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Copy paths015_fp_bp.py
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100 lines (82 loc) · 3.42 KB
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# -----------------------------------------------------------------------
# Copyright 2013 Centrum Wiskunde & Informatica, Amsterdam
#
# Author: Daniel M. Pelt
# Contact: [email protected]
# 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/>.
#
# -----------------------------------------------------------------------
# This example demonstrates using the FP and BP primitives with Matlab's lsqr
# solver. Calls to FP (astra_create_sino_cuda) and
# BP (astra_create_backprojection_cuda) are wrapped in a function astra_wrap,
# and a handle to this function is passed to lsqr.
# Because in this case the inputs/outputs of FP and BP have to be vectors
# instead of images (matrices), the calls require reshaping to and from
# vectors.
import astra
import numpy as np
import scipy.io
import scipy.sparse.linalg
import matplotlib.pyplot as plt
# FP/BP wrapper class
class astra_wrap(object):
def __init__(self, proj_geom, vol_geom):
self.proj_id = astra.create_projector('line', proj_geom, vol_geom)
self.shape = (
proj_geom['DetectorCount'] * len(proj_geom['ProjectionAngles']),
vol_geom['GridColCount'] * vol_geom['GridRowCount'])
self.dtype = np.float
def matvec(self, v):
sid, s = astra.create_sino(
np.reshape(v, (vol_geom['GridRowCount'], vol_geom[
'GridColCount'])), self.proj_id) # , useCUDA=True)
astra.data2d.delete(sid)
return s.flatten()
def rmatvec(self, v):
bid, b = astra.create_backprojection(
np.reshape(v, (len(proj_geom['ProjectionAngles']), proj_geom[
'DetectorCount'],)), self.proj_id) # ,useCUDA=True)
astra.data2d.delete(bid)
return b.flatten()
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))
# Create a 256x256 phantom image
P = scipy.io.loadmat('phantom.mat')['phantom256']
# Create a sinogram.
proj_id = astra.create_projector('line', proj_geom, vol_geom)
sinogram_id, sinogram = astra.create_sino(P, proj_id=proj_id)
# Reshape the sinogram into a vector
b = sinogram.flatten()
# Call lsqr with ASTRA FP and BP
wrapper = astra_wrap(proj_geom, vol_geom)
result = scipy.sparse.linalg.lsqr(wrapper, b, atol=1e-4, btol=1e-4,
iter_lim=25)
# Reshape the result into an image
Y = np.reshape(result[0], (vol_geom['GridRowCount'], vol_geom['GridColCount']))
plt.gray()
plt.imshow(Y)
plt.show()
astra.data2d.delete(sinogram_id)
astra.projector.delete(proj_id)
astra.projector.delete(wrapper.proj_id)