"""分歧裁决：对争议候选位置做细粒度尺度扫描 + 分通道 NCC。

用法：.venv/bin/python try/adjudicate.py
对 41ac 的 A2/C 两候选、444d 的共识候选、以及基线对 4db9/d7a7 做细化评估，
输出每候选的最优尺度、NCC 与分通道 NCC，判断真目标。
"""
from pathlib import Path
import sys
import cv2
import numpy as np

sys.path.insert(0, str(Path(__file__).resolve().parent))
from method_c_rotation import load_mark, template_at, patch_ncc  # noqa: E402

ROOT = Path(__file__).resolve().parent.parent
CAP = ROOT / "captchas"

CASES = {
    # 12位前缀: [(标签, 中心, 初始尺度), ...]
    "41ac8781f7e9": [
        ("C  ", (218.0, 223.0), 0.40),
        ("A2 ", (110.0, 216.0), 0.50),
        ("A2#2", (363.0, 214.5), 0.50),
        ("A2#3", (270.0, 223.5), 0.50),
    ],
    "444d0ea7c8bb": [("共识", (232.5, 202.5), 1.10)],
    "4db94793ec6d": [("基线", (351.0, 197.0), 1.05)],
    "d7a7f80c4554": [("基线", (222.8, 201.4), 0.95)],
}
SCALES_FINE = np.arange(-0.12, 0.121, 0.02)


def find_files(prefix):
    """按 12 位前缀 glob 完整文件名。"""
    bigs = [p for p in CAP.glob(f"{prefix}*.jpeg") if "-mark" not in p.stem]
    if not bigs:
        return None, None
    return bigs[0], CAP / f"{bigs[0].stem}-mark.png"


def channel_ncc(big_f, center, tpl, mask):
    """分通道 NCC（模板与 patch 中心对齐）。失败返回 None。"""
    try:
        h, w = tpl.shape[:2]
        x0 = int(round(center[0] - w / 2.0))
        y0 = int(round(center[1] - h / 2.0))
        if x0 < 0 or y0 < 0 or x0 + w > big_f.shape[1] or y0 + h > big_f.shape[0]:
            return None
        patch = big_f[y0:y0 + h, x0:x0 + w]
        vals = []
        for c in range(3):
            res = cv2.matchTemplate(patch[..., c:c + 1].copy(), tpl[..., c:c + 1].copy(),
                                    cv2.TM_CCOEFF_NORMED, mask=mask[..., None].copy())
            v = float(np.nan_to_num(res[0, 0], nan=-1.0))
            vals.append(v)
        return vals
    except cv2.error as e:
        print(f"  分通道失败: {e}")
        return None


def refine(big_f, bgr, alpha, center, scale0):
    """细粒度尺度扫描，返回 (best_ncc, best_scale, 分通道NCC)。"""
    try:
        best_ncc = -1.0
        best_scale = scale0
        best_ch = None
        for ds in SCALES_FINE:
            s = float(scale0 + ds)
            tpl, mask, _, _ = template_at(bgr, alpha, s, 0.0)
            if tpl is None:
                continue
            ncc = patch_ncc(big_f, center[0], center[1], tpl, mask)
            if ncc > best_ncc:
                ch = channel_ncc(big_f, center, tpl, mask)
                best_ncc = ncc
                best_scale = s
                best_ch = ch
        return best_ncc, best_scale, best_ch
    except Exception as e:
        print(f"  细化失败: {e}")
        return (-1.0, scale0, None)


def process_case(prefix, cands):
    try:
        big_path, mark_path = find_files(prefix)
        if big_path is None:
            print(f"{prefix}  样本缺失")
            return
        big = cv2.imread(str(big_path), cv2.IMREAD_COLOR)
        if big is None:
            print(f"{prefix}  大图读取失败")
            return
        big_f = big.astype(np.float32)
        bgr, alpha = load_mark(mark_path)
        if bgr is None or alpha is None:
            print(f"{prefix}  mark 读取失败")
            return
        print(f"== {prefix}")
        for label, center, scale0 in cands:
            ncc, s, ch = refine(big_f, bgr, alpha, center, scale0)
            chs = f"BGR={ch[0]:.2f}/{ch[1]:.2f}/{ch[2]:.2f}" if ch else "N/A"
            print(f"  {label}  center=({center[0]:.0f},{center[1]:.0f}) "
                  f"scale*={s:.2f} ncc={ncc:.3f} {chs}")
    except Exception as e:
        print(f"{prefix}  处理失败: {e}")


def main():
    for prefix, cands in CASES.items():
        process_case(prefix, cands)


if __name__ == "__main__":
    main()