def on_the_truck_cost(cf):
state, am = waverly_state_truck()
print state.orientation
#state.orientation = 3.14/6
cf.initialize_state(state)
extractor = Extractor()
esdcs = extractor.extractEsdcs("next to the truck.")
place_esdc = esdcs[0]
place_esdc.type = "PLACE"
truck_esdc = place_esdc.l[0]
print "place", place_esdc, place_esdc.type
print "truck", truck_esdc
searcher = BeamSearch(cf)
annotation = initial_annotation(state, esdcs[0])
truck = [
state.getGroundableById(o) for o in state.getObjectsSet()
if 'flatbed' in state.getGroundableById(o).tags
][0]
annotation.setGrounding(truck_esdc, truck)
annotation.setGrounding("sequence", [(state, None)])
obj = prism_from_point(0,
0,
truck.centroid3d[2] + .2,
truck.centroid3d[2] + .5,
width=0.5)
#axes = p.axes()
featureBrowser = crfFeatureWeights.MainWindow()
featureBrowser.show()
axes = featureBrowser.axes
for o in state.getObjectsSet():
drawObject(axes, state.getGroundableById(o))
drawPlaceCostMap(featureBrowser, obj, place_esdc, annotation, searcher, -5,
10, 25, 40, .2)
#p.axes().set_aspect('equal')
#p.show()
state, gggs = annotation_to_ggg_map(annotation)
ggg = gggs[place_esdc]
#ggg = ggg_from_esdc(esdcs[0])
factor = ggg.esdc_to_factor(place_esdc)
print "factor", factor, factor.id
print "cache", cf.factor_to_cost
factor_to_cost = cf.factor_to_cost[factor]
print "cache", factor_to_cost
featureBrowser.load(cf.lccrf, factor_to_cost)
def main():
parser = OptionParser()
parser.add_option("--source_corpus",
dest="source_corpus",
help="The file or directory to read from")
parser.add_option("--dest_corpus",
dest="dest_corpus",
help="The yaml file to which data will be written")
(options, args) = parser.parse_args()
corpus = readCorpus.Corpus(options.source_corpus)
extractor = Extractor()
#oldCorpus = annotationIo.load("%s/dataAnnotation/data/forkliftMturkEsdcs.stefie10.groundings.withPaths.yaml" % os.environ['FORKLIFT_HOME'])
#oldAssignmentIds = set(a.assignmentId for a in oldCorpus)
#print len(oldAssignmentIds), "old ids"
yamlData = []
print len(corpus.assignments), "assignments"
filteredAnnotations = [
x for x in corpus.assignments if (x.scenario.platform == "forklift"
and x.scenario.isSimulated == True)
]
print len(filteredAnnotations), "filtered annotations"
output_annotations = []
for i, a in enumerate(filteredAnnotations):
esdcs = extractor.extractEsdcs(a.command)
assignmentId = a.assignmentId
agent = a.agent
context = a.context
esdcSource = [assignmentId for esdc in esdcs.flattenedEsdcs]
output_annotations.append(
Annotation(assignmentId=assignmentId,
esdcs=esdcs,
objectGroundings=None,
groundingIsCorrect=None,
agent=agent,
context=context,
esdcSource=esdcSource))
esdcYaml = toYaml(esdcs)
print "******"
print a, esdcYaml
print esdcs
yamlData.append({"command": esdcYaml, "assignmentId": a.assignmentId})
print "dumped", i, "commands."
yaml.dump(yamlData, open(options.dest_corpus, "w"))
def approach_the_truck_cost_path(cf):
state, am = waverly_state_truck()
cf.initialize_state(state)
extractor = Extractor()
esdcs = extractor.extractEsdcs("Approach the truck.")
event_esdc = esdcs[0]
truck_esdc = event_esdc.l[0]
annotation = initial_annotation(state, esdcs[0])
truck = [o for o in state.objects if 'flatbed' in o.tags][0]
annotation.setGrounding(truck_esdc, truck)
featureBrowser = crfFeatureWeights.MainWindow()
featureBrowser.show()
ggg = drawAgentPathCostMap(featureBrowser, [event_esdc], annotation, cf,
-15, 25, 15, 50, 1)
factor = ggg.esdc_to_factor(event_esdc)
featureBrowser.load(annotation.context, cf, factor)
def pick_up_the_pallet_cost_pickup(cf):
state, am = waverly_state_truck()
cf.initialize_state(state)
extractor = Extractor()
esdcs = extractor.extractEsdcs("Pick up the tire pallet.")
event_esdc = esdcs[0]
print 'event', event_esdc
# pickup_esdc = event_esdc.r
# print 'pickup', pickup_esdc
pallet_esdc = event_esdc.l[0]
print "pallet", pallet_esdc
annotation = initial_annotation(state, esdcs[0])
pallet = [o for o in state.objects if 'tire' in o.tags][0]
annotation.setGrounding(pallet_esdc, pallet)
featureBrowser = crfFeatureWeights.MainWindow()
featureBrowser.show()
ggg = drawObjectPathCostMap(featureBrowser, pallet_esdc, [event_esdc],
annotation, cf, -20, 40, 15, 50, 1)
factor = ggg.esdc_to_factor(event_esdc)
featureBrowser.load(annotation.context, cf, factor)
def main():
extractor = Extractor()
oldCorpus = annotationIo.load("%s/dataAnnotation/data/forkliftMturkEsdcs.stefie10.groundings.withPaths.yaml" % os.environ['FORKLIFT_HOME'])
annotations = []
for i, a in enumerate(oldCorpus):
# if i != 140:
# continue
print "doing", i, a.entireText
automatic_esdcs_groups = extractor.extractTopNEsdcs(a.entireText, n=10)
for automatic_esdc_group in automatic_esdcs_groups:
annotation = annotationIo.Annotation(a.assignmentId,
automatic_esdc_group)
for automatic_esdc in automatic_esdc_group.flattenedEsdcs:
if automatic_esdc in a.flattenedEsdcs:
annotation.setGroundingIsCorrect(automatic_esdc, True)
else:
annotation.setGroundingIsCorrect(automatic_esdc, False)
annotations.append(annotation)
annotationIo.save(annotationIo.Corpus(annotations), "negativeEsdcs.yaml")
def tp_test():
ex = Extractor()
#parse = ex.extractEsdcs("Pick up the tire pallet next to the truck.")
parse = ex.extractEsdcs("Put the tire pallet on storage.")
esdc = parse[0]
am = action_map.action_map(
"../../data/directions/forklift/partitions/forklift_full_part.pck")
tp = task_planner(am, gen_cost, gen_cost, gen_cost, gen_cost)
s1 = fork_state.fork_state()
s1.pallets = [pallet_t(), pallet_t()]
s1.objects = [object_t(), object_t()]
s1.pallets[0].id = 0
s1.pallets[0].label = pallet_t.LABEL_TIRE
s1.pallets[0].pos = [88, 125, 0]
s1.pallets[0].bbox_min = [88, 125, 0]
s1.pallets[0].bbox_max = [89, 126, 0]
s1.pallets[1].id = 1
s1.pallets[1].label = pallet_t.LABEL_ENGINE
s1.pallets[1].pos = [113, 41, 0]
s1.pallets[1].bbox_min = [113, 41, 0]
s1.pallets[1].bbox_max = [114, 42, 0]
s1.objects[0].object_type = object_enum_t.FLATBED_TRUCK
s1.objects[0].pos = [88, 125, 0]
s1.objects[0].bbox_min = [88, 125, 0]
s1.objects[0].bbox_max = [89, 126, 0]
s1.objects[1].object_type = object_enum_t.FLATBED_TRAILER
s1.objects[1].pos = [88, 125, 0]
s1.objects[1].bbox_min = [88, 125, 0]
s1.objects[1].bbox_max = [89, 126, 0]
rndf_map = rndf("../../data/directions/forklift/Lee_RNDF_demo.txt")
s1.zones = rndf_map.zone_polygons[:]
s1.agent = 0
plan = tp.find_plan(esdc, s1, 100)
c, p = plan[0]
def printMapping(x):
if hasattr(p[x], 'tag'):
print 'mapped ', x.type, map(lambda s: s.text,
x.f), ' to ', p[x].tag
elif x.type == 'EVENT':
print 'mapped', x.type, 'to:'
ss = p[x]
for s, a in ss:
if a != None:
print a, am.index_to_location(a.to_loc)
else:
print 'mapped', x.type, '(' + str(x) + ')', ' to ', p[x]
print '---------------------'
print '-----------------'
ds.breadthFirstTraverse(esdc, printMapping)
print c
def setUpClass(cls):
cls.extractor = Extractor()
from esdcs.extractor.stanfordParserExtractor import Extractor as StanfordExtractor
from g3.esdcs_to_ggg import ggg_from_esdc_group
from yn_dialog.graph import YNGGG
from coreference.merge_coreferences import renumber_ggg_ids, merge_gggs
from dialog.question_answer_corpus import QACorpus
qa_corpus = QACorpus([])
sentence = 'Pick up the tire pallet near the truck.'
# sentence = 'Turn away from the truck and move forward'
# sentence = "I went to the store."
# sentence = 'Raise the forks, turn right, and proceed forward for a few seconds.'
stanford_extractor = StanfordExtractor()
stanford_esdcs = stanford_extractor.extractEsdcs(sentence)
ggg = ggg_from_esdc_group(stanford_esdcs)
ggg, max_id = renumber_ggg_ids(ggg, 0)
target_node_id = '4'
target_node = ggg.node_from_id(target_node_id)
target_factors = ggg.factors_for_node(target_node, 'top')
target_esdc = ggg.factor_to_esdc(target_factors[0])
target_text = target_esdc.text
command_text = ggg.entireText
oid = '??'
question_text = qa_corpus.generate_base_question_text(
target_text) + " " + oid + "?"
qa_text = question_text + " " + "Yes."
yn_ggg = YNGGG.from_dialog(ggg.entireText, qa_text, True)
yn_ggg, max_id = renumber_ggg_ids(yn_ggg, max_id)