mirror of
https://github.com/wassname/scikit-image.git
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Andreas Mueller
44 lines
1.5 KiB
Python
44 lines
1.5 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, visualize_boundaries
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from skimage.util import img_as_float
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from skimage.color import rgb2lab
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img = img_as_float(lena())[::2, ::2, :].copy("C")
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segments = quickshift(rgb2lab(img), kernel_size=5, max_dist=20)
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segments_rgb = quickshift(img, kernel_size=5, max_dist=20)
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print("number of segments: %d" % len(np.unique(segments)))
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boundaries = visualize_boundaries(img, segments)
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boundaries_rgb = visualize_boundaries(img, segments_rgb)
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plt.imshow(boundaries)
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plt.figure()
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plt.imshow(boundaries_rgb)
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plt.axis("off")
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plt.show()
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