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
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), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
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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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