mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-03-10 18:43:09 +03:00
update maintainer
This commit is contained in:
Josh Hawkins
parent
826735bee2
commit
306725a8d8
@ -9,7 +9,6 @@ from typing import Any
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from peewee import DoesNotExist
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from frigate.comms.config_updater import ConfigSubscriber
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from frigate.comms.detections_updater import DetectionSubscriber, DetectionTypeEnum
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from frigate.comms.embeddings_updater import (
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EmbeddingsRequestEnum,
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@ -33,6 +32,10 @@ from frigate.config.camera.updater import (
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CameraConfigUpdateEnum,
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CameraConfigUpdateSubscriber,
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)
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from frigate.config.enrichment_updater import (
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EnrichmentConfigEnum,
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EnrichmentConfigSubscriber,
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)
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from frigate.data_processing.common.license_plate.model import (
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LicensePlateModelRunner,
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)
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@ -96,16 +99,7 @@ class EmbeddingMaintainer(threading.Thread):
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CameraConfigUpdateEnum.semantic_search,
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],
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)
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self.classification_config_subscriber = ConfigSubscriber(
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"config/classification/custom/"
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)
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self.bird_classification_config_subscriber = ConfigSubscriber(
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"config/classification", exact=True
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)
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self.face_recognition_config_subscriber = ConfigSubscriber(
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"config/face_recognition", exact=True
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)
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self.lpr_config_subscriber = ConfigSubscriber("config/lpr", exact=True)
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self.enrichment_config_subscriber = EnrichmentConfigSubscriber()
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# Configure Frigate DB
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db = SqliteVecQueueDatabase(
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@ -279,10 +273,7 @@ class EmbeddingMaintainer(threading.Thread):
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"""Maintain a SQLite-vec database for semantic search."""
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while not self.stop_event.is_set():
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self.config_updater.check_for_updates()
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self._check_classification_config_updates()
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self._check_bird_classification_config_updates()
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self._check_face_recognition_config_updates()
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self._check_lpr_config_updates()
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self._check_enrichment_config_updates()
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self._process_requests()
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self._process_updates()
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self._process_recordings_updates()
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@ -293,10 +284,7 @@ class EmbeddingMaintainer(threading.Thread):
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self._process_event_metadata()
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self.config_updater.stop()
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self.classification_config_subscriber.stop()
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self.bird_classification_config_subscriber.stop()
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self.face_recognition_config_subscriber.stop()
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self.lpr_config_subscriber.stop()
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self.enrichment_config_subscriber.stop()
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self.event_subscriber.stop()
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self.event_end_subscriber.stop()
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self.recordings_subscriber.stop()
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@ -307,124 +295,108 @@ class EmbeddingMaintainer(threading.Thread):
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self.requestor.stop()
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logger.info("Exiting embeddings maintenance...")
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def _check_classification_config_updates(self) -> None:
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"""Check for classification config updates and add/remove processors."""
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topic, model_config = self.classification_config_subscriber.check_for_update()
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def _check_enrichment_config_updates(self) -> None:
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"""Check for enrichment config updates and delegate to processors."""
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update_type, topic, payload = (
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self.enrichment_config_subscriber.check_for_update()
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)
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if topic:
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model_name = topic.split("/")[-1]
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if update_type is None:
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return
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if model_config is None:
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self.realtime_processors = [
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processor
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for processor in self.realtime_processors
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if not (
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isinstance(
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processor,
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(
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CustomStateClassificationProcessor,
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CustomObjectClassificationProcessor,
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),
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)
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and processor.model_config.name == model_name
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)
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]
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# Custom classification add/remove requires managing the processor list
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if update_type == EnrichmentConfigEnum.classification_custom:
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self._handle_custom_classification_update(topic, payload)
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return
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logger.info(
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f"Successfully removed classification processor for model: {model_name}"
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)
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else:
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self.config.classification.custom[model_name] = model_config
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# Apply global config updates before notifying processors
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if update_type == EnrichmentConfigEnum.classification:
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self.config.classification = payload
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logger.debug("Applied dynamic bird classification config update")
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elif update_type == EnrichmentConfigEnum.face_recognition:
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previous_min_area = self.config.face_recognition.min_area
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self.config.face_recognition = payload
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# Check if processor already exists
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for processor in self.realtime_processors:
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if isinstance(
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for camera_config in self.config.cameras.values():
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if camera_config.face_recognition.min_area == previous_min_area:
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camera_config.face_recognition.min_area = payload.min_area
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elif update_type == EnrichmentConfigEnum.lpr:
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previous_min_area = self.config.lpr.min_area
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self.config.lpr = payload
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for camera_config in self.config.cameras.values():
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if camera_config.lpr.min_area == previous_min_area:
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camera_config.lpr.min_area = payload.min_area
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# Broadcast to all processors — each decides if the update is relevant
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for processor in self.realtime_processors:
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processor.update_config(update_type, payload)
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for processor in self.post_processors:
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processor.update_config(update_type, payload)
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def _handle_custom_classification_update(
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self, topic: str, model_config: Any
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) -> None:
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"""Handle add/remove of custom classification processors."""
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model_name = topic.split("/")[-1]
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if model_config is None:
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self.realtime_processors = [
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processor
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for processor in self.realtime_processors
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if not (
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isinstance(
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processor,
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(
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CustomStateClassificationProcessor,
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CustomObjectClassificationProcessor,
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),
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):
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if processor.model_config.name == model_name:
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logger.debug(
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f"Classification processor for model {model_name} already exists, skipping"
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)
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return
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if model_config.state_config is not None:
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processor = CustomStateClassificationProcessor(
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self.config, model_config, self.requestor, self.metrics
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)
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else:
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processor = CustomObjectClassificationProcessor(
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self.config,
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model_config,
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self.event_metadata_publisher,
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self.requestor,
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self.metrics,
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)
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self.realtime_processors.append(processor)
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logger.info(
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f"Added classification processor for model: {model_name} (type: {type(processor).__name__})"
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and processor.model_config.name == model_name
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)
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]
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def _check_bird_classification_config_updates(self) -> None:
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"""Check for bird classification config updates."""
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topic, classification_config = (
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self.bird_classification_config_subscriber.check_for_update()
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logger.info(
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f"Successfully removed classification processor for model: {model_name}"
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)
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return
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self.config.classification.custom[model_name] = model_config
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# Check if processor already exists
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for processor in self.realtime_processors:
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if isinstance(
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processor,
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(
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CustomStateClassificationProcessor,
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CustomObjectClassificationProcessor,
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),
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):
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if processor.model_config.name == model_name:
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logger.debug(
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f"Classification processor for model {model_name} already exists, skipping"
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)
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return
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if model_config.state_config is not None:
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processor = CustomStateClassificationProcessor(
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self.config, model_config, self.requestor, self.metrics
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)
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else:
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processor = CustomObjectClassificationProcessor(
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self.config,
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model_config,
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self.event_metadata_publisher,
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self.requestor,
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self.metrics,
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)
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self.realtime_processors.append(processor)
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logger.info(
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f"Added classification processor for model: {model_name} (type: {type(processor).__name__})"
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)
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if topic is None:
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return
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self.config.classification = classification_config
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logger.debug("Applied dynamic bird classification config update")
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def _check_face_recognition_config_updates(self) -> None:
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"""Check for face recognition config updates."""
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topic, face_config = self.face_recognition_config_subscriber.check_for_update()
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if topic is None:
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return
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previous_min_area = self.config.face_recognition.min_area
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self.config.face_recognition = face_config
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for camera_config in self.config.cameras.values():
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if camera_config.face_recognition.min_area == previous_min_area:
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camera_config.face_recognition.min_area = face_config.min_area
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for processor in self.realtime_processors:
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if isinstance(processor, FaceRealTimeProcessor):
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processor.update_config(face_config)
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logger.debug("Applied dynamic face recognition config update")
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def _check_lpr_config_updates(self) -> None:
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"""Check for LPR config updates."""
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topic, lpr_config = self.lpr_config_subscriber.check_for_update()
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if topic is None:
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return
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previous_min_area = self.config.lpr.min_area
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self.config.lpr = lpr_config
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for camera_config in self.config.cameras.values():
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if camera_config.lpr.min_area == previous_min_area:
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camera_config.lpr.min_area = lpr_config.min_area
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for processor in self.realtime_processors:
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if isinstance(processor, LicensePlateRealTimeProcessor):
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processor.update_config(lpr_config)
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for processor in self.post_processors:
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if isinstance(processor, LicensePlatePostProcessor):
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processor.update_config(lpr_config)
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logger.debug("Applied dynamic LPR config update")
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def _process_requests(self) -> None:
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"""Process embeddings requests"""
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