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)

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.