rembg-cpu-rust

A fast background removal service written in Rust, using the U2Net model and ONNX Runtime for CPU inference.

Features

• Fast background removal using U2Net model
• CPU-based inference using ONNX Runtime
• RESTful API endpoint for image processing
• Docker support for easy deployment
• Handles images of any size while maintaining aspect ratio
• Returns PNG images with transparency

Prerequisites

• Rust 1.70 or higher
• Docker (optional, for containerized deployment)
• wget or curl (for downloading the model)

Quick Start

1. Clone the repository:

git clone https://github.com/yourusername/rembg-cpu-rust
cd rembg-cpu-rust

2. Download the U2Net model:

./scripts/download-model.sh

3. Build and run locally:

cargo run --release

The server will start at http://localhost:8000

Docker Deployment

Build and run using Docker:

docker build -t rembg-cpu-rust .
docker run -p 8000:8000 rembg-cpu-rust

API Usage

Remove background from an image:

curl -X POST -F "image=@/path/to/your/image.jpg" http://localhost:8000/api/rem-bg -o output.png

Or use the provided api.http file with REST Client extensions in VS Code/IntelliJ.

Configuration

• Default port: 8000
• Max file size: 10MB
• Model path: models/u2net.onnx

Project Structure

├── src/
│   ├── application/      # Application logic
│   ├── domain/          # Domain models and errors
│   ├── infrastructure/  # Server setup and configuration
│   └── presentation/    # API handlers
├── models/             # Model storage
├── scripts/           # Utility scripts
└── Dockerfile        # Docker configuration

Performance Considerations

• Images are processed at 320x320 resolution for inference
• Original image dimensions are preserved in the output
• CPU-optimized for broad compatibility

Error Handling

The service handles various error cases:

• Invalid image format
• Missing image file
• Model loading errors
• Processing errors

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• U2Net - Original U2Net model
• ONNX Runtime - Machine learning inference engine
• Axum - Web framework