feat(接口99): 人脸特殊点检测 + 网关转发路由；文档同步至当前代码
- worker: POST /api/v1/face/special-points，60 个固定编号关键点（6组） + 5 个发际线特殊点（沿指定关键点方向射线步进，与 BiSeNet 头发 mask 首次相交； 双射线取中点，left_left=mid(333→332, 298→284) 与右侧镜像） - 网关: 同路径盲转发路由（调用频率低，维持转发不占网关依赖； 响应无 *_base64 字段，改写逻辑天然跳过） - 测试页 static/test_interface99.html：分组着色小点 + 点ID标注（可开关） + 发际线点大圆标签 + JSON 展示 - 文档: CLAUDE.md 重写至当前架构；实现说明/接口文档补接口 9~12/99 契约； 网关待改动补 §7(9/10已加) §8(11/12待加) §9(接口99部署步骤)
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"""接口99：人脸特殊点检测。
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返回 MediaPipe Face Mesh 关键点中一组固定编号的像素坐标（英文组名_点ID 为字段名），
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外加 5 个发际线特殊点：从指定关键点沿指定方向射线步进，与 BiSeNet 头发分割 mask
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首次相交处即发际线点（参考接口1 方案B 的发际线定位方式，见 hair_segmenter.py）。
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组与编号（需求方给定）：
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- eyebrow 107 66 105 63 70 336 296 334 293 300
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- eye 189 222 224 113 226 413 442 444 342 446
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- mouth 0 37 40 67 91 84 17 405 375 409 269
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- nose_midline 9 168 197 5 4 194 278 48 437 217
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- cheek 101 117 123 187 205 50 330 346 352 280 425
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- face_edge 162 234 58 136 149 152 378 365 288 401 366 389 251
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发际线点（字段名需求方给定；A/B 两射线时取中点，仅一条命中则返回该点，均未命中为 null）：
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- hair_line_center 151 → 10 方向与发际线交点
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- hair_line_right mid(108→109 交点A, 69→67 交点B)
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- hair_line_right_right mid(104→103 交点A, 68→54 交点B)
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- hair_line_left mid(337→338 交点A, 299→297 交点B)
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- hair_line_left_left mid(333→332 交点A, 298→284 交点B) # 与 right_right 镜像对称
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"""
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import math
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import numpy as np
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# 固定关键点组：组英文名 → MediaPipe 点 ID 列表（顺序即需求方给定顺序）
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GROUPS = {
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"eyebrow": [107, 66, 105, 63, 70, 336, 296, 334, 293, 300],
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"eye": [189, 222, 224, 113, 226, 413, 442, 444, 342, 446],
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"mouth": [0, 37, 40, 67, 91, 84, 17, 405, 375, 409, 269],
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"nose_midline": [9, 168, 197, 5, 4, 194, 278, 48, 437, 217],
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"cheek": [101, 117, 123, 187, 205, 50, 330, 346, 352, 280, 425],
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"face_edge": [162, 234, 58, 136, 149, 152, 378, 365, 288, 401, 366, 389, 251],
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}
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# 发际线点定义：字段名 → 参与计算的 (起点ID, 方向点ID) 射线组；单射线直接取交点，
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# 双射线取两交点中点。
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HAIRLINE_RAYS = {
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"hair_line_center": [(151, 10)],
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"hair_line_right": [(108, 109), (69, 67)],
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"hair_line_right_right": [(104, 103), (68, 54)],
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"hair_line_left": [(337, 338), (299, 297)],
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"hair_line_left_left": [(333, 332), (298, 284)],
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}
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class NoFaceError(Exception):
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"""未检出人脸。"""
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class NonFrontalError(Exception):
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"""非正面照（复用接口1 姿态阈值）。"""
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def ray_hairline_intersection(p1, p2, hair_mask):
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"""从 p1 沿 p1→p2 方向逐像素步进，返回首次进入头发 mask 的整数像素坐标。
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p1/p2: (x, y) 像素坐标（float）。起点本身已在头发内时直接返回起点（已到发际线）。
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步进直到出图或步数上限（3×图对角线，防极端死循环）；未命中返回 None。
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"""
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mask = np.asarray(hair_mask).astype(bool)
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if mask.size == 0:
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return None
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h, w = mask.shape
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x0, y0 = float(p1[0]), float(p1[1])
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dx, dy = float(p2[0]) - x0, float(p2[1]) - y0
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length = math.hypot(dx, dy)
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if length < 1e-6:
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return None
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ux, uy = dx / length, dy / length
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max_steps = int(3 * math.hypot(h, w))
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def _hit(x, y):
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xi, yi = int(round(x)), int(round(y))
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if 0 <= xi < w and 0 <= yi < h:
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return mask[yi, xi]
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return False
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x, y = x0, y0
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for _ in range(max_steps):
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if _hit(x, y):
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return int(round(x)), int(round(y))
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x += ux
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y += uy
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if not (0 <= int(round(x)) < w and 0 <= int(round(y)) < h):
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return None # 已步出图像，方向上无头发
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return None
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def detect_special_points(image_bgr):
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"""主入口：图片 → {字段名: {"x","y"} 或 None} + 元信息。
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Returns: {"points": {...}, "image_size": {"width","height"},
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"hairline_source": "segmentation"|"unavailable"}
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无人脸抛 NoFaceError；非正面抛 NonFrontalError。
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"""
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from face_analysis.detector import detector
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from face_analysis.calibration import normalized_to_pixel
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from face_analysis.pose import check_frontal_face
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h, w = image_bgr.shape[:2]
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landmarks = detector.detect(image_bgr)
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if landmarks is None:
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raise NoFaceError("未检出人脸")
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if not check_frontal_face(landmarks, w, h):
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raise NonFrontalError("非正面照")
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lm = landmarks.landmark
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px = {i: normalized_to_pixel(lm[i], w, h) for i in range(len(lm))}
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points = {}
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for group, ids in GROUPS.items():
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for idx in ids:
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x, y = px[idx]
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points[f"{group}_{idx}"] = {"x": int(round(x)), "y": int(round(y))}
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# 头发分割（与接口1 相同：按人脸框裁剪后再分割，脸外背景不干扰）
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hair_mask = None
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try:
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from face_analysis.hair_segmenter import get_segmenter
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face_box = (min(p[0] for p in px.values()), min(p[1] for p in px.values()),
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max(p[0] for p in px.values()), max(p[1] for p in px.values()))
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hair_mask, _ = get_segmenter().segment_hair_and_ears(
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image_bgr, face_box=face_box)
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except Exception: # noqa: BLE001 分割不可用 → 发际线点置 null，不阻塞关键点
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hair_mask = None
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for name, rays in HAIRLINE_RAYS.items():
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hits = []
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if hair_mask is not None:
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for a, b in rays:
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hits.append(ray_hairline_intersection(px[a], px[b], hair_mask))
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found = [p for p in hits if p is not None]
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if not found:
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points[name] = None
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elif len(found) == 1:
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points[name] = {"x": found[0][0], "y": found[0][1]}
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else:
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points[name] = {
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"x": int(round(sum(p[0] for p in found) / len(found))),
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"y": int(round(sum(p[1] for p in found) / len(found))),
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}
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return {
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"points": points,
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"image_size": {"width": w, "height": h},
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"hairline_source": "segmentation" if hair_mask is not None else "unavailable",
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}
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if __name__ == "__main__":
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import sys
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import cv2
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import json
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path = sys.argv[1] if len(sys.argv) > 1 else "tests/fixtures/frontal.jpg"
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img = cv2.imread(path)
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if img is None:
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print(f"无法读取图片: {path}")
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sys.exit(1)
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try:
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out = detect_special_points(img)
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except (NoFaceError, NonFrontalError) as e:
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print(f"检测失败: {e}")
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sys.exit(1)
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print(json.dumps(out, ensure_ascii=False, indent=2))
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