Deep Illuminator

Deep Illuminator is a data augmentation tool designed for image relighting. It can be used to easily and efficiently generate a wide range of illumination variants of a single image. It has been tested with several datasets and models and has been shown to succesfully improve performance. It has a built in visualizer created with Streamlit to preview how the target image can be relit. This tool has an accompanying paper.

Example Augmentations

Usage

The simplest method to use this tool is through Docker Hub:

docker pull kartvel/deep-illuminator

Visualizer

Once you have the Deep Illuminator image run the following command to launch the visualizer:

docker run -it --rm  --gpus all \
-p 8501:8501 --entrypoint streamlit \ 
kartvel/deep-illuminator run streamlit/streamlit_app.py

You will be able to interact with it on localhost:8501. Note: If you do not have NVIDIA gpu support enabled for docker simply remove the --gpus all option.

Generating Variants

It is possible to quickly generate multiple variants for images contained in a directory by using the following command:

docker run -it --rm --gpus all \                                                                                               ─╯
-v /path/to/input/images:/app/probe_relighting/originals \
-v /path/to/save/directory:/app/probe_relighting/output \
kartvel/deep-illuminator --[options]

Options

Buidling Docker image or running without a container

Please read the following for other options: instructions

Benchmarks

Improved performance of R2D2 for [email protected] on HPatches

Improved performance of R2D2 for the Long-Term Visual Localization challenge on Aachen v1.1

Acknowledgment

The developpement of the VAE for the visualizer was made possible by the PyTorch-VAE repository.

Bibtex

If you use this code in your project, please consider citing the following paper:

@misc{chogovadze2021controllable,
      title={Controllable Data Augmentation Through Deep Relighting}, 
      author={George Chogovadze and Rémi Pautrat and Marc Pollefeys},
      year={2021},
      eprint={2110.13996},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}