Image Extender

A C++ application for image outpainting and enhancement using LibTorch and OpenCV with CUDA acceleration.

Features

• CUDA-accelerated image processing
• Deep learning-based image outpainting
• Seamless integration with LibTorch and OpenCV

Requirements

• CUDA-compatible GPU
• Docker (for containerized usage)
• CMake 3.18+ (for local development)

Quick Start

Using Docker

The easiest way to run Image Extender is via Docker:

docker pull ghcr.io/magyargergo/image-extender:latest
docker run --gpus all ghcr.io/magyargergo/image-extender:latest

Building Locally

1. Clone the repository:

   git clone https://github.com/magyargergo/ImageExtender.git
   cd ImageExtender

2. Install dependencies:

   • Install CUDA 12.x and cuDNN
   • Install vcpkg: https://github.com/microsoft/vcpkg

3. Build the project:

   mkdir build && cd build
   cmake .. -DCMAKE_TOOLCHAIN_FILE=[path/to/vcpkg]/scripts/buildsystems/vcpkg.cmake -DBUILD_WITH_CUDA=ON
   cmake --build . --config Release

Development

Project Structure

• src/ - Source code files
• include/ - Header files
• models/ - Pre-trained model files
• docker/ - Docker-related files

Dependencies

The project uses vcpkg for dependency management. Key dependencies include:

• LibTorch (PyTorch C++ API) 2.1.2
• OpenCV 4.11.0 with CUDA support
• nlohmann-json for JSON parsing
• Eigen3 for matrix operations

License

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

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

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