# -----------------------------------------------------------------------
# Copyright: 2010-2018, imec Vision Lab, University of Antwerp
# 2013-2018, CWI, Amsterdam
#
# Contact: astra@astra-toolbox.com
# Website: http://www.astra-toolbox.com/
#
# This file is part of the ASTRA Toolbox.
#
#
# The ASTRA Toolbox is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# The ASTRA Toolbox is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with the ASTRA Toolbox. If not, see <http://www.gnu.org/licenses/>.
#
# -----------------------------------------------------------------------
from astra import data2d
from astra import data3d
from astra import projector
from astra import projector3d
from astra import creators
from astra import algorithm
from astra import functions
import numpy as np
from six.moves import reduce
try:
from six.moves import range
except ImportError:
# six 1.3.0
from six.moves import xrange as range
import operator
import scipy.sparse.linalg
[docs]class OpTomo(scipy.sparse.linalg.LinearOperator):
"""Object that imitates a projection matrix with a given projector.
This object can do forward projection by using the ``*`` operator::
W = astra.OpTomo(proj_id)
fp = W*image
bp = W.T*sinogram
It can also be used in minimization methods of the :mod:`scipy.sparse.linalg` module::
W = astra.OpTomo(proj_id)
output = scipy.sparse.linalg.lsqr(W,sinogram)
:param proj_id: ID to a projector.
:type proj_id: :class:`int`
"""
def __init__(self,proj_id):
self.dtype = np.float32
try:
self.vg = projector.volume_geometry(proj_id)
self.pg = projector.projection_geometry(proj_id)
self.data_mod = data2d
self.appendString = ""
if projector.is_cuda(proj_id):
self.appendString += "_CUDA"
except Exception:
self.vg = projector3d.volume_geometry(proj_id)
self.pg = projector3d.projection_geometry(proj_id)
self.data_mod = data3d
self.appendString = "3D"
if projector3d.is_cuda(proj_id):
self.appendString += "_CUDA"
self.vshape = functions.geom_size(self.vg)
self.vsize = reduce(operator.mul,self.vshape)
self.sshape = functions.geom_size(self.pg)
self.ssize = reduce(operator.mul,self.sshape)
self.shape = (self.ssize, self.vsize)
self.proj_id = proj_id
self.transposeOpTomo = OpTomoTranspose(self)
try:
self.T = self.transposeOpTomo
except AttributeError:
# Scipy >= 0.16 defines self.T using self._transpose()
pass
def _transpose(self):
return self.transposeOpTomo
def __checkArray(self, arr, shp):
if len(arr.shape)==1:
arr = arr.reshape(shp)
if arr.dtype != np.float32:
arr = arr.astype(np.float32)
if arr.flags['C_CONTIGUOUS']==False:
arr = np.ascontiguousarray(arr)
return arr
def _matvec(self,v):
"""Implements the forward operator.
:param v: Volume to forward project.
:type v: :class:`numpy.ndarray`
"""
return self.FP(v, out=None).ravel()
[docs] def rmatvec(self,s):
"""Implements the transpose operator.
:param s: The projection data.
:type s: :class:`numpy.ndarray`
"""
return self.BP(s, out=None).ravel()
def __mul__(self,v):
"""Provides easy forward operator by *.
:param v: Volume to forward project.
:type v: :class:`numpy.ndarray`
"""
# Catch the case of a forward projection of a 2D/3D image
if isinstance(v, np.ndarray) and v.shape==self.vshape:
return self._matvec(v)
return scipy.sparse.linalg.LinearOperator.__mul__(self, v)
[docs] def reconstruct(self, method, s, iterations=1, extraOptions = None):
"""Reconstruct an object.
:param method: Method to use for reconstruction.
:type method: :class:`string`
:param s: The projection data.
:type s: :class:`numpy.ndarray`
:param iterations: Number of iterations to use.
:type iterations: :class:`int`
:param extraOptions: Extra options to use during reconstruction (i.e. for cfg['option']).
:type extraOptions: :class:`dict`
"""
if extraOptions == {}:
opts={}
if extraOptions is None:
opts={}
s = self.__checkArray(s, self.sshape)
sid = self.data_mod.link('-sino',self.pg,s)
v = np.zeros(self.vshape,dtype=np.float32)
vid = self.data_mod.link('-vol',self.vg,v)
cfg = creators.astra_dict(method)
cfg['ProjectionDataId'] = sid
cfg['ReconstructionDataId'] = vid
cfg['ProjectorId'] = self.proj_id
if 'FilterType' in list(extraOptions.keys()):
cfg['FilterType'] = extraOptions['FilterType']
opts = {key: extraOptions[key] for key in extraOptions if key != 'FilterType'}
else:
opts = extraOptions
cfg['option'] = opts
alg_id = algorithm.create(cfg)
algorithm.run(alg_id,iterations)
algorithm.delete(alg_id)
self.data_mod.delete([vid,sid])
return v
[docs] def FP(self,v,out=None):
"""Perform forward projection.
Output must have the right 2D/3D shape. Input may also be flattened.
Output must also be contiguous and float32. This isn't required for the
input, but it is more efficient if it is.
:param v: Volume to forward project.
:type v: :class:`numpy.ndarray`
:param out: Array to store result in.
:type out: :class:`numpy.ndarray`
"""
v = self.__checkArray(v, self.vshape)
vid = self.data_mod.link('-vol',self.vg,v)
if out is None:
out = np.zeros(self.sshape,dtype=np.float32)
sid = self.data_mod.link('-sino',self.pg,out)
cfg = creators.astra_dict('FP'+self.appendString)
cfg['ProjectionDataId'] = sid
cfg['VolumeDataId'] = vid
cfg['ProjectorId'] = self.proj_id
fp_id = algorithm.create(cfg)
algorithm.run(fp_id)
algorithm.delete(fp_id)
self.data_mod.delete([vid,sid])
return out
[docs] def BP(self,s,out=None):
"""Perform backprojection.
Output must have the right 2D/3D shape. Input may also be flattened.
Output must also be contiguous and float32. This isn't required for the
input, but it is more efficient if it is.
:param : The projection data.
:type s: :class:`numpy.ndarray`
:param out: Array to store result in.
:type out: :class:`numpy.ndarray`
"""
s = self.__checkArray(s, self.sshape)
sid = self.data_mod.link('-sino',self.pg,s)
if out is None:
out = np.zeros(self.vshape,dtype=np.float32)
vid = self.data_mod.link('-vol',self.vg,out)
cfg = creators.astra_dict('BP'+self.appendString)
cfg['ProjectionDataId'] = sid
cfg['ReconstructionDataId'] = vid
cfg['ProjectorId'] = self.proj_id
bp_id = algorithm.create(cfg)
algorithm.run(bp_id)
algorithm.delete(bp_id)
self.data_mod.delete([vid,sid])
return out
[docs]class OpTomoTranspose(scipy.sparse.linalg.LinearOperator):
"""This object provides the transpose operation (``.T``) of the OpTomo object.
Do not use directly, since it can be accessed as member ``.T`` of
an :class:`OpTomo` object.
"""
def __init__(self,parent):
self.parent = parent
self.dtype = np.float32
self.shape = (parent.shape[1], parent.shape[0])
try:
self.T = self.parent
except AttributeError:
# Scipy >= 0.16 defines self.T using self._transpose()
pass
def _matvec(self, s):
return self.parent.rmatvec(s)
[docs] def rmatvec(self, v):
return self.parent.matvec(v)
def _transpose(self):
return self.parent
def __mul__(self,s):
# Catch the case of a backprojection of 2D/3D data
if isinstance(s, np.ndarray) and s.shape==self.parent.sshape:
return self._matvec(s)
return scipy.sparse.linalg.LinearOperator.__mul__(self, s)