RLE2image.py

import cv2
import numpy as np
import math

# import zigzag functions
from zigzag import *

QUANTIZATION_MAT = np.array([[16,11,10,16,24,40,51,61],[12,12,14,19,26,58,60,55],[14,13,16,24,40,57,69,56 ],[14,17,22,29,51,87,80,62],[18,22,37,56,68,109,103,77],[24,35,55,64,81,104,113,92],[49,64,78,87,103,121,120,101],[72,92,95,98,112,100,103,99]])

# defining block size
block_size = 8

# Reading image.txt to decode it as image
with open('image.txt', 'r') as myfile:
    image=myfile.read()

# spplits into tokens seperated by space characters
details = image.split()

# just python-crap to get integer from tokens : h and w are height and width of image (first two items)
h = int(''.join(filter(str.isdigit, details[0])))
w = int(''.join(filter(str.isdigit, details[1])))

# declare an array of zeros (It helps to reconstruct bigger array on which IDCT and all has to be applied)
array = np.zeros(h*w).astype(int)


# some loop var initialisation
k = 0
i = 2
x = 0
j = 0


# This loop gives us reconstructed array of size of image

while k < array.shape[0]:
# Oh! image has ended
    if(details[i] == ';'):
        break
# This is imp! note that to get negative numbers in array check for - sign in string
    if "-" not in details[i]:
        array[k] = int(''.join(filter(str.isdigit, details[i])))        
    else:
        array[k] = -1*int(''.join(filter(str.isdigit, details[i])))        

    if(i+3 < len(details)):
        j = int(''.join(filter(str.isdigit, details[i+3])))

    if j == 0:
        k = k + 1
    else:                
        k = k + j + 1        

    i = i + 2

array = np.reshape(array,(h,w))

# loop for constructing intensity matrix form frequency matrix (IDCT and all)
i = 0
j = 0
k = 0

# initialisation of compressed image
padded_img = np.zeros((h,w))

while i < h:
    j = 0
    while j < w:        
        temp_stream = array[i:i+8,j:j+8]                
        block = inverse_zigzag(temp_stream.flatten(), int(block_size),int(block_size))            
        de_quantized = np.multiply(block,QUANTIZATION_MAT)                
        padded_img[i:i+8,j:j+8] = cv2.idct(de_quantized)        
        j = j + 8        
    i = i + 8

# clamping to  8-bit max-min values
padded_img[padded_img > 255] = 255
padded_img[padded_img < 0] = 0

# compressed image is written into compressed_image.mp file
cv2.imwrite("compressed_image.bmp",np.uint8(padded_img))

# DONE!