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emmanuelle
a07fc6d172
Removed the See also part in watershed's docstrings, as it made the doc compilation crash...
60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
"""
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======================
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Watershed segmentation
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======================
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The watershed is a classical algorithm used for **segmentation**, that is, for
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separating different objects in an image.
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Starting from user-defined markers, the watershed algorithm treats pixels
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values as a local topography (elevation). The algorithm floods basins from the
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markers, until basins attributed to different markers meet on watershed lines.
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In many cases, markers are chosen as local minima of the image, from which
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basins are flooded.
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In the example below, two overlapping circles are to be separated. To do so,
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one computes an image that is the distance to the background. The maxima of
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this distance (i.e., the minima of the opposite of the distance) are chosen as
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markers, and the flooding of basins from such markers separates the two circles
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along a watershed line.
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See http://en.wikipedia.org/wiki/Watershed_(image_processing) for more details
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on the algorithm.
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"""
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import numpy as np
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from scipy import ndimage
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import matplotlib.pyplot as plt
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from scikits.image.morphology import watershed, is_local_maximum
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# Generate an initial image with two overlapping circles
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x, y = np.indices((80, 80))
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x1, y1, x2, y2 = 28, 28, 44, 52
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r1, r2 = 16, 20
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mask_circle1 = (x - x1)**2 + (y - y1)**2 < r1**2
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mask_circle2 = (x - x2)**2 + (y - y2)**2 < r2**2
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image = np.logical_or(mask_circle1, mask_circle2)
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# Now we want to separate the two objects in image
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# Generate the markers as local maxima of the distance
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# to the background
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from scipy import ndimage
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distance = ndimage.distance_transform_edt(image)
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local_maxi = is_local_maximum(distance, image, np.ones((3, 3)))
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markers = ndimage.label(local_maxi)[0]
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labels = watershed(-distance, markers, mask=image)
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plt.figure(figsize=(9, 3))
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plt.subplot(131)
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plt.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
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plt.axis('off')
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plt.subplot(132)
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plt.imshow(-distance, cmap=plt.cm.jet, interpolation='nearest')
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plt.axis('off')
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plt.subplot(133)
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plt.imshow(labels, cmap=plt.cm.spectral, interpolation='nearest')
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plt.axis('off')
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plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
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right=1)
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plt.show()
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