Fix order of init

frigate/app.py

@@ -581,12 +581,12 @@ class FrigateApp:
         self.init_recording_manager()
         self.init_review_segment_manager()
         self.init_go2rtc()
+        self.start_detectors()
+        self.init_embeddings_manager()
         self.bind_database()
         self.check_db_data_migrations()
         self.init_inter_process_communicator()
         self.init_dispatcher()
-        self.start_detectors()
-        self.init_embeddings_manager()
         self.init_embeddings_client()
         self.start_video_output_processor()
         self.start_ptz_autotracker()

frigate/comms/dispatcher.py

@@ -64,6 +64,9 @@ class Dispatcher:
         self.onvif = onvif
         self.ptz_metrics = ptz_metrics
         self.comms = communicators
+        self.camera_activity = {}
+        self.model_state = {}
+        self.embeddings_reindex = {}
 
         self._camera_settings_handlers: dict[str, Callable] = {
             "audio": self._on_audio_command,
@@ -85,10 +88,6 @@ class Dispatcher:
         for comm in self.comms:
             comm.subscribe(self._receive)
 
-        self.camera_activity = {}
-        self.model_state = {}
-        self.embeddings_reindex = {}
-
     def _receive(self, topic: str, payload: str) -> Optional[Any]:
         """Handle receiving of payload from communicators."""
 

frigate/embeddings/__init__.py

@@ -43,7 +43,6 @@ def manage_embeddings(config: FrigateConfig) -> None:
     listen()
 
     # Configure Frigate DB
-    print("connecting to db in embed")
     db = SqliteVecQueueDatabase(
         config.database.path,
         pragmas={
@@ -55,10 +54,8 @@ def manage_embeddings(config: FrigateConfig) -> None:
         load_vec_extension=True,
     )
     models = [Event]
-    print("binding db to model")
     db.bind(models)
 
-    print("creating embedding maintainer")
     maintainer = EmbeddingMaintainer(
         db,
         config,

frigate/embeddings/functions/onnx.py

@@ -61,37 +61,32 @@ class GenericONNXEmbedding:
         self.tokenizer = None
         self.feature_extractor = None
         self.session = None
+        files_names = list(self.download_urls.keys()) + (
+            [self.tokenizer_file] if self.tokenizer_file else []
+        )
 
         if not all(
-            os.path.exists(os.path.join(self.download_path, n))
+            os.path.exists(os.path.join(self.download_path, n)) for n in files_names
-            for n in self.download_urls.keys()
         ):
-            print("starting model download")
+            logger.debug(f"starting model download for {self.model_name}")
             self.downloader = ModelDownloader(
                 model_name=self.model_name,
                 download_path=self.download_path,
-                file_names=list(self.download_urls.keys())
+                file_names=files_names,
-                + ([self.tokenizer_file] if self.tokenizer_file else []),
                 requestor=self.requestor,
                 download_func=self._download_model,
             )
             self.downloader.ensure_model_files()
         else:
             self.downloader = None
-            for file_name in self.download_urls.keys():
+            ModelDownloader.mark_files_downloaded(
-                self.requestor.send_data(
+                self.requestor, self.model_name, files_names
-                    UPDATE_MODEL_STATE,
+            )
-                    {
-                        "model": f"{self.model_name}-{file_name}",
-                        "state": ModelStatusTypesEnum.downloaded,
-                    },
-                )
             self._load_model_and_tokenizer()
-            print("models are already downloaded")
+            print(f"models are already downloaded for {self.model_name}")
 
     def _download_model(self, path: str):
         try:
-            print("beginning model download process")
             file_name = os.path.basename(path)
             if file_name in self.download_urls:
                 ModelDownloader.download_from_url(self.download_urls[file_name], path)
@@ -130,11 +125,9 @@ class GenericONNXEmbedding:
                 self.tokenizer = self._load_tokenizer()
             else:
                 self.feature_extractor = self._load_feature_extractor()
-            print("creating onnx session")
             self.session = self._load_model(
                 os.path.join(self.download_path, self.model_file)
             )
-            print("successfully loaded model.")
 
     def _load_tokenizer(self):
         tokenizer_path = os.path.join(f"{MODEL_CACHE_DIR}/{self.model_name}/tokenizer")
@@ -151,16 +144,11 @@ class GenericONNXEmbedding:
         )
 
     def _load_model(self, path: str) -> Optional[ort.InferenceSession]:
-        print(f"checking if path exists {path}")
         if os.path.exists(path):
-            print(
-                f"loading ORT session with providers {self.providers} and options {self.provider_options}"
-            )
             return ort.InferenceSession(
                 path, providers=self.providers, provider_options=self.provider_options
             )
         else:
-            print(f"{self.model_name} model file {path} not found.")
             return None
 
     def _process_image(self, image):
@@ -174,7 +162,6 @@ class GenericONNXEmbedding:
     def __call__(
         self, inputs: Union[List[str], List[Image.Image], List[str]]
     ) -> List[np.ndarray]:
-        print("beginning call for onnx embedding")
         self._load_model_and_tokenizer()
 
         if self.session is None or (

frigate/embeddings/maintainer.py

@@ -43,9 +43,7 @@ class EmbeddingMaintainer(threading.Thread):
     ) -> None:
         super().__init__(name="embeddings_maintainer")
         self.config = config
-        print("creating embeddings")
         self.embeddings = Embeddings(config.semantic_search, db)
-        print("finished creating embeddings")
 
         # Check if we need to re-index events
         if config.semantic_search.reindex:
@@ -63,12 +61,10 @@ class EmbeddingMaintainer(threading.Thread):
         self.stop_event = stop_event
         self.tracked_events = {}
         self.genai_client = get_genai_client(config.genai)
-        print("finished embed maintainer setup")
 
     def run(self) -> None:
         """Maintain a SQLite-vec database for semantic search."""
         while not self.stop_event.is_set():
-            print("Doing another embeddings loop.")
             self._process_requests()
             self._process_updates()
             self._process_finalized()
@@ -85,8 +81,6 @@ class EmbeddingMaintainer(threading.Thread):
         """Process embeddings requests"""
 
         def _handle_request(topic: str, data: str) -> str:
-            print(f"Handling embeddings request of type {topic} with data {data}")
-
             try:
                 if topic == EmbeddingsRequestEnum.embed_description.value:
                     return serialize(
@@ -122,7 +116,6 @@ class EmbeddingMaintainer(threading.Thread):
         if not camera or source_type != EventTypeEnum.tracked_object:
             return
 
-        print(f"Processing object update of type {source_type} on {camera}")
         camera_config = self.config.cameras[camera]
         if data["id"] not in self.tracked_events:
             self.tracked_events[data["id"]] = []
@@ -130,23 +123,15 @@ class EmbeddingMaintainer(threading.Thread):
         # Create our own thumbnail based on the bounding box and the frame time
         try:
             frame_id = f"{camera}{data['frame_time']}"
-            print("trying to get frame from manager")
             yuv_frame = self.frame_manager.get(frame_id, camera_config.frame_shape_yuv)
-            print(f"got frame from manager and it is valid {yuv_frame is not None}")
 
             if yuv_frame is not None:
                 data["thumbnail"] = self._create_thumbnail(yuv_frame, data["box"])
                 self.tracked_events[data["id"]].append(data)
                 self.frame_manager.close(frame_id)
-            else:
-                print(
-                    f"Unable to create embedding for thumbnail from {camera} because frame is missing."
-                )
         except FileNotFoundError:
             pass
 
-        print("Finished processing object update")
-
     def _process_finalized(self) -> None:
         """Process the end of an event."""
         while True:
@@ -156,9 +141,6 @@ class EmbeddingMaintainer(threading.Thread):
                 break
 
             event_id, camera, updated_db = ended
-            print(
-                f"Processing finalized event for {camera} which updated the db: {updated_db}"
-            )
             camera_config = self.config.cameras[camera]
 
             if updated_db:
@@ -190,9 +172,6 @@ class EmbeddingMaintainer(threading.Thread):
                         or set(event.zones) & set(camera_config.genai.required_zones)
                     )
                 ):
-                    print(
-                        f"Description generation for {event}, has_snapshot: {event.has_snapshot}"
-                    )
                     if event.has_snapshot and camera_config.genai.use_snapshot:
                         with open(
                             os.path.join(CLIPS_DIR, f"{event.camera}-{event.id}.jpg"),
@@ -245,13 +224,13 @@ class EmbeddingMaintainer(threading.Thread):
 
     def _process_event_metadata(self):
         # Check for regenerate description requests
-        (topic, event_id, source) = self.event_metadata_subscriber.check_for_update(timeout=0.1)
+        (topic, event_id, source) = self.event_metadata_subscriber.check_for_update(
+            timeout=0.1
+        )
 
         if topic is None:
             return
 
-        print(f"Handling event metadata for id {event_id} and source {source}")
-
         if event_id:
             self.handle_regenerate_description(event_id, source)
 
@@ -284,7 +263,7 @@ class EmbeddingMaintainer(threading.Thread):
         )
 
         if not description:
-            print("Failed to generate description for %s", event.id)
+            logger.debug("Failed to generate description for %s", event.id)
             return
 
         # fire and forget description update
@@ -296,7 +275,7 @@ class EmbeddingMaintainer(threading.Thread):
         # Encode the description
         self.embeddings.upsert_description(event.id, description)
 
-        print(
+        logger.debug(
             "Generated description for %s (%d images): %s",
             event.id,
             len(thumbnails),
@@ -317,7 +296,7 @@ class EmbeddingMaintainer(threading.Thread):
 
         thumbnail = base64.b64decode(event.thumbnail)
 
-        print(
+        logger.debug(
             f"Trying {source} regeneration for {event}, has_snapshot: {event.has_snapshot}"
         )
 

frigate/util/downloader.py

@@ -120,6 +120,18 @@ class ModelDownloader:
         if not silent:
             logger.info(f"Downloading complete: {url}")
 
+    @staticmethod
+    def mark_files_downloaded(
+        requestor: InterProcessRequestor, model_name: str, files: list[str]
+    ) -> None:
+        for file_name in files:
+            requestor.send_data(
+                UPDATE_MODEL_STATE,
+                {
+                    "model": f"{model_name}-{file_name}",
+                    "state": ModelStatusTypesEnum.downloaded,
+                },
+            )
+
     def wait_for_download(self):
-        print("waiting for model download")
         self.download_complete.wait()