adding regularisers

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)