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#-----------------------------------------------------------------------
#Copyright 2015 Centrum Wiskunde & Informatica, Amsterdam
#
#Author: Daniel M. Pelt
#Contact: D.M.Pelt@cwi.nl
#Website: http://dmpelt.github.io/pyastratoolbox/
#
#
#This file is part of the Python interface to the
#All Scale Tomographic Reconstruction Antwerp Toolbox ("ASTRA Toolbox").
#
#The Python interface to 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 Python interface to 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 Python interface to the ASTRA Toolbox. If not, see <http://www.gnu.org/licenses/>.
#
#-----------------------------------------------------------------------
import astra
import numpy as np
import six
# Define the plugin class (has to subclass astra.plugin.base)
# Note that usually, these will be defined in a separate package/module
class SIRTPlugin(astra.plugin.base):
"""Example of an ASTRA plugin class, implementing a simple 2D SIRT algorithm.
Options:
'rel_factor': relaxation factor (optional)
"""
# The astra_name variable defines the name to use to
# call the plugin from ASTRA
astra_name = "SIRT-PLUGIN"
def initialize(self,cfg, rel_factor = 1):
self.W = astra.OpTomo(cfg['ProjectorId'])
self.vid = cfg['ReconstructionDataId']
self.sid = cfg['ProjectionDataId']
self.rel = rel_factor
def run(self, its):
v = astra.data2d.get_shared(self.vid)
s = astra.data2d.get_shared(self.sid)
W = self.W
for i in range(its):
v[:] += self.rel*(W.T*(s - (W*v).reshape(s.shape))).reshape(v.shape)/s.size
if __name__=='__main__':
vol_geom = astra.create_vol_geom(256, 256)
proj_geom = astra.create_proj_geom('parallel', 1.0, 384, np.linspace(0,np.pi,180,False))
# As before, create a sinogram from a phantom
import scipy.io
P = scipy.io.loadmat('phantom.mat')['phantom256']
proj_id = astra.create_projector('cuda',proj_geom,vol_geom)
# construct the OpTomo object
W = astra.OpTomo(proj_id)
sinogram = W * P
sinogram = sinogram.reshape([180, 384])
# Register the plugin with ASTRA
# First we import the package that contains the plugin
import s018_plugin
# Then, we register the plugin class with ASTRA
astra.plugin.register(s018_plugin.SIRTPlugin)
# Get a list of registered plugins
six.print_(astra.plugin.get_registered())
# To get help on a registered plugin, use get_help
six.print_(astra.plugin.get_help('SIRT-PLUGIN'))
# Create data structures
sid = astra.data2d.create('-sino', proj_geom, sinogram)
vid = astra.data2d.create('-vol', vol_geom)
# Create config using plugin name
cfg = astra.astra_dict('SIRT-PLUGIN')
cfg['ProjectorId'] = proj_id
cfg['ProjectionDataId'] = sid
cfg['ReconstructionDataId'] = vid
# Create algorithm object
alg_id = astra.algorithm.create(cfg)
# Run algorithm for 100 iterations
astra.algorithm.run(alg_id, 100)
# Get reconstruction
rec = astra.data2d.get(vid)
# Options for the plugin go in cfg['option']
cfg = astra.astra_dict('SIRT-PLUGIN')
cfg['ProjectorId'] = proj_id
cfg['ProjectionDataId'] = sid
cfg['ReconstructionDataId'] = vid
cfg['option'] = {}
cfg['option']['rel_factor'] = 1.5
alg_id_rel = astra.algorithm.create(cfg)
astra.algorithm.run(alg_id_rel, 100)
rec_rel = astra.data2d.get(vid)
# We can also use OpTomo to call the plugin
rec_op = W.reconstruct('SIRT-PLUGIN', sinogram, 100, extraOptions={'rel_factor':1.5})
import pylab as pl
pl.gray()
pl.figure(1)
pl.imshow(rec,vmin=0,vmax=1)
pl.figure(2)
pl.imshow(rec_rel,vmin=0,vmax=1)
pl.figure(3)
pl.imshow(rec_op,vmin=0,vmax=1)
pl.show()
# Clean up.
astra.projector.delete(proj_id)
astra.algorithm.delete([alg_id, alg_id_rel])
astra.data2d.delete([vid, sid])
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