A pipeline covers downloading videos from YouTube and extracting frames using ffmpeg.
Our goal is to build a fast pipeline to produce new datasets for deep learning practitioners in the field of video understanding. We have already done some action recognition and video target segmentation datasets with this tool.
Personally suggest Conda. Please follow its guidance.
yt-dlp is a youtube-dl fork based on the now inactive youtube-dlc. The main focus of this project is adding new features and patches while also keeping up to date with the original project.
FFmpeg is a complete, cross-platform solution to record, convert and stream audio and video.
We suggest creating a virtual environment. The python version doesn't matter.
conda create -n name python=3.x
Activate the virtual environment you created before installing the following things and using the script.
Install yt-dlp with pip. Follow the guidance if there is any problem with that.
python3 -m pip install -U yt-dlp
Install other packages with pip by using the command:
pip install -r requirements.txt
Download FFmpeg with your version. You can also retrieve the source code through Git by using the command:
git clone https://git.ffmpeg.org/ffmpeg.git ffmpeg
- Store the target url list in a .txt file as example.
- Run the script
main.pywith arguments input.python main.py --url ${URL FILE}\ --output ${OUTPUT PATH}\ --suffix ${SUFFIX}\ --h ${H}\ --w ${W}python main.py --url ./example.txt --output ./output --suffix .png --h 360 --w 640- - The dataset will be organized as:
├── output ├── 0 │ ├── 0001.png │ ├── 0002.png │ ├── 0003.png │ ├── 0004.png │ ├── ... │ └── 0116.png ├── 1 │ ├── 0001.png │ ├── 0002.png │ ├── 0003.png │ ├── 0004.png │ ├── ... │ └── 0116.png ├── 0.mp4 └── 1.mp4
For YouTube-VOS dataset, this repo provide the tool to convert the frames and masks to the given format, which is
train.zip
|- JPEGImages
|- <video_id>
|- <frame_id>.jpg
|- <frame_id>.jpg
|- <video_id>
|- <frame_id>.jpg
|- <frame_id>.jpg
|- Annotations
|- <video_id>
|- <frame_id>.png
|- <frame_id>.png
|- <video_id>
|- <frame_id>.png
|- <frame_id>.png
meta.json
{
"videos": {
"<video_id>": {
"objects": {
"<object_id>": {
"category": "<category>",
"frames": [
"<frame_id>",
"<frame_id>",
]
}
}
}
}
}
# <object_id> is the same as the pixel values of object in annotated segmentation PNG files.
# <frame_id> is not necessary to start from 0.