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
+7
-3
@@ -19,10 +19,14 @@
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|------|------|------|
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| model.safetensors | `hairline/models/face-parsing/model.safetensors` | 338,580,732 B (~323MB) |
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下载命令：
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下载命令（国内用 hf-mirror 镜像，快很多）：
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```bash
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# 国内镜像（推荐）
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curl -L -o hairline/models/face-parsing/model.safetensors \
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"https://huggingface.co/jonathandinu/face-parsing/resolve/main/model.safetensors"
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"https://hf-mirror.com/jonathandinu/face-parsing/resolve/main/model.safetensors"
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# 官方源
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# curl -L -o hairline/models/face-parsing/model.safetensors \
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# "https://huggingface.co/jonathandinu/face-parsing/resolve/main/model.safetensors"
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```
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sha256 校验：
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@@ -77,7 +81,7 @@ model.safetensors https://huggingface.co/jonathandinu/face-parsing/resol
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模型已就位，但**内网机还需要 Python 依赖的离线 wheel 包**，否则 `pip install` 在内网无法联网安装。这部分**与目标机的操作系统、Python 版本、CUDA 版本强相关**，需确认后单独打包：
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- **worker（GPU 机）**：`mediapipe` / `opencv-python` / `numpy<2` / `Pillow` / **`torch`+`torchvision` 的 CUDA 版**（按 GPU 的 CUDA 版本选 cu118/cu121 等）+ FastAPI/uvicorn 全家桶。
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- **worker（GPU 机）**：`mediapipe` / `opencv-python` / `numpy<2` / `Pillow` / **`torch`+`torchvision` 的 CUDA 版**（按 GPU 的 CUDA 版本选 cu118/cu121 等）/ `transformers`（接口2 SegFormer）+ FastAPI/uvicorn 全家桶。
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- **网关机**：很轻，只需 FastAPI/uvicorn/httpx 等代理依赖，**不需要 torch/mediapipe**。
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> 架构已拆分（见 `docs/系统架构-网关与高性能后端.md`）：算法依赖只装在 worker，网关保持轻量。
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@@ -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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@@ -162,4 +162,9 @@ PARSE_NECK_L = 16
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PARSE_NECK = 17
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PARSE_CLOTH = 18
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HF_FACE_PARSER_MODEL = "jonathandinu/face-parsing"
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# 内网/离线：指向本地权重目录（transformers from_pretrained 支持本地路径）。
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# 在线 id 为 "jonathandinu/face-parsing"，权重已放到 hairline/models/face-parsing/。
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import os as _os
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HF_FACE_PARSER_MODEL = _os.path.join(
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_os.path.dirname(_os.path.abspath(__file__)), "models", "face-parsing"
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)
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@@ -11,8 +11,9 @@ from __future__ import annotations
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import os
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import numpy as np
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# 模型在 hairline/models/ 下（模块即在 hairline/ 根），故只取一层 dirname。
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DEFAULT_MODEL_PATH = os.path.join(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
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os.path.dirname(os.path.abspath(__file__)),
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"models", "face_landmarker.task",
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)
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@@ -0,0 +1,107 @@
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"""接口2 渲染器：把发际线类型贴图按 502 点 mesh 贴到照片上（OpenCV 逐三角仿射 warp）。
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原理（技术方案 §4）：face_ext.obj 的 502 顶点里，[468..485) 中间行 + [485..502) 发际线行
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与 17 个 MP 顶部锚点连成 ~64 个 ribbon 三角形，其 UV 落在贴图顶部条带（发际线曲线所在）。
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只 warp 这些扩展三角形，即可把贴图里的发际线曲线贴到额头/发际线区域，且天然只画在 ribbon 区。
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"""
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from __future__ import annotations
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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 PIL import Image
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from .obj_io import read_obj
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from ._index_map_data import INDEX_MAP_468
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_MESH_PATH = os.path.join(os.path.dirname(__file__), "mesh", "face_ext.obj")
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_N_MP = 468
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_mesh_cache = None
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_INDEX_MAP = np.asarray(INDEX_MAP_468, dtype=np.int64) # OBJ顶点i → MP点索引
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def mp_order_to_obj_order(points502_mp: np.ndarray) -> np.ndarray:
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"""把 MP 顺序的 502 点重排成 face_ext.obj 的顶点顺序。
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extract_hairline 输出为 MP 顺序：[0..468) MP / [468..485) middle / [485..502) hairline。
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而 face_ext.obj 的前 468 顶点经 INDEX_MAP_468 重排（obj_i → mp_i）；扩展顶点
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[468..502) 两侧同序，直接对应。
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"""
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out = np.empty_like(points502_mp)
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out[:_N_MP] = points502_mp[_INDEX_MAP] # obj[0..468) = mp[INDEX_MAP]
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out[_N_MP:] = points502_mp[_N_MP:] # 扩展行同序
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return out
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def load_ext_mesh(obj_path: str = _MESH_PATH):
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"""解析 face_ext.obj，返回 (uv502, ext_faces)。结果缓存。
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- uv502: (502, 2) float32，每个顶点的 UV（V_raw，V=1 对应贴图顶部）。
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- ext_faces: list[(i,j,k)]，仅保留顶点索引含 ≥468 的扩展三角形（ribbon）。
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obj 中 v 与 vt 一一对应（face 用相同索引），故按位置索引取 UV。
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"""
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global _mesh_cache
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if _mesh_cache is not None:
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return _mesh_cache
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mesh = read_obj(obj_path)
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n_v = len(mesh.positions)
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uv = np.zeros((n_v, 2), dtype=np.float32)
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for face in mesh.faces:
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for pi, ti, _ni in face:
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if ti >= 0 and pi >= 0:
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uv[pi] = mesh.texcoords[ti]
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ext_faces = []
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for face in mesh.faces:
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idx = [pi for (pi, _t, _n) in face]
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if max(idx) >= _N_MP: # 含扩展顶点 → ribbon 三角形
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ext_faces.append(tuple(idx))
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_mesh_cache = (uv, ext_faces)
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return _mesh_cache
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def load_texture_rgba(path: str) -> np.ndarray:
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"""读发际线贴图为 (H, W, 4) uint8 RGBA。"""
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return np.array(Image.open(path).convert("RGBA"))
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def render_hairline_overlay(photo_bgr: np.ndarray,
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points502_norm: np.ndarray,
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ext_faces,
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uv502: np.ndarray,
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texture_rgba: np.ndarray) -> np.ndarray:
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"""把 texture_rgba 的发际线曲线渲染到 photo_bgr 上，返回 BGR 预览图。
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points502_norm: (502, 3) 归一化坐标（x,y ∈ [0,1]），**MP 顺序**（extract_hairline 输出）。
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"""
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H, W = photo_bgr.shape[:2]
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TH, TW = texture_rgba.shape[:2]
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pts_obj = mp_order_to_obj_order(points502_norm) # MP序 → OBJ序
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img_xy = pts_obj[:, :2] * np.array([W, H], dtype=np.float32) # (502,2)
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overlay = np.zeros((H, W, 4), np.float32) # 累积曲线层 RGBA
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tex = texture_rgba.astype(np.float32)
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for (i, j, k) in ext_faces:
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dst = img_xy[[i, j, k]].astype(np.float32)
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# UV → 贴图像素；flipY：贴图 y = (1 - v_raw) * TH（与 head3d Three.js flipY=true 一致）
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src = np.array([[uv502[v][0] * TW, (1.0 - uv502[v][1]) * TH] for v in (i, j, k)],
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dtype=np.float32)
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# 退化三角形（投影到一条线）跳过，避免 getAffineTransform 奇异
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if cv2.contourArea(dst.astype(np.int32)) < 1.0:
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continue
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M = cv2.getAffineTransform(src, dst)
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warped = cv2.warpAffine(tex, M, (W, H), flags=cv2.INTER_LINEAR,
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borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0, 0))
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tri_mask = np.zeros((H, W), np.uint8)
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cv2.fillConvexPoly(tri_mask, dst.astype(np.int32), 255)
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sel = tri_mask > 0
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overlay[sel] = warped[sel]
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# alpha 合成（RGBA→BGR：贴图 RGB 顺序需反成 BGR）
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a = overlay[:, :, 3:4] / 255.0
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rgb = overlay[:, :, :3][..., ::-1] # RGB→BGR
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out = photo_bgr.astype(np.float32) * (1.0 - a) + rgb * a
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return np.clip(out, 0, 255).astype(np.uint8)
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@@ -0,0 +1,123 @@
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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。
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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:
|
||||
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}")
|
||||
@@ -18,5 +18,9 @@ numpy==1.26.4 # 必须 <2，否则 mediapipe 0.10.x import 崩溃
|
||||
torch==2.2.2 # 当前在 5090 上仅 CPU 可用；GPU 需 cu128(≥2.7)
|
||||
torchvision==0.17.2
|
||||
|
||||
# 接口2：C端生发（发际线预览）
|
||||
# MediaPipe Tasks(FaceLandmarker) 用已装的 mediapipe；新增 SegFormer 人脸分割：
|
||||
transformers==4.45.2 # SegFormer 人脸分割（jonathandinu/face-parsing，本地权重）
|
||||
|
||||
# 测试
|
||||
pytest==8.3.3
|
||||
|
||||
@@ -72,6 +72,27 @@ def test_corrupt_1008(client):
|
||||
assert r.json()["code"] == 1008
|
||||
|
||||
|
||||
GROW = "/api/v1/hair/grow"
|
||||
|
||||
|
||||
def test_grow_missing_gender_1004(client):
|
||||
files = {"image_file": ("frontal.jpg", open(fixture("frontal.jpg"), "rb"), "application/octet-stream")}
|
||||
r = client.post(GROW, headers=H, files=files)
|
||||
assert r.json()["code"] == 1004
|
||||
|
||||
|
||||
def test_grow_female_returns_5(client):
|
||||
files = {"image_file": ("frontal.jpg", open(fixture("frontal.jpg"), "rb"), "application/octet-stream")}
|
||||
r = client.post(GROW, headers=H, files=files, data={"gender": "female"})
|
||||
body = r.json()
|
||||
assert body["code"] == 0, body
|
||||
results = body["data"]["results"]
|
||||
assert [x["hairline_type"] for x in results] == ["ellipse", "flower", "heart", "straight", "wave"]
|
||||
assert [x["order"] for x in results] == [1, 2, 3, 4, 5]
|
||||
assert base64.b64decode(results[0]["image_base64"])[:8] == b"\x89PNG\r\n\x1a\n"
|
||||
assert "image_url" not in results[0]
|
||||
|
||||
|
||||
def test_success_structure(client):
|
||||
r = _post(client, "frontal.jpg")
|
||||
body = r.json()
|
||||
|
||||
@@ -0,0 +1,31 @@
|
||||
"""接口2 单元测试：mesh 解析 / MP→OBJ 重排 / 性别贴图映射（不需 SegFormer）。"""
|
||||
import numpy as np
|
||||
|
||||
from hairline.render import load_ext_mesh, mp_order_to_obj_order
|
||||
from hairline.service import get_texture_map
|
||||
from hairline import constants as C
|
||||
|
||||
|
||||
def test_ext_mesh():
|
||||
uv, ext_faces = load_ext_mesh()
|
||||
assert uv.shape == (502, 2)
|
||||
assert len(ext_faces) == 64 # ribbon 扩展三角形
|
||||
# 扩展面至少含一个 ≥468 的顶点
|
||||
assert all(max(f) >= 468 for f in ext_faces)
|
||||
|
||||
|
||||
def test_mp_to_obj_anchor_mapping():
|
||||
"""OBJ 序重排后，ribbon 引用的 obj 锚点应映射回 MP_TOP_ANCHORS。"""
|
||||
pts = np.zeros((502, 3), np.float32)
|
||||
pts[:468, 0] = np.arange(468) # 用 x 编码 MP 索引
|
||||
obj = mp_order_to_obj_order(pts)
|
||||
_uv, ext_faces = load_ext_mesh()
|
||||
obj_anchor_ids = sorted({pi for f in ext_faces for pi in f if pi < 468})
|
||||
mapped = sorted(int(obj[i, 0]) for i in obj_anchor_ids) # 还原成 MP 索引
|
||||
assert mapped == sorted(C.MP_TOP_ANCHORS)
|
||||
|
||||
|
||||
def test_texture_map():
|
||||
m = get_texture_map()
|
||||
assert [k for k, _ in m["female"]] == ["ellipse", "flower", "heart", "straight", "wave"]
|
||||
assert [k for k, _ in m["male"]] == ["ellipse", "inverse_arc", "m", "straight"]
|
||||
Reference in New Issue
Block a user