Complete deprecations targeting release 0.20 or 1.0

TODO.txt

@@ -6,18 +6,15 @@ Version 0.17
 * Finalize ``skimage.future.graph`` API.
 * Finalize ``skimage.future.manual_segmentation`` API.
 
-Version 1.0
------------
-
+Version 0.20
+------------
 * consider removing the argument `coordinates` in
   `skimage.segmentation.active_contour`, which has not effect.
-* In ``skimage/morphology/misc.py`` remove the deprecated parameter
-  ``in_place`` in remove_small_holes(), remove_small_objects() and
-  clear_border(), and update the tests.
-* Remove the deprecation warning for `input` kwarg of the `label`
-  function in `skimage/measure/_label.py`
 * Change the warning about poorly conditioned data to a ValueError within
   `skimage.measure.CircleModel.estimate` and update the tests in `test_circle_model_insufficient_data` accordingly.
+
+Version 0.21
+------------
 * In ``skimage/filters/lpi_filter.py``, remove the deprecated function
   inverse().
 

benchmarks/benchmark_rank.py

@@ -12,10 +12,10 @@ class RankSuite:
 
     def setup(self, filter_func, shape):
         self.image = np.random.randint(0, 255, size=shape, dtype=np.uint8)
-        self.selem = disk(1)
+        self.footprint = disk(1)
 
     def time_filter(self, filter_func, shape):
-        getattr(rank, filter_func)(self.image, self.selem)
+        getattr(rank, filter_func)(self.image, self.footprint)
 
 
 class Rank3DSuite:
@@ -25,7 +25,7 @@ class Rank3DSuite:
 
     def setup(self, filter3d, shape3d):
         self.volume = np.random.randint(0, 255, size=shape3d, dtype=np.uint8)
-        self.selem_3d = ball(1)
+        self.footprint_3d = ball(1)
 
     def time_3d_filters(self, filter3d, shape3d):
-        getattr(rank, filter3d)(self.volume, self.selem_3d)
+        getattr(rank, filter3d)(self.volume, self.footprint_3d)

skimage/_shared/filters.py

@@ -9,13 +9,11 @@
 import numpy as np
 from scipy import ndimage as ndi
 
-from .._shared import utils
 from .._shared.utils import _supported_float_type, convert_to_float, warn
 
 
-@utils.deprecate_multichannel_kwarg(multichannel_position=5)
 def gaussian(image, sigma=1, output=None, mode='nearest', cval=0,
-             multichannel=None, preserve_range=False, truncate=4.0, *,
+             preserve_range=False, truncate=4.0, *,
              channel_axis=None):
     """Multi-dimensional Gaussian filter.
 
@@ -38,13 +36,6 @@ def gaussian(image, sigma=1, output=None, mode='nearest', cval=0,
     cval : scalar, optional
         Value to fill past edges of input if ``mode`` is 'constant'. Default
         is 0.0
-    multichannel : bool, optional (default: None)
-        Whether the last axis of the image is to be interpreted as multiple
-        channels. If True, each channel is filtered separately (channels are
-        not mixed together). Only 3 channels are supported. If ``None``,
-        the function will attempt to guess this, and raise a warning if
-        ambiguous, when the array has shape (M, N, 3).
-        This argument is deprecated: specify `channel_axis` instead.
     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``

skimage/_shared/tests/test_utils.py

@@ -11,8 +11,6 @@
                                    change_default_value, remove_arg,
                                    _supported_float_type,
                                    channel_as_last_axis)
-from skimage.feature import hog
-from skimage.transform import pyramid_gaussian
 
 complex_dtypes = [np.complex64, np.complex128]
 if hasattr(np, 'complex256'):
@@ -280,23 +278,17 @@ def test_decorated_channel_axis_shape(channel_axis):
         assert size == x.shape[channel_axis]
 
 
-def test_decorator_warnings():
-    """Assert that warning message issued by decorator points to
-    expected file and line number.
-    """
-
-    with pytest.warns(FutureWarning) as record:
-        pyramid_gaussian(None, multichannel=True)
-        expected_lineno = inspect.currentframe().f_lineno - 1
-
-    assert record[0].lineno == expected_lineno
-    assert record[0].filename == __file__
+@deprecate_kwarg({"old_kwarg": "new_kwarg"}, deprecated_version="x.y.z")
+def _function_with_deprecated_kwarg(*, new_kwarg):
+    pass
 
-    img = np.random.rand(100, 100, 3)
 
+def test_deprecate_kwarg_location():
+    """Assert that warning message issued by deprecate_kwarg points to
+    file and line number where decorated function is called.
+    """
     with pytest.warns(FutureWarning) as record:
-        hog(img, multichannel=True)
+        _function_with_deprecated_kwarg(old_kwarg=True)
         expected_lineno = inspect.currentframe().f_lineno - 1
-
     assert record[0].lineno == expected_lineno
     assert record[0].filename == __file__

skimage/_shared/utils.py

@@ -288,72 +288,6 @@ def fixed_func(*args, **kwargs):
         return fixed_func
 
 
-class deprecate_multichannel_kwarg(deprecate_kwarg):
-    """Decorator for deprecating multichannel keyword in favor of channel_axis.
-
-    Parameters
-    ----------
-    removed_version : str
-        The package version in which the deprecated argument will be
-        removed.
-
-    """
-
-    def __init__(self, removed_version='1.0', multichannel_position=None):
-        super().__init__(
-            kwarg_mapping={'multichannel': 'channel_axis'},
-            deprecated_version='0.19',
-            warning_msg=None,
-            removed_version=removed_version)
-        self.position = multichannel_position
-
-    def __call__(self, func):
-
-        stack_rank = _get_stack_rank(func)
-
-        @functools.wraps(func)
-        def fixed_func(*args, **kwargs):
-            stacklevel = 1 + self.get_stack_length(func) - stack_rank
-
-            if self.position is not None and len(args) > self.position:
-                warning_msg = (
-                    "Providing the `multichannel` argument positionally to "
-                    "{func_name} is deprecated. Use the `channel_axis` kwarg "
-                    "instead."
-                )
-                warnings.warn(warning_msg.format(func_name=func.__name__),
-                              FutureWarning,
-                              stacklevel=stacklevel)
-                if 'channel_axis' in kwargs:
-                    raise ValueError(
-                        "Cannot provide both a `channel_axis` kwarg and a "
-                        "positional `multichannel` value."
-                    )
-                else:
-                    channel_axis = -1 if args[self.position] else None
-                    kwargs['channel_axis'] = channel_axis
-
-            if 'multichannel' in kwargs:
-                #  warn that the function interface has changed:
-                warnings.warn(self.warning_msg.format(
-                    old_arg='multichannel', func_name=func.__name__,
-                    new_arg='channel_axis'), FutureWarning,
-                    stacklevel=stacklevel)
-
-                # multichannel = True -> last axis corresponds to channels
-                convert = {True: -1, False: None}
-                kwargs['channel_axis'] = convert[kwargs.pop('multichannel')]
-
-            # Call the function with the fixed arguments
-            return func(*args, **kwargs)
-
-        if func.__doc__ is not None:
-            newdoc = docstring_add_deprecated(
-                func, {'multichannel': 'channel_axis'}, '0.19')
-            fixed_func.__doc__ = newdoc
-        return fixed_func
-
-
 class channel_as_last_axis:
     """Decorator for automatically making channels axis last for all arrays.
 

skimage/draw/_random_shapes.py

@@ -3,7 +3,7 @@
 
 from . import (polygon as draw_polygon, disk as draw_disk,
                ellipse as draw_ellipse)
-from .._shared.utils import deprecate_multichannel_kwarg, warn
+from .._shared.utils import warn
 
 
 def _generate_rectangle_mask(point, image, shape, random):
@@ -291,13 +291,11 @@ def _generate_random_colors(num_colors, num_channels, intensity_range, random):
     return np.transpose(colors)
 
 
-@deprecate_multichannel_kwarg(multichannel_position=5)
 def random_shapes(image_shape,
                   max_shapes,
                   min_shapes=1,
                   min_size=2,
                   max_size=None,
-                  multichannel=True,
                   num_channels=3,
                   shape=None,
                   intensity_range=None,
@@ -330,10 +328,6 @@ def random_shapes(image_shape,
         The minimum dimension of each shape to fit into the image.
     max_size : int, optional
         The maximum dimension of each shape to fit into the image.
-    multichannel : bool, optional
-        If True, the generated image has ``num_channels`` color channels,
-        otherwise generates grayscale image. This argument is deprecated:
-        specify `channel_axis` instead.
     num_channels : int, optional
         Number of channels in the generated image. If 1, generate monochrome
         images, else color images with multiple channels. Ignored if

skimage/draw/tests/test_random_shapes.py

@@ -19,14 +19,8 @@ def test_generates_gray_images_with_correct_shape():
 
 
 def test_generates_gray_images_with_correct_shape_deprecated_multichannel():
-    with expected_warnings(["`multichannel` is a deprecated argument"]):
-        image, _ = random_shapes(
-            (4567, 123), min_shapes=3, max_shapes=20, multichannel=False)
-    assert image.shape == (4567, 123)
-
-    # repeat prior test, but check for positional multichannel warning
-    with expected_warnings(["Providing the `multichannel` argument"]):
-        image, _ = random_shapes((4567, 123), 20, 3, 2, None, False)
+    image, _ = random_shapes(
+        (4567, 123), min_shapes=3, max_shapes=20, channel_axis=None)
     assert image.shape == (4567, 123)
 
 
@@ -167,9 +161,8 @@ def test_can_generate_one_by_one_rectangle():
 
 def test_throws_when_intensity_range_out_of_range():
     with testing.raises(ValueError):
-        with expected_warnings(["`multichannel` is a deprecated argument"]):
-            random_shapes((1000, 1234), max_shapes=1, multichannel=False,
-                          intensity_range=(0, 256))
+        random_shapes((1000, 1234), max_shapes=1, channel_axis=None,
+                      intensity_range=(0, 256))
     with testing.raises(ValueError):
         random_shapes((2, 2), max_shapes=1,
                       intensity_range=((-1, 255),))

skimage/exposure/histogram_matching.py

@@ -32,9 +32,7 @@ def _match_cumulative_cdf(source, template):
 
 
 @utils.channel_as_last_axis(channel_arg_positions=(0, 1))
-@utils.deprecate_multichannel_kwarg()
-def match_histograms(image, reference, *, channel_axis=None,
-                     multichannel=False):
+def match_histograms(image, reference, *, channel_axis=None):
     """Adjust an image so that its cumulative histogram matches that of another.
 
     The adjustment is applied separately for each channel.
@@ -50,9 +48,6 @@ def match_histograms(image, reference, *, channel_axis=None,
         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.
-    multichannel : bool, optional
-        Apply the matching separately for each channel. This argument is
-        deprecated: specify `channel_axis` instead.
 
     Returns
     -------

skimage/exposure/tests/test_histogram_matching.py

@@ -4,7 +4,6 @@
 
 from skimage import data
 from skimage import exposure
-from skimage._shared.testing import expected_warnings
 from skimage._shared.utils import _supported_float_type
 from skimage.exposure import histogram_matching
 
@@ -26,17 +25,16 @@ class TestMatchHistogram:
     image_rgb = data.chelsea()
     template_rgb = data.astronaut()
 
-    @pytest.mark.parametrize('image, reference, multichannel', [
-        (image_rgb, template_rgb, True),
-        (image_rgb[:, :, 0], template_rgb[:, :, 0], False)
+    @pytest.mark.parametrize('image, reference, channel_axis', [
+        (image_rgb, template_rgb, -1),
+        (image_rgb[:, :, 0], template_rgb[:, :, 0], None)
     ])
-    def test_match_histograms(self, image, reference, multichannel):
+    def test_match_histograms(self, image, reference, channel_axis):
         """Assert that pdf of matched image is close to the reference's pdf for
         all channels and all values of matched"""
 
-        with expected_warnings(["`multichannel` is a deprecated argument"]):
-            matched = exposure.match_histograms(image, reference,
-                                                multichannel=multichannel)
+        matched = exposure.match_histograms(image, reference,
+                                            channel_axis=channel_axis)
 
         matched_pdf = self._calculate_image_empirical_pdf(matched)
         reference_pdf = self._calculate_image_empirical_pdf(reference)

skimage/feature/__init__.py

@@ -1,5 +1,3 @@
-from .._shared.utils import deprecated
-
 from ._canny import canny
 from ._cascade import Cascade
 from ._daisy import daisy
@@ -30,27 +28,12 @@
 from ._basic_features import multiscale_basic_features
 
 
-@deprecated(alt_func='skimage.feature.graycomatrix',
-            removed_version='1.0')
-def greycomatrix(image, distances, angles, levels=None, symmetric=False,
-                 normed=False):
-    return graycomatrix(image, distances, angles, levels, symmetric, normed)
-
-
-@deprecated(alt_func='skimage.feature.graycoprops',
-            removed_version='1.0')
-def greycoprops(P, prop='contrast'):
-    return graycoprops(P, prop)
-
-
 __all__ = ['canny',
            'Cascade',
            'daisy',
            'hog',
            'graycomatrix',
            'graycoprops',
-           'greycomatrix',
-           'greycoprops',
            'local_binary_pattern',
            'multiblock_lbp',
            'draw_multiblock_lbp',

skimage/feature/_basic_features.py

@@ -3,7 +3,6 @@
 import numpy as np
 from skimage import filters, feature
 from skimage.util.dtype import img_as_float32
-from skimage._shared import utils
 from concurrent.futures import ThreadPoolExecutor
 
 
@@ -97,10 +96,8 @@ def _mutiscale_basic_features_singlechannel(
     return features
 
 
-@utils.deprecate_multichannel_kwarg(multichannel_position=1)
 def multiscale_basic_features(
     image,
-    multichannel=False,
     intensity=True,
     edges=True,
     texture=True,
@@ -120,9 +117,6 @@ def multiscale_basic_features(
     ----------
     image : ndarray
         Input image, which can be grayscale or multichannel.
-    multichannel : bool, default False
-        True if the last dimension corresponds to color channels.
-        This argument is deprecated: specify `channel_axis` instead.
     intensity : bool, default True
         If True, pixel intensities averaged over the different scales
         are added to the feature set.

skimage/feature/_hog.py

@@ -46,10 +46,9 @@ def _hog_channel_gradient(channel):
 
 
 @utils.channel_as_last_axis(multichannel_output=False)
-@utils.deprecate_multichannel_kwarg(multichannel_position=8)
 def hog(image, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(3, 3),
         block_norm='L2-Hys', visualize=False, transform_sqrt=False,
-        feature_vector=True, multichannel=None, *, channel_axis=None):
+        feature_vector=True, *, channel_axis=None):
     """Extract Histogram of Oriented Gradients (HOG) for a given image.
 
     Compute a Histogram of Oriented Gradients (HOG) by
@@ -98,10 +97,6 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(3, 3),
     feature_vector : bool, optional
         Return the data as a feature vector by calling .ravel() on the result
         just before returning.
-    multichannel : boolean, optional
-        If True, the last `image` dimension is considered as a color channel,
-        otherwise as spatial. This argument is deprecated: specify
-        `channel_axis` instead.
     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