Add_Gaussian_Salt&Pepper_Poisson_Speckle_Noise

# Parameters

# image : ndarray
#     Input image data. Will be converted to float.
# mode : str
#     One of the following strings, selecting the type of noise to add:

#     'gauss'     Gaussian-distributed additive noise.
#     'poisson'   Poisson-distributed noise generated from the data.
#     'saltandpepper'       Replaces random pixels with 0 or 1.
#     'speckle'   Multiplicative noise using out = image + n*image,where
#                 n is uniform noise with specified mean & variance.


import numpy as np
import os
import cv2
import matplotlib.pyplot as plt
from PIL import Image


def noisy(noise_typ,image):
    if noise_typ == "gauss":
      row,col,ch= image.shape
      mean = 0
      var = 0.1
      sigma = var**0.5
      gauss = np.random.normal(mean,sigma,(row,col,ch))
      gauss = gauss.reshape(row,col,ch)
      noisy = image + gauss
      return noisy
    
    elif noise_typ == "saltandpepper":
      row,col,ch = image.shape
      s_vs_p = 0.5
      amount = 0.004
      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
    
    elif noise_typ == "poisson":
      vals = len(np.unique(image))
      vals = 2 ** np.ceil(np.log2(vals))
      noisy = np.random.poisson(image * vals) / float(vals)
      return noisy
    
    elif noise_typ =="speckle":
      row,col,ch = image.shape
      gauss = np.random.randn(row,col,ch)
      gauss = gauss.reshape(row,col,ch)        
      noisy = image + image * gauss
      return noisy


img = Image.open('/home/cat.jpg')
img = np.array(img)

img_noisy=noisy("saltandpepper",img)
plt.imshow(img_noisy)
plt.show()