def datasources(self, iterator, cached):
"""
Returns the data sources that load data.
{
'image:' CachedImageDataSource that loads the image files.
'labels:' CachedImageDataSource that loads the groundtruth labels.
'landmarks:' LandmarkDataSource that loads the landmark coordinates.
}
:param iterator: The dataset iterator.
:param cached: If true, use CachedImageDataSource, else ImageDataSource.
:return: A dict of data sources.
"""
datasources_dict = {}
image_data_source = CachedImageDataSource if cached else ImageDataSource
datasources_dict['image'] = image_data_source(
self.image_base_folder,
'',
'',
'.nii.gz',
set_zero_origin=False,
set_identity_direction=False,
set_identity_spacing=False,
sitk_pixel_type=sitk.sitkInt16,
preprocessing=self.preprocessing,
name='image',
parents=[iterator])
if self.generate_landmark_mask:
datasources_dict['landmark_mask'] = LambdaNode(
self.landmark_mask_preprocessing,
name='image',
parents=[datasources_dict['image']])
if self.generate_labels or self.generate_single_vertebrae:
datasources_dict['labels'] = image_data_source(
self.image_base_folder,
'',
'_seg',
'.nii.gz',
set_zero_origin=False,
set_identity_direction=False,
set_identity_spacing=False,
sitk_pixel_type=sitk.sitkUInt8,
name='labels',
parents=[iterator])
if self.generate_landmarks or self.generate_heatmaps or self.generate_spine_heatmap or self.generate_single_vertebrae or self.generate_single_vertebrae_heatmap or (
self.translate_to_center_landmarks
and not self.load_spine_landmarks):
datasources_dict['landmarks'] = LandmarkDataSource(
self.landmarks_file,
self.num_landmarks,
self.dim,
name='landmarks',
parents=[iterator])
if self.load_spine_landmarks:
datasources_dict['spine_landmarks'] = LandmarkDataSource(
self.spine_landmarks_file,
1,
self.dim,
name='spine_landmarks',
parents=[iterator])
return datasources_dict
def landmark_datasources(self, iterator):
"""
Returns all LandmarkDataSources as a dictionary. The entry 'landmark_data_source' is set to the value set in self.landmark_source.
Other entries: 'junior_landmarks_datasource',
'senior_landmarks_datasource',
'challenge_landmarks_datasource',
'mean_landmarks_datasource',
'random_landmarks_datasource'.
:param iterator: The iterator that is used as the parent node.
:return: Dictionary of all LandmarkDataSources.
"""
senior_landmarks_datasource = LandmarkDataSource(
self.senior_point_list_file_name,
self.num_landmarks,
self.dim,
parents=[iterator],
name='senior_landmarks_datasource')
junior_landmarks_datasource = LandmarkDataSource(
self.junior_point_list_file_name,
self.num_landmarks,
self.dim,
parents=[iterator],
name='junior_landmarks_datasource')
challenge_landmarks_datasource = LandmarkDataSource(
self.challenge_point_list_file_name,
self.num_landmarks,
self.dim,
parents=[iterator],
name='challenge_landmarks_datasource')
mean_landmarks_datasource = LambdaNode(
lambda junior, senior: get_mean_landmark_list(junior, senior),
parents=[junior_landmarks_datasource, senior_landmarks_datasource],
name='mean_landmarks_datasource')
random_landmarks_datasource = LambdaNode(
lambda junior, senior: senior if bool_bernoulli(0.5) else junior,
parents=[junior_landmarks_datasource, senior_landmarks_datasource],
name='random_landmarks_datasource')
if self.landmark_source == 'senior':
landmark_datasource = senior_landmarks_datasource
elif self.landmark_source == 'junior':
landmark_datasource = junior_landmarks_datasource
elif self.landmark_source == 'challenge':
landmark_datasource = challenge_landmarks_datasource
elif self.landmark_source == 'mean':
landmark_datasource = mean_landmarks_datasource
elif self.landmark_source == 'random':
landmark_datasource = random_landmarks_datasource
return {
'junior_landmarks_datasource': junior_landmarks_datasource,
'senior_landmarks_datasource': senior_landmarks_datasource,
'challenge_landmarks_datasource': challenge_landmarks_datasource,
'mean_landmarks_datasource': mean_landmarks_datasource,
'random_landmarks_datasource': random_landmarks_datasource,
'landmark_datasource': landmark_datasource
}
def data_sources(self, cached, image_extension='.mha'):
"""
Returns the data sources that load data.
{
'image_datasource:' ImageDataSource that loads the image files.
'landmarks_datasource:' LandmarkDataSource that loads the landmark coordinates.
}
:param cached: If true, use a CachedImageDataSource instead of an ImageDataSource.
:param image_extension: The image extension of the input data.
:return: A dict of data sources.
"""
if cached:
image_datasource = CachedImageDataSource(
self.image_base_folder,
'',
'',
image_extension,
preprocessing=reduce_dimension,
set_identity_spacing=True,
cache_maxsize=16384)
else:
image_datasource = ImageDataSource(self.image_base_folder,
'',
'',
image_extension,
preprocessing=reduce_dimension,
set_identity_spacing=True)
landmarks_datasource = LandmarkDataSource(self.point_list_file_name,
self.num_landmarks, self.dim)
return {
'image_datasource': image_datasource,
'landmarks_datasource': landmarks_datasource
}
def datasources(self, iterator, cached):
"""
Returns the data sources that load data.
{
'image:' CachedImageDataSource that loads the image files.
'labels:' CachedImageDataSource that loads the groundtruth labels.
'landmarks:' LandmarkDataSource that loads the landmark coordinates.
}
:param iterator: The dataset iterator.
:param cached: If true, use CachedImageDataSource, else ImageDataSource.
:return: A dict of data sources.
"""
datasources_dict = {}
image_data_source = CachedImageDataSource if cached else ImageDataSource
datasources_dict['image'] = image_data_source(
self.image_base_folder,
'',
'_image',
'.mha',
set_zero_origin=True,
set_identity_direction=True,
set_identity_spacing=False,
sitk_pixel_type=sitk.sitkInt16,
preprocessing=self.preprocessing,
name='image',
parents=[iterator])
datasources_dict['landmarks'] = LandmarkDataSource(self.landmarks_file,
1,
self.dim,
name='landmarks',
parents=[iterator])
return datasources_dict
def datasources(self):
"""
Returns the data sources that load data.
{
'image:' CachedImageDataSource that loads the image files.
'landmarks:' LandmarkDataSource that loads the landmark coordinates.
'mask:' CachedImageDataSource that loads the groundtruth labels.
}
:return: A dict of data sources.
"""
preprocessing = lambda image: gaussian_sitk(image, self.
input_gaussian_sigma)
image_datasource = CachedImageDataSource(self.image_base_folder,
'',
'_image',
'.mha',
preprocessing=preprocessing)
landmark_datasource = LandmarkDataSource(self.landmarks_file, 1,
self.dim)
mask_datasource = CachedImageDataSource(self.image_base_folder,
'',
'_label_sorted',
'.mha',
sitk_pixel_type=sitk.sitkUInt8)
return {
'image': image_datasource,
'landmarks': landmark_datasource,
'mask': mask_datasource
}
def data_sources(self,
iterator,
cached,
use_landmarks=True,
image_extension='.nii.gz'):
"""
Returns the data sources that load data.
{
'image_datasource:' ImageDataSource that loads the image files.
'landmarks_datasource:' LandmarkDataSource that loads the landmark coordinates.
}
:param iterator: The iterator node object.
:param cached: If True, use CachedImageDataSource instead of ImageDataSource.
:param use_landmarks: If True, create a landmarks datasource.
:param image_extension: The extension of the image files.
:return: A dict of data sources.
"""
if self.smoothing_sigma is not None and self.smoothing_sigma > 0:
preprocessing = lambda x: sitk.Cast(
gaussian(x, self.smoothing_sigma), sitk.sitkInt16)
else:
preprocessing = None
if cached:
image_datasource = CachedImageDataSource(
self.image_base_folder,
'',
'',
image_extension,
preprocessing=preprocessing,
cache_maxsize=25000,
name='image_datasource',
parents=[iterator])
else:
image_datasource = ImageDataSource(self.image_base_folder,
'',
'',
image_extension,
preprocessing=preprocessing,
name='image_datasource',
parents=[iterator])
data_sources_dict = {}
data_sources_dict['image_datasource'] = image_datasource
if use_landmarks:
landmark_datasource = LandmarkDataSource(
self.point_list_file_name,
self.num_landmarks,
self.dim,
name='landmarks_datasource',
parents=[iterator])
data_sources_dict['landmarks_datasource'] = landmark_datasource
return data_sources_dict
def datasources(self, iterator, image_cached, labels_cached, image_preprocessing, cache_size):
"""
Returns the data sources that load data.
{
'image:' CachedImageDataSource that loads the image files.
'labels:' CachedImageDataSource that loads the groundtruth labels.
'landmarks:' LandmarkDataSource that loads the landmark coordinates.
}
:param iterator: The dataset iterator.
:param cached: If true, use CachedImageDataSource, else ImageDataSource.
:return: A dict of data sources.
"""
datasources_dict = {}
if image_cached:
image_data_source = CachedImageDataSource
image_source_kwargs = {'cache_maxsize': cache_size}
else:
image_data_source = ImageDataSource
image_source_kwargs = {}
datasources_dict['image'] = image_data_source(self.image_base_folder,
'',
'',
'.nii.gz',
set_zero_origin=False,
set_identity_direction=False,
set_identity_spacing=False,
sitk_pixel_type=sitk.sitkInt16,
preprocessing=image_preprocessing,
name='image',
parents=[iterator],
**image_source_kwargs)
if self.generate_landmark_mask:
datasources_dict['landmark_mask'] = LambdaNode(self.landmark_mask_preprocessing,
name='landmark_mask',
parents=[datasources_dict['image']])
if self.generate_labels or self.generate_single_vertebrae:
if labels_cached:
image_data_source = CachedImageDataSource
image_source_kwargs = {'cache_maxsize': cache_size}
else:
image_data_source = ImageDataSource
image_source_kwargs = {}
datasources_dict['labels'] = image_data_source(self.image_base_folder,
'',
'_seg',
'.nii.gz',
set_zero_origin=False,
set_identity_direction=False,
set_identity_spacing=False,
sitk_pixel_type=sitk.sitkUInt8,
name='labels',
parents=[iterator],
**image_source_kwargs)
if self.generate_landmarks or self.generate_heatmaps or self.generate_spine_heatmap or self.generate_single_vertebrae or self.generate_single_vertebrae_heatmap or (self.translate_to_center_landmarks and not (self.load_spine_landmarks or self.load_spine_bbs)):
datasources_dict['landmarks'] = LandmarkDataSource(self.landmarks_file,
self.num_landmarks,
self.dim,
name='landmarks',
parents=[iterator])
datasources_dict['landmarks_bb'] = LambdaNode(self.image_landmark_bounding_box, name='landmarks_bb', parents=[datasources_dict['image'], datasources_dict['landmarks']])
datasources_dict['landmarks_bb_start'] = LambdaNode(lambda x: x[0], name='landmarks_bb_start', parents=[datasources_dict['landmarks_bb']])
datasources_dict['landmarks_bb_extent'] = LambdaNode(lambda x: x[1], name='landmarks_bb_extent', parents=[datasources_dict['landmarks_bb']])
if self.load_spine_landmarks:
datasources_dict['spine_landmarks'] = LandmarkDataSource(self.spine_landmarks_file, 1, self.dim, name='spine_landmarks', parents=[iterator])
if self.load_spine_bbs:
datasources_dict['spine_bb'] = LabelDatasource(self.spine_bbs_file, name='spine_landmarks', parents=[iterator])
datasources_dict['landmarks_bb'] = LambdaNode(self.image_bounding_box, name='landmarks_bb', parents=[datasources_dict['image'], datasources_dict['spine_bb']])
datasources_dict['landmarks_bb_start'] = LambdaNode(lambda x: x[0], name='landmarks_bb_start', parents=[datasources_dict['landmarks_bb']])
datasources_dict['landmarks_bb_extent'] = LambdaNode(lambda x: x[1], name='landmarks_bb_extent', parents=[datasources_dict['landmarks_bb']])
return datasources_dict