old_contents: """
  ============
  Thresholding
  ============
  
  Thresholding is used to create a binary image from a grayscale image [1]_.
  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.
  
  .. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29
  
  .. seealso::
      :ref:`sphx_glr_auto_examples_xx_applications_plot_thresholding.py`
  
  """
  import matplotlib
  import matplotlib.pyplot as plt
  
  from skimage import data
  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()
  
  ######################################################################
  # How to apply a threshold?
  # =========================
  #
  # Now, 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.
  
  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()
  
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.
  # 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.
  
  import matplotlib.pyplot as plt
  
  from skimage import data
  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: swap examples to show a different image inthe gallery