mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 01:27:57 +08:00
Andreas Mueller
32 lines
1000 B
Python
32 lines
1000 B
Python
import matplotlib.pyplot as plt
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import numpy as np
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from skimage.data import lena
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from skimage.segmentation import quickshift
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from skimage.util import img_as_float
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from IPython.core.debugger import Tracer
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tracer = Tracer()
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img = img_as_float(lena())[::2, ::2, :].copy("C")
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segments = quickshift(img)
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segments = np.unique(segments, return_inverse=True)[1].reshape(img.shape[:2])
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plt.subplot(131, title="original")
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plt.imshow(img, interpolation='nearest')
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plt.subplot(132, title="superpixels")
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# shuffle the labels for better visualization
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permuted_labels = np.random.permutation(segments.max() + 1)
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plt.imshow(permuted_labels[segments], interpolation='nearest')
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plt.subplot(133, title="mean color")
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colors = [np.bincount(segments.ravel(), img[:, :, c].ravel()) for c in
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xrange(img.shape[2])]
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counts = np.bincount(segments.ravel())
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colors = np.vstack(colors) / counts
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plt.imshow(colors.T[segments], interpolation='nearest')
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plt.show()
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print("num segments: %d" % len(np.unique(segments)))
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