mirror of
https://github.com/wassname/scikit-image.git
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Andreas Mueller
58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
"""
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=============================
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Quickshift image segmentation
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=============================
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Quickshift is a relatively recent 2d image segmentation algorithm, based on an
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approximation of kernelized mean-shift. Therefore it belongs to the family
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of local mode-seeking algorithms and is applied to the color+coordinate space,
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see [1]_ It is often used to extract "superpixels", small homogeneous image
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regions, which build the basis for further processing.
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One of the benefits of quickshift is that it actually computes a
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hierarchical segmentation on multiple scales simultaneously.
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Quickshift has two parameters, one controlling the scale of the local
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density approximation, the other selecting a level in the hierarchical
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segmentation that is produced.
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.. [1] Quick shift and kernel methods for mode seeking, Vedaldi, A. and Soatto, S.
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European Conference on Computer Vision, 2008
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"""
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print __doc__
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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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img = img_as_float(lena())[::2, ::2, :].copy("C")
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segments = quickshift(img, sigma=5, tau=20)
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print("number of segments: %d" % len(np.unique(segments)))
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plt.subplot(131, title="original")
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plt.imshow(img, interpolation='nearest')
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plt.axis("off")
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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.axis("off")
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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.axis("off")
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plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9,
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bottom=0.02, left=0.02, right=0.98)
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plt.show()
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