def compute_pointcloud(self, sketchup_model, theta, phi):
self.load_model(sketchup_model)
print "Computing view for model {} : t={}, p={}".format(
sketchup_model.google_id, theta, phi)
cloud = PointCloud()
cloud._cpp_pointcloud = self._cpp_computer.compute_view(theta, phi)
return cloud
def compute_pointcloud(self, sketchup_model, theta, phi):
self.load_model(sketchup_model)
print "Computing view for model {} : t={}, p={}".format(
sketchup_model.google_id, theta, phi)
cloud = PointCloud()
cloud._cpp_pointcloud = self._cpp_computer.compute_view(theta, phi)
return cloud
def save_pointclouds(example_object, options):
""" Extract pointclouds from tar archive. """
sequences = list(example_object.sequences.all())
frames = Frame.objects.filter(video_sequence__in=sequences)
if not options['force']:
frames = frames.filter(_pointcloud__isnull=True)
if not frames.exists():
return # Nothing to process
if options['download_folder'] is None:
archive_dir = dowload_archives([example_object])
else:
archive_dir = options['download_folder']
pcd_tar_path = "{}/pcd_tar/{}.tar".format(archive_dir, example_object.name)
pcd_tar = tarfile.open(pcd_tar_path)
tmp_dir = tempfile.mkdtemp()
pcd_tar.extractall(tmp_dir)
# pcd_members = dict((m.name, m) for m in pcd_tar.getmembers())
for frame in frames.iterator():
current_pcd = os.path.join(tmp_dir, frame.pcd_member_name())
pointcloud = PointCloud.load_pcd(current_pcd)
frame._pointcloud = pointcloud
frame.save()
shutil.rmtree(tmp_dir)
if options['download_folder'] is None:
shutil.rmtree(archive_dir)
def save_pointclouds(example_object, options):
""" Extract pointclouds from tar archive. """
sequences = list(example_object.sequences.all())
frames = Frame.objects.filter(video_sequence__in=sequences)
if not options['force']:
frames = frames.filter(_pointcloud__isnull=True)
if not frames.exists():
return # Nothing to process
if options['download_folder'] is None:
archive_dir = dowload_archives([example_object])
else:
archive_dir = options['download_folder']
pcd_tar_path = "{}/pcd_tar/{}.tar".format(archive_dir,
example_object.name)
pcd_tar = tarfile.open(pcd_tar_path)
tmp_dir = tempfile.mkdtemp()
pcd_tar.extractall(tmp_dir)
# pcd_members = dict((m.name, m) for m in pcd_tar.getmembers())
for frame in frames.iterator():
current_pcd = os.path.join(tmp_dir, frame.pcd_member_name())
pointcloud = PointCloud.load_pcd(current_pcd)
frame._pointcloud = pointcloud
frame.save()
shutil.rmtree(tmp_dir)
if options['download_folder'] is None:
shutil.rmtree(archive_dir)
def test_identification_banana_vs_bowl_vs_food_can(self):
""" Try to identify with 3 categories. """
# Getting the dataset
bowl_ids = ['fa61e604661d4aa66658ecd96794a1cd',
'f74bba9a22e044dea3769fcd5f96f4',
'd2e1dc9ee02834c71621c7edb823fc53']
banana_ids = ['f6e6117261dca163713c042b393cc65b',
'ba0d56295321002718ddbf38fa69c501',
'7d78e217e0ba160fe2b248b8bb97d290']
bowls = []
for bowl_id in bowl_ids:
bowls.append(SketchupModel.find_google_id(bowl_id))
bananas = []
for banana_id in banana_ids:
bananas.append(SketchupModel.find_google_id(banana_id))
# Training
iden = Identifier()
iden.add_models(bananas, 'banana')
iden.add_models(bowls, 'bowl')
iden.train()
# Identification
for i in range(20):
example = Example.get_random(['banana', 'bowl'])
pcd_file = example.pcd_file()
print "Identification of file {}".format(example)
cloud = PointCloud.load_pcd(pcd_file.name)
iden.identify(cloud)
def test_save_load_pcd(self):
""" Test that PointClouds can be save and load functions. """
pcd_file = open("pointcloud/fixtures/cloud.pcd")
pointcloud = PointCloud.load_pcd(pcd_file.name)
self.assertEqual(pointcloud.size(), 8)
with tempfile.NamedTemporaryFile() as temp_file:
pointcloud.save_pcd(temp_file.name)
temp_file.seek(0)
self.assertTrue(temp_file.read() == pcd_file.read())
def test_save_load_pcd(self):
""" Test that PointClouds can be save and load functions. """
pcd_file = open("pointcloud/fixtures/cloud.pcd")
pointcloud = PointCloud.load_pcd(pcd_file.name)
self.assertEqual(pointcloud.size(), 8)
with tempfile.NamedTemporaryFile() as temp_file:
pointcloud.save_pcd(temp_file.name)
temp_file.seek(0)
self.assertTrue(temp_file.read() == pcd_file.read())
def compute_pointcloud(self):
""" Extract the pointcloud from the ExampleObject archive. """
import tarfile
from tempfile import NamedTemporaryFile
from django_server.settings import MEDIA_ROOT
example_object = self.video_sequence.example_object
temp_file = NamedTemporaryFile(delete=True)
pcd_tar = tarfile.open(MEDIA_ROOT + example_object.pcd_tar.name)
temp_file.write(pcd_tar.extractfile(self.pcd_member_name()).read())
temp_file.flush()
pointcloud = PointCloud.load_pcd(temp_file.name)
return pointcloud
def compute_pointcloud(self):
""" Extract the pointcloud from the ExampleObject archive. """
import tarfile
from tempfile import NamedTemporaryFile
from django_server.settings import MEDIA_ROOT
example_object = self.video_sequence.example_object
temp_file = NamedTemporaryFile(delete=True)
pcd_tar = tarfile.open(MEDIA_ROOT + example_object.pcd_tar.name)
temp_file.write(pcd_tar.extractfile(self.pcd_member_name()).read())
temp_file.flush()
pointcloud = PointCloud.load_pcd(temp_file.name)
return pointcloud
def test_exception_when_no_existing_category(self):
""" Test that if the identifier is empty it throws. """
pointcloud = PointCloud.load_pcd("pointcloud/fixtures/cloud.pcd")
identifier = Identifier()
self.assertRaises(IndexError, identifier.identify, pointcloud)
# training will add a category :
model = SketchupModel()
model.google_id = "test1"
model.mesh = file("sketchup_models/fixtures/mesh_can.tri").read()
identifier.add_models([model], "test_category")
try:
identifier.identify(pointcloud)
except IndexError:
self.fail("identifier.identify() raised IndexError unexpectedly!")
except:
# can raise if Indentification failed
pass
def test_basis(self):
""" Test init of a PointCloud. """
cloud = PointCloud()
self.assertEqual(cloud.size(), 0)
def test_basis(self):
""" Test init of a PointCloud. """
cloud = PointCloud()
self.assertEqual(cloud.size(), 0)
