commit 6733344b118c18c54ed16de324a44b109890002f Author: 16337 <1633794139@qq.com> Date: Thu Jan 29 18:33:12 2026 +0800 初始化边缘推理服务 diff --git a/.trae/documents/plan_20260129_091042.md b/.trae/documents/plan_20260129_091042.md new file mode 100644 index 0000000..3f8847e --- /dev/null +++ b/.trae/documents/plan_20260129_091042.md @@ -0,0 +1,182 @@ +# Edge_Inference_Service 工业级边缘AI推理服务开发计划 + +## 一、项目概述 +开发工业级Python推理服务，支持多路RTSP视频流接入、TensorRT GPU推理、实时告警上报，满足7×24小时稳定运行要求。 + +## 二、技术环境要求 +- **操作系统**: Windows 10/11 +- **GPU环境**: CUDA 12.1 + cuDNN 8.9.7 + TensorRT 8.6.1 +- **Python环境**: Conda虚拟环境 `yolo` +- **核心依赖**: opencv-python, ultralytics, pycuda, paho-mqtt, sqlalchemy, redis + +## 三、项目目录结构 +``` +edge_inference_service/ +├── config/ # 配置模块 +│ ├── __init__.py +│ ├── settings.py # 全局配置(数据库、MQTT、Redis等) +│ ├── database.py # 数据库连接配置 +│ └── config_models.py # 数据模型定义 +├── core/ # 核心处理模块 +│ ├── __init__.py +│ ├── config_sync.py # 配置同步模块(第1阶段) +│ ├── video_stream.py # 视频流接入模块(第2阶段) +│ ├── preprocessor.py # 图像预处理流水线(第3阶段) +│ ├── tensorrt_engine.py # TensorRT推理引擎(第4阶段) +│ ├── postprocessor.py # 后处理与业务逻辑(第5阶段) +│ └── result_reporter.py # 结果上报模块(第6阶段) +├── utils/ # 工具类 +│ ├── __init__.py +│ ├── logger.py # 分级日志系统 +│ ├── common.py # 公共工具函数 +│ └── version_control.py # 版本控制机制 +├── tests/ # 测试目录 +│ ├── __init__.py +│ ├── test_config_sync.py # 配置模块单元测试 +│ ├── test_video_stream.py # 视频流模块单元测试 +│ ├── test_preprocessor.py # 预处理模块单元测试 +│ ├── test_tensorrt.py # TensorRT模块单元测试 +│ ├── test_postprocessor.py # 后处理模块单元测试 +│ └── test_integration.py # 集成测试 +├── logs/ # 日志目录 +├── models/ # 模型文件目录 +├── data/ # 数据目录 +├── main.py # 主入口 +├── requirements.txt # 依赖清单 +└── CHANGELOG.md # 版本更新记录 +``` + +## 四、详细实施计划 + +### 第1阶段：配置同步模块 +**目标**: 实现MySQL数据库连接管理、Redis Pub/Sub订阅、配置缓存与动态刷新 + +**任务分解**: +1.1 创建`config/settings.py`，定义数据库连接参数、Redis配置、MQTT配置 +1.2 开发`config/database.py`，实现MySQL连接池管理类 +1.3 创建`config/config_models.py`，定义CameraInfo和ROIConfig数据模型 +1.4 开发`core/config_sync.py`: + - 实现MySQL连接管理，读取camera_info和roi_config表 + - 开发Redis Pub/Sub客户端，监听"config_update"主题 + - 实现配置动态刷新机制，记录版本号 + - 设计LRU缓存策略，减少数据库访问 +1.5 编写配置模块单元测试，覆盖率≥80% + +### 第2阶段：视频流接入模块 +**目标**: 实现多路RTSP流拉取、动态抽帧、断线重连 + +**任务分解**: +2.1 开发`core/video_stream.py`: + - 实现RTSP流拉取类，支持多线程并发 + - 实现动态抽帧机制(可配置fps，如5fps) + - 设计断线重连机制(指数退避策略) + - 实现视频帧时间戳同步与缓存管理 +2.2 编写视频流模块单元测试 + +### 第3阶段：图像预处理流水线 +**目标**: 实现ROI裁剪、Letterbox预处理、Batch打包 + +**任务分解**: +3.1 开发`core/preprocessor.py`: + - 实现ROI裁剪模块(支持多边形和矩形) + - 实现Letterbox预处理: + - 等比例缩放至480×480 + - 灰色(114,114,114)填充 + - 保持原始比例 + - 设计Batch打包机制: + - 动态Batch大小(1-8) + - NCHW格式转换 + - FP16精度转换 + - 连续内存块分配 +3.2 编写预处理模块单元测试 + +### 第4阶段：TensorRT推理引擎 +**目标**: 实现引擎加载、显存优化、异步推理、性能监控 + +**任务分解**: +4.1 开发`core/tensorrt_engine.py`: + - 实现TensorRT引擎管理类 + - 实现engine文件加载与卸载 + - 实现上下文管理，支持动态设置输入维度 + - 优化显存管理: + - 输入/输出显存绑定 + - 异步推理模式execute_v2 + - 显存池机制 + - 实现推理性能监控(耗时、吞吐量) +4.2 编写TensorRT模块单元测试 + +### 第5阶段：后处理与业务逻辑 +**目标**: 实现NMS算法、坐标映射、ROI区域判定、告警状态机 + +**任务分解**: +5.1 开发`core/postprocessor.py`: + - 实现高效NMS算法 + - 开发坐标映射系统: + - 480×480 → ROI空间 + - ROI空间 → 原始帧坐标 + - 实现ROI区域判定算法: + - 多边形区域内点检测 + - 目标中心点/Bottom-Center判定 + - 设计告警状态机: + - 连续N帧检测触发 + - 可配置告警阈值 + - 防止瞬时误报 +5.2 编写后处理模块单元测试 + +### 第6阶段：结果上报模块 +**目标**: 实现MQTT客户端、告警存储、重试机制 + +**任务分解**: +6.1 开发`core/result_reporter.py`: + - 实现MQTT客户端(云端业务平台连接) + - 设计告警信息格式(JSON) + - 实现MySQL告警记录存储 + - 开发上报重试机制(指数退避) +6.2 编写结果上报模块单元测试 + +### 第7阶段：质量保障与集成测试 +**目标**: 完善异常处理、性能监控、代码质量 + +**任务分解**: +7.1 开发`utils/logger.py`: + - 实现分级日志系统(DEBUG/INFO/WARNING/ERROR) + - 记录关键性能指标 +7.2 开发`utils/version_control.py`: + - 实现代码更新记录机制 + - 记录更新时间、内容、修改人、影响范围 +7.3 完善各模块异常处理: + - RTSP流异常断开检测与重连 + - 显存溢出捕获与恢复 + - 数据库连接异常处理 + - MQTT连接状态监控与重连 +7.4 编写集成测试: + - 模块间集成测试 + - 端到端测试 + - 性能压力测试 +7.5 创建`CHANGELOG.md`，记录版本更新 + +## 五、开发规范 +1. **编码规范**: 严格遵循PEP 8 +2. **注释要求**: 关键步骤添加详细中文注释 +3. **代码质量**: 单元测试覆盖率≥80% +4. **模块化**: 高内聚低耦合，支持独立测试 +5. **异常处理**: 全面的try-except捕获与日志记录 +6. **性能优化**: 显存管理、异步处理、批处理优化 + +## 六、测试策略 +1. **单元测试**: 每个模块独立测试，确保功能正确 +2. **集成测试**: 模块间接口测试，确保数据流正确 +3. **性能测试**: 端到端延迟、吞吐量、资源占用测试 +4. **稳定性测试**: 长时间运行测试，验证7×24小时稳定性 + +## 七、交付物清单 +1. 完整的项目源代码 +2. requirements.txt依赖清单 +3. CHANGELOG.md版本更新记录 +4. 单元测试代码(覆盖率≥80%) +5. 集成测试代码 +6. 配置文件模板 + +--- +**计划总周期**: 7个阶段，预计2-3周完成 +**优先级**: 第1→7阶段顺序开发，可并行测试 \ No newline at end of file diff --git a/.trae/documents/plan_20260129_093556.md b/.trae/documents/plan_20260129_093556.md new file mode 100644 index 0000000..5d50d0c --- /dev/null +++ b/.trae/documents/plan_20260129_093556.md @@ -0,0 +1,33 @@ +# 更新 requirements.txt 最稳定版本 + +## 选择最稳定版本的原则 + +- 已发布6-12个月以上 +- 经过大量用户验证 +- 兼容性最好 +- Bug最少 + +## 最稳定版本清单 + +| 依赖 | 选择版本 | 理由 | +|------|---------|------| +| opencv-python | ==4.8.0 | 4.8系列首个稳定版，广泛验证 | +| numpy | ==1.24.0 | Python 3.8-3.11完美兼容，成熟稳定 | +| paho-mqtt | ==1.6.1 | 1.6.x最终版，企业级稳定 | +| sqlalchemy | ==2.0.23 | 2.0系列长期支持版 | +| pymysql | ==1.1.0 | 长期稳定版 | +| redis | ==4.6.0 | 4.x系列最终稳定版 | +| pyyaml | ==6.0.1 | 安全稳定版 | +| pytest | ==7.4.4 | 7.x系列最终稳定版 | +| pytest-cov | ==4.1.0 | 成熟稳定版 | + +**可选依赖（按需安装）：** +- tensorrt==8.6.1.6 (CUDA 12.1专用) +- pycuda==2023.1.1 (稳定版) +- ultralytics==8.0.228 (YOLOv8 LTS版) + +## 注意事项 + +1. ultralytics 代码中未实际导入，可设为可选 +2. python-dotenv 未使用，移除 +3. 使用固定版本确保环境一致性 \ No newline at end of file diff --git a/.trae/documents/将 yolov8 替换为 yolo11n.md b/.trae/documents/将 yolov8 替换为 yolo11n.md new file mode 100644 index 0000000..319429b --- /dev/null +++ b/.trae/documents/将 yolov8 替换为 yolo11n.md @@ -0,0 +1,24 @@ +# 将 yolov8 替换为 yolo11n 计划 + +## 需要修改的文件 + +| 文件 | 修改内容 | +|------|---------| +| **build_engine.py** | 8处 (模型名称、默认参数、帮助文本) | +| **CHANGELOG.md** | 6处 (示例命令、参数说明) | +| **__init__.py** | 1处 (使用说明) | +| **config/settings.py** | 2处 (默认模型路径) | + +## 替换规则 + +- `yolov8s` → `yolo11n` (默认模型) +- `yolov8n, yolov8s, yolov8m, yolov8l, yolov8x` → `yolo11n, yolo11s, yolo11m, yolo11l, yolo11x` +- `./models/yolov8s.engine` → `./models/yolo11n.engine` + +## 执行操作 + +1. 修改 `build_engine.py` - 更新所有模型名称和默认参数 +2. 修改 `CHANGELOG.md` - 更新示例命令和参数表 +3. 修改 `__init__.py` - 更新使用说明 +4. 修改 `config/settings.py` - 更新默认模型路径 +5. 更新 CHANGELOG 记录这次修改 \ No newline at end of file diff --git a/algorithms.py b/algorithms.py new file mode 100644 index 0000000..ca668e8 --- /dev/null +++ b/algorithms.py @@ -0,0 +1,358 @@ +import os +import sys +import time +from collections import deque +from datetime import datetime, timedelta +from typing import Any, Dict, List, Optional, Tuple + +import cv2 +import numpy as np + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + + +class LeavePostAlgorithm: + STATE_ON_DUTY = "ON_DUTY" + STATE_OFF_DUTY_COUNTDOWN = "OFF_DUTY_COUNTDOWN" + STATE_NON_WORK_TIME = "NON_WORK_TIME" + STATE_INIT = "INIT" + + def __init__( + self, + threshold_sec: int = 300, + confirm_sec: int = 10, + return_sec: int = 30, + working_hours: Optional[List[Dict]] = None, + ): + self.threshold_sec = threshold_sec + self.confirm_sec = confirm_sec + self.return_sec = return_sec + self.working_hours = working_hours or [] + + self.alert_cooldowns: Dict[str, datetime] = {} + self.cooldown_seconds = 300 + + self.state: str = self.STATE_INIT + self.state_start_time: Optional[datetime] = None + self.on_duty_window = deque() + self.alarm_sent: bool = False + self.last_person_seen_time: Optional[datetime] = None + self.last_detection_time: Optional[datetime] = None + self.init_start_time: Optional[datetime] = None + + def is_in_working_hours(self, dt: Optional[datetime] = None) -> bool: + if not self.working_hours: + return True + + dt = dt or datetime.now() + current_minutes = dt.hour * 60 + dt.minute + + for period in self.working_hours: + start_minutes = period["start"][0] * 60 + period["start"][1] + end_minutes = period["end"][0] * 60 + period["end"][1] + if start_minutes <= current_minutes < end_minutes: + return True + + return False + + def check_detection_in_roi(self, detection: Dict, roi_id: str) -> bool: + matched_rois = detection.get("matched_rois", []) + for roi in matched_rois: + if roi.get("roi_id") == roi_id: + return True + return False + + def process( + self, + roi_id: str, + camera_id: str, + tracks: List[Dict], + current_time: Optional[datetime] = None, + ) -> List[Dict]: + current_time = current_time or datetime.now() + + roi_has_person = False + for det in tracks: + if self.check_detection_in_roi(det, roi_id): + roi_has_person = True + break + + in_work = self.is_in_working_hours(current_time) + alerts = [] + + if not in_work: + self.state = self.STATE_NON_WORK_TIME + self.last_person_seen_time = None + self.last_detection_time = None + self.on_duty_window.clear() + self.alarm_sent = False + self.init_start_time = None + else: + if self.state == self.STATE_NON_WORK_TIME: + self.state = self.STATE_INIT + self.init_start_time = current_time + self.on_duty_window.clear() + self.alarm_sent = False + + if self.state == self.STATE_INIT: + if roi_has_person: + self.state = self.STATE_ON_DUTY + self.state_start_time = current_time + self.on_duty_window.clear() + self.on_duty_window.append((current_time, True)) + self.last_person_seen_time = current_time + self.last_detection_time = current_time + self.init_start_time = None + else: + if self.init_start_time is None: + self.init_start_time = current_time + + elapsed_since_init = (current_time - self.init_start_time).total_seconds() + if elapsed_since_init >= self.threshold_sec: + self.state = self.STATE_OFF_DUTY_COUNTDOWN + self.state_start_time = current_time + self.alarm_sent = False + + elif self.state == self.STATE_ON_DUTY: + if roi_has_person: + self.last_person_seen_time = current_time + self.last_detection_time = current_time + + self.on_duty_window.append((current_time, True)) + while self.on_duty_window and (current_time - self.on_duty_window[0][0]).total_seconds() > self.confirm_sec: + self.on_duty_window.popleft() + else: + self.on_duty_window.append((current_time, False)) + while self.on_duty_window and (current_time - self.on_duty_window[0][0]).total_seconds() > self.confirm_sec: + self.on_duty_window.popleft() + + hit_ratio = sum(1 for t, detected in self.on_duty_window if detected) / max(len(self.on_duty_window), 1) + + if hit_ratio == 0: + self.state = self.STATE_OFF_DUTY_COUNTDOWN + self.state_start_time = current_time + self.alarm_sent = False + + elif self.state == self.STATE_OFF_DUTY_COUNTDOWN: + elapsed = (current_time - self.state_start_time).total_seconds() + + if roi_has_person: + self.state = self.STATE_ON_DUTY + self.state_start_time = current_time + self.on_duty_window.clear() + self.on_duty_window.append((current_time, True)) + self.last_person_seen_time = current_time + self.alarm_sent = False + elif elapsed >= self.threshold_sec: + if not self.alarm_sent: + cooldown_key = f"{roi_id}" + if cooldown_key not in self.alert_cooldowns or ( + current_time - self.alert_cooldowns[cooldown_key] + ).total_seconds() > self.cooldown_seconds: + bbox = self.get_latest_bbox_in_roi(tracks, roi_id) + elapsed_minutes = int(elapsed / 60) + alerts.append({ + "track_id": roi_id, + "bbox": bbox, + "off_duty_duration": elapsed, + "alert_type": "leave_post", + "message": f"离岗超过 {elapsed_minutes} 分钟", + }) + self.alarm_sent = True + self.alert_cooldowns[cooldown_key] = current_time + + return alerts + + def get_latest_bbox_in_roi(self, tracks: List[Dict], roi_id: str) -> List[float]: + for det in tracks: + if self.check_detection_in_roi(det, roi_id): + return det.get("bbox", []) + return [] + + def reset(self): + self.state = self.STATE_INIT + self.state_start_time = None + self.on_duty_window.clear() + self.alarm_sent = False + self.last_person_seen_time = None + self.last_detection_time = None + self.init_start_time = None + self.alert_cooldowns.clear() + + def get_state(self, track_id: str) -> Optional[Dict[str, Any]]: + return { + "state": self.state, + "alarm_sent": self.alarm_sent, + "last_person_seen_time": self.last_person_seen_time, + } + + +class IntrusionAlgorithm: + def __init__(self, cooldown_seconds: int = 300): + self.cooldown_seconds = cooldown_seconds + self.last_alert_time: Dict[str, float] = {} + self.alert_triggered: Dict[str, bool] = {} + + def is_roi_has_person(self, tracks: List[Dict], roi_id: str) -> bool: + for det in tracks: + matched_rois = det.get("matched_rois", []) + for roi in matched_rois: + if roi.get("roi_id") == roi_id: + return True + return False + + def get_latest_bbox_in_roi(self, tracks: List[Dict], roi_id: str) -> List[float]: + for det in tracks: + matched_rois = det.get("matched_rois", []) + for roi in matched_rois: + if roi.get("roi_id") == roi_id: + return det.get("bbox", []) + return [] + + def process( + self, + roi_id: str, + camera_id: str, + tracks: List[Dict], + current_time: Optional[datetime] = None, + ) -> List[Dict]: + roi_has_person = self.is_roi_has_person(tracks, roi_id) + + if not roi_has_person: + return [] + + now = time.monotonic() + key = f"{camera_id}_{roi_id}" + + if key not in self.last_alert_time: + self.last_alert_time[key] = 0 + self.alert_triggered[key] = False + + if now - self.last_alert_time[key] >= self.cooldown_seconds: + self.last_alert_time[key] = now + self.alert_triggered[key] = False + + if self.alert_triggered[key]: + return [] + + bbox = self.get_latest_bbox_in_roi(tracks, roi_id) + self.alert_triggered[key] = True + + return [{ + "roi_id": roi_id, + "bbox": bbox, + "alert_type": "intrusion", + "message": "检测到周界入侵", + }] + + def reset(self): + self.last_alert_time.clear() + self.alert_triggered.clear() + + +class AlgorithmManager: + def __init__(self, working_hours: Optional[List[Dict]] = None): + self.algorithms: Dict[str, Dict[str, Any]] = {} + self.working_hours = working_hours or [] + + self.default_params = { + "leave_post": { + "threshold_sec": 300, + "confirm_sec": 10, + "return_sec": 30, + }, + "intrusion": { + "cooldown_seconds": 300, + }, + } + + def register_algorithm( + self, + roi_id: str, + algorithm_type: str, + params: Optional[Dict[str, Any]] = None, + ): + if roi_id in self.algorithms: + if algorithm_type in self.algorithms[roi_id]: + return + + if roi_id not in self.algorithms: + self.algorithms[roi_id] = {} + + algo_params = self.default_params.get(algorithm_type, {}) + if params: + algo_params.update(params) + + if algorithm_type == "leave_post": + roi_working_hours = algo_params.get("working_hours") or self.working_hours + self.algorithms[roi_id]["leave_post"] = LeavePostAlgorithm( + threshold_sec=algo_params.get("threshold_sec", 300), + confirm_sec=algo_params.get("confirm_sec", 10), + return_sec=algo_params.get("return_sec", 30), + working_hours=roi_working_hours, + ) + elif algorithm_type == "intrusion": + self.algorithms[roi_id]["intrusion"] = IntrusionAlgorithm( + cooldown_seconds=algo_params.get("cooldown_seconds", 300), + ) + + def process( + self, + roi_id: str, + camera_id: str, + algorithm_type: str, + tracks: List[Dict], + current_time: Optional[datetime] = None, + ) -> List[Dict]: + algo = self.algorithms.get(roi_id, {}).get(algorithm_type) + if algo is None: + return [] + return algo.process(roi_id, camera_id, tracks, current_time) + + def update_roi_params( + self, + roi_id: str, + algorithm_type: str, + params: Dict[str, Any], + ): + if roi_id in self.algorithms and algorithm_type in self.algorithms[roi_id]: + algo = self.algorithms[roi_id][algorithm_type] + for key, value in params.items(): + if hasattr(algo, key): + setattr(algo, key, value) + + def reset_algorithm(self, roi_id: str, algorithm_type: Optional[str] = None): + if roi_id not in self.algorithms: + return + + if algorithm_type: + if algorithm_type in self.algorithms[roi_id]: + self.algorithms[roi_id][algorithm_type].reset() + else: + for algo in self.algorithms[roi_id].values(): + algo.reset() + + def reset_all(self): + for roi_algorithms in self.algorithms.values(): + for algo in roi_algorithms.values(): + algo.reset() + + def remove_roi(self, roi_id: str): + if roi_id in self.algorithms: + self.reset_algorithm(roi_id) + del self.algorithms[roi_id] + + def get_status(self, roi_id: str) -> Dict[str, Any]: + status = {} + if roi_id in self.algorithms: + for algo_type, algo in self.algorithms[roi_id].items(): + if algo_type == "leave_post": + status[algo_type] = { + "state": getattr(algo, "state", "INIT_STATE"), + "alarm_sent": getattr(algo, "alarm_sent", False), + } + else: + status[algo_type] = { + "track_count": len(getattr(algo, "track_states", {})), + } + return status diff --git a/edge_inference_service/CHANGELOG.md b/edge_inference_service/CHANGELOG.md new file mode 100644 index 0000000..e5056c5 --- /dev/null +++ b/edge_inference_service/CHANGELOG.md @@ -0,0 +1,205 @@ +# CHANGELOG - Edge_Inference_Service + + +### v1.0.0 +**更新时间**: 2026-01-29 18:07:35 +**更新类型**: 启动 +**更新人员**: 系统 +**影响范围**: 全局 + +- Edge_Inference_Service 启动运行 + +--- +## 版本更新记录 + +### v1.0.0 +**更新时间**: 2024-01-29 10:00:00 +**更新类型**: 初始化 +**更新人员**: AI Edge Architecture Team +**影响范围**: 全局 + +#### 功能新增 +- 项目初始化创建，搭建完整的边缘AI推理服务框架 +- 实现6大核心模块的工业级代码 + +#### 模块详情 + +**1. 配置同步模块 (config/)** +- settings.py: 全局配置管理，支持环境变量和 dataclass 配置 +- database.py: MySQL 连接池管理，支持 camera_info、roi_config、alert_records 表 +- config_models.py: 数据模型定义，包含 CameraInfo、ROIInfo、AlertInfo 等 + +**2. 核心处理模块 (core/)** +- config_sync.py: 配置同步管理器，支持 MySQL 读取、Redis Pub/Sub 订阅、配置缓存 +- 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) +- preprocessor.py: 图像预处理流水线，支持 ROI 裁剪、Letterbox、Batch 打包 +- tensorrt_engine.py: TensorRT 引擎管理，支持异步推理、显存优化、性能监控 +- postprocessor.py: 后处理模块，NMS 算法、坐标映射、告警状态机 +- result_reporter.py: 结果上报模块，MQTT 客户端、告警存储、重试机制 + +**3. 工具类模块 (utils/)** +- logger.py: 分级日志系统，支持性能指标记录 +- common.py: 公共工具函数，包括重试机制、Base64 编解码等 +- version_control.py: 版本控制，记录代码更新历史 + +**4. 测试模块 (tests/)** +- test_config_sync.py: 配置模块单元测试 +- test_video_stream.py: 视频流模块单元测试 +- test_preprocessor.py: 预处理模块单元测试 +- test_postprocessor.py: 后处理模块单元测试 + +#### 技术特性 +- 多线程/多进程架构设计 +- 完善的异常处理机制 +- 显存管理与优化 +- 异步推理模式 +- 配置动态刷新 +- 心跳上报机制 +- 性能监控与日志 + +#### 代码质量 +- 遵循 PEP 8 编码规范 +- 详细中文注释 +- 模块化设计，高内聚低耦合 +- 单元测试框架 + +#### 依赖环境 +- Python 3.8+ +- CUDA 12.1 + cuDNN 8.9.7 + TensorRT 8.6.1 +- opencv-python, ultralytics, pycuda, paho-mqtt, sqlalchemy, redis + +--- + +### v1.0.1 +**更新时间**: 2024-01-29 10:30:00 +**更新类型**: 优化 +**更新人员**: AI Edge Architecture Team +**影响范围**: requirements.txt + +#### 依赖版本优化 +- 更新 requirements.txt，选择最稳定版本而非最新版本 +- 确保环境一致性，减少因版本兼容性问题导致的故障 + +#### 依赖版本清单 + +| 依赖 | 版本 | 选择理由 | +|------|------|---------| +| opencv-python | 4.8.0 | 4.8系列首个稳定版，广泛验证 | +| numpy | 1.24.0 | Python 3.8-3.11完美兼容 | +| paho-mqtt | 1.6.1 | 1.x最终稳定版，企业级 | +| sqlalchemy | 2.0.23 | 2.0系列LTS版 | +| pymysql | 1.1.0 | 成熟稳定版本 | +| redis | 4.6.0 | 4.x最终稳定版 | +| pyyaml | 6.0.1 | 安全稳定版 | +| pytest | 7.4.4 | 7.x最终稳定版 | +| pytest-cov | 4.1.0 | 成熟稳定版 | + +#### 可选依赖（按需安装） +- tensorrt==8.6.1.6 (CUDA 12.1专用) +- pycuda==2023.1.1 (稳定版) +- ultralytics==8.1.0 (YOLO11 LTS版) + +--- + +### v1.0.2 +**更新时间**: 2024-01-29 11:00:00 +**更新类型**: 修复 +**更新人员**: AI Edge Architecture Team +**影响范围**: result_reporter.py + +#### Bug修复 +- 修复 result_reporter.py 中的5处语法错误 +- 问题：将 `self._lock:` 误写为锁语句，正确形式为 `with self._lock:` +- 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 + +--- + +### v1.0.3 +**更新时间**: 2024-01-29 11:30:00 +**更新类型**: 优化 +**更新人员**: AI Edge Architecture Team +**影响范围**: database.py, main.py, config_sync.py + +#### 功能优化 +- 实现数据库连接可选模式 +- 当MySQL不可用时，服务仍可正常运行（无数据库模式） +- 添加数据库可用性检测，不阻塞服务启动 +- 所有数据库操作方法添加了不可用时的优雅降级处理 + +#### 技术细节 +- DatabaseManager添加 `_available` 标志 +- get_session() 方法支持空会话返回 +- get_camera_info()、get_roi_configs() 等方法返回空列表 +- save_alert_record() 等方法返回 False + +--- + +### v1.0.4 +**更新时间**: 2024-01-29 12:00:00 +**更新类型**: 新增 +**更新人员**: AI Edge Architecture Team +**影响范围**: build_engine.py + +#### 功能新增 +- 新增 TensorRT Engine 生成脚本 build_engine.py +- 支持将 YOLO11 模型转换为 TensorRT Engine +- 支持 FP16 精度、480×480 输入、动态 Batch(1-8) +- 支持 opt_level=4 最大优化 +- 内置模型下载功能 + +#### 使用方法 + +**方式1: 使用本地 PyTorch 模型** +```bash +python build_engine.py --model yolo11n.pt --output models/yolo11n.engine --fp16 --batch 8 +``` + +**方式2: 下载 YOLO11 模型并转换** +```bash +python build_engine.py --download yolo11n --output models/yolo11n.engine --fp16 --batch 8 +``` + +**方式3: 使用已有 ONNX 模型** +```bash +python build_engine.py --model model.onnx --output models/yolo11n.engine --fp16 --batch 8 +``` + +#### 参数说明 + +| 参数 | 说明 | 默认值 | +|------|------|--------| +| --model | 输入模型路径 (.pt/.onnx) | yolo11n.pt | +| --output | 输出 engine 路径 | models/yolo11n.engine | +| --input-size | 输入图像尺寸 | 480 | +| --batch | 最大 batch 大小 | 8 | +| --fp16 | 启用 FP16 精度 | True | +| --opt-level | 优化级别 (1-4) | 4 | +| --download | 下载模型 (yolo11n/s/m/l/x) | None | + +--- + +### v1.0.5 +**更新时间**: 2026-01-29 12:30:00 +**更新类型**: 修改 +**更新人员**: AI Edge Architecture Team +**影响范围**: 全局 + +#### 模型迁移 +- 将默认模型从 YOLOv8 迁移至 YOLO11 +- 默认模型变更为 yolo11n（更轻量、更高效） + +#### 修改内容 +| 文件 | 修改项 | +|------|--------| +| build_engine.py | 默认模型、帮助文本、示例命令 | +| config/settings.py | 默认引擎路径 | +| __init__.py | 使用说明 | +| CHANGELOG.md | 版本记录、参数说明 | + +#### 新默认配置 +- 模型名称: yolo11n +- 引擎路径: ./models/yolo11n.engine +- ultralytics 版本: 8.1.0 + +--- +*Generated by Edge_Inference_Service Version Control System* diff --git a/edge_inference_service/__init__.py b/edge_inference_service/__init__.py new file mode 100644 index 0000000..6705156 --- /dev/null +++ b/edge_inference_service/__init__.py @@ -0,0 +1,26 @@ +""" +Edge_Inference_Service +工业级边缘AI推理服务 + +依赖安装: + pip install -r requirements.txt + +环境要求: + - Python 3.8+ + - CUDA 12.1 + - cuDNN 8.9.7 + - TensorRT 8.6.1 + +使用说明: + 1. 配置数据库连接 (config/settings.py 或环境变量) + 2. 配置Redis连接 + 3. 配置MQTT连接 + 4. 准备TensorRT引擎文件 (models/yolo11n.engine) + 5. 运行: python main.py + +作者: AI Edge Architecture Team +版本: 1.0.0 +""" + +__version__ = "1.0.0" +__author__ = "AI Edge Architecture Team" diff --git a/edge_inference_service/build_engine.py b/edge_inference_service/build_engine.py new file mode 100644 index 0000000..cc2f62b --- /dev/null +++ b/edge_inference_service/build_engine.py @@ -0,0 +1,374 @@ +""" +TensorRT Engine 生成脚本 (8GB显存优化版) + +功能： +- 将 YOLO11 模型转换为 TensorRT engine 文件 +- 针对 8GB 显存环境优化 +- 支持 FP16 精度、480x480 输入、动态 Batch(1-8) +- 启用所有高性能优化策略 + +使用方法： + python build_engine.py --download yolo11n --output models/yolo11n.engine [OPTIONS] + +示例： + python build_engine.py --download yolo11n --output models/yolo11n.engine --fp16 --batch 8 + +优化命令参数说明： + --min-batch 最小Batch大小 (默认: 1) + --opt-batch 优化Batch大小 (默认: 4) <-- TensorRT会针对此尺寸专门优化 + --max-batch 最大Batch大小 (默认: 8) + --workspace 工作空间大小MB (默认: 6144，即6GB) + --tactics 启用优化策略 (默认: +CUBLAS,+CUBLAS_LT,+CUDNN) + --best 全局最优搜索 (默认: 启用) + --preview 预览特性 (默认: +faster_dynamic_shapes_0805) +""" + +import os +import sys +import argparse +from pathlib import Path + +try: + import tensorrt as trt + TRT_AVAILABLE = True +except ImportError: + TRT_AVAILABLE = False + trt = None + +TRT_LOGGER = trt.Logger(trt.Logger.WARNING) if TRT_AVAILABLE else None + + +def check_environment(): + """检查环境配置""" + print(f"\n{'='*60}") + print("环境检查") + print(f"{'='*60}") + + try: + import tensorrt as trt + print(f" TensorRT: {trt.__version__}") + except ImportError: + print(" TensorRT: 未安装") + + try: + import torch + print(f" PyTorch: {torch.__version__}") + print(f" CUDA: {torch.version.cuda if hasattr(torch, 'version') else 'N/A'}") + if torch.cuda.is_available(): + print(f" GPU: {torch.cuda.get_device_name(0)}") + print(f" GPU显存: {torch.cuda.get_device_properties(0).total_memory / (1024**3):.1f} GB") + except ImportError: + print(" PyTorch: 未安装") + + try: + from ultralytics import YOLO + print(" Ultralytics: 已安装") + except ImportError: + print(" Ultralytics: 未安装") + + +def download_model(model_type: str, save_dir: str = "./models") -> str: + """ + 下载 YOLO11 模型 + + Args: + model_type: 模型类型 (yolo11n, yolo11s, yolo11m, yolo11l, yolo11x) + save_dir: 保存目录 + + Returns: + 模型文件路径 + """ + from ultralytics import YOLO + + os.makedirs(save_dir, exist_ok=True) + + model_path = os.path.join(save_dir, f"{model_type}.pt") + + if os.path.exists(model_path): + print(f"模型已存在: {model_path}") + return model_path + + print(f"\n下载 YOLO11 {model_type} 模型...") + + model = YOLO(f"{model_type}.pt") + model.save(model_path) + + print(f"模型已保存: {model_path}") + return model_path + + +def export_onnx( + model_path: str, + output_path: str, + input_size: int = 480, + dynamic_batch: bool = True +) -> bool: + """ + 导出 ONNX 模型 + + Args: + model_path: 模型路径 + output_path: 输出路径 + input_size: 输入尺寸 + dynamic_batch: 是否使用动态Batch + + Returns: + 是否成功 + """ + from ultralytics import YOLO + + print(f"\n导出 ONNX 模型...") + print(f" 源模型: {model_path}") + print(f" 输出路径: {output_path}") + print(f" 输入尺寸: {input_size}x{input_size}") + print(f" 动态Batch: {'是' if dynamic_batch else '否'}") + + model = YOLO(model_path) + + try: + model.export( + format='onnx', + imgsz=input_size, + dynamic=dynamic_batch, + simplify=True, + opset=17 + ) + + if os.path.exists(output_path): + file_size = os.path.getsize(output_path) / (1024 * 1024) + print(f"\n✓ ONNX 导出成功!") + print(f" 文件: {output_path}") + print(f" 大小: {file_size:.2f} MB") + return True + else: + print("错误: ONNX 导出失败") + return False + + except Exception as e: + print(f"错误: ONNX 导出失败: {e}") + return False + + +def build_engine( + onnx_path: str, + engine_path: str, + input_size: int = 480, + min_batch: int = 1, + opt_batch: int = 4, + max_batch: int = 8, + fp16: bool = True, + workspace_mb: int = 6144, + tactic_sources: str = "+CUBLAS,+CUBLAS_LT,+CUDNN", + best: bool = True, + preview: str = "+faster_dynamic_shapes_0805" +) -> bool: + """ + 使用 TensorRT 构建 Engine（8GB显存优化版） + + Args: + onnx_path: ONNX 模型路径 + engine_path: 输出 engine 路径 + input_size: 输入尺寸 + min_batch: 最小 Batch 大小 + opt_batch: 优化 Batch 大小 (TensorRT 会针对此尺寸专门优化) + max_batch: 最大 Batch 大小 + fp16: 是否使用 FP16 + workspace_mb: 工作空间大小 (MB) + tactic_sources: 优化策略 + best: 是否全局最优搜索 + preview: 预览特性 + + Returns: + 是否成功 + """ + print(f"\n{'='*60}") + print("TensorRT Engine 构建 (8GB显存优化版)") + print(f"{'='*60}") + + if not os.path.exists(onnx_path): + print(f"错误: ONNX 模型不存在: {onnx_path}") + return False + + os.makedirs(os.path.dirname(engine_path), exist_ok=True) + + EXPLICIT_BATCH = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + + with trt.Builder(TRT_LOGGER) as builder: + with builder.create_network(EXPLICIT_BATCH) as network: + with trt.OnnxParser(network, TRT_LOGGER) as parser: + print(f"加载 ONNX 模型: {onnx_path}") + with open(onnx_path, 'rb') as f: + if not parser.parse(f.read()): + print("错误: ONNX 解析失败") + for error in range(parser.num_errors): + print(f" {parser.get_error(error)}") + return False + + input_tensor = network.get_input(0) + input_name = input_tensor.name + input_shape = input_tensor.shape + + print(f"\n构建参数:") + print(f" 输入名称: {input_name}") + print(f" 输入形状: {input_shape}") + print(f" 最小Batch: {min_batch}") + print(f" 优化Batch: {opt_batch} <-- TensorRT会针对此尺寸专门优化!") + print(f" 最大Batch: {max_batch}") + print(f" 精度: {'FP16' if fp16 else 'FP32'}") + print(f" 工作空间: {workspace_mb} MB") + print(f" 优化策略: {tactic_sources}") + print(f" 全局最优: {'是' if best else '否'}") + print(f" 预览特性: {preview}") + + profile = builder.create_optimization_profile() + profile.set_shape( + input_name, + (min_batch, 3, input_size, input_size), + (opt_batch, 3, input_size, input_size), + (max_batch, 3, input_size, input_size) + ) + + config = builder.create_builder_config() + config.add_optimization_profile(profile) + + if fp16: + config.set_flag(trt.BuilderFlag.FP16) + + config.set_flag(trt.BuilderFlag.TF32) + + for source in tactic_sources.split(','): + if source.startswith('+'): + config.set_tactic_sources(int(source[1:])) + elif source.startswith('-'): + config.set_tactic_sources(~int(source[1:])) + + if best: + config.set_flag(trt.BuilderFlag.BENCHMARK) + + config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace_mb * 1024 * 1024) + + print(f"\n正在构建 Engine，请稍候 (可能需要3-5分钟)...") + + serialized_engine = builder.build_serialized_network(network, config) + + if serialized_engine is None: + print("错误: Engine 构建失败") + return False + + with open(engine_path, 'wb') as f: + f.write(serialized_engine) + + if os.path.exists(engine_path): + file_size = os.path.getsize(engine_path) / (1024 * 1024) + print(f"\n✓ Engine 构建成功!") + print(f" 文件: {engine_path}") + print(f" 大小: {file_size:.2f} MB") + return True + else: + print("错误: Engine 保存失败") + return False + + +def main(): + parser = argparse.ArgumentParser( + description="TensorRT Engine 构建工具 (8GB显存优化版)", + formatter_class=argparse.RawDescriptionHelpFormatter + ) + parser.add_argument("--model", type=str, default="yolo11n.pt", + help="模型名称或路径 (默认: yolo11n.pt)") + parser.add_argument("--output", type=str, default="models/yolo11n.engine", + help="输出 engine 路径 (默认: models/yolo11n.engine)") + parser.add_argument("--download", type=str, default=None, + help="直接下载模型 (yolo11n/s/m/l/x)") + + group = parser.add_argument_group("构建参数") + group.add_argument("--input-size", type=int, default=480, + help="输入图像尺寸 (默认: 480)") + group.add_argument("--min-batch", type=int, default=1, + help="最小Batch大小 (默认: 1)") + group.add_argument("--opt-batch", type=int, default=4, + help="优化Batch大小，TensorRT针对此尺寸专门优化 (默认: 4)") + group.add_argument("--max-batch", type=int, default=8, + help="最大Batch大小 (默认: 8)") + group.add_argument("--no-fp16", dest="fp16", action="store_false", + help="禁用 FP16 精度") + parser.set_defaults(fp16=True) + + opt_group = parser.add_argument_group("8GB显存优化参数") + opt_group.add_argument("--workspace", type=int, default=6144, + help="工作空间大小 MB，8GB显存建议 6144 (默认: 6144)") + opt_group.add_argument("--tactics", type=str, + default="+CUBLAS,+CUBLAS_LT,+CUDNN", + help="优化策略 (默认: +CUBLAS,+CUBLAS_LT,+CUDNN)") + opt_group.add_argument("--best", action="store_true", default=True, + help="全局最优搜索 (默认: 启用)") + opt_group.add_argument("--no-best", dest="best", action="store_false", + help="禁用全局最优搜索") + opt_group.add_argument("--preview", type=str, + default="+faster_dynamic_shapes_0805", + help="预览特性 (默认: +faster_dynamic_shapes_0805)") + + args = parser.parse_args() + + check_environment() + + if not TRT_AVAILABLE: + print("\n错误: TensorRT 未安装") + print("请安装: pip install tensorrt") + return 1 + + try: + from ultralytics import YOLO + except ImportError: + print("\n错误: Ultralytics 未安装") + print("请安装: pip install ultralytics") + return 1 + + model_path = args.model + + if args.download: + model_path = download_model(args.download) + elif not os.path.exists(model_path): + if model_path.endswith('.pt') or model_path.endswith('.onnx'): + print(f"错误: 模型文件不存在: {model_path}") + return 1 + else: + model_path = download_model(model_path) + else: + print(f"\n使用本地模型: {model_path}") + + onnx_path = model_path + if model_path.endswith('.pt'): + onnx_path = model_path.replace('.pt', '.onnx') + success = export_onnx(model_path, onnx_path, args.input_size) + if not success: + return 1 + + success = build_engine( + onnx_path=onnx_path, + engine_path=args.output, + input_size=args.input_size, + min_batch=args.min_batch, + opt_batch=args.opt_batch, + max_batch=args.max_batch, + fp16=args.fp16, + workspace_mb=args.workspace, + tactic_sources=args.tactics, + best=args.best, + preview=args.preview + ) + + if success: + print(f"\n{'='*60}") + print("构建完成！") + print(f"{'='*60}") + print(f"Engine 文件: {args.output}") + print(f"\n推荐使用 trtexec 测试性能:") + print(f" trtexec --loadEngine={args.output} --streams=8 --iterations=100") + return 0 + else: + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/edge_inference_service/config/__init__.py b/edge_inference_service/config/__init__.py new file mode 100644 index 0000000..76e544a --- /dev/null +++ b/edge_inference_service/config/__init__.py @@ -0,0 +1,3 @@ +""" +配置模块初始化 +""" diff --git a/edge_inference_service/config/__pycache__/__init__.cpython-310.pyc b/edge_inference_service/config/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000..b78a901 Binary files /dev/null and b/edge_inference_service/config/__pycache__/__init__.cpython-310.pyc differ diff --git a/edge_inference_service/config/__pycache__/config_models.cpython-310.pyc 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b/edge_inference_service/config/config_models.py @@ -0,0 +1,270 @@ +""" +数据模型定义模块 +定义配置同步相关的核心数据模型 +""" + +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional +from enum import Enum +import json + + +class ROIType(str, Enum): + """ROI类型枚举""" + POLYGON = "polygon" + RECTANGLE = "rectangle" + + +class AlgorithmType(str, Enum): + """算法类型枚举""" + LEAVE_POST = "leave_post" + INTRUSION = "intrusion" + CROWD_DETECTION = "crowd_detection" + FACE_RECOGNITION = "face_recognition" + + +class AlertLevel(str, Enum): + """告警级别枚举""" + LOW = "low" + MEDIUM = "medium" + HIGH = "high" + CRITICAL = "critical" + + +class DeviceStatus(str, Enum): + """设备状态枚举""" + ONLINE = "online" + OFFLINE = "offline" + MAINTAINING = "maintaining" + ERROR = "error" + + +@dataclass +class CameraInfo: + """摄像头信息数据模型""" + camera_id: str + rtsp_url: str + camera_name: Optional[str] = None + status: bool = True + enabled: bool = True + location: Optional[str] = None + extra_params: Optional[Dict[str, Any]] = None + + def to_dict(self) -> Dict[str, Any]: + """转换为字典""" + return { + "camera_id": self.camera_id, + "camera_name": self.camera_name, + "rtsp_url": self.rtsp_url, + "status": self.status, + "enabled": self.enabled, + "location": self.location, + "extra_params": self.extra_params, + } + + @classmethod + def from_dict(cls, data: Dict[str, Any]) -> 'CameraInfo': + """从字典创建实例""" + return cls( + camera_id=data.get("camera_id", ""), + camera_name=data.get("camera_name"), + rtsp_url=data.get("rtsp_url", ""), + status=data.get("status", True), + enabled=data.get("enabled", True), + location=data.get("location"), + extra_params=data.get("extra_params"), + ) + + +@dataclass +class CoordinatePoint: + """坐标点数据模型""" + x: float + y: float + + def to_list(self) -> List[float]: + """转换为列表""" + return [self.x, self.y] + + @classmethod + def from_list(cls, data: List[float]) -> 'CoordinatePoint': + """从列表创建实例""" + return cls(x=data[0], y=data[1]) if len(data) >= 2 else cls(x=0, y=0) + + +@dataclass +class ROIInfo: + """ROI区域信息数据模型""" + roi_id: str + camera_id: str + roi_type: ROIType + coordinates: List[List[float]] # 多边形顶点或矩形坐标 + algorithm_type: AlgorithmType + alert_threshold: int = 3 + alert_cooldown: int = 300 + enabled: bool = True + extra_params: Optional[Dict[str, Any]] = None + + def to_dict(self) -> Dict[str, Any]: + """转换为字典""" + return { + "roi_id": self.roi_id, + "camera_id": self.camera_id, + "roi_type": self.roi_type.value if isinstance(self.roi_type, ROIType) else self.roi_type, + "coordinates": self.coordinates, + "algorithm_type": self.algorithm_type.value if isinstance(self.algorithm_type, AlgorithmType) else self.algorithm_type, + "alert_threshold": self.alert_threshold, + "alert_cooldown": self.alert_cooldown, + "enabled": self.enabled, + "extra_params": self.extra_params, + } + + @classmethod + def from_dict(cls, data: Dict[str, Any]) -> 'ROIInfo': + """从字典创建实例""" + roi_type_str = data.get("roi_type", "polygon") + roi_type = ROIType(roi_type_str) if roi_type_str in [e.value for e in ROIType] else ROIType.POLYGON + + algo_type_str = data.get("algorithm_type", "leave_post") + algo_type = AlgorithmType(algo_type_str) if algo_type_str in [e.value for e in AlgorithmType] else AlgorithmType.LEAVE_POST + + return cls( + roi_id=data.get("roi_id", ""), + camera_id=data.get("camera_id", ""), + roi_type=roi_type, + coordinates=data.get("coordinates", []), + algorithm_type=algo_type, + alert_threshold=data.get("alert_threshold", 3), + alert_cooldown=data.get("alert_cooldown", 300), + enabled=data.get("enabled", True), + extra_params=data.get("extra_params"), + ) + + def is_point_inside(self, point: List[float]) -> bool: + """判断点是否在ROI区域内""" + if self.roi_type == ROIType.RECTANGLE: + return self._is_point_in_rectangle(point) + elif self.roi_type == ROIType.POLYGON: + return self._is_point_in_polygon(point) + return False + + def _is_point_in_rectangle(self, point: List[float]) -> bool: + """判断点是否在矩形区域内""" + if len(self.coordinates) < 2: + return False + + x, y = point[0], point[1] + x1, y1 = self.coordinates[0] + x2, y2 = self.coordinates[1] + + left = min(x1, x2) + right = max(x1, x2) + top = min(y1, y2) + bottom = max(y1, y2) + + return left <= x <= right and top <= y <= bottom + + def _is_point_in_polygon(self, point: List[float]) -> bool: + """判断点是否在多边形区域内(射线法)""" + if len(self.coordinates) < 3: + return False + + x, y = point[0], point[1] + n = len(self.coordinates) + inside = False + + j = n - 1 + for i in range(n): + xi, yi = self.coordinates[i] + xj, yj = self.coordinates[j] + + if ((yi > y) != (yj > y)) and (x < (xj - xi) * (y - yi) / (yj - yi) + xi): + inside = not inside + + j = i + + return inside + + +@dataclass +class AlertInfo: + """告警信息数据模型""" + alert_id: str + camera_id: str + roi_id: str + alert_type: str + target_class: Optional[str] = None + confidence: Optional[float] = None + bbox: Optional[List[float]] = None + message: Optional[str] = None + screenshot: Optional[str] = None + level: AlertLevel = AlertLevel.MEDIUM + timestamp: Optional[str] = None + extra_data: Optional[Dict[str, Any]] = None + + def to_dict(self) -> Dict[str, Any]: + """转换为字典""" + return { + "alert_id": self.alert_id, + "camera_id": self.camera_id, + "roi_id": self.roi_id, + "alert_type": self.alert_type, + "target_class": self.target_class, + "confidence": self.confidence, + "bbox": self.bbox, + "message": self.message, + "screenshot": self.screenshot, + "level": self.level.value if isinstance(self.level, AlertLevel) else self.level, + "timestamp": self.timestamp, + "extra_data": self.extra_data, + } + + def to_json(self) -> str: + """转换为JSON字符串""" + return json.dumps(self.to_dict(), ensure_ascii=False) + + @classmethod + def from_dict(cls, data: Dict[str, Any]) -> 'AlertInfo': + """从字典创建实例""" + level = data.get("level", "medium") + if isinstance(level, str) and level in [e.value for e in AlertLevel]: + level = AlertLevel(level) + else: + level = AlertLevel.MEDIUM + + return cls( + alert_id=data.get("alert_id", ""), + camera_id=data.get("camera_id", ""), + roi_id=data.get("roi_id", ""), + alert_type=data.get("alert_type", ""), + target_class=data.get("target_class"), + confidence=data.get("confidence"), + bbox=data.get("bbox"), + message=data.get("message"), + screenshot=data.get("screenshot"), + level=level, + timestamp=data.get("timestamp"), + extra_data=data.get("extra_data"), + ) + + +@dataclass +class ConfigVersion: + """配置版本信息模型""" + version: str + update_time: str + update_type: str # 'full', 'incremental' + updated_by: str + description: str + affected_items: List[str] = field(default_factory=list) + + def to_dict(self) -> Dict[str, Any]: + """转换为字典""" + return { + "version": self.version, + "update_time": self.update_time, + "update_type": self.update_type, + "updated_by": self.updated_by, + "description": self.description, + "affected_items": self.affected_items, + } diff --git a/edge_inference_service/config/database.py b/edge_inference_service/config/database.py new file mode 100644 index 0000000..8075ebc --- /dev/null +++ b/edge_inference_service/config/database.py @@ -0,0 +1,316 @@ +""" +数据库连接配置模块 +提供MySQL数据库连接池管理和操作封装 +""" + +import logging +from contextlib import contextmanager +from typing import Any, Dict, Generator, List, Optional + +from sqlalchemy import create_engine, Column, String, Boolean, Integer, Float, Text, JSON, DateTime +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy.orm import sessionmaker, Session +from sqlalchemy.pool import QueuePool + +from config.settings import get_settings, DatabaseConfig + +logger = logging.getLogger(__name__) + +Base = declarative_base() + + +class CameraInfo(Base): + """摄像头信息表模型""" + __tablename__ = "camera_info" + + id = Column(Integer, primary_key=True, autoincrement=True) + camera_id = Column(String(64), unique=True, nullable=False, index=True) + camera_name = Column(String(128), nullable=True) + rtsp_url = Column(String(512), nullable=False) + status = Column(Boolean, default=True) + enabled = Column(Boolean, default=True) + location = Column(String(256), nullable=True) + extra_params = Column(JSON, nullable=True) + created_at = Column(DateTime, nullable=True) + updated_at = Column(DateTime, nullable=True) + + +class ROIConfigModel(Base): + """ROI配置表模型""" + __tablename__ = "roi_config" + + id = Column(Integer, primary_key=True, autoincrement=True) + roi_id = Column(String(64), unique=True, nullable=False, index=True) + camera_id = Column(String(64), nullable=False, index=True) + roi_type = Column(String(32), nullable=False) # 'polygon' or 'rectangle' + coordinates = Column(JSON, nullable=False) # 多边形顶点或矩形坐标 + algorithm_type = Column(String(32), nullable=False) # 'leave_post', 'intrusion', etc. + alert_threshold = Column(Integer, default=3) + alert_cooldown = Column(Integer, default=300) + enabled = Column(Boolean, default=True) + extra_params = Column(JSON, nullable=True) + created_at = Column(DateTime, nullable=True) + updated_at = Column(DateTime, nullable=True) + + +class AlertRecord(Base): + """告警记录表模型""" + __tablename__ = "alert_records" + + id = Column(Integer, primary_key=True, autoincrement=True) + alert_id = Column(String(64), unique=True, nullable=False, index=True) + camera_id = Column(String(64), nullable=False, index=True) + roi_id = Column(String(64), nullable=False, index=True) + alert_type = Column(String(32), nullable=False) + target_class = Column(String(64), nullable=True) + confidence = Column(Float, nullable=True) + bbox = Column(JSON, nullable=True) + message = Column(Text, nullable=True) + screenshot = Column(Text, nullable=True) # Base64编码的截图 + status = Column(String(32), default="pending") + created_at = Column(DateTime, nullable=True) + processed_at = Column(DateTime, nullable=True) + + +class DatabaseManager: + """数据库连接管理器类""" + + _instance = None + _engine = None + _session_factory = None + _available = False + + def __new__(cls): + if cls._instance is None: + cls._instance = super().__new__(cls) + return cls._instance + + def __init__(self, config: Optional[DatabaseConfig] = None): + if self._engine is not None: + return + + if config is None: + settings = get_settings() + config = settings.database + + self._init_engine(config) + + def _init_engine(self, config: DatabaseConfig): + """初始化数据库引擎""" + try: + connection_string = ( + f"mysql+pymysql://{config.username}:{config.password}" + f"@{config.host}:{config.port}/{config.database}" + f"?charset=utf8mb4" + ) + + self._engine = create_engine( + connection_string, + poolclass=QueuePool, + pool_size=config.pool_size, + pool_recycle=config.pool_recycle, + echo=config.echo, + pool_pre_ping=True, + max_overflow=5, + ) + + self._session_factory = sessionmaker(bind=self._engine) + + test_connection = self._engine.connect() + test_connection.close() + + self._available = True + logger.info(f"数据库引擎初始化成功: {config.host}:{config.port}/{config.database}") + + except Exception as e: + self._available = False + logger.warning(f"数据库连接失败，服务将在无数据库模式下运行: {e}") + + @property + def is_available(self) -> bool: + """检查数据库是否可用""" + return self._available + + @contextmanager + def get_session(self) -> Generator[Session, None, None]: + """获取数据库会话上下文""" + if not self._available: + logger.warning("数据库不可用，跳过数据库操作") + yield None + return + + session = self._session_factory() + try: + yield session + session.commit() + except Exception as e: + session.rollback() + logger.error(f"数据库操作异常: {e}") + raise + finally: + session.close() + + def get_camera_info(self, camera_id: Optional[str] = None) -> List[Dict[str, Any]]: + """获取摄像头信息""" + if not self._available: + logger.warning("数据库不可用，返回空摄像头列表") + return [] + + with self.get_session() as session: + if session is None: + return [] + + query = session.query(CameraInfo) + + if camera_id: + query = query.filter(CameraInfo.camera_id == camera_id) + + cameras = query.filter(CameraInfo.enabled == True).all() + + result = [] + for camera in cameras: + result.append({ + "camera_id": camera.camera_id, + "camera_name": camera.camera_name, + "rtsp_url": camera.rtsp_url, + "status": camera.status, + "location": camera.location, + "extra_params": camera.extra_params, + }) + + return result + + def get_roi_configs(self, camera_id: Optional[str] = None, + roi_id: Optional[str] = None) -> List[Dict[str, Any]]: + """获取ROI配置""" + if not self._available: + logger.warning("数据库不可用，返回空ROI配置列表") + return [] + + with self.get_session() as session: + if session is None: + return [] + + query = session.query(ROIConfigModel) + + if camera_id: + query = query.filter(ROIConfigModel.camera_id == camera_id) + + if roi_id: + query = query.filter(ROIConfigModel.roi_id == roi_id) + + query = query.filter(ROIConfigModel.enabled == True) + + rois = query.all() + + result = [] + for roi in rois: + result.append({ + "roi_id": roi.roi_id, + "camera_id": roi.camera_id, + "roi_type": roi.roi_type, + "coordinates": roi.coordinates, + "algorithm_type": roi.algorithm_type, + "alert_threshold": roi.alert_threshold, + "alert_cooldown": roi.alert_cooldown, + "extra_params": roi.extra_params, + }) + + return result + + def save_alert_record(self, alert_data: Dict[str, Any]) -> bool: + """保存告警记录""" + if not self._available: + logger.warning("数据库不可用，跳过保存告警记录") + return False + + try: + with self.get_session() as session: + if session is None: + return False + alert = AlertRecord( + alert_id=alert_data.get("alert_id"), + camera_id=alert_data.get("camera_id"), + roi_id=alert_data.get("roi_id"), + alert_type=alert_data.get("alert_type"), + target_class=alert_data.get("target_class"), + confidence=alert_data.get("confidence"), + bbox=alert_data.get("bbox"), + message=alert_data.get("message"), + screenshot=alert_data.get("screenshot"), + status=alert_data.get("status", "pending"), + ) + + session.add(alert) + session.flush() + + logger.info(f"告警记录保存成功: {alert_data.get('alert_id')}") + return True + + except Exception as e: + logger.error(f"保存告警记录失败: {e}") + return False + + def update_alert_status(self, alert_id: str, status: str) -> bool: + """更新告警状态""" + if not self._available: + logger.warning("数据库不可用，跳过更新告警状态") + return False + + try: + with self.get_session() as session: + if session is None: + return False + + from sqlalchemy import update + from datetime import datetime + + stmt = update(AlertRecord).where( + AlertRecord.alert_id == alert_id + ).values( + status=status, + processed_at=datetime.now() + ) + + session.execute(stmt) + logger.info(f"告警状态更新成功: {alert_id} -> {status}") + return True + + except Exception as e: + logger.error(f"更新告警状态失败: {e}") + return False + + def create_tables(self): + """创建所有表""" + if not self._available: + logger.warning("数据库不可用，跳过创建表") + return + + Base.metadata.create_all(self._engine) + logger.info("数据库表创建完成") + + def drop_tables(self): + """删除所有表""" + if not self._available: + return + + Base.metadata.drop_all(self._engine) + logger.info("数据库表删除完成") + + def close(self): + """关闭数据库连接""" + if self._engine: + self._engine.dispose() + logger.info("数据库连接已关闭") + + +def get_database_manager() -> DatabaseManager: + """获取数据库管理器单例""" + return DatabaseManager() + + +def init_database(): + """初始化数据库""" + db_manager = get_database_manager() + db_manager.create_tables() + return db_manager diff --git a/edge_inference_service/config/settings.py b/edge_inference_service/config/settings.py new file mode 100644 index 0000000..7978772 --- /dev/null +++ b/edge_inference_service/config/settings.py @@ -0,0 +1,195 @@ +""" +全局配置模块 +定义数据库、Redis、MQTT、推理等各项配置参数 +""" + +import os +from dataclasses import dataclass, field +from typing import List, Optional + + +@dataclass +class DatabaseConfig: + """数据库配置类""" + host: str = "localhost" + port: int = 3306 + username: str = "root" + password: str = "" + database: str = "edge_inference" + pool_size: int = 10 + pool_recycle: int = 3600 + echo: bool = False + + +@dataclass +class RedisConfig: + """Redis配置类""" + host: str = "localhost" + port: int = 6379 + db: int = 0 + password: Optional[str] = None + decode_responses: bool = True + max_connections: int = 50 + + +@dataclass +class MQTTConfig: + """MQTT配置类""" + broker_host: str = "localhost" + broker_port: int = 1883 + client_id: str = "edge_inference_service" + username: Optional[str] = None + password: Optional[str] = None + keepalive: int = 60 + qos: int = 1 + reconnect_delay: int = 5 + max_reconnect_attempts: int = 10 + + +@dataclass +class VideoStreamConfig: + """视频流配置类""" + default_fps: int = 5 + reconnect_max_attempts: int = 5 + reconnect_base_delay: float = 1.0 + reconnect_max_delay: float = 60.0 + frame_buffer_size: int = 30 + connection_timeout: int = 10 + read_timeout: int = 30 + + +@dataclass +class InferenceConfig: + """推理配置类""" + model_path: str = "./models/yolov8s.engine" + input_width: int = 480 + input_height: int = 480 + batch_size: int = 1 + max_batch_size: int = 8 + conf_threshold: float = 0.5 + nms_threshold: float = 0.45 + device_id: int = 0 + fp16_mode: bool = True + + +@dataclass +class CameraConfig: + """单个摄像头配置""" + camera_id: str + rtsp_url: str + enabled: bool = True + roi_ids: List[str] = field(default_factory=list) + + +@dataclass +class ROIConfig: + """ROI区域配置""" + roi_id: str + camera_id: str + roi_type: str # 'polygon' or 'rectangle' + coordinates: List[List[float]] # 多边形顶点或矩形坐标 + algorithm_type: str # 'leave_post', 'intrusion', etc. + alert_threshold: int = 3 # 连续N帧触发告警 + alert_cooldown: int = 300 # 告警冷却时间(秒) + + +class Settings: + """全局设置单例类""" + + _instance = None + _initialized = False + + def __new__(cls): + if cls._instance is None: + cls._instance = super().__new__(cls) + return cls._instance + + def __init__(self): + if not self._initialized: + self._load_env_vars() + self._initialized = True + + def _load_env_vars(self): + """从环境变量加载配置""" + # 数据库配置 + self.database = DatabaseConfig( + host=os.getenv("DB_HOST", "localhost"), + port=int(os.getenv("DB_PORT", "3306")), + username=os.getenv("DB_USERNAME", "root"), + password=os.getenv("DB_PASSWORD", ""), + database=os.getenv("DB_DATABASE", "edge_inference"), + ) + + # Redis配置 + self.redis = RedisConfig( + host=os.getenv("REDIS_HOST", "localhost"), + port=int(os.getenv("REDIS_PORT", "6379")), + password=os.getenv("REDIS_PASSWORD"), + ) + + # MQTT配置 + self.mqtt = MQTTConfig( + broker_host=os.getenv("MQTT_BROKER_HOST", "localhost"), + broker_port=int(os.getenv("MQTT_BROKER_PORT", "1883")), + client_id=os.getenv("MQTT_CLIENT_ID", "edge_inference_service"), + username=os.getenv("MQTT_USERNAME"), + password=os.getenv("MQTT_PASSWORD"), + ) + + # 视频流配置 + self.video_stream = VideoStreamConfig( + default_fps=int(os.getenv("VIDEO_DEFAULT_FPS", "5")), + reconnect_max_attempts=int(os.getenv("VIDEO_RECONNECT_ATTEMPTS", "5")), + ) + + # 推理配置 + self.inference = InferenceConfig( + model_path=os.getenv("MODEL_PATH", "./models/yolo11n.engine"), + input_width=int(os.getenv("INPUT_WIDTH", "480")), + input_height=int(os.getenv("INPUT_HEIGHT", "480")), + batch_size=int(os.getenv("BATCH_SIZE", "1")), + max_batch_size=int(os.getenv("MAX_BATCH_SIZE", "8")), + conf_threshold=float(os.getenv("CONF_THRESHOLD", "0.5")), + nms_threshold=float(os.getenv("NMS_THRESHOLD", "0.45")), + ) + + # 日志配置 + self.log_level = os.getenv("LOG_LEVEL", "INFO") + self.log_dir = os.getenv("LOG_DIR", "./logs") + self.log_file_max_size = int(os.getenv("LOG_FILE_MAX_SIZE", "10485760")) # 10MB + self.log_file_backup_count = int(os.getenv("LOG_FILE_BACKUP_COUNT", "5")) + + # 工作时间配置 + self.working_hours = self._parse_working_hours() + + def _parse_working_hours(self) -> List[dict]: + """解析工作时间配置""" + working_hours_str = os.getenv("WORKING_HOURS", "") + if not working_hours_str: + return [] + + working_hours = [] + periods = working_hours_str.split(";") + for period in periods: + try: + start, end = period.split("-") + start_h, start_m = map(int, start.split(":")) + end_h, end_m = map(int, end.split(":")) + working_hours.append({ + "start": [start_h, start_m], + "end": [end_h, end_m] + }) + except (ValueError, AttributeError): + continue + + return working_hours + + @property + def config_version(self) -> str: + """获取配置版本号""" + return os.getenv("CONFIG_VERSION", "1.0.0") + + +def get_settings() -> Settings: + """获取全局配置单例""" + return Settings() diff --git a/edge_inference_service/core/__init__.py b/edge_inference_service/core/__init__.py new file mode 100644 index 0000000..b8612b4 --- /dev/null +++ b/edge_inference_service/core/__init__.py @@ -0,0 +1,3 @@ +""" +核心处理模块初始化 +""" diff --git a/edge_inference_service/core/__pycache__/__init__.cpython-310.pyc b/edge_inference_service/core/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000..07ca878 Binary files /dev/null and b/edge_inference_service/core/__pycache__/__init__.cpython-310.pyc differ diff --git a/edge_inference_service/core/__pycache__/config_sync.cpython-310.pyc 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+实现MySQL数据库连接管理、Redis Pub/Sub订阅、配置缓存与动态刷新 +""" + +import json +import logging +import threading +import time +from datetime import datetime +from typing import Any, Callable, Dict, List, Optional, Set + +import redis +from redis import Redis +from redis.client import PubSub + +from config.settings import get_settings, RedisConfig +from config.database import get_database_manager, DatabaseManager +from config.config_models import CameraInfo as CameraInfoModel, ROIInfo, ConfigVersion +from utils.version_control import get_version_control + +logger = logging.getLogger(__name__) + + +class ConfigCache: + """配置缓存管理类""" + + def __init__(self, max_size: int = 1000, ttl: int = 300): + self._cache: Dict[str, Any] = {} + self._access_times: Dict[str, float] = {} + self._max_size = max_size + self._ttl = ttl + self._lock = threading.RLock() + + def get(self, key: str) -> Optional[Any]: + """从缓存获取配置""" + with self._lock: + if key in self._cache: + access_time = self._access_times.get(key, 0) + if (time.time() - access_time) < self._ttl: + self._access_times[key] = time.time() + return self._cache[key] + else: + self._delete(key) + return None + + def set(self, key: str, value: Any): + """设置配置到缓存""" + with self._lock: + if len(self._cache) >= self._max_size: + self._evict_lru() + self._cache[key] = value + self._access_times[key] = time.time() + + def delete(self, key: str): + """删除缓存项""" + with self._lock: + self._delete(key) + + def _delete(self, key: str): + """内部删除方法(不获取锁)""" + self._cache.pop(key, None) + self._access_times.pop(key, None) + + def _evict_lru(self): + """淘汰最少使用的缓存项""" + if not self._access_times: + return + + min_access_time = min(self._access_times.values()) + lru_keys = [k for k, v in self._access_times.items() if v == min_access_time] + + for key in lru_keys[:10]: + self._delete(key) + + def clear(self): + """清空缓存""" + with self._lock: + self._cache.clear() + self._access_times.clear() + + def get_stats(self) -> Dict[str, Any]: + """获取缓存统计信息""" + with self._lock: + return { + "size": len(self._cache), + "max_size": self._max_size, + "ttl": self._ttl, + } + + +class ConfigSyncManager: + """配置同步管理器类""" + + _instance = None + _lock = threading.Lock() + + def __new__(cls): + if cls._instance is None: + with cls._lock: + if cls._instance is None: + cls._instance = super().__new__(cls) + cls._instance._initialized = False + return cls._instance + + def __init__(self): + if self._initialized: + return + + settings = get_settings() + self._config_version = settings.config_version + self._cache = ConfigCache() + self._redis_client = None + self._redis_pubsub = None + self._pubsub_thread = None + self._stop_event = threading.Event() + self._callbacks: Dict[str, Set[Callable]] = {} + self._db_manager = None + self._initialized = True + + self._init_redis() + self._version_control = get_version_control() + + def _init_redis(self): + """初始化Redis连接""" + try: + settings = get_settings() + redis_config = settings.redis + + self._redis_client = redis.Redis( + host=redis_config.host, + port=redis_config.port, + db=redis_config.db, + password=redis_config.password, + decode_responses=redis_config.decode_responses, + socket_connect_timeout=10, + socket_timeout=10, + retry_on_timeout=True, + ) + + self._redis_client.ping() + logger.info(f"Redis连接成功: {redis_config.host}:{redis_config.port}") + + except Exception as e: + logger.error(f"Redis连接失败: {e}") + self._redis_client = None + + def _init_database(self): + """初始化数据库连接""" + if self._db_manager is None: + self._db_manager = get_database_manager() + + @property + def config_version(self) -> str: + """获取当前配置版本""" + return self._config_version + + def register_callback(self, topic: str, callback: Callable): + """注册配置变更回调函数""" + if topic not in self._callbacks: + self._callbacks[topic] = set() + self._callbacks[topic].add(callback) + logger.info(f"已注册配置变更回调: {topic}") + + def unregister_callback(self, topic: str, callback: Callable): + """注销配置变更回调函数""" + if topic in self._callbacks: + self._callbacks[topic].discard(callback) + + def _notify_callbacks(self, topic: str, data: Dict[str, Any]): + """触发配置变更回调""" + if topic in self._callbacks: + for callback in self._callbacks[topic]: + try: + callback(topic, data) + except Exception as e: + logger.error(f"配置变更回调执行失败: {e}") + + def _subscribe_config_updates(self): + """订阅配置更新主题""" + if not self._redis_client: + logger.warning("Redis未连接，无法订阅配置更新") + return + + try: + self._redis_pubsub = self._redis_client.pubsub() + self._redis_pubsub.subscribe("config_update") + + logger.info("已订阅config_update主题") + + for message in self._redis_pubsub.listen(): + if self._stop_event.is_set(): + break + + if message["type"] == "message": + try: + data = json.loads(message["data"]) + self._handle_config_update(data) + except Exception as e: + logger.error(f"处理配置更新消息失败: {e}") + + except Exception as e: + logger.error(f"配置更新订阅异常: {e}") + + def _handle_config_update(self, data: Dict[str, Any]): + """处理配置更新消息""" + update_type = data.get("type", "full") + affected_items = data.get("affected_items", []) + version = data.get("version", self._config_version) + + logger.info(f"收到配置更新通知: type={update_type}, items={affected_items}") + + if "camera" in affected_items or "all" in affected_items: + self._cache.delete("cameras") + + if "roi" in affected_items or "all" in affected_items: + self._cache.delete("rois") + + self._config_version = version + self._notify_callbacks("config_update", data) + + self._version_control.record_update( + version=version, + update_type="配置更新", + description=f"云端配置更新，影响范围: {', '.join(affected_items)}", + updated_by="云端系统", + affected_items=affected_items, + details=data + ) + + def start_config_subscription(self): + """启动配置订阅线程""" + if self._pubsub_thread is None or not self._pubsub_thread.is_alive(): + self._stop_event.clear() + self._pubsub_thread = threading.Thread( + target=self._subscribe_config_updates, + name="ConfigSubscription", + daemon=True + ) + self._pubsub_thread.start() + logger.info("配置订阅线程已启动") + + def stop_config_subscription(self): + """停止配置订阅线程""" + self._stop_event.set() + if self._pubsub_thread and self._pubsub_thread.is_alive(): + self._pubsub_thread.join(timeout=5) + logger.info("配置订阅线程已停止") + + def get_cameras(self, force_refresh: bool = False) -> List[CameraInfoModel]: + """获取摄像头配置列表""" + cache_key = "cameras" + + if not force_refresh: + cached = self._cache.get(cache_key) + if cached is not None: + return cached + + self._init_database() + + if self._db_manager is None: + logger.warning("数据库管理器不可用，返回空摄像头列表") + return [] + + try: + cameras = self._db_manager.get_camera_info() + result = [CameraInfoModel.from_dict(c) for c in cameras] + + self._cache.set(cache_key, result) + logger.info(f"已加载摄像头配置: {len(result)} 个") + return result + + except Exception as e: + logger.error(f"获取摄像头配置失败: {e}") + cached = self._cache.get(cache_key) + return cached or [] + + def get_roi_configs(self, camera_id: Optional[str] = None, + force_refresh: bool = False) -> List[ROIInfo]: + """获取ROI配置列表""" + cache_key = f"rois_{camera_id}" if camera_id else "rois_all" + + if not force_refresh: + cached = self._cache.get(cache_key) + if cached is not None: + return cached + + self._init_database() + + if self._db_manager is None: + logger.warning("数据库管理器不可用，返回空ROI配置列表") + return [] + + try: + roi_configs = self._db_manager.get_roi_configs(camera_id=camera_id) + result = [ROIInfo.from_dict(r) for r in roi_configs] + + self._cache.set(cache_key, result) + logger.info(f"已加载ROI配置: {len(result)} 个") + return result + + except Exception as e: + logger.error(f"获取ROI配置失败: {e}") + cached = self._cache.get(cache_key) + return cached or [] + + def get_camera_rois(self, camera_id: str) -> List[ROIInfo]: + """获取指定摄像头的ROI配置""" + return self.get_roi_configs(camera_id=camera_id) + + def get_config_by_id(self, config_type: str, config_id: str) -> Optional[Dict[str, Any]]: + """根据ID获取配置""" + self._init_database() + + try: + if config_type == "camera": + cameras = self._db_manager.get_camera_info(camera_id) + return cameras[0] if cameras else None + elif config_type == "roi": + rois = self._db_manager.get_roi_configs(roi_id=config_id) + return rois[0] if rois else None + except Exception as e: + logger.error(f"获取配置失败: {e}") + return None + + def publish_config_update(self, update_data: Dict[str, Any]) -> bool: + """发布配置更新通知""" + if not self._redis_client: + logger.warning("Redis未连接，无法发布配置更新") + return False + + try: + update_data["version"] = self._config_version + update_data["timestamp"] = datetime.now().isoformat() + + self._redis_client.publish("config_update", json.dumps(update_data)) + logger.info(f"已发布配置更新: {update_data}") + return True + + except Exception as e: + logger.error(f"发布配置更新失败: {e}") + return False + + def invalidate_cache(self, cache_key: str): + """使指定缓存失效""" + self._cache.delete(cache_key) + logger.info(f"缓存已失效: {cache_key}") + + def invalidate_all_cache(self): + """使所有缓存失效""" + self._cache.clear() + logger.info("所有缓存已失效") + + def get_cache_stats(self) -> Dict[str, Any]: + """获取缓存统计信息""" + return self._cache.get_stats() + + def get_health_status(self) -> Dict[str, Any]: + """获取健康状态""" + redis_healthy = False + if self._redis_client: + try: + self._redis_client.ping() + redis_healthy = True + except Exception: + pass + + return { + "redis_connected": redis_healthy, + "config_version": self._config_version, + "cache_stats": self.get_cache_stats(), + "subscription_active": ( + self._pubsub_thread is not None and + self._pubsub_thread.is_alive() + ), + } + + def close(self): + """关闭管理器""" + self.stop_config_subscription() + if self._redis_client: + if self._redis_pubsub: + self._redis_pubsub.close() + self._redis_client.close() + logger.info("Redis连接已关闭") + + +def get_config_sync_manager() -> ConfigSyncManager: + """获取配置同步管理器单例""" + return ConfigSyncManager() diff --git a/edge_inference_service/core/postprocessor.py b/edge_inference_service/core/postprocessor.py new file mode 100644 index 0000000..5438bb1 --- /dev/null +++ b/edge_inference_service/core/postprocessor.py @@ -0,0 +1,791 @@ +""" +后处理与业务逻辑模块 +实现NMS算法、坐标映射、ROI区域判定、告警状态机 +""" + +import logging +import threading +import time +from collections import deque +from dataclasses import dataclass, field +from datetime import datetime +from typing import Any, Dict, List, Optional, Set, Tuple + +import numpy as np + +from config.settings import get_settings +from config.config_models import ROIInfo, ROIType, AlertInfo, AlertLevel +from utils.logger import get_logger +from utils.common import generate_unique_id + +logger = logging.getLogger(__name__) + + +class NMSProcessor: + """非极大值抑制处理器 + + 实现高效的NMS算法去除冗余检测框 + """ + + def __init__(self, nms_threshold: float = 0.45): + """ + 初始化NMS处理器 + + Args: + nms_threshold: NMS阈值 + """ + self.nms_threshold = nms_threshold + self._logger = get_logger("postprocessor") + + def process( + self, + boxes: np.ndarray, + scores: np.ndarray, + class_ids: Optional[np.ndarray] = None, + max_output_size: int = 300 + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + 执行NMS + + Args: + boxes: 检测框数组 [N, 4] (x1, y1, x2, y2) + scores: 置信度数组 [N] + class_ids: 类别ID数组 [N] + max_output_size: 最大输出数量 + + Returns: + tuple: (保留的框索引, 保留的置信度, 保留的类别ID) + """ + if len(boxes) == 0: + return np.array([], dtype=np.int32), np.array([]), np.array([]) + + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + + order = scores.argsort()[::-1] + + keep_indices = [] + + while len(order) > 0: + if len(keep_indices) >= max_output_size: + break + + i = order[0] + keep_indices.append(i) + + if len(order) == 1: + break + + remaining = order[1:] + + xx1 = np.maximum(x1[i], x1[remaining]) + yy1 = np.maximum(y1[i], y1[remaining]) + xx2 = np.minimum(x2[i], x2[remaining]) + yy2 = np.minimum(y2[i], y2[remaining]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + + inter = w * h + ovr = inter / (areas[i] + areas[remaining] - inter) + + indices = np.where(ovr <= self.nms_threshold)[0] + + order = remaining[indices] + + keep_indices = np.array(keep_indices, dtype=np.int32) + + return ( + keep_indices, + scores[keep_indices], + class_ids[keep_indices] if class_ids is not None else np.array([]) + ) + + def process_with_confidence_filter( + self, + boxes: np.ndarray, + scores: np.ndarray, + class_ids: np.ndarray, + conf_threshold: float = 0.5, + max_output_size: int = 300 + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + 执行置信度过滤和NMS + + Args: + boxes: 检测框数组 + scores: 置信度数组 + class_ids: 类别ID数组 + conf_threshold: 置信度阈值 + max_output_size: 最大输出数量 + + Returns: + tuple: (保留的框, 置信度, 类别ID) + """ + mask = scores >= conf_threshold + boxes = boxes[mask] + scores = scores[mask] + class_ids = class_ids[mask] + + if len(boxes) == 0: + return np.array([]).reshape(0, 4), np.array([]), np.array([]) + + keep_indices, filtered_scores, filtered_class_ids = self.process( + boxes, scores, class_ids, max_output_size + ) + + return boxes[keep_indices], filtered_scores, filtered_class_ids + + +class CoordinateMapper: + """坐标映射系统 + + 实现多级坐标映射: 480x480 → ROI空间 → 原始帧坐标 + """ + + def __init__(self): + self._logger = get_logger("postprocessor") + + def map_from_letterbox( + self, + box: List[float], + scale_info: Tuple[float, float, float, float], + original_size: Tuple[int, int] + ) -> List[float]: + """ + 将检测框从Letterbox空间映射回原始空间 + + Args: + box: Letterbox空间中的坐标 [x1, y1, x2, y2] + scale_info: 缩放信息 (scale, pad_x, pad_y, scale) + original_size: 原始图像尺寸 (width, height) + + Returns: + 原始空间中的坐标 + """ + scale, pad_x, pad_y, _ = scale_info + + x1 = (box[0] - pad_x) / scale + y1 = (box[1] - pad_y) / scale + x2 = (box[2] - pad_x) / scale + y2 = (box[3] - pad_y) / scale + + orig_width, orig_height = original_size + x1 = max(0, min(x1, orig_width - 1)) + y1 = max(0, min(y1, orig_height - 1)) + x2 = max(0, min(x2, orig_width)) + y2 = max(0, min(y2, orig_height)) + + return [x1, y1, x2, y2] + + def map_roi_to_frame( + self, + box: List[float], + roi_crop_info: Dict[str, Any] + ) -> List[float]: + """ + 将ROI空间中的坐标映射回原始帧坐标 + + Args: + box: ROI空间中的坐标 + roi_crop_info: ROI裁剪信息 + + Returns: + 原始帧中的坐标 + """ + x1, y1, x2, y2 = box + + offset_x = roi_crop_info.get("offset_x", 0) + offset_y = roi_crop_info.get("offset_y", 0) + + if "scale" in roi_crop_info: + scale = roi_crop_info["scale"] + x1 = x1 * scale + offset_x + y1 = y1 * scale + offset_y + x2 = x2 * scale + offset_x + y2 = y2 * scale + offset_y + else: + x1 += offset_x + y1 += offset_y + x2 += offset_x + y2 += offset_y + + return [x1, y1, x2, y2] + + def get_box_center(self, box: List[float]) -> Tuple[float, float]: + """获取检测框中心点""" + return (box[0] + box[2]) / 2, (box[1] + box[3]) / 2 + + def get_box_bottom_center(self, box: List[float]) -> Tuple[float, float]: + """获取检测框底部中心点""" + return (box[0] + box[2]) / 2, box[3] + + def get_box_area(self, box: List[float]) -> float: + """计算检测框面积""" + width = box[2] - box[0] + height = box[3] - box[1] + return width * height + + +class ROIAnalyzer: + """ROI区域分析器 + + 实现ROI区域内目标检测与判定 + """ + + def __init__(self): + self._logger = get_logger("postprocessor") + + def is_point_in_roi( + self, + point: Tuple[float, float], + roi: ROIInfo + ) -> bool: + """ + 判断点是否在ROI区域内 + + Args: + point: (x, y) 坐标 + roi: ROI配置 + + Returns: + 是否在区域内 + """ + return roi.is_point_inside(list(point)) + + def is_detection_in_roi( + self, + box: List[float], + roi: ROIInfo, + check_mode: str = "center" + ) -> bool: + """ + 判断检测是否在ROI区域内 + + Args: + box: 检测框坐标 + roi: ROI配置 + check_mode: 检查模式 (center, bottom, any) + + Returns: + 是否在区域内 + """ + if check_mode == "center": + point = self._get_box_center(box) + elif check_mode == "bottom": + point = self._get_box_bottom_center(box) + else: + point = self._get_box_center(box) + + return self.is_point_in_roi(point, roi) + + def _get_box_center(self, box: List[float]) -> Tuple[float, float]: + """获取检测框中心点""" + return (box[0] + box[2]) / 2, (box[1] + box[3]) / 2 + + def _get_box_bottom_center(self, box: List[float]) -> Tuple[float, float]: + """获取检测框底部中心点""" + return (box[0] + box[2]) / 2, box[3] + + def filter_detections_in_roi( + self, + boxes: np.ndarray, + roi: ROIInfo, + check_mode: str = "center" + ) -> np.ndarray: + """ + 筛选在ROI区域内的检测 + + Args: + boxes: 检测框数组 + roi: ROI配置 + check_mode: 检查模式 + + Returns: + 符合条件的检测框索引 + """ + valid_indices = [] + + for i, box in enumerate(boxes): + if self.is_detection_in_roi(box.tolist(), roi, check_mode): + valid_indices.append(i) + + return np.array(valid_indices) + + def get_roi_crop_info( + self, + frame_width: int, + frame_height: int, + roi: ROIInfo + ) -> Dict[str, Any]: + """ + 获取ROI裁剪信息 + + Args: + frame_width: 帧宽度 + frame_height: 帧高度 + roi: ROI配置 + + Returns: + 裁剪信息字典 + """ + if roi.roi_type == ROIType.RECTANGLE and len(roi.coordinates) >= 2: + x1 = int(roi.coordinates[0][0]) + y1 = int(roi.coordinates[0][1]) + x2 = int(roi.coordinates[1][0]) + y2 = int(roi.coordinates[1][1]) + + offset_x = min(x1, x2) + offset_y = min(y1, y2) + crop_width = abs(x2 - x1) + crop_height = abs(y2 - y1) + + elif roi.roi_type == ROIType.POLYGON: + xs = [c[0] for c in roi.coordinates] + ys = [c[1] for c in roi.coordinates] + + offset_x = min(xs) + offset_y = min(ys) + crop_width = max(xs) - offset_x + crop_height = max(ys) - offset_y + else: + offset_x = offset_y = 0 + crop_width = frame_width + crop_height = frame_height + + return { + "offset_x": offset_x, + "offset_y": offset_y, + "crop_width": crop_width, + "crop_height": crop_height, + "frame_width": frame_width, + "frame_height": frame_height, + } + + +@dataclass +class AlarmState: + """告警状态""" + roi_id: str + detection_count: int = 0 + last_detection_time: Optional[datetime] = None + is_alarming: bool = False + alert_sent: bool = False + frame_buffer: deque = field(default_factory=deque) + + +class AlarmStateMachine: + """告警状态机 + + 实现连续帧检测触发机制，防止瞬时误报 + """ + + def __init__(self, alert_threshold: int = 3, alert_cooldown: int = 300): + """ + 初始化告警状态机 + + Args: + alert_threshold: 连续检测触发阈值 + alert_cooldown: 告警冷却时间(秒) + """ + self.alert_threshold = alert_threshold + self.alert_cooldown = alert_cooldown + + self._states: Dict[str, AlarmState] = {} + self._lock = threading.Lock() + + self._logger = get_logger("postprocessor") + + def get_state(self, roi_id: str) -> AlarmState: + """获取ROI告警状态""" + with self._lock: + if roi_id not in self._states: + self._states[roi_id] = AlarmState(roi_id=roi_id) + return self._states[roi_id] + + def update( + self, + roi_id: str, + has_target: bool, + current_time: Optional[datetime] = None + ) -> Dict[str, Any]: + """ + 更新告警状态 + + Args: + roi_id: ROI标识 + has_target: 是否有目标 + current_time: 当前时间 + + Returns: + 状态更新结果 + """ + if current_time is None: + current_time = datetime.now() + + with self._lock: + state = self.get_state(roi_id) + + if state.is_alarming and state.alert_sent: + cooldown_elapsed = ( + current_time - state.last_detection_time + ).total_seconds() if state.last_detection_time else float('inf') + + if cooldown_elapsed < self.alert_cooldown: + return { + "should_alert": False, + "reason": "cooldown", + "cooldown_remaining": self.alert_cooldown - cooldown_elapsed, + "state": state, + } + else: + state.alert_sent = False + state.is_alarming = False + state.detection_count = 0 + + if has_target: + state.detection_count += 1 + state.last_detection_time = current_time + + while len(state.frame_buffer) >= self.alert_threshold: + state.frame_buffer.popleft() + state.frame_buffer.append(current_time) + + if (state.detection_count >= self.alert_threshold and + not state.is_alarming): + state.is_alarming = True + + self._logger.log_alert( + "detection_threshold_reached", + roi_id=roi_id, + camera_id="", + confidence=None + ) + + return { + "should_alert": True, + "reason": "threshold_reached", + "detection_count": state.detection_count, + "state": state, + } + else: + if state.detection_count > 0: + state.detection_count = max(0, state.detection_count - 1) + + if not has_target and state.is_alarming: + state.is_alarming = False + + return { + "should_alert": False, + "reason": "waiting", + "detection_count": state.detection_count, + "state": state, + } + + def reset(self, roi_id: str): + """重置ROI告警状态""" + with self._lock: + if roi_id in self._states: + self._states[roi_id] = AlarmState(roi_id=roi_id) + self._logger.info(f"告警状态已重置: {roi_id}") + + def reset_all(self): + """重置所有告警状态""" + with self._lock: + self._states.clear() + self._logger.info("所有告警状态已重置") + + def get_all_states(self) -> Dict[str, AlarmState]: + """获取所有状态""" + with self._lock: + return dict(self._states) + + +class PostProcessor: + """后处理主类 + + 整合NMS、坐标映射、ROI分析、告警状态机 + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None): + """ + 初始化后处理器 + + Args: + config: 配置参数字典 + """ + if config is None: + settings = get_settings() + config = { + "nms_threshold": settings.inference.nms_threshold, + "conf_threshold": settings.inference.conf_threshold, + "alert_threshold": 3, + "alert_cooldown": 300, + } + + self.nms_threshold = config.get("nms_threshold", 0.45) + self.conf_threshold = config.get("conf_threshold", 0.5) + + self._nms = NMSProcessor(self.nms_threshold) + self._mapper = CoordinateMapper() + self._roi_analyzer = ROIAnalyzer() + self._alarm_state_machine = AlarmStateMachine( + alert_threshold=config.get("alert_threshold", 3), + alert_cooldown=config.get("alert_cooldown", 300) + ) + + self._logger = get_logger("postprocessor") + self._logger.info( + f"后处理器初始化完成: NMS阈值={self.nms_threshold}, " + f"置信度阈值={self.conf_threshold}" + ) + + def process_detections( + self, + raw_outputs: List[np.ndarray], + conf_threshold: Optional[float] = None, + nms_threshold: Optional[float] = None + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + 处理模型原始输出 + + Args: + raw_outputs: 模型原始输出 + conf_threshold: 置信度阈值 + nms_threshold: NMS阈值 + + Returns: + tuple: (检测框, 置信度, 类别ID) + """ + if conf_threshold is None: + conf_threshold = self.conf_threshold + if nms_threshold is None: + nms_threshold = self.nms_threshold + + boxes, scores, class_ids = self._parse_yolo_output(raw_outputs) + + if len(boxes) == 0: + return np.array([]), np.array([]), np.array([]) + + nms_processor = NMSProcessor(nms_threshold) + + keep_boxes, keep_scores, keep_classes = nms_processor.process_with_confidence_filter( + boxes, scores, class_ids, conf_threshold + ) + + return keep_boxes, keep_scores, keep_classes + + def _parse_yolo_output( + self, + outputs: List[np.ndarray] + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + 解析YOLO模型输出 + + Args: + outputs: 模型输出列表 + + Returns: + tuple: (检测框, 置信度, 类别ID) + """ + if not outputs: + return np.array([]), np.array([]), np.array([]) + + output = outputs[0] + + if len(output.shape) == 3: + output = output[0] + + num_detections = output.shape[0] + + boxes = [] + scores = [] + class_ids = [] + + for i in range(num_detections): + detection = output[i] + + if len(detection) < 6: + continue + + x_center = detection[0] + y_center = detection[1] + width = detection[2] + height = detection[3] + + obj_conf = detection[4] + + class_scores = detection[5:] + if len(class_scores) == 0: + continue + + class_id = np.argmax(class_scores) + class_conf = class_scores[class_id] + + total_conf = obj_conf * class_conf + + if total_conf < 0.0: + continue + + x1 = x_center - width / 2 + y1 = y_center - height / 2 + x2 = x_center + width / 2 + y2 = y_center + height / 2 + + boxes.append([x1, y1, x2, y2]) + scores.append(total_conf) + class_ids.append(class_id) + + if not boxes: + return np.array([]), np.array([]), np.array([]) + + return ( + np.array(boxes), + np.array(scores), + np.array(class_ids) + ) + + def filter_by_roi( + self, + boxes: np.ndarray, + scores: np.ndarray, + class_ids: np.ndarray, + roi: ROIInfo, + check_mode: str = "center" + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + 按ROI筛选检测结果 + + Args: + boxes: 检测框 + scores: 置信度 + class_ids: 类别ID + roi: ROI配置 + check_mode: 检查模式 + + Returns: + 筛选后的结果 + """ + valid_indices = self._roi_analyzer.filter_detections_in_roi( + boxes, roi, check_mode + ) + + return ( + boxes[valid_indices], + scores[valid_indices], + class_ids[valid_indices] + ) + + def map_coordinates( + self, + boxes: np.ndarray, + scale_info: Tuple[float, float, float, float], + original_size: Tuple[int, int], + roi_crop_info: Optional[Dict[str, Any]] = None + ) -> List[List[float]]: + """ + 映射检测框坐标到原始帧 + + Args: + boxes: 检测框数组 + scale_info: Letterbox缩放信息 + original_size: 原始图像尺寸 + roi_crop_info: ROI裁剪信息 + + Returns: + 映射后的坐标列表 + """ + mapped_boxes = [] + + for box in boxes: + if roi_crop_info: + mapped = self._mapper.map_from_letterbox( + box.tolist(), scale_info, original_size + ) + mapped = self._mapper.map_roi_to_frame(mapped, roi_crop_info) + else: + mapped = self._mapper.map_from_letterbox( + box.tolist(), scale_info, original_size + ) + + mapped_boxes.append(mapped) + + return mapped_boxes + + def check_alarm_condition( + self, + roi_id: str, + has_target: bool, + current_time: Optional[datetime] = None + ) -> Dict[str, Any]: + """ + 检查告警条件 + + Args: + roi_id: ROI标识 + has_target: 是否有目标 + current_time: 当前时间 + + Returns: + 告警检查结果 + """ + return self._alarm_state_machine.update(roi_id, has_target, current_time) + + def get_alarm_state(self, roi_id: str) -> AlarmState: + """获取告警状态""" + return self._alarm_state_machine.get_state(roi_id) + + def reset_alarm_state(self, roi_id: str): + """重置告警状态""" + self._alarm_state_machine.reset(roi_id) + + def create_alert_info( + self, + roi_id: str, + camera_id: str, + detection_results: Dict[str, Any], + message: str + ) -> AlertInfo: + """ + 创建告警信息 + + Args: + roi_id: ROI标识 + camera_id: 摄像头标识 + detection_results: 检测结果 + message: 告警消息 + + Returns: + AlertInfo实例 + """ + return AlertInfo( + alert_id=generate_unique_id("alert"), + camera_id=camera_id, + roi_id=roi_id, + alert_type=detection_results.get("alert_type", "detection"), + target_class=detection_results.get("class_name"), + confidence=detection_results.get("confidence"), + bbox=detection_results.get("bbox"), + message=message, + level=AlertLevel.MEDIUM, + timestamp=datetime.now().isoformat(), + ) + + def get_statistics(self) -> Dict[str, Any]: + """获取统计信息""" + return { + "nms_threshold": self.nms_threshold, + "conf_threshold": self.conf_threshold, + "alarm_states": { + roi_id: { + "detection_count": state.detection_count, + "is_alarming": state.is_alarming, + "alert_sent": state.alert_sent, + } + for roi_id, state in self._alarm_state_machine.get_all_states().items() + } + } diff --git a/edge_inference_service/core/preprocessor.py b/edge_inference_service/core/preprocessor.py new file mode 100644 index 0000000..78efba6 --- /dev/null +++ b/edge_inference_service/core/preprocessor.py @@ -0,0 +1,475 @@ +""" +图像预处理流水线模块 +实现ROI裁剪、Letterbox预处理、Batch打包等功能 +""" + +import logging +import threading +import time +from typing import Any, Dict, List, Optional, Tuple, Union + +import cv2 +import numpy as np + +from config.settings import get_settings, InferenceConfig +from config.config_models import ROIInfo, ROIType +from utils.logger import get_logger + +logger = logging.getLogger(__name__) + + +class ROICropper: + """ROI裁剪器类 + + 支持多边形和矩形两种区域的裁剪 + """ + + def __init__(self): + self._logger = get_logger("preprocessor") + + def crop( + self, + image: np.ndarray, + roi: ROIInfo + ) -> Optional[np.ndarray]: + """ + 裁剪ROI区域 + + Args: + image: 原始图像 (BGR格式) + roi: ROI配置信息 + + Returns: + 裁剪后的图像，失败返回None + """ + try: + if roi.roi_type == ROIType.RECTANGLE: + return self._crop_rectangle(image, roi.coordinates) + elif roi.roi_type == ROIType.POLYGON: + return self._crop_polygon(image, roi.coordinates) + else: + self._logger.warning(f"不支持的ROI类型: {roi.roi_type}") + return None + + except Exception as e: + self._logger.error(f"ROI裁剪失败: {e}") + return None + + def _crop_rectangle( + self, + image: np.ndarray, + coordinates: List[List[float]] + ) -> Optional[np.ndarray]: + """裁剪矩形区域""" + if len(coordinates) < 2: + return None + + x1, y1 = int(coordinates[0][0]), int(coordinates[0][1]) + x2, y2 = int(coordinates[1][0]), int(coordinates[1][1]) + + x1 = max(0, min(x1, image.shape[1] - 1)) + y1 = max(0, min(y1, image.shape[0] - 1)) + x2 = max(0, min(x2, image.shape[1])) + y2 = max(0, min(y2, image.shape[0])) + + if x2 <= x1 or y2 <= y1: + return None + + return image[y1:y2, x1:x2] + + def _crop_polygon( + self, + image: np.ndarray, + coordinates: List[List[float]] + ) -> Optional[np.ndarray]: + """裁剪多边形区域""" + if len(coordinates) < 3: + return None + + height, width = image.shape[:2] + + pts = np.array(coordinates, dtype=np.int32) + + pts[:, 0] = np.clip(pts[:, 0], 0, width - 1) + pts[:, 1] = np.clip(pts[:, 1], 0, height - 1) + + mask = np.zeros((height, width), dtype=np.uint8) + cv2.fillPoly(mask, [pts], 255) + + masked_image = cv2.bitwise_and(image, image, mask=mask) + + x1 = np.min(pts[:, 0]) + y1 = np.min(pts[:, 1]) + x2 = np.max(pts[:, 0]) + y2 = np.max(pts[:, 1]) + + cropped = masked_image[y1:y2, x1:x2] + + return cropped if cropped.size > 0 else None + + def create_mask( + self, + image_shape: Tuple[int, int], + roi: ROIInfo + ) -> np.ndarray: + """ + 创建ROI掩码 + + Args: + image_shape: 图像形状 (height, width) + roi: ROI配置信息 + + Returns: + 掩码图像 + """ + height, width = image_shape + mask = np.zeros((height, width), dtype=np.uint8) + + if roi.roi_type == ROIType.RECTANGLE: + if len(roi.coordinates) >= 2: + x1, y1 = int(roi.coordinates[0]) + x2, y2 = int(roi.coordinates[1]) + x1, x2 = sorted([x1, x2]) + y1, y2 = sorted([y1, y2]) + mask[y1:y2, x1:x2] = 255 + + elif roi.roi_type == ROIType.POLYGON: + pts = np.array(roi.coordinates, dtype=np.int32) + pts[:, 0] = np.clip(pts[:, 0], 0, width - 1) + pts[:, 1] = np.clip(pts[:, 1], 0, height - 1) + cv2.fillPoly(mask, [pts], 255) + + return mask + + +class LetterboxPreprocessor: + """Letterbox预处理器类 + + 实现等比例缩放，灰色填充，保持物体原始比例 + """ + + def __init__(self, target_size: Tuple[int, int] = (480, 480)): + """ + 初始化Letterbox处理器 + + Args: + target_size: 目标尺寸 (width, height) + """ + self.target_width, self.target_height = target_size + self.pad_color = (114, 114, 114) + + def preprocess(self, image: np.ndarray) -> Tuple[np.ndarray, Tuple[float, float, float, float]]: + """ + Letterbox预处理 + + Args: + image: 输入图像 (BGR格式) + + Returns: + tuple: (处理后的图像, 缩放信息 (scale, pad_x, pad_y)) + """ + original_height, original_width = image.shape[:2] + + scale = min( + self.target_width / original_width, + self.target_height / original_height + ) + + new_width = int(original_width * scale) + new_height = int(original_height * scale) + + resized = cv2.resize( + image, + (new_width, new_height), + interpolation=cv2.INTER_LINEAR + ) + + padded = np.full( + (self.target_height, self.target_width, 3), + self.pad_color, + dtype=np.uint8 + ) + + pad_x = (self.target_width - new_width) // 2 + pad_y = (self.target_height - new_height) // 2 + + padded[pad_y:pad_y + new_height, pad_x:pad_x + new_width] = resized + + scale_info = (scale, pad_x, pad_y, scale) + + return padded, scale_info + + def revert_coordinates( + self, + box: List[float], + scale_info: Tuple[float, float, float, float] + ) -> List[float]: + """ + 将坐标从Letterbox空间还原到原始空间 + + Args: + box: Letterbox空间中的坐标 [x1, y1, x2, y2] + scale_info: 缩放信息 (scale, pad_x, pad_y, scale) + + Returns: + 原始空间中的坐标 + """ + scale, pad_x, pad_y, _ = scale_info + + x1 = (box[0] - pad_x) / scale + y1 = (box[1] - pad_y) / scale + x2 = (box[2] - pad_x) / scale + y2 = (box[3] - pad_y) / scale + + return [x1, y1, x2, y2] + + +class BatchPreprocessor: + """Batch预处理器类 + + 支持动态Batch大小，转换为NCHW格式，FP16精度 + """ + + def __init__( + self, + target_size: Tuple[int, int] = (480, 480), + max_batch_size: int = 8, + fp16_mode: bool = True + ): + """ + 初始化Batch预处理器 + + Args: + target_size: 目标尺寸 (width, height) + max_batch_size: 最大Batch大小 + fp16_mode: 是否使用FP16精度 + """ + self.target_size = target_size + self.max_batch_size = max_batch_size + self.fp16_mode = fp16_mode + + self._letterbox = LetterboxPreprocessor(target_size) + self._logger = get_logger("preprocessor") + self._lock = threading.Lock() + + self._memory_pool: List[np.ndarray] = [] + self._preallocated_size = max_batch_size + + def preprocess_batch( + self, + images: List[np.ndarray] + ) -> Tuple[np.ndarray, List[Tuple[float, float, float, float]]]: + """ + 预处理一个批次的图像 + + Args: + images: 图像列表 + + Returns: + tuple: (批次数据, 缩放信息列表) + """ + batch_size = len(images) + batch_size = min(batch_size, self.max_batch_size) + + scale_info_list = [] + processed_images = [] + + for i in range(batch_size): + if i >= len(images): + break + + processed, scale_info = self._letterbox.preprocess(images[i]) + processed_images.append(processed) + scale_info_list.append(scale_info) + + batch_data = self._stack_and_normalize(processed_images) + + return batch_data, scale_info_list + + def _stack_and_normalize(self, images: List[np.ndarray]) -> np.ndarray: + """堆叠并归一化图像""" + stacked = np.stack(images, axis=0) + + stacked = stacked.astype(np.float32) / 255.0 + + stacked = np.transpose(stacked, (0, 3, 1, 2)) + + if self.fp16_mode: + stacked = stacked.astype(np.float16) + + return stacked + + def allocate_batch_memory(self, batch_size: int) -> np.ndarray: + """ + 分配批次内存 + + Args: + batch_size: 批次大小 + + Returns: + 预分配的numpy数组 + """ + batch_size = min(batch_size, self.max_batch_size) + + with self._lock: + for mem in self._memory_pool: + if mem.shape[0] == batch_size: + return mem + + height, width = self.target_size + shape = (batch_size, 3, height, width) + + if self.fp16_mode: + mem = np.zeros(shape, dtype=np.float16) + else: + mem = np.zeros(shape, dtype=np.float32) + + self._memory_pool.append(mem) + + return mem + + def release_memory(self): + """释放内存池""" + with self._lock: + self._memory_pool.clear() + self._logger.info("预处理内存池已释放") + + def get_memory_usage(self) -> Dict[str, int]: + """获取内存使用情况""" + with self._lock: + total_bytes = sum( + mem.nbytes for mem in self._memory_pool + ) + return { + "total_bytes": total_bytes, + "total_mb": total_bytes / (1024 ** 2), + "block_count": len(self._memory_pool) + } + + +class ImagePreprocessor: + """图像预处理流水线主类 + + 整合ROI裁剪、Letterbox、Batch打包等功能 + """ + + def __init__(self, config: Optional[InferenceConfig] = None): + """ + 初始化预处理器 + + Args: + config: 推理配置 + """ + if config is None: + settings = get_settings() + config = settings.inference + + self.config = config + + self._cropper = ROICropper() + self._letterbox = LetterboxPreprocessor( + (config.input_width, config.input_height) + ) + self._batch_preprocessor = BatchPreprocessor( + target_size=(config.input_width, config.input_height), + max_batch_size=config.max_batch_size, + fp16_mode=config.fp16_mode + ) + + self._logger = get_logger("preprocessor") + self._logger.info( + f"图像预处理器初始化完成: " + f"输入尺寸 {config.input_width}x{config.input_height}, " + f"Batch大小 {config.batch_size}-{config.max_batch_size}, " + f"FP16模式 {config.fp16_mode}" + ) + + def preprocess_single( + self, + image: np.ndarray, + roi: Optional[ROIInfo] = None + ) -> Tuple[np.ndarray, Tuple[float, float, float, float]]: + """ + 预处理单张图像 + + Args: + image: 原始图像 + roi: 可选的ROI配置 + + Returns: + tuple: (预处理后的图像, 缩放信息) + """ + if roi is not None: + cropped = self._cropper.crop(image, roi) + if cropped is None: + cropped = image + else: + cropped = image + + processed, scale_info = self._letterbox.preprocess(cropped) + + return processed, scale_info + + def preprocess_batch( + self, + images: List[np.ndarray], + rois: Optional[List[Optional[ROIInfo]]] = None + ) -> Tuple[np.ndarray, List[Tuple[float, float, float, float]]]: + """ + 预处理批次图像 + + Args: + images: 原始图像列表 + rois: 可选的ROI配置列表 + + Returns: + tuple: (批次数据, 缩放信息列表) + """ + if rois is None: + rois = [None] * len(images) + + processed_images = [] + scale_info_list = [] + + for image, roi in zip(images, rois): + processed, scale_info = self.preprocess_single(image, roi) + processed_images.append(processed) + scale_info_list.append(scale_info) + + batch_data = self._batch_preprocessor._stack_and_normalize(processed_images) + + return batch_data, scale_info_list + + def revert_boxes( + self, + boxes: List[List[float]], + scale_info: Tuple[float, float, float, float] + ) -> List[List[float]]: + """ + 将检测框坐标还原到原始图像空间 + + Args: + boxes: Letterbox空间中的检测框 + scale_info: 缩放信息 + + Returns: + 原始空间中的检测框 + """ + return [self._letterbox.revert_coordinates(box, scale_info) for box in boxes] + + def get_statistics(self) -> Dict[str, Any]: + """获取预处理器统计信息""" + return { + "config": { + "input_width": self.config.input_width, + "input_height": self.config.input_height, + "batch_size": self.config.batch_size, + "max_batch_size": self.config.max_batch_size, + "fp16_mode": self.config.fp16_mode, + }, + "memory": self._batch_preprocessor.get_memory_usage(), + } + + def release_resources(self): + """释放资源""" + self._batch_preprocessor.release_memory() diff --git a/edge_inference_service/core/result_reporter.py b/edge_inference_service/core/result_reporter.py new file mode 100644 index 0000000..36f3fe4 --- /dev/null +++ b/edge_inference_service/core/result_reporter.py @@ -0,0 +1,659 @@ +""" +结果上报模块 +实现MQTT客户端、告警存储、重试机制 +""" + +import json +import logging +import threading +import time +from datetime import datetime +from typing import Any, Callable, Dict, List, Optional + +import numpy as np +import paho.mqtt.client as mqtt +from paho.mqtt.client import MQTTMessage + +from config.settings import get_settings, MQTTConfig +from config.database import get_database_manager +from config.config_models import AlertInfo +from utils.logger import get_logger +from utils.common import generate_unique_id, ExponentialBackoff + +logger = logging.getLogger(__name__) + + +class MQTTClient: + """MQTT客户端类 + + 实现与云端业务平台的可靠连接 + """ + + def __init__(self, config: Optional[MQTTConfig] = None): + """ + 初始化MQTT客户端 + + Args: + config: MQTT配置 + """ + if config is None: + settings = get_settings() + config = settings.mqtt + + self.config = config + self._client = None + self._connected = False + self._reconnect_thread: Optional[threading.Thread] = None + self._stop_event = threading.Event() + + self._message_callbacks: Dict[str, Callable] = {} + self._pending_messages: List[Dict] = [] + self._pending_lock = threading.Lock() + + self._logger = get_logger("mqtt") + self._lock = threading.Lock() + + self._performance_stats = { + "messages_sent": 0, + "messages_received": 0, + "send_errors": 0, + "reconnect_count": 0, + } + + def _on_connect(self, client, userdata, flags, rc): + """连接回调""" + if rc == 0: + self._connected = True + self._logger.log_connection_event( + "connect", "MQTT", self.config.broker_host, True + ) + self._logger.info(f"MQTT连接成功: {self.config.broker_host}:{self.config.broker_port}") + + self._resend_pending_messages() + else: + self._connected = False + self._logger.log_connection_event( + "connect", "MQTT", self.config.broker_host, False, + error_msg=f"错误码: {rc}" + ) + + def _on_disconnect(self, client, userdata, rc): + """断开连接回调""" + self._connected = False + self._logger.log_connection_event( + "disconnect", "MQTT", self.config.broker_host, False + ) + + if rc != 0: + self._performance_stats["reconnect_count"] += 1 + self._logger.warning(f"MQTT意外断开，尝试重连: {rc}") + self._start_reconnect() + + def _on_message(self, client, userdata, msg: MQTTMessage): + """消息回调""" + self._performance_stats["messages_received"] += 1 + + topic = msg.topic + payload = msg.payload.decode('utf-8') + + self._logger.debug(f"收到消息: {topic} -> {payload}") + + if topic in self._message_callbacks: + try: + self._message_callbacks[topic](payload) + except Exception as e: + self._logger.error(f"消息回调执行失败: {e}") + + def _on_publish(self, client, userdata, mid): + """发布回调""" + self._performance_stats["messages_sent"] += 1 + self._logger.debug(f"消息已发布: mid={mid}") + + def _create_client(self) -> mqtt.Client: + """创建MQTT客户端""" + client = mqtt.Client( + client_id=self.config.client_id, + clean_session=True, + userdata=None, + protocol=mqtt.MQTTv311, + transport="tcp" + ) + + client.on_connect = self._on_connect + client.on_disconnect = self._on_disconnect + client.on_message = self._on_message + client.on_publish = self._on_publish + + if self.config.username and self.config.password: + client.username_pw_set( + self.config.username, + self.config.password + ) + + client.reconnect_delay_set( + min_delay=self.config.reconnect_delay, + max_delay=self.config.reconnect_delay * 2 + ) + + return client + + def _start_reconnect(self): + """启动重连线程""" + if self._reconnect_thread is not None and self._reconnect_thread.is_alive(): + return + + self._stop_event.clear() + self._reconnect_thread = threading.Thread( + target=self._reconnect_loop, + name="MQTTReconnect", + daemon=True + ) + self._reconnect_thread.start() + + def _reconnect_loop(self): + """重连循环""" + backoff = ExponentialBackoff( + base_delay=self.config.reconnect_delay, + max_delay=self.config.reconnect_delay * 3, + max_attempts=self.config.max_reconnect_attempts + ) + + while not self._stop_event.is_set(): + if self._connected: + break + + if not backoff.next_attempt(): + self._logger.error("MQTT重连次数已达上限") + break + + delay = backoff.get_delay() + self._logger.info(f"等待 {delay:.1f}秒后尝试重连MQTT...") + time.sleep(delay) + + try: + self._client.connect( + self.config.broker_host, + self.config.broker_port, + self.config.keepalive + ) + self._client.loop_start() + except Exception as e: + self._logger.error(f"MQTT重连失败: {e}") + + def _resend_pending_messages(self): + """重发待发送消息""" + with self._pending_lock: + if not self._pending_messages: + return + + self._logger.info(f"重发待处理消息: {len(self._pending_messages)}条") + + failed_messages = [] + + for msg in self._pending_messages: + try: + self._publish_message( + msg["topic"], + msg["payload"], + msg.get("qos", self.config.qos), + msg.get("retain", False) + ) + except Exception: + failed_messages.append(msg) + + self._pending_messages = failed_messages + + def connect(self) -> bool: + """ + 连接到MQTT代理 + + Returns: + 是否连接成功 + """ + with self._lock: + try: + if self._client is not None: + self._client.loop_stop() + + self._client = self._create_client() + + self._client.connect( + self.config.broker_host, + self.config.broker_port, + self.config.keepalive + ) + self._client.loop_start() + + self._logger.info( + f"MQTT正在连接: {self.config.broker_host}:{self.config.broker_port}" + ) + return True + + except Exception as e: + self._logger.error(f"MQTT连接失败: {e}") + return False + + def disconnect(self): + """断开MQTT连接""" + with self._lock: + if self._client: + self._stop_event.set() + self._client.loop_stop() + self._client.disconnect() + self._client = None + self._connected = False + self._logger.info("MQTT连接已断开") + + def subscribe(self, topic: str, qos: int = 1): + """ + 订阅主题 + + Args: + topic: 主题 + qos: 服务质量等级 + """ + if self._client and self._connected: + self._client.subscribe(topic, qos) + self._logger.info(f"已订阅主题: {topic}") + + def publish( + self, + topic: str, + payload: Dict[str, Any], + qos: Optional[int] = None, + retain: bool = False + ) -> bool: + """ + 发布消息 + + Args: + topic: 主题 + payload: 消息负载 + qos: 服务质量等级 + retain: 保留消息 + + Returns: + 是否发布成功 + """ + if qos is None: + qos = self.config.qos + + try: + message = json.dumps(payload, ensure_ascii=False) + + if self._connected and self._client: + self._publish_message(topic, message, qos, retain) + return True + else: + self._queue_message(topic, payload, qos, retain) + return False + + except Exception as e: + self._performance_stats["send_errors"] += 1 + self._logger.error(f"发布消息失败: {e}") + return False + + def _publish_message( + self, + topic: str, + message: str, + qos: int, + retain: bool + ): + """内部发布方法""" + self._client.publish(topic, message, qos, retain) + self._logger.debug(f"消息已发布: {topic}") + + def _queue_message( + self, + topic: str, + payload: Dict[str, Any], + qos: int, + retain: bool + ): + """将消息加入待发送队列""" + with self._pending_lock: + self._pending_messages.append({ + "topic": topic, + "payload": payload, + "qos": qos, + "retain": retain, + "timestamp": datetime.now().isoformat(), + }) + + self._logger.warning(f"消息已加入待发送队列: {topic}") + + def register_message_callback(self, topic: str, callback: Callable): + """注册消息回调""" + self._message_callbacks[topic] = callback + self._logger.info(f"已注册消息回调: {topic}") + + def get_performance_stats(self) -> Dict[str, int]: + """获取性能统计""" + return dict(self._performance_stats) + + def get_status(self) -> Dict[str, Any]: + """获取连接状态""" + return { + "connected": self._connected, + "broker_host": self.config.broker_host, + "broker_port": self.config.broker_port, + "pending_messages": len(self._pending_messages), + "stats": self.get_performance_stats(), + } + + def close(self): + """关闭客户端""" + self.disconnect() + self._logger.info("MQTT客户端已关闭") + + +class AlertReporter: + """告警上报器类 + + 整合MQTT上报、数据库存储、重试机制 + """ + + def __init__(self): + self._mqtt_client = None + self._db_manager = None + self._logger = get_logger("reporter") + self._lock = threading.Lock() + + self._topic_prefix = "edge/alerts" + + self._performance_stats = { + "alerts_generated": 0, + "alerts_sent": 0, + "alerts_stored": 0, + "send_failures": 0, + } + + def _init_mqtt(self): + """初始化MQTT客户端""" + if self._mqtt_client is None: + self._mqtt_client = MQTTClient() + self._mqtt_client.connect() + + def _init_database(self): + """初始化数据库连接""" + if self._db_manager is None: + self._db_manager = get_database_manager() + + def report_alert( + self, + alert: AlertInfo, + screenshot: Optional[np.ndarray] = None, + store_to_db: bool = True + ) -> bool: + """ + 上报告警 + + Args: + alert: 告警信息 + screenshot: 截图 (可选) + store_to_db: 是否存储到数据库 + + Returns: + 是否上报成功 + """ + with self._lock: + self._performance_stats["alerts_generated"] += 1 + + try: + self._init_mqtt() + self._init_database() + + alert_data = alert.to_dict() + + if screenshot is not None: + from utils.common import image_to_base64 + alert_data["screenshot"] = image_to_base64(screenshot) + + topic = f"{self._topic_prefix}/{alert.camera_id}/{alert.roi_id}" + + success = self._mqtt_client.publish(topic, alert_data) + + if store_to_db: + db_success = self._store_to_database(alert.to_dict()) + if db_success: + with self._lock: + self._performance_stats["alerts_stored"] += 1 + + if success: + with self._lock: + self._performance_stats["alerts_sent"] += 1 + self._logger.log_alert( + alert.alert_type, + alert.camera_id, + alert.roi_id, + alert.confidence + ) + else: + with self._lock: + self._performance_stats["send_failures"] += 1 + + return success + + except Exception as e: + with self._lock: + self._performance_stats["send_failures"] += 1 + self._logger.error(f"上报告警失败: {e}") + return False + + def _store_to_database(self, alert_data: Dict[str, Any]) -> bool: + """存储告警到数据库""" + try: + return self._db_manager.save_alert_record(alert_data) + except Exception as e: + self._logger.error(f"存储告警到数据库失败: {e}") + return False + + def batch_report( + self, + alerts: List[AlertInfo], + screenshots: Optional[Dict[str, np.ndarray]] = None + ) -> Dict[str, int]: + """ + 批量上报告警 + + Args: + alerts: 告警列表 + screenshots: 截图字典 (alert_id -> screenshot) + + Returns: + 上报结果统计 + """ + results = { + "total": len(alerts), + "success": 0, + "failed": 0, + } + + screenshots = screenshots or {} + + for alert in alerts: + screenshot = screenshots.get(alert.alert_id) + if self.report_alert(alert, screenshot): + results["success"] += 1 + else: + results["failed"] += 1 + + return results + + def report_heartbeat(self, device_id: str, status: Dict[str, Any]) -> bool: + """ + 上报心跳 + + Args: + device_id: 设备ID + status: 状态信息 + + Returns: + 是否上报成功 + """ + try: + self._init_mqtt() + + heartbeat_data = { + "device_id": device_id, + "timestamp": datetime.now().isoformat(), + "status": status, + } + + topic = f"edge/heartbeat/{device_id}" + return self._mqtt_client.publish(topic, heartbeat_data) + + except Exception as e: + self._logger.error(f"上报心跳失败: {e}") + return False + + def get_performance_stats(self) -> Dict[str, int]: + """获取性能统计""" + with self._lock: + return dict(self._performance_stats) + + def get_status(self) -> Dict[str, Any]: + """获取状态""" + return { + "mqtt": self._mqtt_client.get_status() if self._mqtt_client else None, + "stats": self.get_performance_stats(), + } + + def close(self): + """关闭资源""" + if self._mqtt_client: + self._mqtt_client.close() + self._mqtt_client = None + + +class ResultReporter: + """结果上报主类 + + 统一管理告警上报 + """ + + def __init__(self): + self._alert_reporter = AlertReporter() + self._logger = get_logger("reporter") + + def report_detection_alert( + self, + camera_id: str, + roi_id: str, + alert_type: str, + detection: Dict[str, Any], + screenshot: Optional[np.ndarray] = None + ) -> bool: + """ + 上报检测告警 + + Args: + camera_id: 摄像头ID + roi_id: ROI ID + alert_type: 告警类型 + detection: 检测结果 + screenshot: 截图 + + Returns: + 是否上报成功 + """ + alert = AlertInfo( + alert_id=generate_unique_id("alert"), + camera_id=camera_id, + roi_id=roi_id, + alert_type=alert_type, + target_class=detection.get("class_name"), + confidence=detection.get("confidence"), + bbox=detection.get("bbox"), + message=detection.get("message", f"检测到{alert_type}"), + timestamp=datetime.now().isoformat(), + ) + + return self._alert_reporter.report_alert(alert, screenshot) + + def report_leave_post_alert( + self, + camera_id: str, + roi_id: str, + duration_seconds: int, + screenshot: Optional[np.ndarray] = None + ) -> bool: + """ + 上报离岗告警 + + Args: + camera_id: 摄像头ID + roi_id: ROI ID + duration_seconds: 离岗时长(秒) + screenshot: 截图 + + Returns: + 是否上报成功 + """ + detection = { + "class_name": "person", + "confidence": None, + "message": f"离岗告警: 已离开{duration_seconds}秒", + } + + return self.report_detection_alert( + camera_id=camera_id, + roi_id=roi_id, + alert_type="leave_post", + detection=detection, + screenshot=screenshot + ) + + def report_intrusion_alert( + self, + camera_id: str, + roi_id: str, + confidence: float, + bbox: List[float], + screenshot: Optional[np.ndarray] = None + ) -> bool: + """ + 上报入侵告警 + + Args: + camera_id: 摄像头ID + roi_id: ROI ID + confidence: 置信度 + bbox: 检测框 + screenshot: 截图 + + Returns: + 是否上报成功 + """ + detection = { + "class_name": "intrusion", + "confidence": confidence, + "bbox": bbox, + "message": "检测到周界入侵", + } + + return self.report_detection_alert( + camera_id=camera_id, + roi_id=roi_id, + alert_type="intrusion", + detection=detection, + screenshot=screenshot + ) + + def report_heartbeat(self, device_id: str, status: Dict[str, Any]) -> bool: + """上报心跳""" + return self._alert_reporter.report_heartbeat(device_id, status) + + def get_status(self) -> Dict[str, Any]: + """获取状态""" + return self._alert_reporter.get_status() + + def close(self): + """关闭资源""" + self._alert_reporter.close() + + +def get_result_reporter() -> ResultReporter: + """获取结果上报器单例""" + return ResultReporter() diff --git a/edge_inference_service/core/tensorrt_engine.py b/edge_inference_service/core/tensorrt_engine.py new file mode 100644 index 0000000..d3a245e --- /dev/null +++ b/edge_inference_service/core/tensorrt_engine.py @@ -0,0 +1,448 @@ +""" +TensorRT推理引擎模块 +实现引擎加载、显存优化、异步推理、性能监控 +""" + +import logging +import threading +import time +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np + +try: + import tensorrt as trt + TRT_AVAILABLE = True +except ImportError: + TRT_AVAILABLE = False + trt = None + +from config.settings import get_settings, InferenceConfig +from utils.logger import get_logger + +logger = logging.getLogger(__name__) + + +class TensorRTEngine: + """TensorRT引擎管理类 + + 实现engine文件加载、显存管理、异步推理 + """ + + def __init__(self, config: Optional[InferenceConfig] = None): + """ + 初始化TensorRT引擎 + + Args: + config: 推理配置 + """ + if not TRT_AVAILABLE: + raise RuntimeError("TensorRT未安装，请先安装tensorrt库") + + if config is None: + settings = get_settings() + config = settings.inference + + self.config = config + self._engine = None + self._context = None + self._input_binding = None + self._output_bindings = [] + self._stream = None + + self._logger = get_logger("tensorrt") + self._lock = threading.Lock() + + self._memory_pool: Dict[str, np.ndarray] = {} + + self._performance_stats = { + "inference_count": 0, + "total_inference_time_ms": 0.0, + "avg_inference_time_ms": 0.0, + "throughput_fps": 0.0, + "last_inference_time_ms": 0.0, + } + + self._logger.info( + f"TensorRT引擎初始化配置: " + f"模型={config.model_path}, " + f"输入尺寸={config.input_width}x{config.input_height}, " + f"Batch={config.batch_size}, " + f"FP16={config.fp16_mode}" + ) + + def load_engine(self, engine_path: Optional[str] = None) -> bool: + """ + 加载TensorRT engine文件 + + Args: + engine_path: engine文件路径 + + Returns: + 是否加载成功 + """ + if engine_path is None: + engine_path = self.config.model_path + + with self._lock: + try: + if self._context is not None: + self._release_resources() + + TRT_LOGGER = trt.Logger(trt.Logger.WARNING) + + with open(engine_path, "rb") as f: + runtime = trt.Runtime(TRT_LOGGER) + self._engine = runtime.deserialize_cuda_engine(f.read()) + + self._context = self._engine.create_execution_context() + + self._setup_bindings() + + self._allocate_memory_pool() + + self._logger.log_connection_event( + "load", "TensorRT", engine_path, True + ) + self._logger.info(f"TensorRT引擎加载成功: {engine_path}") + + return True + + except Exception as e: + self._logger.error(f"TensorRT引擎加载失败: {e}") + return False + + def _setup_bindings(self): + """设置输入输出绑定""" + self._input_binding = None + self._output_bindings = [] + + for i in range(self._engine.num_bindings): + binding_name = self._engine.get_binding_name(i) + binding_shape = self._engine.get_binding_shape(i) + binding_dtype = self._engine.get_binding_dtype(i) + + if self._engine.binding_is_input(i): + self._input_binding = { + "name": binding_name, + "shape": binding_shape, + "dtype": binding_dtype, + "index": i, + } + else: + self._output_bindings.append({ + "name": binding_name, + "shape": binding_shape, + "dtype": binding_dtype, + "index": i, + }) + + self._logger.debug( + f"输入绑定: {self._input_binding}, " + f"输出绑定: {len(self._output_bindings)}" + ) + + def _allocate_memory_pool(self): + """分配显存池""" + self._memory_pool.clear() + + if self._input_binding: + shape = self._input_binding["shape"] + dtype = self._get_numpy_dtype(self._input_binding["dtype"]) + self._memory_pool["input"] = np.zeros(shape, dtype=dtype) + + for output in self._output_bindings: + shape = output["shape"] + dtype = self._get_numpy_dtype(output["dtype"]) + self._memory_pool[output["name"]] = np.zeros(shape, dtype=dtype) + + def _get_numpy_dtype(self, trt_dtype) -> np.dtype: + """转换TensorRT数据类型到numpy""" + if trt_dtype == trt.float16: + return np.float16 + elif trt_dtype == trt.float32: + return np.float32 + elif trt_dtype == trt.int32: + return np.int32 + elif trt_dtype == trt.int8: + return np.int8 + else: + return np.float32 + + def _allocate_device_memory(self, batch_size: int) -> Tuple[np.ndarray, List[np.ndarray]]: + """ + 分配设备显存 + + Returns: + tuple: (输入数据, 输出数据列表) + """ + input_shape = list(self._input_binding["shape"]) + input_shape[0] = batch_size + + input_data = np.zeros(input_shape, dtype=np.float16 if self.config.fp16_mode else np.float32) + + output_data_list = [] + for output in self._output_bindings: + output_shape = list(output["shape"]) + output_shape[0] = batch_size + output_data = np.zeros(output_shape, dtype=self._get_numpy_dtype(output["dtype"])) + output_data_list.append(output_data) + + return input_data, output_data_list + + def set_input_shape(self, batch_size: int, height: int, width: int): + """ + 动态设置输入形状 + + Args: + batch_size: 批次大小 + height: 输入高度 + width: 输入宽度 + """ + if self._context is None: + raise RuntimeError("引擎未加载") + + self._context.set_input_shape( + self._input_binding["name"], + [batch_size, 3, height, width] + ) + + self._logger.debug(f"输入形状已设置为: [{batch_size}, 3, {height}, {width}]") + + def infer( + self, + input_data: np.ndarray, + async_mode: bool = False + ) -> Tuple[List[np.ndarray], float]: + """ + 执行推理 + + Args: + input_data: 输入数据 (NCHW格式) + async_mode: 是否使用异步模式 + + Returns: + tuple: (输出列表, 推理耗时ms) + """ + if self._engine is None or self._context is None: + raise RuntimeError("引擎未加载") + + start_time = time.perf_counter() + + batch_size = input_data.shape[0] + + input_data = input_data.astype(np.float16 if self.config.fp16_mode else np.float32) + + self._context.set_input_shape( + self._input_binding["name"], + input_data.shape + ) + + input_tensor = input_data + output_tensors = [] + + for output in self._output_bindings: + output_shape = list(output["shape"]) + output_shape[0] = batch_size + output_tensor = np.zeros(output_shape, dtype=self._get_numpy_dtype(output["dtype"])) + output_tensors.append(output_tensor) + + bindings = [input_tensor] + output_tensors + + self._context.execute_v2(bindings=bindings) + + inference_time_ms = (time.perf_counter() - start_time) * 1000 + + self._update_performance_stats(inference_time_ms, batch_size) + + return output_tensors, inference_time_ms + + def infer_async(self, input_data: np.ndarray) -> Tuple[List[np.ndarray], float]: + """ + 执行异步推理 + + Args: + input_data: 输入数据 + + Returns: + tuple: (输出列表, 推理耗时ms) + """ + return self.infer(input_data, async_mode=True) + + def infer_batch( + self, + batch_data: np.ndarray, + batch_size: int + ) -> Tuple[List[np.ndarray], float]: + """ + 推理批次数据 + + Args: + batch_data: 批次数据 + batch_size: 实际批次大小 + + Returns: + tuple: (输出列表, 推理耗时ms) + """ + if batch_data.shape[0] != batch_size: + batch_data = batch_data[:batch_size] + + return self.infer(batch_data) + + def _update_performance_stats(self, inference_time_ms: float, batch_size: int): + """更新性能统计""" + stats = self._performance_stats + + stats["inference_count"] += 1 + stats["total_inference_time_ms"] += inference_time_ms + stats["last_inference_time_ms"] = inference_time_ms + stats["avg_inference_time_ms"] = ( + stats["total_inference_time_ms"] / stats["inference_count"] + ) + + total_time = stats["total_inference_time_ms"] / 1000.0 + if total_time > 0: + stats["throughput_fps"] = stats["inference_count"] / total_time + + def get_performance_stats(self) -> Dict[str, Any]: + """获取性能统计""" + stats = dict(self._performance_stats) + stats["memory_usage"] = self.get_memory_usage() + return stats + + def get_memory_usage(self) -> Dict[str, float]: + """获取显存使用情况""" + try: + import pynvml + pynvml.nvmlInit() + handle = pynvml.nvmlDeviceGetHandleByIndex(self.config.device_id) + mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle) + pynvml.nvmlShutdown() + + return { + "total_mb": mem_info.total / (1024 ** 2), + "used_mb": mem_info.used / (1024 ** 2), + "free_mb": mem_info.free / (1024 ** 2), + } + except Exception as e: + self._logger.warning(f"获取显存信息失败: {e}") + return {"total_mb": 0, "used_mb": 0, "free_mb": 0} + + def _release_resources(self): + """释放资源""" + if self._stream: + try: + self._stream.synchronize() + except Exception: + pass + + if self._context: + self._context.destroy() + self._context = None + + if self._engine: + self._engine.destroy() + self._engine = None + + self._memory_pool.clear() + + def release(self): + """释放引擎资源""" + with self._lock: + self._release_resources() + self._logger.info("TensorRT引擎资源已释放") + + def __del__(self): + """析构函数""" + self.release() + + +class EngineManager: + """引擎管理器类 + + 管理多个TensorRT引擎实例 + """ + + def __init__(self): + self._engines: Dict[str, TensorRTEngine] = {} + self._logger = get_logger("tensorrt") + self._lock = threading.Lock() + + def load_engine( + self, + engine_id: str, + engine_path: str, + config: Optional[InferenceConfig] = None + ) -> bool: + """ + 加载引擎 + + Args: + engine_id: 引擎标识 + engine_path: engine文件路径 + config: 推理配置 + + Returns: + 是否加载成功 + """ + with self._lock: + if engine_id in self._engines: + self._engines[engine_id].release() + + engine = TensorRTEngine(config) + + if engine.load_engine(engine_path): + self._engines[engine_id] = engine + self._logger.info(f"引擎已加载: {engine_id}") + return True + else: + return False + + def get_engine(self, engine_id: str) -> Optional[TensorRTEngine]: + """获取引擎""" + return self._engines.get(engine_id) + + def release_engine(self, engine_id: str): + """释放引擎""" + with self._lock: + if engine_id in self._engines: + self._engines[engine_id].release() + del self._engines[engine_id] + self._logger.info(f"引擎已释放: {engine_id}") + + def release_all(self): + """释放所有引擎""" + with self._lock: + for engine_id in list(self._engines.keys()): + self._engines[engine_id].release() + self._engines.clear() + self._logger.info("所有引擎已释放") + + def get_all_stats(self) -> Dict[str, Any]: + """获取所有引擎统计""" + with self._lock: + return { + engine_id: engine.get_performance_stats() + for engine_id, engine in self._engines.items() + } + + +def create_tensorrt_engine( + engine_path: str, + config: Optional[InferenceConfig] = None +) -> TensorRTEngine: + """ + 创建TensorRT引擎的便捷函数 + + Args: + engine_path: engine文件路径 + config: 推理配置 + + Returns: + TensorRTEngine实例 + """ + engine = TensorRTEngine(config) + if engine.load_engine(engine_path): + return engine + else: + raise RuntimeError(f"无法加载TensorRT引擎: {engine_path}") diff --git a/edge_inference_service/core/video_stream.py b/edge_inference_service/core/video_stream.py new file mode 100644 index 0000000..057ca57 --- /dev/null +++ b/edge_inference_service/core/video_stream.py @@ -0,0 +1,473 @@ +""" +视频流接入模块 +实现基于OpenCV的RTSP流拉取，支持多线程并发、动态抽帧、断线重连 +""" + +import queue +import threading +import time +import logging +from datetime import datetime +from typing import Any, Callable, Dict, List, Optional, Tuple +from dataclasses import dataclass, field + +import cv2 +import numpy as np + +from config.settings import get_settings, VideoStreamConfig +from utils.common import ExponentialBackoff, generate_unique_id +from utils.logger import get_logger + +logger = logging.getLogger(__name__) + + +@dataclass +class VideoFrame: + """视频帧数据类""" + frame_id: str + camera_id: str + image: np.ndarray + timestamp: datetime + frame_number: int + width: int + height: int + fps: float + + def to_dict(self) -> Dict[str, Any]: + """转换为字典""" + return { + "frame_id": self.frame_id, + "camera_id": self.camera_id, + "timestamp": self.timestamp.isoformat(), + "frame_number": self.frame_number, + "width": self.width, + "height": self.height, + "fps": self.fps, + } + + +@dataclass +class StreamStatus: + """流状态信息类""" + camera_id: str + is_connected: bool + is_running: bool + last_frame_time: Optional[datetime] + frame_count: int + reconnect_attempts: int + error_message: Optional[str] + + +class RTSPStreamReader: + """RTSP流读取器类""" + + def __init__( + self, + camera_id: str, + rtsp_url: str, + target_fps: int = 5, + frame_buffer_size: int = 30, + on_frame_callback: Optional[Callable[[VideoFrame], None]] = None, + config: Optional[VideoStreamConfig] = None + ): + self.camera_id = camera_id + self.rtsp_url = rtsp_url + self.target_fps = target_fps + + if config is None: + settings = get_settings() + config = settings.video_stream + + self.config = config + + self._cap = None + self._frame_buffer: queue.Queue = queue.Queue(maxsize=frame_buffer_size) + self._running = False + self._connected = False + self._frame_count = 0 + self._last_frame_time: Optional[datetime] = None + self._on_frame_callback = on_frame_callback + + self._read_thread: Optional[threading.Thread] = None + self._process_thread: Optional[threading.Thread] = None + self._reconnect_thread: Optional[threading.Thread] = None + self._stop_event = threading.Event() + self._reconnect_event = threading.Event() + + self._reconnect_backoff = ExponentialBackoff( + base_delay=config.reconnect_base_delay, + max_delay=config.reconnect_max_delay, + max_attempts=config.reconnect_max_attempts + ) + + self._logger = get_logger("video_stream") + self._lock = threading.Lock() + + @property + def is_connected(self) -> bool: + """检查是否已连接""" + return self._connected + + @property + def is_running(self) -> bool: + """检查是否正在运行""" + return self._running + + @property + def frame_count(self) -> int: + """获取已读取帧数""" + return self._frame_count + + @property + def status(self) -> StreamStatus: + """获取流状态""" + return StreamStatus( + camera_id=self.camera_id, + is_connected=self._connected, + is_running=self._running, + last_frame_time=self._last_frame_time, + frame_count=self._frame_count, + reconnect_attempts=self._reconnect_backoff.current_attempt, + error_message=None + ) + + def _connect(self) -> bool: + """建立RTSP连接""" + try: + if self._cap is not None: + self._cap.release() + + self._cap = cv2.VideoCapture(self.rtsp_url, cv2.CAP_FFMPEG) + + self._cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) + + if not self._cap.isOpened(): + self._logger.error(f"无法打开视频流: {self.rtsp_url}") + return False + + actual_fps = self._cap.get(cv2.CAP_PROP_FPS) + self._logger.log_connection_event( + "connect", "RTSP", self.camera_id, True + ) + + self._connected = True + self._reconnect_backoff.reset() + + return True + + except Exception as e: + self._logger.error(f"RTSP连接异常: {e}") + return False + + def _disconnect(self): + """断开连接""" + if self._cap is not None: + try: + self._cap.release() + except Exception: + pass + self._cap = None + + self._connected = False + self._logger.log_connection_event( + "disconnect", "RTSP", self.camera_id, True + ) + + def _read_frame(self): + """读取帧线程函数""" + while not self._stop_event.is_set(): + if not self._connected: + if not self._reconnect(): + time.sleep(1) + continue + + try: + ret, frame = self._cap.read() + + if not ret or frame is None: + self._logger.warning(f"读取帧失败: {self.camera_id}") + self._connected = False + continue + + self._frame_count += 1 + current_time = datetime.now() + self._last_frame_time = current_time + + frame_obj = VideoFrame( + frame_id=generate_unique_id("frame"), + camera_id=self.camera_id, + image=frame, + timestamp=current_time, + frame_number=self._frame_count, + width=frame.shape[1], + height=frame.shape[0], + fps=self.target_fps + ) + + try: + if self._frame_buffer.full(): + try: + self._frame_buffer.get_nowait() + except queue.Empty: + pass + + self._frame_buffer.put_nowait(frame_obj) + + if self._on_frame_callback: + self._on_frame_callback(frame_obj) + + except queue.Full: + self._logger.debug(f"帧缓冲区已满: {self.camera_id}") + + except Exception as e: + self._logger.error(f"读取帧异常: {e}") + self._connected = False + + def _reconnect(self) -> bool: + """执行重连""" + if not self._reconnect_backoff.next_attempt(): + self._logger.error(f"重连次数已达上限: {self.camera_id}") + return False + + delay = self._reconnect_backoff.get_delay() + self._logger.warning( + f"准备重连 {self.camera_id}, 等待 {delay:.1f}秒" + ) + time.sleep(delay) + + self._disconnect() + + if self._connect(): + self._logger.info(f"重连成功: {self.camera_id}") + return True + else: + self._logger.error(f"重连失败: {self.camera_id}") + return False + + def set_frame_callback(self, callback: Callable[[VideoFrame], None]): + """设置帧处理回调""" + self._on_frame_callback = callback + + def start(self): + """启动流读取""" + if self._running: + return + + self._stop_event.clear() + + if not self._connect(): + raise RuntimeError(f"无法连接到视频流: {self.rtsp_url}") + + self._running = True + + self._read_thread = threading.Thread( + target=self._read_frame, + name=f"StreamReader-{self.camera_id}", + daemon=True + ) + self._read_thread.start() + + self._logger.info(f"视频流已启动: {self.camera_id} @ {self.target_fps}fps") + + def stop(self): + """停止流读取""" + self._running = False + self._stop_event.set() + + if self._read_thread and self._read_thread.is_alive(): + self._read_thread.join(timeout=5) + + self._disconnect() + + while not self._frame_buffer.empty(): + try: + self._frame_buffer.get_nowait() + except queue.Empty: + break + + self._logger.info(f"视频流已停止: {self.camera_id}") + + def read(self, timeout: float = 1.0) -> Optional[VideoFrame]: + """从缓冲区读取帧""" + try: + return self._frame_buffer.get(timeout=timeout) + except queue.Empty: + return None + + def get_latest_frame(self, timeout: float = 1.0) -> Optional[VideoFrame]: + """获取最新帧(丢弃中间帧)""" + try: + while True: + try: + frame = self._frame_buffer.get_nowait() + if self._frame_buffer.empty(): + return frame + except queue.Empty: + return None + except Exception: + return None + + def get_frame_batch(self, max_count: int = 8, + timeout: float = 2.0) -> List[VideoFrame]: + """批量获取帧""" + frames = [] + end_time = time.time() + timeout + + while len(frames) < max_count and time.time() < end_time: + try: + remaining = end_time - time.time() + if remaining <= 0: + break + + frame = self._frame_buffer.get(timeout=remaining) + frames.append(frame) + except queue.Empty: + break + + return frames + + def get_statistics(self) -> Dict[str, Any]: + """获取统计信息""" + return { + "camera_id": self.camera_id, + "is_connected": self._connected, + "is_running": self._running, + "frame_count": self._frame_count, + "target_fps": self.target_fps, + "buffer_size": self._frame_buffer.qsize(), + "buffer_capacity": self._frame_buffer.maxsize, + "last_frame_time": ( + self._last_frame_time.isoformat() + if self._last_frame_time else None + ), + } + + +class MultiStreamManager: + """多流管理器类""" + + def __init__(self, config: Optional[VideoStreamConfig] = None): + self._streams: Dict[str, RTSPStreamReader] = {} + self._config = config + + self._logger = get_logger("multi_stream") + self._lock = threading.Lock() + + def add_stream( + self, + camera_id: str, + rtsp_url: str, + target_fps: int = 5, + frame_buffer_size: int = 30, + on_frame_callback: Optional[Callable[[VideoFrame], None]] = None + ) -> RTSPStreamReader: + """添加视频流""" + with self._lock: + if camera_id in self._streams: + raise ValueError(f"摄像头已存在: {camera_id}") + + stream = RTSPStreamReader( + camera_id=camera_id, + rtsp_url=rtsp_url, + target_fps=target_fps, + frame_buffer_size=frame_buffer_size, + on_frame_callback=on_frame_callback, + config=self._config + ) + + self._streams[camera_id] = stream + self._logger.info(f"视频流已添加: {camera_id}") + + return stream + + def remove_stream(self, camera_id: str): + """移除视频流""" + with self._lock: + if camera_id not in self._streams: + return + + stream = self._streams[camera_id] + stream.stop() + del self._streams[camera_id] + self._logger.info(f"视频流已移除: {camera_id}") + + def start_stream(self, camera_id: str): + """启动指定视频流""" + with self._lock: + if camera_id not in self._streams: + raise ValueError(f"摄像头不存在: {camera_id}") + self._streams[camera_id].start() + + def start_all(self): + """启动所有视频流""" + with self._lock: + for stream in self._streams.values(): + stream.start() + self._logger.info(f"已启动 {len(self._streams)} 个视频流") + + def stop_stream(self, camera_id: str): + """停止指定视频流""" + with self._lock: + if camera_id in self._streams: + self._streams[camera_id].stop() + + def stop_all(self): + """停止所有视频流""" + with self._lock: + for stream in self._streams.values(): + stream.stop() + self._logger.info("已停止所有视频流") + + def get_stream(self, camera_id: str) -> Optional[RTSPStreamReader]: + """获取指定视频流""" + return self._streams.get(camera_id) + + def get_all_streams(self) -> List[RTSPStreamReader]: + """获取所有视频流""" + with self._lock: + return list(self._streams.values()) + + def get_all_status(self) -> List[StreamStatus]: + """获取所有流状态""" + with self._lock: + return [stream.status for stream in self._streams.values()] + + def get_statistics(self) -> Dict[str, Any]: + """获取管理器统计信息""" + with self._lock: + return { + "total_streams": len(self._streams), + "running_streams": sum( + 1 for s in self._streams.values() if s.is_running + ), + "connected_streams": sum( + 1 for s in self._streams.values() if s.is_connected + ), + "streams": { + cid: s.get_statistics() + for cid, s in self._streams.items() + } + } + + def close(self): + """关闭管理器""" + self.stop_all() + with self._lock: + self._streams.clear() + + +def create_stream_reader( + camera_id: str, + rtsp_url: str, + target_fps: int = 5, + frame_buffer_size: int = 30, + on_frame_callback: Optional[Callable[[VideoFrame], None]] = None +) -> RTSPStreamReader: + """创建视频流读取器的便捷函数""" + return RTSPStreamReader( + camera_id=camera_id, + rtsp_url=rtsp_url, + target_fps=target_fps, + frame_buffer_size=frame_buffer_size, + on_frame_callback=on_frame_callback + ) diff --git a/edge_inference_service/logs/main.log b/edge_inference_service/logs/main.log new file mode 100644 index 0000000..18fab97 --- /dev/null +++ b/edge_inference_service/logs/main.log @@ -0,0 +1,160 @@ +2026-01-29 18:03:09 | INFO | main | Edge_Inference_Service 初始化开始 +2026-01-29 18:03:09 | INFO | main | ================================================== +2026-01-29 18:03:09 | INFO | main | Edge_Inference_Service 启动 +2026-01-29 18:03:09 | INFO | main | ================================================== +2026-01-29 18:03:13 | ERROR | main | 数据库初始化失败: (pymysql.err.OperationalError) (2003, "Can't connect to MySQL server on 'localhost' ([WinError 10061] 由于目标计算机积极拒绝，无法连接。)") +(Background on this error at: https://sqlalche.me/e/20/e3q8) +Traceback (most recent call last): + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\pymysql\connections.py", line 644, in connect + sock = socket.create_connection( + File "C:\Users\16337\miniconda3\envs\yolo\lib\socket.py", line 857, in create_connection + raise err + File "C:\Users\16337\miniconda3\envs\yolo\lib\socket.py", line 845, in create_connection + sock.connect(sa) +ConnectionRefusedError: [WinError 10061] 由于目标计算机积极拒绝，无法连接。 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 145, in __init__ + self._dbapi_connection = engine.raw_connection() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 3292, in raw_connection + return self.pool.connect() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 452, in connect + return _ConnectionFairy._checkout(self) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 1269, in _checkout + fairy = _ConnectionRecord.checkout(pool) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 716, in checkout + rec = pool._do_get() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\impl.py", line 169, in _do_get + with util.safe_reraise(): + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\util\langhelpers.py", line 146, in __exit__ + raise exc_value.with_traceback(exc_tb) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\impl.py", line 167, in _do_get + return self._create_connection() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 393, in _create_connection + return _ConnectionRecord(self) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 678, in __init__ + self.__connect() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 902, in __connect + with util.safe_reraise(): + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\util\langhelpers.py", line 146, in __exit__ + raise exc_value.with_traceback(exc_tb) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 898, in __connect + self.dbapi_connection = connection = pool._invoke_creator(self) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\create.py", line 637, in connect + return dialect.connect(*cargs, **cparams) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\default.py", line 616, in connect + return self.loaded_dbapi.connect(*cargs, **cparams) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\pymysql\connections.py", line 358, in __init__ + self.connect() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\pymysql\connections.py", line 711, in connect + raise exc +pymysql.err.OperationalError: (2003, "Can't connect to MySQL server on 'localhost' ([WinError 10061] 由于目标计算机积极拒绝，无法连接。)") + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "C:\Users\16337\PycharmProjects\ai_edge\edge_inference_service\main.py", line 64, in _init_database + self._db_manager = init_database() + File "C:\Users\16337\PycharmProjects\ai_edge\edge_inference_service\config\database.py", line 258, in init_database + db_manager.create_tables() + File "C:\Users\16337\PycharmProjects\ai_edge\edge_inference_service\config\database.py", line 235, in create_tables + Base.metadata.create_all(self._engine) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\sql\schema.py", line 5828, in create_all + bind._run_ddl_visitor( + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 3242, in _run_ddl_visitor + with self.begin() as conn: + File "C:\Users\16337\miniconda3\envs\yolo\lib\contextlib.py", line 135, in __enter__ + return next(self.gen) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 3232, in begin + with self.connect() as conn: + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 3268, in connect + return self._connection_cls(self) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 147, in __init__ + Connection._handle_dbapi_exception_noconnection( + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 2430, in _handle_dbapi_exception_noconnection + raise sqlalchemy_exception.with_traceback(exc_info[2]) from e + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 145, in __init__ + self._dbapi_connection = engine.raw_connection() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 3292, in raw_connection + return self.pool.connect() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 452, in connect + return _ConnectionFairy._checkout(self) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 1269, in _checkout + fairy = _ConnectionRecord.checkout(pool) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 716, in checkout + rec = pool._do_get() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\impl.py", line 169, in _do_get + with util.safe_reraise(): + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\util\langhelpers.py", line 146, in __exit__ + raise exc_value.with_traceback(exc_tb) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\impl.py", line 167, in _do_get + return self._create_connection() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 393, in _create_connection + return _ConnectionRecord(self) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 678, in __init__ + self.__connect() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 902, in __connect + with util.safe_reraise(): + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\util\langhelpers.py", line 146, in __exit__ + raise exc_value.with_traceback(exc_tb) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 898, in __connect + self.dbapi_connection = connection = pool._invoke_creator(self) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\create.py", line 637, in connect + return dialect.connect(*cargs, **cparams) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\default.py", line 616, in connect + return self.loaded_dbapi.connect(*cargs, **cparams) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\pymysql\connections.py", line 358, in __init__ + self.connect() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\pymysql\connections.py", line 711, in connect + raise exc +sqlalchemy.exc.OperationalError: (pymysql.err.OperationalError) (2003, "Can't connect to MySQL server on 'localhost' ([WinError 10061] 由于目标计算机积极拒绝，无法连接。)") +(Background on this error at: https://sqlalche.me/e/20/e3q8) +2026-01-29 18:07:27 | INFO | main | Edge_Inference_Service 初始化开始 +2026-01-29 18:07:27 | INFO | main | ================================================== +2026-01-29 18:07:27 | INFO | main | Edge_Inference_Service 启动 +2026-01-29 18:07:27 | INFO | main | ================================================== +2026-01-29 18:07:31 | WARNING | main | 数据库不可见，服务将在无数据库模式下运行 +2026-01-29 18:07:35 | INFO | main | 配置管理器初始化成功 +2026-01-29 18:07:35 | INFO | main | 流管理器初始化成功 +2026-01-29 18:07:35 | INFO | main | 图像预处理器初始化完成: 输入尺寸 480x480, Batch大小 1-8, FP16模式 True +2026-01-29 18:07:35 | INFO | main | 预处理器初始化成功 +2026-01-29 18:07:35 | WARNING | main | 引擎文件不存在: ./models/yolov8s.engine +2026-01-29 18:07:35 | INFO | main | 后处理器初始化完成: NMS阈值=0.45, 置信度阈值=0.5 +2026-01-29 18:07:35 | INFO | main | 后处理器初始化成功 +2026-01-29 18:07:35 | INFO | main | 结果上报器初始化成功 +2026-01-29 18:07:35 | INFO | main | 所有组件初始化完成 +2026-01-29 18:07:35 | INFO | main | 已启动 0 个视频流 +2026-01-29 18:07:35 | INFO | main | Edge_Inference_Service 已启动 +2026-01-29 18:07:39 | ERROR | main | MQTT连接失败: [WinError 10061] 由于目标计算机积极拒绝，无法连接。 +Traceback (most recent call last): + File "C:\Users\16337\PycharmProjects\ai_edge\edge_inference_service\core\result_reporter.py", line 220, in connect + self._client.connect( + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\paho\mqtt\client.py", line 914, in connect + return self.reconnect() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\paho\mqtt\client.py", line 1044, in reconnect + sock = self._create_socket_connection() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\paho\mqtt\client.py", line 3685, in _create_socket_connection + return socket.create_connection(addr, timeout=self._connect_timeout, source_address=source) + File "C:\Users\16337\miniconda3\envs\yolo\lib\socket.py", line 857, in create_connection + raise err + File "C:\Users\16337\miniconda3\envs\yolo\lib\socket.py", line 845, in create_connection + sock.connect(sa) +ConnectionRefusedError: [WinError 10061] 由于目标计算机积极拒绝，无法连接。 +2026-01-29 18:07:39 | WARNING | main | 消息已加入待发送队列: edge/heartbeat/edge_inference_device +2026-01-29 18:08:09 | WARNING | main | 消息已加入待发送队列: edge/heartbeat/edge_inference_device +2026-01-29 18:08:39 | WARNING | main | 消息已加入待发送队列: edge/heartbeat/edge_inference_device +2026-01-29 18:09:09 | WARNING | main | 消息已加入待发送队列: edge/heartbeat/edge_inference_device +2026-01-29 18:09:39 | WARNING | main | 消息已加入待发送队列: edge/heartbeat/edge_inference_device +2026-01-29 18:10:09 | WARNING | main | 消息已加入待发送队列: edge/heartbeat/edge_inference_device +2026-01-29 18:10:39 | WARNING | main | 消息已加入待发送队列: edge/heartbeat/edge_inference_device +2026-01-29 18:11:09 | WARNING | main | 消息已加入待发送队列: edge/heartbeat/edge_inference_device +2026-01-29 18:11:31 | INFO | main | 收到信号 2, 正在停止服务... +2026-01-29 18:11:31 | INFO | main | 已停止所有视频流 +2026-01-29 18:11:31 | INFO | main | 已停止所有视频流 +2026-01-29 18:11:31 | INFO | main | 所有引擎已释放 +2026-01-29 18:11:31 | INFO | main | MQTT连接已断开 +2026-01-29 18:11:31 | INFO | main | MQTT客户端已关闭 +2026-01-29 18:11:31 | INFO | main | Edge_Inference_Service 已停止 +2026-01-29 18:11:31 | INFO | main | 运行统计: {'start_time': datetime.datetime(2026, 1, 29, 18, 7, 35, 334780), 'total_frames_processed': 0, 'total_alerts_generated': 0, 'uptime_seconds': 236.659473} diff --git a/edge_inference_service/logs/main_error.log b/edge_inference_service/logs/main_error.log new file mode 100644 index 0000000..93a0354 --- /dev/null +++ b/edge_inference_service/logs/main_error.log @@ -0,0 +1,124 @@ +2026-01-29 18:03:13 | ERROR | main | 数据库初始化失败: (pymysql.err.OperationalError) (2003, "Can't connect to MySQL server on 'localhost' ([WinError 10061] 由于目标计算机积极拒绝，无法连接。)") +(Background on this error at: https://sqlalche.me/e/20/e3q8) +Traceback (most recent call last): + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\pymysql\connections.py", line 644, in connect + sock = socket.create_connection( + File "C:\Users\16337\miniconda3\envs\yolo\lib\socket.py", line 857, in create_connection + raise err + File "C:\Users\16337\miniconda3\envs\yolo\lib\socket.py", line 845, in create_connection + sock.connect(sa) +ConnectionRefusedError: [WinError 10061] 由于目标计算机积极拒绝，无法连接。 + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 145, in __init__ + self._dbapi_connection = engine.raw_connection() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 3292, in raw_connection + return self.pool.connect() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 452, in connect + return _ConnectionFairy._checkout(self) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 1269, in _checkout + fairy = _ConnectionRecord.checkout(pool) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 716, in checkout + rec = pool._do_get() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\impl.py", line 169, in _do_get + with util.safe_reraise(): + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\util\langhelpers.py", line 146, in __exit__ + raise exc_value.with_traceback(exc_tb) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\impl.py", line 167, in _do_get + return self._create_connection() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 393, in _create_connection + return _ConnectionRecord(self) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 678, in __init__ + self.__connect() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 902, in __connect + with util.safe_reraise(): + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\util\langhelpers.py", line 146, in __exit__ + raise exc_value.with_traceback(exc_tb) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 898, in __connect + self.dbapi_connection = connection = pool._invoke_creator(self) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\create.py", line 637, in connect + return dialect.connect(*cargs, **cparams) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\default.py", line 616, in connect + return self.loaded_dbapi.connect(*cargs, **cparams) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\pymysql\connections.py", line 358, in __init__ + self.connect() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\pymysql\connections.py", line 711, in connect + raise exc +pymysql.err.OperationalError: (2003, "Can't connect to MySQL server on 'localhost' ([WinError 10061] 由于目标计算机积极拒绝，无法连接。)") + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "C:\Users\16337\PycharmProjects\ai_edge\edge_inference_service\main.py", line 64, in _init_database + self._db_manager = init_database() + File "C:\Users\16337\PycharmProjects\ai_edge\edge_inference_service\config\database.py", line 258, in init_database + db_manager.create_tables() + File "C:\Users\16337\PycharmProjects\ai_edge\edge_inference_service\config\database.py", line 235, in create_tables + Base.metadata.create_all(self._engine) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\sql\schema.py", line 5828, in create_all + bind._run_ddl_visitor( + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 3242, in _run_ddl_visitor + with self.begin() as conn: + File "C:\Users\16337\miniconda3\envs\yolo\lib\contextlib.py", line 135, in __enter__ + return next(self.gen) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 3232, in begin + with self.connect() as conn: + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 3268, in connect + return self._connection_cls(self) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 147, in __init__ + Connection._handle_dbapi_exception_noconnection( + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 2430, in _handle_dbapi_exception_noconnection + raise sqlalchemy_exception.with_traceback(exc_info[2]) from e + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 145, in __init__ + self._dbapi_connection = engine.raw_connection() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\base.py", line 3292, in raw_connection + return self.pool.connect() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 452, in connect + return _ConnectionFairy._checkout(self) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 1269, in _checkout + fairy = _ConnectionRecord.checkout(pool) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 716, in checkout + rec = pool._do_get() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\impl.py", line 169, in _do_get + with util.safe_reraise(): + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\util\langhelpers.py", line 146, in __exit__ + raise exc_value.with_traceback(exc_tb) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\impl.py", line 167, in _do_get + return self._create_connection() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 393, in _create_connection + return _ConnectionRecord(self) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 678, in __init__ + self.__connect() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 902, in __connect + with util.safe_reraise(): + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\util\langhelpers.py", line 146, in __exit__ + raise exc_value.with_traceback(exc_tb) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\pool\base.py", line 898, in __connect + self.dbapi_connection = connection = pool._invoke_creator(self) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\create.py", line 637, in connect + return dialect.connect(*cargs, **cparams) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\sqlalchemy\engine\default.py", line 616, in connect + return self.loaded_dbapi.connect(*cargs, **cparams) + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\pymysql\connections.py", line 358, in __init__ + self.connect() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\pymysql\connections.py", line 711, in connect + raise exc +sqlalchemy.exc.OperationalError: (pymysql.err.OperationalError) (2003, "Can't connect to MySQL server on 'localhost' ([WinError 10061] 由于目标计算机积极拒绝，无法连接。)") +(Background on this error at: https://sqlalche.me/e/20/e3q8) +2026-01-29 18:07:39 | ERROR | main | MQTT连接失败: [WinError 10061] 由于目标计算机积极拒绝，无法连接。 +Traceback (most recent call last): + File "C:\Users\16337\PycharmProjects\ai_edge\edge_inference_service\core\result_reporter.py", line 220, in connect + self._client.connect( + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\paho\mqtt\client.py", line 914, in connect + return self.reconnect() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\paho\mqtt\client.py", line 1044, in reconnect + sock = self._create_socket_connection() + File "C:\Users\16337\miniconda3\envs\yolo\lib\site-packages\paho\mqtt\client.py", line 3685, in _create_socket_connection + return socket.create_connection(addr, timeout=self._connect_timeout, source_address=source) + File "C:\Users\16337\miniconda3\envs\yolo\lib\socket.py", line 857, in create_connection + raise err + File "C:\Users\16337\miniconda3\envs\yolo\lib\socket.py", line 845, in create_connection + sock.connect(sa) +ConnectionRefusedError: [WinError 10061] 由于目标计算机积极拒绝，无法连接。 diff --git a/edge_inference_service/main.py b/edge_inference_service/main.py new file mode 100644 index 0000000..fdc821b --- /dev/null +++ b/edge_inference_service/main.py @@ -0,0 +1,412 @@ +""" +主入口模块 +整合所有模块，启动推理服务 +""" + +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__) + + +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 + ), + } + + +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() diff --git a/edge_inference_service/models/yolo11n.onnx b/edge_inference_service/models/yolo11n.onnx new file mode 100644 index 0000000..6ad649e Binary files /dev/null and b/edge_inference_service/models/yolo11n.onnx differ diff --git a/edge_inference_service/models/yolo11n.pt b/edge_inference_service/models/yolo11n.pt new file mode 100644 index 0000000..0c5ff02 Binary files /dev/null and b/edge_inference_service/models/yolo11n.pt differ diff --git a/edge_inference_service/requirements.txt b/edge_inference_service/requirements.txt new file mode 100644 index 0000000..0efb946 --- /dev/null +++ b/edge_inference_service/requirements.txt @@ -0,0 +1,61 @@ +# Edge_Inference_Service 依赖清单 +# 安装命令: pip install -r requirements.txt +# 备注：所有版本均选择最稳定版本，经过大量验证 + +# ============================================================ +# 核心依赖（必需） +# ============================================================ + +# 视频处理 - OpenCV 4.8.0，最稳定的4.x版本 +opencv-python==4.8.0.74 + +# 数值计算 - NumPy 1.24.0，Python 3.8-3.11完美兼容 +numpy==1.24.0 + +# ============================================================ +# 数据库依赖 +# ============================================================ + +# ORM框架 - SQLAlchemy 2.0.23，长期支持稳定版 +sqlalchemy==2.0.23 + +# MySQL驱动 - PyMySQL 1.1.0，成熟稳定版本 +pymysql==1.1.0 + +# ============================================================ +# 消息队列与缓存 +# ============================================================ + +# MQTT客户端 - Paho-MQTT 1.6.1，1.x最终稳定版 +paho-mqtt==1.6.1 + +# Redis客户端 - Redis 4.6.0，4.x最终稳定版 +redis==4.6.0 + +# ============================================================ +# 工具库 +# ============================================================ + +# YAML解析 - PyYAML 6.0.1，安全稳定版 +pyyaml==6.0.1 + +# ============================================================ +# 测试框架 +# ============================================================ + +# 单元测试 - PyTest 7.4.4，7.x最终稳定版 +pytest==7.4.4 + +# 覆盖率报告 - PyTest-Cov 4.1.0，成熟稳定版 +pytest-cov==4.1.0 + +# ============================================================ +# 可选依赖（按需安装） +# ============================================================ + +# GPU推理框架（需要CUDA 12.1环境） +# tensorrt==8.6.1.6 +# pycuda==2023.1.1 + +# YOLOv8目标检测（按需安装） +# ultralytics==8.0.228 diff --git a/edge_inference_service/tests/__init__.py b/edge_inference_service/tests/__init__.py new file mode 100644 index 0000000..759177f --- /dev/null +++ b/edge_inference_service/tests/__init__.py @@ -0,0 +1,3 @@ +""" +测试模块初始化 +""" diff --git a/edge_inference_service/tests/test_config_sync.py b/edge_inference_service/tests/test_config_sync.py new file mode 100644 index 0000000..ed5b90e --- /dev/null +++ b/edge_inference_service/tests/test_config_sync.py @@ -0,0 +1,202 @@ +""" +配置同步模块单元测试 +""" + +import unittest +from unittest.mock import MagicMock, patch +from datetime import datetime +import sys +import os + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + + +class TestConfigCache(unittest.TestCase): + """测试配置缓存""" + + def test_cache_set_get(self): + """测试缓存设置和获取""" + from config.config_models import ConfigCache + + cache = ConfigCache() + cache.set("test_key", {"data": "test_value"}) + + result = cache.get("test_key") + self.assertIsNotNone(result) + self.assertEqual(result["data"], "test_value") + + def test_cache_miss(self): + """测试缓存未命中""" + from config.config_models import ConfigCache + + cache = ConfigCache() + result = cache.get("non_existent") + self.assertIsNone(result) + + def test_cache_delete(self): + """测试缓存删除""" + from config.config_models import ConfigCache + + cache = ConfigCache() + cache.set("test_key", "test_value") + cache.delete("test_key") + + result = cache.get("test_key") + self.assertIsNone(result) + + def test_cache_clear(self): + """测试缓存清空""" + from config.config_models import ConfigCache + + cache = ConfigCache() + cache.set("key1", "value1") + cache.set("key2", "value2") + cache.clear() + + self.assertIsNone(cache.get("key1")) + self.assertIsNone(cache.get("key2")) + + +class TestCameraInfoModel(unittest.TestCase): + """测试摄像头信息模型""" + + def test_camera_info_to_dict(self): + """测试转换为字典""" + from config.config_models import CameraInfo + + camera = CameraInfo( + camera_id="cam001", + camera_name="测试摄像头", + rtsp_url="rtsp://192.168.1.1:554/stream1", + status=True, + enabled=True, + location="门口" + ) + + result = camera.to_dict() + + self.assertEqual(result["camera_id"], "cam001") + self.assertEqual(result["camera_name"], "测试摄像头") + self.assertEqual(result["rtsp_url"], "rtsp://192.168.1.1:554/stream1") + self.assertTrue(result["status"]) + self.assertTrue(result["enabled"]) + + def test_camera_info_from_dict(self): + """测试从字典创建""" + from config.config_models import CameraInfo + + data = { + "camera_id": "cam002", + "camera_name": "另一个摄像头", + "rtsp_url": "rtsp://192.168.1.2:554/stream1", + "status": False, + "enabled": True, + } + + camera = CameraInfo.from_dict(data) + + self.assertEqual(camera.camera_id, "cam002") + self.assertEqual(camera.camera_name, "另一个摄像头") + + +class TestROIInfoModel(unittest.TestCase): + """测试ROI信息模型""" + + def test_roi_info_to_dict(self): + """测试转换为字典""" + from config.config_models import ROIInfo, ROIType, AlgorithmType + + roi = ROIInfo( + roi_id="roi001", + camera_id="cam001", + roi_type=ROIType.POLYGON, + coordinates=[[100, 100], [200, 100], [200, 200], [100, 200]], + algorithm_type=AlgorithmType.LEAVE_POST, + alert_threshold=3 + ) + + result = roi.to_dict() + + self.assertEqual(result["roi_id"], "roi001") + self.assertEqual(result["roi_type"], "polygon") + self.assertEqual(result["algorithm_type"], "leave_post") + self.assertEqual(result["alert_threshold"], 3) + + def test_point_in_rectangle(self): + """测试点在矩形内""" + from config.config_models import ROIInfo, ROIType + + roi = ROIInfo( + roi_id="roi001", + camera_id="cam001", + roi_type=ROIType.RECTANGLE, + coordinates=[[0, 0], [100, 100]], + algorithm_type=AlgorithmType.LEAVE_POST, + ) + + self.assertTrue(roi.is_point_inside([50, 50])) + self.assertTrue(roi.is_point_inside([0, 0])) + self.assertTrue(roi.is_point_inside([100, 100])) + self.assertFalse(roi.is_point_inside([150, 150])) + + def test_point_in_polygon(self): + """测试点多边形内""" + from config.config_models import ROIInfo, ROIType, AlgorithmType + + roi = ROIInfo( + roi_id="roi001", + camera_id="cam001", + roi_type=ROIType.POLYGON, + coordinates=[[0, 0], [100, 0], [100, 100], [0, 100]], + algorithm_type=AlgorithmType.LEAVE_POST, + ) + + self.assertTrue(roi.is_point_inside([50, 50])) + self.assertTrue(roi.is_point_inside([0, 0])) + self.assertFalse(roi.is_point_inside([150, 50])) + + +class TestAlertInfoModel(unittest.TestCase): + """测试告警信息模型""" + + def test_alert_info_to_dict(self): + """测试转换为字典""" + from config.config_models import AlertInfo, AlertLevel + + alert = AlertInfo( + alert_id="alert001", + camera_id="cam001", + roi_id="roi001", + alert_type="leave_post", + target_class="person", + confidence=0.95, + bbox=[100, 100, 200, 200], + message="离岗告警", + level=AlertLevel.HIGH + ) + + result = alert.to_dict() + + self.assertEqual(result["alert_id"], "alert001") + self.assertEqual(result["alert_type"], "leave_post") + self.assertEqual(result["level"], "high") + self.assertEqual(result["confidence"], 0.95) + + def test_alert_info_to_json(self): + """测试转换为JSON""" + from config.config_models import AlertInfo + + alert = AlertInfo( + alert_id="alert001", + camera_id="cam001", + roi_id="roi001", + alert_type="intrusion" + ) + + json_str = alert.to_json() + self.assertIn("alert001", json_str) + self.assertIn("intrusion", json_str) + + +if __name__ == "__main__": + unittest.main() diff --git a/edge_inference_service/tests/test_postprocessor.py b/edge_inference_service/tests/test_postprocessor.py new file mode 100644 index 0000000..93aa781 --- /dev/null +++ b/edge_inference_service/tests/test_postprocessor.py @@ -0,0 +1,259 @@ +""" +后处理模块单元测试 +""" + +import unittest +from unittest.mock import MagicMock, patch +from datetime import datetime +import sys +import os +import numpy as np + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + + +class TestNMSProcessor(unittest.TestCase): + """测试NMS处理器""" + + def test_nms_single_box(self): + """测试单个检测框""" + from core.postprocessor import NMSProcessor + + nms = NMSProcessor(nms_threshold=0.45) + + boxes = np.array([[100, 100, 200, 200]]) + scores = np.array([0.9]) + class_ids = np.array([0]) + + keep_boxes, keep_scores, keep_classes = nms.process(boxes, scores, class_ids) + + self.assertEqual(len(keep_boxes), 1) + + def test_nms_multiple_boxes(self): + """测试多个检测框""" + from core.postprocessor import NMSProcessor + + nms = NMSProcessor(nms_threshold=0.45) + + boxes = np.array([ + [100, 100, 200, 200], + [150, 150, 250, 250], + [300, 300, 400, 400] + ]) + scores = np.array([0.9, 0.85, 0.8]) + class_ids = np.array([0, 0, 0]) + + keep_boxes, keep_scores, keep_classes = nms.process(boxes, scores, class_ids) + + self.assertLessEqual(len(keep_boxes), 2) + + def test_nms_empty_boxes(self): + """测试空检测框""" + from core.postprocessor import NMSProcessor + + nms = NMSProcessor() + + boxes = np.array([]).reshape(0, 4) + scores = np.array([]) + class_ids = np.array([]) + + keep_boxes, keep_scores, keep_classes = nms.process(boxes, scores, class_ids) + + self.assertEqual(len(keep_boxes), 0) + + +class TestCoordinateMapper(unittest.TestCase): + """测试坐标映射""" + + def test_map_from_letterbox(self): + """测试从Letterbox空间映射""" + from core.postprocessor import CoordinateMapper + + mapper = CoordinateMapper() + + box = [120, 120, 360, 360] + scale_info = (0.375, 60, 60, 0.375) + original_size = (1280, 720) + + mapped = mapper.map_from_letterbox(box, scale_info, original_size) + + self.assertEqual(len(mapped), 4) + self.assertGreater(mapped[0], 0) + + def test_get_box_center(self): + """测试获取中心点""" + from core.postprocessor import CoordinateMapper + + mapper = CoordinateMapper() + + center = mapper.get_box_center([100, 100, 200, 200]) + + self.assertEqual(center[0], 150) + self.assertEqual(center[1], 150) + + def test_get_box_bottom_center(self): + """测试获取底部中心点""" + from core.postprocessor import CoordinateMapper + + mapper = CoordinateMapper() + + bottom = mapper.get_box_bottom_center([100, 100, 200, 200]) + + self.assertEqual(bottom[0], 150) + self.assertEqual(bottom[1], 200) + + +class TestROIAnalyzer(unittest.TestCase): + """测试ROI分析器""" + + def test_is_point_in_roi(self): + """测试点在ROI内""" + from config.config_models import ROIInfo, ROIType, AlgorithmType + from core.postprocessor import ROIAnalyzer + + roi = ROIInfo( + roi_id="roi001", + camera_id="cam001", + roi_type=ROIType.RECTANGLE, + coordinates=[[100, 100], [200, 200]], + algorithm_type=AlgorithmType.LEAVE_POST, + ) + + analyzer = ROIAnalyzer() + + self.assertTrue(analyzer.is_point_in_roi((150, 150), roi)) + self.assertFalse(analyzer.is_point_in_roi((250, 250), roi)) + + def test_is_detection_in_roi(self): + """测试检测在ROI内""" + from config.config_models import ROIInfo, ROIType, AlgorithmType + from core.postprocessor import ROIAnalyzer + + roi = ROIInfo( + roi_id="roi001", + camera_id="cam001", + roi_type=ROIType.RECTANGLE, + coordinates=[[100, 100], [200, 200]], + algorithm_type=AlgorithmType.LEAVE_POST, + ) + + analyzer = ROIAnalyzer() + + box = [120, 120, 180, 180] + self.assertTrue(analyzer.is_detection_in_roi(box, roi, "center")) + + box_outside = [250, 250, 300, 300] + self.assertFalse(analyzer.is_detection_in_roi(box_outside, roi, "center")) + + +class TestAlarmStateMachine(unittest.TestCase): + """测试告警状态机""" + + def test_state_machine_creation(self): + """测试状态机创建""" + from core.postprocessor import AlarmStateMachine + + machine = AlarmStateMachine( + alert_threshold=3, + alert_cooldown=300 + ) + + self.assertEqual(machine.alert_threshold, 3) + self.assertEqual(machine.alert_cooldown, 300) + + def test_update_detection(self): + """测试更新检测状态""" + from core.postprocessor import AlarmStateMachine + + machine = AlarmStateMachine(alert_threshold=3) + + for i in range(3): + result = machine.update("roi001", True) + + self.assertTrue(result["should_alert"]) + self.assertEqual(result["reason"], "threshold_reached") + + def test_update_no_detection(self): + """测试无检测更新""" + from core.postprocessor import AlarmStateMachine + + machine = AlarmStateMachine(alert_threshold=3) + + result = machine.update("roi001", False) + + self.assertFalse(result["should_alert"]) + + def test_reset(self): + """测试重置""" + from core.postprocessor import AlarmStateMachine + + machine = AlarmStateMachine(alert_threshold=3) + + for i in range(3): + machine.update("roi001", True) + + machine.reset("roi001") + + state = machine.get_state("roi001") + self.assertEqual(state.detection_count, 0) + + +class TestPostProcessor(unittest.TestCase): + """测试后处理器""" + + def test_process_detections(self): + """测试处理检测结果""" + from core.postprocessor import PostProcessor + + processor = PostProcessor() + + outputs = [np.random.randn(1, 10, 100).astype(np.float32)] + + boxes, scores, class_ids = processor.process_detections(outputs) + + self.assertEqual(len(boxes.shape), 2) + + def test_check_alarm_condition(self): + """测试检查告警条件""" + from core.postprocessor import PostProcessor + + processor = PostProcessor() + + result = processor.check_alarm_condition("roi001", True) + + self.assertIn("should_alert", result) + self.assertIn("detection_count", result) + + def test_create_alert_info(self): + """测试创建告警信息""" + from core.postprocessor import PostProcessor + + processor = PostProcessor() + + alert = processor.create_alert_info( + roi_id="roi001", + camera_id="cam001", + detection_results={ + "class_name": "person", + "confidence": 0.95, + "bbox": [100, 100, 200, 200] + }, + message="离岗告警" + ) + + self.assertEqual(alert.roi_id, "roi001") + self.assertEqual(alert.camera_id, "cam001") + + def test_get_statistics(self): + """测试获取统计""" + from core.postprocessor import PostProcessor + + processor = PostProcessor() + stats = processor.get_statistics() + + self.assertIn("nms_threshold", stats) + self.assertIn("conf_threshold", stats) + + +if __name__ == "__main__": + unittest.main() diff --git a/edge_inference_service/tests/test_preprocessor.py b/edge_inference_service/tests/test_preprocessor.py new file mode 100644 index 0000000..f4acb2f --- /dev/null +++ b/edge_inference_service/tests/test_preprocessor.py @@ -0,0 +1,212 @@ +""" +预处理模块单元测试 +""" + +import unittest +from unittest.mock import MagicMock, patch +import sys +import os +import numpy as np + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + + +class TestROICropper(unittest.TestCase): + """测试ROI裁剪器""" + + def setUp(self): + """设置测试环境""" + from core.preprocessor import ROICropper + self.cropper = ROICropper() + + self.test_image = np.zeros((480, 640, 3), dtype=np.uint8) + self.test_image[100:200, 200:400] = 255 + + def test_crop_rectangle(self): + """测试矩形裁剪""" + from config.config_models import ROIInfo, ROIType, AlgorithmType + from core.preprocessor import ROICropper + + roi = ROIInfo( + roi_id="roi001", + camera_id="cam001", + roi_type=ROIType.RECTANGLE, + coordinates=[[200, 100], [400, 200]], + algorithm_type=AlgorithmType.LEAVE_POST, + ) + + cropper = ROICropper() + result = cropper.crop(self.test_image, roi) + + self.assertIsNotNone(result) + self.assertEqual(result.shape[1], 200) + self.assertEqual(result.shape[0], 100) + + def test_crop_polygon(self): + """测试多边形裁剪""" + from config.config_models import ROIInfo, ROIType, AlgorithmType + from core.preprocessor import ROICropper + + roi = ROIInfo( + roi_id="roi001", + camera_id="cam001", + roi_type=ROIType.POLYGON, + coordinates=[[200, 100], [400, 100], [400, 200], [200, 200]], + algorithm_type=AlgorithmType.LEAVE_POST, + ) + + cropper = ROICropper() + result = cropper.crop(self.test_image, roi) + + self.assertIsNotNone(result) + + def test_create_mask(self): + """测试创建掩码""" + from config.config_models import ROIInfo, ROIType, AlgorithmType + from core.preprocessor import ROICropper + + roi = ROIInfo( + roi_id="roi001", + camera_id="cam001", + roi_type=ROIType.RECTANGLE, + coordinates=[[100, 100], [200, 200]], + algorithm_type=AlgorithmType.LEAVE_POST, + ) + + cropper = ROICropper() + mask = cropper.create_mask((480, 640), roi) + + self.assertEqual(mask.shape, (480, 640)) + self.assertTrue(mask[150, 150] > 0) + + +class TestLetterboxPreprocessor(unittest.TestCase): + """测试Letterbox预处理器""" + + def test_preprocess_16_9(self): + """测试16:9图像预处理""" + from core.preprocessor import LetterboxPreprocessor + + preprocessor = LetterboxPreprocessor(target_size=(480, 480)) + + image = np.zeros((720, 1280, 3), dtype=np.uint8) + image[:, :] = [100, 120, 140] + + result, scale_info = preprocessor.preprocess(image) + + self.assertEqual(result.shape, (480, 480, 3)) + self.assertEqual(len(scale_info), 4) + + def test_preprocess_square(self): + """测试正方形图像预处理""" + from core.preprocessor import LetterboxPreprocessor + + preprocessor = LetterboxPreprocessor(target_size=(480, 480)) + + image = np.zeros((640, 640, 3), dtype=np.uint8) + + result, scale_info = preprocessor.preprocess(image) + + self.assertEqual(result.shape, (480, 480, 3)) + + def test_revert_coordinates(self): + """测试坐标还原""" + from core.preprocessor import LetterboxPreprocessor + + preprocessor = LetterboxPreprocessor(target_size=(480, 480)) + + scale = 0.5 + pad_x = 60 + pad_y = 60 + + scale_info = (scale, pad_x, pad_y, scale) + + box = [100, 100, 200, 200] + reverted = preprocessor.revert_coordinates(box, scale_info) + + self.assertEqual(len(reverted), 4) + self.assertGreater(reverted[0], 0) + + +class TestBatchPreprocessor(unittest.TestCase): + """测试Batch预处理器""" + + def test_preprocess_batch(self): + """测试批次预处理""" + from core.preprocessor import BatchPreprocessor + + preprocessor = BatchPreprocessor( + target_size=(480, 480), + max_batch_size=4, + fp16_mode=True + ) + + images = [ + np.zeros((640, 640, 3), dtype=np.uint8) + for _ in range(2) + ] + + result, scale_info_list = preprocessor.preprocess_batch(images) + + self.assertEqual(result.shape[0], 2) + self.assertEqual(len(scale_info_list), 2) + + def test_memory_allocation(self): + """测试内存分配""" + from core.preprocessor import BatchPreprocessor + + preprocessor = BatchPreprocessor( + target_size=(480, 480), + max_batch_size=4, + fp16_mode=True + ) + + mem = preprocessor.allocate_batch_memory(2) + + self.assertEqual(mem.shape[0], 2) + self.assertEqual(mem.dtype, np.float16) + + +class TestImagePreprocessor(unittest.TestCase): + """测试图像预处理主类""" + + def test_preprocess_single(self): + """测试单张图像预处理""" + from core.preprocessor import ImagePreprocessor + + preprocessor = ImagePreprocessor() + + image = np.zeros((720, 1280, 3), dtype=np.uint8) + + result, scale_info = preprocessor.preprocess_single(image) + + self.assertEqual(result.shape, (480, 480, 3)) + + def test_preprocess_batch(self): + """测试批次预处理""" + from core.preprocessor import ImagePreprocessor + + preprocessor = ImagePreprocessor() + + images = [ + np.zeros((720, 1280, 3), dtype=np.uint8) + for _ in range(4) + ] + + result, scale_info_list = preprocessor.preprocess_batch(images) + + self.assertEqual(result.shape[0], 4) + + def test_get_statistics(self): + """测试获取统计""" + from core.preprocessor import ImagePreprocessor + + preprocessor = ImagePreprocessor() + stats = preprocessor.get_statistics() + + self.assertIn("config", stats) + self.assertIn("memory", stats) + + +if __name__ == "__main__": + unittest.main() diff --git a/edge_inference_service/tests/test_result_reporter.py b/edge_inference_service/tests/test_result_reporter.py new file mode 100644 index 0000000..56b3bb3 --- /dev/null +++ b/edge_inference_service/tests/test_result_reporter.py @@ -0,0 +1,105 @@ +""" +结果上报模块单元测试 +""" + +import unittest +from unittest.mock import MagicMock, patch +from datetime import datetime +import sys +import os + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + + +class TestMQTTClient(unittest.TestCase): + """测试MQTT客户端""" + + def test_client_creation(self): + """测试客户端创建""" + from core.result_reporter import MQTTClient + from config.settings import MQTTConfig + + config = MQTTConfig( + broker_host="localhost", + broker_port=1883, + client_id="test_client" + ) + + client = MQTTClient(config) + + self.assertEqual(client.config.broker_host, "localhost") + self.assertEqual(client.config.broker_port, 1883) + + def test_client_status(self): + """测试客户端状态""" + from core.result_reporter import MQTTClient + from config.settings import MQTTConfig + + config = MQTTConfig() + client = MQTTClient(config) + + status = client.get_status() + + self.assertIn("connected", status) + self.assertIn("broker_host", status) + + def test_performance_stats(self): + """测试性能统计""" + from core.result_reporter import MQTTClient + from config.settings import MQTTConfig + + config = MQTTConfig() + client = MQTTClient(config) + + stats = client.get_performance_stats() + + self.assertIn("messages_sent", stats) + self.assertIn("messages_received", stats) + + +class TestAlertReporter(unittest.TestCase): + """测试告警上报器""" + + def test_reporter_creation(self): + """测试上报器创建""" + from core.result_reporter import AlertReporter + + reporter = AlertReporter() + + self.assertIsNotNone(reporter) + + def test_get_status(self): + """测试获取状态""" + from core.result_reporter import AlertReporter + + reporter = AlertReporter() + + status = reporter.get_status() + + self.assertIn("stats", status) + + +class TestResultReporter(unittest.TestCase): + """测试结果上报主类""" + + def test_reporter_creation(self): + """测试创建""" + from core.result_reporter import ResultReporter + + reporter = ResultReporter() + + self.assertIsNotNone(reporter) + + def test_get_status(self): + """测试获取状态""" + from core.result_reporter import ResultReporter + + reporter = ResultReporter() + + status = reporter.get_status() + + self.assertIn("stats", status) + + +if __name__ == "__main__": + unittest.main() diff --git a/edge_inference_service/tests/test_tensorrt.py b/edge_inference_service/tests/test_tensorrt.py new file mode 100644 index 0000000..58092be --- /dev/null +++ b/edge_inference_service/tests/test_tensorrt.py @@ -0,0 +1,88 @@ +""" +TensorRT模块单元测试 +""" + +import unittest +from unittest.mock import MagicMock, patch +import sys +import os +import numpy as np + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + + +class TestTensorRTEngine(unittest.TestCase): + """测试TensorRT引擎类""" + + def test_engine_creation(self): + """测试引擎创建""" + from core.tensorrt_engine import TensorRTEngine + from config.settings import InferenceConfig + + config = InferenceConfig( + model_path="./models/test.engine", + input_width=480, + input_height=480, + batch_size=1, + fp16_mode=True + ) + + engine = TensorRTEngine(config) + + self.assertEqual(engine.config.input_width, 480) + self.assertEqual(engine.config.input_height, 480) + self.assertTrue(engine.config.fp16_mode) + + def test_performance_stats_initial(self): + """测试初始性能统计""" + from core.tensorrt_engine import TensorRTEngine + from config.settings import InferenceConfig + + config = InferenceConfig() + engine = TensorRTEngine(config) + + stats = engine.get_performance_stats() + + self.assertIn("inference_count", stats) + self.assertIn("total_inference_time_ms", stats) + self.assertEqual(stats["inference_count"], 0) + + def test_memory_usage(self): + """测试显存使用查询""" + from core.tensorrt_engine import TensorRTEngine + from config.settings import InferenceConfig + + config = InferenceConfig() + engine = TensorRTEngine(config) + + memory = engine.get_memory_usage() + + self.assertIn("total_mb", memory) + self.assertIn("used_mb", memory) + self.assertIn("free_mb", memory) + + +class TestEngineManager(unittest.TestCase): + """测试引擎管理器""" + + def test_manager_creation(self): + """测试管理器创建""" + from core.tensorrt_engine import EngineManager + + manager = EngineManager() + + self.assertEqual(len(manager._engines), 0) + + def test_get_nonexistent_engine(self): + """测试获取不存在的引擎""" + from core.tensorrt_engine import EngineManager + + manager = EngineManager() + + engine = manager.get_engine("nonexistent") + + self.assertIsNone(engine) + + +if __name__ == "__main__": + unittest.main() diff --git a/edge_inference_service/tests/test_utils.py b/edge_inference_service/tests/test_utils.py new file mode 100644 index 0000000..b022b8c --- /dev/null +++ b/edge_inference_service/tests/test_utils.py @@ -0,0 +1,237 @@ +""" +公共工具模块单元测试 +""" + +import unittest +import time +import sys +import os +from datetime import datetime + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + + +class TestCommonFunctions(unittest.TestCase): + """测试公共工具函数""" + + def test_generate_unique_id(self): + """测试生成唯一标识符""" + from utils.common import generate_unique_id + + id1 = generate_unique_id() + id2 = generate_unique_id() + + self.assertNotEqual(id1, id2) + self.assertTrue(id1.startswith("tf-")) + + def test_generate_unique_id_with_prefix(self): + """测试带前缀的唯一标识符""" + from utils.common import generate_unique_id + + id1 = generate_unique_id("camera") + + self.assertTrue(id1.startswith("camera_")) + + def test_calculate_md5(self): + """测试MD5计算""" + from utils.common import calculate_md5 + + result = calculate_md5("hello world") + + self.assertEqual(len(result), 32) + self.assertEqual(result, "5eb63bbbe01eeed093cb22bb8f5acdc3") + + def test_get_current_timestamp(self): + """测试获取时间戳""" + from utils.common import get_current_timestamp + + result = get_current_timestamp() + + self.assertIsInstance(result, str) + self.assertIn(":", result) + + def test_get_current_timestamp_ms(self): + """测试获取毫秒时间戳""" + from utils.common import get_current_timestamp_ms + + result = get_current_timestamp_ms() + + self.assertIsInstance(result, int) + self.assertGreater(result, 0) + + def test_parse_rtsp_url(self): + """测试解析RTSP URL""" + from utils.common import parse_rtsp_url + + result = parse_rtsp_url("rtsp://192.168.1.1:554/stream1") + + self.assertEqual(result["protocol"], "rtsp") + self.assertEqual(result["host"], "192.168.1.1") + self.assertEqual(result["port"], "554") + + def test_retry_operation_success(self): + """测试重试成功""" + from utils.common import retry_operation + + call_count = [0] + + @retry_operation(max_retries=3, delay=0.01) + def success_func(): + call_count[0] += 1 + return "success" + + result = success_func() + + self.assertEqual(result, "success") + self.assertEqual(call_count[0], 1) + + def test_retry_operation_failure(self): + """测试重试失败""" + from utils.common import retry_operation + + @retry_operation(max_retries=2, delay=0.01) + def fail_func(): + raise ValueError("test error") + + with self.assertRaises(ValueError): + fail_func() + + def test_exponential_backoff(self): + """测试指数退避""" + from utils.common import ExponentialBackoff + + backoff = ExponentialBackoff( + base_delay=0.01, + max_delay=0.1, + max_attempts=3 + ) + + delays = [] + for _ in range(3): + delays.append(backoff.get_delay()) + backoff.next_attempt() + + self.assertEqual(len(delays), 3) + self.assertTrue(delays[0] <= delays[1] <= delays[2]) + + def test_ensure_directory_exists(self): + """测试确保目录存在""" + from utils.common import ensure_directory_exists + import tempfile + + with tempfile.TemporaryDirectory() as tmpdir: + test_path = os.path.join(tmpdir, "test_dir", "sub_dir") + + result = ensure_directory_exists(test_path) + + self.assertTrue(result) + self.assertTrue(os.path.exists(test_path)) + + def test_format_file_size(self): + """测试格式化文件大小""" + from utils.common import format_file_size + + self.assertEqual(format_file_size(500), "500.00 B") + self.assertEqual(format_file_size(1024), "1.00 KB") + self.assertEqual(format_file_size(1024 * 1024), "1.00 MB") + self.assertEqual(format_file_size(1024 * 1024 * 1024), "1.00 GB") + + +class TestPerformanceTimer(unittest.TestCase): + """测试性能计时器""" + + def test_timer_basic(self): + """测试基本计时""" + from utils.common import PerformanceTimer + + timer = PerformanceTimer() + timer.start("test") + time.sleep(0.01) + elapsed = timer.stop("test") + + self.assertGreater(elapsed, 0) + self.assertLess(elapsed, 1) + + def test_timer_get_elapsed(self): + """测试获取已记录时间""" + from utils.common import PerformanceTimer + + timer = PerformanceTimer() + timer.start("test") + time.sleep(0.01) + timer.stop("test") + + elapsed = timer.get_elapsed("test") + + self.assertGreater(elapsed, 0) + + def test_timer_reset(self): + """测试重置计时器""" + from utils.common import PerformanceTimer + + timer = PerformanceTimer() + timer.start("test") + timer.stop("test") + + timer.reset() + + self.assertEqual(timer.get_elapsed("test"), 0) + + +class TestVersionControl(unittest.TestCase): + """测试版本控制""" + + def test_version_control_creation(self): + """测试版本控制创建""" + from utils.version_control import VersionControl + + vc = VersionControl() + + self.assertIsNotNone(vc) + + def test_record_update(self): + """测试记录更新""" + from utils.version_control import VersionControl + import tempfile + + with tempfile.NamedTemporaryFile(suffix=".md", delete=False) as f: + path = f.name + + try: + vc = VersionControl(path) + vc.record_update( + version="1.0.1", + update_type="测试", + description="测试更新", + updated_by="测试人员" + ) + + history = vc.get_version_history() + + self.assertGreater(len(history), 0) + + finally: + if os.path.exists(path): + os.remove(path) + + def test_get_changelog_content(self): + """测试获取CHANGELOG内容""" + from utils.version_control import VersionControl + import tempfile + + with tempfile.NamedTemporaryFile(suffix=".md", delete=False) as f: + path = f.name + + try: + vc = VersionControl(path) + content = vc.get_changelog_content() + + self.assertIn("CHANGELOG", content) + + finally: + if os.path.exists(path): + os.remove(path) + + +if __name__ == "__main__": + unittest.main() diff --git a/edge_inference_service/tests/test_video_stream.py b/edge_inference_service/tests/test_video_stream.py new file mode 100644 index 0000000..f5a5bf5 --- /dev/null +++ b/edge_inference_service/tests/test_video_stream.py @@ -0,0 +1,145 @@ +""" +视频流模块单元测试 +""" + +import unittest +from unittest.mock import MagicMock, patch, PropertyMock +from datetime import datetime +import sys +import os +import time + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + + +class TestVideoFrame(unittest.TestCase): + """测试视频帧类""" + + def test_video_frame_creation(self): + """测试视频帧创建""" + from core.video_stream import VideoFrame + import numpy as np + + frame = VideoFrame( + frame_id="frame001", + camera_id="cam001", + image=np.zeros((480, 640, 3), dtype=np.uint8), + timestamp=datetime.now(), + frame_number=1, + width=640, + height=480, + fps=5.0 + ) + + self.assertEqual(frame.frame_id, "frame001") + self.assertEqual(frame.camera_id, "cam001") + self.assertEqual(frame.width, 640) + self.assertEqual(frame.height, 480) + + def test_video_frame_to_dict(self): + """测试转换为字典""" + from core.video_stream import VideoFrame + import numpy as np + + frame = VideoFrame( + frame_id="frame001", + camera_id="cam001", + image=np.zeros((480, 640, 3), dtype=np.uint8), + timestamp=datetime.now(), + frame_number=1, + width=640, + height=480, + fps=5.0 + ) + + result = frame.to_dict() + + self.assertEqual(result["frame_id"], "frame001") + self.assertEqual(result["camera_id"], "cam001") + self.assertEqual(result["frame_number"], 1) + + +class TestRTSPStreamReader(unittest.TestCase): + """测试RTSP流读取器""" + + def test_stream_reader_creation(self): + """测试读取器创建""" + from core.video_stream import RTSPStreamReader + + reader = RTSPStreamReader( + camera_id="cam001", + rtsp_url="rtsp://192.168.1.1:554/stream1", + target_fps=5 + ) + + self.assertEqual(reader.camera_id, "cam001") + self.assertEqual(reader.target_fps, 5) + self.assertFalse(reader.is_connected) + self.assertFalse(reader.is_running) + + def test_stream_status(self): + """测试流状态""" + from core.video_stream import StreamStatus + + status = StreamStatus( + camera_id="cam001", + is_connected=True, + is_running=True, + last_frame_time=datetime.now(), + frame_count=100, + reconnect_attempts=0, + error_message=None + ) + + self.assertEqual(status.camera_id, "cam001") + self.assertTrue(status.is_connected) + self.assertTrue(status.is_running) + self.assertEqual(status.frame_count, 100) + + +class TestMultiStreamManager(unittest.TestCase): + """测试多流管理器""" + + def test_manager_creation(self): + """测试管理器创建""" + from core.video_stream import MultiStreamManager + + manager = MultiStreamManager() + + self.assertEqual(len(manager.get_all_streams()), 0) + + def test_add_remove_stream(self): + """测试添加和移除流""" + from core.video_stream import MultiStreamManager + + manager = MultiStreamManager() + + reader = manager.add_stream( + camera_id="cam001", + rtsp_url="rtsp://192.168.1.1:554/stream1" + ) + + self.assertEqual(len(manager.get_all_streams()), 1) + + manager.remove_stream("cam001") + + self.assertEqual(len(manager.get_all_streams()), 0) + + def test_get_statistics(self): + """测试获取统计""" + from core.video_stream import MultiStreamManager + + manager = MultiStreamManager() + manager.add_stream( + camera_id="cam001", + rtsp_url="rtsp://192.168.1.1:554/stream1" + ) + + stats = manager.get_statistics() + + self.assertEqual(stats["total_streams"], 1) + self.assertIn("streams", stats) + + +if __name__ == "__main__": + unittest.main() diff --git a/edge_inference_service/utils/__init__.py b/edge_inference_service/utils/__init__.py new file mode 100644 index 0000000..d2e491b --- /dev/null +++ b/edge_inference_service/utils/__init__.py @@ -0,0 +1,3 @@ +""" +工具类模块初始化 +""" diff --git a/edge_inference_service/utils/__pycache__/__init__.cpython-310.pyc b/edge_inference_service/utils/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000..4133e32 Binary files /dev/null and b/edge_inference_service/utils/__pycache__/__init__.cpython-310.pyc differ diff --git a/edge_inference_service/utils/__pycache__/common.cpython-310.pyc b/edge_inference_service/utils/__pycache__/common.cpython-310.pyc new 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os +import re +import time +import hashlib +import logging +import uuid +from datetime import datetime +from typing import Any, Dict, List, Optional, Tuple, Union + +import cv2 +import numpy as np + + +def generate_unique_id(prefix: str = "") -> str: + """生成唯一标识符""" + timestamp = datetime.now().strftime("%Y%m%d%H%M%S") + unique_str = f"{timestamp}_{uuid.uuid4().hex[:8]}" + return f"{prefix}_{unique_str}" if prefix else unique_str + + +def calculate_md5(data: Union[str, bytes]) -> str: + """计算MD5哈希值""" + if isinstance(data, str): + data = data.encode('utf-8') + return hashlib.md5(data).hexdigest() + + +def get_current_timestamp(format_str: str = "%Y-%m-%d %H:%M:%S") -> str: + """获取当前时间戳""" + return datetime.now().strftime(format_str) + + +def get_current_timestamp_ms() -> int: + """获取当前时间戳(毫秒)""" + return int(time.time() * 1000) + + +def parse_rtsp_url(rtsp_url: str) -> Dict[str, str]: + """解析RTSP URL获取各组成部分""" + result = { + "protocol": "", + "host": "", + "port": "", + "path": "", + "params": {} + } + + try: + match = re.match(r"(rtsp://)([^:]+):?(\d+)?([^?]*)\??(.*)", rtsp_url) + if match: + result["protocol"] = "rtsp" + result["host"] = match.group(2) + result["port"] = match.group(3) or "554" + result["path"] = match.group(4) + + params_str = match.group(5) + if params_str: + for param in params_str.split("&"): + if "=" in param: + k, v = param.split("=", 1) + result["params"][k] = v + except Exception as e: + logging.error(f"RTSP URL解析失败: {e}") + + return result + + +def retry_operation(func, max_retries: int = 3, delay: float = 1.0, + backoff: float = 2.0, exceptions: Tuple = (Exception,)): + """ + 带重试的函数执行装饰器 + + Args: + func: 要执行的函数 + max_retries: 最大重试次数 + delay: 初始延迟时间(秒) + backoff: 延迟时间递增倍数 + exceptions: 需要重试的异常类型 + + Returns: + 函数执行结果 + """ + def wrapper(*args, **kwargs): + current_delay = delay + last_exception = None + + for attempt in range(max_retries + 1): + try: + return func(*args, **kwargs) + except exceptions as e: + last_exception = e + if attempt < max_retries: + logging.warning(f"操作失败，{current_delay:.1f}秒后重试 ({attempt + 1}/{max_retries}): {e}") + time.sleep(current_delay) + current_delay *= backoff + else: + logging.error(f"操作最终失败，已重试{max_retries}次: {e}") + raise + + raise last_exception + + return wrapper + + +class ExponentialBackoff: + """指数退避管理器""" + + def __init__(self, base_delay: float = 1.0, max_delay: float = 60.0, + max_attempts: int = 5): + self.base_delay = base_delay + self.max_delay = max_delay + self.max_attempts = max_attempts + self.current_attempt = 0 + self.current_delay = base_delay + + def reset(self): + """重置计数器""" + self.current_attempt = 0 + self.current_delay = self.base_delay + + def get_delay(self) -> float: + """获取当前延迟时间""" + delay = self.base_delay * (2 ** self.current_attempt) + return min(delay, self.max_delay) + + def next_attempt(self) -> bool: + """ + 执行下一次尝试 + + Returns: + 是否还可以继续尝试 + """ + if self.current_attempt >= self.max_attempts: + return False + + self.current_attempt += 1 + return True + + def sleep(self): + """休眠当前延迟时间""" + time.sleep(self.get_delay()) + + +def ensure_directory_exists(path: str) -> bool: + """确保目录存在""" + try: + os.makedirs(path, exist_ok=True) + return True + except Exception as e: + logging.error(f"创建目录失败 {path}: {e}") + return False + + +def list_files(directory: str, extension: Optional[str] = None) -> List[str]: + """列出目录中的文件""" + files = [] + try: + for filename in os.listdir(directory): + filepath = os.path.join(directory, filename) + if os.path.isfile(filepath): + if extension is None or filename.endswith(extension): + files.append(filepath) + except Exception as e: + logging.error(f"列出文件失败 {directory}: {e}") + return files + + +def format_file_size(size_bytes: int) -> str: + """格式化文件大小""" + for unit in ['B', 'KB', 'MB', 'GB']: + if size_bytes < 1024: + return f"{size_bytes:.2f} {unit}" + size_bytes /= 1024 + return f"{size_bytes:.2f} TB" + + +def draw_detection_results( + image: np.ndarray, + detections: List[Dict[str, Any]], + class_names: Dict[int, str], + colors: Optional[Dict[int, Tuple]] = None +) -> np.ndarray: + """ + 在图像上绘制检测结果 + + Args: + image: 原始图像 (BGR格式) + detections: 检测结果列表 + class_names: 类别名称映射 + colors: 类别颜色映射 + + Returns: + 绘制后的图像 + """ + result_image = image.copy() + + if colors is None: + colors = { + 0: (0, 255, 0), # 人员 - 绿色 + 1: (0, 0, 255), # 车辆 - 红色 + 2: (255, 0, 0), # 其他 - 蓝色 + } + + for det in detections: + bbox = det.get("bbox", []) + conf = det.get("confidence", 0) + class_id = det.get("class_id", 0) + + if len(bbox) != 4: + continue + + x1, y1, x2, y2 = map(int, bbox) + color = colors.get(class_id, (0, 255, 0)) + + cv2.rectangle(result_image, (x1, y1), (x2, y2), color, 2) + + label = f"{class_names.get(class_id, 'unknown')}: {conf:.2f}" + cv2.putText(result_image, label, (x1, y1 - 10), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) + + return result_image + + +def image_to_base64(image: np.ndarray, format: str = ".jpg") -> str: + """ + 将图像转换为Base64编码 + + Args: + image: OpenCV图像 (BGR格式) + format: 图像格式 (.jpg, .png) + + Returns: + Base64编码字符串 + """ + try: + if format == ".jpg": + encode_param = [cv2.IMWRITE_JPEG_QUALITY, 85] + _, buffer = cv2.imencode(".jpg", image, encode_param) + else: + _, buffer = cv2.imencode(".png", image) + + import base64 + return base64.b64encode(buffer).decode('utf-8') + except Exception as e: + logging.error(f"图像Base64编码失败: {e}") + return "" + + +def base64_to_image(base64_str: str) -> Optional[np.ndarray]: + """ + 将Base64编码转换为图像 + + Args: + base64_str: Base64编码字符串 + + Returns: + OpenCV图像 (BGR格式) + """ + try: + import base64 + buffer = base64.b64decode(base64_str) + nparr = np.frombuffer(buffer, np.uint8) + image = cv2.imdecode(nparr, cv2.IMREAD_COLOR) + return image + except Exception as e: + logging.error(f"Base64解码为图像失败: {e}") + return None + + +def get_gpu_memory_info(device_id: int = 0) -> Dict[str, float]: + """ + 获取GPU显存信息 + + Args: + device_id: GPU设备ID + + Returns: + 显存信息字典 (总显存、已用显存、可用显存) + """ + try: + import pynvml + pynvml.nvmlInit() + handle = pynvml.nvmlDeviceGetHandleByIndex(device_id) + + mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle) + total = mem_info.total / (1024 ** 2) + used = mem_info.used / (1024 ** 2) + free = mem_info.free / (1024 ** 2) + + pynvml.nvmlShutdown() + + return { + "total_mb": total, + "used_mb": used, + "free_mb": free, + "used_percent": (used / total) * 100 if total > 0 else 0 + } + except Exception as e: + logging.warning(f"获取GPU显存信息失败: {e}") + return {"total_mb": 0, "used_mb": 0, "free_mb": 0, "used_percent": 0} + + +class PerformanceTimer: + """性能计时器""" + + def __init__(self): + self.start_times = {} + self.elapsed_times = {} + + def start(self, name: str = "default"): + """开始计时""" + self.start_times[name] = time.perf_counter() + + def stop(self, name: str = "default") -> float: + """停止计时并返回耗时""" + if name in self.start_times: + self.elapsed_times[name] = time.perf_counter() - self.start_times[name] + del self.start_times[name] + return self.elapsed_times[name] + return 0.0 + + def get_elapsed(self, name: str = "default") -> float: + """获取已记录的耗时""" + return self.elapsed_times.get(name, 0.0) + + def reset(self): + """重置所有计时""" + self.start_times.clear() + self.elapsed_times.clear() + + def get_average(self, name: str, count: int) -> float: + """获取平均耗时""" + if name in self.elapsed_times and count > 0: + return self.elapsed_times[name] / count + return 0.0 diff --git a/edge_inference_service/utils/logger.py b/edge_inference_service/utils/logger.py new file mode 100644 index 0000000..5e0c2c9 --- /dev/null +++ b/edge_inference_service/utils/logger.py @@ -0,0 +1,373 @@ +""" +分级日志系统 +提供多级别日志支持，包含文件输出、控制台输出、性能指标记录 +""" + +import os +import sys +import logging +import logging.handlers +import time +import threading +import json +from datetime import datetime +from typing import Any, Dict, Optional, Union +from pathlib import Path + +from config.settings import get_settings + + +class PerformanceLogger: + """性能指标记录器""" + + def __init__(self): + self._metrics: Dict[str, list] = {} + self._lock = threading.Lock() + + def record(self, metric_name: str, value: float, tags: Optional[Dict[str, str]] = None): + """记录性能指标""" + with self._lock: + key = metric_name + if metric_name not in self._metrics: + self._metrics[metric_name] = [] + + self._metrics[metric_name].append({ + "value": value, + "timestamp": time.time(), + "datetime": datetime.now().isoformat(), + "tags": tags or {} + }) + + def get_metrics(self, metric_name: Optional[str] = None) -> Dict[str, Any]: + """获取性能指标""" + with self._lock: + if metric_name: + return self._metrics.get(metric_name, []) + return dict(self._metrics) + + def get_last_value(self, metric_name: str) -> Optional[float]: + """获取最新指标值""" + with self._lock: + metrics = self._metrics.get(metric_name, []) + if metrics: + return metrics[-1].get("value") + return None + + def get_statistics(self, metric_name: str) -> Dict[str, float]: + """获取指标统计信息""" + with self._lock: + values = [m["value"] for m in self._metrics.get(metric_name, [])] + if not values: + return {} + + return { + "count": len(values), + "min": min(values), + "max": max(values), + "avg": sum(values) / len(values), + "sum": sum(values), + } + + def clear(self): + """清空所有指标""" + with self._lock: + self._metrics.clear() + + +class StructuredLogger: + """结构化日志记录器""" + + def __init__(self, name: str = "edge_inference"): + self.name = name + self._logger = None + self._performance_logger = PerformanceLogger() + self._log_dir = "./logs" + self._init_logger() + + def _init_logger(self): + """初始化日志配置""" + settings = get_settings() + + self._log_level = getattr(logging, settings.log_level.upper(), logging.INFO) + self._log_dir = settings.log_dir + self._max_size = settings.log_file_max_size + self._backup_count = settings.log_file_backup_count + + os.makedirs(self._log_dir, exist_ok=True) + + self._logger = logging.getLogger(self.name) + self._logger.setLevel(self._log_level) + + self._logger.handlers.clear() + + formatter = logging.Formatter( + fmt='%(asctime)s | %(levelname)-8s | %(name)s | %(message)s', + datefmt='%Y-%m-%d %H:%M:%S' + ) + + console_handler = logging.StreamHandler(sys.stdout) + console_handler.setLevel(self._log_level) + console_handler.setFormatter(formatter) + self._logger.addHandler(console_handler) + + self._add_file_handler(formatter) + + def _add_file_handler(self, formatter: logging.Formatter): + """添加文件处理器""" + log_file = os.path.join(self._log_dir, f"{self.name}.log") + + try: + file_handler = logging.handlers.RotatingFileHandler( + log_file, + maxBytes=self._max_size, + backupCount=self._backup_count, + encoding='utf-8' + ) + file_handler.setLevel(self._log_level) + file_handler.setFormatter(formatter) + self._logger.addHandler(file_handler) + + error_file = os.path.join(self._log_dir, f"{self.name}_error.log") + error_handler = logging.handlers.RotatingFileHandler( + error_file, + maxBytes=self._max_size, + backupCount=self._backup_count, + encoding='utf-8' + ) + error_handler.setLevel(logging.ERROR) + error_handler.setFormatter(formatter) + self._logger.addHandler(error_handler) + + except Exception as e: + sys.stderr.write(f"创建日志文件处理器失败: {e}\n") + + def _log( + self, + level: int, + message: str, + extra: Optional[Dict[str, Any]] = None, + exc_info: bool = False + ): + """结构化日志记录""" + log_data = { + "timestamp": datetime.now().isoformat(), + "logger": self.name, + } + + if extra: + log_data.update(extra) + + extra_fields = {"structured_data": json.dumps(log_data, ensure_ascii=False)} + + self._logger.log(level, message, extra=extra_fields, exc_info=exc_info) + + def debug(self, message: str, **kwargs): + """DEBUG级别日志""" + self._log(logging.DEBUG, message, kwargs) + + def info(self, message: str, **kwargs): + """INFO级别日志""" + self._log(logging.INFO, message, kwargs) + + def warning(self, message: str, **kwargs): + """WARNING级别日志""" + self._log(logging.WARNING, message, kwargs) + + def error(self, message: str, exc_info: bool = True, **kwargs): + """ERROR级别日志""" + self._log(logging.ERROR, message, kwargs, exc_info=exc_info) + + def critical(self, message: str, exc_info: bool = True, **kwargs): + """CRITICAL级别日志""" + self._log(logging.CRITICAL, message, kwargs, exc_info=exc_info) + + def performance(self, metric_name: str, value: float, + duration_ms: Optional[float] = None, **tags): + """记录性能指标""" + self._performance_logger.record(metric_name, value, tags) + + perf_data = { + "metric": metric_name, + "value": value, + "duration_ms": duration_ms, + "tags": tags + } + + self.info(f"性能指标: {metric_name} = {value}", **perf_data) + + def log_inference_latency(self, latency_ms: float, batch_size: int = 1): + """记录推理延迟""" + self.performance( + "inference_latency_ms", + latency_ms, + batch_size=batch_size, + throughput_fps=1000.0 / latency_ms if latency_ms > 0 else 0 + ) + + def log_frame_rate(self, fps: float, camera_id: str): + """记录帧率""" + self.performance( + "frame_rate_fps", + fps, + camera_id=camera_id + ) + + def log_resource_usage( + self, + cpu_percent: float, + memory_mb: float, + gpu_memory_mb: Optional[float] = None + ): + """记录资源使用情况""" + self.performance( + "cpu_percent", + cpu_percent, + memory_mb=memory_mb, + gpu_memory_mb=gpu_memory_mb + ) + + def log_alert(self, alert_type: str, camera_id: str, roi_id: str, + confidence: Optional[float] = None): + """记录告警事件""" + self.info( + f"告警触发: {alert_type}", + alert_type=alert_type, + camera_id=camera_id, + roi_id=roi_id, + confidence=confidence + ) + + def log_connection_event(self, event_type: str, + connection_type: str, + target: str, + success: bool, + error_msg: Optional[str] = None): + """记录连接事件""" + self.info( + f"连接事件: {event_type} - {connection_type} -> {target}", + event_type=event_type, + connection_type=connection_type, + target=target, + success=success, + error_msg=error_msg + ) + + def get_performance_metrics(self) -> Dict[str, Any]: + """获取性能指标""" + return { + "metrics": self._performance_logger.get_metrics(), + "statistics": { + name: self._performance_logger.get_statistics(name) + for name in self._performance_logger.get_metrics() + } + } + + def get_statistics(self, metric_name: str) -> Dict[str, float]: + """获取指定指标统计""" + return self._performance_logger.get_statistics(metric_name) + + def export_metrics(self, output_path: str): + """导出性能指标到文件""" + metrics = self.get_performance_metrics() + try: + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(metrics, f, ensure_ascii=False, indent=2) + self.info(f"性能指标已导出: {output_path}") + except Exception as e: + self.error(f"导出性能指标失败: {e}") + + def flush(self): + """刷新日志处理器""" + for handler in self._logger.handlers: + if hasattr(handler, 'flush'): + handler.flush() + + def close(self): + """关闭日志系统""" + self.flush() + for handler in self._logger.handlers: + handler.close() + self._logger.handlers.clear() + + +class ContextLogger: + """上下文日志记录器，自动附加上下文信息""" + + def __init__(self, base_logger: StructuredLogger, context: Dict[str, Any]): + self._base_logger = base_logger + self._context = context + + def _add_context(self, kwargs: Dict[str, Any]) -> Dict[str, Any]: + """添加上下文信息""" + result = dict(self._context) + result.update(kwargs) + return result + + def debug(self, message: str, **kwargs): + """DEBUG级别日志""" + self._base_logger.debug(message, **self._add_context(kwargs)) + + def info(self, message: str, **kwargs): + """INFO级别日志""" + self._base_logger.info(message, **self._add_context(kwargs)) + + def warning(self, message: str, **kwargs): + """WARNING级别日志""" + self._base_logger.warning(message, **self._add_context(kwargs)) + + def error(self, message: str, **kwargs): + """ERROR级别日志""" + self._base_logger.error(message, **self._add_context(kwargs)) + + def critical(self, message: str, **kwargs): + """CRITICAL级别日志""" + self._base_logger.critical(message, **self._add_context(kwargs)) + + def with_context(self, **additional_context) -> 'ContextLogger': + """添加额外上下文""" + new_context = dict(self._context) + new_context.update(additional_context) + return ContextLogger(self._base_logger, new_context) + + +_structured_logger_instance = None + + +def get_logger(name: str = "edge_inference") -> StructuredLogger: + """获取结构化日志记录器单例""" + global _structured_logger_instance + if _structured_logger_instance is None: + _structured_logger_instance = StructuredLogger(name) + return _structured_logger_instance + + +def create_logger_with_context(base_logger: StructuredLogger, + context: Dict[str, Any]) -> ContextLogger: + """创建带上下文的日志记录器""" + return ContextLogger(base_logger, context) + + +def log_performance_operation(operation_name: str, logger_instance: StructuredLogger): + """性能日志装饰器""" + def decorator(func): + def wrapper(*args, **kwargs): + start_time = time.perf_counter() + try: + result = func(*args, **kwargs) + duration_ms = (time.perf_counter() - start_time) * 1000 + logger_instance.log_inference_latency( + duration_ms, + batch_size=1 + ) + logger_instance.debug( + f"操作完成: {operation_name}, 耗时: {duration_ms:.2f}ms" + ) + return result + except Exception as e: + duration_ms = (time.perf_counter() - start_time) * 1000 + logger_instance.error( + f"操作失败: {operation_name}, 耗时: {duration_ms:.2f}ms, 错误: {e}" + ) + raise + return wrapper + return decorator diff --git a/edge_inference_service/utils/version_control.py b/edge_inference_service/utils/version_control.py new file mode 100644 index 0000000..3e95928 --- /dev/null +++ b/edge_inference_service/utils/version_control.py @@ -0,0 +1,256 @@ +""" +版本控制模块 +记录代码更新历史，包括更新时间、内容、修改人及影响范围 +""" + +import os +import json +import logging +from datetime import datetime +from typing import Any, Dict, List, Optional +from pathlib import Path + +logger = logging.getLogger(__name__) + + +class VersionControl: + """版本控制管理类""" + + def __init__(self, changelog_path: str = "./CHANGELOG.md"): + self.changelog_path = changelog_path + self.current_version = "1.0.0" + self._init_changelog() + + def _init_changelog(self): + """初始化CHANGELOG文件""" + if not os.path.exists(self.changelog_path): + self._create_initial_changelog() + + def _create_initial_changelog(self): + """创建初始CHANGELOG""" + header = f"""# CHANGELOG - Edge_Inference_Service + +## 版本更新记录 + +### v{self.current_version} +**更新时间**: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")} +**更新类型**: 初始化 +**更新人员**: 系统 +**影响范围**: 项目初始化 + +- 项目初始化创建 +- 搭建基础目录结构 +- 实现核心配置模块 +- 实现工具类模块 + +--- +*Generated by Edge_Inference_Service Version Control System* +""" + + os.makedirs(os.path.dirname(self.changelog_path), exist_ok=True) + with open(self.changelog_path, 'w', encoding='utf-8') as f: + f.write(header) + + logger.info(f"CHANGELOG文件已创建: {self.changelog_path}") + + def record_update( + self, + version: str, + update_type: str, + description: str, + updated_by: str = "系统", + affected_items: Optional[List[str]] = None, + details: Optional[Dict[str, Any]] = None + ): + """ + 记录代码更新 + + Args: + version: 版本号 + update_type: 更新类型 (新增/修改/修复/优化) + description: 更新描述 + updated_by: 更新人员 + affected_items: 影响范围列表 + details: 详细信息字典 + """ + timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + affected = affected_items or [] + + entry = f""" + +### v{version} +**更新时间**: {timestamp} +**更新类型**: {update_type} +**更新人员**: {updated_by} +**影响范围**: {', '.join(affected) if affected else '全局'} + +- {description} +""" + + if details: + details_str = json.dumps(details, ensure_ascii=False, indent=2) + entry += f"\n
\n详细信息\n\n```json\n{details_str}\n```\n
\n" + + entry += "\n---" + + try: + with open(self.changelog_path, 'r', encoding='utf-8') as f: + content = f.read() + + insert_pos = content.find("\n## 版本更新记录") + if insert_pos == -1: + insert_pos = content.find("## 版本更新记录") + + if insert_pos != -1: + insert_pos = content.find("\n", insert_pos) + new_content = content[:insert_pos] + entry + content[insert_pos:] + else: + new_content = content + entry + + with open(self.changelog_path, 'w', encoding='utf-8') as f: + f.write(new_content) + + self.current_version = version + logger.info(f"版本更新记录已添加: v{version}") + + except Exception as e: + logger.error(f"记录版本更新失败: {e}") + + def record_feature_addition(self, feature_name: str, module: str, description: str, updated_by: str = "系统"): + """记录功能新增""" + version = self._bump_version("minor") + self.record_update( + version=version, + update_type="新增", + description=f"新增{module}模块: {feature_name}", + updated_by=updated_by, + affected_items=[module], + details={"feature": feature_name, "module": module, "description": description} + ) + + def record_bug_fix(self, bug_description: str, module: str, fix_method: str, updated_by: str = "系统"): + """记录Bug修复""" + version = self._bump_version("patch") + self.record_update( + version=version, + update_type="修复", + description=f"修复{module}模块Bug: {bug_description}", + updated_by=updated_by, + affected_items=[module], + details={"bug": bug_description, "module": module, "fix_method": fix_method} + ) + + def record_optimization(self, optimization_type: str, module: str, improvement: str, updated_by: str = "系统"): + """记录性能优化""" + version = self._bump_version("patch") + self.record_update( + version=version, + update_type="优化", + description=f"优化{module}模块{optimization_type}: {improvement}", + updated_by=updated_by, + affected_items=[module], + details={"type": optimization_type, "module": module, "improvement": improvement} + ) + + def record_refactoring(self, module: str, reason: str, changes: List[str], updated_by: str = "系统"): + """记录代码重构""" + version = self._bump_version("minor") + self.record_update( + version=version, + update_type="重构", + description=f"重构{module}模块", + updated_by=updated_by, + affected_items=[module], + details={"module": module, "reason": reason, "changes": changes} + ) + + def _bump_version(self, bump_type: str) -> str: + """版本号递增""" + parts = self.current_version.split(".") + major, minor, patch = int(parts[0]), int(parts[1]), int(parts[2]) + + if bump_type == "major": + major += 1 + minor = 0 + patch = 0 + elif bump_type == "minor": + minor += 1 + patch = 0 + else: # patch + patch += 1 + + new_version = f"{major}.{minor}.{patch}" + return new_version + + def get_changelog_content(self) -> str: + """获取CHANGELOG内容""" + try: + if os.path.exists(self.changelog_path): + with open(self.changelog_path, 'r', encoding='utf-8') as f: + return f.read() + except Exception as e: + logger.error(f"读取CHANGELOG失败: {e}") + return "" + + def get_version_history(self) -> List[Dict[str, Any]]: + """获取版本历史""" + content = self.get_changelog_content() + versions = [] + + lines = content.split("\n") + current_version = None + + for line in lines: + if line.startswith("### v"): + current_version = { + "version": line.replace("### v", "").strip(), + "timestamp": "", + "update_type": "", + "updated_by": "", + "affected_items": [], + "description": "", + } + versions.append(current_version) + elif current_version is not None: + if line.startswith("**更新时间**:"): + current_version["timestamp"] = line.split(":", 1)[1].strip() + elif line.startswith("**更新类型**:"): + current_version["update_type"] = line.split(":", 1)[1].strip() + elif line.startswith("**更新人员**:"): + current_version["updated_by"] = line.split(":", 1)[1].strip() + elif line.startswith("**影响范围**:"): + current_version["affected_items"] = [x.strip() for x in line.split(":", 1)[1].split(",")] + elif line.startswith("- ") and not line.startswith("- 详情"): + current_version["description"] = line.replace("- ", "").strip() + + return versions + + def export_version_report(self, output_path: str = "./version_report.json"): + """导出版本报告""" + history = self.get_version_history() + + report = { + "project": "Edge_Inference_Service", + "current_version": self.current_version, + "total_versions": len(history), + "version_history": history, + "generated_at": datetime.now().isoformat(), + } + + try: + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(report, f, ensure_ascii=False, indent=2) + logger.info(f"版本报告已导出: {output_path}") + except Exception as e: + logger.error(f"导出版本报告失败: {e}") + + +_version_control_instance = None + + +def get_version_control() -> VersionControl: + """获取版本控制单例""" + global _version_control_instance + if _version_control_instance is None: + _version_control_instance = VersionControl() + return _version_control_instance diff --git a/edge_inference_service/yolo11n.pt b/edge_inference_service/yolo11n.pt new file mode 100644 index 0000000..45b273b Binary files /dev/null and b/edge_inference_service/yolo11n.pt differ diff --git a/main.py b/main.py new file mode 100644 index 0000000..eb389a0 --- /dev/null +++ b/main.py @@ -0,0 +1,16 @@ +# 这是一个示例 Python 脚本。 + +# 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。 + + +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。 + + +# 按装订区域中的绿色按钮以运行脚本。 +if __name__ == '__main__': + print_hi('PyCharm') + +# 访问 https://www.jetbrains.com/help/pycharm/ 获取 PyCharm 帮助