Face recognition reprocess (#16212)

* Implement update topic

* Add API for reprocessing face

* Get reprocess working

* Fix crash when no faces exist

* Simplify

frigate/api/classification.py

@@ -100,6 +100,39 @@ def train_face(request: Request, name: str, body: dict = None):
     )
 
 
+@router.post("/faces/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(training_file)
+
+    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/comms/embeddings_updater.py

@@ -14,6 +14,7 @@ class EmbeddingsRequestEnum(Enum):
     embed_thumbnail = "embed_thumbnail"
     generate_search = "generate_search"
     register_face = "register_face"
+    reprocess_face = "reprocess_face"
 
 
 class EmbeddingsResponder:

frigate/data_processing/real_time/face_processor.py

@@ -5,6 +5,7 @@ import datetime
 import logging
 import os
 import random
+import shutil
 import string
 from typing import Optional
 
@@ -32,7 +33,7 @@ class FaceProcessor(RealTimeProcessorApi):
         self.face_config = config.face_recognition
         self.face_detector: cv2.FaceDetectorYN = None
         self.landmark_detector: cv2.face.FacemarkLBF = None
-        self.face_recognizer: cv2.face.LBPHFaceRecognizer = None
+        self.recognizer: cv2.face.LBPHFaceRecognizer = None
         self.requires_face_detection = "face" not in self.config.objects.all_objects
         self.detected_faces: dict[str, float] = {}
 
@@ -113,6 +114,9 @@ class FaceProcessor(RealTimeProcessorApi):
                 faces.append(img)
                 labels.append(idx)
 
+        if not faces:
+            return
+
         self.recognizer: cv2.face.LBPHFaceRecognizer = (
             cv2.face.LBPHFaceRecognizer_create(
                 radius=2, threshold=(1 - self.face_config.min_score) * 1000
@@ -211,9 +215,12 @@ class FaceProcessor(RealTimeProcessorApi):
         if not self.landmark_detector:
             return None
 
-        if not self.label_map:
+        if not self.recognizer:
             self.__build_classifier()
 
+            if not self.recognizer:
+                return None
+
         img = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)
         img = self.__align_face(img, img.shape[1], img.shape[0])
         index, distance = self.recognizer.predict(img)
@@ -400,6 +407,35 @@ class FaceProcessor(RealTimeProcessorApi):
                 "message": "Successfully registered face.",
                 "success": True,
             }
+        elif topic == EmbeddingsRequestEnum.reprocess_face.value:
+            current_file: str = request_data["image_file"]
+            id = current_file[0 : current_file.index("-", current_file.index("-") + 1)]
+            face_score = current_file[current_file.rfind("-") : current_file.rfind(".")]
+            img = None
+
+            if current_file:
+                img = cv2.imread(current_file)
+
+            if img is None:
+                return {
+                    "message": "Invalid image file.",
+                    "success": False,
+                }
+
+            res = self.__classify_face(img)
+
+            if not res:
+                return
+
+            sub_label, score = res
+
+            if self.config.face_recognition.save_attempts:
+                # write face to library
+                folder = os.path.join(FACE_DIR, "train")
+                new_file = os.path.join(
+                    folder, f"{id}-{sub_label}-{score}-{face_score}.webp"
+                )
+                shutil.move(current_file, new_file)
 
     def expire_object(self, object_id: str):
         if object_id in self.detected_faces:

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