def astrarecon(tilt_data,tilt_angles,iterations=1,geometry='parallel3d',SO_dist=1.0,OD_dist=1.0):
proj_shape = np.shape(tilt_data)
recon_shape = (proj_shape[2],proj_shape[2],proj_shape[1])
vol_geom = astra.create_vol_geom(recon_shape)
angles = np.pi*tilt_angles/180
if geometry == 'parallel3d':
proj_geom = astra.create_proj_geom(geometry, 1.0, 1.0, proj_shape[1], proj_shape[2], angles)
cfg = astra.astra_dict('SIRT3D_CUDA')
elif geometry == 'cone':
proj_geom = astra.create_proj_geom(geometry, 1.0, 1.0, proj_shape[1], proj_shape[2], angles, SO_dist, OD_dist)
cfg = astra.astra_dict('FDK_CUDA')
proj_id = astra.data3d.create('-proj3d', proj_geom, np.swapaxes(tilt_data,0,1))
rec_id = astra.data3d.create('-vol', vol_geom)
cfg['ReconstructionDataId'] = rec_id
cfg['ProjectionDataId'] = proj_id
alg_id = astra.algorithm.create(cfg)
astra.algorithm.run(alg_id, iterations)
rec = astra.data3d.get(rec_id)
astra.algorithm.delete(alg_id)
astra.data3d.delete(rec_id)
astra.data3d.delete(proj_id)
return(rec)
def geom_setup_2D(self, sino, angles, shape, cors):
p_low, p_high = self.array_pad(cors, self.nCols)
sino = np.pad(sino, ((0, 0), (p_low, p_high)), mode='reflect')
vol_geom = astra.create_vol_geom(shape[0], shape[1])
proj_geom = astra.create_proj_geom('parallel', 1.0, sino.shape[1],
np.deg2rad(angles))
return sino, vol_geom, proj_geom
def recon_mr_fbp(im, angles, cor_shift):
"""Reconstruct a sinogram with the Minimum Residual FBP algorithm (Pelt, 2013).
Parameters
----------
im : array_like
Sinogram image data as numpy array.
angles : double
Value in radians representing the number of angles of the input sinogram.
"""
# Create ASTRA geometries:
vol_geom = astra.create_vol_geom(im.shape[1], im.shape[1])
proj_geom = astra.create_proj_geom('parallel', 1.0, im.shape[1],
linspace(0, angles, im.shape[0], False))
# Projection geometry with shifted center of rotation (doesn't work apparently):
#proj_geom = astra.geom_postalignment(proj_geom, cor_shift);
# Create the ASTRA projector:
p = mrfbp.ASTRAProjector.ASTRAProjector2D(proj_geom, vol_geom)
# Create the MR-FBP Reconstructor:
rec = mrfbp.Reconstructor(p)
# Reconstruct the image using MR-FBP:
im_rec = rec.reconstruct(im)
return im_rec.astype(float32)
def bp0(sino, det_col=111, num_angles=222, voxel_size_mm=1):
"""Wrapper for astra forward projector
:param sino:
:param projector_id:
:return: backprojected sinogram
"""
vol_geom = astra.create_vol_geom(sino.shape)
proj_id = astra.create_projector(
'cuda',
astra.create_proj_geom('parallel', 1.0, det_col,
np.linspace(0, np.pi, num_angles, False)),
vol_geom)
rec_id, backprojection = astra.create_backprojection(sino * voxel_size_mm,
proj_id)
# rec_id, backprojection = astra.create_backprojection(sino, projector_id)
# backprojection /= sino.shape[0]
# backprojection *= np.pi
astra.data2d.delete(rec_id)
astra.projector.delete(proj_id)
return backprojection
def pid(number_of_angles):
"""Return projector ID.
:param number_of_angles:
:return:
"""
return astra.create_projector('cuda', astra.create_proj_geom('parallel',
1.0, vshape[0], np.linspace(0, np.pi, number_of_angles, False)), vol_geom)
def run(self, iterations):
self.fn = getFilterFile(self.fd, self.pg, iterations, self.rg, self.osz)
if os.path.exists(self.fn):
flt = np.load(self.fn)
self.v[:] = self.customFBP(flt)
return
nd = self.nd
if self.osz:
nds = self.osz
else:
nds = nd
na = len(self.ang)
pgc = astra.create_proj_geom('parallel',1.0,nd,self.ang)
vgc = astra.create_vol_geom((nds,nds))
pidc = astra.create_projector('strip',pgc,vgc)
x = np.zeros((nds,nds))
xs = np.zeros((nds,nds))
sf = np.zeros((na,nd))
vid = astra.data2d.create('-vol',vgc)
sid = astra.data2d.create('-sino',pgc)
cfg = astra.astra_dict('FP')
cfg['ProjectorId']=pidc
cfg['ProjectionDataId']=sid
cfg['VolumeDataId']=vid
fpid = astra.algorithm.create(cfg)
cfg = astra.astra_dict('BP')
cfg['ProjectorId']=pidc
cfg['ProjectionDataId']=sid
cfg['ReconstructionDataId']=vid
bpid = astra.algorithm.create(cfg)
vc = astra.data2d.get_shared(vid)
sc = astra.data2d.get_shared(sid)
x[nds//2,nds//2]=1
alp = 1./(na*nds)
if self.rg:
if self.rg*alp >=0.1:
alp = 0.1/self.rg
astra.log.info('Computing filter...')
for i in range(iterations):
if i%10==0: astra.log.info('{:.2f} % done'.format(100*float(i)/iterations))
xs+=x
vc[:] = x
astra.algorithm.run(fpid)
astra.algorithm.run(bpid)
if self.rg:
dx = x[:-1,:] - x[1:,:]
dy = x[:,:-1] - x[:,1:]
x[:-1,:] -= self.rg*dx*alp
x[1:,:] += self.rg*dx*alp
x[:,:-1] -= self.rg*dy*alp
x[:,1:] += self.rg*dy*alp
x -= vc*alp
vc[:] = xs
astra.algorithm.run(fpid)
flt = sc.copy()*alp
astra.algorithm.delete([fpid,bpid])
astra.algorithm.delete([vid,sid])
np.save(self.fn,flt)
self.v[:] = self.customFBP(flt)
astra.projector.delete(pidc)
def FBP_reconstruction(projections, scanner_params, proj_vecs, voxel_size=.1, rec_shape=501, vol_center=0, **kwargs):
astra.astra.set_gpu_index(globals().get('GPU_ID', kwargs.get('gpu_id', -1)))
# from [proj_slc,rows,cols] to [rows,proj_slc,cols]
projections = np.transpose(projections, (1,0,2))
proj_geom = astra.create_proj_geom('cone_vec', *scanner_params.detector_effective_size, proj_vecs)
projections_id = astra.data3d.create('-sino', proj_geom, projections)
# [z,x,y] to [y,x,z] axis transposition
vol_center = tuple([vol_center[i] for i in [2,1,0]]) if isinstance(vol_center, tuple) else (vol_center,) *3
reconstructed_shape = tuple([rec_shape[i] for i in [2,1,0]]) if isinstance(rec_shape, tuple) else (rec_shape,) *3
vol_geom = astra.creators.create_vol_geom(*reconstructed_shape,
*[center+sign*size/2*voxel_size for center, size in zip(vol_center, reconstructed_shape) for sign in [-1, 1]]
)
reconstruction_id = astra.data3d.create('-vol', vol_geom, data=0)
alg_cfg = astra.astra_dict('BP3D_CUDA')
alg_cfg['ProjectionDataId'] = projections_id
alg_cfg['ReconstructionDataId'] = reconstruction_id
algorithm_id = astra.algorithm.create(alg_cfg)
astra.algorithm.run(algorithm_id)
reconstruction = astra.data3d.get(reconstruction_id)
# Free ressources
astra.algorithm.delete(algorithm_id)
astra.data3d.delete([projections_id, reconstruction_id])
return reconstruction
def run_soft_ineq(data, name, alpha, bound):
d_pr, M, m = check_bounds(data['data'], bound, 0)
pixel_size = 1.0
proj_geom = astra.create_proj_geom(
'parallel',
pixel_size,
d_pr.shape[1],
data['angles']
)
rec_pix = d_pr.shape[1]
vol_geom = astra.create_vol_geom(rec_pix, rec_pix)
projector = astra.create_projector('linear', proj_geom, vol_geom)
res = ineq_linear_least_squares(1.0,
proj_geom, vol_geom,
np.exp(-d_pr), projector, np.exp(-bound),
alpha)
plt.figure()
plt.imshow(res, cmap=plt.cm.viridis)
plt.colorbar(orientation='horizontal')
name_suffix = '%s_a%.1f' % (name, alpha)
plt.title("%s, alpha %.1f" % (name, alpha))
plt.savefig('soft_ineq_' + name_suffix + '.png')
# plt.show(block=False)
np.savetxt(name_suffix, res)
def astra_rec_cuda(tomo, center, recon, theta, vol_geom, niter, proj_type, gpu_index, opts):
# Lazy import ASTRA
import astra as astra_mod
nslices, nang, ndet = tomo.shape
cfg = astra_mod.astra_dict(opts['method'])
if 'extra_options' in opts:
# NOTE: we are modifying 'extra_options' and so need to make a copy
cfg['option'] = copy.deepcopy(opts['extra_options'])
else:
cfg['option'] = {}
if gpu_index is not None:
cfg['option']['GPUindex'] = gpu_index
oc = None
const_theta = np.ones(nang)
proj_geom = astra_mod.create_proj_geom(
'parallel', 1.0, ndet, theta.astype(np.float64))
for i in range(nslices):
if center[i] != oc:
oc = center[i]
proj_geom['option'] = {
'ExtraDetectorOffset':
(center[i] - ndet / 2.) * const_theta}
pid = astra_mod.create_projector(proj_type, proj_geom, vol_geom)
cfg['ProjectorId'] = pid
sid = astra_mod.data2d.link('-sino', proj_geom, tomo[i])
cfg['ProjectionDataId'] = sid
vid = astra_mod.data2d.link('-vol', vol_geom, recon[i])
cfg['ReconstructionDataId'] = vid
alg_id = astra_mod.algorithm.create(cfg)
astra_mod.algorithm.run(alg_id, niter)
astra_mod.algorithm.delete(alg_id)
astra_mod.data2d.delete(vid)
astra_mod.data2d.delete(sid)
astra_mod.projector.delete(pid)
def recon_sirt_fbp(im, angles, iter, temppath ):
"""Reconstruct a sinogram with the SIRT-FBP algorithm (Pelt, 2015).
Parameters
----------
im : array_like
Sinogram image data as numpy array.
iter : int
Number of iterations to be used for the computation of SIRT filter.
angles : double
Value in radians representing the number of angles of the input sinogram.
"""
# Create ASTRA geometries:
vol_geom = astra.create_vol_geom(im.shape[1] , im.shape[1])
proj_geom = astra.create_proj_geom('parallel', 1.0, im.shape[1], linspace(0,angles,im.shape[0],False))
proj = astra.create_projector('cuda', proj_geom, vol_geom)
p = astra.OpTomo(proj)
# Register plugin with ASTRA
astra.plugin.register(sirtfbp.plugin)
# Create the ASTRA projector
im_rec = p.reconstruct('SIRT-FBP',im, iter, extraOptions={'filter_dir':temppath})
return im_rec.astype(float32)
def run_fbp(data, bound):
d_pr, M, m = check_bounds(data['data'], bound, 0)
pixel_size = 1.0
proj_geom = astra.create_proj_geom(
'parallel',
pixel_size,
d_pr.shape[1],
data['angles']
)
rec_pix = d_pr.shape[1]
vol_geom = astra.create_vol_geom(rec_pix, rec_pix)
projector = astra.create_projector('linear', proj_geom, vol_geom)
res = fbp(1.0, proj_geom, vol_geom, np.exp(-d_pr), projector, np.exp(-bound))
plt.figure()
plt.imshow(res, cmap=plt.cm.viridis)
plt.colorbar(orientation='vertical')
name_suffix = 'FBP'
plt.title("FBP")
plt.savefig(name_suffix + '.png')
np.savetxt(name_suffix, res)
return res
def __init__(self, angles, img_size, det_count, sdist, ddist, det_spacing):
self.angles = angles
self.vol_geom = astra.create_vol_geom(img_size, img_size)
self.proj_geom = astra.create_proj_geom('fanflat', det_spacing, det_count, self.angles, sdist, ddist)
self.proj_id = astra.create_projector('cuda', self.proj_geom, self.vol_geom)
self.data2d = []
def __init__(self, angles, img_size, det_count):
self.angles = angles
self.vol_geom = astra.create_vol_geom(img_size, img_size)
self.proj_geom = astra.create_proj_geom('parallel', 1.0, det_count, self.angles)
self.proj_id = astra.create_projector('cuda', self.proj_geom, self.vol_geom)
self.data2d = []
def recon_mr_fbp(im, angles):
"""Reconstruct a sinogram with the Minimum Residual FBP algorithm (Pelt, 2013).
Parameters
----------
im : array_like
Sinogram image data as numpy array.
angles : double
Value in radians representing the number of angles of the input sinogram.
"""
# Create ASTRA geometries:
vol_geom = astra.create_vol_geom(im.shape[1] , im.shape[1])
proj_geom = astra.create_proj_geom('parallel', 1.0, im.shape[1], linspace(0,angles,im.shape[0],False))
# Create the ASTRA projector:
p = mrfbp.ASTRAProjector.ASTRAProjector2D(proj_geom,vol_geom)
# Create the MR-FBP Reconstructor:
rec = mrfbp.Reconstructor(p)
# Reconstruct the image using MR-FBP:
im_rec = rec.reconstruct(im)
return im_rec.astype(float32)
def gen_random_geometry_parallel():
if not NONUNITDET:
w = 1.0
else:
w = 0.8 + 0.4 * np.random.random()
pg = astra.create_proj_geom('parallel', w, np.random.randint(*range2d), np.linspace(0, 2*np.pi, np.random.randint(*range2d), endpoint=False))
return pg
def fbp(sinogram, geo):
vol_geom = astra.create_vol_geom(geo["nVoxelY"], geo["nVoxelX"],
-1 * geo["sVoxelY"] / 2,
geo["sVoxelY"] / 2,
-1 * geo["sVoxelX"] / 2,
geo["sVoxelX"] / 2)
proj_geom = astra.create_proj_geom(
geo["mode"], geo["dDetecU"], geo["nDetecU"],
np.linspace(geo["start_angle"], geo["end_angle"], geo["sino_views"],
False), geo["DSO"], geo["DOD"])
if geo["mode"] is "parallel":
proj_id = astra.create_projector("linear", proj_geom, vol_geom)
elif geo["mode"] is "fanflat":
proj_id = astra.create_projector("line_fanflat", proj_geom_full,
vol_geom)
rec_id = astra.data2d.create('-vol', vol_geom)
sinogram_id = astra.data2d.create('-sino', proj_geom, sinogram)
cfg = astra.astra_dict('FBP')
cfg['ProjectorId'] = proj_id
cfg["FilterType"] = "Ram-Lak"
cfg['ReconstructionDataId'] = rec_id
cfg['ProjectionDataId'] = sinogram_id
alg_id = astra.algorithm.create(cfg)
astra.algorithm.run(alg_id)
image_recon = astra.data2d.get(rec_id)
astra.algorithm.delete(alg_id)
astra.data2d.delete(rec_id)
astra.data2d.delete(sinogram_id)
return image_recon
def __init__(self,
geometry_obj=Geometry(1),
vol_vector=None,
proj_vector=None, gpu_index=0):
self.geom = geometry_obj
if vol_vector is None:
self.vol = Rn(self.geom.vol_size)
else:
self.vol = vol_vector
if proj_vector is None:
self.proj = Rn(self.geom.proj_size)
else:
self.proj = proj_vector
self.gpu_index = gpu_index
self.bp_id = None
self.fp_id = None
# Create volume geometry
self.vol_geom = astra.create_vol_geom(self.geom.vol_shape)
# Create projection geometry
self.proj_geom = astra.create_proj_geom(
self.geom.geom_type,
self.geom.detector_spacing_x, self.geom.detector_spacing_y,
self.geom.det_row_count, self.geom.det_col_count,
self.geom.angles,
self.geom.source_origin, self.geom.origin_detector)
# Allocate ASTRA memory for volume data
self.volume_id = astra.data3d.create('-vol', self.vol_geom)
# Allocate ASTRA memory for projection data
self.proj_id = astra.data3d.create('-sino', self.proj_geom)
def gen_random_geometry_fanflat():
if not NONUNITDET:
w = 1.0
else:
w = 0.6 + 0.8 * np.random.random()
pg = astra.create_proj_geom('fanflat', w, np.random.randint(*range2d), np.linspace(0, 2*np.pi, np.random.randint(*range2d), endpoint=False), 256 * (0.5 + np.random.random()), 256 * np.random.random())
return pg
def _initialize_astra(self,
sz=None,
pixel_size=None,
det2obj=None,
src2obj=None,
theta=None):
if sz is None: sz = self._parent.data.shape()
if pixel_size is None:
pixel_size = self._parent.meta.geometry['det_pixel']
if det2obj is None: det2obj = self._parent.meta.geometry['det2obj']
if src2obj is None: src2obj = self._parent.meta.geometry['src2obj']
if theta is None: theta = self._parent.meta.theta
# Initialize ASTRA (3D):
det_count_x = sz[2]
det_count_z = sz[0]
# Make volume count x > detector count to include corneres of the object:
vol_count_x = sz[2]
vol_count_z = sz[0]
tot_dist = det2obj + src2obj
magnification = tot_dist / src2obj
self.vol_geom = astra.create_vol_geom(vol_count_x, vol_count_x,
vol_count_z)
self.proj_geom = astra.create_proj_geom(
'cone', magnification, magnification, det_count_z, det_count_x,
theta, (src2obj * magnification) / pixel_size,
(det2obj * magnification) / pixel_size)
def single_sino(sino: np.ndarray,
cor: ScalarCoR,
proj_angles: ProjectionAngles,
recon_params: ReconstructionParameters,
progress: Optional[Progress] = None) -> np.ndarray:
assert sino.ndim == 2, "Sinogram must be a 2D image"
sino = BaseRecon.prepare_sinogram(sino, recon_params)
image_width = sino.shape[1]
if astra_mutex.locked():
LOG.warning("Astra recon already in progress. Waiting")
with astra_mutex:
vectors = vec_geom_init2d(proj_angles, 1.0,
cor.to_vec(image_width).value)
vol_geom = astra.create_vol_geom((image_width, image_width))
proj_geom = astra.create_proj_geom('parallel_vec', image_width,
vectors)
cfg = astra.astra_dict(recon_params.algorithm)
cfg['FilterType'] = recon_params.filter_name
with _managed_recon(sino, cfg, proj_geom,
vol_geom) as (alg_id, rec_id):
astra.algorithm.run(alg_id, iterations=recon_params.num_iter)
return astra.data2d.get(rec_id)
def astra_rec_cuda(tomo, center, recon, theta, vol_geom, niter, proj_type,
gpu_index, opts):
# Lazy import ASTRA
import astra as astra_mod
nslices, nang, ndet = tomo.shape
cfg = astra_mod.astra_dict(opts['method'])
if 'extra_options' in opts:
#NOTE: we are modifying 'extra_options' and so need to make a copy
cfg['option'] = copy.deepcopy(opts['extra_options'])
else:
cfg['option'] = {}
if gpu_index is not None:
cfg['option']['GPUindex'] = gpu_index
oc = None
const_theta = np.ones(nang)
proj_geom = astra_mod.create_proj_geom('parallel', 1.0, ndet,
theta.astype(np.float64))
for i in range(nslices):
if center[i] != oc:
oc = center[i]
proj_geom['option'] = {
'ExtraDetectorOffset': (center[i] - ndet / 2.) * const_theta
}
pid = astra_mod.create_projector(proj_type, proj_geom, vol_geom)
cfg['ProjectorId'] = pid
sid = astra_mod.data2d.link('-sino', proj_geom, tomo[i])
cfg['ProjectionDataId'] = sid
vid = astra_mod.data2d.link('-vol', vol_geom, recon[i])
cfg['ReconstructionDataId'] = vid
alg_id = astra_mod.algorithm.create(cfg)
astra_mod.algorithm.run(alg_id, niter)
astra_mod.algorithm.delete(alg_id)
astra_mod.data2d.delete(vid)
astra_mod.data2d.delete(sid)
astra_mod.projector.delete(pid)
def recon_sirt_fbp(im, angles, iter, temppath):
"""Reconstruct a sinogram with the SIRT-FBP algorithm (Pelt, 2015).
Parameters
----------
im : array_like
Sinogram image data as numpy array.
iter : int
Number of iterations to be used for the computation of SIRT filter.
angles : double
Value in radians representing the number of angles of the input sinogram.
"""
# Create ASTRA geometries:
vol_geom = astra.create_vol_geom(im.shape[1], im.shape[1])
proj_geom = astra.create_proj_geom('parallel', 1.0, im.shape[1],
linspace(0, angles, im.shape[0], False))
proj = astra.create_projector('cuda', proj_geom, vol_geom)
p = astra.OpTomo(proj)
# Register plugin with ASTRA
astra.plugin.register(sirtfbp.plugin)
# Create the ASTRA projector
im_rec = p.reconstruct('SIRT-FBP',
im,
iter,
extraOptions={'filter_dir': temppath})
return im_rec.astype(float32)
def astra_rec_cpu(tomo, center, recon, theta, vol_geom, niter, proj_type, opts):
# Lazy import ASTRA
import astra as astra_mod
nslices, nang, ndet = tomo.shape
cfg = astra_mod.astra_dict(opts['method'])
if 'extra_options' in opts:
cfg['option'] = opts['extra_options']
proj_geom = astra_mod.create_proj_geom('parallel', 1.0, ndet, theta.astype(np.float64))
pid = astra_mod.create_projector(proj_type, proj_geom, vol_geom)
sino = np.zeros((nang, ndet), dtype=np.float32)
sid = astra_mod.data2d.link('-sino', proj_geom, sino)
cfg['ProjectorId'] = pid
cfg['ProjectionDataId'] = sid
for i in range(nslices):
shft = int(np.round(ndet / 2. - center[i]))
if not shft == 0:
sino[:] = np.roll(tomo[i], shft)
l = shft
r = ndet + shft
if l < 0:
l = 0
if r > ndet:
r = ndet
sino[:, :l] = 0
sino[:, r:] = 0
else:
sino[:] = tomo[i]
vid = astra_mod.data2d.link('-vol', vol_geom, recon[i])
cfg['ReconstructionDataId'] = vid
alg_id = astra_mod.algorithm.create(cfg)
astra_mod.algorithm.run(alg_id, niter)
astra_mod.algorithm.delete(alg_id)
astra_mod.data2d.delete(vid)
astra_mod.data2d.delete(sid)
astra_mod.projector.delete(pid)
def init(self, volume_data=1, projection_data=1):
# Create volume geometry
self.volume_geom = astra.create_vol_geom(self.num_voxel)
# Create projection geometry
self.projection_geom = astra.create_proj_geom(self.geometry_type,
self.detector_spacing_x, self.detector_spacing_y,
self.det_row_count, self.det_col_count,
self.angles,
self.source_origin, self.origin_detector)
# Allocate and store volume data in ASTRA memory
self.volume_id = astra.data3d.create('-vol', self.volume_geom, volume_data)
# Allocate and store projection data in ASTRA memeory
self.projection_id = astra.data3d.create('-sino', self.projection_geom, projection_data)
# Create algorithm object: forward projector
cfg = astra.astra_dict('FP3D_CUDA')
cfg['option'] = {'GPUindex': self.gpu_index}
cfg['ProjectionDataId'] = self.projection_id
cfg['VolumeDataId'] = self.volume_id
self.forward_alg_id = astra.algorithm.create(cfg)
# Create algorithm object: backward projector
cfg = astra.astra_dict('BP3D_CUDA')
cfg['option'] = {'GPUindex': self.gpu_index}
cfg['ProjectionDataId'] = self.projection_id
cfg['ReconstructionDataId'] = self.volume_id
self.backward_alg_id = astra.algorithm.create(cfg)
def __init__(self,
geometry_obj=Geometry(1),
vol_vector=None,
proj_vector=None,
gpu_index=0):
self.geom = geometry_obj
if vol_vector is None:
self.vol = Rn(self.geom.vol_size)
else:
self.vol = vol_vector
if proj_vector is None:
self.proj = Rn(self.geom.proj_size)
else:
self.proj = proj_vector
self.gpu_index = gpu_index
self.bp_id = None
self.fp_id = None
# Create volume geometry
self.vol_geom = astra.create_vol_geom(self.geom.vol_shape)
# Create projection geometry
self.proj_geom = astra.create_proj_geom(
self.geom.geom_type, self.geom.detector_spacing_x,
self.geom.detector_spacing_y, self.geom.det_row_count,
self.geom.det_col_count, self.geom.angles, self.geom.source_origin,
self.geom.origin_detector)
# Allocate ASTRA memory for volume data
self.volume_id = astra.data3d.create('-vol', self.vol_geom)
# Allocate ASTRA memory for projection data
self.proj_id = astra.data3d.create('-sino', self.proj_geom)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--width', type=int, required=True)
parser.add_argument('--num-projections', type=int, required=True)
parser.add_argument('--num-slices', type=int, required=True)
args = parser.parse_args()
angles = np.linspace(0,
np.pi,
args.num_projections,
False,
dtype=np.float64)
vol_geom = astra.create_vol_geom(args.width, args.width, args.num_slices)
proj_geom = astra.create_proj_geom('parallel3d', 1.0, 1.0, args.num_slices,
args.width, angles)
vid = astra.data3d.create('-vol', vol_geom)
pid = astra.data3d.create('-proj3d', proj_geom, 0.0)
config = astra.astra_dict('BP3D_CUDA')
config['ProjectionDataId'] = pid
config['ReconstructionDataId'] = vid
aid = astra.algorithm.create(config)
astra.algorithm.run(aid)
def create_example_parallel3d(example_type=''):
"""
:brief: create an example of a parallel3d geometry
:param example_type: for the examples, there are different options
- '' or 'normal':
example of a standard parallel geometry with 100 angles
- 'vec':
same geometry as for normal, but converted to a vector geometry
this gives a different plot visualizing the individual angles on
top of the geometry
:return: the example geometry
"""
det_spacing = [0.035, 0.035]
detector_px = [1000, 1000]
angles = np.linspace(0, 2 * np.pi, 100)
if example_type == '' or example_type == 'normal':
proj_geom = astra.create_proj_geom('parallel3d', det_spacing[0],
det_spacing[1], detector_px[0],
detector_px[1], angles)
elif example_type == 'vec':
proj_geom = astra.geom_2vec(create_example_parallel3d())
else:
raise ValueError(
"ASTRA: example type {0} not recognized for parallel3d geometry".
format(example_type))
return proj_geom
def __init__(self, DetColumnCount, DetRowCount, AnglesVec, CenterRotOffset,
ObjSize, OS):
self.ObjSize = ObjSize
self.DetectorsDimV = DetRowCount
if type(ObjSize) == tuple:
Y, X, Z = [int(i) for i in ObjSize]
else:
Y = X = ObjSize
Z = DetRowCount
self.vol_geom = astra.create_vol_geom(Y, X, Z)
################ arrange ordered-subsets ################
import numpy as np
AnglesTot = np.size(AnglesVec) # total number of angles
self.NumbProjBins = (int)(np.ceil(
float(AnglesTot) /
float(OS))) # get the number of projections per bin (subset)
self.newInd_Vec = np.zeros(
[OS, self.NumbProjBins],
dtype='int') # 2D array of OS-sorted indeces
for sub_ind in range(OS):
ind_sel = 0
for proj_ind in range(self.NumbProjBins):
indexS = ind_sel + sub_ind
if (indexS < AnglesTot):
self.newInd_Vec[sub_ind, proj_ind] = indexS
ind_sel += OS
# create full ASTRA geometry (to calculate Lipshitz constant)
vectors = vec_geom_init3D(AnglesVec, 1.0, 1.0, CenterRotOffset)
self.proj_geom = astra.create_proj_geom('parallel3d_vec', DetRowCount,
DetColumnCount, vectors)
# create OS-specific ASTRA geometry
self.proj_geom_OS = {}
for sub_ind in range(OS):
self.indVec = self.newInd_Vec[sub_ind, :]
if (self.indVec[self.NumbProjBins - 1] == 0):
self.indVec = self.indVec[:-1] #shrink vector size
anglesOS = AnglesVec[self.indVec] # OS-specific angles
if np.ndim(CenterRotOffset) == 0: # CenterRotOffset is a _scalar_
vectors = vec_geom_init3D(anglesOS, 1.0, 1.0, CenterRotOffset)
else: # CenterRotOffset is a _vector_
vectors = vec_geom_init3D(anglesOS, 1.0, 1.0,
CenterRotOffset[self.indVec])
self.proj_geom_OS[sub_ind] = astra.create_proj_geom(
'parallel3d_vec', DetRowCount, DetColumnCount, vectors)
def FDK_reconstruction(projections,
scanner_params,
proj_vecs,
voxel_size=.1,
rec_shape=501,
**kwargs):
"""Uses FDK method to reconstruct CT volume from CB projections
Args:
-----
projections (np.ndarray): [proj_slc, rows, cols] CBCT projections
scanner_params (class): class containing scanner data
proj_vecs (np.ndarray): vects describing the scanning used for reconstruction
voxel_size (float): size of the voxels in the reconstructed volume
rec_shape (int/tuple): shape of the reconstructed volume tuple with 3 dims [z,x,y] or int if isotropic
--optional--
gpu_id (int): GPU for astra to use if not set globaly, defaults to -1
Returns:
--------
reconstruction (np.ndarray): [z,x,y] recontructed CT volume
"""
astra.astra.set_gpu_index(globals().get('GPU_ID', kwargs.get('gpu_id',
-1)))
# from [proj_slc,rows,cols] to [rows,proj_slc,cols]
projections = np.transpose(projections, (1, 0, 2))
proj_geom = astra.create_proj_geom('cone_vec',
*scanner_params.detector_binned_size,
proj_vecs)
projections_id = astra.data3d.create('-sino', proj_geom, projections)
# [z,x,y] to [y,x,z] axis transposition
reconstructed_shape = tuple([rec_shape[i]
for i in [2, 1, 0]]) if isinstance(
rec_shape, tuple) else (rec_shape, ) * 3
vol_geom = astra.creators.create_vol_geom(
*reconstructed_shape, *[
sign * size / 2 * voxel_size for size in reconstructed_shape
for sign in [-1, 1]
])
reconstruction_id = astra.data3d.create('-vol', vol_geom, data=0)
alg_cfg = astra.astra_dict('FDK_CUDA')
alg_cfg['ProjectionDataId'] = projections_id
alg_cfg['ReconstructionDataId'] = reconstruction_id
alg_cfg['option'] = {'ShortScan': False}
algorithm_id = astra.algorithm.create(alg_cfg)
astra.algorithm.run(algorithm_id)
reconstruction = astra.data3d.get(reconstruction_id)
# Free ressources
astra.algorithm.delete(algorithm_id)
astra.data3d.delete([projections_id, reconstruction_id])
return reconstruction
def __init__(self, n_pixels, n_angles, rayperdetec=None):
'''
Initialize the ASTRA toolbox with a simple parallel configuration.
The image is assumed to be square, and the detector count is equal to the number of rows/columns.
'''
self.vol_geom = astra.create_vol_geom(n_pixels, n_pixels)
self.proj_geom = astra.create_proj_geom('parallel', 1.0, n_pixels, np.linspace(0,np.pi,n_angles,False))
self.proj_id = astra.create_projector('cuda', self.proj_geom, self.vol_geom)
def astrafp(im, ang, prj="cuda"):
proj_geom = astra.create_proj_geom("parallel", 1.0, im.shape[0], np.array([ang, 0]))
vol_geom = astra.create_vol_geom(im.shape)
pid = astra.create_projector(prj, proj_geom, vol_geom)
w = astra.OpTomo(pid)
fpim = w * im
astra.projector.delete(pid)
return fpim[0 : im.shape[0]]
def __init__(self, angles, img_size):
self.angles = angles
self.vol_geom = astra.create_vol_geom(img_size, img_size)
self.proj_geom = astra.create_proj_geom('fanflat', 1.0, img_size,
self.angles, img_size,
img_size)
self.proj_id = astra.create_projector('cuda', self.proj_geom,
self.vol_geom)
def __init__(self, geometry_type='cone',
num_voxel=(100, 100, 100),
det_row_count=100, det_col_count=100,
angles=np.linspace(0, 2 * np.pi, 180, endpoint=False),
det_col_spacing=1.0, det_row_spacing=1.0,
source_origin=100.0, origin_detector=10.0,
volume_data=1, projection_data=1,
gpu_index=0):
self.geometry_type = geometry_type
self.num_voxel = num_voxel
self.detector_spacing_x = det_col_spacing
self.detector_spacing_y = det_row_spacing
self.det_row_count = det_row_count
self.det_col_count = det_col_count
self.angles = angles
self.source_origin = source_origin
self.origin_detector = origin_detector
self.volume_data = volume_data
self.projection_data = projection_data
self.gpu_index = gpu_index
# Create volume geometry
self.volume_geom = astra.create_vol_geom(self.num_voxel)
# Create projection geometry
self.projection_geom = astra.create_proj_geom(
self.geometry_type,
self.detector_spacing_x, self.detector_spacing_y,
self.det_row_count, self.det_col_count,
self.angles,
self.source_origin, self.origin_detector)
# Allocate and store volume data in ASTRA memory
self.volume_id = astra.data3d.create(
'-vol',
self.volume_geom,
self.volume_data)
# Allocate and store projection data in ASTRA memory
self.projection_id = astra.data3d.create(
'-sino',
self.projection_geom,
self.projection_data)
# Create algorithm object: forward projector
cfg = astra.astra_dict('FP3D_CUDA')
cfg['option'] = {'GPUindex': self.gpu_index}
cfg['ProjectionDataId'] = self.projection_id
cfg['VolumeDataId'] = self.volume_id
self.forward_alg_id = astra.algorithm.create(cfg)
# Create algorithm object: backward projector
cfg = astra.astra_dict('BP3D_CUDA')
# classmethod?
cfg['option'] = {'GPUindex': self.gpu_index}
cfg['ProjectionDataId'] = self.projection_id
cfg['ReconstructionDataId'] = self.volume_id
self.backward_alg_id = astra.algorithm.create(cfg)
def DT(W,p, vol_size, proj_size, angles, Ngv, lamb=10, PU = 'cuda', K = 4, Niter = 50, epsilon=1e-4):
[Nx,Nz] = vol_size
[Ndetx, Ndety] = proj_size
Nan = len(angles)
if Ndety==1:
sinogram = p.reshape([Nan, Ndetx])
else:
sinogram = p.reshape([Nan, Ndetx, Ndety])
# create projection geometry and operator
print('TVR-DART ...')
print('Create projection geometry and operator...')
proj_geom = astra.create_proj_geom('parallel', 1.0, Ndetx, angles)
vol_geom = astra.create_vol_geom(Nz,Nx)
proj_id = astra.create_projector(PU,proj_geom,vol_geom)
W = astra.OpTomo(proj_id)
# initial reconstruction
print('Initial reconstruction...')
recsirt = SIRT.recon(sinogram, 200, proj_geom, vol_geom, PU)
sf = np.max(recsirt)
p = p/sf
if Ndety==1:
sinogram = p.reshape([Nan, Ndetx])
else:
sinogram = p.reshape([Nan, Ndetx, Ndety])
recsirt = recsirt/sf
# set initial TVR-DART parameters
K = K*np.ones(Ngv-1)
gv = np.linspace(0, 1, Ngv,True)
param0 = gv2param(gv,K)
# Esimating parameter using a small section of the dataset
print('Estimation of optimal parameters...')
if Ndety==1:
Segrec,param_esti = joint(W,p, recsirt, param0 ,lamb)
else:
Elist = np.sum(recsirt,axis=(0,2))
index = np.argmax(Elist)
index1 = np.max(np.hstack((0,index-1)))
index2 = np.min(np.hstack((index+1,Ndety-1)))
x0_esti = recsirt[:,index1:index2+1,:]
sinogram_esti = sinogram[:,:,index1:index2+1]
p_esti = sinogram_esti.reshape(Nan*Ndetx*(index2-index1+1))
Segrec,param_esti = joint(W,p_esti, x0_esti, param0 ,lamb)
# Reconstruction with estimated parameter
print('Reconstruction with estimated parameters...')
Segrec,rec = recon(W,p, recsirt, param_esti, lamb, Niter, epsilon)
[gv,K] = param2gv(param_esti)
param_esti = gv2param(gv*sf,K)
Segrec = Segrec*sf;
return Segrec, param_esti;
def astra_project(obj, angles, cuda=False):
"""
Calculate projection of a 3D object using Astra-toolbox.
Args
----------
obj : NumPy array
Either a 2D or 3D array containing the object to project.
If 2D, the structure is of the form [z, x] where z is the
projection axis. If 3D, the strucutre is [y, z, x] where y is
the tilt axis.
angles : list or NumPy array
The projection angles in degrees
cuda : boolean
If True, perform reconstruction using the GPU-accelerated algorithm.
Returns
----------
sino : Numpy array
3D array of the form [y, angle, x] containing the projection of
the input object
"""
if len(obj.shape) == 2:
obj = np.expand_dims(obj, 0)
thetas = np.pi * angles / 180.
y_pix, thickness, x_pix = obj.shape
if cuda:
vol_geom = astra.create_vol_geom(thickness, x_pix, y_pix)
proj_geom = astra.create_proj_geom('parallel3d', 1.0, 1.0, y_pix,
x_pix, thetas)
sino_id, sino = astra.create_sino3d_gpu(obj, proj_geom, vol_geom)
astra.data3d.delete(sino_id)
else:
sino = np.zeros([y_pix, len(angles), x_pix], np.float32)
vol_geom = astra.create_vol_geom(thickness, x_pix)
proj_geom = astra.create_proj_geom('parallel', 1.0, x_pix, thetas)
proj_id = astra.create_projector('strip', proj_geom, vol_geom)
for i in range(0, y_pix):
sino_id, sino[i, :, :] = astra.create_sino(obj[i, :, :], proj_id,
vol_geom)
astra.data2d.delete(sino_id)
sino = np.rollaxis(sino, 1)
return sino
def __init__(self, vol_shape, angles, beam_shape="parallel"):
if len(vol_shape) > 2:
raise ValueError("Only 2D volumes")
self.proj_id = []
self.dispose_projector()
self.vol_geom = astra.create_vol_geom(vol_shape)
self.proj_geom = astra.create_proj_geom(beam_shape, 1, np.max(vol_shape), angles)
def forward_single(self, x):
vol_geom = astra.create_vol_geom(x.shape[0], x.shape[1])
proj_geom = astra.create_proj_geom('parallel', 1.0, x.shape[0],
self.angles)
proj_id = astra.create_projector('cuda', proj_geom, vol_geom)
self.projectors.append(proj_id)
return astra.create_sino(x, proj_id)
def __init__(self,
geometry_type='cone',
num_voxel=(100, 100, 100),
det_row_count=100,
det_col_count=100,
angles=np.linspace(0, 2 * np.pi, 180, endpoint=False),
det_col_spacing=1.0,
det_row_spacing=1.0,
source_origin=100.0,
origin_detector=10.0,
volume_data=1,
projection_data=1,
gpu_index=0):
self.geometry_type = geometry_type
self.num_voxel = num_voxel
self.detector_spacing_x = det_col_spacing
self.detector_spacing_y = det_row_spacing
self.det_row_count = det_row_count
self.det_col_count = det_col_count
self.angles = angles
self.source_origin = source_origin
self.origin_detector = origin_detector
self.volume_data = volume_data
self.projection_data = projection_data
self.gpu_index = gpu_index
# Create volume geometry
self.volume_geom = astra.create_vol_geom(self.num_voxel)
# Create projection geometry
self.projection_geom = astra.create_proj_geom(
self.geometry_type, self.detector_spacing_x,
self.detector_spacing_y, self.det_row_count, self.det_col_count,
self.angles, self.source_origin, self.origin_detector)
# Allocate and store volume data in ASTRA memory
self.volume_id = astra.data3d.create('-vol', self.volume_geom,
self.volume_data)
# Allocate and store projection data in ASTRA memory
self.projection_id = astra.data3d.create('-sino', self.projection_geom,
self.projection_data)
# Create algorithm object: forward projector
cfg = astra.astra_dict('FP3D_CUDA')
cfg['option'] = {'GPUindex': self.gpu_index}
cfg['ProjectionDataId'] = self.projection_id
cfg['VolumeDataId'] = self.volume_id
self.forward_alg_id = astra.algorithm.create(cfg)
# Create algorithm object: backward projector
cfg = astra.astra_dict('BP3D_CUDA')
# classmethod?
cfg['option'] = {'GPUindex': self.gpu_index}
cfg['ProjectionDataId'] = self.projection_id
cfg['ReconstructionDataId'] = self.volume_id
self.backward_alg_id = astra.algorithm.create(cfg)
def reconstruct(self, sinogram, centre_of_rotation, angles, shape, center):
ctr = centre_of_rotation
width = sinogram.shape[1]
pad = 50
sino = np.nan_to_num(sinogram)
# pad the array so that the centre of rotation is in the middle
alen = ctr
blen = width - ctr
mid = width / 2.0
if (ctr > mid):
plow = pad
phigh = (alen - blen) + pad
else:
plow = (blen - alen) + pad
phigh = pad
logdata = np.log(sino+1)
sinogram = np.pad(logdata, ((0, 0), (int(plow), int(phigh))),
mode='reflect')
width = sinogram.shape[1]
vol_geom = astra.create_vol_geom(shape[0], shape[1])
proj_geom = astra.create_proj_geom('parallel', 1.0, width,
np.deg2rad(angles))
sinogram_id = astra.data2d.create("-sino", proj_geom, sinogram)
# Create a data object for the reconstruction
rec_id = astra.data2d.create('-vol', vol_geom)
proj_id = astra.create_projector('strip', proj_geom, vol_geom)
cfg = astra.astra_dict(self.parameters['reconstruction_type'])
cfg['ReconstructionDataId'] = rec_id
cfg['ProjectionDataId'] = sinogram_id
cfg['ProjectorId'] = proj_id
# Create the algorithm object from the configuration structure
alg_id = astra.algorithm.create(cfg)
# Run 20 iterations of the algorithm
itterations = int(self.parameters['number_of_iterations'])
# This will have a runtime in the order of 10 seconds.
astra.algorithm.run(alg_id, itterations)
# Get the result
rec = astra.data2d.get(rec_id)
# Clean up.
astra.algorithm.delete(alg_id)
astra.data2d.delete(rec_id)
astra.data2d.delete(sinogram_id)
return rec
def __init__(self, DetRows, DetColumns, AnglesVec, ObjSize):
self.ObjSize = ObjSize
self.proj_geom = astra.create_proj_geom('parallel3d', 1.0, 1.0,
DetRows, DetColumns, AnglesVec)
if type(ObjSize) == tuple:
N1, N2, N3 = [int(i) for i in ObjSize]
else:
N1 = N2 = N3 = ObjSize
self.vol_geom = astra.create_vol_geom(N3, N2, N1)
def check_recon(folder, alg, iterations, bin_num):
raw_data = np.load(os.path.join(directory, folder, 'Data',
'data_corr.npy'))[:, :, :, 6]
raw_data = np.transpose(raw_data,
axes=(1, 0,
2)) # Transpose to (rows, angles, columns)
# Change if isocentre is not directly in the center of the detector
# raw_data = np.roll(raw_data, -2, axis=2)
# Create a 3D projection geometry with our cone-beam data
# Parameters: 'acquisition type', number of detector rows, number of detector columns, data ndarray
proj_geom = astra.create_proj_geom('cone', pixel_pitch, pixel_pitch,
det_row_count, det_col_count, angles,
source_origin, origin_det)
proj_id = astra.data3d.create('-proj3d', proj_geom, raw_data)
# Create a 3D volume geometry.
# Parameter order: rows, columns, slices (y, x, z)
vol_geom = astra.create_vol_geom(det_col_count, det_col_count,
det_row_count)
# Create a data object for the reconstruction
if alg == 'FDK_CUDA':
recon_id = astra.data3d.create('-vol', vol_geom)
else:
ct_data = np.load(os.path.join(directory, folder, 'CT', 'FDK_CT.npy'))
recon_id = astra.data3d.create('-vol', vol_geom, data=ct_data)
# Set up the parameters for a reconstruction algorithm using the GPU
cfg = astra.astra_dict(alg)
cfg['ReconstructionDataId'] = recon_id
cfg['ProjectionDataId'] = proj_id
# Create the algorithm object from the configuration structure
alg_id = astra.algorithm.create(cfg)
# Run the specified number of iterations of the algorithm
rec_data = np.zeros((iterations // 5, 576, 576))
for i in range(0, iterations, 5):
astra.algorithm.run(alg_id, 5)
# Get the result
rec = astra.data3d.get(recon_id)[11]
print(alg[0:4] + f': {i + 5} Iterations')
rec_data[i // 5] = rec
np.save(
os.path.join(directory, folder, 'CT',
alg[0:4] + '_iteration_check.npy'), rec_data)
# Clean up. Note that GPU memory is tied up in the algorithm object,
# and main RAM in the data objects.
astra.algorithm.delete(alg_id)
astra.data3d.delete(recon_id)
astra.data3d.delete(proj_id)
def test_fanbeam_error(device, batch_size, image_size, angles, spacing,
distances, det_count, clip_to_circle):
# generate random images
# generate random images
det_count = int(det_count * image_size)
mask_radius = det_count / 2.0 if clip_to_circle else -1
x = generate_random_images(1, image_size, mask_radius)[0]
s_dist, d_dist = distances
s_dist *= image_size
d_dist *= image_size
# astra
vol_geom = astra.create_vol_geom(x.shape[0], x.shape[1])
proj_geom = astra.create_proj_geom('fanflat', spacing, det_count, angles,
s_dist, d_dist)
proj_id = astra.create_projector('cuda', proj_geom, vol_geom)
id, astra_y = astra.create_sino(x, proj_id)
_, astra_bp = astra.create_backprojection(astra_y, proj_id)
if clip_to_circle:
astra_bp *= circle_mask(image_size, mask_radius)
# TODO clean astra structures
# our implementation
radon = RadonFanbeam(image_size,
angles,
s_dist,
d_dist,
det_count=det_count,
det_spacing=spacing,
clip_to_circle=clip_to_circle)
x = torch.FloatTensor(x).to(device).view(1, x.shape[0], x.shape[1])
# repeat data to fill batch size
x = torch.cat([x] * batch_size, dim=0)
our_fp = radon.forward(x)
our_bp = radon.backprojection(our_fp)
forward_error = relative_error(astra_y, our_fp[0].cpu().numpy())
back_error = relative_error(astra_bp, our_bp[0].cpu().numpy())
# if back_error > 5e-3:
# plt.imshow(astra_bp)
# plt.figure()
# plt.imshow(our_bp[0].cpu().numpy())
# plt.show()
print(np.max(our_fp.cpu().numpy()), np.max(our_bp.cpu().numpy()))
print(
f"batch: {batch_size}, size: {image_size}, angles: {len(angles)}, spacing: {spacing}, distances: {distances} circle: {clip_to_circle}, forward: {forward_error}, back: {back_error}"
)
# TODO better checks
assert_less(forward_error, 1e-2)
assert_less(back_error, 5e-3)
def pid(number_of_angles):
"""Return projector ID.
:param number_of_angles:
:return:
"""
return astra.create_projector(
'cuda',
astra.create_proj_geom('parallel', 1.0, vshape[0],
np.linspace(0, np.pi, number_of_angles, False)),
vol_geom)
def _basic_par3d_fp():
import astra
import numpy as np
vg = astra.create_vol_geom(2, 32, 32)
pg = astra.create_proj_geom('parallel3d', 1, 1, 32, 32, [0])
vol = np.random.rand(32, 2, 32)
(sino_id, sino) = astra.create_sino3d_gpu(vol, pg, vg)
astra.data3d.delete(sino_id)
err = np.max(np.abs(sino[:,0,:] - np.sum(vol,axis=1)))
return err < 1e-6
def __init__(self,
geometry_obj=Geometry(),
volume_space=Rn(Geometry().vol_size),
projections_space=Rn(Geometry().proj_size),
gpu_index=0):
self.geom = geometry_obj
self.vol_space = volume_space
self.proj_space = projections_space
self.gpu_index = gpu_index
self.bp_id = None
self.fp_id = None
# Create volume geometry
self.vol_geom = astra.create_vol_geom(self.geom.vol_shape)
# Create projection geometry
if self.geom.geom_type == 'cone':
self.proj_geom = astra.create_proj_geom(
self.geom.geom_type,
self.geom.detector_spacing_x, self.geom.detector_spacing_y,
self.geom.det_row_count, self.geom.det_col_count,
self.geom.angles,
self.geom.source_origin, self.geom.origin_detector)
elif self.geom.geom_type == 'parallel':
self.proj_geom = astra.create_proj_geom(
'parallel', self.geom.detector_spacing_x,
self.geom.det_col_count, self.geom.angles)
# Allocate ASTRA memory for volume data and projection data
if self.geom.vol_ndim == 2:
self.volume_id = astra.data2d.create('-vol', self.vol_geom)
self.proj_id = astra.data2d.create('-sino', self.proj_geom)
elif self.geom.vol_ndim == 3:
self.volume_id = astra.data3d.create('-vol', self.vol_geom)
self.proj_id = astra.data3d.create('-sino', self.proj_geom)
else:
raise Exception("Invalid number of dimensions 'ndim'.")
# self.scal_fac = self.geom.full_angle_rad / self.geom.angles.size
# self.scal_fac = 1.0 / self.geom.angles.size
self.scal_fac = self.geom.voxel_size[0] / self.geom.angles.size
def _basic_par2d_fp(type):
import astra
import numpy as np
vg = astra.create_vol_geom(2, 32)
pg = astra.create_proj_geom('parallel', 1, 32, [0])
proj_id = astra.create_projector(type, pg, vg)
vol = np.random.rand(2, 32)
(sino_id, sino) = astra.create_sino(vol, proj_id)
astra.data2d.delete(sino_id)
astra.projector.delete(proj_id)
err = np.max(np.abs(sino[0,:] - np.sum(vol,axis=0)))
return err < 1e-6
def geom_setup_3D(self, sino, angles, shape, cors):
nSinos = sino.shape[self.slice_dim]
length = len(angles)
angles = np.deg2rad(angles)
vectors = np.zeros((length, 12))
for i in range(len(angles)):
# ray direction
vectors[i, 0] = np.sin(angles[i])
vectors[i, 1] = -np.cos(angles[i])
vectors[i, 2] = 0
# center of detector
vectors[i, 3:6] = 0
# vector from detector pixel (0,0) to (0,1)
vectors[i, 6] = np.cos(angles[i])
vectors[i, 7] = np.sin(angles[i])
vectors[i, 8] = 0
# vector from detector pixel (0,0) to (1,0)
vectors[i, 9] = 0
vectors[i, 10] = 0
vectors[i, 11] = 1
# i = range(length)
# # ray direction
# vectors[i, 0] = np.sin(theta[i])
# vectors[i, 1] = -np.cos(theta[i])
# vectors[i, 2] = 0
# # detector centre (use this for translation)
# # assuming all sinograms are translated by the same amount for now
# #det_vec = [cors[0], 0, 0]
# det_vec = [0, 0, 0]
# vectors[i, 3:6] = det_vec
# # (use the following vectors for rotation)
# # vector from detector pixel (0,0) to (0,1)
# vectors[i, 6] = np.cos(theta[i])
# vectors[i, 7] = np.sin(theta[i])
# vectors[i, 8] = 0
# # vector from detector pixel (0,0) to (1,0)
# vectors[i, 9:12] = [0, 0, 1]
# Parameters: #rows, #columns, vectors
vol_geom = astra.create_vol_geom(nSinos, shape[0], shape[2])
proj_geom = astra.create_proj_geom('parallel3d_vec', sino.shape[1],
sino.shape[2], vectors)
return vol_geom, proj_geom
def astra_2D_recon(self, data):
sino = data[0]
cor, angles, vol_shape, init = self.get_frame_params()
angles = np.deg2rad(angles)
if self.res:
res = np.zeros(self.len_res)
# create volume geom
vol_geom = astra.create_vol_geom(vol_shape)
# create projection geom
det_width = sino.shape[self.dim_detX]
proj_geom = astra.create_proj_geom('parallel', 1.0, det_width, angles)
sino = np.transpose(sino, (self.dim_rot, self.dim_detX))
# create sinogram id
sino_id = astra.data2d.create("-sino", proj_geom, sino)
# create reconstruction id
if init is not None:
rec_id = astra.data2d.create('-vol', vol_geom, init)
else:
rec_id = astra.data2d.create('-vol', vol_geom)
# if self.mask_id:
# self.mask_id = astra.data2d.create('-vol', vol_geom, self.mask)
# setup configuration options
cfg = self.set_config(rec_id, sino_id, proj_geom, vol_geom)
# create algorithm id
alg_id = astra.algorithm.create(cfg)
# run algorithm
if self.res:
for j in range(self.iters):
# Run a single iteration
astra.algorithm.run(alg_id, 1)
res[j] = astra.algorithm.get_res_norm(alg_id)
else:
astra.algorithm.run(alg_id, self.iters)
# get reconstruction matrix
if self.manual_mask is not False:
recon = self.manual_mask*astra.data2d.get(rec_id)
else:
recon = astra.data2d.get(rec_id)
# delete geometry
self.delete(alg_id, sino_id, rec_id, False)
return [recon, res] if self.res else recon
def reconstruct3D(self, sinogram, angles, shape, depth, alg_name, iterations):
det_rows = sinogram.shape[0]
det_cols = sinogram.shape[2]
# sinogram = np.transpose(sinogram, (2,1,0))
vol_geom = astra.create_vol_geom(shape[0], depth, shape[1])
proj_geom = astra.create_proj_geom('parallel3d', 1.0, 1.0, det_cols, \
det_rows, np.deg2rad(angles))
sinogram_id = astra.data3d.create("-sino", proj_geom, sinogram)
# Create a data object for the reconstruction
rec_id = astra.data3d.create('-vol', vol_geom)
cfg = astra.astra_dict(alg_name)
cfg['ReconstructionDataId'] = rec_id
cfg['ProjectionDataId'] = sinogram_id
# Create the algorithm object from the configuration structure
alg_id = astra.algorithm.create(cfg)
# This will have a runtime in the order of 10 seconds.
astra.algorithm.run(alg_id, iterations)
#if "CUDA" in params[0] and "FBP" not in params[0]:
#self.res += astra.algorithm.get_res_norm(alg_id)**2
#print math.sqrt(self.res)
# Get the result
rec = astra.data3d.get(rec_id)
astra.algorithm.delete(alg_id)
astra.data3d.delete(rec_id)
astra.data3d.delete(sinogram_id)
rec = rec[:160,:160,1]
return rec
def gen_random_geometry_fanflat_vec():
Vectors = np.zeros([16,6])
# We assume constant detector width in these tests
if not NONUNITDET:
w = 1.0
else:
w = 0.6 + 0.8 * np.random.random()
for i in range(Vectors.shape[0]):
angle1 = 2*np.pi*np.random.random()
if OBLIQUE:
angle2 = angle1 + 0.5 * np.random.random()
else:
angle2 = angle1
dist1 = 256 * (0.5 + np.random.random())
detc = 10 * np.random.random(size=2)
detu = [ math.cos(angle1) * w, math.sin(angle1) * w ]
src = [ math.sin(angle2) * dist1, -math.cos(angle2) * dist1 ]
Vectors[i, :] = [ src[0], src[1], detc[0], detc[1], detu[0], detu[1] ]
pg = astra.create_proj_geom('fanflat_vec', np.random.randint(*range2d), Vectors)
return pg
def gen_random_geometry_parallel_vec():
Vectors = np.zeros([16,6])
# We assume constant detector width in these tests
if not NONUNITDET:
w = 1.0
else:
w = 0.6 + 0.8 * np.random.random()
for i in range(Vectors.shape[0]):
l = 0.6 + 0.8 * np.random.random()
angle1 = 2*np.pi*np.random.random()
if OBLIQUE:
angle2 = angle1 + 0.5 * np.random.random()
else:
angle2 = angle1
detc = 10 * np.random.random(size=2)
detu = [ math.cos(angle1) * w, math.sin(angle1) * w ]
ray = [ math.sin(angle2) * l, -math.cos(angle2) * l ]
Vectors[i, :] = [ ray[0], ray[1], detc[0], detc[1], detu[0], detu[1] ]
pg = astra.create_proj_geom('parallel_vec', np.random.randint(*range2d), Vectors)
return pg
def geom_setup_2D(self, dim_det_cols):
in_pData = self.get_plugin_in_datasets()[0]
cor = self.cor + self.pad_amount
sino_shape = in_pData.get_shape()
nCols = sino_shape[dim_det_cols]
self.p_low, self.p_high = \
self.array_pad(cor[0], nCols + 2*self.pad_amount)
sino_width = \
sino_shape[1] + self.p_low + self.p_high + 2*self.pad_amount
vol_geom = astra.create_vol_geom(self.vol_shape[0], self.vol_shape[1])
self.proj_geom = astra.create_proj_geom('parallel', 1.0, sino_width,
np.deg2rad(self.angles))
self.rec_id = self.astra_function.create('-vol', vol_geom)
self.cfg = self.cfg_setup()
if self.alg_type is '2D' and "CUDA" not in self.name:
proj_id = astra.create_projector('strip', self.proj_geom, vol_geom)
self.cfg['ProjectorId'] = proj_id
def fp0(image, det_col=111, num_angles=222, voxel_size_mm=1):
"""Wrapper for astra forward projector
:param image:
:return: sinogram
"""
vol_geom = astra.create_vol_geom(image.shape)
proj_id = astra.create_projector(
'cuda',
astra.create_proj_geom('parallel', 1.0, det_col,
np.linspace(0, np.pi, num_angles, False)),
vol_geom)
sino_id, sino = astra.create_sino(image, proj_id)
sino *= voxel_size_mm
astra.data2d.delete(sino_id)
astra.projector.delete(proj_id)
return sino
def reconstruct2D(self, sinogram, angles, shape, alg_name, iterations):
vol_geom = astra.create_vol_geom(shape[0], shape[1])
proj_geom = astra.create_proj_geom('parallel', 1.0, sinogram.shape[1],
np.deg2rad(angles))
sinogram_id = astra.data2d.create("-sino", proj_geom, sinogram)
# Create a data object for the reconstruction
rec_id = astra.data2d.create('-vol', vol_geom)
cfg = astra.astra_dict(alg_name)
cfg['ReconstructionDataId'] = rec_id
cfg['ProjectionDataId'] = sinogram_id
if not "CUDA" in alg_name:
proj_id = astra.create_projector('strip', proj_geom, vol_geom)
cfg['ProjectorId'] = proj_id
# Create the algorithm object from the configuration structure
alg_id = astra.algorithm.create(cfg)
# This will have a runtime in the order of 10 seconds.
astra.algorithm.run(alg_id, iterations)
if "CUDA" in alg_name and "FBP" not in alg_name:
self.res += astra.algorithm.get_res_norm(alg_id)**2
print math.sqrt(self.res)
# Get the result
rec = astra.data2d.get(rec_id)
astra.algorithm.delete(alg_id)
astra.data2d.delete(rec_id)
astra.data2d.delete(sinogram_id)
return rec
def recon_fista_tv(im, angles, lam, fista_iter, iter):
"""Reconstruct the input sinogram by using the FISTA-TV algorithm
Parameters
----------
im : array_like
Image data (sinogram) as numpy array.
angles : double
Value in radians representing the number of angles of the input sinogram.
lam : double
Regularization parameter of the FISTA algorithm.
fista_iter : int
Number of iterations of the FISTA algorihtm.
iter : int
Number of iterations of the TV minimization.
"""
# Create ASTRA geometries:
vol_geom = astra.create_vol_geom(im.shape[1] , im.shape[1])
proj_geom = astra.create_proj_geom('parallel', 1.0, im.shape[1], linspace(0, angles, im.shape[0], False))
# Create the ASTRA projector:
p = tvtomo.ProjectorASTRA2D(proj_geom,vol_geom)
# Define parameters and FISTA object that performs reconstruction:
#lam = 1**-14
f = tvtomo.FISTA(p, lam, fista_iter)
# Actual reconstruction (takes time):
im_rec = f.reconstruct(im.astype(float32), iter)
return im_rec.astype(float32)
def astra(*args):
"""
Reconstruct object using the ASTRA toolbox
Extra options
----------
method : str
ASTRA reconstruction method to use.
num_iter : int, optional
Number of algorithm iterations performed.
proj_type : str, optional
ASTRA projector type to use: 'line', 'linear', or 'strip' (CPU only).
extra_options : dict, optional
Extra options for the ASTRA config (i.e. those in cfg['option'])
Warning
-------
When using CUDA, only 1 CPU core can be used.
Example
-------
>>> import tomopy
>>> obj = tomopy.shepp3d() # Generate an object.
>>> ang = tomopy.angles(180) # Generate uniformly spaced tilt angles.
>>> sim = tomopy.project(obj, ang) # Calculate projections.
>>>
>>> # Reconstruct object:
>>> rec = tomopy.recon(sim, ang, algorithm=tomopy.astra,
>>> options={'method':'SART', 'num_iter':10*180, 'proj_type':'linear',
>>> 'extra_options':{'MinConstraint':0}})
>>>
>>> # Show 64th slice of the reconstructed object.
>>> import pylab
>>> pylab.imshow(rec[64], cmap='gray')
>>> pylab.show()
"""
# Lazy import ASTRA
import astra as astra_mod
# Get shared arrays
tomo = mproc.SHARED_TOMO
recon = mproc.SHARED_ARRAY
# Unpack arguments
nang = args[0]
nslices = args[1]
ndet = args[2]
centers = args[3]
angles = args[4]
num_gridx = args[5]['num_gridx']
num_gridy = args[5]['num_gridy']
opts = args[5]['options']
istart = args[6]
iend = args[7]
# Check options
for o in needed_options['astra']:
if not o in opts:
logger.error("Option %s needed for ASTRA reconstruction." % (o,))
raise ValueError()
for o in default_options['astra']:
if not o in opts:
opts[o] = default_options['astra'][o]
# Create ASTRA geometries
vol_geom = astra_mod.create_vol_geom((num_gridx,num_gridy))
proj_geom = astra_mod.create_proj_geom('parallel',1.0,ndet,angles.astype(np.float64))
# Create ASTRA data id
sino = np.zeros((nang, ndet), dtype=np.float32)
# Create ASTRA config
cfg = astra_mod.astra_dict(opts['method'])
if opts['proj_type']!='cuda':
pi = astra_mod.create_projector(opts['proj_type'], proj_geom, vol_geom)
sid = astra_mod.data2d.link('-sino', proj_geom, sino)
cfg['ProjectorId'] = pi
cfg['ProjectionDataId'] = sid
use_cuda=False
else:
use_cuda=True
if 'extra_options' in opts:
cfg['option'] = opts['extra_options']
else:
cfg['option'] = {}
# Perform reconstruction
for i in xrange(istart, iend):
sino[:] = tomo[:,i,:]
cfg['option']['z_id']=i
# Fix center of rotation
if use_cuda:
proj_geom['option'] = {'ExtraDetectorOffset': (centers[i]-ndet/2.)*np.ones(nang)}
sid = astra_mod.data2d.link('-sino', proj_geom, sino)
cfg['ProjectionDataId'] = sid
pi = astra_mod.create_projector(opts['proj_type'], proj_geom, vol_geom)
cfg['ProjectorId'] = pi
else:
# Temporary workaround, will be fixed in later ASTRA version
shft = int(np.round(ndet/2.-centers[i]))
sino[:] = np.roll(sino,shft)
l = shft
r = sino.shape[1]+shft
if l<0: l=0
if r>sino.shape[1]: r=sino.shape[1]
sino[:,0:l]=0
sino[:,r:sino.shape[1]]=0
vid = astra_mod.data2d.link('-vol', vol_geom, recon[i])
cfg['ReconstructionDataId'] = vid
alg_id = astra_mod.algorithm.create(cfg)
astra_mod.algorithm.run(alg_id, opts['num_iter'])
astra_mod.algorithm.delete(alg_id)
astra_mod.data2d.delete(vid)
if use_cuda:
astra_mod.projector.delete(pi)
astra_mod.data2d.delete(sid)
# Clean up
if not use_cuda:
astra_mod.projector.delete(pi)
astra_mod.data2d.delete(sid)