# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import sys from copy import deepcopy from typing import Union, Tuple import numpy as np import SimpleITK as sitk from batchgenerators.augmentations.utils import resize_segmentation from nnunet.preprocessing.preprocessing import get_lowres_axis, get_do_separate_z, resample_data_or_seg from batchgenerators.utilities.file_and_folder_operations import * def save_segmentation_nifti_from_softmax(segmentation_softmax: Union[str, np.ndarray], out_fname: str, properties_dict: dict, order: int = 1, region_class_order: Tuple[Tuple[int]] = None, seg_postprogess_fn: callable = None, seg_postprocess_args: tuple = None, resampled_npz_fname: str = None, non_postprocessed_fname: str = None, force_separate_z: bool = None, interpolation_order_z: int = 0, verbose: bool = True): """ This is a utility for writing segmentations to nifto and npz. It requires the data to have been preprocessed by GenericPreprocessor because it depends on the property dictionary output (dct) to know the geometry of the original data. segmentation_softmax does not have to have the same size in pixels as the original data, it will be resampled to match that. This is generally useful because the spacings our networks operate on are most of the time not the native spacings of the image data. If seg_postprogess_fn is not None then seg_postprogess_fnseg_postprogess_fn(segmentation, *seg_postprocess_args) will be called before nifto export There is a problem with python process communication that prevents us from communicating obejcts larger than 2 GB between processes (basically when the length of the pickle string that will be sent is communicated by the multiprocessing.Pipe object then the placeholder (\%i I think) does not allow for long enough strings (lol). This could be fixed by changing i to l (for long) but that would require manually patching system python code.) We circumvent that problem here by saving softmax_pred to a npy file that will then be read (and finally deleted) by the Process. save_segmentation_nifti_from_softmax can take either filename or np.ndarray for segmentation_softmax and will handle this automatically :param segmentation_softmax: :param out_fname: :param properties_dict: :param order: :param region_class_order: :param seg_postprogess_fn: :param seg_postprocess_args: :param resampled_npz_fname: :param non_postprocessed_fname: :param force_separate_z: if None then we dynamically decide how to resample along z, if True/False then always /never resample along z separately. Do not touch unless you know what you are doing :param interpolation_order_z: if separate z resampling is done then this is the order for resampling in z :param verbose: :return: """ if verbose: print("force_separate_z:", force_separate_z, "interpolation order:", order) if isinstance(segmentation_softmax, str): assert isfile(segmentation_softmax), "If isinstance(segmentation_softmax, str) then " \ "isfile(segmentation_softmax) must be True" del_file = deepcopy(segmentation_softmax) segmentation_softmax = np.load(segmentation_softmax) os.remove(del_file) # first resample, then put result into bbox of cropping, then save current_shape = segmentation_softmax.shape shape_original_after_cropping = properties_dict.get('size_after_cropping') shape_original_before_cropping = properties_dict.get('original_size_of_raw_data') # current_spacing = dct.get('spacing_after_resampling') # original_spacing = dct.get('original_spacing') if np.any([i != j for i, j in zip(np.array(current_shape[1:]), np.array(shape_original_after_cropping))]): if force_separate_z is None: if get_do_separate_z(properties_dict.get('original_spacing')): do_separate_z = True lowres_axis = get_lowres_axis(properties_dict.get('original_spacing')) elif get_do_separate_z(properties_dict.get('spacing_after_resampling')): do_separate_z = True lowres_axis = get_lowres_axis(properties_dict.get('spacing_after_resampling')) else: do_separate_z = False lowres_axis = None else: do_separate_z = force_separate_z if do_separate_z: lowres_axis = get_lowres_axis(properties_dict.get('original_spacing')) else: lowres_axis = None if lowres_axis is not None and len(lowres_axis) != 1: # this happens for spacings like (0.24, 1.25, 1.25) for example. In that case we do not want to resample # separately in the out of plane axis do_separate_z = False if verbose: print("separate z:", do_separate_z, "lowres axis", lowres_axis) seg_old_spacing = resample_data_or_seg(segmentation_softmax, shape_original_after_cropping, is_seg=False, axis=lowres_axis, order=order, do_separate_z=do_separate_z, order_z=interpolation_order_z) # seg_old_spacing = resize_softmax_output(segmentation_softmax, shape_original_after_cropping, order=order) else: if verbose: print("no resampling necessary") seg_old_spacing = segmentation_softmax if resampled_npz_fname is not None: np.savez_compressed(resampled_npz_fname, softmax=seg_old_spacing.astype(np.float16)) # this is needed for ensembling if the nonlinearity is sigmoid if region_class_order is not None: properties_dict['regions_class_order'] = region_class_order save_pickle(properties_dict, resampled_npz_fname[:-4] + ".pkl") if region_class_order is None: labels = properties_dict['classes'] shape = np.array(seg_old_spacing.shape)[1:] shape[0] = len(labels) seg_old_space = np.zeros(shape) counter = 0 for i, label in enumerate(labels): classes = len(label) if classes == 1: seg_old_space[i] = seg_old_spacing[counter:classes + counter] else: seg_old_space[i] = seg_old_spacing[counter:classes+counter].argmax(0)[0] counter += classes seg_old_spacing = seg_old_space else: seg_old_spacing_final = np.zeros(seg_old_spacing.shape[1:]) for i, c in enumerate(region_class_order): seg_old_spacing_final[seg_old_spacing[i] > 0.5] = c seg_old_spacing = seg_old_spacing_final bbox = properties_dict.get('crop_bbox') if bbox is not None: shape_original_before_cropping[0] = len(properties_dict['classes']) seg_old_size = np.zeros(shape_original_before_cropping) for c in range(3): bbox[c][1] = np.min((bbox[c][0] + seg_old_spacing.shape[c], shape_original_before_cropping[c])) seg_old_size[:, bbox[1][0]:bbox[1][1], bbox[2][0]:bbox[2][1]] = seg_old_spacing else: seg_old_size = seg_old_spacing if seg_postprogess_fn is not None: seg_old_size_postprocessed = seg_postprogess_fn(np.copy(seg_old_size), *seg_postprocess_args) else: seg_old_size_postprocessed = seg_old_size seg_resized_itk = sitk.GetImageFromArray(seg_old_size_postprocessed.astype(float)) seg_resized_itk.SetSpacing(properties_dict['itk_spacing']) seg_resized_itk.SetOrigin(properties_dict['itk_origin']) seg_resized_itk.SetDirection(properties_dict['itk_direction']) sitk.WriteImage(seg_resized_itk, out_fname) if (non_postprocessed_fname is not None) and (seg_postprogess_fn is not None): seg_resized_itk = sitk.GetImageFromArray(seg_old_size.astype(np.uint8)) seg_resized_itk.SetSpacing(properties_dict['itk_spacing']) seg_resized_itk.SetOrigin(properties_dict['itk_origin']) seg_resized_itk.SetDirection(properties_dict['itk_direction']) sitk.WriteImage(seg_resized_itk, non_postprocessed_fname) def save_segmentation_nifti(segmentation, out_fname, dct, order=1, force_separate_z=None, order_z=0): """ faster and uses less ram than save_segmentation_nifti_from_softmax, but maybe less precise and also does not support softmax export (which is needed for ensembling). So it's a niche function that may be useful in some cases. :param segmentation: :param out_fname: :param dct: :param order: :param force_separate_z: :return: """ # suppress output print("force_separate_z:", force_separate_z, "interpolation order:", order) sys.stdout = open(os.devnull, 'w') if isinstance(segmentation, str): assert isfile(segmentation), "If isinstance(segmentation_softmax, str) then " \ "isfile(segmentation_softmax) must be True" del_file = deepcopy(segmentation) segmentation = np.load(segmentation) os.remove(del_file) # first resample, then put result into bbox of cropping, then save current_shape = segmentation.shape shape_original_after_cropping = dct.get('size_after_cropping') shape_original_before_cropping = dct.get('original_size_of_raw_data') # current_spacing = dct.get('spacing_after_resampling') # original_spacing = dct.get('original_spacing') if np.any(np.array(current_shape) != np.array(shape_original_after_cropping)): if order == 0: seg_old_spacing = resize_segmentation(segmentation, shape_original_after_cropping, 0) else: if force_separate_z is None: if get_do_separate_z(dct.get('original_spacing')): do_separate_z = True lowres_axis = get_lowres_axis(dct.get('original_spacing')) elif get_do_separate_z(dct.get('spacing_after_resampling')): do_separate_z = True lowres_axis = get_lowres_axis(dct.get('spacing_after_resampling')) else: do_separate_z = False lowres_axis = None else: do_separate_z = force_separate_z if do_separate_z: lowres_axis = get_lowres_axis(dct.get('original_spacing')) else: lowres_axis = None print("separate z:", do_separate_z, "lowres axis", lowres_axis) seg_old_spacing = resample_data_or_seg(segmentation[None], shape_original_after_cropping, is_seg=True, axis=lowres_axis, order=order, do_separate_z=do_separate_z, order_z=order_z)[0] else: seg_old_spacing = segmentation bbox = dct.get('crop_bbox') if bbox is not None: seg_old_size = np.zeros(shape_original_before_cropping) for c in range(3): bbox[c][1] = np.min((bbox[c][0] + seg_old_spacing.shape[c], shape_original_before_cropping[c])) seg_old_size[bbox[0][0]:bbox[0][1], bbox[1][0]:bbox[1][1], bbox[2][0]:bbox[2][1]] = seg_old_spacing else: seg_old_size = seg_old_spacing seg_resized_itk = sitk.GetImageFromArray(seg_old_size.astype(np.uint8)) seg_resized_itk.SetSpacing(dct['itk_spacing']) seg_resized_itk.SetOrigin(dct['itk_origin']) seg_resized_itk.SetDirection(dct['itk_direction']) sitk.WriteImage(seg_resized_itk, out_fname) sys.stdout = sys.__stdout__