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ultralytics 8.2.69 FastSAM prompt inference refactor (#14724)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Laughing 2024-07-30 07:17:23 +08:00 • committed by GitHub
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commit 9532ad7cae
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11 changed files with 187 additions and 427 deletions
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18
ultralytics/models/fastsam/model.py
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@ -28,6 +28,24 @@ class FastSAM(Model):
assert Path(model).suffix not in {".yaml", ".yml"}, "FastSAM models only support pre-trained models."
super().__init__(model=model, task="segment")
def predict(self, source, stream=False, bboxes=None, points=None, labels=None, texts=None, **kwargs):
"""
Performs segmentation prediction on the given image or video source.
Args:
source (str): Path to the image or video file, or a PIL.Image object, or a numpy.ndarray object.
stream (bool, optional): If True, enables real-time streaming. Defaults to False.
bboxes (list, optional): List of bounding box coordinates for prompted segmentation. Defaults to None.
points (list, optional): List of points for prompted segmentation. Defaults to None.
labels (list, optional): List of labels for prompted segmentation. Defaults to None.
texts (list, optional): List of texts for prompted segmentation. Defaults to None.
Returns:
(list): The model predictions.
"""
prompts = dict(bboxes=bboxes, points=points, labels=labels, texts=texts)
return super().predict(source, stream, prompts=prompts, **kwargs)
@property
def task_map(self):
"""Returns a dictionary mapping segment task to corresponding predictor and validator classes."""