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import cv2 import os # Step 1: Load images image_path = "./image" out_noise_path = "./noise_image" # Size of pixelized block block_size = 8 # Specified noise RGB value noise_value = [10, 10, 10] # Check output path integrity if not os.path.exists(out_noise_path): os.mkdir(out_noise_path) def pixelize_and_add_noise(image, block_size, noise_value): row, col, ch = image.shape noisy_image = image.copy() for i in range(0, row, block_size): for j in range(0, col, block_size): # Pixelization: use the mean value for each block block_mean = noisy_image[i:i+block_size, j:j+block_size].mean(axis=(0, 1)) noisy_image[i:i+block_size, j:j+block_size] = block_mean # Add the specified noise to the block noisy_image[i:i+block_size, j:j+block_size] += noise_value # Clip values to be in valid range 0-255 noisy_image = np.clip(noisy_image, 0, 255).astype('uint8') return noisy_image for image_path1 in os.listdir(image_path): for image_path2 in os.listdir(image_path+"/"+image_path1): image = cv2.imread(image_path+"/"+image_path1+"/"+image_path2) # Step 2: Pixelize and add noise to image noisy_image = pixelize_and_add_noise(image, block_size, noise_value) # Step 3: Save the noisy image # Check path integrity if not os.path.exists(out_noise_path+"/"+image_path1): os.mkdir(out_noise_path+"/"+image_path1) cv2.imwrite(out_noise_path+"/"+image_path1+"/"+image_path2, noisy_image)