from pathlib import Path
import json
import numpy as np
from PIL import Image

root = Path(__file__).parent
first = np.asarray(Image.open(root / 'k2-normal-a.jpg').convert('RGB')).astype(np.int16)
second = np.asarray(Image.open(root / 'k2-normal-b.jpg').convert('RGB')).astype(np.int16)
assert first.shape == second.shape == (1540, 2723, 3)
# Fixed boxes from the observed normalized screenshots. All exclude the clock,
# detail panel and dashboard; the control box contains only static ground imagery.
boxes = {'device_and_halo': (1320, 805, 1395, 885), 'static_ground_control': (1050, 800, 1225, 900)}
results = {}
for name, (left, top, right, bottom) in boxes.items():
    delta = np.abs(first[top:bottom, left:right] - second[top:bottom, left:right])
    per_pixel = delta.max(axis=2)
    results[name] = {'box_xyxy': [left, top, right, bottom], 'pixels': int(per_pixel.size),
                     'changed_over_10': int((per_pixel > 10).sum()),
                     'changed_over_20': int((per_pixel > 20).sum()),
                     'mean_channel_delta': float(delta.mean()), 'max_channel_delta': int(delta.max())}
results['evidence_limits'] = ['JPEG normalized tool screenshots, not original PNG pixels.',
    'Two wall-clock captures ~35 seconds apart, not a controlled RAF trace.',
    'Local image change supports visible motion, not a measured frame rate or GPU timing.',
    'No live page script or browser process was used; this only analyzes saved images.']
(root / 'pixel-diff.json').write_text(json.dumps(results, ensure_ascii=False, indent=2), encoding='utf-8')
print(json.dumps(results, ensure_ascii=False, indent=2))