def set_scene(self,scene,speaker):
self.scene = scene
self.speaker = speaker
self.table = self.scene.landmarks['table']
# there is a person standing at this location
# he will be our reference
if speaker is None:
self.speaker = Speaker(Vec2(5.5, 4.5))
else:
self.speaker = speaker
# NOTE we need to keep around the list of landmarks so that we can
# access them by id, which is the index of the landmark in this list
# collect all possible landmarks
self.landmarks = []
for scene_lmk in self.scene.landmarks.itervalues():
self.landmarks.append(scene_lmk)
# a scene can be represented as a plane, line, etc
# each representation of a scene has different landmarks
rs = [scene_lmk.representation]
rs.extend(scene_lmk.representation.get_alt_representations())
for r in rs:
for lmk in r.get_landmarks():
self.landmarks.append(lmk)
# FIXME we are using sentences with 1 or 2 LANDMARK-PHRASEs
# so we need to restrict the landmarks to 0 or 1 ancestors
self.landmarks = [l for l in self.landmarks if l.get_ancestor_count() < 2]
class ModelScene(object):
def __init__(self, scene=None, speaker=None):
self.scene = scene
if scene is None:
self.scene = Scene(3)
# not a very furnished scene, we only have one table
table = Landmark('table',
RectangleRepresentation(rect=BoundingBox([Vec2(5,5), Vec2(6,7)])),
None,
ObjectClass.TABLE)
self.scene.add_landmark(table)
self.table = table
self.set_scene(scene,speaker)
def set_scene(self,scene,speaker):
self.scene = scene
self.speaker = speaker
self.table = self.scene.landmarks['table']
# there is a person standing at this location
# he will be our reference
if speaker is None:
self.speaker = Speaker(Vec2(5.5, 4.5))
else:
self.speaker = speaker
# NOTE we need to keep around the list of landmarks so that we can
# access them by id, which is the index of the landmark in this list
# collect all possible landmarks
self.landmarks = []
for scene_lmk in self.scene.landmarks.itervalues():
self.landmarks.append(scene_lmk)
# a scene can be represented as a plane, line, etc
# each representation of a scene has different landmarks
rs = [scene_lmk.representation]
rs.extend(scene_lmk.representation.get_alt_representations())
for r in rs:
for lmk in r.get_landmarks():
self.landmarks.append(lmk)
# FIXME we are using sentences with 1 or 2 LANDMARK-PHRASEs
# so we need to restrict the landmarks to 0 or 1 ancestors
self.landmarks = [l for l in self.landmarks if l.get_ancestor_count() < 2]
def get_rand_loc(self):
"""returns a random location on the table"""
bb = self.table.representation.get_geometry()
xmin, ymin = bb.min_point
xmax, ymax = bb.max_point
return random.uniform(xmin, xmax), random.uniform(ymin, ymax)
def get_landmark_id(self, lmk):
return self.landmarks.index(lmk)
def get_landmark_by_id(self, lmk_id):
return self.landmarks[lmk_id]
def sample_lmk_rel(self, loc, num_ancestors=None, usebest=False):
"""gets a location and returns a landmark and a relation
that can be used to describe the given location"""
landmarks = self.landmarks
if num_ancestors is not None:
landmarks = [l for l in landmarks if l.get_ancestor_count() == num_ancestors]
loc = Landmark(None, PointRepresentation(loc), None, None)
lmk, lmk_prob, lmk_entropy, head_on = self.speaker.sample_landmark( landmarks, loc, usebest=usebest)
rel, rel_prob, rel_entropy = self.speaker.sample_relation(loc, self.table.representation.get_geometry(), head_on, lmk, step=0.5, usebest=usebest)
rel = rel(head_on,lmk,loc)
return (lmk, lmk_prob, lmk_entropy), (rel, rel_prob, rel_entropy)