Class BoxToMask

monai/apps/detection/transforms/array.py:400–434

Convert box to int16 mask image, which has the same size with the input image. Args: bg_label: background labels for the output mask image, make sure it is smaller than any foreground(fg) labels. ellipse_mask: bool. - If True, it assumes the object shape is clo

class BoxToMask(Transform):
    """
    Convert box to int16 mask image, which has the same size with the input image.

    Args:
        bg_label: background labels for the output mask image, make sure it is smaller than any foreground(fg) labels.
        ellipse_mask: bool.

            - If True, it assumes the object shape is close to ellipse or ellipsoid.
            - If False, it assumes the object shape is close to rectangle or cube and well occupies the bounding box.
            - If the users are going to apply random rotation as data augmentation, we suggest setting ellipse_mask=True
              See also Kalra et al. "Towards Rotation Invariance in Object Detection", ICCV 2021.
    """

    backend = [TransformBackends.NUMPY]

    def __init__(self, bg_label: int = -1, ellipse_mask: bool = False) -> None:
        self.bg_label = bg_label
        self.ellipse_mask = ellipse_mask

    def __call__(  # type: ignore
        self, boxes: NdarrayOrTensor, labels: NdarrayOrTensor, spatial_size: Sequence[int] | int
    ) -> NdarrayOrTensor:
        """
        Args:
            boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``.
            labels: classification foreground(fg) labels corresponding to `boxes`, dtype should be int, sized (N,).
            spatial_size: image spatial size.

        Return:
            - int16 array, sized (num_box, H, W). Each channel represents a box.
                The foreground region in channel c has intensity of labels[c].
                The background intensity is bg_label.
        """
        return convert_box_to_mask(boxes, labels, spatial_size, self.bg_label, self.ellipse_mask)


class MaskToBox(Transform):