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authorWillem Jan Palenstijn <wjp@usecode.org>2015-02-26 16:30:43 +0100
committerWillem Jan Palenstijn <wjp@usecode.org>2015-02-26 16:30:43 +0100
commit9e2bb413a937aefe57f4fcf343413543ae57258a (patch)
treef4d87d40ae17775e4e3c744476d31d56b5dba64b /samples/python/s016_plots.py
parent0ca00f4c671d6d583ae77838d3e0d4fcd411f077 (diff)
parente0aca18f687e9f49223ffb24b9be354bed4b150a (diff)
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Merge pull request #16 from dmpelt/python-interface
Add Python interface
Diffstat (limited to 'samples/python/s016_plots.py')
-rw-r--r--samples/python/s016_plots.py86
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diff --git a/samples/python/s016_plots.py b/samples/python/s016_plots.py
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+#-----------------------------------------------------------------------
+#Copyright 2013 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/>.
+#
+#-----------------------------------------------------------------------
+
+from six.moves import range
+import astra
+import numpy as np
+
+
+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('line',proj_geom,vol_geom)
+sinogram_id, sinogram = astra.create_sino(P, proj_id,useCUDA=True)
+
+import pylab
+pylab.gray()
+pylab.figure(1)
+pylab.imshow(P)
+pylab.figure(2)
+pylab.imshow(sinogram)
+
+# Create a data object for the reconstruction
+rec_id = astra.data2d.create('-vol', vol_geom)
+
+# Set up the parameters for a reconstruction algorithm using the GPU
+cfg = astra.astra_dict('SIRT_CUDA')
+cfg['ReconstructionDataId'] = rec_id
+cfg['ProjectionDataId'] = sinogram_id
+
+# Create the algorithm object from the configuration structure
+alg_id = astra.algorithm.create(cfg)
+
+# Run 1500 iterations of the algorithm one at a time, keeping track of errors
+nIters = 1500
+phantom_error = np.zeros(nIters)
+residual_error = np.zeros(nIters)
+for i in range(nIters):
+ # Run a single iteration
+ astra.algorithm.run(alg_id, 1)
+ residual_error[i] = astra.algorithm.get_res_norm(alg_id)
+ rec = astra.data2d.get(rec_id)
+ phantom_error[i] = np.sqrt(((rec - P)**2).sum())
+
+# Get the result
+rec = astra.data2d.get(rec_id)
+pylab.figure(3)
+pylab.imshow(rec)
+
+pylab.figure(4)
+pylab.plot(residual_error)
+pylab.figure(5)
+pylab.plot(phantom_error)
+
+pylab.show()
+
+# Clean up.
+astra.algorithm.delete(alg_id)
+astra.data2d.delete(rec_id)
+astra.data2d.delete(sinogram_id)
+astra.projector.delete(proj_id)