xslandCursor
b78da0e0cd
基于 MediaPipe 468 关键点提取几何特征，转 z 分数后与各脸型原型加权匹配。 参考分布与原型靶心取自 1093 张测试集的实测画像，不再靠人工设定绝对阈值。 方形脸占比从 29.6% 降到 13.8%，两处原因：一是参考统计量原先只由 50 张样本 估得，相对全量人群有系统性偏移，且三项偏移都在给方形脸加分；二是原型把 aspect_ratio 当作方形脸的主特征，但实测方脸组该值中位仅 +0.16，真正"宽"的 是圆脸(+1.01)，等于在拿脸宽找方脸。 原型参数在「6 张基准标注图判定不变、且领先第二名 >=3 分」的约束下搜索得到。 余量约束是必要的：早前一版余量仅 0.008 分，权重写码时四舍五入就会翻转结论。 测试素材(人像照片)与报告输出体积大，一并加入 .gitignore。 Co-authored-by: Cursor <cursoragent@cursor.com>
84 lines
2.6 KiB
Python
84 lines
2.6 KiB
Python
"""
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把各数据集的人脸特征抽取一次并缓存为 JSON，供调参脚本反复使用。
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MediaPipe 关键点检测是调参循环里唯一的耗时环节，缓存后调参可以秒级迭代。
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"""
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from __future__ import annotations
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import json
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import sys
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from pathlib import Path
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from typing import Dict, List
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import cv2
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sys.path.insert(0, str(Path(__file__).resolve().parent))
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from face_shape_classifier import ( # noqa: E402
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_get_face_mesh,
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extract_face_features,
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)
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ROOT = Path(__file__).resolve().parent
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IMG_EXT = {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
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def iter_images(root: Path) -> List[Path]:
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return sorted(p for p in root.rglob("*") if p.suffix.lower() in IMG_EXT)
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def features_for(path: Path) -> Dict[str, float] | None:
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bgr = cv2.imread(str(path))
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if bgr is None:
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return None
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h, w = bgr.shape[:2]
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rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
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res = _get_face_mesh().process(rgb)
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if not res.multi_face_landmarks:
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return None
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return extract_face_features(res.multi_face_landmarks[0].landmark, image_size=(w, h))
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def main() -> None:
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out_path = Path(sys.argv[1]) if len(sys.argv) > 1 else ROOT / "cache" / "features.json"
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out_path.parent.mkdir(parents=True, exist_ok=True)
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sources = {
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# 6 张带标注的基准图，文件名即期望脸型
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"benchmark": [p for p in ROOT.joinpath("test_img").glob("*.png")],
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"girl": iter_images(ROOT / "test_img" / "girl"),
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"man": iter_images(ROOT / "test_img" / "man"),
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"dataset": iter_images(ROOT / "test_img" / "脸型测试集合"),
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}
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records = []
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failed = 0
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for source, paths in sources.items():
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for i, path in enumerate(paths, 1):
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feats = features_for(path)
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if feats is None:
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failed += 1
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continue
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rel = path.relative_to(ROOT)
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records.append(
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{
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"source": source,
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"path": rel.as_posix(),
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"file": path.name,
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# dataset 的上级目录名即原始分组（弱标签，非可信真值）
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"group": path.parent.name if source == "dataset" else source,
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"expected": path.stem if source == "benchmark" else None,
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"features": feats,
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}
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)
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if i % 50 == 0 or i == len(paths):
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print(f"[{source}] {i}/{len(paths)}", flush=True)
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out_path.write_text(json.dumps(records, ensure_ascii=False), encoding="utf-8")
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print(f"\n写入 {out_path}：{len(records)} 条，检测失败 {failed} 张")
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if __name__ == "__main__":
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main()
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