def positive_proximal(self, x, tau, out=None):
pars = {'algorithm' : TGV, \
'input' : np.asarray(x.as_array(), dtype=np.float32),\
'regularisation_parameter':self.regularisation_parameter, \
'alpha1':self.alpha1,\
'alpha0':self.alpha2,\
'number_of_iterations' :self.iter_TGV ,\
'LipshitzConstant' :self.LipshitzConstant ,\
'tolerance_constant':self.torelance}
res, info = regularisers.TGV(pars['input'],
pars['regularisation_parameter'],
pars['alpha1'], pars['alpha0'],
pars['number_of_iterations'],
pars['LipshitzConstant'],
pars['tolerance_constant'], self.device)
self.info = info
res[res < 0] = 0
if out is not None:
out.fill(res)
else:
out = x.copy()
out.fill(res)
return out
def proximal(self, x, tau, out=None):
pars = {'algorithm' : TGV, \
'input' : np.asarray(x.as_array(), dtype=np.float32),\
'regularisation_parameter':self.regularisation_parameter, \
'alpha1':self.alpha1,\
'alpha0':self.alpha2,\
'number_of_iterations' :self.iter_TGV ,\
'LipshitzConstant' :self.LipshitzConstant ,\
'tolerance_constant':self.torelance}
res, info = regularisers.TGV(pars['input'],
pars['regularisation_parameter'],
pars['alpha1'], pars['alpha0'],
pars['number_of_iterations'],
pars['LipshitzConstant'],
pars['tolerance_constant'], self.device)
# info: return number of iteration and reached tolerance
# https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/src/Core/regularisers_CPU/TGV_core.c#L168
# Stopping Criteria || u^k - u^(k-1) ||_{2} / || u^{k} ||_{2}
self.info = info
if out is not None:
out.fill(res)
else:
out = x.copy()
out.fill(res)
return out
def proximal_numpy(self, in_arr, tau, out = None):
res , info = regularisers.TGV(in_arr,
self.regularisation_parameter,
self.alpha1,
self.alpha2,
self.iter_TGV,
self.LipshitzConstant,
self.torelance,
self.device)
# info: return number of iteration and reached tolerance
# https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/src/Core/regularisers_CPU/TGV_core.c#L168
# Stopping Criteria || u^k - u^(k-1) ||_{2} / || u^{k} ||_{2}
return res, info
def test_functionality_TGV(self):
data = dataexample.CAMERA.get(size=(256,256))
datarr = data.as_array()
from cil.plugins.ccpi_regularisation.functions import TGV
from ccpi.filters import regularisers
tau = 1.
fcil = TGV()
outcil = fcil.proximal(data, tau=tau)
# use CIL defaults
outrgl, info = regularisers.TGV(datarr, fcil.alpha*tau, 1,1, fcil.max_iteration, 12, fcil.tolerance, 'cpu' )
np.testing.assert_almost_equal(outrgl, outcil.as_array())
def proximal(self, x, tau, out=None):
in_arr = np.asarray(x.as_array(), dtype=np.float32, order='C')
res, info = regularisers.TGV(in_arr, self.regularisation_parameter,
self.alpha1, self.alpha2, self.iter_TGV,
self.LipshitzConstant, self.torelance,
self.device)
# info: return number of iteration and reached tolerance
# https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/src/Core/regularisers_CPU/TGV_core.c#L168
# Stopping Criteria || u^k - u^(k-1) ||_{2} / || u^{k} ||_{2}
self.info = info
if out is not None:
out.fill(res)
else:
out = x.copy()
out.fill(res)
return out