batermj/rpg_vid2e

Open source implementation of CVPR 2020 "Video to Events: Recycling Video Dataset for Event Cameras"

Video to Events: Recycling Video Datasets for Event Cameras

This repository contains code that implements video to events conversion as described in Gehrig et al. CVPR'20. The paper can be found here

If you use this code in an academic context, please cite the following work:

Daniel Gehrig, Mathias Gehrig, Javier Hidalgo-Carrió, Davide Scaramuzza, "Video to Events: Recycling Video Datasets for Event Cameras", The International Conference on Computer Vision and Pattern Recognition (CVPR), 2020

@InProceedings{Gehrig_2020_CVPR,
  author = {Daniel Gehrig and Mathias Gehrig and Javier Hidalgo-Carri\'o and Davide Scaramuzza},
  title = {Video to Events: Recycling Video Datasets for Event Cameras},
  booktitle = {{IEEE} Conf. Comput. Vis. Pattern Recog. (CVPR)},
  month = {June},
  year = {2020}
}

Installation

Clone the repo recursively with submodules

git clone [email protected]:uzh-rpg/rpg_vid2e.git --recursive

Installation with Anaconda

Adapt the CUDA toolkit version according to your setup.

cuda_version=10.1

conda create -y -n vid2e python=3.7
conda activate vid2e
conda install -y pytorch torchvision cudatoolkit=$cuda_version -c pytorch
conda install -y -c conda-forge opencv
conda install -y -c conda-forge tqdm
conda install -y -c conda-forge scikit-video

Download Eigen headers

cd rpg_vid2e/
wget http://bitbucket.org/eigen/eigen/get/3.3.7.tar.bz2
tar -xjf 3.3.7.tar.bz2
rm 3.3.7.tar.bz2
mv eigen-eigen-323c052e1731 esim_py/include/eigen3.3.7

Build the python bindings for ESIM

pip install esim_py

Adaptive Upsampling

This package provides code for adaptive upsampling with frame interpolation based on Super-SloMo

Consult the README for detailed instructions and examples.

esim_py

This package exposes python bindings for ESIM which can be used within a training loop.

For detailed instructions and example consult the README