README.md

---
license: mit
title: InspectechSegmentation
sdk: gradio
emoji: 📚
colorFrom: blue
---
# Binary Image Segmentation - FastAPI Service

Professional background removal service with web interface and REST API, ready for Hugging Face Spaces deployment.

## 🚀 Quick Start

### Local Development

```bash
# 1. Install dependencies
pip install -r requirements.txt

# 2. Download U2NETP model weights
mkdir -p .model_cache
wget https://github.com/xuebinqin/U-2-Net/raw/master/saved_models/u2netp/u2netp.pth -O .model_cache/u2netp.pth

# 3. Run the server
uvicorn app:app --host 0.0.0.0 --port 7860

# 4. Open browser
# Visit: http://localhost:7860
```

### Test the API

```bash
python test_api.py
```

## 📁 Project Structure

```
.
├── app.py                    # FastAPI application (main entry point)
├── binary_segmentation.py    # Core segmentation module
├── requirements.txt          # Python dependencies
├── Dockerfile               # Docker configuration for deployment
├── README_HF.md             # Hugging Face Space README
├── DEPLOYMENT.md            # Detailed deployment guide
├── client_examples.py       # API usage examples (Python, JS, curl)
├── test_api.py             # Test script
├── .gitignore              # Git ignore file
└── static/
    └── index.html          # Web interface
```

## 🎨 Features

### Web Interface
- Drag & drop image upload
- 3 AI model options (U2NETP, BiRefNet, RMBG)
- Adjustable threshold
- Multiple output formats (transparent PNG, binary mask, or both)
- Real-time preview
- Download results

### REST API
- **POST /segment** - Segment image → transparent PNG
- **POST /segment/mask** - Get binary mask only
- **POST /segment/base64** - Get base64 encoded results
- **POST /segment/batch** - Process multiple images
- **GET /models** - List available models
- **GET /health** - Health check

### Supported Models

| Model | Speed | Accuracy | Size | Best For |
|-------|-------|----------|------|----------|
| **U2NETP** | ⚡⚡⚡ | ⭐⭐ | 4.7 MB | Speed, simple objects |
| **BiRefNet** | ⚡ | ⭐⭐⭐ | ~400 MB | Best quality |
| **RMBG** | ⚡⚡ | ⭐⭐⭐ | ~200 MB | Balanced |

## 🔧 API Usage Examples

### Python

```python
import requests

# Segment image
with open('input.jpg', 'rb') as f:
    response = requests.post(
        'http://localhost:7860/segment',
        files={'file': f},
        data={'model': 'u2netp', 'threshold': 0.5}
    )

# Save result
with open('output.png', 'wb') as out:
    out.write(response.content)
```

### JavaScript

```javascript
async function removeBackground(file) {
    const formData = new FormData();
    formData.append('file', file);
    formData.append('model', 'u2netp');
    formData.append('threshold', '0.5');
    
    const response = await fetch('/segment', {
        method: 'POST',
        body: formData
    });
    
    const blob = await response.blob();
    return URL.createObjectURL(blob);
}
```

### cURL

```bash
curl -X POST "http://localhost:7860/segment" \
  -F "file=@input.jpg" \
  -F "model=u2netp" \
  -F "threshold=0.5" \
  --output result.png
```

See `client_examples.py` for more!

## 🌐 Deploy to Hugging Face Spaces

See `DEPLOYMENT.md` for complete guide!

## 📝 License

Apache 2.0

## 🙏 Credits

- U2-Net, BiRefNet, RMBG models
- FastAPI framework