douyin-captcha/try/method_a2_ccoeff.py

"""方法A2：蒙版 CCOEFF_NORMED 多尺度匹配 + 跨尺度峰显著度。

修正方法A的两个问题：
1. CCORR_NORMED 不减均值 → 换 CCOEFF_NORMED（有蒙版），区分度更高
2. 跨尺度直接比分数不公平（小模板占便宜）→ 每尺度算峰显著度 (max-median)/std

输出：
- 每对：1.0 尺度参考、最优显著度尺度、该尺度 top-3 峰（NMS）→ 验证干扰结构
- 可视化：mark | top-3 峰局部裁剪 并排图 → try/out/A2/
- 汇总表 → try/out/A2/summary.txt
"""
from pathlib import Path
import cv2
import numpy as np

ROOT = Path(__file__).resolve().parent.parent
CAP = ROOT / "captchas"
OUT = ROOT / "try" / "out" / "A2"
OUT.mkdir(parents=True, exist_ok=True)

SCALES = np.arange(0.50, 1.51, 0.05)
TOP_K = 3


def load_mark(path):
    """返回 (BGR float32, alpha float32 0~1) 或 (None, None)。"""
    try:
        m = cv2.imread(str(path), cv2.IMREAD_UNCHANGED)
        if m is None or m.ndim < 2:
            return None, None
        if m.ndim == 3 and m.shape[2] == 4:
            return m[..., :3].astype(np.float32), m[..., 3].astype(np.float32) / 255.0
        return m.astype(np.float32), np.ones(m.shape[:2], np.float32)
    except Exception as e:
        print(f"  mark 读取失败: {e}")
        return None, None


def match_ccoeff(big, tpl_f, mask_f):
    """蒙版 CCOEFF_NORMED。返回响应图或 None。"""
    try:
        res = cv2.matchTemplate(big, tpl_f, cv2.TM_CCOEFF_NORMED, mask=mask_f)
        return np.nan_to_num(res, nan=-1.0, posinf=-1.0, neginf=-1.0).astype(np.float32)
    except cv2.error as e:
        print(f"  匹配失败: {e}")
        return None


def scale_response(big, bgr_f, alpha_f, scale):
    """单尺度匹配，返回 (响应图, 模板宽, 模板高)；失败返回 (None, 0, 0)。"""
    try:
        if abs(scale - 1.0) < 1e-9:
            tpl, mask = bgr_f, alpha_f
        else:
            w = max(12, round(bgr_f.shape[1] * scale))
            h = max(12, round(bgr_f.shape[0] * scale))
            if w >= big.shape[1] or h >= big.shape[0]:
                return None, 0, 0
            tpl = cv2.resize(bgr_f, (w, h), interpolation=cv2.INTER_AREA).astype(np.float32)
            mask = cv2.resize(alpha_f, (w, h), interpolation=cv2.INTER_AREA).astype(np.float32)
        res = match_ccoeff(big, tpl, mask)
        if res is None:
            return None, 0, 0
        return res, int(mask.shape[1]), int(mask.shape[0])
    except cv2.error as e:
        print(f"  尺度失败(scale={scale}): {e}")
        return None, 0, 0


def prominence(res):
    """峰显著度：峰高相对响应图整体的突出程度，跨尺度可比。"""
    try:
        mx = float(res.max())
        med = float(np.median(res))
        sd = float(res.std())
        return (mx - med) / sd if sd > 1e-6 else 0.0
    except Exception:
        return 0.0


def topk_peaks(res, tpl_w, tpl_h, k=TOP_K):
    """NMS 取前 k 个峰：返回 [(score, (x, y)), ...]。"""
    try:
        r = res.copy()
        peaks = []
        for _ in range(k):
            _, score, _, loc = cv2.minMaxLoc(r)
            if score <= -1.0:
                break
            peaks.append((float(score), loc))
            x0 = max(0, loc[0] - int(tpl_w * 0.7))
            y0 = max(0, loc[1] - int(tpl_h * 0.7))
            x1 = min(r.shape[1], loc[0] + int(tpl_w * 0.7))
            y1 = min(r.shape[0], loc[1] + int(tpl_h * 0.7))
            r[y0:y1, x0:x1] = -1.0
        return peaks
    except Exception as e:
        print(f"  峰提取失败: {e}")
        return []


def side_by_side(name, mark_bgr, big, peaks, tpl_w, tpl_h):
    """mark | top-k 峰裁剪 并排图。"""
    try:
        mark_u8 = np.clip(mark_bgr, 0, 255).astype(np.uint8)
        mark_u8 = cv2.copyMakeBorder(mark_u8, 2, 2, 2, 2, cv2.BORDER_CONSTANT,
                                     value=(255, 255, 255))
        tiles = [cv2.resize(mark_u8, (tpl_w, tpl_h))]
        for score, (x, y) in peaks:
            x0, y0 = max(0, x), max(0, y)
            x1, y1 = min(big.shape[1], x + tpl_w), min(big.shape[0], y + tpl_h)
            crop = big[y0:y1, x0:x1].copy()
            if crop.shape[0] != tpl_h or crop.shape[1] != tpl_w:
                crop = cv2.copyMakeBorder(crop, 0, tpl_h - crop.shape[0],
                                          0, tpl_w - crop.shape[1],
                                          cv2.BORDER_CONSTANT, value=(128, 128, 128))
            cv2.putText(crop, f"{score:.3f}", (2, 12), cv2.FONT_HERSHEY_SIMPLEX,
                        0.4, (0, 0, 255), 1)
            tiles.append(crop)
        sep = np.full((tpl_h, 4, 3), 255, np.uint8)
        row = tiles[0]
        for t in tiles[1:]:
            row = np.hstack([row, sep, t])
        cv2.imwrite(str(OUT / f"{name[:12]}-crops.png"), row)
    except Exception as e:
        print(f"  可视化失败: {e}")


def process_pair(name, lines):
    try:
        big = cv2.imread(str(CAP / f"{name}.jpeg"), cv2.IMREAD_COLOR)
        if big is None:
            lines.append(f"{name}  SKIP 大图读取失败")
            return
        big_f = big.astype(np.float32)
        bgr_f, alpha_f = load_mark(CAP / f"{name}-mark.png")
        if bgr_f is None:
            lines.append(f"{name}  SKIP mark 读取失败")
            return

        # 参考：1.0 尺度
        res10, _, _ = scale_response(big_f, bgr_f, alpha_f, 1.0)
        if res10 is not None:
            _, _, _, loc10 = cv2.minMaxLoc(res10)
            ref = f"1.0尺度峰={float(res10.max()):.3f}@{loc10}"
        else:
            ref = "1.0尺度N/A"

        # 扫尺度，选显著度最高者
        best = None  # (显著度, 尺度, 响应图, 模板宽, 模板高)
        curve = []
        for sc in SCALES:
            res, tw_, th_ = scale_response(big_f, bgr_f, alpha_f, float(sc))
            if res is None:
                curve.append(0.0)
                continue
            p = prominence(res)
            curve.append(p)
            if best is None or p > best[0]:
                best = (p, float(sc), res, tw_, th_)
        curve = np.array(curve)
        if best is None:
            lines.append(f"{name}  SKIP 无有效尺度")
            return
        p, sc, res, tw, th = best
        peaks = topk_peaks(res, tw, th)

        top = "  ".join(f"#{i+1}:{s:.3f}@{loc}" for i, (s, loc) in enumerate(peaks))
        print(f"{name[:12]}  {ref}  最优尺度={sc:.2f} 显著度={p:.2f}  {top}")
        lines.append(f"{name[:12]}\t{ref}\t尺度={sc:.2f}\t显著度={p:.2f}\t{top}")
        side_by_side(name, bgr_f, big, peaks, tw, th)
        # 大图上画 top-3 框
        vis = big.copy()
        colors = [(0, 0, 255), (0, 165, 255), (255, 0, 0)]
        for i, (s, (x, y)) in enumerate(peaks):
            cv2.rectangle(vis, (x, y), (x + tw, y + th), colors[i % 3], 2)
        cv2.imwrite(str(OUT / f"{name[:12]}-match.png"), vis)
    except Exception as e:
        print(f"{name[:12]}  处理失败: {e}")
        lines.append(f"{name[:12]}  ERROR {e}")


def main():
    lines = []
    pairs = sorted({p.name.replace("-mark.png", "") for p in CAP.glob("*-mark.png")})
    for name in pairs:
        process_pair(name, lines)
    (OUT / "summary.txt").write_text("\n".join(lines) + "\n", encoding="utf-8")
    print(f"\n结果与 summary 已写入 {OUT}/")


if __name__ == "__main__":
    main()