nlm ¶

@NLMeans
def nl_means(
    clip: vs.VideoNode,
    h: float | Sequence[float] = 1.2,
    tr: int | Sequence[int] = 1,
    a: int | Sequence[int] = 2,
    s: int | Sequence[int] = 4,
    backend: NLMeans.Backend = NLMeans.Backend.AUTO,
    ref: vs.VideoNode | None = None,
    wmode: NLMeans.WeightMode = NLMeans.WeightMode.WELSCH,
    planes: PlanesT = None,
    **kwargs: Any
) -> vs.VideoNode:
    """
    Convenience wrapper for NLMeans implementations.

    Filter description [here](https://github.com/Khanattila/KNLMeansCL/wiki/Filter-description).

    Example:
        ```py
        denoised = nl_means(clip, 0.4, backend=nl_means.Backend.CUDA, ...)
        ```

    :param clip:            Source clip.
    :param h:               Controls the strength of the filtering.
                            Larger values will remove more noise.
    :param tr:              Temporal Radius. Temporal size = `(2 * d + 1)`.
                            Sets the number of past and future frames to uses for denoising the current frame.
                            d=0 uses 1 frame, while d=1 uses 3 frames and so on.
                            Usually, larger values result in better denoising.
                            Also known as the `d` parameter.
    :param a:               Search Radius. Spatial size = `(2 * a + 1)^2`.
                            Sets the radius of the search window.
                            a=1 uses 9 pixel, while a=2 uses 25 pixels and so on.
                            Usually, larger values result in better denoising.
    :param s:               Similarity Radius. Similarity neighbourhood size = `(2 * s + 1) ** 2`.
                            Sets the radius of the similarity neighbourhood window.
                            The impact on performance is low, therefore it depends on the nature of the noise.
    :param backend:         Set the backend to use for processing.
    :param ref:             Reference clip to do weighting calculation.
                            Also known as the `rclip` parameter.
    :param wmode:           Weighting function to use.
    :param planes:          Which planes to process.
    :param kwargs:          Additional arguments passed to the plugin.

    :return:                Denoised clip.
    """

    assert check_variable(clip, nl_means)

    planes = normalize_planes(clip, planes)

    if not planes:
        return clip

    params = dict[str, list[float] | list[int]](h=to_arr(h), d=to_arr(tr), a=to_arr(a), s=to_arr(s))

    # TODO: Remove legacy support for old arguments.
    for sargs, kargs in zip(
        ["strength", "sr", "simr"],
        ["h", "a", "s"]
    ):
        if sargs in kwargs:
            warnings.warn(
                f"nl_means: '{sargs}' argument is deprecated, use '{kargs}' instead",
                DeprecationWarning
            )
            params[kargs] = to_arr(kwargs.pop(sargs))

    def _nl_means(i: int, channels: str) -> vs.VideoNode:
        return backend.NLMeans(
            clip,
            **{k: p[i] for k, p in params.items()},
            **dict(channels=channels, rclip=ref, wmode=wmode, wref=wmode.wref) | kwargs
        )

    if clip.format.color_family in {vs.GRAY, vs.RGB}:
        for doc, p in params.items():
            if len(set(p)) > 1:
                warnings.warn(
                    f'nl_means: only "{doc}" first value will be used since clip is {clip.format.color_family.name}',
                    UserWarning
                )

        return _nl_means(0, 'AUTO')

    if (
        all(len(p) < 2 for p in params.values())
        and clip.format.subsampling_w == clip.format.subsampling_h == 0
        and planes == [0, 1, 2]
    ):
        return _nl_means(0, 'YUV')

    for k, p in params.items():
        params[k] = normalize_seq(p, 2)

    luma = _nl_means(0, 'Y') if 0 in planes else None
    chroma = _nl_means(1, 'UV') if 1 in planes or 2 in planes else None

    return join({None: clip, tuple(planes): chroma, 0: luma})