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  1. import numpy as np
  2. import cv2
  3. import pickle
  4.  
  5. # def arith_coding(fileVector, blockSize, probability):
  6. #     cumulative_p = {}
  7. #     cumulative_p_prev = {}
  8. #     tags = []
  9.  
  10. #     cum_sum = 0
  11. #     for p in probability:
  12. #         cumulative_p[p] = (probability[p] + cum_sum)
  13. #         cumulative_p_prev[p] = cum_sum
  14. #         cum_sum += probability[p]
  15.  
  16. #     idx = 0
  17. #     while(idx < len(fileVector)):
  18. #         l = 0
  19. #         u = 1
  20. #         for blockNum in range(blockSize):
  21. #             letter = fileVector[idx]
  22. #             new_l = l + (u-l) * cumulative_p_prev[letter]
  23. #             new_u = l + (u-l) * cumulative_p[letter]
  24. #             u = new_u
  25. #             l = new_l
  26. #             idx += 1
  27. #         tags.append((u+l)/2)
  28.  
  29. #     return tags
  30.  
  31.  
  32. def get_binaryStr_within_range(l, u):
  33.     i = 1
  34.     num = 0
  35.     binaryStr = ''
  36.     while (not(num >= l and num < u)):
  37.         decimal_pnt = 2**(-i)
  38.         new_decimal = num + decimal_pnt
  39.         if(new_decimal > u):
  40.             binaryStr += '0'
  41.         else:
  42.             binaryStr += '1'
  43.             num = new_decimal
  44.         i += 1
  45.     return binaryStr
  46.  
  47.  
  48. def arith_coding(fileVector, blockSize, probability):
  49.     cumulative_p = {}
  50.     cumulative_p_prev = {}
  51.     tags = []
  52.  
  53.     last_prob = 0
  54.     for p in probability:
  55.         cumulative_p_prev[p] = last_prob
  56.         cumulative_p[p] = last_prob + probability[p]
  57.         last_prob = cumulative_p[p]
  58.  
  59.     idx = 0
  60.     while(idx < len(fileVector)):
  61.         l = 0.0
  62.         u = 1.0
  63.         tag = ''
  64.         blockNum = 0
  65.         c = 0
  66.         while (blockNum < blockSize):
  67.             if(l >= 0 and u < 0.5):
  68.                 l = 2 * l
  69.                 u = 2 * u
  70.                 tag += '0'
  71.                 for ic in range(c):
  72.                     tag += '1'
  73.                 c = 0
  74.             elif (l >= 0.5 and u <= 1.0):
  75.                 l = 2 * (l - 0.5)
  76.                 u = 2 * (u - 0.5)
  77.                 tag += '1'
  78.                 for ic in range(c):
  79.                     tag += '0'
  80.                 c = 0
  81.             elif(l >= 0.25 and u < 0.75):
  82.                 l = 2 * (l - 0.25)
  83.                 u = 2 * (u - 0.25)
  84.                 c += 1
  85.             else:
  86.                 if(blockNum == blockSize-1):
  87.                     letter = fileVector[idx]
  88.                     new_l = l + (u-l) * cumulative_p_prev[letter]
  89.                     new_u = l + (u-l) * cumulative_p[letter]
  90.                     extra_bits = get_binaryStr_within_range(new_l, new_u)
  91.                     tag += extra_bits
  92.                 else:
  93.                     letter = fileVector[idx]
  94.                     delta = u-l
  95.                     new_l = l + delta * cumulative_p_prev[letter]
  96.                     new_u = min(
  97.                         l + delta * cumulative_p[letter], 1.0)
  98.                     l = new_l
  99.                     u = new_u
  100.                 idx += 1
  101.                 blockNum += 1
  102.         tags.append(tag)
  103.     return tags
  104.  
  105.  
  106. img = cv2.imread('shoma.jpg', cv2.IMREAD_GRAYSCALE)
  107.  
  108. dimensions = np.array([img.shape[0], img.shape[1]])  # height x width
  109.  
  110. img = img.flatten()
  111. blockSize = 16
  112.  
  113. extraPixels = blockSize - (img.size % blockSize)
  114. img = np.pad(img, (0, extraPixels), 'constant', constant_values=(0))
  115.  
  116. probability = {}
  117.  
  118. for pix in img:
  119.     if pix in probability.keys():
  120.         probability[pix] += 1
  121.     else:
  122.         probability[pix] = 1
  123.  
  124. sortedProbability = {}
  125. for shade in range(256):
  126.     if shade in probability.keys():
  127.         sortedProbability[shade] = probability[shade]
  128.  
  129. probability = sortedProbability
  130.  
  131. for p in probability:
  132.     probability[p] /= len(img)
  133.  
  134. probabilityVector = []
  135.  
  136. for shade in range(256):
  137.     if shade in probability.keys():
  138.         probabilityVector.append(probability[shade])
  139.     else:
  140.         probabilityVector.append(0)
  141.  
  142. probabilityVector = np.array(probabilityVector, dtype=float)
  143.  
  144. # tags = np.array(arith_coding(img, blockSize, sortedProbability),
  145. #                 dtype=float)
  146.  
  147. tags = arith_coding(img, blockSize, sortedProbability)
  148.  
  149. with open("test.txt", "wb") as fp:  # Pickling
  150.     pickle.dump(tags, fp)
  151.  
  152. # tags.tofile('tags.dat')
  153. probabilityVector.tofile('probabilities.dat')
  154. dimensions.tofile('dimensions.dat')
  155. np.array([blockSize]).tofile('blocksize.dat')
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