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| -rw-r--r-- | Wrappers/Python/ccpi/plugins/regularisers.py | 115 |
1 files changed, 115 insertions, 0 deletions
diff --git a/Wrappers/Python/ccpi/plugins/regularisers.py b/Wrappers/Python/ccpi/plugins/regularisers.py new file mode 100644 index 0000000..e9c88a4 --- /dev/null +++ b/Wrappers/Python/ccpi/plugins/regularisers.py @@ -0,0 +1,115 @@ +# -*- 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 + +# Copyright 2018 Jakob Jorgensen, Daniil Kazantsev and Edoardo Pasca + +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# 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.cpu_regularisers import TV_ENERGY +from ccpi.framework import DataContainer +from ccpi.optimisation.ops import Operator +import numpy as np + + + +class _ROF_TV_(Operator): + def __init__(self,lambdaReg,iterationsTV,tolerance,time_marchstep,device): + # set parameters + self.lambdaReg = lambdaReg + self.iterationsTV = iterationsTV + self.time_marchstep = time_marchstep + self.device = device # string for 'cpu' or 'gpu' + def __call__(self,x): + # evaluate objective function of TV gradient + EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2) + return EnergyValTV + def prox(self,x,Lipshitz): + pars = {'algorithm' : ROF_TV, \ + 'input' : np.asarray(x.as_array(), dtype=np.float32),\ + 'regularization_parameter':self.lambdaReg*Lipshitz, \ + 'number_of_iterations' :self.iterationsTV ,\ + 'time_marching_parameter':self.time_marchstep} + + out = ROF_TV(pars['input'], + pars['regularization_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'], self.device) + return DataContainer(out) + +class _FGP_TV_(Operator): + def __init__(self,lambdaReg,iterationsTV,tolerance,methodTV,nonnegativity,printing,device): + # set parameters + self.lambdaReg = lambdaReg + self.iterationsTV = iterationsTV + self.tolerance = tolerance + self.methodTV = methodTV + self.nonnegativity = nonnegativity + self.printing = printing + self.device = device # string for 'cpu' or 'gpu' + def __call__(self,x): + # evaluate objective function of TV gradient + EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2) + return EnergyValTV + def prox(self,x,Lipshitz): + pars = {'algorithm' : FGP_TV, \ + 'input' : np.asarray(x.as_array(), dtype=np.float32),\ + 'regularization_parameter':self.lambdaReg*Lipshitz, \ + 'number_of_iterations' :self.iterationsTV ,\ + 'tolerance_constant':self.tolerance,\ + 'methodTV': self.methodTV ,\ + 'nonneg': self.nonnegativity ,\ + 'printingOut': self.printing} + + out = FGP_TV(pars['input'], + pars['regularization_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'], self.device) + return DataContainer(out) + + +class _SB_TV_(Operator): + def __init__(self,lambdaReg,iterationsTV,tolerance,methodTV,printing,device): + # set parameters + self.lambdaReg = lambdaReg + self.iterationsTV = iterationsTV + self.tolerance = tolerance + self.methodTV = methodTV + self.printing = printing + self.device = device # string for 'cpu' or 'gpu' + def __call__(self,x): + # evaluate objective function of TV gradient + EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2) + return EnergyValTV + def prox(self,x,Lipshitz): + pars = {'algorithm' : SB_TV, \ + 'input' : np.asarray(x.as_array(), dtype=np.float32),\ + 'regularization_parameter':self.lambdaReg*Lipshitz, \ + 'number_of_iterations' :self.iterationsTV ,\ + 'tolerance_constant':self.tolerance,\ + 'methodTV': self.methodTV ,\ + 'printingOut': self.printing} + + out = SB_TV(pars['input'], + pars['regularization_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['printingOut'], self.device) + return DataContainer(out) |