Background Remover API

A powerful Python FastAPI application that intelligently removes image backgrounds using the state-of-the-art U²-Net deep learning model. The API processes images entirely in memory and returns clean results with white backgrounds in JPEG format.

✨ Features

• 🖼️ Smart Background Removal: Uses U²-Net for accurate human and object segmentation
• 🚀 Dual Interface: Upload images via web form or programmatic API
• ⚡ Memory Efficient: Processes images entirely in memory without temporary files
• 🎯 High Accuracy: Leverages deep learning for precise edge detection
• 📱 RESTful API: Easy integration with any application
• 🔄 Optional Preprocessing: Uses rembg for faster initial processing when available
• 🖱️ User-Friendly: Simple web interface for quick testing

🛠️ Technical Stack

• FastAPI: Modern, fast web framework
• U²-Net: Deep learning model for salient object detection
• PyTorch: Deep learning framework
• OpenCV: Computer vision operations
• Pillow: Image processing
• rembg: Optional preprocessing library

📋 Requirements

torch>=2.0.0
torchvision>=0.15.0
numpy>=1.24.0
Pillow>=9.5.0
opencv-python>=4.7.0
rembg>=2.0.50
fastapi>=0.104.0
uvicorn>=0.24.0
python-multipart>=0.0.6

🚀 Quick Start

1. Clone and Setup

git clone https://github.com/Team-Exopy/bgremover.git
cd bgremover

2. Create Virtual Environment

# Create virtual environment
python3 -m venv venv

# Activate virtual environment
# On Linux/macOS:
source venv/bin/activate

# On Windows:
venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Download U²-Net Model

Download the pre-trained U²-Net model and place it in the correct directory:

# Create model directory
mkdir -p saved_models/u2net

# Download the model (replace with actual download command or manual download)
# Download u2net-human-seg.pth from the official U²-Net repository
# Save it to: saved_models/u2net/u2net-human-seg.pth

Note: Download u2net-human-seg.pth from the official U²-Net repository and save it in saved_models/u2net/ directory.

5. Run the Application

python3 main.py

The API will be available at:

• Web Interface: http://localhost:8000
• API Documentation: http://localhost:8000/docs
• Alternative Docs: http://localhost:8000/redoc

📚 API Usage

Web Interface

1. Open your browser and go to http://localhost:8000
2. Upload an image using the web form
3. Click "Remove Background"
4. Download the processed image

Programmatic API

Upload Image

curl -X POST "http://localhost:8000/remove-bg" \
     -H "accept: image/jpeg" \
     -H "Content-Type: multipart/form-data" \
     -F "file=@your-image.jpg"

Python Example

import requests

url = "http://localhost:8000/remove-bg"
files = {"file": open("your-image.jpg", "rb")}

response = requests.post(url, files=files)

if response.status_code == 200:
    with open("output-image.jpg", "wb") as f:
        f.write(response.content)
    print("Background removed successfully!")
else:
    print(f"Error: {response.status_code}")

JavaScript Example

const formData = new FormData();
formData.append("file", fileInput.files[0]);

fetch("http://localhost:8000/remove-bg", {
  method: "POST",
  body: formData,
})
  .then((response) => response.blob())
  .then((blob) => {
    const url = URL.createObjectURL(blob);
    // Use the URL to display or download the image
  });

📁 Project Structure

background-remover-api/
├── main.py                 # FastAPI application
├── requirements.txt        # Python dependencies
├── README.md              # This file
├── saved_models/
│   └── u2net/
│       └── u2net-human-seg.pth  # U²-Net model file
├── templates/             # HTML templates (if any)
├── u2Net                   # u2net repo

⚙️ Configuration

The application can be configured by modifying the following parameters in main.py:

• Host: Default 0.0.0.0
• Port: Default 8000
• Model Path: saved_models/u2net/u2net-human-seg.pth
• Maximum File Size: Configure in FastAPI settings
• Allowed File Types: JPG, PNG, JPEG, etc.

🐳 Docker Support (Optional)

Create a Dockerfile for containerized deployment:

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

EXPOSE 8000

CMD ["python", "main.py"]

Build and run:

docker build -t background-remover .
docker run -p 8000:8000 background-remover

🔧 Troubleshooting

Common Issues

1. Model Not Found Error

   • Ensure u2net-human-seg.pth is in saved_models/u2net/ directory
   • Check file permissions

2. Memory Issues

   • Reduce image size before processing
   • Increase system RAM or use GPU acceleration

3. Slow Processing

   • Install rembg for faster preprocessing
   • Consider using GPU-enabled PyTorch

Performance Tips

• Use GPU acceleration if available
• Resize large images before processing
• Consider batch processing for multiple images
• Implement caching for frequently processed images

🤝 Contributing

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

📄 License

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

📖 Citation

If you use this project in your research, please cite the original U²-Net paper:

@InProceedings{Qin_2020_PR,
    title = {U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection},
    author = {Qin, Xuebin and Zhang, Zichen and Huang, Chenyang and Dehghan, Masood and Zaiane, Osmar and Jagersand, Martin},
    journal = {Pattern Recognition},
    volume = {106},
    pages = {107404},
    year = {2020}
}

🔗 Related Links

• U²-Net Official Repository
• FastAPI Documentation
• PyTorch Documentation
• rembg Library

📞 Support

If you encounter any issues or have questions:

1. Check the Issues section
2. Create a new issue with detailed description
3. Include error logs and system information

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Made with ❤️ using U²-Net and FastAPI