Add sample scripts

1 files changed, 116 insertions, 0 deletions

diff --git a/samples/python/s023_FBP_filters.py b/samples/python/s023_FBP_filters.py
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+# -----------------------------------------------------------------------
+# Copyright: 2010-2018, imec 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
+import scipy.io
+
+
+# This sample script illustrates three ways of passing filters to FBP.
+# They work with both the FBP (CPU) and the FBP_CUDA (GPU) algorithms.
+
+
+N = 256
+
+vol_geom = astra.create_vol_geom(N, N)
+proj_geom = astra.create_proj_geom('parallel', 1.0, N, np.linspace(0,np.pi,180,False))
+
+P = scipy.io.loadmat('phantom.mat')['phantom256']
+
+proj_id = astra.create_projector('strip',proj_geom,vol_geom)
+sinogram_id, sinogram = astra.create_sino(P, proj_id)
+
+rec_id = astra.data2d.create('-vol', vol_geom)
+cfg = astra.astra_dict('FBP')
+cfg['ReconstructionDataId'] = rec_id
+cfg['ProjectionDataId'] = sinogram_id
+cfg['ProjectorId'] = proj_id
+
+
+
+# 1. Use a standard Ram-Lak filter
+cfg['FilterType'] = 'ram-lak'
+
+alg_id = astra.algorithm.create(cfg)
+astra.algorithm.run(alg_id)
+rec_RL = astra.data2d.get(rec_id)
+astra.algorithm.delete(alg_id)
+
+# 2. Define a filter in Fourier space
+# This is assumed to be symmetric, and ASTRA therefore expects only half
+
+# The full filter size should be the smallest power of two that is at least
+# twice the number of detector pixels.
+fullFilterSize = 2*N
+kernel = np.append( np.linspace(0, 1, fullFilterSize//2, endpoint=False), np.linspace(1, 0, fullFilterSize//2, endpoint=False) )
+halfFilterSize = fullFilterSize // 2 + 1
+filter = np.reshape(kernel[0:halfFilterSize], (1, halfFilterSize))
+
+filter_geom = astra.create_proj_geom('parallel', 1.0, halfFilterSize, [0]);
+filter_id = astra.data2d.create('-sino', filter_geom, filter);
+
+cfg['FilterType'] = 'projection'
+cfg['FilterSinogramId'] = filter_id
+alg_id = astra.algorithm.create(cfg)
+astra.algorithm.run(alg_id)
+rec_filter = astra.data2d.get(rec_id)
+astra.algorithm.delete(alg_id)
+
+
+# 3. Define a (spatial) convolution kernel directly
+# For a kernel of odd size 2*k+1, the central component is at kernel[k]
+# For a kernel of even size 2*k, the central component is at kernel[k]
+kernel = np.zeros((1, N))
+for i in range(0,N//4):
+    f = np.pi * (2*i + 1)
+    val = -2.0 / (f * f)
+    kernel[0, N//2 + (2*i+1)] = val
+    kernel[0, N//2 - (2*i+1)] = val
+kernel[0, N//2] = 0.5
+kernel_geom = astra.create_proj_geom('parallel', 1.0, N, [0]);
+kernel_id = astra.data2d.create('-sino', kernel_geom, kernel);
+
+cfg['FilterType'] = 'rprojection'
+cfg['FilterSinogramId'] = kernel_id
+alg_id = astra.algorithm.create(cfg)
+astra.algorithm.run(alg_id)
+rec_kernel = astra.data2d.get(rec_id)
+astra.algorithm.delete(alg_id)
+
+import pylab
+pylab.figure()
+pylab.imshow(P)
+pylab.figure()
+pylab.imshow(rec_RL)
+pylab.figure()
+pylab.imshow(rec_filter)
+pylab.figure()
+pylab.imshow(rec_kernel)
+pylab.show()
+
+astra.data2d.delete(rec_id)
+astra.data2d.delete(sinogram_id)
+astra.projector.delete(proj_id)
+