DOC document and export felzenszwalb_segmentation_grey, prettify plots for the web.

doc/examples/plot_felzenszwalb_segmentation.py

@@ -27,13 +27,17 @@ 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)))
+
 plt.subplot(131, title="original")
 plt.imshow(img, interpolation='nearest')
+plt.axis("off")
 
-plt.subplot(132, title="superpixels")
+plt.subplot(132, title="segmentation")
 # shuffle the labels for better visualization
 permuted_labels = np.random.permutation(segments.max() + 1)
 plt.imshow(permuted_labels[segments], interpolation='nearest')
+plt.axis("off")
 
 plt.subplot(133, title="mean color")
 colors = [np.bincount(segments.ravel(), img[:, :, c].ravel()) for c in
@@ -41,5 +45,8 @@ colors = [np.bincount(segments.ravel(), img[:, :, c].ravel()) for c in
 counts = np.bincount(segments.ravel())
 colors = np.vstack(colors) / counts
 plt.imshow(colors.T[segments], interpolation='nearest')
-print("number of segments: %d" % len(np.unique(segments)))
+plt.axis("off")
+
+plt.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

@@ -33,13 +33,17 @@ img = img_as_float(lena())[::2, ::2, :].copy("C")
 segments = quickshift(img, sigma=5, tau=20)
 segments = np.unique(segments, return_inverse=True)[1].reshape(img.shape[:2])
 
+print("number of segments: %d" % len(np.unique(segments)))
+
 plt.subplot(131, title="original")
 plt.imshow(img, interpolation='nearest')
+plt.axis("off")
 
 plt.subplot(132, title="superpixels")
 # shuffle the labels for better visualization
 permuted_labels = np.random.permutation(segments.max() + 1)
 plt.imshow(permuted_labels[segments], interpolation='nearest')
+plt.axis("off")
 
 plt.subplot(133, title="mean color")
 colors = [np.bincount(segments.ravel(), img[:, :, c].ravel()) for c in
@@ -47,5 +51,8 @@ colors = [np.bincount(segments.ravel(), img[:, :, c].ravel()) for c in
 counts = np.bincount(segments.ravel())
 colors = np.vstack(colors) / counts
 plt.imshow(colors.T[segments], interpolation='nearest')
-print("number of segments: %d" % len(np.unique(segments)))
+plt.axis("off")
+
+plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9,
+                    bottom=0.02, left=0.02, right=0.98)
 plt.show()

skimage/segmentation/__init__.py

@@ -1,5 +1,7 @@
 from .random_walker_segmentation import random_walker
 from .felzenszwalb import felzenszwalb_segmentation
+from .felzenszwalb import felzenszwalb_segmentation_grey
 from .quickshift import quickshift
 
-__all__ = [random_walker, quickshift, felzenszwalb_segmentation]
+__all__ = [random_walker, quickshift, felzenszwalb_segmentation,
+        felzenszwalb_segmentation_grey]

skimage/segmentation/_felzenszwalb.pyx

@@ -54,6 +54,16 @@ cdef join_trees(np.int_t *forest, np.int_t n, np.int_t m):
 def felzenszwalb_segmentation_grey(image, scale=200, sigma=0.8):
     """Computes Felsenszwalb's efficient graph based segmentation for a single channel.
 
+    Produces an oversegmentation of a 2d image using a fast, minimum spanning
+    tree based clustering on the image grid.  The parameter ``scale`` sets an
+    observation level. Higher scale means less and larger segments. ``sigma``
+    is the diameter of a Gaussian kernel, used for smoothing the image prior to
+    segmentation.
+
+    The number of produced segments as well as their size can only be
+    controlled indirectly through ``scale``. Segment size within an image can
+    vary greatly depending on local contrast.
+
     Parameters
     ----------
     image: ndarray, [width, height]