feat(gateway): 接入42和72并为42/52/72启用可刷新令牌

hairline/face_landmarks.py

@@ -44,18 +44,18 @@ class FaceLandmarker:
         self._detector = vision.FaceLandmarker.create_from_options(options)
         self._mp = mp
 
-    def detect(self, image_rgb: np.ndarray) -> np.ndarray | None:
-        """Returns (468, 3) float32 of normalized x, y and relative z, or None.
+    def detect(self, image_rgb: np.ndarray, include_iris: bool = False) -> np.ndarray | None:
+        """Returns (468, 3), or (478, 3) with iris, normalized float32 points.
 
-        The task model produces 478 landmarks (468 face + 10 iris); we return
-        only the first 468 to match the canonical FaceMesh topology used by
-        the SDK's OBJ file.
+        The task model produces 478 landmarks (468 face + 10 iris). By default
+        only the first 468 match the SDK's canonical FaceMesh topology;
+        include_iris=True retains all 478 for face-measurement calibration.
         """
         mp_image = self._mp.Image(image_format=self._mp.ImageFormat.SRGB, data=image_rgb)
         result = self._detector.detect(mp_image)
         if not result.face_landmarks:
             return None
-        lm = result.face_landmarks[0][:468]
+        lm = result.face_landmarks[0] if include_iris else result.face_landmarks[0][:468]
         arr = np.array([[p.x, p.y, p.z] for p in lm], dtype=np.float32)
         return arr
 
@@ -66,6 +66,25 @@ class FaceLandmarker:
             pass
 
 
+def to_landmark_list(landmarks):
+    from mediapipe.framework.formats import landmark_pb2
+
+    result = landmark_pb2.NormalizedLandmarkList()
+    for x, y, z in landmarks:
+        result.landmark.add(x=float(x), y=float(y), z=float(z))
+    return result
+
+
+_landmarker = None
+
+
+def get_landmarker() -> FaceLandmarker:
+    global _landmarker
+    if _landmarker is None:
+        _landmarker = FaceLandmarker(static_image_mode=True)
+    return _landmarker
+
+
 class SolutionsFaceLandmarker:
     """CPU-oriented fallback using mediapipe.solutions.face_mesh."""