merge segmentation examples

doc/examples/plot_segmentations.py

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+"""
+====================================================
+Comparison of segmentation and superpixel algorithms
+====================================================
+
+This example compares three popular low-level image segmentation methods.
+As it is difficult do obtain good segmentations, and the definition of "good"
+often depends on the application, these methods are usually used
+for optaining an oversegmentation, also known as superpixels. These superpixels
+then serve as the level of operation for more sophisticated algorithms such as CRFs.
+
+
+
+Felzenszwalb's efficient graph based segmentation
+-------------------------------------------------
+This fast 2d image segmentation algorithm, proposed in [1]_ is popular in the
+computer vision community.
+The algorithm has a single ``scale`` parameter that influences the segment
+size. The actual size and number of segments can vary greatly, depending on
+local contrast.
+
+.. [1] Efficient graph-based image segmentation, Felzenszwalb, P.F. and
+       Huttenlocher, D.P.  International Journal of Computer Vision, 2004
+
+
+Quickshift image segmentation
+-----------------------------
+
+Quickshift is a relatively recent 2d image segmentation algorithm, based on an
+approximation of kernelized mean-shift. Therefore it belongs to the family
+of local mode-seeking algorithms and is applied to the color+coordinate space,
+see [2]_.
+
+One of the benefits of quickshift is that it actually computes a
+hierarchical segmentation on multiple scales simultaneously.
+
+Quickshift has two parameters, one controlling the scale of the local
+density approximation, the other selecting a level in the hierarchical
+segmentation that is produced.
+
+.. [2] Quick shift and kernel methods for mode seeking,
+       Vedaldi, A. and Soatto, S.
+       European Conference on Computer Vision, 2008
+
+
+SLIC - K-Means based image segmentation
+---------------------------------------
+This algorithm simply performs K-kmeans in the 5d color-coordinate space and is
+therefore closely related to quickshift. As the clustering method is simpler,
+it is very efficient. It is essential for this algorithm to work in Lab color
+space to obtain good results.  The algorithm quickly gained momentum and is now
+widely used. See [3] for details.
+
+.. [3] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi,
+    Pascal Fua, and Sabine Suesstrunk, SLIC Superpixels Compared to
+    State-of-the-art Superpixel Methods, TPAMI, May 2012.
+"""
+print __doc__
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from skimage.data import lena
+from skimage.segmentation import felzenszwalb_segmentation, \
+    visualize_boundaries, slic, quickshift
+from skimage.util import img_as_float
+
+img = img_as_float(lena()[::2, ::2])
+segments_fz = felzenszwalb_segmentation(img, scale=100, sigma=0.5, min_size=50)
+segments_slic = slic(img, ratio=10, n_segments=250, sigma=1)
+segments_quick = quickshift(img, kernel_size=3, max_dist=6, ratio=0.5)
+
+print("Felzenszwalb's number of segments: %d" % len(np.unique(segments_fz)))
+print("Slic number of segments: %d" % len(np.unique(segments_slic)))
+print("Quickshift number of segments: %d" % len(np.unique(segments_quick)))
+
+fig, ax = plt.subplots(1, 3)
+
+ax[0].imshow(visualize_boundaries(img, segments_fz))
+ax[1].imshow(visualize_boundaries(img, segments_slic))
+ax[2].imshow(visualize_boundaries(img, segments_quick))
+for a in ax:
+    a.set_xticks(())
+    a.set_yticks(())
+plt.show()