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# imageio.plugins.pyav.PyAVPlugin.iter

PyAVPlugin.iter(*, format: str = 'rgb24', filter_sequence: List[Tuple[str, str | dict]] = None, filter_graph: Tuple[dict, List] = None, thread_count: int = 0, thread_type: str = None) → ndarray

  Yield frames from the video.

  Parameters:
    frame np.ndarray

      A numpy array containing loaded frame data.

    format str

      Convert the data into the given format before returning it. If None, return the data in the encoded format if it can be expressed as a strided array; otherwise raise an Exception.

    filter_sequence List[str, str, dict]

      Set the returned colorspace. If not None (default: rgb24), convert the data into the given format before returning it. If `None` return the data in the encoded format if it can be expressed as a strided array; otherwise raise an Exception.

    filter_graph(dict, List)

      If not None, apply the given graph of FFmpeg filters to each ndimage. The graph is given as a tuple of two dicts. The first dict contains a (named) set of nodes, and the second dict contains a set of edges between nodes of the previous dict. Check the (module-level) plugin docs for details and examples.

    thread_count int

      How many threads to use when decoding a frame. The default is 0, which will set the number using ffmpeg’s default, which is based on the codec, number of available cores, threadding model, and other considerations.

    thread_type str

      The threading model to be used. One of

      - “SLICE” (default): threads assemble parts of the current frame

      - “FRAME”: threads may assemble future frames (faster for bulk reading)

  Yields:
    frame np.ndarray

      A (decoded) video frame.