mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 01:00:23 +08:00
Juan Nunez-Iglesias
0d134987f9
Only two forms remain in use: - `from scipy import ndimage as ndi` - `from scipy.ndimage import function`
66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
"""
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===========================
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Medial axis skeletonization
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===========================
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The medial axis of an object is the set of all points having more than one
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closest point on the object's boundary. It is often called the **topological
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skeleton**, because it is a 1-pixel wide skeleton of the object, with the same
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connectivity as the original object.
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Here, we use the medial axis transform to compute the width of the foreground
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objects. As the function ``medial_axis`` (``skimage.morphology.medial_axis``)
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returns the distance transform in addition to the medial axis (with the keyword
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argument ``return_distance=True``), it is possible to compute the distance to
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the background for all points of the medial axis with this function. This gives
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an estimate of the local width of the objects.
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For a skeleton with fewer branches, there exists another skeletonization
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algorithm in ``skimage``: ``skimage.morphology.skeletonize``, that computes
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a skeleton by iterative morphological thinnings.
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"""
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import numpy as np
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from scipy import ndimage as ndi
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from skimage.morphology import medial_axis
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import matplotlib.pyplot as plt
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def microstructure(l=256):
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"""
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Synthetic binary data: binary microstructure with blobs.
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Parameters
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----------
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l: int, optional
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linear size of the returned image
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"""
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n = 5
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x, y = np.ogrid[0:l, 0:l]
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mask = np.zeros((l, l))
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generator = np.random.RandomState(1)
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points = l * generator.rand(2, n**2)
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mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
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mask = ndi.gaussian_filter(mask, sigma=l/(4.*n))
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return mask > mask.mean()
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data = microstructure(l=64)
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# Compute the medial axis (skeleton) and the distance transform
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skel, distance = medial_axis(data, return_distance=True)
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# Distance to the background for pixels of the skeleton
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dist_on_skel = distance * skel
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
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ax1.imshow(data, cmap=plt.cm.gray, interpolation='nearest')
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ax1.axis('off')
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ax2.imshow(dist_on_skel, cmap=plt.cm.spectral, interpolation='nearest')
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ax2.contour(data, [0.5], colors='w')
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ax2.axis('off')
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fig.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
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plt.show()
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