source_code/SegMamba/light_training/preprocessing/cropping/cropping.py

import numpy as np


# Hello! crop_to_nonzero is the function you are looking for. Ignore the rest.
from acvl_utils.cropping_and_padding.bounding_boxes import get_bbox_from_mask, crop_to_bbox, bounding_box_to_slice


def create_nonzero_mask(data):
    """

    :param data:
    :return: the mask is True where the data is nonzero
    """
    from scipy.ndimage import binary_fill_holes
    assert len(data.shape) == 4 or len(data.shape) == 3, "data must have shape (C, X, Y, Z) or shape (C, X, Y)"
    nonzero_mask = np.zeros(data.shape[1:], dtype=bool)
    for c in range(data.shape[0]):
        this_mask = data[c] != 0
        nonzero_mask = nonzero_mask | this_mask
    nonzero_mask = binary_fill_holes(nonzero_mask)
    return nonzero_mask


def crop_to_nonzero(data, seg=None, nonzero_label=-1):
    """

    :param data:
    :param seg:
    :param nonzero_label: this will be written into the segmentation map
    :return:
    """
    nonzero_mask = create_nonzero_mask(data)
    bbox = get_bbox_from_mask(nonzero_mask)

    slicer = bounding_box_to_slice(bbox)
    data = data[tuple([slice(None), *slicer])]

    if seg is not None:
        seg = seg[tuple([slice(None), *slicer])]

    nonzero_mask = nonzero_mask[slicer][None]
    if seg is not None:
        seg[(seg == 0) & (~nonzero_mask)] = nonzero_label
    else:
        nonzero_mask = nonzero_mask.astype(np.int8)
        nonzero_mask[nonzero_mask == 0] = nonzero_label
        nonzero_mask[nonzero_mask > 0] = 0
        seg = nonzero_mask
    return data, seg, bbox