Prototype type hints for skimage.filters with mypy

.directory

@@ -0,0 +1,5 @@
+[Dolphin]
+PreviewsShown=false
+Timestamp=2023,9,17,13,54,2.458
+Version=4
+ViewMode=1

mypy.ini

@@ -0,0 +1,138 @@
+
+[mypy]
+allow_redefinition = True
+strict_optional = False
+
+# External libraries
+[mypy-scipy.*]
+ignore_missing_imports = True
+[mypy-pooch.*]
+ignore_missing_imports = True
+[mypy-networkx.*]
+ignore_missing_imports = True
+[mypy-matplotlib.*]
+ignore_missing_imports = True
+[mypy-sklearn.*]
+ignore_missing_imports = True
+[mypy-pywt.*]
+ignore_missing_imports = True
+[mypy-pyamg.*]
+ignore_missing_imports = True
+[mypy-numpydoc.*]
+ignore_missing_imports = True
+[mypy-PIL.*]
+ignore_missing_imports = True
+[mypy-tifffile.*]
+ignore_missing_imports = True
+
+# Cython files
+[mypy-skimage.util._remap]
+ignore_missing_imports = True
+[mypy-skimage.filters._multiotsu]
+ignore_missing_imports = True
+[mypy-skimage.filters.rank.bilateral_cy]
+ignore_missing_imports = True
+[mypy-skimage.filters.rank.generic_cy]
+ignore_missing_imports = True
+[mypy-skimage.filters.rank.core_cy]
+ignore_missing_imports = True
+[mypy-skimage.filters.rank.core_cy_3d]
+ignore_missing_imports = True
+[mypy-skimage.filters.rank.percentile_cy]
+ignore_missing_imports = True
+[mypy-skimage.restoration._rolling_ball_cy]
+ignore_missing_imports = True
+[mypy-skimage.restoration._unwrap_1d]
+ignore_missing_imports = True
+[mypy-skimage.restoration._denoise_cy]
+ignore_missing_imports = True
+[mypy-skimage.restoration._nl_means_denoising]
+ignore_missing_imports = True
+[mypy-skimage.restoration._unwrap_2d]
+ignore_missing_imports = True
+[mypy-skimage.restoration._inpaint]
+ignore_missing_imports = True
+[mypy-skimage.restoration._unwrap_3d]
+ignore_missing_imports = True
+[mypy-skimage.io._plugins._histograms]
+ignore_missing_imports = True
+[mypy-skimage.io._plugins._colormixer]
+ignore_missing_imports = True
+[mypy-skimage.feature.censure_cy]
+ignore_missing_imports = True
+[mypy-skimage.feature.corner_cy]
+ignore_missing_imports = True
+[mypy-skimage.feature.orb_cy]
+ignore_missing_imports = True
+[mypy-skimage.feature._canny_cy]
+ignore_missing_imports = True
+[mypy-skimage.feature._hoghistogram]
+ignore_missing_imports = True
+[mypy-skimage.feature._sift]
+ignore_missing_imports = True
+[mypy-skimage.feature._texture]
+ignore_missing_imports = True
+[mypy-skimage.feature._haar]
+ignore_missing_imports = True
+[mypy-skimage.feature.brief_cy]
+ignore_missing_imports = True
+[mypy-skimage.feature._hessian_det_appx]
+ignore_missing_imports = True
+[mypy-skimage.feature._cascade]
+ignore_missing_imports = True
+[mypy-skimage.morphology._extrema_cy]
+ignore_missing_imports = True
+[mypy-skimage.morphology._skeletonize_cy]
+ignore_missing_imports = True
+[mypy-skimage.morphology._max_tree]
+ignore_missing_imports = True
+[mypy-skimage.morphology._grayreconstruct]
+ignore_missing_imports = True
+[mypy-skimage.morphology._flood_fill_cy]
+ignore_missing_imports = True
+[mypy-skimage.morphology._convex_hull]
+ignore_missing_imports = True
+[mypy-skimage._shared.transform]
+ignore_missing_imports = True
+[mypy-skimage._shared.fast_exp]
+ignore_missing_imports = True
+[mypy-skimage._shared.interpolation]
+ignore_missing_imports = True
+[mypy-skimage._shared.geometry]
+ignore_missing_imports = True
+[mypy-skimage.draw._draw]
+ignore_missing_imports = True
+[mypy-skimage.measure._moments_cy]
+ignore_missing_imports = True
+[mypy-skimage.measure._find_contours_cy]
+ignore_missing_imports = True
+[mypy-skimage.measure._pnpoly]
+ignore_missing_imports = True
+[mypy-skimage.measure._marching_cubes_lewiner_cy]
+ignore_missing_imports = True
+[mypy-skimage.measure._ccomp]
+ignore_missing_imports = True
+[mypy-skimage.morphology._skeletonize_3d_cy]
+ignore_missing_imports = True
+[mypy-skimage.transform._radon_transform]
+ignore_missing_imports = True
+[mypy-skimage.transform._warps_cy]
+ignore_missing_imports = True
+[mypy-skimage.transform._hough_transform]
+ignore_missing_imports = True
+[mypy-skimage.graph._ncut_cy]
+ignore_missing_imports = True
+[mypy-skimage.graph.heap]
+ignore_missing_imports = True
+[mypy-skimage.graph._spath]
+ignore_missing_imports = True
+[mypy-skimage.graph._mcp]
+ignore_missing_imports = True
+[mypy-skimage.segmentation._slic]
+ignore_missing_imports = True
+[mypy-skimage.segmentation._watershed_cy]
+ignore_missing_imports = True
+[mypy-skimage.segmentation._quickshift_cy]
+ignore_missing_imports = True
+[mypy-skimage.segmentation._felzenszwalb_cy]
+ignore_missing_imports = True

pyproject.toml

@@ -105,6 +105,7 @@ test = [
     'pytest-cov>=2.11.0',
     'pytest-localserver',
     'pytest-faulthandler',
+    'mypy',
 ]
 
 [project.urls]

requirements/test.txt

@@ -8,3 +8,4 @@ pytest>=7.0
 pytest-cov>=2.11.0
 pytest-localserver
 pytest-faulthandler
+mypy

skimage/__init__.pyi

@@ -39,3 +39,5 @@ from . import (
     transform,
     util,
 )
+
+__version__ = ""

skimage/_shared/testing.py

@@ -50,14 +50,16 @@
 arch32 = struct.calcsize("P") * 8 == 32
 
 
-_error_on_warnings = os.environ.get('SKIMAGE_TEST_STRICT_WARNINGS_GLOBAL', '0')
-if _error_on_warnings.lower() == 'true':
+SKIMAGE_TEST_STRICT_WARNINGS_GLOBAL = os.environ.get(
+    'SKIMAGE_TEST_STRICT_WARNINGS_GLOBAL', '0'
+)
+if SKIMAGE_TEST_STRICT_WARNINGS_GLOBAL.lower() == 'true':
     _error_on_warnings = True
-elif _error_on_warnings.lower() == 'false':
+elif SKIMAGE_TEST_STRICT_WARNINGS_GLOBAL.lower() == 'false':
     _error_on_warnings = False
 else:
     try:
-        _error_on_warnings = bool(int(_error_on_warnings))
+        _error_on_warnings = bool(int(SKIMAGE_TEST_STRICT_WARNINGS_GLOBAL))
     except ValueError:
         _error_on_warnings = False
 

skimage/_shared/utils.py

@@ -50,7 +50,7 @@ class _DecoratorBaseClass:
     `stacklevel = 1 + stack_length - stack_rank`.
     """
 
-    _stack_length = {}
+    _stack_length: dict = {}
 
     def get_stack_length(self, func):
         return self._stack_length.get(func.__name__, _get_stack_length(func))

skimage/exposure/exposure.py

@@ -15,7 +15,7 @@
 ]
 
 
-DTYPE_RANGE = dtype_range.copy()
+DTYPE_RANGE: dict = dtype_range.copy()
 DTYPE_RANGE.update((d.__name__, limits) for d, limits in dtype_range.items())
 DTYPE_RANGE.update(
     {

skimage/feature/_hog.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from . import _hoghistogram
+from . import _hoghistogram  # type: ignore
 from .._shared import utils
 
 

skimage/filters/rank/_percentile.py

@@ -23,7 +23,7 @@
 """
 
 from ..._shared.utils import check_nD
-from . import percentile_cy
+from . import percentile_cy  # type: ignore
 from .generic import _preprocess_input
 
 __all__ = [

skimage/filters/rank/bilateral.py

@@ -24,7 +24,7 @@
 """
 
 from ..._shared.utils import check_nD
-from . import bilateral_cy
+from . import bilateral_cy  # type: ignore
 from .generic import _preprocess_input
 
 __all__ = ['mean_bilateral', 'pop_bilateral', 'sum_bilateral']

skimage/filters/rank/generic.py

@@ -54,7 +54,7 @@
 from ..._shared.utils import check_nD, warn
 from ...morphology.footprints import _footprint_is_sequence
 from ...util import img_as_ubyte
-from . import generic_cy
+from . import generic_cy  # type: ignore
 
 
 __all__ = [

skimage/filters/tests/test_ridges.py

@@ -2,7 +2,7 @@
 import pytest
 from numpy.testing import assert_allclose, assert_array_less, assert_equal
 
-from skimage import img_as_float
+from skimage.util import img_as_float
 from skimage._shared.utils import _supported_float_type
 from skimage.color import rgb2gray
 from skimage.data import camera, retina

skimage/future/trainable_segmentation.py

@@ -8,7 +8,7 @@
 except ImportError:
     has_sklearn = False
 
-    class NotFittedError(Exception):
+    class NotFittedError(Exception):  # type: ignore
         pass
 
 

skimage/graph/_graph_cut.py

@@ -3,7 +3,8 @@
 from scipy.sparse import linalg
 
 from .._shared.utils import deprecate_kwarg
-from . import _ncut, _ncut_cy
+from . import _ncut
+from . import _ncut_cy  # type: ignore
 
 
 def cut_threshold(labels, rag, thresh, in_place=True):

skimage/graph/_ncut.py

@@ -1,7 +1,7 @@
 import networkx as nx
 import numpy as np
 from scipy import sparse
-from . import _ncut_cy
+from . import _ncut_cy  # type: ignore
 
 
 def DW_matrices(graph):

skimage/graph/spath.py

@@ -1,5 +1,5 @@
 import numpy as np
-from . import _spath
+from . import _spath  # type: ignore
 
 
 def shortest_path(arr, reach=1, axis=-1, output_indexlist=False):

skimage/measure/_marching_cubes_lewiner.py

@@ -3,7 +3,7 @@
 import numpy as np
 
 from . import _marching_cubes_lewiner_luts as mcluts
-from . import _marching_cubes_lewiner_cy
+from . import _marching_cubes_lewiner_cy  # type: ignore
 
 
 def marching_cubes(

skimage/measure/_moments.py

@@ -3,7 +3,7 @@
 import numpy as np
 
 from .._shared.utils import _supported_float_type, check_nD
-from . import _moments_cy
+from . import _moments_cy  # type: ignore
 from ._moments_analytical import moments_raw_to_central
 
 

skimage/morphology/max_tree.py

@@ -42,7 +42,7 @@
 from ._util import _validate_connectivity, _offsets_to_raveled_neighbors
 from ..util import invert
 
-from . import _max_tree
+from . import _max_tree  # type: ignore
 
 unsigned_int_types = [np.uint8, np.uint16, np.uint32, np.uint64]
 signed_int_types = [np.int8, np.int16, np.int32, np.int64]

skimage/restoration/_cycle_spin.py

@@ -1,5 +1,8 @@
+import typing as ty
 from itertools import product
+
 import numpy as np
+from numpy.typing import ArrayLike
 from .._shared import utils
 from .._shared.utils import warn
 
@@ -11,7 +14,12 @@
     dask_available = False
 
 
-def _generate_shifts(ndim, multichannel, max_shifts, shift_steps=1):
+def _generate_shifts(
+    ndim: int,
+    multichannel: bool,
+    max_shifts: int | tuple[int, ...],
+    shift_steps: int | tuple[int, ...] = 1,
+) -> product[tuple[int, ...]]:
     """Returns all combinations of shifts in n dimensions over the specified
     max_shifts and step sizes.
 
@@ -22,14 +30,14 @@ def _generate_shifts(ndim, multichannel, max_shifts, shift_steps=1):
     [(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2)]
     """
     mc = int(multichannel)
-    if np.isscalar(max_shifts):
+    if isinstance(max_shifts, int):
         max_shifts = (max_shifts,) * (ndim - mc) + (0,) * mc
     elif multichannel and len(max_shifts) == ndim - 1:
         max_shifts = tuple(max_shifts) + (0,)
     elif len(max_shifts) != ndim:
         raise ValueError("max_shifts should have length ndim")
 
-    if np.isscalar(shift_steps):
+    if isinstance(shift_steps, int):
         shift_steps = (shift_steps,) * (ndim - mc) + (1,) * mc
     elif multichannel and len(shift_steps) == ndim - 1:
         shift_steps = tuple(shift_steps) + (1,)
@@ -49,15 +57,15 @@ def _generate_shifts(ndim, multichannel, max_shifts, shift_steps=1):
 
 @utils.channel_as_last_axis()
 def cycle_spin(
-    x,
-    func,
-    max_shifts,
-    shift_steps=1,
-    num_workers=None,
-    func_kw=None,
+    x: ArrayLike,
+    func: ty.Callable,
+    max_shifts: int | tuple[int, ...],
+    shift_steps: int | tuple[int, ...] = 1,
+    num_workers: int = None,
+    func_kw: dict = None,
     *,
-    channel_axis=None,
-):
+    channel_axis: int = None,
+) -> np.ndarray:
     """Cycle spinning (repeatedly apply func to shifted versions of x).
 
     Parameters
@@ -128,7 +136,6 @@ def cycle_spin(
     """
     if func_kw is None:
         func_kw = {}
-
     x = np.asanyarray(x)
     multichannel = channel_axis is not None
     all_shifts = _generate_shifts(x.ndim, multichannel, max_shifts, shift_steps)

skimage/segmentation/_watershed.py

@@ -18,7 +18,7 @@
 import numpy as np
 from scipy import ndimage as ndi
 
-from . import _watershed_cy
+from . import _watershed_cy  # type: ignore
 from ..morphology.extrema import local_minima
 from ..morphology._util import _validate_connectivity, _offsets_to_raveled_neighbors
 from ..util import crop, regular_seeds