+import numpy as np

+import cv2

+import glob

+

+def shift_img(img, x, y):

+    M = np.float32([[1, 0, x],

+

+                    [0, 1, y]])

+

+    shifted = cv2.warpAffine(img, M, (img.shape[1], img.shape[0]))

+

+    return shifted

+

+def binary_diff_mask(clean, dirty, thresold=0.2):

+    # this parts corrects gamma, and always remember, sRGB values are not in linear scale with lights intensity,

+    clean = np.power(clean, 1/2.2)

+    dirty = np.power(dirty, 1/2.2)

+

+    # averaged_per_pixel = np.abs(dirty / shift_img(clean, 5, 0) - 1)

+    # print(averaged_per_pixel)

+    diff = np.abs(clean - dirty)

+

+    bin_diff = (diff > thresold).astype(np.uint8)

+

+    return bin_diff

+

+clean = glob.glob("data/source/clean/*.png")

+clean = sorted(clean)

+dirty = glob.glob("data/source/dirty/*.png")

+dirty = sorted(dirty)

+

+clean_img = cv2.imread(clean[0])

+dirty_img = cv2.imread(dirty[0])

+

+binary_diff_mask_img = binary_diff_mask(dirty_img/255, clean_img/255, thresold=0.05)

+

+k = 40

+

+for i in range(k*2):

+    print(i)

+    clean_img_copy = shift_img(clean_img, (i-k)/4, 0)

+    binary_diff_mask_img = binary_diff_mask(dirty_img / 255, clean_img_copy / 255, thresold=0.2)

+    cv2.imwrite(f"test/test_img{(i-k)/4}.png", binary_diff_mask_img*255)