def download_lodopab():
global DATA_PATH
print('Before downloading, please make sure to have enough free disk '
'space (~150GB). After unpacking, 114.7GB will be used.')
print("path to store LoDoPaB-CT dataset (default '{}'):".format(DATA_PATH))
inp = input()
if inp:
DATA_PATH = inp
set_config('lodopab_dataset/data_path', DATA_PATH)
os.makedirs(DATA_PATH, exist_ok=True)
ZENODO_RECORD_ID = '3384092'
success = download_zenodo_record(ZENODO_RECORD_ID, DATA_PATH)
print('download of LoDoPaB-CT dataset {}'.format(
'successful' if success else 'failed'))
if not success:
return False
file_list = [
'observation_train.zip', 'ground_truth_train.zip',
'observation_validation.zip', 'ground_truth_validation.zip',
'observation_test.zip', 'ground_truth_test.zip'
]
print('unzipping zip files, this can take several minutes', flush=True)
for file in tqdm(file_list, desc='unzip'):
filename = os.path.join(DATA_PATH, file)
with ZipFile(filename, 'r') as f:
f.extractall(DATA_PATH)
os.remove(filename)
return True
def __init__(self,
min_pt=None,
max_pt=None,
observation_model='post-log',
min_photon_count=None,
sorted_by_patient=False,
impl='astra_cuda'):
"""
Parameters
----------
min_pt : [float, float], optional
Minimum values of the lp space. Default: ``[-0.13, -0.13]``.
max_pt : [float, float], optional
Maximum values of the lp space. Default: ``[0.13, 0.13]``.
observation_model : {'post-log', 'pre-log'}, optional
The observation model to use.
The default is ``'post-log'``.
``'post-log'``
Observations are linearly related to the normalized ground
truth via the ray transform, ``obs = ray_trafo(gt) + noise``.
Note that the scaling of the observations matches the
normalized ground truth, i.e., they are divided by the linear
attenuation of 3071 HU.
``'pre-log'``
Observations are non-linearly related to the ground truth, as
given by the Beer-Lambert law.
The model is
``obs = exp(-ray_trafo(gt * MU(3071 HU))) + noise``,
where `MU(3071 HU)` is the factor, by which the ground truth
was normalized.
min_photon_count : float, optional
Replacement value for a simulated photon count of zero.
If ``observation_model == 'post-log'``, a value greater than zero
is required in order to avoid undefined values. The default is 0.1,
both for ``'post-log'`` and ``'pre-log'`` model.
sorted_by_patient : bool, optional
Whether to sort the samples by patient id.
Useful to resplit the dataset.
See also :meth:`get_indices_for_patient`.
Note that the slices of each patient are ordered randomly wrt.
the z-location in any case.
Default: ``False``.
impl : {``'skimage'``, ``'astra_cpu'``, ``'astra_cuda'``},\
optional
Implementation passed to :class:`odl.tomo.RayTransform` to
construct :attr:`ray_trafo`.
"""
global DATA_PATH
NUM_ANGLES = 1000
NUM_DET_PIXELS = 513
self.shape = ((NUM_ANGLES, NUM_DET_PIXELS), (362, 362))
self.num_elements_per_sample = 2
if min_pt is None:
min_pt = MIN_PT
if max_pt is None:
max_pt = MAX_PT
domain = uniform_discr(min_pt, max_pt, self.shape[1], dtype=np.float32)
if observation_model == 'post-log':
self.post_log = True
elif observation_model == 'pre-log':
self.post_log = False
else:
raise ValueError("`observation_model` must be 'post-log' or "
"'pre-log', not '{}'".format(observation_model))
if min_photon_count is None or min_photon_count <= 1.:
self.min_photon_count = min_photon_count
else:
self.min_photon_count = 1.
warn('`min_photon_count` changed from {} to 1.'.format(
min_photon_count))
self.sorted_by_patient = sorted_by_patient
self.train_len = LEN['train']
self.validation_len = LEN['validation']
self.test_len = LEN['test']
self.random_access = True
while not LoDoPaBDataset.check_for_lodopab():
print('The LoDoPaB-CT dataset could not be found under the '
"configured path '{}'.".format(
CONFIG['lodopab_dataset']['data_path']))
print('Do you want to download it now? (y: download, n: input '
'other path)')
download = input_yes_no()
if download:
success = download_lodopab()
if not success:
raise RuntimeError('lodopab dataset not available, '
'download failed')
else:
print('Path to LoDoPaB dataset:')
DATA_PATH = input()
set_config('lodopab_dataset/data_path', DATA_PATH)
self.rel_patient_ids = None
try:
self.rel_patient_ids = LoDoPaBDataset.get_patient_ids()
except OSError as e:
if self.sorted_by_patient:
raise RuntimeError(
'Can not load patient ids, required for sorting. '
'OSError: {}'.format(e))
warn(
'Can not load patient ids (OSError: {}). '
'Therefore sorting is not possible, so please keep the '
'attribute `sorted_by_patient = False` for the LoDoPaBDataset.'
.format(e))
if self.rel_patient_ids is not None:
self._idx_sorted_by_patient = (
LoDoPaBDataset.get_idx_sorted_by_patient(self.rel_patient_ids))
self.geometry = odl.tomo.parallel_beam_geometry(
domain, num_angles=NUM_ANGLES, det_shape=(NUM_DET_PIXELS, ))
range_ = uniform_discr(self.geometry.partition.min_pt,
self.geometry.partition.max_pt,
self.shape[0],
dtype=np.float32)
super().__init__(space=(range_, domain))
self.ray_trafo = self.get_ray_trafo(impl=impl)