Segment Anything Model (SAM) 的接口参数

class SAM2AutomaticMaskGenerator:
    def __init__(
        self,
        model: SAM2Base,
        points_per_side: Optional[int] = 32,
        points_per_batch: int = 64,
        pred_iou_thresh: float = 0.8,
        stability_score_thresh: float = 0.95,
        stability_score_offset: float = 1.0,
        mask_threshold: float = 0.0,
        box_nms_thresh: float = 0.7,
        crop_n_layers: int = 0,
        crop_nms_thresh: float = 0.7,
        crop_overlap_ratio: float = 512 / 1500,
        crop_n_points_downscale_factor: int = 1,
        point_grids: Optional[List[np.ndarray]] = None,
        min_mask_region_area: int = 0,
        output_mode: str = "binary_mask",
        use_m2m: bool = False,
        multimask_output: bool = True,
        **kwargs,
    ) -> None:
        """
        Using a SAM 2 model, generates masks for the entire image.
        Generates a grid of point prompts over the image, then filters
        low quality and duplicate masks. The default settings are chosen
        for SAM 2 with a HieraL backbone.

        Arguments:
          model (Sam): The SAM 2 model to use for mask prediction.
          points_per_side (int or None): The number of points to be sampled
            along one side of the image. The total number of points is
            points_per_side**2. If None, 'point_grids' must provide explicit
            point sampling.
          points_per_batch (int): Sets the number of points run simultaneously
            by the model. Higher numbers may be faster but use more GPU memory.
          pred_iou_thresh (float): A filtering threshold in [0,1], using the
            model's predicted mask quality.
          stability_score_thresh (float): A filtering threshold in [0,1], using
            the stability of the mask under changes to the cutoff used to binarize
            the model's mask predictions.
          stability_score_offset (float): The amount to shift the cutoff when
            calculated the stability score.
          mask_threshold (float): Threshold for binarizing the mask logits
          box_nms_thresh (float): The box IoU cutoff used by non-maximal
            suppression to filter duplicate masks.
          crop_n_layers (int): If >0, mask prediction will be run again on
            crops of the image. Sets the number of layers to run, where each
            layer has 2**i_layer number of image crops.
          crop_nms_thresh (float): The box IoU cutoff used by non-maximal
            suppression to filter duplicate masks between different crops.
          crop_overlap_ratio (float): Sets the degree to which crops overlap.
            In the first crop layer, crops will overlap by this fraction of
            the image length. Later layers with more crops scale down this overlap.
          crop_n_points_downscale_factor (int): The number of points-per-side
            sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
          point_grids (list(np.ndarray) or None): A list over explicit grids
            of points used for sampling, normalized to [0,1]. The nth grid in the
            list is used in the nth crop layer. Exclusive with points_per_side.
          min_mask_region_area (int): If >0, postprocessing will be applied
            to remove disconnected regions and holes in masks with area smaller
            than min_mask_region_area. Requires opencv.
          output_mode (str): The form masks are returned in. Can be 'binary_mask',
            'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
            For large resolutions, 'binary_mask' may consume large amounts of
            memory.
          use_m2m (bool): Whether to add a one step refinement using previous mask predictions.
          multimask_output (bool): Whether to output multimask at each point of the grid.
        """
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