Harmonize all ndimage usage across the library

Only two forms remain in use:

- `from scipy import ndimage as ndi`
- `from scipy.ndimage import function`

skimage/transform/_geometric.py

@@ -2,11 +2,10 @@ import six
 import math
 import warnings
 import numpy as np
-from scipy import ndimage, spatial
+from scipy import ndimage as ndi, spatial
 
 from .._shared.utils import get_bound_method_class, safe_as_int
 from ..util import img_as_float
-from ..exposure import rescale_intensity
 from ._warps_cy import _warp_fast
 
 
@@ -1054,7 +1053,7 @@ def warp_coords(coord_map, shape, dtype=np.float64):
     users who would like, for example, to re-use a particular coordinate
     mapping, to use specific dtypes at various points along the the
     image-warping process, or to implement different post-processing logic
-    than `warp` performs after the call to `ndimage.map_coordinates`.
+    than `warp` performs after the call to `ndi.map_coordinates`.
 
 
     Examples
@@ -1352,7 +1351,7 @@ def warp(image, inverse_map=None, map_args={}, output_shape=None, order=1,
                 warped = np.dstack(dims)
 
     if warped is None:
-        # use ndimage.map_coordinates
+        # use ndi.map_coordinates
 
         if (isinstance(inverse_map, np.ndarray)
                 and inverse_map.shape == (3, 3)):
@@ -1388,8 +1387,8 @@ def warp(image, inverse_map=None, map_args={}, output_shape=None, order=1,
         # Pre-filtering not necessary for order 0, 1 interpolation
         prefilter = order > 1
 
-        warped = ndimage.map_coordinates(image, coords, prefilter=prefilter,
-                                      mode=mode, order=order, cval=cval)
+        warped = ndi.map_coordinates(image, coords, prefilter=prefilter,
+                                     mode=mode, order=order, cval=cval)
 
 
     _clip_warp_output(image, warped, order, mode, cval, clip)

skimage/transform/_warps.py

@@ -1,5 +1,5 @@
 import numpy as np
-from scipy import ndimage
+from scipy import ndimage as ndi
 
 from ..measure import block_reduce
 from ._geometric import (warp, SimilarityTransform, AffineTransform,
@@ -81,8 +81,8 @@ def resize(image, output_shape, order=1, mode='constant', cval=0, clip=True,
 
         image = _convert_warp_input(image, preserve_range)
 
-        out = ndimage.map_coordinates(image, coord_map, order=order,
-                                      mode=mode, cval=cval)
+        out = ndi.map_coordinates(image, coord_map, order=order,
+                                  mode=mode, cval=cval)
 
         _clip_warp_output(image, out, order, mode, cval, clip)
 

skimage/transform/hough_transform.py

@@ -1,5 +1,5 @@
 import numpy as np
-from scipy import ndimage
+from scipy import ndimage as ndi
 from .. import measure, morphology
 from ._hough_transform import _hough_circle
 
@@ -63,10 +63,10 @@ def hough_line_peaks(hspace, angles, dists, min_distance=9, min_angle=10,
 
     distance_size = 2 * min_distance + 1
     angle_size = 2 * min_angle + 1
-    hspace_max = ndimage.maximum_filter1d(hspace, size=distance_size, axis=0,
-                                          mode='constant', cval=0)
-    hspace_max = ndimage.maximum_filter1d(hspace_max, size=angle_size, axis=1,
-                                          mode='constant', cval=0)
+    hspace_max = ndi.maximum_filter1d(hspace, size=distance_size, axis=0,
+                                      mode='constant', cval=0)
+    hspace_max = ndi.maximum_filter1d(hspace_max, size=angle_size, axis=1,
+                                      mode='constant', cval=0)
     mask = (hspace == hspace_max)
     hspace *= mask
     hspace_t = hspace > threshold

skimage/transform/pyramids.py

@@ -1,6 +1,6 @@
 import math
 import numpy as np
-from scipy import ndimage
+from scipy import ndimage as ndi
 from ..transform import resize
 from ..util import img_as_float
 
@@ -13,12 +13,12 @@ def _smooth(image, sigma, mode, cval):
     # apply Gaussian filter to all dimensions independently
     if image.ndim == 3:
         for dim in range(image.shape[2]):
-            ndimage.gaussian_filter(image[..., dim], sigma,
-                                    output=smoothed[..., dim],
-                                    mode=mode, cval=cval)
-    else:
-        ndimage.gaussian_filter(image, sigma, output=smoothed,
+            ndi.gaussian_filter(image[..., dim], sigma,
+                                output=smoothed[..., dim],
                                 mode=mode, cval=cval)
+    else:
+        ndi.gaussian_filter(image, sigma, output=smoothed,
+                            mode=mode, cval=cval)
 
     return smoothed