xslandClaude Opus 4.8
554b64a916
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>
124 lines
4.5 KiB
Python
124 lines
4.5 KiB
Python
"""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。
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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
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对每张贴图调 render.render_hairline_overlay 生成预览图。
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"""
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from __future__ import annotations
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import glob
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import os
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import cv2
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import numpy as np
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from . import constants as C
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from .face_landmarks import FaceLandmarker
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from .face_parsing import FaceParser
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from .hairline_2d import sample_hairline, smooth_hairline
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from .lift_3d import lift_hairline_to_3d, build_middle_row, assemble_full
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from .render import load_ext_mesh, load_texture_rgba, render_hairline_overlay
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_TEXTURE_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "hairline_texture")
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# ⚠️ 本 worker 是 RTX 5090(sm_120)，torch 2.2.2(cu121) 只编到 sm_90，CUDA 跑算子会报
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# "no kernel image"。SegFormer 默认走 CPU（~2.5s/张）。换 torch cu128 后可设 SEG_DEVICE=cuda。
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_SEG_DEVICE = os.getenv("SEG_DEVICE", "cpu")
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_landmarker = None
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_parser = None
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_texture_map = None
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def get_landmarker() -> FaceLandmarker:
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global _landmarker
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if _landmarker is None:
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_landmarker = FaceLandmarker(static_image_mode=True)
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return _landmarker
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def get_parser() -> FaceParser:
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global _parser
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if _parser is None:
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_parser = FaceParser(device=_SEG_DEVICE)
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return _parser
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def _gender_key(stem: str):
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"""文件名 stem → (gender, key)；非 girl_/man_ 前缀返回 (None, None)。"""
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if stem.startswith("girl_"):
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return "female", stem[5:].replace(" ", "").strip()
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if stem.startswith("man_"):
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return "male", stem[4:].replace(" ", "").strip()
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return None, None
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def get_texture_map() -> dict:
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"""扫描 hairline_texture/ 建 {gender: [(key, path)]}，按 key 排序、缓存。
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文件名规范化去空格（如 `man_ inverse_arc.png` → key `inverse_arc`）。
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"""
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global _texture_map
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if _texture_map is not None:
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return _texture_map
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mapping: dict[str, list] = {"female": [], "male": []}
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for path in sorted(glob.glob(os.path.join(_TEXTURE_DIR, "*.png"))):
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stem = os.path.splitext(os.path.basename(path))[0]
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gender, key = _gender_key(stem)
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if gender:
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mapping[gender].append((key, path))
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for g in mapping:
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mapping[g].sort(key=lambda kp: kp[0])
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_texture_map = mapping
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return _texture_map
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def extract_502(image_bgr: np.ndarray):
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"""照片(BGR) → (points502 MP序, valid17)。无人脸返回 (None, None)。"""
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rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
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landmarks = get_landmarker().detect(rgb)
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if landmarks is None:
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return None, None
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parse_map = get_parser().parse(rgb)
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hairline_2d, valid = sample_hairline(landmarks, parse_map)
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hairline_2d = smooth_hairline(hairline_2d, valid)
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hairline_3d = lift_hairline_to_3d(landmarks, hairline_2d)
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middle_3d = build_middle_row(landmarks, hairline_3d)
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points = assemble_full(landmarks, middle_3d, hairline_3d)
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return points, valid
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def generate_previews(image_bgr: np.ndarray, gender: str):
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"""生成该性别全部发际线预览图。
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Returns: list[dict]，每项 {"hairline_type": key, "image_bgr": ndarray, "order": 1..N}。
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无人脸返回 None。gender 必须是 male/female。
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"""
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if gender not in ("male", "female"):
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raise ValueError(f"gender 必须是 male/female，收到 {gender!r}")
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points, _valid = extract_502(image_bgr)
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if points is None:
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return None
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uv, ext_faces = load_ext_mesh()
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results = []
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for order, (key, path) in enumerate(get_texture_map()[gender], start=1):
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tex = load_texture_rgba(path)
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preview = render_hairline_overlay(image_bgr, points, ext_faces, uv, tex)
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results.append({"hairline_type": key, "image_bgr": preview, "order": order})
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return results
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if __name__ == "__main__":
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import sys
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g = sys.argv[2] if len(sys.argv) > 2 else "female"
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img = cv2.imread(sys.argv[1] if len(sys.argv) > 1 else "tests/fixtures/frontal.jpg")
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os.makedirs("tests/output", exist_ok=True)
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print("texture map:", {k: [kp[0] for kp in v] for k, v in get_texture_map().items()})
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res = generate_previews(img, g)
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if res is None:
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print("无人脸")
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sys.exit(1)
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for r in res:
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out = f"tests/output/preview_{g}_{r['hairline_type']}.png"
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cv2.imwrite(out, r["image_bgr"])
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print(f" order={r['order']} type={r['hairline_type']} -> {out}")
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