feat: 新增42号人脸特征与72号B端关键点接口

gateway/app.py

@@ -527,6 +527,12 @@ async def hair_grow_b(request: Request):
     return await _proxy(request, "/api/v1/hair/grow-b")
 
 
+@app.post("/api/v1/face/b-end-points", summary="72号接口：B 端五个关键点", tags=["人脸分析"])
+async def face_b_end_points(request: Request):
+    """返回扶正裁切后的图片和透明标注层。"""
+    return await _proxy(request, "/api/v1/face/b-end-points")
+
+
 @app.post("/api/v1/face/features", tags=["人脸分析"])
 async def face_features(
     image_file: Optional[UploadFile] = File(default=None, description="上传图片文件（JPG/PNG）"),
@@ -534,6 +540,42 @@ async def face_features(
     image_base64: Optional[str] = Form(default=None, description="图片 base64（需带前缀）"),
 ):
     """接口4：用户特征分析 — 本机直接调豆包视觉模型，不经过 worker。"""
+    return await _face_features_response(image_file, image_url, image_base64)
+
+
+@app.post(
+    "/api/v1/face/features-42",
+    summary="42号接口-加两个特征点",
+    tags=["人脸分析"],
+)
+async def face_features_42(
+    image_file: Optional[UploadFile] = File(default=None, description="上传图片文件（JPG/PNG）"),
+    image_url: Optional[str] = Form(default=None, description="图片 URL"),
+    image_base64: Optional[str] = Form(default=None, description="图片 base64（需带前缀）"),
+):
+    """沿用接口 4。图片参数 `image_file`、`image_url`、`image_base64` 必须且只能传一个。
+
+    先检查原图人脸完整性和俯拍角度，再按接口 52 扶正裁切。俯拍角度超过服务端
+    `FRONTAL_PITCH_THR`（默认 30°）时拦截。
+
+    | 业务码 | 情况 |
+    |---|---|
+    | 1001 | 未检测到人脸 |
+    | 1003 | 人脸不完整、俯拍角度过大或无法扶正裁切 |
+    | 1008 | 图片 URL 下载失败、Base64 解码失败或图片无法读取 |
+    | 1007 | 图片参数错误、预处理内部异常或视觉模型调用失败 |
+
+    业务失败使用 HTTP 200，返回 `code`、`message`、`request_id` 和 `data: null`。
+    同一业务码下的具体原因见 `message`，响应不含独立的质检原因或角度字段。
+
+    成功时 `data.features` 为 JSON 字符串，在接口 4 的字段上新增
+    `zygoma_expansion`（颧弓外扩）和 `temple_hollowing`（太阳穴凹陷）。
+    Ft ÷ Zy 小于 0.8 时两项均为 true，否则均为 false，无法测量时均为 null。
+    """
+    return await _face_features_response(image_file, image_url, image_base64, include_ratios=True)
+
+
+async def _face_features_response(image_file, image_url, image_base64, *, include_ratios=False):
     import uuid as _uuid
 
     # 三选一校验
@@ -562,6 +604,25 @@ async def face_features(
     from fastapi.concurrency import run_in_threadpool
     from face_features import analyze_features
 
+    ratio_features = {}
+    if include_ratios:
+        from face_analysis.face_ratios import PortraitQualityError
+
+        try:
+            img_bytes, ratio_features = await _prepare_features_42(img_bytes, image_url)
+        except PortraitQualityError as ex:
+            return JSONResponse(status_code=200, content={
+                "code": ex.code, "message": str(ex),
+                "request_id": f"gw-{_uuid.uuid4().hex[:8]}", "data": None,
+            })
+        except Exception:
+            logger.exception("接口42 图片预处理失败")
+            return JSONResponse(status_code=200, content={
+                "code": 1007, "message": "图片预处理失败",
+                "request_id": f"gw-{_uuid.uuid4().hex[:8]}", "data": None,
+            })
+        image_url = None
+
     try:
         feats = await run_in_threadpool(analyze_features, img_bytes, image_url)
     except Exception as ex:
@@ -581,10 +642,42 @@ async def face_features(
         "code": 0,
         "message": "success",
         "request_id": f"gw-{_uuid.uuid4().hex[:8]}",
-        "data": {"features": json.dumps(feats, ensure_ascii=False)},
+        "data": {"features": json.dumps({**feats, **ratio_features}, ensure_ascii=False)},
     })
 
 
+async def _prepare_features_42(image_bytes, image_url):
+    from fastapi.concurrency import run_in_threadpool
+
+    if image_url:
+        import httpx
+
+        try:
+            async with httpx.AsyncClient(timeout=30.0, follow_redirects=True) as client:
+                response = await client.get(image_url)
+                response.raise_for_status()
+                image_bytes = response.content
+        except httpx.HTTPError as exc:
+            from face_analysis.face_ratios import PortraitQualityError
+
+            raise PortraitQualityError(1008, "图片下载失败（URL 不可达）") from exc
+
+    async with _if99_lock:
+        return await run_in_threadpool(_preprocess_and_measure_42, image_bytes)
+
+
+def _preprocess_and_measure_42(image_bytes):
+    import cv2
+    from face_analysis.face_ratios import analyze_ratio_features, prepare_feature_portrait
+
+    image, landmarks = prepare_feature_portrait(image_bytes)
+    success, encoded = cv2.imencode(".png", image)
+    if not success:
+        raise RuntimeError("处理后图片编码失败")
+    ratios = analyze_ratio_features(image, landmarks)
+    return encoded.tobytes(), ratios
+
+
 @app.post("/api/v1/hairline/generate", tags=["人脸分析"])
 async def hairline_generate(request: Request):
     """接口5：发际线PNG生成"""