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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SkyLi
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# 项目根目录（修改为你的实际路径）
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PROJECT_ROOT=/path/to/Infrared-ai-inference-service
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__pycache__/
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*.py[cod]
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*.egg-info/
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dist/
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build/
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*.egg
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.pytest_cache/
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.ruff_cache/
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.mypy_cache/
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.tox/
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cov.xml
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*.cover
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*.log
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# Model weights
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weights/
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*.pt
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*.pth
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*.onnx
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*.ckpt
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# Lib (manually placed)
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lib/
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# Environment
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.python-version
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.venv/
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venv/
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.env
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.env.local
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# Lock files
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uv.lock
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# IDE
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.vscode/
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.idea/
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*.swp
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*.swo
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# OS
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.DS_Store
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Thumbs.db
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# Docker
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.dockerignore
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# Claude
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.claude/
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CLAUDE.md
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# Tests
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tests/
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FROM python:3.10-slim
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WORKDIR /app
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# 安装 uv
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RUN pip install uv
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# 复制本地 whl 文件（预下载）
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COPY lib/ /app/lib/
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# 复制依赖配置（Docker 专用）
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COPY pyproject.docker.toml /app/pyproject.toml
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# 安装依赖（包括 infrared-data 从本地 whl）
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RUN uv pip install --system -r /app/pyproject.toml
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# 复制源码
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COPY src/ ./src/
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# 复制预下载的权重文件
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COPY weights/ /app/weights/
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EXPOSE 8000
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CMD ["python", "-m", "uvicorn", "src.main:app", "--host", "0.0.0.0", "--port", "8000"]
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# 手动部署说明
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由于网络限制，权重文件和 whl 包需要手动放置到本地目录。
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## 必需文件
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### 1. whl 包
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- **文件名**: `infrared_data-0.1.0-py3-none-any.whl`
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- **存放路径**: `lib/infrared_data-0.1.0-py3-none-any.whl`
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- **下载地址**: 联系项目维护者获取
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### 2. 模型权重文件
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- **文件名**: `best.pt`
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- **存放路径**: `weights/best.pt`
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- **下载地址**: 联系项目维护者获取
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## 目录结构
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```
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.
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├── lib/
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│ └── infrared_data-0.1.0-py3-none-any.whl
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├── weights/
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│ └── best.pt
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├── src/
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│ ├── __init__.py
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│ ├── config.py
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│ └── main.py
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├── tests/
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├── pyproject.toml
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└── Dockerfile
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```
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## 安装步骤
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```bash
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# 1. 放置上述文件到对应目录
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# 2. 创建 .env 文件（包含项目路径）
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# 注意：此文件包含绝对路径，每个开发者需要根据实际路径修改
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cp .env.example .env # 或手动创建
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# 3. 创建虚拟环境并安装依赖
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set -a && source .env && uv sync
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# 4. 启动服务
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set -a && source .env && INFERENCE_DEVICE=cpu uv run uvicorn src.main:app --host 0.0.0.0 --port 8000
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```
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## 验证
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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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# 测试推理
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curl -X POST http://localhost:8000/infer -F "image=@your_image.jpg"
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```
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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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[project]
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name = "infrared-inference"
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version = "0.1.0"
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description = "HTTP microservice for infrared equipment fault detection"
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requires-python = ">=3.10"
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dependencies = [
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"fastapi>=0.115.0",
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"uvicorn[standard]>=0.30.0",
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"python-multipart>=0.0.12",
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"aiofiles>=24.0.0",
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"infrared-data @ file:///app/lib/infrared_data-0.1.0-py3-none-any.whl",
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]
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[project]
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name = "infrared-inference"
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version = "0.1.0"
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description = "HTTP microservice for infrared equipment fault detection"
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requires-python = ">=3.10"
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dependencies = [
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"fastapi>=0.115.0",
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"uvicorn[standard]>=0.30.0",
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"python-multipart>=0.0.12",
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"aiofiles>=24.0.0",
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"infrared-data @ file://${PROJECT_ROOT}/lib/infrared_data-0.1.0-py3-none-any.whl",
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]
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import os
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from pathlib import Path
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# 权重文件地址
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WEIGHTS_URL = "http://193.112.94.100:9000/SkyLiCoder/Infrared-ai-research-lab/releases/download/v0.1.0/best.pt"
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# 权重本地路径
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WEIGHTS_DIR = Path(__file__).parent.parent / "weights"
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WEIGHTS_PATH = WEIGHTS_DIR / "best.pt"
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# whl 文件地址
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WHL_URL = "http://193.112.94.100:9000/SkyLiCoder/Infrared-ai-research-lab/releases/download/v0.1.0/infrared_data-0.1.0-py3-none-any.whl"
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# whl 本地路径
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WHL_DIR = Path(__file__).parent.parent / "lib"
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WHL_PATH = WHL_DIR / "infrared_data-0.1.0-py3-none-any.whl"
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# 推理设备: "cuda:0" / "cpu"
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DEVICE = os.getenv("INFERENCE_DEVICE", "cpu")
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import os
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import io
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from pathlib import Path
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from typing import Optional
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import numpy as np
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from fastapi import FastAPI, UploadFile, File, HTTPException
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from PIL import Image
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import uvicorn
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from infrared_data import FaultDiagnoser
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from src.config import WEIGHTS_PATH, DEVICE
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app = FastAPI(title="Infrared Equipment Fault Detection API")
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# 全局诊断器实例
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_diagnoser: Optional[FaultDiagnoser] = None
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def get_diagnoser() -> FaultDiagnoser:
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global _diagnoser
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if _diagnoser is None:
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if not WEIGHTS_PATH.exists():
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raise RuntimeError(
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f"Weight file not found at {WEIGHTS_PATH}. "
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"Please run: python scripts/download_assets.py"
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)
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_diagnoser = FaultDiagnoser(str(WEIGHTS_PATH))
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return _diagnoser
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@app.post("/infer")
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async def infer(image: UploadFile = File(...)) -> dict:
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"""
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接收一张红外图片，返回故障诊断结果（JSON）。
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返回结构:
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{
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"image": str,
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"detections": [...],
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"faults": [...],
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"summary": {
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"status": str,
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"detection_count": int,
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"abnormal_count": int,
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"fault_types": [...]
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}
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}
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"""
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# 读取图片内容
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contents = await image.read()
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img = Image.open(io.BytesIO(contents)).convert("RGB")
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img_array = np.array(img)
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diagnoser = get_diagnoser()
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result = diagnoser.diagnose(img_array, device=DEVICE)
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return result
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@app.get("/health")
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async def health() -> dict:
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return {"status": "ok", "device": DEVICE}
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=8000)
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