Source code for cutcutcodec.core.analysis.video.complexity.dct

"""Compute a differenciable batched torch spacial dtc complexity."""

import torch

from cutcutcodec.core.opti.parallel.threading import TorchThreads

from .utils import batched_frames




@batched_frames
def spacial_dct(img: torch.Tensor, threads: int = 0) -> torch.Tensor:
    r"""Compute the spacial dct complexity for the image.

    Parameters
    ----------
    img : arraylike
        The Y[UV] images, of shape ([*batch], [1], height, width, [channels]).
        Only the Y component is used. It has to be in range [0, 1].
        As there is no padding on the edges, the image must be at least 64x64 pixels.
    threads : int, optional
        Defines the number of threads.
        The value -1 means that the function uses as many calculation threads as there are cores.
        The default value (0) allows the same behavior as (-1) if the function
        is called in the main thread, otherwise (1) to avoid nested threads.
        Any other positive value corresponds to the number of threads used.

    Returns
    -------
    spacial_dct : arraylike
        The :math:`C_{sob} \in \mathbb{R}^+` scalar for each image (of shape batch).

    Examples
    --------
    >>> import numpy as np
    >>> from cutcutcodec.core.analysis.video.complexity import spacial_dct
    >>> np.random.seed(0)
    >>> img = np.random.random((720, 1080, 3))  # It could also be a torch array list...
    >>> spacial_dct(img).round(1)
    np.float64(1.4)
    >>>

    """
    _, _, height, width, _ = img.shape
    assert (height, width) >= (64, 64), f"the image {img.shape} is to small"
    with TorchThreads(threads):
        raise NotImplementedError