Added Python interface

1 files changed, 85 insertions, 0 deletions

diff --git a/samples/python/s010_supersampling.py b/samples/python/s010_supersampling.py
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+++ b/samples/python/s010_supersampling.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/>.
+#
+#-----------------------------------------------------------------------
+
+import astra
+import numpy as np
+
+vol_geom = astra.create_vol_geom(256, 256)
+proj_geom = astra.create_proj_geom('parallel', 3.0, 128, np.linspace(0,np.pi,180,False))
+import scipy.io
+P = scipy.io.loadmat('phantom.mat')['phantom256']
+
+# Because the astra.create_sino method does not have support for
+# all possible algorithm options, we manually create a sinogram
+phantom_id = astra.data2d.create('-vol', vol_geom, P)
+sinogram_id = astra.data2d.create('-sino', proj_geom)
+cfg = astra.astra_dict('FP_CUDA')
+cfg['VolumeDataId'] = phantom_id
+cfg['ProjectionDataId'] = sinogram_id
+
+# Set up 3 rays per detector element
+cfg['option'] = {}
+cfg['option']['DetectorSuperSampling'] = 3
+
+alg_id = astra.algorithm.create(cfg)
+astra.algorithm.run(alg_id)
+astra.algorithm.delete(alg_id)
+astra.data2d.delete(phantom_id)
+
+sinogram3 = astra.data2d.get(sinogram_id)
+
+import pylab
+pylab.gray()
+pylab.figure(1)
+pylab.imshow(P)
+pylab.figure(2)
+pylab.imshow(sinogram3)
+
+# Create a reconstruction, also using supersampling
+rec_id = astra.data2d.create('-vol', vol_geom)
+cfg = astra.astra_dict('SIRT_CUDA')
+cfg['ReconstructionDataId'] = rec_id
+cfg['ProjectionDataId'] = sinogram_id
+# Set up 3 rays per detector element
+cfg['option'] = {}
+cfg['option']['DetectorSuperSampling'] = 3
+
+# There is also an option for supersampling during the backprojection step.
+# This should be used if your detector pixels are smaller than the voxels.
+
+# Set up 2 rays per image pixel dimension, for 4 rays total per image pixel.
+# cfg['option']['PixelSuperSampling'] = 2
+
+
+alg_id = astra.algorithm.create(cfg)
+astra.algorithm.run(alg_id, 150)
+astra.algorithm.delete(alg_id)
+
+rec = astra.data2d.get(rec_id)
+pylab.figure(3)
+pylab.imshow(rec)
+pylab.show()
+