AutoAndroidController/py/venv/Lib/site-packages/skimage/morphology/_util.py

"""Utility functions used in the morphology subpackage."""

import numpy as np
from scipy import ndimage as ndi


def _validate_connectivity(image_dim, connectivity, offset):
    """Convert any valid connectivity to a footprint and offset.

    Parameters
    ----------
    image_dim : int
        The number of dimensions of the input image.
    connectivity : int, array, or None
        The neighborhood connectivity. An integer is interpreted as in
        ``scipy.ndimage.generate_binary_structure``, as the maximum number
        of orthogonal steps to reach a neighbor. An array is directly
        interpreted as a footprint and its shape is validated against
        the input image shape. ``None`` is interpreted as a connectivity of 1.
    offset : tuple of int, or None
        The coordinates of the center of the footprint.

    Returns
    -------
    c_connectivity : array of bool
        The footprint (structuring element) corresponding to the input
        `connectivity`.
    offset : array of int
        The offset corresponding to the center of the footprint.

    Raises
    ------
    ValueError:
        If the image dimension and the connectivity or offset dimensions don't
        match.
    """
    if connectivity is None:
        connectivity = 1

    if np.isscalar(connectivity):
        c_connectivity = ndi.generate_binary_structure(image_dim, connectivity)
    else:
        c_connectivity = np.array(connectivity, bool)
        if c_connectivity.ndim != image_dim:
            raise ValueError("Connectivity dimension must be same as image")

    if offset is None:
        if any([x % 2 == 0 for x in c_connectivity.shape]):
            raise ValueError("Connectivity array must have an unambiguous " "center")

        offset = np.array(c_connectivity.shape) // 2

    return c_connectivity, offset


def _raveled_offsets_and_distances(
    image_shape,
    *,
    footprint=None,
    connectivity=1,
    center=None,
    spacing=None,
    order='C',
):
    """Compute offsets to neighboring pixels in raveled coordinate space.

    This function also returns the corresponding distances from the center
    pixel given a spacing (assumed to be 1 along each axis by default).

    Parameters
    ----------
    image_shape : tuple of int
        The shape of the image for which the offsets are being computed.
    footprint : array of bool
        The footprint of the neighborhood, expressed as an n-dimensional array
        of 1s and 0s. If provided, the connectivity argument is ignored.
    connectivity : {1, ..., ndim}
        The square connectivity of the neighborhood: the number of orthogonal
        steps allowed to consider a pixel a neighbor. See
        `scipy.ndimage.generate_binary_structure`. Ignored if footprint is
        provided.
    center : tuple of int
        Tuple of indices to the center of the footprint. If not provided, it
        is assumed to be the center of the footprint, either provided or
        generated by the connectivity argument.
    spacing : tuple of float
        The spacing between pixels/voxels along each axis.
    order : 'C' or 'F'
        The ordering of the array, either C or Fortran ordering.

    Returns
    -------
    raveled_offsets : ndarray
        Linear offsets to a samples neighbors in the raveled image, sorted by
        their distance from the center.
    distances : ndarray
        The pixel distances corresponding to each offset.

    Notes
    -----
    This function will return values even if `image_shape` contains a dimension
    length that is smaller than `footprint`.

    Examples
    --------
    >>> off, d = _raveled_offsets_and_distances(
    ...         (4, 5), footprint=np.ones((4, 3)), center=(1, 1)
    ...         )
    >>> off
    array([-5, -1,  1,  5, -6, -4,  4,  6, 10,  9, 11])
    >>> d[0]
    1.0
    >>> d[-1]  # distance from (1, 1) to (3, 2)
    2.236...
    """
    ndim = len(image_shape)
    if footprint is None:
        footprint = ndi.generate_binary_structure(rank=ndim, connectivity=connectivity)
    if center is None:
        center = tuple(s // 2 for s in footprint.shape)

    if not footprint.ndim == ndim == len(center):
        raise ValueError(
            "number of dimensions in image shape, footprint and its"
            "center index does not match"
        )

    offsets = np.stack(
        [(idx - c) for idx, c in zip(np.nonzero(footprint), center)], axis=-1
    )

    if order == 'F':
        offsets = offsets[:, ::-1]
        image_shape = image_shape[::-1]
    elif order != 'C':
        raise ValueError("order must be 'C' or 'F'")

    # Scale offsets in each dimension and sum
    ravel_factors = image_shape[1:] + (1,)
    ravel_factors = np.cumprod(ravel_factors[::-1])[::-1]
    raveled_offsets = (offsets * ravel_factors).sum(axis=1)

    # Sort by distance
    if spacing is None:
        spacing = np.ones(ndim)
    weighted_offsets = offsets * spacing
    distances = np.sqrt(np.sum(weighted_offsets**2, axis=1))
    sorted_raveled_offsets = raveled_offsets[np.argsort(distances, kind="stable")]
    sorted_distances = np.sort(distances, kind="stable")

    # If any dimension in image_shape is smaller than footprint.shape
    # duplicates might occur, remove them
    if any(x < y for x, y in zip(image_shape, footprint.shape)):
        # np.unique reorders, which we don't want
        _, indices = np.unique(sorted_raveled_offsets, return_index=True)
        indices = np.sort(indices, kind="stable")
        sorted_raveled_offsets = sorted_raveled_offsets[indices]
        sorted_distances = sorted_distances[indices]

    # Remove "offset to center"
    sorted_raveled_offsets = sorted_raveled_offsets[1:]
    sorted_distances = sorted_distances[1:]

    return sorted_raveled_offsets, sorted_distances


def _offsets_to_raveled_neighbors(image_shape, footprint, center, order='C'):
    """Compute offsets to a samples neighbors if the image would be raveled.

    Parameters
    ----------
    image_shape : tuple
        The shape of the image for which the offsets are computed.
    footprint : ndarray
        The footprint (structuring element) determining the neighborhood
        expressed as an n-D array of 1's and 0's.
    center : tuple
        Tuple of indices to the center of `footprint`.
    order : {"C", "F"}, optional
        Whether the image described by `image_shape` is in row-major (C-style)
        or column-major (Fortran-style) order.

    Returns
    -------
    raveled_offsets : ndarray
        Linear offsets to a samples neighbors in the raveled image, sorted by
        their distance from the center.

    Notes
    -----
    This function will return values even if `image_shape` contains a dimension
    length that is smaller than `footprint`.

    Examples
    --------
    >>> _offsets_to_raveled_neighbors((4, 5), np.ones((4, 3)), (1, 1))
    array([-5, -1,  1,  5, -6, -4,  4,  6, 10,  9, 11])
    >>> _offsets_to_raveled_neighbors((2, 3, 2), np.ones((3, 3, 3)), (1, 1, 1))
    array([-6, -2, -1,  1,  2,  6, -8, -7, -5, -4, -3,  3,  4,  5,  7,  8, -9,
            9])
    """
    raveled_offsets = _raveled_offsets_and_distances(
        image_shape, footprint=footprint, center=center, order=order
    )[0]

    return raveled_offsets


def _resolve_neighborhood(footprint, connectivity, ndim, enforce_adjacency=True):
    """Validate or create a footprint (structuring element).

    Depending on the values of `connectivity` and `footprint` this function
    either creates a new footprint (`footprint` is None) using `connectivity`
    or validates the given footprint (`footprint` is not None).

    Parameters
    ----------
    footprint : ndarray
        The footprint (structuring) element used to determine the neighborhood
        of each evaluated pixel (``True`` denotes a connected pixel). It must
        be a boolean array and have the same number of dimensions as `image`.
        If neither `footprint` nor `connectivity` are given, all adjacent
        pixels are considered as part of the neighborhood.
    connectivity : int
        A number used to determine the neighborhood of each evaluated pixel.
        Adjacent pixels whose squared distance from the center is less than or
        equal to `connectivity` are considered neighbors. Ignored if
        `footprint` is not None.
    ndim : int
        Number of dimensions `footprint` ought to have.
    enforce_adjacency : bool
        A boolean that determines whether footprint must only specify direct
        neighbors.

    Returns
    -------
    footprint : ndarray
        Validated or new footprint specifying the neighborhood.

    Examples
    --------
    >>> _resolve_neighborhood(None, 1, 2)
    array([[False,  True, False],
           [ True,  True,  True],
           [False,  True, False]])
    >>> _resolve_neighborhood(None, None, 3).shape
    (3, 3, 3)
    """
    if footprint is None:
        if connectivity is None:
            connectivity = ndim
        footprint = ndi.generate_binary_structure(ndim, connectivity)
    else:
        # Validate custom structured element
        footprint = np.asarray(footprint, dtype=bool)
        # Must specify neighbors for all dimensions
        if footprint.ndim != ndim:
            raise ValueError(
                "number of dimensions in image and footprint do not" "match"
            )
        # Must only specify direct neighbors
        if enforce_adjacency and any(s != 3 for s in footprint.shape):
            raise ValueError("dimension size in footprint is not 3")
        elif any((s % 2 != 1) for s in footprint.shape):
            raise ValueError("footprint size must be odd along all dimensions")

    return footprint


def _set_border_values(image, value, border_width=1):
    """Set edge values along all axes to a constant value.

    Parameters
    ----------
    image : ndarray
        The array to modify inplace.
    value : scalar
        The value to use. Should be compatible with `image`'s dtype.
    border_width : int or sequence of tuples
        A sequence with one 2-tuple per axis where the first and second values
        are the width of the border at the start and end of the axis,
        respectively. If an int is provided, a uniform border width along all
        axes is used.

    Examples
    --------
    >>> image = np.zeros((4, 5), dtype=int)
    >>> _set_border_values(image, 1)
    >>> image
    array([[1, 1, 1, 1, 1],
           [1, 0, 0, 0, 1],
           [1, 0, 0, 0, 1],
           [1, 1, 1, 1, 1]])
    >>> image = np.zeros((8, 8), dtype=int)
    >>> _set_border_values(image, 1, border_width=((1, 1), (2, 3)))
    >>> image
    array([[1, 1, 1, 1, 1, 1, 1, 1],
           [1, 1, 0, 0, 0, 1, 1, 1],
           [1, 1, 0, 0, 0, 1, 1, 1],
           [1, 1, 0, 0, 0, 1, 1, 1],
           [1, 1, 0, 0, 0, 1, 1, 1],
           [1, 1, 0, 0, 0, 1, 1, 1],
           [1, 1, 0, 0, 0, 1, 1, 1],
           [1, 1, 1, 1, 1, 1, 1, 1]])
    """
    if np.isscalar(border_width):
        border_width = ((border_width, border_width),) * image.ndim
    elif len(border_width) != image.ndim:
        raise ValueError('length of `border_width` must match image.ndim')
    for axis, npad in enumerate(border_width):
        if len(npad) != 2:
            raise ValueError('each sequence in `border_width` must have ' 'length 2')
        w_start, w_end = npad
        if w_start == w_end == 0:
            continue
        elif w_start == w_end == 1:
            # Index first and last element in the current dimension
            sl = (slice(None),) * axis + ((0, -1),) + (...,)
            image[sl] = value
            continue
        if w_start > 0:
            # set first w_start entries along axis to value
            sl = (slice(None),) * axis + (slice(0, w_start),) + (...,)
            image[sl] = value
        if w_end > 0:
            # set last w_end entries along axis to value
            sl = (slice(None),) * axis + (slice(-w_end, None),) + (...,)
            image[sl] = value