def add_noise_to_sino(sinogram_in, I0):
"""Adds Poisson noise to a sinogram.
:param sinogram_in: Sinogram to add noise to.
:type sinogram_in: :class:`numpy.ndarray`
:param I0: Background intensity. Lower values lead to higher noise.
:type I0: :class:`float`
:returns: :class:`numpy.ndarray` -- the sinogram with added noise.
"""
if isinstance(sinogram_in, np.ndarray):
sinogramRaw = sinogram_in
else:
sinogramRaw = data2d.get(sinogram_in)
max_sinogramRaw = sinogramRaw.max()
sinogramRawScaled = sinogramRaw / max_sinogramRaw
# to detector count
sinogramCT = I0 * np.exp(-sinogramRawScaled)
# add poison noise
sinogramCT_C = np.zeros_like(sinogramCT)
for i in xrange(sinogramCT_C.shape[0]):
for j in xrange(sinogramCT_C.shape[1]):
sinogramCT_C[i, j] = np.random.poisson(sinogramCT[i, j])
# to density
sinogramCT_D = sinogramCT_C / I0
sinogram_out = -max_sinogramRaw * np.log(sinogramCT_D)
if not isinstance(sinogram_in, np.ndarray):
at.data2d.store(sinogram_in, sinogram_out)
return sinogram_out
def create_sino(data, proj_id, useCUDA=False, returnData=True, gpuIndex=None):
"""Create a forward projection of an image (2D).
:param data: Image data or ID.
:type data: :class:`numpy.ndarray` or :class:`int`
:param proj_id: ID of the projector to use.
:type proj_id: :class:`int`
:param useCUDA: If ``True``, use CUDA for the calculation.
:type useCUDA: :class:`bool`
:param returnData: If False, only return the ID of the forward projection.
:type returnData: :class:`bool`
:param gpuIndex: Optional GPU index.
:type gpuIndex: :class:`int`
:returns: :class:`int` or (:class:`int`, :class:`numpy.ndarray`) -- If ``returnData=False``, returns the ID of the forward projection. Otherwise, returns a tuple containing the ID of the forward projection and the forward projection itself, in that order.
"""
proj_geom = projector.projection_geometry(proj_id)
vol_geom = projector.volume_geometry(proj_id)
if isinstance(data, np.ndarray):
volume_id = data2d.create("-vol", vol_geom, data)
else:
volume_id = data
sino_id = data2d.create("-sino", proj_geom, 0)
algString = "FP"
if useCUDA:
algString = algString + "_CUDA"
cfg = astra_dict(algString)
if not useCUDA:
cfg["ProjectorId"] = proj_id
if not gpuIndex == None:
cfg["option"] = {"GPUindex": gpuIndex}
cfg["ProjectionDataId"] = sino_id
cfg["VolumeDataId"] = volume_id
alg_id = algorithm.create(cfg)
algorithm.run(alg_id)
algorithm.delete(alg_id)
if isinstance(data, np.ndarray):
data2d.delete(volume_id)
if returnData:
return sino_id, data2d.get(sino_id)
else:
return sino_id
def create_sino(data, proj_id, useCUDA=False, returnData=True, gpuIndex=None):
"""Create a forward projection of an image (2D).
:param data: Image data or ID.
:type data: :class:`numpy.ndarray` or :class:`int`
:param proj_id: ID of the projector to use.
:type proj_id: :class:`int`
:param useCUDA: If ``True``, use CUDA for the calculation.
:type useCUDA: :class:`bool`
:param returnData: If False, only return the ID of the forward projection.
:type returnData: :class:`bool`
:param gpuIndex: Optional GPU index.
:type gpuIndex: :class:`int`
:returns: :class:`int` or (:class:`int`, :class:`numpy.ndarray`) -- If ``returnData=False``, returns the ID of the forward projection. Otherwise, returns a tuple containing the ID of the forward projection and the forward projection itself, in that order.
"""
proj_geom = projector.projection_geometry(proj_id)
vol_geom = projector.volume_geometry(proj_id)
if isinstance(data, np.ndarray):
volume_id = data2d.create('-vol', vol_geom, data)
else:
volume_id = data
sino_id = data2d.create('-sino', proj_geom, 0)
algString = 'FP'
if useCUDA:
algString = algString + '_CUDA'
cfg = astra_dict(algString)
if not useCUDA:
cfg['ProjectorId'] = proj_id
if not gpuIndex==None:
cfg['option']={'GPUindex':gpuIndex}
cfg['ProjectionDataId'] = sino_id
cfg['VolumeDataId'] = volume_id
alg_id = algorithm.create(cfg)
algorithm.run(alg_id)
algorithm.delete(alg_id)
if isinstance(data, np.ndarray):
data2d.delete(volume_id)
if returnData:
return sino_id, data2d.get(sino_id)
else:
return sino_id
def create_backprojection(data, proj_id, useCUDA=False, returnData=True):
"""Create a backprojection of a sinogram (2D).
:param data: Sinogram data or ID.
:type data: :class:`numpy.ndarray` or :class:`int`
:param proj_id: ID of the projector to use.
:type proj_id: :class:`int`
:param useCUDA: If ``True``, use CUDA for the calculation.
:type useCUDA: :class:`bool`
:param returnData: If False, only return the ID of the backprojection.
:type returnData: :class:`bool`
:returns: :class:`int` or (:class:`int`, :class:`numpy.ndarray`) -- If ``returnData=False``, returns the ID of the backprojection. Otherwise, returns a tuple containing the ID of the backprojection and the backprojection itself, in that order.
"""
proj_geom = projector.projection_geometry(proj_id)
vol_geom = projector.volume_geometry(proj_id)
if isinstance(data, np.ndarray):
sino_id = data2d.create("-sino", proj_geom, data)
else:
sino_id = data
vol_id = data2d.create("-vol", vol_geom, 0)
algString = "BP"
if useCUDA:
algString = algString + "_CUDA"
cfg = astra_dict(algString)
if not useCUDA:
cfg["ProjectorId"] = proj_id
cfg["ProjectionDataId"] = sino_id
cfg["ReconstructionDataId"] = vol_id
alg_id = algorithm.create(cfg)
algorithm.run(alg_id)
algorithm.delete(alg_id)
if isinstance(data, np.ndarray):
data2d.delete(sino_id)
if returnData:
return vol_id, data2d.get(vol_id)
else:
return vol_id
def create_backprojection(data, proj_id, useCUDA=False, returnData=True):
"""Create a backprojection of a sinogram (2D).
:param data: Sinogram data or ID.
:type data: :class:`numpy.ndarray` or :class:`int`
:param proj_id: ID of the projector to use.
:type proj_id: :class:`int`
:param useCUDA: If ``True``, use CUDA for the calculation.
:type useCUDA: :class:`bool`
:param returnData: If False, only return the ID of the backprojection.
:type returnData: :class:`bool`
:returns: :class:`int` or (:class:`int`, :class:`numpy.ndarray`) -- If ``returnData=False``, returns the ID of the backprojection. Otherwise, returns a tuple containing the ID of the backprojection and the backprojection itself, in that order.
"""
proj_geom = projector.projection_geometry(proj_id)
vol_geom = projector.volume_geometry(proj_id)
if isinstance(data, np.ndarray):
sino_id = data2d.create('-sino', proj_geom, data)
else:
sino_id = data
vol_id = data2d.create('-vol', vol_geom, 0)
algString = 'BP'
if useCUDA:
algString = algString + '_CUDA'
cfg = astra_dict(algString)
if not useCUDA:
cfg['ProjectorId'] = proj_id
cfg['ProjectionDataId'] = sino_id
cfg['ReconstructionDataId'] = vol_id
alg_id = algorithm.create(cfg)
algorithm.run(alg_id)
algorithm.delete(alg_id)
if isinstance(data, np.ndarray):
data2d.delete(sino_id)
if returnData:
return vol_id, data2d.get(vol_id)
else:
return vol_id
def create_reconstruction(
rec_type,
proj_id,
sinogram,
iterations=1,
use_mask="no",
mask=np.array([]),
use_minc="no",
minc=0,
use_maxc="no",
maxc=255,
returnData=True,
filterType=None,
filterData=None,
):
"""Create a reconstruction of a sinogram (2D).
:param rec_type: Name of the reconstruction algorithm.
:type rec_type: :class:`string`
:param proj_id: ID of the projector to use.
:type proj_id: :class:`int`
:param sinogram: Sinogram data or ID.
:type sinogram: :class:`numpy.ndarray` or :class:`int`
:param iterations: Number of iterations to run.
:type iterations: :class:`int`
:param use_mask: Whether to use a mask.
:type use_mask: ``'yes'`` or ``'no'``
:param mask: Mask data or ID
:type mask: :class:`numpy.ndarray` or :class:`int`
:param use_minc: Whether to force a minimum value on the reconstruction pixels.
:type use_minc: ``'yes'`` or ``'no'``
:param minc: Minimum value to use.
:type minc: :class:`float`
:param use_maxc: Whether to force a maximum value on the reconstruction pixels.
:type use_maxc: ``'yes'`` or ``'no'``
:param maxc: Maximum value to use.
:type maxc: :class:`float`
:param returnData: If False, only return the ID of the reconstruction.
:type returnData: :class:`bool`
:param filterType: Which type of filter to use for filter-based methods.
:type filterType: :class:`string`
:param filterData: Optional filter data for filter-based methods.
:type filterData: :class:`numpy.ndarray`
:returns: :class:`int` or (:class:`int`, :class:`numpy.ndarray`) -- If ``returnData=False``, returns the ID of the reconstruction. Otherwise, returns a tuple containing the ID of the reconstruction and reconstruction itself, in that order.
"""
proj_geom = projector.projection_geometry(proj_id)
if isinstance(sinogram, np.ndarray):
sino_id = data2d.create("-sino", proj_geom, sinogram)
else:
sino_id = sinogram
vol_geom = projector.volume_geometry(proj_id)
recon_id = data2d.create("-vol", vol_geom, 0)
cfg = astra_dict(rec_type)
if not "CUDA" in rec_type:
cfg["ProjectorId"] = proj_id
cfg["ProjectionDataId"] = sino_id
cfg["ReconstructionDataId"] = recon_id
cfg["options"] = {}
if use_mask == "yes":
if isinstance(mask, np.ndarray):
mask_id = data2d.create("-vol", vol_geom, mask)
else:
mask_id = mask
cfg["options"]["ReconstructionMaskId"] = mask_id
if not filterType == None:
cfg["FilterType"] = filterType
if not filterData == None:
if isinstance(filterData, np.ndarray):
nexpow = int(pow(2, math.ceil(math.log(2 * proj_geom["DetectorCount"], 2))))
filtSize = nexpow / 2 + 1
filt_proj_geom = create_proj_geom("parallel", 1.0, filtSize, proj_geom["ProjectionAngles"])
filt_id = data2d.create("-sino", filt_proj_geom, filterData)
else:
filt_id = filterData
cfg["FilterSinogramId"] = filt_id
cfg["options"]["UseMinConstraint"] = use_minc
cfg["options"]["MinConstraintValue"] = minc
cfg["options"]["UseMaxConstraint"] = use_maxc
cfg["options"]["MaxConstraintValue"] = maxc
cfg["options"]["ProjectionOrder"] = "random"
alg_id = algorithm.create(cfg)
algorithm.run(alg_id, iterations)
algorithm.delete(alg_id)
if isinstance(sinogram, np.ndarray):
data2d.delete(sino_id)
if use_mask == "yes" and isinstance(mask, np.ndarray):
data2d.delete(mask_id)
if not filterData == None:
if isinstance(filterData, np.ndarray):
data2d.delete(filt_id)
if returnData:
return recon_id, data2d.get(recon_id)
else:
return recon_id
def create_reconstruction(rec_type, proj_id, sinogram, iterations=1, use_mask='no', mask=np.array([]), use_minc='no', minc=0, use_maxc='no', maxc=255, returnData=True, filterType=None, filterData=None):
"""Create a reconstruction of a sinogram (2D).
:param rec_type: Name of the reconstruction algorithm.
:type rec_type: :class:`string`
:param proj_id: ID of the projector to use.
:type proj_id: :class:`int`
:param sinogram: Sinogram data or ID.
:type sinogram: :class:`numpy.ndarray` or :class:`int`
:param iterations: Number of iterations to run.
:type iterations: :class:`int`
:param use_mask: Whether to use a mask.
:type use_mask: ``'yes'`` or ``'no'``
:param mask: Mask data or ID
:type mask: :class:`numpy.ndarray` or :class:`int`
:param use_minc: Whether to force a minimum value on the reconstruction pixels.
:type use_minc: ``'yes'`` or ``'no'``
:param minc: Minimum value to use.
:type minc: :class:`float`
:param use_maxc: Whether to force a maximum value on the reconstruction pixels.
:type use_maxc: ``'yes'`` or ``'no'``
:param maxc: Maximum value to use.
:type maxc: :class:`float`
:param returnData: If False, only return the ID of the reconstruction.
:type returnData: :class:`bool`
:param filterType: Which type of filter to use for filter-based methods.
:type filterType: :class:`string`
:param filterData: Optional filter data for filter-based methods.
:type filterData: :class:`numpy.ndarray`
:returns: :class:`int` or (:class:`int`, :class:`numpy.ndarray`) -- If ``returnData=False``, returns the ID of the reconstruction. Otherwise, returns a tuple containing the ID of the reconstruction and reconstruction itself, in that order.
"""
proj_geom = projector.projection_geometry(proj_id)
if isinstance(sinogram, np.ndarray):
sino_id = data2d.create('-sino', proj_geom, sinogram)
else:
sino_id = sinogram
vol_geom = projector.volume_geometry(proj_id)
recon_id = data2d.create('-vol', vol_geom, 0)
cfg = astra_dict(rec_type)
if not 'CUDA' in rec_type:
cfg['ProjectorId'] = proj_id
cfg['ProjectionDataId'] = sino_id
cfg['ReconstructionDataId'] = recon_id
cfg['options'] = {}
if use_mask == 'yes':
if isinstance(mask, np.ndarray):
mask_id = data2d.create('-vol', vol_geom, mask)
else:
mask_id = mask
cfg['options']['ReconstructionMaskId'] = mask_id
if not filterType == None:
cfg['FilterType'] = filterType
if not filterData == None:
if isinstance(filterData, np.ndarray):
nexpow = int(
pow(2, math.ceil(math.log(2 * proj_geom['DetectorCount'], 2))))
filtSize = nexpow / 2 + 1
filt_proj_geom = create_proj_geom(
'parallel', 1.0, filtSize, proj_geom['ProjectionAngles'])
filt_id = data2d.create('-sino', filt_proj_geom, filterData)
else:
filt_id = filterData
cfg['FilterSinogramId'] = filt_id
cfg['options']['UseMinConstraint'] = use_minc
cfg['options']['MinConstraintValue'] = minc
cfg['options']['UseMaxConstraint'] = use_maxc
cfg['options']['MaxConstraintValue'] = maxc
cfg['options']['ProjectionOrder'] = 'random'
alg_id = algorithm.create(cfg)
algorithm.run(alg_id, iterations)
algorithm.delete(alg_id)
if isinstance(sinogram, np.ndarray):
data2d.delete(sino_id)
if use_mask == 'yes' and isinstance(mask, np.ndarray):
data2d.delete(mask_id)
if not filterData == None:
if isinstance(filterData, np.ndarray):
data2d.delete(filt_id)
if returnData:
return recon_id, data2d.get(recon_id)
else:
return recon_id
def create_sino(data, proj_id=None, proj_geom=None, vol_geom=None,
useCUDA=False, returnData=True, gpuIndex=None):
"""Create a forward projection of an image (2D).
:param data: Image data or ID.
:type data: :class:`numpy.ndarray` or :class:`int`
:param proj_id: ID of the projector to use.
:type proj_id: :class:`int`
:param proj_geom: Projection geometry.
:type proj_geom: :class:`dict`
:param vol_geom: Volume geometry.
:type vol_geom: :class:`dict`
:param useCUDA: If ``True``, use CUDA for the calculation.
:type useCUDA: :class:`bool`
:param returnData: If False, only return the ID of the forward projection.
:type returnData: :class:`bool`
:param gpuIndex: Optional GPU index.
:type gpuIndex: :class:`int`
:returns: :class:`int` or (:class:`int`, :class:`numpy.ndarray`)
If ``returnData=False``, returns the ID of the forward
projection. Otherwise, returns a tuple containing the ID of the
forward projection and the forward projection itself, in that
order.
The geometry of setup is defined by ``proj_id`` or with
``proj_geom`` and ``vol_geom``. If ``proj_id`` is given, then
``proj_geom`` and ``vol_geom`` must be None and vice versa.
"""
if proj_id is not None:
proj_geom = projector.projection_geometry(proj_id)
vol_geom = projector.volume_geometry(proj_id)
elif proj_geom is not None and vol_geom is not None:
if not useCUDA:
# We need more parameters to create projector.
raise ValueError(
"""A ``proj_id`` is needed when CUDA is not used.""")
else:
raise Exception("""The geometry setup is not defined.
The geometry of setup is defined by ``proj_id`` or with
``proj_geom`` and ``vol_geom``. If ``proj_id`` is given, then
``proj_geom`` and ``vol_geom`` must be None and vice versa.""")
if isinstance(data, np.ndarray):
volume_id = data2d.create('-vol', vol_geom, data)
else:
volume_id = data
sino_id = data2d.create('-sino', proj_geom, 0)
algString = 'FP'
if useCUDA:
algString = algString + '_CUDA'
cfg = astra_dict(algString)
if not useCUDA:
cfg['ProjectorId'] = proj_id
if gpuIndex is not None:
cfg['option'] = {'GPUindex': gpuIndex}
cfg['ProjectionDataId'] = sino_id
cfg['VolumeDataId'] = volume_id
alg_id = algorithm.create(cfg)
algorithm.run(alg_id)
algorithm.delete(alg_id)
if isinstance(data, np.ndarray):
data2d.delete(volume_id)
if returnData:
return sino_id, data2d.get(sino_id)
else:
return sino_id
