Add API for reprocessing face

frigate/api/classification.py

@@ -100,6 +100,39 @@ def train_face(request: Request, name: str, body: dict = None):
     )
 
 
+@router.post("/faces/train/{name}/reprocess")
+def reclassify_face(request: Request, name: str, body: dict = None):
+    if not request.app.frigate_config.face_recognition.enabled:
+        return JSONResponse(
+            status_code=400,
+            content={"message": "Face recognition is not enabled.", "success": False},
+        )
+
+    json: dict[str, any] = body or {}
+    training_file = os.path.join(
+        FACE_DIR, f"train/{sanitize_filename(json.get('training_file', ''))}"
+    )
+
+    if not training_file or not os.path.isfile(training_file):
+        return JSONResponse(
+            content=(
+                {
+                    "success": False,
+                    "message": f"Invalid filename or no file exists: {training_file}",
+                }
+            ),
+            status_code=404,
+        )
+
+    context: EmbeddingsContext = request.app.embeddings
+    response = context.reprocess_face()
+
+    return JSONResponse(
+        content=response,
+        status_code=200,
+    )
+
+
 @router.post("/faces/{name}/delete")
 def deregister_faces(request: Request, name: str, body: dict = None):
     if not request.app.frigate_config.face_recognition.enabled:

frigate/data_processing/real_time/face_processor.py

@@ -401,7 +401,7 @@ class FaceProcessor(RealTimeProcessorApi):
                 "message": "Successfully registered face.",
                 "success": True,
             }
-        elif topic == EmbeddingsRequestEnum.reprocess_face:
+        elif topic == EmbeddingsRequestEnum.reprocess_face.value:
             current_file = request_data["image_file"]
             face_score = current_file[current_file.rfind("-") : current_file.rfind(".")]
             img = None

frigate/embeddings/__init__.py

@@ -211,6 +211,11 @@ class EmbeddingsContext:
 
         return self.db.execute_sql(sql_query).fetchall()
 
+    def reprocess_face(self, face_file: str) -> dict[str, any]:
+        return self.requestor.send_data(
+            EmbeddingsRequestEnum.reprocess_face.value, {"image_file": face_file}
+        )
+
     def clear_face_classifier(self) -> None:
         self.requestor.send_data(
             EmbeddingsRequestEnum.clear_face_classifier.value, None