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# -*- coding: utf-8 -*-
"""
Created on Tue Aug 8 14:26:00 2017
@author: ofn77899
"""
import regularizers
import numpy as np
from enum import Enum
import timeit
class Regularizer():
'''Class to handle regularizer algorithms to be used during reconstruction
Currently 5 CPU (OMP) regularization algorithms are available:
1) SplitBregman_TV
2) FGP_TV
3) LLT_model
4) PatchBased_Regul
5) TGV_PD
Usage:
the regularizer can be invoked as object or as static method
Depending on the actual regularizer the input parameter may vary, and
a different default setting is defined.
reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
out = reg(input=u0, regularization_parameter=10., number_of_iterations=30,
tolerance_constant=1e-4,
TV_Penalty=Regularizer.TotalVariationPenalty.l1)
out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10.,
number_of_iterations=30, tolerance_constant=1e-4,
TV_Penalty=Regularizer.TotalVariationPenalty.l1)
A number of optional parameters can be passed or skipped
out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
'''
class Algorithm(Enum):
SplitBregman_TV = regularizers.SplitBregman_TV
FGP_TV = regularizers.FGP_TV
LLT_model = regularizers.LLT_model
PatchBased_Regul = regularizers.PatchBased_Regul
TGV_PD = regularizers.TGV_PD
# Algorithm
class TotalVariationPenalty(Enum):
isotropic = 0
l1 = 1
# TotalVariationPenalty
def __init__(self , algorithm, debug = True):
self.setAlgorithm ( algorithm )
self.debug = debug
# __init__
def setAlgorithm(self, algorithm):
self.algorithm = algorithm
self.pars = self.getDefaultParsForAlgorithm(algorithm)
# setAlgorithm
def getDefaultParsForAlgorithm(self, algorithm):
pars = dict()
if algorithm == Regularizer.Algorithm.SplitBregman_TV :
pars['algorithm'] = algorithm
pars['input'] = None
pars['regularization_parameter'] = None
pars['number_of_iterations'] = 35
pars['tolerance_constant'] = 0.0001
pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic
elif algorithm == Regularizer.Algorithm.FGP_TV :
pars['algorithm'] = algorithm
pars['input'] = None
pars['regularization_parameter'] = None
pars['number_of_iterations'] = 50
pars['tolerance_constant'] = 0.001
pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic
elif algorithm == Regularizer.Algorithm.LLT_model:
pars['algorithm'] = algorithm
pars['input'] = None
pars['regularization_parameter'] = None
pars['time_step'] = None
pars['number_of_iterations'] = None
pars['tolerance_constant'] = None
pars['restrictive_Z_smoothing'] = 0
elif algorithm == Regularizer.Algorithm.PatchBased_Regul:
pars['algorithm'] = algorithm
pars['input'] = None
pars['searching_window_ratio'] = None
pars['similarity_window_ratio'] = None
pars['PB_filtering_parameter'] = None
pars['regularization_parameter'] = None
elif algorithm == Regularizer.Algorithm.TGV_PD:
pars['algorithm'] = algorithm
pars['input'] = None
pars['first_order_term'] = None
pars['second_order_term'] = None
pars['number_of_iterations'] = None
pars['regularization_parameter'] = None
else:
raise Exception('Unknown regularizer algorithm')
return pars
# parsForAlgorithm
def setParameter(self, **kwargs):
'''set named parameter for the regularization engine
raises Exception if the named parameter is not recognized
Typical usage is:
reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
reg.setParameter(input=u0)
reg.setParameter(regularization_parameter=10.)
it can be also used as
reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
reg.setParameter(input=u0 , regularization_parameter=10.)
'''
for key , value in kwargs.items():
if key in self.pars.keys():
self.pars[key] = value
else:
raise Exception('Wrong parameter {0} for regularizer algorithm'.format(key))
# setParameter
def getParameter(self, **kwargs):
ret = {}
for key , value in kwargs.items():
if key in self.pars.keys():
ret[key] = self.pars[key]
else:
raise Exception('Wrong parameter {0} for regularizer algorithm'.format(key))
# setParameter
def __call__(self, input = None, regularization_parameter = None, **kwargs):
'''Actual call for the regularizer.
One can either set the regularization parameters first and then call the
algorithm or set the regularization parameter during the call (as
is done in the static methods).
'''
if kwargs is not None:
for key, value in kwargs.items():
#print("{0} = {1}".format(key, value))
self.pars[key] = value
if input is not None:
self.pars['input'] = input
if regularization_parameter is not None:
self.pars['regularization_parameter'] = regularization_parameter
if self.debug:
print ("--------------------------------------------------")
for key, value in self.pars.items():
if key== 'algorithm' :
print("{0} = {1}".format(key, value.__name__))
elif key == 'input':
print("{0} = {1}".format(key, np.shape(value)))
else:
print("{0} = {1}".format(key, value))
if None in self.pars:
raise Exception("Not all parameters have been provided")
input = self.pars['input']
regularization_parameter = self.pars['regularization_parameter']
if self.algorithm == Regularizer.Algorithm.SplitBregman_TV :
return self.algorithm(input, regularization_parameter,
self.pars['number_of_iterations'],
self.pars['tolerance_constant'],
self.pars['TV_penalty'].value )
elif self.algorithm == Regularizer.Algorithm.FGP_TV :
return self.algorithm(input, regularization_parameter,
self.pars['number_of_iterations'],
self.pars['tolerance_constant'],
self.pars['TV_penalty'].value )
elif self.algorithm == Regularizer.Algorithm.LLT_model :
#LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
# no default
return self.algorithm(input,
regularization_parameter,
self.pars['time_step'] ,
self.pars['number_of_iterations'],
self.pars['tolerance_constant'],
self.pars['restrictive_Z_smoothing'] )
elif self.algorithm == Regularizer.Algorithm.PatchBased_Regul :
#LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
# no default
return self.algorithm(input, regularization_parameter,
self.pars['searching_window_ratio'] ,
self.pars['similarity_window_ratio'] ,
self.pars['PB_filtering_parameter'])
elif self.algorithm == Regularizer.Algorithm.TGV_PD :
#LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
# no default
if len(np.shape(input)) == 2:
return self.algorithm(input, regularization_parameter,
self.pars['first_order_term'] ,
self.pars['second_order_term'] ,
self.pars['number_of_iterations'])
elif len(np.shape(input)) == 3:
#assuming it's 3D
# run independent calls on each slice
out3d = input.copy()
for i in range(np.shape(input)[2]):
out = self.algorithm(input, regularization_parameter,
self.pars['first_order_term'] ,
self.pars['second_order_term'] ,
self.pars['number_of_iterations'])
# copy the result in the 3D image
out3d.T[i] = out[0].copy()
# append the rest of the info that the algorithm returns
output = [out3d]
for i in range(1,len(out)):
output.append(out[i])
return output
# __call__
@staticmethod
def SplitBregman_TV(input, regularization_parameter , **kwargs):
start_time = timeit.default_timer()
reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
out = list( reg(input, regularization_parameter, **kwargs) )
out.append(reg.pars)
txt = reg.printParametersToString()
txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
out.append(txt)
return out
@staticmethod
def FGP_TV(input, regularization_parameter , **kwargs):
start_time = timeit.default_timer()
reg = Regularizer(Regularizer.Algorithm.FGP_TV)
out = list( reg(input, regularization_parameter, **kwargs) )
out.append(reg.pars)
txt = reg.printParametersToString()
txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
out.append(txt)
return out
@staticmethod
def LLT_model(input, regularization_parameter , time_step, number_of_iterations,
tolerance_constant, restrictive_Z_smoothing=0):
start_time = timeit.default_timer()
reg = Regularizer(Regularizer.Algorithm.LLT_model)
out = list( reg(input, regularization_parameter, time_step=time_step,
number_of_iterations=number_of_iterations,
tolerance_constant=tolerance_constant,
restrictive_Z_smoothing=restrictive_Z_smoothing) )
out.append(reg.pars)
txt = reg.printParametersToString()
txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
out.append(txt)
return out
@staticmethod
def PatchBased_Regul(input, regularization_parameter,
searching_window_ratio,
similarity_window_ratio,
PB_filtering_parameter):
start_time = timeit.default_timer()
reg = Regularizer(Regularizer.Algorithm.PatchBased_Regul)
out = list( reg(input,
regularization_parameter,
searching_window_ratio=searching_window_ratio,
similarity_window_ratio=similarity_window_ratio,
PB_filtering_parameter=PB_filtering_parameter )
)
out.append(reg.pars)
txt = reg.printParametersToString()
txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
out.append(txt)
return out
@staticmethod
def TGV_PD(input, regularization_parameter , first_order_term,
second_order_term, number_of_iterations):
start_time = timeit.default_timer()
reg = Regularizer(Regularizer.Algorithm.TGV_PD)
out = list( reg(input, regularization_parameter,
first_order_term=first_order_term,
second_order_term=second_order_term,
number_of_iterations=number_of_iterations) )
out.append(reg.pars)
txt = reg.printParametersToString()
txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
out.append(txt)
return out
def printParametersToString(self):
txt = r''
for key, value in self.pars.items():
if key== 'algorithm' :
txt += "{0} = {1}".format(key, value.__name__)
elif key == 'input':
txt += "{0} = {1}".format(key, np.shape(value))
else:
txt += "{0} = {1}".format(key, value)
txt += '\n'
return txt
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