Contrast Image.sagews

#----------------------------------------------------------------
# Sample code for UEET2533 Information Theory and Coding
# by Chong Zan Kai
# Email [email protected]
# Universiti Tunku Abdul Rahman, Malaysia.
#
# We will demonstrate how to contrast an image file.
#
# Last modified on 9-Jan-2015.
#----------------------------------------------------------------

#----------------------------------------------------------------
# Functions to convert image to matrix and vice versa.
#----------------------------------------------------------------
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np

#
# Convert image 2darray to GF2 matrix.
#
def img_arr_to_matrix(img_2darr):
    #img_2darr = mpimg.imread(bmp_file)
    img_2dlist = []
    #
    # Convert to 0 and 1 first.
    for p4_arr in img_2darr:
        a_list = [int(p[0]>0) for p in p4_arr]
        img_2dlist.append(a_list)
    #
    img_mat = matrix(GF(2), img_2dlist)
    return img_mat

#
# Convert GF2 matrix back to image 2darr.
#
def img_matrix_to_arr(img_mat):
    img_2darr = []
    for row in img_mat.rows():
        pixel_list = [ np.uint8([int(r) * 255, int(r) * 255, int(r) * 255, 255 ]) for r in row]
        img_2darr.append(pixel_list)
    return np.array(img_2darr)

#----------------------------------------------------------------

#----------------------------------------------------------------
# Main
#----------------------------------------------------------------

sample_file = 'sample01.bmp'

# Read the image file as array
img_arr = mpimg.imread(sample_file)


#
# Display the image
#
print 'Original image:'
plt.imshow(img_arr)
#plt.show()

# Convert image array to GF2 matrix
img_mat = img_arr_to_matrix (img_arr)
print


#
# Manipulate the matrix.
# Basically I just inverse the image
#
total_row, total_col = img_mat.dimensions()
for r in range(total_row):
    for c in range(total_col):
        img_mat[r, c] = img_mat[r, c] + 1 # GF2's plus 1 == XOR.

#
# Convert the GF2 matrix to image array again
#
new_img_arr = img_matrix_to_arr (img_mat)

print 'New image:'
plt.imshow(new_img_arr)

Original image:

New image: