Minimal code for Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy, CVPR'21.
This project uses Python >= 3.7.3. After setting up your virtual environment, please install the required python libraries through:
pip install -r requirements.txt
Code is formatted with Black (PEP8) using a pre-commit hook. To configure it, run:
pre-commit install
Similarly to researchers from Monash University, this project processes events through the HDF5 data format. Details about the structure of these files can be found in datasets/tools/.
Download our pre-trained models from here.
Our HDF5 version of sequences from the Event Camera Dataset can also be downloaded from here for evaluation purposes.
To estimate optical flow from the input events:
python eval_flow.py <path_to_model_dir>
To perform image reconstruction from the input events:
python eval_reconstruction.py <path_to_model_dir>
In configs/, you can find the configuration files associated to these scripts and vary the inference settings (e.g., number of input events, dataset).
Our framework can be trained using any event camera dataset. However, if you are interested in using our training data, you can download it from here. The datasets are expected at datasets/data/, but this location can be modified in the configuration files.
To train an image reconstruction and optical flow model, you need to adapt the training settings in configs/train_reconstruction.yml. Here, you can choose the training dataset, the number of input events, the neural networks to be used (EV-FlowNet or FireFlowNet for optical flow; E2VID or FireNet for image reconstruction), the number of epochs, the optimizer and learning rate, etc. To start the training from scratch, run:
python train_reconstruction.py
Alternatively, if you have a model that you would like to keep training from, you can use
python train_reconstruction.py --prev_model <path_to_model_dir>
This is handy if, for instance, you just want to train the image reconstruction model and use a pre-trained optical flow network. For this, you can set train_flow: False in configs/train_reconstruction.yml, and run:
python train_reconstruction.py --prev_model <path_to_optical_flow_model_dir>
If you just want to train an optical flow network, adapt configs/train_flow.yml, and run:
python train_flow.py
Note that we use MLflow to keep track of all the experiments.
If you use this library in an academic context, please cite the following:
@article{paredes2020back,
title={Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy},
author={Paredes-Vall{\'e}s, Federico and de Croon, Guido C. H. E.},
journal={arXiv preprint arXiv:2009.08283},
year={2020}
}
This code borrows from the following open source projects, whom we would like to thank: