def collapse_mask(mask, auto_label=True, custom_mask=None): """collapse separate masks into one mask with multiple integers overlapping areas are ignored Args: mask: nibabel or Brain_Data instance custom_mask: nibabel instance or string to file path; optional Returns: out: Brain_Data instance of a mask with different integers indicating different masks """ from nltools.data import Brain_Data if not isinstance(mask, Brain_Data): if isinstance(mask, nib.Nifti1Image): mask = Brain_Data(mask, mask=custom_mask) else: raise ValueError("Make sure mask is a nibabel or Brain_Data " "instance.") if len(mask.shape()) > 1: if len(mask) > 1: out = mask.empty() # Create list of masks and find any overlaps m_list = [] for x in range(len(mask)): m_list.append(mask[x].to_nifti()) intersect = intersect_masks(m_list, threshold=1, connected=False) intersect = Brain_Data( nib.Nifti1Image(np.abs(intersect.get_data() - 1), intersect.get_affine()), mask=custom_mask, ) merge = [] if auto_label: # Combine all masks into sequential order # ignoring any areas of overlap for i in range(len(m_list)): merge.append( np.multiply( Brain_Data(m_list[i], mask=custom_mask).data, intersect.data) * (i + 1)) out.data = np.sum(np.array(merge).T, 1).astype(int) else: # Collapse masks using value as label for i in range(len(m_list)): merge.append( np.multiply( Brain_Data(m_list[i], mask=custom_mask).data, intersect.data)) out.data = np.sum(np.array(merge).T, 1) return out else: warnings.warn("Doesn't need to be collapased")