mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-16 11:21:25 +08:00
Evgeni Burovski
2bc8538f9f
* typos in comments * handle the corner case of width-1 2D image * vanity * change up leftover <= to correct ==
81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
from __future__ import division, print_function, absolute_import
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import numpy as np
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from ..util import img_as_ubyte
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from ._skeletonize_3d_cy import _compute_thin_image
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def skeletonize_3d(img):
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"""Compute the skeleton of a binary image.
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Thinning is used to reduce each connected component in a binary image
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to a single-pixel wide skeleton.
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Parameters
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----------
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img : ndarray, 2D or 3D
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A binary image containing the objects to be skeletonized. Zeros
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represent background, nonzero values are foreground.
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Returns
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-------
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skeleton : ndarray
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The thinned image.
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See also
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--------
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skeletonize, medial_axis
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Notes
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-----
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The method of [Lee94]_ uses an octree data structure to examine a 3x3x3
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neighborhood of a pixel. The algorithm proceeds by iteratively sweeping
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over the image, and removing pixels at each iteration until the image
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stops changing. Each iteration consists of two steps: first, a list of
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candidates for removal is assembled; then pixels from this list are
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rechecked sequentially, to better preserve connectivity of the image.
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The algorithm this function implements is different from the algorithms
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used by either `skeletonize` or `medial_axis`, thus for 2D images the
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results produced by this function are generally different.
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References
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----------
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.. [Lee94] T.-C. Lee, R.L. Kashyap and C.-N. Chu, Building skeleton models
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via 3-D medial surface/axis thinning algorithms.
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Computer Vision, Graphics, and Image Processing, 56(6):462-478, 1994.
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"""
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# make sure the image is 3D or 2D (if it is, temporarily upcast to 3D)
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if img.ndim < 2 or img.ndim > 3:
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raise ValueError('expect 2D, got ndim = %s' % img.ndim)
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img = np.ascontiguousarray(img)
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img = img_as_ubyte(img, force_copy=False)
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# make an in image 3D pad w/ zeros to simplify dealing w/ boundaries
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# NB: careful to not clobber the original *and* minimize copying
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if img.ndim == 2:
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if img.shape[0] == 1 or img.shape[1] == 1:
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# nothing to do, image is already thin. Bail out.
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return img.copy()
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img_o = np.pad(img[None, ...], pad_width=1, mode='constant')
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else:
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img_o = np.pad(img, pad_width=1, mode='constant')
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# normalize to binary
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maxval = img_o.max()
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img_o[img_o != 0] = 1
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# do the computation
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img_o = np.asarray(_compute_thin_image(img_o))
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# clip it back and restore the original intensity range
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img_o = img_o[1:-1, 1:-1, 1:-1]
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img_o = img_o.squeeze()
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img_o *= maxval
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return img_o
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