'''
Contains code from scikit-image v0.18.3
'''
import numpy as np
from .dtype import img_as_ubyte
from .arraycrop import crop
from ._skeletonize_3d_cy import _compute_thin_image
[docs]def skeletonize(image, periodic=False):
"""Compute the skeleton of a binary image.
Thinning is used to reduce each connected component in a binary image
to a single-pixel wide skeleton.
Parameters
----------
image : ndarray, 2D or 3D
A binary image containing the objects to be skeletonized. Zeros
represent background, nonzero values are foreground.
periodic: bool
If True, the skeletonization uses periodic boundary conditions
for the input array. Input array must be 3D.
Returns
-------
skeleton : ndarray
The thinned image.
Notes
-----
The method of [Lee94]_ uses an octree data structure to examine a 3x3x3
neighborhood of a pixel. The algorithm proceeds by iteratively sweeping
over the image, and removing pixels at each iteration until the image
stops changing. Each iteration consists of two steps: first, a list of
candidates for removal is assembled; then pixels from this list are
rechecked sequentially, to better preserve connectivity of the image.
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.
"""
# make sure the image is 3D or 2D
if image.ndim < 2 or image.ndim > 3:
raise ValueError("skeletonize can only handle 2D or 3D images; "
"got image.ndim = %s instead." % image.ndim)
image = np.ascontiguousarray(image)
image = img_as_ubyte(image, force_copy=False)
if type(periodic) != bool:
raise TypeError("keyword argument periodic must of of type bool; "
"got type %s instead." % type(periodic))
if periodic and image.ndim != 3:
raise ValueError("periodic boundaries currently only work for 3D "
"data. image.ndim = %s." % image.ndim)
# 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
image_o = image
if image.ndim == 2:
image_o = image[np.newaxis, ...]
image_o = np.pad(image_o, pad_width=1, mode='constant')
# normalize to binary
# maxval = image_o.max()
image_o[image_o != 0] = 1
# do the computation
image_o = np.asarray(_compute_thin_image(image_o, periodic=periodic))
# crop it back and restore the original intensity range
image_o = crop(image_o, crop_width=1)
if image.ndim == 2:
image_o = image_o[0]
# image_o *= maxval
return image_o