How to add noise (Gaussian/salt and pepper etc) to image in Python with OpenCV [duplicate]

Question

I am wondering if there exists some functions in Python with OpenCV or any other python image processing library that adds Gaussian or salt and pepper noise to an image? For example, in MATLAB there exists straight-forward functions that do the same job.

Or, how to add noise to an image using Python with OpenCV?

Answer 1

The Function adds gaussian , salt-pepper , poisson and speckle noise in an image

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.     's&p'       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 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 == "s&p":       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 

Answer 2

I don't know is there any method in Python API.But you can use this simple code to add Salt-and-Pepper noise to an image.

import numpy as np import random import cv2  def sp_noise(image,prob):     '''     Add salt and pepper noise to image     prob: Probability of the noise     '''     output = np.zeros(image.shape,np.uint8)     thres = 1 - prob      for i in range(image.shape[0]):         for j in range(image.shape[1]):             rdn = random.random()             if rdn < prob:                 output[i][j] = 0             elif rdn > thres:                 output[i][j] = 255             else:                 output[i][j] = image[i][j]     return output  image = cv2.imread('image.jpg',0) # Only for grayscale image noise_img = sp_noise(image,0.05) cv2.imwrite('sp_noise.jpg', noise_img)