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MAINT: address review comments

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* typos in comments
* handle the corner case of width-1 2D image
* vanity
* change up leftover <= to correct ==
This commit is contained in:
Evgeni Burovski
2016-02-06 18:46:12 +00:00
parent dc702a83ee
commit 2bc8538f9f
3 changed files with 18 additions and 14 deletions
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CONTRIBUTORS.txt
+3
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@@ -221,3 +221,6 @@
- Egor Panfilov
Inpainting with biharmonic equation
- Evgeni Burovski
Adaptation of ImageJ 3D skeletonization algorithm.
skimage/morphology/_skeletonize_3d.py
+5
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@@ -56,6 +56,11 @@ def skeletonize_3d(img):
# make an in image 3D pad w/ zeros to simplify dealing w/ boundaries
# NB: careful to not clobber the original *and* minimize copying
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')
skimage/morphology/_skeletonize_3d_cy.pyx.in
+10 -14
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@@ -16,16 +16,12 @@ The original Java code [IAC15]_ carries the following message:
* @version 1.0 11/13/2015 (unique BSD licensed version for scikit-image)
* @author Ignacio Arganda-Carreras (iargandacarreras at gmail.com)
Porting to Cython was done by Evgeni Burovski (evgeny.burovskiy@gmail.com).
References
----------
.. [Lee94] T.-C. Lee, R.L. Kashyap and C.-N. Chu, Building skeleton models
via 3-D medial surface/axis thinning algorithms.
Computer Vision, Graphics, and Image Processing, 56(6):462-478, 1994.
.. [IAC15] Ignacio Arganda-Carreras, 2015. Skeletonize3D plugin for ImageJ(C).
http://fiji.sc/Skeletonize3D
@@ -50,7 +46,7 @@ def _compute_thin_image(pixel_type[:, :, ::1] img not None):
those point which can be removed without changing local connectivity in the
3x3x3 neighborhood of a point.
This routine implements the two-pass algorthim of [Lee94]_. Namely,
This routine implements the two-pass algorithm of [Lee94]_. Namely,
for each of the six border types (positive and negative x-, y- and z-),
the algorithm first collects all possibly deletable points, and then
performs a sequential rechecking.
@@ -58,7 +54,7 @@ def _compute_thin_image(pixel_type[:, :, ::1] img not None):
The input, `img`, is assumed to be a 3D binary image in the
(p, r, c) format [i.e., C ordered array], filled by zeros (background) and
ones. Furthermore, `img` is assumed to be padded by zeros from all
directions --- this way the zero boundary conditions are authomatic
directions --- this way the zero boundary conditions are automatic
and there is need to guard against out-of-bounds access.
"""
@@ -69,7 +65,7 @@ def _compute_thin_image(pixel_type[:, :, ::1] img not None):
bint no_change
list simple_border_points
Py_ssize_t num_simple_points, i, j
Py_ssize_t num_border_points, i, j
(npy_intp, npy_intp, npy_intp) point
pixel_type neighb[27]
@@ -146,12 +142,12 @@ cdef list _loop_through(pixel_type[:, :, ::1] img,
if img[p, r, c] != 1:
continue
is_border_pt = (curr_border == 1 and img[p, r, c-1] <= 0 or #N
curr_border == 2 and img[p, r, c+1] <= 0 or #S
curr_border == 3 and img[p, r+1, c] <= 0 or #E
curr_border == 4 and img[p, r-1, c] <= 0 or #W
curr_border == 5 and img[p+1, r, c] <= 0 or #U
curr_border == 6 and img[p-1, r, c] <= 0) #B
is_border_pt = (curr_border == 1 and img[p, r, c-1] == 0 or #N
curr_border == 2 and img[p, r, c+1] == 0 or #S
curr_border == 3 and img[p, r+1, c] == 0 or #E
curr_border == 4 and img[p, r-1, c] == 0 or #W
curr_border == 5 and img[p+1, r, c] == 0 or #U
curr_border == 6 and img[p-1, r, c] == 0) #B
if not is_border_pt:
# current point is not deletable
continue
@@ -271,7 +267,7 @@ cdef bint is_Euler_invariant(pixel_type neighbors[],
int[::1] lut):
"""Check if a point is Euler invariant.
Calculate Euler characteristc for each octant and sum up.
Calculate Euler characteristic for each octant and sum up.
Parameters
----------