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
194 lines
7.3 KiB
Python
194 lines
7.3 KiB
Python
"""方法B：SIFT 特征匹配 + RANSAC 相似变换聚类。
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思路：mark(带 alpha 蒙版)与大图各提 SIFT 特征，ratio test 后用
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estimateAffinePartial2D(RANSAC) 迭代提取多个变换簇 —— 每簇对应大图中一个
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相似区域（目标或干扰项）。按 AGENTS.md 断言：目标 = 旋转≈0 的簇。
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输出：
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- 每簇：scale / rotation / 平移(大图坐标系下 mark 左上角) / 内点数
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- 判定：|rotation| 最小的簇为候选目标
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- 可视化：变换后 mark 包围盒画到大图 → try/out/B/
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- 汇总 → try/out/B/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" / "B"
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OUT.mkdir(parents=True, exist_ok=True)
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RATIO = 0.90
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RANSAC_TH = 3.0
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MAX_CLUSTERS = 3
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MIN_MATCHES = 6
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def load_mark(path):
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"""返回 (灰度图, alpha蒙版uint8) 或 (None, None)。"""
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try:
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m = cv2.imread(str(path), cv2.IMREAD_UNCHANGED)
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if m is None or m.ndim < 2:
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return None, None
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if m.ndim == 3 and m.shape[2] == 4:
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gray = cv2.cvtColor(m[..., :3], cv2.COLOR_BGR2GRAY)
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alpha = m[..., 3]
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else:
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gray = cv2.cvtColor(m, cv2.COLOR_BGR2GRAY)
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alpha = np.full(m.shape[:2], 255, np.uint8)
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return gray, alpha
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except Exception as e:
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print(f" mark 读取失败: {e}")
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return None, None
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def good_matches(de_m, de_b):
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"""BF knn + ratio test。返回 (queryIdx, trainIdx) 对列表。"""
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try:
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matcher = cv2.BFMatcher(cv2.NORM_L2)
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knn = matcher.knnMatch(de_m, de_b, k=2)
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return [(m1.queryIdx, m1.trainIdx)
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for m1, m2 in knn if m1.distance < RATIO * m2.distance]
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except Exception as e:
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print(f" 匹配失败: {e}")
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return []
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def extract_clusters(kp_m, kp_b, good):
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"""迭代 RANSAC，最多 MAX_CLUSTERS 个变换簇。返回 [(M, 内点数), ...]。"""
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clusters = []
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try:
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pts_m = np.array([kp.pt for kp in kp_m], dtype=np.float32).reshape(-1, 1, 2)
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pts_b = np.array([kp.pt for kp in kp_b], dtype=np.float32).reshape(-1, 1, 2)
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remaining = np.arange(len(good))
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for _ in range(MAX_CLUSTERS):
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if len(remaining) < MIN_MATCHES:
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break
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idx_pairs = np.array(good, dtype=np.int64)[remaining]
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src = pts_m[idx_pairs[:, 0]]
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dst = pts_b[idx_pairs[:, 1]]
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M, inl = cv2.estimateAffinePartial2D(
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src, dst, method=cv2.RANSAC, ransacReprojThreshold=RANSAC_TH,
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maxIters=5000)
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if M is None or inl is None:
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break
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n_inl = int(inl.sum())
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if n_inl < 4:
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break
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clusters.append((M.copy(), n_inl))
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inlier_pos = np.where(inl.ravel() > 0)[0]
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used = remaining[inlier_pos]
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keep = np.array([i for i, g in enumerate(remaining)
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if not np.any(used == g)], dtype=np.int64)
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remaining = remaining[keep]
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except Exception as e:
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print(f" 聚类失败: {e}")
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return clusters
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def describe(M):
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"""2x3 相似变换 → (scale, rot_deg, tx, ty)；失败返回 (0,0,0,0)。"""
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try:
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a, b = float(M[0, 0]), float(M[0, 1])
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tx, ty = float(M[0, 2]), float(M[1, 2])
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scale = math.hypot(a, b)
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rot = math.degrees(math.atan2(b, a))
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while rot <= -180:
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rot += 360
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while rot > 180:
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rot -= 360
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return scale, rot, tx, ty
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except Exception as e:
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print(f" 变换解析失败: {e}")
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return 0.0, 0.0, 0.0, 0.0
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def draw_cluster(big, M, mark_shape, color):
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"""把 mark 四角经 M 映射后画到大图上。"""
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try:
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h, w = mark_shape[:2]
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corners = np.array([[0, 0], [w, 0], [w, h], [0, h]], dtype=np.float32)
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proj = cv2.transform(corners.reshape(-1, 1, 2), M)
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pts = proj.reshape(-1, 2).astype(np.int32)
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cv2.polylines(big, [pts], True, color, 2)
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scale, rot, _, _ = describe(M)
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cv2.putText(big, f"s={scale:.2f} r={rot:.0f}d",
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(int(pts[:, 0].min()), max(12, int(pts[:, 1].min()) - 4)),
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cv2.FONT_HERSHEY_SIMPLEX, 0.4, color, 1)
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except cv2.error as e:
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print(f" 绘制失败: {e}")
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def process_pair(name, sift, lines):
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try:
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big = cv2.imread(str(CAP / f"{name}.jpeg"), cv2.IMREAD_COLOR)
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if big is None:
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lines.append(f"{name[:12]}\tSKIP\t大图读取失败")
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return
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gray_m, alpha_m = load_mark(CAP / f"{name}-mark.png")
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if gray_m is None or alpha_m is None:
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lines.append(f"{name[:12]}\tSKIP\tmark 读取失败")
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return
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gray_b = cv2.cvtColor(big, cv2.COLOR_BGR2GRAY)
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kp_m, de_m = sift.detectAndCompute(gray_m, alpha_m)
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kp_b, de_b = sift.detectAndCompute(gray_b, None)
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n_kp_m = 0 if kp_m is None else len(kp_m)
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if de_m is None or de_b is None or kp_m is None or kp_b is None or n_kp_m < MIN_MATCHES:
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lines.append(f"{name[:12]}\tSKIP\t特征不足 mk={n_kp_m}")
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return
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good = good_matches(de_m, de_b)
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if len(good) < MIN_MATCHES:
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lines.append(f"{name[:12]}\tSKIP\tratio后匹配不足 {len(good)}")
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return
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clusters = extract_clusters(kp_m, kp_b, good)
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if not clusters:
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lines.append(f"{name[:12]}\tSKIP\t无变换簇")
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return
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# 目标候选：排除退化簇（scale 过小/过大），在余下中选 |rotation| 最小者
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def cluster_scale(i):
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return describe(clusters[i][0])[0]
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valid = [i for i in range(len(clusters))
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if 0.2 <= cluster_scale(i) <= 3.0]
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if not valid:
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lines.append(f"{name[:12]}\tSKIP\t仅退化簇")
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return
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best_idx = min(valid, key=lambda i: abs(describe(clusters[i][0])[1]))
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vis = big.copy()
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for i, (M, n_inl) in enumerate(clusters):
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scale, rot, tx, ty = describe(M)
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is_target = (i == best_idx)
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color = (0, 0, 255) if is_target else (255, 0, 0)
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draw_cluster(vis, M, gray_m.shape, color)
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tag = "<==目标候选" if is_target else ""
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print(f"{name[:12]} 簇{i}: s={scale:.3f} r={rot:+7.1f}° "
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f"t=({tx:6.1f},{ty:6.1f}) 内点={n_inl} {tag}")
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lines.append(f"{name[:12]}\t簇{i}\tscale={scale:.3f}\trot={rot:+.1f}\t"
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f"t=({tx:.1f},{ty:.1f})\tinliers={n_inl}\t{tag}")
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cv2.imwrite(str(OUT / f"{name[:12]}-clusters.png"), vis)
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except Exception as e:
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print(f"{name[:12]} 处理失败: {e}")
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lines.append(f"{name[:12]}\tERROR\t{e}")
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def main():
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# cartoon 平面图形对比度低，放低对比度阈值、提高特征上限
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sift = cv2.SIFT_create(nfeatures=2000, contrastThreshold=0.02)
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lines = []
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pairs = sorted({p.name.replace("-mark.png", "") for p in CAP.glob("*-mark.png")})
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for name in pairs:
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process_pair(name, sift, lines)
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(OUT / "summary.txt").write_text("\n".join(lines) + "\n", encoding="utf-8")
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n_ok = sum(1 for l in lines if "目标候选" in l)
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print(f"\n{n_ok}/{len(pairs)} 对给出目标候选；结果已写入 {OUT}/")
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if __name__ == "__main__":
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main()
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