old_contents: """
  ============
  Thresholding
  ============
  
  Thresholding is used to create a binary image from a grayscale image [1]_.
  
  .. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29
  
  .. seealso::
      A more comprehensive presentation on
      :ref:`sphx_glr_auto_examples_xx_applications_plot_thresholding.py`
  
  """
  
  ######################################################################
  # We illustrate how to apply one of these thresholding algorithms.
  # This example uses the mean value of pixel intensities. It is a simple
  # and naive threshold value, which is sometimes used as a guess value.
  
  import matplotlib.pyplot as plt
  from skimage.filters.thresholding import threshold_mean
  from skimage import data
  
  image = data.camera()
  thresh = threshold_mean(image)
  binary = image > thresh
  
  fig, axes = plt.subplots(ncols=2, figsize=(8, 3))
  ax = axes.ravel()
  
  ax[0].imshow(image, cmap=plt.cm.gray)
  ax[0].set_title('Original image')
  
  ax[1].imshow(binary, cmap=plt.cm.gray)
  ax[1].set_title('Result')
  
  for a in ax:
      a.axis('off')
  
  plt.show()
  
  
  ######################################################################
  # If you are not familiar with the details of the different algorithms and the
  # underlying assumptions, it is often difficult to know which algorithm will give
  # the best results. Therefore, Scikit-image includes a function to evaluate
  # thresholding algorithms provided by the library. At a glance, you can select
  # the best algorithm for you data without a deep understanding of their
  # mechanisms.
  
  from skimage.filters import thresholding
  
  img = data.page()
  
  # Here, we specify a radius for local thresholding algorithms.
  # If it is not specified, only global algorithms are called.
  fig, ax = thresholding.try_all_threshold(img, radius=20,
                                           figsize=(10, 8), verbose=False)
  plt.show()
  
new_contents: """
  ============
  Thresholding
  ============
  
  Thresholding is used to create a binary image from a grayscale image [1]_.
  
  .. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29
  
  .. seealso::
      A more comprehensive presentation on
      :ref:`sphx_glr_auto_examples_xx_applications_plot_thresholding.py`
  
  """
  
  ######################################################################
  # We illustrate how to apply one of these thresholding algorithms.
  # Otsu's method [2]_ calculates an "optimal" threshold (marked by a red line in the
  # histogram below) by maximizing the variance between two classes of pixels,
  # which are separated by the threshold. Equivalently, this threshold minimizes
  # the intra-class variance.
  #
  # .. [2] http://en.wikipedia.org/wiki/Otsu's_method
  
  import matplotlib.pyplot as plt
  from skimage import data
  from skimage.filters import threshold_otsu
  
  
  image = data.camera()
  thresh = threshold_otsu(image)
  binary = image > thresh
  
  fig, axes = plt.subplots(ncols=3, figsize=(8, 2.5))
  ax = axes.ravel()
  ax[0] = plt.subplot(1, 3, 1, adjustable='box-forced')
  ax[1] = plt.subplot(1, 3, 2)
  ax[2] = plt.subplot(1, 3, 3, sharex=ax[0], sharey=ax[0], adjustable='box-forced')
  
  ax[0].imshow(image, cmap=plt.cm.gray)
  ax[0].set_title('Original')
  ax[0].axis('off')
  
  ax[1].hist(image.ravel(), bins=256)
  ax[1].set_title('Histogram')
  ax[1].axvline(thresh, color='r')
  
  ax[2].imshow(binary, cmap=plt.cm.gray)
  ax[2].set_title('Thresholded')
  ax[2].axis('off')
  
  plt.show()
  
  
  ######################################################################
  # If you are not familiar with the details of the different algorithms and the
  # underlying assumptions, it is often difficult to know which algorithm will give
  # the best results. Therefore, Scikit-image includes a function to evaluate
  # thresholding algorithms provided by the library. At a glance, you can select
  # the best algorithm for you data without a deep understanding of their
  # mechanisms.
  
  from skimage.filters import thresholding
  
  img = data.page()
  
  # Here, we specify a radius for local thresholding algorithms.
  # If it is not specified, only global algorithms are called.
  fig, ax = thresholding.try_all_threshold(img, radius=20,
                                           figsize=(10, 8), verbose=False)
  plt.show()
  
subject: Switch to Otsu instead of mean