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https://github.com/blakeblackshear/frigate.git
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4b4053d174
* Send tracked object updates for face and license_plate objects * Update docs * Add to type enum * Add camera to object description update * Formatting * Consolidate yue-Hant * Add missing
56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
"""Handle processing images for face detection and recognition."""
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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 ..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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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):
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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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