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
54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
"""
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===========
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Convex Hull
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===========
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The convex hull of a binary image is the set of pixels included in the
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smallest convex polygon that surround all white pixels in the input.
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In this example, we show how the input pixels (white) get filled in by the
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convex hull (white and grey).
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A good overview of the algorithm is given on `Steve Eddin's blog
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<http://blogs.mathworks.com/steve/2011/10/04/binary-image-convex-hull-algorithm-notes/>`__.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from skimage.morphology import convex_hull_image
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image = np.array(
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[[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 1, 0, 0, 0, 0],
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[0, 0, 0, 1, 0, 1, 0, 0, 0],
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[0, 0, 1, 0, 0, 0, 1, 0, 0],
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[0, 1, 0, 0, 0, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=float)
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original_image = np.copy(image)
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chull = convex_hull_image(image)
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image[chull] += 1
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# image is now:
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# [[ 0. 0. 0. 0. 0. 0. 0. 0. 0.]
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# [ 0. 0. 0. 0. 2. 0. 0. 0. 0.]
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# [ 0. 0. 0. 2. 1. 2. 0. 0. 0.]
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# [ 0. 0. 2. 1. 1. 1. 2. 0. 0.]
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# [ 0. 2. 1. 1. 1. 1. 1. 2. 0.]
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# [ 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 6))
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ax1.set_title('Original picture')
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ax1.imshow(original_image, cmap=plt.cm.gray, interpolation='nearest')
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ax1.set_xticks([]), ax1.set_yticks([])
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ax2.set_title('Transformed picture')
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ax2.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
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ax2.set_xticks([]), ax2.set_yticks([])
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plt.show()
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