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 __make_graph(self):
self.ilang_model = SSD_ilang()
# The following is an alternative use of ilang models: define the log_p and gradient functions here in order to have access to
# the local variables.
self.ilang_model.log_conditional_probability_transformation = self.__P
self.ilang_model.log_conditional_probability_gradient_transformation = self.__G
self.ilang_graph = ProbabilisticGraphicalModel(
['source', 'target', 'transformation', 'sigma'])
self.ilang_graph.set_nodes_given(['sigma', 'source', 'target'], True)
self.ilang_graph.add_dependence(
self.ilang_model, {
'source': 'source',
'target': 'target',
'sigma': 'sigma',
'transformation': 'transformation'
})
self.ilang_sampler = Sampler(self.ilang_graph)
def __make_graph(self):
self.ilang_graph = ProbabilisticGraphicalModel()
for i in range(len(self.__images)):
self.ilang_graph.add_node('im_%d' % i)
self.ilang_graph.set_nodes_given([
'im_%d' % i,
], True)
for i in range(len(self.__images) - 1):
self.ilang_graph.add_nodes(['sigma_%d' % i, 'T_%d' % i])
self.ilang_graph.set_nodes_given([
'sigma_%d' % i,
], True)
model = SSD_ilang('SSD_%d' % i)
self.ilang_graph.add_dependence(
model, {
'sigma': 'sigma_%d' % i,
'transformation': 'T_%d' % i,
'source': 'im_%d' % i,
'target': 'im_%d' % (i + 1)
})
self.ilang_sampler = Sampler(self.ilang_graph)
