README.md

RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments

License + Attribution

This code is licensed under CC BY-NC-SA 4.0. Commercial usage is not permitted; please contact info@tobiasfischer.info or y.demiris@imperial.ac.uk regarding commercial licensing. If you use this dataset or the code in a scientific publication, please cite the following paper:

@inproceedings{FischerECCV2018,
author = {Tobias Fischer and Hyung Jin Chang and Yiannis Demiris},
title = {{RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments}},
booktitle = {European Conference on Computer Vision},
year = {2018},
month = {September},
pages = {339--357}
}

This work was supported in part by the Samsung Global Research Outreach program, and in part by the EU Horizon 2020 Project PAL (643783-RIA).

More information can be found on the Personal Robotic Lab's website: https://www.imperial.ac.uk/personal-robotics/software/.

Requirements

• pip install tensorflow-gpu numpy scipy tqdm matplotlib h5py scikit-learn pytorch_lightning torch torchvision Pillow
• Run the rt_gene/scripts/download_models.py script to download the required model files

Model training code (tensorflow)

This code was used to train the eye gaze estimation CNN for RT-GENE.

• First, the h5 files need to be created from the RAW images. We use the prepare_dataset.m MATLAB script for this purpose. Please adjust the load_path and save_path variables. The augmented variable can be set to 0 to disable image image augmentations described in the paper. The with_faces variable can be set to 1 to also store the face images in the *.h5 files (warning: this requires a lot of memory).
• Then, the train_model.py file can be used to train the models in the 3-Fold setting as described in the paper. An example to call this script is given in the train_models_run.sh file.
• Finally, the evaluate_model.py can be used to get the individual models' performance as well as the ensemble performance. An example to call this script is given in the evaluate_models.sh file.

Model training code (pytorch)

• First, generate the new patches from the new RT-GENE pipeline using GenerateEyePatchesRTGENEDataset.py. This will create new directories inside "RT_GENE/subject/inpainted": "left_new" and "right_new".
• Compile the left_new and right_new patches along the labels into a h5 file using GenerateRTGENEH5Dataset.py, optionally augment the patches here to make it as similar to prepare_dataset.m from the tensorflow preparation stage.
• Run train_model.py on the h5 dataset generated. This will take a while. Available options can be viewed by running train_model.py --help.
• Finally, run post_process_ckpt.py on the generated ckpt files to turn them into models (reduces file size)

List of libraries

• Tensorflow; Apache License 2.0, Link to website