This code is licensed under CC BY-NC-SA 4.0. Commercial usage is not permitted; please contact [email protected] or [email protected] regarding commercial licensing. If you use this dataset or the code in a scientific publication, please cite the following paper:
@inproceedings{CortaceroICCV2019W,
author={Kevin Cortacero and Tobias Fischer and Yiannis Demiris},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision Workshops},
title = {RT-BENE: A Dataset and Baselines for Real-Time Blink Estimation in Natural Environments},
year = {2019},
}
RT-BENE was supported by the EU Horizon 2020 Project PAL (643783-RIA) and a Royal Academy of Engineering Chair in Emerging Technologies to Yiannis Demiris.
More information can be found on the Personal Robotic Lab's website: https://www.imperial.ac.uk/personal-robotics/software/.
For pip users: pip install tensorflow-gpu numpy tqdm opencv-python scikit-learn or for conda users: conda install tensorflow-gpu numpy tqdm opencv scikit-learn
For conda users: conda install -c conda-forge numpy scipy tqdm pillow rospkg opencv scikit-learn h5py matplotlib pytorch-lightning && conda install -c pytorch pytorch torchvision
This code was used to train the blink estimator for RT-BENE. The labels for the RT-BENE blink dataset are contained in the rt_bene_dataset directory. The images corresponding to the labels can be downloaded from the RT-GENE dataset (labels are only available for the "noglasses" part): download (alternative link). Please run python train_blink_model.py --help to see the required arguments to train the model.
This code attempts to duplicate the Tensorflow version using Pytorch and Pytorch-Lightning. It uses the same dataset. An HDF5 file is required, to generate this run GenerateRTBENEH5Dataset.py with the argument to locate the RT-BENE dataset. train_model contains the code required to train the model in pytorch.
See [https://github.com/Tobias-Fischer/rt_gene/blob/master/rt_gene_model_training/pytorch/utils/GenerateEyePatchesRTGENEDataset.py](this script) to generate eye patches for model training (only needed on custom datasets)
Evaluation code for a 3-fold evaluation is provided in the evaluate_blink_model.py file. An example to train and evaluate an ensemble of models can be found in train_and_evaluate.py. Please run python train_and_evaluate.py --help to see the required arguments.
Evaluation code for 3-fold validation is in evaluate_model.py
