Bump node-forge from 1.3.3 to 1.4.0 in /docs

Bumps [node-forge](https://github.com/digitalbazaar/forge) from 1.3.3 to 1.4.0.
- [Changelog](https://github.com/digitalbazaar/forge/blob/main/CHANGELOG.md)
- [Commits](https://github.com/digitalbazaar/forge/compare/v1.3.3...v1.4.0)

---
updated-dependencies:
- dependency-name: node-forge
  dependency-version: 1.4.0
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>

.github/workflows/pr_template_check.yml

@@ -16,7 +16,7 @@ jobs:
         uses: actions/github-script@v7
         with:
           script: |
-            const maintainers = ['blakeblackshear', 'NickM-27', 'hawkeye217'];
+            const maintainers = ['blakeblackshear', 'NickM-27', 'hawkeye217', 'dependabot[bot]'];
             const author = context.payload.pull_request.user.login;
 
             if (maintainers.includes(author)) {

docs/package-lock.json

@@ -15952,9 +15952,9 @@
       }
     },
     "node_modules/node-forge": {
-      "version": "1.3.3",
-      "resolved": "https://registry.npmjs.org/node-forge/-/node-forge-1.3.3.tgz",
-      "integrity": "sha512-rLvcdSyRCyouf6jcOIPe/BgwG/d7hKjzMKOas33/pHEr6gbq18IK9zV7DiPvzsz0oBJPme6qr6H6kGZuI9/DZg==",
+      "version": "1.4.0",
+      "resolved": "https://registry.npmjs.org/node-forge/-/node-forge-1.4.0.tgz",
+      "integrity": "sha512-LarFH0+6VfriEhqMMcLX2F7SwSXeWwnEAJEsYm5QKWchiVYVvJyV9v7UDvUv+w5HO23ZpQTXDv/GxdDdMyOuoQ==",
       "license": "(BSD-3-Clause OR GPL-2.0)",
       "engines": {
         "node": ">= 6.13.0"

frigate/api/app.py

@@ -613,6 +613,34 @@ def config_set(request: Request, body: AppConfigSetBody):
 
                 try:
                     config = FrigateConfig.parse(new_raw_config)
+                except ValidationError as e:
+                    with open(config_file, "w") as f:
+                        f.write(old_raw_config)
+                        f.close()
+                    logger.error(
+                        f"Config Validation Error:\n\n{str(traceback.format_exc())}"
+                    )
+                    error_messages = []
+                    for err in e.errors():
+                        msg = err.get("msg", "")
+                        # Strip pydantic "Value error, " prefix for cleaner display
+                        if msg.startswith("Value error, "):
+                            msg = msg[len("Value error, ") :]
+                        error_messages.append(msg)
+                    message = (
+                        "; ".join(error_messages)
+                        if error_messages
+                        else "Check logs for error message."
+                    )
+                    return JSONResponse(
+                        content=(
+                            {
+                                "success": False,
+                                "message": f"Error saving config: {message}",
+                            }
+                        ),
+                        status_code=400,
+                    )
                 except Exception:
                     with open(config_file, "w") as f:
                         f.write(old_raw_config)

frigate/camera/__init__.py

@@ -1,26 +1,27 @@
 import multiprocessing as mp
-from multiprocessing.managers import SyncManager
+import queue
+from multiprocessing.managers import SyncManager, ValueProxy
 from multiprocessing.sharedctypes import Synchronized
 from multiprocessing.synchronize import Event
 
 
 class CameraMetrics:
-    camera_fps: Synchronized
-    detection_fps: Synchronized
-    detection_frame: Synchronized
-    process_fps: Synchronized
-    skipped_fps: Synchronized
-    read_start: Synchronized
-    audio_rms: Synchronized
-    audio_dBFS: Synchronized
+    camera_fps: ValueProxy[float]
+    detection_fps: ValueProxy[float]
+    detection_frame: ValueProxy[float]
+    process_fps: ValueProxy[float]
+    skipped_fps: ValueProxy[float]
+    read_start: ValueProxy[float]
+    audio_rms: ValueProxy[float]
+    audio_dBFS: ValueProxy[float]
 
-    frame_queue: mp.Queue
+    frame_queue: queue.Queue
 
-    process_pid: Synchronized
-    capture_process_pid: Synchronized
-    ffmpeg_pid: Synchronized
-    reconnects_last_hour: Synchronized
-    stalls_last_hour: Synchronized
+    process_pid: ValueProxy[int]
+    capture_process_pid: ValueProxy[int]
+    ffmpeg_pid: ValueProxy[int]
+    reconnects_last_hour: ValueProxy[int]
+    stalls_last_hour: ValueProxy[int]
 
     def __init__(self, manager: SyncManager):
         self.camera_fps = manager.Value("d", 0)
@@ -56,14 +57,14 @@ class PTZMetrics:
     reset: Event
 
     def __init__(self, *, autotracker_enabled: bool):
-        self.autotracker_enabled = mp.Value("i", autotracker_enabled)
+        self.autotracker_enabled = mp.Value("i", autotracker_enabled)  # type: ignore[assignment]
 
-        self.start_time = mp.Value("d", 0)
-        self.stop_time = mp.Value("d", 0)
-        self.frame_time = mp.Value("d", 0)
-        self.zoom_level = mp.Value("d", 0)
-        self.max_zoom = mp.Value("d", 0)
-        self.min_zoom = mp.Value("d", 0)
+        self.start_time = mp.Value("d", 0)  # type: ignore[assignment]
+        self.stop_time = mp.Value("d", 0)  # type: ignore[assignment]
+        self.frame_time = mp.Value("d", 0)  # type: ignore[assignment]
+        self.zoom_level = mp.Value("d", 0)  # type: ignore[assignment]
+        self.max_zoom = mp.Value("d", 0)  # type: ignore[assignment]
+        self.min_zoom = mp.Value("d", 0)  # type: ignore[assignment]
 
         self.tracking_active = mp.Event()
         self.motor_stopped = mp.Event()

frigate/camera/activity_manager.py

@@ -37,6 +37,9 @@ class CameraActivityManager:
             self.__init_camera(camera_config)
 
     def __init_camera(self, camera_config: CameraConfig) -> None:
+        if camera_config.name is None:
+            return
+
         self.last_camera_activity[camera_config.name] = {}
         self.camera_all_object_counts[camera_config.name] = Counter()
         self.camera_active_object_counts[camera_config.name] = Counter()
@@ -114,7 +117,7 @@ class CameraActivityManager:
         self.last_camera_activity = new_activity
 
     def compare_camera_activity(
-        self, camera: str, new_activity: dict[str, Any]
+        self, camera: str, new_activity: list[dict[str, Any]]
     ) -> None:
         all_objects = Counter(
             obj["label"].replace("-verified", "") for obj in new_activity
@@ -175,6 +178,9 @@ class AudioActivityManager:
             self.__init_camera(camera_config)
 
     def __init_camera(self, camera_config: CameraConfig) -> None:
+        if camera_config.name is None:
+            return
+
         self.current_audio_detections[camera_config.name] = {}
 
     def update_activity(self, new_activity: dict[str, dict[str, Any]]) -> None:
@@ -202,7 +208,7 @@ class AudioActivityManager:
 
     def compare_audio_activity(
         self, camera: str, new_detections: list[tuple[str, float]], now: float
-    ) -> None:
+    ) -> bool:
         camera_config = self.config.cameras.get(camera)
         if camera_config is None:
             return False

frigate/camera/maintainer.py

@@ -102,7 +102,7 @@ class CameraMaintainer(threading.Thread):
                 f"recommend increasing it to at least {shm_stats['min_shm']}MB."
             )
 
-        return shm_stats["shm_frame_count"]
+        return int(shm_stats["shm_frame_count"])
 
     def __start_camera_processor(
         self, name: str, config: CameraConfig, runtime: bool = False
@@ -152,10 +152,10 @@ class CameraMaintainer(threading.Thread):
             camera_stop_event,
             self.config.logger,
         )
-        self.camera_processes[config.name] = camera_process
+        self.camera_processes[name] = camera_process
         camera_process.start()
-        self.camera_metrics[config.name].process_pid.value = camera_process.pid
-        logger.info(f"Camera processor started for {config.name}: {camera_process.pid}")
+        self.camera_metrics[name].process_pid.value = camera_process.pid
+        logger.info(f"Camera processor started for {name}: {camera_process.pid}")
 
     def __start_camera_capture(
         self, name: str, config: CameraConfig, runtime: bool = False
@@ -219,7 +219,7 @@ class CameraMaintainer(threading.Thread):
             logger.info(f"Closing frame queue for {camera}")
             empty_and_close_queue(self.camera_metrics[camera].frame_queue)
 
-    def run(self):
+    def run(self) -> None:
         self.__init_historical_regions()
 
         # start camera processes

frigate/camera/state.py

@@ -31,26 +31,26 @@ logger = logging.getLogger(__name__)
 class CameraState:
     def __init__(
         self,
-        name,
+        name: str,
         config: FrigateConfig,
         frame_manager: SharedMemoryFrameManager,
         ptz_autotracker_thread: PtzAutoTrackerThread,
-    ):
+    ) -> None:
         self.name = name
         self.config = config
         self.camera_config = config.cameras[name]
         self.frame_manager = frame_manager
         self.best_objects: dict[str, TrackedObject] = {}
         self.tracked_objects: dict[str, TrackedObject] = {}
-        self.frame_cache = {}
-        self.zone_objects = defaultdict(list)
+        self.frame_cache: dict[float, dict[str, Any]] = {}
+        self.zone_objects: defaultdict[str, list[Any]] = defaultdict(list)
         self._current_frame = np.zeros(self.camera_config.frame_shape_yuv, np.uint8)
         self.current_frame_lock = threading.Lock()
         self.current_frame_time = 0.0
-        self.motion_boxes = []
-        self.regions = []
-        self.previous_frame_id = None
-        self.callbacks = defaultdict(list)
+        self.motion_boxes: list[tuple[int, int, int, int]] = []
+        self.regions: list[tuple[int, int, int, int]] = []
+        self.previous_frame_id: str | None = None
+        self.callbacks: defaultdict[str, list[Callable]] = defaultdict(list)
         self.ptz_autotracker_thread = ptz_autotracker_thread
         self.prev_enabled = self.camera_config.enabled
 
@@ -62,10 +62,10 @@ class CameraState:
             motion_boxes = self.motion_boxes.copy()
             regions = self.regions.copy()
 
-        frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_YUV2BGR_I420)
+        frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_YUV2BGR_I420)  # type: ignore[assignment]
         # draw on the frame
         if draw_options.get("mask"):
-            mask_overlay = np.where(self.camera_config.motion.rasterized_mask == [0])
+            mask_overlay = np.where(self.camera_config.motion.rasterized_mask == [0])  # type: ignore[attr-defined]
             frame_copy[mask_overlay] = [0, 0, 0]
 
         if draw_options.get("bounding_boxes"):
@@ -97,7 +97,7 @@ class CameraState:
                     and obj["id"]
                     == self.ptz_autotracker_thread.ptz_autotracker.tracked_object[
                         self.name
-                    ].obj_data["id"]
+                    ].obj_data["id"]  # type: ignore[attr-defined]
                     and obj["frame_time"] == frame_time
                 ):
                     thickness = 5
@@ -109,10 +109,12 @@ class CameraState:
                     if (
                         self.camera_config.onvif.autotracking.zooming
                         != ZoomingModeEnum.disabled
+                        and self.camera_config.detect.width is not None
+                        and self.camera_config.detect.height is not None
                     ):
                         max_target_box = self.ptz_autotracker_thread.ptz_autotracker.tracked_object_metrics[
                             self.name
-                        ]["max_target_box"]
+                        ]["max_target_box"]  # type: ignore[index]
                         side_length = max_target_box * (
                             max(
                                 self.camera_config.detect.width,
@@ -221,14 +223,14 @@ class CameraState:
                 )
 
         if draw_options.get("timestamp"):
-            color = self.camera_config.timestamp_style.color
+            ts_color = self.camera_config.timestamp_style.color
             draw_timestamp(
                 frame_copy,
                 frame_time,
                 self.camera_config.timestamp_style.format,
                 font_effect=self.camera_config.timestamp_style.effect,
                 font_thickness=self.camera_config.timestamp_style.thickness,
-                font_color=(color.blue, color.green, color.red),
+                font_color=(ts_color.blue, ts_color.green, ts_color.red),
                 position=self.camera_config.timestamp_style.position,
             )
 
@@ -273,10 +275,10 @@ class CameraState:
 
         return frame_copy
 
-    def finished(self, obj_id):
+    def finished(self, obj_id: str) -> None:
         del self.tracked_objects[obj_id]
 
-    def on(self, event_type: str, callback: Callable):
+    def on(self, event_type: str, callback: Callable[..., Any]) -> None:
         self.callbacks[event_type].append(callback)
 
     def update(
@@ -286,7 +288,7 @@ class CameraState:
         current_detections: dict[str, dict[str, Any]],
         motion_boxes: list[tuple[int, int, int, int]],
         regions: list[tuple[int, int, int, int]],
-    ):
+    ) -> None:
         current_frame = self.frame_manager.get(
             frame_name, self.camera_config.frame_shape_yuv
         )
@@ -313,7 +315,7 @@ class CameraState:
                 f"{self.name}: New object, adding {frame_time} to frame cache for {id}"
             )
             self.frame_cache[frame_time] = {
-                "frame": np.copy(current_frame),
+                "frame": np.copy(current_frame),  # type: ignore[arg-type]
                 "object_id": id,
             }
 
@@ -356,7 +358,8 @@ class CameraState:
             if thumb_update and current_frame is not None:
                 # ensure this frame is stored in the cache
                 if (
-                    updated_obj.thumbnail_data["frame_time"] == frame_time
+                    updated_obj.thumbnail_data is not None
+                    and updated_obj.thumbnail_data["frame_time"] == frame_time
                     and frame_time not in self.frame_cache
                 ):
                     logger.debug(
@@ -397,7 +400,7 @@ class CameraState:
 
         # TODO: can i switch to looking this up and only changing when an event ends?
         # maintain best objects
-        camera_activity: dict[str, list[Any]] = {
+        camera_activity: dict[str, Any] = {
             "motion": len(motion_boxes) > 0,
             "objects": [],
         }
@@ -411,10 +414,7 @@ class CameraState:
                 sub_label = None
 
                 if obj.obj_data.get("sub_label"):
-                    if (
-                        obj.obj_data.get("sub_label")[0]
-                        in self.config.model.all_attributes
-                    ):
+                    if obj.obj_data["sub_label"][0] in self.config.model.all_attributes:
                         label = obj.obj_data["sub_label"][0]
                     else:
                         label = f"{object_type}-verified"
@@ -449,14 +449,19 @@ class CameraState:
                 # if the object is a higher score than the current best score
                 # or the current object is older than desired, use the new object
                 if (
-                    is_better_thumbnail(
+                    current_best.thumbnail_data is not None
+                    and obj.thumbnail_data is not None
+                    and is_better_thumbnail(
                         object_type,
                         current_best.thumbnail_data,
                         obj.thumbnail_data,
                         self.camera_config.frame_shape,
                     )
-                    or (now - current_best.thumbnail_data["frame_time"])
-                    > self.camera_config.best_image_timeout
+                    or (
+                        current_best.thumbnail_data is not None
+                        and (now - current_best.thumbnail_data["frame_time"])
+                        > self.camera_config.best_image_timeout
+                    )
                 ):
                     self.send_mqtt_snapshot(obj, object_type)
             else:
@@ -472,7 +477,9 @@ class CameraState:
             if obj.thumbnail_data is not None
         }
         current_best_frames = {
-            obj.thumbnail_data["frame_time"] for obj in self.best_objects.values()
+            obj.thumbnail_data["frame_time"]
+            for obj in self.best_objects.values()
+            if obj.thumbnail_data is not None
         }
         thumb_frames_to_delete = [
             t
@@ -540,7 +547,7 @@ class CameraState:
             with open(
                 os.path.join(
                     CLIPS_DIR,
-                    f"{self.camera_config.name}-{event_id}-clean.webp",
+                    f"{self.name}-{event_id}-clean.webp",
                 ),
                 "wb",
             ) as p:
@@ -549,7 +556,7 @@ class CameraState:
         # create thumbnail with max height of 175 and save
         width = int(175 * img_frame.shape[1] / img_frame.shape[0])
         thumb = cv2.resize(img_frame, dsize=(width, 175), interpolation=cv2.INTER_AREA)
-        thumb_path = os.path.join(THUMB_DIR, self.camera_config.name)
+        thumb_path = os.path.join(THUMB_DIR, self.name)
         os.makedirs(thumb_path, exist_ok=True)
         cv2.imwrite(os.path.join(thumb_path, f"{event_id}.webp"), thumb)
 

frigate/comms/webpush.py

@@ -542,9 +542,9 @@ class WebPushClient(Communicator):
 
         self.check_registrations()
 
-        reasoning: str = payload.get("reasoning", "")
+        text: str = payload.get("message") or payload.get("reasoning", "")
         title = f"{camera_name}: Monitoring Alert"
-        message = (reasoning[:197] + "...") if len(reasoning) > 200 else reasoning
+        message = (text[:197] + "...") if len(text) > 200 else text
 
         logger.debug(f"Sending camera monitoring push notification for {camera_name}")
 

frigate/config/camera/detect.py

@@ -71,6 +71,7 @@ class DetectConfig(FrigateBaseModel):
         default=None,
         title="Minimum initialization frames",
         description="Number of consecutive detection hits required before creating a tracked object. Increase to reduce false initializations. Default value is fps divided by 2.",
+        ge=2,
     )
     max_disappeared: Optional[int] = Field(
         default=None,

frigate/config/camera/review.py

@@ -188,7 +188,7 @@ class ReviewConfig(FrigateBaseModel):
     detections: DetectionsConfig = Field(
         default_factory=DetectionsConfig,
         title="Detections config",
-        description="Settings for creating detection events (non-alert) and how long to keep them.",
+        description="Settings for which tracked objects generate detections (non-alert) and how detections are retained.",
     )
     genai: GenAIReviewConfig = Field(
         default_factory=GenAIReviewConfig,

frigate/config/config.py

@@ -614,6 +614,21 @@ class FrigateConfig(FrigateBaseModel):
         if self.ffmpeg.hwaccel_args == "auto":
             self.ffmpeg.hwaccel_args = auto_detect_hwaccel()
 
+        # Populate global audio filters for all audio labels
+        all_audio_labels = {
+            label
+            for label in load_labels("/audio-labelmap.txt", prefill=521).values()
+            if label
+        }
+
+        if self.audio.filters is None:
+            self.audio.filters = {}
+
+        for key in sorted(all_audio_labels - self.audio.filters.keys()):
+            self.audio.filters[key] = AudioFilterConfig()
+
+        self.audio.filters = dict(sorted(self.audio.filters.items()))
+
         # Global config to propagate down to camera level
         global_config = self.model_dump(
             include={
@@ -672,12 +687,6 @@ class FrigateConfig(FrigateBaseModel):
             detector_config.model = model
             self.detectors[key] = detector_config
 
-        all_audio_labels = {
-            label
-            for label in load_labels("/audio-labelmap.txt", prefill=521).values()
-            if label
-        }
-
         for name, camera in self.cameras.items():
             modified_global_config = global_config.copy()
 
@@ -755,7 +764,7 @@ class FrigateConfig(FrigateBaseModel):
                 )
 
             # Default min_initialized configuration
-            min_initialized = int(camera_config.detect.fps / 2)
+            min_initialized = max(int(camera_config.detect.fps / 2), 2)
             if camera_config.detect.min_initialized is None:
                 camera_config.detect.min_initialized = min_initialized
 
@@ -801,11 +810,13 @@ class FrigateConfig(FrigateBaseModel):
             if camera_config.audio.filters is None:
                 camera_config.audio.filters = {}
 
-            audio_keys = all_audio_labels
-            audio_keys = audio_keys - camera_config.audio.filters.keys()
-            for key in audio_keys:
+            for key in sorted(all_audio_labels - camera_config.audio.filters.keys()):
                 camera_config.audio.filters[key] = AudioFilterConfig()
 
+            camera_config.audio.filters = dict(
+                sorted(camera_config.audio.filters.items())
+            )
+
             # Add default filters
             object_keys = camera_config.objects.track
             if camera_config.objects.filters is None:

frigate/data_processing/common/audio_transcription/model.py

@@ -53,7 +53,7 @@ class AudioTranscriptionModelRunner:
                 self.downloader = ModelDownloader(
                     model_name="sherpa-onnx",
                     download_path=download_path,
-                    file_names=self.model_files.keys(),
+                    file_names=list(self.model_files.keys()),
                     download_func=self.__download_models,
                 )
                 self.downloader.ensure_model_files()

frigate/data_processing/common/face/model.py

@@ -21,7 +21,7 @@ class FaceRecognizer(ABC):
 
     def __init__(self, config: FrigateConfig) -> None:
         self.config = config
-        self.landmark_detector: cv2.face.FacemarkLBF = None
+        self.landmark_detector: cv2.face.Facemark | None = None
         self.init_landmark_detector()
 
     @abstractmethod
@@ -38,13 +38,14 @@ class FaceRecognizer(ABC):
     def classify(self, face_image: np.ndarray) -> tuple[str, float] | None:
         pass
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
+    @redirect_output_to_logger(logger, logging.DEBUG)  # type: ignore[misc]
     def init_landmark_detector(self) -> None:
         landmark_model = os.path.join(MODEL_CACHE_DIR, "facedet/landmarkdet.yaml")
 
         if os.path.exists(landmark_model):
-            self.landmark_detector = cv2.face.createFacemarkLBF()
-            self.landmark_detector.loadModel(landmark_model)
+            landmark_detector = cv2.face.createFacemarkLBF()
+            landmark_detector.loadModel(landmark_model)
+            self.landmark_detector = landmark_detector
 
     def align_face(
         self,
@@ -52,8 +53,10 @@ class FaceRecognizer(ABC):
         output_width: int,
         output_height: int,
     ) -> np.ndarray:
-        # landmark is run on grayscale images
+        if not self.landmark_detector:
+            raise ValueError("Landmark detector not initialized")
 
+        # landmark is run on grayscale images
         if image.ndim == 3:
             land_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
         else:
@@ -131,8 +134,11 @@ class FaceRecognizer(ABC):
 
 
 def similarity_to_confidence(
-    cosine_similarity: float, median=0.3, range_width=0.6, slope_factor=12
-):
+    cosine_similarity: float,
+    median: float = 0.3,
+    range_width: float = 0.6,
+    slope_factor: float = 12,
+) -> float:
     """
     Default sigmoid function to map cosine similarity to confidence.
 
@@ -151,14 +157,14 @@ def similarity_to_confidence(
     bias = median
 
     # Calculate confidence
-    confidence = 1 / (1 + np.exp(-slope * (cosine_similarity - bias)))
+    confidence: float = 1 / (1 + np.exp(-slope * (cosine_similarity - bias)))
     return confidence
 
 
 class FaceNetRecognizer(FaceRecognizer):
     def __init__(self, config: FrigateConfig):
         super().__init__(config)
-        self.mean_embs: dict[int, np.ndarray] = {}
+        self.mean_embs: dict[str, np.ndarray] = {}
         self.face_embedder: FaceNetEmbedding = FaceNetEmbedding()
         self.model_builder_queue: queue.Queue | None = None
 
@@ -168,7 +174,7 @@ class FaceNetRecognizer(FaceRecognizer):
     def run_build_task(self) -> None:
         self.model_builder_queue = queue.Queue()
 
-        def build_model():
+        def build_model() -> None:
             face_embeddings_map: dict[str, list[np.ndarray]] = {}
             idx = 0
 
@@ -187,7 +193,7 @@ class FaceNetRecognizer(FaceRecognizer):
                     img = cv2.imread(os.path.join(face_folder, image))
 
                     if img is None:
-                        continue
+                        continue  # type: ignore[unreachable]
 
                     img = self.align_face(img, img.shape[1], img.shape[0])
                     emb = self.face_embedder([img])[0].squeeze()
@@ -195,12 +201,13 @@ class FaceNetRecognizer(FaceRecognizer):
 
                 idx += 1
 
+            assert self.model_builder_queue is not None
             self.model_builder_queue.put(face_embeddings_map)
 
         thread = threading.Thread(target=build_model, daemon=True)
         thread.start()
 
-    def build(self):
+    def build(self) -> None:
         if not self.landmark_detector:
             self.init_landmark_detector()
             return None
@@ -226,7 +233,7 @@ class FaceNetRecognizer(FaceRecognizer):
 
         logger.debug("Finished building ArcFace model")
 
-    def classify(self, face_image):
+    def classify(self, face_image: np.ndarray) -> tuple[str, float] | None:
         if not self.landmark_detector:
             return None
 
@@ -245,7 +252,7 @@ class FaceNetRecognizer(FaceRecognizer):
         img = self.align_face(face_image, face_image.shape[1], face_image.shape[0])
         embedding = self.face_embedder([img])[0].squeeze()
 
-        score = 0
+        score: float = 0
         label = ""
 
         for name, mean_emb in self.mean_embs.items():
@@ -268,7 +275,7 @@ class FaceNetRecognizer(FaceRecognizer):
 class ArcFaceRecognizer(FaceRecognizer):
     def __init__(self, config: FrigateConfig):
         super().__init__(config)
-        self.mean_embs: dict[int, np.ndarray] = {}
+        self.mean_embs: dict[str, np.ndarray] = {}
         self.face_embedder: ArcfaceEmbedding = ArcfaceEmbedding(config.face_recognition)
         self.model_builder_queue: queue.Queue | None = None
 
@@ -278,7 +285,7 @@ class ArcFaceRecognizer(FaceRecognizer):
     def run_build_task(self) -> None:
         self.model_builder_queue = queue.Queue()
 
-        def build_model():
+        def build_model() -> None:
             face_embeddings_map: dict[str, list[np.ndarray]] = {}
             idx = 0
 
@@ -297,20 +304,21 @@ class ArcFaceRecognizer(FaceRecognizer):
                     img = cv2.imread(os.path.join(face_folder, image))
 
                     if img is None:
-                        continue
+                        continue  # type: ignore[unreachable]
 
                     img = self.align_face(img, img.shape[1], img.shape[0])
-                    emb = self.face_embedder([img])[0].squeeze()
+                    emb = self.face_embedder([img])[0].squeeze()  # type: ignore[arg-type]
                     face_embeddings_map[name].append(emb)
 
                 idx += 1
 
+            assert self.model_builder_queue is not None
             self.model_builder_queue.put(face_embeddings_map)
 
         thread = threading.Thread(target=build_model, daemon=True)
         thread.start()
 
-    def build(self):
+    def build(self) -> None:
         if not self.landmark_detector:
             self.init_landmark_detector()
             return None
@@ -336,7 +344,7 @@ class ArcFaceRecognizer(FaceRecognizer):
 
         logger.debug("Finished building ArcFace model")
 
-    def classify(self, face_image):
+    def classify(self, face_image: np.ndarray) -> tuple[str, float] | None:
         if not self.landmark_detector:
             return None
 
@@ -353,9 +361,9 @@ class ArcFaceRecognizer(FaceRecognizer):
 
         # align face and run recognition
         img = self.align_face(face_image, face_image.shape[1], face_image.shape[0])
-        embedding = self.face_embedder([img])[0].squeeze()
+        embedding = self.face_embedder([img])[0].squeeze()  # type: ignore[arg-type]
 
-        score = 0
+        score: float = 0
         label = ""
 
         for name, mean_emb in self.mean_embs.items():

frigate/data_processing/common/license_plate/mixin.py

@@ -10,7 +10,7 @@ import random
 import re
 import string
 from pathlib import Path
-from typing import Any, List, Optional, Tuple
+from typing import Any, List, Tuple
 
 import cv2
 import numpy as np
@@ -22,19 +22,35 @@ from frigate.comms.event_metadata_updater import (
     EventMetadataPublisher,
     EventMetadataTypeEnum,
 )
+from frigate.comms.inter_process import InterProcessRequestor
+from frigate.config import FrigateConfig
+from frigate.config.classification import LicensePlateRecognitionConfig
 from frigate.const import CLIPS_DIR, MODEL_CACHE_DIR
+from frigate.data_processing.common.license_plate.model import LicensePlateModelRunner
 from frigate.embeddings.onnx.lpr_embedding import LPR_EMBEDDING_SIZE
 from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import EventsPerSecond, InferenceSpeed
 from frigate.util.image import area
 
+from ...types import DataProcessorMetrics
+
 logger = logging.getLogger(__name__)
 
 WRITE_DEBUG_IMAGES = False
 
 
 class LicensePlateProcessingMixin:
-    def __init__(self, *args, **kwargs):
+    # Attributes expected from consuming classes (set before super().__init__)
+    config: FrigateConfig
+    metrics: DataProcessorMetrics
+    model_runner: LicensePlateModelRunner
+    lpr_config: LicensePlateRecognitionConfig
+    requestor: InterProcessRequestor
+    detected_license_plates: dict[str, dict[str, Any]]
+    camera_current_cars: dict[str, list[str]]
+    sub_label_publisher: EventMetadataPublisher
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
         super().__init__(*args, **kwargs)
         self.plate_rec_speed = InferenceSpeed(self.metrics.alpr_speed)
         self.plates_rec_second = EventsPerSecond()
@@ -97,7 +113,7 @@ class LicensePlateProcessingMixin:
             )
 
         try:
-            outputs = self.model_runner.detection_model([normalized_image])[0]
+            outputs = self.model_runner.detection_model([normalized_image])[0]  # type: ignore[arg-type]
         except Exception as e:
             logger.warning(f"Error running LPR box detection model: {e}")
             return []
@@ -105,18 +121,18 @@ class LicensePlateProcessingMixin:
         outputs = outputs[0, :, :]
 
         if False:
-            current_time = int(datetime.datetime.now().timestamp())
+            current_time = int(datetime.datetime.now().timestamp())  # type: ignore[unreachable]
             cv2.imwrite(
                 f"debug/frames/probability_map_{current_time}.jpg",
                 (outputs * 255).astype(np.uint8),
             )
 
         boxes, _ = self._boxes_from_bitmap(outputs, outputs > self.mask_thresh, w, h)
-        return self._filter_polygon(boxes, (h, w))
+        return self._filter_polygon(boxes, (h, w))  # type: ignore[return-value,arg-type]
 
     def _classify(
         self, images: List[np.ndarray]
-    ) -> Tuple[List[np.ndarray], List[Tuple[str, float]]]:
+    ) -> Tuple[List[np.ndarray], List[Tuple[str, float]]] | None:
         """
         Classify the orientation or category of each detected license plate.
 
@@ -138,15 +154,15 @@ class LicensePlateProcessingMixin:
                 norm_images.append(norm_img)
 
         try:
-            outputs = self.model_runner.classification_model(norm_images)
+            outputs = self.model_runner.classification_model(norm_images)  # type: ignore[arg-type]
         except Exception as e:
             logger.warning(f"Error running LPR classification model: {e}")
-            return
+            return None
 
         return self._process_classification_output(images, outputs)
 
     def _recognize(
-        self, camera: string, images: List[np.ndarray]
+        self, camera: str, images: List[np.ndarray]
     ) -> Tuple[List[str], List[List[float]]]:
         """
         Recognize the characters on the detected license plates using the recognition model.
@@ -179,7 +195,7 @@ class LicensePlateProcessingMixin:
                 norm_images.append(norm_image)
 
         try:
-            outputs = self.model_runner.recognition_model(norm_images)
+            outputs = self.model_runner.recognition_model(norm_images)  # type: ignore[arg-type]
         except Exception as e:
             logger.warning(f"Error running LPR recognition model: {e}")
             return [], []
@@ -410,7 +426,8 @@ class LicensePlateProcessingMixin:
             )
 
             if sorted_data:
-                return map(list, zip(*sorted_data))
+                plates, confs, areas_list = zip(*sorted_data)
+                return list(plates), list(confs), list(areas_list)
 
         return [], [], []
 
@@ -532,7 +549,7 @@ class LicensePlateProcessingMixin:
         # Add the last box
         merged_boxes.append(current_box)
 
-        return np.array(merged_boxes, dtype=np.int32)
+        return np.array(merged_boxes, dtype=np.int32)  # type: ignore[return-value]
 
     def _boxes_from_bitmap(
         self, output: np.ndarray, mask: np.ndarray, dest_width: int, dest_height: int
@@ -560,38 +577,42 @@ class LicensePlateProcessingMixin:
         boxes = []
         scores = []
 
-        for index in range(len(contours)):
-            contour = contours[index]
+        for index in range(len(contours)):  # type: ignore[arg-type]
+            contour = contours[index]  # type: ignore[index]
 
             # get minimum bounding box (rotated rectangle) around the contour and the smallest side length.
             points, sside = self._get_min_boxes(contour)
             if sside < self.min_size:
                 continue
 
-            points = np.array(points, dtype=np.float32)
+            points = np.array(points, dtype=np.float32)  # type: ignore[assignment]
 
             score = self._box_score(output, contour)
             if self.box_thresh > score:
                 continue
 
-            points = self._expand_box(points)
+            points = self._expand_box(points)  # type: ignore[assignment]
 
             # Get the minimum area rectangle again after expansion
-            points, sside = self._get_min_boxes(points.reshape(-1, 1, 2))
+            points, sside = self._get_min_boxes(points.reshape(-1, 1, 2))  # type: ignore[attr-defined]
             if sside < self.min_size + 2:
                 continue
 
-            points = np.array(points, dtype=np.float32)
+            points = np.array(points, dtype=np.float32)  # type: ignore[assignment]
 
             # normalize and clip box coordinates to fit within the destination image size.
-            points[:, 0] = np.clip(
-                np.round(points[:, 0] / width * dest_width), 0, dest_width
+            points[:, 0] = np.clip(  # type: ignore[call-overload]
+                np.round(points[:, 0] / width * dest_width),  # type: ignore[call-overload]
+                0,
+                dest_width,
             )
-            points[:, 1] = np.clip(
-                np.round(points[:, 1] / height * dest_height), 0, dest_height
+            points[:, 1] = np.clip(  # type: ignore[call-overload]
+                np.round(points[:, 1] / height * dest_height),  # type: ignore[call-overload]
+                0,
+                dest_height,
             )
 
-            boxes.append(points.astype("int32"))
+            boxes.append(points.astype("int32"))  # type: ignore[attr-defined]
             scores.append(score)
 
         return np.array(boxes, dtype="int32"), scores
@@ -632,7 +653,7 @@ class LicensePlateProcessingMixin:
         x1, y1 = np.clip(contour.min(axis=0), 0, [w - 1, h - 1])
         x2, y2 = np.clip(contour.max(axis=0), 0, [w - 1, h - 1])
         mask = np.zeros((y2 - y1 + 1, x2 - x1 + 1), dtype=np.uint8)
-        cv2.fillPoly(mask, [contour - [x1, y1]], 1)
+        cv2.fillPoly(mask, [contour - [x1, y1]], 1)  # type: ignore[call-overload]
         return cv2.mean(bitmap[y1 : y2 + 1, x1 : x2 + 1], mask)[0]
 
     @staticmethod
@@ -690,7 +711,7 @@ class LicensePlateProcessingMixin:
         Returns:
             bool: Whether the polygon is valid or not.
         """
-        return (
+        return bool(
             point[:, 0].min() >= 0
             and point[:, 0].max() < width
             and point[:, 1].min() >= 0
@@ -735,7 +756,7 @@ class LicensePlateProcessingMixin:
         return np.array([tl, tr, br, bl])
 
     @staticmethod
-    def _sort_boxes(boxes):
+    def _sort_boxes(boxes: list[np.ndarray]) -> list[np.ndarray]:
         """
         Sort polygons based on their position in the image. If boxes are close in vertical
         position (within 5 pixels), sort them by horizontal position.
@@ -837,16 +858,16 @@ class LicensePlateProcessingMixin:
         results = [["", 0.0]] * len(images)
         indices = np.argsort(np.array([x.shape[1] / x.shape[0] for x in images]))
 
-        outputs = np.stack(outputs)
+        stacked_outputs = np.stack(outputs)
 
-        outputs = [
-            (labels[idx], outputs[i, idx])
-            for i, idx in enumerate(outputs.argmax(axis=1))
+        stacked_outputs = [
+            (labels[idx], stacked_outputs[i, idx])
+            for i, idx in enumerate(stacked_outputs.argmax(axis=1))
         ]
 
         for i in range(0, len(images), self.batch_size):
-            for j in range(len(outputs)):
-                label, score = outputs[j]
+            for j in range(len(stacked_outputs)):
+                label, score = stacked_outputs[j]
                 results[indices[i + j]] = [label, score]
                 # make sure we have high confidence if we need to flip a box
                 if "180" in label and score >= 0.7:
@@ -854,10 +875,10 @@ class LicensePlateProcessingMixin:
                         images[indices[i + j]], cv2.ROTATE_180
                     )
 
-        return images, results
+        return images, results  # type: ignore[return-value]
 
     def _preprocess_recognition_image(
-        self, camera: string, image: np.ndarray, max_wh_ratio: float
+        self, camera: str, image: np.ndarray, max_wh_ratio: float
     ) -> np.ndarray:
         """
         Preprocess an image for recognition by dynamically adjusting its width.
@@ -925,7 +946,7 @@ class LicensePlateProcessingMixin:
         input_w = int(input_h * max_wh_ratio)
 
         # check for model-specific input width
-        model_input_w = self.model_runner.recognition_model.runner.get_input_width()
+        model_input_w = self.model_runner.recognition_model.runner.get_input_width()  # type: ignore[union-attr]
         if isinstance(model_input_w, int) and model_input_w > 0:
             input_w = model_input_w
 
@@ -945,7 +966,7 @@ class LicensePlateProcessingMixin:
         padded_image[:, :, :resized_w] = resized_image
 
         if False:
-            current_time = int(datetime.datetime.now().timestamp() * 1000)
+            current_time = int(datetime.datetime.now().timestamp() * 1000)  # type: ignore[unreachable]
             cv2.imwrite(
                 f"debug/frames/preprocessed_recognition_{current_time}.jpg",
                 image,
@@ -983,8 +1004,9 @@ class LicensePlateProcessingMixin:
                 np.linalg.norm(points[1] - points[2]),
             )
         )
-        pts_std = np.float32(
-            [[0, 0], [crop_width, 0], [crop_width, crop_height], [0, crop_height]]
+        pts_std = np.array(
+            [[0, 0], [crop_width, 0], [crop_width, crop_height], [0, crop_height]],
+            dtype=np.float32,
         )
         matrix = cv2.getPerspectiveTransform(points, pts_std)
         image = cv2.warpPerspective(
@@ -1000,15 +1022,15 @@ class LicensePlateProcessingMixin:
         return image
 
     def _detect_license_plate(
-        self, camera: string, input: np.ndarray
-    ) -> tuple[int, int, int, int]:
+        self, camera: str, input: np.ndarray
+    ) -> tuple[int, int, int, int] | None:
         """
         Use a lightweight YOLOv9 model to detect license plates for users without Frigate+
 
         Return the dimensions of the detected plate as [x1, y1, x2, y2].
         """
         try:
-            predictions = self.model_runner.yolov9_detection_model(input)
+            predictions = self.model_runner.yolov9_detection_model(input)  # type: ignore[arg-type]
         except Exception as e:
             logger.warning(f"Error running YOLOv9 license plate detection model: {e}")
             return None
@@ -1073,7 +1095,7 @@ class LicensePlateProcessingMixin:
             logger.debug(
                 f"{camera}: Found license plate. Bounding box: {expanded_box.astype(int)}"
             )
-            return tuple(expanded_box.astype(int))
+            return tuple(expanded_box.astype(int))  # type: ignore[return-value]
         else:
             return None  # No detection above the threshold
 
@@ -1097,7 +1119,7 @@ class LicensePlateProcessingMixin:
                 f"  Variant {i + 1}: '{p['plate']}' (conf: {p['conf']:.3f}, area: {p['area']})"
             )
 
-        clusters = []
+        clusters: list[list[dict[str, Any]]] = []
         for i, plate in enumerate(plates):
             merged = False
             for j, cluster in enumerate(clusters):
@@ -1132,7 +1154,7 @@ class LicensePlateProcessingMixin:
             )
 
         # Best cluster: largest size, tiebroken by max conf
-        def cluster_score(c):
+        def cluster_score(c: list[dict[str, Any]]) -> tuple[int, float]:
             return (len(c), max(v["conf"] for v in c))
 
         best_cluster_idx = max(
@@ -1178,7 +1200,7 @@ class LicensePlateProcessingMixin:
 
     def lpr_process(
         self, obj_data: dict[str, Any], frame: np.ndarray, dedicated_lpr: bool = False
-    ):
+    ) -> None:
         """Look for license plates in image."""
         self.metrics.alpr_pps.value = self.plates_rec_second.eps()
         self.metrics.yolov9_lpr_pps.value = self.plates_det_second.eps()
@@ -1195,7 +1217,7 @@ class LicensePlateProcessingMixin:
             rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
 
             # apply motion mask
-            rgb[self.config.cameras[obj_data].motion.rasterized_mask == 0] = [0, 0, 0]
+            rgb[self.config.cameras[camera].motion.rasterized_mask == 0] = [0, 0, 0]  # type: ignore[attr-defined]
 
             if WRITE_DEBUG_IMAGES:
                 cv2.imwrite(
@@ -1261,7 +1283,7 @@ class LicensePlateProcessingMixin:
                 "stationary", False
             ):
                 logger.debug(
-                    f"{camera}: Skipping LPR for non-stationary {obj_data['label']} object {id} with no position changes.  (Detected in {self.config.cameras[camera].detect.min_initialized + 1} concurrent frames, threshold to run is {self.config.cameras[camera].detect.min_initialized + 2} frames)"
+                    f"{camera}: Skipping LPR for non-stationary {obj_data['label']} object {id} with no position changes.  (Detected in {self.config.cameras[camera].detect.min_initialized + 1} concurrent frames, threshold to run is {self.config.cameras[camera].detect.min_initialized + 2} frames)"  # type: ignore[operator]
                 )
                 return
 
@@ -1288,7 +1310,7 @@ class LicensePlateProcessingMixin:
                     if time_since_stationary > self.stationary_scan_duration:
                         return
 
-            license_plate: Optional[dict[str, Any]] = None
+            license_plate = None
 
             if "license_plate" not in self.config.cameras[camera].objects.track:
                 logger.debug(f"{camera}: Running manual license_plate detection.")
@@ -1301,7 +1323,7 @@ class LicensePlateProcessingMixin:
                 rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
 
                 # apply motion mask
-                rgb[self.config.cameras[camera].motion.rasterized_mask == 0] = [0, 0, 0]
+                rgb[self.config.cameras[camera].motion.rasterized_mask == 0] = [0, 0, 0]  # type: ignore[attr-defined]
 
                 left, top, right, bottom = car_box
                 car = rgb[top:bottom, left:right]
@@ -1378,10 +1400,10 @@ class LicensePlateProcessingMixin:
                         if attr.get("label") != "license_plate":
                             continue
 
-                        if license_plate is None or attr.get(
+                        if license_plate is None or attr.get(  # type: ignore[unreachable]
                             "score", 0.0
                         ) > license_plate.get("score", 0.0):
-                            license_plate = attr
+                            license_plate = attr  # type: ignore[assignment]
 
                     # no license plates detected in this frame
                     if not license_plate:
@@ -1389,9 +1411,9 @@ class LicensePlateProcessingMixin:
 
                 # we are using dedicated lpr with frigate+
                 if obj_data.get("label") == "license_plate":
-                    license_plate = obj_data
+                    license_plate = obj_data  # type: ignore[assignment]
 
-                license_plate_box = license_plate.get("box")
+                license_plate_box = license_plate.get("box")  # type: ignore[attr-defined]
 
                 # check that license plate is valid
                 if (
@@ -1420,7 +1442,7 @@ class LicensePlateProcessingMixin:
                     0, [license_plate_frame.shape[1], license_plate_frame.shape[0]] * 2
                 )
 
-                plate_box = tuple(int(x) for x in expanded_box)
+                plate_box = tuple(int(x) for x in expanded_box)  # type: ignore[assignment]
 
                 # Crop using the expanded box
                 license_plate_frame = license_plate_frame[
@@ -1596,7 +1618,7 @@ class LicensePlateProcessingMixin:
             sub_label = next(
                 (
                     label
-                    for label, plates_list in self.lpr_config.known_plates.items()
+                    for label, plates_list in self.lpr_config.known_plates.items()  # type: ignore[union-attr]
                     if any(
                         re.match(f"^{plate}$", rep_plate)
                         or Levenshtein.distance(plate, rep_plate)
@@ -1649,14 +1671,16 @@ class LicensePlateProcessingMixin:
             frame_bgr = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
             _, encoded_img = cv2.imencode(".jpg", frame_bgr)
             self.sub_label_publisher.publish(
-                (base64.b64encode(encoded_img).decode("ASCII"), id, camera),
+                (base64.b64encode(encoded_img.tobytes()).decode("ASCII"), id, camera),
                 EventMetadataTypeEnum.save_lpr_snapshot.value,
             )
 
-    def handle_request(self, topic, request_data) -> dict[str, Any] | None:
-        return
+    def handle_request(
+        self, topic: str, request_data: dict[str, Any]
+    ) -> dict[str, Any] | None:
+        return None
 
-    def lpr_expire(self, object_id: str, camera: str):
+    def lpr_expire(self, object_id: str, camera: str) -> None:
         if object_id in self.detected_license_plates:
             self.detected_license_plates.pop(object_id)
 
@@ -1673,7 +1697,7 @@ class CTCDecoder:
     for each decoded character sequence.
     """
 
-    def __init__(self, character_dict_path=None):
+    def __init__(self, character_dict_path: str | None = None) -> None:
         """
         Initializes the CTCDecoder.
         :param character_dict_path: Path to the character dictionary file.

frigate/data_processing/common/license_plate/model.py

@@ -1,3 +1,4 @@
+from frigate.comms.inter_process import InterProcessRequestor
 from frigate.embeddings.onnx.lpr_embedding import (
     LicensePlateDetector,
     PaddleOCRClassification,
@@ -9,7 +10,12 @@ from ...types import DataProcessorModelRunner
 
 
 class LicensePlateModelRunner(DataProcessorModelRunner):
-    def __init__(self, requestor, device: str = "CPU", model_size: str = "small"):
+    def __init__(
+        self,
+        requestor: InterProcessRequestor,
+        device: str = "CPU",
+        model_size: str = "small",
+    ):
         super().__init__(requestor, device, model_size)
         self.detection_model = PaddleOCRDetection(
             model_size=model_size, requestor=requestor, device=device

frigate/data_processing/post/api.py

@@ -17,7 +17,7 @@ class PostProcessorApi(ABC):
         self,
         config: FrigateConfig,
         metrics: DataProcessorMetrics,
-        model_runner: DataProcessorModelRunner,
+        model_runner: DataProcessorModelRunner | None,
     ) -> None:
         self.config = config
         self.metrics = metrics
@@ -41,7 +41,7 @@ class PostProcessorApi(ABC):
     @abstractmethod
     def handle_request(
         self, topic: str, request_data: dict[str, Any]
-    ) -> dict[str, Any] | None:
+    ) -> dict[str, Any] | str | None:
         """Handle metadata requests.
         Args:
             request_data (dict): containing data about requested change to process.

frigate/data_processing/post/audio_transcription.py

@@ -4,7 +4,7 @@ import logging
 import os
 import threading
 import time
-from typing import Optional
+from typing import Any, Optional
 
 from peewee import DoesNotExist
 
@@ -17,6 +17,7 @@ from frigate.const import (
     UPDATE_EVENT_DESCRIPTION,
 )
 from frigate.data_processing.types import PostProcessDataEnum
+from frigate.embeddings.embeddings import Embeddings
 from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.audio import get_audio_from_recording
 
@@ -31,7 +32,7 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
         self,
         config: FrigateConfig,
         requestor: InterProcessRequestor,
-        embeddings,
+        embeddings: Embeddings,
         metrics: DataProcessorMetrics,
     ):
         super().__init__(config, metrics, None)
@@ -40,7 +41,7 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
         self.embeddings = embeddings
         self.recognizer = None
         self.transcription_lock = threading.Lock()
-        self.transcription_thread = None
+        self.transcription_thread: threading.Thread | None = None
         self.transcription_running = False
 
         # faster-whisper handles model downloading automatically
@@ -69,7 +70,7 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
             self.recognizer = None
 
     def process_data(
-        self, data: dict[str, any], data_type: PostProcessDataEnum
+        self, data: dict[str, Any], data_type: PostProcessDataEnum
     ) -> None:
         """Transcribe audio from a recording.
 
@@ -141,13 +142,13 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
         except Exception as e:
             logger.error(f"Error in audio transcription post-processing: {e}")
 
-    def __transcribe_audio(self, audio_data: bytes) -> Optional[tuple[str, float]]:
+    def __transcribe_audio(self, audio_data: bytes) -> Optional[str]:
         """Transcribe WAV audio data using faster-whisper."""
         if not self.recognizer:
             logger.debug("Recognizer not initialized")
             return None
 
-        try:
+        try:  # type: ignore[unreachable]
             # Save audio data to a temporary wav (faster-whisper expects a file)
             temp_wav = os.path.join(CACHE_DIR, f"temp_audio_{int(time.time())}.wav")
             with open(temp_wav, "wb") as f:
@@ -176,7 +177,7 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
             logger.error(f"Error transcribing audio: {e}")
             return None
 
-    def _transcription_wrapper(self, event: dict[str, any]) -> None:
+    def _transcription_wrapper(self, event: dict[str, Any]) -> None:
         """Wrapper to run transcription and reset running flag when done."""
         try:
             self.process_data(
@@ -194,7 +195,7 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
 
             self.requestor.send_data(UPDATE_AUDIO_TRANSCRIPTION_STATE, "idle")
 
-    def handle_request(self, topic: str, request_data: dict[str, any]) -> str | None:
+    def handle_request(self, topic: str, request_data: dict[str, Any]) -> str | None:
         if topic == "transcribe_audio":
             event = request_data["event"]
 

frigate/data_processing/post/license_plate.py

@@ -29,7 +29,7 @@ from .api import PostProcessorApi
 logger = logging.getLogger(__name__)
 
 
-class LicensePlatePostProcessor(LicensePlateProcessingMixin, PostProcessorApi):
+class LicensePlatePostProcessor(LicensePlateProcessingMixin, PostProcessorApi):  # type: ignore[misc]
     def __init__(
         self,
         config: FrigateConfig,
@@ -71,7 +71,7 @@ class LicensePlatePostProcessor(LicensePlateProcessingMixin, PostProcessorApi):
         # don't run LPR post processing for now
         return
 
-        event_id = data["event_id"]
+        event_id = data["event_id"]  # type: ignore[unreachable]
         camera_name = data["camera"]
 
         if data_type == PostProcessDataEnum.recording:
@@ -225,7 +225,7 @@ class LicensePlatePostProcessor(LicensePlateProcessingMixin, PostProcessorApi):
         logger.debug(f"Post processing plate: {event_id}, {frame_time}")
         self.lpr_process(keyframe_obj_data, frame)
 
-    def handle_request(self, topic, request_data) -> dict[str, Any] | None:
+    def handle_request(self, topic: str, request_data: dict) -> dict[str, Any] | None:
         if topic == EmbeddingsRequestEnum.reprocess_plate.value:
             event = request_data["event"]
 
@@ -242,3 +242,5 @@ class LicensePlatePostProcessor(LicensePlateProcessingMixin, PostProcessorApi):
                 "message": "Successfully requested reprocessing of license plate.",
                 "success": True,
             }
+
+        return None

frigate/data_processing/post/object_descriptions.py

@@ -24,7 +24,7 @@ from frigate.util.file import get_event_thumbnail_bytes, load_event_snapshot_ima
 from frigate.util.image import create_thumbnail, ensure_jpeg_bytes
 
 if TYPE_CHECKING:
-    from frigate.embeddings import Embeddings
+    from frigate.embeddings.embeddings import Embeddings
 
 from ..post.api import PostProcessorApi
 from ..types import DataProcessorMetrics
@@ -139,7 +139,7 @@ class ObjectDescriptionProcessor(PostProcessorApi):
         ):
             self._process_genai_description(event, camera_config, thumbnail)
         else:
-            self.cleanup_event(event.id)
+            self.cleanup_event(str(event.id))
 
     def __regenerate_description(self, event_id: str, source: str, force: bool) -> None:
         """Regenerate the description for an event."""
@@ -149,17 +149,17 @@ class ObjectDescriptionProcessor(PostProcessorApi):
             logger.error(f"Event {event_id} not found for description regeneration")
             return
 
-        if self.genai_client is None:
-            logger.error("GenAI not enabled")
-            return
-
-        camera_config = self.config.cameras[event.camera]
+        camera_config = self.config.cameras[str(event.camera)]
         if not camera_config.objects.genai.enabled and not force:
             logger.error(f"GenAI not enabled for camera {event.camera}")
             return
 
         thumbnail = get_event_thumbnail_bytes(event)
 
+        if thumbnail is None:
+            logger.error("No thumbnail available for %s", event.id)
+            return
+
         # ensure we have a jpeg to pass to the model
         thumbnail = ensure_jpeg_bytes(thumbnail)
 
@@ -187,7 +187,9 @@ class ObjectDescriptionProcessor(PostProcessorApi):
             )
         )
 
-        self._genai_embed_description(event, embed_image)
+        self._genai_embed_description(
+            event, [img for img in embed_image if img is not None]
+        )
 
     def process_data(self, frame_data: dict, data_type: PostProcessDataEnum) -> None:
         """Process a frame update."""
@@ -241,7 +243,7 @@ class ObjectDescriptionProcessor(PostProcessorApi):
             # Crop snapshot based on region
             # provide full image if region doesn't exist (manual events)
             height, width = img.shape[:2]
-            x1_rel, y1_rel, width_rel, height_rel = event.data.get(
+            x1_rel, y1_rel, width_rel, height_rel = event.data.get(  # type: ignore[attr-defined]
                 "region", [0, 0, 1, 1]
             )
             x1, y1 = int(x1_rel * width), int(y1_rel * height)
@@ -258,14 +260,16 @@ class ObjectDescriptionProcessor(PostProcessorApi):
             return None
 
     def _process_genai_description(
-        self, event: Event, camera_config: CameraConfig, thumbnail
+        self, event: Event, camera_config: CameraConfig, thumbnail: bytes
     ) -> None:
+        event_id = str(event.id)
+
         if event.has_snapshot and camera_config.objects.genai.use_snapshot:
             snapshot_image = self._read_and_crop_snapshot(event)
             if not snapshot_image:
                 return
 
-        num_thumbnails = len(self.tracked_events.get(event.id, []))
+        num_thumbnails = len(self.tracked_events.get(event_id, []))
 
         # ensure we have a jpeg to pass to the model
         thumbnail = ensure_jpeg_bytes(thumbnail)
@@ -277,7 +281,7 @@ class ObjectDescriptionProcessor(PostProcessorApi):
             else (
                 [
                     data["thumbnail"][:] if data.get("thumbnail") else None
-                    for data in self.tracked_events[event.id]
+                    for data in self.tracked_events[event_id]
                     if data.get("thumbnail")
                 ]
                 if num_thumbnails > 0
@@ -286,22 +290,22 @@ class ObjectDescriptionProcessor(PostProcessorApi):
         )
 
         if camera_config.objects.genai.debug_save_thumbnails and num_thumbnails > 0:
-            logger.debug(f"Saving {num_thumbnails} thumbnails for event {event.id}")
+            logger.debug(f"Saving {num_thumbnails} thumbnails for event {event_id}")
 
-            Path(os.path.join(CLIPS_DIR, f"genai-requests/{event.id}")).mkdir(
+            Path(os.path.join(CLIPS_DIR, f"genai-requests/{event_id}")).mkdir(
                 parents=True, exist_ok=True
             )
 
-            for idx, data in enumerate(self.tracked_events[event.id], 1):
+            for idx, data in enumerate(self.tracked_events[event_id], 1):
                 jpg_bytes: bytes | None = data["thumbnail"]
 
                 if jpg_bytes is None:
-                    logger.warning(f"Unable to save thumbnail {idx} for {event.id}.")
+                    logger.warning(f"Unable to save thumbnail {idx} for {event_id}.")
                 else:
                     with open(
                         os.path.join(
                             CLIPS_DIR,
-                            f"genai-requests/{event.id}/{idx}.jpg",
+                            f"genai-requests/{event_id}/{idx}.jpg",
                         ),
                         "wb",
                     ) as j:
@@ -310,7 +314,7 @@ class ObjectDescriptionProcessor(PostProcessorApi):
         # Generate the description. Call happens in a thread since it is network bound.
         threading.Thread(
             target=self._genai_embed_description,
-            name=f"_genai_embed_description_{event.id}",
+            name=f"_genai_embed_description_{event_id}",
             daemon=True,
             args=(
                 event,
@@ -319,12 +323,12 @@ class ObjectDescriptionProcessor(PostProcessorApi):
         ).start()
 
         # Clean up tracked events and early request state
-        self.cleanup_event(event.id)
+        self.cleanup_event(event_id)
 
     def _genai_embed_description(self, event: Event, thumbnails: list[bytes]) -> None:
         """Embed the description for an event."""
         start = datetime.datetime.now().timestamp()
-        camera_config = self.config.cameras[event.camera]
+        camera_config = self.config.cameras[str(event.camera)]
         description = self.genai_client.generate_object_description(
             camera_config, thumbnails, event
         )
@@ -346,7 +350,7 @@ class ObjectDescriptionProcessor(PostProcessorApi):
 
         # Embed the description
         if self.config.semantic_search.enabled:
-            self.embeddings.embed_description(event.id, description)
+            self.embeddings.embed_description(str(event.id), description)
 
             # Check semantic trigger for this description
             if self.semantic_trigger_processor is not None:

frigate/data_processing/post/review_descriptions.py

@@ -48,8 +48,8 @@ class ReviewDescriptionProcessor(PostProcessorApi):
         self.metrics = metrics
         self.genai_client = client
         self.review_desc_speed = InferenceSpeed(self.metrics.review_desc_speed)
-        self.review_descs_dps = EventsPerSecond()
-        self.review_descs_dps.start()
+        self.review_desc_dps = EventsPerSecond()
+        self.review_desc_dps.start()
 
     def calculate_frame_count(
         self,
@@ -59,7 +59,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
     ) -> int:
         """Calculate optimal number of frames based on context size, image source, and resolution.
 
-        Token usage varies by resolution: larger images (ultrawide aspect ratios) use more tokens.
+        Token usage varies by resolution: larger images (ultra-wide aspect ratios) use more tokens.
         Estimates ~1 token per 1250 pixels. Targets 98% context utilization with safety margin.
         Capped at 20 frames.
         """
@@ -68,7 +68,11 @@ class ReviewDescriptionProcessor(PostProcessorApi):
 
         detect_width = camera_config.detect.width
         detect_height = camera_config.detect.height
-        aspect_ratio = detect_width / detect_height
+
+        if not detect_width or not detect_height:
+            aspect_ratio = 16 / 9
+        else:
+            aspect_ratio = detect_width / detect_height
 
         if image_source == ImageSourceEnum.recordings:
             if aspect_ratio >= 1:
@@ -99,8 +103,10 @@ class ReviewDescriptionProcessor(PostProcessorApi):
 
         return min(max(max_frames, 3), 20)
 
-    def process_data(self, data, data_type):
-        self.metrics.review_desc_dps.value = self.review_descs_dps.eps()
+    def process_data(
+        self, data: dict[str, Any], data_type: PostProcessDataEnum
+    ) -> None:
+        self.metrics.review_desc_dps.value = self.review_desc_dps.eps()
 
         if data_type != PostProcessDataEnum.review:
             return
@@ -186,7 +192,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                 )
 
             # kickoff analysis
-            self.review_descs_dps.update()
+            self.review_desc_dps.update()
             threading.Thread(
                 target=run_analysis,
                 args=(
@@ -202,7 +208,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                 ),
             ).start()
 
-    def handle_request(self, topic, request_data):
+    def handle_request(self, topic: str, request_data: dict[str, Any]) -> str | None:
         if topic == EmbeddingsRequestEnum.summarize_review.value:
             start_ts = request_data["start_ts"]
             end_ts = request_data["end_ts"]
@@ -327,7 +333,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
         file_start = f"preview_{camera}-"
         start_file = f"{file_start}{start_time}.webp"
         end_file = f"{file_start}{end_time}.webp"
-        all_frames = []
+        all_frames: list[str] = []
 
         for file in sorted(os.listdir(preview_dir)):
             if not file.startswith(file_start):
@@ -465,7 +471,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
             thumb_data = cv2.imread(thumb_path)
 
             if thumb_data is None:
-                logger.warning(
+                logger.warning(  # type: ignore[unreachable]
                     "Could not read preview frame at %s, skipping", thumb_path
                 )
                 continue
@@ -488,13 +494,12 @@ class ReviewDescriptionProcessor(PostProcessorApi):
         return thumbs
 
 
-@staticmethod
 def run_analysis(
     requestor: InterProcessRequestor,
     genai_client: GenAIClient,
     review_inference_speed: InferenceSpeed,
     camera_config: CameraConfig,
-    final_data: dict[str, str],
+    final_data: dict[str, Any],
     thumbs: list[bytes],
     genai_config: GenAIReviewConfig,
     labelmap_objects: list[str],

frigate/data_processing/post/semantic_trigger.py

@@ -19,6 +19,7 @@ from frigate.config import FrigateConfig
 from frigate.const import CONFIG_DIR
 from frigate.data_processing.types import PostProcessDataEnum
 from frigate.db.sqlitevecq import SqliteVecQueueDatabase
+from frigate.embeddings.embeddings import Embeddings
 from frigate.embeddings.util import ZScoreNormalization
 from frigate.models import Event, Trigger
 from frigate.util.builtin import cosine_distance
@@ -40,8 +41,8 @@ class SemanticTriggerProcessor(PostProcessorApi):
         requestor: InterProcessRequestor,
         sub_label_publisher: EventMetadataPublisher,
         metrics: DataProcessorMetrics,
-        embeddings,
-    ):
+        embeddings: Embeddings,
+    ) -> None:
         super().__init__(config, metrics, None)
         self.db = db
         self.embeddings = embeddings
@@ -236,11 +237,14 @@ class SemanticTriggerProcessor(PostProcessorApi):
                     return
 
                 # Skip the event if not an object
-                if event.data.get("type") != "object":
+                if event.data.get("type") != "object":  # type: ignore[attr-defined]
                     return
 
                 thumbnail_bytes = get_event_thumbnail_bytes(event)
 
+                if thumbnail_bytes is None:
+                    return
+
                 nparr = np.frombuffer(thumbnail_bytes, np.uint8)
                 thumbnail = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
 
@@ -262,8 +266,10 @@ class SemanticTriggerProcessor(PostProcessorApi):
                     thumbnail,
                 )
 
-    def handle_request(self, topic, request_data):
+    def handle_request(
+        self, topic: str, request_data: dict[str, Any]
+    ) -> dict[str, Any] | str | None:
         return None
 
-    def expire_object(self, object_id, camera):
+    def expire_object(self, object_id: str, camera: str) -> None:
         pass

frigate/data_processing/real_time/audio_transcription.py

@@ -4,7 +4,7 @@ import logging
 import os
 import queue
 import threading
-from typing import Optional
+from typing import Any, Optional
 
 import numpy as np
 
@@ -39,11 +39,11 @@ class AudioTranscriptionRealTimeProcessor(RealTimeProcessorApi):
         self.config = config
         self.camera_config = camera_config
         self.requestor = requestor
-        self.stream = None
-        self.whisper_model = None
+        self.stream: Any = None
+        self.whisper_model: FasterWhisperASR | None = None
         self.model_runner = model_runner
-        self.transcription_segments = []
-        self.audio_queue = queue.Queue()
+        self.transcription_segments: list[str] = []
+        self.audio_queue: queue.Queue[tuple[dict[str, Any], np.ndarray]] = queue.Queue()
         self.stop_event = stop_event
 
     def __build_recognizer(self) -> None:
@@ -142,10 +142,10 @@ class AudioTranscriptionRealTimeProcessor(RealTimeProcessorApi):
             logger.error(f"Error processing audio stream: {e}")
             return None
 
-    def process_frame(self, obj_data: dict[str, any], frame: np.ndarray) -> None:
+    def process_frame(self, obj_data: dict[str, Any], frame: np.ndarray) -> None:
         pass
 
-    def process_audio(self, obj_data: dict[str, any], audio: np.ndarray) -> bool | None:
+    def process_audio(self, obj_data: dict[str, Any], audio: np.ndarray) -> bool | None:
         if audio is None or audio.size == 0:
             logger.debug("No audio data provided for transcription")
             return None
@@ -269,13 +269,13 @@ class AudioTranscriptionRealTimeProcessor(RealTimeProcessorApi):
         )
 
     def handle_request(
-        self, topic: str, request_data: dict[str, any]
-    ) -> dict[str, any] | None:
+        self, topic: str, request_data: dict[str, Any]
+    ) -> dict[str, Any] | None:
         if topic == "clear_audio_recognizer":
             self.stream = None
             self.__build_recognizer()
             return {"message": "Audio recognizer cleared and rebuilt", "success": True}
         return None
 
-    def expire_object(self, object_id: str) -> None:
+    def expire_object(self, object_id: str, camera: str) -> None:
         pass

frigate/data_processing/real_time/bird.py

@@ -14,7 +14,7 @@ from frigate.comms.event_metadata_updater import (
 from frigate.config import FrigateConfig
 from frigate.const import MODEL_CACHE_DIR
 from frigate.log import suppress_stderr_during
-from frigate.util.object import calculate_region
+from frigate.util.image import calculate_region
 
 from ..types import DataProcessorMetrics
 from .api import RealTimeProcessorApi
@@ -35,10 +35,10 @@ class BirdRealTimeProcessor(RealTimeProcessorApi):
         metrics: DataProcessorMetrics,
     ):
         super().__init__(config, metrics)
-        self.interpreter: Interpreter = None
+        self.interpreter: Interpreter | None = None
         self.sub_label_publisher = sub_label_publisher
-        self.tensor_input_details: dict[str, Any] = None
-        self.tensor_output_details: dict[str, Any] = None
+        self.tensor_input_details: list[dict[str, Any]] | None = None
+        self.tensor_output_details: list[dict[str, Any]] | None = None
         self.detected_birds: dict[str, float] = {}
         self.labelmap: dict[int, str] = {}
 
@@ -61,7 +61,7 @@ class BirdRealTimeProcessor(RealTimeProcessorApi):
             self.downloader = ModelDownloader(
                 model_name="bird",
                 download_path=download_path,
-                file_names=self.model_files.keys(),
+                file_names=list(self.model_files.keys()),
                 download_func=self.__download_models,
                 complete_func=self.__build_detector,
             )
@@ -102,8 +102,12 @@ class BirdRealTimeProcessor(RealTimeProcessorApi):
                 i += 1
                 line = f.readline()
 
-    def process_frame(self, obj_data, frame):
-        if not self.interpreter:
+    def process_frame(self, obj_data: dict[str, Any], frame: np.ndarray) -> None:
+        if (
+            not self.interpreter
+            or not self.tensor_input_details
+            or not self.tensor_output_details
+        ):
             return
 
         if obj_data["label"] != "bird":
@@ -145,7 +149,7 @@ class BirdRealTimeProcessor(RealTimeProcessorApi):
             self.tensor_output_details[0]["index"]
         )[0]
         probs = res / res.sum(axis=0)
-        best_id = np.argmax(probs)
+        best_id = int(np.argmax(probs))
 
         if best_id == 964:
             logger.debug("No bird classification was detected.")
@@ -179,9 +183,11 @@ class BirdRealTimeProcessor(RealTimeProcessorApi):
         self.config.classification = payload
         logger.debug("Bird classification config updated dynamically")
 
-    def handle_request(self, topic, request_data):
+    def handle_request(
+        self, topic: str, request_data: dict[str, Any]
+    ) -> dict[str, Any] | None:
         return None
 
-    def expire_object(self, object_id, camera):
+    def expire_object(self, object_id: str, camera: str) -> None:
         if object_id in self.detected_birds:
             self.detected_birds.pop(object_id)

frigate/data_processing/real_time/custom_classification.py

@@ -24,7 +24,8 @@ from frigate.const import CLIPS_DIR, MODEL_CACHE_DIR
 from frigate.log import suppress_stderr_during
 from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import EventsPerSecond, InferenceSpeed, load_labels
-from frigate.util.object import box_overlaps, calculate_region
+from frigate.util.image import calculate_region
+from frigate.util.object import box_overlaps
 
 from ..types import DataProcessorMetrics
 from .api import RealTimeProcessorApi
@@ -49,12 +50,16 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
     ):
         super().__init__(config, metrics)
         self.model_config = model_config
+
+        if not self.model_config.name:
+            raise ValueError("Custom classification model name must be set.")
+
         self.requestor = requestor
         self.model_dir = os.path.join(MODEL_CACHE_DIR, self.model_config.name)
         self.train_dir = os.path.join(CLIPS_DIR, self.model_config.name, "train")
-        self.interpreter: Interpreter = None
-        self.tensor_input_details: dict[str, Any] | None = None
-        self.tensor_output_details: dict[str, Any] | None = None
+        self.interpreter: Interpreter | None = None
+        self.tensor_input_details: list[dict[str, Any]] | None = None
+        self.tensor_output_details: list[dict[str, Any]] | None = None
         self.labelmap: dict[int, str] = {}
         self.classifications_per_second = EventsPerSecond()
         self.state_history: dict[str, dict[str, Any]] = {}
@@ -63,7 +68,7 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
             self.metrics
             and self.model_config.name in self.metrics.classification_speeds
         ):
-            self.inference_speed = InferenceSpeed(
+            self.inference_speed: InferenceSpeed | None = InferenceSpeed(
                 self.metrics.classification_speeds[self.model_config.name]
             )
         else:
@@ -172,12 +177,20 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
 
         return None
 
-    def process_frame(self, frame_data: dict[str, Any], frame: np.ndarray):
+    def process_frame(self, frame_data: dict[str, Any], frame: np.ndarray) -> None:
+        if (
+            not self.model_config.name
+            or not self.model_config.state_config
+            or not self.tensor_input_details
+            or not self.tensor_output_details
+        ):
+            return
+
         if self.metrics and self.model_config.name in self.metrics.classification_cps:
             self.metrics.classification_cps[
                 self.model_config.name
             ].value = self.classifications_per_second.eps()
-        camera = frame_data.get("camera")
+        camera = str(frame_data.get("camera"))
 
         if camera not in self.model_config.state_config.cameras:
             return
@@ -283,7 +296,7 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         logger.debug(
             f"{self.model_config.name} Ran state classification with probabilities: {probs}"
         )
-        best_id = np.argmax(probs)
+        best_id = int(np.argmax(probs))
         score = round(probs[best_id], 2)
         self.__update_metrics(datetime.datetime.now().timestamp() - now)
 
@@ -319,7 +332,9 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
                 verified_state,
             )
 
-    def handle_request(self, topic, request_data):
+    def handle_request(
+        self, topic: str, request_data: dict[str, Any]
+    ) -> dict[str, Any] | None:
         if topic == EmbeddingsRequestEnum.reload_classification_model.value:
             if request_data.get("model_name") == self.model_config.name:
                 self.__build_detector()
@@ -335,7 +350,7 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         else:
             return None
 
-    def expire_object(self, object_id, camera):
+    def expire_object(self, object_id: str, camera: str) -> None:
         pass
 
 
@@ -350,13 +365,17 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
     ):
         super().__init__(config, metrics)
         self.model_config = model_config
+
+        if not self.model_config.name:
+            raise ValueError("Custom classification model name must be set.")
+
         self.model_dir = os.path.join(MODEL_CACHE_DIR, self.model_config.name)
         self.train_dir = os.path.join(CLIPS_DIR, self.model_config.name, "train")
-        self.interpreter: Interpreter = None
+        self.interpreter: Interpreter | None = None
         self.sub_label_publisher = sub_label_publisher
         self.requestor = requestor
-        self.tensor_input_details: dict[str, Any] | None = None
-        self.tensor_output_details: dict[str, Any] | None = None
+        self.tensor_input_details: list[dict[str, Any]] | None = None
+        self.tensor_output_details: list[dict[str, Any]] | None = None
         self.classification_history: dict[str, list[tuple[str, float, float]]] = {}
         self.labelmap: dict[int, str] = {}
         self.classifications_per_second = EventsPerSecond()
@@ -365,7 +384,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             self.metrics
             and self.model_config.name in self.metrics.classification_speeds
         ):
-            self.inference_speed = InferenceSpeed(
+            self.inference_speed: InferenceSpeed | None = InferenceSpeed(
                 self.metrics.classification_speeds[self.model_config.name]
             )
         else:
@@ -431,8 +450,8 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             )
             return None, 0.0
 
-        label_counts = {}
-        label_scores = {}
+        label_counts: dict[str, int] = {}
+        label_scores: dict[str, list[float]] = {}
         total_attempts = len(history)
 
         for label, score, timestamp in history:
@@ -443,7 +462,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             label_counts[label] += 1
             label_scores[label].append(score)
 
-        best_label = max(label_counts, key=label_counts.get)
+        best_label = max(label_counts, key=lambda k: label_counts[k])
         best_count = label_counts[best_label]
 
         consensus_threshold = total_attempts * 0.6
@@ -470,7 +489,15 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         )
         return best_label, avg_score
 
-    def process_frame(self, obj_data, frame):
+    def process_frame(self, obj_data: dict[str, Any], frame: np.ndarray) -> None:
+        if (
+            not self.model_config.name
+            or not self.model_config.object_config
+            or not self.tensor_input_details
+            or not self.tensor_output_details
+        ):
+            return
+
         if self.metrics and self.model_config.name in self.metrics.classification_cps:
             self.metrics.classification_cps[
                 self.model_config.name
@@ -555,7 +582,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         logger.debug(
             f"{self.model_config.name} Ran object classification with probabilities: {probs}"
         )
-        best_id = np.argmax(probs)
+        best_id = int(np.argmax(probs))
         score = round(probs[best_id], 2)
         self.__update_metrics(datetime.datetime.now().timestamp() - now)
 
@@ -650,7 +677,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
                     ),
                 )
 
-    def handle_request(self, topic, request_data):
+    def handle_request(self, topic: str, request_data: dict) -> dict | None:
         if topic == EmbeddingsRequestEnum.reload_classification_model.value:
             if request_data.get("model_name") == self.model_config.name:
                 self.__build_detector()
@@ -666,12 +693,11 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         else:
             return None
 
-    def expire_object(self, object_id, camera):
+    def expire_object(self, object_id: str, camera: str) -> None:
         if object_id in self.classification_history:
             self.classification_history.pop(object_id)
 
 
-@staticmethod
 def write_classification_attempt(
     folder: str,
     frame: np.ndarray,

frigate/data_processing/real_time/face.py

@@ -52,11 +52,11 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
         self.face_config = config.face_recognition
         self.requestor = requestor
         self.sub_label_publisher = sub_label_publisher
-        self.face_detector: cv2.FaceDetectorYN = None
+        self.face_detector: cv2.FaceDetectorYN | None = None
         self.requires_face_detection = "face" not in self.config.objects.all_objects
         self.person_face_history: dict[str, list[tuple[str, float, int]]] = {}
         self.camera_current_people: dict[str, list[str]] = {}
-        self.recognizer: FaceRecognizer | None = None
+        self.recognizer: FaceRecognizer
         self.faces_per_second = EventsPerSecond()
         self.inference_speed = InferenceSpeed(self.metrics.face_rec_speed)
 
@@ -78,7 +78,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
             self.downloader = ModelDownloader(
                 model_name="facedet",
                 download_path=download_path,
-                file_names=self.model_files.keys(),
+                file_names=list(self.model_files.keys()),
                 download_func=self.__download_models,
                 complete_func=self.__build_detector,
             )
@@ -134,7 +134,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
 
     def __detect_face(
         self, input: np.ndarray, threshold: float
-    ) -> tuple[int, int, int, int]:
+    ) -> tuple[int, int, int, int] | None:
         """Detect faces in input image."""
         if not self.face_detector:
             return None
@@ -153,7 +153,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
         faces = self.face_detector.detect(input)
 
         if faces is None or faces[1] is None:
-            return None
+            return None  # type: ignore[unreachable]
 
         face = None
 
@@ -168,7 +168,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
             h: int = int(raw_bbox[3] / scale_factor)
             bbox = (x, y, x + w, y + h)
 
-            if face is None or area(bbox) > area(face):
+            if face is None or area(bbox) > area(face):  # type: ignore[unreachable]
                 face = bbox
 
         return face
@@ -177,7 +177,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
         self.faces_per_second.update()
         self.inference_speed.update(duration)
 
-    def process_frame(self, obj_data: dict[str, Any], frame: np.ndarray):
+    def process_frame(self, obj_data: dict[str, Any], frame: np.ndarray) -> None:
         """Look for faces in image."""
         self.metrics.face_rec_fps.value = self.faces_per_second.eps()
         camera = obj_data["camera"]
@@ -349,7 +349,9 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
 
         self.__update_metrics(datetime.datetime.now().timestamp() - start)
 
-    def handle_request(self, topic, request_data) -> dict[str, Any] | None:
+    def handle_request(
+        self, topic: str, request_data: dict[str, Any]
+    ) -> dict[str, Any] | None:
         if topic == EmbeddingsRequestEnum.clear_face_classifier.value:
             self.recognizer.clear()
             return {"success": True, "message": "Face classifier cleared."}
@@ -432,7 +434,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
                 img = cv2.imread(current_file)
 
             if img is None:
-                return {
+                return {  # type: ignore[unreachable]
                     "message": "Invalid image file.",
                     "success": False,
                 }
@@ -469,7 +471,9 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
                 "score": score,
             }
 
-    def expire_object(self, object_id: str, camera: str):
+        return None
+
+    def expire_object(self, object_id: str, camera: str) -> None:
         if object_id in self.person_face_history:
             self.person_face_history.pop(object_id)
 
@@ -478,7 +482,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
 
     def weighted_average(
         self, results_list: list[tuple[str, float, int]], max_weight: int = 4000
-    ):
+    ) -> tuple[str | None, float]:
         """
         Calculates a robust weighted average, capping the area weight and giving more weight to higher scores.
 
@@ -493,8 +497,8 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
             return None, 0.0
 
         counts: dict[str, int] = {}
-        weighted_scores: dict[str, int] = {}
-        total_weights: dict[str, int] = {}
+        weighted_scores: dict[str, float] = {}
+        total_weights: dict[str, float] = {}
 
         for name, score, face_area in results_list:
             if name == "unknown":
@@ -509,7 +513,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
             counts[name] += 1
 
             # Capped weight based on face area
-            weight = min(face_area, max_weight)
+            weight: float = min(face_area, max_weight)
 
             # Score-based weighting (higher scores get more weight)
             weight *= (score - self.face_config.unknown_score) * 10
@@ -519,7 +523,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
         if not weighted_scores:
             return None, 0.0
 
-        best_name = max(weighted_scores, key=weighted_scores.get)
+        best_name = max(weighted_scores, key=lambda k: weighted_scores[k])
 
         # If the number of faces for this person < min_faces, we are not confident it is a correct result
         if counts[best_name] < self.face_config.min_faces:

frigate/data_processing/real_time/license_plate.py

@@ -61,14 +61,16 @@ class LicensePlateRealTimeProcessor(LicensePlateProcessingMixin, RealTimeProcess
         self,
         obj_data: dict[str, Any],
         frame: np.ndarray,
-        dedicated_lpr: bool | None = False,
-    ):
+        dedicated_lpr: bool = False,
+    ) -> None:
         """Look for license plates in image."""
         self.lpr_process(obj_data, frame, dedicated_lpr)
 
-    def handle_request(self, topic, request_data) -> dict[str, Any] | None:
-        return
+    def handle_request(
+        self, topic: str, request_data: dict[str, Any]
+    ) -> dict[str, Any] | None:
+        return None
 
-    def expire_object(self, object_id: str, camera: str):
+    def expire_object(self, object_id: str, camera: str) -> None:
         """Expire lpr objects."""
         self.lpr_expire(object_id, camera)

frigate/data_processing/types.py

@@ -1,8 +1,10 @@
 """Embeddings types."""
 
+from __future__ import annotations
+
 from enum import Enum
-from multiprocessing.managers import SyncManager
-from multiprocessing.sharedctypes import Synchronized
+from multiprocessing.managers import DictProxy, SyncManager, ValueProxy
+from typing import Any
 
 import sherpa_onnx
 
@@ -10,22 +12,22 @@ from frigate.data_processing.real_time.whisper_online import FasterWhisperASR
 
 
 class DataProcessorMetrics:
-    image_embeddings_speed: Synchronized
-    image_embeddings_eps: Synchronized
-    text_embeddings_speed: Synchronized
-    text_embeddings_eps: Synchronized
-    face_rec_speed: Synchronized
-    face_rec_fps: Synchronized
-    alpr_speed: Synchronized
-    alpr_pps: Synchronized
-    yolov9_lpr_speed: Synchronized
-    yolov9_lpr_pps: Synchronized
-    review_desc_speed: Synchronized
-    review_desc_dps: Synchronized
-    object_desc_speed: Synchronized
-    object_desc_dps: Synchronized
-    classification_speeds: dict[str, Synchronized]
-    classification_cps: dict[str, Synchronized]
+    image_embeddings_speed: ValueProxy[float]
+    image_embeddings_eps: ValueProxy[float]
+    text_embeddings_speed: ValueProxy[float]
+    text_embeddings_eps: ValueProxy[float]
+    face_rec_speed: ValueProxy[float]
+    face_rec_fps: ValueProxy[float]
+    alpr_speed: ValueProxy[float]
+    alpr_pps: ValueProxy[float]
+    yolov9_lpr_speed: ValueProxy[float]
+    yolov9_lpr_pps: ValueProxy[float]
+    review_desc_speed: ValueProxy[float]
+    review_desc_dps: ValueProxy[float]
+    object_desc_speed: ValueProxy[float]
+    object_desc_dps: ValueProxy[float]
+    classification_speeds: DictProxy[str, ValueProxy[float]]
+    classification_cps: DictProxy[str, ValueProxy[float]]
 
     def __init__(self, manager: SyncManager, custom_classification_models: list[str]):
         self.image_embeddings_speed = manager.Value("d", 0.0)
@@ -52,7 +54,7 @@ class DataProcessorMetrics:
 
 
 class DataProcessorModelRunner:
-    def __init__(self, requestor, device: str = "CPU", model_size: str = "large"):
+    def __init__(self, requestor: Any, device: str = "CPU", model_size: str = "large"):
         self.requestor = requestor
         self.device = device
         self.model_size = model_size

frigate/db/sqlitevecq.py

@@ -1,18 +1,21 @@
 import re
 import sqlite3
+from typing import Any
 
 from playhouse.sqliteq import SqliteQueueDatabase
 
 
 class SqliteVecQueueDatabase(SqliteQueueDatabase):
-    def __init__(self, *args, load_vec_extension: bool = False, **kwargs) -> None:
+    def __init__(
+        self, *args: Any, load_vec_extension: bool = False, **kwargs: Any
+    ) -> None:
         self.load_vec_extension: bool = load_vec_extension
         # no extension necessary, sqlite will load correctly for each platform
         self.sqlite_vec_path = "/usr/local/lib/vec0"
         super().__init__(*args, **kwargs)
 
-    def _connect(self, *args, **kwargs) -> sqlite3.Connection:
-        conn: sqlite3.Connection = super()._connect(*args, **kwargs)
+    def _connect(self, *args: Any, **kwargs: Any) -> sqlite3.Connection:
+        conn: sqlite3.Connection = super()._connect(*args, **kwargs)  # type: ignore[misc]
         if self.load_vec_extension:
             self._load_vec_extension(conn)
 
@@ -27,7 +30,7 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
         conn.enable_load_extension(False)
 
     def _register_regexp(self, conn: sqlite3.Connection) -> None:
-        def regexp(expr: str, item: str) -> bool:
+        def regexp(expr: str, item: str | None) -> bool:
             if item is None:
                 return False
             try:

frigate/detectors/detector_config.py

@@ -47,7 +47,7 @@ class ModelTypeEnum(str, Enum):
 class ModelConfig(BaseModel):
     path: Optional[str] = Field(
         None,
-        title="Custom Object detection model path",
+        title="Custom object detector model path",
         description="Path to a custom detection model file (or plus://<model_id> for Frigate+ models).",
     )
     labelmap_path: Optional[str] = Field(

frigate/events/audio.py

@@ -2,17 +2,19 @@
 
 import datetime
 import logging
+import subprocess
 import threading
 import time
 from multiprocessing.managers import DictProxy
 from multiprocessing.synchronize import Event as MpEvent
-from typing import Tuple
+from typing import Any, Tuple
 
 import numpy as np
 
 from frigate.comms.detections_updater import DetectionPublisher, DetectionTypeEnum
 from frigate.comms.inter_process import InterProcessRequestor
-from frigate.config import CameraConfig, CameraInput, FfmpegConfig, FrigateConfig
+from frigate.config import CameraConfig, CameraInput, FrigateConfig
+from frigate.config.camera.ffmpeg import CameraFfmpegConfig
 from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdateSubscriber,
@@ -35,8 +37,7 @@ from frigate.data_processing.real_time.audio_transcription import (
 )
 from frigate.ffmpeg_presets import parse_preset_input
 from frigate.log import LogPipe, suppress_stderr_during
-from frigate.object_detection.base import load_labels
-from frigate.util.builtin import get_ffmpeg_arg_list
+from frigate.util.builtin import get_ffmpeg_arg_list, load_labels
 from frigate.util.ffmpeg import start_or_restart_ffmpeg, stop_ffmpeg
 from frigate.util.process import FrigateProcess
 
@@ -49,7 +50,7 @@ except ModuleNotFoundError:
 logger = logging.getLogger(__name__)
 
 
-def get_ffmpeg_command(ffmpeg: FfmpegConfig) -> list[str]:
+def get_ffmpeg_command(ffmpeg: CameraFfmpegConfig) -> list[str]:
     ffmpeg_input: CameraInput = [i for i in ffmpeg.inputs if "audio" in i.roles][0]
     input_args = get_ffmpeg_arg_list(ffmpeg.global_args) + (
         parse_preset_input(ffmpeg_input.input_args, 1)
@@ -102,9 +103,11 @@ class AudioProcessor(FrigateProcess):
         threading.current_thread().name = "process:audio_manager"
 
         if self.config.audio_transcription.enabled:
-            self.transcription_model_runner = AudioTranscriptionModelRunner(
-                self.config.audio_transcription.device,
-                self.config.audio_transcription.model_size,
+            self.transcription_model_runner: AudioTranscriptionModelRunner | None = (
+                AudioTranscriptionModelRunner(
+                    self.config.audio_transcription.device or "AUTO",
+                    self.config.audio_transcription.model_size,
+                )
             )
         else:
             self.transcription_model_runner = None
@@ -118,7 +121,7 @@ class AudioProcessor(FrigateProcess):
                 self.config,
                 self.camera_metrics,
                 self.transcription_model_runner,
-                self.stop_event,
+                self.stop_event,  # type: ignore[arg-type]
             )
             audio_threads.append(audio_thread)
             audio_thread.start()
@@ -162,7 +165,7 @@ class AudioEventMaintainer(threading.Thread):
         self.logger = logging.getLogger(f"audio.{self.camera_config.name}")
         self.ffmpeg_cmd = get_ffmpeg_command(self.camera_config.ffmpeg)
         self.logpipe = LogPipe(f"ffmpeg.{self.camera_config.name}.audio")
-        self.audio_listener = None
+        self.audio_listener: subprocess.Popen[Any] | None = None
         self.audio_transcription_model_runner = audio_transcription_model_runner
         self.transcription_processor = None
         self.transcription_thread = None
@@ -171,7 +174,7 @@ class AudioEventMaintainer(threading.Thread):
         self.requestor = InterProcessRequestor()
         self.config_subscriber = CameraConfigUpdateSubscriber(
             None,
-            {self.camera_config.name: self.camera_config},
+            {str(self.camera_config.name): self.camera_config},
             [
                 CameraConfigUpdateEnum.audio,
                 CameraConfigUpdateEnum.enabled,
@@ -180,7 +183,10 @@ class AudioEventMaintainer(threading.Thread):
         )
         self.detection_publisher = DetectionPublisher(DetectionTypeEnum.audio.value)
 
-        if self.config.audio_transcription.enabled:
+        if (
+            self.config.audio_transcription.enabled
+            and self.audio_transcription_model_runner is not None
+        ):
             # init the transcription processor for this camera
             self.transcription_processor = AudioTranscriptionRealTimeProcessor(
                 config=self.config,
@@ -200,11 +206,11 @@ class AudioEventMaintainer(threading.Thread):
 
         self.was_enabled = camera.enabled
 
-    def detect_audio(self, audio) -> None:
+    def detect_audio(self, audio: np.ndarray) -> None:
         if not self.camera_config.audio.enabled or self.stop_event.is_set():
             return
 
-        audio_as_float = audio.astype(np.float32)
+        audio_as_float: np.ndarray = audio.astype(np.float32)
         rms, dBFS = self.calculate_audio_levels(audio_as_float)
 
         self.camera_metrics[self.camera_config.name].audio_rms.value = rms
@@ -261,7 +267,7 @@ class AudioEventMaintainer(threading.Thread):
             else:
                 self.transcription_processor.check_unload_model()
 
-    def calculate_audio_levels(self, audio_as_float: np.float32) -> Tuple[float, float]:
+    def calculate_audio_levels(self, audio_as_float: np.ndarray) -> Tuple[float, float]:
         # Calculate RMS (Root-Mean-Square) which represents the average signal amplitude
         # Note: np.float32 isn't serializable, we must use np.float64 to publish the message
         rms = np.sqrt(np.mean(np.absolute(np.square(audio_as_float))))
@@ -296,6 +302,10 @@ class AudioEventMaintainer(threading.Thread):
             self.logpipe.dump()
             self.start_or_restart_ffmpeg()
 
+        if self.audio_listener is None or self.audio_listener.stdout is None:
+            log_and_restart()
+            return
+
         try:
             chunk = self.audio_listener.stdout.read(self.chunk_size)
 
@@ -341,7 +351,10 @@ class AudioEventMaintainer(threading.Thread):
                     self.requestor.send_data(
                         EXPIRE_AUDIO_ACTIVITY, self.camera_config.name
                     )
-                    stop_ffmpeg(self.audio_listener, self.logger)
+
+                    if self.audio_listener:
+                        stop_ffmpeg(self.audio_listener, self.logger)
+
                     self.audio_listener = None
                 self.was_enabled = enabled
                 continue
@@ -367,7 +380,7 @@ class AudioEventMaintainer(threading.Thread):
 
 
 class AudioTfl:
-    def __init__(self, stop_event: threading.Event, num_threads=2):
+    def __init__(self, stop_event: threading.Event, num_threads: int = 2) -> None:
         self.stop_event = stop_event
         self.num_threads = num_threads
         self.labels = load_labels("/audio-labelmap.txt", prefill=521)
@@ -382,7 +395,7 @@ class AudioTfl:
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
 
-    def _detect_raw(self, tensor_input):
+    def _detect_raw(self, tensor_input: np.ndarray) -> np.ndarray:
         self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
         self.interpreter.invoke()
         detections = np.zeros((20, 6), np.float32)
@@ -410,8 +423,10 @@ class AudioTfl:
 
         return detections
 
-    def detect(self, tensor_input, threshold=AUDIO_MIN_CONFIDENCE):
-        detections = []
+    def detect(
+        self, tensor_input: np.ndarray, threshold: float = AUDIO_MIN_CONFIDENCE
+    ) -> list[tuple[str, float, tuple[float, float, float, float]]]:
+        detections: list[tuple[str, float, tuple[float, float, float, float]]] = []
 
         if self.stop_event.is_set():
             return detections

frigate/events/cleanup.py

@@ -29,7 +29,7 @@ class EventCleanup(threading.Thread):
         self.stop_event = stop_event
         self.db = db
         self.camera_keys = list(self.config.cameras.keys())
-        self.removed_camera_labels: list[str] = None
+        self.removed_camera_labels: list[Event] | None = None
         self.camera_labels: dict[str, dict[str, Any]] = {}
 
     def get_removed_camera_labels(self) -> list[Event]:
@@ -37,7 +37,7 @@ class EventCleanup(threading.Thread):
         if self.removed_camera_labels is None:
             self.removed_camera_labels = list(
                 Event.select(Event.label)
-                .where(Event.camera.not_in(self.camera_keys))
+                .where(Event.camera.not_in(self.camera_keys))  # type: ignore[arg-type,call-arg,misc]
                 .distinct()
                 .execute()
             )
@@ -61,7 +61,7 @@ class EventCleanup(threading.Thread):
                 ),
             }
 
-        return self.camera_labels[camera]["labels"]
+        return self.camera_labels[camera]["labels"]  # type: ignore[no-any-return]
 
     def expire_snapshots(self) -> list[str]:
         ## Expire events from unlisted cameras based on the global config
@@ -74,7 +74,9 @@ class EventCleanup(threading.Thread):
         # loop over object types in db
         for event in distinct_labels:
             # get expiration time for this label
-            expire_days = retain_config.objects.get(event.label, retain_config.default)
+            expire_days = retain_config.objects.get(
+                str(event.label), retain_config.default
+            )
 
             expire_after = (
                 datetime.datetime.now() - datetime.timedelta(days=expire_days)
@@ -87,7 +89,7 @@ class EventCleanup(threading.Thread):
                     Event.thumbnail,
                 )
                 .where(
-                    Event.camera.not_in(self.camera_keys),
+                    Event.camera.not_in(self.camera_keys),  # type: ignore[arg-type,call-arg,misc]
                     Event.start_time < expire_after,
                     Event.label == event.label,
                     Event.retain_indefinitely == False,
@@ -109,16 +111,16 @@ class EventCleanup(threading.Thread):
 
             # update the clips attribute for the db entry
             query = Event.select(Event.id).where(
-                Event.camera.not_in(self.camera_keys),
+                Event.camera.not_in(self.camera_keys),  # type: ignore[arg-type,call-arg,misc]
                 Event.start_time < expire_after,
                 Event.label == event.label,
                 Event.retain_indefinitely == False,
             )
 
-            events_to_update = []
+            events_to_update: list[str] = []
 
             for event in query.iterator():
-                events_to_update.append(event.id)
+                events_to_update.append(str(event.id))
                 if len(events_to_update) >= CHUNK_SIZE:
                     logger.debug(
                         f"Updating {update_params} for {len(events_to_update)} events"
@@ -150,7 +152,7 @@ class EventCleanup(threading.Thread):
             for event in distinct_labels:
                 # get expiration time for this label
                 expire_days = retain_config.objects.get(
-                    event.label, retain_config.default
+                    str(event.label), retain_config.default
                 )
 
                 expire_after = (
@@ -177,7 +179,7 @@ class EventCleanup(threading.Thread):
                 # only snapshots are stored in /clips
                 # so no need to delete mp4 files
                 for event in expired_events:
-                    events_to_update.append(event.id)
+                    events_to_update.append(str(event.id))
                     deleted = delete_event_snapshot(event)
 
                     if not deleted:
@@ -214,7 +216,7 @@ class EventCleanup(threading.Thread):
                 Event.camera,
             )
             .where(
-                Event.camera.not_in(self.camera_keys),
+                Event.camera.not_in(self.camera_keys),  # type: ignore[arg-type,call-arg,misc]
                 Event.start_time < expire_after,
                 Event.retain_indefinitely == False,
             )
@@ -245,7 +247,7 @@ class EventCleanup(threading.Thread):
 
         # update the clips attribute for the db entry
         query = Event.select(Event.id).where(
-            Event.camera.not_in(self.camera_keys),
+            Event.camera.not_in(self.camera_keys),  # type: ignore[arg-type,call-arg,misc]
             Event.start_time < expire_after,
             Event.retain_indefinitely == False,
         )
@@ -358,7 +360,7 @@ class EventCleanup(threading.Thread):
 
             logger.debug(f"Found {len(events_to_delete)} events that can be expired")
             if len(events_to_delete) > 0:
-                ids_to_delete = [e.id for e in events_to_delete]
+                ids_to_delete = [str(e.id) for e in events_to_delete]
                 for i in range(0, len(ids_to_delete), CHUNK_SIZE):
                     chunk = ids_to_delete[i : i + CHUNK_SIZE]
                     logger.debug(f"Deleting {len(chunk)} events from the database")

frigate/events/maintainer.py

@@ -2,7 +2,7 @@ import logging
 import threading
 from multiprocessing import Queue
 from multiprocessing.synchronize import Event as MpEvent
-from typing import Dict
+from typing import Any, Dict
 
 from frigate.comms.events_updater import EventEndPublisher, EventUpdateSubscriber
 from frigate.config import FrigateConfig
@@ -15,7 +15,7 @@ from frigate.util.builtin import to_relative_box
 logger = logging.getLogger(__name__)
 
 
-def should_update_db(prev_event: Event, current_event: Event) -> bool:
+def should_update_db(prev_event: dict[str, Any], current_event: dict[str, Any]) -> bool:
     """If current_event has updated fields and (clip or snapshot)."""
     # If event is ending and was previously saved, always update to set end_time
     # This ensures events are properly ended even when alerts/detections are disabled
@@ -47,7 +47,9 @@ def should_update_db(prev_event: Event, current_event: Event) -> bool:
     return False
 
 
-def should_update_state(prev_event: Event, current_event: Event) -> bool:
+def should_update_state(
+    prev_event: dict[str, Any], current_event: dict[str, Any]
+) -> bool:
     """If current event should update state, but not necessarily update the db."""
     if prev_event["stationary"] != current_event["stationary"]:
         return True
@@ -74,7 +76,7 @@ class EventProcessor(threading.Thread):
         super().__init__(name="event_processor")
         self.config = config
         self.timeline_queue = timeline_queue
-        self.events_in_process: Dict[str, Event] = {}
+        self.events_in_process: Dict[str, dict[str, Any]] = {}
         self.stop_event = stop_event
 
         self.event_receiver = EventUpdateSubscriber()
@@ -92,7 +94,7 @@ class EventProcessor(threading.Thread):
             if update == None:
                 continue
 
-            source_type, event_type, camera, _, event_data = update
+            source_type, event_type, camera, _, event_data = update  # type: ignore[misc]
 
             logger.debug(
                 f"Event received: {source_type} {event_type} {camera} {event_data['id']}"
@@ -140,7 +142,7 @@ class EventProcessor(threading.Thread):
         self,
         event_type: str,
         camera: str,
-        event_data: Event,
+        event_data: dict[str, Any],
     ) -> None:
         """handle tracked object event updates."""
         updated_db = False
@@ -150,8 +152,13 @@ class EventProcessor(threading.Thread):
             camera_config = self.config.cameras.get(camera)
             if camera_config is None:
                 return
+
             width = camera_config.detect.width
             height = camera_config.detect.height
+
+            if width is None or height is None:
+                return
+
             first_detector = list(self.config.detectors.values())[0]
 
             start_time = event_data["start_time"]
@@ -222,8 +229,12 @@ class EventProcessor(threading.Thread):
                 Event.thumbnail: event_data.get("thumbnail"),
                 Event.has_clip: event_data["has_clip"],
                 Event.has_snapshot: event_data["has_snapshot"],
-                Event.model_hash: first_detector.model.model_hash,
-                Event.model_type: first_detector.model.model_type,
+                Event.model_hash: first_detector.model.model_hash
+                if first_detector.model
+                else None,
+                Event.model_type: first_detector.model.model_type
+                if first_detector.model
+                else None,
                 Event.detector_type: first_detector.type,
                 Event.data: {
                     "box": box,
@@ -287,10 +298,10 @@ class EventProcessor(threading.Thread):
 
         if event_type == EventStateEnum.end:
             del self.events_in_process[event_data["id"]]
-            self.event_end_publisher.publish((event_data["id"], camera, updated_db))
+            self.event_end_publisher.publish((event_data["id"], camera, updated_db))  # type: ignore[arg-type]
 
     def handle_external_detection(
-        self, event_type: EventStateEnum, event_data: Event
+        self, event_type: EventStateEnum, event_data: dict[str, Any]
     ) -> None:
         # Skip replay cameras
         if event_data.get("camera", "").startswith(REPLAY_CAMERA_PREFIX):

frigate/ffmpeg_presets.py

@@ -3,7 +3,7 @@
 import logging
 import os
 from enum import Enum
-from typing import Any
+from typing import Any, Optional
 
 from frigate.const import (
     FFMPEG_HVC1_ARGS,
@@ -215,7 +215,7 @@ def parse_preset_hardware_acceleration_decode(
     width: int,
     height: int,
     gpu: int,
-) -> list[str]:
+) -> Optional[list[str]]:
     """Return the correct preset if in preset format otherwise return None."""
     if not isinstance(arg, str):
         return None
@@ -242,9 +242,9 @@ def parse_preset_hardware_acceleration_scale(
     else:
         scale = PRESETS_HW_ACCEL_SCALE.get(arg, PRESETS_HW_ACCEL_SCALE["default"])
 
-    scale = scale.format(fps, width, height).split(" ")
-    scale.extend(detect_args)
-    return scale
+    scale_args = scale.format(fps, width, height).split(" ")
+    scale_args.extend(detect_args)
+    return scale_args
 
 
 class EncodeTypeEnum(str, Enum):
@@ -420,7 +420,7 @@ PRESETS_INPUT = {
 }
 
 
-def parse_preset_input(arg: Any, detect_fps: int) -> list[str]:
+def parse_preset_input(arg: Any, detect_fps: int) -> Optional[list[str]]:
     """Return the correct preset if in preset format otherwise return None."""
     if not isinstance(arg, str):
         return None
@@ -465,6 +465,16 @@ PRESETS_RECORD_OUTPUT = {
         "-c:a",
         "aac",
     ],
+    # NOTE: This preset originally used "-c:a copy" to pass through audio
+    # without re-encoding. FFmpeg 7.x introduced a threaded pipeline where
+    # demuxing, encoding, and muxing run in parallel via a Scheduler. This
+    # broke audio streamcopy from RTSP sources: packets are demuxed correctly
+    # but silently dropped before reaching the muxer (0 bytes written). The
+    # issue is specific to RTSP + streamcopy; file inputs and transcoding both
+    # work. Transcoding AAC audio is very lightweight (~30KiB per 10s segment)
+    # and adds negligible CPU overhead, so this is an acceptable workaround.
+    # The benefits of FFmpeg 7.x — particularly the removal of gamma correction
+    # hacks required by earlier versions — outweigh this trade-off.
     "preset-record-generic-audio-copy": [
         "-f",
         "segment",
@@ -476,8 +486,10 @@ PRESETS_RECORD_OUTPUT = {
         "1",
         "-strftime",
         "1",
-        "-c",
+        "-c:v",
         "copy",
+        "-c:a",
+        "aac",
     ],
     "preset-record-mjpeg": [
         "-f",
@@ -530,7 +542,9 @@ PRESETS_RECORD_OUTPUT = {
 }
 
 
-def parse_preset_output_record(arg: Any, force_record_hvc1: bool) -> list[str]:
+def parse_preset_output_record(
+    arg: Any, force_record_hvc1: bool
+) -> Optional[list[str]]:
     """Return the correct preset if in preset format otherwise return None."""
     if not isinstance(arg, str):
         return None

frigate/genai/gemini.py

@@ -50,9 +50,9 @@ class GeminiClient(GenAIClient):
         response_format: Optional[dict] = None,
     ) -> Optional[str]:
         """Submit a request to Gemini."""
-        contents = [
+        contents = [prompt] + [
             types.Part.from_bytes(data=img, mime_type="image/jpeg") for img in images
-        ] + [prompt]
+        ]
         try:
             # Merge runtime_options into generation_config if provided
             generation_config_dict: dict[str, Any] = {"candidate_count": 1}

frigate/genai/openai.py

@@ -44,7 +44,12 @@ class OpenAIClient(GenAIClient):
     ) -> Optional[str]:
         """Submit a request to OpenAI."""
         encoded_images = [base64.b64encode(image).decode("utf-8") for image in images]
-        messages_content = []
+        messages_content: list[dict] = [
+            {
+                "type": "text",
+                "text": prompt,
+            }
+        ]
         for image in encoded_images:
             messages_content.append(
                 {
@@ -55,12 +60,6 @@ class OpenAIClient(GenAIClient):
                     },
                 }
             )
-        messages_content.append(
-            {
-                "type": "text",
-                "text": prompt,
-            }
-        )
         try:
             request_params = {
                 "model": self.genai_config.model,

frigate/jobs/vlm_watch.py

@@ -25,6 +25,9 @@ logger = logging.getLogger(__name__)
 _MIN_INTERVAL = 1
 _MAX_INTERVAL = 300
 
+# Minimum seconds between VLM iterations when woken by detections (no zone filter)
+_DETECTION_COOLDOWN_WITHOUT_ZONE = 10
+
 # Max user/assistant turn pairs to keep in conversation history
 _MAX_HISTORY = 10
 
@@ -40,6 +43,7 @@ class VLMWatchJob(Job):
     labels: list = field(default_factory=list)
     zones: list = field(default_factory=list)
     last_reasoning: str = ""
+    notification_message: str = ""
     iteration_count: int = 0
 
     def to_dict(self) -> dict[str, Any]:
@@ -196,6 +200,7 @@ class VLMWatchRunner(threading.Thread):
                 min(_MAX_INTERVAL, int(parsed.get("next_run_in", 30))),
             )
             reasoning = str(parsed.get("reasoning", ""))
+            notification_message = str(parsed.get("notification_message", ""))
         except (json.JSONDecodeError, ValueError, TypeError) as e:
             logger.warning(
                 "VLM watch job %s: failed to parse VLM response: %s", self.job.id, e
@@ -203,6 +208,7 @@ class VLMWatchRunner(threading.Thread):
             return 30
 
         self.job.last_reasoning = reasoning
+        self.job.notification_message = notification_message
         self.job.iteration_count += 1
         self._broadcast_status()
 
@@ -213,19 +219,35 @@ class VLMWatchRunner(threading.Thread):
                 self.job.camera,
                 reasoning,
             )
-            self._send_notification(reasoning)
+            self._send_notification(notification_message or reasoning)
             self.job.status = JobStatusTypesEnum.success
             return 0
 
         return next_run_in
 
     def _wait_for_trigger(self, max_wait: float) -> None:
-        """Wait up to max_wait seconds, returning early if a relevant detection fires on the target camera."""
-        deadline = time.time() + max_wait
+        """Wait up to max_wait seconds, returning early if a relevant detection fires on the target camera.
+
+        With zones configured, a matching detection wakes immediately (events
+        are already filtered).  Without zones, detections are frequent so a
+        cooldown is enforced: messages are continuously drained to prevent
+        queue backup, but the loop only exits once a match has been seen
+        *and* the cooldown period has elapsed.
+        """
+        now = time.time()
+        deadline = now + max_wait
+        use_cooldown = not self.job.zones
+        earliest_wake = now + _DETECTION_COOLDOWN_WITHOUT_ZONE if use_cooldown else 0
+        triggered = False
+
         while not self.cancel_event.is_set():
             remaining = deadline - time.time()
             if remaining <= 0:
                 break
+
+            if triggered and time.time() >= earliest_wake:
+                break
+
             result = self.detection_subscriber.check_for_update(
                 timeout=min(1.0, remaining)
             )
@@ -250,12 +272,22 @@ class VLMWatchRunner(threading.Thread):
             if cam != self.job.camera or not tracked_objects:
                 continue
             if self._detection_matches_filters(tracked_objects):
-                logger.debug(
-                    "VLM watch job %s: woken early by detection event on %s",
-                    self.job.id,
-                    self.job.camera,
-                )
-                break
+                if not use_cooldown:
+                    logger.debug(
+                        "VLM watch job %s: woken early by detection event on %s",
+                        self.job.id,
+                        self.job.camera,
+                    )
+                    break
+
+                if not triggered:
+                    logger.debug(
+                        "VLM watch job %s: detection match on %s, draining for %.0fs",
+                        self.job.id,
+                        self.job.camera,
+                        max(0, earliest_wake - time.time()),
+                    )
+                    triggered = True
 
     def _detection_matches_filters(self, tracked_objects: list) -> bool:
         """Return True if any tracked object passes the label and zone filters."""
@@ -284,7 +316,11 @@ class VLMWatchRunner(threading.Thread):
             f"You will receive a sequence of frames over time. Use the conversation history to understand "
             f"what is stationary vs. actively changing.\n\n"
             f"For each frame respond with JSON only:\n"
-            f'{{"condition_met": <true/false>, "next_run_in": <integer seconds 1-300>, "reasoning": "<brief explanation>"}}\n\n'
+            f'{{"condition_met": <true/false>, "next_run_in": <integer seconds 1-300>, "reasoning": "<brief explanation>", "notification_message": "<natural language notification>"}}\n\n'
+            f"Guidelines for notification_message:\n"
+            f"- Only required when condition_met is true.\n"
+            f"- Write a short, natural notification a user would want to receive on their phone.\n"
+            f'- Example: "Your package has been delivered to the front porch."\n\n'
             f"Guidelines for next_run_in:\n"
             f"- Scene is empty / nothing of interest visible: 60-300.\n"
             f"- Relevant object(s) visible anywhere in frame (even outside the target zone): 3-10. "
@@ -294,12 +330,13 @@ class VLMWatchRunner(threading.Thread):
             f"- Keep reasoning to 1-2 sentences."
         )
 
-    def _send_notification(self, reasoning: str) -> None:
+    def _send_notification(self, message: str) -> None:
         """Publish a camera_monitoring event so downstream handlers (web push, MQTT) can notify users."""
         payload = {
             "camera": self.job.camera,
             "condition": self.job.condition,
-            "reasoning": reasoning,
+            "message": message,
+            "reasoning": self.job.last_reasoning,
             "job_id": self.job.id,
         }
 

frigate/mypy.ini

@@ -22,68 +22,43 @@ warn_unreachable = true
 no_implicit_reexport = true
 
 [mypy-frigate.*]
+ignore_errors = false
+
+# Third-party code imported from https://github.com/ufal/whisper_streaming
+[mypy-frigate.data_processing.real_time.whisper_online]
 ignore_errors = true
 
-[mypy-frigate.__main__]
-ignore_errors = false
-disallow_untyped_calls = false
+# TODO: Remove ignores for these modules as they are updated with type annotations.
 
-[mypy-frigate.app]
-ignore_errors = false
-disallow_untyped_calls = false
+[mypy-frigate.api.*]
+ignore_errors = true
 
-[mypy-frigate.const]
-ignore_errors = false
+[mypy-frigate.config.*]
+ignore_errors = true
 
-[mypy-frigate.comms.*]
-ignore_errors = false
+[mypy-frigate.debug_replay]
+ignore_errors = true
 
-[mypy-frigate.events]
-ignore_errors = false
+[mypy-frigate.detectors.*]
+ignore_errors = true
 
-[mypy-frigate.genai.*]
-ignore_errors = false
+[mypy-frigate.embeddings.*]
+ignore_errors = true
 
-[mypy-frigate.jobs.*]
-ignore_errors = false
+[mypy-frigate.http]
+ignore_errors = true
 
-[mypy-frigate.motion.*]
-ignore_errors = false
+[mypy-frigate.ptz.*]
+ignore_errors = true
 
-[mypy-frigate.object_detection.*]
-ignore_errors = false
+[mypy-frigate.stats.*]
+ignore_errors = true
 
-[mypy-frigate.output.*]
-ignore_errors = false
+[mypy-frigate.test.*]
+ignore_errors = true
 
-[mypy-frigate.ptz]
-ignore_errors = false
+[mypy-frigate.util.*]
+ignore_errors = true
 
-[mypy-frigate.log]
-ignore_errors = false
-
-[mypy-frigate.models]
-ignore_errors = false
-
-[mypy-frigate.plus]
-ignore_errors = false
-
-[mypy-frigate.stats]
-ignore_errors = false
-
-[mypy-frigate.track.*]
-ignore_errors = false
-
-[mypy-frigate.types]
-ignore_errors = false
-
-[mypy-frigate.version]
-ignore_errors = false
-
-[mypy-frigate.watchdog]
-ignore_errors = false
-disallow_untyped_calls = false
-
-
-[mypy-frigate.service_manager.*]
-ignore_errors = false
+[mypy-frigate.video.*]
+ignore_errors = true

frigate/record/cleanup.py

@@ -7,6 +7,7 @@ import os
 import threading
 from multiprocessing.synchronize import Event as MpEvent
 from pathlib import Path
+from typing import Any
 
 from playhouse.sqlite_ext import SqliteExtDatabase
 
@@ -60,7 +61,9 @@ class RecordingCleanup(threading.Thread):
             db.execute_sql("PRAGMA wal_checkpoint(TRUNCATE);")
             db.close()
 
-    def expire_review_segments(self, config: CameraConfig, now: datetime) -> set[Path]:
+    def expire_review_segments(
+        self, config: CameraConfig, now: datetime.datetime
+    ) -> set[Path]:
         """Delete review segments that are expired"""
         alert_expire_date = (
             now - datetime.timedelta(days=config.record.alerts.retain.days)
@@ -68,7 +71,7 @@ class RecordingCleanup(threading.Thread):
         detection_expire_date = (
             now - datetime.timedelta(days=config.record.detections.retain.days)
         ).timestamp()
-        expired_reviews: ReviewSegment = (
+        expired_reviews = (
             ReviewSegment.select(ReviewSegment.id, ReviewSegment.thumb_path)
             .where(ReviewSegment.camera == config.name)
             .where(
@@ -109,13 +112,13 @@ class RecordingCleanup(threading.Thread):
         continuous_expire_date: float,
         motion_expire_date: float,
         config: CameraConfig,
-        reviews: ReviewSegment,
+        reviews: list[Any],
     ) -> set[Path]:
         """Delete recordings for existing camera based on retention config."""
         # Get the timestamp for cutoff of retained days
 
         # Get recordings to check for expiration
-        recordings: Recordings = (
+        recordings = (
             Recordings.select(
                 Recordings.id,
                 Recordings.start_time,
@@ -148,13 +151,12 @@ class RecordingCleanup(threading.Thread):
         review_start = 0
         deleted_recordings = set()
         kept_recordings: list[tuple[float, float]] = []
-        recording: Recordings
         for recording in recordings:
             keep = False
             mode = None
             # Now look for a reason to keep this recording segment
             for idx in range(review_start, len(reviews)):
-                review: ReviewSegment = reviews[idx]
+                review = reviews[idx]
                 severity = review.severity
                 pre_capture = config.record.get_review_pre_capture(severity)
                 post_capture = config.record.get_review_post_capture(severity)
@@ -214,7 +216,7 @@ class RecordingCleanup(threading.Thread):
                 Recordings.id << deleted_recordings_list[i : i + max_deletes]
             ).execute()
 
-        previews: list[Previews] = (
+        previews = (
             Previews.select(
                 Previews.id,
                 Previews.start_time,
@@ -290,13 +292,13 @@ class RecordingCleanup(threading.Thread):
         expire_before = (
             datetime.datetime.now() - datetime.timedelta(days=expire_days)
         ).timestamp()
-        no_camera_recordings: Recordings = (
+        no_camera_recordings = (
             Recordings.select(
                 Recordings.id,
                 Recordings.path,
             )
             .where(
-                Recordings.camera.not_in(list(self.config.cameras.keys())),
+                Recordings.camera.not_in(list(self.config.cameras.keys())),  # type: ignore[call-arg, arg-type, misc]
                 Recordings.end_time < expire_before,
             )
             .namedtuples()
@@ -341,7 +343,7 @@ class RecordingCleanup(threading.Thread):
             ).timestamp()
 
             # Get all the reviews to check against
-            reviews: ReviewSegment = (
+            reviews = (
                 ReviewSegment.select(
                     ReviewSegment.start_time,
                     ReviewSegment.end_time,

frigate/record/export.py

@@ -85,10 +85,6 @@ def validate_ffmpeg_args(args: str) -> tuple[bool, str]:
     return True, ""
 
 
-def lower_priority():
-    os.nice(PROCESS_PRIORITY_LOW)
-
-
 class PlaybackSourceEnum(str, Enum):
     recordings = "recordings"
     preview = "preview"
@@ -150,7 +146,7 @@ class RecordingExporter(threading.Thread):
         ):
             # has preview mp4
             try:
-                preview: Previews = (
+                preview = (
                     Previews.select(
                         Previews.camera,
                         Previews.path,
@@ -231,20 +227,19 @@ class RecordingExporter(threading.Thread):
 
     def get_record_export_command(
         self, video_path: str, use_hwaccel: bool = True
-    ) -> list[str]:
+    ) -> tuple[list[str], str | list[str]]:
         # handle case where internal port is a string with ip:port
         internal_port = self.config.networking.listen.internal
         if type(internal_port) is str:
             internal_port = int(internal_port.split(":")[-1])
 
+        playlist_lines: list[str] = []
         if (self.end_time - self.start_time) <= MAX_PLAYLIST_SECONDS:
-            playlist_lines = f"http://127.0.0.1:{internal_port}/vod/{self.camera}/start/{self.start_time}/end/{self.end_time}/index.m3u8"
+            playlist_url = f"http://127.0.0.1:{internal_port}/vod/{self.camera}/start/{self.start_time}/end/{self.end_time}/index.m3u8"
             ffmpeg_input = (
-                f"-y -protocol_whitelist pipe,file,http,tcp -i {playlist_lines}"
+                f"-y -protocol_whitelist pipe,file,http,tcp -i {playlist_url}"
             )
         else:
-            playlist_lines = []
-
             # get full set of recordings
             export_recordings = (
                 Recordings.select(
@@ -305,7 +300,7 @@ class RecordingExporter(threading.Thread):
 
     def get_preview_export_command(
         self, video_path: str, use_hwaccel: bool = True
-    ) -> list[str]:
+    ) -> tuple[list[str], list[str]]:
         playlist_lines = []
         codec = "-c copy"
 
@@ -355,7 +350,6 @@ class RecordingExporter(threading.Thread):
             .iterator()
         )
 
-        preview: Previews
         for preview in export_previews:
             playlist_lines.append(f"file '{preview.path}'")
 
@@ -441,10 +435,9 @@ class RecordingExporter(threading.Thread):
             return
 
         p = sp.run(
-            ffmpeg_cmd,
+            ["nice", "-n", str(PROCESS_PRIORITY_LOW)] + ffmpeg_cmd,
             input="\n".join(playlist_lines),
             encoding="ascii",
-            preexec_fn=lower_priority,
             capture_output=True,
         )
 
@@ -469,10 +462,9 @@ class RecordingExporter(threading.Thread):
                 )
 
             p = sp.run(
-                ffmpeg_cmd,
+                ["nice", "-n", str(PROCESS_PRIORITY_LOW)] + ffmpeg_cmd,
                 input="\n".join(playlist_lines),
                 encoding="ascii",
-                preexec_fn=lower_priority,
                 capture_output=True,
             )
 
@@ -493,7 +485,7 @@ class RecordingExporter(threading.Thread):
         logger.debug(f"Finished exporting {video_path}")
 
 
-def migrate_exports(ffmpeg: FfmpegConfig, camera_names: list[str]):
+def migrate_exports(ffmpeg: FfmpegConfig, camera_names: list[str]) -> None:
     Path(os.path.join(CLIPS_DIR, "export")).mkdir(exist_ok=True)
 
     exports = []

frigate/record/maintainer.py

@@ -266,7 +266,7 @@ class RecordingMaintainer(threading.Thread):
 
             # get all reviews with the end time after the start of the oldest cache file
             # or with end_time None
-            reviews: ReviewSegment = (
+            reviews = (
                 ReviewSegment.select(
                     ReviewSegment.start_time,
                     ReviewSegment.end_time,
@@ -301,7 +301,9 @@ class RecordingMaintainer(threading.Thread):
                 RecordingsDataTypeEnum.saved.value,
             )
 
-        recordings_to_insert: list[Optional[Recordings]] = await asyncio.gather(*tasks)
+        recordings_to_insert: list[Optional[dict[str, Any]]] = await asyncio.gather(
+            *tasks
+        )
 
         # fire and forget recordings entries
         self.requestor.send_data(
@@ -314,8 +316,8 @@ class RecordingMaintainer(threading.Thread):
         self.end_time_cache.pop(cache_path, None)
 
     async def validate_and_move_segment(
-        self, camera: str, reviews: list[ReviewSegment], recording: dict[str, Any]
-    ) -> Optional[Recordings]:
+        self, camera: str, reviews: Any, recording: dict[str, Any]
+    ) -> Optional[dict[str, Any]]:
         cache_path: str = recording["cache_path"]
         start_time: datetime.datetime = recording["start_time"]
 
@@ -456,6 +458,8 @@ class RecordingMaintainer(threading.Thread):
             if end_time < retain_cutoff:
                 self.drop_segment(cache_path)
 
+        return None
+
     def _compute_motion_heatmap(
         self, camera: str, motion_boxes: list[tuple[int, int, int, int]]
     ) -> dict[str, int] | None:
@@ -481,7 +485,7 @@ class RecordingMaintainer(threading.Thread):
         frame_width = camera_config.detect.width
         frame_height = camera_config.detect.height
 
-        if frame_width <= 0 or frame_height <= 0:
+        if not frame_width or frame_width <= 0 or not frame_height or frame_height <= 0:
             return None
 
         GRID_SIZE = 16
@@ -575,13 +579,13 @@ class RecordingMaintainer(threading.Thread):
         duration: float,
         cache_path: str,
         store_mode: RetainModeEnum,
-    ) -> Optional[Recordings]:
+    ) -> Optional[dict[str, Any]]:
         segment_info = self.segment_stats(camera, start_time, end_time)
 
         # check if the segment shouldn't be stored
         if segment_info.should_discard_segment(store_mode):
             self.drop_segment(cache_path)
-            return
+            return None
 
         # directory will be in utc due to start_time being in utc
         directory = os.path.join(
@@ -620,7 +624,8 @@ class RecordingMaintainer(threading.Thread):
 
                 if p.returncode != 0:
                     logger.error(f"Unable to convert {cache_path} to {file_path}")
-                    logger.error((await p.stderr.read()).decode("ascii"))
+                    if p.stderr:
+                        logger.error((await p.stderr.read()).decode("ascii"))
                     return None
                 else:
                     logger.debug(
@@ -684,11 +689,16 @@ class RecordingMaintainer(threading.Thread):
             stale_frame_count_threshold = 10
             # empty the object recordings info queue
             while True:
-                (topic, data) = self.detection_subscriber.check_for_update(
+                result = self.detection_subscriber.check_for_update(
                     timeout=FAST_QUEUE_TIMEOUT
                 )
 
-                if not topic:
+                if not result:
+                    break
+
+                topic, data = result
+
+                if not topic or not data:
                     break
 
                 if topic == DetectionTypeEnum.video.value:

frigate/review/maintainer.py

@@ -31,7 +31,7 @@ from frigate.const import (
 )
 from frigate.models import ReviewSegment
 from frigate.review.types import SeverityEnum
-from frigate.track.object_processing import ManualEventState, TrackedObject
+from frigate.track.object_processing import ManualEventState
 from frigate.util.image import SharedMemoryFrameManager, calculate_16_9_crop
 
 logger = logging.getLogger(__name__)
@@ -69,7 +69,9 @@ class PendingReviewSegment:
             self.last_alert_time = frame_time
 
         # thumbnail
-        self._frame = np.zeros((THUMB_HEIGHT * 3 // 2, THUMB_WIDTH), np.uint8)
+        self._frame: np.ndarray[Any, Any] = np.zeros(
+            (THUMB_HEIGHT * 3 // 2, THUMB_WIDTH), np.uint8
+        )
         self.has_frame = False
         self.frame_active_count = 0
         self.frame_path = os.path.join(
@@ -77,8 +79,11 @@ class PendingReviewSegment:
         )
 
     def update_frame(
-        self, camera_config: CameraConfig, frame, objects: list[TrackedObject]
-    ):
+        self,
+        camera_config: CameraConfig,
+        frame: np.ndarray,
+        objects: list[dict[str, Any]],
+    ) -> None:
         min_x = camera_config.frame_shape[1]
         min_y = camera_config.frame_shape[0]
         max_x = 0
@@ -114,7 +119,7 @@ class PendingReviewSegment:
                 self.frame_path, self._frame, [int(cv2.IMWRITE_WEBP_QUALITY), 60]
             )
 
-    def save_full_frame(self, camera_config: CameraConfig, frame):
+    def save_full_frame(self, camera_config: CameraConfig, frame: np.ndarray) -> None:
         color_frame = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
         width = int(THUMB_HEIGHT * color_frame.shape[1] / color_frame.shape[0])
         self._frame = cv2.resize(
@@ -165,13 +170,13 @@ class ActiveObjects:
         self,
         frame_time: float,
         camera_config: CameraConfig,
-        all_objects: list[TrackedObject],
+        all_objects: list[dict[str, Any]],
     ):
         self.camera_config = camera_config
 
         # get current categorization of objects to know if
         # these objects are currently being categorized
-        self.categorized_objects = {
+        self.categorized_objects: dict[str, list[dict[str, Any]]] = {
             "alerts": [],
             "detections": [],
         }
@@ -250,7 +255,7 @@ class ActiveObjects:
 
         return False
 
-    def get_all_objects(self) -> list[TrackedObject]:
+    def get_all_objects(self) -> list[dict[str, Any]]:
         return (
             self.categorized_objects["alerts"] + self.categorized_objects["detections"]
         )
@@ -309,7 +314,7 @@ class ReviewSegmentMaintainer(threading.Thread):
             "reviews",
             json.dumps(review_update),
         )
-        self.review_publisher.publish(review_update, segment.camera)
+        self.review_publisher.publish(review_update, segment.camera)  # type: ignore[arg-type]
         self.requestor.send_data(
             f"{segment.camera}/review_status", segment.severity.value.upper()
         )
@@ -318,8 +323,8 @@ class ReviewSegmentMaintainer(threading.Thread):
         self,
         segment: PendingReviewSegment,
         camera_config: CameraConfig,
-        frame,
-        objects: list[TrackedObject],
+        frame: Optional[np.ndarray],
+        objects: list[dict[str, Any]],
         prev_data: dict[str, Any],
     ) -> None:
         """Update segment."""
@@ -337,7 +342,7 @@ class ReviewSegmentMaintainer(threading.Thread):
             "reviews",
             json.dumps(review_update),
         )
-        self.review_publisher.publish(review_update, segment.camera)
+        self.review_publisher.publish(review_update, segment.camera)  # type: ignore[arg-type]
         self.requestor.send_data(
             f"{segment.camera}/review_status", segment.severity.value.upper()
         )
@@ -346,7 +351,7 @@ class ReviewSegmentMaintainer(threading.Thread):
         self,
         segment: PendingReviewSegment,
         prev_data: dict[str, Any],
-    ) -> float:
+    ) -> Any:
         """End segment."""
         final_data = segment.get_data(ended=True)
         end_time = final_data[ReviewSegment.end_time.name]
@@ -360,24 +365,25 @@ class ReviewSegmentMaintainer(threading.Thread):
             "reviews",
             json.dumps(review_update),
         )
-        self.review_publisher.publish(review_update, segment.camera)
+        self.review_publisher.publish(review_update, segment.camera)  # type: ignore[arg-type]
         self.requestor.send_data(f"{segment.camera}/review_status", "NONE")
         self.active_review_segments[segment.camera] = None
         return end_time
 
-    def forcibly_end_segment(self, camera: str) -> float:
+    def forcibly_end_segment(self, camera: str) -> Any:
         """Forcibly end the pending segment for a camera."""
         segment = self.active_review_segments.get(camera)
         if segment:
             prev_data = segment.get_data(False)
             return self._publish_segment_end(segment, prev_data)
+        return None
 
     def update_existing_segment(
         self,
         segment: PendingReviewSegment,
         frame_name: str,
         frame_time: float,
-        objects: list[TrackedObject],
+        objects: list[dict[str, Any]],
     ) -> None:
         """Validate if existing review segment should continue."""
         camera_config = self.config.cameras[segment.camera]
@@ -492,8 +498,11 @@ class ReviewSegmentMaintainer(threading.Thread):
                 except FileNotFoundError:
                     return
 
-            if segment.severity == SeverityEnum.alert and frame_time > (
-                segment.last_alert_time + camera_config.review.alerts.cutoff_time
+            if (
+                segment.severity == SeverityEnum.alert
+                and segment.last_alert_time is not None
+                and frame_time
+                > (segment.last_alert_time + camera_config.review.alerts.cutoff_time)
             ):
                 needs_new_detection = (
                     segment.last_detection_time > segment.last_alert_time
@@ -516,23 +525,18 @@ class ReviewSegmentMaintainer(threading.Thread):
                         new_zones.update(o["current_zones"])
 
                     if new_detections:
-                        self.active_review_segments[activity.camera_config.name] = (
-                            PendingReviewSegment(
-                                activity.camera_config.name,
-                                end_time,
-                                SeverityEnum.detection,
-                                new_detections,
-                                sub_labels={},
-                                audio=set(),
-                                zones=list(new_zones),
-                            )
+                        new_segment = PendingReviewSegment(
+                            segment.camera,
+                            end_time,
+                            SeverityEnum.detection,
+                            new_detections,
+                            sub_labels={},
+                            audio=set(),
+                            zones=list(new_zones),
                         )
-                        self._publish_segment_start(
-                            self.active_review_segments[activity.camera_config.name]
-                        )
-                        self.active_review_segments[
-                            activity.camera_config.name
-                        ].last_detection_time = last_detection_time
+                        self.active_review_segments[segment.camera] = new_segment
+                        self._publish_segment_start(new_segment)
+                        new_segment.last_detection_time = last_detection_time
             elif segment.severity == SeverityEnum.detection and frame_time > (
                 segment.last_detection_time
                 + camera_config.review.detections.cutoff_time
@@ -544,7 +548,7 @@ class ReviewSegmentMaintainer(threading.Thread):
         camera: str,
         frame_name: str,
         frame_time: float,
-        objects: list[TrackedObject],
+        objects: list[dict[str, Any]],
     ) -> None:
         """Check if a new review segment should be created."""
         camera_config = self.config.cameras[camera]
@@ -581,7 +585,7 @@ class ReviewSegmentMaintainer(threading.Thread):
                         zones.append(zone)
 
             if severity:
-                self.active_review_segments[camera] = PendingReviewSegment(
+                new_segment = PendingReviewSegment(
                     camera,
                     frame_time,
                     severity,
@@ -590,6 +594,7 @@ class ReviewSegmentMaintainer(threading.Thread):
                     audio=set(),
                     zones=zones,
                 )
+                self.active_review_segments[camera] = new_segment
 
                 try:
                     yuv_frame = self.frame_manager.get(
@@ -600,11 +605,11 @@ class ReviewSegmentMaintainer(threading.Thread):
                         logger.debug(f"Failed to get frame {frame_name} from SHM")
                         return
 
-                    self.active_review_segments[camera].update_frame(
+                    new_segment.update_frame(
                         camera_config, yuv_frame, activity.get_all_objects()
                     )
                     self.frame_manager.close(frame_name)
-                    self._publish_segment_start(self.active_review_segments[camera])
+                    self._publish_segment_start(new_segment)
                 except FileNotFoundError:
                     return
 
@@ -621,9 +626,14 @@ class ReviewSegmentMaintainer(threading.Thread):
                 for camera in updated_topics["enabled"]:
                     self.forcibly_end_segment(camera)
 
-            (topic, data) = self.detection_subscriber.check_for_update(timeout=1)
+            result = self.detection_subscriber.check_for_update(timeout=1)
 
-            if not topic:
+            if not result:
+                continue
+
+            topic, data = result
+
+            if not topic or not data:
                 continue
 
             if topic == DetectionTypeEnum.video.value:
@@ -712,10 +722,13 @@ class ReviewSegmentMaintainer(threading.Thread):
                         if topic == DetectionTypeEnum.api:
                             # manual_info["label"] contains 'label: sub_label'
                             # so split out the label without modifying manual_info
+                            det_labels = self.config.cameras[
+                                camera
+                            ].review.detections.labels
                             if (
                                 self.config.cameras[camera].review.detections.enabled
-                                and manual_info["label"].split(": ")[0]
-                                in self.config.cameras[camera].review.detections.labels
+                                and det_labels is not None
+                                and manual_info["label"].split(": ")[0] in det_labels
                             ):
                                 current_segment.last_detection_time = manual_info[
                                     "end_time"
@@ -744,14 +757,15 @@ class ReviewSegmentMaintainer(threading.Thread):
                         ):
                             # manual_info["label"] contains 'label: sub_label'
                             # so split out the label without modifying manual_info
+                            det_labels = self.config.cameras[
+                                camera
+                            ].review.detections.labels
                             if (
                                 not self.config.cameras[
                                     camera
                                 ].review.detections.enabled
-                                or manual_info["label"].split(": ")[0]
-                                not in self.config.cameras[
-                                    camera
-                                ].review.detections.labels
+                                or det_labels is None
+                                or manual_info["label"].split(": ")[0] not in det_labels
                             ):
                                 current_segment.severity = SeverityEnum.alert
                         elif (
@@ -828,17 +842,18 @@ class ReviewSegmentMaintainer(threading.Thread):
                     severity = None
                     # manual_info["label"] contains 'label: sub_label'
                     # so split out the label without modifying manual_info
+                    det_labels = self.config.cameras[camera].review.detections.labels
                     if (
                         self.config.cameras[camera].review.detections.enabled
-                        and manual_info["label"].split(": ")[0]
-                        in self.config.cameras[camera].review.detections.labels
+                        and det_labels is not None
+                        and manual_info["label"].split(": ")[0] in det_labels
                     ):
                         severity = SeverityEnum.detection
                     elif self.config.cameras[camera].review.alerts.enabled:
                         severity = SeverityEnum.alert
 
                     if severity:
-                        self.active_review_segments[camera] = PendingReviewSegment(
+                        api_segment = PendingReviewSegment(
                             camera,
                             frame_time,
                             severity,
@@ -847,32 +862,25 @@ class ReviewSegmentMaintainer(threading.Thread):
                             [],
                             set(),
                         )
+                        self.active_review_segments[camera] = api_segment
 
                         if manual_info["state"] == ManualEventState.start:
                             self.indefinite_events[camera][manual_info["event_id"]] = (
                                 manual_info["label"]
                             )
                             # temporarily make it so this event can not end
-                            self.active_review_segments[
-                                camera
-                            ].last_alert_time = sys.maxsize
-                            self.active_review_segments[
-                                camera
-                            ].last_detection_time = sys.maxsize
+                            api_segment.last_alert_time = sys.maxsize
+                            api_segment.last_detection_time = sys.maxsize
                         elif manual_info["state"] == ManualEventState.complete:
-                            self.active_review_segments[
-                                camera
-                            ].last_alert_time = manual_info["end_time"]
-                            self.active_review_segments[
-                                camera
-                            ].last_detection_time = manual_info["end_time"]
+                            api_segment.last_alert_time = manual_info["end_time"]
+                            api_segment.last_detection_time = manual_info["end_time"]
                     else:
                         logger.warning(
                             f"Manual event API has been called for {camera}, but alerts and detections are disabled. This manual event will not appear as an alert or detection."
                         )
                 elif topic == DetectionTypeEnum.lpr:
                     if self.config.cameras[camera].review.detections.enabled:
-                        self.active_review_segments[camera] = PendingReviewSegment(
+                        lpr_segment = PendingReviewSegment(
                             camera,
                             frame_time,
                             SeverityEnum.detection,
@@ -881,25 +889,18 @@ class ReviewSegmentMaintainer(threading.Thread):
                             [],
                             set(),
                         )
+                        self.active_review_segments[camera] = lpr_segment
 
                         if manual_info["state"] == ManualEventState.start:
                             self.indefinite_events[camera][manual_info["event_id"]] = (
                                 manual_info["label"]
                             )
                             # temporarily make it so this event can not end
-                            self.active_review_segments[
-                                camera
-                            ].last_alert_time = sys.maxsize
-                            self.active_review_segments[
-                                camera
-                            ].last_detection_time = sys.maxsize
+                            lpr_segment.last_alert_time = sys.maxsize
+                            lpr_segment.last_detection_time = sys.maxsize
                         elif manual_info["state"] == ManualEventState.complete:
-                            self.active_review_segments[
-                                camera
-                            ].last_alert_time = manual_info["end_time"]
-                            self.active_review_segments[
-                                camera
-                            ].last_detection_time = manual_info["end_time"]
+                            lpr_segment.last_alert_time = manual_info["end_time"]
+                            lpr_segment.last_detection_time = manual_info["end_time"]
                     else:
                         logger.warning(
                             f"Dedicated LPR camera API has been called for {camera}, but detections are disabled. LPR events will not appear as a detection."

frigate/stats/util.py

@@ -19,6 +19,7 @@ from frigate.types import StatsTrackingTypes
 from frigate.util.services import (
     calculate_shm_requirements,
     get_amd_gpu_stats,
+    get_axcl_npu_stats,
     get_bandwidth_stats,
     get_cpu_stats,
     get_fs_type,
@@ -324,6 +325,10 @@ async def set_npu_usages(config: FrigateConfig, all_stats: dict[str, Any]) -> No
             # OpenVINO NPU usage
             ov_usage = get_openvino_npu_stats()
             stats["openvino"] = ov_usage
+        elif detector.type == "axengine":
+            # AXERA NPU usage
+            axcl_usage = get_axcl_npu_stats()
+            stats["axengine"] = axcl_usage
 
     if stats:
         all_stats["npu_usages"] = stats

frigate/storage.py

@@ -3,6 +3,7 @@
 import logging
 import shutil
 import threading
+from multiprocessing.synchronize import Event as MpEvent
 from pathlib import Path
 
 from peewee import SQL, fn
@@ -23,7 +24,7 @@ MAX_CALCULATED_BANDWIDTH = 10000  # 10Gb/hr
 class StorageMaintainer(threading.Thread):
     """Maintain frigates recording storage."""
 
-    def __init__(self, config: FrigateConfig, stop_event) -> None:
+    def __init__(self, config: FrigateConfig, stop_event: MpEvent) -> None:
         super().__init__(name="storage_maintainer")
         self.config = config
         self.stop_event = stop_event
@@ -114,7 +115,7 @@ class StorageMaintainer(threading.Thread):
         logger.debug(
             f"Storage cleanup check: {hourly_bandwidth} hourly with remaining storage: {remaining_storage}."
         )
-        return remaining_storage < hourly_bandwidth
+        return remaining_storage < float(hourly_bandwidth)
 
     def reduce_storage_consumption(self) -> None:
         """Remove oldest hour of recordings."""
@@ -124,7 +125,7 @@ class StorageMaintainer(threading.Thread):
             [b["bandwidth"] for b in self.camera_storage_stats.values()]
         )
 
-        recordings: Recordings = (
+        recordings = (
             Recordings.select(
                 Recordings.id,
                 Recordings.camera,
@@ -138,7 +139,7 @@ class StorageMaintainer(threading.Thread):
             .iterator()
         )
 
-        retained_events: Event = (
+        retained_events = (
             Event.select(
                 Event.start_time,
                 Event.end_time,
@@ -278,7 +279,7 @@ class StorageMaintainer(threading.Thread):
                 Recordings.id << deleted_recordings_list[i : i + max_deletes]
             ).execute()
 
-    def run(self):
+    def run(self) -> None:
         """Check every 5 minutes if storage needs to be cleaned up."""
         if self.config.safe_mode:
             logger.info("Safe mode enabled, skipping storage maintenance")

frigate/timeline.py

@@ -8,7 +8,7 @@ from multiprocessing.synchronize import Event as MpEvent
 from typing import Any
 
 from frigate.config import FrigateConfig
-from frigate.events.maintainer import EventStateEnum, EventTypeEnum
+from frigate.events.types import EventStateEnum, EventTypeEnum
 from frigate.models import Timeline
 from frigate.util.builtin import to_relative_box
 
@@ -28,7 +28,7 @@ class TimelineProcessor(threading.Thread):
         self.config = config
         self.queue = queue
         self.stop_event = stop_event
-        self.pre_event_cache: dict[str, list[dict[str, Any]]] = {}
+        self.pre_event_cache: dict[str, list[dict[Any, Any]]] = {}
 
     def run(self) -> None:
         while not self.stop_event.is_set():
@@ -56,7 +56,7 @@ class TimelineProcessor(threading.Thread):
 
     def insert_or_save(
         self,
-        entry: dict[str, Any],
+        entry: dict[Any, Any],
         prev_event_data: dict[Any, Any],
         event_data: dict[Any, Any],
     ) -> None:
@@ -84,11 +84,15 @@ class TimelineProcessor(threading.Thread):
         event_type: str,
         prev_event_data: dict[Any, Any],
         event_data: dict[Any, Any],
-    ) -> bool:
+    ) -> None:
         """Handle object detection."""
         camera_config = self.config.cameras.get(camera)
-        if camera_config is None:
-            return False
+        if (
+            camera_config is None
+            or camera_config.detect.width is None
+            or camera_config.detect.height is None
+        ):
+            return
         event_id = event_data["id"]
 
         # Base timeline entry data that all entries will share

frigate/track/tracked_object.py

@@ -67,8 +67,8 @@ class TrackedObject:
         self.has_snapshot = False
         self.top_score = self.computed_score = 0.0
         self.thumbnail_data: dict[str, Any] | None = None
-        self.last_updated = 0
-        self.last_published = 0
+        self.last_updated: float = 0
+        self.last_published: float = 0
         self.frame = None
         self.active = True
         self.pending_loitering = False

frigate/util/builtin.py

@@ -12,7 +12,7 @@ import shlex
 import struct
 import urllib.parse
 from collections.abc import Mapping
-from multiprocessing.sharedctypes import Synchronized
+from multiprocessing.managers import ValueProxy
 from pathlib import Path
 from typing import Any, Dict, Optional, Tuple, Union
 
@@ -64,7 +64,7 @@ class EventsPerSecond:
 
 
 class InferenceSpeed:
-    def __init__(self, metric: Synchronized) -> None:
+    def __init__(self, metric: ValueProxy[float]) -> None:
         self.__metric = metric
         self.__initialized = False
 

frigate/util/services.py

@@ -488,6 +488,43 @@ def get_rockchip_npu_stats() -> Optional[dict[str, float | str]]:
     return stats
 
 
+def get_axcl_npu_stats() -> Optional[dict[str, str | float]]:
+    """Get NPU stats using axcl."""
+    # Check if axcl-smi exists
+    axcl_smi_path = "/usr/bin/axcl/axcl-smi"
+    if not os.path.exists(axcl_smi_path):
+        return None
+
+    try:
+        # Run axcl-smi command to get NPU stats
+        axcl_command = [axcl_smi_path, "sh", "cat", "/proc/ax_proc/npu/top"]
+        p = sp.run(
+            axcl_command,
+            capture_output=True,
+            text=True,
+        )
+
+        if p.returncode != 0:
+            pass
+        else:
+            utilization = None
+
+            for line in p.stdout.strip().splitlines():
+                line = line.strip()
+                if line.startswith("utilization:"):
+                    match = re.search(r"utilization:(\d+)%", line)
+                    if match:
+                        utilization = float(match.group(1))
+
+            if utilization is not None:
+                stats: dict[str, str | float] = {"npu": utilization, "mem": "-%"}
+                return stats
+    except Exception:
+        pass
+
+    return None
+
+
 def try_get_info(f, h, default="N/A", sensor=None):
     try:
         if h:

web/public/locales/en/config/cameras.json

@@ -529,7 +529,7 @@
     },
     "detections": {
       "label": "Detections config",
-      "description": "Settings for creating detection events (non-alert) and how long to keep them.",
+      "description": "Settings for which tracked objects generate detections (non-alert) and how detections are retained.",
       "enabled": {
         "label": "Enable detections",
         "description": "Enable or disable detection events for this camera."

web/public/locales/en/config/global.json

@@ -293,7 +293,7 @@
       "label": "Detector specific model configuration",
       "description": "Detector-specific model configuration options (path, input size, etc.).",
       "path": {
-        "label": "Custom Object detection model path",
+        "label": "Custom object detector model path",
         "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
       },
       "labelmap_path": {
@@ -466,7 +466,7 @@
     "label": "Detection model",
     "description": "Settings to configure a custom object detection model and its input shape.",
     "path": {
-      "label": "Custom Object detection model path",
+      "label": "Custom object detector model path",
       "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
     },
     "labelmap_path": {
@@ -1044,7 +1044,7 @@
     },
     "detections": {
       "label": "Detections config",
-      "description": "Settings for creating detection events (non-alert) and how long to keep them.",
+      "description": "Settings for which tracked objects generate detections (non-alert) and how detections are retained.",
       "enabled": {
         "label": "Enable detections",
         "description": "Enable or disable detection events for all cameras; can be overridden per-camera."

web/public/locales/en/views/settings.json

@@ -1431,6 +1431,11 @@
       "summary": "{{count}} object types selected",
       "empty": "No object labels available"
     },
+    "reviewLabels": {
+      "summary": "{{count}} labels selected",
+      "empty": "No labels available",
+      "allNonAlertDetections": "All non-alert activity will be included as detections."
+    },
     "filters": {
       "objectFieldLabel": "{{field}} for {{label}}"
     },
@@ -1474,7 +1479,8 @@
     "timestamp_style": {
       "title": "Timestamp Settings"
     },
-    "searchPlaceholder": "Search..."
+    "searchPlaceholder": "Search...",
+    "addCustomLabel": "Add custom label..."
   },
   "globalConfig": {
     "title": "Global Configuration",
@@ -1521,6 +1527,8 @@
     "noOverrides": "No overrides",
     "cameraCount_one": "{{count}} camera",
     "cameraCount_other": "{{count}} cameras",
+    "columnCamera": "Camera",
+    "columnOverrides": "Profile Overrides",
     "baseConfig": "Base Config",
     "addProfile": "Add Profile",
     "newProfile": "New Profile",

web/src/components/config-form/section-configs/record.ts

@@ -23,7 +23,7 @@ const record: SectionConfigOverrides = {
     uiSchema: {
       export: {
         hwaccel_args: {
-          "ui:options": { size: "lg" },
+          "ui:options": { suppressMultiSchema: true, size: "lg" },
         },
       },
     },

web/src/components/config-form/section-configs/review.ts

@@ -3,14 +3,16 @@ import type { SectionConfigOverrides } from "./types";
 const review: SectionConfigOverrides = {
   base: {
     sectionDocs: "/configuration/review",
+    fieldDocs: {
+      "alerts.labels": "/configuration/review/#alerts-and-detections",
+      "detections.labels": "/configuration/review/#alerts-and-detections",
+    },
     restartRequired: [],
     fieldOrder: ["alerts", "detections", "genai"],
     fieldGroups: {},
     hiddenFields: [
       "enabled_in_config",
-      "alerts.labels",
       "alerts.enabled_in_config",
-      "detections.labels",
       "detections.enabled_in_config",
       "genai.enabled_in_config",
     ],
@@ -18,20 +20,35 @@ const review: SectionConfigOverrides = {
     uiSchema: {
       alerts: {
         "ui:before": { render: "CameraReviewStatusToggles" },
+        labels: {
+          "ui:widget": "reviewLabels",
+          "ui:options": {
+            suppressMultiSchema: true,
+          },
+        },
         required_zones: {
           "ui:widget": "hidden",
         },
       },
       detections: {
+        labels: {
+          "ui:widget": "reviewLabels",
+          "ui:options": {
+            suppressMultiSchema: true,
+            emptySelectionHintKey:
+              "configForm.reviewLabels.allNonAlertDetections",
+          },
+        },
         required_zones: {
           "ui:widget": "hidden",
         },
       },
       genai: {
         additional_concerns: {
-          "ui:widget": "textarea",
+          "ui:widget": "ArrayAsTextWidget",
           "ui:options": {
             size: "full",
+            multiline: true,
           },
         },
         activity_context_prompt: {

web/src/components/config-form/sections/BaseSection.tsx

@@ -152,6 +152,10 @@ export interface BaseSectionProps {
   profileBorderColor?: string;
   /** Callback to delete the current profile's overrides for this section */
   onDeleteProfileSection?: () => void;
+  /** Whether a SaveAll operation is in progress (disables individual Save) */
+  isSavingAll?: boolean;
+  /** Callback when this section's saving state changes */
+  onSavingChange?: (isSaving: boolean) => void;
 }
 
 export interface CreateSectionOptions {
@@ -186,6 +190,8 @@ export function ConfigSection({
   profileFriendlyName,
   profileBorderColor,
   onDeleteProfileSection,
+  isSavingAll = false,
+  onSavingChange,
 }: ConfigSectionProps) {
   // For replay level, treat as camera-level config access
   const effectiveLevel = level === "replay" ? "camera" : level;
@@ -246,6 +252,7 @@ export function ConfigSection({
     [onPendingDataChange, effectiveSectionPath, cameraName],
   );
   const [isSaving, setIsSaving] = useState(false);
+  const [isResettingToDefault, setIsResettingToDefault] = useState(false);
   const [hasValidationErrors, setHasValidationErrors] = useState(false);
   const [extraHasChanges, setExtraHasChanges] = useState(false);
   const [formKey, setFormKey] = useState(0);
@@ -577,6 +584,7 @@ export function ConfigSection({
     if (!pendingData) return;
 
     setIsSaving(true);
+    onSavingChange?.(true);
     try {
       const basePath =
         effectiveLevel === "camera" && cameraName
@@ -699,6 +707,7 @@ export function ConfigSection({
       }
     } finally {
       setIsSaving(false);
+      onSavingChange?.(false);
     }
   }, [
     sectionPath,
@@ -718,12 +727,14 @@ export function ConfigSection({
     setPendingData,
     requiresRestartForOverrides,
     skipSave,
+    onSavingChange,
   ]);
 
   // Handle reset to global/defaults - removes camera-level override or resets global to defaults
   const handleResetToGlobal = useCallback(async () => {
     if (effectiveLevel === "camera" && !cameraName) return;
 
+    setIsResettingToDefault(true);
     try {
       const basePath =
         effectiveLevel === "camera" && cameraName
@@ -758,6 +769,8 @@ export function ConfigSection({
           defaultValue: "Failed to reset settings",
         }),
       );
+    } finally {
+      setIsResettingToDefault(false);
     }
   }, [
     effectiveSectionPath,
@@ -945,9 +958,12 @@ export function ConfigSection({
                 <Button
                   onClick={() => setIsResetDialogOpen(true)}
                   variant="outline"
-                  disabled={isSaving || disabled}
+                  disabled={isSaving || isResettingToDefault || disabled}
                   className="flex flex-1 gap-2"
                 >
+                  {isResettingToDefault && (
+                    <ActivityIndicator className="h-4 w-4" />
+                  )}
                   {effectiveLevel === "global"
                     ? t("button.resetToDefault", {
                         ns: "common",
@@ -980,7 +996,7 @@ export function ConfigSection({
               <Button
                 onClick={handleReset}
                 variant="outline"
-                disabled={isSaving || disabled}
+                disabled={isSaving || isSavingAll || disabled}
                 className="flex min-w-36 flex-1 gap-2"
               >
                 {t("button.undo", { ns: "common", defaultValue: "Undo" })}
@@ -990,7 +1006,11 @@ export function ConfigSection({
               onClick={handleSave}
               variant="select"
               disabled={
-                !hasChanges || hasValidationErrors || isSaving || disabled
+                !hasChanges ||
+                hasValidationErrors ||
+                isSaving ||
+                isSavingAll ||
+                disabled
               }
               className="flex min-w-36 flex-1 gap-2"
             >

web/src/components/config-form/theme/frigateTheme.ts

@@ -20,6 +20,7 @@ import { TextareaWidget } from "./widgets/TextareaWidget";
 import { SwitchesWidget } from "./widgets/SwitchesWidget";
 import { ObjectLabelSwitchesWidget } from "./widgets/ObjectLabelSwitchesWidget";
 import { AudioLabelSwitchesWidget } from "./widgets/AudioLabelSwitchesWidget";
+import { ReviewLabelSwitchesWidget } from "./widgets/ReviewLabelSwitchesWidget";
 import { ZoneSwitchesWidget } from "./widgets/ZoneSwitchesWidget";
 import { ArrayAsTextWidget } from "./widgets/ArrayAsTextWidget";
 import { FfmpegArgsWidget } from "./widgets/FfmpegArgsWidget";
@@ -76,6 +77,7 @@ export const frigateTheme: FrigateTheme = {
     switches: SwitchesWidget,
     objectLabels: ObjectLabelSwitchesWidget,
     audioLabels: AudioLabelSwitchesWidget,
+    reviewLabels: ReviewLabelSwitchesWidget,
     zoneNames: ZoneSwitchesWidget,
     timezoneSelect: TimezoneSelectWidget,
     optionalField: OptionalFieldWidget,

web/src/components/config-form/theme/widgets/ArrayAsTextWidget.tsx

@@ -1,33 +1,101 @@
-// Widget that displays an array as a concatenated text string
+// Widget that displays an array as editable text.
+// Single-line mode (default): space-separated in an Input.
+// Multiline mode (options.multiline): one item per line in a Textarea.
 import type { WidgetProps } from "@rjsf/utils";
 import { Input } from "@/components/ui/input";
-import { useCallback } from "react";
+import { Textarea } from "@/components/ui/textarea";
+import { cn } from "@/lib/utils";
+import { getSizedFieldClassName } from "../utils";
+import { useCallback, useEffect, useState } from "react";
+
+function arrayToText(value: unknown, multiline: boolean): string {
+  const sep = multiline ? "\n" : " ";
+  if (Array.isArray(value) && value.length > 0) {
+    return value.join(sep);
+  }
+  if (typeof value === "string") {
+    return value;
+  }
+  return "";
+}
+
+function textToArray(text: string, multiline: boolean): string[] {
+  if (text.trim() === "") {
+    return [];
+  }
+  return multiline
+    ? text.split("\n").filter((line) => line.trim() !== "")
+    : text.trim().split(/\s+/);
+}
 
 export function ArrayAsTextWidget(props: WidgetProps) {
-  const { value, onChange, disabled, readonly, placeholder } = props;
+  const {
+    id,
+    value,
+    disabled,
+    readonly,
+    onChange,
+    onBlur,
+    onFocus,
+    placeholder,
+    schema,
+    options,
+  } = props;
 
-  // Convert array or string to text
-  let textValue = "";
-  if (typeof value === "string" && value.length > 0) {
-    textValue = value;
-  } else if (Array.isArray(value) && value.length > 0) {
-    textValue = value.join(" ");
-  }
+  const multiline = !!(options.multiline as boolean);
 
-  const handleChange = useCallback(
-    (event: React.ChangeEvent<HTMLInputElement>) => {
-      const newText = event.target.value;
-      // Convert space-separated string back to array
-      const newArray = newText.trim() ? newText.trim().split(/\s+/) : [];
-      onChange(newArray);
+  // Local state keeps raw text so newlines aren't stripped mid-typing
+  const [text, setText] = useState(() => arrayToText(value, multiline));
+
+  useEffect(() => {
+    setText(arrayToText(value, multiline));
+  }, [value, multiline]);
+
+  const fieldClassName = multiline
+    ? getSizedFieldClassName(options, "md")
+    : undefined;
+
+  const handleInputChange = useCallback(
+    (e: React.ChangeEvent<HTMLInputElement | HTMLTextAreaElement>) => {
+      const raw = e.target.value;
+      setText(raw);
+      onChange(textToArray(raw, multiline));
     },
-    [onChange],
+    [onChange, multiline],
   );
 
+  const handleBlur = useCallback(
+    (e: React.FocusEvent<HTMLInputElement | HTMLTextAreaElement>) => {
+      // Clean up: strip empty entries and sync
+      const cleaned = textToArray(e.target.value, multiline);
+      onChange(cleaned);
+      setText(arrayToText(cleaned, multiline));
+      onBlur?.(id, e.target.value);
+    },
+    [id, onChange, onBlur, multiline],
+  );
+
+  if (multiline) {
+    return (
+      <Textarea
+        id={id}
+        className={cn("text-md", fieldClassName)}
+        value={text}
+        disabled={disabled || readonly}
+        rows={(options.rows as number) || 3}
+        onChange={handleInputChange}
+        onBlur={handleBlur}
+        onFocus={(e) => onFocus?.(id, e.target.value)}
+        aria-label={schema.title}
+      />
+    );
+  }
+
   return (
     <Input
-      value={textValue}
-      onChange={handleChange}
+      value={text}
+      onChange={handleInputChange}
+      onBlur={handleBlur}
       disabled={disabled}
       readOnly={readonly}
       placeholder={placeholder}

web/src/components/config-form/theme/widgets/ReviewLabelSwitchesWidget.tsx

@@ -0,0 +1,84 @@
+// Review Label Switches Widget - For selecting review alert/detection labels via switches.
+// Combines object labels (from objects.track) and audio labels (from audio.listen)
+// since review labels can include both types.
+import type { WidgetProps } from "@rjsf/utils";
+import { SwitchesWidget } from "./SwitchesWidget";
+import type { FormContext } from "./SwitchesWidget";
+import { getTranslatedLabel } from "@/utils/i18n";
+import type { FrigateConfig } from "@/types/frigateConfig";
+import type { JsonObject } from "@/types/configForm";
+
+function getReviewLabels(context: FormContext): string[] {
+  const labels = new Set<string>();
+  const fullConfig = context.fullConfig as FrigateConfig | undefined;
+  const fullCameraConfig = context.fullCameraConfig;
+
+  // Object labels from tracked objects (camera-level, falling back to global)
+  const trackLabels =
+    fullCameraConfig?.objects?.track ?? fullConfig?.objects?.track;
+  if (Array.isArray(trackLabels)) {
+    trackLabels.forEach((label: string) => labels.add(label));
+  }
+
+  // Audio labels from listen config, only if audio detection is enabled
+  const audioEnabled =
+    fullCameraConfig?.audio?.enabled_in_config ??
+    fullConfig?.audio?.enabled_in_config;
+  if (audioEnabled) {
+    const audioLabels =
+      fullCameraConfig?.audio?.listen ?? fullConfig?.audio?.listen;
+    if (Array.isArray(audioLabels)) {
+      audioLabels.forEach((label: string) => labels.add(label));
+    }
+  }
+
+  // Include any labels already in the review form data (alerts + detections)
+  // so that previously saved labels remain visible even if tracking config changed
+  if (context.formData && typeof context.formData === "object") {
+    const formData = context.formData as JsonObject;
+    for (const section of ["alerts", "detections"] as const) {
+      const sectionData = formData[section];
+      if (sectionData && typeof sectionData === "object") {
+        const sectionLabels = (sectionData as JsonObject).labels;
+        if (Array.isArray(sectionLabels)) {
+          sectionLabels.forEach((label) => {
+            if (typeof label === "string") {
+              labels.add(label);
+            }
+          });
+        }
+      }
+    }
+  }
+
+  return [...labels].sort();
+}
+
+function getReviewLabelDisplayName(
+  label: string,
+  context?: FormContext,
+): string {
+  const fullCameraConfig = context?.fullCameraConfig;
+  const fullConfig = context?.fullConfig as FrigateConfig | undefined;
+  const audioLabels =
+    fullCameraConfig?.audio?.listen ?? fullConfig?.audio?.listen;
+  const isAudio = Array.isArray(audioLabels) && audioLabels.includes(label);
+  return getTranslatedLabel(label, isAudio ? "audio" : "object");
+}
+
+export function ReviewLabelSwitchesWidget(props: WidgetProps) {
+  return (
+    <SwitchesWidget
+      {...props}
+      options={{
+        ...props.options,
+        getEntities: getReviewLabels,
+        getDisplayLabel: getReviewLabelDisplayName,
+        i18nKey: "reviewLabels",
+        allowCustomEntries: true,
+        listClassName:
+          "relative max-h-none overflow-visible md:max-h-64 md:overflow-y-auto md:overscroll-contain md:scrollbar-container",
+      }}
+    />
+  );
+}

web/src/components/config-form/theme/widgets/SwitchesWidget.tsx

@@ -1,6 +1,6 @@
 // Generic Switches Widget - Reusable component for selecting from any list of entities
 import { WidgetProps } from "@rjsf/utils";
-import { useMemo, useState } from "react";
+import { useCallback, useMemo, useState } from "react";
 import { Switch } from "@/components/ui/switch";
 import { Button } from "@/components/ui/button";
 import { Input } from "@/components/ui/input";
@@ -43,6 +43,10 @@ export type SwitchesWidgetOptions = {
   listClassName?: string;
   /** Enable search input to filter the list */
   enableSearch?: boolean;
+  /** Allow users to add custom entries not in the predefined list */
+  allowCustomEntries?: boolean;
+  /** i18n key for a hint shown when no entities are selected */
+  emptySelectionHintKey?: string;
 };
 
 function normalizeValue(value: unknown): string[] {
@@ -122,20 +126,51 @@ export function SwitchesWidget(props: WidgetProps) {
     [props.options],
   );
 
+  const allowCustomEntries = useMemo(
+    () => props.options?.allowCustomEntries as boolean | undefined,
+    [props.options],
+  );
+
+  const emptySelectionHintKey = useMemo(
+    () => props.options?.emptySelectionHintKey as string | undefined,
+    [props.options],
+  );
+
   const selectedEntities = useMemo(() => normalizeValue(value), [value]);
   const [isOpen, setIsOpen] = useState(selectedEntities.length > 0);
   const [searchTerm, setSearchTerm] = useState("");
+  const [customEntries, setCustomEntries] = useState<string[]>([]);
+  const [customInput, setCustomInput] = useState("");
+
+  const allEntities = useMemo(() => {
+    if (customEntries.length === 0) {
+      return availableEntities;
+    }
+    const merged = new Set([...availableEntities, ...customEntries]);
+    return [...merged].sort();
+  }, [availableEntities, customEntries]);
 
   const filteredEntities = useMemo(() => {
     if (!enableSearch || !searchTerm.trim()) {
-      return availableEntities;
+      return allEntities;
     }
     const term = searchTerm.toLowerCase();
-    return availableEntities.filter((entity) => {
+    return allEntities.filter((entity) => {
       const displayLabel = getDisplayLabel(entity, context);
       return displayLabel.toLowerCase().includes(term);
     });
-  }, [availableEntities, searchTerm, enableSearch, getDisplayLabel, context]);
+  }, [allEntities, searchTerm, enableSearch, getDisplayLabel, context]);
+
+  const addCustomEntry = useCallback(() => {
+    const trimmed = customInput.trim().toLowerCase();
+    if (!trimmed || allEntities.includes(trimmed)) {
+      setCustomInput("");
+      return;
+    }
+    setCustomEntries((prev) => [...prev, trimmed]);
+    onChange([...selectedEntities, trimmed]);
+    setCustomInput("");
+  }, [customInput, allEntities, selectedEntities, onChange]);
 
   const toggleEntity = (entity: string, enabled: boolean) => {
     if (enabled) {
@@ -163,7 +198,7 @@ export function SwitchesWidget(props: WidgetProps) {
 
   return (
     <Collapsible open={isOpen} onOpenChange={setIsOpen}>
-      <div className="space-y-3">
+      <div className="space-y-2">
         <CollapsibleTrigger asChild>
           <Button
             type="button"
@@ -180,8 +215,14 @@ export function SwitchesWidget(props: WidgetProps) {
           </Button>
         </CollapsibleTrigger>
 
-        <CollapsibleContent className="rounded-lg bg-secondary p-2 pr-0 md:max-w-md">
-          {availableEntities.length === 0 ? (
+        {emptySelectionHintKey && selectedEntities.length === 0 && t && (
+          <div className="mt-0 pb-2 text-sm text-success">
+            {t(emptySelectionHintKey, { ns: namespace })}
+          </div>
+        )}
+
+        <CollapsibleContent className="rounded-lg border border-input bg-secondary pb-1 pr-0 pt-2 md:max-w-md">
+          {allEntities.length === 0 && !allowCustomEntries ? (
             <div className="text-sm text-muted-foreground">{emptyMessage}</div>
           ) : (
             <>
@@ -223,6 +264,26 @@ export function SwitchesWidget(props: WidgetProps) {
                   );
                 })}
               </div>
+              {allowCustomEntries && !disabled && !readonly && (
+                <div className="mx-2 mt-2 pb-1">
+                  <Input
+                    type="text"
+                    placeholder={t?.("configForm.addCustomLabel", {
+                      ns: "views/settings",
+                      defaultValue: "Add custom label...",
+                    })}
+                    value={customInput}
+                    onChange={(e) => setCustomInput(e.target.value)}
+                    onKeyDown={(e) => {
+                      if (e.key === "Enter") {
+                        e.preventDefault();
+                        addCustomEntry();
+                      }
+                    }}
+                    onBlur={addCustomEntry}
+                  />
+                </div>
+              )}
             </>
           )}
         </CollapsibleContent>

web/src/components/overlay/detail/AnnotationOffsetSlider.tsx

@@ -9,15 +9,16 @@ import { toast } from "sonner";
 import { Trans, useTranslation } from "react-i18next";
 import { LuExternalLink, LuInfo, LuMinus, LuPlus } from "react-icons/lu";
 import { cn } from "@/lib/utils";
+import {
+  ANNOTATION_OFFSET_MAX,
+  ANNOTATION_OFFSET_MIN,
+  ANNOTATION_OFFSET_STEP,
+} from "@/lib/const";
 import { isMobile } from "react-device-detect";
 import { useIsAdmin } from "@/hooks/use-is-admin";
 import { useDocDomain } from "@/hooks/use-doc-domain";
 import { Link } from "react-router-dom";
 
-const OFFSET_MIN = -2500;
-const OFFSET_MAX = 2500;
-const OFFSET_STEP = 50;
-
 type Props = {
   className?: string;
 };
@@ -43,7 +44,10 @@ export default function AnnotationOffsetSlider({ className }: Props) {
     (delta: number) => {
       setAnnotationOffset((prev) => {
         const next = prev + delta;
-        return Math.max(OFFSET_MIN, Math.min(OFFSET_MAX, next));
+        return Math.max(
+          ANNOTATION_OFFSET_MIN,
+          Math.min(ANNOTATION_OFFSET_MAX, next),
+        );
       });
     },
     [setAnnotationOffset],
@@ -114,17 +118,17 @@ export default function AnnotationOffsetSlider({ className }: Props) {
           size="icon"
           className="size-8 shrink-0"
           aria-label="-50ms"
-          onClick={() => stepOffset(-OFFSET_STEP)}
-          disabled={annotationOffset <= OFFSET_MIN}
+          onClick={() => stepOffset(-ANNOTATION_OFFSET_STEP)}
+          disabled={annotationOffset <= ANNOTATION_OFFSET_MIN}
         >
           <LuMinus className="size-4" />
         </Button>
         <div className="w-full flex-1 landscape:flex">
           <Slider
             value={[annotationOffset]}
-            min={OFFSET_MIN}
-            max={OFFSET_MAX}
-            step={OFFSET_STEP}
+            min={ANNOTATION_OFFSET_MIN}
+            max={ANNOTATION_OFFSET_MAX}
+            step={ANNOTATION_OFFSET_STEP}
             onValueChange={handleChange}
           />
         </div>
@@ -134,8 +138,8 @@ export default function AnnotationOffsetSlider({ className }: Props) {
           size="icon"
           className="size-8 shrink-0"
           aria-label="+50ms"
-          onClick={() => stepOffset(OFFSET_STEP)}
-          disabled={annotationOffset >= OFFSET_MAX}
+          onClick={() => stepOffset(ANNOTATION_OFFSET_STEP)}
+          disabled={annotationOffset >= ANNOTATION_OFFSET_MAX}
         >
           <LuPlus className="size-4" />
         </Button>

web/src/components/overlay/detail/AnnotationSettingsPane.tsx

@@ -13,10 +13,11 @@ import { Slider } from "@/components/ui/slider";
 import { Trans, useTranslation } from "react-i18next";
 import { useDocDomain } from "@/hooks/use-doc-domain";
 import { useIsAdmin } from "@/hooks/use-is-admin";
-
-const OFFSET_MIN = -2500;
-const OFFSET_MAX = 2500;
-const OFFSET_STEP = 50;
+import {
+  ANNOTATION_OFFSET_MAX,
+  ANNOTATION_OFFSET_MIN,
+  ANNOTATION_OFFSET_STEP,
+} from "@/lib/const";
 
 type AnnotationSettingsPaneProps = {
   event: Event;
@@ -49,7 +50,10 @@ export function AnnotationSettingsPane({
     (delta: number) => {
       setAnnotationOffset((prev) => {
         const next = prev + delta;
-        return Math.max(OFFSET_MIN, Math.min(OFFSET_MAX, next));
+        return Math.max(
+          ANNOTATION_OFFSET_MIN,
+          Math.min(ANNOTATION_OFFSET_MAX, next),
+        );
       });
     },
     [setAnnotationOffset],
@@ -128,16 +132,16 @@ export function AnnotationSettingsPane({
             size="icon"
             className="size-8 shrink-0"
             aria-label="-50ms"
-            onClick={() => stepOffset(-OFFSET_STEP)}
-            disabled={annotationOffset <= OFFSET_MIN}
+            onClick={() => stepOffset(-ANNOTATION_OFFSET_STEP)}
+            disabled={annotationOffset <= ANNOTATION_OFFSET_MIN}
           >
             <LuMinus className="size-4" />
           </Button>
           <Slider
             value={[annotationOffset]}
-            min={OFFSET_MIN}
-            max={OFFSET_MAX}
-            step={OFFSET_STEP}
+            min={ANNOTATION_OFFSET_MIN}
+            max={ANNOTATION_OFFSET_MAX}
+            step={ANNOTATION_OFFSET_STEP}
             onValueChange={handleSliderChange}
             className="flex-1"
           />
@@ -147,8 +151,8 @@ export function AnnotationSettingsPane({
             size="icon"
             className="size-8 shrink-0"
             aria-label="+50ms"
-            onClick={() => stepOffset(OFFSET_STEP)}
-            disabled={annotationOffset >= OFFSET_MAX}
+            onClick={() => stepOffset(ANNOTATION_OFFSET_STEP)}
+            disabled={annotationOffset >= ANNOTATION_OFFSET_MAX}
           >
             <LuPlus className="size-4" />
           </Button>

web/src/lib/const.ts

@@ -1,6 +1,10 @@
 /** ONNX embedding models that require local model downloads. GenAI providers are not in this list. */
 export const JINA_EMBEDDING_MODELS = ["jinav1", "jinav2"] as const;
 
+export const ANNOTATION_OFFSET_MIN = -10000;
+export const ANNOTATION_OFFSET_MAX = 5000;
+export const ANNOTATION_OFFSET_STEP = 50;
+
 export const supportedLanguageKeys = [
   "en",
   "es",

web/src/pages/Settings.tsx

@@ -724,6 +724,7 @@ export default function Settings() {
 
   // Save All state
   const [isSavingAll, setIsSavingAll] = useState(false);
+  const [isAnySectionSaving, setIsAnySectionSaving] = useState(false);
   const [restartDialogOpen, setRestartDialogOpen] = useState(false);
   const { send: sendRestart } = useRestart();
   const { data: fullSchema } = useSWR<RJSFSchema>("config/schema.json");
@@ -1299,6 +1300,10 @@ export default function Settings() {
     [],
   );
 
+  const handleSectionSavingChange = useCallback((saving: boolean) => {
+    setIsAnySectionSaving(saving);
+  }, []);
+
   // The active profile being edited for the selected camera
   const activeEditingProfile = selectedCamera
     ? (editingProfile[selectedCamera] ?? null)
@@ -1508,7 +1513,7 @@ export default function Settings() {
                     onClick={handleUndoAll}
                     variant="outline"
                     size="sm"
-                    disabled={isSavingAll}
+                    disabled={isSavingAll || isAnySectionSaving}
                     className="flex w-full items-center justify-center gap-2"
                   >
                     {t("button.undoAll", {
@@ -1520,7 +1525,11 @@ export default function Settings() {
                     onClick={handleSaveAll}
                     variant="select"
                     size="sm"
-                    disabled={isSavingAll || hasPendingValidationErrors}
+                    disabled={
+                      isSavingAll ||
+                      isAnySectionSaving ||
+                      hasPendingValidationErrors
+                    }
                     className="flex w-full items-center justify-center gap-2"
                   >
                     {isSavingAll ? (
@@ -1606,6 +1615,8 @@ export default function Settings() {
                     }
                     profilesUIEnabled={profilesUIEnabled}
                     setProfilesUIEnabled={setProfilesUIEnabled}
+                    isSavingAll={isSavingAll}
+                    onSectionSavingChange={handleSectionSavingChange}
                   />
                 );
               })()}
@@ -1674,7 +1685,7 @@ export default function Settings() {
                 onClick={handleUndoAll}
                 variant="outline"
                 size="sm"
-                disabled={isSavingAll}
+                disabled={isSavingAll || isAnySectionSaving}
                 className="flex items-center justify-center gap-2"
               >
                 {t("button.undoAll", {
@@ -1686,7 +1697,11 @@ export default function Settings() {
                 variant="select"
                 size="sm"
                 onClick={handleSaveAll}
-                disabled={isSavingAll || hasPendingValidationErrors}
+                disabled={
+                  isSavingAll ||
+                  isAnySectionSaving ||
+                  hasPendingValidationErrors
+                }
                 className="flex items-center justify-center gap-2"
               >
                 {isSavingAll ? (
@@ -1843,6 +1858,8 @@ export default function Settings() {
                   onDeleteProfileSection={handleDeleteProfileForCurrentSection}
                   profilesUIEnabled={profilesUIEnabled}
                   setProfilesUIEnabled={setProfilesUIEnabled}
+                  isSavingAll={isSavingAll}
+                  onSectionSavingChange={handleSectionSavingChange}
                 />
               );
             })()}

web/src/views/settings/ProfilesView.tsx

@@ -542,6 +542,20 @@ export default function ProfilesView({
                   <CollapsibleContent>
                     {cameras.length > 0 ? (
                       <div className="mx-4 mb-3 ml-11 border-l border-border/50 pl-4">
+                        <div className="flex items-baseline gap-3 border-b border-border/30 pb-1.5">
+                          <span className="min-w-[120px] shrink-0 text-xs font-semibold uppercase text-muted-foreground">
+                            {t("profiles.columnCamera", {
+                              ns: "views/settings",
+                              defaultValue: "Camera",
+                            })}
+                          </span>
+                          <span className="text-xs font-semibold uppercase text-muted-foreground">
+                            {t("profiles.columnOverrides", {
+                              ns: "views/settings",
+                              defaultValue: "Profile Overrides",
+                            })}
+                          </span>
+                        </div>
                         {cameras.map((camera) => {
                           const sections = cameraData[camera];
                           return (

web/src/views/settings/SingleSectionPage.tsx

@@ -39,6 +39,10 @@ export type SettingsPageProps = {
   onDeleteProfileSection?: (profileName: string) => void;
   profilesUIEnabled?: boolean;
   setProfilesUIEnabled?: React.Dispatch<React.SetStateAction<boolean>>;
+  /** Whether a SaveAll operation is in progress */
+  isSavingAll?: boolean;
+  /** Callback when a section's saving state changes */
+  onSectionSavingChange?: (isSaving: boolean) => void;
 };
 
 export type SectionStatus = {
@@ -73,6 +77,8 @@ export function SingleSectionPage({
   onPendingDataChange,
   profileState,
   onDeleteProfileSection,
+  isSavingAll,
+  onSectionSavingChange,
 }: SingleSectionPageProps) {
   const sectionNamespace =
     level === "camera" ? "config/cameras" : "config/global";
@@ -95,9 +101,10 @@ export function SingleSectionPage({
     ? getLocaleDocUrl(resolvedSectionConfig.sectionDocs)
     : undefined;
 
-  const currentEditingProfile = selectedCamera
-    ? (profileState?.editingProfile[selectedCamera] ?? null)
-    : null;
+  const currentEditingProfile =
+    level === "camera" && selectedCamera
+      ? (profileState?.editingProfile[selectedCamera] ?? null)
+      : null;
 
   const profileColor = useMemo(
     () =>
@@ -273,6 +280,8 @@ export function SingleSectionPage({
         onDeleteProfileSection={
           currentEditingProfile ? handleDeleteProfileSection : undefined
         }
+        isSavingAll={isSavingAll}
+        onSavingChange={onSectionSavingChange}
       />
     </div>
   );