From 3c36f3b10e2b747eb5b1bf32c9320885a5494b56 Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Tue, 8 May 2018 16:22:07 +0100
Subject: adding regularisers
---
Wrappers/Python/ccpi/plugins/regularisers.py | 115 +++++++++++++++++++++++++++
1 file changed, 115 insertions(+)
create mode 100644 Wrappers/Python/ccpi/plugins/regularisers.py
(limited to 'Wrappers')
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)
--
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