wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-06-27 19:48:43 +08:00
Code Issues Packages Projects Releases Wiki Activity

Harmonize all ndimage usage across the library

Browse Source 
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
Download Patch File Download Diff File Expand all files Collapse all files
doc/examples/applications/plot_coins_segmentation.py
+4 -4
View File
@@ -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
+1 -1
View File
@@ -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
+3 -3
View File
@@ -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
+7 -8
View File
@@ -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
+2 -2
View File
@@ -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
+2 -2
View File
@@ -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
+2 -2
View File
@@ -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
+2 -2
View File
@@ -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
-1
View File
@@ -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
+1 -1
View File
@@ -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
+3 -3
View File
@@ -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))
doc/source/user_guide/numpy_images.txt
+2 -2
View File
@@ -169,8 +169,8 @@ Many functions in scikit-image operate on 3D images directly:
>>> im3d = np.random.rand(100, 1000, 1000)
>>> from skimage import morphology
>>> from scipy import ndimage as nd
>>> seeds = nd.label(im3d < 0.1)[0]
>>> from scipy import ndimage as ndi
>>> seeds = ndi.label(im3d < 0.1)[0]
>>> ws = morphology.watershed(im3d, seeds)
In many cases,
doc/source/user_guide/tutorial_segmentation.txt
+8 -8
View File
@@ -50,8 +50,8 @@ boundary of the coins, or inside the coins.
::
>>> from scipy import ndimage
>>> fill_coins = ndimage.binary_fill_holes(edges)
>>> from scipy import ndimage as ndi
>>> fill_coins = ndi.binary_fill_holes(edges)
.. image:: ../auto_examples/applications/images/plot_coins_segmentation_3.png
:target: ../auto_examples/applications/plot_coins_segmentation.html
@@ -59,7 +59,7 @@ boundary of the coins, or inside the coins.
Now that we have contours that delineate the outer boundary of the coins,
we fill the inner part of the coins using the
``ndimage.binary_fill_holes`` function, which uses mathematical morphology
``ndi.binary_fill_holes`` function, which uses mathematical morphology
to fill the holes.
.. image:: ../auto_examples/applications/images/plot_coins_segmentation_4.png
@@ -67,12 +67,12 @@ to fill the holes.
:align: center
Most coins are well segmented out of the background. Small objects from
the background can be easily removed using the ``ndimage.label``
the background can be easily removed using the ``ndi.label``
function to remove objects smaller than a small threshold.
::
>>> label_objects, nb_labels = ndimage.label(fill_coins)
>>> label_objects, nb_labels = ndi.label(fill_coins)
>>> sizes = np.bincount(label_objects.ravel())
>>> mask_sizes = sizes > 20
>>> mask_sizes[0] = 0
@@ -159,11 +159,11 @@ background.
We remove a few small holes with mathematical morphology::
>>> segmentation = ndimage.binary_fill_holes(segmentation - 1)
>>> segmentation = ndi.binary_fill_holes(segmentation - 1)
We can now label all the coins one by one using ``ndimage.label``::
We can now label all the coins one by one using ``ndi.label``::
>>> labeled_coins, _ = ndimage.label(segmentation)
>>> labeled_coins, _ = ndi.label(segmentation)
.. image:: ../auto_examples/applications/images/plot_coins_segmentation_9.png
:target: ../auto_examples/applications/plot_coins_segmentation.html
skimage/feature/blob.py
+1 -1
View File
@@ -1,6 +1,6 @@
import numpy as np
from scipy.ndimage.filters import gaussian_filter, gaussian_laplace
from scipy.ndimage import gaussian_filter, gaussian_laplace
import itertools as itt
import math
from math import sqrt, hypot, log
skimage/feature/brief.py
+1 -1
View File
@@ -1,5 +1,5 @@
import numpy as np
from scipy.ndimage.filters import gaussian_filter
from scipy.ndimage import gaussian_filter
from .util import (DescriptorExtractor, _mask_border_keypoints,
_prepare_grayscale_input_2D)
skimage/feature/censure.py
+1 -1
View File
@@ -1,5 +1,5 @@
import numpy as np
from scipy.ndimage.filters import maximum_filter, minimum_filter, convolve
from scipy.ndimage import maximum_filter, minimum_filter, convolve
from ..transform import integral_image
from ..feature import structure_tensor
skimage/feature/corner.py
+9 -9
View File
@@ -1,5 +1,5 @@
import numpy as np
from scipy import ndimage
from scipy import ndimage as ndi
from scipy import stats
from ..util import img_as_float, pad
@@ -33,8 +33,8 @@ def _compute_derivatives(image, mode='constant', cval=0):
"""
imy = ndimage.sobel(image, axis=0, mode=mode, cval=cval)
imx = ndimage.sobel(image, axis=1, mode=mode, cval=cval)
imy = ndi.sobel(image, axis=0, mode=mode, cval=cval)
imx = ndi.sobel(image, axis=1, mode=mode, cval=cval)
return imx, imy
@@ -92,9 +92,9 @@ def structure_tensor(image, sigma=1, mode='constant', cval=0):
imx, imy = _compute_derivatives(image, mode=mode, cval=cval)
# structure tensore
Axx = ndimage.gaussian_filter(imx * imx, sigma, mode=mode, cval=cval)
Axy = ndimage.gaussian_filter(imx * imy, sigma, mode=mode, cval=cval)
Ayy = ndimage.gaussian_filter(imy * imy, sigma, mode=mode, cval=cval)
Axx = ndi.gaussian_filter(imx * imx, sigma, mode=mode, cval=cval)
Axy = ndi.gaussian_filter(imx * imy, sigma, mode=mode, cval=cval)
Ayy = ndi.gaussian_filter(imy * imy, sigma, mode=mode, cval=cval)
return Axx, Axy, Ayy
@@ -165,9 +165,9 @@ def hessian_matrix(image, sigma=1, mode='constant', cval=0):
kernel_xy /= kernel_xx.sum()
kernel_yy = kernel_xx.transpose()
Hxx = ndimage.convolve(image, kernel_xx, mode=mode, cval=cval)
Hxy = ndimage.convolve(image, kernel_xy, mode=mode, cval=cval)
Hyy = ndimage.convolve(image, kernel_yy, mode=mode, cval=cval)
Hxx = ndi.convolve(image, kernel_xx, mode=mode, cval=cval)
Hxy = ndi.convolve(image, kernel_xy, mode=mode, cval=cval)
Hyy = ndi.convolve(image, kernel_yy, mode=mode, cval=cval)
return Hxx, Hxy, Hyy
skimage/feature/tests/test_hog.py
+2 -2
View File
@@ -1,5 +1,5 @@
import numpy as np
from scipy import ndimage
from scipy import ndimage as ndi
from skimage import data
from skimage import feature
from skimage import img_as_float
@@ -137,7 +137,7 @@ def test_hog_orientations_circle():
image = np.zeros((height, width))
rr, cc = draw.circle(int(height / 2), int(width / 2), int(width / 3))
image[rr, cc] = 100
image = ndimage.gaussian_filter(image, 2)
image = ndi.gaussian_filter(image, 2)
for orientations in range(2, 15):
(hog, hog_img) = feature.hog(image, orientations=orientations,
skimage/feature/tests/test_peak.py
+5 -5
View File
@@ -1,7 +1,7 @@
import numpy as np
from numpy.testing import (assert_array_almost_equal as assert_close,
assert_equal)
import scipy.ndimage
from scipy import ndimage as ndi
from skimage.feature import peak
@@ -101,8 +101,8 @@ def test_reorder_labels():
expected = np.zeros(image.shape, float)
for imin, imax in ((0, 20), (20, 40)):
for jmin, jmax in ((0, 30), (30, 60)):
expected[imin:imax, jmin:jmax] = scipy.ndimage.maximum_filter(
image[imin:imax, jmin:jmax], footprint=footprint)
expected[imin:imax, jmin:jmax] = ndi.maximum_filter(
image[imin:imax, jmin:jmax], footprint=footprint)
expected = (expected == image)
result = peak.peak_local_max(image, labels=labels, min_distance=1,
threshold_rel=0, footprint=footprint,
@@ -119,7 +119,7 @@ def test_indices_with_labels():
expected = np.zeros(image.shape, float)
for imin, imax in ((0, 20), (20, 40)):
for jmin, jmax in ((0, 30), (30, 60)):
expected[imin:imax, jmin:jmax] = scipy.ndimage.maximum_filter(
expected[imin:imax, jmin:jmax] = ndi.maximum_filter(
image[imin:imax, jmin:jmax], footprint=footprint)
expected = (expected == image)
result = peak.peak_local_max(image, labels=labels, min_distance=1,
@@ -251,7 +251,7 @@ def test_four_quadrants():
expected = np.zeros(image.shape, float)
for imin, imax in ((0, 20), (20, 40)):
for jmin, jmax in ((0, 30), (30, 60)):
expected[imin:imax, jmin:jmax] = scipy.ndimage.maximum_filter(
expected[imin:imax, jmin:jmax] = ndi.maximum_filter(
image[imin:imax, jmin:jmax], footprint=footprint)
expected = (expected == image)
result = peak.peak_local_max(image, labels=labels, footprint=footprint,
skimage/feature/tests/test_register_translation.py
+1 -1
View File
@@ -4,7 +4,7 @@ from numpy.testing import assert_allclose, assert_raises
from skimage.feature.register_translation import (register_translation,
_upsampled_dft)
from skimage.data import camera
from scipy.ndimage.fourier import fourier_shift
from scipy.ndimage import fourier_shift
def test_correlation():
skimage/filters/_gabor.py
+5 -5
View File
@@ -1,5 +1,5 @@
import numpy as np
from scipy import ndimage
from scipy import ndimage as ndi
from .._shared.utils import assert_nD
@@ -130,10 +130,10 @@ def gabor_filter(image, frequency, theta=0, bandwidth=1, sigma_x=None,
offset : float, optional
Phase offset of harmonic function in radians.
mode : string, optional
Mode used to convolve image with a kernel, passed to `ndimage.convolve`
Mode used to convolve image with a kernel, passed to `ndi.convolve`
cval : scalar, optional
Value to fill past edges of input if `mode` of convolution is
'constant'. The parameter is passed to `ndimage.convolve`.
'constant'. The parameter is passed to `ndi.convolve`.
Returns
-------
@@ -169,7 +169,7 @@ def gabor_filter(image, frequency, theta=0, bandwidth=1, sigma_x=None,
g = gabor_kernel(frequency, theta, bandwidth, sigma_x, sigma_y, n_stds,
offset)
filtered_real = ndimage.convolve(image, np.real(g), mode=mode, cval=cval)
filtered_imag = ndimage.convolve(image, np.imag(g), mode=mode, cval=cval)
filtered_real = ndi.convolve(image, np.real(g), mode=mode, cval=cval)
filtered_imag = ndi.convolve(image, np.imag(g), mode=mode, cval=cval)
return filtered_real, filtered_imag
skimage/filters/_gaussian.py
+3 -3
View File
@@ -1,6 +1,6 @@
import collections as coll
import numpy as np
from scipy import ndimage
from scipy import ndimage as ndi
import warnings
from ..util import img_as_float
@@ -46,7 +46,7 @@ def gaussian_filter(image, sigma, output=None, mode='nearest', cval=0,
Notes
-----
This function is a wrapper around :func:`scipy.ndimage.gaussian_filter`.
This function is a wrapper around :func:`scipy.ndi.gaussian_filter`.
Integer arrays are converted to float.
@@ -100,4 +100,4 @@ def gaussian_filter(image, sigma, output=None, mode='nearest', cval=0,
if len(sigma) != image.ndim:
sigma = np.concatenate((np.asarray(sigma), [0]))
image = img_as_float(image)
return ndimage.gaussian_filter(image, sigma, mode=mode, cval=cval)
return ndi.gaussian_filter(image, sigma, mode=mode, cval=cval)
skimage/filters/thresholding.py
+8 -11
View File
@@ -5,7 +5,7 @@ __all__ = ['threshold_adaptive',
'threshold_li', ]
import numpy as np
import scipy.ndimage
from scipy import ndimage as ndi
from ..exposure import histogram
from .._shared.utils import assert_nD
@@ -70,26 +70,23 @@ def threshold_adaptive(image, block_size, method='gaussian', offset=0,
assert_nD(image, 2)
thresh_image = np.zeros(image.shape, 'double')
if method == 'generic':
scipy.ndimage.generic_filter(image, param, block_size,
output=thresh_image, mode=mode)
ndi.generic_filter(image, param, block_size,
output=thresh_image, mode=mode)
elif method == 'gaussian':
if param is None:
# automatically determine sigma which covers > 99% of distribution
sigma = (block_size - 1) / 6.0
else:
sigma = param
scipy.ndimage.gaussian_filter(image, sigma, output=thresh_image,
mode=mode)
ndi.gaussian_filter(image, sigma, output=thresh_image, mode=mode)
elif method == 'mean':
mask = 1. / block_size * np.ones((block_size,))
# separation of filters to speedup convolution
scipy.ndimage.convolve1d(image, mask, axis=0, output=thresh_image,
mode=mode)
scipy.ndimage.convolve1d(thresh_image, mask, axis=1,
output=thresh_image, mode=mode)
ndi.convolve1d(image, mask, axis=0, output=thresh_image, mode=mode)
ndi.convolve1d(thresh_image, mask, axis=1,
output=thresh_image, mode=mode)
elif method == 'median':
scipy.ndimage.median_filter(image, block_size, output=thresh_image,
mode=mode)
ndi.median_filter(image, block_size, output=thresh_image, mode=mode)
return image > (thresh_image - offset)
skimage/future/graph/rag.py
+3 -4
View File
@@ -11,8 +11,7 @@ except ImportError:
warnings.warn(msg)
import numpy as np
from scipy.ndimage import filters
from scipy import ndimage as nd
from scipy import ndimage as ndi
import math
from ... import draw, measure, segmentation, util, color
try:
@@ -269,7 +268,7 @@ def rag_mean_color(image, labels, connectivity=2, mode='distance',
# The footprint is constructed in such a way that the first
# element in the array being passed to _add_edge_filter is
# the central value.
fp = nd.generate_binary_structure(labels.ndim, connectivity)
fp = ndi.generate_binary_structure(labels.ndim, connectivity)
for d in range(fp.ndim):
fp = fp.swapaxes(0, d)
fp[0, ...] = 0
@@ -286,7 +285,7 @@ def rag_mean_color(image, labels, connectivity=2, mode='distance',
# [0,1,1],
# [0,1,1]]
filters.generic_filter(
ndi.generic_filter(
labels,
function=_add_edge_filter,
footprint=fp,
skimage/measure/_regionprops.py
+8 -9
View File
@@ -1,8 +1,7 @@
# coding: utf-8
import warnings
from math import sqrt, atan2, pi as PI
import numpy as np
from scipy import ndimage
from scipy import ndimage as ndi
from ._label import label
from . import _moments
@@ -172,7 +171,7 @@ class _RegionProperties(object):
@_cached_property
def filled_image(self):
return ndimage.binary_fill_holes(self.image, STREL_8)
return ndi.binary_fill_holes(self.image, STREL_8)
@_cached_property
def image(self):
@@ -494,7 +493,7 @@ def regionprops(label_image, intensity_image=None, cache=True):
regions = []
objects = ndimage.find_objects(label_image)
objects = ndi.find_objects(label_image)
for i, sl in enumerate(objects):
if sl is None:
continue
@@ -534,7 +533,7 @@ def perimeter(image, neighbourhood=4):
else:
strel = STREL_8
image = image.astype(np.uint8)
eroded_image = ndimage.binary_erosion(image, strel, border_value=0)
eroded_image = ndi.binary_erosion(image, strel, border_value=0)
border_image = image - eroded_image
perimeter_weights = np.zeros(50, dtype=np.double)
@@ -543,10 +542,10 @@ def perimeter(image, neighbourhood=4):
perimeter_weights[[13, 23]] = (1 + sqrt(2)) / 2
perimeter_image = ndimage.convolve(border_image, np.array([[10, 2, 10],
[ 2, 1, 2],
[10, 2, 10]]),
mode='constant', cval=0)
perimeter_image = ndi.convolve(border_image, np.array([[10, 2, 10],
[ 2, 1, 2],
[10, 2, 10]]),
mode='constant', cval=0)
# You can also write
# return perimeter_weights[perimeter_image].sum()
skimage/measure/_structural_similarity.py
+1 -1
View File
@@ -3,7 +3,7 @@ from __future__ import division
__all__ = ['structural_similarity']
import numpy as np
from scipy.ndimage.filters import uniform_filter, gaussian_filter
from scipy.ndimage import uniform_filter, gaussian_filter
from ..util.dtype import dtype_range
from ..util.arraypad import crop
skimage/measure/profile.py
+5 -5
View File
@@ -1,5 +1,5 @@
import numpy as np
import scipy.ndimage as nd
from scipy import ndimage as ndi
def profile_line(img, src, dst, linewidth=1,
@@ -52,13 +52,13 @@ def profile_line(img, src, dst, linewidth=1,
"""
perp_lines = _line_profile_coordinates(src, dst, linewidth=linewidth)
if img.ndim == 3:
pixels = [nd.map_coordinates(img[..., i], perp_lines,
order=order, mode=mode, cval=cval)
pixels = [ndi.map_coordinates(img[..., i], perp_lines,
order=order, mode=mode, cval=cval)
for i in range(img.shape[2])]
pixels = np.transpose(np.asarray(pixels), (1, 2, 0))
else:
pixels = nd.map_coordinates(img, perp_lines,
order=order, mode=mode, cval=cval)
pixels = ndi.map_coordinates(img, perp_lines,
order=order, mode=mode, cval=cval)
intensities = pixels.mean(axis=1)
return intensities
skimage/morphology/_skeletonize.py
+8 -8
View File
@@ -3,7 +3,7 @@ Algorithms for computing the skeleton of a binary image
"""
import numpy as np
from scipy import ndimage
from scipy import ndimage as ndi
from ._skeletonize_cy import _skeletonize_loop, _table_lookup_index
@@ -119,7 +119,7 @@ def skeletonize(image):
pixel_removed = False
# assign each pixel a unique value based on its foreground neighbours
neighbours = ndimage.correlate(skeleton, mask, mode='constant')
neighbours = ndi.correlate(skeleton, mask, mode='constant')
# ignore background
neighbours *= skeleton
@@ -138,7 +138,7 @@ def skeletonize(image):
skeleton[code_mask] = 0
# pass 2 - remove the 2's and 3's
neighbours = ndimage.correlate(skeleton, mask, mode='constant')
neighbours = ndi.correlate(skeleton, mask, mode='constant')
neighbours *= skeleton
codes = np.take(lut, neighbours)
code_mask = (codes == 2)
@@ -154,7 +154,7 @@ def skeletonize(image):
# --------- Skeletonization by medial axis transform --------
_eight_connect = ndimage.generate_binary_structure(2, 2)
_eight_connect = ndi.generate_binary_structure(2, 2)
def medial_axis(image, mask=None, return_distance=False):
@@ -247,17 +247,17 @@ def medial_axis(image, mask=None, return_distance=False):
center_is_foreground = (np.arange(512) & 2**4).astype(bool)
table = (center_is_foreground # condition 1.
&
(np.array([ndimage.label(_pattern_of(index), _eight_connect)[1] !=
ndimage.label(_pattern_of(index & ~ 2**4),
(np.array([ndi.label(_pattern_of(index), _eight_connect)[1] !=
ndi.label(_pattern_of(index & ~ 2**4),
_eight_connect)[1]
for index in range(512)]) # condition 2
for index in range(512)]) # condition 2
|
np.array([np.sum(_pattern_of(index)) < 3 for index in range(512)]))
# condition 3
)
# Build distance transform
distance = ndimage.distance_transform_edt(masked_image)
distance = ndi.distance_transform_edt(masked_image)
if return_distance:
store_distance = distance.copy()
skimage/morphology/binary.py
+3 -3
View File
@@ -2,7 +2,7 @@
Binary morphological operations
"""
import numpy as np
from scipy import ndimage as nd
from scipy import ndimage as ndi
from .misc import default_selem
@@ -39,7 +39,7 @@ def binary_erosion(image, selem=None, out=None):
"""
if out is None:
out = np.empty(image.shape, dtype=np.bool)
nd.binary_erosion(image, structure=selem, output=out)
ndi.binary_erosion(image, structure=selem, output=out)
return out
@@ -74,7 +74,7 @@ def binary_dilation(image, selem=None, out=None):
"""
if out is None:
out = np.empty(image.shape, dtype=np.bool)
nd.binary_dilation(image, structure=selem, output=out)
ndi.binary_dilation(image, structure=selem, output=out)
return out
skimage/morphology/grey.py
+6 -6
View File
@@ -3,7 +3,7 @@ Grayscale morphological operations
"""
import functools
import numpy as np
from scipy import ndimage as nd
from scipy import ndimage as ndi
from .misc import default_selem
from ..util import pad, crop
@@ -53,7 +53,7 @@ def _invert_selem(selem):
"""Change the order of the values in `selem`.
This is a patch for the *weird* footprint inversion in
`nd.grey_morphology` [1]_.
`ndi.grey_morphology` [1]_.
Parameters
----------
@@ -182,7 +182,7 @@ def erosion(image, selem=None, out=None, shift_x=False, shift_y=False):
selem = _shift_selem(selem, shift_x, shift_y)
if out is None:
out = np.empty_like(image)
nd.grey_erosion(image, footprint=selem, output=out)
ndi.grey_erosion(image, footprint=selem, output=out)
return out
@@ -243,12 +243,12 @@ def dilation(image, selem=None, out=None, shift_x=False, shift_y=False):
# Inside ndimage.grey_dilation, the structuring element is inverted,
# eg. `selem = selem[::-1, ::-1]` for 2D [1]_, for reasons unknown to
# this author (@jni). To "patch" this behaviour, we invert our own
# selem before passing it to `nd.grey_dilation`.
# selem before passing it to `ndi.grey_dilation`.
# [1] https://github.com/scipy/scipy/blob/ec20ababa400e39ac3ffc9148c01ef86d5349332/scipy/ndimage/morphology.py#L1285
selem = _invert_selem(selem)
if out is None:
out = np.empty_like(image)
nd.grey_dilation(image, footprint=selem, output=out)
ndi.grey_dilation(image, footprint=selem, output=out)
return out
@@ -401,7 +401,7 @@ def white_tophat(image, selem=None, out=None):
return out
elif out is None:
out = np.empty_like(image)
out = nd.white_tophat(image, footprint=selem, output=out)
out = ndi.white_tophat(image, footprint=selem, output=out)
return out
skimage/morphology/misc.py
+3 -3
View File
@@ -1,7 +1,7 @@
import numpy as np
import functools
import warnings
import scipy.ndimage as nd
from scipy import ndimage as ndi
from .selem import _default_selem
# Our function names don't exactly correspond to ndimages.
@@ -102,9 +102,9 @@ def remove_small_objects(ar, min_size=64, connectivity=1, in_place=False):
return out
if out.dtype == bool:
selem = nd.generate_binary_structure(ar.ndim, connectivity)
selem = ndi.generate_binary_structure(ar.ndim, connectivity)
ccs = np.zeros_like(ar, dtype=np.int32)
nd.label(ar, selem, output=ccs)
ndi.label(ar, selem, output=ccs)
else:
ccs = out
skimage/morphology/selem.py
+2 -2
View File
@@ -4,7 +4,7 @@
"""
import numpy as np
from scipy import ndimage
from scipy import ndimage as ndi
from .. import draw
def square(width, dtype=np.uint8):
@@ -360,4 +360,4 @@ def _default_selem(ndim):
are 1 and 0 otherwise.
"""
return ndimage.morphology.generate_binary_structure(ndim, 1)
return ndi.morphology.generate_binary_structure(ndim, 1)
skimage/morphology/tests/test_binary.py
+5 -5
View File
@@ -4,7 +4,7 @@ from numpy import testing
from skimage import data, color
from skimage.util import img_as_bool
from skimage.morphology import binary, grey, selem
from scipy import ndimage
from scipy import ndimage as ndi
img = color.rgb2gray(data.astronaut())
@@ -96,7 +96,7 @@ def test_3d_fallback_default_selem():
# expect a "hyper-cross" centered in the 5x5x5:
image_expected = np.zeros((7, 7, 7), dtype=bool)
image_expected[2:5, 2:5, 2:5] = ndimage.generate_binary_structure(3, 1)
image_expected[2:5, 2:5, 2:5] = ndi.generate_binary_structure(3, 1)
testing.assert_array_equal(opened, image_expected)
def test_3d_fallback_cube_selem():
@@ -119,9 +119,9 @@ def test_2d_ndimage_equivalence():
bin_opened = binary.binary_opening(image)
bin_closed = binary.binary_closing(image)
selem = ndimage.generate_binary_structure(2, 1)
ndimage_opened = ndimage.binary_opening(image, structure=selem)
ndimage_closed = ndimage.binary_closing(image, structure=selem)
selem = ndi.generate_binary_structure(2, 1)
ndimage_opened = ndi.binary_opening(image, structure=selem)
ndimage_closed = ndi.binary_closing(image, structure=selem)
testing.assert_array_equal(bin_opened, ndimage_opened)
testing.assert_array_equal(bin_closed, ndimage_closed)
skimage/morphology/tests/test_grey.py
+9 -10
View File
@@ -2,9 +2,8 @@ import os.path
import numpy as np
from numpy import testing
from scipy import ndimage
from scipy import ndimage as ndi
import skimage
from skimage import data_dir, img_as_uint
from skimage.morphology import grey, selem
from skimage._shared._warnings import expected_warnings
@@ -152,7 +151,7 @@ def test_3d_fallback_default_selem():
# expect a "hyper-cross" centered in the 5x5x5:
image_expected = np.zeros((7, 7, 7), dtype=bool)
image_expected[2:5, 2:5, 2:5] = ndimage.generate_binary_structure(3, 1)
image_expected[2:5, 2:5, 2:5] = ndi.generate_binary_structure(3, 1)
testing.assert_array_equal(opened, image_expected)
def test_3d_fallback_cube_selem():
@@ -174,9 +173,9 @@ def test_3d_fallback_white_tophat():
with expected_warnings(['operator.*deprecated|\A\Z']):
new_image = grey.white_tophat(image)
footprint = ndimage.generate_binary_structure(3,1)
footprint = ndi.generate_binary_structure(3,1)
with expected_warnings(['operator.*deprecated|\A\Z']):
image_expected = ndimage.white_tophat(image,footprint=footprint)
image_expected = ndi.white_tophat(image,footprint=footprint)
testing.assert_array_equal(new_image, image_expected)
def test_3d_fallback_black_tophat():
@@ -187,9 +186,9 @@ def test_3d_fallback_black_tophat():
with expected_warnings(['operator.*deprecated|\A\Z']):
new_image = grey.black_tophat(image)
footprint = ndimage.generate_binary_structure(3,1)
footprint = ndi.generate_binary_structure(3,1)
with expected_warnings(['operator.*deprecated|\A\Z']):
image_expected = ndimage.black_tophat(image,footprint=footprint)
image_expected = ndi.black_tophat(image,footprint=footprint)
testing.assert_array_equal(new_image, image_expected)
def test_2d_ndimage_equivalence():
@@ -201,9 +200,9 @@ def test_2d_ndimage_equivalence():
opened = grey.opening(image)
closed = grey.closing(image)
selem = ndimage.generate_binary_structure(2, 1)
ndimage_opened = ndimage.grey_opening(image, footprint=selem)
ndimage_closed = ndimage.grey_closing(image, footprint=selem)
selem = ndi.generate_binary_structure(2, 1)
ndimage_opened = ndi.grey_opening(image, footprint=selem)
ndimage_closed = ndi.grey_closing(image, footprint=selem)
testing.assert_array_equal(opened, ndimage_opened)
testing.assert_array_equal(closed, ndimage_closed)
skimage/morphology/tests/test_watershed.py
+3 -4
View File
@@ -46,7 +46,7 @@ import math
import unittest
import numpy as np
import scipy.ndimage
from scipy import ndimage as ndi
from skimage.morphology.watershed import watershed, _slow_watershed
@@ -380,10 +380,9 @@ class TestWatershed(unittest.TestCase):
markers[x, y] = idx
idx += 1
image = scipy.ndimage.gaussian_filter(image, 4)
image = ndi.gaussian_filter(image, 4)
watershed(image, markers, self.eight)
scipy.ndimage.watershed_ift(image.astype(np.uint16), markers,
self.eight)
ndi.watershed_ift(image.astype(np.uint16), markers, self.eight)
def test_watershed10(self):
"watershed 10"
skimage/morphology/watershed.py
+5 -6
View File
@@ -26,9 +26,8 @@ Original author: Lee Kamentsky
from _heapq import heappush, heappop
import numpy as np
import scipy.ndimage
from scipy import ndimage as ndi
from ..filters import rank_order
from .._shared.utils import deprecated
from . import _watershed
@@ -114,13 +113,13 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
>>> # Now we want to separate the two objects in image
>>> # Generate the markers as local maxima of the distance
>>> # to the background
>>> from scipy import ndimage
>>> distance = ndimage.distance_transform_edt(image)
>>> from scipy import ndimage as ndi
>>> distance = ndi.distance_transform_edt(image)
>>> from skimage.feature import peak_local_max
>>> local_maxi = peak_local_max(distance, labels=image,
... footprint=np.ones((3, 3)),
... indices=False)
>>> markers = ndimage.label(local_maxi)[0]
>>> markers = ndi.label(local_maxi)[0]
>>> labels = watershed(-distance, markers, mask=image)
The algorithm works also for 3-D images, and can be used for example to
@@ -128,7 +127,7 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
"""
if connectivity is None:
c_connectivity = scipy.ndimage.generate_binary_structure(image.ndim, 1)
c_connectivity = ndi.generate_binary_structure(image.ndim, 1)
else:
c_connectivity = np.array(connectivity, bool)
if c_connectivity.ndim != image.ndim:
skimage/restoration/tests/test_denoise.py
+3 -4
View File
@@ -24,10 +24,9 @@ def test_denoise_tv_chambolle_2d():
denoised_astro = restoration.denoise_tv_chambolle(img, weight=60.0)
# which dtype?
assert denoised_astro.dtype in [np.float, np.float32, np.float64]
from scipy import ndimage
grad = ndimage.morphological_gradient(img, size=((3, 3)))
grad_denoised = ndimage.morphological_gradient(
denoised_astro, size=((3, 3)))
from scipy import ndimage as ndi
grad = ndi.morphological_gradient(img, size=((3, 3)))
grad_denoised = ndi.morphological_gradient(denoised_astro, size=((3, 3)))
# test if the total variation has decreased
assert grad_denoised.dtype == np.float
assert (np.sqrt((grad_denoised**2).sum())
skimage/restoration/tests/test_restoration.py
+3 -3
View File
@@ -2,7 +2,7 @@ from os.path import abspath, dirname, join as pjoin
import numpy as np
from scipy.signal import convolve2d
from scipy import ndimage as nd
from scipy import ndimage as ndi
import skimage
from skimage.data import camera
@@ -61,10 +61,10 @@ def test_image_shape():
"""
point = np.zeros((5, 5), np.float)
point[2, 2] = 1.
psf = nd.gaussian_filter(point, sigma=1.)
psf = ndi.gaussian_filter(point, sigma=1.)
# image shape: (45, 45), as reported in #1172
image = skimage.img_as_float(camera()[110:155, 225:270]) # just the face
image_conv = nd.convolve(image, psf)
image_conv = ndi.convolve(image, psf)
deconv_sup = restoration.wiener(image_conv, psf, 1)
deconv_un = restoration.unsupervised_wiener(image_conv, psf)[0]
# test the shape
skimage/segmentation/_felzenszwalb_cy.pyx
+2 -2
View File
@@ -3,7 +3,7 @@
#cython: nonecheck=False
#cython: wraparound=False
import numpy as np
import scipy
from scipy import ndimage as ndi
cimport cython
cimport numpy as cnp
@@ -47,7 +47,7 @@ def _felzenszwalb_grey(image, double scale=1, sigma=0.8,
# rescale scale to behave like in reference implementation
scale = float(scale) / 255.
image = scipy.ndimage.gaussian_filter(image, sigma=sigma)
image = ndi.gaussian_filter(image, sigma=sigma)
# compute edge weights in 8 connectivity:
right_cost = np.abs((image[1:, :] - image[:-1, :]))
skimage/segmentation/_quickshift.pyx
+2 -2
View File
@@ -3,7 +3,7 @@
#cython: nonecheck=False
#cython: wraparound=False
import numpy as np
from scipy import ndimage
from scipy import ndimage as ndi
from itertools import product
cimport numpy as cnp
@@ -69,7 +69,7 @@ def quickshift(image, ratio=1., float kernel_size=5, max_dist=10,
ValueError("Only RGB images can be converted to Lab space.")
image = rgb2lab(image)
image = ndimage.gaussian_filter(img_as_float(image), [sigma, sigma, 0])
image = ndi.gaussian_filter(img_as_float(image), [sigma, sigma, 0])
cdef cnp.ndarray[dtype=cnp.float_t, ndim=3, mode="c"] image_c \
= np.ascontiguousarray(image) * ratio
skimage/segmentation/boundaries.py
+6 -6
View File
@@ -1,7 +1,7 @@
from __future__ import division
import numpy as np
from scipy import ndimage as nd
from scipy import ndimage as ndi
from ..morphology import dilation, erosion, square
from ..util import img_as_float, view_as_windows, pad
from ..color import gray2rgb
@@ -146,7 +146,7 @@ def find_boundaries(label_img, connectivity=1, mode='thick', background=0):
[0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
"""
ndim = label_img.ndim
selem = nd.generate_binary_structure(ndim, connectivity)
selem = ndi.generate_binary_structure(ndim, connectivity)
if mode != 'subpixel':
boundaries = dilation(label_img, selem) != erosion(label_img, selem)
if mode == 'inner':
@@ -155,7 +155,7 @@ def find_boundaries(label_img, connectivity=1, mode='thick', background=0):
elif mode == 'outer':
max_label = np.iinfo(label_img.dtype).max
background_image = (label_img == background)
selem = nd.generate_binary_structure(ndim, ndim)
selem = ndi.generate_binary_structure(ndim, ndim)
inverted_background = np.array(label_img, copy=True)
inverted_background[background_image] = max_label
adjacent_objects = ((dilation(label_img, selem) !=
@@ -205,10 +205,10 @@ def mark_boundaries(image, label_img, color=(1, 1, 0),
if mode == 'subpixel':
# Here, we want to interpose an extra line of pixels between
# each original line - except for the last axis which holds
# the RGB information. ``nd.zoom`` then performs the (cubic)
# the RGB information. ``ndi.zoom`` then performs the (cubic)
# interpolation, filling in the values of the interposed pixels
marked = nd.zoom(marked, [2 - 1/s for s in marked.shape[:-1]] + [1],
mode='reflect')
marked = ndi.zoom(marked, [2 - 1/s for s in marked.shape[:-1]] + [1],
mode='reflect')
boundaries = find_boundaries(label_img, mode=mode,
background=background_label)
if outline_color is not None:
skimage/segmentation/random_walker_segmentation.py
+2 -2
View File
@@ -10,7 +10,7 @@ significantly the performance.
import warnings
import numpy as np
from scipy import sparse, ndimage
from scipy import sparse, ndimage as ndi
# executive summary for next code block: try to import umfpack from
# scipy, but make sure not to raise a fuss if it fails since it's only
@@ -417,7 +417,7 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
# If the array has pruned zones, be sure that no isolated pixels
# exist between pruned zones (they could not be determined)
if np.any(labels < 0):
filled = ndimage.binary_propagation(labels > 0, mask=labels >= 0)
filled = ndi.binary_propagation(labels > 0, mask=labels >= 0)
labels[np.logical_and(np.logical_not(filled), labels == 0)] = -1
del filled
labels = np.atleast_3d(labels)
skimage/segmentation/slic_superpixels.py
+2 -2
View File
@@ -2,7 +2,7 @@
import collections as coll
import numpy as np
from scipy import ndimage
from scipy import ndimage as ndi
import warnings
from ..util import img_as_float, regular_grid
@@ -139,7 +139,7 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=0,
if (sigma > 0).any():
# add zero smoothing for multichannel dimension
sigma = list(sigma) + [0]
image = ndimage.gaussian_filter(image, sigma)
image = ndi.gaussian_filter(image, sigma)
if multichannel and (convert2lab or convert2lab is None):
if image.shape[-1] != 3 and convert2lab:
skimage/transform/_geometric.py
+5 -6
View File
@@ -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
+3 -3
View File
@@ -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
+5 -5
View File
@@ -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
+6 -6
View File
@@ -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