def test_astra_algorithm_gpu():
"""Create ASTRA algorithm object on GPU."""
direction = 'forward'
ndim = 2
vol_id = astra_data(VOL_GEOM_2D, 'volume', ndim=ndim)
rec_id = astra_data(VOL_GEOM_2D, 'volume', ndim=ndim)
sino_id = astra_data(PROJ_GEOM_2D, 'projection', ndim=ndim)
# 2D CUDA
proj_id = astra_projector('cuda', VOL_GEOM_2D, PROJ_GEOM_2D, ndim=ndim)
# 2D CUDA FP
alg_id = astra_algorithm('forward',
ndim,
vol_id,
sino_id,
proj_id=proj_id,
impl='cuda')
astra.algorithm.delete(alg_id)
# 2D CUDA BP
alg_id = astra_algorithm('backward',
ndim,
rec_id,
sino_id,
proj_id=proj_id,
impl='cuda')
astra.algorithm.delete(alg_id)
ndim = 3
vol_id = astra_data(VOL_GEOM_3D, 'volume', ndim=ndim)
sino_id = astra_data(PROJ_GEOM_3D, 'projection', ndim=ndim)
proj_id = astra_projector('cuda3d', VOL_GEOM_3D, PROJ_GEOM_3D, ndim=ndim)
with pytest.raises(NotImplementedError):
astra_algorithm(direction,
ndim,
vol_id,
sino_id,
proj_id=proj_id,
impl='cpu')
for direction in {'forward', 'backward'}:
astra_algorithm(direction,
ndim,
vol_id,
sino_id,
proj_id=proj_id,
impl='cuda')
def test_astra_algorithm():
"""Create ASTRA algorithm object."""
direction = 'forward'
ndim = 2
impl = 'cpu'
vol_id = astra_data(VOL_GEOM_2D, 'volume', ndim=ndim)
sino_id = astra_data(PROJ_GEOM_2D, 'projection', ndim=ndim)
proj_id = astra_projector('linear', VOL_GEOM_2D, PROJ_GEOM_2D, ndim=ndim)
# Checks
with pytest.raises(ValueError):
astra_algorithm('none', ndim, vol_id, sino_id, proj_id, impl)
with pytest.raises(ValueError):
astra_algorithm(direction, 0, vol_id, sino_id, proj_id, impl)
with pytest.raises(ValueError):
astra_algorithm('none', ndim, vol_id, sino_id, proj_id, 'none')
with pytest.raises(ValueError):
astra_algorithm(
'backward', ndim, vol_id, sino_id, proj_id=None, impl='cpu'
)
alg_id = astra_algorithm(direction, ndim, vol_id, sino_id, proj_id, impl)
astra.algorithm.delete(alg_id)
# 2D CPU
ndim = 2
impl = 'cpu'
for direction in {'forward', 'backward'}:
alg_id = astra_algorithm(
direction, ndim, vol_id, sino_id, proj_id, impl
)
astra.algorithm.delete(alg_id)
def test_parallel_2d_projector():
"""Create ASTRA 2D projectors."""
# We can just test if it runs
astra_projector('line', VOL_GEOM_2D, PROJ_GEOM_2D, ndim=2)
astra_projector('linear', VOL_GEOM_2D, PROJ_GEOM_2D, ndim=2)
with pytest.raises(ValueError):
astra_projector('line_fanflat', VOL_GEOM_2D, PROJ_GEOM_2D, ndim=2)
with pytest.raises(ValueError):
astra_projector('linearcone', VOL_GEOM_2D, PROJ_GEOM_2D, ndim=2)
def create_ids(self):
"""Create ASTRA objects."""
# Create input and output arrays
if self.geometry.motion_partition.ndim == 1:
motion_shape = self.geometry.motion_partition.shape
else:
# Need to flatten 2- or 3-dimensional angles into one axis
motion_shape = (np.prod(self.geometry.motion_partition.shape), )
proj_shape = motion_shape + self.geometry.det_partition.shape
proj_ndim = len(proj_shape)
if proj_ndim == 2:
astra_proj_shape = proj_shape
astra_vol_shape = self.reco_space.shape
elif proj_ndim == 3:
# The `u` and `v` axes of the projection data are swapped,
# see explanation in `astra_*_3d_geom_to_vec`.
astra_proj_shape = (proj_shape[1], proj_shape[0], proj_shape[2])
astra_vol_shape = self.reco_space.shape
self.in_array = np.empty(astra_vol_shape, dtype='float32', order='C')
self.out_array = np.empty(astra_proj_shape, dtype='float32', order='C')
# Create ASTRA data structures
vol_geom = astra_volume_geometry(self.reco_space)
proj_geom = astra_projection_geometry(self.geometry)
self.vol_id = astra_data(vol_geom,
datatype='volume',
ndim=self.reco_space.ndim,
data=self.in_array,
allow_copy=False)
self.proj_id = astra_projector('nearest',
vol_geom,
proj_geom,
ndim=proj_ndim,
impl='cuda')
self.sino_id = astra_data(proj_geom,
datatype='projection',
ndim=proj_ndim,
data=self.out_array,
allow_copy=False)
# Create algorithm
self.algo_id = astra_algorithm('forward',
proj_ndim,
self.vol_id,
self.sino_id,
proj_id=self.proj_id,
impl='cuda')
def create_ids(self):
"""Create ASTRA objects."""
ndim = self.geometry.ndim
# Create input and output arrays
if ndim == 2:
astra_proj_shape = self.proj_space.shape
elif ndim == 3:
astra_proj_shape = (self.proj_space.shape[2],
self.proj_space.shape[0],
self.proj_space.shape[1])
self.in_array = np.empty(self.reco_space.shape,
dtype='float32',
order='C')
self.out_array = np.empty(astra_proj_shape, dtype='float32', order='C')
# Create ASTRA data structures
vol_geom = astra_volume_geometry(self.reco_space)
proj_geom = astra_projection_geometry(self.geometry)
self.vol_id = astra_data(vol_geom,
datatype='volume',
ndim=self.reco_space.ndim,
data=self.in_array,
allow_copy=False)
self.proj_id = astra_projector('nearest',
vol_geom,
proj_geom,
ndim,
impl='cuda')
self.sino_id = astra_data(proj_geom,
datatype='projection',
ndim=self.proj_space.ndim,
data=self.out_array,
allow_copy=False)
# Create algorithm
self.algo_id = astra_algorithm('forward',
ndim,
self.vol_id,
self.sino_id,
proj_id=self.proj_id,
impl='cuda')
def create_ids(self):
"""Create ASTRA objects."""
ndim = self.geometry.ndim
# Create input and output arrays
if ndim == 2:
astra_proj_shape = self.proj_space.shape
elif ndim == 3:
astra_proj_shape = (self.proj_space.shape[2],
self.proj_space.shape[0],
self.proj_space.shape[1])
self.in_array = np.empty(self.reco_space.shape,
dtype='float32', order='C')
self.out_array = np.empty(astra_proj_shape,
dtype='float32', order='C')
# Create ASTRA data structures
vol_geom = astra_volume_geometry(self.reco_space)
proj_geom = astra_projection_geometry(self.geometry)
self.vol_id = astra_data(vol_geom,
datatype='volume',
ndim=self.reco_space.ndim,
data=self.in_array,
allow_copy=False)
self.proj_id = astra_projector('nearest', vol_geom, proj_geom,
ndim, impl='cuda')
self.sino_id = astra_data(proj_geom,
datatype='projection',
ndim=self.proj_space.ndim,
data=self.out_array,
allow_copy=False)
# Create algorithm
self.algo_id = astra_algorithm(
'forward', ndim, self.vol_id, self.sino_id,
proj_id=self.proj_id, impl='cuda')
def astra_cpu_forward_projector(vol_data, geometry, proj_space, out=None):
"""Run an ASTRA forward projection on the given data using the CPU.
Parameters
----------
vol_data : `DiscreteLpVector`
Volume data to which the forward projector is applied
geometry : `Geometry`
Geometry defining the tomographic setup
proj_space : `DiscreteLp`
Space to which the calling operator maps
out : `DiscreteLpVector`, optional
Vector in the projection space to which the result is written. If
`None` creates an element in the projection space ``proj_space``
Returns
-------
out : ``proj_space`` `element`
Projection data resulting from the application of the projector
"""
if not isinstance(vol_data, DiscreteLpVector):
raise TypeError('volume data {!r} is not a DiscreteLpVector '
'instance'.format(vol_data))
if not isinstance(vol_data.space.dspace, Ntuples):
raise TypeError('data type {!r} of the volume space is not an '
'instance of Ntuples'.format(vol_data.space.dspace))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if not isinstance(proj_space, DiscreteLp):
raise TypeError('projection space {!r} is not a DiscreteLp '
'instance'.format(proj_space))
if not isinstance(proj_space.dspace, Ntuples):
raise TypeError('data type {!r} of the reconstruction space is not an '
'instance of Ntuples'.format(proj_space.dspace))
if vol_data.ndim != geometry.ndim:
raise ValueError('dimensions {} of volume data and {} of geometry '
'do not match'
''.format(vol_data.ndim, geometry.ndim))
if out is None:
out = proj_space.element()
else:
if not isinstance(out, DiscreteLpVector):
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpVector instance'.format(out))
ndim = vol_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(vol_data.space)
proj_geom = astra_projection_geometry(geometry)
# Create ASTRA data structures
vol_id = astra_data(vol_geom, datatype='volume', data=vol_data)
sino_id = astra_data(proj_geom,
datatype='projection',
data=out,
ndim=proj_space.ndim)
# Create projector
if not all(s == vol_data.space.interp[0] for s in vol_data.space.interp):
raise ValueError('volume interpolation must be the same in each '
'dimension, got {}'.format(vol_data.space.interp))
vol_interp = vol_data.space.interp[0]
proj_id = astra_projector(vol_interp,
vol_geom,
proj_geom,
ndim,
impl='cpu')
# Create algorithm
algo_id = astra_algorithm('forward',
ndim,
vol_id,
sino_id,
proj_id,
impl='cpu')
# Run algorithm
astra.algorithm.run(algo_id)
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
return out
def test_parallel_3d_projector():
"""Create ASTRA 3D projectors."""
# We can just test if it runs
astra_projector('linear3d', VOL_GEOM_3D, PROJ_GEOM_3D, ndim=3)
def astra_cpu_forward_projector(vol_data, geometry, proj_space, out=None):
"""Run an ASTRA forward projection on the given data using the CPU.
Parameters
----------
vol_data : `DiscreteLpVector`
Volume data to which the forward projector is applied
geometry : `Geometry`
Geometry defining the tomographic setup
proj_space : `DiscreteLp`
Space to which the calling operator maps
out : `DiscreteLpVector`, optional
Vector in the projection space to which the result is written. If
`None` creates an element in the projection space ``proj_space``
Returns
-------
out : ``proj_space`` `element`
Projection data resulting from the application of the projector
"""
if not isinstance(vol_data, DiscreteLpVector):
raise TypeError('volume data {!r} is not a DiscreteLpVector '
'instance'.format(vol_data))
if not isinstance(vol_data.space.dspace, Ntuples):
raise TypeError('data type {!r} of the volume space is not an '
'instance of Ntuples'.format(vol_data.space.dspace))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if not isinstance(proj_space, DiscreteLp):
raise TypeError('projection space {!r} is not a DiscreteLp '
'instance'.format(proj_space))
if not isinstance(proj_space.dspace, Ntuples):
raise TypeError('data type {!r} of the reconstruction space is not an '
'instance of Ntuples'.format(proj_space.dspace))
if vol_data.ndim != geometry.ndim:
raise ValueError('dimensions {} of volume data and {} of geometry '
'do not match'
''.format(vol_data.ndim, geometry.ndim))
if out is None:
out = proj_space.element()
else:
if not isinstance(out, DiscreteLpVector):
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpVector instance'.format(out))
ndim = vol_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(vol_data.space)
proj_geom = astra_projection_geometry(geometry)
# Create ASTRA data structures
vol_id = astra_data(vol_geom, datatype='volume', data=vol_data)
sino_id = astra_data(proj_geom, datatype='projection', data=out,
ndim=proj_space.ndim)
# Create projector
if not all(s == vol_data.space.interp[0] for s in vol_data.space.interp):
raise ValueError('volume interpolation must be the same in each '
'dimension, got {}'.format(vol_data.space.interp))
vol_interp = vol_data.space.interp[0]
proj_id = astra_projector(vol_interp, vol_geom, proj_geom, ndim,
impl='cpu')
# Create algorithm
algo_id = astra_algorithm('forward', ndim, vol_id, sino_id, proj_id,
impl='cpu')
# Run algorithm
astra.algorithm.run(algo_id)
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
return out
def astra_cpu_forward_projector(vol_data, geometry, proj_space, out=None,
astra_proj_type=None):
"""Run an ASTRA forward projection on the given data using the CPU.
Parameters
----------
vol_data : `DiscretizedSpaceElement`
Volume data to which the forward projector is applied.
geometry : `Geometry`
Geometry defining the tomographic setup.
proj_space : `DiscretizedSpace`
Space to which the calling operator maps.
out : ``proj_space`` element, optional
Element of the projection space to which the result is written. If
``None``, an element in ``proj_space`` is created.
astra_proj_type : str, optional
Type of projector that should be used. See `the ASTRA documentation
<http://www.astra-toolbox.com/docs/proj2d.html>`_ for details.
By default, a suitable projector type for the given geometry is
selected, see `default_astra_proj_type`.
Returns
-------
out : ``proj_space`` element
Projection data resulting from the application of the projector.
If ``out`` was provided, the returned object is a reference to it.
"""
if not isinstance(vol_data, DiscretizedSpaceElement):
raise TypeError(
'volume data {!r} is not a `DiscretizedSpaceElement` instance'
''.format(vol_data)
)
if vol_data.space.impl != 'numpy':
raise TypeError(
"`vol_data.space.impl` must be 'numpy', got {!r}"
"".format(vol_data.space.impl)
)
if not isinstance(geometry, Geometry):
raise TypeError(
'geometry {!r} is not a Geometry instance'.format(geometry)
)
if not isinstance(proj_space, DiscretizedSpace):
raise TypeError(
'`proj_space` {!r} is not a DiscretizedSpace instance.'
''.format(proj_space)
)
if proj_space.impl != 'numpy':
raise TypeError(
"`proj_space.impl` must be 'numpy', got {!r}"
"".format(proj_space.impl)
)
if vol_data.ndim != geometry.ndim:
raise ValueError(
'dimensions {} of volume data and {} of geometry do not match'
''.format(vol_data.ndim, geometry.ndim)
)
if out is None:
out = proj_space.element()
else:
if out not in proj_space:
raise TypeError(
'`out` {} is neither None nor a `DiscretizedSpaceElement` '
'instance'.format(out)
)
ndim = vol_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(vol_data.space)
proj_geom = astra_projection_geometry(geometry)
# Create projector
if astra_proj_type is None:
astra_proj_type = default_astra_proj_type(geometry)
proj_id = astra_projector(astra_proj_type, vol_geom, proj_geom, ndim)
# Create ASTRA data structures
vol_data_arr = np.asarray(vol_data)
vol_id = astra_data(vol_geom, datatype='volume', data=vol_data_arr,
allow_copy=True)
with writable_array(out, dtype='float32', order='C') as out_arr:
sino_id = astra_data(proj_geom, datatype='projection', data=out_arr,
ndim=proj_space.ndim)
# Create algorithm
algo_id = astra_algorithm('forward', ndim, vol_id, sino_id, proj_id,
impl='cpu')
# Run algorithm
astra.algorithm.run(algo_id)
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
return out
def astra_cpu_forward_projector(vol_data, geometry, proj_space, out=None):
"""Run an ASTRA forward projection on the given data using the CPU.
Parameters
----------
vol_data : `DiscreteLpElement`
Volume data to which the forward projector is applied
geometry : `Geometry`
Geometry defining the tomographic setup
proj_space : `DiscreteLp`
Space to which the calling operator maps
out : ``proj_space`` element, optional
Element of the projection space to which the result is written. If
``None``, an element in ``proj_space`` is created.
Returns
-------
out : ``proj_space`` element
Projection data resulting from the application of the projector.
If ``out`` was provided, the returned object is a reference to it.
"""
if not isinstance(vol_data, DiscreteLpElement):
raise TypeError('volume data {!r} is not a `DiscreteLpElement` '
'instance.'.format(vol_data))
if vol_data.space.impl != 'numpy':
raise TypeError('dspace {!r} of the volume is not an '
'instance of `NumpyNtuples`'
''.format(vol_data.space.dspace))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if not isinstance(proj_space, DiscreteLp):
raise TypeError('projection space {!r} is not a DiscreteLp '
'instance.'.format(proj_space))
if proj_space.impl != 'numpy':
raise TypeError('data type {!r} of the reconstruction space is not an '
'instance of NumpyNtuples'.format(proj_space.dspace))
if vol_data.ndim != geometry.ndim:
raise ValueError('dimensions {} of volume data and {} of geometry '
'do not match'
''.format(vol_data.ndim, geometry.ndim))
if out is None:
out = proj_space.element()
else:
if out not in proj_space:
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpElement instance'.format(out))
ndim = vol_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(vol_data.space)
proj_geom = astra_projection_geometry(geometry)
# Create projector
if not all(s == vol_data.space.interp_by_axis[0]
for s in vol_data.space.interp_by_axis):
raise ValueError('volume interpolation must be the same in each '
'dimension, got {}'.format(vol_data.space.interp))
vol_interp = vol_data.space.interp
proj_id = astra_projector(vol_interp, vol_geom, proj_geom, ndim,
impl='cpu')
# Create ASTRA data structures
vol_id = astra_data(vol_geom, datatype='volume', data=vol_data,
allow_copy=True)
with writable_array(out, dtype='float32', order='C') as arr:
sino_id = astra_data(proj_geom, datatype='projection', data=arr,
ndim=proj_space.ndim)
# Create algorithm
algo_id = astra_algorithm('forward', ndim, vol_id, sino_id, proj_id,
impl='cpu')
# Run algorithm
astra.algorithm.run(algo_id)
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
return out
def astra_cpu_back_projector(proj_data, geometry, reco_space, out=None):
"""Run an ASTRA backward projection on the given data using the CPU.
Parameters
----------
proj_data : `DiscreteLpVector`
Projection data to which the backward projector is applied
geometry : `Geometry`
Geometry defining the tomographic setup
reco_space : `DiscreteLp`
Space to which the calling operator maps
out : `DiscreteLpVector` or `None`, optional
Vector in the reconstruction space to which the result is written. If
`None` creates an element in the reconstruction space ``reco_space``
Returns
-------
out : ``reco_space`` element
Reconstruction data resulting from the application of the backward
projector
"""
if not isinstance(proj_data, DiscreteLpVector):
raise TypeError('projection data {!r} is not a DiscreteLpVector '
'instance'.format(proj_data))
if not isinstance(proj_data.space.dspace, Ntuples):
raise TypeError('data type {!r} of the projection space is not an '
'instance of Ntuples'.format(proj_data.shape.dspace))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if not isinstance(reco_space, DiscreteLp):
raise TypeError('reconstruction space {!r} is not a DiscreteLp '
'instance'.format(reco_space))
if not isinstance(reco_space.dspace, Ntuples):
raise TypeError('data type {!r} of the reconstruction space is not an '
'instance of Ntuples'.format(reco_space.dspace))
if reco_space.ndim != geometry.ndim:
raise ValueError('dimensions {} of reconstruction space and {} of '
'geometry do not match'.format(
reco_space.ndim, geometry.ndim))
if out is None:
out = reco_space.element()
else:
if not isinstance(out, DiscreteLpVector):
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpVector instance'.format(out))
ndim = proj_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(reco_space)
proj_geom = astra_projection_geometry(geometry)
# Create ASTRA data structures
vol_id = astra_data(vol_geom, datatype='volume', data=out,
ndim=reco_space.ndim)
sino_id = astra_data(proj_geom, datatype='projection', data=proj_data)
# Create projector
# TODO: implement with different schemes for angles and detector
if not all(s == proj_data.space.interp[0] for s in proj_data.space.interp):
raise ValueError('data interpolation must be the same in each '
'dimension, got {}'.format(proj_data.space.interp))
proj_interp = proj_data.space.interp[0]
proj_id = astra_projector(proj_interp, vol_geom, proj_geom, ndim,
impl='cpu')
# Create algorithm
algo_id = astra_algorithm('backward', ndim, vol_id, sino_id, proj_id,
impl='cpu')
# Run algorithm and delete it
astra.algorithm.run(algo_id)
# Angular integration weighting factor
# angle interval weight by approximate cell volume
extent = float(geometry.motion_partition.extent())
size = float(geometry.motion_partition.size)
scaling_factor = extent / size
# Fix inconsistent scaling: parallel2d & fanflat scale with (voxel
# stride)**2 / (pixel stride), currently only square voxels are supported
scaling_factor *= float(geometry.det_partition.cell_sides[0])
scaling_factor /= float(reco_space.partition.cell_sides[0]) ** 2
out *= scaling_factor
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
return out
def astra_cpu_back_projector(proj_data, geometry, reco_space, out=None):
"""Run an ASTRA backward projection on the given data using the CPU.
Parameters
----------
proj_data : `DiscreteLpVector`
Projection data to which the backward projector is applied
geometry : `Geometry`
Geometry defining the tomographic setup
reco_space : `DiscreteLp`
Space to which the calling operator maps
out : `DiscreteLpVector` or `None`, optional
Vector in the reconstruction space to which the result is written. If
`None` creates an element in the reconstruction space ``reco_space``
Returns
-------
out : ``reco_space`` element
Reconstruction data resulting from the application of the backward
projector
"""
if not isinstance(proj_data, DiscreteLpVector):
raise TypeError('projection data {!r} is not a DiscreteLpVector '
'instance'.format(proj_data))
if not isinstance(proj_data.space.dspace, Ntuples):
raise TypeError('data type {!r} of the projection space is not an '
'instance of Ntuples'.format(proj_data.shape.dspace))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if not isinstance(reco_space, DiscreteLp):
raise TypeError('reconstruction space {!r} is not a DiscreteLp '
'instance'.format(reco_space))
if not isinstance(reco_space.dspace, Ntuples):
raise TypeError('data type {!r} of the reconstruction space is not an '
'instance of Ntuples'.format(reco_space.dspace))
if reco_space.ndim != geometry.ndim:
raise ValueError('dimensions {} of reconstruction space and {} of '
'geometry do not match'.format(
reco_space.ndim, geometry.ndim))
if out is None:
out = reco_space.element()
else:
if not isinstance(out, DiscreteLpVector):
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpVector instance'.format(out))
ndim = proj_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(reco_space)
proj_geom = astra_projection_geometry(geometry)
# Create ASTRA data structures
vol_id = astra_data(vol_geom,
datatype='volume',
data=out,
ndim=reco_space.ndim)
sino_id = astra_data(proj_geom, datatype='projection', data=proj_data)
# Create projector
# TODO: implement with different schemes for angles and detector
if not all(s == proj_data.space.interp[0] for s in proj_data.space.interp):
raise ValueError('data interpolation must be the same in each '
'dimension, got {}'.format(proj_data.space.interp))
proj_interp = proj_data.space.interp[0]
proj_id = astra_projector(proj_interp,
vol_geom,
proj_geom,
ndim,
impl='cpu')
# Create algorithm
algo_id = astra_algorithm('backward',
ndim,
vol_id,
sino_id,
proj_id,
impl='cpu')
# Run algorithm and delete it
astra.algorithm.run(algo_id)
# Angular integration weighting factor
# angle interval weight by approximate cell volume
extent = float(geometry.motion_partition.extent())
size = float(geometry.motion_partition.size)
scaling_factor = extent / size
# Fix inconsistent scaling: parallel2d & fanflat scale with (voxel
# stride)**2 / (pixel stride), currently only square voxels are supported
scaling_factor *= float(geometry.det_partition.cell_sides[0])
scaling_factor /= float(reco_space.partition.cell_sides[0])**2
out *= scaling_factor
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
return out
def astra_cpu_back_projector(proj_data, geometry, reco_space, out=None):
"""Run an ASTRA back-projection on the given data using the CPU.
Parameters
----------
proj_data : `DiscreteLpElement`
Projection data to which the back-projector is applied
geometry : `Geometry`
Geometry defining the tomographic setup
reco_space : `DiscreteLp`
Space to which the calling operator maps
out : ``reco_space`` element, optional
Element of the reconstruction space to which the result is written.
If ``None``, an element in ``reco_space`` is created.
Returns
-------
out : ``reco_space`` element
Reconstruction data resulting from the application of the backward
projector. If ``out`` was provided, the returned object is a
reference to it.
"""
if not isinstance(proj_data, DiscreteLpElement):
raise TypeError('projection data {!r} is not a DiscreteLpElement '
'instance'.format(proj_data))
if proj_data.space.impl != 'numpy':
raise TypeError('data type {!r} of the projection space is not an '
'instance of NumpyNtuples'
''.format(proj_data.shape.dspace))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if not isinstance(reco_space, DiscreteLp):
raise TypeError('reconstruction space {!r} is not a DiscreteLp '
'instance'.format(reco_space))
if reco_space.impl != 'numpy':
raise TypeError('data type {!r} of the reconstruction space is not an '
'instance of NumpyNtuples'.format(reco_space.dspace))
if reco_space.ndim != geometry.ndim:
raise ValueError('dimensions {} of reconstruction space and {} of '
'geometry do not match'.format(
reco_space.ndim, geometry.ndim))
if out is None:
out = reco_space.element()
else:
if out not in reco_space:
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpElement instance'.format(out))
ndim = proj_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(reco_space)
proj_geom = astra_projection_geometry(geometry)
# Create ASTRA data structure
sino_id = astra_data(proj_geom,
datatype='projection',
data=proj_data,
allow_copy=True)
# Create projector
# TODO: implement with different schemes for angles and detector
if not all(s == proj_data.space.interp_byaxis[0]
for s in proj_data.space.interp_byaxis):
raise ValueError('data interpolation must be the same in each '
'dimension, got {}'
''.format(proj_data.space.interp_byaxis))
proj_interp = proj_data.space.interp
proj_id = astra_projector(proj_interp,
vol_geom,
proj_geom,
ndim,
impl='cpu')
# convert out to correct dtype and order if needed
with writable_array(out, dtype='float32', order='C') as arr:
vol_id = astra_data(vol_geom,
datatype='volume',
data=arr,
ndim=reco_space.ndim)
# Create algorithm
algo_id = astra_algorithm('backward',
ndim,
vol_id,
sino_id,
proj_id,
impl='cpu')
# Run algorithm and delete it
astra.algorithm.run(algo_id)
# Weight the adjoint by appropriate weights
scaling_factor = float(proj_data.space.weighting.const)
scaling_factor /= float(reco_space.weighting.const)
out *= scaling_factor
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
return out
def test_parallel_3d_projector_gpu():
"""Create ASTRA 3D projectors on GPU."""
astra_projector('cuda3d', VOL_GEOM_3D, PROJ_GEOM_3D, ndim=3)
def astra_cuda_forward_projector(vol_data, geometry, proj_space, out=None):
"""Run an ASTRA forward projection on the given data using the GPU.
Parameters
----------
vol_data : `DiscreteLpElement`
Volume data to which the projector is applied
geometry : `Geometry`
Geometry defining the tomographic setup
proj_space : `DiscreteLp`
Space to which the calling operator maps
out : ``proj_space`` element, optional
Element of the projection space to which the result is written. If
``None``, an element in ``proj_space`` is created.
Returns
-------
out : ``proj_space`` element
Projection data resulting from the application of the projector.
If ``out`` was provided, the returned object is a reference to it.
"""
if not isinstance(vol_data, DiscreteLpElement):
raise TypeError('volume data {!r} is not a DiscreteLpElement '
'instance'.format(vol_data))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if vol_data.ndim != geometry.ndim:
raise ValueError('dimensions {} of volume data and {} of geometry '
'do not match'
''.format(vol_data.ndim, geometry.ndim))
if not isinstance(proj_space, DiscreteLp):
raise TypeError('projection space {!r} is not a DiscreteLp '
'instance'.format(proj_space))
if out is not None:
if not isinstance(out, DiscreteLpElement):
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpElement instance'.format(out))
ndim = vol_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(vol_data.space)
proj_geom = astra_projection_geometry(geometry)
# Create ASTRA data structures
# In the case dim == 3, we need to swap axes, so can't perform the FP
# in-place
vol_id = astra_data(vol_geom, datatype='volume', data=vol_data,
allow_copy=True)
# Create projector
proj_id = astra_projector('nearest', vol_geom, proj_geom, ndim,
impl='cuda')
if ndim == 2:
if out is None:
out = proj_space.element()
# Wrap the array in correct dtype etc if needed
with writable_array(out, dtype='float32', order='C') as arr:
sino_id = astra_data(proj_geom, datatype='projection', data=arr)
# Create algorithm
algo_id = astra_algorithm('forward', ndim, vol_id, sino_id,
proj_id=proj_id, impl='cuda')
# Run algorithm
astra.algorithm.run(algo_id)
elif ndim == 3:
sino_id = astra_data(proj_geom, datatype='projection',
ndim=proj_space.ndim)
# Create algorithm
algo_id = astra_algorithm('forward', ndim, vol_id, sino_id,
proj_id=proj_id, impl='cuda')
# Run algorithm
astra.algorithm.run(algo_id)
if out is None:
out = proj_space.element(np.rollaxis(astra.data3d.get(sino_id),
0, 3))
else:
out[:] = np.rollaxis(astra.data3d.get(sino_id), 0, 3)
else:
raise RuntimeError('unknown ndim')
# Fix inconsistent scaling
if isinstance(geometry, Parallel2dGeometry):
# parallel2d scales with pixel stride
out *= 1 / float(geometry.det_partition.cell_sides[0])
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
if ndim == 2:
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
else:
astra.data3d.delete((vol_id, sino_id))
astra.projector3d.delete(proj_id)
return out
def astra_cuda_back_projector(proj_data, geometry, reco_space, out=None):
"""Run an ASTRA backward projection on the given data using the GPU.
Parameters
----------
proj_data : `DiscreteLp` element
Projection data to which the backward projector is applied
geometry : `Geometry`
Geometry defining the tomographic setup
reco_space : `DiscreteLp`
Space to which the calling operator maps
out : ``reco_space`` element, optional
Element of the reconstruction space to which the result is written.
If ``None``, an element in ``reco_space`` is created.
Returns
-------
out : ``reco_space`` element
Reconstruction data resulting from the application of the backward
projector. If ``out`` was provided, the returned object is a
reference to it.
"""
if not isinstance(proj_data, DiscreteLpElement):
raise TypeError('projection data {!r} is not a DiscreteLpElement '
'instance'.format(proj_data))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if not isinstance(reco_space, DiscreteLp):
raise TypeError('reconstruction space {!r} is not a DiscreteLp '
'instance'.format(reco_space))
if reco_space.ndim != geometry.ndim:
raise ValueError('dimensions {} of reconstruction space and {} of '
'geometry do not match'.format(reco_space.ndim,
geometry.ndim))
if out is not None:
if not isinstance(out, DiscreteLpElement):
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpElement instance'.format(out))
ndim = proj_data.ndim
# Create geometries
vol_geom = astra_volume_geometry(reco_space)
proj_geom = astra_projection_geometry(geometry)
if ndim == 2:
swapped_proj_data = proj_data
else:
swapped_proj_data = np.ascontiguousarray(
np.rollaxis(proj_data.asarray(), 2, 0))
sino_id = astra_data(proj_geom, datatype='projection',
data=swapped_proj_data, allow_copy=True)
# Create projector
proj_id = astra_projector('nearest', vol_geom, proj_geom, ndim,
impl='cuda')
# Reconstruction volume
if out is None:
out = reco_space.element()
# Wrap the array in correct dtype etc if needed
with writable_array(out, dtype='float32', order='C') as arr:
vol_id = astra_data(vol_geom, datatype='volume', data=arr,
ndim=reco_space.ndim)
# Create algorithm
algo_id = astra_algorithm('backward', ndim, vol_id, sino_id,
proj_id=proj_id, impl='cuda')
# Run algorithm
astra.algorithm.run(algo_id)
out *= astra_cuda_bp_scaling_factor(reco_space, geometry)
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
if ndim == 2:
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
else:
astra.data3d.delete((vol_id, sino_id))
astra.projector3d.delete(proj_id)
return out
def astra_cuda_back_projector(proj_data, geometry, reco_space, out=None):
"""Run an ASTRA backward projection on the given data using the GPU.
Parameters
----------
proj_data : `DiscreteLp` element
Projection data to which the backward projector is applied
geometry : `Geometry`
Geometry defining the tomographic setup
reco_space : `DiscreteLp`
Space to which the calling operator maps
out : `DiscreteLpVector`, optional
Vector in the reconstruction space to which the result is written.
If `None` creates an element in the reconstruction space
``reco_space``
Returns
-------
out : ``reco_space`` element
Reconstruction data resulting from the application of the backward
projector
"""
if not isinstance(proj_data, DiscreteLpVector):
raise TypeError('projection data {!r} is not a DiscreteLpVector '
'instance'.format(proj_data))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if not isinstance(reco_space, DiscreteLp):
raise TypeError('reconstruction space {!r} is not a DiscreteLp '
'instance'.format(reco_space))
if reco_space.ndim != geometry.ndim:
raise ValueError('dimensions {} of reconstruction space and {} of '
'geometry do not match'.format(reco_space.ndim,
geometry.ndim))
if out is not None:
if not isinstance(out, DiscreteLpVector):
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpVector instance'.format(out))
ndim = proj_data.ndim
# Create geometries
vol_geom = astra_volume_geometry(reco_space)
proj_geom = astra_projection_geometry(geometry)
# Create data structures
if out is None:
out = reco_space.element()
vol_id = astra_data(vol_geom, datatype='volume', data=out,
ndim=reco_space.ndim)
if ndim == 2:
swapped_proj_data = proj_data
else:
swapped_proj_data = np.ascontiguousarray(
np.rollaxis(proj_data.asarray(), 2, 0))
sino_id = astra_data(proj_geom, datatype='projection',
data=swapped_proj_data)
# Create projector
proj_id = astra_projector('nearest', vol_geom, proj_geom, ndim,
impl='cuda')
# Create algorithm
algo_id = astra_algorithm('backward', ndim, vol_id, sino_id,
proj_id=proj_id, impl='cuda')
# Run algorithm
astra.algorithm.run(algo_id)
# Angular integration weighting factor
# angle interval weight by approximate cell volume
extent = float(geometry.motion_partition.extent())
size = float(geometry.motion_partition.size)
scaling_factor = extent / size
# Fix inconsistent scaling
if isinstance(geometry, Parallel2dGeometry):
# scales with 1 / (cell volume)
scaling_factor *= float(reco_space.cell_volume)
elif isinstance(geometry, FanFlatGeometry):
# scales with 1 / (cell volume)
scaling_factor *= float(reco_space.cell_volume)
# magnification correction
src_radius = geometry.src_radius
det_radius = geometry.det_radius
scaling_factor *= ((src_radius + det_radius) / src_radius)
elif isinstance(geometry, Parallel3dAxisGeometry):
# scales with voxel stride
# currently only square voxels are supported
extent = reco_space.partition.extent()
shape = np.array(reco_space.shape, dtype=float)
scaling_factor /= float(extent[0] / shape[0])
elif isinstance(geometry, HelicalConeFlatGeometry):
# cone scales with voxel stride
# currently only square voxels are supported
extent = reco_space.partition.extent()
shape = np.array(reco_space.shape, dtype=float)
scaling_factor /= float(extent[0] / shape[0])
# magnification correction
src_radius = geometry.src_radius
det_radius = geometry.det_radius
scaling_factor *= ((src_radius + det_radius) / src_radius) ** 2
out *= scaling_factor
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
if ndim == 2:
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
else:
astra.data3d.delete((vol_id, sino_id))
astra.projector3d.delete(proj_id)
return out
def astra_cuda_forward_projector(vol_data, geometry, proj_space, out=None):
"""Run an ASTRA forward projection on the given data using the GPU.
Parameters
----------
vol_data : `DiscreteLpVector`
Volume data to which the projector is applied
geometry : `Geometry`
Geometry defining the tomographic setup
proj_space : `DiscreteLp`
Space to which the calling operator maps
out : `DiscreteLpVector`, optional
Vector in the projection space to which the result is written. If
`None` creates an element in the projection space ``proj_space``
Returns
-------
out : ``proj_space`` element
Projection data resulting from the application of the projector
"""
if not isinstance(vol_data, DiscreteLpVector):
raise TypeError('volume data {!r} is not a DiscreteLpVector '
'instance'.format(vol_data))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if vol_data.ndim != geometry.ndim:
raise ValueError('dimensions {} of volume data and {} of geometry '
'do not match'
''.format(vol_data.ndim, geometry.ndim))
if not isinstance(proj_space, DiscreteLp):
raise TypeError('projection space {!r} is not a DiscreteLp '
'instance'.format(proj_space))
if out is not None:
if not isinstance(out, DiscreteLpVector):
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpVector instance'.format(out))
ndim = vol_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(vol_data.space)
proj_geom = astra_projection_geometry(geometry)
# Create ASTRA data structures
# In the case dim == 3, we need to swap axes, so can't perform the FP
# in-place
if out is None and ndim == 2:
out = proj_space.element()
vol_id = astra_data(vol_geom, datatype='volume', data=vol_data)
# needs to be improved, cuda does not use out anyway
data_out = out if ndim == 2 else None
sino_id = astra_data(proj_geom, datatype='projection', data=data_out,
ndim=proj_space.ndim)
# Create projector
proj_id = astra_projector('nearest', vol_geom, proj_geom, ndim,
impl='cuda')
# Create algorithm
algo_id = astra_algorithm('forward', ndim, vol_id, sino_id,
proj_id=proj_id, impl='cuda')
# Run algorithm
astra.algorithm.run(algo_id)
# Wrap data
if ndim == 3:
if out is None:
out = proj_space.element(np.rollaxis(astra.data3d.get(sino_id),
0, 3))
else:
out[:] = np.rollaxis(astra.data3d.get(sino_id), 0, 3)
# Fix inconsistent scaling
if isinstance(geometry, Parallel2dGeometry):
# parallel2d scales with pixel stride
out *= 1 / float(geometry.det_partition.cell_sides[0])
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
if ndim == 2:
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
else:
astra.data3d.delete((vol_id, sino_id))
astra.projector3d.delete(proj_id)
return out
def astra_cpu_forward_projector(vol_data, geometry, proj_space, out=None):
"""Run an ASTRA forward projection on the given data using the CPU.
Parameters
----------
vol_data : `DiscreteLpElement`
Volume data to which the forward projector is applied
geometry : `Geometry`
Geometry defining the tomographic setup
proj_space : `DiscreteLp`
Space to which the calling operator maps
out : ``proj_space`` element, optional
Element of the projection space to which the result is written. If
``None``, an element in ``proj_space`` is created.
Returns
-------
out : ``proj_space`` element
Projection data resulting from the application of the projector.
If ``out`` was provided, the returned object is a reference to it.
"""
if not isinstance(vol_data, DiscreteLpElement):
raise TypeError('volume data {!r} is not a `DiscreteLpElement` '
'instance.'.format(vol_data))
if vol_data.space.impl != 'numpy':
raise TypeError('dspace {!r} of the volume is not an '
'instance of `NumpyNtuples`'
''.format(vol_data.space.dspace))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if not isinstance(proj_space, DiscreteLp):
raise TypeError('projection space {!r} is not a DiscreteLp '
'instance.'.format(proj_space))
if proj_space.impl != 'numpy':
raise TypeError('data type {!r} of the reconstruction space is not an '
'instance of NumpyNtuples'.format(proj_space.dspace))
if vol_data.ndim != geometry.ndim:
raise ValueError('dimensions {} of volume data and {} of geometry '
'do not match'
''.format(vol_data.ndim, geometry.ndim))
if out is None:
out = proj_space.element()
else:
if out not in proj_space:
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpElement instance'.format(out))
ndim = vol_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(vol_data.space)
proj_geom = astra_projection_geometry(geometry)
# Create projector
if not all(s == vol_data.space.interp_byaxis[0]
for s in vol_data.space.interp_byaxis):
raise ValueError('volume interpolation must be the same in each '
'dimension, got {}'.format(vol_data.space.interp))
vol_interp = vol_data.space.interp
proj_id = astra_projector(vol_interp,
vol_geom,
proj_geom,
ndim,
impl='cpu')
# Create ASTRA data structures
vol_id = astra_data(vol_geom,
datatype='volume',
data=vol_data,
allow_copy=True)
with writable_array(out, dtype='float32', order='C') as arr:
sino_id = astra_data(proj_geom,
datatype='projection',
data=arr,
ndim=proj_space.ndim)
# Create algorithm
algo_id = astra_algorithm('forward',
ndim,
vol_id,
sino_id,
proj_id,
impl='cpu')
# Run algorithm
astra.algorithm.run(algo_id)
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
return out
def astra_cpu_back_projector(proj_data,
geometry,
vol_space,
out=None,
astra_proj_type=None):
"""Run an ASTRA back-projection on the given data using the CPU.
Parameters
----------
proj_data : `DiscreteLpElement`
Projection data to which the back-projector is applied.
geometry : `Geometry`
Geometry defining the tomographic setup.
vol_space : `DiscreteLp`
Space to which the calling operator maps.
out : ``vol_space`` element, optional
Element of the reconstruction space to which the result is written.
If ``None``, an element in ``vol_space`` is created.
astra_proj_type : str, optional
Type of projector that should be used. See `the ASTRA documentation
<http://www.astra-toolbox.com/docs/proj2d.html>`_ for details.
By default, a suitable projector type for the given geometry is
selected, see `default_astra_proj_type`.
Returns
-------
out : ``vol_space`` element
Reconstruction data resulting from the application of the backward
projector. If ``out`` was provided, the returned object is a
reference to it.
"""
if not isinstance(proj_data, DiscreteLpElement):
raise TypeError('projection data {!r} is not a DiscreteLpElement '
'instance'.format(proj_data))
if proj_data.space.impl != 'numpy':
raise TypeError('`proj_data` must be a `numpy.ndarray` based, '
"container got `impl` {!r}"
"".format(proj_data.space.impl))
if not isinstance(geometry, Geometry):
raise TypeError('geometry {!r} is not a Geometry instance'
''.format(geometry))
if not isinstance(vol_space, DiscreteLp):
raise TypeError('volume space {!r} is not a DiscreteLp '
'instance'.format(vol_space))
if vol_space.impl != 'numpy':
raise TypeError("`vol_space.impl` must be 'numpy', got {!r}"
"".format(vol_space.impl))
if vol_space.ndim != geometry.ndim:
raise ValueError('dimensions {} of reconstruction space and {} of '
'geometry do not match'.format(
vol_space.ndim, geometry.ndim))
if out is None:
out = vol_space.element()
else:
if out not in vol_space:
raise TypeError('`out` {} is neither None nor a '
'DiscreteLpElement instance'.format(out))
ndim = proj_data.ndim
# Create astra geometries
vol_geom = astra_volume_geometry(vol_space)
proj_geom = astra_projection_geometry(geometry)
# Create ASTRA data structure
sino_id = astra_data(proj_geom,
datatype='projection',
data=proj_data,
allow_copy=True)
# Create projector
if astra_proj_type is None:
astra_proj_type = default_astra_proj_type(geometry)
proj_id = astra_projector(astra_proj_type, vol_geom, proj_geom, ndim)
# Convert out to correct dtype and order if needed.
with writable_array(out, dtype='float32', order='C') as out_arr:
vol_id = astra_data(vol_geom,
datatype='volume',
data=out_arr,
ndim=vol_space.ndim)
# Create algorithm
algo_id = astra_algorithm('backward',
ndim,
vol_id,
sino_id,
proj_id,
impl='cpu')
# Run algorithm
astra.algorithm.run(algo_id)
# Weight the adjoint by appropriate weights
scaling_factor = float(proj_data.space.weighting.const)
scaling_factor /= float(vol_space.weighting.const)
out *= scaling_factor
# Delete ASTRA objects
astra.algorithm.delete(algo_id)
astra.data2d.delete((vol_id, sino_id))
astra.projector.delete(proj_id)
return out