Don't save redundant state changes

frigate/data_processing/real_time/custom_classification.py

@@ -99,6 +99,31 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         if self.inference_speed:
             self.inference_speed.update(duration)
 
+    def _should_save_image(self, camera: str, detected_state: str) -> bool:
+        """
+        Determine if we should save the image for training.
+        Only save when the state is changing or being verified, not when it's stable.
+        """
+        if camera not in self.state_history:
+            # First detection for this camera, save it
+            return True
+
+        verification = self.state_history[camera]
+        current_state = verification.get("current_state")
+        pending_state = verification.get("pending_state")
+
+        # Save if there's a pending state change being verified
+        if pending_state is not None:
+            return True
+
+        # Save if the detected state differs from the current verified state
+        # (state is changing)
+        if current_state is not None and detected_state != current_state:
+            return True
+
+        # Don't save if state is stable (detected_state == current_state)
+        return False
+
     def verify_state_change(self, camera: str, detected_state: str) -> str | None:
         """
         Verify state change requires 3 consecutive identical states before publishing.
@@ -212,6 +237,7 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
                 return
 
         if self.interpreter is None:
+            if self._should_save_image(camera, "unknown"):
                 write_classification_attempt(
                     self.train_dir,
                     cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
@@ -236,12 +262,15 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         score = round(probs[best_id], 2)
         self.__update_metrics(datetime.datetime.now().timestamp() - now)
 
+        detected_state = self.labelmap[best_id]
+
+        if self._should_save_image(camera, detected_state):
             write_classification_attempt(
                 self.train_dir,
                 cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
                 "none-none",
                 now,
-            self.labelmap[best_id],
+                detected_state,
                 score,
             )
 
@@ -251,7 +280,6 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
             )
             return
 
-        detected_state = self.labelmap[best_id]
         verified_state = self.verify_state_change(camera, detected_state)
 
         if verified_state is not None: