def project(obj, theta, center=None, emission=True, sinogram_order=False, ncore=None, nchunk=None):
"""
Project x-rays through a given 3D object.
Parameters
----------
obj : ndarray
Voxelized 3D object.
theta : array
Projection angles in radian.
center: array, optional
Location of rotation axis.
emission : bool, optional
Determines whether output data is emission or transmission type.
sinogram_order: bool, optional
Determins whether output data is a stack of sinograms (True, y-axis first axis)
or a stack of radiographs (False, theta first axis).
ncore : int, optional
Number of cores that will be assigned to jobs.
nchunk : int, optional
Chunk size for each core.
Returns
-------
ndarray
3D tomographic data.
"""
obj = dtype.as_float32(obj)
theta = dtype.as_float32(theta)
# Estimate data dimensions.
oy, ox, oz = obj.shape
dt = theta.size
dy = oy
dx = _round_to_even(np.sqrt(ox * ox + oz * oz) + 2)
shape = dy, dt, dx
tomo = dtype.empty_shared_array(shape)
tomo[:] = 0.0
center = get_center(shape, center)
tomo = mproc.distribute_jobs(
(obj, center, tomo),
func=extern.c_project,
args=(theta,),
axis=0,
ncore=ncore,
nchunk=nchunk)
# NOTE: returns sinogram order with emmission=True
if not emission:
# convert data to be transmission type
np.exp(-tomo, tomo)
if not sinogram_order:
# rotate to radiograph order
tomo = np.swapaxes(tomo, 0, 1) #doesn't copy data
# copy data to sharedmem
tomo = dtype.as_sharedmem(tomo, copy=True)
return tomo
def init_tomo(tomo, sinogram_order, sharedmem=True):
tomo = dtype.as_float32(tomo)
if not sinogram_order:
tomo = np.swapaxes(tomo, 0, 1) #doesn't copy data
if sharedmem:
# copy data to sharedmem (if not already or not contiguous)
tomo = dtype.as_sharedmem(tomo, copy=not dtype.is_contiguous(tomo))
else:
# ensure contiguous
tomo = np.require(tomo, requirements="AC")
return tomo
def init_tomo(tomo, sinogram_order, sharedmem=True):
tomo = dtype.as_float32(tomo)
if not sinogram_order:
tomo = np.swapaxes(tomo, 0, 1) # doesn't copy data
if sharedmem:
# copy data to sharedmem (if not already or not contiguous)
tomo = dtype.as_sharedmem(tomo, copy=not dtype.is_contiguous(tomo))
else:
# ensure contiguous
tomo = np.require(tomo, requirements="AC")
return tomo
def _init_recon(shape, init_recon, val=1e-6, sharedmem=True):
if init_recon is None:
if sharedmem:
recon = dtype.empty_shared_array(shape)
recon[:] = val
else:
recon = np.full(shape, val, dtype=np.float32)
else:
recon = np.require(init_recon, dtype=np.float32, requirements="AC")
if sharedmem:
recon = dtype.as_sharedmem(recon)
return recon
def _init_recon(shape, init_recon, val=1e-6, sharedmem=True):
if init_recon is None:
if sharedmem:
recon = dtype.empty_shared_array(shape)
recon[:] = val
else:
recon = np.full(shape, val, dtype=np.float32)
else:
recon = np.require(init_recon, dtype=np.float32, requirements="AC")
if sharedmem:
recon = dtype.as_sharedmem(recon)
return recon
def project(obj,
theta,
center=None,
emission=True,
pad=True,
sinogram_order=False,
ncore=None,
nchunk=None):
"""
Project x-rays through a given 3D object.
Parameters
----------
obj : ndarray
Voxelized 3D object.
theta : array
Projection angles in radian.
center: array, optional
Location of rotation axis.
emission : bool, optional
Determines whether output data is emission or transmission type.
pad : bool, optional
Determines if the projection image width will be padded or not. If True,
then the diagonal length of the object cross-section will be used for the
output size of the projection image width.
sinogram_order: bool, optional
Determines whether output data is a stack of sinograms (True, y-axis first axis)
or a stack of radiographs (False, theta first axis).
ncore : int, optional
Number of cores that will be assigned to jobs.
nchunk : int, optional
Chunk size for each core.
Returns
-------
ndarray
3D tomographic data.
"""
obj = dtype.as_float32(obj)
theta = dtype.as_float32(theta)
# Estimate data dimensions.
oy, ox, oz = obj.shape
dt = theta.size
dy = oy
if pad == True:
dx = _round_to_even(np.sqrt(ox * ox + oz * oz) + 2)
elif pad == False:
dx = ox
shape = dy, dt, dx
tomo = dtype.empty_shared_array(shape)
tomo[:] = 0.0
center = get_center(shape, center)
tomo = mproc.distribute_jobs((obj, center, tomo),
func=extern.c_project,
args=(theta, ),
axis=0,
ncore=ncore,
nchunk=nchunk)
# NOTE: returns sinogram order with emmission=True
if not emission:
# convert data to be transmission type
np.exp(-tomo, tomo)
if not sinogram_order:
# rotate to radiograph order
tomo = np.swapaxes(tomo, 0, 1) #doesn't copy data
# copy data to sharedmem
tomo = dtype.as_sharedmem(tomo, copy=True)
return tomo
def project2(
objx, objy, theta, center=None, emission=True, pad=True,
sinogram_order=False, ncore=None, nchunk=None):
"""
Project x-rays through a given 3D object.
Parameters
----------
objx, objy : ndarray
(x, y) components of vector of a voxelized 3D object.
theta : array
Projection angles in radian.
center: array, optional
Location of rotation axis.
emission : bool, optional
Determines whether output data is emission or transmission type.
pad : bool, optional
Determines if the projection image width will be padded or not. If True,
then the diagonal length of the object cross-section will be used for the
output size of the projection image width.
sinogram_order: bool, optional
Determines whether output data is a stack of sinograms (True, y-axis first axis)
or a stack of radiographs (False, theta first axis).
ncore : int, optional
Number of cores that will be assigned to jobs.
nchunk : int, optional
Chunk size for each core.
Returns
-------
ndarray
3D tomographic data.
"""
objx = dtype.as_float32(objx)
objy = dtype.as_float32(objy)
theta = dtype.as_float32(theta)
# Estimate data dimensions.
oy, ox, oz = objx.shape
dt = theta.size
dy = oy
if pad is True:
dx = _round_to_even(np.sqrt(ox * ox + oz * oz) + 2)
elif pad is False:
dx = ox
shape = dy, dt, dx
tomo = dtype.empty_shared_array(shape)
tomo[:] = 0.0
center = get_center(shape, center)
extern.c_project2(objx, objy, center, tomo, theta)
# NOTE: returns sinogram order with emmission=True
if not emission:
# convert data to be transmission type
np.exp(-tomo, tomo)
if not sinogram_order:
# rotate to radiograph order
tomo = np.swapaxes(tomo, 0, 1) # doesn't copy data
# copy data to sharedmem
tomo = dtype.as_sharedmem(tomo, copy=True)
return tomo
