TimeLens: Event-based Video Frame Interpolation
This repository is about the High Speed Event and RGB (HS-ERGB) dataset, used in the 2021 CVPR paper TimeLens: Event-based Video Frame Interpolation by Stepan Tulyakov*, Daniel Gehrig*, Stamatios Georgoulis, Julius Erbach, Mathias Gehrig, Yuanyou Li, and Davide Scaramuzza.
For more information, visit our project page.
Citation
A pdf of the paper is available here. If you use this dataset, please cite this publication as follows:
@Article{Tulyakov21CVPR,
author = {Stepan Tulyakov and Daniel Gehrig and Stamatios Georgoulis and Julius Erbach and Mathias Gehrig and Yuanyou Li and
Davide Scaramuzza},
title = {{TimeLens}: Event-based Video Frame Interpolation},
journal = "IEEE Conference on Computer Vision and Pattern Recognition",
year = 2021,
}
Google Colab
A Google Colab notebook is now available here. You can upsample your own video and events from you gdrive.
Gallery
For more examples, visit our project page.
Installation
Install the dependencies with
cuda_version=10.2
conda create -y -n timelens python=3.7
conda activate timelens
conda install -y pytorch torchvision cudatoolkit=$cuda_version -c pytorch
conda install -y -c conda-forge opencv scipy tqdm click
Test TimeLens
First start by cloning this repo into a new folder
mkdir ~/timelens/
cd ~/timelens
git clone https://github.com/uzh-rpg/rpg_timelens
Then download the checkpoint and data to the repo
cd rpg_timelens
wget http://rpg.ifi.uzh.ch/timelens/data/checkpoint.bin
wget http://rpg.ifi.uzh.ch/timelens/data/example_github.zip
unzip example_github.zip
rm -rf example_github.zip
Running Timelens
To run timelens simply call
skip=0
insert=7
python -m timelens.run_timelens checkpoint.bin example/events example/images example/output $skip $insert
This will generate the output in example/output. The first four variables are the checkpoint file, image folder and event folder and output folder respectively. The variables skip and insert determine the number of skipped vs. inserted frames, i.e. to generate a video with an 8 higher framerate, 7 frames need to be inserted, and 0 skipped.
The resulting images can be converted to a video with
ffmpeg -i example/output/%06d.png timelens.mp4
the resulting video is timelens.mp4.
Dataset
Download the dataset from our project page. The dataset structure is as follows
.
├── close
│ └── test
│ ├── baloon_popping
│ │ ├── events_aligned
│ │ └── images_corrected
│ ├── candle
│ │ ├── events_aligned
│ │ └── images_corrected
│ ...
│
└── far
└── test
├── bridge_lake_01
│ ├── events_aligned
│ └── images_corrected
├── bridge_lake_03
│ ├── events_aligned
│ └── images_corrected
...
Each events_aligned folder contains events files with template filename %06d.npz, and images_corrected contains image files with template filename %06d.png. In events_aligned each event file with index n contains events between images with index n-1 and n, i.e. event file 000001.npz contains events between images 000000.png and 000001.png. Moreover, images_corrected also contains timestamp.txt where image timestamps are stored. Note that in some folders there are more image files than event files. However, the image stamps in timestamp.txt should match with the event files and the additional images can be ignored.
For a quick test download the dataset to a folder using the link sent by email.
wget download_link.zip -O /tmp/dataset.zip
unzip /tmp/dataset.zip -d hsergb/
And run the test
python test_loader.py --dataset_root hsergb/ \
--dataset_type close \
--sequence spinning_umbrella \
--sample_index 400
This should open a window visualizing aligned events with a single image.
3 Feb 15, 2022
1 Nov 8, 2021
6 Dec 6, 2022
91 Nov 22, 2022
213 Nov 12, 2022
3 Feb 10, 2022
235 Dec 18, 2022
138 Dec 9, 2022
225 Dec 25, 2022
4 Mar 19, 2022
0 Oct 30, 2021
4 Nov 10, 2021
6 Feb 3, 2022
99 Jan 6, 2023
185 Jan 1, 2023
1 Jun 28, 2022
11 Dec 12, 2022
840 Dec 26, 2022
948 Dec 23, 2022