Merge pull request #1970 from alexandrejaguar/master

Format clean-ups in convex hull and otsu

doc/examples/edges/plot_convex_hull.py

@@ -13,12 +13,12 @@ A good overview of the algorithm is given on `Steve Eddin's blog
 <http://blogs.mathworks.com/steve/2011/10/04/binary-image-convex-hull-algorithm-notes/>`__.
 
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 
 from skimage.morphology import convex_hull_image
 
-
 image = np.array(
     [[0, 0, 0, 0, 0, 0, 0, 0, 0],
      [0, 0, 0, 0, 1, 0, 0, 0, 0],
@@ -31,6 +31,7 @@ original_image = np.copy(image)
 
 chull = convex_hull_image(image)
 image[chull] += 1
+
 # image is now:
 # [[ 0.  0.  0.  0.  0.  0.  0.  0.  0.]
 #  [ 0.  0.  0.  0.  2.  0.  0.  0.  0.]
@@ -39,15 +40,15 @@ image[chull] += 1
 #  [ 0.  2.  1.  1.  1.  1.  1.  2.  0.]
 #  [ 0.  0.  0.  0.  0.  0.  0.  0.  0.]]
 
+fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(12, 4))
+plt.tight_layout()
 
-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 6))
+ax0.set_title('Original picture')
+ax0.imshow(original_image, cmap=plt.cm.gray, interpolation='nearest')
+ax0.set_xticks([]), ax0.set_yticks([])
 
-ax1.set_title('Original picture')
-ax1.imshow(original_image, cmap=plt.cm.gray, interpolation='nearest')
+ax1.set_title('Transformed picture')
+ax1.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
 ax1.set_xticks([]), ax1.set_yticks([])
 
-ax2.set_title('Transformed picture')
-ax2.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
-ax2.set_xticks([]), ax2.set_yticks([])
-
 plt.show()

doc/examples/features_detection/plot_censure.py

@@ -6,36 +6,35 @@ CENSURE feature detector
 The CENSURE feature detector is a scale-invariant center-surround detector
 (CENSURE) that claims to outperform other detectors and is capable of real-time
 implementation.
-
 """
+
 from skimage import data
 from skimage import transform as tf
 from skimage.feature import CENSURE
 from skimage.color import rgb2gray
+
 import matplotlib.pyplot as plt
 
-
-img1 = rgb2gray(data.astronaut())
+img_orig = rgb2gray(data.astronaut())
 tform = tf.AffineTransform(scale=(1.5, 1.5), rotation=0.5,
                            translation=(150, -200))
-img2 = tf.warp(img1, tform)
+img_warp = tf.warp(img_orig, tform)
 
 detector = CENSURE()
 
-fig, ax = plt.subplots(nrows=1, ncols=2)
+fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))
+plt.tight_layout()
 
-plt.gray()
+detector.detect(img_orig)
 
-detector.detect(img1)
-
-ax[0].imshow(img1)
+ax[0].imshow(img_orig, cmap=plt.cm.gray)
 ax[0].axis('off')
 ax[0].scatter(detector.keypoints[:, 1], detector.keypoints[:, 0],
               2 ** detector.scales, facecolors='none', edgecolors='r')
 
-detector.detect(img2)
+detector.detect(img_warp)
 
-ax[1].imshow(img2)
+ax[1].imshow(img_warp, cmap=plt.cm.gray)
 ax[1].axis('off')
 ax[1].scatter(detector.keypoints[:, 1], detector.keypoints[:, 0],
               2 ** detector.scales, facecolors='none', edgecolors='r')

doc/examples/segmentation/plot_local_otsu.py

@@ -10,7 +10,7 @@ structuring element.
 
 The example compares the local threshold with the global threshold.
 
-.. Note: local is much slower than global thresholding
+.. note: local is much slower than global thresholding
 
 .. [1] http://en.wikipedia.org/wiki/Otsu's_method
 
@@ -37,6 +37,7 @@ global_otsu = img >= threshold_global_otsu
 fig, ax = plt.subplots(2, 2, figsize=(8, 5), sharex=True, sharey=True,
                        subplot_kw={'adjustable': 'box-forced'})
 ax0, ax1, ax2, ax3 = ax.ravel()
+plt.tight_layout()
 
 fig.colorbar(ax0.imshow(img, cmap=plt.cm.gray),
              ax=ax0, orientation='horizontal')