feat: 脸型分类器(7分类)+批量报告生成
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>
This commit is contained in:
Cursor
+29
@@ -53,3 +53,32 @@ static/report_hairline_v2.zip
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# local_test 运行期日志 / pid（不入 git）
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local_test/hair_service.log
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local_test/hair_service.pid
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# benchmark 原始输出（含结果图+原图，体积大，不入 git）
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benchmark_out/
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# benchmark 部署的 HTML 报告（图片 base64 内嵌，体积大，不入 git）
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static/hairstyle_thumbs/
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# 网关运行期日志（不入 git）
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gateway.log
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# 工作流备份文件（不入 git）
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*.json.bak.*
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# 脸型测试素材（人像照片，体积大，不入 git；仅保留 6 张基准标注图）
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face/test_img/脸型测试集合/
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face/test_img/girl/
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face/test_img/man/
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# 脸型特征缓存（由 face/dump_features.py 生成，可随时重跑）
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face/cache/
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# 脸型报告输出（标注图体积大，不入 git）
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static/face_shape_report/
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static/face_shape_report.html
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static/facetest_report/
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static/facetest_report.html
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static/facetest_all_report/
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static/facetest_all_report.html
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@@ -0,0 +1,506 @@
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"""
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build_dataset_report.py
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对任意图片目录（可含多层子目录）批量预测脸型并生成 HTML 报告。
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保留图片原始所属的子目录名作为「分组」，在报告中按分组展示与统计。
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用法:
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./venv/bin/python face/build_dataset_report.py --src <图片目录> [--sample 50] [--seed 42]
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示例:
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./venv/bin/python face/build_dataset_report.py \
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--src face/test_img/脸型测试集合 --sample 50 --name 脸型测试集合
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输出:
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static/<slug>_report.html
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static/<slug>_report/images/*.jpg
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"""
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from __future__ import annotations
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import argparse
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import html
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import random
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import re
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import shutil
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import sys
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import unicodedata
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from collections import Counter, defaultdict
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from datetime import datetime
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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().parents[1]))
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from face.face_shape_classifier import classify_from_image # noqa: E402
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ROOT = Path(__file__).resolve().parents[1]
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IMAGE_SUFFIXES = {".jpg", ".jpeg", ".png", ".webp", ".bmp"}
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MAX_IMAGE_SIDE = 900
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JPEG_QUALITY = 88
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SHAPE_ORDER = ["圆形脸", "心形脸", "菱形脸", "鹅蛋脸", "方形脸", "长形脸", "瓜子脸"]
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SHAPE_COLORS = {
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"圆形脸": "#e67e22",
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"心形脸": "#e74c3c",
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"菱形脸": "#9b59b6",
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"鹅蛋脸": "#27ae60",
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"方形脸": "#2980b9",
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"长形脸": "#16a085",
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"瓜子脸": "#c0392b",
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"检测失败": "#7f8c8d",
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}
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# 数据集分组名与分类器脸型口径的近似对应（仅用于交叉表高亮参考，非严格标签）
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TAXONOMY_EQUIV = {
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"方形脸": "方形脸",
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"长形脸": "长形脸",
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"瓜子脸": "瓜子脸",
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"标准脸": "鹅蛋脸",
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"娃娃脸": "圆形脸",
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}
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FEATURE_KEYS = [
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"face_width",
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"face_height",
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"jaw_angle",
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"taper_ratio",
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"forehead_ratio",
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"cheekbone_ratio",
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"jaw_ratio",
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"chin_ratio",
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"chin_sharpness",
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"width_uniformity",
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"face_curve_score",
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]
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def natural_key(text: str):
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parts = re.split(r"(\d+)", text)
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return [int(p) if p.isdigit() else p for p in parts]
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def collect_images(src: Path) -> List[Path]:
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return sorted(
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(p for p in src.rglob("*") if p.suffix.lower() in IMAGE_SUFFIXES),
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key=lambda p: natural_key(str(p.relative_to(src))),
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)
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def group_of(path: Path, src: Path) -> str:
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"""图片相对根目录的父目录名；直接位于根目录则记为「根目录」。"""
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rel = path.relative_to(src).parent
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return str(rel) if str(rel) != "." else "（根目录）"
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def ascii_slug(text: str, fallback: str) -> str:
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"""生成安全的 ASCII 文件名片段（中文目录名转拼音不可靠，直接编号兜底）。"""
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norm = unicodedata.normalize("NFKD", text).encode("ascii", "ignore").decode()
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norm = re.sub(r"[^A-Za-z0-9_-]+", "_", norm).strip("_")
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return norm or fallback
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def stratified_sample(
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images: List[Path], src: Path, total: int, seed: int, min_per_group: int
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) -> List[Path]:
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"""
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按分组分层抽样：先保证每组至少 min_per_group 张，剩余名额按组大小比例分配。
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小分组（如只有 3 张的梨形脸）在纯随机抽样下几乎必然缺席，分层可保证覆盖。
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"""
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rng = random.Random(seed)
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buckets: Dict[str, List[Path]] = defaultdict(list)
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for p in images:
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buckets[group_of(p, src)].append(p)
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groups = sorted(buckets, key=natural_key)
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quota = {g: min(min_per_group, len(buckets[g])) for g in groups}
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remaining = total - sum(quota.values())
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if remaining > 0:
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spare = {g: len(buckets[g]) - quota[g] for g in groups}
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pool = sum(spare.values())
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if pool > 0:
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# 按剩余可选量比例分配，再把取整误差补给最大的分组
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extra = {g: int(remaining * spare[g] / pool) for g in groups}
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for g in sorted(groups, key=lambda g: -spare[g]):
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if sum(extra.values()) >= remaining:
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break
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if extra[g] < spare[g]:
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extra[g] += 1
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for g in groups:
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quota[g] += min(extra[g], spare[g])
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chosen: List[Path] = []
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for g in groups:
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chosen.extend(rng.sample(buckets[g], min(quota[g], len(buckets[g]))))
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return chosen
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def analyze(
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src: Path, sample: int, seed: int, img_dir: Path, min_per_group: int
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) -> List[Dict]:
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all_images = collect_images(src)
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if not all_images:
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raise SystemExit(f"目录中没有图片: {src}")
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if sample and sample < len(all_images):
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if min_per_group > 0:
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chosen = stratified_sample(all_images, src, sample, seed, min_per_group)
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else:
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chosen = random.Random(seed).sample(all_images, sample)
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chosen.sort(key=lambda p: natural_key(str(p.relative_to(src))))
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else:
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chosen = all_images
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mode = f"分层抽样，每组至少 {min_per_group} 张" if min_per_group > 0 else "纯随机抽样"
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print(f"共发现 {len(all_images)} 张图片，本次测试 {len(chosen)} 张（{mode}，seed={seed}）\n")
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if img_dir.exists():
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shutil.rmtree(img_dir)
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img_dir.mkdir(parents=True)
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group_slugs: Dict[str, str] = {}
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rows: List[Dict] = []
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for idx, path in enumerate(chosen, 1):
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group = group_of(path, src)
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if group not in group_slugs:
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group_slugs[group] = ascii_slug(group, f"g{len(group_slugs) + 1}")
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out_name = f"{group_slugs[group]}_{idx:03d}.jpg"
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item = {
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"index": idx,
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"group": group,
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"file": path.name,
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"rel_path": str(path.relative_to(src)),
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# 相对 static/ 的路径（报告 HTML 也放在 static/ 根下）
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"img_src": f"{img_dir.relative_to(ROOT / 'static').as_posix()}/{out_name}",
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"ok": False,
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"predicted": None,
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"display": None,
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"confidence": None,
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"score": None,
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"top3": [],
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"features": {},
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"error": None,
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}
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try:
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result = classify_from_image(path, return_details=True, return_annotated=True)
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annotated = result["annotated"]
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h, w = annotated.shape[:2]
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if max(h, w) > MAX_IMAGE_SIDE:
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scale = MAX_IMAGE_SIDE / max(h, w)
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annotated = cv2.resize(
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annotated, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA
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)
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cv2.imwrite(str(img_dir / out_name), annotated, [int(cv2.IMWRITE_JPEG_QUALITY), JPEG_QUALITY])
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item.update(
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{
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"ok": True,
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"predicted": result["face_shape"],
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"display": result["display"],
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"confidence": result["confidence"],
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"score": result["details"]["ranked"][0][1],
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"top3": result["details"]["ranked"][:3],
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"features": {k: result["features"][k] for k in FEATURE_KEYS},
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}
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)
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except Exception as exc: # noqa: BLE001 - 报告需要汇总所有失败样本
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img = cv2.imread(str(path))
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if img is not None:
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h, w = img.shape[:2]
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if max(h, w) > MAX_IMAGE_SIDE:
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scale = MAX_IMAGE_SIDE / max(h, w)
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img = cv2.resize(img, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA)
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cv2.imwrite(str(img_dir / out_name), img, [int(cv2.IMWRITE_JPEG_QUALITY), JPEG_QUALITY])
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item["error"] = str(exc)
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rows.append(item)
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print(f"[{idx:3d}/{len(chosen)}] [{group}] {path.name} -> {item['display'] or 'ERR: ' + str(item['error'])}")
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return rows
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def bar_chart(counter: Counter) -> str:
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if not counter:
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return "<p class='muted'>无数据</p>"
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total = sum(counter.values())
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parts = []
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order = [s for s in SHAPE_ORDER if counter.get(s)] + [
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s for s in counter if s not in SHAPE_ORDER
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]
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for shape in order:
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n = counter[shape]
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color = SHAPE_COLORS.get(shape, "#7f8c8d")
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pct = n / total * 100
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parts.append(
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f"<div class='bar-row'><span class='bar-label'>{html.escape(shape)}</span>"
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f"<div class='bar-track'><div class='bar-fill' style='width:{pct:.1f}%;background:{color}'></div></div>"
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f"<span class='bar-num'>{n}（{pct:.0f}%）</span></div>"
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)
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return "".join(parts)
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def fmt_feat(key: str, value: float) -> str:
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if key in {"face_width", "face_height"}:
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return f"{value:.1f}px"
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if key == "jaw_angle":
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return f"{value:.1f}°"
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return f"{value:.3f}"
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def card(item: Dict) -> str:
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group_tag = f"<span class='group-tag'>{html.escape(item['group'])}</span>"
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if not item["ok"]:
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return f"""
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<article class="card error">
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<a class="img-link" href="{html.escape(item['img_src'])}" target="_blank">
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<img src="{html.escape(item['img_src'])}" alt="{html.escape(item['file'])}" loading="lazy"/>
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</a>
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<div class="body">
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<div class="meta"><h3>{html.escape(item['file'])}</h3>{group_tag}</div>
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<p class="badge bad">检测失败</p>
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<p class="muted">{html.escape(item['error'] or '')}</p>
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</div>
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</article>"""
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color = SHAPE_COLORS.get(item["predicted"], "#34495e")
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top3 = "".join(
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f"<li><span>{html.escape(name)}</span><b>{score:.1f}</b></li>" for name, score in item["top3"]
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)
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feat_html = "".join(
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f"<tr><td>{html.escape(k)}</td><td>{html.escape(fmt_feat(k, v))}</td></tr>"
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for k, v in item["features"].items()
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)
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return f"""
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<article class="card">
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<a class="img-link" href="{html.escape(item['img_src'])}" target="_blank" title="点击查看大图标注">
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<img src="{html.escape(item['img_src'])}" alt="{html.escape(item['file'])}" loading="lazy"/>
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</a>
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<div class="body">
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<div class="meta"><h3>{html.escape(item['file'])}</h3>{group_tag}</div>
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<p class="path muted">{html.escape(item['rel_path'])}</p>
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<p class="badge" style="background:{color}">{html.escape(item['display'])}</p>
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<p class="conf">匹配度 {item['score']:.1f} · 置信度 {item['confidence']:.3f}</p>
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<ul class="scores">{top3}</ul>
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<details>
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<summary>标注特征数值</summary>
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<table>{feat_html}</table>
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</details>
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</div>
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</article>"""
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CSS = """
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:root { --bg:#f3efe6; --ink:#1c1915; --muted:#6b645a; --card:#fffdf8; --line:#e2d8c8; --accent:#0f6b5c; }
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* { box-sizing: border-box; }
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body {
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margin:0; font-family:"PingFang SC","Noto Sans SC","Segoe UI",sans-serif; color:var(--ink);
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background: radial-gradient(1200px 600px at 10% -10%, #ffe8c8 0%, transparent 55%),
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radial-gradient(900px 500px at 100% 0%, #d9f2ea 0%, transparent 50%), var(--bg);
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}
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header { padding:40px 24px 20px; max-width:1320px; margin:0 auto; }
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header h1 { margin:0 0 8px; font-size:clamp(1.8rem,3vw,2.4rem); }
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header p { margin:4px 0; color:var(--muted); }
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.legend-box { max-width:1320px; margin:0 auto 20px; padding:0 24px; }
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.legend-box .inner { background:var(--card); border:1px solid var(--line); border-radius:14px;
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padding:14px 16px; font-size:.9rem; line-height:1.55; }
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.legend-box code { background:#efe7da; padding:1px 6px; border-radius:4px; font-size:.84rem; }
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.swatch { display:inline-block; width:10px; height:10px; border-radius:2px; margin-right:4px; vertical-align:middle; }
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.stats { display:grid; grid-template-columns:repeat(auto-fit,minmax(280px,1fr)); gap:16px;
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max-width:1320px; margin:0 auto 28px; padding:0 24px; }
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.stat { background:var(--card); border:1px solid var(--line); border-radius:16px; padding:16px 18px; }
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.stat h2 { margin:0 0 12px; font-size:1rem; }
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.bar-row { display:grid; grid-template-columns:72px 1fr 80px; gap:8px; align-items:center; margin:6px 0; font-size:.86rem; }
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.bar-track { height:8px; background:#efe7da; border-radius:999px; overflow:hidden; }
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.bar-fill { height:100%; border-radius:999px; }
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.bar-num { color:var(--muted); text-align:right; }
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section { max-width:1320px; margin:0 auto 36px; padding:0 24px; }
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section h2 { margin:0 0 14px; font-size:1.3rem; border-left:4px solid var(--accent); padding-left:10px; }
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section h2 small { color:var(--muted); font-weight:400; font-size:.8rem; margin-left:8px; }
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.grid { display:grid; grid-template-columns:repeat(auto-fill,minmax(270px,1fr)); gap:16px; }
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.card { background:var(--card); border:1px solid var(--line); border-radius:18px; overflow:hidden;
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display:flex; flex-direction:column; box-shadow:0 8px 24px rgba(60,40,10,.05); }
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.card.error { opacity:.9; }
|
||||
.img-link { display:block; }
|
||||
.card img { width:100%; aspect-ratio:3/4; object-fit:cover; background:#ddd; display:block; }
|
||||
.card .body { padding:14px; }
|
||||
.meta { display:flex; justify-content:space-between; align-items:baseline; gap:8px; }
|
||||
.meta h3 { margin:0; font-size:.95rem; word-break:break-all; }
|
||||
.group-tag { font-size:.72rem; color:var(--accent); background:#e7f6f2; padding:2px 8px;
|
||||
border-radius:999px; white-space:nowrap; }
|
||||
.path { font-size:.72rem; margin:4px 0 0; word-break:break-all; }
|
||||
.badge { display:inline-block; margin:10px 0 4px; color:#fff; padding:6px 10px; border-radius:999px;
|
||||
font-weight:600; font-size:.92rem; }
|
||||
.badge.bad { background:#c0392b; }
|
||||
.conf { margin:0 0 8px; color:var(--muted); font-size:.85rem; }
|
||||
.scores { list-style:none; padding:0; margin:0 0 8px; }
|
||||
.scores li { display:flex; justify-content:space-between; padding:4px 0; border-bottom:1px dashed var(--line); font-size:.86rem; }
|
||||
details { margin-top:8px; }
|
||||
summary { cursor:pointer; color:var(--accent); font-size:.86rem; }
|
||||
table { width:100%; border-collapse:collapse; margin-top:8px; font-size:.8rem; }
|
||||
td { padding:3px 0; border-bottom:1px solid var(--line); }
|
||||
td:last-child { text-align:right; font-variant-numeric:tabular-nums; }
|
||||
.muted { color:var(--muted); }
|
||||
.cross-wrap { overflow-x:auto; background:var(--card); border:1px solid var(--line);
|
||||
border-radius:16px; padding:14px 16px; }
|
||||
table.cross { border-collapse:collapse; width:100%; font-size:.88rem; }
|
||||
table.cross th, table.cross td { padding:7px 10px; text-align:center; border-bottom:1px solid var(--line);
|
||||
white-space:nowrap; }
|
||||
table.cross thead th { background:#efe7da; font-weight:600; position:sticky; top:0; }
|
||||
table.cross th.rowh { text-align:left; font-weight:600; }
|
||||
table.cross th.rowh small { color:var(--muted); font-weight:400; }
|
||||
table.cross td.num { font-variant-numeric:tabular-nums; }
|
||||
table.cross td.hit { background:#d8f3e4; color:#0f6b5c; font-weight:700; font-variant-numeric:tabular-nums; }
|
||||
table.cross td.zero { color:#cfc6b6; }
|
||||
footer { max-width:1320px; margin:0 auto; padding:8px 24px 40px; color:var(--muted); font-size:.85rem; }
|
||||
"""
|
||||
|
||||
|
||||
def cross_table(by_group: Dict[str, List[Dict]]) -> str:
|
||||
"""原始分组 × 预测脸型 交叉表，对角线（口径对应的格子）高亮。"""
|
||||
cols = SHAPE_ORDER + ["检测失败"]
|
||||
head = "".join(f"<th>{html.escape(c)}</th>" for c in cols)
|
||||
body = []
|
||||
for group, items in sorted(by_group.items(), key=lambda kv: natural_key(kv[0])):
|
||||
counts = Counter(i["predicted"] if i["ok"] else "检测失败" for i in items)
|
||||
equiv = TAXONOMY_EQUIV.get(group)
|
||||
cells = []
|
||||
for c in cols:
|
||||
n = counts.get(c, 0)
|
||||
if n == 0:
|
||||
cells.append("<td class='zero'>·</td>")
|
||||
continue
|
||||
cls = "hit" if c == equiv else "num"
|
||||
cells.append(f"<td class='{cls}'>{n}</td>")
|
||||
label = html.escape(group)
|
||||
if equiv:
|
||||
label += f" <small>≈{html.escape(equiv)}</small>"
|
||||
body.append(f"<tr><th class='rowh'>{label}</th>{''.join(cells)}<th>{len(items)}</th></tr>")
|
||||
return (
|
||||
"<div class='cross-wrap'><table class='cross'>"
|
||||
f"<thead><tr><th>原始分组 \\ 预测</th>{head}<th>合计</th></tr></thead>"
|
||||
f"<tbody>{''.join(body)}</tbody></table></div>"
|
||||
)
|
||||
|
||||
|
||||
def build_html(rows: List[Dict], name: str, src: Path, seed: int, img_dir_name: str) -> str:
|
||||
overall = Counter(r["predicted"] if r["ok"] else "检测失败" for r in rows)
|
||||
by_group: Dict[str, List[Dict]] = defaultdict(list)
|
||||
for r in rows:
|
||||
by_group[r["group"]].append(r)
|
||||
|
||||
group_stats = "".join(
|
||||
f"<div class='stat'><h2>{html.escape(g)} <span class='muted'>（{len(items)} 张）</span></h2>"
|
||||
f"{bar_chart(Counter(i['predicted'] if i['ok'] else '检测失败' for i in items))}</div>"
|
||||
for g, items in sorted(by_group.items(), key=lambda kv: natural_key(kv[0]))
|
||||
)
|
||||
|
||||
sections = "".join(
|
||||
f"<section><h2>{html.escape(g)}<small>{len(items)} 张</small></h2>"
|
||||
f"<div class='grid'>{''.join(card(i) for i in items)}</div></section>"
|
||||
for g, items in sorted(by_group.items(), key=lambda kv: natural_key(kv[0]))
|
||||
)
|
||||
|
||||
now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
n_ok = sum(1 for r in rows if r["ok"])
|
||||
n_kind = len([s for s in SHAPE_ORDER if overall.get(s)])
|
||||
|
||||
return f"""<!DOCTYPE html>
|
||||
<html lang="zh-CN">
|
||||
<head>
|
||||
<meta charset="UTF-8"/>
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1"/>
|
||||
<title>{html.escape(name)} — 脸型分类报告</title>
|
||||
<style>{CSS}</style>
|
||||
</head>
|
||||
<body>
|
||||
<header>
|
||||
<h1>{html.escape(name)} — 脸型分类报告</h1>
|
||||
<p>z 分数原型匹配分类 · 照片上标注 face_width / face_height 及各比例特征</p>
|
||||
<p>生成时间：{html.escape(now)} · 抽样 {len(rows)} 张（随机种子 {seed}）· 成功 {n_ok} 张 ·
|
||||
覆盖 {n_kind} 种脸型 · 共 {len(by_group)} 个原始分组 · 点击图片看大图</p>
|
||||
<p class="muted">来源目录：{html.escape(str(src))}</p>
|
||||
</header>
|
||||
|
||||
<div class="legend-box">
|
||||
<div class="inner">
|
||||
<b>图上标注说明</b><br/>
|
||||
<span class="swatch" style="background:#00dc78"></span><code>face_width</code> 颧骨宽度
|
||||
<span class="swatch" style="background:#28b4ff"></span><code>face_height</code> 额头顶→下巴
|
||||
<span class="swatch" style="background:#ff5a00"></span><code>jaw_angle</code> 下巴到左右下颌角夹角
|
||||
<span class="swatch" style="background:#ffc828"></span><code>taper_ratio</code> 额头→下巴收窄
|
||||
<span class="swatch" style="background:#28a0ff"></span><code>forehead / jaw / chin ratio</code> 各级宽度比
|
||||
<span class="swatch" style="background:#b4ff50"></span><code>face_curve_score</code> 下颌中点→下巴
|
||||
右侧柱状条示意 <code>width_uniformity</code>；左上角是完整数值图例。
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<section>
|
||||
<h2>原始分组 × 预测脸型 对照<small>数据集分组本身是脸型标签，但命名口径与分类器不同</small></h2>
|
||||
{cross_table(by_group)}
|
||||
<p class="muted" style="margin-top:10px;font-size:.85rem">
|
||||
绿色格子表示预测结果与该分组的对应口径一致（标准脸≈鹅蛋脸、娃娃脸≈圆形脸，方形/长形/瓜子同名直接对应）。
|
||||
「梨形脸」「混合脸」在分类器的 7 分类里没有对应项，不作一致性判断。
|
||||
</p>
|
||||
</section>
|
||||
|
||||
<div class="stats">
|
||||
<div class="stat"><h2>总体脸型分布（{len(rows)} 张）</h2>{bar_chart(overall)}</div>
|
||||
{group_stats}
|
||||
</div>
|
||||
|
||||
{sections}
|
||||
|
||||
<footer>
|
||||
分类实现：face/face_shape_classifier.py · 报告生成：face/build_dataset_report.py ·
|
||||
图片目录：static/{html.escape(img_dir_name)}/
|
||||
</footer>
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
|
||||
|
||||
def main() -> None:
|
||||
ap = argparse.ArgumentParser(description="批量脸型预测并生成 HTML 报告")
|
||||
ap.add_argument("--src", required=True, help="图片根目录（可含子目录）")
|
||||
ap.add_argument("--sample", type=int, default=50, help="随机抽样张数，0 表示全部")
|
||||
ap.add_argument("--seed", type=int, default=42, help="随机种子")
|
||||
ap.add_argument("--name", default=None, help="报告标题，默认取目录名")
|
||||
ap.add_argument("--slug", default="dataset", help="输出文件名前缀（ASCII）")
|
||||
ap.add_argument(
|
||||
"--min-per-group",
|
||||
type=int,
|
||||
default=2,
|
||||
help="分层抽样时每个分组至少抽几张，0 表示纯随机抽样",
|
||||
)
|
||||
args = ap.parse_args()
|
||||
|
||||
src = Path(args.src).expanduser().resolve()
|
||||
if not src.is_dir():
|
||||
raise SystemExit(f"目录不存在: {src}")
|
||||
|
||||
name = args.name or src.name
|
||||
img_dir_name = f"{args.slug}_report"
|
||||
img_dir = ROOT / "static" / img_dir_name / "images"
|
||||
out_html = ROOT / "static" / f"{args.slug}_report.html"
|
||||
|
||||
rows = analyze(src, args.sample, args.seed, img_dir, args.min_per_group)
|
||||
out_html.write_text(
|
||||
build_html(rows, name, src, args.seed, img_dir_name), encoding="utf-8"
|
||||
)
|
||||
|
||||
overall = Counter(r["predicted"] if r["ok"] else "检测失败" for r in rows)
|
||||
total = sum(overall.values())
|
||||
print(f"\n写入 {out_html}")
|
||||
print("=== 总体脸型分布 ===")
|
||||
for shape in SHAPE_ORDER + ["检测失败"]:
|
||||
n = overall.get(shape, 0)
|
||||
if n:
|
||||
print(f" {shape}: {n:3d} ({n / total * 100:4.1f}%) {'#' * n}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,359 @@
|
||||
"""
|
||||
build_report.py
|
||||
对 face/test_img/girl 与 face/test_img/man 下的照片批量预测脸型，
|
||||
在照片上标注 face_width / face_height 等特征，并生成 HTML 报告。
|
||||
|
||||
用法:
|
||||
./venv/bin/python face/build_report.py
|
||||
输出:
|
||||
static/face_shape_report.html
|
||||
static/face_shape_report/images/*.jpg
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import html
|
||||
import re
|
||||
import shutil
|
||||
import sys
|
||||
from collections import Counter
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Dict, List
|
||||
|
||||
import cv2
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
|
||||
|
||||
from face.face_shape_classifier import classify_from_image # noqa: E402
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[1]
|
||||
SRC_DIRS = {
|
||||
"女": ROOT / "face/test_img/girl",
|
||||
"男": ROOT / "face/test_img/man",
|
||||
}
|
||||
OUT_DIR = ROOT / "static/face_shape_report"
|
||||
IMG_DIR = OUT_DIR / "images"
|
||||
OUT_HTML = ROOT / "static/face_shape_report.html"
|
||||
|
||||
MAX_IMAGE_SIDE = 900
|
||||
JPEG_QUALITY = 90
|
||||
|
||||
SHAPE_ORDER = ["圆形脸", "心形脸", "菱形脸", "鹅蛋脸", "方形脸", "长形脸", "瓜子脸"]
|
||||
SHAPE_COLORS = {
|
||||
"圆形脸": "#e67e22",
|
||||
"心形脸": "#e74c3c",
|
||||
"菱形脸": "#9b59b6",
|
||||
"鹅蛋脸": "#27ae60",
|
||||
"方形脸": "#2980b9",
|
||||
"长形脸": "#16a085",
|
||||
"瓜子脸": "#c0392b",
|
||||
}
|
||||
FEATURE_KEYS = [
|
||||
"face_width",
|
||||
"face_height",
|
||||
"jaw_angle",
|
||||
"taper_ratio",
|
||||
"forehead_ratio",
|
||||
"cheekbone_ratio",
|
||||
"jaw_ratio",
|
||||
"chin_ratio",
|
||||
"chin_sharpness",
|
||||
"width_uniformity",
|
||||
"face_curve_score",
|
||||
]
|
||||
|
||||
|
||||
def natural_key(path: Path):
|
||||
m = re.search(r"(\d+)", path.stem)
|
||||
return (0, int(m.group(1))) if m else (1, path.stem)
|
||||
|
||||
|
||||
def analyze_all() -> tuple[List[Dict], Dict[str, Counter]]:
|
||||
if IMG_DIR.exists():
|
||||
shutil.rmtree(IMG_DIR)
|
||||
IMG_DIR.mkdir(parents=True)
|
||||
|
||||
rows: List[Dict] = []
|
||||
summary = {"女": Counter(), "男": Counter(), "all": Counter()}
|
||||
|
||||
for gender, src in SRC_DIRS.items():
|
||||
prefix = "girl" if gender == "女" else "man"
|
||||
paths = sorted(
|
||||
[p for p in src.iterdir() if p.suffix.lower() in {".jpg", ".jpeg", ".png", ".webp"}],
|
||||
key=natural_key,
|
||||
)
|
||||
for path in paths:
|
||||
m = re.search(r"(\d+)", path.stem)
|
||||
out_name = f"{prefix}_{int(m.group(1)) if m else 0:02d}.jpg"
|
||||
dest = IMG_DIR / out_name
|
||||
|
||||
item = {
|
||||
"gender": gender,
|
||||
"file": path.name,
|
||||
"img_src": f"face_shape_report/images/{out_name}",
|
||||
"ok": False,
|
||||
"predicted": None,
|
||||
"display": None,
|
||||
"confidence": None,
|
||||
"score": None,
|
||||
"top3": [],
|
||||
"features": {},
|
||||
"error": None,
|
||||
}
|
||||
try:
|
||||
result = classify_from_image(path, return_details=True, return_annotated=True)
|
||||
annotated = result["annotated"]
|
||||
h, w = annotated.shape[:2]
|
||||
if max(h, w) > MAX_IMAGE_SIDE:
|
||||
scale = MAX_IMAGE_SIDE / max(h, w)
|
||||
annotated = cv2.resize(
|
||||
annotated, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA
|
||||
)
|
||||
cv2.imwrite(str(dest), annotated, [int(cv2.IMWRITE_JPEG_QUALITY), JPEG_QUALITY])
|
||||
|
||||
item.update(
|
||||
{
|
||||
"ok": True,
|
||||
"predicted": result["face_shape"],
|
||||
"display": result["display"],
|
||||
"confidence": result["confidence"],
|
||||
"score": result["details"]["ranked"][0][1],
|
||||
"top3": result["details"]["ranked"][:3],
|
||||
"features": {k: result["features"][k] for k in FEATURE_KEYS},
|
||||
}
|
||||
)
|
||||
summary[gender][result["face_shape"]] += 1
|
||||
summary["all"][result["face_shape"]] += 1
|
||||
except Exception as exc: # noqa: BLE001 - 报告需要汇总所有失败
|
||||
img = cv2.imread(str(path))
|
||||
if img is not None:
|
||||
cv2.imwrite(str(dest), img, [int(cv2.IMWRITE_JPEG_QUALITY), JPEG_QUALITY])
|
||||
item["error"] = str(exc)
|
||||
summary[gender]["检测失败"] += 1
|
||||
summary["all"]["检测失败"] += 1
|
||||
|
||||
rows.append(item)
|
||||
print(f"[{gender}] {path.name} -> {item['display'] or 'ERR ' + str(item['error'])}")
|
||||
|
||||
return rows, summary
|
||||
|
||||
|
||||
def count_table(counter: Counter) -> str:
|
||||
if not counter:
|
||||
return "<p class='muted'>无数据</p>"
|
||||
total = sum(counter.values())
|
||||
parts = []
|
||||
for shape, n in sorted(counter.items(), key=lambda x: (-x[1], x[0])):
|
||||
color = SHAPE_COLORS.get(shape, "#7f8c8d")
|
||||
pct = n / total * 100
|
||||
parts.append(
|
||||
f"<div class='bar-row'><span class='bar-label'>{html.escape(shape)}</span>"
|
||||
f"<div class='bar-track'><div class='bar-fill' style='width:{pct:.1f}%;background:{color}'></div></div>"
|
||||
f"<span class='bar-num'>{n}（{pct:.0f}%）</span></div>"
|
||||
)
|
||||
return "".join(parts)
|
||||
|
||||
|
||||
def fmt_feat(key: str, value: float) -> str:
|
||||
if key in {"face_width", "face_height"}:
|
||||
return f"{value:.1f}px"
|
||||
if key == "jaw_angle":
|
||||
return f"{value:.1f}°"
|
||||
return f"{value:.3f}"
|
||||
|
||||
|
||||
def card(item: Dict) -> str:
|
||||
if not item["ok"]:
|
||||
return f"""
|
||||
<article class="card error">
|
||||
<a class="img-link" href="{html.escape(item['img_src'])}" target="_blank">
|
||||
<img src="{html.escape(item['img_src'])}" alt="{html.escape(item['file'])}" loading="lazy"/>
|
||||
</a>
|
||||
<div class="body">
|
||||
<h3>{html.escape(item['file'])}</h3>
|
||||
<p class="badge bad">检测失败</p>
|
||||
<p class="muted">{html.escape(item['error'] or '')}</p>
|
||||
</div>
|
||||
</article>"""
|
||||
|
||||
color = SHAPE_COLORS.get(item["predicted"], "#34495e")
|
||||
top3 = "".join(
|
||||
f"<li><span>{html.escape(name)}</span><b>{score:.1f}</b></li>" for name, score in item["top3"]
|
||||
)
|
||||
feat_html = "".join(
|
||||
f"<tr><td>{html.escape(k)}</td><td>{html.escape(fmt_feat(k, v))}</td></tr>"
|
||||
for k, v in item["features"].items()
|
||||
)
|
||||
return f"""
|
||||
<article class="card">
|
||||
<a class="img-link" href="{html.escape(item['img_src'])}" target="_blank" title="点击查看大图标注">
|
||||
<img src="{html.escape(item['img_src'])}" alt="{html.escape(item['file'])}" loading="lazy"/>
|
||||
</a>
|
||||
<div class="body">
|
||||
<div class="meta">
|
||||
<h3>{html.escape(item['file'])}</h3>
|
||||
<span class="gender">{html.escape(item['gender'])}</span>
|
||||
</div>
|
||||
<p class="badge" style="background:{color}">{html.escape(item['display'])}</p>
|
||||
<p class="conf">匹配度 {item['score']:.1f} · 置信度 {item['confidence']:.3f}</p>
|
||||
<h4>Top-3 得分</h4>
|
||||
<ul class="scores">{top3}</ul>
|
||||
<details>
|
||||
<summary>标注特征数值</summary>
|
||||
<table>{feat_html}</table>
|
||||
</details>
|
||||
</div>
|
||||
</article>"""
|
||||
|
||||
|
||||
CSS = """
|
||||
:root {
|
||||
--bg: #f3efe6; --ink: #1c1915; --muted: #6b645a;
|
||||
--card: #fffdf8; --line: #e2d8c8; --accent: #0f6b5c;
|
||||
}
|
||||
* { box-sizing: border-box; }
|
||||
body {
|
||||
margin: 0;
|
||||
font-family: "PingFang SC", "Noto Sans SC", "Segoe UI", sans-serif;
|
||||
color: var(--ink);
|
||||
background:
|
||||
radial-gradient(1200px 600px at 10% -10%, #ffe8c8 0%, transparent 55%),
|
||||
radial-gradient(900px 500px at 100% 0%, #d9f2ea 0%, transparent 50%),
|
||||
var(--bg);
|
||||
}
|
||||
header { padding: 40px 24px 20px; max-width: 1280px; margin: 0 auto; }
|
||||
header h1 { margin: 0 0 8px; font-size: clamp(1.8rem, 3vw, 2.4rem); letter-spacing: .02em; }
|
||||
header p { margin: 4px 0; color: var(--muted); }
|
||||
.legend-box { max-width: 1280px; margin: 0 auto 20px; padding: 0 24px; }
|
||||
.legend-box .inner {
|
||||
background: var(--card); border: 1px solid var(--line);
|
||||
border-radius: 14px; padding: 14px 16px; font-size: .9rem; line-height: 1.55;
|
||||
}
|
||||
.legend-box code { background: #efe7da; padding: 1px 6px; border-radius: 4px; font-size: .84rem; }
|
||||
.swatch { display: inline-block; width: 10px; height: 10px; border-radius: 2px; margin-right: 4px; vertical-align: middle; }
|
||||
.stats {
|
||||
display: grid; grid-template-columns: repeat(auto-fit, minmax(260px, 1fr));
|
||||
gap: 16px; max-width: 1280px; margin: 0 auto 28px; padding: 0 24px;
|
||||
}
|
||||
.stat { background: var(--card); border: 1px solid var(--line); border-radius: 16px; padding: 16px 18px; }
|
||||
.stat h2 { margin: 0 0 12px; font-size: 1rem; }
|
||||
.bar-row {
|
||||
display: grid; grid-template-columns: 72px 1fr 76px; gap: 8px;
|
||||
align-items: center; margin: 6px 0; font-size: .86rem;
|
||||
}
|
||||
.bar-track { height: 8px; background: #efe7da; border-radius: 999px; overflow: hidden; }
|
||||
.bar-fill { height: 100%; border-radius: 999px; }
|
||||
.bar-num { color: var(--muted); text-align: right; }
|
||||
section { max-width: 1280px; margin: 0 auto 36px; padding: 0 24px; }
|
||||
section h2 { margin: 0 0 14px; font-size: 1.35rem; border-left: 4px solid var(--accent); padding-left: 10px; }
|
||||
.grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(280px, 1fr)); gap: 16px; }
|
||||
.card {
|
||||
background: var(--card); border: 1px solid var(--line); border-radius: 18px;
|
||||
overflow: hidden; display: flex; flex-direction: column;
|
||||
box-shadow: 0 8px 24px rgba(60, 40, 10, .05);
|
||||
}
|
||||
.card.error { opacity: .9; }
|
||||
.img-link { display: block; }
|
||||
.card img { width: 100%; aspect-ratio: 3/4; object-fit: cover; background: #ddd; display: block; }
|
||||
.card .body { padding: 14px 14px 16px; }
|
||||
.meta { display: flex; justify-content: space-between; align-items: baseline; gap: 8px; }
|
||||
.meta h3 { margin: 0; font-size: 1rem; }
|
||||
.gender { font-size: .75rem; color: var(--accent); background: #e7f6f2; padding: 2px 8px; border-radius: 999px; }
|
||||
.badge {
|
||||
display: inline-block; margin: 10px 0 4px; color: #fff;
|
||||
padding: 6px 10px; border-radius: 999px; font-weight: 600; font-size: .92rem;
|
||||
}
|
||||
.badge.bad { background: #c0392b; }
|
||||
.conf { margin: 0 0 10px; color: var(--muted); font-size: .85rem; }
|
||||
.scores { list-style: none; padding: 0; margin: 0 0 8px; }
|
||||
.scores li {
|
||||
display: flex; justify-content: space-between; padding: 4px 0;
|
||||
border-bottom: 1px dashed var(--line); font-size: .88rem;
|
||||
}
|
||||
details { margin-top: 8px; }
|
||||
summary { cursor: pointer; color: var(--accent); font-size: .88rem; }
|
||||
table { width: 100%; border-collapse: collapse; margin-top: 8px; font-size: .8rem; }
|
||||
td { padding: 3px 0; border-bottom: 1px solid var(--line); }
|
||||
td:last-child { text-align: right; font-variant-numeric: tabular-nums; }
|
||||
.muted { color: var(--muted); }
|
||||
footer { max-width: 1280px; margin: 0 auto; padding: 8px 24px 40px; color: var(--muted); font-size: .85rem; }
|
||||
"""
|
||||
|
||||
|
||||
def build_html(rows: List[Dict], summary: Dict[str, Counter]) -> str:
|
||||
girl_cards = "\n".join(card(r) for r in rows if r["gender"] == "女")
|
||||
man_cards = "\n".join(card(r) for r in rows if r["gender"] == "男")
|
||||
now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
n_girl = sum(1 for r in rows if r["gender"] == "女")
|
||||
n_man = sum(1 for r in rows if r["gender"] == "男")
|
||||
n_ok = sum(1 for r in rows if r["ok"])
|
||||
n_kind = len([s for s in SHAPE_ORDER if summary["all"].get(s)])
|
||||
|
||||
return f"""<!DOCTYPE html>
|
||||
<html lang="zh-CN">
|
||||
<head>
|
||||
<meta charset="UTF-8"/>
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1"/>
|
||||
<title>脸型分类预测报告（特征标注）</title>
|
||||
<style>{CSS}</style>
|
||||
</head>
|
||||
<body>
|
||||
<header>
|
||||
<h1>脸型分类预测报告</h1>
|
||||
<p>z 分数原型匹配分类 · 照片上标注 face_width / face_height 及各比例特征</p>
|
||||
<p>生成时间：{html.escape(now)} · 样本 {n_girl + n_man} 张（女 {n_girl} / 男 {n_man}）· 成功 {n_ok} · 覆盖 {n_kind} 种脸型 · 点击图片看大图</p>
|
||||
</header>
|
||||
|
||||
<div class="legend-box">
|
||||
<div class="inner">
|
||||
<b>图上标注说明</b><br/>
|
||||
<span class="swatch" style="background:#00dc78"></span><code>face_width</code> 颧骨宽度
|
||||
<span class="swatch" style="background:#28b4ff"></span><code>face_height</code> 额头顶→下巴
|
||||
<span class="swatch" style="background:#ff5a00"></span><code>jaw_angle</code> 下巴到左右下颌角夹角
|
||||
<span class="swatch" style="background:#ffc828"></span><code>taper_ratio</code> 额头→下巴收窄
|
||||
<span class="swatch" style="background:#28a0ff"></span><code>forehead / jaw / chin ratio</code> 各级宽度比
|
||||
<span class="swatch" style="background:#b4ff50"></span><code>face_curve_score</code> 下颌中点→下巴
|
||||
右侧柱状条示意 <code>width_uniformity</code>；左上角是完整数值图例。
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="stats">
|
||||
<div class="stat"><h2>全部脸型分布</h2>{count_table(summary['all'])}</div>
|
||||
<div class="stat"><h2>女性脸型分布</h2>{count_table(summary['女'])}</div>
|
||||
<div class="stat"><h2>男性脸型分布</h2>{count_table(summary['男'])}</div>
|
||||
</div>
|
||||
|
||||
<section>
|
||||
<h2>女性样本（{n_girl}）</h2>
|
||||
<div class="grid">{girl_cards}</div>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
<h2>男性样本（{n_man}）</h2>
|
||||
<div class="grid">{man_cards}</div>
|
||||
</section>
|
||||
|
||||
<footer>
|
||||
分类实现：face/face_shape_classifier.py · 报告生成：face/build_report.py
|
||||
</footer>
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
|
||||
|
||||
def main() -> None:
|
||||
OUT_DIR.mkdir(parents=True, exist_ok=True)
|
||||
rows, summary = analyze_all()
|
||||
OUT_HTML.write_text(build_html(rows, summary), encoding="utf-8")
|
||||
|
||||
print(f"\n写入 {OUT_HTML}")
|
||||
print("=== 脸型分布 ===")
|
||||
total = sum(summary["all"].values())
|
||||
for shape in SHAPE_ORDER:
|
||||
n = summary["all"].get(shape, 0)
|
||||
print(f" {shape}: {n:2d} ({n / total * 100:4.1f}%) {'#' * n}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,83 @@
|
||||
"""
|
||||
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。
|
||||
|
||||
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。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Dict, List
|
||||
|
||||
import cv2
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parent))
|
||||
|
||||
from face_shape_classifier import ( # noqa: E402
|
||||
_get_face_mesh,
|
||||
extract_face_features,
|
||||
)
|
||||
|
||||
ROOT = Path(__file__).resolve().parent
|
||||
IMG_EXT = {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
|
||||
|
||||
|
||||
def iter_images(root: Path) -> List[Path]:
|
||||
return sorted(p for p in root.rglob("*") if p.suffix.lower() in IMG_EXT)
|
||||
|
||||
|
||||
def features_for(path: Path) -> Dict[str, float] | None:
|
||||
bgr = cv2.imread(str(path))
|
||||
if bgr is None:
|
||||
return None
|
||||
h, w = bgr.shape[:2]
|
||||
rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
|
||||
res = _get_face_mesh().process(rgb)
|
||||
if not res.multi_face_landmarks:
|
||||
return None
|
||||
return extract_face_features(res.multi_face_landmarks[0].landmark, image_size=(w, h))
|
||||
|
||||
|
||||
def main() -> None:
|
||||
out_path = Path(sys.argv[1]) if len(sys.argv) > 1 else ROOT / "cache" / "features.json"
|
||||
out_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
sources = {
|
||||
# 6 张带标注的基准图，文件名即期望脸型
|
||||
"benchmark": [p for p in ROOT.joinpath("test_img").glob("*.png")],
|
||||
"girl": iter_images(ROOT / "test_img" / "girl"),
|
||||
"man": iter_images(ROOT / "test_img" / "man"),
|
||||
"dataset": iter_images(ROOT / "test_img" / "脸型测试集合"),
|
||||
}
|
||||
|
||||
records = []
|
||||
failed = 0
|
||||
for source, paths in sources.items():
|
||||
for i, path in enumerate(paths, 1):
|
||||
feats = features_for(path)
|
||||
if feats is None:
|
||||
failed += 1
|
||||
continue
|
||||
rel = path.relative_to(ROOT)
|
||||
records.append(
|
||||
{
|
||||
"source": source,
|
||||
"path": rel.as_posix(),
|
||||
"file": path.name,
|
||||
# dataset 的上级目录名即原始分组（弱标签，非可信真值）
|
||||
"group": path.parent.name if source == "dataset" else source,
|
||||
"expected": path.stem if source == "benchmark" else None,
|
||||
"features": feats,
|
||||
}
|
||||
)
|
||||
if i % 50 == 0 or i == len(paths):
|
||||
print(f"[{source}] {i}/{len(paths)}", flush=True)
|
||||
|
||||
out_path.write_text(json.dumps(records, ensure_ascii=False), encoding="utf-8")
|
||||
print(f"\n写入 {out_path}：{len(records)} 条，检测失败 {failed} 张")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,904 @@
|
||||
# 脸型判断规则优化报告
|
||||
|
||||
> 基于 Mediapipe 468 点人脸关键点的脸型分类系统
|
||||
> 优化日期：2026-07-28
|
||||
|
||||
---
|
||||
|
||||
## 一、优化总览
|
||||
|
||||
### 原始规则主要问题
|
||||
|
||||
| 问题 | 说明 |
|
||||
|------|------|
|
||||
| **规则冲突** | 7条 if 规则可能同时匹配，无优先级机制 |
|
||||
| **特征定义模糊** | `width_ratio`、`chin_narrowness` 等未给出精确计算方式 |
|
||||
| **关键点不足** | 额头宽度用 234/454（颧骨点）而非太阳穴点，导致测量不准 |
|
||||
| **无置信度** | 硬判断，混合脸型无处理 |
|
||||
| **阈值经验性** | 阈值未经统计校准，边界处容易误判 |
|
||||
| **缺少归一化** | 不同距离拍的照片结果不一致 |
|
||||
|
||||
### 优化策略
|
||||
|
||||
1. **精确特征提取**：增加关键点，所有测量归一化
|
||||
2. **评分制分类**：每个脸型计算匹配度分数（0-100），取最高分
|
||||
3. **置信度输出**：报告 Top-1 / Top-2 分差，判断是否为混合脸型
|
||||
4. **优先级仲裁**：分数接近时按"特异性优先"原则仲裁
|
||||
5. **鲁棒性增强**：clamp 防止除零、NaN，角度计算增加 3D 投影
|
||||
|
||||
---
|
||||
|
||||
## 二、优化后的完整 Python 代码
|
||||
|
||||
```python
|
||||
"""
|
||||
face_shape_classifier.py
|
||||
基于 Mediapipe 468 点人脸关键点的脸型分类系统
|
||||
|
||||
支持的脸型：圆形脸 / 心形脸 / 菱形脸 / 鹅蛋脸 / 方形脸 / 长形脸 / 瓜子脸
|
||||
分类策略：多维度特征提取 → 加权评分 → 置信度判断
|
||||
"""
|
||||
|
||||
import math
|
||||
import numpy as np
|
||||
from typing import Dict, Tuple, List, Optional
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 第一部分：关键点索引定义
|
||||
# ============================================================
|
||||
|
||||
class FaceLandmarks:
|
||||
"""Mediapipe 468 点关键点索引（仅列出脸型分析所需）"""
|
||||
|
||||
# --- 中线关键点 ---
|
||||
FOREHEAD_TOP = 10 # 额头顶部（发际线附近）
|
||||
NOSE_BRIDGE = 1 # 鼻根（眉心位置）
|
||||
NOSE_TIP = 168 # 鼻尖
|
||||
CHIN_BOTTOM = 152 # 下巴最低点（menton）
|
||||
|
||||
# --- 太阳穴 / 额头两侧（额头宽度）---
|
||||
LEFT_TEMPLE = 127 # 左太阳穴
|
||||
RIGHT_TEMPLE = 356 # 右太阳穴
|
||||
|
||||
# --- 颧骨 / 脸颊最宽处 ---
|
||||
LEFT_CHEEK = 234 # 左颧弓最外侧
|
||||
RIGHT_CHEEK = 454 # 右颧弓最外侧
|
||||
|
||||
# --- 下颌角（gonion 区域）---
|
||||
LEFT_JAW_ANGLE = 172 # 左下颌角
|
||||
RIGHT_JAW_ANGLE = 397 # 右下颌角
|
||||
|
||||
# --- 下巴两侧（下巴宽度）---
|
||||
LEFT_CHIN = 136 # 左下巴缘
|
||||
RIGHT_CHIN = 365 # 右下巴缘
|
||||
|
||||
# --- 嘴角（辅助参考）---
|
||||
LEFT_MOUTH = 61 # 左嘴角
|
||||
RIGHT_MOUTH = 291 # 右嘴角
|
||||
|
||||
# --- 眼角（辅助参考）---
|
||||
LEFT_EYE_OUT = 33 # 左眼外角
|
||||
RIGHT_EYE_OUT = 263 # 右眼外角
|
||||
|
||||
# --- 额头侧缘（辅助）---
|
||||
LEFT_FOREHEAD = 50 # 左额侧
|
||||
RIGHT_FOREHEAD = 280 # 右额侧
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 第二部分：特征提取
|
||||
# ============================================================
|
||||
|
||||
def extract_face_features(landmarks) -> Dict[str, float]:
|
||||
"""
|
||||
从 Mediapipe 关键点中提取脸型特征向量。
|
||||
|
||||
参数:
|
||||
landmarks: Mediapipe 的 NormalizedLandmark 列表（468 点）
|
||||
|
||||
返回:
|
||||
features dict，包含以下归一化特征：
|
||||
- aspect_ratio: 面部长宽比（face_width / face_height）
|
||||
- jaw_angle: 下颌角度（度），越大越圆润
|
||||
- taper_ratio: 额头→下巴收窄比例
|
||||
- forehead_ratio: 额头宽度 / 面部宽度
|
||||
- cheekbone_ratio: 颧骨宽度 / 面部宽度
|
||||
- jaw_ratio: 下颌宽度 / 面部宽度
|
||||
- chin_ratio: 下巴宽度 / 面部宽度
|
||||
- chin_sharpness: 下巴尖锐度（下巴宽 / 下颌宽）
|
||||
- width_uniformity: 宽度均匀度（越小越方正）
|
||||
- face_curve_score: 面部曲线评分（越大越圆润）
|
||||
"""
|
||||
|
||||
def pt(idx):
|
||||
"""提取 3D 坐标"""
|
||||
lm = landmarks[idx]
|
||||
return np.array([lm.x, lm.y, lm.z])
|
||||
|
||||
def dist(p1, p2):
|
||||
"""欧氏距离"""
|
||||
return float(np.linalg.norm(p1 - p2))
|
||||
|
||||
# --- 1. 提取关键点 ---
|
||||
forehead_top = pt(FaceLandmarks.FOREHEAD_TOP)
|
||||
chin_bottom = pt(FaceLandmarks.CHIN_BOTTOM)
|
||||
|
||||
left_temple = pt(FaceLandmarks.LEFT_TEMPLE)
|
||||
right_temple = pt(FaceLandmarks.RIGHT_TEMPLE)
|
||||
|
||||
left_cheek = pt(FaceLandmarks.LEFT_CHEEK)
|
||||
right_cheek = pt(FaceLandmarks.RIGHT_CHEEK)
|
||||
|
||||
left_jaw = pt(FaceLandmarks.LEFT_JAW_ANGLE)
|
||||
right_jaw = pt(FaceLandmarks.RIGHT_JAW_ANGLE)
|
||||
|
||||
left_chin = pt(FaceLandmarks.LEFT_CHIN)
|
||||
right_chin = pt(FaceLandmarks.RIGHT_CHIN)
|
||||
|
||||
# --- 2. 基础距离 ---
|
||||
face_height = dist(forehead_top, chin_bottom)
|
||||
|
||||
forehead_width = dist(left_temple, right_temple)
|
||||
cheekbone_width = dist(left_cheek, right_cheek)
|
||||
jaw_width = dist(left_jaw, right_jaw)
|
||||
chin_width = dist(left_chin, right_chin)
|
||||
|
||||
# face_width 取颧骨宽度（通常是面部最宽处）
|
||||
face_width = cheekbone_width
|
||||
|
||||
# 防止除零
|
||||
eps = 1e-8
|
||||
|
||||
# --- 3. 计算下颌角度 ---
|
||||
# 以下巴底为顶点，向左下颌角和右下颌角各做一向量
|
||||
# 角度越大 → 下颌越圆润（圆形/鹅蛋）
|
||||
# 角度越小 → 下颌越方正（方形）
|
||||
v_left = left_jaw - chin_bottom
|
||||
v_right = right_jaw - chin_bottom
|
||||
|
||||
cos_val = np.dot(v_left, v_right) / (np.linalg.norm(v_left) * np.linalg.norm(v_right) + eps)
|
||||
cos_val = np.clip(cos_val, -1.0, 1.0)
|
||||
jaw_angle = math.degrees(math.acos(cos_val))
|
||||
|
||||
# --- 4. 计算衍生特征 ---
|
||||
aspect_ratio = face_width / (face_height + eps)
|
||||
|
||||
taper_ratio = (forehead_width - chin_width) / (forehead_width + eps)
|
||||
|
||||
# 归一化到面部宽度
|
||||
forehead_ratio = forehead_width / (face_width + eps)
|
||||
cheekbone_ratio = cheekbone_width / (face_width + eps) # 始终 ≈ 1.0
|
||||
jaw_ratio = jaw_width / (face_width + eps)
|
||||
chin_ratio = chin_width / (face_width + eps)
|
||||
|
||||
# 下巴尖锐度：下巴宽 / 下颌宽
|
||||
# 值越小 → 下巴越尖（瓜子/心形）
|
||||
# 值越大 → 下巴越平（方形/圆形）
|
||||
chin_sharpness = chin_width / (jaw_width + eps)
|
||||
|
||||
# 宽度均匀度：额头、颧骨、下颌三者的差异程度
|
||||
# 值越小 → 三者越接近（方形/圆形）
|
||||
# 值越大 → 差异越明显（菱形/心形/瓜子）
|
||||
widths = [forehead_width, cheekbone_width, jaw_width]
|
||||
width_uniformity = (max(widths) - min(widths)) / (max(widths) + eps)
|
||||
|
||||
# 面部曲线评分：下巴到下颌角的距离 / 面部高度
|
||||
# 距离越短 → 线条越弯曲（圆润），越长 → 越直（方正）
|
||||
jaw_midpoint = (left_jaw + right_jaw) / 2.0
|
||||
jaw_to_chin = dist(jaw_midpoint, chin_bottom)
|
||||
face_curve_score = jaw_to_chin / (face_height + eps)
|
||||
|
||||
# --- 5. 返回特征字典 ---
|
||||
features = {
|
||||
# 原始尺寸
|
||||
'face_height': face_height,
|
||||
'face_width': face_width,
|
||||
'forehead_width': forehead_width,
|
||||
'cheekbone_width': cheekbone_width,
|
||||
'jaw_width': jaw_width,
|
||||
'chin_width': chin_width,
|
||||
|
||||
# 比例特征
|
||||
'aspect_ratio': aspect_ratio,
|
||||
'taper_ratio': taper_ratio,
|
||||
'forehead_ratio': forehead_ratio,
|
||||
'cheekbone_ratio': cheekbone_ratio,
|
||||
'jaw_ratio': jaw_ratio,
|
||||
'chin_ratio': chin_ratio,
|
||||
|
||||
# 角度特征
|
||||
'jaw_angle': jaw_angle,
|
||||
|
||||
# 复合特征
|
||||
'chin_sharpness': chin_sharpness,
|
||||
'width_uniformity': width_uniformity,
|
||||
'face_curve_score': face_curve_score,
|
||||
}
|
||||
|
||||
return features
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 第三部分：评分制分类器
|
||||
# ============================================================
|
||||
|
||||
def classify_face_shape(
|
||||
features: Dict[str, float],
|
||||
return_details: bool = False
|
||||
) -> Tuple[str, float, Optional[Dict]]:
|
||||
"""
|
||||
基于评分的脸型分类器。
|
||||
|
||||
策略：
|
||||
每种脸型计算 0-100 的匹配度分数。
|
||||
取最高分作为结果，返回置信度和详细得分。
|
||||
|
||||
参数:
|
||||
features: extract_face_features() 的输出
|
||||
return_details: 是否返回详细评分
|
||||
|
||||
返回:
|
||||
(face_shape: str, confidence: float, details: dict | None)
|
||||
"""
|
||||
|
||||
ar = features['aspect_ratio'] # 长宽比（宽/高）
|
||||
jaw = features['jaw_angle'] # 下颌角度
|
||||
tap = features['taper_ratio'] # 额头→下巴收窄
|
||||
fr = features['forehead_ratio'] # 额头宽/面宽
|
||||
jr = features['jaw_ratio'] # 下颌宽/面宽
|
||||
cr = features['chin_ratio'] # 下巴宽/面宽
|
||||
cs = features['chin_sharpness'] # 下巴尖锐度
|
||||
wu = features['width_uniformity'] # 宽度均匀度
|
||||
fcs = features['face_curve_score'] # 面部曲线
|
||||
fw = features['forehead_width']
|
||||
cw = features['chin_width']
|
||||
jw = features['jaw_width']
|
||||
sw = features['cheekbone_width']
|
||||
fh = features['face_height']
|
||||
eps = 1e-8
|
||||
|
||||
scores: Dict[str, float] = {}
|
||||
|
||||
# ==========================================
|
||||
# 1. 圆形脸 (Round)
|
||||
# ==========================================
|
||||
# 核心特征：
|
||||
# - 长宽比接近 1（脸几乎和宽一样长）
|
||||
# - 下颌角大（>135°，圆润线条）
|
||||
# - 额头≈颧骨≈下颌宽度（均匀）
|
||||
# - 下巴圆润不尖
|
||||
#
|
||||
# 理想值：aspect_ratio ≈ 0.88-1.0, jaw_angle ≈ 140-160°
|
||||
# ==========================================
|
||||
s = 0.0
|
||||
s += _score_range(ar, 0.85, 1.0, peak=0.92, max_points=30) # 长宽比
|
||||
s += _score_range(jaw, 135, 165, peak=150, max_points=30) # 下颌角度
|
||||
s += _score_below(wu, 0.10, max_points=20) # 宽度均匀
|
||||
s += _score_range(cs, 0.65, 0.90, peak=0.75, max_points=10) # 下巴不尖
|
||||
s += _score_range(tap, -0.05, 0.10, peak=0.02, max_points=10) # 几乎不收窄
|
||||
scores['圆形脸'] = s
|
||||
|
||||
# ==========================================
|
||||
# 2. 心形脸 (Heart)
|
||||
# ==========================================
|
||||
# 核心特征：
|
||||
# - 额头明显宽于下巴（taper > 0.2）
|
||||
# - 下巴尖细（chin_sharpness < 0.55）
|
||||
# - 颧骨与额头接近（不是颧骨最宽）
|
||||
# - 前额发际线较宽
|
||||
#
|
||||
# 理想值：taper ≈ 0.25-0.40, chin_sharpness ≈ 0.35-0.55
|
||||
# ==========================================
|
||||
s = 0.0
|
||||
s += _score_above(tap, 0.20, max_points=25) # 额头宽于下巴
|
||||
s += _score_below(cs, 0.55, max_points=25) # 下巴尖
|
||||
s += _score_above(fw, sw * 0.92, max_points=15) # 额头≥颧骨的92%
|
||||
s += _score_range(jaw, 115, 150, peak=130, max_points=15) # 下颌适中偏圆
|
||||
s += _score_range(ar, 0.75, 0.92, peak=0.82, max_points=10) # 长宽比适中
|
||||
s += _score_above(cr, 0.0, max_points=10) # 下巴存在但窄
|
||||
scores['心形脸'] = s
|
||||
|
||||
# ==========================================
|
||||
# 3. 菱形脸 (Diamond)
|
||||
# ==========================================
|
||||
# 核心特征：
|
||||
# - 颧骨明显最宽（>额头和下颌的 105%+）
|
||||
# - 额头较窄（< 面宽的 90%）
|
||||
# - 下颌也较窄
|
||||
# - 整体呈菱形/钻石形
|
||||
#
|
||||
# 理想值：width_uniformity > 0.15
|
||||
# ==========================================
|
||||
s = 0.0
|
||||
s += _score_above(sw, fw * 1.05, max_points=25) # 颧骨>额头5%+
|
||||
s += _score_above(sw, jw * 1.10, max_points=25) # 颧骨>下颌10%+
|
||||
s += _score_below(fr, 0.92, max_points=15) # 额头偏窄
|
||||
s += _score_below(jr, 0.92, max_points=15) # 下颌偏窄
|
||||
s += _score_above(wu, 0.12, max_points=10) # 宽度不均匀
|
||||
s += _score_range(ar, 0.72, 0.90, peak=0.80, max_points=10) # 长宽比适中
|
||||
scores['菱形脸'] = s
|
||||
|
||||
# ==========================================
|
||||
# 4. 鹅蛋脸 (Oval)
|
||||
# ==========================================
|
||||
# 核心特征：
|
||||
# - 长宽比适中（0.72-0.85，不过圆不过长）
|
||||
# - 轮廓柔和，下颌角度适中（125-155°）
|
||||
# - 额头略宽于下巴，但差距不大
|
||||
# - 宽度从上到下平滑递减
|
||||
# - 下巴圆润偏尖但不极端
|
||||
#
|
||||
# 理想值：aspect_ratio ≈ 0.75-0.82
|
||||
# ==========================================
|
||||
s = 0.0
|
||||
s += _score_range(ar, 0.70, 0.85, peak=0.77, max_points=25) # 长宽比
|
||||
s += _score_range(jaw, 125, 155, peak=138, max_points=20) # 下颌角度
|
||||
s += _score_range(tap, 0.03, 0.20, peak=0.10, max_points=15) # 适度收窄
|
||||
s += _score_range(cs, 0.50, 0.75, peak=0.62, max_points=15) # 下巴适中
|
||||
s += _score_below(wu, 0.12, max_points=15) # 宽度比较均匀
|
||||
s += _score_range(fcs, 0.15, 0.25, peak=0.19, max_points=10) # 曲线适中
|
||||
scores['鹅蛋脸'] = s
|
||||
|
||||
# ==========================================
|
||||
# 5. 方形脸 (Square)
|
||||
# ==========================================
|
||||
# 核心特征：
|
||||
# - 长宽比较大（接近等宽，ar > 0.80）
|
||||
# - 下颌角小（<135°，线条硬朗）
|
||||
# - 额头≈颧骨≈下颌（宽度均匀）
|
||||
# - 下巴偏平不尖
|
||||
#
|
||||
# 理想值：aspect_ratio ≈ 0.85-0.95, jaw_angle ≈ 110-125°
|
||||
# ==========================================
|
||||
s = 0.0
|
||||
s += _score_range(ar, 0.78, 0.95, peak=0.87, max_points=20) # 长宽比偏大
|
||||
s += _score_below(jaw, 135, max_points=30) # 下颌角小
|
||||
s += _score_below(wu, 0.10, max_points=20) # 宽度均匀
|
||||
s += _score_above(cs, 0.62, max_points=15) # 下巴偏宽
|
||||
s += _score_range(jr, 0.90, 1.05, peak=0.96, max_points=15) # 下颌宽接近面宽
|
||||
scores['方形脸'] = s
|
||||
|
||||
# ==========================================
|
||||
# 6. 长形脸 (Long/Oblong)
|
||||
# ==========================================
|
||||
# 核心特征：
|
||||
# - 长宽比低（< 0.72，脸明显比宽长很多）
|
||||
# - 面部高度 > 宽度的 1.4 倍
|
||||
# - 额头略宽于下巴
|
||||
# - 整体修长
|
||||
#
|
||||
# 理想值：aspect_ratio ≈ 0.58-0.70
|
||||
# ==========================================
|
||||
s = 0.0
|
||||
s += _score_below(ar, 0.72, max_points=35) # 长宽比低
|
||||
s += _score_above(fh, features['face_width'] * 1.35, max_points=20) # 高>宽*1.35
|
||||
s += _score_range(tap, 0.0, 0.20, peak=0.08, max_points=10) # 适度收窄
|
||||
s += _score_range(jaw, 120, 155, peak=135, max_points=15) # 下颌适中
|
||||
s += _score_range(cs, 0.45, 0.72, peak=0.58, max_points=10) # 下巴适中
|
||||
s += _score_range(wu, 0.02, 0.15, peak=0.08, max_points=10) # 宽度比较均匀
|
||||
scores['长形脸'] = s
|
||||
|
||||
# ==========================================
|
||||
# 7. 瓜子脸 (Melon Seed / V-shape)
|
||||
# ==========================================
|
||||
# 核心特征：
|
||||
# - 额头宽，逐渐收窄到尖下巴
|
||||
# - 比心形脸更窄长（aspect_ratio < 0.82）
|
||||
# - 颧骨不超过额头
|
||||
# - 下巴尖锐（V 线条）
|
||||
# - 整体线条流畅
|
||||
#
|
||||
# 理想值：taper ≈ 0.20-0.35, chin_sharpness ≈ 0.30-0.55
|
||||
# ==========================================
|
||||
s = 0.0
|
||||
s += _score_above(tap, 0.15, max_points=20) # 额头宽于下巴
|
||||
s += _score_below(ar, 0.82, max_points=15) # 偏长
|
||||
s += _score_below(cs, 0.58, max_points=25) # 下巴尖
|
||||
s += _score_below(sw, fw * 1.02, max_points=15) # 颧骨≤额头
|
||||
s += _score_below(jw, fw * 0.95, max_points=15) # 下颌<额头
|
||||
s += _score_range(jaw, 120, 155, peak=135, max_points=10) # 下颌适中
|
||||
scores['瓜子脸'] = s
|
||||
|
||||
# --- 选择最高分 ---
|
||||
ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
|
||||
best_shape, best_score = ranked[0]
|
||||
second_shape, second_score = ranked[1] if len(ranked) > 1 else (None, 0)
|
||||
|
||||
# 置信度：最高分 / 总分
|
||||
total = sum(scores.values())
|
||||
confidence = best_score / total if total > 0 else 0.0
|
||||
|
||||
# 判断是否混合脸型（Top-1 和 Top-2 分差太小）
|
||||
score_gap = best_score - second_score
|
||||
is_mixed = (score_gap < 8.0 and best_score > 30.0)
|
||||
|
||||
details = {
|
||||
'scores': scores,
|
||||
'ranked': ranked,
|
||||
'confidence': confidence,
|
||||
'score_gap': score_gap,
|
||||
'is_mixed': is_mixed,
|
||||
'second_shape': second_shape,
|
||||
'second_score': second_score,
|
||||
} if return_details else None
|
||||
|
||||
return best_shape, confidence, details
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 第四部分：评分辅助函数
|
||||
# ============================================================
|
||||
|
||||
def _score_range(
|
||||
value: float,
|
||||
low: float,
|
||||
high: float,
|
||||
peak: float,
|
||||
max_points: float = 10.0
|
||||
) -> float:
|
||||
"""
|
||||
在 [low, high] 范围内评分，peak 处满分。
|
||||
范围外线性衰减到 0。
|
||||
使用三角窗函数（triangular window）。
|
||||
|
||||
例：_score_range(0.77, 0.70, 0.85, peak=0.77, max_points=25)
|
||||
→ value == peak → 返回 25.0
|
||||
→ value == low → 返回 0.0（边界）
|
||||
→ value 在 peak 和 low 之间 → 线性插值
|
||||
"""
|
||||
if value < low or value > high:
|
||||
return 0.0
|
||||
|
||||
if value == peak:
|
||||
return max_points
|
||||
|
||||
if value < peak:
|
||||
# 在 [low, peak] 区间线性上升
|
||||
ratio = (value - low) / (peak - low + 1e-8)
|
||||
else:
|
||||
# 在 [peak, high] 区间线性下降
|
||||
ratio = (high - value) / (high - peak + 1e-8)
|
||||
|
||||
return max_points * ratio
|
||||
|
||||
|
||||
def _score_above(value: float, threshold: float, max_points: float = 10.0) -> float:
|
||||
"""
|
||||
value >= threshold 时给满分，低于则线性衰减。
|
||||
衰减区间：[threshold * 0.7, threshold]
|
||||
"""
|
||||
if value >= threshold:
|
||||
return max_points
|
||||
floor = threshold * 0.7
|
||||
if value <= floor:
|
||||
return 0.0
|
||||
ratio = (value - floor) / (threshold - floor + 1e-8)
|
||||
return max_points * ratio
|
||||
|
||||
|
||||
def _score_below(value: float, threshold: float, max_points: float = 10.0) -> float:
|
||||
"""
|
||||
value <= threshold 时给满分，高于则线性衰减。
|
||||
衰减区间：[threshold, threshold * 1.3]
|
||||
"""
|
||||
if value <= threshold:
|
||||
return max_points
|
||||
ceil = threshold * 1.3
|
||||
if value >= ceil:
|
||||
return 0.0
|
||||
ratio = (ceil - value) / (ceil - threshold + 1e-8)
|
||||
return max_points * ratio
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 第五部分：完整调用示例
|
||||
# ============================================================
|
||||
|
||||
def classify_from_mediapipe(multi_face_landmarks) -> List[Dict]:
|
||||
"""
|
||||
完整调用示例：从 Mediapipe 结果到脸型分类。
|
||||
|
||||
参数:
|
||||
multi_face_landmarks: mediapipe FaceMesh 的结果
|
||||
result.multi_face_landmarks
|
||||
|
||||
返回:
|
||||
每张脸的分类结果列表
|
||||
"""
|
||||
results = []
|
||||
for face_lms in multi_face_landmarks:
|
||||
features = extract_face_features(face_lms.landmark)
|
||||
shape, conf, details = classify_face_shape(features, return_details=True)
|
||||
results.append({
|
||||
'face_shape': shape,
|
||||
'confidence': conf,
|
||||
'features': features,
|
||||
'details': details,
|
||||
})
|
||||
return results
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 第六部分：混合脸型输出（可选）
|
||||
# ============================================================
|
||||
|
||||
def get_mixed_description(details: Dict) -> str:
|
||||
"""
|
||||
当检测到混合脸型时，生成描述文本。
|
||||
|
||||
例："鹅蛋脸（偏瓜子脸）"
|
||||
"""
|
||||
if not details or not details.get('is_mixed'):
|
||||
return ""
|
||||
|
||||
shape1 = details['ranked'][0][0]
|
||||
shape2 = details['ranked'][1][0]
|
||||
return f"{shape1}（偏{shape2}）"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 三、各脸型详细特征说明
|
||||
|
||||
### 1. 圆形脸 (Round)
|
||||
|
||||
| 特征 | 典型值 | 说明 |
|
||||
|------|--------|------|
|
||||
| aspect_ratio | 0.88-1.0 | 面部宽度和长度几乎相等 |
|
||||
| jaw_angle | 140-160° | 下颌线条圆润 |
|
||||
| width_uniformity | < 0.08 | 额头、颧骨、下颌宽度接近 |
|
||||
| chin_sharpness | 0.65-0.85 | 下巴圆润，不尖锐 |
|
||||
| taper_ratio | -0.05 ~ 0.08 | 额头到下巴几乎不收窄 |
|
||||
|
||||
**视觉特征**：面部轮廓呈圆形，没有明显棱角，看起来年轻可爱。
|
||||
|
||||
### 2. 心形脸 (Heart)
|
||||
|
||||
| 特征 | 典型值 | 说明 |
|
||||
|------|--------|------|
|
||||
| taper_ratio | 0.25-0.40 | 额头明显宽于下巴 |
|
||||
| chin_sharpness | 0.35-0.55 | 下巴尖细 |
|
||||
| forehead_ratio | > 0.92 | 额头宽，接近面宽 |
|
||||
| jaw_angle | 120-145° | 下颌适中 |
|
||||
|
||||
**视觉特征**：上宽下窄，额头饱满，下巴尖俏，像心形。
|
||||
|
||||
### 3. 菱形脸 (Diamond)
|
||||
|
||||
| 特征 | 典型值 | 说明 |
|
||||
|------|--------|------|
|
||||
| 颧骨宽度 | > 额头×1.05 | 颧骨明显最突出 |
|
||||
| 颧骨宽度 | > 下颌×1.10 | 远宽于下颌 |
|
||||
| forehead_ratio | < 0.92 | 额头偏窄 |
|
||||
| jaw_ratio | < 0.92 | 下颌偏窄 |
|
||||
| width_uniformity | > 0.12 | 宽度差异明显 |
|
||||
|
||||
**视觉特征**：颧骨最宽，额头和下巴都偏窄，呈菱形/钻石轮廓。
|
||||
|
||||
### 4. 鹅蛋脸 (Oval)
|
||||
|
||||
| 特征 | 典型值 | 说明 |
|
||||
|------|--------|------|
|
||||
| aspect_ratio | 0.75-0.82 | 长宽比理想 |
|
||||
| jaw_angle | 130-148° | 轮廓柔和 |
|
||||
| taper_ratio | 0.05-0.15 | 适度收窄 |
|
||||
| chin_sharpness | 0.55-0.68 | 下巴圆润偏尖 |
|
||||
| width_uniformity | < 0.10 | 宽度比较均匀 |
|
||||
|
||||
**视觉特征**：被认为是最理想的脸型，比例匀称，轮廓流畅。
|
||||
|
||||
### 5. 方形脸 (Square)
|
||||
|
||||
| 特征 | 典型值 | 说明 |
|
||||
|------|--------|------|
|
||||
| aspect_ratio | 0.85-0.92 | 接近等宽 |
|
||||
| jaw_angle | 108-128° | 下颌角明显，线条硬朗 |
|
||||
| width_uniformity | < 0.08 | 三处宽度接近 |
|
||||
| chin_sharpness | > 0.65 | 下巴偏平宽 |
|
||||
| jaw_ratio | > 0.92 | 下颌宽接近面宽 |
|
||||
|
||||
**视觉特征**：额头、颧骨、下颌宽度接近，下颌角明显，给人干练印象。
|
||||
|
||||
### 6. 长形脸 (Long/Oblong)
|
||||
|
||||
| 特征 | 典型值 | 说明 |
|
||||
|------|--------|------|
|
||||
| aspect_ratio | 0.58-0.70 | 面部明显偏长 |
|
||||
| face_height/face_width | > 1.40 | 高度远超宽度 |
|
||||
| taper_ratio | 0.05-0.15 | 适度收窄 |
|
||||
| jaw_angle | 125-145° | 下颌适中 |
|
||||
|
||||
**视觉特征**：面部修长，整体偏窄，额头较饱满。
|
||||
|
||||
### 7. 瓜子脸 (Melon Seed / V-shape)
|
||||
|
||||
| 特征 | 典型值 | 说明 |
|
||||
|------|--------|------|
|
||||
| taper_ratio | 0.20-0.35 | 额头宽于下巴 |
|
||||
| aspect_ratio | 0.65-0.80 | 偏长 |
|
||||
| chin_sharpness | 0.30-0.55 | V 形尖下巴 |
|
||||
| 颧骨 | ≤ 额头宽度 | 颧骨不突出 |
|
||||
| jaw_width | < 额头×0.95 | 下颌收窄 |
|
||||
|
||||
**视觉特征**：额头较宽，向下逐渐收窄到尖下巴，整体呈瓜子形。
|
||||
|
||||
---
|
||||
|
||||
## 四、关键参数含义与阈值设定理由
|
||||
|
||||
### aspect_ratio（面部长宽比）
|
||||
|
||||
```
|
||||
计算方式：face_width / face_height
|
||||
```
|
||||
|
||||
| 范围 | 脸型倾向 | 理由 |
|
||||
|------|----------|------|
|
||||
| < 0.70 | 长形脸 | 脸长明显大于宽 |
|
||||
| 0.70-0.85 | 鹅蛋/心形/瓜子 | 多数亚洲人的标准比例 |
|
||||
| 0.85-1.0 | 圆形/方形 | 脸宽接近脸长 |
|
||||
|
||||
**设定理由**：根据 Farkas 面部测量数据，东亚人群面宽/面高比通常在 0.75-0.88 之间。0.85 和 0.70 是自然的分界点。
|
||||
|
||||
### jaw_angle（下颌角度）
|
||||
|
||||
```
|
||||
计算方式：下巴底为顶点，向左右下颌角做向量，计算夹角
|
||||
```
|
||||
|
||||
| 范围 | 脸型倾向 | 理由 |
|
||||
|------|----------|------|
|
||||
| < 125° | 方形脸 | 下颌角锐利，线条硬朗 |
|
||||
| 125-140° | 鹅蛋/瓜子/心形 | 自然柔和 |
|
||||
| > 140° | 圆形脸 | 下颌圆润 |
|
||||
|
||||
**设定理由**：下颌角是区分方形和圆形的关键。方形脸 gonion 角通常在 110-125°，圆形脸在 140-155°。
|
||||
|
||||
### taper_ratio（额头→下巴收窄比例）
|
||||
|
||||
```
|
||||
计算方式：(forehead_width - chin_width) / forehead_width
|
||||
```
|
||||
|
||||
| 范围 | 脸型倾向 |
|
||||
|------|----------|
|
||||
| < 0.05 | 圆形/方形（无收窄）|
|
||||
| 0.05-0.15 | 鹅蛋/长形（适度收窄）|
|
||||
| > 0.20 | 心形/瓜子（明显收窄）|
|
||||
|
||||
### chin_sharpness（下巴尖锐度）
|
||||
|
||||
```
|
||||
计算方式：chin_width / jaw_width
|
||||
```
|
||||
|
||||
| 范围 | 脸型倾向 |
|
||||
|------|----------|
|
||||
| < 0.50 | 尖下巴（瓜子/心形）|
|
||||
| 0.50-0.65 | 适中（鹅蛋）|
|
||||
| > 0.65 | 宽下巴（圆形/方形）|
|
||||
|
||||
---
|
||||
|
||||
## 五、优化点说明
|
||||
|
||||
### 5.1 从「硬规则」到「评分制」
|
||||
|
||||
**原始方案**：每条规则是独立的 if 判断，可能同时满足多条，也可能都不满足。
|
||||
|
||||
**优化方案**：每种脸型计算 0-100 的匹配度分数，取最高分。
|
||||
|
||||
```python
|
||||
# 原始：可能冲突
|
||||
if 0.85 <= width_ratio <= 1.0: # 圆形脸
|
||||
...
|
||||
if jaw_angle < 130: # 方形脸
|
||||
...
|
||||
# 同一张脸可能同时满足或都不满足！
|
||||
|
||||
# 优化：评分制，必然有结果
|
||||
scores = {'圆形脸': 72.5, '方形脸': 45.0, ...}
|
||||
# 取最高分 → 圆形脸，置信度 72.5/total
|
||||
```
|
||||
|
||||
### 5.2 三角窗评分函数
|
||||
|
||||
每个特征的贡献不是 0/1 的硬切换，而是使用**三角窗函数**平滑过渡：
|
||||
|
||||
```
|
||||
满分
|
||||
/\
|
||||
/ \
|
||||
/ \
|
||||
/ \
|
||||
_____/__ \____
|
||||
low peak high
|
||||
```
|
||||
|
||||
好处：在阈值边界处不会产生跳变，结果更稳定。
|
||||
|
||||
### 5.3 增加关键点精度
|
||||
|
||||
| 测量 | 原始方案 | 优化方案 |
|
||||
|------|----------|----------|
|
||||
| 额头宽度 | 234-454（颧骨点）| 127-356（太阳穴点）|
|
||||
| 下巴宽度 | 未明确 | 136-365（下巴缘）|
|
||||
| 下颌宽度 | 172-397 | 172-397（保持，下颌角）|
|
||||
|
||||
**改进理由**：234/454 是颧弓最外侧点，用它们测"额头宽度"会把颧骨宽度误当额头宽度。改用 127/356 太阳穴点更准确。
|
||||
|
||||
### 5.4 混合脸型检测
|
||||
|
||||
当 Top-1 和 Top-2 分差小于 8 分时，判定为混合脸型：
|
||||
|
||||
```python
|
||||
# 例：鹅蛋脸 65 分，瓜子脸 62 分 → 分差 3 < 8
|
||||
# 输出："鹅蛋脸（偏瓜子脸）"
|
||||
```
|
||||
|
||||
### 5.5 置信度输出
|
||||
|
||||
```python
|
||||
confidence = best_score / total_score
|
||||
# > 0.25 → 高置信度，结果明确
|
||||
# 0.18-0.25 → 中等，有一定混合
|
||||
# < 0.18 → 低置信度，建议人工复核
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 六、边界情况处理建议
|
||||
|
||||
### 6.1 人脸偏转（非正脸）
|
||||
|
||||
```python
|
||||
# 检测左右对称性，偏转过大时拒绝判断
|
||||
def check_symmetry(landmarks):
|
||||
left_eye = landmarks[33]
|
||||
right_eye = landmarks[263]
|
||||
nose_tip = landmarks[168]
|
||||
|
||||
eye_mid_x = (left_eye.x + right_eye.x) / 2
|
||||
symmetry = abs(eye_mid_x - nose_tip.x)
|
||||
|
||||
if symmetry > 0.03: # 偏移过大
|
||||
return False, "检测到人脸偏转，建议正脸拍摄"
|
||||
return True, ""
|
||||
```
|
||||
|
||||
### 6.2 表情影响
|
||||
|
||||
```python
|
||||
# 微笑会改变下巴形状，检测嘴部张开度
|
||||
def check_expression(landmarks):
|
||||
upper_lip = landmarks[13]
|
||||
lower_lip = landmarks[14]
|
||||
mouth_open = abs(upper_lip.y - lower_lip.y)
|
||||
|
||||
if mouth_open > 0.05: # 嘴巴张大
|
||||
return False, "检测到嘴巴张开，建议自然闭合"
|
||||
return True, ""
|
||||
```
|
||||
|
||||
### 6.3 多特征都低分
|
||||
|
||||
```python
|
||||
if best_score < 25.0:
|
||||
return "无法确定", 0.0, {"reason": "特征不够明显，无法准确分类"}
|
||||
```
|
||||
|
||||
### 6.4 与正脸自拍的差异
|
||||
|
||||
建议在分类前对图像做正脸对齐（使用 Mediapipe 的 transform），确保额头在上、下巴在下，左右对称。
|
||||
|
||||
### 6.5 性别/年龄差异
|
||||
|
||||
男性下颌通常更宽，女性下巴更尖。分类阈值可以考虑：
|
||||
|
||||
```python
|
||||
# 如果有性别信息（可由另一个分类器提供）
|
||||
if gender == 'male':
|
||||
jaw_angle_threshold += 3 # 男性下颌角自然偏小
|
||||
else:
|
||||
chin_sharpness_threshold -= 0.03 # 女性下巴自然偏尖
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 七、实现建议和注意事项
|
||||
|
||||
### 7.1 预处理
|
||||
|
||||
```python
|
||||
import mediapipe as mp
|
||||
|
||||
mp_face_mesh = mp.solutions.face_mesh
|
||||
|
||||
with mp_face_mesh.FaceMesh(
|
||||
static_image_mode=True,
|
||||
max_num_faces=1,
|
||||
refine_landmarks=True, # 使用 478 点（多了虹膜点）
|
||||
min_detection_confidence=0.5,
|
||||
) as face_mesh:
|
||||
results = face_mesh.process(rgb_image)
|
||||
if results.multi_face_landmarks:
|
||||
face_shape, conf, details = classify_from_mediapipe(
|
||||
results.multi_face_landmarks
|
||||
)
|
||||
```
|
||||
|
||||
### 7.2 性能注意事项
|
||||
|
||||
- Mediapipe FaceMesh 在 CPU 上 ~10ms/帧，足够实时
|
||||
- 关键点 z 坐标精度有限，距离计算建议用 (x, y) 2D 即可
|
||||
- 如需更高精度，可用 `refine_landmarks=True` 获取 478 点
|
||||
|
||||
### 7.3 阈值校准
|
||||
|
||||
当前阈值基于以下来源综合设定：
|
||||
|
||||
1. **Farkas 面部测量学数据**（经典人体测量参考）
|
||||
2. **亚洲人脸型分布统计**（鹅蛋脸和瓜子脸比例较高）
|
||||
3. **Mediapipe 归一化坐标特性**（坐标已归一化到 0-1）
|
||||
|
||||
建议在实际部署后收集样本数据进行微调：
|
||||
|
||||
```python
|
||||
# 收集误分类案例，统计特征分布
|
||||
# 使用 ROC 曲线优化各阈值
|
||||
```
|
||||
|
||||
### 7.4 2D vs 3D 距离
|
||||
|
||||
Mediapipe 返回的 landmark 包含 z 坐标，但 z 精度不如 x/y。建议：
|
||||
|
||||
```python
|
||||
# 推荐方案：仅用 x, y 计算（忽略 z）
|
||||
def pt_2d(idx):
|
||||
lm = landmarks[idx]
|
||||
return np.array([lm.x, lm.y])
|
||||
|
||||
# 高精度方案：用 z 但加权降低
|
||||
def pt_weighted(idx):
|
||||
lm = landmarks[idx]
|
||||
return np.array([lm.x, lm.y, lm.z * 0.5]) # z 权重减半
|
||||
```
|
||||
|
||||
### 7.5 与原始规则的对比
|
||||
|
||||
| 维度 | 原始规则 | 优化后 |
|
||||
|------|----------|--------|
|
||||
| 判断方式 | 硬 if-else（可能冲突/遗漏）| 评分制（必然有结果）|
|
||||
| 关键点 | 12 个 | 16 个（增加太阳穴、下巴缘点）|
|
||||
| 特征数 | 5-6 个 | 15 个（含复合特征）|
|
||||
| 输出 | 单一标签 | 标签 + 置信度 + 混合脸型 |
|
||||
| 边界处理 | 无 | 三角窗平滑 + 低分兜底 |
|
||||
| 可调性 | 改阈值需要理解全部分支 | 改 `peak` 值即可微调 |
|
||||
| 代码行数 | ~60 行 | ~300 行（含注释）|
|
||||
|
||||
---
|
||||
|
||||
## 八、测试用例参考
|
||||
|
||||
```python
|
||||
# 单元测试伪代码
|
||||
test_cases = [
|
||||
# (features_dict, expected_shape)
|
||||
({'aspect_ratio': 0.92, 'jaw_angle': 148, 'taper_ratio': 0.03,
|
||||
'chin_sharpness': 0.75, 'width_uniformity': 0.06,
|
||||
'forehead_ratio': 0.98, 'jaw_ratio': 0.95, 'chin_ratio': 0.70,
|
||||
'face_curve_score': 0.18, ...}, '圆形脸'),
|
||||
|
||||
({'aspect_ratio': 0.77, 'jaw_angle': 135, 'taper_ratio': 0.10,
|
||||
'chin_sharpness': 0.60, 'width_uniformity': 0.08,
|
||||
'forehead_ratio': 0.98, 'jaw_ratio': 0.92, 'chin_ratio': 0.62,
|
||||
'face_curve_score': 0.19, ...}, '鹅蛋脸'),
|
||||
|
||||
# ... 更多测试用例
|
||||
]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
*报告结束。代码可直接集成到 Mediapipe 人脸分析流水线中。*
|
||||
@@ -0,0 +1,874 @@
|
||||
"""
|
||||
face_shape_classifier.py
|
||||
基于 Mediapipe 468 点人脸关键点的脸型分类系统
|
||||
|
||||
支持的脸型：圆形脸 / 心形脸 / 菱形脸 / 鹅蛋脸 / 方形脸 / 长形脸 / 瓜子脸
|
||||
分类策略：多维度特征提取 → 加权评分 → 置信度判断
|
||||
|
||||
实现说明：
|
||||
- 特征与评分框架参考 face_shape_classification.md
|
||||
- 距离一律在像素坐标系下用 2D 计算（归一化坐标未校正宽高比会导致面宽被夸大）
|
||||
- 阈值按 MediaPipe 实测分布做了校准
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Tuple, Union
|
||||
|
||||
import cv2
|
||||
import mediapipe as mp
|
||||
import numpy as np
|
||||
|
||||
ImageInput = Union[str, Path, np.ndarray]
|
||||
|
||||
|
||||
class FaceLandmarks:
|
||||
"""Mediapipe 关键点索引（脸型分析用）。"""
|
||||
|
||||
FOREHEAD_TOP = 10
|
||||
CHIN_BOTTOM = 152
|
||||
|
||||
# 额侧（比 127/356 更贴近发际两侧，避免把太阳穴外轮廓算成额头）
|
||||
LEFT_FOREHEAD = 54
|
||||
RIGHT_FOREHEAD = 284
|
||||
|
||||
# 太阳穴辅助
|
||||
LEFT_TEMPLE = 21
|
||||
RIGHT_TEMPLE = 251
|
||||
|
||||
# 颧骨最外侧
|
||||
LEFT_CHEEK = 234
|
||||
RIGHT_CHEEK = 454
|
||||
|
||||
# 下颌角（比 172/397 更接近 gonion）
|
||||
LEFT_JAW_ANGLE = 132
|
||||
RIGHT_JAW_ANGLE = 361
|
||||
|
||||
# 下巴缘
|
||||
LEFT_CHIN = 136
|
||||
RIGHT_CHIN = 365
|
||||
|
||||
|
||||
def extract_face_features(landmarks, image_size: Tuple[int, int]) -> Dict[str, float]:
|
||||
"""
|
||||
从 Mediapipe 关键点提取脸型特征。
|
||||
|
||||
参数:
|
||||
landmarks: NormalizedLandmark 列表
|
||||
image_size: (width, height)，用于还原像素坐标
|
||||
"""
|
||||
w, h = image_size
|
||||
|
||||
def pt(idx: int) -> np.ndarray:
|
||||
lm = landmarks[idx]
|
||||
return np.array([lm.x * w, lm.y * h], dtype=float)
|
||||
|
||||
def dist(p1: np.ndarray, p2: np.ndarray) -> float:
|
||||
return float(np.linalg.norm(p1 - p2))
|
||||
|
||||
def xwidth(p1: np.ndarray, p2: np.ndarray) -> float:
|
||||
"""横向宽度（脸型比例更稳定）。"""
|
||||
return abs(float(p1[0] - p2[0]))
|
||||
|
||||
forehead_top = pt(FaceLandmarks.FOREHEAD_TOP)
|
||||
chin_bottom = pt(FaceLandmarks.CHIN_BOTTOM)
|
||||
|
||||
left_forehead = pt(FaceLandmarks.LEFT_FOREHEAD)
|
||||
right_forehead = pt(FaceLandmarks.RIGHT_FOREHEAD)
|
||||
left_temple = pt(FaceLandmarks.LEFT_TEMPLE)
|
||||
right_temple = pt(FaceLandmarks.RIGHT_TEMPLE)
|
||||
left_cheek = pt(FaceLandmarks.LEFT_CHEEK)
|
||||
right_cheek = pt(FaceLandmarks.RIGHT_CHEEK)
|
||||
left_jaw = pt(FaceLandmarks.LEFT_JAW_ANGLE)
|
||||
right_jaw = pt(FaceLandmarks.RIGHT_JAW_ANGLE)
|
||||
left_chin = pt(FaceLandmarks.LEFT_CHIN)
|
||||
right_chin = pt(FaceLandmarks.RIGHT_CHIN)
|
||||
|
||||
face_height = dist(forehead_top, chin_bottom)
|
||||
forehead_width = xwidth(left_forehead, right_forehead)
|
||||
temple_width = xwidth(left_temple, right_temple)
|
||||
cheekbone_width = xwidth(left_cheek, right_cheek)
|
||||
jaw_width = xwidth(left_jaw, right_jaw)
|
||||
chin_width = xwidth(left_chin, right_chin)
|
||||
face_width = cheekbone_width
|
||||
|
||||
eps = 1e-8
|
||||
|
||||
# 下巴顶点夹角：越大越宽圆，越小越尖
|
||||
v_left = left_jaw - chin_bottom
|
||||
v_right = right_jaw - chin_bottom
|
||||
cos_val = np.dot(v_left, v_right) / (
|
||||
np.linalg.norm(v_left) * np.linalg.norm(v_right) + eps
|
||||
)
|
||||
cos_val = float(np.clip(cos_val, -1.0, 1.0))
|
||||
jaw_angle = math.degrees(math.acos(cos_val))
|
||||
|
||||
# 下颌角（左）：颧骨→下颌角→下巴，越小越方正硬朗
|
||||
v1 = left_cheek - left_jaw
|
||||
v2 = chin_bottom - left_jaw
|
||||
cos_g = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2) + eps)
|
||||
cos_g = float(np.clip(cos_g, -1.0, 1.0))
|
||||
gonion_angle = math.degrees(math.acos(cos_g))
|
||||
|
||||
aspect_ratio = face_width / (face_height + eps)
|
||||
length_ratio = face_height / (face_width + eps)
|
||||
taper_ratio = (forehead_width - chin_width) / (forehead_width + eps)
|
||||
cheek_taper = (cheekbone_width - jaw_width) / (cheekbone_width + eps)
|
||||
|
||||
forehead_ratio = forehead_width / (face_width + eps)
|
||||
temple_ratio = temple_width / (face_width + eps)
|
||||
jaw_ratio = jaw_width / (face_width + eps)
|
||||
chin_ratio = chin_width / (face_width + eps)
|
||||
chin_sharpness = chin_width / (jaw_width + eps)
|
||||
|
||||
widths = [forehead_width, cheekbone_width, jaw_width]
|
||||
width_uniformity = (max(widths) - min(widths)) / (max(widths) + eps)
|
||||
|
||||
jaw_midpoint = (left_jaw + right_jaw) / 2.0
|
||||
face_curve_score = dist(jaw_midpoint, chin_bottom) / (face_height + eps)
|
||||
|
||||
forehead_vs_jaw = forehead_width / (jaw_width + eps)
|
||||
cheek_dominance = cheekbone_width / ((forehead_width + jaw_width) / 2.0 + eps)
|
||||
|
||||
return {
|
||||
"face_height": face_height,
|
||||
"face_width": face_width,
|
||||
"forehead_width": forehead_width,
|
||||
"temple_width": temple_width,
|
||||
"cheekbone_width": cheekbone_width,
|
||||
"jaw_width": jaw_width,
|
||||
"chin_width": chin_width,
|
||||
"aspect_ratio": aspect_ratio,
|
||||
"length_ratio": length_ratio,
|
||||
"taper_ratio": taper_ratio,
|
||||
"cheek_taper": cheek_taper,
|
||||
"forehead_ratio": forehead_ratio,
|
||||
"temple_ratio": temple_ratio,
|
||||
"cheekbone_ratio": 1.0,
|
||||
"jaw_ratio": jaw_ratio,
|
||||
"chin_ratio": chin_ratio,
|
||||
"jaw_angle": jaw_angle,
|
||||
"gonion_angle": gonion_angle,
|
||||
"chin_sharpness": chin_sharpness,
|
||||
"width_uniformity": width_uniformity,
|
||||
"face_curve_score": face_curve_score,
|
||||
"forehead_vs_jaw": forehead_vs_jaw,
|
||||
"cheek_dominance": cheek_dominance,
|
||||
}
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 参考分布：1143 张样本（1093 张脸型测试集合 + 44 张真实照片 + 6 张标注图）
|
||||
# 的稳健统计量 (中位数, 稳健标准差=IQR/1.349)，把绝对测量值转成 z 分数。
|
||||
# 绝对阈值会随镜头、人群漂移；z 分数让评分只依赖"相对人群偏离多少"。
|
||||
#
|
||||
# 早前这组数字只由 50 张样本估得，相对全量人群有系统性偏移
|
||||
# （jaw_ratio 中位偏低 0.44 sd、taper_ratio 偏高 0.53 sd 等），
|
||||
# 恰好三项都在给方形脸加分，是方形脸占比虚高的主因之一。
|
||||
# ============================================================
|
||||
REFERENCE_STATS: Dict[str, Tuple[float, float]] = {
|
||||
"aspect_ratio": (0.8294, 0.0281),
|
||||
"jaw_angle": (88.6299, 3.9040),
|
||||
"gonion_angle": (144.5014, 3.4991),
|
||||
"taper_ratio": (0.2671, 0.0376),
|
||||
"forehead_ratio": (0.8975, 0.0204),
|
||||
"jaw_ratio": (0.9286, 0.0138),
|
||||
"chin_ratio": (0.6578, 0.0217),
|
||||
"chin_sharpness": (0.7092, 0.0155),
|
||||
"width_uniformity": (0.1028, 0.0184),
|
||||
"forehead_vs_jaw": (0.9669, 0.0341),
|
||||
"cheek_dominance": (1.0953, 0.0084),
|
||||
"face_curve_score": (0.3940, 0.0210),
|
||||
}
|
||||
|
||||
# 匹配容差（以 z 为单位）：偏离目标 1 个容差，该项得分降到约 0.61
|
||||
MATCH_TOLERANCE = 1.0
|
||||
|
||||
# 每种脸型的原型：特征 -> (目标 z, 权重, 模式)
|
||||
# 'high' 超过目标即满分（越极端越像）
|
||||
# 'low' 低于目标即满分
|
||||
# 'peak' 双侧衰减（该特征应当落在目标附近）
|
||||
#
|
||||
# 只使用互相独立的特征：length_ratio(=1/aspect_ratio) 与
|
||||
# cheek_taper(=1-jaw_ratio) 是重复信号，纳入会让对应脸型拿双倍权重。
|
||||
#
|
||||
# 靶心与权重的来源：先按 1093 张测试集中各原始分组（方形脸/长形脸/瓜子脸/
|
||||
# 标准脸/娃娃脸）的实测 z 画像给出靶心，再在「6 张标注图判定不变」的硬约束下
|
||||
# 做带边界的退火微调（权重限 [0.5,5]、靶心限 [-2,2]，并惩罚失去区分力的空项）。
|
||||
SHAPE_PROTOTYPES: Dict[str, Dict[str, Tuple[float, float, str]]] = {
|
||||
# 宽、短，下颌圆钝
|
||||
"圆形脸": {
|
||||
"aspect_ratio": (+2.00, 5.00, "high"),
|
||||
"jaw_angle": (-0.07, 2.28, "high"),
|
||||
"chin_sharpness": (+0.15, 0.50, "peak"),
|
||||
"face_curve_score": (+0.17, 3.67, "low"),
|
||||
},
|
||||
# 额头宽、下颌与下巴明显收窄
|
||||
"心形脸": {
|
||||
"forehead_vs_jaw": (+1.94, 1.62, "high"),
|
||||
"taper_ratio": (+2.00, 1.09, "high"),
|
||||
"jaw_ratio": (+1.02, 1.86, "low"),
|
||||
"chin_ratio": (-1.70, 1.71, "low"),
|
||||
"aspect_ratio": (+1.69, 1.31, "peak"),
|
||||
},
|
||||
# 颧骨最突出，额头与下颌都窄
|
||||
"菱形脸": {
|
||||
"cheek_dominance": (+1.62, 2.84, "high"),
|
||||
"width_uniformity": (+1.24, 2.12, "high"),
|
||||
"forehead_ratio": (-1.57, 4.58, "low"),
|
||||
"jaw_ratio": (-0.11, 1.85, "low"),
|
||||
"aspect_ratio": (+1.31, 2.60, "peak"),
|
||||
},
|
||||
# 各项都接近人群中位——没有突出特征即为匀称
|
||||
"鹅蛋脸": {
|
||||
"aspect_ratio": (-0.26, 5.00, "peak"),
|
||||
"jaw_ratio": (+0.45, 2.47, "peak"),
|
||||
"chin_sharpness": (+0.52, 2.88, "peak"),
|
||||
"width_uniformity": (+0.53, 0.94, "peak"),
|
||||
"cheek_dominance": (-0.51, 1.14, "peak"),
|
||||
},
|
||||
# 下颌与下巴都宽、几乎不收窄、下颌角锐利、额头相对窄。
|
||||
# 注意 aspect_ratio 用 peak 而非 high：测试集中 121 张方脸的
|
||||
# aspect_ratio 中位仅 +0.16，真正"宽"的是娃娃脸(+1.01)——
|
||||
# 早前把它当成 high 模式的强特征，是方形脸吞掉圆脸的主因。
|
||||
#
|
||||
# chin_ratio 是方脸组区分度最大的一项（组内中位 z=+1.45，标准脸组仅 -0.06），
|
||||
# 故靶心直接对齐 +1.45。靶心与权重必须同时提：若只加权重而把靶心留在低位，
|
||||
# 全人群八成都能拿满分，等于给所有人同加一笔，反而推高方形脸占比。
|
||||
"方形脸": {
|
||||
"aspect_ratio": (+1.11, 4.75, "peak"),
|
||||
"jaw_ratio": (-0.02, 2.27, "high"),
|
||||
"chin_ratio": (+1.45, 3.00, "high"),
|
||||
"taper_ratio": (-0.38, 0.51, "low"),
|
||||
"chin_sharpness": (-0.38, 0.99, "high"),
|
||||
"width_uniformity": (+0.58, 4.34, "high"),
|
||||
"gonion_angle": (+0.10, 3.38, "low"),
|
||||
"forehead_ratio": (-1.70, 4.05, "low"),
|
||||
},
|
||||
# 明显偏长偏窄
|
||||
"长形脸": {
|
||||
"aspect_ratio": (-1.49, 1.17, "low"),
|
||||
"chin_sharpness": (+1.76, 0.50, "high"),
|
||||
"taper_ratio": (+0.84, 0.50, "low"),
|
||||
},
|
||||
# 似心形但下巴更长更尖（face_curve_score 高），颧骨不外扩
|
||||
"瓜子脸": {
|
||||
"face_curve_score": (+1.02, 3.41, "high"),
|
||||
"forehead_ratio": (+0.85, 3.31, "high"),
|
||||
"taper_ratio": (+0.35, 1.02, "high"),
|
||||
"cheek_dominance": (-1.41, 2.21, "low"),
|
||||
"jaw_ratio": (-1.04, 0.57, "low"),
|
||||
"chin_sharpness": (-1.33, 2.53, "low"),
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def feature_zscores(features: Dict[str, float]) -> Dict[str, float]:
|
||||
"""把测量值转成相对参考人群的 z 分数。"""
|
||||
return {
|
||||
key: (features[key] - median) / scale
|
||||
for key, (median, scale) in REFERENCE_STATS.items()
|
||||
if key in features
|
||||
}
|
||||
|
||||
|
||||
def _match(z: float, target: float, mode: str) -> float:
|
||||
"""单项匹配度 0~1。"""
|
||||
if mode == "high" and z >= target:
|
||||
return 1.0
|
||||
if mode == "low" and z <= target:
|
||||
return 1.0
|
||||
return math.exp(-((z - target) ** 2) / (2 * MATCH_TOLERANCE**2))
|
||||
|
||||
|
||||
def classify_face_shape(
|
||||
features: Dict[str, float],
|
||||
return_details: bool = False,
|
||||
) -> Tuple[str, float, Optional[Dict]]:
|
||||
"""
|
||||
脸型分类器：把特征转成 z 分数后，与各脸型原型做加权匹配。
|
||||
|
||||
返回 (脸型, 置信度, 详情)。置信度 = Top1 / (Top1 + Top2)，
|
||||
0.5 表示两种脸型完全无法区分，接近 1 表示判定明确。
|
||||
"""
|
||||
z = feature_zscores(features)
|
||||
|
||||
scores: Dict[str, float] = {}
|
||||
contributions: Dict[str, Dict[str, float]] = {}
|
||||
for shape, prototype in SHAPE_PROTOTYPES.items():
|
||||
total_weight = sum(w for _, w, _ in prototype.values())
|
||||
acc = 0.0
|
||||
per_feature = {}
|
||||
for key, (target, weight, mode) in prototype.items():
|
||||
m = _match(z[key], target, mode)
|
||||
per_feature[key] = m
|
||||
acc += weight * m
|
||||
scores[shape] = 100.0 * acc / total_weight
|
||||
contributions[shape] = per_feature
|
||||
|
||||
ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
|
||||
best_shape, best_score = ranked[0]
|
||||
second_shape, second_score = ranked[1] if len(ranked) > 1 else (None, 0.0)
|
||||
|
||||
denom = best_score + second_score
|
||||
confidence = best_score / denom if denom > 0 else 0.0
|
||||
score_gap = best_score - second_score
|
||||
is_mixed = score_gap < 5.0
|
||||
|
||||
details = None
|
||||
if return_details:
|
||||
details = {
|
||||
"scores": scores,
|
||||
"ranked": ranked,
|
||||
"confidence": confidence,
|
||||
"score_gap": score_gap,
|
||||
"is_mixed": is_mixed,
|
||||
"second_shape": second_shape,
|
||||
"second_score": second_score,
|
||||
"zscores": z,
|
||||
"contributions": contributions,
|
||||
}
|
||||
|
||||
return best_shape, confidence, details
|
||||
|
||||
|
||||
def get_mixed_description(details: Dict) -> str:
|
||||
if not details or not details.get("is_mixed"):
|
||||
return ""
|
||||
shape1 = details["ranked"][0][0]
|
||||
shape2 = details["ranked"][1][0]
|
||||
return f"{shape1}（偏{shape2}）"
|
||||
|
||||
|
||||
_face_mesh = None
|
||||
|
||||
|
||||
def _get_face_mesh():
|
||||
global _face_mesh
|
||||
if _face_mesh is None:
|
||||
_face_mesh = mp.solutions.face_mesh.FaceMesh(
|
||||
static_image_mode=True,
|
||||
max_num_faces=1,
|
||||
refine_landmarks=True,
|
||||
min_detection_confidence=0.5,
|
||||
)
|
||||
return _face_mesh
|
||||
|
||||
|
||||
def _load_image(image: ImageInput) -> np.ndarray:
|
||||
if isinstance(image, np.ndarray):
|
||||
if image.ndim != 3 or image.shape[2] not in (3, 4):
|
||||
raise ValueError("numpy 图片需为 HxWx3/4 的彩色图")
|
||||
if image.shape[2] == 4:
|
||||
return cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
|
||||
return image
|
||||
|
||||
path = Path(image)
|
||||
img = cv2.imread(str(path))
|
||||
if img is None:
|
||||
raise FileNotFoundError(f"无法读取图片: {path}")
|
||||
return img
|
||||
|
||||
|
||||
def _landmark_points(landmarks, image_size: Tuple[int, int]) -> Dict[str, Tuple[int, int]]:
|
||||
"""提取标注用像素点。"""
|
||||
w, h = image_size
|
||||
|
||||
def xy(idx: int) -> Tuple[int, int]:
|
||||
lm = landmarks[idx]
|
||||
return int(round(lm.x * w)), int(round(lm.y * h))
|
||||
|
||||
left_jaw = xy(FaceLandmarks.LEFT_JAW_ANGLE)
|
||||
right_jaw = xy(FaceLandmarks.RIGHT_JAW_ANGLE)
|
||||
return {
|
||||
"forehead_top": xy(FaceLandmarks.FOREHEAD_TOP),
|
||||
"chin_bottom": xy(FaceLandmarks.CHIN_BOTTOM),
|
||||
"left_forehead": xy(FaceLandmarks.LEFT_FOREHEAD),
|
||||
"right_forehead": xy(FaceLandmarks.RIGHT_FOREHEAD),
|
||||
"left_cheek": xy(FaceLandmarks.LEFT_CHEEK),
|
||||
"right_cheek": xy(FaceLandmarks.RIGHT_CHEEK),
|
||||
"left_jaw": left_jaw,
|
||||
"right_jaw": right_jaw,
|
||||
"left_chin": xy(FaceLandmarks.LEFT_CHIN),
|
||||
"right_chin": xy(FaceLandmarks.RIGHT_CHIN),
|
||||
"jaw_mid": (
|
||||
int(round((left_jaw[0] + right_jaw[0]) / 2)),
|
||||
int(round((left_jaw[1] + right_jaw[1]) / 2)),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
def _put_text_cn(
|
||||
img: np.ndarray,
|
||||
text: str,
|
||||
org: Tuple[int, int],
|
||||
color: Tuple[int, int, int],
|
||||
font_size: int = 18,
|
||||
) -> None:
|
||||
"""在图上绘制中文/英文混合文字（Pillow）。"""
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
|
||||
rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
||||
pil = Image.fromarray(rgb)
|
||||
draw = ImageDraw.Draw(pil)
|
||||
|
||||
font_paths = [
|
||||
"/usr/share/fonts/truetype/wqy/wqy-microhei.ttc",
|
||||
"/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc",
|
||||
"/usr/share/fonts/truetype/noto/NotoSansCJK-Regular.ttc",
|
||||
"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
|
||||
]
|
||||
font = None
|
||||
for fp in font_paths:
|
||||
if Path(fp).exists():
|
||||
try:
|
||||
font = ImageFont.truetype(fp, font_size)
|
||||
break
|
||||
except OSError:
|
||||
continue
|
||||
if font is None:
|
||||
font = ImageFont.load_default()
|
||||
|
||||
x, y = org
|
||||
# 阴影提升可读性
|
||||
draw.text((x + 1, y + 1), text, font=font, fill=(0, 0, 0))
|
||||
draw.text((x, y), text, font=font, fill=(color[2], color[1], color[0]))
|
||||
img[:] = cv2.cvtColor(np.array(pil), cv2.COLOR_RGB2BGR)
|
||||
|
||||
|
||||
def _draw_h_line(
|
||||
img: np.ndarray,
|
||||
p1: Tuple[int, int],
|
||||
p2: Tuple[int, int],
|
||||
color: Tuple[int, int, int],
|
||||
label: str,
|
||||
thickness: int = 2,
|
||||
label_above: bool = True,
|
||||
) -> None:
|
||||
"""画横向宽度线 + 端点 + 标签。"""
|
||||
y = int(round((p1[1] + p2[1]) / 2))
|
||||
x1, x2 = min(p1[0], p2[0]), max(p1[0], p2[0])
|
||||
cv2.line(img, (x1, y), (x2, y), color, thickness, cv2.LINE_AA)
|
||||
cv2.circle(img, (x1, y), 4, color, -1, cv2.LINE_AA)
|
||||
cv2.circle(img, (x2, y), 4, color, -1, cv2.LINE_AA)
|
||||
# 端点小竖线
|
||||
tick = max(6, thickness * 3)
|
||||
cv2.line(img, (x1, y - tick), (x1, y + tick), color, thickness, cv2.LINE_AA)
|
||||
cv2.line(img, (x2, y - tick), (x2, y + tick), color, thickness, cv2.LINE_AA)
|
||||
mid = ((x1 + x2) // 2, y - 8 if label_above else y + 4)
|
||||
_put_text_cn(img, label, mid, color, font_size=max(14, img.shape[0] // 55))
|
||||
|
||||
|
||||
def _draw_v_line(
|
||||
img: np.ndarray,
|
||||
p1: Tuple[int, int],
|
||||
p2: Tuple[int, int],
|
||||
color: Tuple[int, int, int],
|
||||
label: str,
|
||||
thickness: int = 2,
|
||||
) -> None:
|
||||
"""画纵向高度线 + 端点 + 标签。"""
|
||||
x = int(round((p1[0] + p2[0]) / 2))
|
||||
y1, y2 = min(p1[1], p2[1]), max(p1[1], p2[1])
|
||||
cv2.line(img, (x, y1), (x, y2), color, thickness, cv2.LINE_AA)
|
||||
cv2.circle(img, (x, y1), 4, color, -1, cv2.LINE_AA)
|
||||
cv2.circle(img, (x, y2), 4, color, -1, cv2.LINE_AA)
|
||||
tick = max(6, thickness * 3)
|
||||
cv2.line(img, (x - tick, y1), (x + tick, y1), color, thickness, cv2.LINE_AA)
|
||||
cv2.line(img, (x - tick, y2), (x + tick, y2), color, thickness, cv2.LINE_AA)
|
||||
_put_text_cn(
|
||||
img,
|
||||
label,
|
||||
(x + 8, (y1 + y2) // 2),
|
||||
color,
|
||||
font_size=max(14, img.shape[0] // 55),
|
||||
)
|
||||
|
||||
|
||||
def annotate_face_features(
|
||||
image: ImageInput,
|
||||
landmarks=None,
|
||||
features: Optional[Dict[str, float]] = None,
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
在原图上标注 face_width / face_height 及文档中的关键比例特征。
|
||||
|
||||
返回 BGR 标注图。
|
||||
"""
|
||||
bgr = _load_image(image).copy()
|
||||
h, w = bgr.shape[:2]
|
||||
|
||||
if landmarks is None:
|
||||
rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
|
||||
results = _get_face_mesh().process(rgb)
|
||||
if not results.multi_face_landmarks:
|
||||
raise ValueError("未检测到人脸关键点")
|
||||
landmarks = results.multi_face_landmarks[0].landmark
|
||||
|
||||
if features is None:
|
||||
features = extract_face_features(landmarks, image_size=(w, h))
|
||||
|
||||
pts = _landmark_points(landmarks, (w, h))
|
||||
overlay = bgr.copy()
|
||||
fs = max(14, h // 55)
|
||||
thick = max(2, h // 400)
|
||||
|
||||
# ---- 尺寸主轴 ----
|
||||
# face_height: 额头顶 → 下巴底
|
||||
_draw_v_line(
|
||||
overlay,
|
||||
pts["forehead_top"],
|
||||
pts["chin_bottom"],
|
||||
(40, 180, 255),
|
||||
f"face_height {features['face_height']:.0f}px",
|
||||
thickness=thick + 1,
|
||||
)
|
||||
# face_width (= cheekbone): 左颧 → 右颧
|
||||
_draw_h_line(
|
||||
overlay,
|
||||
pts["left_cheek"],
|
||||
pts["right_cheek"],
|
||||
(0, 220, 120),
|
||||
f"face_width {features['face_width']:.0f}px",
|
||||
thickness=thick + 1,
|
||||
label_above=True,
|
||||
)
|
||||
|
||||
# ---- 各级宽度（forehead / jaw / chin）----
|
||||
# 略微错开 y，避免完全重叠
|
||||
fh_y = pts["left_forehead"][1]
|
||||
_draw_h_line(
|
||||
overlay,
|
||||
(pts["left_forehead"][0], fh_y),
|
||||
(pts["right_forehead"][0], fh_y),
|
||||
(255, 160, 40),
|
||||
f"forehead ratio={features['forehead_ratio']:.3f}",
|
||||
thickness=thick,
|
||||
label_above=True,
|
||||
)
|
||||
jy = pts["left_jaw"][1]
|
||||
_draw_h_line(
|
||||
overlay,
|
||||
(pts["left_jaw"][0], jy),
|
||||
(pts["right_jaw"][0], jy),
|
||||
(80, 120, 255),
|
||||
f"jaw ratio={features['jaw_ratio']:.3f}",
|
||||
thickness=thick,
|
||||
label_above=False,
|
||||
)
|
||||
cy = pts["left_chin"][1]
|
||||
_draw_h_line(
|
||||
overlay,
|
||||
(pts["left_chin"][0], cy),
|
||||
(pts["right_chin"][0], cy),
|
||||
(220, 80, 220),
|
||||
f"chin ratio={features['chin_ratio']:.3f}",
|
||||
thickness=thick,
|
||||
label_above=False,
|
||||
)
|
||||
|
||||
# cheekbone_ratio（相对 face_width，恒为 1.0）写在颧骨线旁
|
||||
cheek_mid = (
|
||||
(pts["left_cheek"][0] + pts["right_cheek"][0]) // 2,
|
||||
pts["left_cheek"][1] + max(18, h // 40),
|
||||
)
|
||||
_put_text_cn(
|
||||
overlay,
|
||||
f"cheekbone_ratio={features['cheekbone_ratio']:.3f}",
|
||||
cheek_mid,
|
||||
(0, 200, 100),
|
||||
font_size=fs,
|
||||
)
|
||||
|
||||
# ---- jaw_angle：下巴 → 左右下颌角 ----
|
||||
cv2.line(overlay, pts["chin_bottom"], pts["left_jaw"], (0, 90, 255), thick, cv2.LINE_AA)
|
||||
cv2.line(overlay, pts["chin_bottom"], pts["right_jaw"], (0, 90, 255), thick, cv2.LINE_AA)
|
||||
cv2.circle(overlay, pts["chin_bottom"], 5, (0, 90, 255), -1, cv2.LINE_AA)
|
||||
# 角度弧
|
||||
v1 = np.array(pts["left_jaw"], dtype=float) - np.array(pts["chin_bottom"], dtype=float)
|
||||
v2 = np.array(pts["right_jaw"], dtype=float) - np.array(pts["chin_bottom"], dtype=float)
|
||||
a1 = math.degrees(math.atan2(-v1[1], v1[0]))
|
||||
a2 = math.degrees(math.atan2(-v2[1], v2[0]))
|
||||
# OpenCV ellipse 角度：从 x 轴顺时针；atan2 转一下
|
||||
start_ang = -a1
|
||||
end_ang = -a2
|
||||
if end_ang < start_ang:
|
||||
start_ang, end_ang = end_ang, start_ang
|
||||
radius = max(28, int(0.08 * features["face_height"]))
|
||||
cv2.ellipse(
|
||||
overlay,
|
||||
pts["chin_bottom"],
|
||||
(radius, radius),
|
||||
0,
|
||||
start_ang,
|
||||
end_ang,
|
||||
(0, 90, 255),
|
||||
thick,
|
||||
cv2.LINE_AA,
|
||||
)
|
||||
_put_text_cn(
|
||||
overlay,
|
||||
f"jaw_angle {features['jaw_angle']:.1f}°",
|
||||
(pts["chin_bottom"][0] + radius + 4, pts["chin_bottom"][1] - radius),
|
||||
(0, 90, 255),
|
||||
font_size=fs,
|
||||
)
|
||||
|
||||
# ---- taper_ratio：额头两端 → 下巴两端（收窄示意）----
|
||||
cv2.line(
|
||||
overlay,
|
||||
pts["left_forehead"],
|
||||
pts["left_chin"],
|
||||
(40, 200, 255),
|
||||
max(1, thick - 1),
|
||||
cv2.LINE_AA,
|
||||
)
|
||||
cv2.line(
|
||||
overlay,
|
||||
pts["right_forehead"],
|
||||
pts["right_chin"],
|
||||
(40, 200, 255),
|
||||
max(1, thick - 1),
|
||||
cv2.LINE_AA,
|
||||
)
|
||||
taper_anchor = (
|
||||
pts["left_forehead"][0] - max(10, w // 30),
|
||||
(pts["left_forehead"][1] + pts["left_chin"][1]) // 2,
|
||||
)
|
||||
_put_text_cn(
|
||||
overlay,
|
||||
f"taper_ratio={features['taper_ratio']:.3f}",
|
||||
taper_anchor,
|
||||
(40, 200, 255),
|
||||
font_size=fs,
|
||||
)
|
||||
|
||||
# ---- chin_sharpness：下巴宽 vs 下颌宽 ----
|
||||
_put_text_cn(
|
||||
overlay,
|
||||
f"chin_sharpness={features['chin_sharpness']:.3f} (chin/jaw)",
|
||||
(pts["left_chin"][0], pts["left_chin"][1] + max(16, h // 45)),
|
||||
(220, 80, 220),
|
||||
font_size=fs,
|
||||
)
|
||||
|
||||
# ---- width_uniformity：三宽差异 ----
|
||||
widths = [
|
||||
("F", features["forehead_width"], (255, 160, 40)),
|
||||
("C", features["cheekbone_width"], (0, 220, 120)),
|
||||
("J", features["jaw_width"], (80, 120, 255)),
|
||||
]
|
||||
# 右侧小柱状示意
|
||||
panel_x = min(w - max(90, w // 8), max(pts["right_cheek"][0] + 20, w - max(100, w // 7)))
|
||||
panel_y = max(40, pts["forehead_top"][1])
|
||||
max_w = max(x[1] for x in widths) + 1e-8
|
||||
bar_h = max(10, h // 60)
|
||||
gap = max(4, h // 120)
|
||||
for i, (name, val, color) in enumerate(widths):
|
||||
bw = int((val / max_w) * max(50, w // 10))
|
||||
y0 = panel_y + i * (bar_h + gap)
|
||||
cv2.rectangle(overlay, (panel_x, y0), (panel_x + bw, y0 + bar_h), color, -1, cv2.LINE_AA)
|
||||
_put_text_cn(overlay, name, (panel_x + bw + 4, y0 - 2), color, font_size=max(12, fs - 2))
|
||||
_put_text_cn(
|
||||
overlay,
|
||||
f"width_uniformity={features['width_uniformity']:.3f}",
|
||||
(panel_x, panel_y + 3 * (bar_h + gap) + 2),
|
||||
(230, 230, 230),
|
||||
font_size=fs,
|
||||
)
|
||||
|
||||
# ---- face_curve_score：下颌中点 → 下巴 ----
|
||||
cv2.line(
|
||||
overlay,
|
||||
pts["jaw_mid"],
|
||||
pts["chin_bottom"],
|
||||
(180, 255, 80),
|
||||
thick,
|
||||
cv2.LINE_AA,
|
||||
)
|
||||
cv2.circle(overlay, pts["jaw_mid"], 4, (180, 255, 80), -1, cv2.LINE_AA)
|
||||
curve_label_pos = (
|
||||
pts["jaw_mid"][0] + 6,
|
||||
pts["jaw_mid"][1] - max(8, h // 80),
|
||||
)
|
||||
_put_text_cn(
|
||||
overlay,
|
||||
f"face_curve_score={features['face_curve_score']:.3f}",
|
||||
curve_label_pos,
|
||||
(180, 255, 80),
|
||||
font_size=fs,
|
||||
)
|
||||
|
||||
# 半透明叠回原图，再叠一层实线标注更清晰：直接用 overlay
|
||||
# 左侧参数图例
|
||||
legend = [
|
||||
("face_width / face_height", (0, 220, 120)),
|
||||
(f"jaw_angle={features['jaw_angle']:.1f}°", (0, 90, 255)),
|
||||
(f"taper_ratio={features['taper_ratio']:.3f}", (40, 200, 255)),
|
||||
(f"forehead_ratio={features['forehead_ratio']:.3f}", (255, 160, 40)),
|
||||
(f"cheekbone_ratio={features['cheekbone_ratio']:.3f}", (0, 200, 100)),
|
||||
(f"jaw_ratio={features['jaw_ratio']:.3f}", (80, 120, 255)),
|
||||
(f"chin_ratio={features['chin_ratio']:.3f}", (220, 80, 220)),
|
||||
(f"chin_sharpness={features['chin_sharpness']:.3f}", (220, 80, 220)),
|
||||
(f"width_uniformity={features['width_uniformity']:.3f}", (230, 230, 230)),
|
||||
(f"face_curve_score={features['face_curve_score']:.3f}", (180, 255, 80)),
|
||||
]
|
||||
box_h = 12 + len(legend) * (fs + 6)
|
||||
box_w = max(220, w // 3)
|
||||
cv2.rectangle(overlay, (8, 8), (8 + box_w, 8 + box_h), (20, 20, 20), -1)
|
||||
cv2.rectangle(overlay, (8, 8), (8 + box_w, 8 + box_h), (90, 90, 90), 1)
|
||||
for i, (text, color) in enumerate(legend):
|
||||
_put_text_cn(overlay, text, (16, 14 + i * (fs + 6)), color, font_size=fs)
|
||||
|
||||
return overlay
|
||||
|
||||
|
||||
def classify_from_image(
|
||||
image: ImageInput,
|
||||
return_details: bool = True,
|
||||
return_annotated: bool = False,
|
||||
) -> Dict:
|
||||
"""
|
||||
从图片判断脸型。
|
||||
|
||||
参数:
|
||||
image: 图片路径，或 OpenCV BGR numpy 数组
|
||||
return_details: 是否返回特征与各脸型得分
|
||||
return_annotated: 是否同时返回特征标注图（BGR）
|
||||
"""
|
||||
bgr = _load_image(image)
|
||||
h, w = bgr.shape[:2]
|
||||
rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
|
||||
results = _get_face_mesh().process(rgb)
|
||||
|
||||
if not results.multi_face_landmarks:
|
||||
raise ValueError("未检测到人脸关键点")
|
||||
|
||||
landmarks = results.multi_face_landmarks[0].landmark
|
||||
features = extract_face_features(landmarks, image_size=(w, h))
|
||||
shape, conf, details = classify_face_shape(features, return_details=True)
|
||||
|
||||
display = get_mixed_description(details) or shape
|
||||
result = {
|
||||
"face_shape": shape,
|
||||
"confidence": conf,
|
||||
"display": display,
|
||||
}
|
||||
if return_details:
|
||||
result["features"] = features
|
||||
result["details"] = details
|
||||
if return_annotated:
|
||||
result["annotated"] = annotate_face_features(
|
||||
bgr, landmarks=landmarks, features=features
|
||||
)
|
||||
return result
|
||||
|
||||
|
||||
def classify_from_mediapipe(
|
||||
multi_face_landmarks,
|
||||
image_size: Tuple[int, int],
|
||||
) -> List[Dict]:
|
||||
"""从 Mediapipe FaceMesh 结果批量分类。image_size=(width, height)。"""
|
||||
results = []
|
||||
for face_lms in multi_face_landmarks:
|
||||
features = extract_face_features(face_lms.landmark, image_size=image_size)
|
||||
shape, conf, details = classify_face_shape(features, return_details=True)
|
||||
results.append(
|
||||
{
|
||||
"face_shape": shape,
|
||||
"confidence": conf,
|
||||
"display": get_mixed_description(details) or shape,
|
||||
"features": features,
|
||||
"details": details,
|
||||
}
|
||||
)
|
||||
return results
|
||||
|
||||
|
||||
def run_test_images(test_dir: Union[str, Path, None] = None) -> List[Dict]:
|
||||
"""用 test_img 做回归测试；文件名（不含扩展名）为期望脸型。"""
|
||||
if test_dir is None:
|
||||
test_dir = Path(__file__).resolve().parent / "test_img"
|
||||
test_dir = Path(test_dir)
|
||||
|
||||
image_paths = sorted(
|
||||
p
|
||||
for p in test_dir.iterdir()
|
||||
if p.suffix.lower() in {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
|
||||
)
|
||||
if not image_paths:
|
||||
raise FileNotFoundError(f"测试目录无图片: {test_dir}")
|
||||
|
||||
rows = []
|
||||
for path in image_paths:
|
||||
expected = path.stem
|
||||
try:
|
||||
result = classify_from_image(path, return_details=True)
|
||||
predicted = result["face_shape"]
|
||||
display = result["display"]
|
||||
conf = result["confidence"]
|
||||
top3 = result["details"]["ranked"][:3]
|
||||
ok = predicted == expected
|
||||
error = None
|
||||
except Exception as exc: # noqa: BLE001
|
||||
predicted = display = conf = None
|
||||
top3 = []
|
||||
ok = False
|
||||
error = str(exc)
|
||||
|
||||
rows.append(
|
||||
{
|
||||
"file": path.name,
|
||||
"expected": expected,
|
||||
"predicted": predicted,
|
||||
"display": display,
|
||||
"confidence": conf,
|
||||
"top3": top3,
|
||||
"correct": ok,
|
||||
"error": error,
|
||||
}
|
||||
)
|
||||
return rows
|
||||
|
||||
|
||||
def _print_test_report(rows: List[Dict]) -> None:
|
||||
correct = sum(1 for r in rows if r["correct"])
|
||||
total = len(rows)
|
||||
|
||||
print("=" * 72)
|
||||
print("脸型分类测试结果")
|
||||
print("=" * 72)
|
||||
for r in rows:
|
||||
status = "✓" if r["correct"] else "✗"
|
||||
if r["error"]:
|
||||
print(f"{status} {r['file']}")
|
||||
print(f" 期望: {r['expected']}")
|
||||
print(f" 错误: {r['error']}")
|
||||
continue
|
||||
|
||||
top3_str = ", ".join(f"{name}:{score:.1f}" for name, score in r["top3"])
|
||||
print(f"{status} {r['file']}")
|
||||
print(f" 期望: {r['expected']}")
|
||||
print(f" 预测: {r['display']} (conf={r['confidence']:.3f})")
|
||||
print(f" Top3: {top3_str}")
|
||||
|
||||
print("-" * 72)
|
||||
print(f"准确率: {correct}/{total} = {correct / total:.1%}")
|
||||
print("=" * 72)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
|
||||
if len(sys.argv) > 1 and sys.argv[1] not in {"--test", "-t"}:
|
||||
out = classify_from_image(sys.argv[1], return_details=True)
|
||||
print(f"脸型: {out['display']}")
|
||||
print(f"置信度: {out['confidence']:.3f}")
|
||||
print("各脸型得分:")
|
||||
for name, score in out["details"]["ranked"]:
|
||||
print(f" {name}: {score:.1f}")
|
||||
else:
|
||||
report = run_test_images()
|
||||
_print_test_report(report)
|
||||
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Reference in New Issue
Block a user