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>
This commit is contained in:
Claude Opus 4.8
@@ -19,6 +19,8 @@ from fastapi.responses import JSONResponse
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from fastapi.staticfiles import StaticFiles
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from pydantic import BaseModel, Field
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logging.basicConfig(level=logging.INFO,
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format="%(asctime)s %(levelname)s %(name)s: %(message)s")
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logger = logging.getLogger("hair.worker")
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# ---------------------------------------------------------------------------
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@@ -68,6 +70,14 @@ async def lifespan(_app: FastAPI):
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except Exception as e: # noqa: BLE001
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# 方案 B 不可用（如 torch 缺失）：降级为方案 A only，不阻塞服务
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logger.warning("头发分割不可用，接口1 将走方案A兜底：%s", e)
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# 接口2（C端生发）单例预热：FaceLandmarker + SegFormer + mesh/贴图映射
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try:
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from hairline.service import get_landmarker, get_parser, get_texture_map
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from hairline.render import load_ext_mesh
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get_landmarker(); get_parser(); load_ext_mesh(); get_texture_map()
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logger.info("接口2 发际线管线就绪")
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except Exception as e: # noqa: BLE001
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logger.warning("接口2 发际线管线初始化失败（该接口将返回错误）：%s", e)
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_STATE["ready"] = True
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yield
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@@ -415,10 +425,11 @@ async def face_measure(
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summary="接口2 C端生发",
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tags=["生发"],
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description=f"""
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输入用户正面照，返回多个生发方案，每个方案包含：
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- 生发后效果图 URL
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- 对应的发际线形（如花瓣形、波浪形）
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- 合适度排序（order=1 最优）
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输入用户正面照 + **性别**，返回该性别对应的多张「建议发际线预览图」（本期为
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**发际线曲线叠加在原照片上的预览图**，非最终文生图生发图）。每个方案包含：
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- 预览图（worker 返回 `image_base64`，网关落盘后改写为 `image_url`）
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- 发际线类型 `hairline_type`（英文 key）
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- 顺序 `order`（本期固定 `1..N`，不排序）
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{_image_fields_desc}
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@@ -426,7 +437,12 @@ async def face_measure(
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---
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**beauty_enabled**：是否对生发后图片开启美颜，默认 `false`。
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- **gender**（必填）：`male` / `female`。决定返回的贴图集合（female 5 张 / male 4 张）。
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非法或缺失返回 `1004`。
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- **beauty_enabled**：本期保留但不生效。
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`hairline_type` 取值：`ellipse` / `flower` / `heart` / `straight` / `wave`（female），
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`ellipse` / `m` / `straight` / `inverse_arc`（male）。
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""",
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responses={
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200: {
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@@ -439,8 +455,8 @@ async def face_measure(
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"request_id": "mock-request-id",
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"data": {
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"results": [
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{"image_url": SAMPLE_IMAGE_URL, "hairline_type": "花瓣形", "order": 1},
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{"image_url": SAMPLE_IMAGE_URL, "hairline_type": "波浪形", "order": 2},
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{"image_base64": "iVBORw0KGgo...", "hairline_type": "ellipse", "order": 1},
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{"image_base64": "iVBORw0KGgo...", "hairline_type": "flower", "order": 2},
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]
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},
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}
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@@ -464,15 +480,48 @@ async def hair_grow(
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image_file: Optional[UploadFile] = File(default=None, description="上传图片文件（JPG/PNG，≤ 1 MB）"),
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image_url: Optional[str] = Form(default=None, description="图片 URL"),
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image_base64: Optional[str] = Form(default=None, description="图片 base64（需带 data:image/...;base64, 前缀）"),
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beauty_enabled: bool = Form(default=False, description="是否开启美颜效果，默认 false"),
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gender: Optional[str] = Form(default=None, description="性别 male/female（必填）"),
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beauty_enabled: bool = Form(default=False, description="是否开启美颜（本期不生效）"),
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):
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data = {
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"results": [
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{"image_url": SAMPLE_IMAGE_URL, "hairline_type": "花瓣形", "order": 1},
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{"image_url": SAMPLE_IMAGE_URL, "hairline_type": "波浪形", "order": 2},
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]
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}
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return ok(data)
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# 1. gender 必填校验（非法/缺失 → 1004）
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if gender not in ("male", "female"):
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return err(1004, "gender 必填且只能为 male / female")
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# 2. 三选一取图
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raw, e = await resolve_image_bytes(image_file, image_url, image_base64)
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if e is not None:
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return e
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if len(raw) > MAX_FILE_BYTES:
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return err(1006, "文件超出 1 MB 限制")
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image = cv2.imdecode(np.frombuffer(raw, np.uint8), cv2.IMREAD_COLOR)
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if image is None:
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return err(1008, "图片格式不支持（仅 JPG / PNG）")
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h, w = image.shape[:2]
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short_side, long_side = min(w, h), max(w, h)
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if short_side < MIN_SHORT_SIDE or long_side < MIN_LONG_SIDE:
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return err(1002, "人像分辨率过低")
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try:
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from hairline.service import generate_previews
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previews = generate_previews(image, gender) # 无人脸 → None
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if previews is None:
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return err(1001, "无法识别人像")
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results = []
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for p in previews:
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ok_enc, png = cv2.imencode(".png", p["image_bgr"])
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results.append({
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"image_base64": base64.b64encode(png.tobytes()).decode(),
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"hairline_type": p["hairline_type"],
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"order": p["order"],
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})
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return ok({"results": results})
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except Exception as ex: # noqa: BLE001
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logger.exception("接口2 处理异常")
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return err(1007, f"处理失败：{ex}")
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# ---------------------------------------------------------------------------
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