- PyAVPlugin.iter(*, format: str = 'rgb24', filter_sequence: Optional[List[Tuple[str, Union[str, dict]]]] = None, filter_graph: Optional[Tuple[dict, List]] = None, thread_count: int = 0, thread_type: Optional[str] = None) ndarray [source]#
Yield frames from the video.
A numpy array containing loaded frame data.
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_sequenceList[str, str, dict]
Set the returned colorspace. If not None (default: rgb24), convert the data into the given format before returning it. If
Nonereturn 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.
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.
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)
A (decoded) video frame.