class smooth_term_OF(Functional):
def __init__(self,
space,
data,
forward,
tau,
alpha_df,
alpha_of,
grad=None,
huber=False,
gamma=1e-7,
aug_lagr=False,
lagr_mult=None):
self.N = round(len(space) / 2) # number of time steps\
self.space = space
self.image_space = self.space[0]
self.space_time = ProductSpace(self.image_space, self.N)
self.data = data
self.forward = forward
self.tau = tau
self.alpha_df = alpha_df
self.alpha_of = alpha_of
self.grad = grad
self.data_fit = DataFitL2TimeDep(self.space, self.data, self.forward,
self.alpha_df)
self.aug_lagr = aug_lagr
if lagr_mult is None:
self.lagr_mult = self.space_time.zero()
else:
self.lagr_mult = lagr_mult
if huber is False:
self.of_constr = L2OpticalFlowConstraint(self.space, self.tau,
self.alpha_of, self.grad)
else:
self.of_constr = HuberL1OpticalFlowConstraint(
self.space, self.tau, self.alpha_of, self.grad, gamma)
if aug_lagr is True:
self.aug_lagr_term = AugmentedLagrangeTerm(self.space,
self.lagr_mult,
self.tau, self.grad)
super(smooth_term_OF, self).__init__(space=space,
linear=False,
grad_lipschitz=np.nan)
def __call__(self, x):
ret = self.data_fit(x) + self.of_constr(x)
if self.aug_lagr is True:
ret += self.aug_lagr_term(x)
return ret
@property
def gradient(self):
grad_df = self.data_fit.gradient
grad_of = self.of_constr.gradient
if self.aug_lagr is True:
grad_al = self.aug_lagr_term.gradient
return BroadcastOperator(*[
grad_df[i] + grad_of[i] + grad_al[i] for i in range(2 * self.N)
])
else:
return BroadcastOperator(
*[grad_df[i] + grad_of[i] for i in range(2 * self.N)])
