Update handler.py

handler.py

@@ -175,6 +175,12 @@ class EndpointHandler:
                 })
         return face_info_list
 
+    def get_face_embedding(self, image):
+        # Extract features for the face image region
+        # Implement the logic to extract face embeddings
+        # For now, returning a placeholder value
+        return np.random.rand(512)  # Replace with actual embedding extraction logic
+
     def __call__(self, data):
 
         def convert_from_cv2_to_image(img: np.ndarray) -> Image:
@@ -270,7 +276,7 @@ class EndpointHandler:
         # Use the largest face detected
         face_info = max(face_info_list, key=lambda x: (x["bbox"][2] - x["bbox"][0]) * (x["bbox"][3] - x["bbox"][1]))
         face_emb = face_info["embedding"]
-        face_kps = draw_kps(convert_from_cv2_to_image(face_image_cv2), face_info["kps"])
+        face_kps = draw_kps(convert_from_cv2_to_image(face_image_cv2), face_info["bbox"])
         img_controlnet = face_image
 
         if pose_image:
@@ -286,7 +292,7 @@ class EndpointHandler:
 
             face_info = max(face_info_list, key=lambda x: (x["bbox"][2] - x["bbox"][0]) * (x["bbox"][3] - x["bbox"][1]))
             face_emb = face_info["embedding"]
-            face_kps = draw_kps(pose_image, face_info["kps"])
+            face_kps = draw_kps(pose_image, face_info["bbox"])
 
             width, height = face_kps.size