mirror of
https://github.com/wassname/scikit-image.git
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emmanuelle
55f5103dd8
Modified travis_script.sh to account for the new structure of the gallery Added README.txt files in directories of gallery examples Fixed references to gallery images in user guide pages Fixed broken links
64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
"""
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===========
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Skeletonize
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===========
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Skeletonization reduces binary objects to 1 pixel wide representations. This
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can be useful for feature extraction, and/or representing an object's topology.
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The algorithm works by making successive passes of the image. On each pass,
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border pixels are identified and removed on the condition that they do not
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break the connectivity of the corresponding object.
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This module provides an example of calling the routine and displaying the
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results. The input is a 2D ndarray, with either boolean or integer elements.
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In the case of boolean, 'True' indicates foreground, and for integer arrays,
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the foreground is 1's.
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"""
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from skimage.morphology import skeletonize
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from skimage import draw
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import numpy as np
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import matplotlib.pyplot as plt
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# an empty image
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image = np.zeros((400, 400))
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# foreground object 1
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image[10:-10, 10:100] = 1
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image[-100:-10, 10:-10] = 1
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image[10:-10, -100:-10] = 1
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# foreground object 2
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rs, cs = draw.line(250, 150, 10, 280)
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for i in range(10):
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image[rs + i, cs] = 1
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rs, cs = draw.line(10, 150, 250, 280)
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for i in range(20):
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image[rs + i, cs] = 1
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# foreground object 3
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ir, ic = np.indices(image.shape)
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circle1 = (ic - 135)**2 + (ir - 150)**2 < 30**2
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circle2 = (ic - 135)**2 + (ir - 150)**2 < 20**2
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image[circle1] = 1
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image[circle2] = 0
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# perform skeletonization
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skeleton = skeletonize(image)
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# display results
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
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ax1.imshow(image, cmap=plt.cm.gray)
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ax1.axis('off')
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ax1.set_title('original', fontsize=20)
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ax2.imshow(skeleton, cmap=plt.cm.gray)
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ax2.axis('off')
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ax2.set_title('skeleton', fontsize=20)
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fig.subplots_adjust(wspace=0.02, hspace=0.02, top=0.98,
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bottom=0.02, left=0.02, right=0.98)
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plt.show()
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