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video

Functions

fastvideo.eval.io.video.extract_frames 

extract_frames(video: Tensor, n_frames: int | None = None) -> Tensor

Uniformly sample n_frames from a (T, C, H, W) video tensor.

Source code in fastvideo/eval/io/video.py 
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def extract_frames(video: torch.Tensor, n_frames: int | None = None) -> torch.Tensor:
    """Uniformly sample *n_frames* from a ``(T, C, H, W)`` video tensor."""
    if n_frames is None or n_frames >= video.shape[0]:
        return video
    indices = torch.linspace(0, video.shape[0] - 1, n_frames).long()
    return video[indices]

fastvideo.eval.io.video.load_video 

load_video(source: str | Tensor | list, **kwargs) -> Tensor

Load a video as a (T, C, H, W) float32 tensor in [0, 1].

Supported source types:

  • str / Path – path to .mp4 / .avi / .gif file, or a directory of frame images (sorted alphabetically).
  • torch.Tensor – returned as-is after shape validation.
  • list[PIL.Image] – stacked into a tensor.
Source code in fastvideo/eval/io/video.py 
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def load_video(source: str | torch.Tensor | list, **kwargs) -> torch.Tensor:
    """Load a video as a ``(T, C, H, W)`` float32 tensor in ``[0, 1]``.

    Supported *source* types:

    * **str / Path** – path to ``.mp4`` / ``.avi`` / ``.gif`` file, or a
      directory of frame images (sorted alphabetically).
    * **torch.Tensor** – returned as-is after shape validation.
    * **list[PIL.Image]** – stacked into a tensor.
    """
    if isinstance(source, torch.Tensor):
        if source.ndim != 4:
            raise ValueError(f"Expected video tensor with 4 dims (T,C,H,W), got {source.ndim}")
        return source.float()

    if isinstance(source, list):
        frames = [_pil_to_tensor(img) for img in source]
        return torch.stack(frames)

    path = Path(source)
    if path.is_dir():
        return _load_frame_dir(path)
    return _load_video_file(str(path))