mirror of
https://github.com/wassname/scikit-image.git
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Josh Warner (Mac)
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
"""
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==========================
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Random walker segmentation
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==========================
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The random walker algorithm [1]_ determines the segmentation of an image from
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a set of markers labeling several phases (2 or more). An anisotropic diffusion
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equation is solved with tracers initiated at the markers' position. The local
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diffusivity coefficient is greater if neighboring pixels have similar values,
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so that diffusion is difficult across high gradients. The label of each unknown
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pixel is attributed to the label of the known marker that has the highest
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probability to be reached first during this diffusion process.
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In this example, two phases are clearly visible, but the data are too
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noisy to perform the segmentation from the histogram only. We determine
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markers of the two phases from the extreme tails of the histogram of gray
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values, and use the random walker for the segmentation.
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.. [1] *Random walks for image segmentation*, Leo Grady, IEEE Trans. Pattern
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Anal. Mach. Intell. 2006 Nov; 28(11):1768-83
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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.segmentation import random_walker
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from skimage.data import binary_blobs
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import skimage
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# Generate noisy synthetic data
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data = skimage.img_as_float(binary_blobs(length=128, seed=1))
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data += 0.35 * np.random.randn(*data.shape)
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markers = np.zeros(data.shape, dtype=np.uint)
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markers[data < -0.3] = 1
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markers[data > 1.3] = 2
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# Run random walker algorithm
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labels = random_walker(data, markers, beta=10, mode='bf')
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# Plot results
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fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 3.2),
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sharex=True, sharey=True)
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ax1.imshow(data, cmap='gray', interpolation='nearest')
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ax1.axis('off')
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ax1.set_adjustable('box-forced')
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ax1.set_title('Noisy data')
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ax2.imshow(markers, cmap='hot', interpolation='nearest')
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ax2.axis('off')
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ax2.set_adjustable('box-forced')
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ax2.set_title('Markers')
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ax3.imshow(labels, cmap='gray', interpolation='nearest')
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ax3.axis('off')
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ax3.set_adjustable('box-forced')
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ax3.set_title('Segmentation')
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fig.tight_layout()
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plt.show()
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