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# -----------------------------------------------------------------------
# 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/>.
#
# -----------------------------------------------------------------------
import astra
import numpy as np
import six
class SIRTPlugin(astra.plugin.base):
"""SIRT.
Options:
'Relaxation': relaxation factor (optional)
'MinConstraint': constrain values to at least this (optional)
'MaxConstraint': constrain values to at most this (optional)
"""
astra_name = "SIRT-PLUGIN"
def initialize(self,cfg, Relaxation = 1, MinConstraint = None, MaxConstraint = None):
self.W = astra.OpTomo(cfg['ProjectorId'])
self.vid = cfg['ReconstructionDataId']
self.sid = cfg['ProjectionDataId']
self.min_constraint = MinConstraint
self.max_constraint = MaxConstraint
try:
v = astra.data2d.get_shared(self.vid)
s = astra.data2d.get_shared(self.sid)
self.data_mod = astra.data2d
except Exception:
v = astra.data3d.get_shared(self.vid)
s = astra.data3d.get_shared(self.sid)
self.data_mod = astra.data3d
self.R = self.W * np.ones(v.shape,dtype=np.float32).ravel();
self.R[self.R < 0.000001] = np.Inf
self.R = 1 / self.R
self.R = self.R.reshape(s.shape)
self.mrC = self.W.T * np.ones(s.shape,dtype=np.float32).ravel();
self.mrC[self.mrC < 0.000001] = np.Inf
self.mrC = -Relaxation / self.mrC
self.mrC = self.mrC.reshape(v.shape)
def run(self, its):
v = self.data_mod.get_shared(self.vid)
s = self.data_mod.get_shared(self.sid)
tv = np.zeros(v.shape, dtype=np.float32)
ts = np.zeros(s.shape, dtype=np.float32)
W = self.W
mrC = self.mrC
R = self.R
for i in range(its):
W.FP(v,out=ts)
ts -= s
ts *= R # ts = R * (W*v - s)
W.BP(ts,out=tv)
tv *= mrC
v += tv # v = v - rel * C * W' * ts
if self.min_constraint is not None or self.max_constraint is not None:
v.clip(min=self.min_constraint, max=self.max_constraint, out=v)
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