merge segmentation examples

doc/examples/plot_felzenszwalb_segmentation.py

@@ -1,51 +0,0 @@
-"""
-=================================================
-Felzenszwalb's efficient graph based segmentation
-=================================================
-
-This fast 2d image segmentation algorithm, proposed in [1]_ is popular in the
-computer vision community. It is often used to extract "superpixels", small
-homogeneous image regions, which build the basis for further processing.
-
-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
-"""
-print __doc__
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-from skimage.data import lena
-from skimage.segmentation import felzenszwalb_segmentation
-from skimage.util import img_as_float
-
-img = img_as_float(lena())
-segments = felzenszwalb_segmentation(img, scale=1)
-segments = np.unique(segments, return_inverse=True)[1].reshape(img.shape[:2])
-
-print("number of segments: %d" % len(np.unique(segments)))
-
-
-fig, (ax_org, ax_sp, ax_mean) = plt.subplots(1, 3)
-ax_org.set_title("original")
-ax_org.imshow(img, interpolation='nearest')
-ax_org.axis("off")
-
-ax_sp.set_title("superpixels")
-ax_sp.imshow(segments, interpolation='nearest', cmap=plt.cm.prism)
-ax_sp.axis("off")
-
-colors = [np.bincount(segments.ravel(), img[:, :, c].ravel()) for c in
-        xrange(img.shape[2])]
-counts = np.bincount(segments.ravel())
-colors = np.vstack(colors) / counts
-ax_mean.set_title("mean color")
-ax_mean.imshow(colors.T[segments], interpolation='nearest')
-ax_mean.axis("off")
-fig.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9,
-                    bottom=0.02, left=0.02, right=0.98)
-plt.show()

doc/examples/plot_quickshift.py

@@ -1,43 +0,0 @@
-"""
-=============================
-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 [1]_ It is often used to extract "superpixels", small homogeneous image
-regions, which build the basis for further processing.
-
-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.
-
-.. [1] Quick shift and kernel methods for mode seeking, Vedaldi, A. and Soatto, S.
-       European Conference on Computer Vision, 2008
-"""
-print __doc__
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-from skimage.data import lena
-from skimage.segmentation import quickshift, visualize_boundaries
-from skimage.util import img_as_float
-from skimage.color import rgb2lab
-
-img = img_as_float(lena())[::2, ::2, :].copy("C")
-segments = quickshift(rgb2lab(img), kernel_size=5, max_dist=20)
-segments_rgb = quickshift(img, kernel_size=5, max_dist=20)
-
-print("number of segments: %d" % len(np.unique(segments)))
-boundaries = visualize_boundaries(img, segments)
-boundaries_rgb = visualize_boundaries(img, segments_rgb)
-plt.imshow(boundaries)
-plt.figure()
-plt.imshow(boundaries_rgb)
-plt.axis("off")
-plt.show()

doc/examples/plot_segmentations.py

@@ -0,0 +1,85 @@
+"""
+====================================================
+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()

doc/examples/plot_slic.py

@@ -1,20 +0,0 @@
-"""
-"""
-print __doc__
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-from skimage.data import lena
-from skimage.segmentation import slic, visualize_boundaries
-from skimage.util import img_as_float
-
-img = img_as_float(lena()).copy("C")
-segments = slic(img, ratio=10.0, n_segments=1000)
-
-print("number of segments: %d" % len(np.unique(segments)))
-
-boundaries_mine = visualize_boundaries(img, segments)
-plt.imshow(boundaries_mine)
-plt.axis("off")
-plt.show()