SkyLi
356a308f9b
- 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
149 lines
2.9 KiB
Markdown
149 lines
2.9 KiB
Markdown
# Infrared Equipment Fault Detection Service
|
|
|
|
红外设备故障检测 HTTP 微服务，基于 `infrared-data` whl 包提供推理接口。
|
|
|
|
## 功能特性
|
|
|
|
- RESTful API 服务
|
|
- 支持红外图像故障诊断
|
|
- 返回检测框、温度特征、故障状态
|
|
- CPU/GPU 灵活切换
|
|
|
|
## 项目结构
|
|
|
|
```
|
|
.
|
|
├── lib/ # whl 包（需手动放置）
|
|
│ └── infrared_data-0.1.0-py3-none-any.whl
|
|
├── weights/ # 模型权重（需手动放置）
|
|
│ └── best.pt
|
|
├── src/
|
|
│ ├── __init__.py
|
|
│ ├── config.py # 配置
|
|
│ └── main.py # FastAPI 应用
|
|
├── tests/
|
|
│ └── valid/ # 测试图片和结果
|
|
├── pyproject.toml # 本地依赖配置
|
|
├── pyproject.docker.toml # Docker 依赖配置
|
|
├── Dockerfile # Docker 镜像
|
|
├── MANUAL_SETUP.md # 手动部署说明
|
|
└── README.md # 本文件
|
|
```
|
|
|
|
## 快速开始
|
|
|
|
### 1. 手动放置文件
|
|
|
|
在部署前，需要手动获取并放置以下文件：
|
|
|
|
| 文件 | 存放路径 |
|
|
|------|----------|
|
|
| whl 包 | `lib/infrared_data-0.1.0-py3-none-any.whl` |
|
|
| 模型权重 | `weights/best.pt` |
|
|
|
|
详细说明请参考 [MANUAL_SETUP.md](MANUAL_SETUP.md)。
|
|
|
|
### 2. 安装依赖
|
|
|
|
```bash
|
|
uv sync
|
|
```
|
|
|
|
### 3. 启动服务
|
|
|
|
```bash
|
|
# CPU 模式
|
|
INFERENCE_DEVICE=cpu uv run uvicorn src.main:app --host 0.0.0.0 --port 8000
|
|
|
|
# GPU 模式
|
|
INFERENCE_DEVICE=cuda:0 uv run uvicorn src.main:app --host 0.0.0.0 --port 8000
|
|
```
|
|
|
|
### 4. 测试
|
|
|
|
```bash
|
|
# 健康检查
|
|
curl http://localhost:8000/health
|
|
|
|
# 推理测试
|
|
curl -X POST http://localhost:8000/infer -F "image=@your_image.jpg"
|
|
```
|
|
|
|
## API 文档
|
|
|
|
启动服务后访问: http://localhost:8000/docs
|
|
|
|
### POST /infer
|
|
|
|
上传红外图像进行故障诊断。
|
|
|
|
**请求**
|
|
- Content-Type: `multipart/form-data`
|
|
- 字段: `image` (图片文件)
|
|
|
|
**响应**
|
|
```json
|
|
{
|
|
"image": "array",
|
|
"detections": [
|
|
{
|
|
"bbox": [x1, y1, x2, y2],
|
|
"bbox_xywh": [cx, cy, w, h],
|
|
"confidence": 0.94,
|
|
"class_id": 4,
|
|
"class_name": "conservator"
|
|
}
|
|
],
|
|
"faults": [
|
|
{
|
|
"temp_max": 255.0,
|
|
"temp_min": 0.0,
|
|
"temp_mean": 81.1,
|
|
"temp_std": 68.3,
|
|
"temp_median": 57.0,
|
|
"fault_status": "abnormal",
|
|
"fault_type": "hotspot"
|
|
}
|
|
],
|
|
"summary": {
|
|
"status": "fault_detected",
|
|
"detection_count": 3,
|
|
"abnormal_count": 3,
|
|
"fault_types": ["hotspot"]
|
|
}
|
|
}
|
|
```
|
|
|
|
### GET /health
|
|
|
|
健康检查接口。
|
|
|
|
**响应**
|
|
```json
|
|
{
|
|
"status": "ok",
|
|
"device": "cpu"
|
|
}
|
|
```
|
|
|
|
## Docker 部署
|
|
|
|
```bash
|
|
# 构建镜像
|
|
docker build -t infrared-inference .
|
|
|
|
# 运行容器
|
|
docker run -p 8000:8000 infrared-inference
|
|
```
|
|
|
|
## 故障类型
|
|
|
|
| 类型 | 说明 |
|
|
|------|------|
|
|
| `none` | 正常 |
|
|
| `hotspot` | 热点异常 |
|
|
| `local_overheating` | 局部过热 |
|
|
| `overheat` | 过热 |
|
|
| `suspect` | 可疑 |
|
|
|