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from pathlib import Path
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from ultralytics.engine.model import Model
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from .predict import FastSAMPredictor
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from .val import FastSAMValidator
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class FastSAM(Model):
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"""
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FastSAM model interface.
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Example:
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```python
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from ultralytics import FastSAM
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model = FastSAM("last.pt")
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results = model.predict("ultralytics/assets/bus.jpg")
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```
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"""
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def __init__(self, model="FastSAM-x.pt"):
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"""Call the __init__ method of the parent class (YOLO) with the updated default model."""
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if str(model) == "FastSAM.pt":
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model = "FastSAM-x.pt"
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assert Path(model).suffix not in {".yaml", ".yml"}, "FastSAM models only support pre-trained models."
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super().__init__(model=model, task="segment")
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def predict(self, source, stream=False, bboxes=None, points=None, labels=None, texts=None, **kwargs):
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"""
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Perform segmentation prediction on image or video source.
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Supports prompted segmentation with bounding boxes, points, labels, and texts.
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Args:
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source (str | PIL.Image | numpy.ndarray): Input source.
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stream (bool): Enable real-time streaming.
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bboxes (list): Bounding box coordinates for prompted segmentation.
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points (list): Points for prompted segmentation.
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labels (list): Labels for prompted segmentation.
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texts (list): Texts for prompted segmentation.
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**kwargs (Any): Additional keyword arguments.
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Returns:
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(list): Model predictions.
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"""
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prompts = dict(bboxes=bboxes, points=points, labels=labels, texts=texts)
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return super().predict(source, stream, prompts=prompts, **kwargs)
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@property
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def task_map(self):
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"""Returns a dictionary mapping segment task to corresponding predictor and validator classes."""
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return {"segment": {"predictor": FastSAMPredictor, "validator": FastSAMValidator}}
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