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52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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"""
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SAM model interface
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"""
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from pathlib import Path
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from ultralytics.engine.model import Model
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from ultralytics.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(Model):
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"""
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SAM model interface.
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"""
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def __init__(self, model='sam_b.pt') -> None:
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if model and Path(model).suffix not in ('.pt', '.pth'):
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raise NotImplementedError('SAM prediction requires pre-trained *.pt or *.pth model.')
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super().__init__(model=model, task='segment')
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def _load(self, weights: str, task=None):
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self.model = build_sam(weights)
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def predict(self, source, stream=False, bboxes=None, points=None, labels=None, **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', imgsz=1024)
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kwargs.update(overrides)
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prompts = dict(bboxes=bboxes, points=points, labels=labels)
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return super().predict(source, stream, prompts=prompts, **kwargs)
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def __call__(self, source=None, stream=False, bboxes=None, points=None, labels=None, **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, bboxes, points, labels, **kwargs)
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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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@property
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def task_map(self):
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return {'segment': {'predictor': Predictor}}
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