scikit-learn · DimitriPapadopoulos · Sep 27, 2021 · Sep 27, 2021 · Sep 27, 2021 · Feb 11, 2022
diff --git a/sklearn/conftest.py b/sklearn/conftest.py
@@ -9,7 +9,7 @@
 
 from sklearn.utils import _IS_32BIT
 from sklearn.utils._openmp_helpers import _openmp_effective_n_threads
-from sklearn.externals import _pilutil
+from sklearn.utils import _pilutil
 from sklearn._min_dependencies import PYTEST_MIN_VERSION
 from sklearn.utils.fixes import np_version, parse_version
 from sklearn.datasets import fetch_20newsgroups

diff --git a/sklearn/datasets/_base.py b/sklearn/datasets/_base.py
@@ -1365,7 +1365,7 @@ def load_sample_images():
     dtype('uint8')
     """
     # import PIL only when needed
-    from ..externals._pilutil import imread
+    from ..utils._pilutil import imread
 
     descr = load_descr("README.txt", descr_module=IMAGES_MODULE)
 

diff --git a/sklearn/datasets/_lfw.py b/sklearn/datasets/_lfw.py
@@ -119,7 +119,7 @@ def _check_fetch_lfw(data_home=None, funneled=True, download_if_missing=True):
 def _load_imgs(file_paths, slice_, color, resize):
     """Internally used to load images"""
     # import PIL only when needed
-    from ..externals._pilutil import imread, imresize
+    from ..utils._pilutil import imread, imresize
 
     # compute the portion of the images to load to respect the slice_ parameter
     # given by the caller

diff --git a/sklearn/datasets/tests/test_base.py b/sklearn/datasets/tests/test_base.py
@@ -31,7 +31,7 @@
 from sklearn.utils._testing import SkipTest
 from sklearn.datasets.tests.test_common import check_as_frame
 
-from sklearn.externals._pilutil import pillow_installed
+from sklearn.utils._pilutil import pillow_installed
 
 from sklearn.utils import IS_PYPY
 

diff --git a/sklearn/datasets/tests/test_lfw.py b/sklearn/datasets/tests/test_lfw.py
@@ -15,7 +15,7 @@
 import numpy as np
 import pytest
 from functools import partial
-from sklearn.externals._pilutil import pillow_installed, imsave
+from sklearn.utils._pilutil import pillow_installed, imsave
 from sklearn.datasets import fetch_lfw_pairs
 from sklearn.datasets import fetch_lfw_people
 

diff --git a/sklearn/externals/_pilutil.py → sklearn/utils/_pilutil.py b/sklearn/externals/_pilutil.py → sklearn/utils/_pilutil.py
@@ -43,8 +43,20 @@
 
 import numpy
 
-from numpy import (amin, amax, ravel, asarray, arange, ones, newaxis,
-                   transpose, iscomplexobj, uint8, issubdtype, array)
+from numpy import (
+    amin,
+    amax,
+    ravel,
+    asarray,
+    arange,
+    ones,
+    newaxis,
+    transpose,
+    iscomplexobj,
+    uint8,
+    issubdtype,
+    array,
+)
 
 # Modification of original scipy pilutil.py to make this module importable if
 # pillow is not installed. If pillow is not installed, functions will raise
@@ -55,12 +67,12 @@
     except ImportError:
         import Image
     pillow_installed = True
-    if not hasattr(Image, 'frombytes'):
+    if not hasattr(Image, "frombytes"):
         Image.frombytes = Image.fromstring
 except ImportError:
     pillow_installed = False
 
-__all__ = ['bytescale', 'imread', 'imsave', 'fromimage', 'toimage', 'imresize']
+__all__ = ["bytescale", "imread", "imsave", "fromimage", "toimage", "imresize"]
 
 
 PILLOW_ERROR_MESSAGE = (
@@ -102,7 +114,7 @@ def bytescale(data, cmin=None, cmax=None, high=255, low=0):
     Examples
     --------
     >>> import numpy as np
-    >>> from scipy.misc import bytescale
+    >>> from sklearn.utils._pilutil import bytescale
     >>> img = np.array([[ 91.06794177,   3.39058326,  84.4221549 ],
     ...                 [ 73.88003259,  80.91433048,   4.88878881],
     ...                 [ 51.53875334,  34.45808177,  27.5873488 ]])
@@ -237,7 +249,7 @@ def imsave(name, arr, format=None):
     Construct an array of gradient intensity values and save to file:
 
     >>> import numpy as np
-    >>> from scipy.misc import imsave
+    >>> from sklearn.utils._pilutil import imsave
     >>> x = np.zeros((255, 255))
     >>> x = np.zeros((255, 255), dtype=np.uint8)
     >>> x[:] = np.arange(255)
@@ -293,35 +305,37 @@ def fromimage(im, flatten=False, mode=None):
     if mode is not None:
         if mode != im.mode:
             im = im.convert(mode)
-    elif im.mode == 'P':
+    elif im.mode == "P":
         # Mode 'P' means there is an indexed "palette".  If we leave the mode
         # as 'P', then when we do `a = array(im)` below, `a` will be a 2-D
         # containing the indices into the palette, and not a 3-D array
         # containing the RGB or RGBA values.
-        if 'transparency' in im.info:
-            im = im.convert('RGBA')
+        if "transparency" in im.info:
+            im = im.convert("RGBA")
         else:
-            im = im.convert('RGB')
+            im = im.convert("RGB")
 
     if flatten:
-        im = im.convert('F')
-    elif im.mode == '1':
+        im = im.convert("F")
+    elif im.mode == "1":
         # Workaround for crash in PIL. When im is 1-bit, the call array(im)
         # can cause a seg. fault, or generate garbage. See
         # https://github.com/scipy/scipy/issues/2138 and
         # https://github.com/python-pillow/Pillow/issues/350.
         #
         # This converts im from a 1-bit image to an 8-bit image.
-        im = im.convert('L')
+        im = im.convert("L")
 
     a = array(im)
     return a
 
+
 _errstr = "Mode is unknown or incompatible with input array shape."
 
 
-def toimage(arr, high=255, low=0, cmin=None, cmax=None, pal=None,
-            mode=None, channel_axis=None):
+def toimage(
+    arr, high=255, low=0, cmin=None, cmax=None, pal=None, mode=None, channel_axis=None
+):
     """Takes a numpy array and returns a PIL image.
 
     This function is only available if Python Imaging Library (PIL) is installed.
@@ -358,39 +372,38 @@ def toimage(arr, high=255, low=0, cmin=None, cmax=None, pal=None,
     if iscomplexobj(data):
         raise ValueError("Cannot convert a complex-valued array.")
     shape = list(data.shape)
-    valid = len(shape) == 2 or ((len(shape) == 3) and
-                                ((3 in shape) or (4 in shape)))
+    valid = len(shape) == 2 or ((len(shape) == 3) and ((3 in shape) or (4 in shape)))
     if not valid:
-        raise ValueError("'arr' does not have a suitable array shape for "
-                         "any mode.")
+        raise ValueError("'arr' does not have a suitable array shape for any mode.")
     if len(shape) == 2:
         shape = (shape[1], shape[0])  # columns show up first
-        if mode == 'F':
+        if mode == "F":
             data32 = data.astype(numpy.float32)
             image = Image.frombytes(mode, shape, data32.tobytes())
             return image
-        if mode in [None, 'L', 'P']:
-            bytedata = bytescale(data, high=high, low=low,
-                                 cmin=cmin, cmax=cmax)
-            image = Image.frombytes('L', shape, bytedata.tobytes())
+        if mode in [None, "L", "P"]:
+            bytedata = bytescale(data, high=high, low=low, cmin=cmin, cmax=cmax)
+            image = Image.frombytes("L", shape, bytedata.tobytes())
             if pal is not None:
                 image.putpalette(asarray(pal, dtype=uint8).tobytes())
                 # Becomes a mode='P' automagically.
-            elif mode == 'P':  # default gray-scale
-                pal = (arange(0, 256, 1, dtype=uint8)[:, newaxis] *
-                       ones((3,), dtype=uint8)[newaxis, :])
+            elif mode == "P":  # default gray-scale
+                pal = (
+                    arange(0, 256, 1, dtype=uint8)[:, newaxis]
+                    * ones((3,), dtype=uint8)[newaxis, :]
+                )
                 image.putpalette(asarray(pal, dtype=uint8).tobytes())
             return image
-        if mode == '1':  # high input gives threshold for 1
-            bytedata = (data > high)
-            image = Image.frombytes('1', shape, bytedata.tobytes())
+        if mode == "1":  # high input gives threshold for 1
+            bytedata = data > high
+            image = Image.frombytes("1", shape, bytedata.tobytes())
             return image
         if cmin is None:
             cmin = amin(ravel(data))
         if cmax is None:
             cmax = amax(ravel(data))
-        data = (data*1.0 - cmin)*(high - low)/(cmax - cmin) + low
-        if mode == 'I':
+        data = (data * 1.0 - cmin) * (high - low) / (cmax - cmin) + low
+        if mode == "I":
             data32 = data.astype(numpy.uint32)
             image = Image.frombytes(mode, shape, data32.tobytes())
         else:
@@ -400,7 +413,7 @@ def toimage(arr, high=255, low=0, cmin=None, cmax=None, pal=None,
     # if here then 3-d array with a 3 or a 4 in the shape length.
     # Check for 3 in datacube shape --- 'RGB' or 'YCbCr'
     if channel_axis is None:
-        if (3 in shape):
+        if 3 in shape:
             ca = numpy.flatnonzero(asarray(shape) == 3)[0]
         else:
             ca = numpy.flatnonzero(asarray(shape) == 4)
@@ -427,17 +440,17 @@ def toimage(arr, high=255, low=0, cmin=None, cmax=None, pal=None,
         shape = (shape[2], shape[1])
     if mode is None:
         if numch == 3:
-            mode = 'RGB'
+            mode = "RGB"
         else:
-            mode = 'RGBA'
+            mode = "RGBA"
 
-    if mode not in ['RGB', 'RGBA', 'YCbCr', 'CMYK']:
+    if mode not in ["RGB", "RGBA", "YCbCr", "CMYK"]:
         raise ValueError(_errstr)
 
-    if mode in ['RGB', 'YCbCr']:
+    if mode in ["RGB", "YCbCr"]:
         if numch != 3:
             raise ValueError("Invalid array shape for mode.")
-    if mode in ['RGBA', 'CMYK']:
+    if mode in ["RGBA", "CMYK"]:
         if numch != 4:
             raise ValueError("Invalid array shape for mode.")
 
@@ -446,7 +459,7 @@ def toimage(arr, high=255, low=0, cmin=None, cmax=None, pal=None,
     return image
 
 
-def imresize(arr, size, interp='bilinear', mode=None):
+def imresize(arr, size, interp="bilinear", mode=None):
     """
     Resize an image.
 
@@ -492,11 +505,11 @@ def imresize(arr, size, interp='bilinear', mode=None):
     ts = type(size)
     if issubdtype(ts, numpy.signedinteger):
         percent = size / 100.0
-        size = tuple((array(im.size)*percent).astype(int))
+        size = tuple((array(im.size) * percent).astype(int))
     elif issubdtype(type(size), numpy.floating):
-        size = tuple((array(im.size)*size).astype(int))
+        size = tuple((array(im.size) * size).astype(int))
     else:
         size = (size[1], size[0])
-    func = {'nearest': 0, 'lanczos': 1, 'bilinear': 2, 'bicubic': 3, 'cubic': 3}
+    func = {"nearest": 0, "lanczos": 1, "bilinear": 2, "bicubic": 3, "cubic": 3}
     imnew = im.resize(size, resample=func[interp])
     return fromimage(imnew)