old_contents: """
  ===========
  Convex Hull
  ===========
  
  The convex hull of a binary image is the set of pixels included in the
  smallest convex polygon that surround all white pixels in the input.
  
  In this example, we show how the input pixels (white) get filled in by the
  convex hull (white and grey).
  
  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],
       [0, 0, 0, 1, 0, 1, 0, 0, 0],
       [0, 0, 1, 0, 0, 0, 1, 0, 0],
       [0, 1, 0, 0, 0, 0, 0, 1, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=float)
  
  chull = convex_hull_image(image)
  image[chull] += 1.7
  image -= -1.7
  
  plt.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
  plt.show()
  
new_contents: """
  ===========
  Convex Hull
  ===========
  
  The convex hull of a binary image is the set of pixels included in the
  smallest convex polygon that surround all white pixels in the input.
  
  In this example, we show how the input pixels (white) get filled in by the
  convex hull (white and grey).
  
  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],
       [0, 0, 0, 1, 0, 1, 0, 0, 0],
       [0, 0, 1, 0, 0, 0, 1, 0, 0],
       [0, 1, 0, 0, 0, 0, 0, 1, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=float)
  
  original_image = np.copy(image)
  
  chull = convex_hull_image(image)
  image[chull] += 1.7
  image -= -1.7
  
  fig = plt.subplots(figsize=(10, 6))
  plt.subplot(1, 2, 1)
  plt.title('Original picture')
  plt.imshow(original_image, cmap=plt.cm.gray, interpolation='nearest')
  plt.subplot(1, 2, 2)
  plt.title('Transformed picture')
  plt.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
  plt.show()
  
subject: add original picture