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Draft for better write_video documentation #8576

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Draft for better write_video documentation #8576

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24 changes: 21 additions & 3 deletions torchvision/io/video.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,20 +62,38 @@ def write_video(
audio_options: Optional[Dict[str, Any]] = None,
) -> None:
"""
Writes a 4d tensor in [T, H, W, C] format in a video file
Writes a 4d tensor in [T, H, W, C] format in a video file.
The default parameters (i.e. `fps`, `audio_fps`) return videos
of a fixed quality & compressing speed, and may not necessarily be suitable for all applications.
Since torchvision relies on `PyAV` (therefore, ultimately `FFmpeg`) to encode videos,
you can get more fine-grained control by referring to the other options at
your disposal within `the FFMpeg wiki <http://trac.ffmpeg.org/wiki#Encoding>`_.

Args:
filename (str): path where the video will be saved
video_array (Tensor[T, H, W, C]): tensor containing the individual frames,
as a uint8 tensor in [T, H, W, C] format
fps (Number): video frames per second
video_codec (str): the name of the video codec, i.e. "libx264", "h264", etc.
options (Dict): dictionary containing options to be passed into the PyAV video stream
options (Dict): dictionary containing options to be passed into the PyAV video stream.
The list of options is codec-dependent and can all
be found from `the FFMpeg wiki <http://trac.ffmpeg.org/wiki#Encoding>`_.
audio_array (Tensor[C, N]): tensor containing the audio, where C is the number of channels
and N is the number of samples
audio_fps (Number): audio sample rate, typically 44100 or 48000
audio_codec (str): the name of the audio codec, i.e. "mp3", "aac", etc.
audio_options (Dict): dictionary containing options to be passed into the PyAV audio stream
audio_options (Dict): dictionary containing options to be passed into the PyAV audio stream.
The list of options is codec-dependent and can all
be found from `the FFMpeg wiki <http://trac.ffmpeg.org/wiki#Encoding>`_.

Examples::
>>> # Creating libx264 video with CRF 17, for visually lossless footage:
>>>
>>> from torchvision.io import write_video
>>> # 1000 frames of 100x100, 3-channel image.
>>> vid = torch.randn(1000, 100, 100, 3, dtype = torch.uint8)
>>> write_video("video.mp4", options = {"crf": "17"})

"""
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
_log_api_usage_once(write_video)
Expand Down