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Ruff Docstring formatting (#15793)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-08-25 04:27:55 +08:00 • committed by GitHub
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commit 776ca86369
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19
ultralytics/models/fastsam/model.py
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@ -30,18 +30,21 @@ class FastSAM(Model):
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.
Perform segmentation prediction on image or video source.
Supports prompted segmentation with bounding boxes, points, labels, and texts.
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.
source (str | PIL.Image | numpy.ndarray): Input source.
stream (bool): Enable real-time streaming.
bboxes (list): Bounding box coordinates for prompted segmentation.
points (list): Points for prompted segmentation.
labels (list): Labels for prompted segmentation.
texts (list): Texts for prompted segmentation.
**kwargs (Any): Additional keyword arguments.
Returns:
(list): The model predictions.
(list): Model predictions.
"""
prompts = dict(bboxes=bboxes, points=points, labels=labels, texts=texts)
return super().predict(source, stream, prompts=prompts, **kwargs)