from PIL import Image
import numpy as np
from numpy.lib.stride_tricks import sliding_window_view

def load_bw(path):
    # Black=1, White=0
    im = Image.open(path).convert("L")
    bw = (np.array(im) < 128).astype(np.uint8)
    return bw

def finder_template(box_size=2):
    # 7x7 finder: outer black, inner white (5x5), center black (3x3)
    F = np.array([
        [1,1,1,1,1,1,1],
        [1,0,0,0,0,0,1],
        [1,0,1,1,1,0,1],
        [1,0,1,1,1,0,1],
        [1,0,1,1,1,0,1],
        [1,0,0,0,0,0,1],
        [1,1,1,1,1,1,1],
    ], dtype=np.uint8)
    # upscale to pixels
    return np.kron(F, np.ones((box_size, box_size), dtype=np.uint8))

def count_finders(img_path, box_size=2, max_mismatch=0):
    bw = load_bw(img_path)
    T = finder_template(box_size=box_size)
    h, w = T.shape
    # slide 14x14 window over the image
    win = sliding_window_view(bw, (h, w))
    # Hamming distance to template per window
    mismatches = (win ^ T).sum(axis=(-2, -1))
    hits = mismatches <= max_mismatch
    # Optional: suppress overlapping duplicates by non-maximum suppression on exact matches
    ys, xs = np.where(hits)
    # Convert to center coordinates
    centers = [(int(y + h/2), int(x + w/2)) for y, x in zip(ys, xs)]

    # Greedy dedup within ~half a finder width
    deduped = []
    r = max(2, h//2)
    for cy, cx in centers:
        if all((abs(cy - y) > r) or (abs(cx - x) > r) for y, x in deduped):
            deduped.append((cy, cx))

    return len(deduped), deduped

if __name__ == "__main__":
    n, centers = count_finders("./scrambled_test_qr_code.png", box_size=2, max_mismatch=0)
    print("Finder count:", n, "centers:", centers, "Exactly three?", n == 3)