Python code to add random Gaussian noise on images

add_gaussian_noise.py

import cv2

def add_gaussian_noise(X_imgs):
    gaussian_noise_imgs = []
    row, col, _ = X_imgs[0].shape
    # Gaussian distribution parameters
    mean = 0
    var = 0.1
    sigma = var ** 0.5
    
    for X_img in X_imgs:
        gaussian = np.random.random((row, col, 1)).astype(np.float32)
        gaussian = np.concatenate((gaussian, gaussian, gaussian), axis = 2)
        gaussian_img = cv2.addWeighted(X_img, 0.75, 0.25 * gaussian, 0.25, 0)
        gaussian_noise_imgs.append(gaussian_img)
    gaussian_noise_imgs = np.array(gaussian_noise_imgs, dtype = np.float32)
    return gaussian_noise_imgs
  
gaussian_noise_imgs = add_gaussian_noise(X_imgs)

Hi, Santykish,

• img[...,::-1] simply reverses the channel order from cv2 BGR to RGB, so it can be shown by matplotlib which is RGB
  theres many ways that can be done, that one is simply an indexing shortcut. You could use the cv2.cvtColor(img, cv2.COLOR_BGR2RGB) or you could open it with PIL.Image.open(), or you could display it with cv2 and never convert the colors.
• /255 - you want a float.

There are a ton of ways in which one can 'add noise', in fact the topic of noise is much greater than what this gist covers.
Do you need to maintain moments of the data? Do you want to denoise a sensor? or generate data from a distribution.

Having a hard time trying to adapt it to a similar problem. I'm firstly testing this noise function to add later a sepia effect, so this looks more like a vintage image, but did not manage to plot it properly yet. Code goes as follows

def old_photo(file_nm):
    img = cv2.imread("HT.jpg")[...,::-1]/255.0
    noise =  np.random.normal(loc=0, scale=1, size=img.shape)
    
    noised_img = np.clip((img*(1 + noise*0.4)),0,1)
    plt.imshow(noised_img)
       
if __name__ == "__main__":
    print(old_photo("HT.jpg"))

Do you know what can be going wrong here? I'm missing a point I guess

Having a hard time trying to adapt it to a similar problem. I'm firstly testing this noise function to add later a sepia effect, so this looks more like a vintage image, but did not manage to plot it properly yet. Code goes as follows

def old_photo(file_nm):
    img = cv2.imread("HT.jpg")[...,::-1]/255.0
    noise =  np.random.normal(loc=0, scale=1, size=img.shape)
    
    noised_img = np.clip((img*(1 + noise*0.4)),0,1)
    plt.imshow(noised_img)
       
if __name__ == "__main__":
    print(old_photo("HT.jpg"))

Do you know what can be going wrong here? I'm missing a point I guess

Your old_photo function is returning None. You need to do it in a jupyter notebook. Or you can save the noised_image. How come you're trying to print it? Even if you output the results, it'll be a numpy array.