Initial commit: Infrared Equipment Fault Detection HTTP Service
- Add FastAPI microservice for infrared equipment fault detection - Add dual pyproject.toml: local development and Docker deployment - Add MANUAL_SETUP.md for manual file placement instructions - Add Dockerfile for containerized deployment - Add sample .env.example for PROJECT_ROOT configuration - Configure gitignore for weights, lib, venv, and environment files
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# Infrared Equipment Fault Detection Service
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红外设备故障检测 HTTP 微服务,基于 `infrared-data` whl 包提供推理接口。
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## 功能特性
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- RESTful API 服务
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- 支持红外图像故障诊断
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- 返回检测框、温度特征、故障状态
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- CPU/GPU 灵活切换
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## 项目结构
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```
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.
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├── lib/ # whl 包(需手动放置)
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│ └── infrared_data-0.1.0-py3-none-any.whl
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├── weights/ # 模型权重(需手动放置)
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│ └── best.pt
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├── src/
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│ ├── __init__.py
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│ ├── config.py # 配置
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│ └── main.py # FastAPI 应用
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├── tests/
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│ └── valid/ # 测试图片和结果
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├── pyproject.toml # 本地依赖配置
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├── pyproject.docker.toml # Docker 依赖配置
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├── Dockerfile # Docker 镜像
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├── MANUAL_SETUP.md # 手动部署说明
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└── README.md # 本文件
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```
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## 快速开始
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### 1. 手动放置文件
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在部署前,需要手动获取并放置以下文件:
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| 文件 | 存放路径 |
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|------|----------|
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| whl 包 | `lib/infrared_data-0.1.0-py3-none-any.whl` |
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| 模型权重 | `weights/best.pt` |
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详细说明请参考 [MANUAL_SETUP.md](MANUAL_SETUP.md)。
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### 2. 安装依赖
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```bash
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uv sync
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```
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### 3. 启动服务
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```bash
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# CPU 模式
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INFERENCE_DEVICE=cpu uv run uvicorn src.main:app --host 0.0.0.0 --port 8000
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# GPU 模式
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INFERENCE_DEVICE=cuda:0 uv run uvicorn src.main:app --host 0.0.0.0 --port 8000
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```
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### 4. 测试
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```bash
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# 健康检查
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curl http://localhost:8000/health
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# 推理测试
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curl -X POST http://localhost:8000/infer -F "image=@your_image.jpg"
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```
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## API 文档
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启动服务后访问: http://localhost:8000/docs
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### POST /infer
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上传红外图像进行故障诊断。
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**请求**
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- Content-Type: `multipart/form-data`
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- 字段: `image` (图片文件)
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**响应**
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```json
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{
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"image": "array",
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"detections": [
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{
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"bbox": [x1, y1, x2, y2],
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"bbox_xywh": [cx, cy, w, h],
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"confidence": 0.94,
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"class_id": 4,
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"class_name": "conservator"
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}
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],
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"faults": [
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{
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"temp_max": 255.0,
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"temp_min": 0.0,
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"temp_mean": 81.1,
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"temp_std": 68.3,
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"temp_median": 57.0,
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"fault_status": "abnormal",
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"fault_type": "hotspot"
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}
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],
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"summary": {
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"status": "fault_detected",
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"detection_count": 3,
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"abnormal_count": 3,
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"fault_types": ["hotspot"]
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}
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}
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```
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### GET /health
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健康检查接口。
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**响应**
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```json
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{
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"status": "ok",
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"device": "cpu"
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}
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```
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## Docker 部署
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```bash
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# 构建镜像
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docker build -t infrared-inference .
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# 运行容器
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docker run -p 8000:8000 infrared-inference
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```
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## 故障类型
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| 类型 | 说明 |
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|------|------|
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| `none` | 正常 |
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| `hotspot` | 热点异常 |
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| `local_overheating` | 局部过热 |
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| `overheat` | 过热 |
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| `suspect` | 可疑 |
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