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# 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 numpy as np
from nnunet.preprocessing.preprocessing import PreprocessorFor2D, resample_patient
class GenericPreprocessor_scale_uint8_to_0_1(PreprocessorFor2D):
"""
For RGB images with a value range of [0, 255]. This preprocessor overwrites the default normalization scheme by
normalizing intensity values through a simple division by 255 which rescales them to [0, 1]
NOTE THAT THIS INHERITS FROM PreprocessorFor2D, SO ITS WRITTEN FOR 2D ONLY! WHEN CREATING A PREPROCESSOR FOR 3D
DATA, USE GenericPreprocessor AS PARENT!
"""
def resample_and_normalize(self, data, target_spacing, properties, seg=None, force_separate_z=None):
############ THIS PART IS IDENTICAL TO PARENT CLASS ################
original_spacing_transposed = np.array(properties["original_spacing"])[self.transpose_forward]
before = {
'spacing': properties["original_spacing"],
'spacing_transposed': original_spacing_transposed,
'data.shape (data is transposed)': data.shape
}
target_spacing[0] = original_spacing_transposed[0]
data, seg = resample_patient(data, seg, np.array(original_spacing_transposed), target_spacing, 3, 1,
force_separate_z=force_separate_z, order_z_data=0, order_z_seg=0,
separate_z_anisotropy_threshold=self.resample_separate_z_anisotropy_threshold)
after = {
'spacing': target_spacing,
'data.shape (data is resampled)': data.shape
}
print("before:", before, "\nafter: ", after, "\n")
if seg is not None: # hippocampus 243 has one voxel with -2 as label. wtf?
seg[seg < -1] = 0
properties["size_after_resampling"] = data[0].shape
properties["spacing_after_resampling"] = target_spacing
use_nonzero_mask = self.use_nonzero_mask
assert len(self.normalization_scheme_per_modality) == len(data), "self.normalization_scheme_per_modality " \
"must have as many entries as data has " \
"modalities"
assert len(self.use_nonzero_mask) == len(data), "self.use_nonzero_mask must have as many entries as data" \
" has modalities"
print("normalization...")
############ HERE IS WHERE WE START CHANGING THINGS!!!!!!!################
# this is where the normalization takes place. We ignore use_nonzero_mask and normalization_scheme_per_modality
for c in range(len(data)):
data[c] = data[c].astype(np.float32) / 255.
return data, seg, properties |