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

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?

just look at cv2.randu() or cv.randn(), it's all pretty similar to matlab already, i guess.

let's play a bit ;) :

import cv2
import numpy as np

>>> im = np.empty((5,5), np.uint8) # needs preallocated input image
>>> im
array([[248, 168,  58,   2,   1],  # uninitialized memory counts as random, too ?  fun ;) 
       [  0, 100,   2,   0, 101],
       [  0,   0, 106,   2,   0],
       [131,   2,   0,  90,   3],
       [  0, 100,   1,   0,  83]], dtype=uint8)
>>> im = np.zeros((5,5), np.uint8) # seriously now.
>>> im
array([[0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0]], dtype=uint8)
>>> cv2.randn(im,(0),(99))         # normal
array([[  0,  76,   0, 129,   0],
       [  0,   0,   0, 188,  27],
       [  0, 152,   0,   0,   0],
       [  0,   0, 134,  79,   0],
       [  0, 181,  36, 128,   0]], dtype=uint8)
>>> cv2.randu(im,(0),(99))         # uniform
array([[19, 53,  2, 86, 82],
       [86, 73, 40, 64, 78],
       [34, 20, 62, 80,  7],
       [24, 92, 37, 60, 72],
       [40, 12, 27, 33, 18]], dtype=uint8)

to apply it to an existing image, just generate noise in the desired range, and add it:

img = ...
noise = ...

image = img + noise

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)