binary-plane-slicing.py

import numpy as np
import cv2

img = cv2.imread('images/lena.jpeg',0)
 
lst = []
for i in range(img.shape[0]):
    for j in range(img.shape[1]):
         lst.append(np.binary_repr(img[i][j] ,width=8)) # width = no. of bits
 
# We have a list of strings where each string represents binary pixel value. To extract bit planes we need to iterate over the strings and store the characters corresponding to bit planes into lists.
# Multiply with 2^(n-1) and reshape to reconstruct the bit image.
eight_bit_img = (np.array([int(i[0]) for i in lst],dtype = np.uint8) * 128).reshape(img.shape[0],img.shape[1])
seven_bit_img = (np.array([int(i[1]) for i in lst],dtype = np.uint8) * 64).reshape(img.shape[0],img.shape[1])
six_bit_img = (np.array([int(i[2]) for i in lst],dtype = np.uint8) * 32).reshape(img.shape[0],img.shape[1])
five_bit_img = (np.array([int(i[3]) for i in lst],dtype = np.uint8) * 16).reshape(img.shape[0],img.shape[1])
four_bit_img = (np.array([int(i[4]) for i in lst],dtype = np.uint8) * 8).reshape(img.shape[0],img.shape[1])
three_bit_img = (np.array([int(i[5]) for i in lst],dtype = np.uint8) * 4).reshape(img.shape[0],img.shape[1])
two_bit_img = (np.array([int(i[6]) for i in lst],dtype = np.uint8) * 2).reshape(img.shape[0],img.shape[1])
one_bit_img = (np.array([int(i[7]) for i in lst],dtype = np.uint8) * 1).reshape(img.shape[0],img.shape[1])
 
#Concatenate these images for ease of display using cv2.hconcat()
finalr = cv2.hconcat([eight_bit_img,seven_bit_img,six_bit_img,five_bit_img])
finalv =cv2.hconcat([four_bit_img,three_bit_img,two_bit_img,one_bit_img])
 
# Vertically concatenate
final = cv2.vconcat([finalr,finalv])
recombined_image = eight_bit_img + seven_bit_img + six_bit_img + five_bit_img + four_bit_img + three_bit_img + two_bit_img + one_bit_img
# Display the images
cv2.imshow("original image", img)
cv2.imshow('8 planes',final*255)
cv2.imshow("recombined image", recombined_image)
cv2.waitKey(0) 
cv2.destroyAllWindows()