feat(project): move edge_inference_service contents to root and update paths
- Moved all project files and directories (config, core, models, etc.) from edge_inference_service/ to the repository root ai_edge/ - Updated model path in config/settings.py to reflect new structure - Revised usage paths in __init__.py documentation
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CHANGELOG.md
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# CHANGELOG - Edge_Inference_Service
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### v1.0.0
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**更新时间**: 2026-01-29 18:07:35
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**更新类型**: 启动
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**更新人员**: 系统
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**影响范围**: 全局
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- Edge_Inference_Service 启动运行
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---
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## 版本更新记录
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### v1.0.0
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**更新时间**: 2024-01-29 10:00:00
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**更新类型**: 初始化
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**更新人员**: AI Edge Architecture Team
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**影响范围**: 全局
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#### 功能新增
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- 项目初始化创建，搭建完整的边缘AI推理服务框架
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- 实现6大核心模块的工业级代码
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#### 模块详情
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**1. 配置同步模块 (config/)**
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- settings.py: 全局配置管理，支持环境变量和 dataclass 配置
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- database.py: MySQL 连接池管理，支持 camera_info、roi_config、alert_records 表
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- config_models.py: 数据模型定义，包含 CameraInfo、ROIInfo、AlertInfo 等
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**2. 核心处理模块 (core/)**
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- config_sync.py: 配置同步管理器，支持 MySQL 读取、Redis Pub/Sub 订阅、配置缓存
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- video_stream.py: RTSP 流读取器，支持多线程、动态抽帧、断线重连(指数退避)
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- preprocessor.py: 图像预处理流水线，支持 ROI 裁剪、Letterbox、Batch 打包
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- tensorrt_engine.py: TensorRT 引擎管理，支持异步推理、显存优化、性能监控
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- postprocessor.py: 后处理模块，NMS 算法、坐标映射、告警状态机
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- result_reporter.py: 结果上报模块，MQTT 客户端、告警存储、重试机制
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**3. 工具类模块 (utils/)**
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- logger.py: 分级日志系统，支持性能指标记录
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- common.py: 公共工具函数，包括重试机制、Base64 编解码等
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- version_control.py: 版本控制，记录代码更新历史
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**4. 测试模块 (tests/)**
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- test_config_sync.py: 配置模块单元测试
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- test_video_stream.py: 视频流模块单元测试
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- test_preprocessor.py: 预处理模块单元测试
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- test_postprocessor.py: 后处理模块单元测试
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#### 技术特性
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- 多线程/多进程架构设计
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- 完善的异常处理机制
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- 显存管理与优化
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- 异步推理模式
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- 配置动态刷新
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- 心跳上报机制
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- 性能监控与日志
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#### 代码质量
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- 遵循 PEP 8 编码规范
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- 详细中文注释
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- 模块化设计，高内聚低耦合
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- 单元测试框架
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#### 依赖环境
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- Python 3.8+
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- CUDA 12.1 + cuDNN 8.9.7 + TensorRT 8.6.1
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- opencv-python, ultralytics, pycuda, paho-mqtt, sqlalchemy, redis
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---
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### v1.0.1
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**更新时间**: 2024-01-29 10:30:00
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**更新类型**: 优化
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**更新人员**: AI Edge Architecture Team
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**影响范围**: requirements.txt
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#### 依赖版本优化
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- 更新 requirements.txt，选择最稳定版本而非最新版本
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- 确保环境一致性，减少因版本兼容性问题导致的故障
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#### 依赖版本清单
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| 依赖 | 版本 | 选择理由 |
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|------|------|---------|
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| opencv-python | 4.8.0 | 4.8系列首个稳定版，广泛验证 |
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| numpy | 1.24.0 | Python 3.8-3.11完美兼容 |
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| paho-mqtt | 1.6.1 | 1.x最终稳定版，企业级 |
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| sqlalchemy | 2.0.23 | 2.0系列LTS版 |
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| pymysql | 1.1.0 | 成熟稳定版本 |
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| redis | 4.6.0 | 4.x最终稳定版 |
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| pyyaml | 6.0.1 | 安全稳定版 |
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| pytest | 7.4.4 | 7.x最终稳定版 |
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| pytest-cov | 4.1.0 | 成熟稳定版 |
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#### 可选依赖（按需安装）
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- tensorrt==8.6.1.6 (CUDA 12.1专用)
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- pycuda==2023.1.1 (稳定版)
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- ultralytics==8.1.0 (YOLO11 LTS版)
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---
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### v1.0.2
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**更新时间**: 2024-01-29 11:00:00
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**更新类型**: 修复
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**更新人员**: AI Edge Architecture Team
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**影响范围**: result_reporter.py
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#### Bug修复
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- 修复 result_reporter.py 中的5处语法错误
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- 问题：将 `self._lock:` 误写为锁语句，正确形式为 `with self._lock:`
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- 修复缺失的 numpy 导入：AlertReporter.report_alert() 方法使用了 `np.ndarray` 类型提示但未导入 numpy
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---
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### v1.0.3
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**更新时间**: 2024-01-29 11:30:00
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**更新类型**: 优化
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**更新人员**: AI Edge Architecture Team
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**影响范围**: database.py, main.py, config_sync.py
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#### 功能优化
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- 实现数据库连接可选模式
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- 当MySQL不可用时，服务仍可正常运行（无数据库模式）
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- 添加数据库可用性检测，不阻塞服务启动
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- 所有数据库操作方法添加了不可用时的优雅降级处理
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#### 技术细节
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- DatabaseManager添加 `_available` 标志
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- get_session() 方法支持空会话返回
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- get_camera_info()、get_roi_configs() 等方法返回空列表
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- save_alert_record() 等方法返回 False
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---
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### v1.0.4
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**更新时间**: 2024-01-29 12:00:00
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**更新类型**: 新增
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**更新人员**: AI Edge Architecture Team
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**影响范围**: build_engine.py
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#### 功能新增
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- 新增 TensorRT Engine 生成脚本 build_engine.py
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- 支持将 YOLO11 模型转换为 TensorRT Engine
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- 支持 FP16 精度、480×480 输入、动态 Batch(1-8)
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- 支持 opt_level=4 最大优化
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- 内置模型下载功能
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#### 使用方法
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**方式1: 使用本地 PyTorch 模型**
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```bash
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python build_engine.py --model yolo11n.pt --output models/yolo11n.engine --fp16 --batch 8
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```
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**方式2: 下载 YOLO11 模型并转换**
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```bash
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python build_engine.py --download yolo11n --output models/yolo11n.engine --fp16 --batch 8
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```
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**方式3: 使用已有 ONNX 模型**
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```bash
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python build_engine.py --model model.onnx --output models/yolo11n.engine --fp16 --batch 8
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```
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#### 参数说明
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| 参数 | 说明 | 默认值 |
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|------|------|--------|
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| --model | 输入模型路径 (.pt/.onnx) | yolo11n.pt |
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| --output | 输出 engine 路径 | models/yolo11n.engine |
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| --input-size | 输入图像尺寸 | 480 |
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| --batch | 最大 batch 大小 | 8 |
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| --fp16 | 启用 FP16 精度 | True |
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| --opt-level | 优化级别 (1-4) | 4 |
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| --download | 下载模型 (yolo11n/s/m/l/x) | None |
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---
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### v1.0.5
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**更新时间**: 2026-01-29 12:30:00
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**更新类型**: 修改
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**更新人员**: AI Edge Architecture Team
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**影响范围**: 全局
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#### 模型迁移
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- 将默认模型从 YOLOv8 迁移至 YOLO11
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- 默认模型变更为 yolo11n（更轻量、更高效）
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#### 修改内容
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| 文件 | 修改项 |
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|------|--------|
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| build_engine.py | 默认模型、帮助文本、示例命令 |
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| config/settings.py | 默认引擎路径 |
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| __init__.py | 使用说明 |
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| CHANGELOG.md | 版本记录、参数说明 |
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#### 新默认配置
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- 模型名称: yolo11n
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- 引擎路径: ./models/yolo11n.engine
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- ultralytics 版本: 8.1.0
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---
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*Generated by Edge_Inference_Service Version Control System*
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@@ -1,412 +0,0 @@
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"""
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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
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from config.settings import get_settings, Settings
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from config.database import init_database
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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 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._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._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 DatabaseManager
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self._db_manager = DatabaseManager()
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if self._db_manager.is_available:
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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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|
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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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|
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def _init_reporter(self):
|
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"""初始化结果上报器"""
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self._reporter = ResultReporter()
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self._logger.info("结果上报器初始化成功")
|
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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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|
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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()
|
||||
self._init_engine()
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self._init_postprocessor()
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||||
self._init_reporter()
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|
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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",
|
||||
update_type="启动",
|
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description="Edge_Inference_Service 启动运行",
|
||||
updated_by="系统",
|
||||
affected_items=["全局"],
|
||||
)
|
||||
|
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self._logger.info("所有组件初始化完成")
|
||||
|
||||
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:
|
||||
try:
|
||||
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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self._logger.info(f"已添加摄像头: {camera.camera_id}")
|
||||
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"""
|
||||
def callback(frame):
|
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self._process_frame(camera_id, frame)
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return callback
|
||||
|
||||
def _process_frame(self, camera_id: str, frame: VideoFrame):
|
||||
"""处理视频帧"""
|
||||
try:
|
||||
start_time = time.perf_counter()
|
||||
|
||||
roi_configs = self._config_manager.get_roi_configs(camera_id)
|
||||
|
||||
for roi in roi_configs:
|
||||
self._process_roi_frame(camera_id, frame, roi)
|
||||
|
||||
processing_time_ms = (time.perf_counter() - start_time) * 1000
|
||||
|
||||
self._performance_stats["total_frames_processed"] += 1
|
||||
|
||||
self._logger.log_inference_latency(
|
||||
processing_time_ms,
|
||||
batch_size=1
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
self._logger.error(f"处理帧失败 {camera_id}: {e}")
|
||||
|
||||
def _process_roi_frame(
|
||||
self,
|
||||
camera_id: str,
|
||||
frame: VideoFrame,
|
||||
roi
|
||||
):
|
||||
"""处理ROI帧"""
|
||||
try:
|
||||
cropped = self._preprocessor.preprocess_single(frame.image, roi)
|
||||
|
||||
processed_image, scale_info = cropped
|
||||
|
||||
batch_data = self._preprocessor._batch_preprocessor._stack_and_normalize(
|
||||
[processed_image]
|
||||
)
|
||||
|
||||
engine = self._engine_manager.get_engine("default")
|
||||
if engine is None:
|
||||
return
|
||||
|
||||
outputs, inference_time_ms = engine.infer(batch_data)
|
||||
|
||||
boxes, scores, class_ids = self._postprocessor.process_detections(
|
||||
outputs,
|
||||
conf_threshold=self._settings.inference.conf_threshold
|
||||
)
|
||||
|
||||
if len(boxes) > 0:
|
||||
self._handle_detections(
|
||||
camera_id, roi.roi_id, frame,
|
||||
boxes, scores, class_ids,
|
||||
scale_info
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
self._logger.error(f"处理ROI帧失败: {e}")
|
||||
|
||||
def _handle_detections(
|
||||
self,
|
||||
camera_id: str,
|
||||
roi_id: str,
|
||||
frame: VideoFrame,
|
||||
boxes: any,
|
||||
scores: any,
|
||||
class_ids: any,
|
||||
scale_info: tuple
|
||||
):
|
||||
"""处理检测结果"""
|
||||
try:
|
||||
from config.config_models import AlgorithmType
|
||||
|
||||
mapped_boxes = self._postprocessor.map_coordinates(
|
||||
boxes, scale_info,
|
||||
(frame.width, frame.height)
|
||||
)
|
||||
|
||||
for i, box in enumerate(mapped_boxes):
|
||||
detection_result = {
|
||||
"class_id": int(class_ids[i]) if len(class_ids) > 0 else 0,
|
||||
"confidence": float(scores[i]),
|
||||
"bbox": box,
|
||||
}
|
||||
|
||||
alert_result = self._postprocessor.check_alarm_condition(
|
||||
roi_id, True, frame.timestamp
|
||||
)
|
||||
|
||||
if alert_result["should_alert"]:
|
||||
self._performance_stats["total_alerts_generated"] += 1
|
||||
|
||||
screenshot = frame.image
|
||||
|
||||
self._reporter.report_detection_alert(
|
||||
camera_id=camera_id,
|
||||
roi_id=roi_id,
|
||||
alert_type="detection",
|
||||
detection={
|
||||
"class_name": f"class_{detection_result['class_id']}",
|
||||
"confidence": detection_result["confidence"],
|
||||
"bbox": detection_result["bbox"],
|
||||
"message": f"检测到目标"
|
||||
},
|
||||
screenshot=screenshot
|
||||
)
|
||||
|
||||
self._logger.log_alert(
|
||||
"detection",
|
||||
camera_id,
|
||||
roi_id,
|
||||
detection_result["confidence"]
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
self._logger.error(f"处理检测结果失败: {e}")
|
||||
|
||||
def start(self):
|
||||
"""启动服务"""
|
||||
if self._running:
|
||||
return
|
||||
|
||||
self._running = True
|
||||
self._stop_event.clear()
|
||||
|
||||
self._load_cameras()
|
||||
|
||||
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("edge_inference_device", 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()
|
||||
|
||||
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._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()
|
||||
Binary file not shown.
416
main.py
416
main.py
@@ -1,16 +1,412 @@
|
||||
# 这是一个示例 Python 脚本。
|
||||
"""
|
||||
主入口模块
|
||||
整合所有模块，启动推理服务
|
||||
"""
|
||||
|
||||
# 按 Shift+F10 执行或将其替换为您的代码。
|
||||
# 按 双击 Shift 在所有地方搜索类、文件、工具窗口、操作和设置。
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
import threading
|
||||
import signal
|
||||
import time
|
||||
from datetime import datetime
|
||||
from typing import Dict, Any, Optional
|
||||
|
||||
from config.settings import get_settings, Settings
|
||||
from config.database import init_database
|
||||
from core.config_sync import get_config_sync_manager, ConfigSyncManager
|
||||
from core.video_stream import MultiStreamManager, VideoFrame
|
||||
from core.preprocessor import ImagePreprocessor
|
||||
from core.tensorrt_engine import TensorRTEngine, EngineManager
|
||||
from core.postprocessor import PostProcessor
|
||||
from core.result_reporter import ResultReporter
|
||||
from utils.logger import get_logger, StructuredLogger
|
||||
from utils.version_control import get_version_control
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def print_hi(name):
|
||||
# 在下面的代码行中使用断点来调试脚本。
|
||||
print(f'Hi, {name}') # 按 Ctrl+F8 切换断点。
|
||||
class EdgeInferenceService:
|
||||
"""边缘推理服务主类
|
||||
|
||||
整合所有模块，提供完整的推理服务
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self._running = False
|
||||
self._settings = get_settings()
|
||||
self._logger = get_logger("main")
|
||||
self._version_control = get_version_control()
|
||||
|
||||
self._db_manager = None
|
||||
self._config_manager: Optional[ConfigSyncManager] = None
|
||||
self._stream_manager: Optional[MultiStreamManager] = None
|
||||
self._preprocessor: Optional[ImagePreprocessor] = None
|
||||
self._engine_manager: Optional[EngineManager] = None
|
||||
self._postprocessor: Optional[PostProcessor] = None
|
||||
self._reporter: Optional[ResultReporter] = None
|
||||
|
||||
self._processing_threads: Dict[str, threading.Thread] = {}
|
||||
self._stop_event = threading.Event()
|
||||
|
||||
self._performance_stats = {
|
||||
"start_time": None,
|
||||
"total_frames_processed": 0,
|
||||
"total_alerts_generated": 0,
|
||||
"uptime_seconds": 0,
|
||||
}
|
||||
|
||||
self._logger.info("Edge_Inference_Service 初始化开始")
|
||||
|
||||
def _init_database(self):
|
||||
"""初始化数据库"""
|
||||
try:
|
||||
from config.database import DatabaseManager
|
||||
self._db_manager = DatabaseManager()
|
||||
if self._db_manager.is_available:
|
||||
self._logger.info("数据库初始化成功")
|
||||
else:
|
||||
self._logger.warning("数据库不可见，服务将在无数据库模式下运行")
|
||||
except Exception as e:
|
||||
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}")
|
||||
self._db_manager = None
|
||||
|
||||
def _init_config_manager(self):
|
||||
"""初始化配置管理器"""
|
||||
try:
|
||||
self._config_manager = get_config_sync_manager()
|
||||
self._config_manager.start_config_subscription()
|
||||
self._logger.info("配置管理器初始化成功")
|
||||
except Exception as e:
|
||||
self._logger.error(f"配置管理器初始化失败: {e}")
|
||||
raise
|
||||
|
||||
def _init_stream_manager(self):
|
||||
"""初始化流管理器"""
|
||||
self._stream_manager = MultiStreamManager()
|
||||
self._logger.info("流管理器初始化成功")
|
||||
|
||||
def _init_preprocessor(self):
|
||||
"""初始化预处理器"""
|
||||
self._preprocessor = ImagePreprocessor()
|
||||
self._logger.info("预处理器初始化成功")
|
||||
|
||||
def _init_engine(self):
|
||||
"""初始化推理引擎"""
|
||||
try:
|
||||
self._engine_manager = EngineManager()
|
||||
|
||||
engine_path = self._settings.inference.model_path
|
||||
if os.path.exists(engine_path):
|
||||
self._engine_manager.load_engine("default", engine_path)
|
||||
self._logger.info(f"推理引擎加载成功: {engine_path}")
|
||||
else:
|
||||
self._logger.warning(f"引擎文件不存在: {engine_path}")
|
||||
|
||||
except Exception as e:
|
||||
self._logger.error(f"推理引擎初始化失败: {e}")
|
||||
raise
|
||||
|
||||
def _init_postprocessor(self):
|
||||
"""初始化后处理器"""
|
||||
self._postprocessor = PostProcessor()
|
||||
self._logger.info("后处理器初始化成功")
|
||||
|
||||
def _init_reporter(self):
|
||||
"""初始化结果上报器"""
|
||||
self._reporter = ResultReporter()
|
||||
self._logger.info("结果上报器初始化成功")
|
||||
|
||||
def initialize(self):
|
||||
"""初始化所有组件"""
|
||||
self._logger.info("=" * 50)
|
||||
self._logger.info("Edge_Inference_Service 启动")
|
||||
self._logger.info("=" * 50)
|
||||
|
||||
self._init_database()
|
||||
self._init_config_manager()
|
||||
self._init_stream_manager()
|
||||
self._init_preprocessor()
|
||||
self._init_engine()
|
||||
self._init_postprocessor()
|
||||
self._init_reporter()
|
||||
|
||||
self._performance_stats["start_time"] = datetime.now()
|
||||
|
||||
self._version_control.record_update(
|
||||
version="1.0.0",
|
||||
update_type="启动",
|
||||
description="Edge_Inference_Service 启动运行",
|
||||
updated_by="系统",
|
||||
affected_items=["全局"],
|
||||
)
|
||||
|
||||
self._logger.info("所有组件初始化完成")
|
||||
|
||||
def _load_cameras(self):
|
||||
"""加载摄像头配置"""
|
||||
cameras = self._config_manager.get_cameras()
|
||||
|
||||
for camera in cameras:
|
||||
try:
|
||||
self._stream_manager.add_stream(
|
||||
camera_id=camera.camera_id,
|
||||
rtsp_url=camera.rtsp_url,
|
||||
target_fps=self._settings.video_stream.default_fps,
|
||||
on_frame_callback=self._create_frame_callback(camera.camera_id)
|
||||
)
|
||||
self._logger.info(f"已添加摄像头: {camera.camera_id}")
|
||||
except Exception as e:
|
||||
self._logger.error(f"添加摄像头失败 {camera.camera_id}: {e}")
|
||||
|
||||
def _create_frame_callback(self, camera_id: str):
|
||||
"""创建帧处理回调"""
|
||||
def callback(frame):
|
||||
self._process_frame(camera_id, frame)
|
||||
return callback
|
||||
|
||||
def _process_frame(self, camera_id: str, frame: VideoFrame):
|
||||
"""处理视频帧"""
|
||||
try:
|
||||
start_time = time.perf_counter()
|
||||
|
||||
roi_configs = self._config_manager.get_roi_configs(camera_id)
|
||||
|
||||
for roi in roi_configs:
|
||||
self._process_roi_frame(camera_id, frame, roi)
|
||||
|
||||
processing_time_ms = (time.perf_counter() - start_time) * 1000
|
||||
|
||||
self._performance_stats["total_frames_processed"] += 1
|
||||
|
||||
self._logger.log_inference_latency(
|
||||
processing_time_ms,
|
||||
batch_size=1
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
self._logger.error(f"处理帧失败 {camera_id}: {e}")
|
||||
|
||||
def _process_roi_frame(
|
||||
self,
|
||||
camera_id: str,
|
||||
frame: VideoFrame,
|
||||
roi
|
||||
):
|
||||
"""处理ROI帧"""
|
||||
try:
|
||||
cropped = self._preprocessor.preprocess_single(frame.image, roi)
|
||||
|
||||
processed_image, scale_info = cropped
|
||||
|
||||
batch_data = self._preprocessor._batch_preprocessor._stack_and_normalize(
|
||||
[processed_image]
|
||||
)
|
||||
|
||||
engine = self._engine_manager.get_engine("default")
|
||||
if engine is None:
|
||||
return
|
||||
|
||||
outputs, inference_time_ms = engine.infer(batch_data)
|
||||
|
||||
boxes, scores, class_ids = self._postprocessor.process_detections(
|
||||
outputs,
|
||||
conf_threshold=self._settings.inference.conf_threshold
|
||||
)
|
||||
|
||||
if len(boxes) > 0:
|
||||
self._handle_detections(
|
||||
camera_id, roi.roi_id, frame,
|
||||
boxes, scores, class_ids,
|
||||
scale_info
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
self._logger.error(f"处理ROI帧失败: {e}")
|
||||
|
||||
def _handle_detections(
|
||||
self,
|
||||
camera_id: str,
|
||||
roi_id: str,
|
||||
frame: VideoFrame,
|
||||
boxes: any,
|
||||
scores: any,
|
||||
class_ids: any,
|
||||
scale_info: tuple
|
||||
):
|
||||
"""处理检测结果"""
|
||||
try:
|
||||
from config.config_models import AlgorithmType
|
||||
|
||||
mapped_boxes = self._postprocessor.map_coordinates(
|
||||
boxes, scale_info,
|
||||
(frame.width, frame.height)
|
||||
)
|
||||
|
||||
for i, box in enumerate(mapped_boxes):
|
||||
detection_result = {
|
||||
"class_id": int(class_ids[i]) if len(class_ids) > 0 else 0,
|
||||
"confidence": float(scores[i]),
|
||||
"bbox": box,
|
||||
}
|
||||
|
||||
alert_result = self._postprocessor.check_alarm_condition(
|
||||
roi_id, True, frame.timestamp
|
||||
)
|
||||
|
||||
if alert_result["should_alert"]:
|
||||
self._performance_stats["total_alerts_generated"] += 1
|
||||
|
||||
screenshot = frame.image
|
||||
|
||||
self._reporter.report_detection_alert(
|
||||
camera_id=camera_id,
|
||||
roi_id=roi_id,
|
||||
alert_type="detection",
|
||||
detection={
|
||||
"class_name": f"class_{detection_result['class_id']}",
|
||||
"confidence": detection_result["confidence"],
|
||||
"bbox": detection_result["bbox"],
|
||||
"message": f"检测到目标"
|
||||
},
|
||||
screenshot=screenshot
|
||||
)
|
||||
|
||||
self._logger.log_alert(
|
||||
"detection",
|
||||
camera_id,
|
||||
roi_id,
|
||||
detection_result["confidence"]
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
self._logger.error(f"处理检测结果失败: {e}")
|
||||
|
||||
def start(self):
|
||||
"""启动服务"""
|
||||
if self._running:
|
||||
return
|
||||
|
||||
self._running = True
|
||||
self._stop_event.clear()
|
||||
|
||||
self._load_cameras()
|
||||
|
||||
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("edge_inference_device", 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()
|
||||
|
||||
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._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
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
# 按装订区域中的绿色按钮以运行脚本。
|
||||
if __name__ == '__main__':
|
||||
print_hi('PyCharm')
|
||||
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
|
||||
|
||||
# 访问 https://www.jetbrains.com/help/pycharm/ 获取 PyCharm 帮助
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
Reference in New Issue
Block a user