# noise overlaid over imagenoisy = np.clip((img + noise*0.2),0,1)noisy2 = np.clip((img + noise*0.4),0,1) Img = cv2.imread(img_path)/255.0noise = np.random.normal(loc=0, scale=1, size=img.shape) Import numpy as npimport cv2import matplotlib.pyplot as plt In every case i blend in 0.2 and 0.4 of the image Noise affects mid values, white and black receiving little noise image folded over and gaussian noise multipled and added to it: peak gaussian noise multiplied then added over image: noise increases gaussian noise added over image: noise is spread throughout On Fri, at 8:30 AM Kanishk Rana commented on this gist. Is there a way to add noise to the bottom half of the image? # norm noise for viz only noise2 = ( noise - noise. # noise multiplied by bottom and top half images, # whites stay white blacks black, noise is added to center img2 = img * 2 n2 = np.
# noise multiplied by image: # whites can go to black but blacks cannot go to white noisy2mul = np. Import numpy as np import cv2 import matplotlib.
