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MAINT: address review comments

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Use recommended idioms in the .py wrapper
This commit is contained in:
Evgeni Burovski
2016-02-20 17:29:13 +00:00
parent 2bc8538f9f
commit ba328e7531
2 changed files with 21 additions and 17 deletions
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skimage/morphology/_skeletonize_3d.py
+12 -16
View File
@@ -1,7 +1,7 @@
from __future__ import division, print_function, absolute_import
import numpy as np
from ..util import img_as_ubyte
from ..util import img_as_ubyte, crop
from ._skeletonize_3d_cy import _compute_thin_image
@@ -46,24 +46,19 @@ def skeletonize_3d(img):
Computer Vision, Graphics, and Image Processing, 56(6):462-478, 1994.
"""
# make sure the image is 3D or 2D (if it is, temporarily upcast to 3D)
# make sure the image is 3D or 2D
if img.ndim < 2 or img.ndim > 3:
raise ValueError('expect 2D, got ndim = %s' % img.ndim)
img = np.ascontiguousarray(img)
img = img_as_ubyte(img, force_copy=False)
# make an in image 3D pad w/ zeros to simplify dealing w/ boundaries
# NB: careful to not clobber the original *and* minimize copying
# make an in image 3D and pad it w/ zeros to simplify dealing w/ boundaries
# NB: careful here to not clobber the original *and* minimize copying
img_o = img
if img.ndim == 2:
if img.shape[0] == 1 or img.shape[1] == 1:
# nothing to do, image is already thin. Bail out.
return img.copy()
img_o = np.pad(img[None, ...], pad_width=1, mode='constant')
else:
img_o = np.pad(img, pad_width=1, mode='constant')
img_o = img[np.newaxis, ...]
img_o = np.pad(img_o, pad_width=1, mode='constant')
# normalize to binary
maxval = img_o.max()
@@ -72,9 +67,10 @@ def skeletonize_3d(img):
# do the computation
img_o = np.asarray(_compute_thin_image(img_o))
# clip it back and restore the original intensity range
img_o = img_o[1:-1, 1:-1, 1:-1]
img_o = img_o.squeeze()
# crop it back and restore the original intensity range
img_o = crop(img_o, crop_width=1)
if img.ndim == 2:
img_o = img_o[0]
img_o *= maxval
return img_o
return img_o
skimage/morphology/tests/test_skeletonize_3d.py
+9 -1
View File
@@ -27,6 +27,13 @@ def test_skeletonize_wrong_dim():
assert_raises(ValueError, skeletonize_3d, im)
def test_skeletonize_1D():
# a corner case of an image of a shape(1, N)
im = np.ones((5, 1), dtype=np.uint8)
res = skeletonize_3d(im)
assert_equal(res, im)
def test_skeletonize_no_foreground():
im = np.zeros((5, 5), dtype=np.uint8)
result = skeletonize_3d(im)
@@ -80,7 +87,8 @@ def test_input():
# check that the input is not clobbered
# for 2D and 3D images of varying dtypes
imgs = [np.ones((8, 8), dtype=float), np.ones((4, 8, 8), dtype=float),
np.ones((8, 8), dtype=np.uint8), np.ones((4, 8, 8), dtype=np.uint8)]
np.ones((8, 8), dtype=np.uint8), np.ones((4, 8, 8), dtype=np.uint8),
np.ones((8, 8), dtype=bool), np.ones((4, 8, 8), dtype=bool)]
for img in imgs:
yield check_input, img