Check consistency of four corners of an image

four_corner_consistency.py

import itertools
import PIL.Image
import numpy as np

def four_corners_similar(image: PIL.Image.Image):
    pixel_value_diff_tolerance = 40 # tolerance for pixel value difference, 0-255
    discrepancy_threshold = 0.01 # max percentage of pixels that can be different between corners to be considered similar
    corner_image_ratio = 0.06
    crop_size = int(corner_image_ratio * min(image.size))
    # print(crop_size)
    w, h = image.size
    img_np = np.asarray(image.convert("RGB"), dtype=np.int16)
    top_left = img_np[0:crop_size, 0:crop_size]
    top_right = img_np[0:crop_size, w - crop_size:w]
    bottom_left = img_np[h - crop_size:h, 0:crop_size]
    bottom_right = img_np[h - crop_size:h, w - crop_size:w]
    corners = [top_left, top_right, bottom_left, bottom_right]
    max_diff = 0
    for i, j in itertools.product(range(len(corners)), range(len(corners))):
        if j <= i:
            continue
        diff = np.absolute(corners[i] - corners[j])
        diff[diff < pixel_value_diff_tolerance] = 0
        percentage_diff = np.count_nonzero(diff) / (crop_size**2 * 3)
        max_diff = max(max_diff, percentage_diff)
        if percentage_diff > discrepancy_threshold:
            # print(f"Corners {i} and {j} are not similar. Percentage: {percentage_diff:.3f}")
            # display(PIL.Image.fromarray(corners[i].astype(np.uint8)))
            # display(PIL.Image.fromarray(corners[j].astype(np.uint8)))
            # display(image.resize((256, 256)))
            return False, max_diff

    return True, max_diff