anchor.diff:          pass # code for computing convex hull
   ----
   
  -Next we iterate over each shape number, count the pixels and filter out shapes that don't have enough pixels (assuming they are artifacts produced by uneven illumination).
  -Then for each shape we select its pixels and calculate total area using convex hull:
  +Next script iterates over each shape found, counts the pixels and filters out shapes that don't have enough pixels (assuming they are artifacts produced by uneven illumination).
  +Then for each shape we select its pixels and calculate total area using link:http://scikit-image.org/docs/dev/auto_examples/plot_convex_hull.html[convex_hull_image] function 
  +and summing up all the pixels.
   
   
   [source,python]
  
positive.code: from skimage import io,  color
  from scipy.ndimage.measurements import label, sum
  from skimage.morphology import convex_hull_image
  # import matplotlib.pyplot as plt
  
  
  def main():
  
negative.code: area = sum(convex_hull)
  ----
  
  Coin on the first image is a polish grosz, which has a diameter of 15,5 mm. Which gives area of 188,692 square milimeters. 
  Using minimum size in pixels = 20 and threshold value = 0.8 we get area of coin roughly equal to 11771 pixels. 
  Using those two values we can compute conversion factor between pixels and square milimetres, 
  for this image one pixel is roughly equal to 0,016 square milimetres.
  
  By using objects with well-known dimensions we can compute conversion factor for other shapes on the image. 
  All we need to do is place a reference object with known or easily computable area.
   
  
  == Final code
  