adding_noise.py

import matplotlib.pyplot as plt 
import numpy as np 
from skimage import data, img_as_float

path = '/home/habray/Image_Processing/Working_Folder/image/'

img = plt.imread(path + 'panda.jpg')

def gaussian_noise(image):

    row,col = image.shape
    mean = 0
    var = 500
    sigma = var**0.5
    gauss = np.random.normal(mean,sigma,(row,col))
    gauss = gauss.reshape(row,col)
    noisy = image + gauss
    return noisy

def salt_pepper_noise(image):
    
    s_vs_p = 0.5
    amount = 0.2
    out = np.copy(image)
    # Salt mode
    num_salt = np.ceil(amount * image.size * s_vs_p)
    coords = [np.random.randint(0, i - 1, int(num_salt))
            for i in image.shape]
    out[coords] = 1

    # Pepper mode
    num_pepper = np.ceil(amount* image.size * (1. - s_vs_p))
    coords = [np.random.randint(0, i - 1, int(num_pepper))
            for i in image.shape]
    out[coords] = 0
    return out

def plot_img_and_hist(image, axes, bins=256):
    # Plot an image along with its histogram and cumulative histogram.

    image = img_as_float(image)
    ax_img, ax_hist = axes

    # Display image
    ax_img.imshow(image, cmap="gray")
    ax_img.set_axis_off()

    # Display histogram
    ax_hist.hist(image.ravel(), bins=bins, histtype='barstacked', color='black')
    ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
    ax_hist.set_xlabel('Pixel intensity')
    ax_hist.set_xlim(0, 1)
    ax_hist.set_yticks([])

    return ax_img, ax_hist

fig = plt.figure(figsize=(8, 5))
axes = np.zeros((2, 4), dtype=np.object)
axes[0, 0] = fig.add_subplot(2, 3, 1)
for i in range(1, 3):
    axes[0, i] = fig.add_subplot(2, 3, 1+i, sharex=axes[0,0], sharey=axes[0,0])
for i in range(0, 3):
    axes[1, i] = fig.add_subplot(2, 3, 4+i)

ax_img, ax_hist = plot_img_and_hist(img, axes[:, 0])
ax_img.set_title('Original image')

y_min, y_max = ax_hist.get_ylim()
ax_hist.set_ylabel('Number of pixels')
ax_hist.set_yticks(np.linspace(0, y_max, 5))

ax_img, ax_hist = plot_img_and_hist(gaussian_noise(img), axes[:, 1])
ax_img.set_title('Gaussian Noise')

ax_img, ax_hist = plot_img_and_hist(salt_pepper_noise(img), axes[:, 2])
ax_img.set_title('Salt & Pepper Noise')

# prevent overlap of y-axis labels
fig.tight_layout()
plt.show()