mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-03-16 02:17:46 +01:00
f39ddbc00d
* Catch error and show toast when failing to delete review items * i18n keys * add link to speed estimation docs in zone edit pane * Implement reset of tracked object update for each camera * Cleanup * register mqtt callbacks for toggling alerts and detections * clarify snapshots docs * clarify semantic search reindexing * add ukrainian * adjust date granularity for last recording time The api endpoint only returns granularity down to the day * Add amd hardware * fix crash in face library on initial start after enabling * Fix recordings view for mobile landscape The events view incorrectly was displaying two columns on landscape view and it only took up 20% of the screen width. Additionally, in landscape view the timeline was too wide (especially on iPads of various screen sizes) and would overlap the main video * face rec overfitting instructions * Clarify * face docs * clarify * clarify --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
"""Handle processing images for face detection and recognition."""
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import json
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import logging
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import numpy as np
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from frigate.comms.event_metadata_updater import EventMetadataPublisher
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from frigate.comms.inter_process import InterProcessRequestor
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from frigate.config import FrigateConfig
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from frigate.data_processing.common.license_plate.mixin import (
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LicensePlateProcessingMixin,
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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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from frigate.types import TrackedObjectUpdateTypesEnum
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from ..types import DataProcessorMetrics
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from .api import RealTimeProcessorApi
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logger = logging.getLogger(__name__)
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class LicensePlateRealTimeProcessor(LicensePlateProcessingMixin, RealTimeProcessorApi):
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def __init__(
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self,
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config: FrigateConfig,
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requestor: InterProcessRequestor,
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sub_label_publisher: EventMetadataPublisher,
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metrics: DataProcessorMetrics,
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model_runner: LicensePlateModelRunner,
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detected_license_plates: dict[str, dict[str, any]],
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):
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self.requestor = requestor
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self.detected_license_plates = detected_license_plates
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self.model_runner = model_runner
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self.lpr_config = config.lpr
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self.config = config
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self.sub_label_publisher = sub_label_publisher
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self.camera_current_cars: dict[str, list[str]] = {}
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super().__init__(config, metrics)
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def process_frame(
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self,
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obj_data: dict[str, any],
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frame: np.ndarray,
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dedicated_lpr: bool | None = False,
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):
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"""Look for license plates in image."""
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self.lpr_process(obj_data, frame, dedicated_lpr)
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def handle_request(self, topic, request_data) -> dict[str, any] | None:
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return
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def expire_object(self, object_id: str, camera: str):
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if object_id in self.detected_license_plates:
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self.detected_license_plates.pop(object_id)
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if object_id in self.camera_current_cars.get(camera, []):
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self.camera_current_cars[camera].remove(object_id)
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if len(self.camera_current_cars[camera]) == 0:
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self.requestor.send_data(
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"tracked_object_update",
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json.dumps(
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{
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"type": TrackedObjectUpdateTypesEnum.lpr,
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"name": None,
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"plate": None,
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"camera": camera,
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}
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),
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)
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