update

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

@@ -0,0 +1,184 @@
+"""Image Processing for Python
+
+``scikit-image`` (a.k.a. ``skimage``) is a collection of algorithms for image
+processing and computer vision.
+
+The main package of ``skimage`` only provides a few utilities for converting
+between image data types; for most features, you need to import one of the
+following subpackages:
+
+Subpackages
+-----------
+color
+    Color space conversion.
+data
+    Test images and example data.
+draw
+    Drawing primitives (lines, text, etc.) that operate on NumPy arrays.
+exposure
+    Image intensity adjustment, e.g., histogram equalization, etc.
+feature
+    Feature detection and extraction, e.g., texture analysis corners, etc.
+filters
+    Sharpening, edge finding, rank filters, thresholding, etc.
+graph
+    Graph-theoretic operations, e.g., shortest paths.
+io
+    Reading, saving, and displaying images and video.
+measure
+    Measurement of image properties, e.g., region properties and contours.
+metrics
+    Metrics corresponding to images, e.g. distance metrics, similarity, etc.
+morphology
+    Morphological operations, e.g., opening or skeletonization.
+restoration
+    Restoration algorithms, e.g., deconvolution algorithms, denoising, etc.
+segmentation
+    Partitioning an image into multiple regions.
+transform
+    Geometric and other transforms, e.g., rotation or the Radon transform.
+util
+    Generic utilities.
+
+Utility Functions
+-----------------
+img_as_float
+    Convert an image to floating point format, with values in [0, 1].
+    Is similar to `img_as_float64`, but will not convert lower-precision
+    floating point arrays to `float64`.
+img_as_float32
+    Convert an image to single-precision (32-bit) floating point format,
+    with values in [0, 1].
+img_as_float64
+    Convert an image to double-precision (64-bit) floating point format,
+    with values in [0, 1].
+img_as_uint
+    Convert an image to unsigned integer format, with values in [0, 65535].
+img_as_int
+    Convert an image to signed integer format, with values in [-32768, 32767].
+img_as_ubyte
+    Convert an image to unsigned byte format, with values in [0, 255].
+img_as_bool
+    Convert an image to boolean format, with values either True or False.
+dtype_limits
+    Return intensity limits, i.e. (min, max) tuple, of the image's dtype.
+
+"""
+
+__version__ = '0.24.0'
+
+import lazy_loader as lazy
+
+__getattr__, __lazy_dir__, _ = lazy.attach_stub(__name__, __file__)
+
+
+def __dir__():
+    return __lazy_dir__() + ['__version__']
+
+
+# Logic for checking for improper install and importing while in the source
+# tree when package has not been installed inplace.
+# Code adapted from scikit-learn's __check_build module.
+_INPLACE_MSG = """
+It appears that you are importing a local scikit-image source tree. For
+this, you need to have an inplace install. Maybe you are in the source
+directory and you need to try from another location."""
+
+_STANDARD_MSG = """
+Your install of scikit-image appears to be broken.
+Try re-installing the package following the instructions at:
+https://scikit-image.org/docs/stable/user_guide/install.html"""
+
+
+def _raise_build_error(e):
+    # Raise a comprehensible error
+    import os.path as osp
+
+    local_dir = osp.split(__file__)[0]
+    msg = _STANDARD_MSG
+    if local_dir == "skimage":
+        # Picking up the local install: this will work only if the
+        # install is an 'inplace build'
+        msg = _INPLACE_MSG
+    raise ImportError(
+        f"{e}\nIt seems that scikit-image has not been built correctly.\n{msg}"
+    )
+
+
+try:
+    # This variable is injected in the __builtins__ by the build
+    # process. It used to enable importing subpackages of skimage when
+    # the binaries are not built
+    __SKIMAGE_SETUP__
+except NameError:
+    __SKIMAGE_SETUP__ = False
+
+if __SKIMAGE_SETUP__:
+    import sys
+
+    sys.stderr.write('Partial import of skimage during the build process.\n')
+    # We are not importing the rest of the scikit during the build
+    # process, as it may not be compiled yet
+else:
+    try:
+        from ._shared import geometry
+
+        del geometry
+    except ImportError as e:
+        _raise_build_error(e)
+
+    # Legacy imports into the root namespace; not advertised in __all__
+    from .util.dtype import (
+        dtype_limits,
+        img_as_float32,
+        img_as_float64,
+        img_as_float,
+        img_as_int,
+        img_as_uint,
+        img_as_ubyte,
+        img_as_bool,
+    )
+
+    from .util.lookfor import lookfor
+
+    from .data import data_dir
+
+
+if 'dev' in __version__:
+    # Append last commit date and hash to dev version information, if available
+
+    import subprocess
+    import os.path
+
+    try:
+        p = subprocess.Popen(
+            ['git', 'log', '-1', '--format="%h %aI"'],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            cwd=os.path.dirname(__file__),
+        )
+    except FileNotFoundError:
+        pass
+    except OSError:
+        pass  # If skimage is built with emscripten which does not support processes
+    else:
+        out, err = p.communicate()
+        if p.returncode == 0:
+            git_hash, git_date = (
+                out.decode('utf-8')
+                .strip()
+                .replace('"', '')
+                .split('T')[0]
+                .replace('-', '')
+                .split()
+            )
+
+            __version__ = '+'.join(
+                [tag for tag in __version__.split('+') if not tag.startswith('git')]
+            )
+            __version__ += f'+git{git_date}.{git_hash}'
+
+from skimage._shared.tester import PytestTester  # noqa
+
+test = PytestTester(__name__)
+del PytestTester

.CondaPkg/env/Lib/site-packages/skimage/__init__.pyi

@@ -0,0 +1,41 @@
+submodules = [
+    'color',
+    'data',
+    'draw',
+    'exposure',
+    'feature',
+    'filters',
+    'future',
+    'graph',
+    'io',
+    'measure',
+    'metrics',
+    'morphology',
+    'registration',
+    'restoration',
+    'segmentation',
+    'transform',
+    'util',
+]
+
+__all__ = submodules + ['__version__']  # noqa: F822
+
+from . import (
+    color,
+    data,
+    draw,
+    exposure,
+    feature,
+    filters,
+    future,
+    graph,
+    io,
+    measure,
+    metrics,
+    morphology,
+    registration,
+    restoration,
+    segmentation,
+    transform,
+    util,
+)

.CondaPkg/env/Lib/site-packages/skimage/_shared/_dependency_checks.py

@@ -0,0 +1,7 @@
+from .version_requirements import is_installed
+import sys
+import platform
+
+has_mpl = is_installed("matplotlib", ">=3.3")
+
+is_wasm = (sys.platform == "emscripten") or (platform.machine() in ["wasm32", "wasm64"])

.CondaPkg/env/Lib/site-packages/skimage/_shared/_geometry.py

@@ -0,0 +1,54 @@
+__all__ = ['polygon_clip', 'polygon_area']
+
+import numpy as np
+
+from .version_requirements import require
+
+
+@require("matplotlib", ">=3.3")
+def polygon_clip(rp, cp, r0, c0, r1, c1):
+    """Clip a polygon to the given bounding box.
+
+    Parameters
+    ----------
+    rp, cp : (K,) ndarray of double
+        Row and column coordinates of the polygon.
+    (r0, c0), (r1, c1) : double
+        Top-left and bottom-right coordinates of the bounding box.
+
+    Returns
+    -------
+    r_clipped, c_clipped : (L,) ndarray of double
+        Coordinates of clipped polygon.
+
+    Notes
+    -----
+    This makes use of Sutherland-Hodgman clipping as implemented in
+    AGG 2.4 and exposed in Matplotlib.
+
+    """
+    from matplotlib import path, transforms
+
+    poly = path.Path(np.vstack((rp, cp)).T, closed=True)
+    clip_rect = transforms.Bbox([[r0, c0], [r1, c1]])
+    poly_clipped = poly.clip_to_bbox(clip_rect).to_polygons()[0]
+
+    return poly_clipped[:, 0], poly_clipped[:, 1]
+
+
+def polygon_area(pr, pc):
+    """Compute the area of a polygon.
+
+    Parameters
+    ----------
+    pr, pc : (K,) array of float
+        Polygon row and column coordinates.
+
+    Returns
+    -------
+    a : float
+        Area of the polygon.
+    """
+    pr = np.asarray(pr)
+    pc = np.asarray(pc)
+    return 0.5 * np.abs(np.sum((pc[:-1] * pr[1:]) - (pc[1:] * pr[:-1])))

.CondaPkg/env/Lib/site-packages/skimage/_shared/_tempfile.py

@@ -0,0 +1,28 @@
+from tempfile import NamedTemporaryFile
+from contextlib import contextmanager
+import os
+
+
+@contextmanager
+def temporary_file(suffix=''):
+    """Yield a writeable temporary filename that is deleted on context exit.
+
+    Parameters
+    ----------
+    suffix : string, optional
+        The suffix for the file.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from skimage import io
+    >>> with temporary_file('.tif') as tempfile:
+    ...     im = np.arange(25, dtype=np.uint8).reshape((5, 5))
+    ...     io.imsave(tempfile, im)
+    ...     assert np.all(io.imread(tempfile) == im)
+    """
+    with NamedTemporaryFile(suffix=suffix, delete=False) as tempfile_stream:
+        tempfile = tempfile_stream.name
+
+    yield tempfile
+    os.remove(tempfile)

.CondaPkg/env/Lib/site-packages/skimage/_shared/_warnings.py

@@ -0,0 +1,149 @@
+from contextlib import contextmanager
+import sys
+import warnings
+import re
+import functools
+import os
+
+__all__ = ['all_warnings', 'expected_warnings', 'warn']
+
+
+# A version of `warnings.warn` with a default stacklevel of 2.
+# functool is used so as not to increase the call stack accidentally
+warn = functools.partial(warnings.warn, stacklevel=2)
+
+
+@contextmanager
+def all_warnings():
+    """
+    Context for use in testing to ensure that all warnings are raised.
+
+    Examples
+    --------
+    >>> import warnings
+    >>> def foo():
+    ...     warnings.warn(RuntimeWarning("bar"), stacklevel=2)
+
+    We raise the warning once, while the warning filter is set to "once".
+    Hereafter, the warning is invisible, even with custom filters:
+
+    >>> with warnings.catch_warnings():
+    ...     warnings.simplefilter('once')
+    ...     foo()                         # doctest: +SKIP
+
+    We can now run ``foo()`` without a warning being raised:
+
+    >>> from numpy.testing import assert_warns
+    >>> foo()                             # doctest: +SKIP
+
+    To catch the warning, we call in the help of ``all_warnings``:
+
+    >>> with all_warnings():
+    ...     assert_warns(RuntimeWarning, foo)
+    """
+    # _warnings.py is on the critical import path.
+    # Since this is a testing only function, we lazy import inspect.
+    import inspect
+
+    # Whenever a warning is triggered, Python adds a __warningregistry__
+    # member to the *calling* module.  The exercise here is to find
+    # and eradicate all those breadcrumbs that were left lying around.
+    #
+    # We proceed by first searching all parent calling frames and explicitly
+    # clearing their warning registries (necessary for the doctests above to
+    # pass).  Then, we search for all submodules of skimage and clear theirs
+    # as well (necessary for the skimage test suite to pass).
+
+    frame = inspect.currentframe()
+    if frame:
+        for f in inspect.getouterframes(frame):
+            f[0].f_locals['__warningregistry__'] = {}
+    del frame
+
+    for mod_name, mod in list(sys.modules.items()):
+        try:
+            mod.__warningregistry__.clear()
+        except AttributeError:
+            pass
+
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        yield w
+
+
+@contextmanager
+def expected_warnings(matching):
+    r"""Context for use in testing to catch known warnings matching regexes
+
+    Parameters
+    ----------
+    matching : None or a list of strings or compiled regexes
+        Regexes for the desired warning to catch
+        If matching is None, this behaves as a no-op.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> rng = np.random.default_rng()
+    >>> image = rng.integers(0, 2**16, size=(100, 100), dtype=np.uint16)
+    >>> # rank filters are slow when bit-depth exceeds 10 bits
+    >>> from skimage import filters
+    >>> with expected_warnings(['Bad rank filter performance']):
+    ...     median_filtered = filters.rank.median(image)
+
+    Notes
+    -----
+    Uses `all_warnings` to ensure all warnings are raised.
+    Upon exiting, it checks the recorded warnings for the desired matching
+    pattern(s).
+    Raises a ValueError if any match was not found or an unexpected
+    warning was raised.
+    Allows for three types of behaviors: `and`, `or`, and `optional` matches.
+    This is done to accommodate different build environments or loop conditions
+    that may produce different warnings.  The behaviors can be combined.
+    If you pass multiple patterns, you get an orderless `and`, where all of the
+    warnings must be raised.
+    If you use the `|` operator in a pattern, you can catch one of several
+    warnings.
+    Finally, you can use `|\A\Z` in a pattern to signify it as optional.
+
+    """
+    if isinstance(matching, str):
+        raise ValueError(
+            '``matching`` should be a list of strings and not ' 'a string itself.'
+        )
+
+    # Special case for disabling the context manager
+    if matching is None:
+        yield None
+        return
+
+    strict_warnings = os.environ.get('SKIMAGE_TEST_STRICT_WARNINGS', '1')
+    if strict_warnings.lower() == 'true':
+        strict_warnings = True
+    elif strict_warnings.lower() == 'false':
+        strict_warnings = False
+    else:
+        strict_warnings = bool(int(strict_warnings))
+
+    with all_warnings() as w:
+        # enter context
+        yield w
+        # exited user context, check the recorded warnings
+        # Allow users to provide None
+        while None in matching:
+            matching.remove(None)
+        remaining = [m for m in matching if r'\A\Z' not in m.split('|')]
+        for warn in w:
+            found = False
+            for match in matching:
+                if re.search(match, str(warn.message)) is not None:
+                    found = True
+                    if match in remaining:
+                        remaining.remove(match)
+            if strict_warnings and not found:
+                raise ValueError(f'Unexpected warning: {str(warn.message)}')
+        if strict_warnings and (len(remaining) > 0):
+            newline = "\n"
+            msg = f"No warning raised matching:{newline}{newline.join(remaining)}"
+            raise ValueError(msg)

.CondaPkg/env/Lib/site-packages/skimage/_shared/compat.py

@@ -0,0 +1,30 @@
+"""Compatibility helpers for dependencies."""
+
+from packaging.version import parse
+
+import numpy as np
+import scipy as sp
+
+
+__all__ = [
+    "NP_COPY_IF_NEEDED",
+    "SCIPY_CG_TOL_PARAM_NAME",
+]
+
+
+NUMPY_LT_2_0_0 = parse(np.__version__) < parse('2.0.0.dev0')
+
+# With NumPy 2.0.0, `copy=False` now raises a ValueError if the copy cannot be
+# made. The previous behavior to only copy if needed is provided with `copy=None`.
+# During the transition period, use this symbol instead.
+# Remove once NumPy 2.0.0 is the minimal required version.
+# https://numpy.org/devdocs/release/2.0.0-notes.html#new-copy-keyword-meaning-for-array-and-asarray-constructors
+# https://github.com/numpy/numpy/pull/25168
+NP_COPY_IF_NEEDED = False if NUMPY_LT_2_0_0 else None
+
+
+SCIPY_LT_1_12 = parse(sp.__version__) < parse('1.12')
+
+# Starting in SciPy v1.12, 'scipy.sparse.linalg.cg' keyword argument `tol` is
+# deprecated in favor of `rtol`.
+SCIPY_CG_TOL_PARAM_NAME = "tol" if SCIPY_LT_1_12 else "rtol"

.CondaPkg/env/Lib/site-packages/skimage/_shared/coord.py

@@ -0,0 +1,125 @@
+import numpy as np
+from scipy.spatial import cKDTree, distance
+
+
+def _ensure_spacing(coord, spacing, p_norm, max_out):
+    """Returns a subset of coord where a minimum spacing is guaranteed.
+
+    Parameters
+    ----------
+    coord : ndarray
+        The coordinates of the considered points.
+    spacing : float
+        the maximum allowed spacing between the points.
+    p_norm : float
+        Which Minkowski p-norm to use. Should be in the range [1, inf].
+        A finite large p may cause a ValueError if overflow can occur.
+        ``inf`` corresponds to the Chebyshev distance and 2 to the
+        Euclidean distance.
+    max_out: int
+        If not None, at most the first ``max_out`` candidates are
+        returned.
+
+    Returns
+    -------
+    output : ndarray
+        A subset of coord where a minimum spacing is guaranteed.
+
+    """
+
+    # Use KDtree to find the peaks that are too close to each other
+    tree = cKDTree(coord)
+
+    indices = tree.query_ball_point(coord, r=spacing, p=p_norm)
+    rejected_peaks_indices = set()
+    naccepted = 0
+    for idx, candidates in enumerate(indices):
+        if idx not in rejected_peaks_indices:
+            # keep current point and the points at exactly spacing from it
+            candidates.remove(idx)
+            dist = distance.cdist(
+                [coord[idx]], coord[candidates], distance.minkowski, p=p_norm
+            ).reshape(-1)
+            candidates = [c for c, d in zip(candidates, dist) if d < spacing]
+
+            # candidates.remove(keep)
+            rejected_peaks_indices.update(candidates)
+            naccepted += 1
+            if max_out is not None and naccepted >= max_out:
+                break
+
+    # Remove the peaks that are too close to each other
+    output = np.delete(coord, tuple(rejected_peaks_indices), axis=0)
+    if max_out is not None:
+        output = output[:max_out]
+
+    return output
+
+
+def ensure_spacing(
+    coords,
+    spacing=1,
+    p_norm=np.inf,
+    min_split_size=50,
+    max_out=None,
+    *,
+    max_split_size=2000,
+):
+    """Returns a subset of coord where a minimum spacing is guaranteed.
+
+    Parameters
+    ----------
+    coords : array_like
+        The coordinates of the considered points.
+    spacing : float
+        the maximum allowed spacing between the points.
+    p_norm : float
+        Which Minkowski p-norm to use. Should be in the range [1, inf].
+        A finite large p may cause a ValueError if overflow can occur.
+        ``inf`` corresponds to the Chebyshev distance and 2 to the
+        Euclidean distance.
+    min_split_size : int
+        Minimum split size used to process ``coords`` by batch to save
+        memory. If None, the memory saving strategy is not applied.
+    max_out : int
+        If not None, only the first ``max_out`` candidates are returned.
+    max_split_size : int
+        Maximum split size used to process ``coords`` by batch to save
+        memory. This number was decided by profiling with a large number
+        of points. Too small a number results in too much looping in
+        Python instead of C, slowing down the process, while too large
+        a number results in large memory allocations, slowdowns, and,
+        potentially, in the process being killed -- see gh-6010. See
+        benchmark results `here
+        <https://github.com/scikit-image/scikit-image/pull/6035#discussion_r751518691>`_.
+
+    Returns
+    -------
+    output : array_like
+        A subset of coord where a minimum spacing is guaranteed.
+
+    """
+
+    output = coords
+    if len(coords):
+        coords = np.atleast_2d(coords)
+        if min_split_size is None:
+            batch_list = [coords]
+        else:
+            coord_count = len(coords)
+            split_idx = [min_split_size]
+            split_size = min_split_size
+            while coord_count - split_idx[-1] > max_split_size:
+                split_size *= 2
+                split_idx.append(split_idx[-1] + min(split_size, max_split_size))
+            batch_list = np.array_split(coords, split_idx)
+
+        output = np.zeros((0, coords.shape[1]), dtype=coords.dtype)
+        for batch in batch_list:
+            output = _ensure_spacing(
+                np.vstack([output, batch]), spacing, p_norm, max_out
+            )
+            if max_out is not None and len(output) >= max_out:
+                break
+
+    return output

.CondaPkg/env/Lib/site-packages/skimage/_shared/dtype.py

@@ -0,0 +1,73 @@
+import numpy as np
+
+# Define classes of supported dtypes and Python scalar types
+# Variables ending in `_dtypes` only contain numpy.dtypes of the respective
+# class; variables ending in `_types` additionally include Python scalar types.
+signed_integer_dtypes = {np.int8, np.int16, np.int32, np.int64}
+signed_integer_types = signed_integer_dtypes | {int}
+
+unsigned_integer_dtypes = {np.uint8, np.uint16, np.uint32, np.uint64}
+
+integer_dtypes = signed_integer_dtypes | unsigned_integer_dtypes
+integer_types = signed_integer_types | unsigned_integer_dtypes
+
+floating_dtypes = {np.float16, np.float32, np.float64}
+floating_types = floating_dtypes | {float}
+
+complex_dtypes = {np.complex64, np.complex128}
+complex_types = complex_dtypes | {complex}
+
+inexact_dtypes = floating_dtypes | complex_dtypes
+inexact_types = floating_types | complex_types
+
+bool_types = {np.dtype(bool), bool}
+
+numeric_dtypes = integer_dtypes | inexact_dtypes | {np.bool_}
+numeric_types = integer_types | inexact_types | bool_types
+
+
+def numeric_dtype_min_max(dtype):
+    """Return minimum and maximum representable value for a given dtype.
+
+    A convenient wrapper around `numpy.finfo` and `numpy.iinfo` that
+    additionally supports numpy.bool as well.
+
+    Parameters
+    ----------
+    dtype : numpy.dtype
+        The dtype. Tries to convert Python "types" such as int or float, to
+        the corresponding NumPy dtype.
+
+    Returns
+    -------
+    min, max : number
+        Minimum and maximum of the given `dtype`. These scalars are themselves
+        of the given `dtype`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> numeric_dtype_min_max(np.uint8)
+    (0, 255)
+    >>> numeric_dtype_min_max(bool)
+    (False, True)
+    >>> numeric_dtype_min_max(np.float64)
+    (-1.7976931348623157e+308, 1.7976931348623157e+308)
+    >>> numeric_dtype_min_max(int)
+    (-9223372036854775808, 9223372036854775807)
+    """
+    dtype = np.dtype(dtype)
+    if np.issubdtype(dtype, np.integer):
+        info = np.iinfo(dtype)
+        min_ = dtype.type(info.min)
+        max_ = dtype.type(info.max)
+    elif np.issubdtype(dtype, np.inexact):
+        info = np.finfo(dtype)
+        min_ = info.min
+        max_ = info.max
+    elif np.issubdtype(dtype, np.dtype(bool)):
+        min_ = dtype.type(False)
+        max_ = dtype.type(True)
+    else:
+        raise ValueError(f"unsupported dtype {dtype!r}")
+    return min_, max_

.CondaPkg/env/Lib/site-packages/skimage/_shared/fast_exp.h

@@ -0,0 +1,47 @@
+/* A fast approximation of the exponential function.
+ * Reference [1]: https://schraudolph.org/pubs/Schraudolph99.pdf
+ * Reference [2]: https://doi.org/10.1162/089976600300015033
+ * Additional improvements by Leonid Bloch. */
+
+#include <stdint.h>
+
+/* use just EXP_A = 1512775 for integer version, to avoid FP calculations */
+#define EXP_A (1512775.3951951856938)  /* 2^20/ln2 */
+/* For min. RMS error */
+#define EXP_BC 1072632447              /* 1023*2^20 - 60801 */
+/* For min. max. relative error */
+/* #define EXP_BC 1072647449 */        /* 1023*2^20 - 45799 */
+/* For min. mean relative error */
+/* #define EXP_BC 1072625005 */        /* 1023*2^20 - 68243 */
+
+__inline double _fast_exp (double y)
+{
+    union
+    {
+        double d;
+        struct { int32_t i, j; } n;
+        char t[8];
+    } _eco;
+
+    _eco.n.i = 1;
+
+    switch(_eco.t[0]) {
+        case 1:
+            /* Little endian */
+            _eco.n.j = (int32_t)(EXP_A*(y)) + EXP_BC;
+            _eco.n.i = 0;
+            break;
+        case 0:
+            /* Big endian */
+            _eco.n.i = (int32_t)(EXP_A*(y)) + EXP_BC;
+            _eco.n.j = 0;
+            break;
+    }
+
+    return _eco.d;
+}
+
+__inline float _fast_expf (float y)
+{
+  return (float)_fast_exp((double)y);
+}

.CondaPkg/env/Lib/site-packages/skimage/_shared/filters.py

@@ -0,0 +1,142 @@
+"""Filters used across multiple skimage submodules.
+
+These are defined here to avoid circular imports.
+
+The unit tests remain under skimage/filters/tests/
+"""
+
+from collections.abc import Iterable
+
+import numpy as np
+from scipy import ndimage as ndi
+
+from .._shared.utils import (
+    _supported_float_type,
+    convert_to_float,
+    deprecate_parameter,
+    DEPRECATED,
+)
+
+
+@deprecate_parameter(
+    "output", new_name="out", start_version="0.23", stop_version="0.25"
+)
+def gaussian(
+    image,
+    sigma=1,
+    output=DEPRECATED,
+    mode='nearest',
+    cval=0,
+    preserve_range=False,
+    truncate=4.0,
+    *,
+    channel_axis=None,
+    out=None,
+):
+    """Multi-dimensional Gaussian filter.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image (grayscale or color) to filter.
+    sigma : scalar or sequence of scalars, optional
+        Standard deviation for Gaussian kernel. The standard
+        deviations of the Gaussian filter are given for each axis as a
+        sequence, or as a single number, in which case it is equal for
+        all axes.
+    mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
+        The ``mode`` parameter determines how the array borders are
+        handled, where ``cval`` is the value when mode is equal to
+        'constant'. Default is 'nearest'.
+    cval : scalar, optional
+        Value to fill past edges of input if ``mode`` is 'constant'. Default
+        is 0.0
+    preserve_range : bool, optional
+        If True, keep the original range of values. Otherwise, the input
+        ``image`` is converted according to the conventions of ``img_as_float``
+        (Normalized first to values [-1.0 ; 1.0] or [0 ; 1.0] depending on
+        dtype of input)
+
+        For more information, see:
+        https://scikit-image.org/docs/dev/user_guide/data_types.html
+    truncate : float, optional
+        Truncate the filter at this many standard deviations.
+    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.
+    out : ndarray, optional
+        If given, the filtered image will be stored in this array.
+
+        .. versionadded:: 0.23
+            `out` was added in 0.23.
+
+    Returns
+    -------
+    filtered_image : ndarray
+        the filtered array
+
+    Notes
+    -----
+    This function is a wrapper around :func:`scipy.ndimage.gaussian_filter`.
+
+    Integer arrays are converted to float.
+
+    `out` should be of floating-point data type since `gaussian` converts the
+    input `image` to float. If `out` is not provided, another array
+    will be allocated and returned as the result.
+
+    The multi-dimensional filter is implemented as a sequence of
+    one-dimensional convolution filters. The intermediate arrays are
+    stored in the same data type as the output. Therefore, for output
+    types with a limited precision, the results may be imprecise
+    because intermediate results may be stored with insufficient
+    precision.
+
+    Examples
+    --------
+    >>> import skimage as ski
+    >>> a = np.zeros((3, 3))
+    >>> a[1, 1] = 1
+    >>> a
+    array([[0., 0., 0.],
+           [0., 1., 0.],
+           [0., 0., 0.]])
+    >>> ski.filters.gaussian(a, sigma=0.4)  # mild smoothing
+    array([[0.00163116, 0.03712502, 0.00163116],
+           [0.03712502, 0.84496158, 0.03712502],
+           [0.00163116, 0.03712502, 0.00163116]])
+    >>> ski.filters.gaussian(a, sigma=1)  # more smoothing
+    array([[0.05855018, 0.09653293, 0.05855018],
+           [0.09653293, 0.15915589, 0.09653293],
+           [0.05855018, 0.09653293, 0.05855018]])
+    >>> # Several modes are possible for handling boundaries
+    >>> ski.filters.gaussian(a, sigma=1, mode='reflect')
+    array([[0.08767308, 0.12075024, 0.08767308],
+           [0.12075024, 0.16630671, 0.12075024],
+           [0.08767308, 0.12075024, 0.08767308]])
+    >>> # For RGB images, each is filtered separately
+    >>> image = ski.data.astronaut()
+    >>> filtered_img = ski.filters.gaussian(image, sigma=1, channel_axis=-1)
+
+    """
+    if np.any(np.asarray(sigma) < 0.0):
+        raise ValueError("Sigma values less than zero are not valid")
+    if channel_axis is not None:
+        # do not filter across channels
+        if not isinstance(sigma, Iterable):
+            sigma = [sigma] * (image.ndim - 1)
+        if len(sigma) == image.ndim - 1:
+            sigma = list(sigma)
+            sigma.insert(channel_axis % image.ndim, 0)
+    image = convert_to_float(image, preserve_range)
+    float_dtype = _supported_float_type(image.dtype)
+    image = image.astype(float_dtype, copy=False)
+    if (out is not None) and (not np.issubdtype(out.dtype, np.floating)):
+        raise ValueError(f"dtype of `out` must be float; got {out.dtype!r}.")
+    return ndi.gaussian_filter(
+        image, sigma, output=out, mode=mode, cval=cval, truncate=truncate
+    )

.CondaPkg/env/Lib/site-packages/skimage/_shared/tester.py

@@ -0,0 +1,131 @@
+import os
+import sys
+
+
+def _show_skimage_info():
+    import skimage
+
+    print(f"skimage version {skimage.__version__}")
+
+
+class PytestTester:
+    """
+    Pytest test runner.
+
+    This class is made available in ``skimage._shared.testing``, and a test
+    function is typically added to a package's __init__.py like so::
+
+      from skimage._shared.testing import PytestTester
+      test = PytestTester(__name__)
+      del PytestTester
+
+    Calling this test function finds and runs all tests associated with the
+    module and all its sub-modules.
+
+    Attributes
+    ----------
+    module_name : str
+        Full path to the package to test.
+
+    Parameters
+    ----------
+    module_name : module name
+        The name of the module to test.
+
+    """
+
+    def __init__(self, module_name):
+        self.module_name = module_name
+
+    def __call__(
+        self,
+        label='fast',
+        verbose=1,
+        extra_argv=None,
+        doctests=False,
+        coverage=False,
+        durations=-1,
+        tests=None,
+    ):
+        """
+        Run tests for module using pytest.
+
+        Parameters
+        ----------
+        label : {'fast', 'full'}, optional
+            Identifies the tests to run. When set to 'fast', tests decorated
+            with `pytest.mark.slow` are skipped, when 'full', the slow marker
+            is ignored.
+        verbose : int, optional
+            Verbosity value for test outputs, in the range 1-3. Default is 1.
+        extra_argv : list, optional
+            List with any extra arguments to pass to pytests.
+        doctests : bool, optional
+            .. note:: Not supported
+        coverage : bool, optional
+            If True, report coverage of scikit-image code. Default is False.
+            Requires installation of (pip) pytest-cov.
+        durations : int, optional
+            If < 0, do nothing, If 0, report time of all tests, if > 0,
+            report the time of the slowest `timer` tests. Default is -1.
+        tests : test or list of tests
+            Tests to be executed with pytest '--pyargs'
+
+        Returns
+        -------
+        result : bool
+            Return True on success, false otherwise.
+        """
+        import pytest
+
+        module = sys.modules[self.module_name]
+        module_path = os.path.abspath(module.__path__[0])
+
+        # setup the pytest arguments
+        pytest_args = ["-l"]
+
+        # offset verbosity. The "-q" cancels a "-v".
+        pytest_args += ["-q"]
+
+        # Filter out annoying import messages. Want these in both develop and
+        # release mode.
+        pytest_args += [
+            "-W ignore:Not importing directory",
+            "-W ignore:numpy.dtype size changed",
+            "-W ignore:numpy.ufunc size changed",
+        ]
+
+        if doctests:
+            raise ValueError("Doctests not supported")
+
+        if extra_argv:
+            pytest_args += list(extra_argv)
+
+        if verbose > 1:
+            pytest_args += ["-" + "v" * (verbose - 1)]
+
+        if coverage:
+            pytest_args += ["--cov=" + module_path]
+
+        if label == "fast":
+            pytest_args += ["-m", "not slow"]
+        elif label != "full":
+            pytest_args += ["-m", label]
+
+        if durations >= 0:
+            pytest_args += [f"--durations={durations}"]
+
+        if tests is None:
+            tests = [self.module_name]
+
+        pytest_args += ["--pyargs"] + list(tests)
+
+        # run tests.
+        _show_skimage_info()
+
+        try:
+            code = pytest.main(pytest_args)
+        except SystemExit as exc:
+            code = exc.code
+
+        return code == 0

.CondaPkg/env/Lib/site-packages/skimage/_shared/testing.py

@@ -0,0 +1,303 @@
+"""
+Testing utilities.
+"""
+
+import os
+import platform
+import re
+import struct
+import sys
+import functools
+import inspect
+from tempfile import NamedTemporaryFile
+
+import numpy as np
+from numpy import testing
+from numpy.testing import (
+    TestCase,
+    assert_,
+    assert_warns,
+    assert_no_warnings,
+    assert_equal,
+    assert_almost_equal,
+    assert_array_equal,
+    assert_allclose,
+    assert_array_almost_equal,
+    assert_array_almost_equal_nulp,
+    assert_array_less,
+)
+
+from .. import data, io
+from ..data._fetchers import _fetch
+from ..util import img_as_uint, img_as_float, img_as_int, img_as_ubyte
+from ._warnings import expected_warnings
+from ._dependency_checks import is_wasm
+
+import pytest
+
+
+skipif = pytest.mark.skipif
+xfail = pytest.mark.xfail
+parametrize = pytest.mark.parametrize
+raises = pytest.raises
+fixture = pytest.fixture
+
+SKIP_RE = re.compile(r"(\s*>>>.*?)(\s*)#\s*skip\s+if\s+(.*)$")
+
+# true if python is running in 32bit mode
+# Calculate the size of a void * pointer in bits
+# https://docs.python.org/3/library/struct.html
+arch32 = struct.calcsize("P") * 8 == 32
+
+
+def assert_less(a, b, msg=None):
+    message = f"{a!r} is not lower than {b!r}"
+    if msg is not None:
+        message += ": " + msg
+    assert a < b, message
+
+
+def assert_greater(a, b, msg=None):
+    message = f"{a!r} is not greater than {b!r}"
+    if msg is not None:
+        message += ": " + msg
+    assert a > b, message
+
+
+def doctest_skip_parser(func):
+    """Decorator replaces custom skip test markup in doctests
+
+    Say a function has a docstring::
+
+        >>> something, HAVE_AMODULE, HAVE_BMODULE = 0, False, False
+        >>> something # skip if not HAVE_AMODULE
+        0
+        >>> something # skip if HAVE_BMODULE
+        0
+
+    This decorator will evaluate the expression after ``skip if``.  If this
+    evaluates to True, then the comment is replaced by ``# doctest: +SKIP``. If
+    False, then the comment is just removed. The expression is evaluated in the
+    ``globals`` scope of `func`.
+
+    For example, if the module global ``HAVE_AMODULE`` is False, and module
+    global ``HAVE_BMODULE`` is False, the returned function will have docstring::
+
+        >>> something # doctest: +SKIP
+        >>> something + else # doctest: +SKIP
+        >>> something # doctest: +SKIP
+
+    """
+    lines = func.__doc__.split('\n')
+    new_lines = []
+    for line in lines:
+        match = SKIP_RE.match(line)
+        if match is None:
+            new_lines.append(line)
+            continue
+        code, space, expr = match.groups()
+
+        try:
+            # Works as a function decorator
+            if eval(expr, func.__globals__):
+                code = code + space + "# doctest: +SKIP"
+        except AttributeError:
+            # Works as a class decorator
+            if eval(expr, func.__init__.__globals__):
+                code = code + space + "# doctest: +SKIP"
+
+        new_lines.append(code)
+    func.__doc__ = "\n".join(new_lines)
+    return func
+
+
+def roundtrip(image, plugin, suffix):
+    """Save and read an image using a specified plugin"""
+    if '.' not in suffix:
+        suffix = '.' + suffix
+    with NamedTemporaryFile(suffix=suffix, delete=False) as temp_file:
+        fname = temp_file.name
+    io.imsave(fname, image, plugin=plugin)
+    new = io.imread(fname, plugin=plugin)
+    try:
+        os.remove(fname)
+    except Exception:
+        pass
+    return new
+
+
+def color_check(plugin, fmt='png'):
+    """Check roundtrip behavior for color images.
+
+    All major input types should be handled as ubytes and read
+    back correctly.
+    """
+    img = img_as_ubyte(data.chelsea())
+    r1 = roundtrip(img, plugin, fmt)
+    testing.assert_allclose(img, r1)
+
+    img2 = img > 128
+    r2 = roundtrip(img2, plugin, fmt)
+    testing.assert_allclose(img2, r2.astype(bool))
+
+    img3 = img_as_float(img)
+    r3 = roundtrip(img3, plugin, fmt)
+    testing.assert_allclose(r3, img)
+
+    img4 = img_as_int(img)
+    if fmt.lower() in (('tif', 'tiff')):
+        img4 -= 100
+        r4 = roundtrip(img4, plugin, fmt)
+        testing.assert_allclose(r4, img4)
+    else:
+        r4 = roundtrip(img4, plugin, fmt)
+        testing.assert_allclose(r4, img_as_ubyte(img4))
+
+    img5 = img_as_uint(img)
+    r5 = roundtrip(img5, plugin, fmt)
+    testing.assert_allclose(r5, img)
+
+
+def mono_check(plugin, fmt='png'):
+    """Check the roundtrip behavior for images that support most types.
+
+    All major input types should be handled.
+    """
+
+    img = img_as_ubyte(data.moon())
+    r1 = roundtrip(img, plugin, fmt)
+    testing.assert_allclose(img, r1)
+
+    img2 = img > 128
+    r2 = roundtrip(img2, plugin, fmt)
+    testing.assert_allclose(img2, r2.astype(bool))
+
+    img3 = img_as_float(img)
+    r3 = roundtrip(img3, plugin, fmt)
+    if r3.dtype.kind == 'f':
+        testing.assert_allclose(img3, r3)
+    else:
+        testing.assert_allclose(r3, img_as_uint(img))
+
+    img4 = img_as_int(img)
+    if fmt.lower() in (('tif', 'tiff')):
+        img4 -= 100
+        r4 = roundtrip(img4, plugin, fmt)
+        testing.assert_allclose(r4, img4)
+    else:
+        r4 = roundtrip(img4, plugin, fmt)
+        testing.assert_allclose(r4, img_as_uint(img4))
+
+    img5 = img_as_uint(img)
+    r5 = roundtrip(img5, plugin, fmt)
+    testing.assert_allclose(r5, img5)
+
+
+def fetch(data_filename):
+    """Attempt to fetch data, but if unavailable, skip the tests."""
+    try:
+        return _fetch(data_filename)
+    except (ConnectionError, ModuleNotFoundError):
+        pytest.skip(f'Unable to download {data_filename}', allow_module_level=True)
+
+
+# Ref: about the lack of threading support in WASM, please see
+# https://github.com/pyodide/pyodide/issues/237
+def run_in_parallel(num_threads=2, warnings_matching=None):
+    """Decorator to run the same function multiple times in parallel.
+
+    This decorator is useful to ensure that separate threads execute
+    concurrently and correctly while releasing the GIL.
+
+    It is currently skipped when running on WASM-based platforms, as
+    the threading module is not supported.
+
+    Parameters
+    ----------
+    num_threads : int, optional
+        The number of times the function is run in parallel.
+
+    warnings_matching: list or None
+        This parameter is passed on to `expected_warnings` so as not to have
+        race conditions with the warnings filters. A single
+        `expected_warnings` context manager is used for all threads.
+        If None, then no warnings are checked.
+
+    """
+
+    assert num_threads > 0
+
+    def wrapper(func):
+        if is_wasm:
+            # Threading isn't supported on WASM, return early
+            return func
+
+        import threading
+
+        @functools.wraps(func)
+        def inner(*args, **kwargs):
+            with expected_warnings(warnings_matching):
+                threads = []
+                for i in range(num_threads - 1):
+                    thread = threading.Thread(target=func, args=args, kwargs=kwargs)
+                    threads.append(thread)
+                for thread in threads:
+                    thread.start()
+
+                func(*args, **kwargs)
+
+                for thread in threads:
+                    thread.join()
+
+        return inner
+
+    return wrapper
+
+
+def assert_stacklevel(warnings, *, offset=-1):
+    """Assert correct stacklevel of captured warnings.
+
+    When scikit-image raises warnings, the stacklevel should ideally be set
+    so that the origin of the warnings will point to the public function
+    that was called by the user and not necessarily the very place where the
+    warnings were emitted (which may be inside of some internal function).
+    This utility function helps with checking that
+    the stacklevel was set correctly on warnings captured by `pytest.warns`.
+
+    Parameters
+    ----------
+    warnings : collections.abc.Iterable[warning.WarningMessage]
+        Warnings that were captured by `pytest.warns`.
+    offset : int, optional
+        Offset from the line this function is called to the line were the
+        warning is supposed to originate from. For multiline calls, the
+        first line is relevant. Defaults to -1 which corresponds to the line
+        right above the one where this function is called.
+
+    Raises
+    ------
+    AssertionError
+        If a warning in `warnings` does not match the expected line number or
+        file name.
+
+    Examples
+    --------
+    >>> def test_something():
+    ...     with pytest.warns(UserWarning, match="some message") as record:
+    ...         something_raising_a_warning()
+    ...     assert_stacklevel(record)
+    ...
+    >>> def test_another_thing():
+    ...     with pytest.warns(UserWarning, match="some message") as record:
+    ...         iam_raising_many_warnings(
+    ...             "A long argument that forces the call to wrap."
+    ...         )
+    ...     assert_stacklevel(record, offset=-3)
+    """
+    frame = inspect.stack()[1].frame  # 0 is current frame, 1 is outer frame
+    line_number = frame.f_lineno + offset
+    filename = frame.f_code.co_filename
+    expected = f"{filename}:{line_number}"
+    for warning in warnings:
+        actual = f"{warning.filename}:{warning.lineno}"
+        assert actual == expected, f"{actual} != {expected}"

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_coord.py

@@ -0,0 +1,91 @@
+import time
+
+import numpy as np
+import pytest
+from scipy.spatial.distance import pdist, minkowski
+
+from skimage._shared.coord import ensure_spacing
+
+
+@pytest.mark.parametrize("p", [1, 2, np.inf])
+@pytest.mark.parametrize("size", [30, 50, None])
+def test_ensure_spacing_trivial(p, size):
+    # --- Empty input
+    assert ensure_spacing([], p_norm=p) == []
+
+    # --- A unique point
+    coord = np.random.randn(1, 2)
+    assert np.array_equal(coord, ensure_spacing(coord, p_norm=p, min_split_size=size))
+
+    # --- Verified spacing
+    coord = np.random.randn(100, 2)
+
+    # --- 0 spacing
+    assert np.array_equal(
+        coord, ensure_spacing(coord, spacing=0, p_norm=p, min_split_size=size)
+    )
+
+    # Spacing is chosen to be half the minimum distance
+    spacing = pdist(coord, metric=minkowski, p=p).min() * 0.5
+
+    out = ensure_spacing(coord, spacing=spacing, p_norm=p, min_split_size=size)
+
+    assert np.array_equal(coord, out)
+
+
+@pytest.mark.parametrize("ndim", [1, 2, 3, 4, 5])
+@pytest.mark.parametrize("size", [2, 10, None])
+def test_ensure_spacing_nD(ndim, size):
+    coord = np.ones((5, ndim))
+
+    expected = np.ones((1, ndim))
+
+    assert np.array_equal(ensure_spacing(coord, min_split_size=size), expected)
+
+
+@pytest.mark.parametrize("p", [1, 2, np.inf])
+@pytest.mark.parametrize("size", [50, 100, None])
+def test_ensure_spacing_batch_processing(p, size):
+    coord = np.random.randn(100, 2)
+
+    # --- Consider the average distance btween the point as spacing
+    spacing = np.median(pdist(coord, metric=minkowski, p=p))
+
+    expected = ensure_spacing(coord, spacing=spacing, p_norm=p)
+
+    assert np.array_equal(
+        ensure_spacing(coord, spacing=spacing, p_norm=p, min_split_size=size), expected
+    )
+
+
+def test_max_batch_size():
+    """Small batches are slow, large batches -> large allocations -> also slow.
+
+    https://github.com/scikit-image/scikit-image/pull/6035#discussion_r751518691
+    """
+    coords = np.random.randint(low=0, high=1848, size=(40000, 2))
+    tstart = time.time()
+    ensure_spacing(coords, spacing=100, min_split_size=50, max_split_size=2000)
+    dur1 = time.time() - tstart
+
+    tstart = time.time()
+    ensure_spacing(coords, spacing=100, min_split_size=50, max_split_size=20000)
+    dur2 = time.time() - tstart
+
+    # Originally checked dur1 < dur2 to assert that the default batch size was
+    # faster than a much larger batch size. However, on rare occasion a CI test
+    # case would fail with dur1 ~5% larger than dur2. To be more robust to
+    # variable load or differences across architectures, we relax this here.
+    assert dur1 < 1.33 * dur2
+
+
+@pytest.mark.parametrize("p", [1, 2, np.inf])
+@pytest.mark.parametrize("size", [30, 50, None])
+def test_ensure_spacing_p_norm(p, size):
+    coord = np.random.randn(100, 2)
+
+    # --- Consider the average distance btween the point as spacing
+    spacing = np.median(pdist(coord, metric=minkowski, p=p))
+    out = ensure_spacing(coord, spacing=spacing, p_norm=p, min_split_size=size)
+
+    assert pdist(out, metric=minkowski, p=p).min() > spacing

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_dtype.py

@@ -0,0 +1,14 @@
+import numpy as np
+import pytest
+
+
+from ..dtype import numeric_dtype_min_max, numeric_types
+
+
+class Test_numeric_dtype_min_max:
+    @pytest.mark.parametrize("dtype", numeric_types)
+    def test_all_numeric_types(self, dtype):
+        min_, max_ = numeric_dtype_min_max(dtype)
+        assert np.isscalar(min_)
+        assert np.isscalar(max_)
+        assert min_ < max_

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_fast_exp.py

@@ -0,0 +1,20 @@
+from ..fast_exp import fast_exp
+import numpy as np
+
+
+def test_fast_exp():
+    X = np.linspace(-5, 0, 5000, endpoint=True)
+
+    # Ground truth
+    Y = np.exp(X)
+
+    # Approximation at double precision
+    _y_f64 = np.array([fast_exp['float64_t'](x) for x in X])
+
+    # Approximation at single precision
+    _y_f32 = np.array(
+        [fast_exp['float32_t'](x) for x in X.astype('float32')], dtype='float32'
+    )
+
+    for _y in [_y_f64, _y_f32]:
+        assert np.abs(Y - _y).mean() < 3e-3

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_geometry.py

@@ -0,0 +1,81 @@
+import pytest
+from skimage._shared._geometry import polygon_clip, polygon_area
+
+import numpy as np
+from numpy.testing import assert_equal, assert_almost_equal
+
+pytest.importorskip("matplotlib")
+
+
+hand = np.array(
+    [
+        [1.64516129, 1.16145833],
+        [1.64516129, 1.59375],
+        [1.35080645, 1.921875],
+        [1.375, 2.18229167],
+        [1.68548387, 1.9375],
+        [1.60887097, 2.55208333],
+        [1.68548387, 2.69791667],
+        [1.76209677, 2.56770833],
+        [1.83064516, 1.97395833],
+        [1.89516129, 2.75],
+        [1.9516129, 2.84895833],
+        [2.01209677, 2.76041667],
+        [1.99193548, 1.99479167],
+        [2.11290323, 2.63020833],
+        [2.2016129, 2.734375],
+        [2.25403226, 2.60416667],
+        [2.14919355, 1.953125],
+        [2.30645161, 2.36979167],
+        [2.39112903, 2.36979167],
+        [2.41532258, 2.1875],
+        [2.1733871, 1.703125],
+        [2.07782258, 1.16666667],
+    ]
+)
+
+
+def test_polygon_area():
+    x = [0, 0, 1, 1]
+    y = [0, 1, 1, 0]
+
+    assert_almost_equal(polygon_area(y, x), 1)
+
+    x = [0, 0, 1]
+    y = [0, 1, 1]
+
+    assert_almost_equal(polygon_area(y, x), 0.5)
+
+    x = [0, 0, 0.5, 1, 1, 0.5]
+    y = [0, 1, 0.5, 1, 0, 0.5]
+
+    assert_almost_equal(polygon_area(y, x), 0.5)
+
+
+def test_poly_clip():
+    x = [0, 1, 2, 1]
+    y = [0, -1, 0, 1]
+
+    yc, xc = polygon_clip(y, x, 0, 0, 1, 1)
+    assert_equal(polygon_area(yc, xc), 0.5)
+
+    x = [-1, 1.5, 1.5, -1]
+    y = [0.5, 0.5, 1.5, 1.5]
+    yc, xc = polygon_clip(y, x, 0, 0, 1, 1)
+    assert_equal(polygon_area(yc, xc), 0.5)
+
+
+def test_hand_clip():
+    (r0, c0, r1, c1) = (1.0, 1.5, 2.1, 2.5)
+    clip_r, clip_c = polygon_clip(hand[:, 1], hand[:, 0], r0, c0, r1, c1)
+    assert_equal(clip_r.size, 19)
+    assert_equal(clip_r[0], clip_r[-1])
+    assert_equal(clip_c[0], clip_c[-1])
+
+    (r0, c0, r1, c1) = (1.0, 1.5, 1.7, 2.5)
+    clip_r, clip_c = polygon_clip(hand[:, 1], hand[:, 0], r0, c0, r1, c1)
+    assert_equal(clip_r.size, 6)
+
+    (r0, c0, r1, c1) = (1.0, 1.5, 1.5, 2.5)
+    clip_r, clip_c = polygon_clip(hand[:, 1], hand[:, 0], r0, c0, r1, c1)
+    assert_equal(clip_r.size, 5)

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_interpolation.py

@@ -0,0 +1,28 @@
+from skimage._shared.interpolation import coord_map_py
+from skimage._shared.testing import assert_array_equal
+
+
+def test_coord_map():
+    symmetric = [coord_map_py(4, n, 'S') for n in range(-6, 6)]
+    expected_symmetric = [2, 3, 3, 2, 1, 0, 0, 1, 2, 3, 3, 2]
+    assert_array_equal(symmetric, expected_symmetric)
+
+    wrap = [coord_map_py(4, n, 'W') for n in range(-6, 6)]
+    expected_wrap = [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1]
+    assert_array_equal(wrap, expected_wrap)
+
+    edge = [coord_map_py(4, n, 'E') for n in range(-6, 6)]
+    expected_edge = [0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 3]
+    assert_array_equal(edge, expected_edge)
+
+    reflect = [coord_map_py(4, n, 'R') for n in range(-6, 6)]
+    expected_reflect = [0, 1, 2, 3, 2, 1, 0, 1, 2, 3, 2, 1]
+    assert_array_equal(reflect, expected_reflect)
+
+    reflect = [coord_map_py(1, n, 'R') for n in range(-6, 6)]
+    expected_reflect = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    assert_array_equal(reflect, expected_reflect)
+
+    other = [coord_map_py(4, n, 'undefined') for n in range(-6, 6)]
+    expected_other = list(range(-6, 6))
+    assert_array_equal(other, expected_other)

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_safe_as_int.py

@@ -0,0 +1,41 @@
+import numpy as np
+from skimage._shared.utils import safe_as_int
+from skimage._shared import testing
+
+
+def test_int_cast_not_possible():
+    with testing.raises(ValueError):
+        safe_as_int(7.1)
+    with testing.raises(ValueError):
+        safe_as_int([7.1, 0.9])
+    with testing.raises(ValueError):
+        safe_as_int(np.r_[7.1, 0.9])
+    with testing.raises(ValueError):
+        safe_as_int((7.1, 0.9))
+    with testing.raises(ValueError):
+        safe_as_int(((3, 4, 1), (2, 7.6, 289)))
+    with testing.raises(ValueError):
+        safe_as_int(7.1, 0.09)
+    with testing.raises(ValueError):
+        safe_as_int([7.1, 0.9], 0.09)
+    with testing.raises(ValueError):
+        safe_as_int(np.r_[7.1, 0.9], 0.09)
+    with testing.raises(ValueError):
+        safe_as_int((7.1, 0.9), 0.09)
+    with testing.raises(ValueError):
+        safe_as_int(((3, 4, 1), (2, 7.6, 289)), 0.25)
+
+
+def test_int_cast_possible():
+    testing.assert_equal(safe_as_int(7.1, atol=0.11), 7)
+    testing.assert_equal(safe_as_int(-7.1, atol=0.11), -7)
+    testing.assert_equal(safe_as_int(41.9, atol=0.11), 42)
+    testing.assert_array_equal(
+        safe_as_int([2, 42, 5789234.0, 87, 4]), np.r_[2, 42, 5789234, 87, 4]
+    )
+    testing.assert_array_equal(
+        safe_as_int(
+            np.r_[[[3, 4, 1.000000001], [7, 2, -8.999999999], [6, 9, -4234918347.0]]]
+        ),
+        np.r_[[[3, 4, 1], [7, 2, -9], [6, 9, -4234918347]]],
+    )

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_testing.py

@@ -0,0 +1,154 @@
+""" Testing decorators module
+"""
+
+import inspect
+import re
+import warnings
+
+import pytest
+from numpy.testing import assert_equal
+from skimage._shared.testing import (
+    doctest_skip_parser,
+    run_in_parallel,
+    assert_stacklevel,
+)
+from skimage._shared import testing
+from skimage._shared._dependency_checks import is_wasm
+
+from skimage._shared._warnings import expected_warnings
+from warnings import warn
+
+
+def test_skipper():
+    def f():
+        pass
+
+    class c:
+        def __init__(self):
+            self.me = "I think, therefore..."
+
+    docstring = """ Header
+
+            >>> something # skip if not HAVE_AMODULE
+            >>> something + else
+            >>> a = 1 # skip if not HAVE_BMODULE
+            >>> something2   # skip if HAVE_AMODULE
+        """
+    f.__doc__ = docstring
+    c.__doc__ = docstring
+
+    global HAVE_AMODULE, HAVE_BMODULE
+    HAVE_AMODULE = False
+    HAVE_BMODULE = True
+
+    f2 = doctest_skip_parser(f)
+    c2 = doctest_skip_parser(c)
+    assert f is f2
+    assert c is c2
+
+    expected = """ Header
+
+            >>> something # doctest: +SKIP
+            >>> something + else
+            >>> a = 1
+            >>> something2
+        """
+    assert_equal(f2.__doc__, expected)
+    assert_equal(c2.__doc__, expected)
+
+    HAVE_AMODULE = True
+    HAVE_BMODULE = False
+    f.__doc__ = docstring
+    c.__doc__ = docstring
+    f2 = doctest_skip_parser(f)
+    c2 = doctest_skip_parser(c)
+
+    assert f is f2
+    expected = """ Header
+
+            >>> something
+            >>> something + else
+            >>> a = 1 # doctest: +SKIP
+            >>> something2   # doctest: +SKIP
+        """
+    assert_equal(f2.__doc__, expected)
+    assert_equal(c2.__doc__, expected)
+
+    del HAVE_AMODULE
+    f.__doc__ = docstring
+    c.__doc__ = docstring
+    with testing.raises(NameError):
+        doctest_skip_parser(f)
+    with testing.raises(NameError):
+        doctest_skip_parser(c)
+
+
+@pytest.mark.skipif(is_wasm, reason="Cannot start threads in WASM")
+def test_run_in_parallel():
+    state = []
+
+    @run_in_parallel()
+    def change_state1():
+        state.append(None)
+
+    change_state1()
+    assert len(state) == 2
+
+    @run_in_parallel(num_threads=1)
+    def change_state2():
+        state.append(None)
+
+    change_state2()
+    assert len(state) == 3
+
+    @run_in_parallel(num_threads=3)
+    def change_state3():
+        state.append(None)
+
+    change_state3()
+    assert len(state) == 6
+
+
+def test_parallel_warning():
+    @run_in_parallel()
+    def change_state_warns_fails():
+        warn("Test warning for test parallel", stacklevel=2)
+
+    with expected_warnings(['Test warning for test parallel']):
+        change_state_warns_fails()
+
+    @run_in_parallel(warnings_matching=['Test warning for test parallel'])
+    def change_state_warns_passes():
+        warn("Test warning for test parallel", stacklevel=2)
+
+    change_state_warns_passes()
+
+
+def test_expected_warnings_noop():
+    # This will ensure the line beolow it behaves like a no-op
+    with expected_warnings(['Expected warnings test']):
+        # This should behave as a no-op
+        with expected_warnings(None):
+            warn('Expected warnings test')
+
+
+class Test_assert_stacklevel:
+    def raise_warning(self, *args, **kwargs):
+        warnings.warn(*args, **kwargs)
+
+    def test_correct_stacklevel(self):
+        # Should pass if stacklevel is set correctly
+        with pytest.warns(UserWarning, match="passes") as record:
+            self.raise_warning("passes", UserWarning, stacklevel=2)
+        assert_stacklevel(record)
+
+    @pytest.mark.parametrize("level", [1, 3])
+    def test_wrong_stacklevel(self, level):
+        # AssertionError should be raised for wrong stacklevel
+        with pytest.warns(UserWarning, match="wrong") as record:
+            self.raise_warning("wrong", UserWarning, stacklevel=level)
+        # Check that message contains expected line on right side
+        line_number = inspect.currentframe().f_lineno - 2
+        regex = ".*" + re.escape(f"!= {__file__}:{line_number}")
+        with pytest.raises(AssertionError, match=regex):
+            assert_stacklevel(record, offset=-5)

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_utils.py

@@ -0,0 +1,516 @@
+import sys
+import warnings
+
+import numpy as np
+import pytest
+
+from skimage._shared import testing
+from skimage._shared.utils import (
+    _supported_float_type,
+    _validate_interpolation_order,
+    change_default_value,
+    channel_as_last_axis,
+    check_nD,
+    deprecate_func,
+    deprecate_parameter,
+    DEPRECATED,
+)
+
+complex_dtypes = [np.complex64, np.complex128]
+if hasattr(np, 'complex256'):
+    complex_dtypes += [np.complex256]
+
+have_numpydoc = False
+try:
+    import numpydoc  # noqa: F401
+
+    have_numpydoc = True
+except ImportError:
+    pass
+
+
+def test_change_default_value():
+    @change_default_value('arg1', new_value=-1, changed_version='0.12')
+    def foo(arg0, arg1=0, arg2=1):
+        """Expected docstring"""
+        return arg0, arg1, arg2
+
+    @change_default_value(
+        'arg1',
+        new_value=-1,
+        changed_version='0.12',
+        warning_msg="Custom warning message",
+    )
+    def bar(arg0, arg1=0, arg2=1):
+        """Expected docstring"""
+        return arg0, arg1, arg2
+
+    # Assert warning messages
+    with pytest.warns(FutureWarning) as record:
+        assert foo(0) == (0, 0, 1)
+        assert bar(0) == (0, 0, 1)
+
+    expected_msg = (
+        "The new recommended value for arg1 is -1. Until "
+        "version 0.12, the default arg1 value is 0. From "
+        "version 0.12, the arg1 default value will be -1. "
+        "To avoid this warning, please explicitly set arg1 value."
+    )
+
+    assert str(record[0].message) == expected_msg
+    assert str(record[1].message) == "Custom warning message"
+
+    # Assert that nothing happens if arg1 is set
+    with warnings.catch_warnings(record=True) as recorded:
+        # No kwargs
+        assert foo(0, 2) == (0, 2, 1)
+        assert foo(0, arg1=0) == (0, 0, 1)
+
+        # Function name and doc is preserved
+        assert foo.__name__ == 'foo'
+        if sys.flags.optimize < 2:
+            # if PYTHONOPTIMIZE is set to 2, docstrings are stripped
+            assert foo.__doc__ == 'Expected docstring'
+    # Assert no warnings were raised
+    assert len(recorded) == 0
+
+
+def test_check_nD():
+    z = np.random.random(200**2).reshape((200, 200))
+    x = z[10:30, 30:10]
+    with testing.raises(ValueError):
+        check_nD(x, 2)
+
+
+@pytest.mark.parametrize(
+    'dtype', [bool, int, np.uint8, np.uint16, float, np.float32, np.float64]
+)
+@pytest.mark.parametrize('order', [None, -1, 0, 1, 2, 3, 4, 5, 6])
+def test_validate_interpolation_order(dtype, order):
+    if order is None:
+        # Default order
+        assert _validate_interpolation_order(dtype, None) == 0 if dtype == bool else 1
+    elif order < 0 or order > 5:
+        # Order not in valid range
+        with testing.raises(ValueError):
+            _validate_interpolation_order(dtype, order)
+    elif dtype == bool and order != 0:
+        # Deprecated order for bool array
+        with pytest.raises(ValueError):
+            _validate_interpolation_order(bool, order)
+    else:
+        # Valid use case
+        assert _validate_interpolation_order(dtype, order) == order
+
+
+@pytest.mark.parametrize(
+    'dtype',
+    [
+        bool,
+        np.float16,
+        np.float32,
+        np.float64,
+        np.uint8,
+        np.uint16,
+        np.uint32,
+        np.uint64,
+        np.int8,
+        np.int16,
+        np.int32,
+        np.int64,
+    ],
+)
+def test_supported_float_dtype_real(dtype):
+    float_dtype = _supported_float_type(dtype)
+    if dtype in [np.float16, np.float32]:
+        assert float_dtype == np.float32
+    else:
+        assert float_dtype == np.float64
+
+
+@pytest.mark.parametrize('dtype', complex_dtypes)
+@pytest.mark.parametrize('allow_complex', [False, True])
+def test_supported_float_dtype_complex(dtype, allow_complex):
+    if allow_complex:
+        float_dtype = _supported_float_type(dtype, allow_complex=allow_complex)
+        if dtype == np.complex64:
+            assert float_dtype == np.complex64
+        else:
+            assert float_dtype == np.complex128
+    else:
+        with testing.raises(ValueError):
+            _supported_float_type(dtype, allow_complex=allow_complex)
+
+
+@pytest.mark.parametrize('dtype', ['f', 'float32', np.float32, np.dtype(np.float32)])
+def test_supported_float_dtype_input_kinds(dtype):
+    assert _supported_float_type(dtype) == np.float32
+
+
+@pytest.mark.parametrize(
+    'dtypes, expected',
+    [
+        ((np.float16, np.float64), np.float64),
+        ((np.float32, np.uint16, np.int8), np.float64),
+        ((np.float32, np.float16), np.float32),
+    ],
+)
+def test_supported_float_dtype_sequence(dtypes, expected):
+    float_dtype = _supported_float_type(dtypes)
+    assert float_dtype == expected
+
+
+@channel_as_last_axis(multichannel_output=False)
+def _decorated_channel_axis_size(x, *, channel_axis=None):
+    if channel_axis is None:
+        return None
+    assert channel_axis == -1
+    return x.shape[-1]
+
+
+@testing.parametrize('channel_axis', [None, 0, 1, 2, -1, -2, -3])
+def test_decorated_channel_axis_shape(channel_axis):
+    # Verify that channel_as_last_axis modifies the channel_axis as expected
+
+    # need unique size per axis here
+    x = np.zeros((2, 3, 4))
+
+    size = _decorated_channel_axis_size(x, channel_axis=channel_axis)
+    if channel_axis is None:
+        assert size is None
+    else:
+        assert size == x.shape[channel_axis]
+
+
+@deprecate_func(
+    deprecated_version="x", removed_version="y", hint="You are on your own."
+)
+def _deprecated_func():
+    """Dummy function used in `test_deprecate_func`.
+
+    The decorated function must be outside the test function, otherwise it
+    seems that the warning does not point at the calling location.
+    """
+
+
+def test_deprecate_func():
+    with pytest.warns(FutureWarning) as record:
+        _deprecated_func()
+        testing.assert_stacklevel(record)
+
+    assert len(record) == 1
+    assert record[0].message.args[0] == (
+        "`_deprecated_func` is deprecated since version x and will be removed in "
+        "version y. You are on your own."
+    )
+
+
+@deprecate_parameter("old1", start_version="0.10", stop_version="0.12")
+@deprecate_parameter("old0", start_version="0.10", stop_version="0.12")
+def _func_deprecated_params(arg0, old0=DEPRECATED, old1=DEPRECATED, arg1=None):
+    """Expected docstring.
+
+    Parameters
+    ----------
+    arg0 : int
+        First unchanged parameter.
+    arg1 : int, optional
+        Second unchanged parameter.
+    """
+    return arg0, old0, old1, arg1
+
+
+@deprecate_parameter("old1", new_name="new0", start_version="0.10", stop_version="0.12")
+@deprecate_parameter("old0", new_name="new1", start_version="0.10", stop_version="0.12")
+def _func_replace_params(
+    arg0, old0=DEPRECATED, old1=DEPRECATED, new0=None, new1=None, arg1=None
+):
+    """Expected docstring.
+
+    Parameters
+    ----------
+    arg0 : int
+        First unchanged parameter.
+    new0 : int, optional
+        First new parameter.
+
+        .. versionadded:: 0.10
+    new1 : int, optional
+        Second new parameter.
+
+        .. versionadded:: 0.10
+    arg1 : int, optional
+        Second unchanged parameter.
+    """
+    return arg0, old0, old1, new0, new1, arg1
+
+
+class Test_deprecate_parameter:
+    @pytest.mark.skipif(not have_numpydoc, reason="requires numpydoc")
+    def test_docstring_removed_param(self):
+        # function name and doc are preserved
+        assert _func_deprecated_params.__name__ == "_func_deprecated_params"
+        if sys.flags.optimize < 2:
+            # if PYTHONOPTIMIZE is set to 2, docstrings are stripped
+            assert (
+                _func_deprecated_params.__doc__
+                == """Expected docstring.
+
+
+    Parameters
+    ----------
+    arg0 : int
+        First unchanged parameter.
+    arg1 : int, optional
+        Second unchanged parameter.
+
+    Other Parameters
+    ----------------
+    old0 : DEPRECATED
+        `old0` is deprecated.
+
+        .. deprecated:: 0.10
+    old1 : DEPRECATED
+        `old1` is deprecated.
+
+        .. deprecated:: 0.10
+"""
+            )
+
+    @pytest.mark.skipif(not have_numpydoc, reason="requires numpydoc")
+    def test_docstring_replaced_param(self):
+        assert _func_replace_params.__name__ == "_func_replace_params"
+        if sys.flags.optimize < 2:
+            # if PYTHONOPTIMIZE is set to 2, docstrings are stripped
+            assert (
+                _func_replace_params.__doc__
+                == """Expected docstring.
+
+
+    Parameters
+    ----------
+    arg0 : int
+        First unchanged parameter.
+    new0 : int, optional
+        First new parameter.
+
+        .. versionadded:: 0.10
+    new1 : int, optional
+        Second new parameter.
+
+        .. versionadded:: 0.10
+    arg1 : int, optional
+        Second unchanged parameter.
+
+    Other Parameters
+    ----------------
+    old0 : DEPRECATED
+        Deprecated in favor of `new1`.
+
+        .. deprecated:: 0.10
+    old1 : DEPRECATED
+        Deprecated in favor of `new0`.
+
+        .. deprecated:: 0.10
+"""
+            )
+
+    def test_warning_removed_param(self):
+        match = (
+            r".*`old[01]` is deprecated since version 0\.10 and will be removed "
+            r"in 0\.12.* see the documentation of .*_func_deprecated_params`."
+        )
+        with pytest.warns(FutureWarning, match=match):
+            assert _func_deprecated_params(1, 2) == (1, DEPRECATED, DEPRECATED, None)
+        with pytest.warns(FutureWarning, match=match):
+            assert _func_deprecated_params(1, 2, 3) == (1, DEPRECATED, DEPRECATED, None)
+        with pytest.warns(FutureWarning, match=match):
+            assert _func_deprecated_params(1, old0=2) == (
+                1,
+                DEPRECATED,
+                DEPRECATED,
+                None,
+            )
+        with pytest.warns(FutureWarning, match=match):
+            assert _func_deprecated_params(1, old1=2) == (
+                1,
+                DEPRECATED,
+                DEPRECATED,
+                None,
+            )
+
+        with warnings.catch_warnings(record=True) as record:
+            assert _func_deprecated_params(1, arg1=3) == (1, DEPRECATED, DEPRECATED, 3)
+        assert len(record) == 0
+
+    def test_warning_replaced_param(self):
+        match = (
+            r".*`old[0,1]` is deprecated since version 0\.10 and will be removed "
+            r"in 0\.12.* see the documentation of .*_func_replace_params`."
+        )
+
+        with pytest.warns(FutureWarning, match=match):
+            assert _func_replace_params(1, 2) == (
+                1,
+                DEPRECATED,
+                DEPRECATED,
+                None,
+                2,
+                None,
+            )
+
+        with pytest.warns(FutureWarning, match=match) as records:
+            assert _func_replace_params(1, 2, 3) == (
+                1,
+                DEPRECATED,
+                DEPRECATED,
+                3,
+                2,
+                None,
+            )
+        assert len(records) == 2
+        assert "`old1` is deprecated" in records[0].message.args[0]
+        assert "`old0` is deprecated" in records[1].message.args[0]
+
+        with pytest.warns(FutureWarning, match=match):
+            assert _func_replace_params(1, old0=2) == (
+                1,
+                DEPRECATED,
+                DEPRECATED,
+                None,
+                2,
+                None,
+            )
+
+        with pytest.warns(FutureWarning, match=match):
+            assert _func_replace_params(1, old1=3) == (
+                1,
+                DEPRECATED,
+                DEPRECATED,
+                3,
+                None,
+                None,
+            )
+
+        # Otherwise, no warnings are emitted!
+        with warnings.catch_warnings(record=True) as record:
+            assert _func_replace_params(1, new0=2, new1=3) == (
+                1,
+                DEPRECATED,
+                DEPRECATED,
+                2,
+                3,
+                None,
+            )
+        assert len(record) == 0
+
+    def test_missing_DEPRECATED(self):
+        decorate = deprecate_parameter(
+            "old", start_version="0.10", stop_version="0.12", stacklevel=2
+        )
+
+        def foo(arg0, old=None):
+            return arg0, old
+
+        with pytest.raises(RuntimeError, match="Expected .* <DEPRECATED>"):
+            decorate(foo)
+
+        def bar(arg0, old=DEPRECATED):
+            return arg0
+
+        assert decorate(bar)(1) == 1
+
+    def test_new_keyword_only(self):
+        @deprecate_parameter(
+            "old",
+            new_name="new",
+            start_version="0.19",
+            stop_version="0.21",
+        )
+        def foo(arg0, old=DEPRECATED, *, new=1, arg3=None):
+            """Expected docstring"""
+            return arg0, new, arg3
+
+        # Assert that nothing happens when the function is called with the
+        # new API
+        with warnings.catch_warnings(record=True) as recorded:
+            # No kwargs
+            assert foo(0) == (0, 1, None)
+            # Kwargs without deprecated argument
+            assert foo(0, new=1, arg3=2) == (0, 1, 2)
+            assert foo(0, new=2) == (0, 2, None)
+            assert foo(0, arg3=2) == (0, 1, 2)
+        assert len(recorded) == 0
+
+    def test_conflicting_old_and_new(self):
+        match = r".*`old[0,1]` is deprecated"
+        with pytest.warns(FutureWarning, match=match):
+            with pytest.raises(ValueError, match=".* avoid conflicting values"):
+                _func_replace_params(1, old0=2, new1=2)
+
+        with pytest.warns(FutureWarning, match=match):
+            with pytest.raises(ValueError, match=".* avoid conflicting values"):
+                _func_replace_params(1, old1=2, new0=2)
+
+        with pytest.warns(FutureWarning, match=match):
+            with pytest.raises(ValueError, match=".* avoid conflicting values"):
+                _func_replace_params(1, old0=1, old1=1, new0=1, new1=1)
+
+    def test_wrong_call_signature(self):
+        """Check that normal errors for faulty calls are unchanged."""
+        with pytest.raises(
+            TypeError, match=r".* required positional argument\: 'arg0'"
+        ):
+            _func_replace_params()
+
+        with pytest.warns(FutureWarning, match=r".*`old[0,1]` is deprecated"):
+            with pytest.raises(
+                TypeError, match=".* multiple values for argument 'old0'"
+            ):
+                _func_deprecated_params(1, 2, old0=2)
+
+    def test_wrong_param_name(self):
+        with pytest.raises(ValueError, match="'old' is not in list"):
+
+            @deprecate_parameter("old", start_version="0.10", stop_version="0.12")
+            def foo(arg0):
+                pass
+
+        with pytest.raises(ValueError, match="'new' is not in list"):
+
+            @deprecate_parameter(
+                "old", new_name="new", start_version="0.10", stop_version="0.12"
+            )
+            def bar(arg0, old, arg1):
+                pass
+
+    def test_warning_location(self):
+        with pytest.warns(FutureWarning) as records:
+            _func_deprecated_params(1, old0=2, old1=2)
+            testing.assert_stacklevel(records)
+        assert len(records) == 2
+
+    def test_stacklevel(self):
+        @deprecate_parameter(
+            "old",
+            start_version="0.19",
+            stop_version="0.21",
+        )
+        def foo(arg0, old=DEPRECATED):
+            pass
+
+        with pytest.raises(RuntimeError, match="Set stacklevel manually"):
+            foo(0, 1)
+
+        @deprecate_parameter(
+            "old",
+            start_version="0.19",
+            stop_version="0.21",
+            stacklevel=2,
+        )
+        def bar(arg0, old=DEPRECATED):
+            pass
+
+        with pytest.warns(FutureWarning, match="`old` is deprecated") as records:
+            bar(0, 1)
+            testing.assert_stacklevel(records)

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_version_requirements.py

@@ -0,0 +1,42 @@
+"""Tests for the version requirement functions.
+
+"""
+
+import numpy as np
+from numpy.testing import assert_equal
+from skimage._shared import version_requirements as version_req
+from skimage._shared import testing
+
+
+def test_get_module_version():
+    assert version_req.get_module_version('numpy')
+    assert version_req.get_module_version('scipy')
+    with testing.raises(ImportError):
+        version_req.get_module_version('fakenumpy')
+
+
+def test_is_installed():
+    assert version_req.is_installed('python', '>=2.7')
+    assert not version_req.is_installed('numpy', '<1.0')
+
+
+def test_require():
+    # A function that only runs on Python >2.7 and numpy > 1.5 (should pass)
+    @version_req.require('python', '>2.7')
+    @version_req.require('numpy', '>1.5')
+    def foo():
+        return 1
+
+    assert_equal(foo(), 1)
+
+    # function that requires scipy < 0.1 (should fail)
+    @version_req.require('scipy', '<0.1')
+    def bar():
+        return 0
+
+    with testing.raises(ImportError):
+        bar()
+
+
+def test_get_module():
+    assert version_req.get_module("numpy") is np

.CondaPkg/env/Lib/site-packages/skimage/_shared/tests/test_warnings.py

@@ -0,0 +1,37 @@
+import os
+from skimage._shared._warnings import expected_warnings
+import pytest
+
+
+@pytest.fixture(scope='function')
+def setup():
+    # Remove any environment variable if it exists
+    old_strictness = os.environ.pop('SKIMAGE_TEST_STRICT_WARNINGS', None)
+    yield
+    # Add the user's desired strictness
+    if old_strictness is not None:
+        os.environ['SKIMAGE_TEST_STRICT_WARNINGS'] = old_strictness
+
+
+def test_strict_warnigns_default(setup):
+    # By default we should fail on missing expected warnings
+    with pytest.raises(ValueError):
+        with expected_warnings(['some warnings']):
+            pass
+
+
+@pytest.mark.parametrize('strictness', ['1', 'true', 'True', 'TRUE'])
+def test_strict_warning_true(setup, strictness):
+    os.environ['SKIMAGE_TEST_STRICT_WARNINGS'] = strictness
+    with pytest.raises(ValueError):
+        with expected_warnings(['some warnings']):
+            pass
+
+
+@pytest.mark.parametrize('strictness', ['0', 'false', 'False', 'FALSE'])
+def test_strict_warning_false(setup, strictness):
+    # If the user doesn't wish to be strict about warnings
+    # the following shouldn't raise any error
+    os.environ['SKIMAGE_TEST_STRICT_WARNINGS'] = strictness
+    with expected_warnings(['some warnings']):
+        pass

.CondaPkg/env/Lib/site-packages/skimage/_shared/utils.py

@@ -0,0 +1,884 @@
+import functools
+import inspect
+import sys
+import warnings
+
+import numpy as np
+
+from ._warnings import all_warnings, warn
+
+__all__ = [
+    'deprecate_func',
+    'get_bound_method_class',
+    'all_warnings',
+    'safe_as_int',
+    'check_shape_equality',
+    'check_nD',
+    'warn',
+    'reshape_nd',
+    'identity',
+    'slice_at_axis',
+    "deprecate_parameter",
+    "DEPRECATED",
+]
+
+
+def _count_wrappers(func):
+    """Count the number of wrappers around `func`."""
+    unwrapped = func
+    count = 0
+    while hasattr(unwrapped, "__wrapped__"):
+        unwrapped = unwrapped.__wrapped__
+        count += 1
+    return count
+
+
+def _warning_stacklevel(func):
+    """Find stacklevel for a warning raised from a wrapper around `func`.
+
+    Try to determine the number of
+
+    Parameters
+    ----------
+    func : Callable
+
+
+    Returns
+    -------
+    stacklevel : int
+        The stacklevel. Minimum of 2.
+    """
+    # Count number of wrappers around `func`
+    wrapped_count = _count_wrappers(func)
+
+    # Count number of total wrappers around global version of `func`
+    module = sys.modules.get(func.__module__)
+    try:
+        for name in func.__qualname__.split("."):
+            global_func = getattr(module, name)
+    except AttributeError as e:
+        raise RuntimeError(
+            f"Could not access `{func.__qualname__}` in {module!r}, "
+            f" may be a closure. Set stacklevel manually. ",
+        ) from e
+    else:
+        global_wrapped_count = _count_wrappers(global_func)
+
+    stacklevel = global_wrapped_count - wrapped_count + 1
+    return max(stacklevel, 2)
+
+
+def _get_stack_length(func):
+    """Return function call stack length."""
+    _func = func.__globals__.get(func.__name__, func)
+    length = _count_wrappers(_func)
+    return length
+
+
+class _DecoratorBaseClass:
+    """Used to manage decorators' warnings stacklevel.
+
+    The `_stack_length` class variable is used to store the number of
+    times a function is wrapped by a decorator.
+
+    Let `stack_length` be the total number of times a decorated
+    function is wrapped, and `stack_rank` be the rank of the decorator
+    in the decorators stack. The stacklevel of a warning is then
+    `stacklevel = 1 + stack_length - stack_rank`.
+    """
+
+    _stack_length = {}
+
+    def get_stack_length(self, func):
+        length = self._stack_length.get(func.__name__, _get_stack_length(func))
+        return length
+
+
+class change_default_value(_DecoratorBaseClass):
+    """Decorator for changing the default value of an argument.
+
+    Parameters
+    ----------
+    arg_name: str
+        The name of the argument to be updated.
+    new_value: any
+        The argument new value.
+    changed_version : str
+        The package version in which the change will be introduced.
+    warning_msg: str
+        Optional warning message. If None, a generic warning message
+        is used.
+
+    """
+
+    def __init__(self, arg_name, *, new_value, changed_version, warning_msg=None):
+        self.arg_name = arg_name
+        self.new_value = new_value
+        self.warning_msg = warning_msg
+        self.changed_version = changed_version
+
+    def __call__(self, func):
+        parameters = inspect.signature(func).parameters
+        arg_idx = list(parameters.keys()).index(self.arg_name)
+        old_value = parameters[self.arg_name].default
+
+        stack_rank = _count_wrappers(func)
+
+        if self.warning_msg is None:
+            self.warning_msg = (
+                f'The new recommended value for {self.arg_name} is '
+                f'{self.new_value}. Until version {self.changed_version}, '
+                f'the default {self.arg_name} value is {old_value}. '
+                f'From version {self.changed_version}, the {self.arg_name} '
+                f'default value will be {self.new_value}. To avoid '
+                f'this warning, please explicitly set {self.arg_name} value.'
+            )
+
+        @functools.wraps(func)
+        def fixed_func(*args, **kwargs):
+            stacklevel = 1 + self.get_stack_length(func) - stack_rank
+            if len(args) < arg_idx + 1 and self.arg_name not in kwargs.keys():
+                # warn that arg_name default value changed:
+                warnings.warn(self.warning_msg, FutureWarning, stacklevel=stacklevel)
+            return func(*args, **kwargs)
+
+        return fixed_func
+
+
+class PatchClassRepr(type):
+    """Control class representations in rendered signatures."""
+
+    def __repr__(cls):
+        return f"<{cls.__name__}>"
+
+
+class DEPRECATED(metaclass=PatchClassRepr):
+    """Signal value to help with deprecating parameters that use None.
+
+    This is a proxy object, used to signal that a parameter has not been set.
+    This is useful if ``None`` is already used for a different purpose or just
+    to highlight a deprecated parameter in the signature.
+    """
+
+
+class deprecate_parameter:
+    """Deprecate a parameter of a function.
+
+    Parameters
+    ----------
+    deprecated_name : str
+        The name of the deprecated parameter.
+    start_version : str
+        The package version in which the warning was introduced.
+    stop_version : str
+        The package version in which the warning will be replaced by
+        an error / the deprecation is completed.
+    template : str, optional
+        If given, this message template is used instead of the default one.
+    new_name : str, optional
+        If given, the default message will recommend the new parameter name and an
+        error will be raised if the user uses both old and new names for the
+        same parameter.
+    modify_docstring : bool, optional
+        If the wrapped function has a docstring, add the deprecated parameters
+        to the "Other Parameters" section.
+    stacklevel : int, optional
+        This decorator attempts to detect the appropriate stacklevel for the
+        deprecation warning automatically. If this fails, e.g., due to
+        decorating a closure, you can set the stacklevel manually. The
+        outermost decorator should have stacklevel 2, the next inner one
+        stacklevel 3, etc.
+
+    Notes
+    -----
+    Assign `DEPRECATED` as the new default value for the deprecated parameter.
+    This marks the status of the parameter also in the signature and rendered
+    HTML docs.
+
+    This decorator can be stacked to deprecate more than one parameter.
+
+    Examples
+    --------
+    >>> from skimage._shared.utils import deprecate_parameter, DEPRECATED
+    >>> @deprecate_parameter(
+    ...     "b", new_name="c", start_version="0.1", stop_version="0.3"
+    ... )
+    ... def foo(a, b=DEPRECATED, *, c=None):
+    ...     return a, c
+
+    Calling ``foo(1, b=2)``  will warn with::
+
+        FutureWarning: Parameter `b` is deprecated since version 0.1 and will
+        be removed in 0.3 (or later). To avoid this warning, please use the
+        parameter `c` instead. For more details, see the documentation of
+        `foo`.
+    """
+
+    DEPRECATED = DEPRECATED  # Make signal value accessible for convenience
+
+    remove_parameter_template = (
+        "Parameter `{deprecated_name}` is deprecated since version "
+        "{deprecated_version} and will be removed in {changed_version} (or "
+        "later). To avoid this warning, please do not use the parameter "
+        "`{deprecated_name}`. For more details, see the documentation of "
+        "`{func_name}`."
+    )
+
+    replace_parameter_template = (
+        "Parameter `{deprecated_name}` is deprecated since version "
+        "{deprecated_version} and will be removed in {changed_version} (or "
+        "later). To avoid this warning, please use the parameter `{new_name}` "
+        "instead. For more details, see the documentation of `{func_name}`."
+    )
+
+    def __init__(
+        self,
+        deprecated_name,
+        *,
+        start_version,
+        stop_version,
+        template=None,
+        new_name=None,
+        modify_docstring=True,
+        stacklevel=None,
+    ):
+        self.deprecated_name = deprecated_name
+        self.new_name = new_name
+        self.template = template
+        self.start_version = start_version
+        self.stop_version = stop_version
+        self.modify_docstring = modify_docstring
+        self.stacklevel = stacklevel
+
+    def __call__(self, func):
+        parameters = inspect.signature(func).parameters
+        deprecated_idx = list(parameters.keys()).index(self.deprecated_name)
+        if self.new_name:
+            new_idx = list(parameters.keys()).index(self.new_name)
+        else:
+            new_idx = False
+
+        if parameters[self.deprecated_name].default is not DEPRECATED:
+            raise RuntimeError(
+                f"Expected `{self.deprecated_name}` to have the value {DEPRECATED!r} "
+                f"to indicate its status in the rendered signature."
+            )
+
+        if self.template is not None:
+            template = self.template
+        elif self.new_name is not None:
+            template = self.replace_parameter_template
+        else:
+            template = self.remove_parameter_template
+        warning_message = template.format(
+            deprecated_name=self.deprecated_name,
+            deprecated_version=self.start_version,
+            changed_version=self.stop_version,
+            func_name=func.__qualname__,
+            new_name=self.new_name,
+        )
+
+        @functools.wraps(func)
+        def fixed_func(*args, **kwargs):
+            deprecated_value = DEPRECATED
+            new_value = DEPRECATED
+
+            # Extract value of deprecated parameter
+            if len(args) > deprecated_idx:
+                deprecated_value = args[deprecated_idx]
+                args = (
+                    args[:deprecated_idx] + (DEPRECATED,) + args[deprecated_idx + 1 :]
+                )
+            if self.deprecated_name in kwargs.keys():
+                deprecated_value = kwargs[self.deprecated_name]
+                kwargs[self.deprecated_name] = DEPRECATED
+            # Extract value of new parameter (if present)
+            if new_idx is not False and len(args) > new_idx:
+                new_value = args[new_idx]
+            if self.new_name and self.new_name in kwargs.keys():
+                new_value = kwargs[self.new_name]
+
+            if deprecated_value is not DEPRECATED:
+                stacklevel = (
+                    self.stacklevel
+                    if self.stacklevel is not None
+                    else _warning_stacklevel(func)
+                )
+                warnings.warn(
+                    warning_message, category=FutureWarning, stacklevel=stacklevel
+                )
+
+                if new_value is not DEPRECATED:
+                    raise ValueError(
+                        f"Both deprecated parameter `{self.deprecated_name}` "
+                        f"and new parameter `{self.new_name}` are used. Use "
+                        f"only the latter to avoid conflicting values."
+                    )
+                elif self.new_name is not None:
+                    # Assign old value to new one
+                    kwargs[self.new_name] = deprecated_value
+
+            return func(*args, **kwargs)
+
+        if self.modify_docstring and func.__doc__ is not None:
+            newdoc = _docstring_add_deprecated(
+                func, {self.deprecated_name: self.new_name}, self.start_version
+            )
+            fixed_func.__doc__ = newdoc
+
+        return fixed_func
+
+
+def _docstring_add_deprecated(func, kwarg_mapping, deprecated_version):
+    """Add deprecated kwarg(s) to the "Other Params" section of a docstring.
+
+    Parameters
+    ----------
+    func : function
+        The function whose docstring we wish to update.
+    kwarg_mapping : dict
+        A dict containing {old_arg: new_arg} key/value pairs, see
+        `deprecate_parameter`.
+    deprecated_version : str
+        A major.minor version string specifying when old_arg was
+        deprecated.
+
+    Returns
+    -------
+    new_doc : str
+        The updated docstring. Returns the original docstring if numpydoc is
+        not available.
+    """
+    if func.__doc__ is None:
+        return None
+    try:
+        from numpydoc.docscrape import FunctionDoc, Parameter
+    except ImportError:
+        # Return an unmodified docstring if numpydoc is not available.
+        return func.__doc__
+
+    Doc = FunctionDoc(func)
+    for old_arg, new_arg in kwarg_mapping.items():
+        desc = []
+        if new_arg is None:
+            desc.append(f'`{old_arg}` is deprecated.')
+        else:
+            desc.append(f'Deprecated in favor of `{new_arg}`.')
+
+        desc += ['', f'.. deprecated:: {deprecated_version}']
+        Doc['Other Parameters'].append(
+            Parameter(name=old_arg, type='DEPRECATED', desc=desc)
+        )
+    new_docstring = str(Doc)
+
+    # new_docstring will have a header starting with:
+    #
+    # .. function:: func.__name__
+    #
+    # and some additional blank lines. We strip these off below.
+    split = new_docstring.split('\n')
+    no_header = split[1:]
+    while not no_header[0].strip():
+        no_header.pop(0)
+
+    # Store the initial description before any of the Parameters fields.
+    # Usually this is a single line, but the while loop covers any case
+    # where it is not.
+    descr = no_header.pop(0)
+    while no_header[0].strip():
+        descr += '\n    ' + no_header.pop(0)
+    descr += '\n\n'
+    # '\n    ' rather than '\n' here to restore the original indentation.
+    final_docstring = descr + '\n    '.join(no_header)
+    # strip any extra spaces from ends of lines
+    final_docstring = '\n'.join([line.rstrip() for line in final_docstring.split('\n')])
+    return final_docstring
+
+
+class channel_as_last_axis:
+    """Decorator for automatically making channels axis last for all arrays.
+
+    This decorator reorders axes for compatibility with functions that only
+    support channels along the last axis. After the function call is complete
+    the channels axis is restored back to its original position.
+
+    Parameters
+    ----------
+    channel_arg_positions : tuple of int, optional
+        Positional arguments at the positions specified in this tuple are
+        assumed to be multichannel arrays. The default is to assume only the
+        first argument to the function is a multichannel array.
+    channel_kwarg_names : tuple of str, optional
+        A tuple containing the names of any keyword arguments corresponding to
+        multichannel arrays.
+    multichannel_output : bool, optional
+        A boolean that should be True if the output of the function is not a
+        multichannel array and False otherwise. This decorator does not
+        currently support the general case of functions with multiple outputs
+        where some or all are multichannel.
+
+    """
+
+    def __init__(
+        self,
+        channel_arg_positions=(0,),
+        channel_kwarg_names=(),
+        multichannel_output=True,
+    ):
+        self.arg_positions = set(channel_arg_positions)
+        self.kwarg_names = set(channel_kwarg_names)
+        self.multichannel_output = multichannel_output
+
+    def __call__(self, func):
+        @functools.wraps(func)
+        def fixed_func(*args, **kwargs):
+            channel_axis = kwargs.get('channel_axis', None)
+
+            if channel_axis is None:
+                return func(*args, **kwargs)
+
+            # TODO: convert scalars to a tuple in anticipation of eventually
+            #       supporting a tuple of channel axes. Right now, only an
+            #       integer or a single-element tuple is supported, though.
+            if np.isscalar(channel_axis):
+                channel_axis = (channel_axis,)
+            if len(channel_axis) > 1:
+                raise ValueError("only a single channel axis is currently supported")
+
+            if channel_axis == (-1,) or channel_axis == -1:
+                return func(*args, **kwargs)
+
+            if self.arg_positions:
+                new_args = []
+                for pos, arg in enumerate(args):
+                    if pos in self.arg_positions:
+                        new_args.append(np.moveaxis(arg, channel_axis[0], -1))
+                    else:
+                        new_args.append(arg)
+                new_args = tuple(new_args)
+            else:
+                new_args = args
+
+            for name in self.kwarg_names:
+                kwargs[name] = np.moveaxis(kwargs[name], channel_axis[0], -1)
+
+            # now that we have moved the channels axis to the last position,
+            # change the channel_axis argument to -1
+            kwargs["channel_axis"] = -1
+
+            # Call the function with the fixed arguments
+            out = func(*new_args, **kwargs)
+            if self.multichannel_output:
+                out = np.moveaxis(out, -1, channel_axis[0])
+            return out
+
+        return fixed_func
+
+
+class deprecate_func(_DecoratorBaseClass):
+    """Decorate a deprecated function and warn when it is called.
+
+    Adapted from <http://wiki.python.org/moin/PythonDecoratorLibrary>.
+
+    Parameters
+    ----------
+    deprecated_version : str
+        The package version when the deprecation was introduced.
+    removed_version : str
+        The package version in which the deprecated function will be removed.
+    hint : str, optional
+        A hint on how to address this deprecation,
+        e.g., "Use `skimage.submodule.alternative_func` instead."
+
+    Examples
+    --------
+    >>> @deprecate_func(
+    ...     deprecated_version="1.0.0",
+    ...     removed_version="1.2.0",
+    ...     hint="Use `bar` instead."
+    ... )
+    ... def foo():
+    ...     pass
+
+    Calling ``foo`` will warn with::
+
+        FutureWarning: `foo` is deprecated since version 1.0.0
+        and will be removed in version 1.2.0. Use `bar` instead.
+    """
+
+    def __init__(self, *, deprecated_version, removed_version=None, hint=None):
+        self.deprecated_version = deprecated_version
+        self.removed_version = removed_version
+        self.hint = hint
+
+    def __call__(self, func):
+        message = (
+            f"`{func.__name__}` is deprecated since version "
+            f"{self.deprecated_version}"
+        )
+        if self.removed_version:
+            message += f" and will be removed in version {self.removed_version}."
+        if self.hint:
+            # Prepend space and make sure it closes with "."
+            message += f" {self.hint.rstrip('.')}."
+
+        stack_rank = _count_wrappers(func)
+
+        @functools.wraps(func)
+        def wrapped(*args, **kwargs):
+            stacklevel = 1 + self.get_stack_length(func) - stack_rank
+            warnings.warn(message, category=FutureWarning, stacklevel=stacklevel)
+            return func(*args, **kwargs)
+
+        # modify docstring to display deprecation warning
+        doc = f'**Deprecated:** {message}'
+        if wrapped.__doc__ is None:
+            wrapped.__doc__ = doc
+        else:
+            wrapped.__doc__ = doc + '\n\n    ' + wrapped.__doc__
+
+        return wrapped
+
+
+def get_bound_method_class(m):
+    """Return the class for a bound method."""
+    return m.im_class if sys.version < '3' else m.__self__.__class__
+
+
+def safe_as_int(val, atol=1e-3):
+    """
+    Attempt to safely cast values to integer format.
+
+    Parameters
+    ----------
+    val : scalar or iterable of scalars
+        Number or container of numbers which are intended to be interpreted as
+        integers, e.g., for indexing purposes, but which may not carry integer
+        type.
+    atol : float
+        Absolute tolerance away from nearest integer to consider values in
+        ``val`` functionally integers.
+
+    Returns
+    -------
+    val_int : NumPy scalar or ndarray of dtype `np.int64`
+        Returns the input value(s) coerced to dtype `np.int64` assuming all
+        were within ``atol`` of the nearest integer.
+
+    Notes
+    -----
+    This operation calculates ``val`` modulo 1, which returns the mantissa of
+    all values. Then all mantissas greater than 0.5 are subtracted from one.
+    Finally, the absolute tolerance from zero is calculated. If it is less
+    than ``atol`` for all value(s) in ``val``, they are rounded and returned
+    in an integer array. Or, if ``val`` was a scalar, a NumPy scalar type is
+    returned.
+
+    If any value(s) are outside the specified tolerance, an informative error
+    is raised.
+
+    Examples
+    --------
+    >>> safe_as_int(7.0)
+    7
+
+    >>> safe_as_int([9, 4, 2.9999999999])
+    array([9, 4, 3])
+
+    >>> safe_as_int(53.1)
+    Traceback (most recent call last):
+        ...
+    ValueError: Integer argument required but received 53.1, check inputs.
+
+    >>> safe_as_int(53.01, atol=0.01)
+    53
+
+    """
+    mod = np.asarray(val) % 1  # Extract mantissa
+
+    # Check for and subtract any mod values > 0.5 from 1
+    if mod.ndim == 0:  # Scalar input, cannot be indexed
+        if mod > 0.5:
+            mod = 1 - mod
+    else:  # Iterable input, now ndarray
+        mod[mod > 0.5] = 1 - mod[mod > 0.5]  # Test on each side of nearest int
+
+    try:
+        np.testing.assert_allclose(mod, 0, atol=atol)
+    except AssertionError:
+        raise ValueError(
+            f'Integer argument required but received ' f'{val}, check inputs.'
+        )
+
+    return np.round(val).astype(np.int64)
+
+
+def check_shape_equality(*images):
+    """Check that all images have the same shape"""
+    image0 = images[0]
+    if not all(image0.shape == image.shape for image in images[1:]):
+        raise ValueError('Input images must have the same dimensions.')
+    return
+
+
+def slice_at_axis(sl, axis):
+    """
+    Construct tuple of slices to slice an array in the given dimension.
+
+    Parameters
+    ----------
+    sl : slice
+        The slice for the given dimension.
+    axis : int
+        The axis to which `sl` is applied. All other dimensions are left
+        "unsliced".
+
+    Returns
+    -------
+    sl : tuple of slices
+        A tuple with slices matching `shape` in length.
+
+    Examples
+    --------
+    >>> slice_at_axis(slice(None, 3, -1), 1)
+    (slice(None, None, None), slice(None, 3, -1), Ellipsis)
+    """
+    return (slice(None),) * axis + (sl,) + (...,)
+
+
+def reshape_nd(arr, ndim, dim):
+    """Reshape a 1D array to have n dimensions, all singletons but one.
+
+    Parameters
+    ----------
+    arr : array, shape (N,)
+        Input array
+    ndim : int
+        Number of desired dimensions of reshaped array.
+    dim : int
+        Which dimension/axis will not be singleton-sized.
+
+    Returns
+    -------
+    arr_reshaped : array, shape ([1, ...], N, [1,...])
+        View of `arr` reshaped to the desired shape.
+
+    Examples
+    --------
+    >>> rng = np.random.default_rng()
+    >>> arr = rng.random(7)
+    >>> reshape_nd(arr, 2, 0).shape
+    (7, 1)
+    >>> reshape_nd(arr, 3, 1).shape
+    (1, 7, 1)
+    >>> reshape_nd(arr, 4, -1).shape
+    (1, 1, 1, 7)
+    """
+    if arr.ndim != 1:
+        raise ValueError("arr must be a 1D array")
+    new_shape = [1] * ndim
+    new_shape[dim] = -1
+    return np.reshape(arr, new_shape)
+
+
+def check_nD(array, ndim, arg_name='image'):
+    """
+    Verify an array meets the desired ndims and array isn't empty.
+
+    Parameters
+    ----------
+    array : array-like
+        Input array to be validated
+    ndim : int or iterable of ints
+        Allowable ndim or ndims for the array.
+    arg_name : str, optional
+        The name of the array in the original function.
+
+    """
+    array = np.asanyarray(array)
+    msg_incorrect_dim = "The parameter `%s` must be a %s-dimensional array"
+    msg_empty_array = "The parameter `%s` cannot be an empty array"
+    if isinstance(ndim, int):
+        ndim = [ndim]
+    if array.size == 0:
+        raise ValueError(msg_empty_array % (arg_name))
+    if array.ndim not in ndim:
+        raise ValueError(
+            msg_incorrect_dim % (arg_name, '-or-'.join([str(n) for n in ndim]))
+        )
+
+
+def convert_to_float(image, preserve_range):
+    """Convert input image to float image with the appropriate range.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    preserve_range : bool
+        Determines if the range of the image should be kept or transformed
+        using img_as_float. Also see
+        https://scikit-image.org/docs/dev/user_guide/data_types.html
+
+    Notes
+    -----
+    * Input images with `float32` data type are not upcast.
+
+    Returns
+    -------
+    image : ndarray
+        Transformed version of the input.
+
+    """
+    if image.dtype == np.float16:
+        return image.astype(np.float32)
+    if preserve_range:
+        # Convert image to double only if it is not single or double
+        # precision float
+        if image.dtype.char not in 'df':
+            image = image.astype(float)
+    else:
+        from ..util.dtype import img_as_float
+
+        image = img_as_float(image)
+    return image
+
+
+def _validate_interpolation_order(image_dtype, order):
+    """Validate and return spline interpolation's order.
+
+    Parameters
+    ----------
+    image_dtype : dtype
+        Image dtype.
+    order : int, optional
+        The order of the spline interpolation. The order has to be in
+        the range 0-5. See `skimage.transform.warp` for detail.
+
+    Returns
+    -------
+    order : int
+        if input order is None, returns 0 if image_dtype is bool and 1
+        otherwise. Otherwise, image_dtype is checked and input order
+        is validated accordingly (order > 0 is not supported for bool
+        image dtype)
+
+    """
+
+    if order is None:
+        return 0 if image_dtype == bool else 1
+
+    if order < 0 or order > 5:
+        raise ValueError("Spline interpolation order has to be in the " "range 0-5.")
+
+    if image_dtype == bool and order != 0:
+        raise ValueError(
+            "Input image dtype is bool. Interpolation is not defined "
+            "with bool data type. Please set order to 0 or explicitly "
+            "cast input image to another data type."
+        )
+
+    return order
+
+
+def _to_np_mode(mode):
+    """Convert padding modes from `ndi.correlate` to `np.pad`."""
+    mode_translation_dict = dict(nearest='edge', reflect='symmetric', mirror='reflect')
+    if mode in mode_translation_dict:
+        mode = mode_translation_dict[mode]
+    return mode
+
+
+def _to_ndimage_mode(mode):
+    """Convert from `numpy.pad` mode name to the corresponding ndimage mode."""
+    mode_translation_dict = dict(
+        constant='constant',
+        edge='nearest',
+        symmetric='reflect',
+        reflect='mirror',
+        wrap='wrap',
+    )
+    if mode not in mode_translation_dict:
+        raise ValueError(
+            f"Unknown mode: '{mode}', or cannot translate mode. The "
+            f"mode should be one of 'constant', 'edge', 'symmetric', "
+            f"'reflect', or 'wrap'. See the documentation of numpy.pad for "
+            f"more info."
+        )
+    return _fix_ndimage_mode(mode_translation_dict[mode])
+
+
+def _fix_ndimage_mode(mode):
+    # SciPy 1.6.0 introduced grid variants of constant and wrap which
+    # have less surprising behavior for images. Use these when available
+    grid_modes = {'constant': 'grid-constant', 'wrap': 'grid-wrap'}
+    return grid_modes.get(mode, mode)
+
+
+new_float_type = {
+    # preserved types
+    np.float32().dtype.char: np.float32,
+    np.float64().dtype.char: np.float64,
+    np.complex64().dtype.char: np.complex64,
+    np.complex128().dtype.char: np.complex128,
+    # altered types
+    np.float16().dtype.char: np.float32,
+    'g': np.float64,  # np.float128 ; doesn't exist on windows
+    'G': np.complex128,  # np.complex256 ; doesn't exist on windows
+}
+
+
+def _supported_float_type(input_dtype, allow_complex=False):
+    """Return an appropriate floating-point dtype for a given dtype.
+
+    float32, float64, complex64, complex128 are preserved.
+    float16 is promoted to float32.
+    complex256 is demoted to complex128.
+    Other types are cast to float64.
+
+    Parameters
+    ----------
+    input_dtype : np.dtype or tuple of np.dtype
+        The input dtype. If a tuple of multiple dtypes is provided, each
+        dtype is first converted to a supported floating point type and the
+        final dtype is then determined by applying `np.result_type` on the
+        sequence of supported floating point types.
+    allow_complex : bool, optional
+        If False, raise a ValueError on complex-valued inputs.
+
+    Returns
+    -------
+    float_type : dtype
+        Floating-point dtype for the image.
+    """
+    if isinstance(input_dtype, tuple):
+        return np.result_type(*(_supported_float_type(d) for d in input_dtype))
+    input_dtype = np.dtype(input_dtype)
+    if not allow_complex and input_dtype.kind == 'c':
+        raise ValueError("complex valued input is not supported")
+    return new_float_type.get(input_dtype.char, np.float64)
+
+
+def identity(image, *args, **kwargs):
+    """Returns the first argument unmodified."""
+    return image
+
+
+def as_binary_ndarray(array, *, variable_name):
+    """Return `array` as a numpy.ndarray of dtype bool.
+
+    Raises
+    ------
+    ValueError:
+        An error including the given `variable_name` if `array` can not be
+        safely cast to a boolean array.
+    """
+    array = np.asarray(array)
+    if array.dtype != bool:
+        if np.any((array != 1) & (array != 0)):
+            raise ValueError(
+                f"{variable_name} array is not of dtype boolean or "
+                f"contains values other than 0 and 1 so cannot be "
+                f"safely cast to boolean array."
+            )
+    return np.asarray(array, dtype=bool)

.CondaPkg/env/Lib/site-packages/skimage/_shared/version_requirements.py

@@ -0,0 +1,138 @@
+import sys
+
+from packaging import version as _version
+
+
+def _check_version(actver, version, cmp_op):
+    """
+    Check version string of an active module against a required version.
+
+    If dev/prerelease tags result in TypeError for string-number comparison,
+    it is assumed that the dependency is satisfied.
+    Users on dev branches are responsible for keeping their own packages up to
+    date.
+    """
+    try:
+        if cmp_op == '>':
+            return _version.parse(actver) > _version.parse(version)
+        elif cmp_op == '>=':
+            return _version.parse(actver) >= _version.parse(version)
+        elif cmp_op == '=':
+            return _version.parse(actver) == _version.parse(version)
+        elif cmp_op == '<':
+            return _version.parse(actver) < _version.parse(version)
+        else:
+            return False
+    except TypeError:
+        return True
+
+
+def get_module_version(module_name):
+    """Return module version or None if version can't be retrieved."""
+    mod = __import__(module_name, fromlist=[module_name.rpartition('.')[-1]])
+    return getattr(mod, '__version__', getattr(mod, 'VERSION', None))
+
+
+def is_installed(name, version=None):
+    """Test if *name* is installed.
+
+    Parameters
+    ----------
+    name : str
+        Name of module or "python"
+    version : str, optional
+        Version string to test against.
+        If version is not None, checking version
+        (must have an attribute named '__version__' or 'VERSION')
+        Version may start with =, >=, > or < to specify the exact requirement
+
+    Returns
+    -------
+    out : bool
+        True if `name` is installed matching the optional version.
+    """
+    if name.lower() == 'python':
+        actver = sys.version[:6]
+    else:
+        try:
+            actver = get_module_version(name)
+        except ImportError:
+            return False
+    if version is None:
+        return True
+    else:
+        # since version_requirements is in the critical import path,
+        # we lazy import re
+        import re
+
+        match = re.search('[0-9]', version)
+        assert match is not None, "Invalid version number"
+        symb = version[: match.start()]
+        if not symb:
+            symb = '='
+        assert symb in ('>=', '>', '=', '<'), f"Invalid version condition '{symb}'"
+        version = version[match.start() :]
+        return _check_version(actver, version, symb)
+
+
+def require(name, version=None):
+    """Return decorator that forces a requirement for a function or class.
+
+    Parameters
+    ----------
+    name : str
+        Name of module or "python".
+    version : str, optional
+        Version string to test against.
+        If version is not None, checking version
+        (must have an attribute named '__version__' or 'VERSION')
+        Version may start with =, >=, > or < to specify the exact requirement
+
+    Returns
+    -------
+    func : function
+        A decorator that raises an ImportError if a function is run
+        in the absence of the input dependency.
+    """
+    # since version_requirements is in the critical import path, we lazy import
+    # functools
+    import functools
+
+    def decorator(obj):
+        @functools.wraps(obj)
+        def func_wrapped(*args, **kwargs):
+            if is_installed(name, version):
+                return obj(*args, **kwargs)
+            else:
+                msg = f'"{obj}" in "{obj.__module__}" requires "{name}'
+                if version is not None:
+                    msg += f" {version}"
+                raise ImportError(msg + '"')
+
+        return func_wrapped
+
+    return decorator
+
+
+def get_module(module_name, version=None):
+    """Return a module object of name *module_name* if installed.
+
+    Parameters
+    ----------
+    module_name : str
+        Name of module.
+    version : str, optional
+        Version string to test against.
+        If version is not None, checking version
+        (must have an attribute named '__version__' or 'VERSION')
+        Version may start with =, >=, > or < to specify the exact requirement
+
+    Returns
+    -------
+    mod : module or None
+        Module if *module_name* is installed matching the optional version
+        or None otherwise.
+    """
+    if not is_installed(module_name, version):
+        return None
+    return __import__(module_name, fromlist=[module_name.rpartition('.')[-1]])

.CondaPkg/env/Lib/site-packages/skimage/_vendored/numpy_lookfor.py

@@ -0,0 +1,293 @@
+# Vendored subset of numpy/lib/utils.py in 1.26.3
+# https://github.com/numpy/numpy/blob/b4bf93b936802618ebb49ee43e382b576b29a0a6/numpy/lib/utils.py
+#
+# Can be removed after deprecation of `skimage.lookfor` is completed.
+
+import sys
+import os
+import re
+
+from numpy import ufunc
+
+
+# Cache for lookfor: {id(module): {name: (docstring, kind, index), ...}...}
+# where kind: "func", "class", "module", "object"
+# and index: index in breadth-first namespace traversal
+_lookfor_caches = {}
+
+
+# regexp whose match indicates that the string may contain a function
+# signature
+_function_signature_re = re.compile(r"[a-z0-9_]+\(.*[,=].*\)", re.I)
+
+
+def _getmembers(item):
+    import inspect
+
+    try:
+        members = inspect.getmembers(item)
+    except Exception:
+        members = [(x, getattr(item, x)) for x in dir(item) if hasattr(item, x)]
+    return members
+
+
+def _lookfor_generate_cache(module, import_modules, regenerate):
+    """
+    Generate docstring cache for given module.
+
+    Parameters
+    ----------
+    module : str, None, module
+        Module for which to generate docstring cache
+    import_modules : bool
+        Whether to import sub-modules in packages.
+    regenerate : bool
+        Re-generate the docstring cache
+
+    Returns
+    -------
+    cache : dict {obj_full_name: (docstring, kind, index), ...}
+        Docstring cache for the module, either cached one (regenerate=False)
+        or newly generated.
+
+    """
+    # Local import to speed up numpy's import time.
+    import inspect
+
+    from io import StringIO
+
+    if module is None:
+        module = "numpy"
+
+    if isinstance(module, str):
+        try:
+            __import__(module)
+        except ImportError:
+            return {}
+        module = sys.modules[module]
+    elif isinstance(module, list) or isinstance(module, tuple):
+        cache = {}
+        for mod in module:
+            cache.update(_lookfor_generate_cache(mod, import_modules, regenerate))
+        return cache
+
+    if id(module) in _lookfor_caches and not regenerate:
+        return _lookfor_caches[id(module)]
+
+    # walk items and collect docstrings
+    cache = {}
+    _lookfor_caches[id(module)] = cache
+    seen = {}
+    index = 0
+    stack = [(module.__name__, module)]
+    while stack:
+        name, item = stack.pop(0)
+        if id(item) in seen:
+            continue
+        seen[id(item)] = True
+
+        index += 1
+        kind = "object"
+
+        if inspect.ismodule(item):
+            kind = "module"
+            try:
+                _all = item.__all__
+            except AttributeError:
+                _all = None
+
+            # import sub-packages
+            if import_modules and hasattr(item, '__path__'):
+                for pth in item.__path__:
+                    for mod_path in os.listdir(pth):
+                        this_py = os.path.join(pth, mod_path)
+                        init_py = os.path.join(pth, mod_path, '__init__.py')
+                        if os.path.isfile(this_py) and mod_path.endswith('.py'):
+                            to_import = mod_path[:-3]
+                        elif os.path.isfile(init_py):
+                            to_import = mod_path
+                        else:
+                            continue
+                        if to_import == '__init__':
+                            continue
+
+                        try:
+                            old_stdout = sys.stdout
+                            old_stderr = sys.stderr
+                            try:
+                                sys.stdout = StringIO()
+                                sys.stderr = StringIO()
+                                __import__("%s.%s" % (name, to_import))  # noqa: UP031
+                            finally:
+                                sys.stdout = old_stdout
+                                sys.stderr = old_stderr
+                        except KeyboardInterrupt:
+                            # Assume keyboard interrupt came from a user
+                            raise
+                        except BaseException:
+                            # Ignore also SystemExit and pytests.importorskip
+                            # `Skipped` (these are BaseExceptions; gh-22345)
+                            continue
+
+            for n, v in _getmembers(item):
+                try:
+                    item_name = getattr(
+                        v, '__name__', "%s.%s" % (name, n)  # noqa: UP031
+                    )  # noqa: UP031
+                    mod_name = getattr(v, '__module__', None)
+                except NameError:
+                    # ref. SWIG's global cvars
+                    #    NameError: Unknown C global variable
+                    item_name = "%s.%s" % (name, n)  # noqa: UP031
+                    mod_name = None
+                if '.' not in item_name and mod_name:
+                    item_name = "%s.%s" % (mod_name, item_name)  # noqa: UP031
+
+                if not item_name.startswith(name + '.'):
+                    # don't crawl "foreign" objects
+                    if isinstance(v, ufunc):
+                        # ... unless they are ufuncs
+                        pass
+                    else:
+                        continue
+                elif not (inspect.ismodule(v) or _all is None or n in _all):
+                    continue
+                stack.append(("%s.%s" % (name, n), v))  # noqa: UP031
+        elif inspect.isclass(item):
+            kind = "class"
+            for n, v in _getmembers(item):
+                stack.append(("%s.%s" % (name, n), v))  # noqa: UP031
+        elif hasattr(item, "__call__"):
+            kind = "func"
+
+        try:
+            doc = inspect.getdoc(item)
+        except NameError:
+            # ref SWIG's NameError: Unknown C global variable
+            doc = None
+        if doc is not None:
+            cache[name] = (doc, kind, index)
+
+    return cache
+
+
+def lookfor(what, module=None, import_modules=True, regenerate=False, output=None):
+    """
+    Do a keyword search on docstrings.
+
+    A list of objects that matched the search is displayed,
+    sorted by relevance. All given keywords need to be found in the
+    docstring for it to be returned as a result, but the order does
+    not matter.
+
+    Parameters
+    ----------
+    what : str
+        String containing words to look for.
+    module : str or list, optional
+        Name of module(s) whose docstrings to go through.
+    import_modules : bool, optional
+        Whether to import sub-modules in packages. Default is True.
+    regenerate : bool, optional
+        Whether to re-generate the docstring cache. Default is False.
+    output : file-like, optional
+        File-like object to write the output to. If omitted, use a pager.
+
+    See Also
+    --------
+    source, info
+
+    Notes
+    -----
+    Relevance is determined only roughly, by checking if the keywords occur
+    in the function name, at the start of a docstring, etc.
+
+    Examples
+    --------
+    >>> np.lookfor('binary representation') # doctest: +SKIP
+    Search results for 'binary representation'
+    ------------------------------------------
+    numpy.binary_repr
+        Return the binary representation of the input number as a string.
+    numpy.core.setup_common.long_double_representation
+        Given a binary dump as given by GNU od -b, look for long double
+    numpy.base_repr
+        Return a string representation of a number in the given base system.
+    ...
+
+    """
+    import pydoc
+
+    # Cache
+    cache = _lookfor_generate_cache(module, import_modules, regenerate)
+
+    # Search
+    # XXX: maybe using a real stemming search engine would be better?
+    found = []
+    whats = str(what).lower().split()
+    if not whats:
+        return
+
+    for name, (docstring, kind, index) in cache.items():
+        if kind in ('module', 'object'):
+            # don't show modules or objects
+            continue
+        doc = docstring.lower()
+        if all(w in doc for w in whats):
+            found.append(name)
+
+    # Relevance sort
+    # XXX: this is full Harrison-Stetson heuristics now,
+    # XXX: it probably could be improved
+
+    kind_relevance = {'func': 1000, 'class': 1000, 'module': -1000, 'object': -1000}
+
+    def relevance(name, docstr, kind, index):
+        r = 0
+        # do the keywords occur within the start of the docstring?
+        first_doc = "\n".join(docstr.lower().strip().split("\n")[:3])
+        r += sum([200 for w in whats if w in first_doc])
+        # do the keywords occur in the function name?
+        r += sum([30 for w in whats if w in name])
+        # is the full name long?
+        r += -len(name) * 5
+        # is the object of bad type?
+        r += kind_relevance.get(kind, -1000)
+        # is the object deep in namespace hierarchy?
+        r += -name.count('.') * 10
+        r += max(-index / 100, -100)
+        return r
+
+    def relevance_value(a):
+        return relevance(a, *cache[a])
+
+    found.sort(key=relevance_value)
+
+    # Pretty-print
+    s = "Search results for '%s'" % (' '.join(whats))
+    help_text = [s, "-" * len(s)]
+    for name in found[::-1]:
+        doc, kind, ix = cache[name]
+
+        doclines = [line.strip() for line in doc.strip().split("\n") if line.strip()]
+
+        # find a suitable short description
+        try:
+            first_doc = doclines[0].strip()
+            if _function_signature_re.search(first_doc):
+                first_doc = doclines[1].strip()
+        except IndexError:
+            first_doc = ""
+        help_text.append("%s\n    %s" % (name, first_doc))  # noqa: UP031
+
+    if not found:
+        help_text.append("Nothing found.")
+
+    # Output
+    if output is not None:
+        output.write("\n".join(help_text))
+    elif len(help_text) > 10:
+        pager = pydoc.getpager()
+        pager("\n".join(help_text))
+    else:
+        print("\n".join(help_text))

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

@@ -0,0 +1,3 @@
+import lazy_loader as lazy
+
+__getattr__, __dir__, __all__ = lazy.attach_stub(__name__, __file__)

.CondaPkg/env/Lib/site-packages/skimage/color/__init__.pyi

@@ -0,0 +1,135 @@
+# Explicitly setting `__all__` is necessary for type inference engines
+# to know which symbols are exported. See
+# https://peps.python.org/pep-0484/#stub-files
+
+__all__ = [
+    'convert_colorspace',
+    'xyz_tristimulus_values',
+    'rgba2rgb',
+    'rgb2hsv',
+    'hsv2rgb',
+    'rgb2xyz',
+    'xyz2rgb',
+    'rgb2rgbcie',
+    'rgbcie2rgb',
+    'rgb2gray',
+    'gray2rgb',
+    'gray2rgba',
+    'xyz2lab',
+    'lab2xyz',
+    'lab2rgb',
+    'rgb2lab',
+    'rgb2hed',
+    'hed2rgb',
+    'lab2lch',
+    'lch2lab',
+    'rgb2yuv',
+    'yuv2rgb',
+    'rgb2yiq',
+    'yiq2rgb',
+    'rgb2ypbpr',
+    'ypbpr2rgb',
+    'rgb2ycbcr',
+    'ycbcr2rgb',
+    'rgb2ydbdr',
+    'ydbdr2rgb',
+    'separate_stains',
+    'combine_stains',
+    'rgb_from_hed',
+    'hed_from_rgb',
+    'rgb_from_hdx',
+    'hdx_from_rgb',
+    'rgb_from_fgx',
+    'fgx_from_rgb',
+    'rgb_from_bex',
+    'bex_from_rgb',
+    'rgb_from_rbd',
+    'rbd_from_rgb',
+    'rgb_from_gdx',
+    'gdx_from_rgb',
+    'rgb_from_hax',
+    'hax_from_rgb',
+    'rgb_from_bro',
+    'bro_from_rgb',
+    'rgb_from_bpx',
+    'bpx_from_rgb',
+    'rgb_from_ahx',
+    'ahx_from_rgb',
+    'rgb_from_hpx',
+    'hpx_from_rgb',
+    'color_dict',
+    'label2rgb',
+    'deltaE_cie76',
+    'deltaE_ciede94',
+    'deltaE_ciede2000',
+    'deltaE_cmc',
+]
+
+from .colorconv import (
+    convert_colorspace,
+    xyz_tristimulus_values,
+    rgba2rgb,
+    rgb2hsv,
+    hsv2rgb,
+    rgb2xyz,
+    xyz2rgb,
+    rgb2rgbcie,
+    rgbcie2rgb,
+    rgb2gray,
+    gray2rgb,
+    gray2rgba,
+    xyz2lab,
+    lab2xyz,
+    lab2rgb,
+    rgb2lab,
+    xyz2luv,
+    luv2xyz,
+    luv2rgb,
+    rgb2luv,
+    rgb2hed,
+    hed2rgb,
+    lab2lch,
+    lch2lab,
+    rgb2yuv,
+    yuv2rgb,
+    rgb2yiq,
+    yiq2rgb,
+    rgb2ypbpr,
+    ypbpr2rgb,
+    rgb2ycbcr,
+    ycbcr2rgb,
+    rgb2ydbdr,
+    ydbdr2rgb,
+    separate_stains,
+    combine_stains,
+    rgb_from_hed,
+    hed_from_rgb,
+    rgb_from_hdx,
+    hdx_from_rgb,
+    rgb_from_fgx,
+    fgx_from_rgb,
+    rgb_from_bex,
+    bex_from_rgb,
+    rgb_from_rbd,
+    rbd_from_rgb,
+    rgb_from_gdx,
+    gdx_from_rgb,
+    rgb_from_hax,
+    hax_from_rgb,
+    rgb_from_bro,
+    bro_from_rgb,
+    rgb_from_bpx,
+    bpx_from_rgb,
+    rgb_from_ahx,
+    ahx_from_rgb,
+    rgb_from_hpx,
+    hpx_from_rgb,
+)
+
+from .colorlabel import color_dict, label2rgb
+from .delta_e import (
+    deltaE_cie76,
+    deltaE_ciede94,
+    deltaE_ciede2000,
+    deltaE_cmc,
+)

.CondaPkg/env/Lib/site-packages/skimage/color/adapt_rgb.py

@@ -0,0 +1,81 @@
+import functools
+
+import numpy as np
+
+from .. import color
+from ..util.dtype import _convert
+
+
+__all__ = ['adapt_rgb', 'hsv_value', 'each_channel']
+
+
+def is_rgb_like(image, channel_axis=-1):
+    """Return True if the image *looks* like it's RGB.
+
+    This function should not be public because it is only intended to be used
+    for functions that don't accept volumes as input, since checking an image's
+    shape is fragile.
+    """
+    return (image.ndim == 3) and (image.shape[channel_axis] in (3, 4))
+
+
+def adapt_rgb(apply_to_rgb):
+    """Return decorator that adapts to RGB images to a gray-scale filter.
+
+    This function is only intended to be used for functions that don't accept
+    volumes as input, since checking an image's shape is fragile.
+
+    Parameters
+    ----------
+    apply_to_rgb : function
+        Function that returns a filtered image from an image-filter and RGB
+        image. This will only be called if the image is RGB-like.
+    """
+
+    def decorator(image_filter):
+        @functools.wraps(image_filter)
+        def image_filter_adapted(image, *args, **kwargs):
+            if is_rgb_like(image):
+                return apply_to_rgb(image_filter, image, *args, **kwargs)
+            else:
+                return image_filter(image, *args, **kwargs)
+
+        return image_filter_adapted
+
+    return decorator
+
+
+def hsv_value(image_filter, image, *args, **kwargs):
+    """Return color image by applying `image_filter` on HSV-value of `image`.
+
+    Note that this function is intended for use with `adapt_rgb`.
+
+    Parameters
+    ----------
+    image_filter : function
+        Function that filters a gray-scale image.
+    image : array
+        Input image. Note that RGBA images are treated as RGB.
+    """
+    # Slice the first three channels so that we remove any alpha channels.
+    hsv = color.rgb2hsv(image[:, :, :3])
+    value = hsv[:, :, 2].copy()
+    value = image_filter(value, *args, **kwargs)
+    hsv[:, :, 2] = _convert(value, hsv.dtype)
+    return color.hsv2rgb(hsv)
+
+
+def each_channel(image_filter, image, *args, **kwargs):
+    """Return color image by applying `image_filter` on channels of `image`.
+
+    Note that this function is intended for use with `adapt_rgb`.
+
+    Parameters
+    ----------
+    image_filter : function
+        Function that filters a gray-scale image.
+    image : array
+        Input image.
+    """
+    c_new = [image_filter(c, *args, **kwargs) for c in np.moveaxis(image, -1, 0)]
+    return np.stack(c_new, axis=-1)

.CondaPkg/env/Lib/site-packages/skimage/color/colorlabel.py

@@ -0,0 +1,299 @@
+import itertools
+
+import numpy as np
+
+from .._shared.utils import _supported_float_type, warn
+from ..util import img_as_float
+from . import rgb_colors
+from .colorconv import gray2rgb, rgb2hsv, hsv2rgb
+
+
+__all__ = ['color_dict', 'label2rgb', 'DEFAULT_COLORS']
+
+
+DEFAULT_COLORS = (
+    'red',
+    'blue',
+    'yellow',
+    'magenta',
+    'green',
+    'indigo',
+    'darkorange',
+    'cyan',
+    'pink',
+    'yellowgreen',
+)
+
+
+color_dict = {k: v for k, v in rgb_colors.__dict__.items() if isinstance(v, tuple)}
+
+
+def _rgb_vector(color):
+    """Return RGB color as (1, 3) array.
+
+    This RGB array gets multiplied by masked regions of an RGB image, which are
+    partially flattened by masking (i.e. dimensions 2D + RGB -> 1D + RGB).
+
+    Parameters
+    ----------
+    color : str or array
+        Color name in ``skimage.color.color_dict`` or RGB float values between [0, 1].
+    """
+    if isinstance(color, str):
+        color = color_dict[color]
+    # Slice to handle RGBA colors.
+    return np.array(color[:3])
+
+
+def _match_label_with_color(label, colors, bg_label, bg_color):
+    """Return `unique_labels` and `color_cycle` for label array and color list.
+
+    Colors are cycled for normal labels, but the background color should only
+    be used for the background.
+    """
+    # Temporarily set background color; it will be removed later.
+    if bg_color is None:
+        bg_color = (0, 0, 0)
+    bg_color = _rgb_vector(bg_color)
+
+    # map labels to their ranks among all labels from small to large
+    unique_labels, mapped_labels = np.unique(label, return_inverse=True)
+    # unique_inverse is no longer flat in NumPy 2.0
+    mapped_labels = mapped_labels.reshape(-1)
+
+    # get rank of bg_label
+    bg_label_rank_list = mapped_labels[label.flat == bg_label]
+
+    # The rank of each label is the index of the color it is matched to in
+    # color cycle. bg_label should always be mapped to the first color, so
+    # its rank must be 0. Other labels should be ranked from small to large
+    # from 1.
+    if len(bg_label_rank_list) > 0:
+        bg_label_rank = bg_label_rank_list[0]
+        mapped_labels[mapped_labels < bg_label_rank] += 1
+        mapped_labels[label.flat == bg_label] = 0
+    else:
+        mapped_labels += 1
+
+    # Modify labels and color cycle so background color is used only once.
+    color_cycle = itertools.cycle(colors)
+    color_cycle = itertools.chain([bg_color], color_cycle)
+
+    return mapped_labels, color_cycle
+
+
+def label2rgb(
+    label,
+    image=None,
+    colors=None,
+    alpha=0.3,
+    bg_label=0,
+    bg_color=(0, 0, 0),
+    image_alpha=1,
+    kind='overlay',
+    *,
+    saturation=0,
+    channel_axis=-1,
+):
+    """Return an RGB image where color-coded labels are painted over the image.
+
+    Parameters
+    ----------
+    label : ndarray
+        Integer array of labels with the same shape as `image`.
+    image : ndarray, optional
+        Image used as underlay for labels. It should have the same shape as
+        `labels`, optionally with an additional RGB (channels) axis. If `image`
+        is an RGB image, it is converted to grayscale before coloring.
+    colors : list, optional
+        List of colors. If the number of labels exceeds the number of colors,
+        then the colors are cycled.
+    alpha : float [0, 1], optional
+        Opacity of colorized labels. Ignored if image is `None`.
+    bg_label : int, optional
+        Label that's treated as the background. If `bg_label` is specified,
+        `bg_color` is `None`, and `kind` is `overlay`,
+        background is not painted by any colors.
+    bg_color : str or array, optional
+        Background color. Must be a name in ``skimage.color.color_dict`` or RGB float
+        values between [0, 1].
+    image_alpha : float [0, 1], optional
+        Opacity of the image.
+    kind : string, one of {'overlay', 'avg'}
+        The kind of color image desired. 'overlay' cycles over defined colors
+        and overlays the colored labels over the original image. 'avg' replaces
+        each labeled segment with its average color, for a stained-class or
+        pastel painting appearance.
+    saturation : float [0, 1], optional
+        Parameter to control the saturation applied to the original image
+        between fully saturated (original RGB, `saturation=1`) and fully
+        unsaturated (grayscale, `saturation=0`). Only applies when
+        `kind='overlay'`.
+    channel_axis : int, optional
+        This parameter indicates which axis of the output array will correspond
+        to channels. If `image` is provided, this must also match the axis of
+        `image` that corresponds to channels.
+
+        .. versionadded:: 0.19
+            ``channel_axis`` was added in 0.19.
+
+    Returns
+    -------
+    result : ndarray of float, same shape as `image`
+        The result of blending a cycling colormap (`colors`) for each distinct
+        value in `label` with the image, at a certain alpha value.
+    """
+    if image is not None:
+        image = np.moveaxis(image, source=channel_axis, destination=-1)
+    if kind == 'overlay':
+        rgb = _label2rgb_overlay(
+            label, image, colors, alpha, bg_label, bg_color, image_alpha, saturation
+        )
+    elif kind == 'avg':
+        rgb = _label2rgb_avg(label, image, bg_label, bg_color)
+    else:
+        raise ValueError("`kind` must be either 'overlay' or 'avg'.")
+    return np.moveaxis(rgb, source=-1, destination=channel_axis)
+
+
+def _label2rgb_overlay(
+    label,
+    image=None,
+    colors=None,
+    alpha=0.3,
+    bg_label=-1,
+    bg_color=None,
+    image_alpha=1,
+    saturation=0,
+):
+    """Return an RGB image where color-coded labels are painted over the image.
+
+    Parameters
+    ----------
+    label : ndarray
+        Integer array of labels with the same shape as `image`.
+    image : ndarray, optional
+        Image used as underlay for labels. It should have the same shape as
+        `labels`, optionally with an additional RGB (channels) axis. If `image`
+        is an RGB image, it is converted to grayscale before coloring.
+    colors : list, optional
+        List of colors. If the number of labels exceeds the number of colors,
+        then the colors are cycled.
+    alpha : float [0, 1], optional
+        Opacity of colorized labels. Ignored if image is `None`.
+    bg_label : int, optional
+        Label that's treated as the background. If `bg_label` is specified and
+        `bg_color` is `None`, background is not painted by any colors.
+    bg_color : str or array, optional
+        Background color. Must be a name in ``skimage.color.color_dict`` or RGB float
+        values between [0, 1].
+    image_alpha : float [0, 1], optional
+        Opacity of the image.
+    saturation : float [0, 1], optional
+        Parameter to control the saturation applied to the original image
+        between fully saturated (original RGB, `saturation=1`) and fully
+        unsaturated (grayscale, `saturation=0`).
+
+    Returns
+    -------
+    result : ndarray of float, same shape as `image`
+        The result of blending a cycling colormap (`colors`) for each distinct
+        value in `label` with the image, at a certain alpha value.
+    """
+    if not 0 <= saturation <= 1:
+        warn(f'saturation must be in range [0, 1], got {saturation}')
+
+    if colors is None:
+        colors = DEFAULT_COLORS
+    colors = [_rgb_vector(c) for c in colors]
+
+    if image is None:
+        image = np.zeros(label.shape + (3,), dtype=np.float64)
+        # Opacity doesn't make sense if no image exists.
+        alpha = 1
+    else:
+        if image.shape[: label.ndim] != label.shape or image.ndim > label.ndim + 1:
+            raise ValueError("`image` and `label` must be the same shape")
+
+        if image.ndim == label.ndim + 1 and image.shape[-1] != 3:
+            raise ValueError("`image` must be RGB (image.shape[-1] must be 3).")
+
+        if image.min() < 0:
+            warn("Negative intensities in `image` are not supported")
+
+        float_dtype = _supported_float_type(image.dtype)
+        image = img_as_float(image).astype(float_dtype, copy=False)
+        if image.ndim > label.ndim:
+            hsv = rgb2hsv(image)
+            hsv[..., 1] *= saturation
+            image = hsv2rgb(hsv)
+        elif image.ndim == label.ndim:
+            image = gray2rgb(image)
+        image = image * image_alpha + (1 - image_alpha)
+
+    # Ensure that all labels are non-negative so we can index into
+    # `label_to_color` correctly.
+    offset = min(label.min(), bg_label)
+    if offset != 0:
+        label = label - offset  # Make sure you don't modify the input array.
+        bg_label -= offset
+
+    new_type = np.min_scalar_type(int(label.max()))
+    if new_type == bool:
+        new_type = np.uint8
+    label = label.astype(new_type)
+
+    mapped_labels_flat, color_cycle = _match_label_with_color(
+        label, colors, bg_label, bg_color
+    )
+
+    if len(mapped_labels_flat) == 0:
+        return image
+
+    dense_labels = range(np.max(mapped_labels_flat) + 1)
+
+    label_to_color = np.stack([c for i, c in zip(dense_labels, color_cycle)])
+
+    mapped_labels = label
+    mapped_labels.flat = mapped_labels_flat
+    result = label_to_color[mapped_labels] * alpha + image * (1 - alpha)
+
+    # Remove background label if its color was not specified.
+    remove_background = 0 in mapped_labels_flat and bg_color is None
+    if remove_background:
+        result[label == bg_label] = image[label == bg_label]
+
+    return result
+
+
+def _label2rgb_avg(label_field, image, bg_label=0, bg_color=(0, 0, 0)):
+    """Visualise each segment in `label_field` with its mean color in `image`.
+
+    Parameters
+    ----------
+    label_field : ndarray of int
+        A segmentation of an image.
+    image : array, shape ``label_field.shape + (3,)``
+        A color image of the same spatial shape as `label_field`.
+    bg_label : int, optional
+        A value in `label_field` to be treated as background.
+    bg_color : 3-tuple of int, optional
+        The color for the background label
+
+    Returns
+    -------
+    out : ndarray, same shape and type as `image`
+        The output visualization.
+    """
+    out = np.zeros(label_field.shape + (3,), dtype=image.dtype)
+    labels = np.unique(label_field)
+    bg = labels == bg_label
+    if bg.any():
+        labels = labels[labels != bg_label]
+        mask = (label_field == bg_label).nonzero()
+        out[mask] = bg_color
+    for label in labels:
+        mask = (label_field == label).nonzero()
+        color = image[mask].mean(axis=0)
+        out[mask] = color
+    return out

.CondaPkg/env/Lib/site-packages/skimage/color/delta_e.py

@@ -0,0 +1,393 @@
+"""
+Functions for calculating the "distance" between colors.
+
+Implicit in these definitions of "distance" is the notion of "Just Noticeable
+Distance" (JND).  This represents the distance between colors where a human can
+perceive different colors.  Humans are more sensitive to certain colors than
+others, which different deltaE metrics correct for with varying degrees of
+sophistication.
+
+The literature often mentions 1 as the minimum distance for visual
+differentiation, but more recent studies (Mahy 1994) peg JND at 2.3
+
+The delta-E notation comes from the German word for "Sensation" (Empfindung).
+
+Reference
+---------
+https://en.wikipedia.org/wiki/Color_difference
+
+"""
+
+import numpy as np
+
+from .._shared.utils import _supported_float_type
+from .colorconv import lab2lch, _cart2polar_2pi
+
+
+def _float_inputs(lab1, lab2, allow_float32=True):
+    lab1 = np.asarray(lab1)
+    lab2 = np.asarray(lab2)
+    if allow_float32:
+        float_dtype = _supported_float_type((lab1.dtype, lab2.dtype))
+    else:
+        float_dtype = np.float64
+    lab1 = lab1.astype(float_dtype, copy=False)
+    lab2 = lab2.astype(float_dtype, copy=False)
+    return lab1, lab2
+
+
+def deltaE_cie76(lab1, lab2, channel_axis=-1):
+    """Euclidean distance between two points in Lab color space
+
+    Parameters
+    ----------
+    lab1 : array_like
+        reference color (Lab colorspace)
+    lab2 : array_like
+        comparison color (Lab colorspace)
+    channel_axis : int, optional
+        This parameter indicates which axis of the arrays corresponds to
+        channels.
+
+        .. versionadded:: 0.19
+           ``channel_axis`` was added in 0.19.
+
+    Returns
+    -------
+    dE : array_like
+        distance between colors `lab1` and `lab2`
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Color_difference
+    .. [2] A. R. Robertson, "The CIE 1976 color-difference formulae,"
+           Color Res. Appl. 2, 7-11 (1977).
+    """
+    lab1, lab2 = _float_inputs(lab1, lab2, allow_float32=True)
+    L1, a1, b1 = np.moveaxis(lab1, source=channel_axis, destination=0)[:3]
+    L2, a2, b2 = np.moveaxis(lab2, source=channel_axis, destination=0)[:3]
+    return np.sqrt((L2 - L1) ** 2 + (a2 - a1) ** 2 + (b2 - b1) ** 2)
+
+
+def deltaE_ciede94(
+    lab1, lab2, kH=1, kC=1, kL=1, k1=0.045, k2=0.015, *, channel_axis=-1
+):
+    """Color difference according to CIEDE 94 standard
+
+    Accommodates perceptual non-uniformities through the use of application
+    specific scale factors (`kH`, `kC`, `kL`, `k1`, and `k2`).
+
+    Parameters
+    ----------
+    lab1 : array_like
+        reference color (Lab colorspace)
+    lab2 : array_like
+        comparison color (Lab colorspace)
+    kH : float, optional
+        Hue scale
+    kC : float, optional
+        Chroma scale
+    kL : float, optional
+        Lightness scale
+    k1 : float, optional
+        first scale parameter
+    k2 : float, optional
+        second scale parameter
+    channel_axis : int, optional
+        This parameter indicates which axis of the arrays corresponds to
+        channels.
+
+        .. versionadded:: 0.19
+           ``channel_axis`` was added in 0.19.
+
+    Returns
+    -------
+    dE : array_like
+        color difference between `lab1` and `lab2`
+
+    Notes
+    -----
+    deltaE_ciede94 is not symmetric with respect to lab1 and lab2.  CIEDE94
+    defines the scales for the lightness, hue, and chroma in terms of the first
+    color.  Consequently, the first color should be regarded as the "reference"
+    color.
+
+    `kL`, `k1`, `k2` depend on the application and default to the values
+    suggested for graphic arts
+
+    ==========  ==============  ==========
+    Parameter    Graphic Arts    Textiles
+    ==========  ==============  ==========
+    `kL`         1.000           2.000
+    `k1`         0.045           0.048
+    `k2`         0.015           0.014
+    ==========  ==============  ==========
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Color_difference
+    .. [2] http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html
+    """
+    lab1, lab2 = _float_inputs(lab1, lab2, allow_float32=True)
+    lab1 = np.moveaxis(lab1, source=channel_axis, destination=0)
+    lab2 = np.moveaxis(lab2, source=channel_axis, destination=0)
+
+    L1, C1 = lab2lch(lab1, channel_axis=0)[:2]
+    L2, C2 = lab2lch(lab2, channel_axis=0)[:2]
+
+    dL = L1 - L2
+    dC = C1 - C2
+    dH2 = get_dH2(lab1, lab2, channel_axis=0)
+
+    SL = 1
+    SC = 1 + k1 * C1
+    SH = 1 + k2 * C1
+
+    dE2 = (dL / (kL * SL)) ** 2
+    dE2 += (dC / (kC * SC)) ** 2
+    dE2 += dH2 / (kH * SH) ** 2
+    return np.sqrt(np.maximum(dE2, 0))
+
+
+def deltaE_ciede2000(lab1, lab2, kL=1, kC=1, kH=1, *, channel_axis=-1):
+    """Color difference as given by the CIEDE 2000 standard.
+
+    CIEDE 2000 is a major revision of CIDE94.  The perceptual calibration is
+    largely based on experience with automotive paint on smooth surfaces.
+
+    Parameters
+    ----------
+    lab1 : array_like
+        reference color (Lab colorspace)
+    lab2 : array_like
+        comparison color (Lab colorspace)
+    kL : float (range), optional
+        lightness scale factor, 1 for "acceptably close"; 2 for "imperceptible"
+        see deltaE_cmc
+    kC : float (range), optional
+        chroma scale factor, usually 1
+    kH : float (range), optional
+        hue scale factor, usually 1
+    channel_axis : int, optional
+        This parameter indicates which axis of the arrays corresponds to
+        channels.
+
+        .. versionadded:: 0.19
+           ``channel_axis`` was added in 0.19.
+
+    Returns
+    -------
+    deltaE : array_like
+        The distance between `lab1` and `lab2`
+
+    Notes
+    -----
+    CIEDE 2000 assumes parametric weighting factors for the lightness, chroma,
+    and hue (`kL`, `kC`, `kH` respectively).  These default to 1.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Color_difference
+    .. [2] http://www.ece.rochester.edu/~gsharma/ciede2000/ciede2000noteCRNA.pdf
+           :DOI:`10.1364/AO.33.008069`
+    .. [3] M. Melgosa, J. Quesada, and E. Hita, "Uniformity of some recent
+           color metrics tested with an accurate color-difference tolerance
+           dataset," Appl. Opt. 33, 8069-8077 (1994).
+    """
+    lab1, lab2 = _float_inputs(lab1, lab2, allow_float32=True)
+
+    channel_axis = channel_axis % lab1.ndim
+    unroll = False
+    if lab1.ndim == 1 and lab2.ndim == 1:
+        unroll = True
+        if lab1.ndim == 1:
+            lab1 = lab1[None, :]
+        if lab2.ndim == 1:
+            lab2 = lab2[None, :]
+        channel_axis += 1
+    L1, a1, b1 = np.moveaxis(lab1, source=channel_axis, destination=0)[:3]
+    L2, a2, b2 = np.moveaxis(lab2, source=channel_axis, destination=0)[:3]
+
+    # distort `a` based on average chroma
+    # then convert to lch coordinates from distorted `a`
+    # all subsequence calculations are in the new coordinates
+    # (often denoted "prime" in the literature)
+    Cbar = 0.5 * (np.hypot(a1, b1) + np.hypot(a2, b2))
+    c7 = Cbar**7
+    G = 0.5 * (1 - np.sqrt(c7 / (c7 + 25**7)))
+    scale = 1 + G
+    C1, h1 = _cart2polar_2pi(a1 * scale, b1)
+    C2, h2 = _cart2polar_2pi(a2 * scale, b2)
+    # recall that c, h are polar coordinates.  c==r, h==theta
+
+    # cide2000 has four terms to delta_e:
+    # 1) Luminance term
+    # 2) Hue term
+    # 3) Chroma term
+    # 4) hue Rotation term
+
+    # lightness term
+    Lbar = 0.5 * (L1 + L2)
+    tmp = (Lbar - 50) ** 2
+    SL = 1 + 0.015 * tmp / np.sqrt(20 + tmp)
+    L_term = (L2 - L1) / (kL * SL)
+
+    # chroma term
+    Cbar = 0.5 * (C1 + C2)  # new coordinates
+    SC = 1 + 0.045 * Cbar
+    C_term = (C2 - C1) / (kC * SC)
+
+    # hue term
+    h_diff = h2 - h1
+    h_sum = h1 + h2
+    CC = C1 * C2
+
+    dH = h_diff.copy()
+    dH[h_diff > np.pi] -= 2 * np.pi
+    dH[h_diff < -np.pi] += 2 * np.pi
+    dH[CC == 0.0] = 0.0  # if r == 0, dtheta == 0
+    dH_term = 2 * np.sqrt(CC) * np.sin(dH / 2)
+
+    Hbar = h_sum.copy()
+    mask = np.logical_and(CC != 0.0, np.abs(h_diff) > np.pi)
+    Hbar[mask * (h_sum < 2 * np.pi)] += 2 * np.pi
+    Hbar[mask * (h_sum >= 2 * np.pi)] -= 2 * np.pi
+    Hbar[CC == 0.0] *= 2
+    Hbar *= 0.5
+
+    T = (
+        1
+        - 0.17 * np.cos(Hbar - np.deg2rad(30))
+        + 0.24 * np.cos(2 * Hbar)
+        + 0.32 * np.cos(3 * Hbar + np.deg2rad(6))
+        - 0.20 * np.cos(4 * Hbar - np.deg2rad(63))
+    )
+    SH = 1 + 0.015 * Cbar * T
+
+    H_term = dH_term / (kH * SH)
+
+    # hue rotation
+    c7 = Cbar**7
+    Rc = 2 * np.sqrt(c7 / (c7 + 25**7))
+    dtheta = np.deg2rad(30) * np.exp(-(((np.rad2deg(Hbar) - 275) / 25) ** 2))
+    R_term = -np.sin(2 * dtheta) * Rc * C_term * H_term
+
+    # put it all together
+    dE2 = L_term**2
+    dE2 += C_term**2
+    dE2 += H_term**2
+    dE2 += R_term
+    ans = np.sqrt(np.maximum(dE2, 0))
+    if unroll:
+        ans = ans[0]
+    return ans
+
+
+def deltaE_cmc(lab1, lab2, kL=1, kC=1, *, channel_axis=-1):
+    """Color difference from the  CMC l:c standard.
+
+    This color difference was developed by the Colour Measurement Committee
+    (CMC) of the Society of Dyers and Colourists (United Kingdom). It is
+    intended for use in the textile industry.
+
+    The scale factors `kL`, `kC` set the weight given to differences in
+    lightness and chroma relative to differences in hue.  The usual values are
+    ``kL=2``, ``kC=1`` for "acceptability" and ``kL=1``, ``kC=1`` for
+    "imperceptibility".  Colors with ``dE > 1`` are "different" for the given
+    scale factors.
+
+    Parameters
+    ----------
+    lab1 : array_like
+        reference color (Lab colorspace)
+    lab2 : array_like
+        comparison color (Lab colorspace)
+    channel_axis : int, optional
+        This parameter indicates which axis of the arrays corresponds to
+        channels.
+
+        .. versionadded:: 0.19
+           ``channel_axis`` was added in 0.19.
+
+    Returns
+    -------
+    dE : array_like
+        distance between colors `lab1` and `lab2`
+
+    Notes
+    -----
+    deltaE_cmc the defines the scales for the lightness, hue, and chroma
+    in terms of the first color.  Consequently
+    ``deltaE_cmc(lab1, lab2) != deltaE_cmc(lab2, lab1)``
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Color_difference
+    .. [2] http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html
+    .. [3] F. J. J. Clarke, R. McDonald, and B. Rigg, "Modification to the
+           JPC79 colour-difference formula," J. Soc. Dyers Colour. 100, 128-132
+           (1984).
+    """
+    lab1, lab2 = _float_inputs(lab1, lab2, allow_float32=True)
+    lab1 = np.moveaxis(lab1, source=channel_axis, destination=0)
+    lab2 = np.moveaxis(lab2, source=channel_axis, destination=0)
+    L1, C1, h1 = lab2lch(lab1, channel_axis=0)[:3]
+    L2, C2, h2 = lab2lch(lab2, channel_axis=0)[:3]
+
+    dC = C1 - C2
+    dL = L1 - L2
+    dH2 = get_dH2(lab1, lab2, channel_axis=0)
+
+    T = np.where(
+        np.logical_and(np.rad2deg(h1) >= 164, np.rad2deg(h1) <= 345),
+        0.56 + 0.2 * np.abs(np.cos(h1 + np.deg2rad(168))),
+        0.36 + 0.4 * np.abs(np.cos(h1 + np.deg2rad(35))),
+    )
+    c1_4 = C1**4
+    F = np.sqrt(c1_4 / (c1_4 + 1900))
+
+    SL = np.where(L1 < 16, 0.511, 0.040975 * L1 / (1.0 + 0.01765 * L1))
+    SC = 0.638 + 0.0638 * C1 / (1.0 + 0.0131 * C1)
+    SH = SC * (F * T + 1 - F)
+
+    dE2 = (dL / (kL * SL)) ** 2
+    dE2 += (dC / (kC * SC)) ** 2
+    dE2 += dH2 / (SH**2)
+
+    return np.sqrt(np.maximum(dE2, 0))
+
+
+def get_dH2(lab1, lab2, *, channel_axis=-1):
+    """squared hue difference term occurring in deltaE_cmc and deltaE_ciede94
+
+    Despite its name, "dH" is not a simple difference of hue values.  We avoid
+    working directly with the hue value, since differencing angles is
+    troublesome.  The hue term is usually written as:
+        c1 = sqrt(a1**2 + b1**2)
+        c2 = sqrt(a2**2 + b2**2)
+        term = (a1-a2)**2 + (b1-b2)**2 - (c1-c2)**2
+        dH = sqrt(term)
+
+    However, this has poor roundoff properties when a or b is dominant.
+    Instead, ab is a vector with elements a and b.  The same dH term can be
+    re-written as:
+        |ab1-ab2|**2 - (|ab1| - |ab2|)**2
+    and then simplified to:
+        2*|ab1|*|ab2| - 2*dot(ab1, ab2)
+    """
+    # This function needs double precision internally for accuracy
+    input_is_float_32 = _supported_float_type((lab1.dtype, lab2.dtype)) == np.float32
+    lab1, lab2 = _float_inputs(lab1, lab2, allow_float32=False)
+
+    a1, b1 = np.moveaxis(lab1, source=channel_axis, destination=0)[1:3]
+    a2, b2 = np.moveaxis(lab2, source=channel_axis, destination=0)[1:3]
+
+    # magnitude of (a, b) is the chroma
+    C1 = np.hypot(a1, b1)
+    C2 = np.hypot(a2, b2)
+
+    term = (C1 * C2) - (a1 * a2 + b1 * b2)
+    out = 2 * term
+    if input_is_float_32:
+        out = out.astype(np.float32)
+    return out

.CondaPkg/env/Lib/site-packages/skimage/color/rgb_colors.py

@@ -0,0 +1,146 @@
+aliceblue = (0.941, 0.973, 1)
+antiquewhite = (0.98, 0.922, 0.843)
+aqua = (0, 1, 1)
+aquamarine = (0.498, 1, 0.831)
+azure = (0.941, 1, 1)
+beige = (0.961, 0.961, 0.863)
+bisque = (1, 0.894, 0.769)
+black = (0, 0, 0)
+blanchedalmond = (1, 0.922, 0.804)
+blue = (0, 0, 1)
+blueviolet = (0.541, 0.169, 0.886)
+brown = (0.647, 0.165, 0.165)
+burlywood = (0.871, 0.722, 0.529)
+cadetblue = (0.373, 0.62, 0.627)
+chartreuse = (0.498, 1, 0)
+chocolate = (0.824, 0.412, 0.118)
+coral = (1, 0.498, 0.314)
+cornflowerblue = (0.392, 0.584, 0.929)
+cornsilk = (1, 0.973, 0.863)
+crimson = (0.863, 0.0784, 0.235)
+cyan = (0, 1, 1)
+darkblue = (0, 0, 0.545)
+darkcyan = (0, 0.545, 0.545)
+darkgoldenrod = (0.722, 0.525, 0.0431)
+darkgray = (0.663, 0.663, 0.663)
+darkgreen = (0, 0.392, 0)
+darkgrey = (0.663, 0.663, 0.663)
+darkkhaki = (0.741, 0.718, 0.42)
+darkmagenta = (0.545, 0, 0.545)
+darkolivegreen = (0.333, 0.42, 0.184)
+darkorange = (1, 0.549, 0)
+darkorchid = (0.6, 0.196, 0.8)
+darkred = (0.545, 0, 0)
+darksalmon = (0.914, 0.588, 0.478)
+darkseagreen = (0.561, 0.737, 0.561)
+darkslateblue = (0.282, 0.239, 0.545)
+darkslategray = (0.184, 0.31, 0.31)
+darkslategrey = (0.184, 0.31, 0.31)
+darkturquoise = (0, 0.808, 0.82)
+darkviolet = (0.58, 0, 0.827)
+deeppink = (1, 0.0784, 0.576)
+deepskyblue = (0, 0.749, 1)
+dimgray = (0.412, 0.412, 0.412)
+dimgrey = (0.412, 0.412, 0.412)
+dodgerblue = (0.118, 0.565, 1)
+firebrick = (0.698, 0.133, 0.133)
+floralwhite = (1, 0.98, 0.941)
+forestgreen = (0.133, 0.545, 0.133)
+fuchsia = (1, 0, 1)
+gainsboro = (0.863, 0.863, 0.863)
+ghostwhite = (0.973, 0.973, 1)
+gold = (1, 0.843, 0)
+goldenrod = (0.855, 0.647, 0.125)
+gray = (0.502, 0.502, 0.502)
+green = (0, 0.502, 0)
+greenyellow = (0.678, 1, 0.184)
+grey = (0.502, 0.502, 0.502)
+honeydew = (0.941, 1, 0.941)
+hotpink = (1, 0.412, 0.706)
+indianred = (0.804, 0.361, 0.361)
+indigo = (0.294, 0, 0.51)
+ivory = (1, 1, 0.941)
+khaki = (0.941, 0.902, 0.549)
+lavender = (0.902, 0.902, 0.98)
+lavenderblush = (1, 0.941, 0.961)
+lawngreen = (0.486, 0.988, 0)
+lemonchiffon = (1, 0.98, 0.804)
+lightblue = (0.678, 0.847, 0.902)
+lightcoral = (0.941, 0.502, 0.502)
+lightcyan = (0.878, 1, 1)
+lightgoldenrodyellow = (0.98, 0.98, 0.824)
+lightgray = (0.827, 0.827, 0.827)
+lightgreen = (0.565, 0.933, 0.565)
+lightgrey = (0.827, 0.827, 0.827)
+lightpink = (1, 0.714, 0.757)
+lightsalmon = (1, 0.627, 0.478)
+lightseagreen = (0.125, 0.698, 0.667)
+lightskyblue = (0.529, 0.808, 0.98)
+lightslategray = (0.467, 0.533, 0.6)
+lightslategrey = (0.467, 0.533, 0.6)
+lightsteelblue = (0.69, 0.769, 0.871)
+lightyellow = (1, 1, 0.878)
+lime = (0, 1, 0)
+limegreen = (0.196, 0.804, 0.196)
+linen = (0.98, 0.941, 0.902)
+magenta = (1, 0, 1)
+maroon = (0.502, 0, 0)
+mediumaquamarine = (0.4, 0.804, 0.667)
+mediumblue = (0, 0, 0.804)
+mediumorchid = (0.729, 0.333, 0.827)
+mediumpurple = (0.576, 0.439, 0.859)
+mediumseagreen = (0.235, 0.702, 0.443)
+mediumslateblue = (0.482, 0.408, 0.933)
+mediumspringgreen = (0, 0.98, 0.604)
+mediumturquoise = (0.282, 0.82, 0.8)
+mediumvioletred = (0.78, 0.0824, 0.522)
+midnightblue = (0.098, 0.098, 0.439)
+mintcream = (0.961, 1, 0.98)
+mistyrose = (1, 0.894, 0.882)
+moccasin = (1, 0.894, 0.71)
+navajowhite = (1, 0.871, 0.678)
+navy = (0, 0, 0.502)
+oldlace = (0.992, 0.961, 0.902)
+olive = (0.502, 0.502, 0)
+olivedrab = (0.42, 0.557, 0.137)
+orange = (1, 0.647, 0)
+orangered = (1, 0.271, 0)
+orchid = (0.855, 0.439, 0.839)
+palegoldenrod = (0.933, 0.91, 0.667)
+palegreen = (0.596, 0.984, 0.596)
+palevioletred = (0.686, 0.933, 0.933)
+papayawhip = (1, 0.937, 0.835)
+peachpuff = (1, 0.855, 0.725)
+peru = (0.804, 0.522, 0.247)
+pink = (1, 0.753, 0.796)
+plum = (0.867, 0.627, 0.867)
+powderblue = (0.69, 0.878, 0.902)
+purple = (0.502, 0, 0.502)
+red = (1, 0, 0)
+rosybrown = (0.737, 0.561, 0.561)
+royalblue = (0.255, 0.412, 0.882)
+saddlebrown = (0.545, 0.271, 0.0745)
+salmon = (0.98, 0.502, 0.447)
+sandybrown = (0.98, 0.643, 0.376)
+seagreen = (0.18, 0.545, 0.341)
+seashell = (1, 0.961, 0.933)
+sienna = (0.627, 0.322, 0.176)
+silver = (0.753, 0.753, 0.753)
+skyblue = (0.529, 0.808, 0.922)
+slateblue = (0.416, 0.353, 0.804)
+slategray = (0.439, 0.502, 0.565)
+slategrey = (0.439, 0.502, 0.565)
+snow = (1, 0.98, 0.98)
+springgreen = (0, 1, 0.498)
+steelblue = (0.275, 0.51, 0.706)
+tan = (0.824, 0.706, 0.549)
+teal = (0, 0.502, 0.502)
+thistle = (0.847, 0.749, 0.847)
+tomato = (1, 0.388, 0.278)
+turquoise = (0.251, 0.878, 0.816)
+violet = (0.933, 0.51, 0.933)
+wheat = (0.961, 0.871, 0.702)
+white = (1, 1, 1)
+whitesmoke = (0.961, 0.961, 0.961)
+yellow = (1, 1, 0)
+yellowgreen = (0.604, 0.804, 0.196)

.CondaPkg/env/Lib/site-packages/skimage/color/tests/test_adapt_rgb.py

@@ -0,0 +1,94 @@
+from functools import partial
+
+import numpy as np
+
+from skimage import img_as_float, img_as_uint
+from skimage import color, data, filters
+from skimage.color.adapt_rgb import adapt_rgb, each_channel, hsv_value
+
+# Down-sample image for quicker testing.
+COLOR_IMAGE = data.astronaut()[::5, ::6]
+GRAY_IMAGE = data.camera()[::5, ::5]
+
+SIGMA = 3
+smooth = partial(filters.gaussian, sigma=SIGMA)
+assert_allclose = partial(np.testing.assert_allclose, atol=1e-8)
+
+
+@adapt_rgb(each_channel)
+def edges_each(image):
+    return filters.sobel(image)
+
+
+@adapt_rgb(each_channel)
+def smooth_each(image, sigma):
+    return filters.gaussian(image, sigma=sigma)
+
+
+@adapt_rgb(each_channel)
+def mask_each(image, mask):
+    result = image.copy()
+    result[mask] = 0
+    return result
+
+
+@adapt_rgb(hsv_value)
+def edges_hsv(image):
+    return filters.sobel(image)
+
+
+@adapt_rgb(hsv_value)
+def smooth_hsv(image, sigma):
+    return filters.gaussian(image, sigma=sigma)
+
+
+@adapt_rgb(hsv_value)
+def edges_hsv_uint(image):
+    return img_as_uint(filters.sobel(image))
+
+
+def test_gray_scale_image():
+    # We don't need to test both `hsv_value` and `each_channel` since
+    # `adapt_rgb` is handling gray-scale inputs.
+    assert_allclose(edges_each(GRAY_IMAGE), filters.sobel(GRAY_IMAGE))
+
+
+def test_each_channel():
+    filtered = edges_each(COLOR_IMAGE)
+    for i, channel in enumerate(np.rollaxis(filtered, axis=-1)):
+        expected = img_as_float(filters.sobel(COLOR_IMAGE[:, :, i]))
+        assert_allclose(channel, expected)
+
+
+def test_each_channel_with_filter_argument():
+    filtered = smooth_each(COLOR_IMAGE, SIGMA)
+    for i, channel in enumerate(np.rollaxis(filtered, axis=-1)):
+        assert_allclose(channel, smooth(COLOR_IMAGE[:, :, i]))
+
+
+def test_each_channel_with_asymmetric_kernel():
+    mask = np.triu(np.ones(COLOR_IMAGE.shape[:2], dtype=bool))
+    mask_each(COLOR_IMAGE, mask)
+
+
+def test_hsv_value():
+    filtered = edges_hsv(COLOR_IMAGE)
+    value = color.rgb2hsv(COLOR_IMAGE)[:, :, 2]
+    assert_allclose(color.rgb2hsv(filtered)[:, :, 2], filters.sobel(value))
+
+
+def test_hsv_value_with_filter_argument():
+    filtered = smooth_hsv(COLOR_IMAGE, SIGMA)
+    value = color.rgb2hsv(COLOR_IMAGE)[:, :, 2]
+    assert_allclose(color.rgb2hsv(filtered)[:, :, 2], smooth(value))
+
+
+def test_hsv_value_with_non_float_output():
+    # Since `rgb2hsv` returns a float image and the result of the filtered
+    # result is inserted into the HSV image, we want to make sure there isn't
+    # a dtype mismatch.
+    filtered = edges_hsv_uint(COLOR_IMAGE)
+    filtered_value = color.rgb2hsv(filtered)[:, :, 2]
+    value = color.rgb2hsv(COLOR_IMAGE)[:, :, 2]
+    # Reduce tolerance because dtype conversion.
+    assert_allclose(filtered_value, filters.sobel(value), rtol=1e-5, atol=1e-5)

.CondaPkg/env/Lib/site-packages/skimage/color/tests/test_colorlabel.py

@@ -0,0 +1,318 @@
+import itertools
+
+import numpy as np
+import pytest
+from numpy.testing import (
+    assert_array_almost_equal,
+    assert_array_equal,
+    assert_no_warnings,
+    assert_warns,
+)
+
+from skimage._shared.testing import expected_warnings
+from skimage.color.colorconv import hsv2rgb, rgb2hsv
+from skimage.color.colorlabel import label2rgb
+
+
+def test_shape_mismatch():
+    image = np.ones((3, 3))
+    label = np.ones((2, 2))
+    with pytest.raises(ValueError):
+        label2rgb(image, label, bg_label=-1)
+
+
+def test_wrong_kind():
+    label = np.ones((3, 3))
+    # Must not raise an error.
+    label2rgb(label, bg_label=-1)
+    # kind='foo' is wrong.
+    with pytest.raises(ValueError):
+        label2rgb(label, kind='foo', bg_label=-1)
+
+
+@pytest.mark.parametrize("channel_axis", [0, 1, -1])
+def test_uint_image(channel_axis):
+    img = np.random.randint(0, 255, (10, 10), dtype=np.uint8)
+    labels = np.zeros((10, 10), dtype=np.int64)
+    labels[1:3, 1:3] = 1
+    labels[6:9, 6:9] = 2
+    output = label2rgb(labels, image=img, bg_label=0, channel_axis=channel_axis)
+    # Make sure that the output is made of floats and in the correct range
+    assert np.issubdtype(output.dtype, np.floating)
+    assert output.max() <= 1
+
+    # size 3 (RGB) along the specified channel_axis
+    new_axis = channel_axis % output.ndim
+    assert output.shape[new_axis] == 3
+
+
+def test_rgb():
+    image = np.ones((1, 3))
+    label = np.arange(3).reshape(1, -1)
+    colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
+    # Set alphas just in case the defaults change
+    rgb = label2rgb(
+        label, image=image, colors=colors, alpha=1, image_alpha=1, bg_label=-1
+    )
+    assert_array_almost_equal(rgb, [colors])
+
+
+def test_alpha():
+    image = np.random.uniform(size=(3, 3))
+    label = np.random.randint(0, 9, size=(3, 3))
+    # If we set `alpha = 0`, then rgb should match image exactly.
+    rgb = label2rgb(label, image=image, alpha=0, image_alpha=1, bg_label=-1)
+    assert_array_almost_equal(rgb[..., 0], image)
+    assert_array_almost_equal(rgb[..., 1], image)
+    assert_array_almost_equal(rgb[..., 2], image)
+
+
+def test_no_input_image():
+    label = np.arange(3).reshape(1, -1)
+    colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
+    rgb = label2rgb(label, colors=colors, bg_label=-1)
+    assert_array_almost_equal(rgb, [colors])
+
+
+def test_image_alpha():
+    image = np.random.uniform(size=(1, 3))
+    label = np.arange(3).reshape(1, -1)
+    colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
+    # If we set `image_alpha = 0`, then rgb should match label colors exactly.
+    rgb = label2rgb(
+        label, image=image, colors=colors, alpha=1, image_alpha=0, bg_label=-1
+    )
+    assert_array_almost_equal(rgb, [colors])
+
+
+def test_color_names():
+    image = np.ones((1, 3))
+    label = np.arange(3).reshape(1, -1)
+    cnames = ['red', 'lime', 'blue']
+    colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
+    # Set alphas just in case the defaults change
+    rgb = label2rgb(
+        label, image=image, colors=cnames, alpha=1, image_alpha=1, bg_label=-1
+    )
+    assert_array_almost_equal(rgb, [colors])
+
+
+def test_bg_and_color_cycle():
+    image = np.zeros((1, 10))  # dummy image
+    label = np.arange(10).reshape(1, -1)
+    colors = [(1, 0, 0), (0, 0, 1)]
+    bg_color = (0, 0, 0)
+    rgb = label2rgb(
+        label, image=image, bg_label=0, bg_color=bg_color, colors=colors, alpha=1
+    )
+    assert_array_almost_equal(rgb[0, 0], bg_color)
+    for pixel, color in zip(rgb[0, 1:], itertools.cycle(colors)):
+        assert_array_almost_equal(pixel, color)
+
+
+def test_negative_labels():
+    labels = np.array([0, -1, -2, 0])
+    rout = np.array(
+        [(0.0, 0.0, 0.0), (0.0, 0.0, 1.0), (1.0, 0.0, 0.0), (0.0, 0.0, 0.0)]
+    )
+    assert_array_almost_equal(
+        rout, label2rgb(labels, bg_label=0, alpha=1, image_alpha=1)
+    )
+
+
+def test_nonconsecutive():
+    labels = np.array([0, 2, 4, 0])
+    colors = [(1, 0, 0), (0, 0, 1)]
+    rout = np.array(
+        [(1.0, 0.0, 0.0), (0.0, 0.0, 1.0), (1.0, 0.0, 0.0), (1.0, 0.0, 0.0)]
+    )
+    assert_array_almost_equal(
+        rout, label2rgb(labels, colors=colors, alpha=1, image_alpha=1, bg_label=-1)
+    )
+
+
+def test_label_consistency():
+    """Assert that the same labels map to the same colors."""
+    label_1 = np.arange(5).reshape(1, -1)
+    label_2 = np.array([0, 1])
+    colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1), (1, 1, 0), (1, 0, 1)]
+    # Set alphas just in case the defaults change
+    rgb_1 = label2rgb(label_1, colors=colors, bg_label=-1)
+    rgb_2 = label2rgb(label_2, colors=colors, bg_label=-1)
+    for label_id in label_2.flat:
+        assert_array_almost_equal(
+            rgb_1[label_1 == label_id], rgb_2[label_2 == label_id]
+        )
+
+
+def test_leave_labels_alone():
+    labels = np.array([-1, 0, 1])
+    labels_saved = labels.copy()
+
+    label2rgb(labels, bg_label=-1)
+    label2rgb(labels, bg_label=1)
+    assert_array_equal(labels, labels_saved)
+
+
+@pytest.mark.parametrize("channel_axis", [0, 1, -1])
+def test_avg(channel_axis):
+    # label image
+    label_field = np.array([[1, 1, 1, 2], [1, 2, 2, 2], [3, 3, 4, 4]], dtype=np.uint8)
+
+    # color image
+    r = np.array([[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0]])
+    g = np.array([[0.0, 0.0, 0.0, 1.0], [1.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]])
+    b = np.array([[0.0, 0.0, 0.0, 1.0], [0.0, 1.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]])
+    image = np.dstack((r, g, b))
+
+    # reference label-colored image
+    rout = np.array([[0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5], [0.0, 0.0, 0.0, 0.0]])
+    gout = np.array(
+        [[0.25, 0.25, 0.25, 0.75], [0.25, 0.75, 0.75, 0.75], [0.0, 0.0, 0.0, 0.0]]
+    )
+    bout = np.array([[0.0, 0.0, 0.0, 1.0], [0.0, 1.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]])
+    expected_out = np.dstack((rout, gout, bout))
+
+    # test standard averaging
+    _image = np.moveaxis(image, source=-1, destination=channel_axis)
+    out = label2rgb(
+        label_field, _image, kind='avg', bg_label=-1, channel_axis=channel_axis
+    )
+    out = np.moveaxis(out, source=channel_axis, destination=-1)
+    assert_array_equal(out, expected_out)
+
+    # test averaging with custom background value
+    out_bg = label2rgb(
+        label_field,
+        _image,
+        bg_label=2,
+        bg_color=(0, 0, 0),
+        kind='avg',
+        channel_axis=channel_axis,
+    )
+    out_bg = np.moveaxis(out_bg, source=channel_axis, destination=-1)
+    expected_out_bg = expected_out.copy()
+    expected_out_bg[label_field == 2] = 0
+    assert_array_equal(out_bg, expected_out_bg)
+
+    # test default background color
+    out_bg = label2rgb(
+        label_field, _image, bg_label=2, kind='avg', channel_axis=channel_axis
+    )
+    out_bg = np.moveaxis(out_bg, source=channel_axis, destination=-1)
+    assert_array_equal(out_bg, expected_out_bg)
+
+
+def test_negative_intensity():
+    labels = np.arange(100).reshape(10, 10)
+    image = np.full((10, 10), -1, dtype='float64')
+    assert_warns(UserWarning, label2rgb, labels, image, bg_label=-1)
+
+
+def test_bg_color_rgb_string():
+    img = np.random.randint(0, 255, (10, 10), dtype=np.uint8)
+    labels = np.zeros((10, 10), dtype=np.int64)
+    labels[1:3, 1:3] = 1
+    labels[6:9, 6:9] = 2
+    output = label2rgb(labels, image=img, alpha=0.9, bg_label=0, bg_color='red')
+    assert output[0, 0, 0] > 0.9  # red channel
+
+
+def test_avg_with_2d_image():
+    img = np.random.randint(0, 255, (10, 10), dtype=np.uint8)
+    labels = np.zeros((10, 10), dtype=np.int64)
+    labels[1:3, 1:3] = 1
+    labels[6:9, 6:9] = 2
+    assert_no_warnings(label2rgb, labels, image=img, bg_label=0, kind='avg')
+
+
+@pytest.mark.parametrize('image_type', ['rgb', 'gray', None])
+def test_label2rgb_nd(image_type):
+    # validate 1D and 3D cases by testing their output relative to the 2D case
+    shape = (10, 10)
+    if image_type == 'rgb':
+        img = np.random.randint(0, 255, shape + (3,), dtype=np.uint8)
+    elif image_type == 'gray':
+        img = np.random.randint(0, 255, shape, dtype=np.uint8)
+    else:
+        img = None
+
+    # add a couple of rectangular labels
+    labels = np.zeros(shape, dtype=np.int64)
+    # Note: Have to choose labels here so that the 1D slice below also contains
+    #       both label values. Otherwise the labeled colors will not match.
+    labels[2:-2, 1:3] = 1
+    labels[3:-3, 6:9] = 2
+
+    # label in the 2D case (correct 2D output is tested in other functions)
+    labeled_2d = label2rgb(labels, image=img, bg_label=0)
+
+    # labeling a single line gives an equivalent result
+    image_1d = img[5] if image_type is not None else None
+    labeled_1d = label2rgb(labels[5], image=image_1d, bg_label=0)
+    expected = labeled_2d[5]
+    assert_array_equal(labeled_1d, expected)
+
+    # Labeling a 3D stack of duplicates gives the same result in each plane
+    image_3d = np.stack((img,) * 4) if image_type is not None else None
+    labels_3d = np.stack((labels,) * 4)
+    labeled_3d = label2rgb(labels_3d, image=image_3d, bg_label=0)
+    for labeled_plane in labeled_3d:
+        assert_array_equal(labeled_plane, labeled_2d)
+
+
+def test_label2rgb_shape_errors():
+    img = np.random.randint(0, 255, (10, 10, 3), dtype=np.uint8)
+    labels = np.zeros((10, 10), dtype=np.int64)
+    labels[2:5, 2:5] = 1
+
+    # mismatched 2D shape
+    with pytest.raises(ValueError):
+        label2rgb(labels, img[1:])
+
+    # too many axes in img
+    with pytest.raises(ValueError):
+        label2rgb(labels, img[..., np.newaxis])
+
+    # too many channels along the last axis
+    with pytest.raises(ValueError):
+        label2rgb(labels, np.concatenate((img, img), axis=-1))
+
+
+def test_overlay_full_saturation():
+    rgb_img = np.random.uniform(size=(10, 10, 3))
+    labels = np.ones((10, 10), dtype=np.int64)
+    labels[5:, 5:] = 2
+    labels[:3, :3] = 0
+    alpha = 0.3
+    rgb = label2rgb(labels, image=rgb_img, alpha=alpha, bg_label=0, saturation=1)
+    # check that rgb part of input image is preserved, where labels=0
+    assert_array_almost_equal(rgb_img[:3, :3] * (1 - alpha), rgb[:3, :3])
+
+
+def test_overlay_custom_saturation():
+    rgb_img = np.random.uniform(size=(10, 10, 3))
+    labels = np.ones((10, 10), dtype=np.int64)
+    labels[5:, 5:] = 2
+    labels[:3, :3] = 0
+    alpha = 0.3
+    saturation = 0.3
+    rgb = label2rgb(
+        labels, image=rgb_img, alpha=alpha, bg_label=0, saturation=saturation
+    )
+
+    hsv = rgb2hsv(rgb_img)
+    hsv[..., 1] *= saturation
+    saturaded_img = hsv2rgb(hsv)
+
+    # check that rgb part of input image is saturated, where labels=0
+    assert_array_almost_equal(saturaded_img[:3, :3] * (1 - alpha), rgb[:3, :3])
+
+
+def test_saturation_warning():
+    rgb_img = np.random.uniform(size=(10, 10, 3))
+    labels = np.ones((10, 10), dtype=np.int64)
+    with expected_warnings(["saturation must be in range"]):
+        label2rgb(labels, image=rgb_img, bg_label=0, saturation=2)
+    with expected_warnings(["saturation must be in range"]):
+        label2rgb(labels, image=rgb_img, bg_label=0, saturation=-1)

.CondaPkg/env/Lib/site-packages/skimage/color/tests/test_delta_e.py

@@ -0,0 +1,299 @@
+"""Test for correctness of color distance functions"""
+
+import numpy as np
+import pytest
+from numpy.testing import assert_allclose, assert_almost_equal, assert_equal
+
+from skimage._shared.testing import fetch
+from skimage._shared.utils import _supported_float_type
+from skimage.color.delta_e import (
+    deltaE_cie76,
+    deltaE_ciede94,
+    deltaE_ciede2000,
+    deltaE_cmc,
+)
+
+
+@pytest.mark.parametrize("channel_axis", [0, 1, -1])
+@pytest.mark.parametrize('dtype', [np.float32, np.float64])
+def test_ciede2000_dE(dtype, channel_axis):
+    data = load_ciede2000_data()
+    N = len(data)
+    lab1 = np.zeros((N, 3), dtype=dtype)
+    lab1[:, 0] = data['L1']
+    lab1[:, 1] = data['a1']
+    lab1[:, 2] = data['b1']
+
+    lab2 = np.zeros((N, 3), dtype=dtype)
+    lab2[:, 0] = data['L2']
+    lab2[:, 1] = data['a2']
+    lab2[:, 2] = data['b2']
+
+    lab1 = np.moveaxis(lab1, source=-1, destination=channel_axis)
+    lab2 = np.moveaxis(lab2, source=-1, destination=channel_axis)
+    dE2 = deltaE_ciede2000(lab1, lab2, channel_axis=channel_axis)
+    assert dE2.dtype == _supported_float_type(dtype)
+
+    assert_allclose(dE2, data['dE'], rtol=1e-2)
+
+
+def load_ciede2000_data():
+    dtype = [
+        ('pair', int),
+        ('1', int),
+        ('L1', float),
+        ('a1', float),
+        ('b1', float),
+        ('a1_prime', float),
+        ('C1_prime', float),
+        ('h1_prime', float),
+        ('hbar_prime', float),
+        ('G', float),
+        ('T', float),
+        ('SL', float),
+        ('SC', float),
+        ('SH', float),
+        ('RT', float),
+        ('dE', float),
+        ('2', int),
+        ('L2', float),
+        ('a2', float),
+        ('b2', float),
+        ('a2_prime', float),
+        ('C2_prime', float),
+        ('h2_prime', float),
+    ]
+
+    # note: ciede_test_data.txt contains several intermediate quantities
+    path = fetch('color/tests/ciede2000_test_data.txt')
+    return np.loadtxt(path, dtype=dtype)
+
+
+@pytest.mark.parametrize("channel_axis", [0, 1, -1])
+@pytest.mark.parametrize('dtype', [np.float32, np.float64])
+def test_cie76(dtype, channel_axis):
+    data = load_ciede2000_data()
+    N = len(data)
+    lab1 = np.zeros((N, 3), dtype=dtype)
+    lab1[:, 0] = data['L1']
+    lab1[:, 1] = data['a1']
+    lab1[:, 2] = data['b1']
+
+    lab2 = np.zeros((N, 3), dtype=dtype)
+    lab2[:, 0] = data['L2']
+    lab2[:, 1] = data['a2']
+    lab2[:, 2] = data['b2']
+
+    lab1 = np.moveaxis(lab1, source=-1, destination=channel_axis)
+    lab2 = np.moveaxis(lab2, source=-1, destination=channel_axis)
+    dE2 = deltaE_cie76(lab1, lab2, channel_axis=channel_axis)
+    assert dE2.dtype == _supported_float_type(dtype)
+    oracle = np.array(
+        [
+            4.00106328,
+            6.31415011,
+            9.1776999,
+            2.06270077,
+            2.36957073,
+            2.91529271,
+            2.23606798,
+            2.23606798,
+            4.98000036,
+            4.9800004,
+            4.98000044,
+            4.98000049,
+            4.98000036,
+            4.9800004,
+            4.98000044,
+            3.53553391,
+            36.86800781,
+            31.91002977,
+            30.25309901,
+            27.40894015,
+            0.89242934,
+            0.7972,
+            0.8583065,
+            0.82982507,
+            3.1819238,
+            2.21334297,
+            1.53890382,
+            4.60630929,
+            6.58467989,
+            3.88641412,
+            1.50514845,
+            2.3237848,
+            0.94413208,
+            1.31910843,
+        ]
+    )
+    rtol = 1e-5 if dtype == np.float32 else 1e-8
+    assert_allclose(dE2, oracle, rtol=rtol)
+
+
+@pytest.mark.parametrize("channel_axis", [0, 1, -1])
+@pytest.mark.parametrize('dtype', [np.float32, np.float64])
+def test_ciede94(dtype, channel_axis):
+    data = load_ciede2000_data()
+    N = len(data)
+    lab1 = np.zeros((N, 3), dtype=dtype)
+    lab1[:, 0] = data['L1']
+    lab1[:, 1] = data['a1']
+    lab1[:, 2] = data['b1']
+
+    lab2 = np.zeros((N, 3), dtype=dtype)
+    lab2[:, 0] = data['L2']
+    lab2[:, 1] = data['a2']
+    lab2[:, 2] = data['b2']
+
+    lab1 = np.moveaxis(lab1, source=-1, destination=channel_axis)
+    lab2 = np.moveaxis(lab2, source=-1, destination=channel_axis)
+    dE2 = deltaE_ciede94(lab1, lab2, channel_axis=channel_axis)
+    assert dE2.dtype == _supported_float_type(dtype)
+    oracle = np.array(
+        [
+            1.39503887,
+            1.93410055,
+            2.45433566,
+            0.68449187,
+            0.6695627,
+            0.69194527,
+            2.23606798,
+            2.03163832,
+            4.80069441,
+            4.80069445,
+            4.80069449,
+            4.80069453,
+            4.80069441,
+            4.80069445,
+            4.80069449,
+            3.40774352,
+            34.6891632,
+            29.44137328,
+            27.91408781,
+            24.93766082,
+            0.82213163,
+            0.71658427,
+            0.8048753,
+            0.75284394,
+            1.39099471,
+            1.24808929,
+            1.29795787,
+            1.82045088,
+            2.55613309,
+            1.42491303,
+            1.41945261,
+            2.3225685,
+            0.93853308,
+            1.30654464,
+        ]
+    )
+    rtol = 1e-5 if dtype == np.float32 else 1e-8
+    assert_allclose(dE2, oracle, rtol=rtol)
+
+
+@pytest.mark.parametrize("channel_axis", [0, 1, -1])
+@pytest.mark.parametrize('dtype', [np.float32, np.float64])
+def test_cmc(dtype, channel_axis):
+    data = load_ciede2000_data()
+    N = len(data)
+    lab1 = np.zeros((N, 3), dtype=dtype)
+    lab1[:, 0] = data['L1']
+    lab1[:, 1] = data['a1']
+    lab1[:, 2] = data['b1']
+
+    lab2 = np.zeros((N, 3), dtype=dtype)
+    lab2[:, 0] = data['L2']
+    lab2[:, 1] = data['a2']
+    lab2[:, 2] = data['b2']
+
+    lab1 = np.moveaxis(lab1, source=-1, destination=channel_axis)
+    lab2 = np.moveaxis(lab2, source=-1, destination=channel_axis)
+    dE2 = deltaE_cmc(lab1, lab2, channel_axis=channel_axis)
+    assert dE2.dtype == _supported_float_type(dtype)
+    oracle = np.array(
+        [
+            1.73873611,
+            2.49660844,
+            3.30494501,
+            0.85735576,
+            0.88332927,
+            0.97822692,
+            3.50480874,
+            2.87930032,
+            6.5783807,
+            6.57838075,
+            6.5783808,
+            6.57838086,
+            6.67492321,
+            6.67492326,
+            6.67492331,
+            4.66852997,
+            42.10875485,
+            39.45889064,
+            38.36005919,
+            33.93663807,
+            1.14400168,
+            1.00600419,
+            1.11302547,
+            1.05335328,
+            1.42822951,
+            1.2548143,
+            1.76838061,
+            2.02583367,
+            3.08695508,
+            1.74893533,
+            1.90095165,
+            1.70258148,
+            1.80317207,
+            2.44934417,
+        ]
+    )
+    rtol = 1e-5 if dtype == np.float32 else 1e-8
+    assert_allclose(dE2, oracle, rtol=rtol)
+
+    # Equal or close colors make `delta_e.get_dH2` function to return
+    # negative values resulting in NaNs when passed to sqrt (see #1908
+    # issue on Github):
+    lab1 = lab2
+    expected = np.zeros_like(oracle)
+    assert_almost_equal(
+        deltaE_cmc(lab1, lab2, channel_axis=channel_axis), expected, decimal=6
+    )
+
+    lab2[0, 0] += np.finfo(float).eps
+    assert_almost_equal(
+        deltaE_cmc(lab1, lab2, channel_axis=channel_axis), expected, decimal=6
+    )
+
+
+def test_cmc_single_item():
+    # Single item case:
+    lab1 = lab2 = np.array([0.0, 1.59607713, 0.87755709])
+    assert_equal(deltaE_cmc(lab1, lab2), 0)
+
+    lab2[0] += np.finfo(float).eps
+    assert_equal(deltaE_cmc(lab1, lab2), 0)
+
+
+def test_single_color_cie76():
+    lab1 = (0.5, 0.5, 0.5)
+    lab2 = (0.4, 0.4, 0.4)
+    deltaE_cie76(lab1, lab2)
+
+
+def test_single_color_ciede94():
+    lab1 = (0.5, 0.5, 0.5)
+    lab2 = (0.4, 0.4, 0.4)
+    deltaE_ciede94(lab1, lab2)
+
+
+def test_single_color_ciede2000():
+    lab1 = (0.5, 0.5, 0.5)
+    lab2 = (0.4, 0.4, 0.4)
+    deltaE_ciede2000(lab1, lab2)
+
+
+def test_single_color_cmc():
+    lab1 = (0.5, 0.5, 0.5)
+    lab2 = (0.4, 0.4, 0.4)
+    deltaE_cmc(lab1, lab2)

.CondaPkg/env/Lib/site-packages/skimage/conftest.py

@@ -0,0 +1,17 @@
+import pytest
+
+# List of files that pytest should ignore
+collect_ignore = [
+    "io/_plugins",
+]
+
+
+@pytest.fixture(autouse=True)
+def handle_np2():
+    # TODO: remove when we require numpy >= 2
+    try:
+        import numpy as np
+
+        np.set_printoptions(legacy="1.21")
+    except ImportError:
+        pass

.CondaPkg/env/Lib/site-packages/skimage/data/README.txt

@@ -0,0 +1,9 @@
+This directory contains sample data from scikit-image.
+
+By default, it only contains a small subset of the entire dataset.
+
+The full detaset can be downloaded by using the following commands from
+a python console.
+
+  >>> from skimage.data import download_all
+  >>> download_all()

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

@@ -0,0 +1,14 @@
+"""
+Test images and datasets.
+
+A curated set of general purpose and scientific images used in tests, examples,
+and documentation.
+
+Newer datasets are no longer included as part of the package, but are
+downloaded on demand. To make data available offline, use :func:`download_all`.
+
+"""
+
+import lazy_loader as lazy
+
+__getattr__, __dir__, __all__ = lazy.attach_stub(__name__, __file__)

.CondaPkg/env/Lib/site-packages/skimage/data/__init__.pyi

@@ -0,0 +1,88 @@
+__all__ = [
+    'astronaut',
+    'binary_blobs',
+    'brain',
+    'brick',
+    'camera',
+    'cat',
+    'cell',
+    'cells3d',
+    'checkerboard',
+    'chelsea',
+    'clock',
+    'coffee',
+    'coins',
+    'colorwheel',
+    'data_dir',
+    'download_all',
+    'eagle',
+    'file_hash',
+    'grass',
+    'gravel',
+    'horse',
+    'hubble_deep_field',
+    'human_mitosis',
+    'immunohistochemistry',
+    'kidney',
+    'lbp_frontal_face_cascade_filename',
+    'lfw_subset',
+    'lily',
+    'logo',
+    'microaneurysms',
+    'moon',
+    'nickel_solidification',
+    'page',
+    'protein_transport',
+    'retina',
+    'rocket',
+    'shepp_logan_phantom',
+    'skin',
+    'stereo_motorcycle',
+    'text',
+    'vortex',
+]
+
+from ._binary_blobs import binary_blobs
+from ._fetchers import (
+    astronaut,
+    brain,
+    brick,
+    camera,
+    cat,
+    cell,
+    cells3d,
+    checkerboard,
+    chelsea,
+    clock,
+    coffee,
+    coins,
+    colorwheel,
+    data_dir,
+    download_all,
+    eagle,
+    file_hash,
+    grass,
+    gravel,
+    horse,
+    hubble_deep_field,
+    human_mitosis,
+    immunohistochemistry,
+    kidney,
+    lbp_frontal_face_cascade_filename,
+    lfw_subset,
+    lily,
+    logo,
+    microaneurysms,
+    moon,
+    nickel_solidification,
+    page,
+    palisades_of_vogt,
+    protein_transport,
+    retina,
+    rocket,
+    shepp_logan_phantom,
+    skin,
+    stereo_motorcycle,
+    text,
+    vortex,
+)

.CondaPkg/env/Lib/site-packages/skimage/data/_binary_blobs.py

@@ -0,0 +1,61 @@
+import numpy as np
+
+from .._shared.filters import gaussian
+
+
+def binary_blobs(
+    length=512, blob_size_fraction=0.1, n_dim=2, volume_fraction=0.5, rng=None
+):
+    """
+    Generate synthetic binary image with several rounded blob-like objects.
+
+    Parameters
+    ----------
+    length : int, optional
+        Linear size of output image.
+    blob_size_fraction : float, optional
+        Typical linear size of blob, as a fraction of ``length``, should be
+        smaller than 1.
+    n_dim : int, optional
+        Number of dimensions of output image.
+    volume_fraction : float, default 0.5
+        Fraction of image pixels covered by the blobs (where the output is 1).
+        Should be in [0, 1].
+    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.
+
+    Returns
+    -------
+    blobs : ndarray of bools
+        Output binary image
+
+    Examples
+    --------
+    >>> from skimage import data
+    >>> data.binary_blobs(length=5, blob_size_fraction=0.2)  # doctest: +SKIP
+    array([[ True, False,  True,  True,  True],
+           [ True,  True,  True, False,  True],
+           [False,  True, False,  True,  True],
+           [ True, False, False,  True,  True],
+           [ True, False, False, False,  True]])
+    >>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.1)
+    >>> # Finer structures
+    >>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.05)
+    >>> # Blobs cover a smaller volume fraction of the image
+    >>> blobs = data.binary_blobs(length=256, volume_fraction=0.3)
+
+    """
+
+    rs = np.random.default_rng(rng)
+    shape = tuple([length] * n_dim)
+    mask = np.zeros(shape)
+    n_pts = max(int(1.0 / blob_size_fraction) ** n_dim, 1)
+    points = (length * rs.random((n_dim, n_pts))).astype(int)
+    mask[tuple(indices for indices in points)] = 1
+    mask = gaussian(
+        mask, sigma=0.25 * length * blob_size_fraction, preserve_range=False
+    )
+    threshold = np.percentile(mask, 100 * (1 - volume_fraction))
+    return np.logical_not(mask < threshold)

.CondaPkg/env/Lib/site-packages/skimage/data/_registry.py

@@ -0,0 +1,187 @@
+# flake8: noqa
+
+# This minimal dataset was available as part of
+# scikit-image 0.15 and will be retained until
+# further notice.
+# Testing data and additional datasets should only
+# be made available by pooch
+legacy_datasets = [
+    'astronaut.png',
+    'brick.png',
+    'camera.png',
+    'chessboard_GRAY.png',
+    'chessboard_RGB.png',
+    'chelsea.png',
+    'clock_motion.png',
+    'coffee.png',
+    'coins.png',
+    'color.png',
+    'cell.png',
+    'grass.png',
+    'gravel.png',
+    'horse.png',
+    'hubble_deep_field.jpg',
+    'ihc.png',
+    'lbpcascade_frontalface_opencv.xml',
+    'lfw_subset.npy',
+    'logo.png',
+    'microaneurysms.png',
+    'moon.png',
+    'page.png',
+    'text.png',
+    'retina.jpg',
+    'rocket.jpg',
+    'phantom.png',
+    'motorcycle_disp.npz',
+    'motorcycle_left.png',
+    'motorcycle_right.png',
+]
+
+# Registry of datafiles that can be downloaded along with their SHA256 hashes
+# To generate the SHA256 hash, use the command
+# openssl sha256 filename
+registry = {
+    "color/tests/data/lab_array_a_10.npy": "a3ef76f1530e374f9121020f1f220bc89767dc866f4bbd1b1f47e5b84891a38c",
+    "color/tests/data/lab_array_a_2.npy": "793d5981cbffceb14b5fb589f998a2b1acdb5ff9c14d364c8e9e8bd45a80b275",
+    "color/tests/data/lab_array_a_r.npy": "3d3613da109d0c87827525fc49b58111aefc12438fa6426654979f66807b9227",
+    "color/tests/data/lab_array_b_10.npy": "e8d648b28077c1bfcef55ec6dc8679819612b56a01647f8c0a78625bb06f99b6",
+    "color/tests/data/lab_array_b_2.npy": "da9c6aa99e4ab3af8ec3107bbf11647cc483a0760285dd5c9fb66988be393ca1",
+    "color/tests/data/lab_array_b_r.npy": "d9eee96f4d65a2fbba82039508aac8c18304752ee8e33233e2a013e65bb91464",
+    "color/tests/data/lab_array_c_10.npy": "88b4ff2a2d2c4f48e7bb265609221d4b9ef439a4e2d8a86989696bfdb47790e6",
+    "color/tests/data/lab_array_c_2.npy": "e1b8acfdc7284ab9cd339de66948134304073b6f734ecf9ad42f8297b83d3405",
+    "color/tests/data/lab_array_c_r.npy": "09ffba2ed69e467864fea883493cd2d2706da028433464e3e858a8086842867e",
+    "color/tests/data/lab_array_d50_10.npy": "42e2ff26cb10e2a98fcf1bc06c2483302ff4fabf971fe8d49b530f490b5d24c7",
+    "color/tests/data/lab_array_d50_2.npy": "4aa03b7018ff7276643d3c082123cf07304f9d8d898ae92a5756a86955de4faf",
+    "color/tests/data/lab_array_d50_r.npy": "57db02009f9a68dade33ce1ecffead0418d8ac8113b2a589fc02a20e6bf7e799",
+    "color/tests/data/lab_array_d55_10.npy": "ab4f21368b6d8351578ab093381c44b49ae87a6b7f25c11aa094b07f215eed7d",
+    "color/tests/data/lab_array_d55_2.npy": "0319723de4632a252bae828b7c96d038fb075a7df05beadfbad653da05efe372",
+    "color/tests/data/lab_array_d55_r.npy": "060ebc446f7b4da4df58a60f0006133dbca735da87ba61854f4a75d28db67a3a",
+    "color/tests/data/lab_array_d65_10.npy": "5cb9e9c384d2577aaf8b7d2d21ff5b505708b80605a2f59d10e89d22c3d308d2",
+    "color/tests/data/lab_array_d65_2.npy": "16e847160f7ba4f19806d8194ed44a6654c9367e5a2cb240aa6e7eece44a6649",
+    "color/tests/data/lab_array_d65_r.npy": "82d0dd7a46741f627b8868793e64cdc2f9944fe1e049b573f752a93760a1577c",
+    "color/tests/data/lab_array_d75_10.npy": "c2d3de5422c785c925926b0c6223aeaf50b9393619d1c30830190d433606cbe1",
+    "color/tests/data/lab_array_d75_2.npy": "c94d53da398d36e076471ff7e0dafcaffc64ce4ba33b4d04849c32d19c87494a",
+    "color/tests/data/lab_array_e_2.npy": "ac05f17a83961b020ceccbdd46bddc86943d43e678dabcc898caf4a1e4be6165",
+    "color/tests/data/luv_array_a_10.npy": "c8af67f9fd64a6e9c610ac0c12c5315a49ca229363f048e5d851409d4a3ae5b6",
+    "color/tests/data/luv_array_a_2.npy": "eaf05dc61f4a70ece367d5e751a14d42b7c397c7b1c2df4cfecec9ddf26e1c1a",
+    "color/tests/data/luv_array_a_r.npy": "2c0891add787ec757601f9c61ad14dd9621dd969af4e32753f2e64df437081b7",
+    "color/tests/data/luv_array_b_10.npy": "a5407736b8a43071139ca178d12cdf930f32f52a0644f0b13f89d8895c8b43db",
+    "color/tests/data/luv_array_b_2.npy": "8e74173d54dc549b6c0ebd1f1d70489d2905cad87744e41ed74384f21f22986d",
+    "color/tests/data/luv_array_b_r.npy": "0a74c41df369cbb5fc0a00c16d60dc6f946ebf144bc5e506545b0d160fa53dfa",
+    "color/tests/data/luv_array_c_10.npy": "3a5f975ffa57f69a1be9e02b153e8161f83040ce3002ea1b0a05b9fbdd0d8ec4",
+    "color/tests/data/luv_array_c_2.npy": "32506cd50ea2181997cb88d3511e275740e8151d6c693cd178f5eafd8b0c6e47",
+    "color/tests/data/luv_array_c_r.npy": "c0fbf98cc0e62ed426ab4d228986d6660089444a7bbfcc64cbb1c632644067bb",
+    "color/tests/data/luv_array_d50_10.npy": "fe223db556222ce3a59198bed3a3324c2c719b8083fb84dc5b00f214b4773b16",
+    "color/tests/data/luv_array_d50_2.npy": "48e8989048904bdf2c3c1ada265c1c29c5eff60f02f848a25cde622982c84901",
+    "color/tests/data/luv_array_d50_r.npy": "f93f0def9c93f872dd10ce4a91fdb3f06eea61ddb6e72387b7669909827d4f9c",
+    "color/tests/data/luv_array_d55_10.npy": "d88d53d2bad230c2331442187712ec52ffdee62bf0f60b200c33411bfed76c60",
+    "color/tests/data/luv_array_d55_2.npy": "c761b40475df591ae9c0475d54ef712d067190ca4652efc6308b69080a652061",
+    "color/tests/data/luv_array_d55_r.npy": "05fbd57e3602ee4d5202b9f18f9b5fc05b545891a9b4456d2a88aa798a5a774a",
+    "color/tests/data/luv_array_d65_10.npy": "41a5452ffac4d31dd579d9528e725432c60d77b5f505d801898d9401429c89bf",
+    "color/tests/data/luv_array_d65_2.npy": "962ce180132c6c11798cbc423b2b204d1d10187670f6eb5dec1058eaad301e0e",
+    "color/tests/data/luv_array_d65_r.npy": "78db8c19af26dd802ce98b039a33855f7c8d6a103a2721d094b1d9c619717449",
+    "color/tests/data/luv_array_d75_10.npy": "e1cc70d56eb6789633d4c2a4059b9533f616a7c8592c9bd342403e41d72f45e4",
+    "color/tests/data/luv_array_d75_2.npy": "07db3bd59bd89de8e5ff62dad786fe5f4b299133495ba9bea30495b375133a98",
+    "color/tests/data/luv_array_e_2.npy": "41b1037d81b267305ffe9e8e97e0affa9fa54b18e60413b01b8f11861cb32213",
+    "color/tests/ciede2000_test_data.txt": "2e005c6f76ddfb7bbcc8f68490f1f7b4b4a2a4b06b36a80c985677a2799c0e40",
+    "data/astronaut.png": "88431cd9653ccd539741b555fb0a46b61558b301d4110412b5bc28b5e3ea6cb5",
+    "data/brick.png": "7966caf324f6ba843118d98f7a07746d22f6a343430add0233eca5f6eaaa8fcf",
+    "data/cell.png": "8d23a7fb81f7cc877cd09f330357fc7f595651306e84e17252f6e0a1b3f61515",
+    "data/camera.png": "b0793d2adda0fa6ae899c03989482bff9a42d3d5690fc7e3648f2795d730c23a",
+    "data/chessboard_GRAY.png": "3e51870774515af4d07d820bd8827364c70839bf9b573c746e485095e893df90",
+    "data/chessboard_RGB.png": "1ac01eff2d4e50f4eda55a2ddecdc28a6576623a58d7a7ef84513c5cc19a0331",
+    "data/chelsea.png": "596aa1e7cb875eb79f437e310381d26b338a81c2da23439704a73c4651e8c4bb",
+    "data/clock_motion.png": "f029226b28b642e80113d86622e9b215ee067a0966feaf5e60604a1e05733955",
+    "data/coffee.png": "cc02f8ca188b167c775a7101b5d767d1e71792cf762c33d6fa15a4599b5a8de7",
+    "data/coins.png": "f8d773fc9cfa6f4d8e5942dc34d0a0788fcaed2a4fefbbed0aef5398d7ef4cba",
+    "data/color.png": "7d2df993de2b4fa2a78e04e5df8050f49a9c511aa75e59ab3bd56ac9c98aef7e",
+    "data/eagle.png": "928f1bbe7403b533265f56db3a6b07c835dfa8e2513f5c5075ca2f1960f6179e",
+    "data/horse.png": "c7fb60789fe394c485f842291ea3b21e50d140f39d6dcb5fb9917cc178225455",
+    "data/grass.png": "b6b6022426b38936c43a4ac09635cd78af074e90f42ffa8227ac8b7452d39f89",
+    "data/hubble_deep_field.jpg": "3a19c5dd8a927a9334bb1229a6d63711b1c0c767fb27e2286e7c84a3e2c2f5f4",
+    "data/ihc.png": "f8dd1aa387ddd1f49d8ad13b50921b237df8e9b262606d258770687b0ef93cef",
+    "data/logo.png": "f2c57fe8af089f08b5ba523d95573c26e62904ac5967f4c8851b27d033690168",
+    "data/lfw_subset.npy": "9560ec2f5edfac01973f63a8a99d00053fecd11e21877e18038fbe500f8e872c",
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+
+legacy_registry = {
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+}