old_contents: """
  =================
  Template Matching
  =================
  
  In this example, we use template matching to identify the occurrence of an
  image patch (in this case, a sub-image centered on the camera man's head).
  Since there's only a single match, the maximum value in the `match_template`
  result` corresponds to the head location. If you expect multiple matches, you
  should use a proper peak-finding function.
  
  """
  
  import numpy as np
  import matplotlib.pyplot as plt
  from skimage import data
  from skimage.feature import match_template
  
  image = data.camera()
  head = image[70:170, 180:280]
  
  result = match_template(image, head)
  
  fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4))
  
  ax1.imshow(head)
  ax1.set_axis_off()
  ax1.set_title('template')
  
  ax2.imshow(image)
  ax2.set_axis_off()
  ax2.set_title('image')
  
  # highlight matched region
  xy = np.unravel_index(np.argmax(result), result.shape)[::-1] #-1 flips ij to xy
  wface, hface = head.shape
  rect = plt.Rectangle(xy, wface, hface, edgecolor='r', facecolor='none')
  ax2.add_patch(rect)
  
  plt.show()
  
  
new_contents: """
  =================
  Template Matching
  =================
  
  In this example, we use template matching to identify the occurrence of an
  image patch (in this case, a sub-image centered on the camera man's head).
  Since there's only a single match, the maximum value in the `match_template`
  result` corresponds to the head location. If you expect multiple matches, you
  should use a proper peak-finding function.
  
  """
  
  import numpy as np
  import matplotlib.pyplot as plt
  from skimage import data
  from skimage.feature import match_template
  
  image = data.camera()
  head = image[70:170, 180:280]
  
  result = match_template(image, head)
  
  fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4))
  
  ax1.imshow(head)
  ax1.set_axis_off()
  ax1.set_title('template')
  
  ax2.imshow(image)
  ax2.set_axis_off()
  ax2.set_title('image')
  
  # highlight matched region
  xy = np.unravel_index(np.argmax(result), result.shape)[::-1] #-1 flips ij to xy
  hface, wface = head.shape
  rect = plt.Rectangle(xy, wface, hface, edgecolor='r', facecolor='none')
  ax2.add_patch(rect)
  
  plt.show()
  
  
subject: Fix shape unpacking ((height, width), not (w, h)).