def test_operations(self):
reader = ImageReader(['image'])
reader.initialise(SINGLE_MOD_DATA, SINGLE_MOD_TASK, single_mod_list)
idx, data, interp_order = reader()
self.assertEqual(
SINGLE_MOD_DATA['lesion'].interp_order, interp_order['image'][0])
self.assertAllClose(data['image'].shape, (256, 168, 256, 1, 1))
def initialise_dataset_loader(self, data_param=None, task_param=None):
self.data_param = data_param
self.gan_param = task_param
# read each line of csv files into an instance of Subject
if self.is_training:
self.reader = ImageReader(['image', 'conditioning'])
else: # in the inference process use image input only
self.reader = ImageReader(['conditioning'])
if self.reader:
self.reader.initialise_reader(data_param, task_param)
if self.net_param.normalise_foreground_only:
foreground_masking_layer = BinaryMaskingLayer(
type_str=self.net_param.foreground_type,
multimod_fusion=self.net_param.multimod_foreground_type,
threshold=0.0)
else:
foreground_masking_layer = None
mean_var_normaliser = MeanVarNormalisationLayer(
image_name='image', binary_masking_func=foreground_masking_layer)
if self.net_param.histogram_ref_file:
histogram_normaliser = HistogramNormalisationLayer(
image_name='image',
modalities=vars(task_param).get('image'),
model_filename=self.net_param.histogram_ref_file,
binary_masking_func=foreground_masking_layer,
norm_type=self.net_param.norm_type,
cutoff=self.net_param.cutoff,
name='hist_norm_layer')
else:
histogram_normaliser = None
normalisation_layers = []
if self.net_param.normalisation:
normalisation_layers.append(histogram_normaliser)
if self.net_param.whitening:
normalisation_layers.append(mean_var_normaliser)
augmentation_layers = []
if self.is_training:
if self.action_param.random_flipping_axes != -1:
augmentation_layers.append(
RandomFlipLayer(
flip_axes=self.action_param.random_flipping_axes))
if self.action_param.scaling_percentage:
augmentation_layers.append(
RandomSpatialScalingLayer(
min_percentage=self.action_param.scaling_percentage[0],
max_percentage=self.action_param.scaling_percentage[1])
)
if self.action_param.rotation_angle:
augmentation_layers.append(RandomRotationLayer())
augmentation_layers[-1].init_uniform_angle(
self.action_param.rotation_angle)
if self.reader:
self.reader.add_preprocessing_layers(normalisation_layers +
augmentation_layers)
def get_3d_reader():
multi_mod_list = data_partitioner.initialise(
MULTI_MOD_DATA).get_file_list()
print(MULTI_MOD_DATA, MULTI_MOD_TASK)
reader = ImageReader(['image', 'label'])
reader.initialise(MULTI_MOD_DATA, MULTI_MOD_TASK, multi_mod_list)
return reader
def test_operations(self):
reader = ImageReader(['image'])
reader.initialise(SINGLE_MOD_DATA, SINGLE_MOD_TASK, single_mod_list)
idx, data, interp_order = reader()
self.assertEqual(SINGLE_MOD_DATA['lesion'].interp_order,
interp_order['image'][0])
self.assertAllClose(data['image'].shape, (256, 168, 256, 1, 1))
def initialise_dataset_loader(
self, data_param=None, task_param=None, data_partitioner=None):
self.data_param = data_param
self.autoencoder_param = task_param
if not self.is_training:
self._infer_type = look_up_operations(
self.autoencoder_param.inference_type, SUPPORTED_INFERENCE)
else:
self._infer_type = None
file_lists = self.get_file_lists(data_partitioner)
# read each line of csv files into an instance of Subject
if self.is_evaluation:
NotImplementedError('Evaluation is not yet '
'supported in this application.')
if self.is_training:
self.readers = []
for file_list in file_lists:
reader = ImageReader(['image'])
reader.initialise(data_param, task_param, file_list)
self.readers.append(reader)
if self._infer_type in ('encode', 'encode-decode'):
self.readers = [ImageReader(['image'])]
self.readers[0].initialise(data_param,
task_param,
file_lists[0])
elif self._infer_type == 'sample':
self.readers = []
elif self._infer_type == 'linear_interpolation':
self.readers = [ImageReader(['feature'])]
self.readers[0].initialise(data_param,
task_param,
[file_lists])
def get_reader(data_param, image_sets_partitioner, phase):
# Using Nifty Reader
if phase == 'training':
image_reader = ImageReader().initialise(
data_param, file_list=image_sets_partitioner.get_file_list(TRAIN))
elif phase == 'validation':
image_reader = ImageReader().initialise(
data_param, file_list=image_sets_partitioner.get_file_list(VALID))
elif phase == 'inference':
image_reader = ImageReader().initialise(
data_param, file_list=image_sets_partitioner.get_file_list(INFER))
else:
raise Exception('Invalid phase choice: {}'.format(
{'phase': ['train', 'validation', 'inference']}))
# Adding preprocessing layers
mean_variance_norm_layer = MeanVarNormalisationLayer(image_name='image')
pad_layer = PadLayer(image_name=('image', 'label'), border=(8, 8, 8))
image_reader.add_preprocessing_layers([mean_variance_norm_layer])
if phase == 'inference':
image_reader.add_preprocessing_layers([pad_layer])
return image_reader
def get_reader(data_param, grouping_param, image_sets_partitioner, phase):
# Using Nifty Reader
if phase == 'training':
image_reader = ImageReader().initialise(
data_param,
grouping_param,
file_list=image_sets_partitioner.get_file_list(TRAIN))
elif phase == 'validation':
image_reader = ImageReader().initialise(
data_param,
grouping_param,
file_list=image_sets_partitioner.get_file_list(VALID))
elif phase == 'inference':
# TODO: need to improve that
if data_param['mask']:
del data_param['mask']
if grouping_param['sampler']:
del grouping_param['sampler']
image_reader = ImageReader().initialise(
data_param,
grouping_param,
file_list=image_sets_partitioner.get_file_list(INFER))
else:
raise Exception('Invalid phase choice: {}'.format(
{'phase': ['training', 'validation', 'inference']}))
return image_reader
def get_2d_reader():
'''
define the 2d reader
:return: 2d reader
'''
reader = ImageReader(['image'])
reader.initialise(MOD_2D_DATA, MOD_2D_TASK, mod_2d_list)
return reader
def get_25d_reader():
'''
define the 2.5 d reader
:return:
'''
reader = ImageReader(['image'])
reader.initialise(SINGLE_25D_DATA, SINGLE_25D_TASK, single_25d_list)
return reader
def get_3d_reader():
'''
define the 3d reader
:return: 3d reader
'''
reader = ImageReader(['image'])
reader.initialise(MULTI_MOD_DATA, MULTI_MOD_TASK, multi_mod_list)
return reader
def initialise_dataset_loader(
self, data_param=None, task_param=None, data_partitioner=None):
self.data_param = data_param
self.segmentation_param = task_param
# read each line of csv files into an instance of Subject
if self.is_training:
file_lists = []
if self.action_param.validation_every_n > 0:
file_lists.append(data_partitioner.train_files)
file_lists.append(data_partitioner.validation_files)
else:
file_lists.append(data_partitioner.train_files)
self.readers = []
for file_list in file_lists:
reader = ImageReader(SUPPORTED_INPUT)
reader.initialise(data_param, task_param, file_list)
self.readers.append(reader)
else: # in the inference process use image input only
inference_reader = ImageReader(['image'])
file_list = data_partitioner.inference_files
inference_reader.initialise(data_param, task_param, file_list)
self.readers = [inference_reader]
foreground_masking_layer = None
if self.net_param.normalise_foreground_only:
foreground_masking_layer = BinaryMaskingLayer(
type_str=self.net_param.foreground_type,
multimod_fusion=self.net_param.multimod_foreground_type,
threshold=0.0)
normalisation_layers = []
if self.net_param.whitening:
mean_var_normaliser = MeanVarNormalisationLayer(
image_name='image',
binary_masking_func=foreground_masking_layer)
normalisation_layers.append(mean_var_normaliser)
if task_param.label_normalisation and \
(self.is_training or not task_param.output_prob):
label_normaliser = DiscreteLabelNormalisationLayer(
image_name='label',
modalities=vars(task_param).get('label'),
model_filename=self.net_param.histogram_ref_file)
normalisation_layers.append(label_normaliser)
volume_padding_layer = []
if self.net_param.volume_padding_size:
volume_padding_layer.append(PadLayer(
image_name=SUPPORTED_INPUT,
border=self.net_param.volume_padding_size))
for reader in self.readers:
reader.add_preprocessing_layers(
normalisation_layers + volume_padding_layer)
def test_properties(self):
reader = ImageReader(['image'])
reader.initialise(SINGLE_MOD_DATA, SINGLE_MOD_TASK, single_mod_list)
self.assertEqual(len(reader.output_list), 4)
self.assertDictEqual(reader.shapes, {'image': (256, 168, 256, 1, 1)})
self.assertDictEqual(reader.tf_dtypes, {'image': tf.float32})
self.assertEqual(reader.names, ['image'])
self.assertDictEqual(reader.input_sources, {'image': ('lesion', )})
self.assertEqual(reader.get_subject_id(1)[:4], 'Fin_')
def test_trainable_preprocessing(self):
label_file = os.path.join('testing_data', 'label_reader.txt')
if os.path.exists(label_file):
os.remove(label_file)
label_normaliser = DiscreteLabelNormalisationLayer(
image_name='label',
modalities=vars(LABEL_TASK).get('label'),
model_filename=os.path.join('testing_data', 'label_reader.txt'))
reader = ImageReader(['label'])
with self.assertRaisesRegexp(AssertionError, ''):
reader.add_preprocessing_layers(label_normaliser)
reader.initialise(LABEL_DATA, LABEL_TASK, label_list)
reader.add_preprocessing_layers(label_normaliser)
reader.add_preprocessing_layers(
[PadLayer(image_name=['label'], border=(10, 5, 5))])
idx, data, interp_order = reader(idx=0)
unique_data = np.unique(data['label'])
expected_v1 = np.array(
[0., 1., 2., 3., 4., 5., 6., 7., 8.,
9., 10., 11., 12., 13., 14., 15., 16., 17.,
18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,
28., 29., 30., 31., 32., 33., 34., 35., 36.,
37., 38., 39., 40., 41., 42., 43., 44., 45.,
46., 47., 48., 49., 50., 51., 52., 53., 54.,
55., 56., 57., 58., 59., 60., 61., 62., 63.,
64., 65., 66., 67., 68., 69., 70., 71., 72.,
73., 74., 75., 76., 77., 78., 79., 80., 81.,
82., 83., 84., 85., 86., 87., 88., 89., 90.,
91., 92., 93., 94., 95., 96., 97., 98., 99.,
100., 101., 102., 103., 104., 105., 106., 107., 108.,
109., 110., 111., 112., 113., 114., 115., 116., 117.,
118., 119., 120., 121., 122., 123., 124., 125., 126.,
127., 128., 129., 130., 131., 132., 133., 134., 135.,
136., 137., 138., 139., 140., 141., 142., 143., 144.,
145., 146., 147., 148., 149., 150., 151., 152., 153.,
154., 155., 156., 157.], dtype=np.float32)
expected_v2 = np.array(
[0., 1., 2., 3., 4., 5., 6., 7., 8.,
9., 10., 11., 12., 13., 14., 15., 16., 17.,
18., 20., 21., 22., 23., 24., 25., 26., 27.,
28., 29., 30., 31., 32., 33., 34., 35., 36.,
37., 38., 39., 40., 41., 42., 43., 44., 45.,
46., 47., 48., 49., 50., 51., 52., 53., 54.,
55., 56., 57., 58., 59., 60., 61., 62., 63.,
64., 65., 66., 67., 68., 69., 70., 71., 72.,
73., 74., 75., 76., 77., 78., 79., 80., 81.,
82., 83., 84., 85., 86., 87., 88., 89., 90.,
91., 92., 93., 94., 95., 96., 97., 98., 99.,
100., 101., 102., 103., 104., 105., 106., 107., 108.,
109., 110., 111., 112., 113., 114., 115., 116., 117.,
118., 119., 120., 121., 122., 123., 124., 125., 126.,
127., 128., 129., 130., 131., 132., 133., 134., 135.,
136., 137., 138., 139., 140., 141., 142., 143., 144.,
145., 146., 147., 148., 149., 150., 151., 152., 153.,
154., 155., 156., 157.], dtype=np.float32)
compatible_assert = \
np.all(unique_data == expected_v1) or \
np.all(unique_data == expected_v2)
self.assertTrue(compatible_assert)
self.assertAllClose(data['label'].shape, (103, 74, 93, 1, 1))
def test_trainable_preprocessing(self):
label_file = os.path.join('testing_data', 'label_reader.txt')
if os.path.exists(label_file):
os.remove(label_file)
label_normaliser = DiscreteLabelNormalisationLayer(
image_name='label',
modalities=vars(LABEL_TASK).get('label'),
model_filename=os.path.join('testing_data', 'label_reader.txt'))
reader = ImageReader(['label'])
with self.assertRaisesRegexp(AssertionError, ''):
reader.add_preprocessing_layers(label_normaliser)
reader.initialise(LABEL_DATA, LABEL_TASK, label_list)
reader.add_preprocessing_layers(label_normaliser)
reader.add_preprocessing_layers(
[PadLayer(image_name=['label'], border=(10, 5, 5))])
idx, data, interp_order = reader(idx=0)
unique_data = np.unique(data['label'])
expected_v1 = np.array([
0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14.,
15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,
28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40.,
41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53.,
54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66.,
67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,
80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92.,
93., 94., 95., 96., 97., 98., 99., 100., 101., 102., 103., 104.,
105., 106., 107., 108., 109., 110., 111., 112., 113., 114., 115.,
116., 117., 118., 119., 120., 121., 122., 123., 124., 125., 126.,
127., 128., 129., 130., 131., 132., 133., 134., 135., 136., 137.,
138., 139., 140., 141., 142., 143., 144., 145., 146., 147., 148.,
149., 150., 151., 152., 153., 154., 155., 156., 157.
],
dtype=np.float32)
expected_v2 = np.array([
0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14.,
15., 16., 17., 18., 20., 21., 22., 23., 24., 25., 26., 27., 28.,
29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41.,
42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54.,
55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67.,
68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80.,
81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93.,
94., 95., 96., 97., 98., 99., 100., 101., 102., 103., 104., 105.,
106., 107., 108., 109., 110., 111., 112., 113., 114., 115., 116.,
117., 118., 119., 120., 121., 122., 123., 124., 125., 126., 127.,
128., 129., 130., 131., 132., 133., 134., 135., 136., 137., 138.,
139., 140., 141., 142., 143., 144., 145., 146., 147., 148., 149.,
150., 151., 152., 153., 154., 155., 156., 157.
],
dtype=np.float32)
compatible_assert = \
np.all(unique_data == expected_v1) or \
np.all(unique_data == expected_v2)
self.assertTrue(compatible_assert)
self.assertAllClose(data['label'].shape, (103, 74, 93, 1, 1))
def test_properties(self):
reader = ImageReader(['image'])
reader.initialise(SINGLE_MOD_DATA, SINGLE_MOD_TASK, single_mod_list)
self.assertEqual(len(reader.output_list), 4)
self.assertDictEqual(reader.shapes,
{'image': (256, 168, 256, 1, 1)})
self.assertDictEqual(reader.tf_dtypes, {'image': tf.float32})
self.assertEqual(reader.names, ['image'])
self.assertDictEqual(reader.input_sources,
{'image': ('lesion',)})
self.assertEqual(reader.get_subject_id(1)[:4], 'Fin_')
def initialise_dataset_loader(self, data_param=None, task_param=None):
self.data_param = data_param
self.regression_param = task_param
# read each line of csv files into an instance of Subject
if self.is_training:
self.reader = ImageReader(SUPPORTED_INPUT)
else: # in the inference process use image input only
self.reader = ImageReader(['image'])
self.reader.initialise_reader(data_param, task_param)
mean_var_normaliser = MeanVarNormalisationLayer(image_name='image')
if self.net_param.histogram_ref_file:
histogram_normaliser = HistogramNormalisationLayer(
image_name='image',
modalities=vars(task_param).get('image'),
model_filename=self.net_param.histogram_ref_file,
norm_type=self.net_param.norm_type,
cutoff=self.net_param.cutoff,
name='hist_norm_layer')
else:
histogram_normaliser = None
normalisation_layers = []
if self.net_param.normalisation:
normalisation_layers.append(histogram_normaliser)
if self.net_param.whitening:
normalisation_layers.append(mean_var_normaliser)
augmentation_layers = []
if self.is_training:
if self.action_param.random_flipping_axes != -1:
augmentation_layers.append(
RandomFlipLayer(
flip_axes=self.action_param.random_flipping_axes))
if self.action_param.scaling_percentage:
augmentation_layers.append(
RandomSpatialScalingLayer(
min_percentage=self.action_param.scaling_percentage[0],
max_percentage=self.action_param.scaling_percentage[1])
)
if self.action_param.rotation_angle:
augmentation_layers.append(RandomRotationLayer())
augmentation_layers[-1].init_uniform_angle(
self.action_param.rotation_angle)
volume_padding_layer = []
if self.net_param.volume_padding_size:
volume_padding_layer.append(
PadLayer(image_name=SUPPORTED_INPUT,
border=self.net_param.volume_padding_size))
self.reader.add_preprocessing_layers(volume_padding_layer +
normalisation_layers +
augmentation_layers)
def test_preprocessing_zero_padding(self):
reader = ImageReader(['image'])
reader.initialise(SINGLE_MOD_DATA, SINGLE_MOD_TASK, single_mod_list)
idx, data, interp_order = reader()
self.assertEqual(SINGLE_MOD_DATA['lesion'].interp_order,
interp_order['image'][0])
self.assertAllClose(data['image'].shape, (256, 168, 256, 1, 1))
reader.add_preprocessing_layers(
[PadLayer(image_name=['image'], border=(0, 0, 0))])
idx, data, interp_order = reader(idx=2)
self.assertEqual(idx, 2)
self.assertAllClose(data['image'].shape, (256, 168, 256, 1, 1))
def initialise_dataset_loader(self,
data_param=None,
task_param=None,
data_partitioner=None):
self.data_param = data_param
self.registration_param = task_param
if self.is_evaluation:
NotImplementedError('Evaluation is not yet '
'supported in this application.')
try:
reader_phase = self.action_param.dataset_to_infer
except AttributeError:
reader_phase = None
file_lists = data_partitioner.get_file_lists_by(phase=reader_phase,
action=self.action)
self.readers = []
for file_list in file_lists:
fixed_reader = ImageReader({'fixed_image', 'fixed_label'})
fixed_reader.initialise(data_param, task_param, file_list)
self.readers.append(fixed_reader)
moving_reader = ImageReader({'moving_image', 'moving_label'})
moving_reader.initialise(data_param, task_param, file_list)
self.readers.append(moving_reader)
def test_existing_csv(self):
reader_for_csv = ImageReader(['image'])
reader_for_csv.initialise(SINGLE_MOD_DATA, SINGLE_MOD_TASK,
single_mod_list)
reader = ImageReader(['image'])
reader.initialise(EXISTING_DATA, SINGLE_MOD_TASK, existing_list)
self.assertEqual(len(reader.output_list), 4)
self.assertDictEqual(reader.spatial_ranks, {'image': 3})
self.assertDictEqual(reader.shapes, {'image': (256, 168, 256, 1, 1)})
self.assertDictEqual(reader.tf_dtypes, {'image': tf.float32})
self.assertEqual(reader.names, ('image', ))
self.assertDictEqual(reader.input_sources, {'image': ('lesion', )})
self.assertEqual(reader.get_subject_id(1)[:4], 'Fin_')
self.assertTrue(isinstance(reader.get_subject(1), dict))
def test_existing_csv(self):
reader_for_csv = ImageReader(['image'])
reader_for_csv.initialise_reader(SINGLE_MOD_DATA, SINGLE_MOD_TASK)
reader = ImageReader(['image'])
reader.initialise_reader(EXISTING_DATA, SINGLE_MOD_TASK)
self.assertEquals(len(reader.output_list), 4)
self.assertDictEqual(reader.shapes, {'image': (256, 168, 256, 1, 1)})
self.assertDictEqual(reader.tf_dtypes, {'image': tf.float32})
self.assertEqual(reader.names, ['image'])
self.assertDictEqual(reader.input_sources, {'image': ('lesion', )})
self.assertEqual(reader.get_subject_id(1)[:4], 'Fin_')
def initialise_dataset_loader(self,
data_param=None,
task_param=None,
data_partitioner=None):
self.data_param = data_param
self.multioutput_param = task_param
# initialise input image readers
if self.is_training:
reader_names = ('image', 'label', 'weight', 'sampler')
elif self.is_inference:
# in the inference process use `image` input only
reader_names = ('image', )
elif self.is_evaluation:
reader_names = ('image', 'label', 'inferred')
else:
tf.logging.fatal('Action `%s` not supported. Expected one of %s',
self.action, self.SUPPORTED_PHASES)
raise ValueError
try:
reader_phase = self.action_param.dataset_to_infer
except AttributeError:
reader_phase = None
file_lists = data_partitioner.get_file_lists_by(phase=reader_phase,
action=self.action)
self.readers = [
ImageReader(reader_names).initialise(data_param, task_param,
file_list)
for file_list in file_lists
]
def test_reader_field(self):
data_param = {'mr': {'path_to_search': IMAGE_PATH_2D}}
group_param = {'ct': ('mr', )}
reader = ImageReader(['ct']).initialise(data_param, group_param)
self.renamed_property_asserts(reader)
idx, data, interp = reader()
# test output
self.assertTrue('ct' in data)
self.assertTrue(idx in range(len(reader.output_list)))
self.assertDictEqual(interp, {'ct': (1, )})
self.assertEqual(data['ct'].shape, (100, 100, 1, 1, 1))
with self.assertRaisesRegexp(ValueError, ''):
# grouping name 'ct' but
reader = ImageReader(['mr']).initialise(data_param, group_param)
def test_3d_concat_properties(self):
"""
loading two modalities, grouping subject names only
"""
data_param = {
'mr': {
'path_to_search': IMAGE_PATH_3D_1,
'filename_contains': 'x_y_z_1_1',
'pixdim': (4, 3, 4),
'axcodes': 'RAS'
},
'ct': {
'path_to_search': IMAGE_PATH_3D_1,
'filename_contains': 'x_y_z_1_1',
'pixdim': (4, 3, 4),
'axcodes': 'RAS'
}
}
grouping_param = {'image': ('mr', 'ct')}
reader = ImageReader().initialise(data_param, grouping_param)
self.assertDictEqual(reader.spatial_ranks, {'image': 3})
self.assertEqual(reader.output_list[0]['image'].output_pixdim,
((4.0, 3.0, 4.0), ) * 2)
self.assertEqual(reader.output_list[0]['image'].output_axcodes,
(('R', 'A', 'S'), ) * 2)
idx, data, interp = reader()
# test output
self.assertTrue('image' in data)
self.assertTrue(idx in range(len(reader.output_list)))
self.assertDictEqual(interp, {'image': (1, 1)})
# allows rounding error spatially
self.assertAllClose(data['image'].shape[:3], (12, 8, 10), atol=1)
self.assertAllClose(data['image'].shape[3:], (1, 2))
def preprocess(
input_path,
model_path,
output_path,
cutoff,
):
input_path = Path(input_path)
output_path = Path(output_path)
input_dir = input_path.parent
DATA_PARAM = {
'Modality0':
ParserNamespace(
path_to_search=str(input_dir),
filename_contains=('nii.gz', ),
interp_order=0,
pixdim=None,
axcodes='RAS',
loader=None,
)
}
TASK_PARAM = ParserNamespace(image=('Modality0', ))
data_partitioner = ImageSetsPartitioner()
file_list = data_partitioner.initialise(DATA_PARAM).get_file_list()
reader = ImageReader(['image'])
reader.initialise(DATA_PARAM, TASK_PARAM, file_list)
binary_masking_func = BinaryMaskingLayer(type_str='mean_plus', )
hist_norm = HistogramNormalisationLayer(
image_name='image',
modalities=['Modality0'],
model_filename=str(model_path),
binary_masking_func=binary_masking_func,
cutoff=cutoff,
name='hist_norm_layer',
)
image = reader.output_list[0]['image']
data = image.get_data()
norm_image_dict, mask_dict = hist_norm({'image': data})
data = norm_image_dict['image']
nii = nib.Nifti1Image(data.squeeze(), image.original_affine[0])
dst = output_path
nii.to_filename(str(dst))
def test_2d_as_5D_multimodal_properties(self):
data_param = {'mr': {'path_to_search': IMAGE_PATH_2D,
'filename_contains': '_u',
'pixdim': (2, 2, 2),
'axcodes': 'RAS'}}
grouping_param = {'ct': ('mr', 'mr', 'mr')}
reader = ImageReader().initialise(data_param, grouping_param)
self.assertEqual(reader.spatial_ranks, {'ct': 2})
def get_3d_reader():
data_param = {
'mr': {
'path_to_search': IMAGE_PATH_3D,
'filename_contains': 'FLAIR',
'interp_order': 1}}
reader = ImageReader().initialise(data_param)
return reader
def test_errors(self):
reader = ImageReader(['image'])
reader.initialise(BAD_DATA, SINGLE_MOD_TASK, bad_data_list)
with self.assertRaisesRegexp(ValueError, ''):
reader = ImageReader(['image'])
reader.initialise(SINGLE_MOD_DATA, BAD_TASK, single_mod_list)
reader = ImageReader(['image'])
reader.initialise(SINGLE_MOD_DATA, SINGLE_MOD_TASK, single_mod_list)
idx, data, interp_order = reader(idx=100)
self.assertEqual(idx, -1)
self.assertEqual(data, None)
idx, data, interp_order = reader(shuffle=True)
self.assertEqual(data['image'].shape, (256, 168, 256, 1, 1))
def get_label_reader():
reader = ImageReader(['label'])
reader.initialise(MOD_LABEL_DATA, MOD_LABEl_TASK, mod_label_list)
label_normaliser = DiscreteLabelNormalisationLayer(
image_name='label',
modalities=vars(SINGLE_25D_TASK).get('label'),
model_filename=os.path.join('testing_data', 'agg_test.txt'))
reader.add_preprocessing_layers(label_normaliser)
pad_layer = PadLayer(image_name=('label',), border=(5, 6, 7))
reader.add_preprocessing_layers([pad_layer])
return reader
def initialise_dataset_loader(
self, data_param=None, task_param=None, data_partitioner=None):
self.data_param = data_param
self.segmentation_param = task_param
# read each line of csv files into an instance of Subject
if self.is_training:
file_lists = []
if self.action_param.validation_every_n > 0:
file_lists.append(data_partitioner.train_files)
file_lists.append(data_partitioner.validation_files)
else:
file_lists.append(data_partitioner.all_files)
self.readers = []
for file_list in file_lists:
reader = ImageReader(SUPPORTED_INPUT)
reader.initialise(data_param, task_param, file_list)
self.readers.append(reader)
else: # in the inference process use image input only
inference_reader = ImageReader(['image'])
file_list = data_partitioner.inference_files
inference_reader.initialise(data_param, task_param, file_list)
self.readers = [inference_reader]
foreground_masking_layer = None
if self.net_param.normalise_foreground_only:
foreground_masking_layer = BinaryMaskingLayer(
type_str=self.net_param.foreground_type,
multimod_fusion=self.net_param.multimod_foreground_type,
threshold=0.0)
mean_var_normaliser = MeanVarNormalisationLayer(
image_name='image', binary_masking_func=foreground_masking_layer)
label_normaliser = DiscreteLabelNormalisationLayer(
image_name='label',
modalities=vars(task_param).get('label'),
model_filename=self.net_param.histogram_ref_file)
normalisation_layers = []
normalisation_layers.append(mean_var_normaliser)
if task_param.label_normalisation:
normalisation_layers.append(label_normaliser)
volume_padding_layer = []
if self.net_param.volume_padding_size:
volume_padding_layer.append(PadLayer(
image_name=SUPPORTED_INPUT,
border=self.net_param.volume_padding_size))
for reader in self.readers:
reader.add_preprocessing_layers(
normalisation_layers + volume_padding_layer)
def test_trainable_preprocessing(self):
label_file = os.path.join('testing_data', 'label_reader.txt')
if os.path.exists(label_file):
os.remove(label_file)
label_normaliser = DiscreteLabelNormalisationLayer(
image_name='label',
modalities=vars(LABEL_TASK).get('label'),
model_filename=os.path.join('testing_data', 'label_reader.txt'))
reader = ImageReader(['label'])
with self.assertRaisesRegexp(AssertionError, ''):
reader.add_preprocessing_layers(label_normaliser)
reader.initialise_reader(LABEL_DATA, LABEL_TASK)
reader.add_preprocessing_layers(label_normaliser)
reader.add_preprocessing_layers(
[PadLayer(image_name=['label'], border=(10, 5, 5))])
idx, data, interp_order = reader(idx=0)
unique_data = np.unique(data['label'])
expected = np.array(range(156), dtype=np.float32)
self.assertAllClose(unique_data, expected)
self.assertAllClose(data['label'].shape, (83, 73, 73, 1, 1))
def initialise_dataset_loader(self,
data_param=None,
task_param=None,
data_partitioner=None):
RegressionApplication.initialise_dataset_loader(
self, data_param, task_param, data_partitioner)
if self.is_training:
return
if not task_param.error_map:
return
file_lists = self.get_file_lists(data_partitioner)
# modifying the original readers in regression application
# as we need ground truth labels to generate error maps
self.readers = []
for file_list in file_lists:
reader = ImageReader(['image', 'output'])
reader.initialise(data_param, task_param, file_list)
self.readers.append(reader)
mean_var_normaliser = MeanVarNormalisationLayer(image_name='image')
histogram_normaliser = None
if self.net_param.histogram_ref_file:
histogram_normaliser = HistogramNormalisationLayer(
image_name='image',
modalities=vars(task_param).get('image'),
model_filename=self.net_param.histogram_ref_file,
norm_type=self.net_param.norm_type,
cutoff=self.net_param.cutoff,
name='hist_norm_layer')
preprocessors = []
if self.net_param.normalisation:
preprocessors.append(histogram_normaliser)
if self.net_param.whitening:
preprocessors.append(mean_var_normaliser)
if self.net_param.volume_padding_size:
preprocessors.append(
PadLayer(image_name=SUPPORTED_INPUT,
border=self.net_param.volume_padding_size))
self.readers[0].add_preprocessing_layers(preprocessors)
def test_simple(self):
data_param = {'mr': {'path_to_search': IMAGE_PATH_2D}}
reader = ImageReader().initialise(data_param)
# test properties
self.default_property_asserts(reader)
idx, data, interp = reader()
# test output
self.assertTrue('mr' in data)
self.assertTrue(idx in range(len(reader.output_list)))
self.assertDictEqual(interp, {'mr': (1, )})
self.assertEqual(data['mr'].shape, (100, 100, 1, 1, 1))
def test_renaming(self):
data_param = {'mr': {'path_to_search': IMAGE_PATH_2D}}
group_param = {'ct': ('mr', )}
reader = ImageReader().initialise(data_param, group_param)
self.renamed_property_asserts(reader)
idx, data, interp = reader()
# test output
self.assertTrue('ct' in data)
self.assertTrue(idx in range(len(reader.output_list)))
self.assertDictEqual(interp, {'ct': (1, )})
self.assertEqual(data['ct'].shape, (100, 100, 1, 1, 1))
def initialise_dataset_loader(self,
data_param=None,
task_param=None,
data_partitioner=None):
self.data_param = data_param
self.autoencoder_param = task_param
if not self.is_training:
self._infer_type = look_up_operations(
self.autoencoder_param.inference_type, SUPPORTED_INFERENCE)
else:
self._infer_type = None
file_lists = self.get_file_lists(data_partitioner)
# read each line of csv files into an instance of Subject
if self.is_evaluation:
NotImplementedError('Evaluation is not yet '
'supported in this application.')
if self.is_training:
self.readers = []
for file_list in file_lists:
reader = ImageReader(['image'])
reader.initialise(data_param, task_param, file_list)
self.readers.append(reader)
if self._infer_type in ('encode', 'encode-decode'):
self.readers = [ImageReader(['image'])]
self.readers[0].initialise(data_param, task_param, file_lists[0])
elif self._infer_type == 'sample':
self.readers = []
elif self._infer_type == 'linear_interpolation':
self.readers = [ImageReader(['feature'])]
self.readers[0].initialise(data_param, task_param, [file_lists])
def get_label_reader():
reader = ImageReader(['label'])
reader.initialise(MOD_LABEL_DATA, MOD_LABEl_TASK, mod_label_list)
label_normaliser = DiscreteLabelNormalisationLayer(
image_name='label',
modalities=vars(SINGLE_25D_TASK).get('label'),
model_filename=os.path.join('testing_data', 'agg_test.txt'))
reader.add_preprocessing_layers(label_normaliser)
pad_layer = PadLayer(image_name=('label',), border=(5, 6, 7))
reader.add_preprocessing_layers([pad_layer])
return reader
def initialise_dataset_loader(
self, data_param=None, task_param=None, data_partitioner=None):
RegressionApplication.initialise_dataset_loader(
self, data_param, task_param, data_partitioner)
if self.is_training:
return
if not task_param.error_map:
return
file_lists = self.get_file_lists(data_partitioner)
# modifying the original readers in regression application
# as we need ground truth labels to generate error maps
self.readers=[]
for file_list in file_lists:
reader = ImageReader(['image', 'output'])
reader.initialise(data_param, task_param, file_list)
self.readers.append(reader)
mean_var_normaliser = MeanVarNormalisationLayer(image_name='image')
histogram_normaliser = None
if self.net_param.histogram_ref_file:
histogram_normaliser = HistogramNormalisationLayer(
image_name='image',
modalities=vars(task_param).get('image'),
model_filename=self.net_param.histogram_ref_file,
norm_type=self.net_param.norm_type,
cutoff=self.net_param.cutoff,
name='hist_norm_layer')
preprocessors = []
if self.net_param.normalisation:
preprocessors.append(histogram_normaliser)
if self.net_param.whitening:
preprocessors.append(mean_var_normaliser)
if self.net_param.volume_padding_size:
preprocessors.append(PadLayer(
image_name=SUPPORTED_INPUT,
border=self.net_param.volume_padding_size))
self.readers[0].add_preprocessing_layers(preprocessors)
def test_existing_csv(self):
reader_for_csv = ImageReader(['image'])
reader_for_csv.initialise(
SINGLE_MOD_DATA, SINGLE_MOD_TASK, single_mod_list)
reader = ImageReader(['image'])
reader.initialise(EXISTING_DATA, SINGLE_MOD_TASK, existing_list)
self.assertEqual(len(reader.output_list), 4)
self.assertDictEqual(reader.spatial_ranks, {'image': 3})
self.assertDictEqual(reader.shapes,
{'image': (256, 168, 256, 1, 1)})
self.assertDictEqual(reader.tf_dtypes, {'image': tf.float32})
self.assertEqual(reader.names, ('image',))
self.assertDictEqual(reader.input_sources,
{'image': ('lesion',)})
self.assertEqual(reader.get_subject_id(1)[:4], 'Fin_')
self.assertTrue(isinstance(reader.get_subject(1), dict))
def initialise_dataset_loader(
self, data_param=None, task_param=None, data_partitioner=None):
self.data_param = data_param
self.autoencoder_param = task_param
if not self.is_training:
self._infer_type = look_up_operations(
self.autoencoder_param.inference_type, SUPPORTED_INFERENCE)
else:
self._infer_type = None
# read each line of csv files into an instance of Subject
if self.is_training:
file_lists = []
if self.action_param.validation_every_n > 0:
file_lists.append(data_partitioner.train_files)
file_lists.append(data_partitioner.validation_files)
else:
file_lists.append(data_partitioner.train_files)
self.readers = []
for file_list in file_lists:
reader = ImageReader(['image'])
reader.initialise(data_param, task_param, file_list)
self.readers.append(reader)
if self._infer_type in ('encode', 'encode-decode'):
self.readers = [ImageReader(['image'])]
self.readers[0].initialise(data_param,
task_param,
data_partitioner.inference_files)
elif self._infer_type == 'sample':
self.readers = []
elif self._infer_type == 'linear_interpolation':
self.readers = [ImageReader(['feature'])]
self.readers[0].initialise(data_param,
task_param,
data_partitioner.inference_files)
def test_no_2d_resampling_properties(self):
data_param = {'mr': {'path_to_search': IMAGE_PATH_2D,
'csv_file': '2d_test.csv',
'pixdim': (2, 2, 2),
'axcodes': 'RAS'}}
reader = ImageReader().initialise(data_param)
self.assertEqual(reader.output_list[0]['mr'].output_pixdim, (None,))
self.assertEqual(reader.output_list[0]['mr'].output_axcodes, (None,))
idx, data, interp = reader()
# test output
self.assertTrue('mr' in data)
self.assertTrue(idx in range(len(reader.output_list)))
self.assertDictEqual(interp, {'mr': (1,)})
self.assertEqual(data['mr'].shape, (100, 100, 1, 1, 1))
def get_first_array(directory: Union[str, Path]) -> np.ndarray:
"""
Use NiftyNet's reader to get RAS images
"""
directory = Path(directory)
image_dir = list(directory.glob('**/*.nii.gz'))[0].parent
input_dict = dict(
path_to_search=str(image_dir),
filename_contains='nii',
axcodes=('R', 'A', 'S'),
)
data_parameters = {
'image': input_dict,
}
reader = ImageReader().initialise(data_parameters)
_, image_dict, _ = reader()
return image_dict['image'].squeeze()
def test_preprocessing_zero_padding(self):
reader = ImageReader(['image'])
reader.initialise(SINGLE_MOD_DATA, SINGLE_MOD_TASK, single_mod_list)
idx, data, interp_order = reader()
self.assertEqual(SINGLE_MOD_DATA['lesion'].interp_order,
interp_order['image'][0])
self.assertAllClose(data['image'].shape, (256, 168, 256, 1, 1))
reader.add_preprocessing_layers(
[PadLayer(image_name=['image'], border=(0, 0, 0))])
idx, data, interp_order = reader(idx=2)
self.assertEqual(idx, 2)
self.assertAllClose(data['image'].shape, (256, 168, 256, 1, 1))
def initialise_dataset_loader(
self, data_param=None, task_param=None, data_partitioner=None):
self.data_param = data_param
self.registration_param = task_param
file_lists = self.get_file_lists(data_partitioner)
if self.is_evaluation:
NotImplementedError('Evaluation is not yet '
'supported in this application.')
self.readers = []
for file_list in file_lists:
fixed_reader = ImageReader({'fixed_image', 'fixed_label'})
fixed_reader.initialise(data_param, task_param, file_list)
self.readers.append(fixed_reader)
moving_reader = ImageReader({'moving_image', 'moving_label'})
moving_reader.initialise(data_param, task_param, file_list)
self.readers.append(moving_reader)
def get_dynamic_window_reader():
reader = ImageReader(['image', 'sampler'])
reader.initialise(DYNAMIC_MOD_DATA, DYNAMIC_MOD_TASK, dynamic_list)
return reader
def get_25d_reader():
reader = ImageReader(['image'])
reader.initialise(SINGLE_25D_DATA, SINGLE_25D_TASK, single_25d_list)
return reader
def get_3d_reader():
reader = ImageReader(['image', 'sampler'])
reader.initialise(MULTI_MOD_DATA, MULTI_MOD_TASK, multi_mod_list)
return reader
def get_2d_reader():
reader = ImageReader(['image', 'sampler'])
reader.initialise(MOD_2D_DATA, MOD_2D_TASK, mod_2d_list)
return reader
def get_dynamic_window_reader():
dynamic_list = data_partitioner.initialise(DYNAMIC_MOD_DATA).get_file_list()
reader = ImageReader(['image', 'label'])
reader.initialise(DYNAMIC_MOD_DATA, DYNAMIC_MOD_TASK, dynamic_list)
return reader
def initialise_dataset_loader(
self, data_param=None, task_param=None, data_partitioner=None):
self.data_param = data_param
self.segmentation_param = task_param
file_lists = self.get_file_lists(data_partitioner)
# read each line of csv files into an instance of Subject
if self.is_training:
self.readers = []
for file_list in file_lists:
reader = ImageReader({'image', 'label', 'weight', 'sampler'})
reader.initialise(data_param, task_param, file_list)
self.readers.append(reader)
elif self.is_inference:
# in the inference process use image input only
inference_reader = ImageReader({'image'})
file_list = data_partitioner.inference_files
inference_reader.initialise(data_param, task_param, file_list)
self.readers = [inference_reader]
elif self.is_evaluation:
file_list = data_partitioner.inference_files
reader = ImageReader({'image', 'label', 'inferred'})
reader.initialise(data_param, task_param, file_list)
self.readers = [reader]
else:
raise ValueError('Action `{}` not supported. Expected one of {}'
.format(self.action, self.SUPPORTED_ACTIONS))
foreground_masking_layer = None
if self.net_param.normalise_foreground_only:
foreground_masking_layer = BinaryMaskingLayer(
type_str=self.net_param.foreground_type,
multimod_fusion=self.net_param.multimod_foreground_type,
threshold=0.0)
mean_var_normaliser = MeanVarNormalisationLayer(
image_name='image', binary_masking_func=foreground_masking_layer)
histogram_normaliser = None
if self.net_param.histogram_ref_file:
histogram_normaliser = HistogramNormalisationLayer(
image_name='image',
modalities=vars(task_param).get('image'),
model_filename=self.net_param.histogram_ref_file,
binary_masking_func=foreground_masking_layer,
norm_type=self.net_param.norm_type,
cutoff=self.net_param.cutoff,
name='hist_norm_layer')
label_normalisers = None
if self.net_param.histogram_ref_file and \
task_param.label_normalisation:
label_normalisers = [DiscreteLabelNormalisationLayer(
image_name='label',
modalities=vars(task_param).get('label'),
model_filename=self.net_param.histogram_ref_file)]
if self.is_evaluation:
label_normalisers.append(
DiscreteLabelNormalisationLayer(
image_name='inferred',
modalities=vars(task_param).get('inferred'),
model_filename=self.net_param.histogram_ref_file))
label_normalisers[-1].key = label_normalisers[0].key
normalisation_layers = []
if self.net_param.normalisation:
normalisation_layers.append(histogram_normaliser)
if self.net_param.whitening:
normalisation_layers.append(mean_var_normaliser)
if task_param.label_normalisation and \
(self.is_training or not task_param.output_prob):
normalisation_layers.extend(label_normalisers)
augmentation_layers = []
if self.is_training:
if self.action_param.random_flipping_axes != -1:
augmentation_layers.append(RandomFlipLayer(
flip_axes=self.action_param.random_flipping_axes))
if self.action_param.scaling_percentage:
augmentation_layers.append(RandomSpatialScalingLayer(
min_percentage=self.action_param.scaling_percentage[0],
max_percentage=self.action_param.scaling_percentage[1]))
if self.action_param.rotation_angle or \
self.action_param.rotation_angle_x or \
self.action_param.rotation_angle_y or \
self.action_param.rotation_angle_z:
rotation_layer = RandomRotationLayer()
if self.action_param.rotation_angle:
rotation_layer.init_uniform_angle(
self.action_param.rotation_angle)
else:
rotation_layer.init_non_uniform_angle(
self.action_param.rotation_angle_x,
self.action_param.rotation_angle_y,
self.action_param.rotation_angle_z)
augmentation_layers.append(rotation_layer)
# add deformation layer
if self.action_param.do_elastic_deformation:
spatial_rank = list(self.readers[0].spatial_ranks.values())[0]
augmentation_layers.append(RandomElasticDeformationLayer(
spatial_rank=spatial_rank,
num_controlpoints=self.action_param.num_ctrl_points,
std_deformation_sigma=self.action_param.deformation_sigma,
proportion_to_augment=self.action_param.proportion_to_deform))
volume_padding_layer = []
if self.net_param.volume_padding_size:
volume_padding_layer.append(PadLayer(
image_name=SUPPORTED_INPUT,
border=self.net_param.volume_padding_size))
# only add augmentation to first reader (not validation reader)
self.readers[0].add_preprocessing_layers(
volume_padding_layer +
normalisation_layers +
augmentation_layers)
for reader in self.readers[1:]:
reader.add_preprocessing_layers(
volume_padding_layer +
normalisation_layers)
def get_3d_reader():
multi_mod_list = data_partitioner.initialise(MULTI_MOD_DATA).get_file_list()
print(MULTI_MOD_DATA, MULTI_MOD_TASK)
reader = ImageReader(['image', 'label'])
reader.initialise(MULTI_MOD_DATA, MULTI_MOD_TASK, multi_mod_list)
return reader
def get_2d_reader():
mod_2d_list = data_partitioner.initialise(MOD_2D_DATA).get_file_list()
reader = ImageReader(['image'])
reader.initialise(MOD_2D_DATA, MOD_2D_TASK, mod_2d_list)
return reader
def test_images2d(self):
reader = ImageReader(['image'])
# COLOR IMAGES
reader.initialise(IMAGE_2D_DATA, IMAGE_2D_TASK_COLOR,
image2d_data_list)
idx, data, interp_order = reader()
image = data['image']
# Check index
self.assertGreaterEqual(idx, 0)
self.assertLess(idx, 10)
# Check data type
self.assertGreaterEqual(image.min(), 0)
self.assertLessEqual(image.max(), 255)
self.assertEqual(image.dtype, np.float32)
# Check shape
self.assertEqual(image.ndim, 5)
self.assertAllEqual(image.shape, (100, 100, 1, 1, 3))
self.assertEqual(interp_order['image'], (1,))
# GRAY IMAGES
reader.initialise(IMAGE_2D_DATA, IMAGE_2D_TASK_GRAY,
image2d_data_list)
idx, data, interp_order = reader()
image = data['image']
# Check index
self.assertGreaterEqual(idx, 0)
self.assertLess(idx, 10)
# Check data type
self.assertGreaterEqual(image.min(), 0)
self.assertLessEqual(image.max(), 255)
self.assertEqual(image.dtype, np.float32)
# Check shape
self.assertEqual(image.ndim, 5)
self.assertAllEqual(image.shape, (100, 100, 1, 1, 1))
self.assertEqual(interp_order['image'], (1,))
gray_idx, gray_data, gray_order = reader(idx=5)
# SEGMENTATION MASKS
reader.initialise(IMAGE_2D_DATA, IMAGE_2D_TASK_MASK,
image2d_data_list)
idx, data, interp_order = reader()
image = data['image']
# Check index
self.assertGreaterEqual(idx, 0)
self.assertLess(idx, 10)
# Check data type
self.assertGreaterEqual(image.min(), 0)
self.assertLessEqual(image.max(), 255)
self.assertEqual(image.dtype, np.float32)
self.assertEqual(np.unique(image).size, 2)
# Check shape
self.assertEqual(image.ndim, 5)
self.assertAllEqual(image.shape, (100, 100, 1, 1, 1))
self.assertEqual(interp_order['image'], (0,))
# Compare segmentation masks to thresholding original image
mask_idx, mask_data, mask_order = reader(idx=5)
gray_data = gray_data['image']
mask_data = mask_data['image']
self.assertEqual(gray_idx, mask_idx)
self.assertEqual(gray_order['image'], (1,))
self.assertEqual(mask_order['image'], (0,))
self.assertAllEqual((gray_data > SEG_THRESHOLD) * 255, mask_data)
def initialise_dataset_loader(
self, data_param=None, task_param=None, data_partitioner=None):
self.data_param = data_param
self.classification_param = task_param
file_lists = self.get_file_lists(data_partitioner)
# read each line of csv files into an instance of Subject
if self.is_training:
self.readers = []
for file_list in file_lists:
reader = ImageReader(['image', 'label', 'sampler'])
reader.initialise(data_param, task_param, file_list)
self.readers.append(reader)
elif self.is_inference:
# in the inference process use image input only
inference_reader = ImageReader(['image'])
inference_reader.initialise(data_param, task_param, file_lists[0])
self.readers = [inference_reader]
elif self.is_evaluation:
reader = ImageReader({'image', 'label', 'inferred'})
reader.initialise(data_param, task_param, file_lists[0])
self.readers = [reader]
else:
raise ValueError('Action `{}` not supported. Expected one of {}'
.format(self.action, self.SUPPORTED_ACTIONS))
foreground_masking_layer = None
if self.net_param.normalise_foreground_only:
foreground_masking_layer = BinaryMaskingLayer(
type_str=self.net_param.foreground_type,
multimod_fusion=self.net_param.multimod_foreground_type,
threshold=0.0)
mean_var_normaliser = MeanVarNormalisationLayer(
image_name='image', binary_masking_func=foreground_masking_layer)
histogram_normaliser = None
if self.net_param.histogram_ref_file:
histogram_normaliser = HistogramNormalisationLayer(
image_name='image',
modalities=vars(task_param).get('image'),
model_filename=self.net_param.histogram_ref_file,
binary_masking_func=foreground_masking_layer,
norm_type=self.net_param.norm_type,
cutoff=self.net_param.cutoff,
name='hist_norm_layer')
label_normaliser = None
if self.net_param.histogram_ref_file:
label_normaliser = DiscreteLabelNormalisationLayer(
image_name='label',
modalities=vars(task_param).get('label'),
model_filename=self.net_param.histogram_ref_file)
normalisation_layers = []
if self.net_param.normalisation:
normalisation_layers.append(histogram_normaliser)
if self.net_param.whitening:
normalisation_layers.append(mean_var_normaliser)
if task_param.label_normalisation:
normalisation_layers.append(label_normaliser)
augmentation_layers = []
if self.is_training:
if self.action_param.random_flipping_axes != -1:
augmentation_layers.append(RandomFlipLayer(
flip_axes=self.action_param.random_flipping_axes))
if self.action_param.scaling_percentage:
augmentation_layers.append(RandomSpatialScalingLayer(
min_percentage=self.action_param.scaling_percentage[0],
max_percentage=self.action_param.scaling_percentage[1]))
if self.action_param.rotation_angle or \
self.action_param.rotation_angle_x or \
self.action_param.rotation_angle_y or \
self.action_param.rotation_angle_z:
rotation_layer = RandomRotationLayer()
if self.action_param.rotation_angle:
rotation_layer.init_uniform_angle(
self.action_param.rotation_angle)
else:
rotation_layer.init_non_uniform_angle(
self.action_param.rotation_angle_x,
self.action_param.rotation_angle_y,
self.action_param.rotation_angle_z)
augmentation_layers.append(rotation_layer)
for reader in self.readers:
reader.add_preprocessing_layers(
normalisation_layers +
augmentation_layers)
def get_concentric_window_reader():
reader = ImageReader(['image', 'label'])
reader.initialise(MULTI_WINDOW_DATA, MULTI_WINDOW_TASK, multi_mod_list)
return reader
def initialise_dataset_loader(
self, data_param=None, task_param=None, data_partitioner=None):
self.data_param = data_param
self.segmentation_param = task_param
# read each line of csv files into an instance of Subject
if self.is_training:
file_lists = []
if self.action_param.validation_every_n > 0:
file_lists.append(data_partitioner.train_files)
file_lists.append(data_partitioner.validation_files)
else:
file_lists.append(data_partitioner.train_files)
self.readers = []
for file_list in file_lists:
reader = ImageReader(SUPPORTED_INPUT)
reader.initialise(data_param, task_param, file_list)
self.readers.append(reader)
else: # in the inference process use image input only
inference_reader = ImageReader(['image'])
file_list = data_partitioner.inference_files
inference_reader.initialise(data_param, task_param, file_list)
self.readers = [inference_reader]
foreground_masking_layer = None
if self.net_param.normalise_foreground_only:
foreground_masking_layer = BinaryMaskingLayer(
type_str=self.net_param.foreground_type,
multimod_fusion=self.net_param.multimod_foreground_type,
threshold=0.0)
mean_var_normaliser = MeanVarNormalisationLayer(
image_name='image', binary_masking_func=foreground_masking_layer)
histogram_normaliser = None
if self.net_param.histogram_ref_file:
histogram_normaliser = HistogramNormalisationLayer(
image_name='image',
modalities=vars(task_param).get('image'),
model_filename=self.net_param.histogram_ref_file,
binary_masking_func=foreground_masking_layer,
norm_type=self.net_param.norm_type,
cutoff=self.net_param.cutoff,
name='hist_norm_layer')
label_normaliser = None
if self.net_param.histogram_ref_file:
label_normaliser = DiscreteLabelNormalisationLayer(
image_name='label',
modalities=vars(task_param).get('label'),
model_filename=self.net_param.histogram_ref_file)
normalisation_layers = []
if self.net_param.normalisation:
normalisation_layers.append(histogram_normaliser)
if self.net_param.whitening:
normalisation_layers.append(mean_var_normaliser)
if task_param.label_normalisation:
normalisation_layers.append(label_normaliser)
augmentation_layers = []
if self.is_training:
if self.action_param.random_flipping_axes != -1:
augmentation_layers.append(RandomFlipLayer(
flip_axes=self.action_param.random_flipping_axes))
if self.action_param.scaling_percentage:
augmentation_layers.append(RandomSpatialScalingLayer(
min_percentage=self.action_param.scaling_percentage[0],
max_percentage=self.action_param.scaling_percentage[1]))
if self.action_param.rotation_angle or \
self.action_param.rotation_angle_x or \
self.action_param.rotation_angle_y or \
self.action_param.rotation_angle_z:
rotation_layer = RandomRotationLayer()
if self.action_param.rotation_angle:
rotation_layer.init_uniform_angle(
self.action_param.rotation_angle)
else:
rotation_layer.init_non_uniform_angle(
self.action_param.rotation_angle_x,
self.action_param.rotation_angle_y,
self.action_param.rotation_angle_z)
augmentation_layers.append(rotation_layer)
volume_padding_layer = []
if self.net_param.volume_padding_size:
volume_padding_layer.append(PadLayer(
image_name=SUPPORTED_INPUT,
border=self.net_param.volume_padding_size))
for reader in self.readers:
reader.add_preprocessing_layers(
volume_padding_layer +
normalisation_layers +
augmentation_layers)
def initialise_dataset_loader(
self, data_param=None, task_param=None, data_partitioner=None):
self.data_param = data_param
self.regression_param = task_param
# read each line of csv files into an instance of Subject
if self.is_training:
file_lists = []
if self.action_param.validation_every_n > 0:
file_lists.append(data_partitioner.train_files)
file_lists.append(data_partitioner.validation_files)
else:
file_lists.append(data_partitioner.train_files)
self.readers = []
for file_list in file_lists:
reader = ImageReader(SUPPORTED_INPUT)
reader.initialise(data_param, task_param, file_list)
self.readers.append(reader)
else:
inference_reader = ImageReader(['image'])
file_list = data_partitioner.inference_files
inference_reader.initialise(data_param, task_param, file_list)
self.readers = [inference_reader]
mean_var_normaliser = MeanVarNormalisationLayer(
image_name='image')
histogram_normaliser = None
if self.net_param.histogram_ref_file:
histogram_normaliser = HistogramNormalisationLayer(
image_name='image',
modalities=vars(task_param).get('image'),
model_filename=self.net_param.histogram_ref_file,
norm_type=self.net_param.norm_type,
cutoff=self.net_param.cutoff,
name='hist_norm_layer')
normalisation_layers = []
if self.net_param.normalisation:
normalisation_layers.append(histogram_normaliser)
if self.net_param.whitening:
normalisation_layers.append(mean_var_normaliser)
augmentation_layers = []
if self.is_training:
if self.action_param.random_flipping_axes != -1:
augmentation_layers.append(RandomFlipLayer(
flip_axes=self.action_param.random_flipping_axes))
if self.action_param.scaling_percentage:
augmentation_layers.append(RandomSpatialScalingLayer(
min_percentage=self.action_param.scaling_percentage[0],
max_percentage=self.action_param.scaling_percentage[1]))
if self.action_param.rotation_angle:
augmentation_layers.append(RandomRotationLayer())
augmentation_layers[-1].init_uniform_angle(
self.action_param.rotation_angle)
volume_padding_layer = []
if self.net_param.volume_padding_size:
volume_padding_layer.append(PadLayer(
image_name=SUPPORTED_INPUT,
border=self.net_param.volume_padding_size))
for reader in self.readers:
reader.add_preprocessing_layers(volume_padding_layer +
normalisation_layers +
augmentation_layers)
def initialise_dataset_loader(
self, data_param=None, task_param=None, data_partitioner=None):
self.data_param = data_param
self.gan_param = task_param
# read each line of csv files into an instance of Subject
if self.is_training:
file_lists = []
if self.action_param.validation_every_n > 0:
file_lists.append(data_partitioner.train_files)
file_lists.append(data_partitioner.validation_files)
else:
file_lists.append(data_partitioner.train_files)
self.readers = []
for file_list in file_lists:
reader = ImageReader(['image', 'conditioning'])
reader.initialise(data_param, task_param, file_list)
self.readers.append(reader)
else:
inference_reader = ImageReader(['conditioning'])
file_list = data_partitioner.inference_files
inference_reader.initialise(data_param, task_param, file_list)
self.readers = [inference_reader]
foreground_masking_layer = None
if self.net_param.normalise_foreground_only:
foreground_masking_layer = BinaryMaskingLayer(
type_str=self.net_param.foreground_type,
multimod_fusion=self.net_param.multimod_foreground_type,
threshold=0.0)
mean_var_normaliser = MeanVarNormalisationLayer(
image_name='image',
binary_masking_func=foreground_masking_layer)
histogram_normaliser = None
if self.net_param.histogram_ref_file:
histogram_normaliser = HistogramNormalisationLayer(
image_name='image',
modalities=vars(task_param).get('image'),
model_filename=self.net_param.histogram_ref_file,
binary_masking_func=foreground_masking_layer,
norm_type=self.net_param.norm_type,
cutoff=self.net_param.cutoff,
name='hist_norm_layer')
normalisation_layers = []
if self.net_param.normalisation:
normalisation_layers.append(histogram_normaliser)
if self.net_param.whitening:
normalisation_layers.append(mean_var_normaliser)
augmentation_layers = []
if self.is_training:
if self.action_param.random_flipping_axes != -1:
augmentation_layers.append(RandomFlipLayer(
flip_axes=self.action_param.random_flipping_axes))
if self.action_param.scaling_percentage:
augmentation_layers.append(RandomSpatialScalingLayer(
min_percentage=self.action_param.scaling_percentage[0],
max_percentage=self.action_param.scaling_percentage[1]))
if self.action_param.rotation_angle:
augmentation_layers.append(RandomRotationLayer())
augmentation_layers[-1].init_uniform_angle(
self.action_param.rotation_angle)
for reader in self.readers:
reader.add_preprocessing_layers(
normalisation_layers + augmentation_layers)
def test_volume_loader(self):
expected_T1 = np.array(
[0.0, 8.24277910972, 21.4917343731,
27.0551695202, 32.6186046672, 43.5081573038,
53.3535675285, 61.9058849776, 70.0929786194,
73.9944243858, 77.7437509974, 88.5331971492,
100.0])
expected_FLAIR = np.array(
[0.0, 5.36540863446, 15.5386130103,
20.7431912042, 26.1536608309, 36.669150376,
44.7821246138, 50.7930589961, 56.1703089214,
59.2393548654, 63.1565641037, 78.7271261392,
100.0])
reader = ImageReader(['image'])
reader.initialise(DATA_PARAM, TASK_PARAM, file_list)
self.assertAllClose(len(reader._file_list), 4)
foreground_masking_layer = BinaryMaskingLayer(
type_str='otsu_plus',
multimod_fusion='or')
hist_norm = HistogramNormalisationLayer(
image_name='image',
modalities=vars(TASK_PARAM).get('image'),
model_filename=MODEL_FILE,
binary_masking_func=foreground_masking_layer,
cutoff=(0.05, 0.95),
name='hist_norm_layer')
if os.path.exists(MODEL_FILE):
os.remove(MODEL_FILE)
hist_norm.train(reader.output_list)
out_map = hist_norm.mapping
self.assertAllClose(out_map['T1'], expected_T1)
self.assertAllClose(out_map['FLAIR'], expected_FLAIR)
# normalise a uniformly sampled random image
test_shape = (20, 20, 20, 3, 2)
rand_image = np.random.uniform(low=-10.0, high=10.0, size=test_shape)
norm_image = np.copy(rand_image)
norm_image_dict, mask_dict = hist_norm({'image': norm_image})
norm_image, mask = hist_norm(norm_image, mask_dict)
self.assertAllClose(norm_image_dict['image'], norm_image)
self.assertAllClose(mask_dict['image'], mask)
# apply mean std normalisation
mv_norm = MeanVarNormalisationLayer(
image_name='image',
binary_masking_func=foreground_masking_layer)
norm_image, _ = mv_norm(norm_image, mask)
self.assertAllClose(norm_image.shape, mask.shape)
mv_norm = MeanVarNormalisationLayer(
image_name='image',
binary_masking_func=None)
norm_image, _ = mv_norm(norm_image)
# mapping should keep at least the order of the images
rand_image = rand_image[:, :, :, 1, 1].flatten()
norm_image = norm_image[:, :, :, 1, 1].flatten()
order_before = rand_image[1:] > rand_image[:-1]
order_after = norm_image[1:] > norm_image[:-1]
self.assertAllClose(np.mean(norm_image), 0.0)
self.assertAllClose(np.std(norm_image), 1.0)
self.assertAllClose(order_before, order_after)
if os.path.exists(MODEL_FILE):
os.remove(MODEL_FILE)
def test_initialisation(self):
with self.assertRaisesRegexp(ValueError, ''):
reader = ImageReader(['test'])
reader.initialise(MULTI_MOD_DATA, MULTI_MOD_TASK, multi_mod_list)
with self.assertRaisesRegexp(AssertionError, ''):
reader = ImageReader(None)
# reader.initialise(MULTI_MOD_DATA, MULTI_MOD_TASK, multi_mod_list)
reader = ImageReader(['image'])
reader.initialise(MULTI_MOD_DATA, MULTI_MOD_TASK, multi_mod_list)
self.assertEqual(len(reader.output_list), 4)
reader = ImageReader(['image'])
reader.initialise(SINGLE_MOD_DATA, SINGLE_MOD_TASK, single_mod_list)
self.assertEqual(len(reader.output_list), 4)
reader = ImageReader(['image'])
with self.assertRaisesRegexp(ValueError, ''):
reader.initialise(SINGLE_MOD_DATA, SINGLE_MOD_TASK, [])
def initialise_dataset_loader(
self, data_param=None, task_param=None, data_partitioner=None):
self.data_param = data_param
self.regression_param = task_param
file_lists = self.get_file_lists(data_partitioner)
# read each line of csv files into an instance of Subject
if self.is_training:
self.readers = []
for file_list in file_lists:
reader = ImageReader({'image', 'output', 'weight', 'sampler'})
reader.initialise(data_param, task_param, file_list)
self.readers.append(reader)
elif self.is_inference:
inference_reader = ImageReader(['image'])
file_list = data_partitioner.inference_files
inference_reader.initialise(data_param, task_param, file_lists[0])
self.readers = [inference_reader]
elif self.is_evaluation:
file_list = data_partitioner.inference_files
reader = ImageReader({'image', 'output', 'inferred'})
reader.initialise(data_param, task_param, file_lists[0])
self.readers = [reader]
else:
raise ValueError('Action `{}` not supported. Expected one of {}'
.format(self.action, self.SUPPORTED_ACTIONS))
mean_var_normaliser = MeanVarNormalisationLayer(
image_name='image')
histogram_normaliser = None
if self.net_param.histogram_ref_file:
histogram_normaliser = HistogramNormalisationLayer(
image_name='image',
modalities=vars(task_param).get('image'),
model_filename=self.net_param.histogram_ref_file,
norm_type=self.net_param.norm_type,
cutoff=self.net_param.cutoff,
name='hist_norm_layer')
normalisation_layers = []
if self.net_param.normalisation:
normalisation_layers.append(histogram_normaliser)
if self.net_param.whitening:
normalisation_layers.append(mean_var_normaliser)
augmentation_layers = []
if self.is_training:
if self.action_param.random_flipping_axes != -1:
augmentation_layers.append(RandomFlipLayer(
flip_axes=self.action_param.random_flipping_axes))
if self.action_param.scaling_percentage:
augmentation_layers.append(RandomSpatialScalingLayer(
min_percentage=self.action_param.scaling_percentage[0],
max_percentage=self.action_param.scaling_percentage[1]))
if self.action_param.rotation_angle:
augmentation_layers.append(RandomRotationLayer())
augmentation_layers[-1].init_uniform_angle(
self.action_param.rotation_angle)
volume_padding_layer = []
if self.net_param.volume_padding_size:
volume_padding_layer.append(PadLayer(
image_name=SUPPORTED_INPUT,
border=self.net_param.volume_padding_size))
for reader in self.readers:
reader.add_preprocessing_layers(volume_padding_layer +
normalisation_layers +
augmentation_layers)
