fix(接口4): worker 移除脸型 Mock + face_shape 改本机 MediaPipe 计算
- worker /api/v1/face/features 不再返回假成功数据，直接告知仅网关实现， 避免本机误打 :8187 被 Mock 结果误导。 - 网关 ark_api_key 加载优先级改为 gateway/config.json 优先（原先误读 worker_config.json 里的失效 key）。 - 接口4 face_shape 不再采信豆包结果，改用本机 face/face_shape_classifier.py （MediaPipe 7 类）计算覆盖；其余 5 项特征仍走豆包。 - 修复 face_shape_classifier 共享 FaceMesh 实例的线程安全问题（加锁）， 避免网关侧接口4 并发请求时崩溃/结果错乱。 - 新增 /api/v1/debug/face-shape 调试接口 + static/test_face_shape.html 单图调试页（worker 侧）。 - 更新文档：网关机现在也需要 mediapipe/opencv-python/numpy<2。 ⚠️ 部署前提醒：网关机需先安装 mediapipe==0.10.14 / opencv-python==4.10.0.84 / numpy==1.26.4，否则接口4 会返回 1007「分析服务异常」。 Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
Cursor
@@ -1,6 +1,6 @@
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"""旷视五接口 — worker 侧（高性能 GPU 后端）。
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接口 1（四庭七眼测量）已为**真实算法实现**（见 face_analysis 包）；接口 2~5 仍为 Mock。
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接口 1 等算法在 worker；接口4（用户特征）已迁到网关本机（调豆包），worker 同路径只返回明确错误、无 Mock。
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拆分架构：worker 跑算法、返回 `annotated_image_base64`（不落盘、不拼 URL，由网关完成）。
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worker 对 `/api/*` 校验内网鉴权头 `X-Internal-Token`，`/health` 供网关探测不校验。
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"""
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@@ -885,56 +885,28 @@ async def hair_grow_b(
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@app.post(
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"/api/v1/face/features",
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summary="接口4 用户特征分析",
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summary="接口4 用户特征分析（仅网关）",
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tags=["人脸分析"],
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description=f"""
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输入用户照片，返回 N 个用户面部特征字段。
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description="""
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**本接口不在 worker 实现。** 请调用网关（本机默认 `http://127.0.0.1:8080`）的同路径；
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网关本机调火山方舟豆包视觉模型，不转发到 worker。
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{_image_fields_desc}
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图片同时支持 `multipart/form-data` 文件上传（字段名 `image_file`）。
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---
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由**火山方舟 豆包视觉模型**分析，返回**固定 6 个英文字段**。
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**返回格式**：`data.features` 为一个 **JSON 字符串**（不是对象），需要在客户端 `JSON.parse()` 后使用。
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| 字段 | 说明 |
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|------|------|
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| face_shape | 脸形（如"鹅蛋脸"） |
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| eyebrow_shape | 眉形（如"平眉"） |
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| facial_age | 面部年龄（区间，如"18-25岁"） |
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| dynamic_static_type | 动静类型（"静态型"/"动态型"） |
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| gender | 性别（"男"/"女"） |
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| gene_style | 基因风格（如"自然型"） |
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> 无人脸返回 `1001`。
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直接打 worker（如 `:8187`）会返回错误，避免误用假数据。
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""",
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responses={
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200: {
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"description": "成功",
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"description": "worker 不提供本接口",
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"content": {
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"application/json": {
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"example": {
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"code": 0,
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"message": "success",
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"code": 1007,
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"message": "接口4 仅在网关实现，请访问网关（本机默认 :8080），worker 不提供本接口",
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"request_id": "mock-request-id",
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"data": {
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"features": '{"face_shape":"鹅蛋脸","eyebrow_shape":"平眉","facial_age":"18-25岁","dynamic_static_type":"静态型","gender":"女","gene_style":"少年型"}',
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},
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"data": None,
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}
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}
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},
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},
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400: {
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"description": "参数错误 / 图片识别失败",
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"content": {
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"application/json": {
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"example": {"code": 1001, "message": "无法识别人像", "request_id": "x", "data": None}
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}
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},
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},
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},
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)
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async def face_features(
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@@ -942,16 +914,12 @@ async def face_features(
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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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):
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# ⚠️ 接口4 已迁到**网关本机**实现（直接调豆包视觉模型，见 gateway/app.py）。
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# 网关不会把本接口转发到 worker，故此处仅留 Mock 占位、保持 worker 无外网依赖。
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features = json.dumps(
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{
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"face_shape": "鹅蛋脸", "eyebrow_shape": "平眉", "facial_age": "18-25岁",
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"dynamic_static_type": "静态型", "gender": "女", "gene_style": "少年型",
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},
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ensure_ascii=False,
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# 接口4 只在网关实现（gateway/app.py → face_features.analyze_features）。
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# 不再返回 Mock 成功数据，避免本机打 :8187 时被假结果误导。
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return err(
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1007,
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"接口4 仅在网关实现，请访问网关（本机默认 :8080），worker 不提供本接口",
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)
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return ok({"features": features})
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# ---------------------------------------------------------------------------
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@@ -1662,6 +1630,84 @@ async def download_hairline_log(rid: Optional[str] = None, tail: int = 500):
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return PlainTextResponse("".join(lines), media_type="text/plain; charset=utf-8")
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# ---------------------------------------------------------------------------
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# 调试：MediaPipe 脸型分类（face/face_shape_classifier.py，非接口4 豆包）
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# ---------------------------------------------------------------------------
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@app.post(
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"/api/v1/debug/face-shape",
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summary="调试 单张脸型分类（MediaPipe）",
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tags=["调试"],
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description="""
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离线脸型分类调试接口（`face/face_shape_classifier.py`），**不是**接口4 的豆包视觉分析。
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上传正面照 → MediaPipe 468 点 → 7 类脸型评分 + 特征标注图。
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""",
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include_in_schema=True,
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)
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async def debug_face_shape(
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image_file: Optional[UploadFile] = File(default=None, description="上传图片文件（JPG/PNG）"),
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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"),
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):
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raw, e = await resolve_image_bytes(image_file, image_url, image_base64)
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if e:
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return e
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try:
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nparr = np.frombuffer(raw, np.uint8)
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bgr = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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if bgr is None:
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return err(1008, "图片格式不支持（仅 JPG / PNG）")
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except Exception: # noqa: BLE001
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return err(1008, "图片格式不支持（仅 JPG / PNG）")
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def _jsonable(obj):
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if isinstance(obj, dict):
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return {k: _jsonable(v) for k, v in obj.items()}
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if isinstance(obj, (list, tuple)):
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return [_jsonable(v) for v in obj]
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if hasattr(obj, "item"):
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return obj.item()
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if isinstance(obj, (float, int, str, bool)) or obj is None:
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return obj
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return obj
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from fastapi.concurrency import run_in_threadpool
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try:
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from face.face_shape_classifier import classify_from_image
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result = await run_in_threadpool(
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classify_from_image, bgr, True, True,
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)
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except ValueError as ex:
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return err(1001, str(ex) or "无法识别人像")
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except Exception as ex: # noqa: BLE001
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return err(1007, f"脸型分类失败：{ex}")
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details = result.get("details") or {}
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ranked = [
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{"shape": name, "score": round(float(score), 2)}
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for name, score in (details.get("ranked") or [])
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]
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annotated = result.pop("annotated", None)
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h, w = bgr.shape[:2]
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data = {
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"face_shape": result["face_shape"],
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"display": result["display"],
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"confidence": round(float(result["confidence"]), 4),
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"is_mixed": bool(details.get("is_mixed")),
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"second_shape": details.get("second_shape"),
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"score_gap": round(float(details["score_gap"]), 2) if details.get("score_gap") is not None else None,
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"ranked": ranked,
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"features": _jsonable(result.get("features") or {}),
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"zscores": _jsonable(details.get("zscores") or {}),
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"image_size": {"width": w, "height": h},
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"annotated_image_base64": _jpg_b64(annotated) if annotated is not None else None,
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}
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return ok(data)
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# ---------------------------------------------------------------------------
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# 健康检查
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# ---------------------------------------------------------------------------
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