def research(self):
params = ClusterParams(eval(self.distance_limit.get()),
eval(self.angle_limit.get()),
eval(self.min_line_length.get()),
eval(self.anglevar_weight.get()),
eval(self.distvar_weight.get()),
1,
self.allow_intersection.get(),
eval(self.beam_width.get()),
self.attempt_dnc.get()
)
self.c.delete("line")
searchMe = []
for o in self.c.find_all():
#this needs to create a landmark now
searchMe.append(Landmark('purple_prism',
RectangleRepresentation(rect=BoundingBox([Vec2(self.c.coords(o)[0],self.c.coords(o)[1]), Vec2(self.c.coords(o)[2],self.c.coords(o)[3])]), landmarks_to_get=[]),
None,
ObjectClass.PRISM,
Color.PURPLE))
print searchMe
results = SceneEval.sceneEval(searchMe,params)
if len(results)>0:
self.chainViz(results)
def adapt(scene):
'''takes a scene object and returns a list of lists of objects that form groups'''
for l in scene.landmarks:
o =PhysicalObject(scene.landmarks[l].uuid,
np.array(scene.landmarks[l].representation.middle),
np.array(scene.landmarks[l].representation.rect.min_point),
np.array(scene.landmarks[l].representation.rect.max_point))
objects.append(o)
results = SceneEval.sceneEval(objects)
return results
def adapt(scene):
'''takes a scene object and returns a list of lists of objects that form groups'''
for l in scene.landmarks:
o =PhysicalObject(scene.landmarks[l].uuid,
np.array(scene.landmarks[l].representation.middle),
np.array(scene.landmarks[l].representation.rect.min_point),
np.array(scene.landmarks[l].representation.rect.max_point))
objects.append(o)
results = SceneEval.findChains(objects)[0:-1]#trim the score from the end of the list
for r in range(len(results)):
for s in range(len(results[r])):
results[r][s] = scene.fetch_landmark(results[r][s])
return results
def research(self):
params = ClusterParams(eval(self.distance_limit.get()),
eval(self.angle_limit.get()),
eval(self.min_line_length.get()),
eval(self.anglevar_weight.get()),
eval(self.distvar_weight.get()),
1,
self.allow_intersection.get()
)
self.c.delete("line")
searchMe = []
for o in self.c.find_all():
searchMe.append(PhysicalObject(o,np.array(self.c.coords(o)[0:2]),np.array(self.c.coords(o)[0:2]),np.array(self.c.coords(o)[2:4])))
results = SceneEval.sceneEval(searchMe,params)
if len(results)>0:
self.chainViz(results)
def adapt(landmarks):
'''takes a scene object and returns a list of lists of objects that form groups'''
landmarkDict = dict()
for l in landmarks:
landmarkDict[l.uuid] = l
o = PhysicalObject(l.uuid, np.array(l.representation.middle),
np.array(l.representation.rect.min_point),
np.array(l.representation.rect.max_point), l.uuid)
objects.append(o)
bundles = SceneEval.sceneEval(objects)
for i in bundles:
print i.convert(landmarkDict)
results = [bundle.convert(landmarkDict) for bundle in bundles]
return results
def adapt(landmarks):
'''takes a scene object and returns a list of lists of objects that form groups'''
landmarkDict = dict()
for l in landmarks:
landmarkDict[l.uuid]=l
o = PhysicalObject(l.uuid,
np.array(l.representation.middle),
np.array(l.representation.rect.min_point),
np.array(l.representation.rect.max_point),
l.uuid)
objects.append(o)
bundles = SceneEval.sceneEval(objects)
for i in bundles:
print i.convert(landmarkDict)
results = [bundle.convert(landmarkDict) for bundle in bundles]
return results
def research(self):
params = ClusterParams(eval(self.distance_limit.get()),
eval(self.angle_limit.get()),
eval(self.min_line_length.get()),
eval(self.anglevar_weight.get()),
eval(self.distvar_weight.get()), 1,
self.allow_intersection.get(),
eval(self.beam_width.get()),
self.attempt_dnc.get())
self.c.delete("line")
searchMe = []
for o in self.c.find_all():
searchMe.append(
PhysicalObject(o,
self.c.coords(o)[0:2],
self.c.coords(o)[0:2],
self.c.coords(o)[2:4]))
results = SceneEval.sceneEval(searchMe, params)
if len(results) > 0:
self.chainViz(results)
ObjectClass.PRISM,
Color.ORANGE)
scene.add_landmark(table)
for obj in (obj1, obj2, obj3, obj4, obj5,obj6):
obj.representation.alt_representations = []
scene.add_landmark(obj)
return scene, speaker
if __name__ == '__main__':
scene, speaker = construct_training_scene()
lmks = [lmk for lmk in scene.landmarks.values() if not lmk.name == 'table']
groups = SceneEval.sceneEval(lmks)
print groups
for i,g in enumerate(groups):
try: scene.add_landmark(Landmark('ol%d'%i, g, None, Landmark.LINE))
except: scene.add_landmark(Landmark('ol%d'%i, g.representation, None, Landmark.LINE))
#perspectives = [ Vec2(5.5,4.5), Vec2(6.5,6.0)]
#speaker.talk_to_baby(scene, perspectives, how_many_each=10)
dozen = 1
couple = 1
while True:
for i in range(couple * dozen):
location = Landmark( 'point', PointRepresentation(Vec2(random()*0.8-0.4,random()*0.6+0.4)), None, Landmark.POINT)
trajector = location#obj2
speaker.describe(trajector, scene, True, 1)
