import numpy as np

from ._felzenszwalb_cy import _felzenszwalb_cython
from .._shared import utils


@utils.channel_as_last_axis(multichannel_output=False)
def felzenszwalb(image, scale=1, sigma=0.8, min_size=20, *, channel_axis=-1):
    """Computes Felsenszwalb's efficient graph based image segmentation.

    Produces an oversegmentation of a multichannel (i.e. RGB) image
    using a fast, minimum spanning tree based clustering on the image grid.
    The parameter ``scale`` sets an observation level. Higher scale means
    less and larger segments. ``sigma`` is the diameter of a Gaussian kernel,
    used for smoothing the image prior to segmentation.

    The number of produced segments as well as their size can only be
    controlled indirectly through ``scale``. Segment size within an image can
    vary greatly depending on local contrast.

    For RGB images, the algorithm uses the euclidean distance between pixels in
    color space.

    Parameters
    ----------
    image : (M, N[, 3]) ndarray
        Input image.
    scale : float
        Free parameter. Higher means larger clusters.
    sigma : float
        Width (standard deviation) of Gaussian kernel used in preprocessing.
    min_size : int
        Minimum component size. Enforced using postprocessing.
    channel_axis : int or None, optional
        If None, the image is assumed to be a grayscale (single channel) image.
        Otherwise, this parameter indicates which axis of the array corresponds
        to channels.

        .. versionadded:: 0.19
           ``channel_axis`` was added in 0.19.

    Returns
    -------
    segment_mask : (M, N) ndarray
        Integer mask indicating segment labels.

    References
    ----------
    .. [1] Efficient graph-based image segmentation, Felzenszwalb, P.F. and
           Huttenlocher, D.P.  International Journal of Computer Vision, 2004

    Notes
    -----
        The `k` parameter used in the original paper renamed to `scale` here.

    Examples
    --------
    >>> from skimage.segmentation import felzenszwalb
    >>> from skimage.data import coffee
    >>> img = coffee()
    >>> segments = felzenszwalb(img, scale=3.0, sigma=0.95, min_size=5)
    """
    if channel_axis is None and image.ndim > 2:
        raise ValueError(
            "This algorithm works only on single or " "multi-channel 2d images. "
        )

    image = np.atleast_3d(image)
    return _felzenszwalb_cython(image, scale=scale, sigma=sigma, min_size=min_size)