text: import numpy as np
  from PIL import Image,ImageChops
  import math,operator
  import os
  cwd = os.getcwd()
  fullHP = cwd+"/screens/37.png"
  damageHP = cwd+"/screens/63.png"
  print(cwd)
  imFULL = Image.fromarray(np.asarray(Image.open(fullHP)) - np.asarray(Image.open(fullHP)))
  imDAM = Image.fromarray(np.asarray(Image.open(fullHP)) - np.asarray(Image.open(damageHP)))
  print(imDAM.size)
  def mse(imageA, imageB):
  	# the 'Mean Squared Error' between the two images is the
  	# sum of the squared difference between the two images;
  	# NOTE: the two images must have the same dimension
  	err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2)
  	err /= float(imageA.shape[0] * imageA.shape[1])
  	
  	# return the MSE, the lower the error, the more "similar"
  	# the two images are
  	return err
  def rmsdiff(im1, im2):
      "Calculate the root-mean-square difference between two images"
      diff = ImageChops.difference(im1, im2)
      #diff.show()
      h = diff.histogram()
      sq = (value*((idx%256)**2) for idx, value in enumerate(h))
      sum_of_squares = sum(sq)
      rms = math.sqrt(sum_of_squares/float(im1.size[0] * im1.size[1]))
      return rms
  def rmsdiffe(im1, im2):
      """Calculates the root mean square error (RSME) between two images"""
      errors = np.asarray(ImageChops.difference(im1, im2)) / 255
      return math.sqrt(np.mean(np.square(errors)))
  #imFULL.show()
  #imDAM.show()
  y = 1- rmsdiffe(imFULL,imDAM)
  x= mse(np.asarray(imFULL),np.asarray(imDAM))
  print("comp",y)