Various fixes (#20785)

* Catch case where detector overflows

* Add more debug logs

* Cleanup

* Adjust no class wording

* Adjustments

frigate/embeddings/maintainer.py

@@ -397,7 +397,14 @@ class EmbeddingMaintainer(threading.Thread):
 
         source_type, _, camera, frame_name, data = update
 
+        logger.debug(
+            f"Received update - source_type: {source_type}, camera: {camera}, data label: {data.get('label') if data else 'None'}"
+        )
+
         if not camera or source_type != EventTypeEnum.tracked_object:
+            logger.debug(
+                f"Skipping update - camera: {camera}, source_type: {source_type}"
+            )
             return
 
         if self.config.semantic_search.enabled:
@@ -407,6 +414,9 @@ class EmbeddingMaintainer(threading.Thread):
 
         # no need to process updated objects if no processors are active
         if len(self.realtime_processors) == 0 and len(self.post_processors) == 0:
+            logger.debug(
+                f"No processors active - realtime: {len(self.realtime_processors)}, post: {len(self.post_processors)}"
+            )
             return
 
         # Create our own thumbnail based on the bounding box and the frame time
@@ -415,6 +425,7 @@ class EmbeddingMaintainer(threading.Thread):
                 frame_name, camera_config.frame_shape_yuv
             )
         except FileNotFoundError:
+            logger.debug(f"Frame {frame_name} not found for camera {camera}")
             pass
 
         if yuv_frame is None:
@@ -423,7 +434,11 @@ class EmbeddingMaintainer(threading.Thread):
             )
             return
 
+        logger.debug(
+            f"Processing {len(self.realtime_processors)} realtime processors for object {data.get('id')} (label: {data.get('label')})"
+        )
         for processor in self.realtime_processors:
+            logger.debug(f"Calling process_frame on {processor.__class__.__name__}")
             processor.process_frame(data, yuv_frame)
 
         for processor in self.post_processors:

frigate/object_detection/base.py

@@ -9,6 +9,7 @@ from multiprocessing import Queue, Value
 from multiprocessing.synchronize import Event as MpEvent
 
 import numpy as np
+import zmq
 
 from frigate.comms.object_detector_signaler import (
     ObjectDetectorPublisher,
@@ -377,6 +378,15 @@ class RemoteObjectDetector:
         if self.stop_event.is_set():
             return detections
 
+        # Drain any stale detection results from the ZMQ buffer before making a new request
+        # This prevents reading detection results from a previous request
+        # NOTE: This should never happen, but can in some rare cases
+        while True:
+            try:
+                self.detector_subscriber.socket.recv_string(flags=zmq.NOBLOCK)
+            except zmq.Again:
+                break
+
         # copy input to shared memory
         self.np_shm[:] = tensor_input[:]
         self.detection_queue.put(self.name)

web/src/components/card/ClassificationCard.tsx

@@ -181,6 +181,7 @@ type GroupedClassificationCardProps = {
   selectedItems: string[];
   i18nLibrary: string;
   objectType: string;
+  noClassificationLabel?: string;
   onClick: (data: ClassificationItemData | undefined) => void;
   children?: (data: ClassificationItemData) => React.ReactNode;
 };
@@ -190,6 +191,7 @@ export function GroupedClassificationCard({
   threshold,
   selectedItems,
   i18nLibrary,
+  noClassificationLabel = "details.none",
   onClick,
   children,
 }: GroupedClassificationCardProps) {
@@ -222,10 +224,14 @@ export function GroupedClassificationCard({
     const bestTyped: ClassificationItemData = best;
     return {
       ...bestTyped,
-      name: event ? (event.sub_label ?? t("details.unknown")) : bestTyped.name,
+      name: event
+        ? event.sub_label && event.sub_label !== "none"
+          ? event.sub_label
+          : t(noClassificationLabel)
+        : bestTyped.name,
       score: event?.data?.sub_label_score || bestTyped.score,
     };
-  }, [group, event, t]);
+  }, [group, event, noClassificationLabel, t]);
 
   const bestScoreStatus = useMemo(() => {
     if (!bestItem?.score || !threshold) {
@@ -311,8 +317,10 @@ export function GroupedClassificationCard({
                     isMobile && "px-2",
                   )}
                 >
-                  {event?.sub_label ? event.sub_label : t("details.unknown")}
+                  {event?.sub_label && event.sub_label !== "none"
-                  {event?.sub_label && (
+                    ? event.sub_label
+                    : t(noClassificationLabel)}
+                  {event?.sub_label && event.sub_label !== "none" && (
                     <div
                       className={cn(
                         "",

web/src/pages/FaceLibrary.tsx

@@ -845,6 +845,7 @@ function FaceAttemptGroup({
       selectedItems={selectedFaces}
       i18nLibrary="views/faceLibrary"
       objectType="person"
+      noClassificationLabel="details.unknown"
       onClick={(data) => {
         if (data) {
           onClickFaces([data.filename], true);

web/src/views/classification/ModelTrainingView.tsx

@@ -961,6 +961,7 @@ function ObjectTrainGrid({
                 selectedItems={selectedImages}
                 i18nLibrary="views/classificationModel"
                 objectType={model.object_config?.objects?.at(0) ?? "Object"}
+                noClassificationLabel="details.none"
                 onClick={(data) => {
                   if (data) {
                     onClickImages([data.filename], true);