Function erode

monai/transforms/utils_morphological_ops.py:26–55

Erode 2D/3D binary mask. Args: mask: input 2D/3D binary mask, [N,C,M,N] or [N,C,M,N,P] torch tensor or ndarray. filter_size: erosion filter size, has to be odd numbers, default to be 3. pad_value: the filled value for padding. We need to pad the input before filteri

(mask: NdarrayOrTensor, filter_size: int | Sequence[int] = 3, pad_value: float = 1.0)

def erode(mask: NdarrayOrTensor, filter_size: int | Sequence[int] = 3, pad_value: float = 1.0) -> NdarrayOrTensor:
    """
    Erode 2D/3D binary mask.

    Args:
        mask: input 2D/3D binary mask, [N,C,M,N] or [N,C,M,N,P] torch tensor or ndarray.
        filter_size: erosion filter size, has to be odd numbers, default to be 3.
        pad_value: the filled value for padding. We need to pad the input before filtering
                   to keep the output with the same size as input. Usually use default value
                   and not changed.

    Return:
        eroded mask, same shape and data type as input.

    Example:

        .. code-block:: python

            # define a naive mask
            mask = torch.zeros(3,2,3,3,3)
            mask[:,:,1,1,1] = 1.0
            filter_size = 3
            erode_result = erode(mask, filter_size)  # expect torch.zeros(3,2,3,3,3)
            dilate_result = dilate(mask, filter_size)  # expect torch.ones(3,2,3,3,3)
    """
    mask_t, *_ = convert_data_type(mask, torch.Tensor)
    res_mask_t = erode_t(mask_t, filter_size=filter_size, pad_value=pad_value)
    res_mask: NdarrayOrTensor
    res_mask, *_ = convert_to_dst_type(src=res_mask_t, dst=mask)
    return res_mask


def dilate(mask: NdarrayOrTensor, filter_size: int | Sequence[int] = 3, pad_value: float = 0.0) -> NdarrayOrTensor: