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Refactor lpr into real time data processor (#16497)
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@@ -131,47 +131,6 @@ class Embeddings:
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device="GPU" if config.semantic_search.model_size == "large" else "CPU",
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)
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self.lpr_detection_model = None
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self.lpr_classification_model = None
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self.lpr_recognition_model = None
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if self.config.lpr.enabled:
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self.lpr_detection_model = GenericONNXEmbedding(
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model_name="paddleocr-onnx",
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model_file="detection.onnx",
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download_urls={
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"detection.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/detection.onnx"
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},
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model_size="large",
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model_type=ModelTypeEnum.lpr_detect,
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requestor=self.requestor,
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device="CPU",
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)
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self.lpr_classification_model = GenericONNXEmbedding(
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model_name="paddleocr-onnx",
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model_file="classification.onnx",
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download_urls={
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"classification.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/classification.onnx"
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},
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model_size="large",
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model_type=ModelTypeEnum.lpr_classify,
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requestor=self.requestor,
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device="CPU",
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)
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self.lpr_recognition_model = GenericONNXEmbedding(
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model_name="paddleocr-onnx",
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model_file="recognition.onnx",
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download_urls={
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"recognition.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/recognition.onnx"
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},
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model_size="large",
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model_type=ModelTypeEnum.lpr_recognize,
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requestor=self.requestor,
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device="CPU",
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)
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def embed_thumbnail(
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self, event_id: str, thumbnail: bytes, upsert: bool = True
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) -> ndarray:
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