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Update facenet model (#14647)
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@ -63,6 +63,6 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
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self.execute_sql("""
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self.execute_sql("""
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CREATE VIRTUAL TABLE IF NOT EXISTS vec_faces USING vec0(
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CREATE VIRTUAL TABLE IF NOT EXISTS vec_faces USING vec0(
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id TEXT PRIMARY KEY,
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id TEXT PRIMARY KEY,
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face_embedding FLOAT[128] distance_metric=cosine
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face_embedding FLOAT[512] distance_metric=cosine
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);
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);
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""")
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""")
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@ -133,7 +133,7 @@ class Embeddings:
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model_name="facenet",
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model_name="facenet",
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model_file="facenet.onnx",
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model_file="facenet.onnx",
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download_urls={
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download_urls={
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"facenet.onnx": "https://github.com/NicolasSM-001/faceNet.onnx-/raw/refs/heads/main/faceNet.onnx",
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"facenet.onnx": "https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/facenet.onnx",
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"facedet.onnx": "https://github.com/opencv/opencv_zoo/raw/refs/heads/main/models/face_detection_yunet/face_detection_yunet_2023mar_int8.onnx",
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"facedet.onnx": "https://github.com/opencv/opencv_zoo/raw/refs/heads/main/models/face_detection_yunet/face_detection_yunet_2023mar_int8.onnx",
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},
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},
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model_size="large",
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model_size="large",
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@ -222,7 +222,7 @@ class GenericONNXEmbedding:
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frame[y_center : y_center + og_h, x_center : x_center + og_w] = og
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frame[y_center : y_center + og_h, x_center : x_center + og_w] = og
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frame = np.expand_dims(frame, axis=0)
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frame = np.expand_dims(frame, axis=0)
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return [{"image_input": frame}]
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return [{"input_2": frame}]
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elif self.model_type == ModelTypeEnum.alpr_detect:
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elif self.model_type == ModelTypeEnum.alpr_detect:
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preprocessed = []
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preprocessed = []
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for x in raw_inputs:
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for x in raw_inputs:
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