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Reference for ultralytics/models/sam/model.py

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This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/sam/model.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!


ultralytics.models.sam.model.SAM 

SAM(model='sam_b.pt')

Bases: Model

SAM (Segment Anything Model) interface class for real-time image segmentation tasks.

This class provides an interface to the Segment Anything Model (SAM) from Ultralytics, designed for promptable segmentation with versatility in image analysis. It supports various prompts such as bounding boxes, points, or labels, and features zero-shot performance capabilities.

Attributes:

Name Type Description
model Module

The loaded SAM model.
is_sam2 bool

Indicates whether the model is SAM2 variant.
task str

The task type, set to "segment" for SAM models.

Methods:

Name Description
predict

Performs segmentation prediction on the given image or video source.
info

Logs information about the SAM model.

Examples:

>>> sam = SAM("sam_b.pt")
>>> results = sam.predict("image.jpg", points=[[500, 375]])
>>> for r in results:
>>>     print(f"Detected {len(r.masks)} masks")

Parameters:

Name Type Description Default
model str

Path to the pre-trained SAM model file. File should have a .pt or .pth extension.

 'sam_b.pt'

Raises:

Type Description
NotImplementedError

If the model file extension is not .pt or .pth.

Examples:

>>> sam = SAM("sam_b.pt")
>>> print(sam.is_sam2)
Source code in ultralytics/models/sam/model.py 
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def __init__(self, model="sam_b.pt") -> None:
    """
    Initializes the SAM (Segment Anything Model) instance.

    Args:
        model (str): Path to the pre-trained SAM model file. File should have a .pt or .pth extension.

    Raises:
        NotImplementedError: If the model file extension is not .pt or .pth.

    Examples:
        >>> sam = SAM("sam_b.pt")
        >>> print(sam.is_sam2)
    """
    if model and Path(model).suffix not in {".pt", ".pth"}:
        raise NotImplementedError("SAM prediction requires pre-trained *.pt or *.pth model.")
    self.is_sam2 = "sam2" in Path(model).stem
    super().__init__(model=model, task="segment")

task_map property 

task_map

Provides a mapping from the 'segment' task to its corresponding 'Predictor'.

Returns:

Type Description
Dict[str, Type[Predictor]]

A dictionary mapping the 'segment' task to its corresponding Predictor class. For SAM2 models, it maps to SAM2Predictor, otherwise to the standard Predictor.

Examples:

>>> sam = SAM("sam_b.pt")
>>> task_map = sam.task_map
>>> print(task_map)
{'segment': <class 'ultralytics.models.sam.predict.Predictor'>}

__call__ 

__call__(source=None, stream=False, bboxes=None, points=None, labels=None, **kwargs)

Performs segmentation prediction on the given image or video source.

This method is an alias for the 'predict' method, providing a convenient way to call the SAM model for segmentation tasks.

Parameters:

Name Type Description Default
source str | Image | ndarray | None

Path to the image or video file, or a PIL.Image object, or a numpy.ndarray object.

 None
stream bool

If True, enables real-time streaming.

 False
bboxes List[List[float]] | None

List of bounding box coordinates for prompted segmentation.

 None
points List[List[float]] | None

List of points for prompted segmentation.

 None
labels List[int] | None

List of labels for prompted segmentation.

 None
**kwargs Any

Additional keyword arguments to be passed to the predict method.

 {}

Returns:

Type Description
List

The model predictions, typically containing segmentation masks and other relevant information.

Examples:

>>> sam = SAM("sam_b.pt")
>>> results = sam("image.jpg", points=[[500, 375]])
>>> print(f"Detected {len(results[0].masks)} masks")
Source code in ultralytics/models/sam/model.py 
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def __call__(self, source=None, stream=False, bboxes=None, points=None, labels=None, **kwargs):
    """
    Performs segmentation prediction on the given image or video source.

    This method is an alias for the 'predict' method, providing a convenient way to call the SAM model
    for segmentation tasks.

    Args:
        source (str | PIL.Image | numpy.ndarray | None): Path to the image or video file, or a PIL.Image
            object, or a numpy.ndarray object.
        stream (bool): If True, enables real-time streaming.
        bboxes (List[List[float]] | None): List of bounding box coordinates for prompted segmentation.
        points (List[List[float]] | None): List of points for prompted segmentation.
        labels (List[int] | None): List of labels for prompted segmentation.
        **kwargs (Any): Additional keyword arguments to be passed to the predict method.

    Returns:
        (List): The model predictions, typically containing segmentation masks and other relevant information.

    Examples:
        >>> sam = SAM("sam_b.pt")
        >>> results = sam("image.jpg", points=[[500, 375]])
        >>> print(f"Detected {len(results[0].masks)} masks")
    """
    return self.predict(source, stream, bboxes, points, labels, **kwargs)

info 

info(detailed=False, verbose=True)

Logs information about the SAM model.

This method provides details about the Segment Anything Model (SAM), including its architecture, parameters, and computational requirements.

Parameters:

Name Type Description Default
detailed bool

If True, displays detailed information about the model layers and operations.

 False
verbose bool

If True, prints the information to the console.

 True

Returns:

Type Description
Tuple

A tuple containing the model's information (string representations of the model).

Examples:

>>> sam = SAM("sam_b.pt")
>>> info = sam.info()
>>> print(info[0])  # Print summary information
Source code in ultralytics/models/sam/model.py 
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def info(self, detailed=False, verbose=True):
    """
    Logs information about the SAM model.

    This method provides details about the Segment Anything Model (SAM), including its architecture,
    parameters, and computational requirements.

    Args:
        detailed (bool): If True, displays detailed information about the model layers and operations.
        verbose (bool): If True, prints the information to the console.

    Returns:
        (Tuple): A tuple containing the model's information (string representations of the model).

    Examples:
        >>> sam = SAM("sam_b.pt")
        >>> info = sam.info()
        >>> print(info[0])  # Print summary information
    """
    return model_info(self.model, detailed=detailed, verbose=verbose)

predict 

predict(source, stream=False, bboxes=None, points=None, labels=None, **kwargs)

Performs segmentation prediction on the given image or video source.

Parameters:

Name Type Description Default
source str | Image | ndarray

Path to the image or video file, or a PIL.Image object, or a numpy.ndarray object.

 required
stream bool

If True, enables real-time streaming.

 False
bboxes List[List[float]] | None

List of bounding box coordinates for prompted segmentation.

 None
points List[List[float]] | None

List of points for prompted segmentation.

 None
labels List[int] | None

List of labels for prompted segmentation.

 None
**kwargs Any

Additional keyword arguments for prediction.

 {}

Returns:

Type Description
List

The model predictions.

Examples:

>>> sam = SAM("sam_b.pt")
>>> results = sam.predict("image.jpg", points=[[500, 375]])
>>> for r in results:
...     print(f"Detected {len(r.masks)} masks")
Source code in ultralytics/models/sam/model.py 
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def predict(self, source, stream=False, bboxes=None, points=None, labels=None, **kwargs):
    """
    Performs segmentation prediction on the given image or video source.

    Args:
        source (str | PIL.Image | numpy.ndarray): Path to the image or video file, or a PIL.Image object, or
            a numpy.ndarray object.
        stream (bool): If True, enables real-time streaming.
        bboxes (List[List[float]] | None): List of bounding box coordinates for prompted segmentation.
        points (List[List[float]] | None): List of points for prompted segmentation.
        labels (List[int] | None): List of labels for prompted segmentation.
        **kwargs (Any): Additional keyword arguments for prediction.

    Returns:
        (List): The model predictions.

    Examples:
        >>> sam = SAM("sam_b.pt")
        >>> results = sam.predict("image.jpg", points=[[500, 375]])
        >>> for r in results:
        ...     print(f"Detected {len(r.masks)} masks")
    """
    overrides = dict(conf=0.25, task="segment", mode="predict", imgsz=1024)
    kwargs = {**overrides, **kwargs}
    prompts = dict(bboxes=bboxes, points=points, labels=labels)
    return super().predict(source, stream, prompts=prompts, **kwargs)




📅 Created 11 months ago ✏️ Updated 1 month ago
jk4e Laughing-q glenn-jocher Burhan-Q