def preprocess_predict_nifti(self,
input_files,
output_file=None,
softmax_ouput_file=None):
"""
Use this to predict new data
:param input_files:
:param output_file:
:param softmax_ouput_file:
:return:
"""
print("preprocessing...")
d, s, properties = self.preprocess_patient(input_files)
print("predicting...")
pred = self.predict_preprocessed_data_return_softmax(
d, self.data_aug_params["mirror"], 1, False, 1,
self.data_aug_params['mirror_axes'], True, True, 2,
self.patch_size, True)
pred = pred.transpose([0] + [i + 1 for i in self.transpose_backward])
print("resampling to original spacing and nifti export...")
save_segmentation_nifti_from_softmax(pred, output_file, properties, 3,
None, None, None,
softmax_ouput_file, None)
print("done")
def merge_files(args):
files, properties_file, out_file, only_keep_largest_connected_component, min_region_size_per_class, override = args
if override or not isfile(out_file):
softmax = [np.load(f)['softmax'][None] for f in files]
softmax = np.vstack(softmax)
softmax = np.mean(softmax, 0)
props = load_pickle(properties_file)
save_segmentation_nifti_from_softmax(softmax, out_file, props, 1, None,
None, None)
def merge(args):
file1, file2, properties_file, out_file = args
if not isfile(out_file):
res1 = np.load(file1)['softmax']
res2 = np.load(file2)['softmax']
props = load_pickle(properties_file)
mn = np.mean((res1, res2), 0)
save_segmentation_nifti_from_softmax(mn, out_file, props, 1, None,
None, None)
def preprocess_predict_nifti(self,
input_files: List[str],
output_file: str = None,
softmax_ouput_file: str = None,
mixed_precision: bool = True) -> None:
"""
Use this to predict new data
:param input_files:
:param output_file:
:param softmax_ouput_file:
:param mixed_precision:
:return:
"""
print("preprocessing...")
d, s, properties = self.preprocess_patient(input_files)
print("predicting...")
pred = self.predict_preprocessed_data_return_seg_and_softmax(
d,
do_mirroring=self.data_aug_params["do_mirror"],
mirror_axes=self.data_aug_params['mirror_axes'],
use_sliding_window=True,
step_size=0.5,
use_gaussian=True,
pad_border_mode='constant',
pad_kwargs={'constant_values': 0},
verbose=True,
all_in_gpu=False,
mixed_precision=mixed_precision)[1]
pred = pred.transpose([0] + [i + 1 for i in self.transpose_backward])
if 'segmentation_export_params' in self.plans.keys():
force_separate_z = self.plans['segmentation_export_params'][
'force_separate_z']
interpolation_order = self.plans['segmentation_export_params'][
'interpolation_order']
interpolation_order_z = self.plans['segmentation_export_params'][
'interpolation_order_z']
else:
force_separate_z = None
interpolation_order = 1
interpolation_order_z = 0
print("resampling to original spacing and nifti export...")
save_segmentation_nifti_from_softmax(
pred,
output_file,
properties,
interpolation_order,
self.regions_class_order,
None,
None,
softmax_ouput_file,
None,
force_separate_z=force_separate_z,
interpolation_order_z=interpolation_order_z)
print("done")
def merge(args):
file1, file2, properties_file, out_file = args
if not isfile(out_file):
res1 = np.load(file1)['softmax']
res2 = np.load(file2)['softmax']
props = load_pickle(properties_file)
mn = np.mean((res1, res2), 0)
save_segmentation_nifti_from_softmax(mn,
out_file,
props,
3,
None,
None,
None,
force_separate_z=None,
interpolation_order_z=0)
def preprocess_predict_nifti(self,
input_files,
output_file=None,
softmax_ouput_file=None,
mixed_precision: bool = True):
"""
Use this to predict new data
:param input_files:
:param output_file:
:param softmax_ouput_file:
:param mixed_precision:
:return:
"""
print("preprocessing...")
d, s, properties = self.preprocess_patient(input_files)
print("predicting...")
pred = self.predict_preprocessed_data_return_seg_and_softmax(
d,
do_mirroring=self.data_aug_params["do_mirror"],
mirror_axes=self.data_aug_params['mirror_axes'],
use_sliding_window=True,
step_size=0.5,
use_gaussian=True,
pad_border_mode='constant',
pad_kwargs={'constant_values': 0},
all_in_gpu=True,
mixed_precision=mixed_precision)[1]
pred = pred.transpose([0] + [i + 1 for i in self.transpose_backward])
print("resampling to original spacing and nifti export...")
save_segmentation_nifti_from_softmax(pred,
output_file,
properties,
3,
None,
None,
None,
softmax_ouput_file,
None,
force_separate_z=False,
interpolation_order_z=3)
print("done")
def merge_files(files, properties_files, out_file, override, store_npz):
if override or not isfile(out_file):
softmax = [np.load(f)['softmax'][None] for f in files]
softmax = np.vstack(softmax)
softmax = np.mean(softmax, 0)
props = [load_pickle(f) for f in properties_files]
reg_class_orders = [
p['regions_class_order']
if 'regions_class_order' in p.keys() else None for p in props
]
if not all([i is None for i in reg_class_orders]):
# if reg_class_orders are not None then they must be the same in all pkls
tmp = reg_class_orders[0]
for r in reg_class_orders[1:]:
assert tmp == r, 'If merging files with regions_class_order, the regions_class_orders of all ' \
'files must be the same. regions_class_order: %s, \n files: %s' % \
(str(reg_class_orders), str(files))
regions_class_order = tmp
else:
regions_class_order = None
# Softmax probabilities are already at target spacing so this will not do any resampling (resampling parameters
# don't matter here)
save_segmentation_nifti_from_softmax(softmax,
out_file,
props[0],
3,
regions_class_order,
None,
None,
force_separate_z=None)
if store_npz:
np.savez_compressed(out_file[:-7] + ".npz", softmax=softmax)
save_pickle(props, out_file[:-7] + ".pkl")