def run_preprocessing(self, num_threads):
if os.path.isdir(join(self.preprocessed_output_folder, "gt_segmentations")):
shutil.rmtree(join(self.preprocessed_output_folder, "gt_segmentations"))
shutil.copytree(join(self.folder_with_cropped_data, "gt_segmentations"), join(self.preprocessed_output_folder,
"gt_segmentations"))
normalization_schemes = self.plans['normalization_schemes']
use_nonzero_mask_for_normalization = self.plans['use_mask_for_norm']
intensityproperties = self.plans['dataset_properties']['intensityproperties']
preprocessor = PreprocessorFor2D(normalization_schemes, use_nonzero_mask_for_normalization, self.transpose_forward,
intensityproperties)
target_spacings = [i["current_spacing"] for i in self.plans_per_stage.values()]
preprocessor.run(target_spacings, self.folder_with_cropped_data, self.preprocessed_output_folder,
self.plans['data_identifier'], num_threads)
def preprocess_patient(self, input_files):
"""
Used to predict new unseen data. Not used for the preprocessing of the training/test data
:param input_files:
:return:
"""
from nnunet.preprocessing.preprocessing import GenericPreprocessor, PreprocessorFor2D
if self.threeD:
preprocessor = GenericPreprocessor(self.normalization_schemes, self.use_mask_for_norm,
self.intensity_properties)
else:
preprocessor = PreprocessorFor2D(self.normalization_schemes, self.use_mask_for_norm,
self.intensity_properties)
d, s, properties = preprocessor.preprocess_test_case(input_files,
self.plans['plans_per_stage'][self.stage]['current_spacing'])
return d, s, properties