update

.CondaPkg/env/Lib/site-packages/skimage/segmentation/__init__.py

@@ -0,0 +1,46 @@
+"""Algorithms to partition images into meaningful regions or boundaries.
+"""
+
+from ._expand_labels import expand_labels
+from .random_walker_segmentation import random_walker
+from .active_contour_model import active_contour
+from ._felzenszwalb import felzenszwalb
+from .slic_superpixels import slic
+from ._quickshift import quickshift
+from .boundaries import find_boundaries, mark_boundaries
+from ._clear_border import clear_border
+from ._join import join_segmentations, relabel_sequential
+from ._watershed import watershed
+from ._chan_vese import chan_vese
+from .morphsnakes import (
+    morphological_geodesic_active_contour,
+    morphological_chan_vese,
+    inverse_gaussian_gradient,
+    disk_level_set,
+    checkerboard_level_set,
+)
+from ..morphology import flood, flood_fill
+
+
+__all__ = [
+    'expand_labels',
+    'random_walker',
+    'active_contour',
+    'felzenszwalb',
+    'slic',
+    'quickshift',
+    'find_boundaries',
+    'mark_boundaries',
+    'clear_border',
+    'join_segmentations',
+    'relabel_sequential',
+    'watershed',
+    'chan_vese',
+    'morphological_geodesic_active_contour',
+    'morphological_chan_vese',
+    'inverse_gaussian_gradient',
+    'disk_level_set',
+    'checkerboard_level_set',
+    'flood',
+    'flood_fill',
+]

.CondaPkg/env/Lib/site-packages/skimage/segmentation/_chan_vese.py

@@ -0,0 +1,365 @@
+import numpy as np
+from scipy.ndimage import distance_transform_edt as distance
+
+from .._shared.utils import _supported_float_type
+
+
+def _cv_calculate_variation(image, phi, mu, lambda1, lambda2, dt):
+    """Returns the variation of level set 'phi' based on algorithm parameters.
+
+    This corresponds to equation (22) of the paper by Pascal Getreuer,
+    which computes the next iteration of the level set based on a current
+    level set.
+
+    A full explanation regarding all the terms is beyond the scope of the
+    present description, but there is one difference of particular import.
+    In the original algorithm, convergence is accelerated, and required
+    memory is reduced, by using a single array. This array, therefore, is a
+    combination of non-updated and updated values. If this were to be
+    implemented in python, this would require a double loop, where the
+    benefits of having fewer iterations would be outweided by massively
+    increasing the time required to perform each individual iteration. A
+    similar approach is used by Rami Cohen, and it is from there that the
+    C1-4 notation is taken.
+    """
+    eta = 1e-16
+    P = np.pad(phi, 1, mode='edge')
+
+    phixp = P[1:-1, 2:] - P[1:-1, 1:-1]
+    phixn = P[1:-1, 1:-1] - P[1:-1, :-2]
+    phix0 = (P[1:-1, 2:] - P[1:-1, :-2]) / 2.0
+
+    phiyp = P[2:, 1:-1] - P[1:-1, 1:-1]
+    phiyn = P[1:-1, 1:-1] - P[:-2, 1:-1]
+    phiy0 = (P[2:, 1:-1] - P[:-2, 1:-1]) / 2.0
+
+    C1 = 1.0 / np.sqrt(eta + phixp**2 + phiy0**2)
+    C2 = 1.0 / np.sqrt(eta + phixn**2 + phiy0**2)
+    C3 = 1.0 / np.sqrt(eta + phix0**2 + phiyp**2)
+    C4 = 1.0 / np.sqrt(eta + phix0**2 + phiyn**2)
+
+    K = P[1:-1, 2:] * C1 + P[1:-1, :-2] * C2 + P[2:, 1:-1] * C3 + P[:-2, 1:-1] * C4
+
+    Hphi = (phi > 0).astype(image.dtype)
+    (c1, c2) = _cv_calculate_averages(image, Hphi)
+
+    difference_from_average_term = (
+        -lambda1 * (image - c1) ** 2 + lambda2 * (image - c2) ** 2
+    )
+    new_phi = phi + (dt * _cv_delta(phi)) * (mu * K + difference_from_average_term)
+    return new_phi / (1 + mu * dt * _cv_delta(phi) * (C1 + C2 + C3 + C4))
+
+
+def _cv_heavyside(x, eps=1.0):
+    """Returns the result of a regularised heavyside function of the
+    input value(s).
+    """
+    return 0.5 * (1.0 + (2.0 / np.pi) * np.arctan(x / eps))
+
+
+def _cv_delta(x, eps=1.0):
+    """Returns the result of a regularised dirac function of the
+    input value(s).
+    """
+    return eps / (eps**2 + x**2)
+
+
+def _cv_calculate_averages(image, Hphi):
+    """Returns the average values 'inside' and 'outside'."""
+    H = Hphi
+    Hinv = 1.0 - H
+    Hsum = np.sum(H)
+    Hinvsum = np.sum(Hinv)
+    avg_inside = np.sum(image * H)
+    avg_oustide = np.sum(image * Hinv)
+    if Hsum != 0:
+        avg_inside /= Hsum
+    if Hinvsum != 0:
+        avg_oustide /= Hinvsum
+    return (avg_inside, avg_oustide)
+
+
+def _cv_difference_from_average_term(image, Hphi, lambda_pos, lambda_neg):
+    """Returns the 'energy' contribution due to the difference from
+    the average value within a region at each point.
+    """
+    (c1, c2) = _cv_calculate_averages(image, Hphi)
+    Hinv = 1.0 - Hphi
+    return lambda_pos * (image - c1) ** 2 * Hphi + lambda_neg * (image - c2) ** 2 * Hinv
+
+
+def _cv_edge_length_term(phi, mu):
+    """Returns the 'energy' contribution due to the length of the
+    edge between regions at each point, multiplied by a factor 'mu'.
+    """
+    P = np.pad(phi, 1, mode='edge')
+    fy = (P[2:, 1:-1] - P[:-2, 1:-1]) / 2.0
+    fx = (P[1:-1, 2:] - P[1:-1, :-2]) / 2.0
+    return mu * _cv_delta(phi) * np.sqrt(fx**2 + fy**2)
+
+
+def _cv_energy(image, phi, mu, lambda1, lambda2):
+    """Returns the total 'energy' of the current level set function.
+
+    This corresponds to equation (7) of the paper by Pascal Getreuer,
+    which is the weighted sum of the following:
+    (A) the length of the contour produced by the zero values of the
+    level set,
+    (B) the area of the "foreground" (area of the image where the
+    level set is positive),
+    (C) the variance of the image inside the foreground,
+    (D) the variance of the image outside of the foreground
+
+    Each value is computed for each pixel, and then summed. The weight
+    of (B) is set to 0 in this implementation.
+    """
+    H = _cv_heavyside(phi)
+    avgenergy = _cv_difference_from_average_term(image, H, lambda1, lambda2)
+    lenenergy = _cv_edge_length_term(phi, mu)
+    return np.sum(avgenergy) + np.sum(lenenergy)
+
+
+def _cv_reset_level_set(phi):
+    """This is a placeholder function as resetting the level set is not
+    strictly necessary, and has not been done for this implementation.
+    """
+    return phi
+
+
+def _cv_checkerboard(image_size, square_size, dtype=np.float64):
+    """Generates a checkerboard level set function.
+
+    According to Pascal Getreuer, such a level set function has fast
+    convergence.
+    """
+    yv = np.arange(image_size[0], dtype=dtype).reshape(image_size[0], 1)
+    xv = np.arange(image_size[1], dtype=dtype)
+    sf = np.pi / square_size
+    xv *= sf
+    yv *= sf
+    return np.sin(yv) * np.sin(xv)
+
+
+def _cv_large_disk(image_size):
+    """Generates a disk level set function.
+
+    The disk covers the whole image along its smallest dimension.
+    """
+    res = np.ones(image_size)
+    centerY = int((image_size[0] - 1) / 2)
+    centerX = int((image_size[1] - 1) / 2)
+    res[centerY, centerX] = 0.0
+    radius = float(min(centerX, centerY))
+    return (radius - distance(res)) / radius
+
+
+def _cv_small_disk(image_size):
+    """Generates a disk level set function.
+
+    The disk covers half of the image along its smallest dimension.
+    """
+    res = np.ones(image_size)
+    centerY = int((image_size[0] - 1) / 2)
+    centerX = int((image_size[1] - 1) / 2)
+    res[centerY, centerX] = 0.0
+    radius = float(min(centerX, centerY)) / 2.0
+    return (radius - distance(res)) / (radius * 3)
+
+
+def _cv_init_level_set(init_level_set, image_shape, dtype=np.float64):
+    """Generates an initial level set function conditional on input arguments."""
+    if isinstance(init_level_set, str):
+        if init_level_set == 'checkerboard':
+            res = _cv_checkerboard(image_shape, 5, dtype)
+        elif init_level_set == 'disk':
+            res = _cv_large_disk(image_shape)
+        elif init_level_set == 'small disk':
+            res = _cv_small_disk(image_shape)
+        else:
+            raise ValueError("Incorrect name for starting level set preset.")
+    else:
+        res = init_level_set
+    return res.astype(dtype, copy=False)
+
+
+def chan_vese(
+    image,
+    mu=0.25,
+    lambda1=1.0,
+    lambda2=1.0,
+    tol=1e-3,
+    max_num_iter=500,
+    dt=0.5,
+    init_level_set='checkerboard',
+    extended_output=False,
+):
+    """Chan-Vese segmentation algorithm.
+
+    Active contour model by evolving a level set. Can be used to
+    segment objects without clearly defined boundaries.
+
+    Parameters
+    ----------
+    image : (M, N) ndarray
+        Grayscale image to be segmented.
+    mu : float, optional
+        'edge length' weight parameter. Higher `mu` values will
+        produce a 'round' edge, while values closer to zero will
+        detect smaller objects.
+    lambda1 : float, optional
+        'difference from average' weight parameter for the output
+        region with value 'True'. If it is lower than `lambda2`, this
+        region will have a larger range of values than the other.
+    lambda2 : float, optional
+        'difference from average' weight parameter for the output
+        region with value 'False'. If it is lower than `lambda1`, this
+        region will have a larger range of values than the other.
+    tol : float, positive, optional
+        Level set variation tolerance between iterations. If the
+        L2 norm difference between the level sets of successive
+        iterations normalized by the area of the image is below this
+        value, the algorithm will assume that the solution was
+        reached.
+    max_num_iter : uint, optional
+        Maximum number of iterations allowed before the algorithm
+        interrupts itself.
+    dt : float, optional
+        A multiplication factor applied at calculations for each step,
+        serves to accelerate the algorithm. While higher values may
+        speed up the algorithm, they may also lead to convergence
+        problems.
+    init_level_set : str or (M, N) ndarray, optional
+        Defines the starting level set used by the algorithm.
+        If a string is inputted, a level set that matches the image
+        size will automatically be generated. Alternatively, it is
+        possible to define a custom level set, which should be an
+        array of float values, with the same shape as 'image'.
+        Accepted string values are as follows.
+
+        'checkerboard'
+            the starting level set is defined as
+            sin(x/5*pi)*sin(y/5*pi), where x and y are pixel
+            coordinates. This level set has fast convergence, but may
+            fail to detect implicit edges.
+        'disk'
+            the starting level set is defined as the opposite
+            of the distance from the center of the image minus half of
+            the minimum value between image width and image height.
+            This is somewhat slower, but is more likely to properly
+            detect implicit edges.
+        'small disk'
+            the starting level set is defined as the
+            opposite of the distance from the center of the image
+            minus a quarter of the minimum value between image width
+            and image height.
+    extended_output : bool, optional
+        If set to True, the return value will be a tuple containing
+        the three return values (see below). If set to False which
+        is the default value, only the 'segmentation' array will be
+        returned.
+
+    Returns
+    -------
+    segmentation : (M, N) ndarray, bool
+        Segmentation produced by the algorithm.
+    phi : (M, N) ndarray of floats
+        Final level set computed by the algorithm.
+    energies : list of floats
+        Shows the evolution of the 'energy' for each step of the
+        algorithm. This should allow to check whether the algorithm
+        converged.
+
+    Notes
+    -----
+    The Chan-Vese Algorithm is designed to segment objects without
+    clearly defined boundaries. This algorithm is based on level sets
+    that are evolved iteratively to minimize an energy, which is
+    defined by weighted values corresponding to the sum of differences
+    intensity from the average value outside the segmented region, the
+    sum of differences from the average value inside the segmented
+    region, and a term which is dependent on the length of the
+    boundary of the segmented region.
+
+    This algorithm was first proposed by Tony Chan and Luminita Vese,
+    in a publication entitled "An Active Contour Model Without Edges"
+    [1]_.
+
+    This implementation of the algorithm is somewhat simplified in the
+    sense that the area factor 'nu' described in the original paper is
+    not implemented, and is only suitable for grayscale images.
+
+    Typical values for `lambda1` and `lambda2` are 1. If the
+    'background' is very different from the segmented object in terms
+    of distribution (for example, a uniform black image with figures
+    of varying intensity), then these values should be different from
+    each other.
+
+    Typical values for mu are between 0 and 1, though higher values
+    can be used when dealing with shapes with very ill-defined
+    contours.
+
+    The 'energy' which this algorithm tries to minimize is defined
+    as the sum of the differences from the average within the region
+    squared and weighed by the 'lambda' factors to which is added the
+    length of the contour multiplied by the 'mu' factor.
+
+    Supports 2D grayscale images only, and does not implement the area
+    term described in the original article.
+
+    References
+    ----------
+    .. [1] An Active Contour Model without Edges, Tony Chan and
+           Luminita Vese, Scale-Space Theories in Computer Vision,
+           1999, :DOI:`10.1007/3-540-48236-9_13`
+    .. [2] Chan-Vese Segmentation, Pascal Getreuer Image Processing On
+           Line, 2 (2012), pp. 214-224,
+           :DOI:`10.5201/ipol.2012.g-cv`
+    .. [3] The Chan-Vese Algorithm - Project Report, Rami Cohen, 2011
+           :arXiv:`1107.2782`
+    """
+    if len(image.shape) != 2:
+        raise ValueError("Input image should be a 2D array.")
+
+    float_dtype = _supported_float_type(image.dtype)
+    phi = _cv_init_level_set(init_level_set, image.shape, dtype=float_dtype)
+
+    if type(phi) != np.ndarray or phi.shape != image.shape:
+        raise ValueError(
+            "The dimensions of initial level set do not "
+            "match the dimensions of image."
+        )
+
+    image = image.astype(float_dtype, copy=False)
+    image = image - np.min(image)
+    if np.max(image) != 0:
+        image = image / np.max(image)
+
+    i = 0
+    old_energy = _cv_energy(image, phi, mu, lambda1, lambda2)
+    energies = []
+    phivar = tol + 1
+    segmentation = phi > 0
+
+    while phivar > tol and i < max_num_iter:
+        # Save old level set values
+        oldphi = phi
+
+        # Calculate new level set
+        phi = _cv_calculate_variation(image, phi, mu, lambda1, lambda2, dt)
+        phi = _cv_reset_level_set(phi)
+        phivar = np.sqrt(((phi - oldphi) ** 2).mean())
+
+        # Extract energy and compare to previous level set and
+        # segmentation to see if continuing is necessary
+        segmentation = phi > 0
+        new_energy = _cv_energy(image, phi, mu, lambda1, lambda2)
+
+        # Save old energy values
+        energies.append(old_energy)
+        old_energy = new_energy
+        i += 1
+
+    if extended_output:
+        return (segmentation, phi, energies)
+    else:
+        return segmentation

.CondaPkg/env/Lib/site-packages/skimage/segmentation/_clear_border.py

@@ -0,0 +1,109 @@
+import numpy as np
+
+from ..measure import label
+
+
+def clear_border(labels, buffer_size=0, bgval=0, mask=None, *, out=None):
+    """Clear objects connected to the label image border.
+
+    Parameters
+    ----------
+    labels : (M[, N[, ..., P]]) array of int or bool
+        Imaging data labels.
+    buffer_size : int, optional
+        The width of the border examined.  By default, only objects
+        that touch the outside of the image are removed.
+    bgval : float or int, optional
+        Cleared objects are set to this value.
+    mask : ndarray of bool, same shape as `image`, optional.
+        Image data mask. Objects in labels image overlapping with
+        False pixels of mask will be removed. If defined, the
+        argument buffer_size will be ignored.
+    out : ndarray
+        Array of the same shape as `labels`, into which the
+        output is placed. By default, a new array is created.
+
+    Returns
+    -------
+    out : (M[, N[, ..., P]]) array
+        Imaging data labels with cleared borders
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from skimage.segmentation import clear_border
+    >>> labels = np.array([[0, 0, 0, 0, 0, 0, 0, 1, 0],
+    ...                    [1, 1, 0, 0, 1, 0, 0, 1, 0],
+    ...                    [1, 1, 0, 1, 0, 1, 0, 0, 0],
+    ...                    [0, 0, 0, 1, 1, 1, 1, 0, 0],
+    ...                    [0, 1, 1, 1, 1, 1, 1, 1, 0],
+    ...                    [0, 0, 0, 0, 0, 0, 0, 0, 0]])
+    >>> clear_border(labels)
+    array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 1, 0, 0, 0, 0],
+           [0, 0, 0, 1, 0, 1, 0, 0, 0],
+           [0, 0, 0, 1, 1, 1, 1, 0, 0],
+           [0, 1, 1, 1, 1, 1, 1, 1, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0]])
+    >>> mask = np.array([[0, 0, 1, 1, 1, 1, 1, 1, 1],
+    ...                  [0, 0, 1, 1, 1, 1, 1, 1, 1],
+    ...                  [1, 1, 1, 1, 1, 1, 1, 1, 1],
+    ...                  [1, 1, 1, 1, 1, 1, 1, 1, 1],
+    ...                  [1, 1, 1, 1, 1, 1, 1, 1, 1],
+    ...                  [1, 1, 1, 1, 1, 1, 1, 1, 1]]).astype(bool)
+    >>> clear_border(labels, mask=mask)
+    array([[0, 0, 0, 0, 0, 0, 0, 1, 0],
+           [0, 0, 0, 0, 1, 0, 0, 1, 0],
+           [0, 0, 0, 1, 0, 1, 0, 0, 0],
+           [0, 0, 0, 1, 1, 1, 1, 0, 0],
+           [0, 1, 1, 1, 1, 1, 1, 1, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0]])
+
+    """
+    if any(buffer_size >= s for s in labels.shape) and mask is None:
+        # ignore buffer_size if mask
+        raise ValueError("buffer size may not be greater than labels size")
+
+    if out is None:
+        out = labels.copy()
+
+    if mask is not None:
+        err_msg = (
+            f'labels and mask should have the same shape but '
+            f'are {out.shape} and {mask.shape}'
+        )
+        if out.shape != mask.shape:
+            raise (ValueError, err_msg)
+        if mask.dtype != bool:
+            raise TypeError("mask should be of type bool.")
+        borders = ~mask
+    else:
+        # create borders with buffer_size
+        borders = np.zeros_like(out, dtype=bool)
+        ext = buffer_size + 1
+        slstart = slice(ext)
+        slend = slice(-ext, None)
+        slices = [slice(None) for _ in out.shape]
+        for d in range(out.ndim):
+            slices[d] = slstart
+            borders[tuple(slices)] = True
+            slices[d] = slend
+            borders[tuple(slices)] = True
+            slices[d] = slice(None)
+
+    # Re-label, in case we are dealing with a binary out
+    # and to get consistent labeling
+    labels, number = label(out, background=0, return_num=True)
+
+    # determine all objects that are connected to borders
+    borders_indices = np.unique(labels[borders])
+    indices = np.arange(number + 1)
+    # mask all label indices that are connected to borders
+    label_mask = np.isin(indices, borders_indices)
+    # create mask for pixels to clear
+    mask = label_mask[labels.reshape(-1)].reshape(labels.shape)
+
+    # clear border pixels
+    out[mask] = bgval
+
+    return out

.CondaPkg/env/Lib/site-packages/skimage/segmentation/_expand_labels.py

@@ -0,0 +1,104 @@
+import numpy as np
+from scipy.ndimage import distance_transform_edt
+
+
+def expand_labels(label_image, distance=1, spacing=1):
+    """Expand labels in label image by ``distance`` pixels without overlapping.
+
+    Given a label image, ``expand_labels`` grows label regions (connected components)
+    outwards by up to ``distance`` units without overflowing into neighboring regions.
+    More specifically, each background pixel that is within Euclidean distance
+    of <= ``distance`` pixels of a connected component is assigned the label of that
+    connected component. The `spacing` parameter can be used to specify the spacing
+    rate of the distance transform used to calculate the Euclidean distance for anisotropic
+    images.
+    Where multiple connected components are within ``distance`` pixels of a background
+    pixel, the label value of the closest connected component will be assigned (see
+    Notes for the case of multiple labels at equal distance).
+
+    Parameters
+    ----------
+    label_image : ndarray of dtype int
+        label image
+    distance : float
+        Euclidean distance in pixels by which to grow the labels. Default is one.
+    spacing : float, or sequence of float, optional
+        Spacing of elements along each dimension. If a sequence, must be of length
+        equal to the input rank; if a single number, this is used for all axes. If
+        not specified, a grid spacing of unity is implied.
+
+    Returns
+    -------
+    enlarged_labels : ndarray of dtype int
+        Labeled array, where all connected regions have been enlarged
+
+    Notes
+    -----
+    Where labels are spaced more than ``distance`` pixels are apart, this is
+    equivalent to a morphological dilation with a disc or hyperball of radius ``distance``.
+    However, in contrast to a morphological dilation, ``expand_labels`` will
+    not expand a label region into a neighboring region.
+
+    This implementation of ``expand_labels`` is derived from CellProfiler [1]_, where
+    it is known as module "IdentifySecondaryObjects (Distance-N)" [2]_.
+
+    There is an important edge case when a pixel has the same distance to
+    multiple regions, as it is not defined which region expands into that
+    space. Here, the exact behavior depends on the upstream implementation
+    of ``scipy.ndimage.distance_transform_edt``.
+
+    See Also
+    --------
+    :func:`skimage.measure.label`, :func:`skimage.segmentation.watershed`, :func:`skimage.morphology.dilation`
+
+    References
+    ----------
+    .. [1] https://cellprofiler.org
+    .. [2] https://github.com/CellProfiler/CellProfiler/blob/082930ea95add7b72243a4fa3d39ae5145995e9c/cellprofiler/modules/identifysecondaryobjects.py#L559
+
+    Examples
+    --------
+    >>> labels = np.array([0, 1, 0, 0, 0, 0, 2])
+    >>> expand_labels(labels, distance=1)
+    array([1, 1, 1, 0, 0, 2, 2])
+
+    Labels will not overwrite each other:
+
+    >>> expand_labels(labels, distance=3)
+    array([1, 1, 1, 1, 2, 2, 2])
+
+    In case of ties, behavior is undefined, but currently resolves to the
+    label closest to ``(0,) * ndim`` in lexicographical order.
+
+    >>> labels_tied = np.array([0, 1, 0, 2, 0])
+    >>> expand_labels(labels_tied, 1)
+    array([1, 1, 1, 2, 2])
+    >>> labels2d = np.array(
+    ...     [[0, 1, 0, 0],
+    ...      [2, 0, 0, 0],
+    ...      [0, 3, 0, 0]]
+    ... )
+    >>> expand_labels(labels2d, 1)
+    array([[2, 1, 1, 0],
+           [2, 2, 0, 0],
+           [2, 3, 3, 0]])
+    >>> expand_labels(labels2d, 1, spacing=[1, 0.5])
+    array([[1, 1, 1, 1],
+           [2, 2, 2, 0],
+           [3, 3, 3, 3]])
+    """
+
+    distances, nearest_label_coords = distance_transform_edt(
+        label_image == 0, sampling=spacing, return_indices=True
+    )
+    labels_out = np.zeros_like(label_image)
+    dilate_mask = distances <= distance
+    # build the coordinates to find nearest labels,
+    # in contrast to [1] this implementation supports label arrays
+    # of any dimension
+    masked_nearest_label_coords = [
+        dimension_indices[dilate_mask] for dimension_indices in nearest_label_coords
+    ]
+    nearest_labels = label_image[tuple(masked_nearest_label_coords)]
+    labels_out[dilate_mask] = nearest_labels
+    return labels_out

.CondaPkg/env/Lib/site-packages/skimage/segmentation/_felzenszwalb.py

@@ -0,0 +1,69 @@
+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)

.CondaPkg/env/Lib/site-packages/skimage/segmentation/_join.py

@@ -0,0 +1,184 @@
+import numpy as np
+
+from ..util._map_array import map_array, ArrayMap
+
+
+def join_segmentations(s1, s2, return_mapping: bool = False):
+    """Return the join of the two input segmentations.
+
+    The join J of S1 and S2 is defined as the segmentation in which two
+    voxels are in the same segment if and only if they are in the same
+    segment in *both* S1 and S2.
+
+    Parameters
+    ----------
+    s1, s2 : numpy arrays
+        s1 and s2 are label fields of the same shape.
+    return_mapping : bool, optional
+        If true, return mappings for joined segmentation labels to the original labels.
+
+    Returns
+    -------
+    j : numpy array
+        The join segmentation of s1 and s2.
+    map_j_to_s1 : ArrayMap, optional
+        Mapping from labels of the joined segmentation j to labels of s1.
+    map_j_to_s2 : ArrayMap, optional
+        Mapping from labels of the joined segmentation j to labels of s2.
+
+    Examples
+    --------
+    >>> from skimage.segmentation import join_segmentations
+    >>> s1 = np.array([[0, 0, 1, 1],
+    ...                [0, 2, 1, 1],
+    ...                [2, 2, 2, 1]])
+    >>> s2 = np.array([[0, 1, 1, 0],
+    ...                [0, 1, 1, 0],
+    ...                [0, 1, 1, 1]])
+    >>> join_segmentations(s1, s2)
+    array([[0, 1, 3, 2],
+           [0, 5, 3, 2],
+           [4, 5, 5, 3]])
+    >>> j, m1, m2 = join_segmentations(s1, s2, return_mapping=True)
+    >>> m1
+    ArrayMap(array([0, 1, 2, 3, 4, 5]), array([0, 0, 1, 1, 2, 2]))
+    >>> np.all(m1[j] == s1)
+    True
+    >>> np.all(m2[j] == s2)
+    True
+    """
+    if s1.shape != s2.shape:
+        raise ValueError(
+            "Cannot join segmentations of different shape. "
+            f"s1.shape: {s1.shape}, s2.shape: {s2.shape}"
+        )
+    # Reindex input label images
+    s1_relabeled, _, backward_map1 = relabel_sequential(s1)
+    s2_relabeled, _, backward_map2 = relabel_sequential(s2)
+    # Create joined label image
+    factor = s2.max() + 1
+    j_initial = factor * s1_relabeled + s2_relabeled
+    j, _, map_j_to_j_initial = relabel_sequential(j_initial)
+    if not return_mapping:
+        return j
+    # Determine label mapping
+    labels_j = np.unique(j_initial)
+    labels_s1_relabeled, labels_s2_relabeled = np.divmod(labels_j, factor)
+    map_j_to_s1 = ArrayMap(
+        map_j_to_j_initial.in_values, backward_map1[labels_s1_relabeled]
+    )
+    map_j_to_s2 = ArrayMap(
+        map_j_to_j_initial.in_values, backward_map2[labels_s2_relabeled]
+    )
+    return j, map_j_to_s1, map_j_to_s2
+
+
+def relabel_sequential(label_field, offset=1):
+    """Relabel arbitrary labels to {`offset`, ... `offset` + number_of_labels}.
+
+    This function also returns the forward map (mapping the original labels to
+    the reduced labels) and the inverse map (mapping the reduced labels back
+    to the original ones).
+
+    Parameters
+    ----------
+    label_field : numpy array of int, arbitrary shape
+        An array of labels, which must be non-negative integers.
+    offset : int, optional
+        The return labels will start at `offset`, which should be
+        strictly positive.
+
+    Returns
+    -------
+    relabeled : numpy array of int, same shape as `label_field`
+        The input label field with labels mapped to
+        {offset, ..., number_of_labels + offset - 1}.
+        The data type will be the same as `label_field`, except when
+        offset + number_of_labels causes overflow of the current data type.
+    forward_map : ArrayMap
+        The map from the original label space to the returned label
+        space. Can be used to re-apply the same mapping. See examples
+        for usage. The output data type will be the same as `relabeled`.
+    inverse_map : ArrayMap
+        The map from the new label space to the original space. This
+        can be used to reconstruct the original label field from the
+        relabeled one. The output data type will be the same as `label_field`.
+
+    Notes
+    -----
+    The label 0 is assumed to denote the background and is never remapped.
+
+    The forward map can be extremely big for some inputs, since its
+    length is given by the maximum of the label field. However, in most
+    situations, ``label_field.max()`` is much smaller than
+    ``label_field.size``, and in these cases the forward map is
+    guaranteed to be smaller than either the input or output images.
+
+    Examples
+    --------
+    >>> from skimage.segmentation import relabel_sequential
+    >>> label_field = np.array([1, 1, 5, 5, 8, 99, 42])
+    >>> relab, fw, inv = relabel_sequential(label_field)
+    >>> relab
+    array([1, 1, 2, 2, 3, 5, 4])
+    >>> print(fw)
+    ArrayMap:
+      1 → 1
+      5 → 2
+      8 → 3
+      42 → 4
+      99 → 5
+    >>> np.array(fw)
+    array([0, 1, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0,
+           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5])
+    >>> np.array(inv)
+    array([ 0,  1,  5,  8, 42, 99])
+    >>> (fw[label_field] == relab).all()
+    True
+    >>> (inv[relab] == label_field).all()
+    True
+    >>> relab, fw, inv = relabel_sequential(label_field, offset=5)
+    >>> relab
+    array([5, 5, 6, 6, 7, 9, 8])
+    """
+    if offset <= 0:
+        raise ValueError("Offset must be strictly positive.")
+    if np.min(label_field) < 0:
+        raise ValueError("Cannot relabel array that contains negative values.")
+    offset = int(offset)
+    in_vals = np.unique(label_field)
+    if in_vals[0] == 0:
+        # always map 0 to 0
+        out_vals = np.concatenate([[0], np.arange(offset, offset + len(in_vals) - 1)])
+    else:
+        out_vals = np.arange(offset, offset + len(in_vals))
+    input_type = label_field.dtype
+    if input_type.kind not in "iu":
+        raise TypeError("label_field must have an integer dtype")
+
+    # Some logic to determine the output type:
+    #  - we don't want to return a smaller output type than the input type,
+    #  ie if we get uint32 as labels input, don't return a uint8 array.
+    #  - but, in some cases, using the input type could result in overflow. The
+    #  input type could be a signed integer (e.g. int32) but
+    #  `np.min_scalar_type` will always return an unsigned type. We check for
+    #  that by casting the largest output value to the input type. If it is
+    #  unchanged, we use the input type, else we use the unsigned minimum
+    #  required type
+    required_type = np.min_scalar_type(out_vals[-1])
+    if input_type.itemsize < required_type.itemsize:
+        output_type = required_type
+    else:
+        if out_vals[-1] < np.iinfo(input_type).max:
+            output_type = input_type
+        else:
+            output_type = required_type
+    out_array = np.empty(label_field.shape, dtype=output_type)
+    out_vals = out_vals.astype(output_type)
+    map_array(label_field, in_vals, out_vals, out=out_array)
+    fw_map = ArrayMap(in_vals, out_vals)
+    inv_map = ArrayMap(out_vals, in_vals)
+    return out_array, fw_map, inv_map

.CondaPkg/env/Lib/site-packages/skimage/segmentation/_quickshift.py

@@ -0,0 +1,104 @@
+import numpy as np
+
+from .._shared.filters import gaussian
+from .._shared.utils import _supported_float_type
+from ..color import rgb2lab
+from ..util import img_as_float
+from ._quickshift_cy import _quickshift_cython
+
+
+def quickshift(
+    image,
+    ratio=1.0,
+    kernel_size=5,
+    max_dist=10,
+    return_tree=False,
+    sigma=0,
+    convert2lab=True,
+    rng=42,
+    *,
+    channel_axis=-1,
+):
+    """Segment image using quickshift clustering in Color-(x,y) space.
+
+    Produces an oversegmentation of the image using the quickshift mode-seeking
+    algorithm.
+
+    Parameters
+    ----------
+    image : (M, N, C) ndarray
+        Input image. The axis corresponding to color channels can be specified
+        via the `channel_axis` argument.
+    ratio : float, optional, between 0 and 1
+        Balances color-space proximity and image-space proximity.
+        Higher values give more weight to color-space.
+    kernel_size : float, optional
+        Width of Gaussian kernel used in smoothing the
+        sample density. Higher means fewer clusters.
+    max_dist : float, optional
+        Cut-off point for data distances.
+        Higher means fewer clusters.
+    return_tree : bool, optional
+        Whether to return the full segmentation hierarchy tree and distances.
+    sigma : float, optional
+        Width for Gaussian smoothing as preprocessing. Zero means no smoothing.
+    convert2lab : bool, optional
+        Whether the input should be converted to Lab colorspace prior to
+        segmentation. For this purpose, the input is assumed to be RGB.
+    rng : {`numpy.random.Generator`, int}, optional
+        Pseudo-random number generator.
+        By default, a PCG64 generator is used (see :func:`numpy.random.default_rng`).
+        If `rng` is an int, it is used to seed the generator.
+
+        The PRNG is used to break ties, and is seeded with 42 by default.
+    channel_axis : int, optional
+        The axis of `image` corresponding to color channels. Defaults to the
+        last axis.
+
+    Returns
+    -------
+    segment_mask : (M, N) ndarray
+        Integer mask indicating segment labels.
+
+    Notes
+    -----
+    The authors advocate to convert the image to Lab color space prior to
+    segmentation, though this is not strictly necessary. For this to work, the
+    image must be given in RGB format.
+
+    References
+    ----------
+    .. [1] Quick shift and kernel methods for mode seeking,
+           Vedaldi, A. and Soatto, S.
+           European Conference on Computer Vision, 2008
+    """
+
+    image = img_as_float(np.atleast_3d(image))
+    float_dtype = _supported_float_type(image.dtype)
+    image = image.astype(float_dtype, copy=False)
+
+    if image.ndim > 3:
+        raise ValueError("Only 2D color images are supported")
+
+    # move channels to last position as expected by the Cython code
+    image = np.moveaxis(image, source=channel_axis, destination=-1)
+
+    if convert2lab:
+        if image.shape[-1] != 3:
+            raise ValueError("Only RGB images can be converted to Lab space.")
+        image = rgb2lab(image)
+
+    if kernel_size < 1:
+        raise ValueError("`kernel_size` should be >= 1.")
+
+    image = gaussian(image, sigma=[sigma, sigma, 0], mode='reflect', channel_axis=-1)
+    image = np.ascontiguousarray(image * ratio)
+
+    segment_mask = _quickshift_cython(
+        image,
+        kernel_size=kernel_size,
+        max_dist=max_dist,
+        return_tree=return_tree,
+        rng=rng,
+    )
+    return segment_mask

.CondaPkg/env/Lib/site-packages/skimage/segmentation/_watershed.py

@@ -0,0 +1,242 @@
+"""watershed.py - watershed algorithm
+
+This module implements a watershed algorithm that apportions pixels into
+marked basins. The algorithm uses a priority queue to hold the pixels
+with the metric for the priority queue being pixel value, then the time
+of entry into the queue - this settles ties in favor of the closest marker.
+
+Some ideas taken from
+Soille, "Automated Basin Delineation from Digital Elevation Models Using
+Mathematical Morphology", Signal Processing 20 (1990) 171-182.
+
+The most important insight in the paper is that entry time onto the queue
+solves two problems: a pixel should be assigned to the neighbor with the
+largest gradient or, if there is no gradient, pixels on a plateau should
+be split between markers on opposite sides.
+"""
+
+import numpy as np
+from scipy import ndimage as ndi
+
+from . import _watershed_cy
+from ..morphology.extrema import local_minima
+from ..morphology._util import _validate_connectivity, _offsets_to_raveled_neighbors
+from ..util import crop, regular_seeds
+
+
+def _validate_inputs(image, markers, mask, connectivity):
+    """Ensure that all inputs to watershed have matching shapes and types.
+
+    Parameters
+    ----------
+    image : array
+        The input image.
+    markers : int or array of int
+        The marker image.
+    mask : array, or None
+        A boolean mask, True where we want to compute the watershed.
+    connectivity : int in {1, ..., image.ndim}
+        The connectivity of the neighborhood of a pixel.
+
+    Returns
+    -------
+    image, markers, mask : arrays
+        The validated and formatted arrays. Image will have dtype float64,
+        markers int32, and mask int8. If ``None`` was given for the mask,
+        it is a volume of all 1s.
+
+    Raises
+    ------
+    ValueError
+        If the shapes of the given arrays don't match.
+    """
+    n_pixels = image.size
+    if mask is None:
+        # Use a complete `True` mask if none is provided
+        mask = np.ones(image.shape, bool)
+    else:
+        mask = np.asanyarray(mask, dtype=bool)
+        n_pixels = np.sum(mask)
+        if mask.shape != image.shape:
+            message = (
+                f'`mask` (shape {mask.shape}) must have same shape '
+                f'as `image` (shape {image.shape})'
+            )
+            raise ValueError(message)
+    if markers is None:
+        markers_bool = local_minima(image, connectivity=connectivity) * mask
+        footprint = ndi.generate_binary_structure(markers_bool.ndim, connectivity)
+        markers = ndi.label(markers_bool, structure=footprint)[0]
+    elif not isinstance(markers, (np.ndarray, list, tuple)):
+        # not array-like, assume int
+        # given int, assume that number of markers *within mask*.
+        markers = regular_seeds(image.shape, int(markers / (n_pixels / image.size)))
+        markers *= mask
+    else:
+        markers = np.asanyarray(markers) * mask
+        if markers.shape != image.shape:
+            message = (
+                f'`markers` (shape {markers.shape}) must have same '
+                f'shape as `image` (shape {image.shape})'
+            )
+            raise ValueError(message)
+    return (image.astype(np.float64), markers, mask.astype(np.int8))
+
+
+def watershed(
+    image,
+    markers=None,
+    connectivity=1,
+    offset=None,
+    mask=None,
+    compactness=0,
+    watershed_line=False,
+):
+    """Find watershed basins in an image flooded from given markers.
+
+    Parameters
+    ----------
+    image : (M, N[, ...]) ndarray
+        Data array where the lowest value points are labeled first.
+    markers : int, or (M, N[, ...]) ndarray of int, optional
+        The desired number of basins, or an array marking the basins with the
+        values to be assigned in the label matrix. Zero means not a marker. If
+        None, the (default) markers are determined as the local minima of
+        `image`. Specifically, the computation is equivalent to applying
+        :func:`skimage.morphology.local_minima` onto `image`, followed by
+        :func:`skimage.measure.label` onto the result (with the same given
+        `connectivity`). Generally speaking, users are encouraged to pass
+        markers explicitly.
+    connectivity : int or ndarray, optional
+        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 (structuring element). Default value is 1.
+        In 2D, 1 gives a 4-neighborhood while 2 gives an 8-neighborhood.
+    offset : array_like of shape image.ndim, optional
+        The coordinates of the center of the footprint.
+    mask : (M, N[, ...]) ndarray of bools or 0's and 1's, optional
+        Array of same shape as `image`. Only points at which mask == True
+        will be labeled.
+    compactness : float, optional
+        Use compact watershed [1]_ with given compactness parameter.
+        Higher values result in more regularly-shaped watershed basins.
+    watershed_line : bool, optional
+        If True, a one-pixel wide line separates the regions
+        obtained by the watershed algorithm. The line has the label 0.
+        Note that the method used for adding this line expects that
+        marker regions are not adjacent; the watershed line may not catch
+        borders between adjacent marker regions.
+
+    Returns
+    -------
+    out : ndarray
+        A labeled matrix of the same type and shape as `markers`.
+
+    See Also
+    --------
+    skimage.segmentation.random_walker
+        A segmentation algorithm based on anisotropic diffusion, usually
+        slower than the watershed but with good results on noisy data and
+        boundaries with holes.
+
+    Notes
+    -----
+    This function implements a watershed algorithm [2]_ [3]_ that apportions
+    pixels into marked basins. The algorithm uses a priority queue to hold
+    the pixels with the metric for the priority queue being pixel value, then
+    the time of entry into the queue -- this settles ties in favor of the
+    closest marker.
+
+    Some ideas are taken from [4]_.
+    The most important insight in the paper is that entry time onto the queue
+    solves two problems: a pixel should be assigned to the neighbor with the
+    largest gradient or, if there is no gradient, pixels on a plateau should
+    be split between markers on opposite sides.
+
+    This implementation converts all arguments to specific, lowest common
+    denominator types, then passes these to a C algorithm.
+
+    Markers can be determined manually, or automatically using for example
+    the local minima of the gradient of the image, or the local maxima of the
+    distance function to the background for separating overlapping objects
+    (see example).
+
+    References
+    ----------
+    .. [1] P. Neubert and P. Protzel, "Compact Watershed and Preemptive SLIC:
+           On Improving Trade-offs of Superpixel Segmentation Algorithms,"
+           2014 22nd International Conference on Pattern Recognition,
+           Stockholm, Sweden, 2014, pp. 996-1001, :DOI:`10.1109/ICPR.2014.181`
+           https://www.tu-chemnitz.de/etit/proaut/publications/cws_pSLIC_ICPR.pdf
+
+    .. [2] https://en.wikipedia.org/wiki/Watershed_%28image_processing%29
+
+    .. [3] http://cmm.ensmp.fr/~beucher/wtshed.html
+
+    .. [4] P. J. Soille and M. M. Ansoult, "Automated basin delineation from
+           digital elevation models using mathematical morphology," Signal
+           Processing, 20(2):171-182, :DOI:`10.1016/0165-1684(90)90127-K`
+
+    Examples
+    --------
+    The watershed algorithm is useful to separate overlapping objects.
+
+    We first generate an initial image with two overlapping circles:
+
+    >>> x, y = np.indices((80, 80))
+    >>> x1, y1, x2, y2 = 28, 28, 44, 52
+    >>> r1, r2 = 16, 20
+    >>> mask_circle1 = (x - x1)**2 + (y - y1)**2 < r1**2
+    >>> mask_circle2 = (x - x2)**2 + (y - y2)**2 < r2**2
+    >>> image = np.logical_or(mask_circle1, mask_circle2)
+
+    Next, we want to separate the two circles. We generate markers at the
+    maxima of the distance to the background:
+
+    >>> from scipy import ndimage as ndi
+    >>> distance = ndi.distance_transform_edt(image)
+    >>> from skimage.feature import peak_local_max
+    >>> max_coords = peak_local_max(distance, labels=image,
+    ...                             footprint=np.ones((3, 3)))
+    >>> local_maxima = np.zeros_like(image, dtype=bool)
+    >>> local_maxima[tuple(max_coords.T)] = True
+    >>> markers = ndi.label(local_maxima)[0]
+
+    Finally, we run the watershed on the image and markers:
+
+    >>> labels = watershed(-distance, markers, mask=image)
+
+    The algorithm works also for 3D images, and can be used for example to
+    separate overlapping spheres.
+    """
+    image, markers, mask = _validate_inputs(image, markers, mask, connectivity)
+    connectivity, offset = _validate_connectivity(image.ndim, connectivity, offset)
+
+    # pad the image, markers, and mask so that we can use the mask to
+    # keep from running off the edges
+    pad_width = [(p, p) for p in offset]
+    image = np.pad(image, pad_width, mode='constant')
+    mask = np.pad(mask, pad_width, mode='constant').ravel()
+    output = np.pad(markers, pad_width, mode='constant')
+
+    flat_neighborhood = _offsets_to_raveled_neighbors(
+        image.shape, connectivity, center=offset
+    )
+    marker_locations = np.flatnonzero(output)
+    image_strides = np.array(image.strides, dtype=np.intp) // image.itemsize
+
+    _watershed_cy.watershed_raveled(
+        image.ravel(),
+        marker_locations,
+        flat_neighborhood,
+        mask,
+        image_strides,
+        compactness,
+        output.ravel(),
+        watershed_line,
+    )
+
+    output = crop(output, pad_width, copy=True)
+
+    return output

.CondaPkg/env/Lib/site-packages/skimage/segmentation/active_contour_model.py

@@ -0,0 +1,250 @@
+import numpy as np
+from scipy.interpolate import RectBivariateSpline
+
+from .._shared.utils import _supported_float_type
+from ..util import img_as_float
+from ..filters import sobel
+
+
+def active_contour(
+    image,
+    snake,
+    alpha=0.01,
+    beta=0.1,
+    w_line=0,
+    w_edge=1,
+    gamma=0.01,
+    max_px_move=1.0,
+    max_num_iter=2500,
+    convergence=0.1,
+    *,
+    boundary_condition='periodic',
+):
+    """Active contour model.
+
+    Active contours by fitting snakes to features of images. Supports single
+    and multichannel 2D images. Snakes can be periodic (for segmentation) or
+    have fixed and/or free ends.
+    The output snake has the same length as the input boundary.
+    As the number of points is constant, make sure that the initial snake
+    has enough points to capture the details of the final contour.
+
+    Parameters
+    ----------
+    image : (M, N) or (M, N, 3) ndarray
+        Input image.
+    snake : (K, 2) ndarray
+        Initial snake coordinates. For periodic boundary conditions, endpoints
+        must not be duplicated.
+    alpha : float, optional
+        Snake length shape parameter. Higher values makes snake contract
+        faster.
+    beta : float, optional
+        Snake smoothness shape parameter. Higher values makes snake smoother.
+    w_line : float, optional
+        Controls attraction to brightness. Use negative values to attract
+        toward dark regions.
+    w_edge : float, optional
+        Controls attraction to edges. Use negative values to repel snake from
+        edges.
+    gamma : float, optional
+        Explicit time stepping parameter.
+    max_px_move : float, optional
+        Maximum pixel distance to move per iteration.
+    max_num_iter : int, optional
+        Maximum iterations to optimize snake shape.
+    convergence : float, optional
+        Convergence criteria.
+    boundary_condition : string, optional
+        Boundary conditions for the contour. Can be one of 'periodic',
+        'free', 'fixed', 'free-fixed', or 'fixed-free'. 'periodic' attaches
+        the two ends of the snake, 'fixed' holds the end-points in place,
+        and 'free' allows free movement of the ends. 'fixed' and 'free' can
+        be combined by parsing 'fixed-free', 'free-fixed'. Parsing
+        'fixed-fixed' or 'free-free' yields same behaviour as 'fixed' and
+        'free', respectively.
+
+    Returns
+    -------
+    snake : (K, 2) ndarray
+        Optimised snake, same shape as input parameter.
+
+    References
+    ----------
+    .. [1]  Kass, M.; Witkin, A.; Terzopoulos, D. "Snakes: Active contour
+            models". International Journal of Computer Vision 1 (4): 321
+            (1988). :DOI:`10.1007/BF00133570`
+
+    Examples
+    --------
+    >>> from skimage.draw import circle_perimeter
+    >>> from skimage.filters import gaussian
+
+    Create and smooth image:
+
+    >>> img = np.zeros((100, 100))
+    >>> rr, cc = circle_perimeter(35, 45, 25)
+    >>> img[rr, cc] = 1
+    >>> img = gaussian(img, sigma=2, preserve_range=False)
+
+    Initialize spline:
+
+    >>> s = np.linspace(0, 2*np.pi, 100)
+    >>> init = 50 * np.array([np.sin(s), np.cos(s)]).T + 50
+
+    Fit spline to image:
+
+    >>> snake = active_contour(img, init, w_edge=0, w_line=1)  # doctest: +SKIP
+    >>> dist = np.sqrt((45-snake[:, 0])**2 + (35-snake[:, 1])**2)  # doctest: +SKIP
+    >>> int(np.mean(dist))  # doctest: +SKIP
+    25
+
+    """
+    max_num_iter = int(max_num_iter)
+    if max_num_iter <= 0:
+        raise ValueError("max_num_iter should be >0.")
+    convergence_order = 10
+    valid_bcs = [
+        'periodic',
+        'free',
+        'fixed',
+        'free-fixed',
+        'fixed-free',
+        'fixed-fixed',
+        'free-free',
+    ]
+    if boundary_condition not in valid_bcs:
+        raise ValueError(
+            "Invalid boundary condition.\n"
+            + "Should be one of: "
+            + ", ".join(valid_bcs)
+            + '.'
+        )
+
+    img = img_as_float(image)
+    float_dtype = _supported_float_type(image.dtype)
+    img = img.astype(float_dtype, copy=False)
+
+    RGB = img.ndim == 3
+
+    # Find edges using sobel:
+    if w_edge != 0:
+        if RGB:
+            edge = [sobel(img[:, :, 0]), sobel(img[:, :, 1]), sobel(img[:, :, 2])]
+        else:
+            edge = [sobel(img)]
+    else:
+        edge = [0]
+
+    # Superimpose intensity and edge images:
+    if RGB:
+        img = w_line * np.sum(img, axis=2) + w_edge * sum(edge)
+    else:
+        img = w_line * img + w_edge * edge[0]
+
+    # Interpolate for smoothness:
+    intp = RectBivariateSpline(
+        np.arange(img.shape[1]), np.arange(img.shape[0]), img.T, kx=2, ky=2, s=0
+    )
+
+    snake_xy = snake[:, ::-1]
+    x = snake_xy[:, 0].astype(float_dtype)
+    y = snake_xy[:, 1].astype(float_dtype)
+    n = len(x)
+    xsave = np.empty((convergence_order, n), dtype=float_dtype)
+    ysave = np.empty((convergence_order, n), dtype=float_dtype)
+
+    # Build snake shape matrix for Euler equation in double precision
+    eye_n = np.eye(n, dtype=float)
+    a = (
+        np.roll(eye_n, -1, axis=0) + np.roll(eye_n, -1, axis=1) - 2 * eye_n
+    )  # second order derivative, central difference
+    b = (
+        np.roll(eye_n, -2, axis=0)
+        + np.roll(eye_n, -2, axis=1)
+        - 4 * np.roll(eye_n, -1, axis=0)
+        - 4 * np.roll(eye_n, -1, axis=1)
+        + 6 * eye_n
+    )  # fourth order derivative, central difference
+    A = -alpha * a + beta * b
+
+    # Impose boundary conditions different from periodic:
+    sfixed = False
+    if boundary_condition.startswith('fixed'):
+        A[0, :] = 0
+        A[1, :] = 0
+        A[1, :3] = [1, -2, 1]
+        sfixed = True
+    efixed = False
+    if boundary_condition.endswith('fixed'):
+        A[-1, :] = 0
+        A[-2, :] = 0
+        A[-2, -3:] = [1, -2, 1]
+        efixed = True
+    sfree = False
+    if boundary_condition.startswith('free'):
+        A[0, :] = 0
+        A[0, :3] = [1, -2, 1]
+        A[1, :] = 0
+        A[1, :4] = [-1, 3, -3, 1]
+        sfree = True
+    efree = False
+    if boundary_condition.endswith('free'):
+        A[-1, :] = 0
+        A[-1, -3:] = [1, -2, 1]
+        A[-2, :] = 0
+        A[-2, -4:] = [-1, 3, -3, 1]
+        efree = True
+
+    # Only one inversion is needed for implicit spline energy minimization:
+    inv = np.linalg.inv(A + gamma * eye_n)
+    # can use float_dtype once we have computed the inverse in double precision
+    inv = inv.astype(float_dtype, copy=False)
+
+    # Explicit time stepping for image energy minimization:
+    for i in range(max_num_iter):
+        # RectBivariateSpline always returns float64, so call astype here
+        fx = intp(x, y, dx=1, grid=False).astype(float_dtype, copy=False)
+        fy = intp(x, y, dy=1, grid=False).astype(float_dtype, copy=False)
+
+        if sfixed:
+            fx[0] = 0
+            fy[0] = 0
+        if efixed:
+            fx[-1] = 0
+            fy[-1] = 0
+        if sfree:
+            fx[0] *= 2
+            fy[0] *= 2
+        if efree:
+            fx[-1] *= 2
+            fy[-1] *= 2
+        xn = inv @ (gamma * x + fx)
+        yn = inv @ (gamma * y + fy)
+
+        # Movements are capped to max_px_move per iteration:
+        dx = max_px_move * np.tanh(xn - x)
+        dy = max_px_move * np.tanh(yn - y)
+        if sfixed:
+            dx[0] = 0
+            dy[0] = 0
+        if efixed:
+            dx[-1] = 0
+            dy[-1] = 0
+        x += dx
+        y += dy
+
+        # Convergence criteria needs to compare to a number of previous
+        # configurations since oscillations can occur.
+        j = i % (convergence_order + 1)
+        if j < convergence_order:
+            xsave[j, :] = x
+            ysave[j, :] = y
+        else:
+            dist = np.min(
+                np.max(np.abs(xsave - x[None, :]) + np.abs(ysave - y[None, :]), 1)
+            )
+            if dist < convergence:
+                break
+
+    return np.stack([y, x], axis=1)

.CondaPkg/env/Lib/site-packages/skimage/segmentation/boundaries.py

@@ -0,0 +1,240 @@
+import numpy as np
+from scipy import ndimage as ndi
+
+from .._shared.utils import _supported_float_type
+from ..morphology import dilation, erosion, square
+from ..util import img_as_float, view_as_windows
+from ..color import gray2rgb
+
+
+def _find_boundaries_subpixel(label_img):
+    """See ``find_boundaries(..., mode='subpixel')``.
+
+    Notes
+    -----
+    This function puts in an empty row and column between each *actual*
+    row and column of the image, for a corresponding shape of ``2s - 1``
+    for every image dimension of size ``s``. These "interstitial" rows
+    and columns are filled as ``True`` if they separate two labels in
+    `label_img`, ``False`` otherwise.
+
+    I used ``view_as_windows`` to get the neighborhood of each pixel.
+    Then I check whether there are two labels or more in that
+    neighborhood.
+    """
+    ndim = label_img.ndim
+    max_label = np.iinfo(label_img.dtype).max
+
+    label_img_expanded = np.zeros(
+        [(2 * s - 1) for s in label_img.shape], label_img.dtype
+    )
+    pixels = (slice(None, None, 2),) * ndim
+    label_img_expanded[pixels] = label_img
+
+    edges = np.ones(label_img_expanded.shape, dtype=bool)
+    edges[pixels] = False
+    label_img_expanded[edges] = max_label
+    windows = view_as_windows(np.pad(label_img_expanded, 1, mode='edge'), (3,) * ndim)
+
+    boundaries = np.zeros_like(edges)
+    for index in np.ndindex(label_img_expanded.shape):
+        if edges[index]:
+            values = np.unique(windows[index].ravel())
+            if len(values) > 2:  # single value and max_label
+                boundaries[index] = True
+    return boundaries
+
+
+def find_boundaries(label_img, connectivity=1, mode='thick', background=0):
+    """Return bool array where boundaries between labeled regions are True.
+
+    Parameters
+    ----------
+    label_img : array of int or bool
+        An array in which different regions are labeled with either different
+        integers or boolean values.
+    connectivity : int in {1, ..., `label_img.ndim`}, optional
+        A pixel is considered a boundary pixel if any of its neighbors
+        has a different label. `connectivity` controls which pixels are
+        considered neighbors. A connectivity of 1 (default) means
+        pixels sharing an edge (in 2D) or a face (in 3D) will be
+        considered neighbors. A connectivity of `label_img.ndim` means
+        pixels sharing a corner will be considered neighbors.
+    mode : string in {'thick', 'inner', 'outer', 'subpixel'}
+        How to mark the boundaries:
+
+        - thick: any pixel not completely surrounded by pixels of the
+          same label (defined by `connectivity`) is marked as a boundary.
+          This results in boundaries that are 2 pixels thick.
+        - inner: outline the pixels *just inside* of objects, leaving
+          background pixels untouched.
+        - outer: outline pixels in the background around object
+          boundaries. When two objects touch, their boundary is also
+          marked.
+        - subpixel: return a doubled image, with pixels *between* the
+          original pixels marked as boundary where appropriate.
+    background : int, optional
+        For modes 'inner' and 'outer', a definition of a background
+        label is required. See `mode` for descriptions of these two.
+
+    Returns
+    -------
+    boundaries : array of bool, same shape as `label_img`
+        A bool image where ``True`` represents a boundary pixel. For
+        `mode` equal to 'subpixel', ``boundaries.shape[i]`` is equal
+        to ``2 * label_img.shape[i] - 1`` for all ``i`` (a pixel is
+        inserted in between all other pairs of pixels).
+
+    Examples
+    --------
+    >>> labels = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+    ...                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+    ...                    [0, 0, 0, 0, 0, 5, 5, 5, 0, 0],
+    ...                    [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
+    ...                    [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
+    ...                    [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
+    ...                    [0, 0, 0, 0, 0, 5, 5, 5, 0, 0],
+    ...                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+    ...                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
+    >>> find_boundaries(labels, mode='thick').astype(np.uint8)
+    array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
+           [0, 1, 1, 1, 1, 1, 0, 1, 1, 0],
+           [0, 1, 1, 0, 1, 1, 0, 1, 1, 0],
+           [0, 1, 1, 1, 1, 1, 0, 1, 1, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
+           [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
+    >>> find_boundaries(labels, mode='inner').astype(np.uint8)
+    array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 0, 1, 0, 0],
+           [0, 0, 1, 0, 1, 1, 0, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 0, 1, 0, 0],
+           [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
+    >>> find_boundaries(labels, mode='outer').astype(np.uint8)
+    array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 0, 0, 1, 0],
+           [0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
+           [0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
+           [0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
+           [0, 0, 1, 1, 1, 1, 0, 0, 1, 0],
+           [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
+    >>> labels_small = labels[::2, ::3]
+    >>> labels_small
+    array([[0, 0, 0, 0],
+           [0, 0, 5, 0],
+           [0, 1, 5, 0],
+           [0, 0, 5, 0],
+           [0, 0, 0, 0]], dtype=uint8)
+    >>> find_boundaries(labels_small, mode='subpixel').astype(np.uint8)
+    array([[0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 1, 1, 1, 0],
+           [0, 0, 0, 1, 0, 1, 0],
+           [0, 1, 1, 1, 0, 1, 0],
+           [0, 1, 0, 1, 0, 1, 0],
+           [0, 1, 1, 1, 0, 1, 0],
+           [0, 0, 0, 1, 0, 1, 0],
+           [0, 0, 0, 1, 1, 1, 0],
+           [0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
+    >>> bool_image = np.array([[False, False, False, False, False],
+    ...                        [False, False, False, False, False],
+    ...                        [False, False,  True,  True,  True],
+    ...                        [False, False,  True,  True,  True],
+    ...                        [False, False,  True,  True,  True]],
+    ...                       dtype=bool)
+    >>> find_boundaries(bool_image)
+    array([[False, False, False, False, False],
+           [False, False,  True,  True,  True],
+           [False,  True,  True,  True,  True],
+           [False,  True,  True, False, False],
+           [False,  True,  True, False, False]])
+    """
+    if label_img.dtype == 'bool':
+        label_img = label_img.astype(np.uint8)
+    ndim = label_img.ndim
+    footprint = ndi.generate_binary_structure(ndim, connectivity)
+    if mode != 'subpixel':
+        boundaries = dilation(label_img, footprint) != erosion(label_img, footprint)
+        if mode == 'inner':
+            foreground_image = label_img != background
+            boundaries &= foreground_image
+        elif mode == 'outer':
+            max_label = np.iinfo(label_img.dtype).max
+            background_image = label_img == background
+            footprint = ndi.generate_binary_structure(ndim, ndim)
+            inverted_background = np.array(label_img, copy=True)
+            inverted_background[background_image] = max_label
+            adjacent_objects = (
+                dilation(label_img, footprint)
+                != erosion(inverted_background, footprint)
+            ) & ~background_image
+            boundaries &= background_image | adjacent_objects
+        return boundaries
+    else:
+        boundaries = _find_boundaries_subpixel(label_img)
+        return boundaries
+
+
+def mark_boundaries(
+    image,
+    label_img,
+    color=(1, 1, 0),
+    outline_color=None,
+    mode='outer',
+    background_label=0,
+):
+    """Return image with boundaries between labeled regions highlighted.
+
+    Parameters
+    ----------
+    image : (M, N[, 3]) array
+        Grayscale or RGB image.
+    label_img : (M, N) array of int
+        Label array where regions are marked by different integer values.
+    color : length-3 sequence, optional
+        RGB color of boundaries in the output image.
+    outline_color : length-3 sequence, optional
+        RGB color surrounding boundaries in the output image. If None, no
+        outline is drawn.
+    mode : string in {'thick', 'inner', 'outer', 'subpixel'}, optional
+        The mode for finding boundaries.
+    background_label : int, optional
+        Which label to consider background (this is only useful for
+        modes ``inner`` and ``outer``).
+
+    Returns
+    -------
+    marked : (M, N, 3) array of float
+        An image in which the boundaries between labels are
+        superimposed on the original image.
+
+    See Also
+    --------
+    find_boundaries
+    """
+    float_dtype = _supported_float_type(image.dtype)
+    marked = img_as_float(image, force_copy=True)
+    marked = marked.astype(float_dtype, copy=False)
+    if marked.ndim == 2:
+        marked = gray2rgb(marked)
+    if mode == 'subpixel':
+        # Here, we want to interpose an extra line of pixels between
+        # each original line - except for the last axis which holds
+        # the RGB information. ``ndi.zoom`` then performs the (cubic)
+        # interpolation, filling in the values of the interposed pixels
+        marked = ndi.zoom(
+            marked, [2 - 1 / s for s in marked.shape[:-1]] + [1], mode='mirror'
+        )
+    boundaries = find_boundaries(label_img, mode=mode, background=background_label)
+    if outline_color is not None:
+        outlines = dilation(boundaries, square(3))
+        marked[outlines] = outline_color
+    marked[boundaries] = color
+    return marked

.CondaPkg/env/Lib/site-packages/skimage/segmentation/morphsnakes.py

@@ -0,0 +1,449 @@
+from itertools import cycle
+
+import numpy as np
+from scipy import ndimage as ndi
+
+from .._shared.utils import check_nD
+
+__all__ = [
+    'morphological_chan_vese',
+    'morphological_geodesic_active_contour',
+    'inverse_gaussian_gradient',
+    'disk_level_set',
+    'checkerboard_level_set',
+]
+
+
+class _fcycle:
+    def __init__(self, iterable):
+        """Call functions from the iterable each time it is called."""
+        self.funcs = cycle(iterable)
+
+    def __call__(self, *args, **kwargs):
+        f = next(self.funcs)
+        return f(*args, **kwargs)
+
+
+# SI and IS operators for 2D and 3D.
+_P2 = [
+    np.eye(3),
+    np.array([[0, 1, 0]] * 3),
+    np.flipud(np.eye(3)),
+    np.rot90([[0, 1, 0]] * 3),
+]
+_P3 = [np.zeros((3, 3, 3)) for i in range(9)]
+
+_P3[0][:, :, 1] = 1
+_P3[1][:, 1, :] = 1
+_P3[2][1, :, :] = 1
+_P3[3][:, [0, 1, 2], [0, 1, 2]] = 1
+_P3[4][:, [0, 1, 2], [2, 1, 0]] = 1
+_P3[5][[0, 1, 2], :, [0, 1, 2]] = 1
+_P3[6][[0, 1, 2], :, [2, 1, 0]] = 1
+_P3[7][[0, 1, 2], [0, 1, 2], :] = 1
+_P3[8][[0, 1, 2], [2, 1, 0], :] = 1
+
+
+def sup_inf(u):
+    """SI operator."""
+
+    if np.ndim(u) == 2:
+        P = _P2
+    elif np.ndim(u) == 3:
+        P = _P3
+    else:
+        raise ValueError("u has an invalid number of dimensions " "(should be 2 or 3)")
+
+    erosions = []
+    for P_i in P:
+        erosions.append(ndi.binary_erosion(u, P_i).astype(np.int8))
+
+    return np.stack(erosions, axis=0).max(0)
+
+
+def inf_sup(u):
+    """IS operator."""
+
+    if np.ndim(u) == 2:
+        P = _P2
+    elif np.ndim(u) == 3:
+        P = _P3
+    else:
+        raise ValueError("u has an invalid number of dimensions " "(should be 2 or 3)")
+
+    dilations = []
+    for P_i in P:
+        dilations.append(ndi.binary_dilation(u, P_i).astype(np.int8))
+
+    return np.stack(dilations, axis=0).min(0)
+
+
+_curvop = _fcycle(
+    [lambda u: sup_inf(inf_sup(u)), lambda u: inf_sup(sup_inf(u))]  # SIoIS
+)  # ISoSI
+
+
+def _check_input(image, init_level_set):
+    """Check that shapes of `image` and `init_level_set` match."""
+    check_nD(image, [2, 3])
+
+    if len(image.shape) != len(init_level_set.shape):
+        raise ValueError(
+            "The dimensions of the initial level set do not "
+            "match the dimensions of the image."
+        )
+
+
+def _init_level_set(init_level_set, image_shape):
+    """Auxiliary function for initializing level sets with a string.
+
+    If `init_level_set` is not a string, it is returned as is.
+    """
+    if isinstance(init_level_set, str):
+        if init_level_set == 'checkerboard':
+            res = checkerboard_level_set(image_shape)
+        elif init_level_set == 'disk':
+            res = disk_level_set(image_shape)
+        else:
+            raise ValueError("`init_level_set` not in " "['checkerboard', 'disk']")
+    else:
+        res = init_level_set
+    return res
+
+
+def disk_level_set(image_shape, *, center=None, radius=None):
+    """Create a disk level set with binary values.
+
+    Parameters
+    ----------
+    image_shape : tuple of positive integers
+        Shape of the image
+    center : tuple of positive integers, optional
+        Coordinates of the center of the disk given in (row, column). If not
+        given, it defaults to the center of the image.
+    radius : float, optional
+        Radius of the disk. If not given, it is set to the 75% of the
+        smallest image dimension.
+
+    Returns
+    -------
+    out : array with shape `image_shape`
+        Binary level set of the disk with the given `radius` and `center`.
+
+    See Also
+    --------
+    checkerboard_level_set
+    """
+
+    if center is None:
+        center = tuple(i // 2 for i in image_shape)
+
+    if radius is None:
+        radius = min(image_shape) * 3.0 / 8.0
+
+    grid = np.mgrid[[slice(i) for i in image_shape]]
+    grid = (grid.T - center).T
+    phi = radius - np.sqrt(np.sum((grid) ** 2, 0))
+    res = np.int8(phi > 0)
+    return res
+
+
+def checkerboard_level_set(image_shape, square_size=5):
+    """Create a checkerboard level set with binary values.
+
+    Parameters
+    ----------
+    image_shape : tuple of positive integers
+        Shape of the image.
+    square_size : int, optional
+        Size of the squares of the checkerboard. It defaults to 5.
+
+    Returns
+    -------
+    out : array with shape `image_shape`
+        Binary level set of the checkerboard.
+
+    See Also
+    --------
+    disk_level_set
+    """
+
+    grid = np.mgrid[[slice(i) for i in image_shape]]
+    grid = grid // square_size
+
+    # Alternate 0/1 for even/odd numbers.
+    grid = grid & 1
+
+    checkerboard = np.bitwise_xor.reduce(grid, axis=0)
+    res = np.int8(checkerboard)
+    return res
+
+
+def inverse_gaussian_gradient(image, alpha=100.0, sigma=5.0):
+    """Inverse of gradient magnitude.
+
+    Compute the magnitude of the gradients in the image and then inverts the
+    result in the range [0, 1]. Flat areas are assigned values close to 1,
+    while areas close to borders are assigned values close to 0.
+
+    This function or a similar one defined by the user should be applied over
+    the image as a preprocessing step before calling
+    `morphological_geodesic_active_contour`.
+
+    Parameters
+    ----------
+    image : (M, N) or (L, M, N) array
+        Grayscale image or volume.
+    alpha : float, optional
+        Controls the steepness of the inversion. A larger value will make the
+        transition between the flat areas and border areas steeper in the
+        resulting array.
+    sigma : float, optional
+        Standard deviation of the Gaussian filter applied over the image.
+
+    Returns
+    -------
+    gimage : (M, N) or (L, M, N) array
+        Preprocessed image (or volume) suitable for
+        `morphological_geodesic_active_contour`.
+    """
+    gradnorm = ndi.gaussian_gradient_magnitude(image, sigma, mode='nearest')
+    return 1.0 / np.sqrt(1.0 + alpha * gradnorm)
+
+
+def morphological_chan_vese(
+    image,
+    num_iter,
+    init_level_set='checkerboard',
+    smoothing=1,
+    lambda1=1,
+    lambda2=1,
+    iter_callback=lambda x: None,
+):
+    """Morphological Active Contours without Edges (MorphACWE)
+
+    Active contours without edges implemented with morphological operators. It
+    can be used to segment objects in images and volumes without well defined
+    borders. It is required that the inside of the object looks different on
+    average than the outside (i.e., the inner area of the object should be
+    darker or lighter than the outer area on average).
+
+    Parameters
+    ----------
+    image : (M, N) or (L, M, N) array
+        Grayscale image or volume to be segmented.
+    num_iter : uint
+        Number of num_iter to run
+    init_level_set : str, (M, N) array, or (L, M, N) array
+        Initial level set. If an array is given, it will be binarized and used
+        as the initial level set. If a string is given, it defines the method
+        to generate a reasonable initial level set with the shape of the
+        `image`. Accepted values are 'checkerboard' and 'disk'. See the
+        documentation of `checkerboard_level_set` and `disk_level_set`
+        respectively for details about how these level sets are created.
+    smoothing : uint, optional
+        Number of times the smoothing operator is applied per iteration.
+        Reasonable values are around 1-4. Larger values lead to smoother
+        segmentations.
+    lambda1 : float, optional
+        Weight parameter for the outer region. If `lambda1` is larger than
+        `lambda2`, the outer region will contain a larger range of values than
+        the inner region.
+    lambda2 : float, optional
+        Weight parameter for the inner region. If `lambda2` is larger than
+        `lambda1`, the inner region will contain a larger range of values than
+        the outer region.
+    iter_callback : function, optional
+        If given, this function is called once per iteration with the current
+        level set as the only argument. This is useful for debugging or for
+        plotting intermediate results during the evolution.
+
+    Returns
+    -------
+    out : (M, N) or (L, M, N) array
+        Final segmentation (i.e., the final level set)
+
+    See Also
+    --------
+    disk_level_set, checkerboard_level_set
+
+    Notes
+    -----
+    This is a version of the Chan-Vese algorithm that uses morphological
+    operators instead of solving a partial differential equation (PDE) for the
+    evolution of the contour. The set of morphological operators used in this
+    algorithm are proved to be infinitesimally equivalent to the Chan-Vese PDE
+    (see [1]_). However, morphological operators are do not suffer from the
+    numerical stability issues typically found in PDEs (it is not necessary to
+    find the right time step for the evolution), and are computationally
+    faster.
+
+    The algorithm and its theoretical derivation are described in [1]_.
+
+    References
+    ----------
+    .. [1] A Morphological Approach to Curvature-based Evolution of Curves and
+           Surfaces, Pablo Márquez-Neila, Luis Baumela, Luis Álvarez. In IEEE
+           Transactions on Pattern Analysis and Machine Intelligence (PAMI),
+           2014, :DOI:`10.1109/TPAMI.2013.106`
+    """
+
+    init_level_set = _init_level_set(init_level_set, image.shape)
+
+    _check_input(image, init_level_set)
+
+    u = np.int8(init_level_set > 0)
+
+    iter_callback(u)
+
+    for _ in range(num_iter):
+        # inside = u > 0
+        # outside = u <= 0
+        c0 = (image * (1 - u)).sum() / float((1 - u).sum() + 1e-8)
+        c1 = (image * u).sum() / float(u.sum() + 1e-8)
+
+        # Image attachment
+        du = np.gradient(u)
+        abs_du = np.abs(du).sum(0)
+        aux = abs_du * (lambda1 * (image - c1) ** 2 - lambda2 * (image - c0) ** 2)
+
+        u[aux < 0] = 1
+        u[aux > 0] = 0
+
+        # Smoothing
+        for _ in range(smoothing):
+            u = _curvop(u)
+
+        iter_callback(u)
+
+    return u
+
+
+def morphological_geodesic_active_contour(
+    gimage,
+    num_iter,
+    init_level_set='disk',
+    smoothing=1,
+    threshold='auto',
+    balloon=0,
+    iter_callback=lambda x: None,
+):
+    """Morphological Geodesic Active Contours (MorphGAC).
+
+    Geodesic active contours implemented with morphological operators. It can
+    be used to segment objects with visible but noisy, cluttered, broken
+    borders.
+
+    Parameters
+    ----------
+    gimage : (M, N) or (L, M, N) array
+        Preprocessed image or volume to be segmented. This is very rarely the
+        original image. Instead, this is usually a preprocessed version of the
+        original image that enhances and highlights the borders (or other
+        structures) of the object to segment.
+        :func:`morphological_geodesic_active_contour` will try to stop the contour
+        evolution in areas where `gimage` is small. See
+        :func:`inverse_gaussian_gradient` as an example function to
+        perform this preprocessing. Note that the quality of
+        :func:`morphological_geodesic_active_contour` might greatly depend on this
+        preprocessing.
+    num_iter : uint
+        Number of num_iter to run.
+    init_level_set : str, (M, N) array, or (L, M, N) array
+        Initial level set. If an array is given, it will be binarized and used
+        as the initial level set. If a string is given, it defines the method
+        to generate a reasonable initial level set with the shape of the
+        `image`. Accepted values are 'checkerboard' and 'disk'. See the
+        documentation of `checkerboard_level_set` and `disk_level_set`
+        respectively for details about how these level sets are created.
+    smoothing : uint, optional
+        Number of times the smoothing operator is applied per iteration.
+        Reasonable values are around 1-4. Larger values lead to smoother
+        segmentations.
+    threshold : float, optional
+        Areas of the image with a value smaller than this threshold will be
+        considered borders. The evolution of the contour will stop in these
+        areas.
+    balloon : float, optional
+        Balloon force to guide the contour in non-informative areas of the
+        image, i.e., areas where the gradient of the image is too small to push
+        the contour towards a border. A negative value will shrink the contour,
+        while a positive value will expand the contour in these areas. Setting
+        this to zero will disable the balloon force.
+    iter_callback : function, optional
+        If given, this function is called once per iteration with the current
+        level set as the only argument. This is useful for debugging or for
+        plotting intermediate results during the evolution.
+
+    Returns
+    -------
+    out : (M, N) or (L, M, N) array
+        Final segmentation (i.e., the final level set)
+
+    See Also
+    --------
+    inverse_gaussian_gradient, disk_level_set, checkerboard_level_set
+
+    Notes
+    -----
+    This is a version of the Geodesic Active Contours (GAC) algorithm that uses
+    morphological operators instead of solving partial differential equations
+    (PDEs) for the evolution of the contour. The set of morphological operators
+    used in this algorithm are proved to be infinitesimally equivalent to the
+    GAC PDEs (see [1]_). However, morphological operators are do not suffer
+    from the numerical stability issues typically found in PDEs (e.g., it is
+    not necessary to find the right time step for the evolution), and are
+    computationally faster.
+
+    The algorithm and its theoretical derivation are described in [1]_.
+
+    References
+    ----------
+    .. [1] A Morphological Approach to Curvature-based Evolution of Curves and
+           Surfaces, Pablo Márquez-Neila, Luis Baumela, Luis Álvarez. In IEEE
+           Transactions on Pattern Analysis and Machine Intelligence (PAMI),
+           2014, :DOI:`10.1109/TPAMI.2013.106`
+    """
+
+    image = gimage
+    init_level_set = _init_level_set(init_level_set, image.shape)
+
+    _check_input(image, init_level_set)
+
+    if threshold == 'auto':
+        threshold = np.percentile(image, 40)
+
+    structure = np.ones((3,) * len(image.shape), dtype=np.int8)
+    dimage = np.gradient(image)
+    # threshold_mask = image > threshold
+    if balloon != 0:
+        threshold_mask_balloon = image > threshold / np.abs(balloon)
+
+    u = np.int8(init_level_set > 0)
+
+    iter_callback(u)
+
+    for _ in range(num_iter):
+        # Balloon
+        if balloon > 0:
+            aux = ndi.binary_dilation(u, structure)
+        elif balloon < 0:
+            aux = ndi.binary_erosion(u, structure)
+        if balloon != 0:
+            u[threshold_mask_balloon] = aux[threshold_mask_balloon]
+
+        # Image attachment
+        aux = np.zeros_like(image)
+        du = np.gradient(u)
+        for el1, el2 in zip(dimage, du):
+            aux += el1 * el2
+        u[aux > 0] = 1
+        u[aux < 0] = 0
+
+        # Smoothing
+        for _ in range(smoothing):
+            u = _curvop(u)
+
+        iter_callback(u)
+
+    return u

.CondaPkg/env/Lib/site-packages/skimage/segmentation/random_walker_segmentation.py

@@ -0,0 +1,580 @@
+"""
+Random walker segmentation algorithm
+
+from *Random walks for image segmentation*, Leo Grady, IEEE Trans
+Pattern Anal Mach Intell. 2006 Nov;28(11):1768-83.
+
+Installing pyamg and using the 'cg_mg' mode of random_walker improves
+significantly the performance.
+"""
+
+import numpy as np
+from scipy import sparse, ndimage as ndi
+
+from .._shared import utils
+from .._shared.utils import warn
+from .._shared.compat import SCIPY_CG_TOL_PARAM_NAME
+
+# executive summary for next code block: try to import umfpack from
+# scipy, but make sure not to raise a fuss if it fails since it's only
+# needed to speed up a few cases.
+# See discussions at:
+# https://groups.google.com/d/msg/scikit-image/FrM5IGP6wh4/1hp-FtVZmfcJ
+# https://stackoverflow.com/questions/13977970/ignore-exceptions-printed-to-stderr-in-del/13977992?noredirect=1#comment28386412_13977992
+try:
+    from scipy.sparse.linalg.dsolve.linsolve import umfpack
+
+    old_del = umfpack.UmfpackContext.__del__
+
+    def new_del(self):
+        try:
+            old_del(self)
+        except AttributeError:
+            pass
+
+    umfpack.UmfpackContext.__del__ = new_del
+    UmfpackContext = umfpack.UmfpackContext()
+except ImportError:
+    UmfpackContext = None
+
+try:
+    from pyamg import ruge_stuben_solver
+
+    amg_loaded = True
+except ImportError:
+    amg_loaded = False
+except AttributeError as error:
+    if "`np.deprecate` was removed" in error.args[0]:
+        warn(
+            "found optional dependency pyamg, which cannot (yet) be imported with "
+            "NumPy >=2 and will be treated as if not available",
+            RuntimeWarning,
+        )
+        amg_loaded = False
+    else:
+        raise error
+
+
+from ..util import img_as_float
+
+from scipy.sparse.linalg import cg, spsolve
+
+
+def _make_graph_edges_3d(n_x, n_y, n_z):
+    """Returns a list of edges for a 3D image.
+
+    Parameters
+    ----------
+    n_x : integer
+        The size of the grid in the x direction.
+    n_y : integer
+        The size of the grid in the y direction
+    n_z : integer
+        The size of the grid in the z direction
+
+    Returns
+    -------
+    edges : (2, N) ndarray
+        with the total number of edges::
+
+            N = n_x * n_y * (nz - 1) +
+                n_x * (n_y - 1) * nz +
+                (n_x - 1) * n_y * nz
+
+        Graph edges with each column describing a node-id pair.
+    """
+    vertices = np.arange(n_x * n_y * n_z).reshape((n_x, n_y, n_z))
+    edges_deep = np.vstack((vertices[..., :-1].ravel(), vertices[..., 1:].ravel()))
+    edges_right = np.vstack((vertices[:, :-1].ravel(), vertices[:, 1:].ravel()))
+    edges_down = np.vstack((vertices[:-1].ravel(), vertices[1:].ravel()))
+    edges = np.hstack((edges_deep, edges_right, edges_down))
+    return edges
+
+
+def _compute_weights_3d(data, spacing, beta, eps, multichannel):
+    # Weight calculation is main difference in multispectral version
+    # Original gradient**2 replaced with sum of gradients ** 2
+    gradients = (
+        np.concatenate(
+            [
+                np.diff(data[..., 0], axis=ax).ravel() / spacing[ax]
+                for ax in [2, 1, 0]
+                if data.shape[ax] > 1
+            ],
+            axis=0,
+        )
+        ** 2
+    )
+    for channel in range(1, data.shape[-1]):
+        gradients += (
+            np.concatenate(
+                [
+                    np.diff(data[..., channel], axis=ax).ravel() / spacing[ax]
+                    for ax in [2, 1, 0]
+                    if data.shape[ax] > 1
+                ],
+                axis=0,
+            )
+            ** 2
+        )
+
+    # All channels considered together in this standard deviation
+    scale_factor = -beta / (10 * data.std())
+    if multichannel:
+        # New final term in beta to give == results in trivial case where
+        # multiple identical spectra are passed.
+        scale_factor /= np.sqrt(data.shape[-1])
+    weights = np.exp(scale_factor * gradients)
+    weights += eps
+    return -weights
+
+
+def _build_laplacian(data, spacing, mask, beta, multichannel):
+    l_x, l_y, l_z = data.shape[:3]
+    edges = _make_graph_edges_3d(l_x, l_y, l_z)
+    weights = _compute_weights_3d(
+        data, spacing, beta=beta, eps=1.0e-10, multichannel=multichannel
+    )
+    if mask is not None:
+        # Remove edges of the graph connected to masked nodes, as well
+        # as corresponding weights of the edges.
+        mask0 = np.hstack(
+            [mask[..., :-1].ravel(), mask[:, :-1].ravel(), mask[:-1].ravel()]
+        )
+        mask1 = np.hstack(
+            [mask[..., 1:].ravel(), mask[:, 1:].ravel(), mask[1:].ravel()]
+        )
+        ind_mask = np.logical_and(mask0, mask1)
+        edges, weights = edges[:, ind_mask], weights[ind_mask]
+
+        # Reassign edges labels to 0, 1, ... edges_number - 1
+        _, inv_idx = np.unique(edges, return_inverse=True)
+        edges = inv_idx.reshape(edges.shape)
+
+    # Build the sparse linear system
+    pixel_nb = l_x * l_y * l_z
+    i_indices = edges.ravel()
+    j_indices = edges[::-1].ravel()
+    data = np.hstack((weights, weights))
+    lap = sparse.coo_matrix((data, (i_indices, j_indices)), shape=(pixel_nb, pixel_nb))
+    lap.setdiag(-np.ravel(lap.sum(axis=0)))
+    return lap.tocsr()
+
+
+def _build_linear_system(data, spacing, labels, nlabels, mask, beta, multichannel):
+    """
+    Build the matrix A and rhs B of the linear system to solve.
+    A and B are two block of the laplacian of the image graph.
+    """
+    if mask is None:
+        labels = labels.ravel()
+    else:
+        labels = labels[mask]
+
+    indices = np.arange(labels.size)
+    seeds_mask = labels > 0
+    unlabeled_indices = indices[~seeds_mask]
+    seeds_indices = indices[seeds_mask]
+
+    lap_sparse = _build_laplacian(
+        data, spacing, mask=mask, beta=beta, multichannel=multichannel
+    )
+
+    rows = lap_sparse[unlabeled_indices, :]
+    lap_sparse = rows[:, unlabeled_indices]
+    B = -rows[:, seeds_indices]
+
+    seeds = labels[seeds_mask]
+    seeds_mask = sparse.csc_matrix(
+        np.hstack([np.atleast_2d(seeds == lab).T for lab in range(1, nlabels + 1)])
+    )
+    rhs = B.dot(seeds_mask)
+
+    return lap_sparse, rhs
+
+
+def _solve_linear_system(lap_sparse, B, tol, mode):
+    if mode is None:
+        mode = 'cg_j'
+
+    if mode == 'cg_mg' and not amg_loaded:
+        warn(
+            '"cg_mg" not available, it requires pyamg to be installed. '
+            'The "cg_j" mode will be used instead.',
+            stacklevel=2,
+        )
+        mode = 'cg_j'
+
+    if mode == 'bf':
+        X = spsolve(lap_sparse, B.toarray()).T
+    else:
+        maxiter = None
+        if mode == 'cg':
+            if UmfpackContext is None:
+                warn(
+                    '"cg" mode may be slow because UMFPACK is not available. '
+                    'Consider building Scipy with UMFPACK or use a '
+                    'preconditioned version of CG ("cg_j" or "cg_mg" modes).',
+                    stacklevel=2,
+                )
+            M = None
+        elif mode == 'cg_j':
+            M = sparse.diags(1.0 / lap_sparse.diagonal())
+        else:
+            # mode == 'cg_mg'
+            lap_sparse = lap_sparse.tocsr()
+            ml = ruge_stuben_solver(lap_sparse, coarse_solver='pinv')
+            M = ml.aspreconditioner(cycle='V')
+            maxiter = 30
+        rtol = {SCIPY_CG_TOL_PARAM_NAME: tol}
+        cg_out = [
+            cg(lap_sparse, B[:, i].toarray(), **rtol, atol=0, M=M, maxiter=maxiter)
+            for i in range(B.shape[1])
+        ]
+        if np.any([info > 0 for _, info in cg_out]):
+            warn(
+                "Conjugate gradient convergence to tolerance not achieved. "
+                "Consider decreasing beta to improve system conditionning.",
+                stacklevel=2,
+            )
+        X = np.asarray([x for x, _ in cg_out])
+
+    return X
+
+
+def _preprocess(labels):
+    label_values, inv_idx = np.unique(labels, return_inverse=True)
+    if max(label_values) <= 0:
+        raise ValueError(
+            'No seeds provided in label image: please ensure '
+            'it contains at least one positive value'
+        )
+
+    if not (label_values == 0).any():
+        warn(
+            'Random walker only segments unlabeled areas, where '
+            'labels == 0. No zero valued areas in labels were '
+            'found. Returning provided labels.',
+            stacklevel=2,
+        )
+
+        return labels, None, None, None, None
+
+    # If some labeled pixels are isolated inside pruned zones, prune them
+    # as well and keep the labels for the final output
+
+    null_mask = labels == 0
+    pos_mask = labels > 0
+    mask = labels >= 0
+
+    fill = ndi.binary_propagation(null_mask, mask=mask)
+    isolated = np.logical_and(pos_mask, np.logical_not(fill))
+
+    pos_mask[isolated] = False
+
+    # If the array has pruned zones, be sure that no isolated pixels
+    # exist between pruned zones (they could not be determined)
+    if label_values[0] < 0 or np.any(isolated):
+        isolated = np.logical_and(
+            np.logical_not(ndi.binary_propagation(pos_mask, mask=mask)), null_mask
+        )
+
+        labels[isolated] = -1
+        if np.all(isolated[null_mask]):
+            warn(
+                'All unlabeled pixels are isolated, they could not be '
+                'determined by the random walker algorithm.',
+                stacklevel=2,
+            )
+            return labels, None, None, None, None
+
+        mask[isolated] = False
+        mask = np.atleast_3d(mask)
+    else:
+        mask = None
+
+    # Reorder label values to have consecutive integers (no gaps)
+    zero_idx = np.searchsorted(label_values, 0)
+    labels = np.atleast_3d(inv_idx.reshape(labels.shape) - zero_idx)
+
+    nlabels = label_values[zero_idx + 1 :].shape[0]
+
+    inds_isolated_seeds = np.nonzero(isolated)
+    isolated_values = labels[inds_isolated_seeds]
+
+    return labels, nlabels, mask, inds_isolated_seeds, isolated_values
+
+
+@utils.channel_as_last_axis(multichannel_output=False)
+def random_walker(
+    data,
+    labels,
+    beta=130,
+    mode='cg_j',
+    tol=1.0e-3,
+    copy=True,
+    return_full_prob=False,
+    spacing=None,
+    *,
+    prob_tol=1e-3,
+    channel_axis=None,
+):
+    """Random walker algorithm for segmentation from markers.
+
+    Random walker algorithm is implemented for gray-level or multichannel
+    images.
+
+    Parameters
+    ----------
+    data : (M, N[, P][, C]) ndarray
+        Image to be segmented in phases. Gray-level `data` can be two- or
+        three-dimensional; multichannel data can be three- or four-
+        dimensional with `channel_axis` specifying the dimension containing
+        channels. Data spacing is assumed isotropic unless the `spacing`
+        keyword argument is used.
+    labels : (M, N[, P]) array of ints
+        Array of seed markers labeled with different positive integers
+        for different phases. Zero-labeled pixels are unlabeled pixels.
+        Negative labels correspond to inactive pixels that are not taken
+        into account (they are removed from the graph). If labels are not
+        consecutive integers, the labels array will be transformed so that
+        labels are consecutive. In the multichannel case, `labels` should have
+        the same shape as a single channel of `data`, i.e. without the final
+        dimension denoting channels.
+    beta : float, optional
+        Penalization coefficient for the random walker motion
+        (the greater `beta`, the more difficult the diffusion).
+    mode : string, available options {'cg', 'cg_j', 'cg_mg', 'bf'}
+        Mode for solving the linear system in the random walker algorithm.
+
+        - 'bf' (brute force): an LU factorization of the Laplacian is
+          computed. This is fast for small images (<1024x1024), but very slow
+          and memory-intensive for large images (e.g., 3-D volumes).
+        - 'cg' (conjugate gradient): the linear system is solved iteratively
+          using the Conjugate Gradient method from scipy.sparse.linalg. This is
+          less memory-consuming than the brute force method for large images,
+          but it is quite slow.
+        - 'cg_j' (conjugate gradient with Jacobi preconditionner): the
+          Jacobi preconditionner is applied during the Conjugate
+          gradient method iterations. This may accelerate the
+          convergence of the 'cg' method.
+        - 'cg_mg' (conjugate gradient with multigrid preconditioner): a
+          preconditioner is computed using a multigrid solver, then the
+          solution is computed with the Conjugate Gradient method. This mode
+          requires that the pyamg module is installed.
+    tol : float, optional
+        Tolerance to achieve when solving the linear system using
+        the conjugate gradient based modes ('cg', 'cg_j' and 'cg_mg').
+    copy : bool, optional
+        If copy is False, the `labels` array will be overwritten with
+        the result of the segmentation. Use copy=False if you want to
+        save on memory.
+    return_full_prob : bool, optional
+        If True, the probability that a pixel belongs to each of the
+        labels will be returned, instead of only the most likely
+        label.
+    spacing : iterable of floats, optional
+        Spacing between voxels in each spatial dimension. If `None`, then
+        the spacing between pixels/voxels in each dimension is assumed 1.
+    prob_tol : float, optional
+        Tolerance on the resulting probability to be in the interval [0, 1].
+        If the tolerance is not satisfied, a warning is displayed.
+    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
+    -------
+    output : ndarray
+        * If `return_full_prob` is False, array of ints of same shape
+          and data type as `labels`, in which each pixel has been
+          labeled according to the marker that reached the pixel first
+          by anisotropic diffusion.
+        * If `return_full_prob` is True, array of floats of shape
+          `(nlabels, labels.shape)`. `output[label_nb, i, j]` is the
+          probability that label `label_nb` reaches the pixel `(i, j)`
+          first.
+
+    See Also
+    --------
+    skimage.segmentation.watershed
+        A segmentation algorithm based on mathematical morphology
+        and "flooding" of regions from markers.
+
+    Notes
+    -----
+    Multichannel inputs are scaled with all channel data combined. Ensure all
+    channels are separately normalized prior to running this algorithm.
+
+    The `spacing` argument is specifically for anisotropic datasets, where
+    data points are spaced differently in one or more spatial dimensions.
+    Anisotropic data is commonly encountered in medical imaging.
+
+    The algorithm was first proposed in [1]_.
+
+    The algorithm solves the diffusion equation at infinite times for
+    sources placed on markers of each phase in turn. A pixel is labeled with
+    the phase that has the greatest probability to diffuse first to the pixel.
+
+    The diffusion equation is solved by minimizing x.T L x for each phase,
+    where L is the Laplacian of the weighted graph of the image, and x is
+    the probability that a marker of the given phase arrives first at a pixel
+    by diffusion (x=1 on markers of the phase, x=0 on the other markers, and
+    the other coefficients are looked for). Each pixel is attributed the label
+    for which it has a maximal value of x. The Laplacian L of the image
+    is defined as:
+
+       - L_ii = d_i, the number of neighbors of pixel i (the degree of i)
+       - L_ij = -w_ij if i and j are adjacent pixels
+
+    The weight w_ij is a decreasing function of the norm of the local gradient.
+    This ensures that diffusion is easier between pixels of similar values.
+
+    When the Laplacian is decomposed into blocks of marked and unmarked
+    pixels::
+
+        L = M B.T
+            B A
+
+    with first indices corresponding to marked pixels, and then to unmarked
+    pixels, minimizing x.T L x for one phase amount to solving::
+
+        A x = - B x_m
+
+    where x_m = 1 on markers of the given phase, and 0 on other markers.
+    This linear system is solved in the algorithm using a direct method for
+    small images, and an iterative method for larger images.
+
+    References
+    ----------
+    .. [1] Leo Grady, Random walks for image segmentation, IEEE Trans Pattern
+        Anal Mach Intell. 2006 Nov;28(11):1768-83.
+        :DOI:`10.1109/TPAMI.2006.233`.
+
+    Examples
+    --------
+    >>> rng = np.random.default_rng()
+    >>> a = np.zeros((10, 10)) + 0.2 * rng.random((10, 10))
+    >>> a[5:8, 5:8] += 1
+    >>> b = np.zeros_like(a, dtype=np.int32)
+    >>> b[3, 3] = 1  # Marker for first phase
+    >>> b[6, 6] = 2  # Marker for second phase
+    >>> random_walker(a, b)  # doctest: +SKIP
+    array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
+           [1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
+           [1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)
+
+    """
+    # Parse input data
+    if mode not in ('cg_mg', 'cg', 'bf', 'cg_j', None):
+        raise ValueError(
+            f"{mode} is not a valid mode. Valid modes are 'cg_mg', "
+            f"'cg', 'cg_j', 'bf', and None"
+        )
+
+    # Spacing kwarg checks
+    if spacing is None:
+        spacing = np.ones(3)
+    elif len(spacing) == labels.ndim:
+        if len(spacing) == 2:
+            # Need a dummy spacing for singleton 3rd dim
+            spacing = np.r_[spacing, 1.0]
+        spacing = np.asarray(spacing)
+    else:
+        raise ValueError(
+            'Input argument `spacing` incorrect, should be an '
+            'iterable with one number per spatial dimension.'
+        )
+
+    # This algorithm expects 4-D arrays of floats, where the first three
+    # dimensions are spatial and the final denotes channels. 2-D images have
+    # a singleton placeholder dimension added for the third spatial dimension,
+    # and single channel images likewise have a singleton added for channels.
+    # The following block ensures valid input and coerces it to the correct
+    # form.
+    multichannel = channel_axis is not None
+    if not multichannel:
+        if data.ndim not in (2, 3):
+            raise ValueError(
+                'For non-multichannel input, data must be of ' 'dimension 2 or 3.'
+            )
+        if data.shape != labels.shape:
+            raise ValueError('Incompatible data and labels shapes.')
+        data = np.atleast_3d(img_as_float(data))[..., np.newaxis]
+    else:
+        if data.ndim not in (3, 4):
+            raise ValueError(
+                'For multichannel input, data must have 3 or 4 ' 'dimensions.'
+            )
+        if data.shape[:-1] != labels.shape:
+            raise ValueError('Incompatible data and labels shapes.')
+        data = img_as_float(data)
+        if data.ndim == 3:  # 2D multispectral, needs singleton in 3rd axis
+            data = data[:, :, np.newaxis, :]
+
+    labels_shape = labels.shape
+    labels_dtype = labels.dtype
+
+    if copy:
+        labels = np.copy(labels)
+
+    (labels, nlabels, mask, inds_isolated_seeds, isolated_values) = _preprocess(labels)
+
+    if isolated_values is None:
+        # No non isolated zero valued areas in labels were
+        # found. Returning provided labels.
+        if return_full_prob:
+            # Return the concatenation of the masks of each unique label
+            return np.concatenate(
+                [np.atleast_3d(labels == lab) for lab in np.unique(labels) if lab > 0],
+                axis=-1,
+            )
+        return labels
+
+    # Build the linear system (lap_sparse, B)
+    lap_sparse, B = _build_linear_system(
+        data, spacing, labels, nlabels, mask, beta, multichannel
+    )
+
+    # Solve the linear system lap_sparse X = B
+    # where X[i, j] is the probability that a marker of label i arrives
+    # first at pixel j by anisotropic diffusion.
+    X = _solve_linear_system(lap_sparse, B, tol, mode)
+
+    if X.min() < -prob_tol or X.max() > 1 + prob_tol:
+        warn(
+            'The probability range is outside [0, 1] given the tolerance '
+            '`prob_tol`. Consider decreasing `beta` and/or decreasing '
+            '`tol`.'
+        )
+
+    # Build the output according to return_full_prob value
+    # Put back labels of isolated seeds
+    labels[inds_isolated_seeds] = isolated_values
+    labels = labels.reshape(labels_shape)
+
+    mask = labels == 0
+    mask[inds_isolated_seeds] = False
+
+    if return_full_prob:
+        out = np.zeros((nlabels,) + labels_shape)
+        for lab, (label_prob, prob) in enumerate(zip(out, X), start=1):
+            label_prob[mask] = prob
+            label_prob[labels == lab] = 1
+    else:
+        X = np.argmax(X, axis=0) + 1
+        out = labels.astype(labels_dtype)
+        out[mask] = X
+
+    return out

.CondaPkg/env/Lib/site-packages/skimage/segmentation/slic_superpixels.py

@@ -0,0 +1,449 @@
+import math
+from collections.abc import Iterable
+from warnings import warn
+
+import numpy as np
+from numpy import random
+from scipy.cluster.vq import kmeans2
+from scipy.spatial.distance import pdist, squareform
+
+from .._shared import utils
+from .._shared.filters import gaussian
+from ..color import rgb2lab
+from ..util import img_as_float, regular_grid
+from ._slic import _enforce_label_connectivity_cython, _slic_cython
+
+
+def _get_mask_centroids(mask, n_centroids, multichannel):
+    """Find regularly spaced centroids on a mask.
+
+    Parameters
+    ----------
+    mask : 3D ndarray
+        The mask within which the centroids must be positioned.
+    n_centroids : int
+        The number of centroids to be returned.
+
+    Returns
+    -------
+    centroids : 2D ndarray
+        The coordinates of the centroids with shape (n_centroids, 3).
+    steps : 1D ndarray
+        The approximate distance between two seeds in all dimensions.
+
+    """
+
+    # Get tight ROI around the mask to optimize
+    coord = np.array(np.nonzero(mask), dtype=float).T
+    # Fix random seed to ensure repeatability
+    # Keep old-style RandomState here as expected results in tests depend on it
+    rng = random.RandomState(123)
+
+    # select n_centroids randomly distributed points from within the mask
+    idx_full = np.arange(len(coord), dtype=int)
+    idx = np.sort(rng.choice(idx_full, min(n_centroids, len(coord)), replace=False))
+
+    # To save time, when n_centroids << len(coords), use only a subset of the
+    # coordinates when calling k-means. Rather than the full set of coords,
+    # we will use a substantially larger subset than n_centroids. Here we
+    # somewhat arbitrarily choose dense_factor=10 to make the samples
+    # 10 times closer together along each axis than the n_centroids samples.
+    dense_factor = 10
+    ndim_spatial = mask.ndim - 1 if multichannel else mask.ndim
+    n_dense = int((dense_factor**ndim_spatial) * n_centroids)
+    if len(coord) > n_dense:
+        # subset of points to use for the k-means calculation
+        # (much denser than idx, but less than the full set)
+        idx_dense = np.sort(rng.choice(idx_full, n_dense, replace=False))
+    else:
+        idx_dense = Ellipsis
+    centroids, _ = kmeans2(coord[idx_dense], coord[idx], iter=5)
+
+    # Compute the minimum distance of each centroid to the others
+    dist = squareform(pdist(centroids))
+    np.fill_diagonal(dist, np.inf)
+    closest_pts = dist.argmin(-1)
+    steps = abs(centroids - centroids[closest_pts, :]).mean(0)
+
+    return centroids, steps
+
+
+def _get_grid_centroids(image, n_centroids):
+    """Find regularly spaced centroids on the image.
+
+    Parameters
+    ----------
+    image : 2D, 3D or 4D ndarray
+        Input image, which can be 2D or 3D, and grayscale or
+        multichannel.
+    n_centroids : int
+        The (approximate) number of centroids to be returned.
+
+    Returns
+    -------
+    centroids : 2D ndarray
+        The coordinates of the centroids with shape (~n_centroids, 3).
+    steps : 1D ndarray
+        The approximate distance between two seeds in all dimensions.
+
+    """
+    d, h, w = image.shape[:3]
+
+    grid_z, grid_y, grid_x = np.mgrid[:d, :h, :w]
+    slices = regular_grid(image.shape[:3], n_centroids)
+
+    centroids_z = grid_z[slices].ravel()[..., np.newaxis]
+    centroids_y = grid_y[slices].ravel()[..., np.newaxis]
+    centroids_x = grid_x[slices].ravel()[..., np.newaxis]
+
+    centroids = np.concatenate([centroids_z, centroids_y, centroids_x], axis=-1)
+
+    steps = np.asarray([float(s.step) if s.step is not None else 1.0 for s in slices])
+    return centroids, steps
+
+
+@utils.channel_as_last_axis(multichannel_output=False)
+def slic(
+    image,
+    n_segments=100,
+    compactness=10.0,
+    max_num_iter=10,
+    sigma=0,
+    spacing=None,
+    convert2lab=None,
+    enforce_connectivity=True,
+    min_size_factor=0.5,
+    max_size_factor=3,
+    slic_zero=False,
+    start_label=1,
+    mask=None,
+    *,
+    channel_axis=-1,
+):
+    """Segments image using k-means clustering in Color-(x,y,z) space.
+
+    Parameters
+    ----------
+    image : (M, N[, P][, C]) ndarray
+        Input image. Can be 2D or 3D, and grayscale or multichannel
+        (see `channel_axis` parameter).
+        Input image must either be NaN-free or the NaN's must be masked out.
+    n_segments : int, optional
+        The (approximate) number of labels in the segmented output image.
+    compactness : float, optional
+        Balances color proximity and space proximity. Higher values give
+        more weight to space proximity, making superpixel shapes more
+        square/cubic. In SLICO mode, this is the initial compactness.
+        This parameter depends strongly on image contrast and on the
+        shapes of objects in the image. We recommend exploring possible
+        values on a log scale, e.g., 0.01, 0.1, 1, 10, 100, before
+        refining around a chosen value.
+    max_num_iter : int, optional
+        Maximum number of iterations of k-means.
+    sigma : float or array-like of floats, optional
+        Width of Gaussian smoothing kernel for pre-processing for each
+        dimension of the image. The same sigma is applied to each dimension in
+        case of a scalar value. Zero means no smoothing.
+        Note that `sigma` is automatically scaled if it is scalar and
+        if a manual voxel spacing is provided (see Notes section). If
+        sigma is array-like, its size must match ``image``'s number
+        of spatial dimensions.
+    spacing : array-like of floats, optional
+        The voxel spacing along each spatial dimension. By default,
+        `slic` assumes uniform spacing (same voxel resolution along
+        each spatial dimension).
+        This parameter controls the weights of the distances along the
+        spatial dimensions during k-means clustering.
+    convert2lab : bool, optional
+        Whether the input should be converted to Lab colorspace prior to
+        segmentation. The input image *must* be RGB. Highly recommended.
+        This option defaults to ``True`` when ``channel_axis` is not None *and*
+        ``image.shape[-1] == 3``.
+    enforce_connectivity : bool, optional
+        Whether the generated segments are connected or not
+    min_size_factor : float, optional
+        Proportion of the minimum segment size to be removed with respect
+        to the supposed segment size ```depth*width*height/n_segments```
+    max_size_factor : float, optional
+        Proportion of the maximum connected segment size. A value of 3 works
+        in most of the cases.
+    slic_zero : bool, optional
+        Run SLIC-zero, the zero-parameter mode of SLIC. [2]_
+    start_label : int, optional
+        The labels' index start. Should be 0 or 1.
+
+        .. versionadded:: 0.17
+           ``start_label`` was introduced in 0.17
+    mask : ndarray, optional
+        If provided, superpixels are computed only where mask is True,
+        and seed points are homogeneously distributed over the mask
+        using a k-means clustering strategy. Mask number of dimensions
+        must be equal to image number of spatial dimensions.
+
+        .. versionadded:: 0.17
+           ``mask`` was introduced in 0.17
+    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
+    -------
+    labels : 2D or 3D array
+        Integer mask indicating segment labels.
+
+    Raises
+    ------
+    ValueError
+        If ``convert2lab`` is set to ``True`` but the last array
+        dimension is not of length 3.
+    ValueError
+        If ``start_label`` is not 0 or 1.
+    ValueError
+        If ``image`` contains unmasked NaN values.
+    ValueError
+        If ``image`` contains unmasked infinite values.
+    ValueError
+        If ``image`` is 2D but ``channel_axis`` is -1 (the default).
+
+    Notes
+    -----
+    * If `sigma > 0`, the image is smoothed using a Gaussian kernel prior to
+      segmentation.
+
+    * If `sigma` is scalar and `spacing` is provided, the kernel width is
+      divided along each dimension by the spacing. For example, if ``sigma=1``
+      and ``spacing=[5, 1, 1]``, the effective `sigma` is ``[0.2, 1, 1]``. This
+      ensures sensible smoothing for anisotropic images.
+
+    * The image is rescaled to be in [0, 1] prior to processing (masked
+      values are ignored).
+
+    * Images of shape (M, N, 3) are interpreted as 2D RGB images by default. To
+      interpret them as 3D with the last dimension having length 3, use
+      `channel_axis=None`.
+
+    * `start_label` is introduced to handle the issue [4]_. Label indexing
+      starts at 1 by default.
+
+    References
+    ----------
+    .. [1] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi,
+        Pascal Fua, and Sabine Süsstrunk, SLIC Superpixels Compared to
+        State-of-the-art Superpixel Methods, TPAMI, May 2012.
+        :DOI:`10.1109/TPAMI.2012.120`
+    .. [2] https://www.epfl.ch/labs/ivrl/research/slic-superpixels/#SLICO
+    .. [3] Irving, Benjamin. "maskSLIC: regional superpixel generation with
+           application to local pathology characterisation in medical images.",
+           2016, :arXiv:`1606.09518`
+    .. [4] https://github.com/scikit-image/scikit-image/issues/3722
+
+    Examples
+    --------
+    >>> from skimage.segmentation import slic
+    >>> from skimage.data import astronaut
+    >>> img = astronaut()
+    >>> segments = slic(img, n_segments=100, compactness=10)
+
+    Increasing the compactness parameter yields more square regions:
+
+    >>> segments = slic(img, n_segments=100, compactness=20)
+
+    """
+    if image.ndim == 2 and channel_axis is not None:
+        raise ValueError(
+            f"channel_axis={channel_axis} indicates multichannel, which is not "
+            "supported for a two-dimensional image; use channel_axis=None if "
+            "the image is grayscale"
+        )
+
+    image = img_as_float(image)
+    float_dtype = utils._supported_float_type(image.dtype)
+    # copy=True so subsequent in-place operations do not modify the
+    # function input
+    image = image.astype(float_dtype, copy=True)
+
+    if mask is not None:
+        # Create masked_image to rescale while ignoring masked values
+        mask = np.ascontiguousarray(mask, dtype=bool)
+        if channel_axis is not None:
+            mask_ = np.expand_dims(mask, axis=channel_axis)
+            mask_ = np.broadcast_to(mask_, image.shape)
+        else:
+            mask_ = mask
+        image_values = image[mask_]
+    else:
+        image_values = image
+
+    # Rescale image to [0, 1] to make choice of compactness insensitive to
+    # input image scale.
+    imin = image_values.min()
+    imax = image_values.max()
+    if np.isnan(imin):
+        raise ValueError("unmasked NaN values in image are not supported")
+    if np.isinf(imin) or np.isinf(imax):
+        raise ValueError("unmasked infinite values in image are not supported")
+    image -= imin
+    if imax != imin:
+        image /= imax - imin
+
+    use_mask = mask is not None
+    dtype = image.dtype
+
+    is_2d = False
+
+    multichannel = channel_axis is not None
+    if image.ndim == 2:
+        # 2D grayscale image
+        image = image[np.newaxis, ..., np.newaxis]
+        is_2d = True
+    elif image.ndim == 3 and multichannel:
+        # Make 2D multichannel image 3D with depth = 1
+        image = image[np.newaxis, ...]
+        is_2d = True
+    elif image.ndim == 3 and not multichannel:
+        # Add channel as single last dimension
+        image = image[..., np.newaxis]
+
+    if multichannel and (convert2lab or convert2lab is None):
+        if image.shape[channel_axis] != 3 and convert2lab:
+            raise ValueError("Lab colorspace conversion requires a RGB image.")
+        elif image.shape[channel_axis] == 3:
+            image = rgb2lab(image)
+
+    if start_label not in [0, 1]:
+        raise ValueError("start_label should be 0 or 1.")
+
+    # initialize cluster centroids for desired number of segments
+    update_centroids = False
+    if use_mask:
+        mask = mask.view('uint8')
+        if mask.ndim == 2:
+            mask = np.ascontiguousarray(mask[np.newaxis, ...])
+        if mask.shape != image.shape[:3]:
+            raise ValueError("image and mask should have the same shape.")
+        centroids, steps = _get_mask_centroids(mask, n_segments, multichannel)
+        update_centroids = True
+    else:
+        centroids, steps = _get_grid_centroids(image, n_segments)
+
+    if spacing is None:
+        spacing = np.ones(3, dtype=dtype)
+    elif isinstance(spacing, Iterable):
+        spacing = np.asarray(spacing, dtype=dtype)
+        if is_2d:
+            if spacing.size != 2:
+                if spacing.size == 3:
+                    warn(
+                        "Input image is 2D: spacing number of "
+                        "elements must be 2. In the future, a ValueError "
+                        "will be raised.",
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+                else:
+                    raise ValueError(
+                        f"Input image is 2D, but spacing has "
+                        f"{spacing.size} elements (expected 2)."
+                    )
+            else:
+                spacing = np.insert(spacing, 0, 1)
+        elif spacing.size != 3:
+            raise ValueError(
+                f"Input image is 3D, but spacing has "
+                f"{spacing.size} elements (expected 3)."
+            )
+        spacing = np.ascontiguousarray(spacing, dtype=dtype)
+    else:
+        raise TypeError("spacing must be None or iterable.")
+
+    if np.isscalar(sigma):
+        sigma = np.array([sigma, sigma, sigma], dtype=dtype)
+        sigma /= spacing
+    elif isinstance(sigma, Iterable):
+        sigma = np.asarray(sigma, dtype=dtype)
+        if is_2d:
+            if sigma.size != 2:
+                if spacing.size == 3:
+                    warn(
+                        "Input image is 2D: sigma number of "
+                        "elements must be 2. In the future, a ValueError "
+                        "will be raised.",
+                        FutureWarning,
+                        stacklevel=2,
+                    )
+                else:
+                    raise ValueError(
+                        f"Input image is 2D, but sigma has "
+                        f"{sigma.size} elements (expected 2)."
+                    )
+            else:
+                sigma = np.insert(sigma, 0, 0)
+        elif sigma.size != 3:
+            raise ValueError(
+                f"Input image is 3D, but sigma has "
+                f"{sigma.size} elements (expected 3)."
+            )
+
+    if (sigma > 0).any():
+        # add zero smoothing for channel dimension
+        sigma = list(sigma) + [0]
+        image = gaussian(image, sigma=sigma, mode='reflect')
+
+    n_centroids = centroids.shape[0]
+    segments = np.ascontiguousarray(
+        np.concatenate([centroids, np.zeros((n_centroids, image.shape[3]))], axis=-1),
+        dtype=dtype,
+    )
+
+    # Scaling of ratio in the same way as in the SLIC paper so the
+    # values have the same meaning
+    step = max(steps)
+    ratio = 1.0 / compactness
+
+    image = np.ascontiguousarray(image * ratio, dtype=dtype)
+
+    if update_centroids:
+        # Step 2 of the algorithm [3]_
+        _slic_cython(
+            image,
+            mask,
+            segments,
+            step,
+            max_num_iter,
+            spacing,
+            slic_zero,
+            ignore_color=True,
+            start_label=start_label,
+        )
+
+    labels = _slic_cython(
+        image,
+        mask,
+        segments,
+        step,
+        max_num_iter,
+        spacing,
+        slic_zero,
+        ignore_color=False,
+        start_label=start_label,
+    )
+
+    if enforce_connectivity:
+        if use_mask:
+            segment_size = mask.sum() / n_centroids
+        else:
+            segment_size = math.prod(image.shape[:3]) / n_centroids
+        min_size = int(min_size_factor * segment_size)
+        max_size = int(max_size_factor * segment_size)
+        labels = _enforce_label_connectivity_cython(
+            labels, min_size, max_size, start_label=start_label
+        )
+
+    if is_2d:
+        labels = labels[0]
+
+    return labels

.CondaPkg/env/Lib/site-packages/skimage/segmentation/tests/test_active_contour_model.py

@@ -0,0 +1,190 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_equal, assert_allclose
+
+from skimage import data
+from skimage._shared.utils import _supported_float_type
+from skimage.color import rgb2gray
+from skimage.filters import gaussian
+from skimage.segmentation import active_contour
+
+
+@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
+def test_periodic_reference(dtype):
+    img = data.astronaut()
+    img = rgb2gray(img)
+    s = np.linspace(0, 2 * np.pi, 400)
+    r = 100 + 100 * np.sin(s)
+    c = 220 + 100 * np.cos(s)
+    init = np.array([r, c]).T
+    img_smooth = gaussian(img, sigma=3, preserve_range=False).astype(dtype, copy=False)
+    snake = active_contour(
+        img_smooth, init, alpha=0.015, beta=10, w_line=0, w_edge=1, gamma=0.001
+    )
+    assert snake.dtype == _supported_float_type(dtype)
+    refr = [98, 99, 100, 101, 102, 103, 104, 105, 106, 108]
+    refc = [299, 298, 298, 298, 298, 297, 297, 296, 296, 295]
+    assert_equal(np.array(snake[:10, 0], dtype=np.int32), refr)
+    assert_equal(np.array(snake[:10, 1], dtype=np.int32), refc)
+
+
+@pytest.mark.parametrize('dtype', [np.float32, np.float64])
+def test_fixed_reference(dtype):
+    img = data.text()
+    r = np.linspace(136, 50, 100)
+    c = np.linspace(5, 424, 100)
+    init = np.array([r, c]).T
+    image_smooth = gaussian(img, sigma=1, preserve_range=False).astype(
+        dtype, copy=False
+    )
+    snake = active_contour(
+        image_smooth,
+        init,
+        boundary_condition='fixed',
+        alpha=0.1,
+        beta=1.0,
+        w_line=-5,
+        w_edge=0,
+        gamma=0.1,
+    )
+    assert snake.dtype == _supported_float_type(dtype)
+    refr = [136, 135, 134, 133, 132, 131, 129, 128, 127, 125]
+    refc = [5, 9, 13, 17, 21, 25, 30, 34, 38, 42]
+    assert_equal(np.array(snake[:10, 0], dtype=np.int32), refr)
+    assert_equal(np.array(snake[:10, 1], dtype=np.int32), refc)
+
+
+@pytest.mark.parametrize('dtype', [np.float32, np.float64])
+def test_free_reference(dtype):
+    img = data.text()
+    r = np.linspace(70, 40, 100)
+    c = np.linspace(5, 424, 100)
+    init = np.array([r, c]).T
+    img_smooth = gaussian(img, sigma=3, preserve_range=False).astype(dtype, copy=False)
+    snake = active_contour(
+        img_smooth,
+        init,
+        boundary_condition='free',
+        alpha=0.1,
+        beta=1.0,
+        w_line=-5,
+        w_edge=0,
+        gamma=0.1,
+    )
+    assert snake.dtype == _supported_float_type(dtype)
+    refr = [76, 76, 75, 74, 73, 72, 71, 70, 69, 69]
+    refc = [10, 13, 16, 19, 23, 26, 29, 32, 36, 39]
+    assert_equal(np.array(snake[:10, 0], dtype=np.int32), refr)
+    assert_equal(np.array(snake[:10, 1], dtype=np.int32), refc)
+
+
+@pytest.mark.parametrize('dtype', [np.float32, np.float64])
+def test_RGB(dtype):
+    img = gaussian(data.text(), sigma=1, preserve_range=False)
+    imgR = np.zeros((img.shape[0], img.shape[1], 3), dtype=dtype)
+    imgG = np.zeros((img.shape[0], img.shape[1], 3), dtype=dtype)
+    imgRGB = np.zeros((img.shape[0], img.shape[1], 3), dtype=dtype)
+    imgR[:, :, 0] = img
+    imgG[:, :, 1] = img
+    imgRGB[:, :, :] = img[:, :, None]
+    r = np.linspace(136, 50, 100)
+    c = np.linspace(5, 424, 100)
+    init = np.array([r, c]).T
+    snake = active_contour(
+        imgR,
+        init,
+        boundary_condition='fixed',
+        alpha=0.1,
+        beta=1.0,
+        w_line=-5,
+        w_edge=0,
+        gamma=0.1,
+    )
+    float_dtype = _supported_float_type(dtype)
+    assert snake.dtype == float_dtype
+    refr = [136, 135, 134, 133, 132, 131, 129, 128, 127, 125]
+    refc = [5, 9, 13, 17, 21, 25, 30, 34, 38, 42]
+    assert_equal(np.array(snake[:10, 0], dtype=np.int32), refr)
+    assert_equal(np.array(snake[:10, 1], dtype=np.int32), refc)
+    snake = active_contour(
+        imgG,
+        init,
+        boundary_condition='fixed',
+        alpha=0.1,
+        beta=1.0,
+        w_line=-5,
+        w_edge=0,
+        gamma=0.1,
+    )
+    assert snake.dtype == float_dtype
+    assert_equal(np.array(snake[:10, 0], dtype=np.int32), refr)
+    assert_equal(np.array(snake[:10, 1], dtype=np.int32), refc)
+    snake = active_contour(
+        imgRGB,
+        init,
+        boundary_condition='fixed',
+        alpha=0.1,
+        beta=1.0,
+        w_line=-5 / 3.0,
+        w_edge=0,
+        gamma=0.1,
+    )
+    assert snake.dtype == float_dtype
+    assert_equal(np.array(snake[:10, 0], dtype=np.int32), refr)
+    assert_equal(np.array(snake[:10, 1], dtype=np.int32), refc)
+
+
+def test_end_points():
+    img = data.astronaut()
+    img = rgb2gray(img)
+    s = np.linspace(0, 2 * np.pi, 400)
+    r = 100 + 100 * np.sin(s)
+    c = 220 + 100 * np.cos(s)
+    init = np.array([r, c]).T
+    snake = active_contour(
+        gaussian(img, sigma=3),
+        init,
+        boundary_condition='periodic',
+        alpha=0.015,
+        beta=10,
+        w_line=0,
+        w_edge=1,
+        gamma=0.001,
+        max_num_iter=100,
+    )
+    assert np.sum(np.abs(snake[0, :] - snake[-1, :])) < 2
+    snake = active_contour(
+        gaussian(img, sigma=3),
+        init,
+        boundary_condition='free',
+        alpha=0.015,
+        beta=10,
+        w_line=0,
+        w_edge=1,
+        gamma=0.001,
+        max_num_iter=100,
+    )
+    assert np.sum(np.abs(snake[0, :] - snake[-1, :])) > 2
+    snake = active_contour(
+        gaussian(img, sigma=3),
+        init,
+        boundary_condition='fixed',
+        alpha=0.015,
+        beta=10,
+        w_line=0,
+        w_edge=1,
+        gamma=0.001,
+        max_num_iter=100,
+    )
+    assert_allclose(snake[0, :], [r[0], c[0]], atol=1e-5)
+
+
+def test_bad_input():
+    img = np.zeros((10, 10))
+    r = np.linspace(136, 50, 100)
+    c = np.linspace(5, 424, 100)
+    init = np.array([r, c]).T
+    with pytest.raises(ValueError):
+        active_contour(img, init, boundary_condition='wrong')
+    with pytest.raises(ValueError):
+        active_contour(img, init, max_num_iter=-15)

.CondaPkg/env/Lib/site-packages/skimage/segmentation/tests/test_boundaries.py

@@ -0,0 +1,159 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_array_equal, assert_allclose
+
+from skimage._shared.utils import _supported_float_type
+from skimage.segmentation import find_boundaries, mark_boundaries
+
+
+white = (1, 1, 1)
+
+
+def test_find_boundaries():
+    image = np.zeros((10, 10), dtype=np.uint8)
+    image[2:7, 2:7] = 1
+
+    ref = np.array(
+        [
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+            [0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+            [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+            [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+            [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+            [0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+            [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        ]
+    )
+
+    result = find_boundaries(image)
+    assert_array_equal(result, ref)
+
+
+def test_find_boundaries_bool():
+    image = np.zeros((5, 5), dtype=bool)
+    image[2:5, 2:5] = True
+
+    ref = np.array(
+        [
+            [False, False, False, False, False],
+            [False, False, True, True, True],
+            [False, True, True, True, True],
+            [False, True, True, False, False],
+            [False, True, True, False, False],
+        ],
+        dtype=bool,
+    )
+    result = find_boundaries(image)
+    assert_array_equal(result, ref)
+
+
+@pytest.mark.parametrize('dtype', [np.uint8, np.float16, np.float32, np.float64])
+def test_mark_boundaries(dtype):
+    image = np.zeros((10, 10), dtype=dtype)
+    label_image = np.zeros((10, 10), dtype=np.uint8)
+    label_image[2:7, 2:7] = 1
+
+    ref = np.array(
+        [
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+            [0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+            [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+            [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+            [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+            [0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+            [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        ]
+    )
+
+    marked = mark_boundaries(image, label_image, color=white, mode='thick')
+    assert marked.dtype == _supported_float_type(dtype)
+    result = np.mean(marked, axis=-1)
+    assert_array_equal(result, ref)
+
+    ref = np.array(
+        [
+            [0, 2, 2, 2, 2, 2, 2, 2, 0, 0],
+            [2, 2, 1, 1, 1, 1, 1, 2, 2, 0],
+            [2, 1, 1, 1, 1, 1, 1, 1, 2, 0],
+            [2, 1, 1, 2, 2, 2, 1, 1, 2, 0],
+            [2, 1, 1, 2, 0, 2, 1, 1, 2, 0],
+            [2, 1, 1, 2, 2, 2, 1, 1, 2, 0],
+            [2, 1, 1, 1, 1, 1, 1, 1, 2, 0],
+            [2, 2, 1, 1, 1, 1, 1, 2, 2, 0],
+            [0, 2, 2, 2, 2, 2, 2, 2, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        ]
+    )
+    marked = mark_boundaries(
+        image, label_image, color=white, outline_color=(2, 2, 2), mode='thick'
+    )
+    result = np.mean(marked, axis=-1)
+    assert_array_equal(result, ref)
+
+
+def test_mark_boundaries_bool():
+    image = np.zeros((10, 10), dtype=bool)
+    label_image = np.zeros((10, 10), dtype=np.uint8)
+    label_image[2:7, 2:7] = 1
+
+    ref = np.array(
+        [
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+            [0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+            [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+            [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+            [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+            [0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+            [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        ]
+    )
+
+    marked = mark_boundaries(image, label_image, color=white, mode='thick')
+    result = np.mean(marked, axis=-1)
+    assert_array_equal(result, ref)
+
+
+@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
+def test_mark_boundaries_subpixel(dtype):
+    labels = np.array(
+        [[0, 0, 0, 0], [0, 0, 5, 0], [0, 1, 5, 0], [0, 0, 5, 0], [0, 0, 0, 0]],
+        dtype=np.uint8,
+    )
+    np.random.seed(0)
+    image = np.round(np.random.rand(*labels.shape), 2)
+    image = image.astype(dtype, copy=False)
+    marked = mark_boundaries(image, labels, color=white, mode='subpixel')
+    assert marked.dtype == _supported_float_type(dtype)
+    marked_proj = np.round(np.mean(marked, axis=-1), 2)
+
+    ref_result = np.array(
+        [
+            [0.55, 0.63, 0.72, 0.69, 0.6, 0.55, 0.54],
+            [0.45, 0.58, 0.72, 1.0, 1.0, 1.0, 0.69],
+            [0.42, 0.54, 0.65, 1.0, 0.44, 1.0, 0.89],
+            [0.69, 1.0, 1.0, 1.0, 0.69, 1.0, 0.83],
+            [0.96, 1.0, 0.38, 1.0, 0.79, 1.0, 0.53],
+            [0.89, 1.0, 1.0, 1.0, 0.38, 1.0, 0.16],
+            [0.57, 0.78, 0.93, 1.0, 0.07, 1.0, 0.09],
+            [0.2, 0.52, 0.92, 1.0, 1.0, 1.0, 0.54],
+            [0.02, 0.35, 0.83, 0.9, 0.78, 0.81, 0.87],
+        ]
+    )
+    assert_allclose(marked_proj, ref_result, atol=0.01)
+
+
+@pytest.mark.parametrize('mode', ['thick', 'inner', 'outer', 'subpixel'])
+def test_boundaries_constant_image(mode):
+    """A constant-valued image has not boundaries."""
+    ones = np.ones((8, 8), dtype=int)
+    b = find_boundaries(ones, mode=mode)
+    assert np.all(b == 0)

.CondaPkg/env/Lib/site-packages/skimage/segmentation/tests/test_chan_vese.py

@@ -0,0 +1,102 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_array_equal
+
+from skimage._shared.utils import _supported_float_type
+from skimage.segmentation import chan_vese
+
+
+@pytest.mark.parametrize('dtype', [np.float32, np.float64])
+def test_chan_vese_flat_level_set(dtype):
+    # because the algorithm evolves the level set around the
+    # zero-level, it the level-set has no zero level, the algorithm
+    # will not produce results in theory. However, since a continuous
+    # approximation of the delta function is used, the algorithm
+    # still affects the entirety of the level-set. Therefore with
+    # infinite time, the segmentation will still converge.
+    img = np.zeros((10, 10), dtype=dtype)
+    img[3:6, 3:6] = 1
+    ls = np.full((10, 10), 1000, dtype=dtype)
+    result = chan_vese(img, mu=0.0, tol=1e-3, init_level_set=ls)
+    assert_array_equal(result.astype(float), np.ones((10, 10)))
+    result = chan_vese(img, mu=0.0, tol=1e-3, init_level_set=-ls)
+    assert_array_equal(result.astype(float), np.zeros((10, 10)))
+
+
+def test_chan_vese_small_disk_level_set():
+    img = np.zeros((10, 10))
+    img[3:6, 3:6] = 1
+    result = chan_vese(img, mu=0.0, tol=1e-3, init_level_set="small disk")
+    assert_array_equal(result.astype(float), img)
+
+
+def test_chan_vese_simple_shape():
+    img = np.zeros((10, 10))
+    img[3:6, 3:6] = 1
+    result = chan_vese(img, mu=0.0, tol=1e-8).astype(float)
+    assert_array_equal(result, img)
+
+
+@pytest.mark.parametrize('dtype', [np.uint8, np.float16, np.float32, np.float64])
+def test_chan_vese_extended_output(dtype):
+    img = np.zeros((10, 10), dtype=dtype)
+    img[3:6, 3:6] = 1
+    result = chan_vese(img, mu=0.0, tol=1e-8, extended_output=True)
+    float_dtype = _supported_float_type(dtype)
+    assert result[1].dtype == float_dtype
+    assert all(arr.dtype == float_dtype for arr in result[2])
+    assert_array_equal(len(result), 3)
+
+
+def test_chan_vese_remove_noise():
+    ref = np.zeros((10, 10))
+    ref[1:6, 1:6] = np.array(
+        [
+            [0, 1, 1, 1, 0],
+            [1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1],
+            [0, 1, 1, 1, 0],
+        ]
+    )
+    img = ref.copy()
+    img[8, 3] = 1
+    result = chan_vese(
+        img, mu=0.3, tol=1e-3, max_num_iter=100, dt=10, init_level_set="disk"
+    ).astype(float)
+    assert_array_equal(result, ref)
+
+
+def test_chan_vese_incorrect_image_type():
+    img = np.zeros((10, 10, 3))
+    ls = np.zeros((10, 9))
+    with pytest.raises(ValueError):
+        chan_vese(img, mu=0.0, init_level_set=ls)
+
+
+def test_chan_vese_gap_closing():
+    ref = np.zeros((20, 20))
+    ref[8:15, :] = np.ones((7, 20))
+    img = ref.copy()
+    img[:, 6] = np.zeros(20)
+    result = chan_vese(
+        img, mu=0.7, tol=1e-3, max_num_iter=1000, dt=1000, init_level_set="disk"
+    ).astype(float)
+    assert_array_equal(result, ref)
+
+
+def test_chan_vese_incorrect_level_set():
+    img = np.zeros((10, 10))
+    ls = np.zeros((10, 9))
+    with pytest.raises(ValueError):
+        chan_vese(img, mu=0.0, init_level_set=ls)
+    with pytest.raises(ValueError):
+        chan_vese(img, mu=0.0, init_level_set="a")
+
+
+def test_chan_vese_blank_image():
+    img = np.zeros((10, 10))
+    level_set = np.random.rand(10, 10)
+    ref = level_set > 0
+    result = chan_vese(img, mu=0.0, tol=0.0, init_level_set=level_set)
+    assert_array_equal(result, ref)

.CondaPkg/env/Lib/site-packages/skimage/segmentation/tests/test_clear_border.py

@@ -0,0 +1,180 @@
+import numpy as np
+from skimage.segmentation import clear_border
+
+from skimage._shared.testing import assert_array_equal, assert_
+
+
+def test_clear_border():
+    image = np.array(
+        [
+            [0, 0, 0, 0, 0, 0, 0, 1, 0],
+            [1, 1, 0, 0, 1, 0, 0, 1, 0],
+            [1, 1, 0, 1, 0, 1, 0, 0, 0],
+            [0, 0, 0, 1, 1, 1, 1, 0, 0],
+            [0, 1, 1, 1, 1, 1, 1, 1, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0],
+        ]
+    )
+
+    # test default case
+    result = clear_border(image.copy())
+    ref = image.copy()
+    ref[1:3, 0:2] = 0
+    ref[0:2, -2] = 0
+    assert_array_equal(result, ref)
+
+    # test buffer
+    result = clear_border(image.copy(), 1)
+    assert_array_equal(result, np.zeros(result.shape))
+
+    # test background value
+    result = clear_border(image.copy(), buffer_size=1, bgval=2)
+    assert_array_equal(result, 2 * np.ones_like(image))
+
+    # test mask
+    mask = np.array(
+        [
+            [0, 0, 1, 1, 1, 1, 1, 1, 1],
+            [0, 0, 1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+            [1, 1, 1, 1, 1, 1, 1, 1, 1],
+        ]
+    ).astype(bool)
+    result = clear_border(image.copy(), mask=mask)
+    ref = image.copy()
+    ref[1:3, 0:2] = 0
+    assert_array_equal(result, ref)
+
+
+def test_clear_border_3d():
+    image = np.array(
+        [
+            [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 0, 0]],
+            [[0, 0, 0, 0], [0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]],
+            [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
+        ]
+    )
+    # test default case
+    result = clear_border(image.copy())
+    ref = image.copy()
+    ref[0, 3, 0] = 0
+    assert_array_equal(result, ref)
+
+    # test buffer
+    result = clear_border(image.copy(), 1)
+    assert_array_equal(result, np.zeros(result.shape))
+
+    # test background value
+    result = clear_border(image.copy(), buffer_size=1, bgval=2)
+    assert_array_equal(result, 2 * np.ones_like(image))
+
+
+def test_clear_border_non_binary():
+    image = np.array(
+        [[1, 2, 3, 1, 2], [3, 3, 5, 4, 2], [3, 4, 5, 4, 2], [3, 3, 2, 1, 2]]
+    )
+
+    result = clear_border(image)
+    expected = np.array(
+        [[0, 0, 0, 0, 0], [0, 0, 5, 4, 0], [0, 4, 5, 4, 0], [0, 0, 0, 0, 0]]
+    )
+
+    assert_array_equal(result, expected)
+    assert_(not np.all(image == result))
+
+
+def test_clear_border_non_binary_3d():
+    image3d = np.array(
+        [
+            [[1, 2, 3, 1, 2], [3, 3, 3, 4, 2], [3, 4, 3, 4, 2], [3, 3, 2, 1, 2]],
+            [[1, 2, 3, 1, 2], [3, 3, 5, 4, 2], [3, 4, 5, 4, 2], [3, 3, 2, 1, 2]],
+            [[1, 2, 3, 1, 2], [3, 3, 3, 4, 2], [3, 4, 3, 4, 2], [3, 3, 2, 1, 2]],
+        ]
+    )
+
+    result = clear_border(image3d)
+    expected = np.array(
+        [
+            [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
+            [[0, 0, 0, 0, 0], [0, 0, 5, 0, 0], [0, 0, 5, 0, 0], [0, 0, 0, 0, 0]],
+            [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
+        ]
+    )
+
+    assert_array_equal(result, expected)
+    assert_(not np.all(image3d == result))
+
+
+def test_clear_border_non_binary_inplace():
+    image = np.array(
+        [[1, 2, 3, 1, 2], [3, 3, 5, 4, 2], [3, 4, 5, 4, 2], [3, 3, 2, 1, 2]]
+    )
+
+    result = clear_border(image, out=image)
+    expected = np.array(
+        [[0, 0, 0, 0, 0], [0, 0, 5, 4, 0], [0, 4, 5, 4, 0], [0, 0, 0, 0, 0]]
+    )
+
+    assert_array_equal(result, expected)
+    assert_array_equal(image, result)
+
+
+def test_clear_border_non_binary_inplace_3d():
+    image3d = np.array(
+        [
+            [[1, 2, 3, 1, 2], [3, 3, 3, 4, 2], [3, 4, 3, 4, 2], [3, 3, 2, 1, 2]],
+            [[1, 2, 3, 1, 2], [3, 3, 5, 4, 2], [3, 4, 5, 4, 2], [3, 3, 2, 1, 2]],
+            [[1, 2, 3, 1, 2], [3, 3, 3, 4, 2], [3, 4, 3, 4, 2], [3, 3, 2, 1, 2]],
+        ]
+    )
+
+    result = clear_border(image3d, out=image3d)
+    expected = np.array(
+        [
+            [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
+            [[0, 0, 0, 0, 0], [0, 0, 5, 0, 0], [0, 0, 5, 0, 0], [0, 0, 0, 0, 0]],
+            [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
+        ]
+    )
+
+    assert_array_equal(result, expected)
+    assert_array_equal(image3d, result)
+
+
+def test_clear_border_non_binary_out():
+    image = np.array(
+        [[1, 2, 3, 1, 2], [3, 3, 5, 4, 2], [3, 4, 5, 4, 2], [3, 3, 2, 1, 2]]
+    )
+    out = np.empty_like(image)
+    result = clear_border(image, out=out)
+    expected = np.array(
+        [[0, 0, 0, 0, 0], [0, 0, 5, 4, 0], [0, 4, 5, 4, 0], [0, 0, 0, 0, 0]]
+    )
+
+    assert_array_equal(result, expected)
+    assert_array_equal(out, result)
+
+
+def test_clear_border_non_binary_out_3d():
+    image3d = np.array(
+        [
+            [[1, 2, 3, 1, 2], [3, 3, 3, 4, 2], [3, 4, 3, 4, 2], [3, 3, 2, 1, 2]],
+            [[1, 2, 3, 1, 2], [3, 3, 5, 4, 2], [3, 4, 5, 4, 2], [3, 3, 2, 1, 2]],
+            [[1, 2, 3, 1, 2], [3, 3, 3, 4, 2], [3, 4, 3, 4, 2], [3, 3, 2, 1, 2]],
+        ]
+    )
+    out = np.empty_like(image3d)
+
+    result = clear_border(image3d, out=out)
+    expected = np.array(
+        [
+            [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
+            [[0, 0, 0, 0, 0], [0, 0, 5, 0, 0], [0, 0, 5, 0, 0], [0, 0, 0, 0, 0]],
+            [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
+        ]
+    )
+
+    assert_array_equal(result, expected)
+    assert_array_equal(out, result)

.CondaPkg/env/Lib/site-packages/skimage/segmentation/tests/test_expand_labels.py

@@ -0,0 +1,182 @@
+from scipy import ndimage as ndi
+from skimage import data
+
+import numpy as np
+
+from skimage import measure
+from skimage.segmentation._expand_labels import expand_labels
+
+from skimage._shared import testing
+from skimage._shared.testing import assert_array_equal
+
+SAMPLE1D = np.array([0, 0, 4, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0])
+SAMPLE1D_EXPANDED_3 = np.array([4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0])
+
+# Some pixels are important edge cases with undefined behaviour:
+# these are the pixels that are at the same distance from
+# multiple labels. Ideally the label would be chosen at random
+# to avoid bias, but as we are relying on the index map returned
+# by the scipy.ndimage distance transform, what actually happens
+# is determined by the upstream implementation of the distance
+# tansform, thus we don't give any guarantees for the edge case pixels.
+#
+# Regardless, it seems prudent to have a test including an edge case
+# so we can detect whether future upstream changes in scipy.ndimage
+# modify the behaviour.
+
+EDGECASE1D = np.array([0, 0, 4, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0])
+EDGECASE1D_EXPANDED_3 = np.array([4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0])
+
+SAMPLE2D = np.array(
+    [
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+        [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+        [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+    ]
+)
+
+SAMPLE2D_EXPANDED_3 = np.array(
+    [
+        [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
+        [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
+        [1, 1, 1, 1, 1, 1, 1, 0, 0, 2, 0],
+        [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 0, 0, 2, 2, 2, 2],
+        [0, 0, 1, 0, 0, 0, 0, 2, 2, 2, 2],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0],
+    ]
+)
+
+# non-integer expansion
+SAMPLE2D_EXPANDED_1_5 = np.array(
+    [
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
+        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
+        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
+        [1, 1, 1, 1, 1, 0, 0, 0, 2, 2, 2],
+        [1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2],
+        [0, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2],
+        [0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+    ]
+)
+
+
+EDGECASE2D = np.array(
+    [
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0],
+        [0, 0, 1, 1, 0, 2, 2, 0, 0, 0, 0],
+        [0, 1, 1, 1, 0, 2, 0, 0, 0, 0, 0],
+        [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
+    ]
+)
+
+EDGECASE2D_EXPANDED_4 = np.array(
+    [
+        [1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 0],
+        [1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2],
+        [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 0],
+        [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 0],
+    ]
+)
+
+SAMPLE3D = np.array(
+    [
+        [[0, 0, 0, 0], [0, 3, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
+        [[0, 0, 0, 0], [0, 3, 3, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
+        [[0, 0, 0, 0], [0, 3, 0, 0], [0, 0, 0, 0], [0, 0, 5, 0]],
+        [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 5, 0]],
+    ]
+)
+
+SAMPLE3D_EXPANDED_2 = np.array(
+    [
+        [[3, 3, 3, 3], [3, 3, 3, 3], [3, 3, 3, 3], [0, 3, 5, 0]],
+        [[3, 3, 3, 3], [3, 3, 3, 3], [3, 3, 3, 3], [0, 5, 5, 5]],
+        [[3, 3, 3, 3], [3, 3, 3, 3], [3, 3, 5, 5], [5, 5, 5, 5]],
+        [[3, 3, 3, 0], [3, 3, 3, 0], [3, 3, 5, 5], [5, 5, 5, 5]],
+    ]
+)
+SAMPLE3D_EXPAND_SPACING = np.array(
+    [
+        [[0, 3, 0, 0], [3, 3, 3, 0], [0, 3, 0, 0], [0, 0, 0, 0]],
+        [[0, 3, 3, 0], [3, 3, 3, 3], [0, 3, 3, 0], [0, 0, 0, 0]],
+        [[0, 3, 0, 0], [3, 3, 3, 0], [0, 3, 5, 0], [0, 5, 5, 5]],
+        [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 5, 0], [0, 5, 5, 5]],
+    ]
+)
+
+SAMPLE_EDGECASE_BEHAVIOUR = np.array([[0, 1, 0, 0], [2, 0, 0, 0], [0, 3, 0, 0]])
+
+
+@testing.parametrize(
+    "input_array, expected_output, expand_distance, spacing",
+    [
+        (SAMPLE1D, SAMPLE1D_EXPANDED_3, 3, 1),
+        (SAMPLE2D, SAMPLE2D_EXPANDED_3, 3, 1),
+        (SAMPLE2D, SAMPLE2D_EXPANDED_1_5, 1.5, 1),
+        (EDGECASE1D, EDGECASE1D_EXPANDED_3, 3, 1),
+        (EDGECASE2D, EDGECASE2D_EXPANDED_4, 4, 1),
+        (SAMPLE3D, SAMPLE3D_EXPANDED_2, 2, 1),
+        (SAMPLE3D, SAMPLE3D_EXPAND_SPACING, 1, [2, 1, 1]),
+    ],
+)
+def test_expand_labels(input_array, expected_output, expand_distance, spacing):
+    expanded = expand_labels(input_array, expand_distance, spacing)
+    assert_array_equal(expanded, expected_output)
+
+
+@testing.parametrize('ndim', [2, 3])
+@testing.parametrize('distance', range(6))
+def test_binary_blobs(ndim, distance):
+    """Check some invariants with label expansion.
+
+    - New labels array should exactly contain the original labels array.
+    - Distance to old labels array within new labels should never exceed input
+      distance.
+    - Distance beyond the expanded labels should always exceed the input
+      distance.
+    """
+    img = data.binary_blobs(length=64, blob_size_fraction=0.05, n_dim=ndim)
+    labels = measure.label(img)
+    expanded = expand_labels(labels, distance=distance)
+    original_mask = labels != 0
+    assert_array_equal(labels[original_mask], expanded[original_mask])
+    expanded_only_mask = (expanded - labels).astype(bool)
+    distance_map = ndi.distance_transform_edt(~original_mask)
+    expanded_distances = distance_map[expanded_only_mask]
+    if expanded_distances.size > 0:
+        assert np.all(expanded_distances <= distance)
+    beyond_expanded_distances = distance_map[~expanded.astype(bool)]
+    if beyond_expanded_distances.size > 0:
+        assert np.all(beyond_expanded_distances > distance)
+
+
+def test_edge_case_behaviour():
+    """Check edge case behavior to detect upstream changes
+
+    For edge cases where a pixel has the same distance to several regions,
+    lexicographical order seems to determine which region gets to expand
+    into this pixel given the current upstream behaviour in
+    scipy.ndimage.distance_map_edt.
+
+    As a result, we expect different results when transposing the array.
+    If this test fails, something has changed upstream.
+    """
+    expanded = expand_labels(SAMPLE_EDGECASE_BEHAVIOUR, 1)
+    expanded_transpose = expand_labels(SAMPLE_EDGECASE_BEHAVIOUR.T, 1)
+    assert not np.all(expanded == expanded_transpose.T)

.CondaPkg/env/Lib/site-packages/skimage/segmentation/tests/test_felzenszwalb.py

@@ -0,0 +1,90 @@
+import numpy as np
+from skimage import data
+from skimage.segmentation import felzenszwalb
+
+from skimage._shared import testing
+from skimage._shared.testing import (
+    assert_greater,
+    run_in_parallel,
+    assert_equal,
+    assert_array_equal,
+    assert_warns,
+    assert_no_warnings,
+)
+
+
+@run_in_parallel()
+def test_grey():
+    # very weak tests.
+    img = np.zeros((20, 21))
+    img[:10, 10:] = 0.2
+    img[10:, :10] = 0.4
+    img[10:, 10:] = 0.6
+    seg = felzenszwalb(img, sigma=0)
+    # we expect 4 segments:
+    assert_equal(len(np.unique(seg)), 4)
+    # that mostly respect the 4 regions:
+    for i in range(4):
+        hist = np.histogram(img[seg == i], bins=[0, 0.1, 0.3, 0.5, 1])[0]
+        assert_greater(hist[i], 40)
+
+
+def test_minsize():
+    # single-channel:
+    img = data.coins()[20:168, 0:128]
+    for min_size in np.arange(10, 100, 10):
+        segments = felzenszwalb(img, min_size=min_size, sigma=3)
+        counts = np.bincount(segments.ravel())
+        # actually want to test greater or equal.
+        assert_greater(counts.min() + 1, min_size)
+    # multi-channel:
+    coffee = data.coffee()[::4, ::4]
+    for min_size in np.arange(10, 100, 10):
+        segments = felzenszwalb(coffee, min_size=min_size, sigma=3)
+        counts = np.bincount(segments.ravel())
+        # actually want to test greater or equal.
+        assert_greater(counts.min() + 1, min_size)
+
+
+@testing.parametrize('channel_axis', [0, -1])
+def test_3D(channel_axis):
+    grey_img = np.zeros((10, 10))
+    rgb_img = np.zeros((10, 10, 3))
+    three_d_img = np.zeros((10, 10, 10))
+
+    rgb_img = np.moveaxis(rgb_img, -1, channel_axis)
+    with assert_no_warnings():
+        felzenszwalb(grey_img, channel_axis=-1)
+        felzenszwalb(grey_img, channel_axis=None)
+        felzenszwalb(rgb_img, channel_axis=channel_axis)
+    with assert_warns(RuntimeWarning):
+        felzenszwalb(three_d_img, channel_axis=channel_axis)
+    with testing.raises(ValueError):
+        felzenszwalb(rgb_img, channel_axis=None)
+        felzenszwalb(three_d_img, channel_axis=None)
+
+
+def test_color():
+    # very weak tests.
+    img = np.zeros((20, 21, 3))
+    img[:10, :10, 0] = 1
+    img[10:, :10, 1] = 1
+    img[10:, 10:, 2] = 1
+    seg = felzenszwalb(img, sigma=0)
+    # we expect 4 segments:
+    assert_equal(len(np.unique(seg)), 4)
+    assert_array_equal(seg[:10, :10], 0)
+    assert_array_equal(seg[10:, :10], 2)
+    assert_array_equal(seg[:10, 10:], 1)
+    assert_array_equal(seg[10:, 10:], 3)
+
+
+def test_merging():
+    # test region merging in the post-processing step
+    img = np.array([[0, 0.3], [0.7, 1]])
+    # With scale=0, only the post-processing is performed.
+    seg = felzenszwalb(img, scale=0, sigma=0, min_size=2)
+    # we expect 2 segments:
+    assert_equal(len(np.unique(seg)), 2)
+    assert_array_equal(seg[0, :], 0)
+    assert_array_equal(seg[1, :], 1)

.CondaPkg/env/Lib/site-packages/skimage/segmentation/tests/test_join.py

@@ -0,0 +1,218 @@
+import numpy as np
+from skimage.segmentation import join_segmentations, relabel_sequential
+
+from skimage._shared import testing
+from skimage._shared.testing import assert_array_equal
+import pytest
+
+
+def test_join_segmentations():
+    s1 = np.array([[0, 0, 1, 1], [0, 2, 1, 1], [2, 2, 2, 1]])
+    s2 = np.array([[0, 1, 1, 0], [0, 1, 1, 0], [0, 1, 1, 1]])
+
+    # test correct join
+    # NOTE: technically, equality to j_ref is not required, only that there
+    # is a one-to-one mapping between j and j_ref. I don't know of an easy way
+    # to check this (i.e. not as error-prone as the function being tested)
+    j = join_segmentations(s1, s2)
+    j_ref = np.array([[0, 1, 3, 2], [0, 5, 3, 2], [4, 5, 5, 3]])
+    assert_array_equal(j, j_ref)
+
+    # test correct mapping
+    j, m1, m2 = join_segmentations(s1, s2, return_mapping=True)
+    assert_array_equal(m1[j], s1)
+    assert_array_equal(m2[j], s2)
+
+    # test correct exception when arrays are different shapes
+    s3 = np.array([[0, 0, 1, 1], [0, 2, 2, 1]])
+    with testing.raises(ValueError):
+        join_segmentations(s1, s3)
+
+
+def _check_maps(ar, ar_relab, fw, inv):
+    assert_array_equal(fw[ar], ar_relab)
+    assert_array_equal(inv[ar_relab], ar)
+
+
+def test_relabel_sequential_offset1():
+    ar = np.array([1, 1, 5, 5, 8, 99, 42])
+    ar_relab, fw, inv = relabel_sequential(ar)
+    _check_maps(ar, ar_relab, fw, inv)
+    ar_relab_ref = np.array([1, 1, 2, 2, 3, 5, 4])
+    assert_array_equal(ar_relab, ar_relab_ref)
+    fw_ref = np.zeros(100, int)
+    fw_ref[1] = 1
+    fw_ref[5] = 2
+    fw_ref[8] = 3
+    fw_ref[42] = 4
+    fw_ref[99] = 5
+    assert_array_equal(fw, fw_ref)
+    inv_ref = np.array([0, 1, 5, 8, 42, 99])
+    assert_array_equal(inv, inv_ref)
+
+
+def test_relabel_sequential_offset5():
+    ar = np.array([1, 1, 5, 5, 8, 99, 42])
+    ar_relab, fw, inv = relabel_sequential(ar, offset=5)
+    _check_maps(ar, ar_relab, fw, inv)
+    ar_relab_ref = np.array([5, 5, 6, 6, 7, 9, 8])
+    assert_array_equal(ar_relab, ar_relab_ref)
+    fw_ref = np.zeros(100, int)
+    fw_ref[1] = 5
+    fw_ref[5] = 6
+    fw_ref[8] = 7
+    fw_ref[42] = 8
+    fw_ref[99] = 9
+    assert_array_equal(fw, fw_ref)
+    inv_ref = np.array([0, 0, 0, 0, 0, 1, 5, 8, 42, 99])
+    assert_array_equal(inv, inv_ref)
+
+
+def test_relabel_sequential_offset5_with0():
+    ar = np.array([1, 1, 5, 5, 8, 99, 42, 0])
+    ar_relab, fw, inv = relabel_sequential(ar, offset=5)
+    _check_maps(ar, ar_relab, fw, inv)
+    ar_relab_ref = np.array([5, 5, 6, 6, 7, 9, 8, 0])
+    assert_array_equal(ar_relab, ar_relab_ref)
+    fw_ref = np.zeros(100, int)
+    fw_ref[1] = 5
+    fw_ref[5] = 6
+    fw_ref[8] = 7
+    fw_ref[42] = 8
+    fw_ref[99] = 9
+    assert_array_equal(fw, fw_ref)
+    inv_ref = np.array([0, 0, 0, 0, 0, 1, 5, 8, 42, 99])
+    assert_array_equal(inv, inv_ref)
+
+
+def test_relabel_sequential_dtype():
+    ar = np.array([1, 1, 5, 5, 8, 99, 42, 0], dtype=np.uint8)
+    ar_relab, fw, inv = relabel_sequential(ar, offset=5)
+    _check_maps(ar.astype(int), ar_relab, fw, inv)
+    ar_relab_ref = np.array([5, 5, 6, 6, 7, 9, 8, 0])
+    assert_array_equal(ar_relab, ar_relab_ref)
+    fw_ref = np.zeros(100, int)
+    fw_ref[1] = 5
+    fw_ref[5] = 6
+    fw_ref[8] = 7
+    fw_ref[42] = 8
+    fw_ref[99] = 9
+    assert_array_equal(fw, fw_ref)
+    inv_ref = np.array([0, 0, 0, 0, 0, 1, 5, 8, 42, 99])
+    assert_array_equal(inv, inv_ref)
+
+
+def test_relabel_sequential_signed_overflow():
+    imax = np.iinfo(np.int32).max
+    labels = np.array([0, 1, 99, 42, 42], dtype=np.int32)
+    output, fw, inv = relabel_sequential(labels, offset=imax)
+    reference = np.array([0, imax, imax + 2, imax + 1, imax + 1], dtype=np.uint32)
+    assert_array_equal(output, reference)
+    assert output.dtype == reference.dtype
+
+
+def test_very_large_labels():
+    imax = np.iinfo(np.int64).max
+    labels = np.array([0, 1, imax, 42, 42], dtype=np.int64)
+    output, fw, inv = relabel_sequential(labels, offset=imax)
+    assert np.max(output) == imax + 2
+
+
+@pytest.mark.parametrize(
+    'dtype',
+    (
+        np.byte,
+        np.short,
+        np.intc,
+        int,
+        np.longlong,
+        np.ubyte,
+        np.ushort,
+        np.uintc,
+        np.uint,
+        np.ulonglong,
+    ),
+)
+@pytest.mark.parametrize('data_already_sequential', (False, True))
+def test_relabel_sequential_int_dtype_stability(data_already_sequential, dtype):
+    if data_already_sequential:
+        ar = np.array([1, 3, 0, 2, 5, 4], dtype=dtype)
+    else:
+        ar = np.array([1, 1, 5, 5, 8, 99, 42, 0], dtype=dtype)
+    assert all(a.dtype == dtype for a in relabel_sequential(ar))
+
+
+def test_relabel_sequential_int_dtype_overflow():
+    ar = np.array([1, 3, 0, 2, 5, 4], dtype=np.uint8)
+    offset = 254
+    ar_relab, fw, inv = relabel_sequential(ar, offset=offset)
+    _check_maps(ar, ar_relab, fw, inv)
+    assert all(a.dtype == np.uint16 for a in (ar_relab, fw))
+    assert inv.dtype == ar.dtype
+    ar_relab_ref = np.where(ar > 0, ar.astype(int) + offset - 1, 0)
+    assert_array_equal(ar_relab, ar_relab_ref)
+
+
+def test_relabel_sequential_negative_values():
+    ar = np.array([1, 1, 5, -5, 8, 99, 42, 0])
+    with pytest.raises(ValueError):
+        relabel_sequential(ar)
+
+
+@pytest.mark.parametrize('offset', (0, -3))
+@pytest.mark.parametrize('data_already_sequential', (False, True))
+def test_relabel_sequential_nonpositive_offset(data_already_sequential, offset):
+    if data_already_sequential:
+        ar = np.array([1, 3, 0, 2, 5, 4])
+    else:
+        ar = np.array([1, 1, 5, 5, 8, 99, 42, 0])
+    with pytest.raises(ValueError):
+        relabel_sequential(ar, offset=offset)
+
+
+@pytest.mark.parametrize('offset', (1, 5))
+@pytest.mark.parametrize('with0', (False, True))
+@pytest.mark.parametrize('input_starts_at_offset', (False, True))
+def test_relabel_sequential_already_sequential(offset, with0, input_starts_at_offset):
+    if with0:
+        ar = np.array([1, 3, 0, 2, 5, 4])
+    else:
+        ar = np.array([1, 3, 2, 5, 4])
+    if input_starts_at_offset:
+        ar[ar > 0] += offset - 1
+    ar_relab, fw, inv = relabel_sequential(ar, offset=offset)
+    _check_maps(ar, ar_relab, fw, inv)
+    if input_starts_at_offset:
+        ar_relab_ref = ar
+    else:
+        ar_relab_ref = np.where(ar > 0, ar + offset - 1, 0)
+    assert_array_equal(ar_relab, ar_relab_ref)
+
+
+def test_incorrect_input_dtype():
+    labels = np.array([0, 2, 2, 1, 1, 8], dtype=float)
+    with testing.raises(TypeError):
+        _ = relabel_sequential(labels)
+
+
+def test_arraymap_call():
+    ar = np.array([1, 1, 5, 5, 8, 99, 42, 0], dtype=np.intp)
+    relabeled, fw, inv = relabel_sequential(ar)
+    testing.assert_array_equal(relabeled, fw(ar))
+    testing.assert_array_equal(ar, inv(relabeled))
+
+
+def test_arraymap_len():
+    ar = np.array([1, 1, 5, 5, 8, 99, 42, 0], dtype=np.intp)
+    relabeled, fw, inv = relabel_sequential(ar)
+    assert len(fw) == 100
+    assert len(fw) == len(np.array(fw))
+    assert len(inv) == 6
+    assert len(inv) == len(np.array(inv))
+
+
+def test_arraymap_set():
+    ar = np.array([1, 1, 5, 5, 8, 99, 42, 0], dtype=np.intp)
+    relabeled, fw, inv = relabel_sequential(ar)
+    fw[72] = 6
+    assert fw[72] == 6

.CondaPkg/env/Lib/site-packages/skimage/segmentation/tests/test_morphsnakes.py

@@ -0,0 +1,152 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_array_equal
+
+from skimage.segmentation import (
+    disk_level_set,
+    inverse_gaussian_gradient,
+    morphological_chan_vese,
+    morphological_geodesic_active_contour,
+)
+
+
+def gaussian_blob():
+    coords = np.mgrid[-5:6, -5:6]
+    sqrdistances = (coords**2).sum(0)
+    return np.exp(-sqrdistances / 10)
+
+
+def test_morphsnakes_incorrect_image_shape():
+    img = np.zeros((10, 10, 3))
+    ls = np.zeros((10, 9))
+
+    with pytest.raises(ValueError):
+        morphological_chan_vese(img, num_iter=1, init_level_set=ls)
+    with pytest.raises(ValueError):
+        morphological_geodesic_active_contour(img, num_iter=1, init_level_set=ls)
+
+
+def test_morphsnakes_incorrect_ndim():
+    img = np.zeros((4, 4, 4, 4))
+    ls = np.zeros((4, 4, 4, 4))
+
+    with pytest.raises(ValueError):
+        morphological_chan_vese(img, num_iter=1, init_level_set=ls)
+    with pytest.raises(ValueError):
+        morphological_geodesic_active_contour(img, num_iter=1, init_level_set=ls)
+
+
+def test_morphsnakes_black():
+    img = np.zeros((11, 11))
+    ls = disk_level_set(img.shape, center=(5, 5), radius=3)
+
+    ref_zeros = np.zeros(img.shape, dtype=np.int8)
+    ref_ones = np.ones(img.shape, dtype=np.int8)
+
+    acwe_ls = morphological_chan_vese(img, num_iter=6, init_level_set=ls)
+    assert_array_equal(acwe_ls, ref_zeros)
+
+    gac_ls = morphological_geodesic_active_contour(img, num_iter=6, init_level_set=ls)
+    assert_array_equal(gac_ls, ref_zeros)
+
+    gac_ls2 = morphological_geodesic_active_contour(
+        img, num_iter=6, init_level_set=ls, balloon=1, threshold=-1, smoothing=0
+    )
+    assert_array_equal(gac_ls2, ref_ones)
+
+    assert acwe_ls.dtype == gac_ls.dtype == gac_ls2.dtype == np.int8
+
+
+def test_morphsnakes_simple_shape_chan_vese():
+    img = gaussian_blob()
+    ls1 = disk_level_set(img.shape, center=(5, 5), radius=3)
+    ls2 = disk_level_set(img.shape, center=(5, 5), radius=6)
+
+    acwe_ls1 = morphological_chan_vese(img, num_iter=10, init_level_set=ls1)
+    acwe_ls2 = morphological_chan_vese(img, num_iter=10, init_level_set=ls2)
+
+    assert_array_equal(acwe_ls1, acwe_ls2)
+
+    assert acwe_ls1.dtype == acwe_ls2.dtype == np.int8
+
+
+def test_morphsnakes_simple_shape_geodesic_active_contour():
+    img = (disk_level_set((11, 11), center=(5, 5), radius=3.5)).astype(float)
+    gimg = inverse_gaussian_gradient(img, alpha=10.0, sigma=1.0)
+    ls = disk_level_set(img.shape, center=(5, 5), radius=6)
+
+    ref = np.array(
+        [
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+            [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+            [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+            [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+            [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+            [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+            [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        ],
+        dtype=np.int8,
+    )
+
+    gac_ls = morphological_geodesic_active_contour(
+        gimg, num_iter=10, init_level_set=ls, balloon=-1
+    )
+    assert_array_equal(gac_ls, ref)
+    assert gac_ls.dtype == np.int8
+
+
+def test_init_level_sets():
+    image = np.zeros((6, 6))
+    checkerboard_ls = morphological_chan_vese(image, 0, 'checkerboard')
+    checkerboard_ref = np.array(
+        [
+            [0, 0, 0, 0, 0, 1],
+            [0, 0, 0, 0, 0, 1],
+            [0, 0, 0, 0, 0, 1],
+            [0, 0, 0, 0, 0, 1],
+            [0, 0, 0, 0, 0, 1],
+            [1, 1, 1, 1, 1, 0],
+        ],
+        dtype=np.int8,
+    )
+
+    disk_ls = morphological_geodesic_active_contour(image, 0, 'disk')
+    disk_ref = np.array(
+        [
+            [0, 0, 0, 0, 0, 0],
+            [0, 0, 1, 1, 1, 0],
+            [0, 1, 1, 1, 1, 1],
+            [0, 1, 1, 1, 1, 1],
+            [0, 1, 1, 1, 1, 1],
+            [0, 0, 1, 1, 1, 0],
+        ],
+        dtype=np.int8,
+    )
+
+    assert_array_equal(checkerboard_ls, checkerboard_ref)
+    assert_array_equal(disk_ls, disk_ref)
+
+
+def test_morphsnakes_3d():
+    image = np.zeros((7, 7, 7))
+
+    evolution = []
+
+    def callback(x):
+        evolution.append(x.sum())
+
+    ls = morphological_chan_vese(image, 5, 'disk', iter_callback=callback)
+
+    # Check that the initial disk level set is correct
+    assert evolution[0] == 81
+
+    # Check that the final level set is correct
+    assert ls.sum() == 0
+
+    # Check that the contour is shrinking at every iteration
+    for v1, v2 in zip(evolution[:-1], evolution[1:]):
+        assert v1 >= v2

.CondaPkg/env/Lib/site-packages/skimage/segmentation/tests/test_quickshift.py

@@ -0,0 +1,79 @@
+import numpy as np
+import pytest
+from skimage.segmentation import quickshift
+
+from skimage._shared import testing
+from skimage._shared.testing import (
+    assert_greater,
+    run_in_parallel,
+    assert_equal,
+    assert_array_equal,
+)
+
+
+@run_in_parallel()
+@testing.parametrize('dtype', [np.float32, np.float64])
+def test_grey(dtype):
+    rng = np.random.default_rng(0)
+    img = np.zeros((20, 21))
+    img[:10, 10:] = 0.2
+    img[10:, :10] = 0.4
+    img[10:, 10:] = 0.6
+    img += 0.05 * rng.normal(size=img.shape)
+    img = img.astype(dtype, copy=False)
+    seg = quickshift(img, kernel_size=2, max_dist=3, rng=0, convert2lab=False, sigma=0)
+    quickshift(img, kernel_size=2, max_dist=3, rng=0, convert2lab=False, sigma=0)
+    # we expect 4 segments:
+    assert_equal(len(np.unique(seg)), 4)
+    # that mostly respect the 4 regions:
+    for i in range(4):
+        hist = np.histogram(img[seg == i], bins=[0, 0.1, 0.3, 0.5, 1])[0]
+        assert_greater(hist[i], 20)
+
+
+@testing.parametrize('dtype', [np.float32, np.float64])
+@testing.parametrize('channel_axis', [-3, -2, -1, 0, 1, 2])
+def test_color(dtype, channel_axis):
+    rng = np.random.default_rng(583428449)
+    img = np.zeros((20, 21, 3))
+    img[:10, :10, 0] = 1
+    img[10:, :10, 1] = 1
+    img[10:, 10:, 2] = 1
+    img += 0.01 * rng.normal(size=img.shape)
+    img[img > 1] = 1
+    img[img < 0] = 0
+    img = img.astype(dtype, copy=False)
+
+    img = np.moveaxis(img, source=-1, destination=channel_axis)
+    seg = quickshift(
+        img, rng=0, max_dist=30, kernel_size=10, sigma=0, channel_axis=channel_axis
+    )
+    # we expect 4 segments:
+    assert_equal(len(np.unique(seg)), 4)
+    assert_array_equal(seg[:10, :10], 1)
+    assert_array_equal(seg[10:, :10], 3)
+    assert_array_equal(seg[:10, 10:], 0)
+    assert_array_equal(seg[10:, 10:], 2)
+
+    seg2 = quickshift(
+        img,
+        kernel_size=1,
+        max_dist=2,
+        rng=0,
+        convert2lab=False,
+        sigma=0,
+        channel_axis=channel_axis,
+    )
+    # very oversegmented:
+    assert len(np.unique(seg2)) > 10
+    # still don't cross lines
+    assert (seg2[9, :] != seg2[10, :]).all()
+    assert (seg2[:, 9] != seg2[:, 10]).all()
+
+
+def test_convert2lab_not_rgb():
+    img = np.zeros((20, 21, 2))
+    with pytest.raises(
+        ValueError, match="Only RGB images can be converted to Lab space"
+    ):
+        quickshift(img, convert2lab=True)

.CondaPkg/env/Lib/site-packages/skimage/segmentation/tests/test_random_walker.py

@@ -0,0 +1,523 @@
+import numpy as np
+
+from skimage._shared import testing
+from skimage._shared._warnings import expected_warnings
+from skimage._shared.testing import xfail, arch32
+from skimage.segmentation import random_walker
+from skimage.transform import resize
+
+PYAMG_MISSING_WARNING = r'pyamg|\A\Z'
+
+
+def make_2d_syntheticdata(lx, ly=None):
+    if ly is None:
+        ly = lx
+    np.random.seed(1234)
+    data = np.zeros((lx, ly)) + 0.1 * np.random.randn(lx, ly)
+    small_l = int(lx // 5)
+    data[
+        lx // 2 - small_l : lx // 2 + small_l, ly // 2 - small_l : ly // 2 + small_l
+    ] = 1
+    data[
+        lx // 2 - small_l + 1 : lx // 2 + small_l - 1,
+        ly // 2 - small_l + 1 : ly // 2 + small_l - 1,
+    ] = 0.1 * np.random.randn(2 * small_l - 2, 2 * small_l - 2)
+    data[lx // 2 - small_l, ly // 2 - small_l // 8 : ly // 2 + small_l // 8] = 0
+    seeds = np.zeros_like(data)
+    seeds[lx // 5, ly // 5] = 1
+    seeds[lx // 2 + small_l // 4, ly // 2 - small_l // 4] = 2
+    return data, seeds
+
+
+def make_3d_syntheticdata(lx, ly=None, lz=None):
+    if ly is None:
+        ly = lx
+    if lz is None:
+        lz = lx
+    np.random.seed(1234)
+    data = np.zeros((lx, ly, lz)) + 0.1 * np.random.randn(lx, ly, lz)
+    small_l = int(lx // 5)
+    data[
+        lx // 2 - small_l : lx // 2 + small_l,
+        ly // 2 - small_l : ly // 2 + small_l,
+        lz // 2 - small_l : lz // 2 + small_l,
+    ] = 1
+    data[
+        lx // 2 - small_l + 1 : lx // 2 + small_l - 1,
+        ly // 2 - small_l + 1 : ly // 2 + small_l - 1,
+        lz // 2 - small_l + 1 : lz // 2 + small_l - 1,
+    ] = 0
+    # make a hole
+    hole_size = np.max([1, small_l // 8])
+    data[
+        lx // 2 - small_l,
+        ly // 2 - hole_size : ly // 2 + hole_size,
+        lz // 2 - hole_size : lz // 2 + hole_size,
+    ] = 0
+    seeds = np.zeros_like(data)
+    seeds[lx // 5, ly // 5, lz // 5] = 1
+    seeds[lx // 2 + small_l // 4, ly // 2 - small_l // 4, lz // 2 - small_l // 4] = 2
+    return data, seeds
+
+
+@testing.parametrize('dtype', [np.float16, np.float32, np.float64])
+def test_2d_bf(dtype):
+    lx = 70
+    ly = 100
+
+    # have to use a smaller beta to avoid warning with lower precision input
+    beta = 90 if dtype == np.float64 else 25
+
+    data, labels = make_2d_syntheticdata(lx, ly)
+    data = data.astype(dtype, copy=False)
+    labels_bf = random_walker(data, labels, beta=beta, mode='bf')
+    assert (labels_bf[25:45, 40:60] == 2).all()
+    assert data.shape == labels.shape
+    full_prob_bf = random_walker(
+        data, labels, beta=beta, mode='bf', return_full_prob=True
+    )
+    assert (full_prob_bf[1, 25:45, 40:60] >= full_prob_bf[0, 25:45, 40:60]).all()
+    assert data.shape == labels.shape
+    # Now test with more than two labels
+    labels[55, 80] = 3
+    full_prob_bf = random_walker(
+        data, labels, beta=beta, mode='bf', return_full_prob=True
+    )
+    assert (full_prob_bf[1, 25:45, 40:60] >= full_prob_bf[0, 25:45, 40:60]).all()
+    assert len(full_prob_bf) == 3
+    assert data.shape == labels.shape
+
+
+@testing.parametrize('dtype', [np.float16, np.float32, np.float64])
+def test_2d_cg(dtype):
+    lx = 70
+    ly = 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    data = data.astype(dtype, copy=False)
+    with expected_warnings(['"cg" mode|scipy.sparse.linalg.cg']):
+        labels_cg = random_walker(data, labels, beta=90, mode='cg')
+    assert (labels_cg[25:45, 40:60] == 2).all()
+    assert data.shape == labels.shape
+    with expected_warnings(['"cg" mode|scipy.sparse.linalg.cg']):
+        full_prob = random_walker(
+            data, labels, beta=90, mode='cg', return_full_prob=True
+        )
+    assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all()
+    assert data.shape == labels.shape
+
+
+@testing.parametrize('dtype', [np.float16, np.float32, np.float64])
+def test_2d_cg_mg(dtype):
+    lx = 70
+    ly = 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    data = data.astype(dtype, copy=False)
+    anticipated_warnings = [
+        f'scipy.sparse.sparsetools|{PYAMG_MISSING_WARNING}|scipy.sparse.linalg.cg'
+    ]
+    with expected_warnings(anticipated_warnings):
+        labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg')
+    assert (labels_cg_mg[25:45, 40:60] == 2).all()
+    assert data.shape == labels.shape
+    with expected_warnings(anticipated_warnings):
+        full_prob = random_walker(
+            data, labels, beta=90, mode='cg_mg', return_full_prob=True
+        )
+    assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all()
+    assert data.shape == labels.shape
+
+
+@testing.parametrize('dtype', [np.float16, np.float32, np.float64])
+def test_2d_cg_j(dtype):
+    lx = 70
+    ly = 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    data = data.astype(dtype, copy=False)
+    labels_cg = random_walker(data, labels, beta=90, mode='cg_j')
+    assert (labels_cg[25:45, 40:60] == 2).all()
+    assert data.shape == labels.shape
+    full_prob = random_walker(data, labels, beta=90, mode='cg_j', return_full_prob=True)
+    assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all()
+    assert data.shape == labels.shape
+
+
+def test_types():
+    lx = 70
+    ly = 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    data = 255 * (data - data.min()) // (data.max() - data.min())
+    data = data.astype(np.uint8)
+    with expected_warnings([f"{PYAMG_MISSING_WARNING}|scipy.sparse.linalg.cg"]):
+        labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg')
+    assert (labels_cg_mg[25:45, 40:60] == 2).all()
+    assert data.shape == labels.shape
+
+
+def test_reorder_labels():
+    lx = 70
+    ly = 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    labels[labels == 2] = 4
+    labels_bf = random_walker(data, labels, beta=90, mode='bf')
+    assert (labels_bf[25:45, 40:60] == 2).all()
+    assert data.shape == labels.shape
+
+
+def test_2d_inactive():
+    lx = 70
+    ly = 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    labels[10:20, 10:20] = -1
+    labels[46:50, 33:38] = -2
+    labels = random_walker(data, labels, beta=90)
+    assert (labels.reshape((lx, ly))[25:45, 40:60] == 2).all()
+    assert data.shape == labels.shape
+
+
+def test_2d_laplacian_size():
+    # test case from: https://github.com/scikit-image/scikit-image/issues/5034
+    # The markers here were modified from the ones in the original issue to
+    # avoid a singular matrix, but still reproduce the issue.
+    data = np.asarray(
+        [[12823, 12787, 12710], [12883, 13425, 12067], [11934, 11929, 12309]]
+    )
+    markers = np.asarray([[0, -1, 2], [0, -1, 0], [1, 0, -1]])
+    expected_labels = np.asarray([[1, -1, 2], [1, -1, 2], [1, 1, -1]])
+    labels = random_walker(data, markers, beta=10)
+    np.testing.assert_array_equal(labels, expected_labels)
+
+
+@testing.parametrize('dtype', [np.float32, np.float64])
+def test_3d(dtype):
+    n = 30
+    lx, ly, lz = n, n, n
+    data, labels = make_3d_syntheticdata(lx, ly, lz)
+    data = data.astype(dtype, copy=False)
+    with expected_warnings(['"cg" mode|scipy.sparse.linalg.cg']):
+        labels = random_walker(data, labels, mode='cg')
+    assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all()
+    assert data.shape == labels.shape
+
+
+def test_3d_inactive():
+    n = 30
+    lx, ly, lz = n, n, n
+    data, labels = make_3d_syntheticdata(lx, ly, lz)
+    labels[5:25, 26:29, 26:29] = -1
+    with expected_warnings(['"cg" mode|CObject type|scipy.sparse.linalg.cg']):
+        labels = random_walker(data, labels, mode='cg')
+    assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all()
+    assert data.shape == labels.shape
+
+
+@testing.parametrize('channel_axis', [0, 1, -1])
+@testing.parametrize('dtype', [np.float32, np.float64])
+def test_multispectral_2d(dtype, channel_axis):
+    lx, ly = 70, 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    data = data.astype(dtype, copy=False)
+    data = data[..., np.newaxis].repeat(2, axis=-1)  # Expect identical output
+
+    data = np.moveaxis(data, -1, channel_axis)
+    with expected_warnings(
+        ['"cg" mode|scipy.sparse.linalg.cg', 'The probability range is outside']
+    ):
+        multi_labels = random_walker(data, labels, mode='cg', channel_axis=channel_axis)
+    data = np.moveaxis(data, channel_axis, -1)
+
+    assert data[..., 0].shape == labels.shape
+    with expected_warnings(['"cg" mode|scipy.sparse.linalg.cg']):
+        random_walker(data[..., 0], labels, mode='cg')
+    assert (multi_labels.reshape(labels.shape)[25:45, 40:60] == 2).all()
+    assert data[..., 0].shape == labels.shape
+
+
+@testing.parametrize('dtype', [np.float32, np.float64])
+def test_multispectral_3d(dtype):
+    n = 30
+    lx, ly, lz = n, n, n
+    data, labels = make_3d_syntheticdata(lx, ly, lz)
+    data = data.astype(dtype, copy=False)
+    data = data[..., np.newaxis].repeat(2, axis=-1)  # Expect identical output
+    with expected_warnings(['"cg" mode|scipy.sparse.linalg.cg']):
+        multi_labels = random_walker(data, labels, mode='cg', channel_axis=-1)
+    assert data[..., 0].shape == labels.shape
+    with expected_warnings(['"cg" mode|scipy.sparse.linalg.cg']):
+        single_labels = random_walker(data[..., 0], labels, mode='cg')
+    assert (multi_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all()
+    assert (single_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all()
+    assert data[..., 0].shape == labels.shape
+
+
+def test_spacing_0():
+    n = 30
+    lx, ly, lz = n, n, n
+    data, _ = make_3d_syntheticdata(lx, ly, lz)
+
+    # Rescale `data` along Z axis
+    data_aniso = np.zeros((n, n, n // 2))
+    for i, yz in enumerate(data):
+        data_aniso[i, :, :] = resize(
+            yz, (n, n // 2), mode='constant', anti_aliasing=False
+        )
+
+    # Generate new labels
+    small_l = int(lx // 5)
+    labels_aniso = np.zeros_like(data_aniso)
+    labels_aniso[lx // 5, ly // 5, lz // 5] = 1
+    labels_aniso[
+        lx // 2 + small_l // 4, ly // 2 - small_l // 4, lz // 4 - small_l // 8
+    ] = 2
+
+    # Test with `spacing` kwarg
+    with expected_warnings(['"cg" mode|scipy.sparse.linalg.cg']):
+        labels_aniso = random_walker(
+            data_aniso, labels_aniso, mode='cg', spacing=(1.0, 1.0, 0.5)
+        )
+
+    assert (labels_aniso[13:17, 13:17, 7:9] == 2).all()
+
+
+@xfail(
+    condition=arch32,
+    reason=(
+        'Known test failure on 32-bit platforms. See links for '
+        'details: '
+        'https://github.com/scikit-image/scikit-image/issues/3091 '
+        'https://github.com/scikit-image/scikit-image/issues/3092'
+    ),
+)
+def test_spacing_1():
+    n = 30
+    lx, ly, lz = n, n, n
+    data, _ = make_3d_syntheticdata(lx, ly, lz)
+
+    # Rescale `data` along Y axis
+    # `resize` is not yet 3D capable, so this must be done by looping in 2D.
+    data_aniso = np.zeros((n, n * 2, n))
+    for i, yz in enumerate(data):
+        data_aniso[i, :, :] = resize(
+            yz, (n * 2, n), mode='constant', anti_aliasing=False
+        )
+
+    # Generate new labels
+    small_l = int(lx // 5)
+    labels_aniso = np.zeros_like(data_aniso)
+    labels_aniso[lx // 5, ly // 5, lz // 5] = 1
+    labels_aniso[lx // 2 + small_l // 4, ly - small_l // 2, lz // 2 - small_l // 4] = 2
+
+    # Test with `spacing` kwarg
+    # First, anisotropic along Y
+    with expected_warnings(['"cg" mode|scipy.sparse.linalg.cg']):
+        labels_aniso = random_walker(
+            data_aniso, labels_aniso, mode='cg', spacing=(1.0, 2.0, 1.0)
+        )
+    assert (labels_aniso[13:17, 26:34, 13:17] == 2).all()
+
+    # Rescale `data` along X axis
+    # `resize` is not yet 3D capable, so this must be done by looping in 2D.
+    data_aniso = np.zeros((n, n * 2, n))
+    for i in range(data.shape[1]):
+        data_aniso[i, :, :] = resize(
+            data[:, 1, :], (n * 2, n), mode='constant', anti_aliasing=False
+        )
+
+    # Generate new labels
+    small_l = int(lx // 5)
+    labels_aniso2 = np.zeros_like(data_aniso)
+    labels_aniso2[lx // 5, ly // 5, lz // 5] = 1
+    labels_aniso2[lx - small_l // 2, ly // 2 + small_l // 4, lz // 2 - small_l // 4] = 2
+
+    # Anisotropic along X
+    with expected_warnings(['"cg" mode|scipy.sparse.linalg.cg']):
+        labels_aniso2 = random_walker(
+            data_aniso, labels_aniso2, mode='cg', spacing=(2.0, 1.0, 1.0)
+        )
+    assert (labels_aniso2[26:34, 13:17, 13:17] == 2).all()
+
+
+def test_trivial_cases():
+    # When all voxels are labeled
+    img = np.ones((10, 10))
+    labels = np.ones((10, 10))
+
+    with expected_warnings(["Returning provided labels"]):
+        pass_through = random_walker(img, labels)
+    np.testing.assert_array_equal(pass_through, labels)
+
+    # When all voxels are labeled AND return_full_prob is True
+    labels[:, :5] = 3
+    expected = np.concatenate(
+        ((labels == 1)[..., np.newaxis], (labels == 3)[..., np.newaxis]), axis=2
+    )
+    with expected_warnings(["Returning provided labels"]):
+        test = random_walker(img, labels, return_full_prob=True)
+    np.testing.assert_array_equal(test, expected)
+
+    # Unlabeled voxels not connected to seed, so nothing can be done
+    img = np.full((10, 10), False)
+    object_A = np.array([(6, 7), (6, 8), (7, 7), (7, 8)])
+    object_B = np.array([(3, 1), (4, 1), (2, 2), (3, 2), (4, 2), (2, 3), (3, 3)])
+    for x, y in np.vstack((object_A, object_B)):
+        img[y][x] = True
+
+    markers = np.zeros((10, 10), dtype=np.int8)
+    for x, y in object_B:
+        markers[y][x] = 1
+
+    markers[img == 0] = -1
+    with expected_warnings(["All unlabeled pixels are isolated"]):
+        output_labels = random_walker(img, markers)
+    assert np.all(output_labels[markers == 1] == 1)
+    # Here 0-labeled pixels could not be determined (no connection to seed)
+    assert np.all(output_labels[markers == 0] == -1)
+    with expected_warnings(["All unlabeled pixels are isolated"]):
+        test = random_walker(img, markers, return_full_prob=True)
+
+
+def test_length2_spacing():
+    # If this passes without raising an exception (warnings OK), the new
+    #   spacing code is working properly.
+    np.random.seed(42)
+    img = np.ones((10, 10)) + 0.2 * np.random.normal(size=(10, 10))
+    labels = np.zeros((10, 10), dtype=np.uint8)
+    labels[2, 4] = 1
+    labels[6, 8] = 4
+    random_walker(img, labels, spacing=(1.0, 2.0))
+
+
+def test_bad_inputs():
+    # Too few dimensions
+    img = np.ones(10)
+    labels = np.arange(10)
+    with testing.raises(ValueError):
+        random_walker(img, labels)
+    with testing.raises(ValueError):
+        random_walker(img, labels, channel_axis=-1)
+
+    # Too many dimensions
+    np.random.seed(42)
+    img = np.random.normal(size=(3, 3, 3, 3, 3))
+    labels = np.arange(3**5).reshape(img.shape)
+    with testing.raises(ValueError):
+        random_walker(img, labels)
+    with testing.raises(ValueError):
+        random_walker(img, labels, channel_axis=-1)
+
+    # Spacing incorrect length
+    img = np.random.normal(size=(10, 10))
+    labels = np.zeros((10, 10))
+    labels[2, 4] = 2
+    labels[6, 8] = 5
+    with testing.raises(ValueError):
+        random_walker(img, labels, spacing=(1,))
+
+    # Invalid mode
+    img = np.random.normal(size=(10, 10))
+    labels = np.zeros((10, 10))
+    with testing.raises(ValueError):
+        random_walker(img, labels, mode='bad')
+
+
+def test_isolated_seeds():
+    np.random.seed(0)
+    a = np.random.random((7, 7))
+    mask = -np.ones(a.shape)
+    # This pixel is an isolated seed
+    mask[1, 1] = 1
+    # Unlabeled pixels
+    mask[3:, 3:] = 0
+    # Seeds connected to unlabeled pixels
+    mask[4, 4] = 2
+    mask[6, 6] = 1
+
+    # Test that no error is raised, and that labels of isolated seeds are OK
+    with expected_warnings(['The probability range is outside|scipy.sparse.linalg.cg']):
+        res = random_walker(a, mask)
+    assert res[1, 1] == 1
+    with expected_warnings(['The probability range is outside|scipy.sparse.linalg.cg']):
+        res = random_walker(a, mask, return_full_prob=True)
+    assert res[0, 1, 1] == 1
+    assert res[1, 1, 1] == 0
+
+
+def test_isolated_area():
+    np.random.seed(0)
+    a = np.random.random((7, 7))
+    mask = -np.ones(a.shape)
+    # This pixel is an isolated seed
+    mask[1, 1] = 0
+    # Unlabeled pixels
+    mask[3:, 3:] = 0
+    # Seeds connected to unlabeled pixels
+    mask[4, 4] = 2
+    mask[6, 6] = 1
+
+    # Test that no error is raised, and that labels of isolated seeds are OK
+    with expected_warnings(['The probability range is outside|scipy.sparse.linalg.cg']):
+        res = random_walker(a, mask)
+    assert res[1, 1] == 0
+    with expected_warnings(['The probability range is outside|scipy.sparse.linalg.cg']):
+        res = random_walker(a, mask, return_full_prob=True)
+    assert res[0, 1, 1] == 0
+    assert res[1, 1, 1] == 0
+
+
+def test_prob_tol():
+    np.random.seed(0)
+    a = np.random.random((7, 7))
+    mask = -np.ones(a.shape)
+    # This pixel is an isolated seed
+    mask[1, 1] = 1
+    # Unlabeled pixels
+    mask[3:, 3:] = 0
+    # Seeds connected to unlabeled pixels
+    mask[4, 4] = 2
+    mask[6, 6] = 1
+
+    with expected_warnings(['The probability range is outside|scipy.sparse.linalg.cg']):
+        res = random_walker(a, mask, return_full_prob=True)
+
+    # Lower beta, no warning is expected.
+    res = random_walker(a, mask, return_full_prob=True, beta=10)
+    assert res[0, 1, 1] == 1
+    assert res[1, 1, 1] == 0
+
+    # Being more prob_tol tolerant, no warning is expected.
+    res = random_walker(a, mask, return_full_prob=True, prob_tol=1e-1)
+    assert res[0, 1, 1] == 1
+    assert res[1, 1, 1] == 0
+
+    # Reduced tol, no warning is expected.
+    res = random_walker(a, mask, return_full_prob=True, tol=1e-9)
+    assert res[0, 1, 1] == 1
+    assert res[1, 1, 1] == 0
+
+
+def test_umfpack_import():
+    from skimage.segmentation import random_walker_segmentation
+
+    UmfpackContext = random_walker_segmentation.UmfpackContext
+    try:
+        # when scikit-umfpack is installed UmfpackContext should not be None
+        import scikits.umfpack  # noqa: F401
+
+        assert UmfpackContext is not None
+    except ImportError:
+        assert UmfpackContext is None
+
+
+def test_empty_labels():
+    image = np.random.random((5, 5))
+    labels = np.zeros((5, 5), dtype=int)
+
+    with testing.raises(ValueError, match="No seeds provided"):
+        random_walker(image, labels)
+
+    labels[1, 1] = -1
+    with testing.raises(ValueError, match="No seeds provided"):
+        random_walker(image, labels)
+
+    # Once seeds are provided, it should run without error
+    labels[3, 3] = 1
+    random_walker(image, labels)

.CondaPkg/env/Lib/site-packages/skimage/segmentation/tests/test_slic.py

@@ -0,0 +1,632 @@
+from itertools import product
+
+import numpy as np
+import pytest
+from numpy.testing import assert_equal
+
+from skimage import data, filters, img_as_float
+from skimage._shared.testing import run_in_parallel, expected_warnings
+from skimage.segmentation import slic
+
+
+@run_in_parallel()
+def test_color_2d():
+    rng = np.random.default_rng(0)
+    img = np.zeros((20, 21, 3))
+    img[:10, :10, 0] = 1
+    img[10:, :10, 1] = 1
+    img[10:, 10:, 2] = 1
+    img += 0.01 * rng.normal(size=img.shape)
+    img[img > 1] = 1
+    img[img < 0] = 0
+    seg = slic(img, n_segments=4, sigma=0, enforce_connectivity=False, start_label=0)
+
+    # we expect 4 segments
+    assert_equal(len(np.unique(seg)), 4)
+    assert_equal(seg.shape, img.shape[:-1])
+    assert_equal(seg[:10, :10], 0)
+    assert_equal(seg[10:, :10], 2)
+    assert_equal(seg[:10, 10:], 1)
+    assert_equal(seg[10:, 10:], 3)
+
+
+def test_multichannel_2d():
+    rng = np.random.default_rng(0)
+    img = np.zeros((20, 20, 8))
+    img[:10, :10, 0:2] = 1
+    img[:10, 10:, 2:4] = 1
+    img[10:, :10, 4:6] = 1
+    img[10:, 10:, 6:8] = 1
+    img += 0.01 * rng.normal(size=img.shape)
+    img = np.clip(img, 0, 1, out=img)
+    seg = slic(img, n_segments=4, enforce_connectivity=False, start_label=0)
+
+    # we expect 4 segments
+    assert_equal(len(np.unique(seg)), 4)
+    assert_equal(seg.shape, img.shape[:-1])
+    assert_equal(seg[:10, :10], 0)
+    assert_equal(seg[10:, :10], 2)
+    assert_equal(seg[:10, 10:], 1)
+    assert_equal(seg[10:, 10:], 3)
+
+
+def test_gray_2d():
+    rng = np.random.default_rng(0)
+    img = np.zeros((20, 21))
+    img[:10, :10] = 0.33
+    img[10:, :10] = 0.67
+    img[10:, 10:] = 1.00
+    img += 0.0033 * rng.normal(size=img.shape)
+    img[img > 1] = 1
+    img[img < 0] = 0
+    seg = slic(
+        img,
+        sigma=0,
+        n_segments=4,
+        compactness=1,
+        channel_axis=None,
+        convert2lab=False,
+        start_label=0,
+    )
+
+    assert_equal(len(np.unique(seg)), 4)
+    assert_equal(seg.shape, img.shape)
+    assert_equal(seg[:10, :10], 0)
+    assert_equal(seg[10:, :10], 2)
+    assert_equal(seg[:10, 10:], 1)
+    assert_equal(seg[10:, 10:], 3)
+
+
+def test_gray2d_default_channel_axis():
+    img = np.zeros((20, 21))
+    img[:10, :10] = 0.33
+    with pytest.raises(ValueError, match="channel_axis=-1 indicates multichannel"):
+        slic(img)
+    slic(img, channel_axis=None)
+
+
+def _check_segment_labels(seg1, seg2, allowed_mismatch_ratio=0.1):
+    size = seg1.size
+    ndiff = np.sum(seg1 != seg2)
+    assert (ndiff / size) < allowed_mismatch_ratio
+
+
+def test_slic_consistency_across_image_magnitude():
+    # verify that that images of various scales across integer and float dtypes
+    # give the same segmentation result
+    img_uint8 = data.cat()[:256, :128]
+    img_uint16 = 256 * img_uint8.astype(np.uint16)
+    img_float32 = img_as_float(img_uint8)
+    img_float32_norm = img_float32 / img_float32.max()
+    img_float32_offset = img_float32 + 1000
+
+    seg1 = slic(img_uint8)
+    seg2 = slic(img_uint16)
+    seg3 = slic(img_float32)
+    seg4 = slic(img_float32_norm)
+    seg5 = slic(img_float32_offset)
+
+    np.testing.assert_array_equal(seg1, seg2)
+    np.testing.assert_array_equal(seg1, seg3)
+    # Assert that offset has no impact on result
+    np.testing.assert_array_equal(seg4, seg5)
+    # Floating point cases can have mismatch due to floating point error
+    # exact match was observed on x86_64, but mismatches seen no i686.
+    # For now just verify that a similar number of superpixels are present in
+    # each case.
+    n_seg1 = seg1.max()
+    n_seg4 = seg4.max()
+    assert abs(n_seg1 - n_seg4) / n_seg1 < 0.5
+
+
+def test_color_3d():
+    rng = np.random.default_rng(0)
+    img = np.zeros((20, 21, 22, 3))
+    slices = []
+    for dim_size in img.shape[:-1]:
+        midpoint = dim_size // 2
+        slices.append((slice(None, midpoint), slice(midpoint, None)))
+    slices = list(product(*slices))
+    colors = list(product(*(([0, 1],) * 3)))
+    for s, c in zip(slices, colors):
+        img[s] = c
+    img += 0.01 * rng.normal(size=img.shape)
+    img[img > 1] = 1
+    img[img < 0] = 0
+    seg = slic(img, sigma=0, n_segments=8, start_label=0)
+
+    assert_equal(len(np.unique(seg)), 8)
+    for s, c in zip(slices, range(8)):
+        assert_equal(seg[s], c)
+
+
+def test_gray_3d():
+    rng = np.random.default_rng(0)
+    img = np.zeros((20, 21, 22))
+    slices = []
+    for dim_size in img.shape:
+        midpoint = dim_size // 2
+        slices.append((slice(None, midpoint), slice(midpoint, None)))
+    slices = list(product(*slices))
+    shades = np.arange(0, 1.000001, 1.0 / 7)
+    for s, sh in zip(slices, shades):
+        img[s] = sh
+    img += 0.001 * rng.normal(size=img.shape)
+    img[img > 1] = 1
+    img[img < 0] = 0
+    seg = slic(
+        img,
+        sigma=0,
+        n_segments=8,
+        compactness=1,
+        channel_axis=None,
+        convert2lab=False,
+        start_label=0,
+    )
+
+    assert_equal(len(np.unique(seg)), 8)
+    for s, c in zip(slices, range(8)):
+        assert_equal(seg[s], c)
+
+
+def test_list_sigma():
+    rng = np.random.default_rng(0)
+    img = np.array([[1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1]], float)
+    img += 0.1 * rng.normal(size=img.shape)
+    result_sigma = np.array([[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]], int)
+    with expected_warnings(
+        ["Input image is 2D: sigma number of " "elements must be 2"]
+    ):
+        seg_sigma = slic(
+            img, n_segments=2, sigma=[1, 50, 1], channel_axis=None, start_label=0
+        )
+    assert_equal(seg_sigma, result_sigma)
+
+
+def test_spacing():
+    rng = np.random.default_rng(0)
+    img = np.array([[1, 1, 1, 0, 0], [1, 1, 0, 0, 0]], float)
+    result_non_spaced = np.array([[0, 0, 0, 1, 1], [0, 0, 1, 1, 1]], int)
+    result_spaced = np.array([[0, 0, 0, 0, 0], [1, 1, 1, 1, 1]], int)
+    img += 0.1 * rng.normal(size=img.shape)
+    seg_non_spaced = slic(
+        img, n_segments=2, sigma=0, channel_axis=None, compactness=1.0, start_label=0
+    )
+    seg_spaced = slic(
+        img,
+        n_segments=2,
+        sigma=0,
+        spacing=[500, 1],
+        compactness=1.0,
+        channel_axis=None,
+        start_label=0,
+    )
+    assert_equal(seg_non_spaced, result_non_spaced)
+    assert_equal(seg_spaced, result_spaced)
+
+
+def test_invalid_lab_conversion():
+    img = np.array([[1, 1, 1, 0, 0], [1, 1, 0, 0, 0]], float) + 1
+    with pytest.raises(ValueError):
+        slic(img, channel_axis=-1, convert2lab=True, start_label=0)
+
+
+def test_enforce_connectivity():
+    img = np.array([[0, 0, 0, 1, 1, 1], [1, 0, 0, 1, 1, 0], [0, 0, 0, 1, 1, 0]], float)
+
+    segments_connected = slic(
+        img,
+        2,
+        compactness=0.0001,
+        enforce_connectivity=True,
+        convert2lab=False,
+        start_label=0,
+        channel_axis=None,
+    )
+    segments_disconnected = slic(
+        img,
+        2,
+        compactness=0.0001,
+        enforce_connectivity=False,
+        convert2lab=False,
+        start_label=0,
+        channel_axis=None,
+    )
+
+    # Make sure nothing fatal occurs (e.g. buffer overflow) at low values of
+    # max_size_factor
+    segments_connected_low_max = slic(
+        img,
+        2,
+        compactness=0.0001,
+        enforce_connectivity=True,
+        convert2lab=False,
+        max_size_factor=0.8,
+        start_label=0,
+        channel_axis=None,
+    )
+
+    result_connected = np.array(
+        [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]], float
+    )
+
+    result_disconnected = np.array(
+        [[0, 0, 0, 1, 1, 1], [1, 0, 0, 1, 1, 0], [0, 0, 0, 1, 1, 0]], float
+    )
+
+    assert_equal(segments_connected, result_connected)
+    assert_equal(segments_disconnected, result_disconnected)
+    assert_equal(segments_connected_low_max, result_connected)
+
+
+def test_slic_zero():
+    # Same as test_color_2d but with slic_zero=True
+    rng = np.random.default_rng(0)
+    img = np.zeros((20, 21, 3))
+    img[:10, :10, 0] = 1
+    img[10:, :10, 1] = 1
+    img[10:, 10:, 2] = 1
+    img += 0.01 * rng.normal(size=img.shape)
+    img[img > 1] = 1
+    img[img < 0] = 0
+    seg = slic(img, n_segments=4, sigma=0, slic_zero=True, start_label=0)
+
+    # we expect 4 segments
+    assert_equal(len(np.unique(seg)), 4)
+    assert_equal(seg.shape, img.shape[:-1])
+    assert_equal(seg[:10, :10], 0)
+    assert_equal(seg[10:, :10], 2)
+    assert_equal(seg[:10, 10:], 1)
+    assert_equal(seg[10:, 10:], 3)
+
+
+def test_more_segments_than_pixels():
+    rng = np.random.default_rng(0)
+    img = np.zeros((20, 21))
+    img[:10, :10] = 0.33
+    img[10:, :10] = 0.67
+    img[10:, 10:] = 1.00
+    img += 0.0033 * rng.normal(size=img.shape)
+    img[img > 1] = 1
+    img[img < 0] = 0
+    seg = slic(
+        img,
+        sigma=0,
+        n_segments=500,
+        compactness=1,
+        channel_axis=None,
+        convert2lab=False,
+        start_label=0,
+    )
+    assert np.all(seg.ravel() == np.arange(seg.size))
+
+
+def test_color_2d_mask():
+    rng = np.random.default_rng(0)
+    msk = np.zeros((20, 21))
+    msk[2:-2, 2:-2] = 1
+    img = np.zeros((20, 21, 3))
+    img[:10, :10, 0] = 1
+    img[10:, :10, 1] = 1
+    img[10:, 10:, 2] = 1
+    img += 0.01 * rng.normal(size=img.shape)
+    np.clip(img, 0, 1, out=img)
+    seg = slic(img, n_segments=4, sigma=0, enforce_connectivity=False, mask=msk)
+
+    # we expect 4 segments + masked area
+    assert_equal(len(np.unique(seg)), 5)
+    assert_equal(seg.shape, img.shape[:-1])
+    # segments
+    assert_equal(seg[2:10, 2:10], 1)
+    assert_equal(seg[10:-2, 2:10], 4)
+    assert_equal(seg[2:10, 10:-2], 2)
+    assert_equal(seg[10:-2, 10:-2], 3)
+    # non masked area
+    assert_equal(seg[:2, :], 0)
+    assert_equal(seg[-2:, :], 0)
+    assert_equal(seg[:, :2], 0)
+    assert_equal(seg[:, -2:], 0)
+
+
+def test_multichannel_2d_mask():
+    rng = np.random.default_rng(0)
+    msk = np.zeros((20, 20))
+    msk[2:-2, 2:-2] = 1
+    img = np.zeros((20, 20, 8))
+    img[:10, :10, 0:2] = 1
+    img[:10, 10:, 2:4] = 1
+    img[10:, :10, 4:6] = 1
+    img[10:, 10:, 6:8] = 1
+    img += 0.01 * rng.normal(size=img.shape)
+    np.clip(img, 0, 1, out=img)
+    seg = slic(img, n_segments=4, enforce_connectivity=False, mask=msk)
+
+    # we expect 4 segments + masked area
+    assert_equal(len(np.unique(seg)), 5)
+    assert_equal(seg.shape, img.shape[:-1])
+    # segments
+    assert_equal(seg[2:10, 2:10], 2)
+    assert_equal(seg[2:10, 10:-2], 1)
+    assert_equal(seg[10:-2, 2:10], 4)
+    assert_equal(seg[10:-2, 10:-2], 3)
+    # non masked area
+    assert_equal(seg[:2, :], 0)
+    assert_equal(seg[-2:, :], 0)
+    assert_equal(seg[:, :2], 0)
+    assert_equal(seg[:, -2:], 0)
+
+
+def test_gray_2d_mask():
+    rng = np.random.default_rng(0)
+    msk = np.zeros((20, 21))
+    msk[2:-2, 2:-2] = 1
+    img = np.zeros((20, 21))
+    img[:10, :10] = 0.33
+    img[10:, :10] = 0.67
+    img[10:, 10:] = 1.00
+    img += 0.0033 * rng.normal(size=img.shape)
+    np.clip(img, 0, 1, out=img)
+    seg = slic(
+        img,
+        sigma=0,
+        n_segments=4,
+        compactness=1,
+        channel_axis=None,
+        convert2lab=False,
+        mask=msk,
+    )
+
+    assert_equal(len(np.unique(seg)), 5)
+    assert_equal(seg.shape, img.shape)
+    # segments
+    assert_equal(seg[2:10, 2:10], 1)
+    assert_equal(seg[2:10, 10:-2], 2)
+    assert_equal(seg[10:-2, 2:10], 3)
+    assert_equal(seg[10:-2, 10:-2], 4)
+    # non masked area
+    assert_equal(seg[:2, :], 0)
+    assert_equal(seg[-2:, :], 0)
+    assert_equal(seg[:, :2], 0)
+    assert_equal(seg[:, -2:], 0)
+
+
+def test_list_sigma_mask():
+    rng = np.random.default_rng(0)
+    msk = np.zeros((2, 6))
+    msk[:, 1:-1] = 1
+    img = np.array([[1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1]], float)
+    img += 0.1 * rng.normal(size=img.shape)
+    result_sigma = np.array([[0, 1, 1, 2, 2, 0], [0, 1, 1, 2, 2, 0]], int)
+    seg_sigma = slic(img, n_segments=2, sigma=[50, 1], channel_axis=None, mask=msk)
+    assert_equal(seg_sigma, result_sigma)
+
+
+def test_spacing_mask():
+    rng = np.random.default_rng(0)
+    msk = np.zeros((2, 5))
+    msk[:, 1:-1] = 1
+    img = np.array([[1, 1, 1, 0, 0], [1, 1, 0, 0, 0]], float)
+    result_non_spaced = np.array([[0, 1, 1, 2, 0], [0, 1, 2, 2, 0]], int)
+    result_spaced = np.array([[0, 1, 1, 1, 0], [0, 2, 2, 2, 0]], int)
+    img += 0.1 * rng.normal(size=img.shape)
+    seg_non_spaced = slic(
+        img, n_segments=2, sigma=0, channel_axis=None, compactness=1.0, mask=msk
+    )
+    seg_spaced = slic(
+        img,
+        n_segments=2,
+        sigma=0,
+        spacing=[50, 1],
+        compactness=1.0,
+        channel_axis=None,
+        mask=msk,
+    )
+    assert_equal(seg_non_spaced, result_non_spaced)
+    assert_equal(seg_spaced, result_spaced)
+
+
+def test_enforce_connectivity_mask():
+    msk = np.zeros((3, 6))
+    msk[:, 1:-1] = 1
+    img = np.array([[0, 0, 0, 1, 1, 1], [1, 0, 0, 1, 1, 0], [0, 0, 0, 1, 1, 0]], float)
+
+    segments_connected = slic(
+        img,
+        2,
+        compactness=0.0001,
+        enforce_connectivity=True,
+        convert2lab=False,
+        mask=msk,
+        channel_axis=None,
+    )
+    segments_disconnected = slic(
+        img,
+        2,
+        compactness=0.0001,
+        enforce_connectivity=False,
+        convert2lab=False,
+        mask=msk,
+        channel_axis=None,
+    )
+
+    # Make sure nothing fatal occurs (e.g. buffer overflow) at low values of
+    # max_size_factor
+    segments_connected_low_max = slic(
+        img,
+        2,
+        compactness=0.0001,
+        enforce_connectivity=True,
+        convert2lab=False,
+        max_size_factor=0.8,
+        mask=msk,
+        channel_axis=None,
+    )
+
+    result_connected = np.array(
+        [[0, 1, 1, 2, 2, 0], [0, 1, 1, 2, 2, 0], [0, 1, 1, 2, 2, 0]], float
+    )
+
+    result_disconnected = np.array(
+        [[0, 1, 1, 2, 2, 0], [0, 1, 1, 2, 2, 0], [0, 1, 1, 2, 2, 0]], float
+    )
+
+    assert_equal(segments_connected, result_connected)
+    assert_equal(segments_disconnected, result_disconnected)
+    assert_equal(segments_connected_low_max, result_connected)
+
+
+def test_slic_zero_mask():
+    rng = np.random.default_rng(0)
+    msk = np.zeros((20, 21))
+    msk[2:-2, 2:-2] = 1
+    img = np.zeros((20, 21, 3))
+    img[:10, :10, 0] = 1
+    img[10:, :10, 1] = 1
+    img[10:, 10:, 2] = 1
+    img += 0.01 * rng.normal(size=img.shape)
+    np.clip(img, 0, 1, out=img)
+    seg = slic(img, n_segments=4, sigma=0, slic_zero=True, mask=msk)
+
+    # we expect 4 segments + masked area
+    assert_equal(len(np.unique(seg)), 5)
+    assert_equal(seg.shape, img.shape[:-1])
+    # segments
+    assert_equal(seg[2:10, 2:10], 1)
+    assert_equal(seg[2:10, 10:-2], 2)
+    assert_equal(seg[10:-2, 2:10], 3)
+    assert_equal(seg[10:-2, 10:-2], 4)
+    # non masked area
+    assert_equal(seg[:2, :], 0)
+    assert_equal(seg[-2:, :], 0)
+    assert_equal(seg[:, :2], 0)
+    assert_equal(seg[:, -2:], 0)
+
+
+def test_more_segments_than_pixels_mask():
+    rng = np.random.default_rng(0)
+    msk = np.zeros((20, 21))
+    msk[2:-2, 2:-2] = 1
+    img = np.zeros((20, 21))
+    img[:10, :10] = 0.33
+    img[10:, :10] = 0.67
+    img[10:, 10:] = 1.00
+    img += 0.0033 * rng.normal(size=img.shape)
+    np.clip(img, 0, 1, out=img)
+    seg = slic(
+        img,
+        sigma=0,
+        n_segments=500,
+        compactness=1,
+        channel_axis=None,
+        convert2lab=False,
+        mask=msk,
+    )
+
+    expected = np.arange(seg[2:-2, 2:-2].size) + 1
+    assert np.all(seg[2:-2, 2:-2].ravel() == expected)
+
+
+def test_color_3d_mask():
+    msk = np.zeros((20, 21, 22))
+    msk[2:-2, 2:-2, 2:-2] = 1
+
+    rng = np.random.default_rng(0)
+    img = np.zeros((20, 21, 22, 3))
+    slices = []
+    for dim_size in msk.shape:
+        midpoint = dim_size // 2
+        slices.append((slice(None, midpoint), slice(midpoint, None)))
+    slices = list(product(*slices))
+    colors = list(product(*(([0, 1],) * 3)))
+    for s, c in zip(slices, colors):
+        img[s] = c
+    img += 0.01 * rng.normal(size=img.shape)
+    np.clip(img, 0, 1, out=img)
+
+    seg = slic(img, sigma=0, n_segments=8, mask=msk)
+
+    # we expect 8 segments + masked area
+    assert_equal(len(np.unique(seg)), 9)
+    for s, c in zip(slices, range(1, 9)):
+        assert_equal(seg[s][2:-2, 2:-2, 2:-2], c)
+
+
+def test_gray_3d_mask():
+    msk = np.zeros((20, 21, 22))
+    msk[2:-2, 2:-2, 2:-2] = 1
+
+    rng = np.random.default_rng(0)
+    img = np.zeros((20, 21, 22))
+    slices = []
+    for dim_size in img.shape:
+        midpoint = dim_size // 2
+        slices.append((slice(None, midpoint), slice(midpoint, None)))
+    slices = list(product(*slices))
+    shades = np.linspace(0, 1, 8)
+    for s, sh in zip(slices, shades):
+        img[s] = sh
+    img += 0.001 * rng.normal(size=img.shape)
+    np.clip(img, 0, 1, out=img)
+    seg = slic(
+        img, sigma=0, n_segments=8, channel_axis=None, convert2lab=False, mask=msk
+    )
+
+    # we expect 8 segments + masked area
+    assert_equal(len(np.unique(seg)), 9)
+    for s, c in zip(slices, range(1, 9)):
+        assert_equal(seg[s][2:-2, 2:-2, 2:-2], c)
+
+
+@pytest.mark.parametrize("dtype", ['float16', 'float32', 'float64', 'uint8', 'int'])
+def test_dtype_support(dtype):
+    img = np.random.rand(28, 28).astype(dtype)
+
+    # Simply run the function to assert that it runs without error
+    slic(img, start_label=1, channel_axis=None)
+
+
+def test_start_label_fix():
+    """Tests the fix for a bug producing a label < start_label (gh-6240).
+
+    For the v0.19.1 release, the `img` and `slic` call as below result in two
+    non-contiguous regions with value 0 despite `start_label=1`. We verify that
+    the minimum label is now `start_label` as expected.
+    """
+
+    # generate bumpy data that gives unexpected label prior to bug fix
+    rng = np.random.default_rng(9)
+    img = rng.standard_normal((8, 13)) > 0
+    img = filters.gaussian(img, sigma=1)
+
+    start_label = 1
+    superp = slic(
+        img,
+        start_label=start_label,
+        channel_axis=None,
+        n_segments=6,
+        compactness=0.01,
+        enforce_connectivity=True,
+        max_num_iter=10,
+    )
+    assert superp.min() == start_label
+
+
+def test_raises_ValueError_if_input_has_NaN():
+    img = np.zeros((4, 5), dtype=float)
+    img[2, 3] = np.nan
+    with pytest.raises(ValueError):
+        slic(img, channel_axis=None)
+
+    mask = ~np.isnan(img)
+    slic(img, mask=mask, channel_axis=None)
+
+
+@pytest.mark.parametrize("inf", [-np.inf, np.inf])
+def test_raises_ValueError_if_input_has_inf(inf):
+    img = np.zeros((4, 5), dtype=float)
+    img[2, 3] = inf
+    with pytest.raises(ValueError):
+        slic(img, channel_axis=None)
+
+    mask = np.isfinite(img)
+    slic(img, mask=mask, channel_axis=None)

.CondaPkg/env/Lib/site-packages/skimage/segmentation/tests/test_watershed.py

@@ -0,0 +1,896 @@
+"""test_watershed.py - tests the watershed function
+"""
+
+import math
+import unittest
+
+import numpy as np
+import pytest
+from scipy import ndimage as ndi
+
+from skimage._shared.filters import gaussian
+from skimage.measure import label
+
+from .._watershed import watershed
+
+eps = 1e-12
+# fmt: off
+blob = np.array([[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
+                 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
+                 [255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 255, 255, 255, 255, 255],
+                 [255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255],
+                 [255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255],
+                 [255, 255, 204, 153, 111,  94,  72,  52,  52,  72,  94, 111, 153, 204, 255, 255],
+                 [255, 255, 204, 153, 111,  72,  39,   1,   1,  39,  72, 111, 153, 204, 255, 255],
+                 [255, 255, 204, 183, 141, 111,  72,  39,  39,  72, 111, 141, 183, 204, 255, 255],
+                 [255, 255, 255, 204, 183, 141, 111,  72,  72, 111, 141, 183, 204, 255, 255, 255],
+                 [255, 255, 255, 255, 204, 183, 141,  94,  94, 141, 183, 204, 255, 255, 255, 255],
+                 [255, 255, 255, 255, 255, 204, 153, 103, 103, 153, 204, 255, 255, 255, 255, 255],
+                 [255, 255, 255, 255, 204, 183, 141,  94,  94, 141, 183, 204, 255, 255, 255, 255],
+                 [255, 255, 255, 204, 183, 141, 111,  72,  72, 111, 141, 183, 204, 255, 255, 255],
+                 [255, 255, 204, 183, 141, 111,  72,  39,  39,  72, 111, 141, 183, 204, 255, 255],
+                 [255, 255, 204, 153, 111,  72,  39,   1,   1,  39,  72, 111, 153, 204, 255, 255],
+                 [255, 255, 204, 153, 111,  94,  72,  52,  52,  72,  94, 111, 153, 204, 255, 255],
+                 [255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255],
+                 [255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255],
+                 [255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 255, 255, 255, 255, 255],
+                 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
+                 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255]])
+# fmt: on
+
+
+def diff(a, b):
+    if not isinstance(a, np.ndarray):
+        a = np.asarray(a)
+    if not isinstance(b, np.ndarray):
+        b = np.asarray(b)
+    if (0 in a.shape) and (0 in b.shape):
+        return 0.0
+    b[a == 0] = 0
+    if a.dtype in [np.complex64, np.complex128] or b.dtype in [
+        np.complex64,
+        np.complex128,
+    ]:
+        a = np.asarray(a, np.complex128)
+        b = np.asarray(b, np.complex128)
+        t = ((a.real - b.real) ** 2).sum() + ((a.imag - b.imag) ** 2).sum()
+    else:
+        a = np.asarray(a)
+        a = a.astype(np.float64)
+        b = np.asarray(b)
+        b = b.astype(np.float64)
+        t = ((a - b) ** 2).sum()
+    return math.sqrt(t)
+
+
+class TestWatershed(unittest.TestCase):
+    eight = np.ones((3, 3), bool)
+
+    def test_watershed01(self):
+        "watershed 1"
+        data = np.array(
+            [
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 1, 1, 1, 1, 1, 0],
+                [0, 1, 0, 0, 0, 1, 0],
+                [0, 1, 0, 0, 0, 1, 0],
+                [0, 1, 0, 0, 0, 1, 0],
+                [0, 1, 1, 1, 1, 1, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+            ],
+            np.uint8,
+        )
+        markers = np.array(
+            [
+                [-1, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 1, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+            ],
+            np.int8,
+        )
+        out = watershed(data, markers, self.eight)
+        expected = np.array(
+            [
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, 1, 1, 1, 1, 1, -1],
+                [-1, 1, 1, 1, 1, 1, -1],
+                [-1, 1, 1, 1, 1, 1, -1],
+                [-1, 1, 1, 1, 1, 1, -1],
+                [-1, 1, 1, 1, 1, 1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+            ]
+        )
+        error = diff(expected, out)
+        assert error < eps
+
+    def test_watershed02(self):
+        "watershed 2"
+        data = np.array(
+            [
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 1, 1, 1, 1, 1, 0],
+                [0, 1, 0, 0, 0, 1, 0],
+                [0, 1, 0, 0, 0, 1, 0],
+                [0, 1, 0, 0, 0, 1, 0],
+                [0, 1, 1, 1, 1, 1, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+            ],
+            np.uint8,
+        )
+        markers = np.array(
+            [
+                [-1, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 1, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+            ],
+            np.int8,
+        )
+        out = watershed(data, markers)
+        error = diff(
+            [
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, 1, 1, 1, -1, -1],
+                [-1, 1, 1, 1, 1, 1, -1],
+                [-1, 1, 1, 1, 1, 1, -1],
+                [-1, 1, 1, 1, 1, 1, -1],
+                [-1, -1, 1, 1, 1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+            ],
+            out,
+        )
+        self.assertTrue(error < eps)
+
+    def test_watershed03(self):
+        "watershed 3"
+        data = np.array(
+            [
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 1, 1, 1, 1, 1, 0],
+                [0, 1, 0, 1, 0, 1, 0],
+                [0, 1, 0, 1, 0, 1, 0],
+                [0, 1, 0, 1, 0, 1, 0],
+                [0, 1, 1, 1, 1, 1, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+            ],
+            np.uint8,
+        )
+        markers = np.array(
+            [
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 2, 0, 3, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, -1],
+            ],
+            np.int8,
+        )
+        out = watershed(data, markers)
+        error = diff(
+            [
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, 0, 2, 0, 3, 0, -1],
+                [-1, 2, 2, 0, 3, 3, -1],
+                [-1, 2, 2, 0, 3, 3, -1],
+                [-1, 2, 2, 0, 3, 3, -1],
+                [-1, 0, 2, 0, 3, 0, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+            ],
+            out,
+        )
+        self.assertTrue(error < eps)
+
+    def test_watershed04(self):
+        "watershed 4"
+        data = np.array(
+            [
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 1, 1, 1, 1, 1, 0],
+                [0, 1, 0, 1, 0, 1, 0],
+                [0, 1, 0, 1, 0, 1, 0],
+                [0, 1, 0, 1, 0, 1, 0],
+                [0, 1, 1, 1, 1, 1, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+            ],
+            np.uint8,
+        )
+        markers = np.array(
+            [
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 2, 0, 3, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, -1],
+            ],
+            np.int8,
+        )
+        out = watershed(data, markers, self.eight)
+        error = diff(
+            [
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, 2, 2, 0, 3, 3, -1],
+                [-1, 2, 2, 0, 3, 3, -1],
+                [-1, 2, 2, 0, 3, 3, -1],
+                [-1, 2, 2, 0, 3, 3, -1],
+                [-1, 2, 2, 0, 3, 3, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+            ],
+            out,
+        )
+        self.assertTrue(error < eps)
+
+    def test_watershed05(self):
+        "watershed 5"
+        data = np.array(
+            [
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 1, 1, 1, 1, 1, 0],
+                [0, 1, 0, 1, 0, 1, 0],
+                [0, 1, 0, 1, 0, 1, 0],
+                [0, 1, 0, 1, 0, 1, 0],
+                [0, 1, 1, 1, 1, 1, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+            ],
+            np.uint8,
+        )
+        markers = np.array(
+            [
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 3, 0, 2, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, -1],
+            ],
+            np.int8,
+        )
+        out = watershed(data, markers, self.eight)
+        error = diff(
+            [
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, 3, 3, 0, 2, 2, -1],
+                [-1, 3, 3, 0, 2, 2, -1],
+                [-1, 3, 3, 0, 2, 2, -1],
+                [-1, 3, 3, 0, 2, 2, -1],
+                [-1, 3, 3, 0, 2, 2, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+            ],
+            out,
+        )
+        self.assertTrue(error < eps)
+
+    def test_watershed06(self):
+        "watershed 6"
+        data = np.array(
+            [
+                [0, 1, 0, 0, 0, 1, 0],
+                [0, 1, 0, 0, 0, 1, 0],
+                [0, 1, 0, 0, 0, 1, 0],
+                [0, 1, 1, 1, 1, 1, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+            ],
+            np.uint8,
+        )
+        markers = np.array(
+            [
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 1, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+                [-1, 0, 0, 0, 0, 0, 0],
+            ],
+            np.int8,
+        )
+        out = watershed(data, markers, self.eight)
+        error = diff(
+            [
+                [-1, 1, 1, 1, 1, 1, -1],
+                [-1, 1, 1, 1, 1, 1, -1],
+                [-1, 1, 1, 1, 1, 1, -1],
+                [-1, 1, 1, 1, 1, 1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1],
+            ],
+            out,
+        )
+        self.assertTrue(error < eps)
+
+    def test_watershed07(self):
+        "A regression test of a competitive case that failed"
+        data = blob
+        mask = data != 255
+        markers = np.zeros(data.shape, int)
+        markers[6, 7] = 1
+        markers[14, 7] = 2
+        out = watershed(data, markers, self.eight, mask=mask)
+        #
+        # The two objects should be the same size, except possibly for the
+        # border region
+        #
+        size1 = np.sum(out == 1)
+        size2 = np.sum(out == 2)
+        self.assertTrue(abs(size1 - size2) <= 6)
+
+    def test_watershed08(self):
+        "The border pixels + an edge are all the same value"
+        data = blob.copy()
+        data[10, 7:9] = 141
+        mask = data != 255
+        markers = np.zeros(data.shape, int)
+        markers[6, 7] = 1
+        markers[14, 7] = 2
+        out = watershed(data, markers, self.eight, mask=mask)
+        #
+        # The two objects should be the same size, except possibly for the
+        # border region
+        #
+        size1 = np.sum(out == 1)
+        size2 = np.sum(out == 2)
+        self.assertTrue(abs(size1 - size2) <= 6)
+
+    def test_watershed09(self):
+        """Test on an image of reasonable size
+
+        This is here both for timing (does it take forever?) and to
+        ensure that the memory constraints are reasonable
+        """
+        image = np.zeros((1000, 1000))
+        coords = np.random.uniform(0, 1000, (100, 2)).astype(int)
+        markers = np.zeros((1000, 1000), int)
+        idx = 1
+        for x, y in coords:
+            image[x, y] = 1
+            markers[x, y] = idx
+            idx += 1
+
+        image = gaussian(image, sigma=4, mode='reflect')
+        watershed(image, markers, self.eight)
+        ndi.watershed_ift(image.astype(np.uint16), markers, self.eight)
+
+    def test_watershed10(self):
+        "watershed 10"
+        data = np.array(
+            [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], np.uint8
+        )
+        markers = np.array(
+            [[1, 0, 0, 2], [0, 0, 0, 0], [0, 0, 0, 0], [3, 0, 0, 4]], np.int8
+        )
+        out = watershed(data, markers, self.eight)
+        error = diff([[1, 1, 2, 2], [1, 1, 2, 2], [3, 3, 4, 4], [3, 3, 4, 4]], out)
+        self.assertTrue(error < eps)
+
+    def test_watershed11(self):
+        '''Make sure that all points on this plateau are assigned to closest seed'''
+        # https://github.com/scikit-image/scikit-image/issues/803
+        #
+        # Make sure that no point in a level image is farther away
+        # from its seed than any other
+        #
+        image = np.zeros((21, 21))
+        markers = np.zeros((21, 21), int)
+        markers[5, 5] = 1
+        markers[5, 10] = 2
+        markers[10, 5] = 3
+        markers[10, 10] = 4
+
+        structure = np.array(
+            [[False, True, False], [True, True, True], [False, True, False]]
+        )
+        out = watershed(image, markers, structure)
+        i, j = np.mgrid[0:21, 0:21]
+        d = np.dstack(
+            [
+                np.sqrt((i.astype(float) - i0) ** 2, (j.astype(float) - j0) ** 2)
+                for i0, j0 in ((5, 5), (5, 10), (10, 5), (10, 10))
+            ]
+        )
+        dmin = np.min(d, 2)
+        self.assertTrue(np.all(d[i, j, out[i, j] - 1] == dmin))
+
+    def test_watershed12(self):
+        "The watershed line"
+        data = np.array(
+            [
+                [
+                    203,
+                    255,
+                    203,
+                    153,
+                    153,
+                    153,
+                    153,
+                    153,
+                    153,
+                    153,
+                    153,
+                    153,
+                    153,
+                    153,
+                    153,
+                    153,
+                ],
+                [
+                    203,
+                    255,
+                    203,
+                    153,
+                    153,
+                    153,
+                    102,
+                    102,
+                    102,
+                    102,
+                    102,
+                    102,
+                    153,
+                    153,
+                    153,
+                    153,
+                ],
+                [
+                    203,
+                    255,
+                    203,
+                    203,
+                    153,
+                    153,
+                    102,
+                    102,
+                    77,
+                    0,
+                    102,
+                    102,
+                    153,
+                    153,
+                    203,
+                    203,
+                ],
+                [
+                    203,
+                    255,
+                    255,
+                    203,
+                    153,
+                    153,
+                    153,
+                    102,
+                    102,
+                    102,
+                    102,
+                    153,
+                    153,
+                    203,
+                    203,
+                    255,
+                ],
+                [
+                    203,
+                    203,
+                    255,
+                    203,
+                    203,
+                    203,
+                    153,
+                    153,
+                    153,
+                    153,
+                    153,
+                    153,
+                    203,
+                    203,
+                    255,
+                    255,
+                ],
+                [
+                    153,
+                    203,
+                    255,
+                    255,
+                    255,
+                    203,
+                    203,
+                    203,
+                    203,
+                    203,
+                    203,
+                    203,
+                    203,
+                    255,
+                    255,
+                    203,
+                ],
+                [
+                    153,
+                    203,
+                    203,
+                    203,
+                    255,
+                    255,
+                    255,
+                    255,
+                    255,
+                    255,
+                    255,
+                    255,
+                    255,
+                    255,
+                    203,
+                    203,
+                ],
+                [
+                    153,
+                    153,
+                    153,
+                    203,
+                    203,
+                    203,
+                    203,
+                    203,
+                    255,
+                    203,
+                    203,
+                    203,
+                    203,
+                    203,
+                    203,
+                    153,
+                ],
+                [
+                    102,
+                    102,
+                    153,
+                    153,
+                    153,
+                    153,
+                    203,
+                    203,
+                    255,
+                    203,
+                    203,
+                    255,
+                    203,
+                    153,
+                    153,
+                    153,
+                ],
+                [
+                    102,
+                    102,
+                    102,
+                    102,
+                    102,
+                    153,
+                    203,
+                    255,
+                    255,
+                    203,
+                    203,
+                    203,
+                    203,
+                    153,
+                    102,
+                    153,
+                ],
+                [
+                    102,
+                    51,
+                    51,
+                    102,
+                    102,
+                    153,
+                    203,
+                    255,
+                    203,
+                    203,
+                    153,
+                    153,
+                    153,
+                    153,
+                    102,
+                    153,
+                ],
+                [
+                    77,
+                    51,
+                    51,
+                    102,
+                    153,
+                    153,
+                    203,
+                    255,
+                    203,
+                    203,
+                    203,
+                    153,
+                    102,
+                    102,
+                    102,
+                    153,
+                ],
+                [
+                    77,
+                    0,
+                    51,
+                    102,
+                    153,
+                    203,
+                    203,
+                    255,
+                    203,
+                    255,
+                    203,
+                    153,
+                    102,
+                    51,
+                    102,
+                    153,
+                ],
+                [
+                    77,
+                    0,
+                    51,
+                    102,
+                    153,
+                    203,
+                    255,
+                    255,
+                    203,
+                    203,
+                    203,
+                    153,
+                    102,
+                    0,
+                    102,
+                    153,
+                ],
+                [
+                    102,
+                    0,
+                    51,
+                    102,
+                    153,
+                    203,
+                    255,
+                    203,
+                    203,
+                    153,
+                    153,
+                    153,
+                    102,
+                    102,
+                    102,
+                    153,
+                ],
+                [
+                    102,
+                    102,
+                    102,
+                    102,
+                    153,
+                    203,
+                    255,
+                    203,
+                    153,
+                    153,
+                    153,
+                    153,
+                    153,
+                    153,
+                    153,
+                    153,
+                ],
+            ]
+        )
+        markerbin = data == 0
+        marker = label(markerbin)
+        ws = watershed(data, marker, connectivity=2, watershed_line=True)
+        for lab, area in zip(range(4), [34, 74, 74, 74]):
+            self.assertTrue(np.sum(ws == lab) == area)
+
+    def test_watershed_input_not_modified(self):
+        """Test to ensure input markers are not modified."""
+        image = np.random.default_rng().random(size=(21, 21))
+        markers = np.zeros((21, 21), dtype=np.uint8)
+        markers[[5, 5, 15, 15], [5, 15, 5, 15]] = [1, 2, 3, 4]
+        original_markers = np.copy(markers)
+        result = watershed(image, markers)
+        np.testing.assert_equal(original_markers, markers)
+        assert not np.all(result == markers)
+
+
+def test_compact_watershed():
+    image = np.zeros((5, 6))
+    image[:, 3:] = 1
+    seeds = np.zeros((5, 6), dtype=int)
+    seeds[2, 0] = 1
+    seeds[2, 3] = 2
+    compact = watershed(image, seeds, compactness=0.01)
+    expected = np.array(
+        [
+            [1, 1, 1, 2, 2, 2],
+            [1, 1, 1, 2, 2, 2],
+            [1, 1, 1, 2, 2, 2],
+            [1, 1, 1, 2, 2, 2],
+            [1, 1, 1, 2, 2, 2],
+        ],
+        dtype=int,
+    )
+    np.testing.assert_equal(compact, expected)
+    normal = watershed(image, seeds)
+    expected = np.ones(image.shape, dtype=int)
+    expected[2, 3:] = 2
+    np.testing.assert_equal(normal, expected)
+
+
+def test_numeric_seed_watershed():
+    """Test that passing just the number of seeds to watershed works."""
+    image = np.zeros((5, 6))
+    image[:, 3:] = 1
+    compact = watershed(image, 2, compactness=0.01)
+    expected = np.array(
+        [
+            [1, 1, 1, 1, 2, 2],
+            [1, 1, 1, 1, 2, 2],
+            [1, 1, 1, 1, 2, 2],
+            [1, 1, 1, 1, 2, 2],
+            [1, 1, 1, 1, 2, 2],
+        ],
+        dtype=np.int32,
+    )
+    np.testing.assert_equal(compact, expected)
+
+
+@pytest.mark.parametrize(
+    'dtype',
+    [np.uint8, np.int8, np.uint16, np.int16, np.uint32, np.int32, np.uint64, np.int64],
+)
+def test_watershed_output_dtype(dtype):
+    image = np.zeros((100, 100))
+    markers = np.zeros((100, 100), dtype)
+    out = watershed(image, markers)
+    assert out.dtype == markers.dtype
+
+
+def test_incorrect_markers_shape():
+    image = np.ones((5, 6))
+    markers = np.ones((5, 7))
+    with pytest.raises(ValueError):
+        watershed(image, markers)
+
+
+def test_incorrect_mask_shape():
+    image = np.ones((5, 6))
+    mask = np.ones((5, 7))
+    with pytest.raises(ValueError):
+        watershed(image, markers=4, mask=mask)
+
+
+def test_markers_in_mask():
+    data = blob
+    mask = data != 255
+    out = watershed(data, 25, connectivity=2, mask=mask)
+    # There should be no markers where the mask is false
+    assert np.all(out[~mask] == 0)
+
+
+def test_no_markers():
+    data = blob
+    mask = data != 255
+    out = watershed(data, mask=mask)
+    assert np.max(out) == 2
+
+
+def test_connectivity():
+    """
+    Watershed segmentation should output different result for
+    different connectivity
+    when markers are calculated where None is supplied.
+    Issue = 5084
+    """
+    # Generate a dummy BrightnessTemperature image
+    x, y = np.indices((406, 270))
+    x1, y1, x2, y2, x3, y3, x4, y4 = 200, 208, 300, 120, 100, 100, 340, 208
+    r1, r2, r3, r4 = 100, 50, 40, 80
+    mask_circle1 = (x - x1) ** 2 + (y - y1) ** 2 < r1**2
+    mask_circle2 = (x - x2) ** 2 + (y - y2) ** 2 < r2**2
+    mask_circle3 = (x - x3) ** 2 + (y - y3) ** 2 < r3**2
+    mask_circle4 = (x - x4) ** 2 + (y - y4) ** 2 < r4**2
+    image = np.logical_or(mask_circle1, mask_circle2)
+    image = np.logical_or(image, mask_circle3)
+    image = np.logical_or(image, mask_circle4)
+
+    # calculate distance in discrete increase
+    DummyBT = ndi.distance_transform_edt(image)
+    DummyBT_dis = np.around(DummyBT / 12, decimals=0) * 12
+    # calculate the mask
+    Img_mask = np.where(DummyBT_dis == 0, 0, 1)
+
+    # segments for connectivity 1 and 2
+    labels_c1 = watershed(
+        200 - DummyBT_dis, mask=Img_mask, connectivity=1, compactness=0.01
+    )
+    labels_c2 = watershed(
+        200 - DummyBT_dis, mask=Img_mask, connectivity=2, compactness=0.01
+    )
+
+    # assertions
+    assert np.unique(labels_c1).shape[0] == 6
+    assert np.unique(labels_c2).shape[0] == 5
+
+    # checking via area of each individual segment.
+    for lab, area in zip(range(6), [61824, 3653, 20467, 11097, 1301, 11278]):
+        assert np.sum(labels_c1 == lab) == area
+
+    for lab, area in zip(range(5), [61824, 3653, 20466, 12386, 11291]):
+        assert np.sum(labels_c2 == lab) == area