mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-06 05:16:40 +08:00
Neil Yager
122 lines
4.2 KiB
Python
122 lines
4.2 KiB
Python
"""skeletonize.py - Use an iterative thinning algorithm to find the
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skeletons of binary objects in an image.
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"""
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import numpy as np
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from scipy.ndimage import correlate
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from .. import util
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def skeletonize(image):
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"""
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Return a single pixel wide skeleton of all connected components
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in a binary image.
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The algorithm works by making successive passes of the image,
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removing pixels on object borders. This continues until no
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more pixels can be removed. The image is correlated with a
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mask that assigns each pixel a number in the range [0...255]
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corresponding to each possible pattern of its 8 neighbouring
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pixels. A look up table is then used to assign the pixels a
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value of 0, 1, 2 or 3, which are selectively removed during
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the iterations.
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Parameters
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----------
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image: ndarray (2D)
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A binary image containing the objects to be skeletonized. '1'
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represents foreground, and '0' represents background. It
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also accepts arrays of boolean values where True is foreground.
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Notes
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-----
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This implementation gives different results than a medial
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axis transformation, which can be can be implemented using
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morphological operations. This implementation is generally much
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faster.
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Returns
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-------
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out: ndarray
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A matrix containing the thinned image
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References
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----------
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A fast parallel algorithm for thinning digital patterns,
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T. Y. ZHANG and C. Y. SUEN, Communications of the ACM,
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March 1984, Volume 27, Number 3
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Examples
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--------
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"""
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# look up table - there is one entry for each of the 2^8=256 possible
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# combinations of 8 binary neighbours. 1's, 2's and 3's are candidates
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# for removal at each iteration of the algorithm.
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lut = [ 0,0,0,1,0,0,1,3,0,0,3,1,1,0,1,3,0,0,0,0,0,0,0,0,2,0,2,0,3,0,3,3,
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0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,2,0,0,0,3,0,2,2,
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0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
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2,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,3,0,2,0,
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0,1,3,1,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,
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3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
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2,3,1,3,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
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2,3,0,1,0,0,0,1,0,0,0,0,0,0,0,0,3,3,0,1,0,0,0,0,2,2,0,0,2,0,0,0]
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# convert to unsigned int (this should work for boolean values)
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skeleton = np.array(image).astype(np.uint8)
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# check some properties of the input image:
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# - 2D
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# - binary image with only 0's and 1's
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if skeleton.ndim != 2:
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raise ValueError('Skeletonize requires a 2D array')
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if not np.all(np.in1d(skeleton.flat, (0, 1))):
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raise ValueError('Image contains values other than 0 and 1')
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# create the mask that will assign a unique value based on the
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# arrangement of neighbouring pixels
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mask = np.array([[ 1, 2, 4],
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[128, 0, 8],
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[ 64, 32, 16]], np.uint8)
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pixelRemoved = True
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while pixelRemoved:
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pixelRemoved = False;
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# assign each pixel a unique value based on its foreground neighbours
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neighbours = correlate(skeleton, mask, mode='constant')
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# ignore background
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neighbours[skeleton == 0] = 0
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# use LUT to categorize each foreground pixel as a 0, 1, 2 or 3
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codes = np.take(lut, neighbours)
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# pass 1 - remove the 1's and 3's
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code_mask = (codes == 1)
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if np.any(code_mask):
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pixelRemoved = True
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skeleton[code_mask] = 0
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code_mask = (codes == 3)
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if np.any(code_mask):
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pixelRemoved = True
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skeleton[code_mask] = 0
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# pass 2 - remove the 2's and 3's
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neighbours = correlate(skeleton, mask, mode='constant')
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neighbours[skeleton == 0] = 0
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codes = np.take(lut, neighbours)
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code_mask = (codes == 2)
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if np.any(code_mask):
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pixelRemoved = True
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skeleton[code_mask] = 0
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code_mask = (codes == 3)
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if np.any(code_mask):
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pixelRemoved = True
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skeleton[code_mask] = 0
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return skeleton
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