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Diffstat (limited to 'samples/python/s016_plots.py')
| -rw-r--r-- | samples/python/s016_plots.py | 86 |
1 files changed, 86 insertions, 0 deletions
diff --git a/samples/python/s016_plots.py b/samples/python/s016_plots.py new file mode 100644 index 0000000..cd4d98c --- /dev/null +++ b/samples/python/s016_plots.py @@ -0,0 +1,86 @@ +#----------------------------------------------------------------------- +#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) |