Fix warnings generated by gallery examples

doc/examples/plot_label.py

@@ -19,7 +19,8 @@ import matplotlib.patches as mpatches
 from skimage import data
 from skimage.filters import threshold_otsu
 from skimage.segmentation import clear_border
-from skimage.morphology import label, closing, square
+from skimage.measure import label
+from skimage.morphology import closing, square
 from skimage.measure import regionprops
 from skimage.color import label2rgb
 

doc/examples/plot_rag_draw.py

@@ -1,5 +1,5 @@
 """
-=====================================
+======================================
 Drawing Region Adjacency Graphs (RAGs)
 ======================================
 

doc/examples/plot_regionprops.py

@@ -11,8 +11,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 
 from skimage.draw import ellipse
-from skimage.morphology import label
-from skimage.measure import regionprops
+from skimage.measure import label, regionprops
 from skimage.transform import rotate
 
 

doc/examples/plot_tinting_grayscale_images.py

@@ -37,7 +37,7 @@ ax2.imshow(yellow_multiplier * image)
 
 In many cases, dealing with RGB values may not be ideal. Because of that, there
 are many other `color spaces`_ in which you can represent a color image. One
-popular color space is called HSV_, which represents hue (~the color),
+popular color space is called HSV, which represents hue (~the color),
 saturation (~colorfulness), and value (~brightness). For example, a color
 (hue) might be green, but its saturation is how intense that green is---where
 olive is on the low end and neon on the high end.
@@ -46,6 +46,9 @@ In some implementations, the hue in HSV goes from 0 to 360, since hues wrap
 around in a circle. In scikit-image, however, hues are float values from 0 to
 1, so that hue, saturation, and value all share the same scale.
 
+.. _color spaces:
+    http://en.wikipedia.org/wiki/List_of_color_spaces_and_their_uses
+
 Below, we plot a linear gradient in the hue, with the saturation and value
 turned all the way up:
 """
@@ -69,6 +72,8 @@ Notice how the colors at the far left and far right are the same. That reflects
 the fact that the hues wrap around like the color wheel (see HSV_ for more
 info).
 
+.. _HSV: http://en.wikipedia.org/wiki/HSL_and_HSV
+
 Now, let's create a little utility function to take an RGB image and:
 
 1. Transform the RGB image to HSV
@@ -147,7 +152,4 @@ plt.show()
 For coloring multiple regions, you may also be interested in
 `skimage.color.label2rgb <http://scikit-image.org/docs/0.9.x/api/skimage.color.html#label2rgb>`_.
 
-.. _color spaces:
-    http://en.wikipedia.org/wiki/List_of_color_spaces_and_their_uses
-.. _HSV: http://en.wikipedia.org/wiki/HSL_and_HSV
 """

doc/examples/plot_windowed_histogram.py

@@ -60,10 +60,12 @@ def windowed_histogram_similarity(image, selem, reference_hist, n_bins):
     # a measure of distance between histograms
     X = px_histograms
     Y = reference_hist
+
     num = (X - Y) ** 2
     denom = X + Y
+    denom[denom == 0] = np.infty
     frac = num / denom
-    frac[denom == 0] = 0
+
     chi_sqr = 0.5 * np.sum(frac, axis=2)
 
     # Generate a similarity measure. It needs to be low when distance is high