Miscellaneous fixes (#23619)

* fix stale active object indicators on the live dashboard

The camera_activity/<camera> snapshot cache is only written when a client sends onConnect, and object "end" events only update the local state of mounted useCameraActivity hooks, never the cache. As a result, a hook that seeded from a stale cache or missed an "end" event while disconnected showed objects that had already left, with no path to correct itself short of a full page reload.

This change will re-request the snapshot on hook mount (collapsed to one onConnect per task across camera cards), and always re-notify camera_activity topics so hooks reconcile against their own local state instead of relying on snapshot-vs-snapshot comparison, and clear the payload dedup cache on reconnect and resync so byte-identical snapshots still apply.

* docs tweaks

* fix mqtt log message

* use consistent values for lpr debug frame filenames

with millisecond resolution

* apply object events through a functional updater to prevent lost updates

The events effect derived a new objects list from the value captured at render time and wrote the whole list back. When events arrived close
together, a run derived from a stale list erased a concurrent run's removal; the resurrected object then had no remaining "end" event to clear it, and the add branch could mint a duplicate entry that no splice could ever remove, leaving the live dashboard showing active objects the backend had already cleared, until a page reload.

The fix is to apply each event inside setObjects so it operates on the true current list exactly once. Unchanged results return the same reference so React bails out of re-rendering, and the label rewrite is hoisted so added objects get the sub_label/verified label directly instead of relying on the effect re-running against its own state update.

frigate/data_processing/common/license_plate/mixin.py

@@ -86,13 +86,15 @@ class LicensePlateProcessingMixin:
         self.similarity_threshold = 0.8
         self.cluster_threshold = 0.85
 
-    def _detect(self, image: np.ndarray) -> List[np.ndarray]:
+    def _detect(self, image: np.ndarray, debug_frame_id: int) -> List[np.ndarray]:
         """
         Detect possible areas of text in the input image by first resizing and normalizing it,
         running a detection model, and filtering out low-probability regions.
 
         Args:
             image (np.ndarray): The input image in which license plates will be detected.
+            debug_frame_id (int): Shared id used to name debug images so all artifacts
+                from a single LPR pass share the same filename suffix.
 
         Returns:
             List[np.ndarray]: A list of bounding box coordinates representing detected license plates.
@@ -106,9 +108,8 @@ class LicensePlateProcessingMixin:
         normalized_image = self._normalize_image(resized_image)
 
         if WRITE_DEBUG_IMAGES:
-            current_time = int(datetime.datetime.now().timestamp())
             cv2.imwrite(
-                f"debug/frames/license_plate_resized_{current_time}.jpg",
+                f"debug/frames/license_plate_resized_{debug_frame_id}.jpg",
                 resized_image,
             )
 
@@ -203,7 +204,7 @@ class LicensePlateProcessingMixin:
         return self.ctc_decoder(outputs)
 
     def _process_license_plate(
-        self, camera: str, id: str, image: np.ndarray
+        self, camera: str, id: str, image: np.ndarray, debug_frame_id: int
     ) -> Tuple[List[str], List[List[float]], List[int]]:
         """
         Complete pipeline for detecting, classifying, and recognizing license plates in the input image.
@@ -214,6 +215,8 @@ class LicensePlateProcessingMixin:
             camera (str): Camera identifier.
             id (str): Event identifier.
             image (np.ndarray): The input image in which to detect, classify, and recognize license plates.
+            debug_frame_id (int): Shared id used to name debug images so all artifacts
+                from a single LPR pass share the same filename suffix.
 
         Returns:
             Tuple[List[str], List[List[float]], List[int]]: Detected license plate texts, character-level confidence scores for each plate (flattened into a single list per plate), and areas of the plates.
@@ -227,7 +230,7 @@ class LicensePlateProcessingMixin:
             logger.debug("Model runners not loaded")
             return [], [], []
 
-        boxes = self._detect(image)
+        boxes = self._detect(image, debug_frame_id)
         if len(boxes) == 0:
             logger.debug(f"{camera}: No boxes found by OCR detector model")
             return [], [], []
@@ -243,7 +246,6 @@ class LicensePlateProcessingMixin:
             boxes, plate_width=plate_width, gap_fraction=0.1
         )
 
-        current_time = int(datetime.datetime.now().timestamp())
         if WRITE_DEBUG_IMAGES:
             debug_image = image.copy()
             for box in boxes:
@@ -259,7 +261,7 @@ class LicensePlateProcessingMixin:
                 )
 
             cv2.imwrite(
-                f"debug/frames/license_plate_boxes_{current_time}.jpg", debug_image
+                f"debug/frames/license_plate_boxes_{debug_frame_id}.jpg", debug_image
             )
 
         boxes = self._sort_boxes(list(boxes))
@@ -322,7 +324,7 @@ class LicensePlateProcessingMixin:
             if WRITE_DEBUG_IMAGES:
                 for i, img in enumerate(group_plate_images):
                     cv2.imwrite(
-                        f"debug/frames/license_plate_cropped_{current_time}_{group_indices[i] + 1}.jpg",
+                        f"debug/frames/license_plate_cropped_{debug_frame_id}_{group_indices[i] + 1}.jpg",
                         img,
                     )
 
@@ -335,7 +337,7 @@ class LicensePlateProcessingMixin:
                     cv2.imwrite(
                         os.path.join(
                             CLIPS_DIR,
-                            f"lpr/{camera}/{id}/{current_time}_{group_indices[i] + 1}.jpg",
+                            f"lpr/{camera}/{id}/{debug_frame_id}_{group_indices[i] + 1}.jpg",
                         ),
                         img,
                     )
@@ -1199,6 +1201,7 @@ class LicensePlateProcessingMixin:
         self.metrics.yolov9_lpr_pps.value = self.plates_det_second.eps()
         camera = obj_data if dedicated_lpr else obj_data["camera"]
         current_time = int(datetime.datetime.now().timestamp())
+        debug_frame_id = int(datetime.datetime.now().timestamp() * 1000)
 
         if not self.config.cameras[camera].lpr.enabled:
             return
@@ -1214,7 +1217,7 @@ class LicensePlateProcessingMixin:
 
             if WRITE_DEBUG_IMAGES:
                 cv2.imwrite(
-                    f"debug/frames/dedicated_lpr_masked_{current_time}.jpg",
+                    f"debug/frames/dedicated_lpr_masked_{debug_frame_id}.jpg",
                     rgb,
                 )
 
@@ -1326,7 +1329,7 @@ class LicensePlateProcessingMixin:
 
                 if WRITE_DEBUG_IMAGES:
                     cv2.imwrite(
-                        f"debug/frames/car_frame_{current_time}.jpg",
+                        f"debug/frames/car_frame_{debug_frame_id}.jpg",
                         car,
                     )
 
@@ -1454,7 +1457,7 @@ class LicensePlateProcessingMixin:
 
             if WRITE_DEBUG_IMAGES:
                 cv2.imwrite(
-                    f"debug/frames/license_plate_frame_{current_time}.jpg",
+                    f"debug/frames/license_plate_frame_{debug_frame_id}.jpg",
                     license_plate_frame,
                 )
 
@@ -1464,7 +1467,7 @@ class LicensePlateProcessingMixin:
         # run detection, returns results sorted by confidence, best first
         start = datetime.datetime.now().timestamp()
         license_plates, confidences, areas = self._process_license_plate(
-            camera, id, license_plate_frame
+            camera, id, license_plate_frame, debug_frame_id
         )
         self.plates_rec_second.update()
         self.plate_rec_speed.update(datetime.datetime.now().timestamp() - start)