anchor.diff:  area = sum(convex_hull)
   ----
   
  -Coin on the first image is a polish grosz, which has a diamater 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 equal to 11771 pixels. 
  +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 case one pixel is roughly equal to 0,016 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
  
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: 
  link:http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.measurements.label.html[label] is a function from *scipy.ndimage.measurements* package. 
  
  It assignes unique integer values to continuous patches of non-zero pixels.
  Number of segments found is stored in *num_segments* variable. New image with shape pixels labeled is stored in *segmented* variable.
  Labels have values from 1 to *num_segments*.
  
  
  