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detector/yolo_utils: indentation, remove unused variable
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@ -6,16 +6,15 @@ import cv2
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logger = logging.getLogger(__name__)
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def yolov8_preprocess(tensor_input, model_input_shape):
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# tensor_input must be nhwc
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assert tensor_input.shape[3] == 3
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if tuple(tensor_input.shape[1:3]) != tuple(model_input_shape[2:4]):
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logger.warn(f"yolov8_preprocess: tensor_input.shape {tensor_input.shape} and model_input_shape {model_input_shape} do not match!")
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# cv2.dnn.blobFromImage is faster than numpying it
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return cv2.dnn.blobFromImage(tensor_input[0], 1.0 / 255, (model_input_shape[3], model_input_shape[2]), None, swapRB=False)
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# tensor_input must be nhwc
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assert tensor_input.shape[3] == 3
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if tuple(tensor_input.shape[1:3]) != tuple(model_input_shape[2:4]):
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logger.warn(f"yolov8_preprocess: tensor_input.shape {tensor_input.shape} and model_input_shape {model_input_shape} do not match!")
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# cv2.dnn.blobFromImage is faster than numpying it
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return cv2.dnn.blobFromImage(tensor_input[0], 1.0 / 255, (model_input_shape[3], model_input_shape[2]), None, swapRB=False)
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def yolov8_postprocess(model_input_shape, tensor_output, box_count = 20):
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model_box_count = tensor_output.shape[2]
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model_class_count = tensor_output.shape[1] - 4
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probs = tensor_output[0, 4:, :]
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all_ids = np.argmax(probs, axis=0)
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all_confidences = probs.T[np.arange(model_box_count), all_ids]
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