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| -rw-r--r-- | samples/matlab/s023_FBP_filters.m | 96 | ||||
| -rw-r--r-- | samples/python/s023_FBP_filters.py | 116 |
2 files changed, 212 insertions, 0 deletions
diff --git a/samples/matlab/s023_FBP_filters.m b/samples/matlab/s023_FBP_filters.m new file mode 100644 index 0000000..4abec7e --- /dev/null +++ b/samples/matlab/s023_FBP_filters.m @@ -0,0 +1,96 @@ +% ----------------------------------------------------------------------- +% This file is part of the ASTRA Toolbox +% +% Copyright: 2010-2018, imec Vision Lab, University of Antwerp +% 2014-2018, CWI, Amsterdam +% License: Open Source under GPLv3 +% Contact: astra@astra-toolbox.com +% Website: http://www.astra-toolbox.com/ +% ----------------------------------------------------------------------- + + +% 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, linspace2(0,pi,180)); + +proj_id = astra_create_projector('strip', proj_geom, vol_geom); + +P = phantom(256); + +[sinogram_id, sinogram] = astra_create_sino(P, proj_id); + +rec_id = astra_mex_data2d('create', '-vol', vol_geom); + +cfg = astra_struct('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_mex_algorithm('create', cfg); +astra_mex_algorithm('run', alg_id); +rec_RL = astra_mex_data2d('get', rec_id); +astra_mex_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 = [linspace2(0, 1, floor(fullFilterSize / 2)) linspace2(1, 0, ceil(fullFilterSize / 2))]; +halfFilterSize = floor(fullFilterSize / 2) + 1; +filter = kernel(1:halfFilterSize); + +filter_geom = astra_create_proj_geom('parallel', 1.0, halfFilterSize, [0]); +filter_id = astra_mex_data2d('create', '-sino', filter_geom, filter); + +cfg.FilterType = 'projection'; +cfg.FilterSinogramId = filter_id; + +alg_id = astra_mex_algorithm('create', cfg); +astra_mex_algorithm('run', alg_id); +rec_filter = astra_mex_data2d('get', rec_id); +astra_mex_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+1) +% For a kernel of even size 2*k, the central component is at kernel(k+1) + +kernel = zeros(1, N); +for i = 0:floor(N/4)-1 + f = pi * (2*i + 1); + val = -2.0 / (f * f); + kernel(floor(N/2) + 1 + (2*i+1)) = val; + kernel(floor(N/2) + 1 - (2*i+1)) = val; +end +kernel(floor(N/2)+1) = 0.5; + +kernel_geom = astra_create_proj_geom('parallel', 1.0, N, [0]); +kernel_id = astra_mex_data2d('create', '-sino', kernel_geom, kernel); + +cfg.FilterType = 'rprojection'; +cfg.FilterSinogramId = kernel_id; + +alg_id = astra_mex_algorithm('create', cfg); +astra_mex_algorithm('run', alg_id); +rec_kernel = astra_mex_data2d('get', rec_id); +astra_mex_algorithm('delete', alg_id); + +figure(1); imshow(P, []); +figure(2); imshow(rec_RL, []); +figure(3); imshow(rec_filter, []); +figure(4); imshow(rec_kernel, []); + + +astra_mex_data2d('delete', rec_id); +astra_mex_data2d('delete', sinogram_id); +astra_mex_projector('delete', proj_id); diff --git a/samples/python/s023_FBP_filters.py b/samples/python/s023_FBP_filters.py new file mode 100644 index 0000000..11518ac --- /dev/null +++ b/samples/python/s023_FBP_filters.py @@ -0,0 +1,116 @@ +# ----------------------------------------------------------------------- +# 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) + |