class RigidTransformationSSD():
def __init__(self,target,source):
self.target = target
self.source = source
self._resampled_source_cache = None
self._resampled_source_need_update = True
self._contruct_ilang_model()
self._set_transformation(numpy.zeros((1,6)))
def _contruct_ilang_model(self):
self.ilang_model = ModelRigidSSD('RigidSSD')
self.graph = ProbabilisticGraphicalModel(['target','source','transformation'])
self.graph.set_nodes_given(['target','source'],True)
self.graph.set_node_value('source',self.source.get_data())
self.graph.set_node_value('target',self.target.get_data())
self.graph.set_node_value('transformation',numpy.zeros((1,6)))
self.graph.add_dependence(self.ilang_model,{'target':'target','source':'source','transformation':'transformation'})
self.sampler = Sampler(self.graph)
def _set_transformation(self,transformation):
self.graph.set_node_value('transformation',transformation)
self._resampled_source_need_update = True
def _get_transformation(self):
return self.graph.get_node_value('transformation')
def estimate_transformation(self,method=None,iterations=30000,trace=True,parameters=None, display_progress=True):
if method!=None:
self.sampler.set_node_sampling_method_manual('transformation',method)
else:
self.sampler.set_node_sampling_method_auto('transformation')
last_sample = self.sampler.sample_node('transformation',nsamples=iterations,trace=trace)
# This is not necessary, but it triggers the cache of the resampled source image:
self._set_transformation(last_sample)
return last_sample
def resample_in_target_space(self,image):
# FIXME
#print "Resampling .."
transformation = self._get_transformation()
return image
def _get_resampled_source(self):
#FIXME: remove the next few lines
if self.transformation.sum() != 0:
return self.target
if self._resampled_source_need_update:
resampled_source = self.resample_in_target_space(self.source)
self._resampled_source_cache = resampled_source
self._resampled_source_need_update = False
else:
resampled_source = self._resampled_source_cache
return resampled_source
def display_in_browser(self,axis=0,shrink=256,rotate=90,subsample_slices=4,scale_factors=None):
self.display(axis,shrink,rotate,scale_factors,open_browser=True)
def display(self,axis=0,shrink=256,rotate=90,subsample_slices=4,scale_factors=None,open_browser=False):
resampled = self.resampled_source
D = MultipleVolumes([resampled,self.target], axis, shrink, rotate, subsample_slices, scale_factors, open_browser)
return D.display()
def _repr_html_(self):
return self.display()._repr_html_()
transformation = property(_get_transformation, _set_transformation, None)
resampled_source = property(_get_resampled_source, None, None)
class RigidTransformationSSD():
def __init__(self, target, source):
self.target = target
self.source = source
self._resampled_source_cache = None
self._resampled_source_need_update = True
self._contruct_ilang_model()
self._set_transformation(numpy.zeros((1, 6)))
def _contruct_ilang_model(self):
self.ilang_model = ModelRigidSSD('RigidSSD')
self.graph = ProbabilisticGraphicalModel(
['target', 'source', 'transformation'])
self.graph.set_nodes_given(['target', 'source'], True)
self.graph.set_node_value('source', self.source.get_data())
self.graph.set_node_value('target', self.target.get_data())
self.graph.set_node_value('transformation', numpy.zeros((1, 6)))
self.graph.add_dependence(
self.ilang_model, {
'target': 'target',
'source': 'source',
'transformation': 'transformation'
})
self.sampler = Sampler(self.graph)
def _set_transformation(self, transformation):
self.graph.set_node_value('transformation', transformation)
self._resampled_source_need_update = True
def _get_transformation(self):
return self.graph.get_node_value('transformation')
def estimate_transformation(self,
method=None,
iterations=30000,
trace=True,
parameters=None,
display_progress=True):
if method != None:
self.sampler.set_node_sampling_method_manual(
'transformation', method)
else:
self.sampler.set_node_sampling_method_auto('transformation')
last_sample = self.sampler.sample_node('transformation',
nsamples=iterations,
trace=trace)
# This is not necessary, but it triggers the cache of the resampled source image:
self._set_transformation(last_sample)
return last_sample
def resample_in_target_space(self, image):
# FIXME
#print "Resampling .."
transformation = self._get_transformation()
return image
def _get_resampled_source(self):
#FIXME: remove the next few lines
if self.transformation.sum() != 0:
return self.target
if self._resampled_source_need_update:
resampled_source = self.resample_in_target_space(self.source)
self._resampled_source_cache = resampled_source
self._resampled_source_need_update = False
else:
resampled_source = self._resampled_source_cache
return resampled_source
def display_in_browser(self,
axis=0,
shrink=256,
rotate=90,
subsample_slices=4,
scale_factors=None):
self.display(axis, shrink, rotate, scale_factors, open_browser=True)
def display(self,
axis=0,
shrink=256,
rotate=90,
subsample_slices=4,
scale_factors=None,
open_browser=False):
resampled = self.resampled_source
D = MultipleVolumes([resampled, self.target], axis, shrink, rotate,
subsample_slices, scale_factors, open_browser)
return D.display()
def _repr_html_(self):
return self.display()._repr_html_()
transformation = property(_get_transformation, _set_transformation, None)
resampled_source = property(_get_resampled_source, None, None)
