mirror of
https://github.com/wassname/scikit-image.git
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Siva Prasad Varma
49 lines
1.4 KiB
Python
49 lines
1.4 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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ax2.set_title('Transformed picture')
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ax2.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
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plt.show()
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