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# -*- 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 import regularisers
from ccpi.filters.cpu_regularisers import TV_ENERGY
from ccpi.framework import DataContainer
from ccpi.optimisation.functions import Function
import numpy as np
class ROF_TV(Function):
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 0.5*EnergyValTV[0]
def prox(self,x,tau):
pars = {'algorithm' : ROF_TV, \
'input' : np.asarray(x.as_array(), dtype=np.float32),\
'regularization_parameter':self.lambdaReg*tau, \
'number_of_iterations' :self.iterationsTV ,\
'time_marching_parameter':self.time_marchstep}
res , info = regularisers.ROF_TV(pars['input'],
pars['regularization_parameter'],
pars['number_of_iterations'],
pars['time_marching_parameter'], self.device)
if out is not None:
out.fill(res)
else:
out = x.copy()
out.fill(res)
return out
class FGP_TV(Function):
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 0.5*EnergyValTV[0]
def proximal(self,x,tau, out=None):
pars = {'algorithm' : FGP_TV, \
'input' : np.asarray(x.as_array(), dtype=np.float32),\
'regularization_parameter':self.lambdaReg*tau, \
'number_of_iterations' :self.iterationsTV ,\
'tolerance_constant':self.tolerance,\
'methodTV': self.methodTV ,\
'nonneg': self.nonnegativity ,\
'printingOut': self.printing}
res , info = regularisers.FGP_TV(pars['input'],
pars['regularization_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'],
pars['methodTV'],
pars['nonneg'],
self.device)
if out is not None:
out.fill(res)
else:
out = x.copy()
out.fill(res)
return out
class FGP_dTV(Function):
def __init__(self, refdata, regularisation_parameter, iterations,
tolerance, eta_const, methodTV, nonneg, device='cpu'):
# set parameters
self.lambdaReg = regularisation_parameter
self.iterationsTV = iterations
self.tolerance = tolerance
self.methodTV = methodTV
self.nonnegativity = nonneg
self.device = device # string for 'cpu' or 'gpu'
self.refData = np.asarray(refdata.as_array(), dtype=np.float32)
self.eta = eta_const
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 0.5*EnergyValTV[0]
def proximal(self,x,tau, out=None):
pars = {'algorithm' : FGP_dTV, \
'input' : np.asarray(x.as_array(), dtype=np.float32),\
'regularization_parameter':self.lambdaReg*tau, \
'number_of_iterations' :self.iterationsTV ,\
'tolerance_constant':self.tolerance,\
'methodTV': self.methodTV ,\
'nonneg': self.nonnegativity ,\
'eta_const' : self.eta,\
'refdata':self.refData}
#inputData, refdata, regularisation_parameter, iterations,
# tolerance_param, eta_const, methodTV, nonneg, device='cpu'
res , info = regularisers.FGP_dTV(pars['input'],
pars['refdata'],
pars['regularization_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'],
pars['eta_const'],
pars['methodTV'],
pars['nonneg'],
self.device)
if out is not None:
out.fill(res)
else:
out = x.copy()
out.fill(res)
return out
class SB_TV(Function):
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 0.5*EnergyValTV[0]
def proximal(self,x,tau, out=None):
pars = {'algorithm' : SB_TV, \
'input' : np.asarray(x.as_array(), dtype=np.float32),\
'regularization_parameter':self.lambdaReg*tau, \
'number_of_iterations' :self.iterationsTV ,\
'tolerance_constant':self.tolerance,\
'methodTV': self.methodTV ,\
'printingOut': self.printing}
res , info = regularisers.SB_TV(pars['input'],
pars['regularization_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'],
pars['methodTV'],
pars['printingOut'], self.device)
if out is not None:
out.fill(res)
else:
out = x.copy()
out.fill(res)
return out
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