From 3cae1d138c53a3fd042de3d2c9d9a07cf0650e0f Mon Sep 17 00:00:00 2001
From: "Daniel M. Pelt" <D.M.Pelt@cwi.nl>
Date: Tue, 24 Feb 2015 12:35:45 +0100
Subject: Added Python interface

---
 samples/python/s012_masks.py | 92 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 92 insertions(+)
 create mode 100644 samples/python/s012_masks.py

(limited to 'samples/python/s012_masks.py')

diff --git a/samples/python/s012_masks.py b/samples/python/s012_masks.py
new file mode 100644
index 0000000..441d11b
--- /dev/null
+++ b/samples/python/s012_masks.py
@@ -0,0 +1,92 @@
+#-----------------------------------------------------------------------
+#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
+
+# In this example we will create a reconstruction in a circular region,
+# instead of the usual rectangle.
+
+# This is done by placing a circular mask on the square reconstruction volume:
+
+c = np.linspace(-127.5,127.5,256)
+x, y = np.meshgrid(c,c)
+mask = np.array((x**2 + y**2 < 127.5**2),dtype=np.float)
+
+import pylab
+pylab.gray()
+pylab.figure(1)
+pylab.imshow(mask)
+
+vol_geom = astra.create_vol_geom(256, 256)
+proj_geom = astra.create_proj_geom('parallel', 1.0, 384, np.linspace(0,np.pi,50,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)
+
+pylab.figure(2)
+pylab.imshow(P)
+pylab.figure(3)
+pylab.imshow(sinogram)
+
+# Create a data object for the reconstruction
+rec_id = astra.data2d.create('-vol', vol_geom)
+
+# Create a data object for the mask
+mask_id = astra.data2d.create('-vol', vol_geom, mask)
+
+# 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
+cfg['option'] = {}
+cfg['option']['ReconstructionMaskId'] = mask_id
+
+# Create the algorithm object from the configuration structure
+alg_id = astra.algorithm.create(cfg)
+
+# Run 150 iterations of the algorithm
+astra.algorithm.run(alg_id, 150)
+
+# Get the result
+rec = astra.data2d.get(rec_id)
+
+pylab.figure(4)
+pylab.imshow(rec)
+
+pylab.show()
+
+# Clean up. Note that GPU memory is tied up in the algorithm object,
+# and main RAM in the data objects.
+astra.algorithm.delete(alg_id)
+astra.data2d.delete(mask_id)
+astra.data2d.delete(rec_id)
+astra.data2d.delete(sinogram_id)
+astra.projector.delete(proj_id)
-- 
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