new_contents: import pypiv
  import numpy as np
  import matplotlib.pyplot as plt
  from glob import glob
  
  def main():
      imgs = ['images/finger1.png', 'images/finger2.png']
      frames = [plt.imread(x) for x in imgs]
      frame_a, frame_b = frames[0], frames[1]
  
      frame_a_inv = np.copy(frame_a[::-1,::-1])
      frame_b_inv = np.copy(frame_b[::-1,::-1])
  
      piv = do_piv(frame_a, frame_b)
      piv_inv = do_piv(frame_a_inv, frame_b_inv)
  
  
      u = piv.u
      ui = piv_inv.u[::-1, ::-1]
  
      u[np.isnan(u)] = 1000.
      ui[np.isnan(ui)] = -1000.
  
      f, (ax1, ax2) = plt.subplots(1, 2)
      ax1.imshow(u, interpolation='nearest')
      ax2.imshow(-ui, interpolation='nearest')
  
      print np.sum(u+ui)
  
      plt.show()
  
  def do_piv(frame_a, frame_b):
  
      #PIV1
      piv = pypiv.DirectPIV(frame_a, frame_b, window_size=32,
                              search_size=32, distance=16)
      piv.correlate_frames()
      #pypiv.filters.outlier_from_local_median(piv, 2.0)
      #pypiv.filters.replace_outliers(piv)
      pypiv.filters.median_filter(piv)
  
      #PIV2
      piv = pypiv.AdaptivePIV(piv, window_size=32,
                                    search_size=32, distance=16)
      piv.correlate_frames()
  
      #pypiv.filters.outlier_from_local_median(piv, 2.0)
      #pypiv.filters.replace_outliers(piv)
      pypiv.filters.median_filter(piv)
  
      #PIV3
      piv = pypiv.AdaptivePIV(piv, window_size=32,
                                    search_size=32, distance=8)
      piv.correlate_frames()
      #pypiv.filters.outlier_from_local_median(piv, 2.0)
      #pypiv.filters.replace_outliers(piv)
      pypiv.filters.median_filter(piv)
  
      return piv
  
  
  if __name__=='__main__':
      main()
  
subject: Add example for inverted image comparison.