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INSTALL
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INSTALL
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#-----------------------------------------------------------------------
#Copyright 2013 Centrum Wiskunde & Informatica, Amsterdam
#
#Author: Daniel M. Pelt
#Contact: [email protected]
#Website: http://dmpelt.github.io/pynnfbp/
#
#
#This file is part of the PyNN-FBP, a Python implementation of the
#NN-FBP tomographic reconstruction method.
#
#PyNN-FBP 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.
#
#PyNN-FBP 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 PyNN-FBP. If not, see <http://www.gnu.org/licenses/>.
#
#-----------------------------------------------------------------------
** Latest version can be found at http://dmpelt.github.io/pynnfbp/ **
To install PyNN-FBP, simply run:
python setup.py install
To use PyNN-FBP, you need installed:
- Numpy and scipy
- PyTables
- (for ASTRAProjector) The ASTRA toolbox (https://code.google.com/p/astra-toolbox/), with Python interface (https://github.com/dmpelt/pyastratoolbox)
- (for SimplePyCUDAProjector) PyCUDA and pyfft.
Examples can be found in the 'examples' directory. After installation, the examples can be run to show how the package is used.
Running 'PaperExample.py' from the 'examples' directory should show comparable results to the threeshape experiment (Fig. 9a and Table 1) of [1].
[1] Pelt, D., & Batenburg, K. (2013). Fast tomographic reconstruction from limited data using artificial neural networks. Image Processing, IEEE Transactions on, 22(12), pp.5238-5251.