mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-27 23:39:49 +08:00
Johannes Schönberger
66 lines
1.7 KiB
Python
66 lines
1.7 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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plt.figure(figsize=(8, 4.5))
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plt.subplot(121)
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plt.imshow(image, cmap=plt.cm.gray)
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plt.axis('off')
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plt.title('original', fontsize=20)
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plt.subplot(122)
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plt.imshow(skeleton, cmap=plt.cm.gray)
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plt.axis('off')
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plt.title('skeleton', fontsize=20)
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plt.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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