44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
"""
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=============================================
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Find Regular Segments Using Compact Watershed
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=============================================
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The watershed transform is commonly used as a starting point for many
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segmentation algorithms. However, without a judicious choice of seeds, it
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can produce very uneven fragment sizes, which can be difficult to deal with
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in downstream analyses.
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The *compact* watershed transform remedies this by favoring seeds that are
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close to the pixel being considered.
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Both algorithms are implemented in the :py:func:`skimage.morphology.watershed`
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function. To use the compact form, simply pass a ``compactness`` value greater
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than 0.
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"""
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import numpy as np
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from skimage import data, util, filters, color
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from skimage.segmentation import watershed
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import matplotlib.pyplot as plt
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coins = data.coins()
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edges = filters.sobel(coins)
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grid = util.regular_grid(coins.shape, n_points=468)
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seeds = np.zeros(coins.shape, dtype=int)
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seeds[grid] = np.arange(seeds[grid].size).reshape(seeds[grid].shape) + 1
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w0 = watershed(edges, seeds)
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w1 = watershed(edges, seeds, compactness=0.01)
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fig, (ax0, ax1) = plt.subplots(1, 2)
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ax0.imshow(color.label2rgb(w0, coins, bg_label=-1))
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ax0.set_title('Classical watershed')
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ax1.imshow(color.label2rgb(w1, coins, bg_label=-1))
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ax1.set_title('Compact watershed')
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plt.show()
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