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A minimal tool to quickly remove or replace backgrounds from images using AI. Fast, local, privacy-focused, and easy to use.

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akhilsahuji/backdrop-off

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Backdrop-Off - Bulk Background Remover

A simple and powerful desktop application for removing backgrounds from images with custom background color options.

Backdrop-Off Platform License

Features

  • πŸ–ΌοΈ Batch Processing: Process multiple images at once
  • 🎨 Custom Background Colors: Choose any background color using color picker or hex codes
  • πŸ“Š Progress Tracking: Real-time progress bar and image counter
  • πŸ“ Flexible Input: Browse folders or manually enter paths
  • πŸš€ Cross-Platform: Works on Windows, macOS, and Linux
  • πŸ“‚ Quick Access: Open output folder directly after processing
  • 🎯 User-Friendly: Clean and intuitive interface

Screenshots

image

Demo

backdrop-off.mp4

Installation

Prerequisites

  • Python 3.7 or higher
  • pip (Python package installer)

Method 1: Install from Source

  1. Clone the repository:

    git clone https://github.com/akhilsahuji/backdrop-off.git
    cd backdrop-off
  2. Install dependencies:

    pip install -r requirements.txt
  3. Run the application:

    python bg_remover_ui.py

Method 2: Download Executable

Pre-built executables for Windows, macOS, and Linux will be available in the Releases section.

Usage

  1. Select Input Folder: Choose the folder containing your images or type the path manually
  2. Select Output Folder: Choose where to save processed images or type the path manually
  3. Choose Background Color:
    • Use the color picker button, or
    • Enter a hex color code (e.g., #FF0000 for red)
  4. Start Processing: Click "Start Processing" and watch the progress
  5. View Results: Click "Open Output Folder" when processing is complete

Supported Image Formats

  • JPEG (.jpg, .jpeg)
  • PNG (.png)

Popular Background Colors

  • White: #FFFFFF
  • Black: #000000
  • Red: #FF0000
  • Green: #00FF00
  • Blue: #0000FF
  • Transparent backgrounds are converted to your chosen solid color

Development

Setting up Development Environment

  1. Clone the repository:

    git clone https://github.com/akhilsahuji/backdrop-off.git
    cd backdrop-off
  2. Create virtual environment (recommended):

    python -m venv venv
    
    # On Windows:
    venv\Scripts\activate
    
    # On macOS/Linux:
    source venv/bin/activate
  3. Install dependencies:

    pip install -r requirements.txt

Building Executables

To create standalone executables for distribution:

# Install PyInstaller
pip install pyinstaller

# Build executable
pyinstaller --onefile --windowed bg_remover_ui.py

The executable will be created in the dist/ folder.

Dependencies

  • customtkinter: Modern UI framework
  • rembg: AI-powered background removal
  • Pillow (PIL): Image processing
  • onnxruntime: AI model runtime

Contributing

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

Roadmap

  • Drag & drop support
  • Batch rename options
  • Custom output formats (PNG with transparency)
  • Image quality settings

Troubleshooting

Common Issues

"No module named 'customtkinter'"

pip install customtkinter

"No module named 'rembg'"

pip install rembg

Performance Issues

  • Processing time depends on image size and quantity
  • Ensure sufficient RAM (4GB+ recommended)
  • Close other heavy applications during processing

License

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

Acknowledgments

  • rembg - For the amazing background removal AI
  • CustomTkinter - For the modern UI framework

Support

If you encounter any issues or have questions:

  1. Check the Issues page
  2. Create a new issue with detailed description
  3. Include your OS, Python version, and error messages

⭐ If you find this project helpful, please give it a star! ⭐

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A minimal tool to quickly remove or replace backgrounds from images using AI. Fast, local, privacy-focused, and easy to use.

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