feat(接口2): C端生发发际线预览(真实实现，替换Mock)

第一步：按性别把发际线类型贴图渲染到照片，输出 N 张发际线叠加预览图。 - hairline/render.py: 解析 face_ext.obj(502 UV + 64 ribbon扩展面) + OpenCV 逐三角仿射 warp 渲染器；关键修复——face_ext.obj 是 OBJ序，用 INDEX_MAP_468 把 MP序 502点重排成 OBJ序后再投影，否则 ribbon 会错贴到中脸 - hairline/service.py: FaceLandmarker+SegFormer 单例 + 性别贴图映射(扫描去空格) + generate_previews 管线(female5/male4) - 集成点修复: face_landmarks DEFAULT_MODEL_PATH 改 hairline/models/; constants HF_FACE_PARSER_MODEL 改本地路径(离线) - app.py: /api/v1/hair/grow 接真实实现，gender 必填(非法→1004)，返回 results[].image_base64(不落盘)，校验/鉴权同接口1；lifespan 预热接口2单例；补 logging.basicConfig - 依赖: transformers==4.45.2；SegFormer 权重走 hf-mirror 下载(见 OFFLINE_ASSETS) - 测试: tests/test_hairline.py(mesh/重排/贴图映射) + test_api 接口2用例，31 全绿注：SegFormer 受 5090/torch 限制走 CPU(~2.5s/张)，换 cu128 可 SEG_DEVICE=cuda。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-14 20:31:41 +08:00
parent 891bc0da8b
commit 554b64a916
9 changed files with 365 additions and 20 deletions
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+"""接口2 服务层：模型单例 + 性别贴图映射 + 「照片→N 张发际线预览图」管线。
+
+把 head3d 的 extract_hairline 步骤包成单例复用（避免每请求重建模型），再按性别
+对每张贴图调 render.render_hairline_overlay 生成预览图。
+"""
+from __future__ import annotations
+import glob
+import os
+
+import cv2
+import numpy as np
+
+from . import constants as C
+from .face_landmarks import FaceLandmarker
+from .face_parsing import FaceParser
+from .hairline_2d import sample_hairline, smooth_hairline
+from .lift_3d import lift_hairline_to_3d, build_middle_row, assemble_full
+from .render import load_ext_mesh, load_texture_rgba, render_hairline_overlay
+
+_TEXTURE_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "hairline_texture")
+
+# ⚠️ 本 worker 是 RTX 5090(sm_120)，torch 2.2.2(cu121) 只编到 sm_90，CUDA 跑算子会报
+#    "no kernel image"。SegFormer 默认走 CPU（~2.5s/张）。换 torch cu128 后可设 SEG_DEVICE=cuda。
+_SEG_DEVICE = os.getenv("SEG_DEVICE", "cpu")
+
+_landmarker = None
+_parser = None
+_texture_map = None
+
+
+def get_landmarker() -> FaceLandmarker:
+    global _landmarker
+    if _landmarker is None:
+        _landmarker = FaceLandmarker(static_image_mode=True)
+    return _landmarker
+
+
+def get_parser() -> FaceParser:
+    global _parser
+    if _parser is None:
+        _parser = FaceParser(device=_SEG_DEVICE)
+    return _parser
+
+
+def _gender_key(stem: str):
+    """文件名 stem → (gender, key)；非 girl_/man_ 前缀返回 (None, None)。"""
+    if stem.startswith("girl_"):
+        return "female", stem[5:].replace(" ", "").strip()
+    if stem.startswith("man_"):
+        return "male", stem[4:].replace(" ", "").strip()
+    return None, None
+
+
+def get_texture_map() -> dict:
+    """扫描 hairline_texture/ 建 {gender: [(key, path)]}，按 key 排序、缓存。
+
+    文件名规范化去空格（如 `man_ inverse_arc.png` → key `inverse_arc`）。
+    """
+    global _texture_map
+    if _texture_map is not None:
+        return _texture_map
+    mapping: dict[str, list] = {"female": [], "male": []}
+    for path in sorted(glob.glob(os.path.join(_TEXTURE_DIR, "*.png"))):
+        stem = os.path.splitext(os.path.basename(path))[0]
+        gender, key = _gender_key(stem)
+        if gender:
+            mapping[gender].append((key, path))
+    for g in mapping:
+        mapping[g].sort(key=lambda kp: kp[0])
+    _texture_map = mapping
+    return _texture_map
+
+
+def extract_502(image_bgr: np.ndarray):
+    """照片(BGR) → (points502 MP序, valid17)。无人脸返回 (None, None)。"""
+    rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
+    landmarks = get_landmarker().detect(rgb)
+    if landmarks is None:
+        return None, None
+    parse_map = get_parser().parse(rgb)
+    hairline_2d, valid = sample_hairline(landmarks, parse_map)
+    hairline_2d = smooth_hairline(hairline_2d, valid)
+    hairline_3d = lift_hairline_to_3d(landmarks, hairline_2d)
+    middle_3d = build_middle_row(landmarks, hairline_3d)
+    points = assemble_full(landmarks, middle_3d, hairline_3d)
+    return points, valid
+
+
+def generate_previews(image_bgr: np.ndarray, gender: str):
+    """生成该性别全部发际线预览图。
+
+    Returns: list[dict]，每项 {"hairline_type": key, "image_bgr": ndarray, "order": 1..N}。
+    无人脸返回 None。gender 必须是 male/female。
+    """
+    if gender not in ("male", "female"):
+        raise ValueError(f"gender 必须是 male/female，收到 {gender!r}")
+    points, _valid = extract_502(image_bgr)
+    if points is None:
+        return None
+
+    uv, ext_faces = load_ext_mesh()
+    results = []
+    for order, (key, path) in enumerate(get_texture_map()[gender], start=1):
+        tex = load_texture_rgba(path)
+        preview = render_hairline_overlay(image_bgr, points, ext_faces, uv, tex)
+        results.append({"hairline_type": key, "image_bgr": preview, "order": order})
+    return results
+
+
+if __name__ == "__main__":
+    import sys
+    g = sys.argv[2] if len(sys.argv) > 2 else "female"
+    img = cv2.imread(sys.argv[1] if len(sys.argv) > 1 else "tests/fixtures/frontal.jpg")
+    os.makedirs("tests/output", exist_ok=True)
+    print("texture map:", {k: [kp[0] for kp in v] for k, v in get_texture_map().items()})
+    res = generate_previews(img, g)
+    if res is None:
+        print("无人脸")
+        sys.exit(1)
+    for r in res:
+        out = f"tests/output/preview_{g}_{r['hairline_type']}.png"
+        cv2.imwrite(out, r["image_bgr"])
+        print(f"  order={r['order']} type={r['hairline_type']} -> {out}")