From ab15b31baf13d3e4528e1cd713ab609a5ed39f0d Mon Sep 17 00:00:00 2001 From: Edoardo Pasca Date: Mon, 14 May 2018 16:57:16 +0200 Subject: Import (#13) * add dependency on ccpi-regulariser * fixed import names --- Wrappers/Python/ccpi/plugins/regularisers.py | 14 +++++++------- Wrappers/Python/conda-recipe/meta.yaml | 1 + Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py | 8 ++++---- Wrappers/Python/wip/demo_simple_RGLTK.py | 8 ++++---- 4 files changed, 16 insertions(+), 15 deletions(-) (limited to 'Wrappers/Python') diff --git a/Wrappers/Python/ccpi/plugins/regularisers.py b/Wrappers/Python/ccpi/plugins/regularisers.py index 46464a9..29d4397 100644 --- a/Wrappers/Python/ccpi/plugins/regularisers.py +++ b/Wrappers/Python/ccpi/plugins/regularisers.py @@ -18,14 +18,14 @@ # limitations under the License. # This requires CCPi-Regularisation toolbox to be installed -from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV +from ccpi.filters import regularisers from ccpi.filters.cpu_regularisers import TV_ENERGY from ccpi.framework import DataContainer from ccpi.optimisation.ops import Operator import numpy as np -class _ROF_TV_(Operator): +class ROF_TV(Operator): def __init__(self,lambdaReg,iterationsTV,tolerance,time_marchstep,device): # set parameters self.lambdaReg = lambdaReg @@ -43,13 +43,13 @@ class _ROF_TV_(Operator): 'number_of_iterations' :self.iterationsTV ,\ 'time_marching_parameter':self.time_marchstep} - out = ROF_TV(pars['input'], + out = regularisers.ROF_TV(pars['input'], pars['regularization_parameter'], pars['number_of_iterations'], pars['time_marching_parameter'], self.device) return DataContainer(out) -class _FGP_TV_(Operator): +class FGP_TV(Operator): def __init__(self,lambdaReg,iterationsTV,tolerance,methodTV,nonnegativity,printing,device): # set parameters self.lambdaReg = lambdaReg @@ -73,7 +73,7 @@ class _FGP_TV_(Operator): 'nonneg': self.nonnegativity ,\ 'printingOut': self.printing} - out = FGP_TV(pars['input'], + out = regularisers.FGP_TV(pars['input'], pars['regularization_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], @@ -83,7 +83,7 @@ class _FGP_TV_(Operator): return DataContainer(out) -class _SB_TV_(Operator): +class SB_TV(Operator): def __init__(self,lambdaReg,iterationsTV,tolerance,methodTV,printing,device): # set parameters self.lambdaReg = lambdaReg @@ -105,7 +105,7 @@ class _SB_TV_(Operator): 'methodTV': self.methodTV ,\ 'printingOut': self.printing} - out = SB_TV(pars['input'], + out = regularisers.SB_TV(pars['input'], pars['regularization_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], diff --git a/Wrappers/Python/conda-recipe/meta.yaml b/Wrappers/Python/conda-recipe/meta.yaml index f97de5e..edec71b 100644 --- a/Wrappers/Python/conda-recipe/meta.yaml +++ b/Wrappers/Python/conda-recipe/meta.yaml @@ -19,6 +19,7 @@ requirements: - numpy - ccpi-framework - ccpi-reconstruction + - ccpi-regulariser - matplotlib about: diff --git a/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py b/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py index bb9b89f..19cd86f 100644 --- a/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py +++ b/Wrappers/Python/wip/demo_compare_RGLTK_TV_denoising.py @@ -10,7 +10,7 @@ from ccpi.optimisation.algs import FISTA, FBPD, CGLS from ccpi.optimisation.funcs import Norm2sq, ZeroFun, Norm1, TV2D from ccpi.optimisation.ops import LinearOperatorMatrix, Identity -from ccpi.plugins.regularisers import _ROF_TV_, _FGP_TV_, _SB_TV_ +from ccpi.plugins.regularisers import ROF_TV, FGP_TV, SB_TV # All external imports import numpy as np @@ -117,7 +117,7 @@ plt.legend() plt.show() #%% FISTA with ROF-TV regularisation -g_rof = _ROF_TV_(lambdaReg = lam_tv, +g_rof = ROF_TV(lambdaReg = lam_tv, iterationsTV=2000, tolerance=0, time_marchstep=0.0009, @@ -136,7 +136,7 @@ plt.show() print(EnergytotalROF) #%% FISTA with FGP-TV regularisation -g_fgp = _FGP_TV_(lambdaReg = lam_tv, +g_fgp = FGP_TV(lambdaReg = lam_tv, iterationsTV=5000, tolerance=0, methodTV=0, @@ -157,7 +157,7 @@ plt.show() print(EnergytotalFGP) #%% Split-Bregman-TV regularisation -g_sb = _SB_TV_(lambdaReg = lam_tv, +g_sb = SB_TV(lambdaReg = lam_tv, iterationsTV=1000, tolerance=0, methodTV=0, diff --git a/Wrappers/Python/wip/demo_simple_RGLTK.py b/Wrappers/Python/wip/demo_simple_RGLTK.py index d92799a..5564503 100644 --- a/Wrappers/Python/wip/demo_simple_RGLTK.py +++ b/Wrappers/Python/wip/demo_simple_RGLTK.py @@ -8,7 +8,7 @@ from ccpi.framework import ImageData , ImageGeometry, AcquisitionGeometry from ccpi.optimisation.algs import FISTA, FBPD, CGLS from ccpi.optimisation.funcs import Norm2sq, Norm1, TV2D from ccpi.astra.ops import AstraProjectorSimple -from ccpi.plugins.regularisers import _ROF_TV_, _FGP_TV_, _SB_TV_ +from ccpi.plugins.regularisers import ROF_TV, FGP_TV, SB_TV # All external imports import numpy as np @@ -108,7 +108,7 @@ plt.show() # Set up the ROF variant of TV from the CCPi Regularisation Toolkit and run # TV-reconstruction using FISTA -g_rof = _ROF_TV_(lambdaReg = lamtv, +g_rof = ROF_TV(lambdaReg = lamtv, iterationsTV=50, tolerance=1e-5, time_marchstep=0.01, @@ -127,7 +127,7 @@ plt.semilogy(criter_rof) plt.show() # Repeat for FGP variant. -g_fgp = _FGP_TV_(lambdaReg = lamtv, +g_fgp = FGP_TV(lambdaReg = lamtv, iterationsTV=50, tolerance=1e-5, methodTV=0, @@ -146,7 +146,7 @@ plt.semilogy(criter_fgp) plt.show() # Repeat for SB variant. -g_sb = _SB_TV_(lambdaReg = lamtv, +g_sb = SB_TV(lambdaReg = lamtv, iterationsTV=50, tolerance=1e-5, methodTV=0, -- cgit v1.2.3