1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
|
# -----------------------------------------------------------------------
# Copyright: 2010-2018, iMinds-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
# In this example we will create a reconstruction constrained to
# greyvalues between 0 and 1
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('cuda',proj_geom,vol_geom)
sinogram_id, sinogram = astra.create_sino(P, proj_id)
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
cfg['option']={}
cfg['option']['MinConstraint'] = 0
cfg['option']['MaxConstraint'] = 1
# 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(3)
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(rec_id)
astra.data2d.delete(sinogram_id)
astra.projector.delete(proj_id)
|
