Add min score

frigate/config/semantic_search.py

@@ -23,17 +23,23 @@ class SemanticSearchConfig(FrigateBaseModel):
 
 class FaceRecognitionConfig(FrigateBaseModel):
     enabled: bool = Field(default=False, title="Enable face recognition.")
+    min_score: float = Field(
+        title="Minimum face distance score required to save the attempt.",
+        default=0.8,
+        gt=0.0,
+        le=1.0,
+    )
     threshold: float = Field(
-        default=170,
-        title="minimum face distance score required to be considered a match.",
+        default=0.9,
+        title="Minimum face distance score required to be considered a match.",
         gt=0.0,
         le=1.0,
     )
     min_area: int = Field(
         default=500, title="Min area of face box to consider running face recognition."
     )
-    debug_save_images: bool = Field(
-        default=False, title="Save images of face detections for debugging."
+    save_attempts: bool = Field(
+        default=True, title="Save images of face detections for training."
     )
 
 

frigate/embeddings/maintainer.py

@@ -515,13 +515,19 @@ class EmbeddingMaintainer(threading.Thread):
             f"Detected best face for person as: {sub_label} with probability {score} and overall face score {face_score}"
         )
 
-        if self.config.face_recognition.debug_save_images:
+        if self.config.face_recognition.save_attempts:
             # write face to library
             folder = os.path.join(FACE_DIR, "debug")
             file = os.path.join(folder, f"{id}-{sub_label}-{score}-{face_score}.webp")
             os.makedirs(folder, exist_ok=True)
             cv2.imwrite(file, face_frame)
 
+        if score < self.config.face_recognition.threshold:
+            logger.debug(
+                f"Recognized face distance {score} is less than threshold {self.config.face_recognition.threshold}"
+            )
+            return
+
         if id in self.detected_faces and face_score <= self.detected_faces[id]:
             logger.debug(
                 f"Recognized face distance {score} and overall score {face_score} is less than previous overall face score ({self.detected_faces.get(id)})."

frigate/util/model.py

@@ -166,7 +166,7 @@ class FaceClassificationModel:
         self.landmark_detector.loadModel("/config/model_cache/facedet/landmarkdet.yaml")
         self.recognizer: cv2.face.LBPHFaceRecognizer = (
             cv2.face.LBPHFaceRecognizer_create(
-                radius=2, threshold=(1 - config.threshold) * 1000
+                radius=2, threshold=(1 - config.min_score) * 1000
             )
         )
         self.label_map: dict[int, str] = {}