杨豪
d15b3c5b43
- src/: 最终交付物（solve.py base64 API、method_l_shape.py 核心算法、verify_result.py 验证工具） - docs/: 方案文档与实验演进记录 - try/: 历史实验脚本（A~K 方法） - 10/10 样本求解成功，3 个独立真值锚点偏差 <=4px
192 lines
7.0 KiB
Python
192 lines
7.0 KiB
Python
"""方法H：受尺度约束的 alpha 轮廓 + 局部对比度匹配。
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前面的 C/D/E 实验暴露出两个系统性误差：
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- 小模板会被背景纹理误选；
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- mark 的 RGB 颜色不是大图目标的真实颜色。
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本方法只使用 mark 的 alpha 轮廓，并把尺度限制在 0.75~1.25（样本中目标
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物体与 110px mark 近似同尺寸）。候选分数由两部分组成：
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1. alpha 轮廓在大图边缘距离图上的贴合度；
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2. alpha 内部与外部环带之间的局部对比度。
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输出：try/out/H/{*-match.png,summary.txt}
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"""
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from pathlib import Path
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import math
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import cv2
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import numpy as np
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ROOT = Path(__file__).resolve().parent.parent
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CAP = ROOT / "captchas"
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OUT = ROOT / "try" / "out" / "H"
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OUT.mkdir(parents=True, exist_ok=True)
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SCALES = np.arange(0.75, 1.26, 0.025)
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TOP_K = 5
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RING = 5
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def files_for(prefix):
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bigs = [p for p in CAP.glob(f"{prefix}*.jpeg") if "-mark" not in p.stem]
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if not bigs:
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return None, None
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big = bigs[0]
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return big, CAP / f"{big.stem}-mark.png"
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def load_data(big_path, mark_path):
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try:
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big = cv2.imread(str(big_path), cv2.IMREAD_GRAYSCALE)
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mark = cv2.imread(str(mark_path), cv2.IMREAD_UNCHANGED)
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if big is None or mark is None:
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return None
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alpha = mark[..., 3] if mark.ndim == 3 and mark.shape[2] == 4 else np.full(mark.shape[:2], 255, np.uint8)
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shape = (alpha > 128).astype(np.uint8)
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edge = cv2.morphologyEx(shape * 255, cv2.MORPH_GRADIENT, np.ones((3, 3), np.uint8)) > 0
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outer = cv2.dilate(shape, np.ones((RING * 2 + 1, RING * 2 + 1), np.uint8)) > 0
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ring = outer & ~shape
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return big, shape, edge, ring
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except Exception as e:
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print(f" 读取失败: {e}")
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return None
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def resize_binary(mask, scale):
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try:
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h, w = mask.shape
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size = (max(12, round(w * scale)), max(12, round(h * scale)))
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return cv2.resize(mask.astype(np.uint8), size, interpolation=cv2.INTER_NEAREST) > 0
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except cv2.error:
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return None
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def distance_fit(distance, edge):
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try:
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pts = edge.astype(np.float32)
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n = float(pts.sum())
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h, w = edge.shape
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if n < 10 or h >= distance.shape[0] or w >= distance.shape[1]:
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return None
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# 每个候选框内，模板边缘点到背景边缘的平均距离
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cost = cv2.matchTemplate(distance.astype(np.float32), pts, cv2.TM_CCORR) / n
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return 1.0 / (1.0 + cost)
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except cv2.error:
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return None
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def contrast_map(gray, shape, ring):
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try:
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inside = shape.astype(np.float32)
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outside = ring.astype(np.float32)
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ni, no = float(inside.sum()), float(outside.sum())
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if ni < 10 or no < 10:
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return None
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mean_i = cv2.matchTemplate(gray.astype(np.float32), inside, cv2.TM_CCORR) / ni
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mean_o = cv2.matchTemplate(gray.astype(np.float32), outside, cv2.TM_CCORR) / no
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return np.abs(mean_i - mean_o)
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except cv2.error:
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return None
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def normalize_map(values):
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try:
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lo, hi = np.percentile(values, (10, 99))
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if hi <= lo:
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return np.zeros_like(values, dtype=np.float32)
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return np.clip((values - lo) / (hi - lo), 0.0, 1.0).astype(np.float32)
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except Exception:
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return np.zeros_like(values, dtype=np.float32)
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def peaks(score_map, w, h):
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result = []
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try:
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r = score_map.copy()
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for _ in range(TOP_K):
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_, score, _, loc = cv2.minMaxLoc(r)
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if score <= 0:
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break
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result.append((float(score), int(loc[0]), int(loc[1])))
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x0 = max(0, loc[0] - int(w * 0.65))
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y0 = max(0, loc[1] - int(h * 0.65))
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x1 = min(r.shape[1], loc[0] + int(w * 0.65))
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y1 = min(r.shape[0], loc[1] + int(h * 0.65))
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r[y0:y1, x0:x1] = 0
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except Exception as e:
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print(f" 峰提取失败: {e}")
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return result
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def process(prefix, lines):
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try:
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big_path, mark_path = files_for(prefix)
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if big_path is None:
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lines.append(f"{prefix}\tSKIP\t文件缺失")
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return
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loaded = load_data(big_path, mark_path)
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if loaded is None:
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lines.append(f"{prefix}\tSKIP\t读取失败")
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return
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big, shape0, edge0, ring0 = loaded
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bg_edges = cv2.Canny(big, 80, 180)
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distance = cv2.distanceTransform((255 - bg_edges).astype(np.uint8), cv2.DIST_L2, 3)
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candidates = []
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for scale in SCALES:
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shape = resize_binary(shape0, float(scale))
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edge = resize_binary(edge0, float(scale))
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ring = resize_binary(ring0, float(scale))
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if shape is None or edge is None or ring is None:
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continue
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fit = distance_fit(distance, edge)
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contrast = contrast_map(big, shape, ring)
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if fit is None or contrast is None or fit.shape != contrast.shape:
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continue
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score_map = 0.55 * normalize_map(fit) + 0.45 * normalize_map(contrast)
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for score, x, y in peaks(score_map, shape.shape[1], shape.shape[0]):
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candidates.append((score, float(scale), x, y, shape.shape[1], shape.shape[0],
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float(fit[y, x]), float(contrast[y, x])))
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if not candidates:
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lines.append(f"{prefix}\tSKIP\t无候选")
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return
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candidates.sort(key=lambda c: -c[0])
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kept = []
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for c in candidates:
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_, _, x, y, w, h, _, _ = c
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cx, cy = x + w / 2.0, y + h / 2.0
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if all(math.hypot(cx - (q[2] + q[4] / 2), cy - (q[3] + q[5] / 2)) > 0.6 * max(w, h)
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for q in kept):
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kept.append(c)
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if len(kept) >= TOP_K:
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break
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best = kept[0]
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score, scale, x, y, w, h, fit_score, contrast_score = best
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print(f"{prefix} score={score:.3f} scale={scale:.3f} offset=({x},{y}) "
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f"edgeFit={fit_score:.2f} contrast={contrast_score:.2f}")
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lines.append(f"{prefix}\tscore={score:.3f}\tscale={scale:.3f}\toffset=({x},{y})\t"
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f"edgeFit={fit_score:.2f}\tcontrast={contrast_score:.2f}")
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vis = cv2.cvtColor(big, cv2.COLOR_GRAY2BGR)
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colors = [(0, 0, 255), (0, 165, 255), (255, 0, 0), (0, 255, 0), (255, 0, 255)]
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for i, item in enumerate(kept):
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_, _, px, py, pw, ph, _, _ = item
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cv2.rectangle(vis, (px, py), (px + pw, py + ph), colors[i], 2)
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cv2.putText(vis, str(i + 1), (px + 2, py + 16), cv2.FONT_HERSHEY_SIMPLEX,
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0.55, colors[i], 2)
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cv2.imwrite(str(OUT / f"{prefix}-match.png"), vis)
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except Exception as e:
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print(f"{prefix} 处理失败: {e}")
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lines.append(f"{prefix}\tERROR\t{e}")
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def main():
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lines = []
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prefixes = sorted({p.name.split("~", 1)[0] for p in CAP.glob("*.jpeg")
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if "-mark" not in p.stem})
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for prefix in prefixes:
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process(prefix, lines)
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(OUT / "summary.txt").write_text("\n".join(lines) + "\n", encoding="utf-8")
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print(f"\n结果已写入 {OUT}/")
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if __name__ == "__main__":
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main()
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