Histogram equalization

gistfile1.txt

from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from skimage import io,color, img_as_float

def brighten(A):
    gamma = 0.4
    c = 255
    A = c*(A**gamma) 
    return A


def histogram_equalization(A):
    rows = A.shape[0]
    columns = A.shape[1]
    histogram, bin_edges = np.histogram(A, range=(0,255), bins=255)
    c = (np.cumsum(histogram/np.max(histogram)))*255
    c = (c*255)/c[-1]
   
    for i in xrange(0, rows):
        for j in xrange(0, columns):
            A[i,j] = c[int(A[i, j])]

    return A


#def contrast_stretching(A, c1, c2, c3, c4):
#    A = c1*np.tanh(c2*A - c3) + c4
#    return A


#def point_transformation(A):
 #   A = contrast_stretching(brighten(A))

#    return A


A = (img_as_float(io.imread('thecar.jpg', plugin='pil')))*255
B = (img_as_float(io.imread('thecar.jpg', plugin='pil')))*255

C = np.empty_like(A)
C[:] = A


bright_A = brighten(A)

equal_A = histogram_equalization(C)

f, (ax0, ax1) = plt.subplots(1, 2)
f.tight_layout()

ax0.imshow(A, cmap=plt.cm.gray)
ax0.set_title('Input image')

ax1.imshow(bright_A, cmap=plt.cm.gray)
ax1.set_title('Image brightened, gamma = , c = ')

f1, (ax0, ax1, ax2, ax3) = plt.subplots(1, 4)
f1.tight_layout()

ax0.imshow(A, cmap=plt.cm.gray)
ax0.set_title('Input image')

ax1.hist(A.flatten(), bins=255, range=(0, 255), histtype='bar')
ax1.set_title('Histogram of input image')

ax2.imshow(equal_A, cmap=plt.cm.gray)
ax2.set_title('Output image')

ax3.hist(equal_A.flatten(), bins=255, range=(0, 255), histtype='bar')
ax3.set_title('Histogram of output image')

plt.show()