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https://github.com/wassname/scikit-image.git
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Andreas Mueller
53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
"""
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=================================================
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Felzenszwalb's efficient graph based segmentation
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=================================================
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This fast 2d image segmentation algorithm, proposed in [1]_ is popular in the
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computer vision community. It is often used to extract "superpixels", small
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homogeneous image regions, which build the basis for further processing.
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The algorithm has a single ``scale`` parameter that influences the segment
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size. The actual size and number of segments can vary greatly, depending on
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local contrast.
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.. [1] Efficient graph-based image segmentation, Felzenszwalb, P.F. and
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Huttenlocher, D.P. International Journal of Computer Vision, 2004
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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 felzenszwalb_segmentation
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from skimage.util import img_as_float
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img = img_as_float(lena())
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segments = felzenszwalb_segmentation(img, scale=1)
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segments = np.unique(segments, return_inverse=True)[1].reshape(img.shape[:2])
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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="segmentation")
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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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