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Harmonize all ndimage usage across the library

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Only two forms remain in use:

- `from scipy import ndimage as ndi`
- `from scipy.ndimage import function`
This commit is contained in:
Juan Nunez-Iglesias
2015-06-09 15:18:37 +10:00
parent 82a5d0c5d9
commit 0d134987f9
47 changed files with 173 additions and 185 deletions
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doc/examples/plot_gabor.py
+7 -8
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@@ -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 = []