"""实拍会话列表，逐行报告名称字形哈希的置位数，并导出取样带截图。

用途：定位为什么某些联系人的 256 位名称哈希退化成全零。
"""
import os
import sys

import numpy as np

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

import cv2  # noqa: E402

from wechat_bot import WeChatBot, _NAME_FP_BYTES  # noqa: E402

OUT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "namehash")


def main() -> None:
    os.makedirs(OUT, exist_ok=True)
    bot = WeChatBot()
    if not bot.connect(activate=False):
        raise SystemExit("挂载企业微信失败")
    img = bot.capture_session_list()
    if img is None:
        raise SystemExit("截取会话列表失败")
    cv2.imwrite(os.path.join(OUT, "list.png"), img)
    scale = max(0.75, float(bot.scale or 1.0))
    print(f"列表尺寸 {img.shape[1]}x{img.shape[0]}  scale={scale}")

    centers = bot._avatar_row_centers(img)
    print(f"检出 {len(centers)} 行头像，中心={centers}")

    # 名称哈希实际取样的两条带（与 _session_name_fingerprint 保持一致）
    bands = {
        "prefix": (60 * scale, 84 * scale, -8 * scale, -2 * scale),
        "main": (80 * scale, img.shape[1] - 60 * scale, -21 * scale, -2 * scale),
    }

    for idx, raw_center in enumerate(centers):
        y_c = bot._avatar_anchor(img, raw_center)
        name_fp = bot._session_name_fingerprint(img, y_c, row_center=True)
        avatar_fp = bot._session_fingerprint(img, y_c, row_center=True)
        bits = int.from_bytes(name_fp or b"", "big").bit_count()
        head = (avatar_fp or b"")[:8].hex()
        print(
            f"行{idx}: 锚点 {raw_center}->{y_c}  名称置位={bits:3d}/256  "
            f"长度={len(name_fp or b'')}  头像={head}"
        )
        for tag, (x1, x2, dy1, dy2) in bands.items():
            xa, xb = int(max(0, x1)), int(min(img.shape[1], x2))
            ya, yb = int(max(0, y_c + dy1)), int(min(img.shape[0], y_c + dy2))
            crop = img[ya:yb, xa:xb]
            if crop.size:
                gray = crop[:, :, :3].astype(np.float32).mean(axis=2)
                background = float(np.median(gray))
                ink = float((np.abs(gray - background) >= 16.0).mean())
                print(
                    f"      {tag:6s} x[{xa},{xb}) y[{ya},{yb})  "
                    f"底色={background:.0f} 墨迹占比={ink:.3f}"
                )
                cv2.imwrite(os.path.join(OUT, f"row{idx}_{tag}.png"), crop)
            else:
                print(f"      {tag:6s} 取样区为空 x[{xa},{xb}) y[{ya},{yb})")
    print(f"\n截图已写入 {OUT}")
    print(f"_NAME_FP_BYTES={_NAME_FP_BYTES}")

    # 不同联系人之间名称哈希的距离下界，决定容差能开到多大
    print("\n当前列表内不同行的名称哈希距离：")
    fps = []
    for idx, raw_center in enumerate(centers):
        y_c = bot._avatar_anchor(img, raw_center)
        fps.append((idx, bot._session_name_fingerprint(img, y_c, row_center=True)))
    worst = 999
    for i in range(len(fps)):
        for j in range(i + 1, len(fps)):
            a, b = fps[i][1], fps[j][1]
            if len(a) != _NAME_FP_BYTES or len(b) != _NAME_FP_BYTES:
                continue
            dist = (int.from_bytes(a, "big") ^ int.from_bytes(b, "big")).bit_count()
            worst = min(worst, dist)
            print(f"  行{fps[i][0]} vs 行{fps[j][0]}: {dist}")
    print(f"不同联系人名称距离下界 = {worst}")


if __name__ == "__main__":
    main()