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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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