equalization_RGB.py

import numpy as np
import matplotlib.pyplot as plt
from skimage.io import imshow, imread
from skimage.color import rgb2gray
from skimage.exposure import equalize_hist
from skimage import img_as_ubyte

image = imread(r'C:\Users\Nina\Documents\Cours-TP\PAF\data\pied_sanslum.jpg')





target_bins = np.arange(255)
target_freq = np.linspace(0, 1, len(target_bins))
freq_bins = [cumulative_distribution(image[:,:,i]) for i in \
                range(3)]
names = ['Reds', 'Greens', 'Blues']
line_color = ['red','green','blue']
adjusted_figures = []
f_size = 20

#Pad frequencies with min frequency
adj_freqs = []
for i in range(len(freq_bins)):
    if len(freq_bins[i][0]) < 256:
        frequencies = list(freq_bins[i][0])
        min_pad = [min(frequencies)] * (256 - len(frequencies))
        frequencies = min_pad + frequencies
    else:
        frequencies = freq_bins[i][0]
    adj_freqs.append(np.array(frequencies))

#Plot RGB Images
fig, ax = plt.subplots(1,3, figsize=[15,5])
for n, ax in enumerate(ax.flatten()):
    interpolation = np.interp(adj_freqs[n], target_freq,
                    target_bins)
    adjusted_image = img_as_ubyte(interpolation[image[:,:,n]].astype(int))
    ax.set_title(f'{names[n]}', fontsize = f_size)
    ax.imshow(adjusted_image, cmap = names[n])
    adjusted_figures.append([adjusted_image])
fig.tight_layout()
#Plot Adjusted CDFs
fig, ax = plt.subplots(1,3, figsize=[15,3])
for n, ax in enumerate(ax.flatten()):
    interpolation = np.interp(adj_freqs[n], target_freq,
                    target_bins)
    adjusted_image = img_as_ubyte(interpolation[image[:,:,n]]
                        .astype(int))
    freq_adj, bins_adj = cumulative_distribution(adjusted_image)

    ax.set_title(f'{names[n]}', fontsize = f_size)
    ax.step(bins_adj, freq_adj, c=line_color[n],
            label='Actual CDF')
    ax.plot(target_bins,
            target_freq,
            c='gray',
            label='Target CDF',
            linestyle = '--')
fig.tight_layout()

adjusted_image = np.dstack((adjusted_figures[0][0],
                            adjusted_figures[1][0],
                            adjusted_figures[2][0]))

#Plot Original Image against Adjusted Image
fig, ax = plt.subplots(1,2, figsize=[17,7])

ax[0].imshow(image);
ax[0].set_title(f'Original Image', fontsize = f_size)

ax[1].imshow(adjusted_image);
ax[1].set_title(f'Adjusted Image', fontsize = f_size)


fig.tight_layout()


plt.show()