def generate_3d_phantom_data(model_number,
L,
H,
n,
m,
geometry,
from_volume_accuracy=256):
N_size = from_volume_accuracy
M_size = int(np.ceil(N_size / n * m))
assert M_size <= N_size
phantom_3Dt = TomoP3D.ModelTemporal(model_number, N_size,
'../resources/phantoms_3D.dat')
timesteps = phantom_3Dt.shape[0]
# make an upscaled xray transform (we need accurate sinograms)
reco_space_copy = odl.uniform_discr(min_pt=[-L, -L, -H],
max_pt=[L, L, H],
shape=[N_size, N_size, M_size])
xray_transform = odl.tomo.RayTransform(reco_space_copy, geometry)
p_lst = list()
for t in range(timesteps):
x = xray_transform.domain.element(phantom_3Dt[t, ..., :M_size])
p = xray_transform(x).data
p_lst.append(p)
# plot_3d(phantom_3Dt[t, ..., :M_size])
# plot_sino(p)
return np.array(p_lst)
def get_ImageData(num_model, geometry):
'''Returns an ImageData relative to geometry with the model num_model from tomophantom
:param num_model: model number
:type num_model: int
:param geometry: geometrical info that describes the phantom
:type geometry: ImageGeometry
Example usage:
.. code-block:: python
ndim = 2
N=128
angles = np.linspace(0, 360, 50, True, dtype=np.float32)
offset = 0.4
channels = 3
if ndim == 2:
ag = AcquisitionGeometry.create_Cone2D((offset,-100), (offset,100))
ag.set_panel(N)
else:
ag = AcquisitionGeometry.create_Cone3D((offset,-100, 0), (offset,100,0))
ag.set_panel((N,N-2))
ag.set_channels(channels)
ag.set_angles(angles, angle_unit=AcquisitionGeometry.DEGREE)
ig = ag.get_ImageGeometry()
num_model = 1
phantom = TomoPhantom.get_ImageData(num_model=num_model, geometry=ig)
'''
ig = geometry.copy()
ig.set_labels(DataOrder.TOMOPHANTOM_IG_LABELS)
num_dims = len(ig.dimension_labels)
if ImageGeometry.CHANNEL in ig.dimension_labels:
if not is_model_temporal(num_model):
raise ValueError('Selected model {} is not a temporal model, please change your selection'.format(num_model))
if num_dims == 4:
# 3D+time for tomophantom
# output dimensions channel and then spatial,
# e.g. [ 'channel', 'vertical', 'horizontal_y', 'horizontal_x' ]
num_model = num_model
shape = tuple(ig.shape[1:])
phantom_arr = TomoP3D.ModelTemporal(num_model, shape, path_library3D)
elif num_dims == 3:
# 2D+time for tomophantom
# output dimensions channel and then spatial,
# e.g. [ 'channel', 'horizontal_y', 'horizontal_x' ]
N = ig.shape[1]
num_model = num_model
phantom_arr = TomoP2D.ModelTemporal(num_model, ig.shape[1], path_library2D)
else:
raise ValueError('Wrong ImageGeometry')
if ig.channels != phantom_arr.shape[0]:
raise ValueError('The required model {} has {} channels. The ImageGeometry you passed has {}. Please update your ImageGeometry.'\
.format(num_model, ig.channels, phantom_arr.shape[0]))
else:
if num_dims == 3:
# 3D
num_model = num_model
phantom_arr = TomoP3D.Model(num_model, ig.shape, path_library3D)
elif num_dims == 2:
# 2D
if ig.shape[0] != ig.shape[1]:
raise ValueError('Can only handle square ImageData, got shape'.format(ig.shape))
N = ig.shape[0]
num_model = num_model
phantom_arr = TomoP2D.Model(num_model, N, path_library2D)
else:
raise ValueError('Wrong ImageGeometry')
im_data = ImageData(phantom_arr, geometry=ig, suppress_warning=True)
im_data.reorder(list(geometry.dimension_labels))
return im_data
import matplotlib.pyplot as plt
import numpy as np
import tomophantom
from tomophantom import TomoP3D
print("Building 4D phantom using TomoPhantom software")
tic = timeit.default_timer()
model = 100 # note that the selected model is temporal (3D + time)
# Define phantom dimensions using a scalar (cubic) or a tuple [N1, N2, N3]
N_size = 256 # or as a tuple of a custom size (256,256,256)
# one can specify an exact path to the parameters file
# path_library2D = '../../../PhantomLibrary/models/Phantom3DLibrary.dat'
path = os.path.dirname(tomophantom.__file__)
path_library3D = os.path.join(path, "Phantom3DLibrary.dat")
#This will generate a Time x N_size x N_size x N_size phantom (4D)
phantom_tm = TomoP3D.ModelTemporal(model, N_size, path_library3D)
toc = timeit.default_timer()
Run_time = toc - tic
print("Phantom has been built in {} seconds".format(Run_time))
for i in range(0, np.size(phantom_tm, 0)):
sliceSel = int(0.5 * N_size)
#plt.gray()
plt.figure(1)
plt.subplot(131)
plt.imshow(phantom_tm[i, sliceSel, :, :], vmin=0, vmax=1)
plt.title('3D Phantom, axial view')
plt.subplot(132)
plt.imshow(phantom_tm[i, :, sliceSel, :], vmin=0, vmax=1)
plt.title('3D Phantom, coronal view')