mirror of
https://github.com/blakeblackshear/frigate.git
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60b34bcfca
* recordings data pub/sub * function to process recording stream frames * model runner * lpr model runner * refactor to mixin class and use model runner * separate out realtime and post processors * move model and mixin folders * basic postprocessor * clean up * docs * postprocessing logic * clean up * return none if recordings are disabled * run postprocessor handle_requests too * tweak expansion * add put endpoint * postprocessor tweaks with endpoint
32 lines
1.2 KiB
Python
32 lines
1.2 KiB
Python
from frigate.embeddings.onnx.lpr_embedding import (
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LicensePlateDetector,
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PaddleOCRClassification,
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PaddleOCRDetection,
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PaddleOCRRecognition,
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)
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from ...types import DataProcessorModelRunner
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class LicensePlateModelRunner(DataProcessorModelRunner):
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def __init__(self, requestor, device: str = "CPU", model_size: str = "large"):
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super().__init__(requestor, device, model_size)
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self.detection_model = PaddleOCRDetection(
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model_size=model_size, requestor=requestor, device=device
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)
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self.classification_model = PaddleOCRClassification(
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model_size=model_size, requestor=requestor, device=device
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)
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self.recognition_model = PaddleOCRRecognition(
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model_size=model_size, requestor=requestor, device=device
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)
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self.yolov9_detection_model = LicensePlateDetector(
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model_size=model_size, requestor=requestor, device=device
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)
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# Load all models once
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self.detection_model._load_model_and_utils()
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self.classification_model._load_model_and_utils()
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self.recognition_model._load_model_and_utils()
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self.yolov9_detection_model._load_model_and_utils()
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