In many cases, raw videos contain several scenes and are too long for training. Thus, it is essential to split them into shorter clips based on scenes. Here, we provide code for scene detection and video splitting.
At this step, you should have a raw video dataset prepared. A meta file of the dataset information is needed for data processing. To create a meta file from a folder, run:
python -m tools.datasets.convert video /path/to/video/folder --output /path/to/save/meta.csvThis should output a .csv file with column path.
If you already have a meta file for the videos and want to keep the information.
Make sure the meta file has column id, which is the id for each video, and the video is named as {id}.mp4.
The following command will add a new column path to the meta file.
python tools/scene_cut/convert_id_to_path.py /path/to/meta.csv --folder_path /path/to/video/folderThis should output
{prefix}_path-filtered.csvwith columnpath(broken videos filtered){prefix}_path_intact.csvwith columnpathandintact(intactindicating a video is intact or not)
The next step is to detect scenes in a video.
We use PySceneDetect for this job.
Make sure the input meta file has column path, which is the path of a video.
python tools/scene_cut/scene_detect.py /path/to/meta.csvThe output is {prefix}_timestamp.csv with column timestamp. Each cell in column timestamp is a list of tuples,
with each tuple indicating the start and end timestamp of a scene
(e.g., [('00:00:01.234', '00:00:02.345'), ('00:00:03.456', '00:00:04.567')]).
After obtaining timestamps for scenes, we conduct video splitting (cutting).
Make sure the meta file contains column timestamp.
python tools/scene_cut/cut.py /path/to/meta.csv --save_dir /path/to/output/dirThis will save video clips to /path/to/output/dir. The video clips are named as {video_id}_scene-{scene_id}.mp4
To create a new meta file for the generated clips, run:
python -m tools.datasets.convert video /path/to/video/folder --output /path/to/save/meta.csv