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# SAM model interface
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from ultralytics.yolo.cfg import get_cfg
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from ...yolo.utils.torch_utils import model_info
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from .build import build_sam
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from .predict import Predictor
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class SAM:
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def __init__(self, model='sam_b.pt') -> None:
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if model and not model.endswith('.pt') and not model.endswith('.pth'):
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# Should raise AssertionError instead?
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raise NotImplementedError('Segment anything prediction requires pre-trained checkpoint')
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self.model = build_sam(model)
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self.predictor = None # reuse predictor
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def predict(self, source, stream=False, **kwargs):
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"""Predicts and returns segmentation masks for given image or video source."""
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overrides = dict(conf=0.25, task='segment', mode='predict')
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overrides.update(kwargs) # prefer kwargs
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if not self.predictor:
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self.predictor = Predictor(overrides=overrides)
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self.predictor.setup_model(model=self.model)
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else: # only update args if predictor is already setup
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self.predictor.args = get_cfg(self.predictor.args, overrides)
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return self.predictor(source, stream=stream)
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def train(self, **kwargs):
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"""Function trains models but raises an error as SAM models do not support training."""
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raise NotImplementedError("SAM models don't support training")
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def val(self, **kwargs):
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"""Run validation given dataset."""
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raise NotImplementedError("SAM models don't support validation")
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def __call__(self, source=None, stream=False, **kwargs):
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"""Calls the 'predict' function with given arguments to perform object detection."""
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return self.predict(source, stream, **kwargs)
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def __getattr__(self, attr):
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"""Raises error if object has no requested attribute."""
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name = self.__class__.__name__
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raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
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def info(self, detailed=False, verbose=True):
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"""
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Logs model info.
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Args:
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detailed (bool): Show detailed information about model.
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verbose (bool): Controls verbosity.
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"""
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return model_info(self.model, detailed=detailed, verbose=verbose)
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