new_contents: """
  ===============
  Contour finding
  ===============
  
  ``skimage.measure.find_contours`` uses a marching squares method to
  find constant valued contours in an image.  Array values are linearly
  interpolated to provide better precision of the output contours.
  Contours which intersect the image edge are open; all others
  are closed.
  
  The `marching squares algorithm
  <http://www.essi.fr/~lingrand/MarchingCubes/algo.html>`__ is a special
  case of the marching cubes algorithm (Lorensen, William and Harvey
  E. Cline. Marching Cubes: A High Resolution 3D Surface Construction
  Algorithm. Computer Graphics (SIGGRAPH 87 Proceedings) 21(4) July
  1987, p. 163-170).
  
  """
  
  from skimage import data
  from skimage import measure
  
  import numpy as np
  import matplotlib.pyplot as plt
  
  # Construct some test data
  x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j]
  r = np.sin(np.exp((np.sin(x)**3 + np.cos(y)**2)))
  
  # Find contours at a constant value of 0.8
  contours = measure.find_contours(r, 0.8)
  
  # Display the image and plot all contours found
  plt.imshow(r, interpolation='nearest')
  
  for n, contour in enumerate(contours):
      plt.plot(contour[:, 1], contour[:, 0], linewidth=2)
  
  plt.axis('image')
  plt.xticks([])
  plt.yticks([])
  plt.show()
  
  
subject: Add contour finding example.