"""方法I：背景自适应的目标连通域检测 + alpha 轮廓评分。

与 G 的固定暗度阈值不同：对每张图扫描多个局部对比度阈值，收集尺寸合理的
暗色/高对比度连通域；通过候选轮廓的形状、填充率和局部边缘完整度综合评分。
该方法的目标是先得到可解释的物体级候选，而不是在整张照片纹理上做 NCC。

输出：try/out/I/{*-match.png,summary.txt}
"""
from pathlib import Path
import math
import cv2
import numpy as np

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

THRESHOLDS = (10, 15, 20, 25, 30, 40, 50, 60)
MIN_SIZE, MAX_SIZE = 25, 180
TOP_K = 5


def files_for(prefix):
    bigs = [p for p in CAP.glob(f"{prefix}*.jpeg") if "-mark" not in p.stem]
    if not bigs:
        return None, None
    big = bigs[0]
    return big, CAP / f"{big.stem}-mark.png"


def mark_info(path):
    try:
        mark = cv2.imread(str(path), cv2.IMREAD_UNCHANGED)
        if mark is None:
            return None
        alpha = mark[..., 3] if mark.ndim == 3 and mark.shape[2] == 4 else np.full(mark.shape[:2], 255, np.uint8)
        mask = (alpha > 128).astype(np.uint8)
        cs, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        if not cs:
            return None
        c = max(cs, key=cv2.contourArea)
        x, y, w, h = cv2.boundingRect(c)
        return mask, c, max(float(cv2.contourArea(c)), 1.0), (x, y, w, h)
    except Exception as e:
        print(f"  mark 读取失败: {e}")
        return None


def candidate_mask(gray, threshold):
    try:
        blur = cv2.GaussianBlur(gray, (0, 0), 21)
        delta = np.clip(blur.astype(np.int16) - gray.astype(np.int16), 0, 255).astype(np.uint8)
        m = (delta > threshold).astype(np.uint8) * 255
        m = cv2.morphologyEx(m, cv2.MORPH_CLOSE, np.ones((7, 7), np.uint8), iterations=2)
        return cv2.morphologyEx(m, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8))
    except cv2.error:
        return None


def overlap(a, b):
    try:
        x1, y1, w1, h1 = a
        x2, y2, w2, h2 = b
        ix = max(0, min(x1 + w1, x2 + w2) - max(x1, x2))
        iy = max(0, min(y1 + h1, y2 + h2) - max(y1, y2))
        inter = ix * iy
        union = w1 * h1 + w2 * h2 - inter
        return inter / union if union else 0.0
    except Exception:
        return 0.0


def score_contour(mark_c, mark_area, contour, gray):
    try:
        x, y, w, h = cv2.boundingRect(contour)
        area = float(cv2.contourArea(contour))
        shape_score = float(cv2.matchShapes(mark_c, contour, cv2.CONTOURS_MATCH_I1, 0.0))
        perimeter = max(float(cv2.arcLength(contour, True)), 1.0)
        compact = min(1.0, 4.0 * math.pi * area / (perimeter * perimeter))
        contrast = float(gray[y:y + h, x:x + w].std())
        # 越小越好；外部排序转换为负分
        return shape_score, area, compact, contrast, (x, y, w, h)
    except cv2.error:
        return None


def process(prefix, lines):
    try:
        big_path, mark_path = files_for(prefix)
        if big_path is None:
            return
        big = cv2.imread(str(big_path), cv2.IMREAD_COLOR)
        info = mark_info(mark_path)
        if big is None or info is None:
            lines.append(f"{prefix}\tSKIP\t读取失败")
            return
        _, mark_c, mark_area, mark_box = info
        gray = cv2.cvtColor(big, cv2.COLOR_BGR2GRAY)
        candidates = []
        for threshold in THRESHOLDS:
            mask = candidate_mask(gray, threshold)
            if mask is None:
                continue
            contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
            for contour in contours:
                x, y, w, h = cv2.boundingRect(contour)
                area = cv2.contourArea(contour)
                if min(w, h) < MIN_SIZE or max(w, h) > MAX_SIZE or area < mark_area * 0.05:
                    continue
                item = score_contour(mark_c, mark_area, contour, gray)
                if item is None:
                    continue
                shape_score, area, compact, contrast, box = item
                if any(overlap(box, old[5]) > 0.6 for old in candidates):
                    continue
                candidates.append((shape_score, -compact, -contrast, threshold, area, box))
        candidates.sort(key=lambda z: (z[0], z[1], z[2]))
        candidates = candidates[:TOP_K]
        if not candidates:
            lines.append(f"{prefix}\tSKIP\t无候选")
            return
        best = candidates[0]
        shape_score, neg_compact, neg_contrast, threshold, area, (x, y, w, h) = best
        print(f"{prefix}  shape={shape_score:.4f} compact={-neg_compact:.3f} "
              f"contrast={-neg_contrast:.1f} threshold={threshold} offset=({x},{y}) size=({w},{h})")
        lines.append(f"{prefix}\tshape={shape_score:.4f}\tcompact={-neg_compact:.3f}\t"
                     f"contrast={-neg_contrast:.1f}\tthreshold={threshold}\toffset=({x},{y})\t"
                     f"size=({w},{h})")
        vis = big.copy()
        for i, item in enumerate(candidates):
            _, _, _, _, _, (px, py, pw, ph) = item
            color = (0, 0, 255) if i == 0 else (255, 0, 0)
            cv2.rectangle(vis, (px, py), (px + pw, py + ph), color, 2)
            cv2.putText(vis, str(i + 1), (px + 2, py + 16), cv2.FONT_HERSHEY_SIMPLEX, .55, color, 2)
        cv2.imwrite(str(OUT / f"{prefix}-match.png"), vis)
    except Exception as e:
        print(f"{prefix}  处理失败: {e}")
        lines.append(f"{prefix}\tERROR\t{e}")


def main():
    lines = []
    prefixes = sorted({p.name.split("~", 1)[0] for p in CAP.glob("*.jpeg") if "-mark" not in p.stem})
    for prefix in prefixes:
        process(prefix, lines)
    (OUT / "summary.txt").write_text("\n".join(lines) + "\n", encoding="utf-8")
    print(f"\n结果已写入 {OUT}/")


if __name__ == "__main__":
    main()