Harmonize all ndimage usage across the library

Only two forms remain in use:

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

doc/examples/applications/plot_coins_segmentation.py

@@ -71,9 +71,9 @@ ax.set_title('Canny detector')
 These contours are then filled using mathematical morphology.
 """
 
-from scipy import ndimage
+from scipy import ndimage as ndi
 
-fill_coins = ndimage.binary_fill_holes(edges)
+fill_coins = ndi.binary_fill_holes(edges)
 
 fig, ax = plt.subplots(figsize=(4, 3))
 ax.imshow(fill_coins, cmap=plt.cm.gray, interpolation='nearest')
@@ -158,8 +158,8 @@ individually.
 
 from skimage.color import label2rgb
 
-segmentation = ndimage.binary_fill_holes(segmentation - 1)
-labeled_coins, _ = ndimage.label(segmentation)
+segmentation = ndi.binary_fill_holes(segmentation - 1)
+labeled_coins, _ = ndi.label(segmentation)
 image_label_overlay = label2rgb(labeled_coins, image=coins)
 
 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))

doc/examples/applications/plot_rank_filters.py

@@ -547,7 +547,7 @@ available in `skimage`.
 
 from time import time
 
-from scipy.ndimage.filters import percentile_filter
+from scipy.ndimage import percentile_filter
 from skimage.morphology import dilation
 from skimage.filters.rank import median, maximum
 

doc/examples/plot_canny.py

@@ -17,7 +17,7 @@ the hysteresis thresholding.
 """
 import numpy as np
 import matplotlib.pyplot as plt
-from scipy import ndimage
+from scipy import ndimage as ndi
 
 from skimage import feature
 
@@ -26,8 +26,8 @@ from skimage import feature
 im = np.zeros((128, 128))
 im[32:-32, 32:-32] = 1
 
-im = ndimage.rotate(im, 15, mode='constant')
-im = ndimage.gaussian_filter(im, 4)
+im = ndi.rotate(im, 15, mode='constant')
+im = ndi.gaussian_filter(im, 4)
 im += 0.2 * np.random.random(im.shape)
 
 # Compute the Canny filter for two values of sigma

doc/examples/plot_gabor.py

@@ -14,10 +14,9 @@ for classification, which is based on the least squared error for simplicity.
 """
 from __future__ import print_function
 
-import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
-from scipy import ndimage as nd
+from scipy import ndimage as ndi
 
 from skimage import data
 from skimage.util import img_as_float
@@ -27,7 +26,7 @@ from skimage.filters import gabor_kernel
 def compute_feats(image, kernels):
     feats = np.zeros((len(kernels), 2), dtype=np.double)
     for k, kernel in enumerate(kernels):
-        filtered = nd.convolve(image, kernel, mode='wrap')
+        filtered = ndi.convolve(image, kernel, mode='wrap')
         feats[k, 0] = filtered.mean()
         feats[k, 1] = filtered.var()
     return feats
@@ -71,23 +70,23 @@ ref_feats[2, :, :] = compute_feats(wall, kernels)
 print('Rotated images matched against references using Gabor filter banks:')
 
 print('original: brick, rotated: 30deg, match result: ', end='')
-feats = compute_feats(nd.rotate(brick, angle=190, reshape=False), kernels)
+feats = compute_feats(ndi.rotate(brick, angle=190, reshape=False), kernels)
 print(image_names[match(feats, ref_feats)])
 
 print('original: brick, rotated: 70deg, match result: ', end='')
-feats = compute_feats(nd.rotate(brick, angle=70, reshape=False), kernels)
+feats = compute_feats(ndi.rotate(brick, angle=70, reshape=False), kernels)
 print(image_names[match(feats, ref_feats)])
 
 print('original: grass, rotated: 145deg, match result: ', end='')
-feats = compute_feats(nd.rotate(grass, angle=145, reshape=False), kernels)
+feats = compute_feats(ndi.rotate(grass, angle=145, reshape=False), kernels)
 print(image_names[match(feats, ref_feats)])
 
 
 def power(image, kernel):
     # Normalize images for better comparison.
     image = (image - image.mean()) / image.std()
-    return np.sqrt(nd.convolve(image, np.real(kernel), mode='wrap')**2 +
-                   nd.convolve(image, np.imag(kernel), mode='wrap')**2)
+    return np.sqrt(ndi.convolve(image, np.real(kernel), mode='wrap')**2 +
+                   ndi.convolve(image, np.imag(kernel), mode='wrap')**2)
 
 # Plot a selection of the filter bank kernels and their responses.
 results = []

doc/examples/plot_join_segmentations.py

@@ -11,7 +11,7 @@ segmentations.
 
 """
 import numpy as np
-from scipy import ndimage as nd
+from scipy import ndimage as ndi
 import matplotlib.pyplot as plt
 
 from skimage.filters import sobel
@@ -30,7 +30,7 @@ markers[coins < 30.0 / 255] = background
 markers[coins > 150.0 / 255] = foreground
 
 ws = watershed(edges, markers)
-seg1 = nd.label(ws == foreground)[0]
+seg1 = ndi.label(ws == foreground)[0]
 
 # make segmentation using SLIC superpixels
 seg2 = slic(coins, n_segments=117, max_iter=160, sigma=1, compactness=0.75,

doc/examples/plot_marked_watershed.py

@@ -14,7 +14,7 @@ See Wikipedia_ for more details on the algorithm.
 
 """
 
-from scipy import ndimage
+from scipy import ndimage as ndi
 import matplotlib.pyplot as plt
 
 from skimage.morphology import watershed, disk
@@ -30,7 +30,7 @@ denoised = rank.median(image, disk(2))
 
 # find continuous region (low gradient) --> markers
 markers = rank.gradient(denoised, disk(5)) < 10
-markers = ndimage.label(markers)[0]
+markers = ndi.label(markers)[0]
 
 #local gradient
 gradient = rank.gradient(denoised, disk(2))

doc/examples/plot_medial_transform.py

@@ -21,7 +21,7 @@ a skeleton by iterative morphological thinnings.
 
 """
 import numpy as np
-from scipy import ndimage
+from scipy import ndimage as ndi
 from skimage.morphology import medial_axis
 import matplotlib.pyplot as plt
 
@@ -43,7 +43,7 @@ def microstructure(l=256):
     generator = np.random.RandomState(1)
     points = l * generator.rand(2, n**2)
     mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
-    mask = ndimage.gaussian_filter(mask, sigma=l/(4.*n))
+    mask = ndi.gaussian_filter(mask, sigma=l/(4.*n))
     return mask > mask.mean()
 
 data = microstructure(l=64)

doc/examples/plot_peak_local_max.py

@@ -10,7 +10,7 @@ size of the dilation. Locations where the original image is equal to the
 dilated image are returned as local maxima.
 
 """
-from scipy import ndimage
+from scipy import ndimage as ndi
 import matplotlib.pyplot as plt
 from skimage.feature import peak_local_max
 from skimage import data, img_as_float
@@ -19,7 +19,7 @@ im = img_as_float(data.coins())
 
 # image_max is the dilation of im with a 20*20 structuring element
 # It is used within peak_local_max function
-image_max = ndimage.maximum_filter(im, size=20, mode='constant')
+image_max = ndi.maximum_filter(im, size=20, mode='constant')
 
 # Comparison between image_max and im to find the coordinates of local maxima
 coordinates = peak_local_max(im, min_distance=20)

doc/examples/plot_random_walker_segmentation.py

@@ -21,7 +21,6 @@ values, and use the random walker for the segmentation.
 
 """
 import numpy as np
-from scipy import ndimage
 import matplotlib.pyplot as plt
 
 from skimage.segmentation import random_walker

doc/examples/plot_register_translation.py

@@ -21,7 +21,7 @@ import matplotlib.pyplot as plt
 from skimage import data
 from skimage.feature import register_translation
 from skimage.feature.register_translation import _upsampled_dft
-from scipy.ndimage.fourier import fourier_shift
+from scipy.ndimage import fourier_shift
 
 image = data.camera()
 shift = (-2.4, 1.32)

doc/examples/plot_watershed.py

@@ -26,7 +26,7 @@ See Wikipedia_ for more details on the algorithm.
 """
 import numpy as np
 import matplotlib.pyplot as plt
-from scipy import ndimage
+from scipy import ndimage as ndi
 
 from skimage.morphology import watershed
 from skimage.feature import peak_local_max
@@ -42,10 +42,10 @@ image = np.logical_or(mask_circle1, mask_circle2)
 
 # Now we want to separate the two objects in image
 # Generate the markers as local maxima of the distance to the background
-distance = ndimage.distance_transform_edt(image)
+distance = ndi.distance_transform_edt(image)
 local_maxi = peak_local_max(distance, indices=False, footprint=np.ones((3, 3)),
                             labels=image)
-markers = ndimage.label(local_maxi)[0]
+markers = ndi.label(local_maxi)[0]
 labels = watershed(-distance, markers, mask=image)
 
 fig, axes = plt.subplots(ncols=3, figsize=(8, 2.7))