16337
93a2278626
1. database.py: 优化数据库连接和错误处理 2. postprocessor.py: 改进后处理逻辑 3. result_reporter.py: 完善告警上报字段 4. video_stream.py: 增强视频流稳定性 5. main.py: 优化启动流程和异常处理 6. logger.py: 改进日志格式和轮转配置 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
601 lines
22 KiB
Python
601 lines
22 KiB
Python
"""
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主入口模块
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整合所有模块，启动推理服务
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"""
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import logging
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import os
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import sys
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import threading
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import signal
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import time
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from datetime import datetime
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from typing import Dict, Any, Optional, List, Tuple
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from config.settings import get_settings, Settings
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from core.config_sync import get_config_sync_manager, ConfigSyncManager
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from core.video_stream import MultiStreamManager, VideoFrame
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from core.preprocessor import ImagePreprocessor
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from core.tensorrt_engine import TensorRTEngine, EngineManager
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from core.postprocessor import PostProcessor
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from core.result_reporter import ResultReporter
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from algorithms import AlgorithmManager
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from utils.logger import get_logger, StructuredLogger
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from utils.version_control import get_version_control
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logger = logging.getLogger(__name__)
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class EdgeInferenceService:
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"""边缘推理服务主类
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整合所有模块，提供完整的推理服务
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"""
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def __init__(self):
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self._running = False
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self._settings = get_settings()
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self._logger = get_logger("main")
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self._version_control = get_version_control()
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self._db_manager = None
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self._config_manager: Optional[ConfigSyncManager] = None
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self._stream_manager: Optional[MultiStreamManager] = None
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self._preprocessor: Optional[ImagePreprocessor] = None
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self._engine_manager: Optional[EngineManager] = None
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self._postprocessor: Optional[PostProcessor] = None
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self._reporter: Optional[ResultReporter] = None
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self._algorithm_manager: Optional[AlgorithmManager] = None
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self._processing_threads: Dict[str, threading.Thread] = {}
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self._stop_event = threading.Event()
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self._performance_stats = {
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"start_time": None,
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"total_frames_processed": 0,
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"total_alerts_generated": 0,
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"uptime_seconds": 0,
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}
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self._batch_roi_queue: List[tuple] = []
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self._batch_lock = threading.Lock()
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self._batch_event = threading.Event()
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self._inference_thread: Optional[threading.Thread] = None
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self._max_batch_size = 8
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self._batch_timeout_sec = 0.05 # 50ms 攒批窗口
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# 摄像头级别告警去重：同一摄像头+告警类型在冷却期内只上报一次
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self._camera_alert_cooldown: Dict[str, datetime] = {}
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self._camera_cooldown_seconds = 30 # 同摄像头同类型告警最小间隔（秒）
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self._logger.info("Edge_Inference_Service 初始化开始")
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def _init_database(self):
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"""初始化数据库"""
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try:
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from config.database import SQLiteManager
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self._db_manager = SQLiteManager()
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if self._db_manager._conn:
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self._logger.info("数据库初始化成功")
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else:
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self._logger.warning("数据库不可见，服务将在无数据库模式下运行")
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except Exception as e:
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self._logger.warning(f"数据库初始化失败，服务将在无数据库模式下运行: {e}")
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self._db_manager = None
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def _init_config_manager(self):
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"""初始化配置管理器"""
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try:
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self._config_manager = get_config_sync_manager()
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self._config_manager.start_config_subscription()
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self._logger.info("配置管理器初始化成功")
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except Exception as e:
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self._logger.error(f"配置管理器初始化失败: {e}")
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raise
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def _init_stream_manager(self):
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"""初始化流管理器"""
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self._stream_manager = MultiStreamManager()
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self._logger.info("流管理器初始化成功")
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def _init_preprocessor(self):
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"""初始化预处理器"""
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self._preprocessor = ImagePreprocessor()
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self._logger.info("预处理器初始化成功")
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def _init_engine(self):
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"""初始化推理引擎"""
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try:
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self._engine_manager = EngineManager()
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engine_path = self._settings.inference.model_path
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if os.path.exists(engine_path):
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self._engine_manager.load_engine("default", engine_path)
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self._logger.info(f"推理引擎加载成功: {engine_path}")
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else:
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self._logger.warning(f"引擎文件不存在: {engine_path}")
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except Exception as e:
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self._logger.error(f"推理引擎初始化失败: {e}")
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raise
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def _init_postprocessor(self):
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"""初始化后处理器"""
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self._postprocessor = PostProcessor()
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self._logger.info("后处理器初始化成功")
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def _init_reporter(self):
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"""初始化结果上报器"""
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try:
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self._reporter = ResultReporter()
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self._logger.info("ResultReporter 对象已创建，准备初始化...")
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self._reporter.initialize() # 初始化存储和MQTT连接
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self._logger.info("结果上报器初始化成功")
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except Exception as e:
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self._logger.error(f"结果上报器初始化失败: {e}")
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import traceback
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self._logger.error(traceback.format_exc())
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def _init_algorithm_manager(self):
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"""初始化算法管理器"""
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try:
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self._algorithm_manager = AlgorithmManager()
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self._algorithm_manager.start_config_subscription()
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self._logger.info("算法管理器初始化成功")
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except Exception as e:
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self._logger.error(f"算法管理器初始化失败: {e}")
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def initialize(self):
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"""初始化所有组件"""
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self._logger.info("=" * 50)
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self._logger.info("Edge_Inference_Service 启动")
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self._logger.info("=" * 50)
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self._init_database()
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self._init_config_manager()
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self._init_stream_manager()
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self._init_preprocessor()
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self._init_engine()
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self._init_postprocessor()
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self._init_reporter()
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self._init_algorithm_manager()
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self._performance_stats["start_time"] = datetime.now()
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self._version_control.record_update(
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version="1.0.0",
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update_type="启动",
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description="Edge_Inference_Service 启动运行",
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updated_by="系统",
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affected_items=["全局"],
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)
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self._logger.info("所有组件初始化完成")
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def _load_cameras(self):
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"""加载摄像头配置"""
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cameras = self._config_manager.get_cameras()
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for camera in cameras:
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try:
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self._stream_manager.add_stream(
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camera_id=camera.camera_id,
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rtsp_url=camera.rtsp_url,
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target_fps=self._settings.video_stream.default_fps,
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on_frame_callback=self._create_frame_callback(camera.camera_id)
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)
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self._logger.info(f"已添加摄像头: {camera.camera_id}")
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except Exception as e:
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self._logger.error(f"添加摄像头失败 {camera.camera_id}: {e}")
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def _create_frame_callback(self, camera_id: str):
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"""创建帧处理回调"""
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def callback(frame):
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self._process_frame(camera_id, frame)
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return callback
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def _process_frame(self, camera_id: str, frame: VideoFrame):
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"""处理视频帧 - 批量处理多 ROI"""
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try:
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roi_configs = self._config_manager.get_roi_configs_with_bindings(camera_id)
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# 每100帧打印一次状态
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if self._performance_stats["total_frames_processed"] % 100 == 0:
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self._logger.info(f"[{camera_id}] 已处理 {self._performance_stats['total_frames_processed']} 帧, ROI数: {len(roi_configs)}")
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roi_items = []
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for roi in roi_configs:
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if not roi.enabled:
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continue
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if not roi.bindings:
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continue
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for bind in roi.bindings:
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if not bind.enabled:
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continue
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try:
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cropped, scale_info = self._preprocessor.preprocess_single(
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frame.image, roi
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)
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roi_items.append((camera_id, roi, bind, frame, cropped, scale_info))
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except Exception as e:
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self._logger.error(f"预处理 ROI 失败 {roi.roi_id}: {e}")
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if not roi_items:
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return
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with self._batch_lock:
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self._batch_roi_queue.extend(roi_items)
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# 通知推理线程有新数据
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self._batch_event.set()
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self._performance_stats["total_frames_processed"] += 1
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except Exception as e:
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self._logger.error(f"处理帧失败 {camera_id}: {e}")
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def _batch_process_rois(self):
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"""批量处理 ROI - 真正的 batch 推理"""
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with self._batch_lock:
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roi_items = self._batch_roi_queue
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if not roi_items:
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return
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self._batch_roi_queue = []
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try:
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images = [item[4] for item in roi_items]
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scale_infos = [item[5] for item in roi_items]
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# 真正的 batch: 将所有 ROI 裁剪图拼成 [N,3,H,W] 一次推理
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batch_data, _ = self._preprocessor._batch_preprocessor.preprocess_batch(
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images
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)
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engine = self._engine_manager.get_engine("default")
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if engine is None:
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return
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# 一次性推理整个 batch
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outputs, inference_time_ms = engine.infer(batch_data)
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# 诊断：输出原始推理结果形状
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import numpy as np
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if isinstance(outputs, np.ndarray):
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self._logger.info(f"[推理诊断] batch_data shape={batch_data.shape}, output shape={outputs.shape}, 耗时={inference_time_ms:.1f}ms")
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elif isinstance(outputs, (list, tuple)):
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shapes = [o.shape if hasattr(o, 'shape') else type(o) for o in outputs]
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self._logger.info(f"[推理诊断] batch_data shape={batch_data.shape}, outputs={shapes}, 耗时={inference_time_ms:.1f}ms")
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batch_size = len(roi_items)
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batch_results = self._postprocessor.batch_process_detections(
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outputs,
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batch_size,
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conf_threshold=self._settings.inference.conf_threshold
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)
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total_detections = sum(len(r[0]) for r in batch_results)
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self._logger.info(f"[推理] batch_size={batch_size}, 总检测数={total_detections}, conf_thresh={self._settings.inference.conf_threshold}")
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for idx, (camera_id, roi, bind, frame, _, scale_info) in enumerate(roi_items):
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boxes, scores, class_ids = batch_results[idx]
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if len(boxes) > 0:
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self._handle_detections(
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camera_id, roi, bind, frame,
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boxes, scores, class_ids,
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scale_info
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)
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except Exception as e:
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self._logger.error(f"批量处理 ROI 失败: {e}")
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def _process_roi_frame(self, camera_id: str, frame: VideoFrame, roi):
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"""收集 ROI 帧数据 - 已集成到 _process_frame 中"""
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pass
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def _build_tracks(
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self,
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roi,
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boxes: any,
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scores: any,
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class_ids: any,
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scale_info: tuple
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) -> list:
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"""将检测结果转换为算法所需的 tracks 格式
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坐标从 letterbox 空间还原到 ROI 裁剪空间
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"""
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tracks = []
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class_names = getattr(self._settings, 'class_names', ['person'])
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# 将 letterbox 坐标还原到 ROI 裁剪空间
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reverted_boxes = self._preprocessor.revert_boxes(
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[box.tolist() if hasattr(box, 'tolist') else list(box) for box in boxes],
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scale_info
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)
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for i, box in enumerate(reverted_boxes):
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class_id = int(class_ids[i]) if class_ids[i] else 0
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track = {
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"track_id": f"{roi.roi_id}_{i}",
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"class": class_names[class_id] if class_id < len(class_names) else f"class_{class_id}",
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"confidence": float(scores[i]),
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"bbox": box,
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"matched_rois": [{"roi_id": roi.roi_id}],
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}
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tracks.append(track)
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return tracks
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def _handle_detections(
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self,
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camera_id: str,
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roi,
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bind,
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frame: VideoFrame,
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boxes: any,
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scores: any,
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class_ids: any,
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scale_info: tuple
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):
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"""处理检测结果 - 算法接管判断权"""
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try:
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if self._algorithm_manager is None:
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self._logger.warning("算法管理器不可用，跳过算法处理")
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return
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roi_id = roi.roi_id
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algo_code = bind.algo_code
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algo_params = bind.params or {}
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# 诊断日志：检测到目标（使用 INFO 级别确保能看到）
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if len(boxes) > 0:
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self._logger.info(f"[{camera_id}] ROI={roi_id[:8]} 检测到 {len(boxes)} 个目标, algo={algo_code}")
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self._algorithm_manager.register_algorithm(
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roi_id=roi_id,
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bind_id=bind.bind_id,
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algorithm_type=algo_code,
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params=algo_params
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)
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tracks = self._build_tracks(roi, boxes, scores, class_ids, scale_info)
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if not tracks:
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return
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# 诊断日志：tracks 内容（INFO 级别）
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self._logger.info(f"[{camera_id}] tracks: {[t.get('class') for t in tracks]}, target_class={bind.target_class}")
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alerts = self._algorithm_manager.process(
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roi_id=roi_id,
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bind_id=bind.bind_id,
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camera_id=camera_id,
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algorithm_type=algo_code,
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tracks=tracks,
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current_time=frame.timestamp
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)
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# 诊断日志：算法处理结果
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if alerts:
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self._logger.info(f"[{camera_id}] 算法 {algo_code} 返回 {len(alerts)} 个告警")
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else:
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# 获取算法状态用于诊断
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algo_status = self._algorithm_manager.get_status(roi_id)
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self._logger.info(f"[{camera_id}] 算法 {algo_code} 无告警, 状态: {algo_status}")
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for alert in alerts:
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alert_type = alert.get("alert_type", "detection")
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# 摄像头级别去重：同一摄像头+告警类型在冷却期内只上报一次
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dedup_key = f"{camera_id}_{alert_type}"
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now = frame.timestamp
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last_alert_time = self._camera_alert_cooldown.get(dedup_key)
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if last_alert_time is not None:
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elapsed = (now - last_alert_time).total_seconds()
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if elapsed < self._camera_cooldown_seconds:
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self._logger.info(
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f"[去重] 跳过告警: camera={camera_id}, type={alert_type}, "
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f"roi={roi_id}, 距上次={elapsed:.1f}s < {self._camera_cooldown_seconds}s"
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)
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continue
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self._camera_alert_cooldown[dedup_key] = now
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self._performance_stats["total_alerts_generated"] += 1
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from core.result_reporter import AlertInfo
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alert_info = AlertInfo(
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alert_id=f"{roi_id}_{bind.bind_id}_{int(frame.timestamp.timestamp())}",
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camera_id=camera_id,
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roi_id=roi_id,
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bind_id=bind.bind_id,
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device_id=self._settings.mqtt.device_id,
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alert_type=alert_type,
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algorithm=algo_code,
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target_class=alert.get("class", bind.target_class or "unknown"),
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confidence=alert.get("confidence", 1.0),
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bbox=alert.get("bbox", []),
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message=alert.get("message", ""),
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timestamp=frame.timestamp,
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duration_minutes=alert.get("duration_minutes"),
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)
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self._reporter.report_alert(alert_info, screenshot=frame.image)
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self._logger.info(
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f"告警已生成: type={alert_type}, "
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f"camera={camera_id}, roi={roi_id}, "
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f"confidence={alert.get('confidence', 1.0)}"
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)
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except Exception as e:
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self._logger.error(f"处理检测结果失败: {e}")
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def _inference_worker(self):
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"""推理线程：攒批窗口内收集 ROI 请求，批量推理"""
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while not self._stop_event.is_set():
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# 等待有新数据到达或超时
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self._batch_event.wait(timeout=self._batch_timeout_sec)
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self._batch_event.clear()
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with self._batch_lock:
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queue_size = len(self._batch_roi_queue)
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# 攒批窗口：等到攒够 max_batch 或超时后处理
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if queue_size > 0 and queue_size < self._max_batch_size:
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# 再等一小段时间凑更多
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self._batch_event.wait(timeout=self._batch_timeout_sec)
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self._batch_event.clear()
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with self._batch_lock:
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if not self._batch_roi_queue:
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continue
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self._batch_process_rois()
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def start(self):
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||
"""启动服务"""
|
||
if self._running:
|
||
return
|
||
|
||
self._running = True
|
||
self._stop_event.clear()
|
||
|
||
self._load_cameras()
|
||
|
||
# 启动独立推理线程（生产者-消费者模式）
|
||
self._inference_thread = threading.Thread(
|
||
target=self._inference_worker,
|
||
name="InferenceWorker",
|
||
daemon=True
|
||
)
|
||
self._inference_thread.start()
|
||
self._logger.info("推理线程已启动")
|
||
|
||
self._stream_manager.start_all()
|
||
|
||
self._logger.info("Edge_Inference_Service 已启动")
|
||
|
||
self._start_heartbeat_thread()
|
||
|
||
self._register_signal_handlers()
|
||
|
||
self._wait_for_shutdown()
|
||
|
||
def _start_heartbeat_thread(self):
|
||
"""启动心跳线程"""
|
||
def heartbeat():
|
||
while not self._stop_event.is_set():
|
||
try:
|
||
uptime = (datetime.now() - self._performance_stats["start_time"]).total_seconds()
|
||
self._performance_stats["uptime_seconds"] = uptime
|
||
|
||
status = {
|
||
"running": True,
|
||
"uptime_seconds": uptime,
|
||
"frames_processed": self._performance_stats["total_frames_processed"],
|
||
"alerts_generated": self._performance_stats["total_alerts_generated"],
|
||
"stream_stats": self._stream_manager.get_statistics() if self._stream_manager else {},
|
||
}
|
||
|
||
if self._reporter:
|
||
self._reporter.report_heartbeat(self._settings.mqtt.device_id, status)
|
||
|
||
except Exception as e:
|
||
self._logger.error(f"心跳上报失败: {e}")
|
||
|
||
time.sleep(30)
|
||
|
||
thread = threading.Thread(target=heartbeat, name="Heartbeat", daemon=True)
|
||
thread.start()
|
||
|
||
def _register_signal_handlers(self):
|
||
"""注册信号处理器"""
|
||
def handle_signal(signum, frame):
|
||
self._logger.info(f"收到信号 {signum}, 正在停止服务...")
|
||
self.stop()
|
||
|
||
signal.signal(signal.SIGINT, handle_signal)
|
||
signal.signal(signal.SIGTERM, handle_signal)
|
||
|
||
def _wait_for_shutdown(self):
|
||
"""等待关闭信号"""
|
||
while not self._stop_event.is_set():
|
||
time.sleep(1)
|
||
|
||
def stop(self):
|
||
"""停止服务"""
|
||
if not self._running:
|
||
return
|
||
|
||
self._running = False
|
||
self._stop_event.set()
|
||
self._batch_event.set() # 唤醒推理线程以退出
|
||
|
||
if self._inference_thread and self._inference_thread.is_alive():
|
||
self._inference_thread.join(timeout=5)
|
||
|
||
if self._stream_manager:
|
||
self._stream_manager.stop_all()
|
||
self._stream_manager.close()
|
||
|
||
if self._engine_manager:
|
||
self._engine_manager.release_all()
|
||
|
||
if self._config_manager:
|
||
self._config_manager.stop_config_subscription()
|
||
self._config_manager.close()
|
||
|
||
if self._algorithm_manager:
|
||
self._algorithm_manager.stop_config_subscription()
|
||
|
||
if self._reporter:
|
||
self._reporter.close()
|
||
|
||
self._performance_stats["uptime_seconds"] = (
|
||
(datetime.now() - self._performance_stats["start_time"]).total_seconds()
|
||
)
|
||
|
||
self._logger.info("Edge_Inference_Service 已停止")
|
||
self._logger.info(f"运行统计: {self._performance_stats}")
|
||
|
||
def get_status(self) -> Dict[str, Any]:
|
||
"""获取服务状态"""
|
||
return {
|
||
"running": self._running,
|
||
"start_time": (
|
||
self._performance_stats["start_time"].isoformat()
|
||
if self._performance_stats["start_time"] else None
|
||
),
|
||
"uptime_seconds": self._performance_stats["uptime_seconds"],
|
||
"total_frames_processed": self._performance_stats["total_frames_processed"],
|
||
"total_alerts_generated": self._performance_stats["total_alerts_generated"],
|
||
"stream_manager": (
|
||
self._stream_manager.get_statistics()
|
||
if self._stream_manager else {}
|
||
),
|
||
"config_version": (
|
||
self._config_manager.config_version
|
||
if self._config_manager else None
|
||
),
|
||
}
|
||
|
||
|
||
def main():
|
||
"""主函数入口"""
|
||
service = EdgeInferenceService()
|
||
|
||
try:
|
||
service.initialize()
|
||
service.start()
|
||
except KeyboardInterrupt:
|
||
service.stop()
|
||
except Exception as e:
|
||
logger.error(f"服务异常: {e}")
|
||
raise
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|