def proximal_numpy(self, in_arr, tau, out = None):
res , info = regularisers.FGP_dTV(\
in_arr,\
self.reference,\
self.alpha*tau,\
self.max_iteration,\
self.tolerance,\
self.eta,\
self.methodTV,\
self.nonnegativity,\
self.device)
return res, info
def test_functionality_FGP_dTV(self):
data = dataexample.CAMERA.get(size=(256,256))
datarr = data.as_array()
ref = data*0.3
from cil.plugins.ccpi_regularisation.functions import FGP_dTV
from ccpi.filters import regularisers
tau = 1.
fcil = FGP_dTV(ref)
outcil = fcil.proximal(data, tau=tau)
# use CIL defaults
outrgl, info = regularisers.FGP_dTV(datarr, ref.as_array(), fcil.alpha*tau, fcil.max_iteration, fcil.tolerance, 0.01, 0, 1, '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.FGP_dTV(\
in_arr,\
self.reference,\
self.alpha*tau,\
self.max_iteration,\
self.tolerance,\
self.eta,\
self.methodTV,\
self.nonnegativity,\
self.device)
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' : 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