def crop(task_string, override=False, num_threads=8):
cropped_out_dir = join(cropped_output_dir, task_string)
maybe_mkdir_p(cropped_out_dir)
if override and isdir(cropped_out_dir):
shutil.rmtree(cropped_out_dir)
maybe_mkdir_p(cropped_out_dir)
splitted_4d_output_dir_task = join(splitted_4d_output_dir, task_string)
lists, _ = create_lists_from_splitted_dataset(splitted_4d_output_dir_task)
imgcrop = ImageCropper(num_threads, cropped_out_dir)
imgcrop.run_cropping(lists, overwrite_existing=override)
shutil.copy(join(splitted_4d_output_dir, task_string, "dataset.json"), cropped_out_dir)
def crop(task_string, override=False, num_threads=default_num_threads):
cropped_out_dir = join(nnUNet_cropped_data, task_string)
os.makedirs(cropped_out_dir, exist_ok=True)
if override and isdir(cropped_out_dir):
shutil.rmtree(cropped_out_dir)
os.makedirs(cropped_out_dir, exist_ok=True)
splitted_4d_output_dir_task = join(nnUNet_raw_data, task_string)
lists, _ = create_lists_from_splitted_dataset(splitted_4d_output_dir_task)
imgcrop = ImageCropper(num_threads, cropped_out_dir)
imgcrop.run_cropping(lists, overwrite_existing=override)
shutil.copy(join(nnUNet_raw_data, task_string, "dataset.json"), cropped_out_dir)
def crop(task_string, override=False, num_threads=default_num_threads):
cropped_out_dir = join(nnUNet_cropped_data, task_string)
maybe_mkdir_p(cropped_out_dir)
if override and isdir(cropped_out_dir):
shutil.rmtree(cropped_out_dir)
maybe_mkdir_p(cropped_out_dir)
splitted_4d_output_dir_task = join(nnUNet_raw_data, task_string)
lists, _ = create_lists_from_splitted_dataset(splitted_4d_output_dir_task)
# from nnunet.preprocessing.cropping import ImageCropper
imgcrop = ImageCropper(num_threads, cropped_out_dir)
# run_cropping
imgcrop.run_cropping(lists, overwrite_existing=override)
shutil.copy(join(nnUNet_raw_data, task_string, "dataset.json"),
cropped_out_dir)
def preprocess_test_case(self, data_files, target_spacing, seg_file=None, force_separate_z=None):
data, seg, properties = ImageCropper.crop_from_list_of_files(data_files, seg_file)
data = data.transpose((0, *[i + 1 for i in self.transpose_forward]))
seg = seg.transpose((0, *[i + 1 for i in self.transpose_forward]))
data, seg, properties = self.resample_and_normalize(data, target_spacing, properties, seg,
force_separate_z=force_separate_z)
return data.astype(np.float32), seg, properties
def generate_data(self, data_path):
# form list of file names
# expected_num_modalities = self.trainer.plans['num_modalities']
# # check input folder integrity
# case_ids = check_input_folder_and_return_caseIDs(data_path, expected_num_modalities)
# all_files = subfiles(data_path, suffix=".nii.gz", join=False, sort=True)
# list_of_lists = [[join(data_path, i) for i in all_files if i[:len(j)].startswith(j) and
# len(i) == (len(j) + 12)] for j in case_ids]
# part_id = 0
# num_parts = 1
file_name = [
'/home/zhiyuan/project/data/nnUNet_raw/nnUNet_raw_data/Task004_Hippocampus/imagesTs/hippocampus_392_0000.nii.gz'
]
data, seg, properties = ImageCropper.crop_from_list_of_files(file_name)
plans = self.trainer.plans
if plans.get('transpose_forward') is None or plans.get(
'transpose_backward') is None:
print(
"WARNING! You seem to have data that was preprocessed with a previous version of nnU-Net. "
"You should rerun preprocessing. We will proceed and assume that both transpose_foward "
"and transpose_backward are [0, 1, 2]. If that is not correct then weird things will happen!"
)
plans['transpose_forward'] = [0, 1, 2]
plans['transpose_backward'] = [0, 1, 2]
# 1. From preprocessing.GenericPreprocessor.preprocess_test_case
preprocessor = GenericPreprocessor(
plans['normalization_schemes'], plans['use_mask_for_norm'],
plans['transpose_forward'],
plans['dataset_properties']['intensityproperties'])
data = data.transpose(
(0, *[i + 1 for i in preprocessor.transpose_forward]))
seg = seg.transpose(
(0, *[i + 1 for i in preprocessor.transpose_forward]))
data, seg, properties = preprocessor.resample_and_normalize(
data,
plans['plans_per_stage'][self.trainer.stage]['current_spacing'],
properties,
seg,
force_separate_z=None)
output_data = data.astype(np.float32)
return output_data, seg, properties