s009_projection_matrix.py

#-----------------------------------------------------------------------
#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', 1.0, 384, np.linspace(0,np.pi,180,False))

# For CPU-based algorithms, a "projector" object specifies the projection
# model used. In this case, we use the "line" model.
proj_id = astra.create_projector('line', proj_geom, vol_geom)

# Generate the projection matrix for this projection model.
# This creates a matrix W where entry w_{i,j} corresponds to the
# contribution of volume element j to detector element i.
matrix_id = astra.projector.matrix(proj_id)

# Get the projection matrix as a Scipy sparse matrix.
W = astra.matrix.get(matrix_id)


# Manually use this projection matrix to do a projection:
import scipy.io
P = scipy.io.loadmat('phantom.mat')['phantom256']
s = W.dot(P.flatten())
s = np.reshape(s, (len(proj_geom['ProjectionAngles']),proj_geom['DetectorCount']))

import pylab
pylab.gray()
pylab.figure(1)
pylab.imshow(s)
pylab.show()

# Each row of the projection matrix corresponds to a detector element.
# Detector t for angle p is for row 1 + t + p*proj_geom.DetectorCount.
# Each column corresponds to a volume pixel.
# Pixel (x,y) corresponds to column 1 + x + y*vol_geom.GridColCount.


astra.projector.delete(proj_id)
astra.matrix.delete(matrix_id)