This repo contains helpful scripts for using the YouTube BoundingBoxes dataset released by Google Research. The only current hosting method provided for the dataset is annotations in csv form. The csv files contain links to the videos on YouTube, but it's up to you to download the video files themselves. For this reason, these scripts are provided for downloading, cutting, and decoding the videos into a usable form.
These scripts were written by Mark Buckler and the YouTube BoundingBoxes dataset was created and curated by Esteban Real, Jonathon Shlens, Stefano Mazzocchi, Xin Pan, and Vincent Vanhoucke. The dataset web page is here and the accompanying whitepaper is here.
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Clone this repository.
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Install majority of dependencies by running
pip install -r requirements.txtin this repo's directory. -
Install wget, ffmpeg and youtube-dl through your package manager. For most platforms this should be straightforward, but for Ubuntu 14.04 users you will need to update your apt-get repository before being able to install ffmpeg as shown here.
Some small tweaks may be needed for different software environments. These scripts were developed and tested on Ubuntu 14.04.
Note: You will need to use at least Python 3.0. This script was developed with Python 3.5.2.
The download.py script is provided for users who are interested in
downloading the videos which accompany the provided annotations. It also
cuts these videos down to the range in which they have been
annotated. Parallel video downloads are supported so that you can
saturate your download bandwith even though YouTube throttles per-video.
I was able to download the Detection Validation videos (412 GB) in
roughly 3 hours.
Run python download.py [VIDEO_DIR] [NUM_THREADS] to download the dataset into the specified
directory. If you don't provide a path, a directory named videos will be
created.
Once your downloading has completed you may be interested in decoding the videos into individual still frames. If this is the case, use the decoding script. The script decodes all frames within the clips at 30 frames per second.
Run python decode.py [VIDEO_DIR] [FRAME_DIR]. The first parameter is the directory where your videos were downloaded to (default: videos), and the second is where you would like the decoded frames to go (default: frames).