diff options
Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Python/ccpi/optimisation/algorithms/SIRT.py | 9 | ||||
| -rw-r--r-- | Wrappers/Python/wip/demo_SIRT.py (renamed from Wrappers/Python/wip/demo_test_sirt.py) | 141 |
2 files changed, 69 insertions, 81 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/algorithms/SIRT.py b/Wrappers/Python/ccpi/optimisation/algorithms/SIRT.py index beba913..30584d4 100644 --- a/Wrappers/Python/ccpi/optimisation/algorithms/SIRT.py +++ b/Wrappers/Python/ccpi/optimisation/algorithms/SIRT.py @@ -1,12 +1,5 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- -""" -Created on Wed Apr 10 13:39:35 2019 - -@author: jakob -""" - -# -*- coding: utf-8 -*- # This work is part of the Core Imaging Library developed by # Visual Analytics and Imaging System Group of the Science Technology # Facilities Council, STFC @@ -26,9 +19,7 @@ Created on Wed Apr 10 13:39:35 2019 # limitations under the License. from ccpi.optimisation.algorithms import Algorithm -from ccpi.framework import ImageData, AcquisitionData -#from collections.abc import Iterable class SIRT(Algorithm): '''Simultaneous Iterative Reconstruction Technique diff --git a/Wrappers/Python/wip/demo_test_sirt.py b/Wrappers/Python/wip/demo_SIRT.py index 8f65f39..66b82a2 100644 --- a/Wrappers/Python/wip/demo_test_sirt.py +++ b/Wrappers/Python/wip/demo_SIRT.py @@ -4,14 +4,9 @@ # First make all imports from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, \ AcquisitionData -from ccpi.optimisation.algs import FISTA, FBPD, CGLS, SIRT -from ccpi.optimisation.funcs import Norm2sq, Norm1, IndicatorBox +from ccpi.optimisation.functions import IndicatorBox from ccpi.astra.ops import AstraProjectorSimple - -from ccpi.optimisation.algorithms import CGLS as CGLSALG -from ccpi.optimisation.algorithms import SIRT as SIRTALG - -from ccpi.optimisation.operators import Identity +from ccpi.optimisation.algorithms import SIRT, CGLS import numpy as np import matplotlib.pyplot as plt @@ -70,8 +65,6 @@ else: # wrapping calls to ASTRA as well as specifying whether to use CPU or GPU. Aop = AstraProjectorSimple(ig, ag, 'gpu') -Aop = Identity(ig,ig) - # Forward and backprojection are available as methods direct and adjoint. Here # generate test data b and do simple backprojection to obtain z. b = Aop.direct(Phantom) @@ -94,115 +87,119 @@ x_init = ImageData(np.zeros(x.shape),geometry=ig) opt = {'tol': 1e-4, 'iter': 100} -# First a CGLS reconstruction can be done: -x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aop, b, opt) +# First run a simple CGLS reconstruction: +CGLS_alg = CGLS() +CGLS_alg.set_up(x_init, Aop, b ) +CGLS_alg.max_iteration = 2000 +CGLS_alg.run(opt['iter']) +x_CGLS_alg = CGLS_alg.get_output() plt.figure() -plt.imshow(x_CGLS.array) -plt.title('CGLS') +plt.imshow(x_CGLS_alg.array) +plt.title('CGLS ALG') plt.colorbar() plt.show() plt.figure() -plt.semilogy(criter_CGLS) +plt.semilogy(CGLS_alg.loss) plt.title('CGLS criterion') plt.show() -my_CGLS_alg = CGLSALG() -my_CGLS_alg.set_up(x_init, Aop, b ) -my_CGLS_alg.max_iteration = 2000 -my_CGLS_alg.run(opt['iter']) -x_CGLS_alg = my_CGLS_alg.get_output() - -plt.figure() -plt.imshow(x_CGLS_alg.array) -plt.title('CGLS ALG') -plt.colorbar() -plt.show() - - -# A SIRT unconstrained reconstruction can be done: similarly: -x_SIRT, it_SIRT, timing_SIRT, criter_SIRT = SIRT(x_init, Aop, b, opt) +# A SIRT reconstruction can be done simply by replacing CGLS by SIRT. +# In the first instance, no constraints are enforced. +SIRT_alg = SIRT() +SIRT_alg.set_up(x_init, Aop, b ) +SIRT_alg.max_iteration = 2000 +SIRT_alg.run(opt['iter']) +x_SIRT_alg = SIRT_alg.get_output() plt.figure() -plt.imshow(x_SIRT.array) +plt.imshow(x_SIRT_alg.array) plt.title('SIRT unconstrained') plt.colorbar() plt.show() plt.figure() -plt.semilogy(criter_SIRT) +plt.semilogy(SIRT_alg.loss) plt.title('SIRT unconstrained criterion') plt.show() +# The SIRT algorithm is stopped after the specified number of iterations has +# been run. It can be resumed by calling the run command again, which will run +# it for the specificed number of iterations +SIRT_alg.run(opt['iter']) +x_SIRT_alg2 = SIRT_alg.get_output() -my_SIRT_alg = SIRTALG() -my_SIRT_alg.set_up(x_init, Aop, b ) -my_SIRT_alg.max_iteration = 2000 -my_SIRT_alg.run(opt['iter']) -x_SIRT_alg = my_SIRT_alg.get_output() - -plt.figure() -plt.imshow(x_SIRT_alg.array) -plt.title('SIRT ALG') -plt.colorbar() -plt.show() - - -# A SIRT nonnegativity constrained reconstruction can be done using the -# additional input "constraint" set to a box indicator function with 0 as the -# lower bound and the default upper bound of infinity: -x_SIRT0, it_SIRT0, timing_SIRT0, criter_SIRT0 = SIRT(x_init, Aop, b, opt, - constraint=IndicatorBox(lower=0)) plt.figure() -plt.imshow(x_SIRT0.array) -plt.title('SIRT nonneg') +plt.imshow(x_SIRT_alg2.array) +plt.title('SIRT unconstrained, extra iterations') plt.colorbar() plt.show() plt.figure() -plt.semilogy(criter_SIRT0) -plt.title('SIRT nonneg criterion') +plt.semilogy(SIRT_alg.loss) +plt.title('SIRT unconstrained criterion, extra iterations') plt.show() -my_SIRT_alg0 = SIRTALG() -my_SIRT_alg0.set_up(x_init, Aop, b, constraint=IndicatorBox(lower=0) ) -my_SIRT_alg0.max_iteration = 2000 -my_SIRT_alg0.run(opt['iter']) -x_SIRT_alg0 = my_SIRT_alg0.get_output() +# A SIRT nonnegativity constrained reconstruction can be done using the +# additional input "constraint" set to a box indicator function with 0 as the +# lower bound and the default upper bound of infinity. First setup a new +# instance of the SIRT algorithm. +SIRT_alg0 = SIRT() +SIRT_alg0.set_up(x_init, Aop, b, constraint=IndicatorBox(lower=0) ) +SIRT_alg0.max_iteration = 2000 +SIRT_alg0.run(opt['iter']) +x_SIRT_alg0 = SIRT_alg0.get_output() plt.figure() plt.imshow(x_SIRT_alg0.array) -plt.title('SIRT ALG0') +plt.title('SIRT nonnegativity constrained') plt.colorbar() plt.show() +plt.figure() +plt.semilogy(SIRT_alg0.loss) +plt.title('SIRT nonnegativity criterion') +plt.show() + -# A SIRT reconstruction with box constraints on [0,1] can also be done: -x_SIRT01, it_SIRT01, timing_SIRT01, criter_SIRT01 = SIRT(x_init, Aop, b, opt, - constraint=IndicatorBox(lower=0,upper=1)) +# A SIRT reconstruction with box constraints on [0,1] can also be done. +SIRT_alg01 = SIRT() +SIRT_alg01.set_up(x_init, Aop, b, constraint=IndicatorBox(lower=0,upper=1) ) +SIRT_alg01.max_iteration = 2000 +SIRT_alg01.run(opt['iter']) +x_SIRT_alg01 = SIRT_alg01.get_output() plt.figure() -plt.imshow(x_SIRT01.array) -plt.title('SIRT box(0,1)') +plt.imshow(x_SIRT_alg01.array) +plt.title('SIRT boc(0,1)') plt.colorbar() plt.show() plt.figure() -plt.semilogy(criter_SIRT01) +plt.semilogy(SIRT_alg01.loss) plt.title('SIRT box(0,1) criterion') plt.show() -my_SIRT_alg01 = SIRTALG() -my_SIRT_alg01.set_up(x_init, Aop, b, constraint=IndicatorBox(lower=0,upper=1) ) -my_SIRT_alg01.max_iteration = 2000 -my_SIRT_alg01.run(opt['iter']) -x_SIRT_alg01 = my_SIRT_alg01.get_output() +# The test image has values in the range [0,1], so enforcing values in the +# reconstruction to be within this interval improves a lot. Just for fun +# we can also easily see what happens if we choose a narrower interval as +# constrint in the reconstruction, lower bound 0.2, upper bound 0.8. +SIRT_alg0208 = SIRT() +SIRT_alg0208.set_up(x_init,Aop,b,constraint=IndicatorBox(lower=0.2,upper=0.8)) +SIRT_alg0208.max_iteration = 2000 +SIRT_alg0208.run(opt['iter']) +x_SIRT_alg0208 = SIRT_alg0208.get_output() plt.figure() -plt.imshow(x_SIRT_alg01.array) -plt.title('SIRT ALG01') +plt.imshow(x_SIRT_alg0208.array) +plt.title('SIRT boc(0.2,0.8)') plt.colorbar() plt.show() + +plt.figure() +plt.semilogy(SIRT_alg0208.loss) +plt.title('SIRT box(0.2,0.8) criterion') +plt.show()
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