def resample(self, shape):
from diffeo2c import diffeo_resample
d2 = diffeo_resample(self.d, shape)
# this might give negative values
v2 = scipy_image_resample(self.variance, shape)
np.clip(v2, 0, 1, out=v2)
return Diffeomorphism2D(d2, v2)
def resample(self, shape):
from diffeo2c import diffeo_resample
d2 = diffeo_resample(self.d, shape)
# this might give negative values
v2 = scipy_image_resample(self.variance, shape)
np.clip(v2, 0, 1, out=v2)
return Diffeomorphism2D(d2, v2)
def next_phase(self):
""" Switches to the next phase. """
cur_estimator = self.estimators[self.current_phase]
cur_diffeo = cur_estimator.get_value()
next_phase = self.phases[self.current_phase + 1]
next_estimator = self._get_phase_estimator(next_phase.max_displ)
# resample
cur_diffeo_upsampled = diffeo_resample(cur_diffeo.d, next_phase.shape)
next_estimator.set_guess(cur_diffeo_upsampled)
self.estimators.append(next_estimator)
self.current_phase += 1
self.phase_obs = 0
self.last_tmp_guess = None
def next_phase(self):
""" Switches to the next phase. """
cur_estimator = self.estimators[self.current_phase]
cur_diffeo = cur_estimator.get_value()
next_phase = self.phases[self.current_phase + 1]
next_estimator = self._get_phase_estimator(next_phase.max_displ)
# resample
cur_diffeo_upsampled = diffeo_resample(cur_diffeo.d, next_phase.shape)
next_estimator.set_guess(cur_diffeo_upsampled)
self.estimators.append(next_estimator)
self.current_phase += 1
self.phase_obs = 0
self.last_tmp_guess = None