = None
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def get_pipeline() -> InferencePipeline:
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global _pipeline
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if _pipeline is None:
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_pipeline = InferencePipeline()
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return _pipeline
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def start_pipeline():
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pipeline = get_pipeline()
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pipeline.start()
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return pipeline
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def stop_pipeline():
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global _pipeline
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if _pipeline is not None:
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_pipeline.stop()
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_pipeline = None
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