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-rw-r--r--python/astra/optomo.py96
1 files changed, 66 insertions, 30 deletions
diff --git a/python/astra/optomo.py b/python/astra/optomo.py
index 5a92998..dde719e 100644
--- a/python/astra/optomo.py
+++ b/python/astra/optomo.py
@@ -111,21 +111,7 @@ class OpTomo(scipy.sparse.linalg.LinearOperator):
:param v: Volume to forward project.
:type v: :class:`numpy.ndarray`
"""
- v = self.__checkArray(v, self.vshape)
- vid = self.data_mod.link('-vol',self.vg,v)
- s = np.zeros(self.sshape,dtype=np.float32)
- sid = self.data_mod.link('-sino',self.pg,s)
-
- 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 s.ravel()
+ return self.FP(v, out=None).ravel()
def rmatvec(self,s):
"""Implements the transpose operator.
@@ -133,21 +119,7 @@ class OpTomo(scipy.sparse.linalg.LinearOperator):
:param s: The projection data.
:type s: :class:`numpy.ndarray`
"""
- 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('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 v.ravel()
+ return self.BP(s, out=None).ravel()
def __mul__(self,v):
"""Provides easy forward operator by *.
@@ -189,6 +161,70 @@ class OpTomo(scipy.sparse.linalg.LinearOperator):
self.data_mod.delete([vid,sid])
return v
+ 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
+
+ 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
+
+
+
+
class OpTomoTranspose(scipy.sparse.linalg.LinearOperator):
"""This object provides the transpose operation (``.T``) of the OpTomo object.