Use ImageChops

tests/test_images.py

@@ -47,16 +47,14 @@ def image_similarity(path1: Path, path2: Path) -> float:
  return 0
 
  # Convert images to pixel data arrays
- pixels1 = list(image1.getdata())
- pixels2 = list(image2.getdata())
+ diff = ImageChops.difference(image1, image2)
+ pixels = list(diff.getdata())
+
  # Calculate the Mean Squared Error (MSE)
- if isinstance(pixels1[0], tuple):
- mse = sum(
- sum((c1 - c2) ** 2 for c1, c2 in zip(p1, p2))
- for p1, p2 in zip(pixels1, pixels2)
- ) / len(pixels1)
+ if isinstance(pixels[0], tuple):
+ mse = sum(sum(c**2 for c in p) / len(p) for p in pixels) / len(pixels)
  else:
- mse = sum((p1 - p2) ** 2 for p1, p2 in zip(pixels1, pixels2)) / len(pixels1)
+ mse = sum(p**2 for p in pixels) / len(pixels)
 
  return 1 - mse