def generate_pointclouds_in_object_space(dbs, session, args):
object_name = dbs[session.object_id]['object_name']
if not os.path.exists(object_name):
os.mkdir(object_name)
obs_ids = models.find_all_observations_for_session(dbs, session.id)
if len(obs_ids) == 0:
raise RuntimeError("There are no observations available.")
db_reader = capture.ObservationReader(
'Database Source', db_params=dbtools.args_to_db_params(args))
#observation dealer will deal out each observation id.
observation_dealer = ecto.Dealer(
tendril=db_reader.inputs.at('observation'), iterable=obs_ids)
depthTo3d = calib.DepthTo3d('Depth ~> 3D')
rescale_depth = capture.RescaledRegisteredDepth(
'Depth scaling') #this is for SXGA mode scale handling.
point_cloud_transform = reconstruction.PointCloudTransform(
'Object Space Transform', do_transform=False
) #keeps the points in camera coordinates, but populates the global sensor position and orientatino.
point_cloud_converter = conversion.MatToPointCloudXYZRGB('To Point Cloud')
to_ecto_pcl = ecto_pcl.PointCloudT2PointCloud('converter',
format=ecto_pcl.XYZRGB)
plasm = ecto.Plasm()
plasm.connect(observation_dealer[:] >> db_reader['observation'],
db_reader['K'] >> depthTo3d['K'],
db_reader['image'] >> rescale_depth['image'],
db_reader['depth'] >> rescale_depth['depth'],
rescale_depth[:] >> depthTo3d['depth'],
depthTo3d['points3d'] >> point_cloud_converter['points'],
db_reader['image'] >> point_cloud_converter['image'],
db_reader['mask'] >> point_cloud_converter['mask'],
db_reader['R', 'T'] >> point_cloud_transform['R', 'T'],
point_cloud_converter['point_cloud'] >> to_ecto_pcl[:],
to_ecto_pcl[:] >> point_cloud_transform['cloud'])
ply_writer = ecto_pcl.PLYWriter('PLY Saver',
filename_format='%s/cloud_%%05d.ply' %
(object_name))
pcd_writer = ecto_pcl.PCDWriter('PCD Saver',
filename_format='%s/cloud_%%05d.pcd' %
(object_name))
plasm.connect(point_cloud_transform['view'] >> (ply_writer['input'],
pcd_writer['input']))
if args.visualize:
global cloud_view
plasm.connect(
point_cloud_transform['view'] >> cloud_view,
db_reader['image'] >> imshows['image'],
db_reader['depth'] >> imshows['depth'],
db_reader['mask'] >> imshows['mask'],
)
from ecto.opts import run_plasm
run_plasm(args, plasm, locals=vars())
def generate_pointclouds_in_object_space(dbs, session, args):
object_name = dbs[session.object_id]['object_name']
if not os.path.exists(object_name):
os.mkdir(object_name)
obs_ids = models.find_all_observations_for_session(dbs, session.id)
if len(obs_ids) == 0:
raise RuntimeError("There are no observations available.")
db_reader = capture.ObservationReader('Database Source', db_params=dbtools.args_to_db_params(args))
#observation dealer will deal out each observation id.
observation_dealer = ecto.Dealer(tendril=db_reader.inputs.at('observation'), iterable=obs_ids)
depthTo3d = calib.DepthTo3d('Depth ~> 3D')
rescale_depth = capture.RescaledRegisteredDepth('Depth scaling') #this is for SXGA mode scale handling.
point_cloud_transform = reconstruction.PointCloudTransform('Object Space Transform',do_transform=False)#keeps the points in camera coordinates, but populates the global sensor position and orientatino.
point_cloud_converter = conversion.MatToPointCloudXYZRGB('To Point Cloud')
to_ecto_pcl = ecto_pcl.PointCloudT2PointCloud('converter', format=ecto_pcl.XYZRGB)
plasm = ecto.Plasm()
plasm.connect(
observation_dealer[:] >> db_reader['observation'],
db_reader['K'] >> depthTo3d['K'],
db_reader['image'] >> rescale_depth['image'],
db_reader['depth'] >> rescale_depth['depth'],
rescale_depth[:] >> depthTo3d['depth'],
depthTo3d['points3d'] >> point_cloud_converter['points'],
db_reader['image'] >> point_cloud_converter['image'],
db_reader['mask'] >> point_cloud_converter['mask'],
db_reader['R', 'T'] >> point_cloud_transform['R', 'T'],
point_cloud_converter['point_cloud'] >> to_ecto_pcl[:],
to_ecto_pcl[:] >> point_cloud_transform['cloud']
)
ply_writer = ecto_pcl.PLYWriter('PLY Saver',
filename_format='%s/cloud_%%05d.ply' % (object_name))
pcd_writer = ecto_pcl.PCDWriter('PCD Saver',
filename_format='%s/cloud_%%05d.pcd' % (object_name))
plasm.connect(point_cloud_transform['view'] >> (ply_writer['input'], pcd_writer['input'])
)
if args.visualize:
global cloud_view
plasm.connect(
point_cloud_transform['view'] >> cloud_view,
db_reader['image'] >> imshows['image'],
db_reader['depth'] >> imshows['depth'],
db_reader['mask'] >> imshows['mask'],
)
from ecto.opts import run_plasm
run_plasm(args, plasm, locals=vars())
args = parse_args()
db = dbtools.init_object_databases(couchdb.Server(args.db_root))
for object_id in args.objects:
#get a list of observation ids for a particular object id.
obs_ids = models.find_all_observations_for_object(db, object_id)
if not obs_ids:
print 'No observations found for object %s.' % object_id
continue
plasm = ecto.Plasm()
#the db_reader transforms observation id into a set of image,depth,mask,K,R,T
db_reader = capture.ObservationReader(
"db_reader", db_params=dbtools.args_to_db_params(args))
#this iterates over all of the observation ids.
observation_dealer = ecto.Dealer(
tendril=db_reader.inputs.at('observation'), iterable=obs_ids)
plasm.connect(observation_dealer[:] >> db_reader['observation'])
path_names = ['image', 'depth', 'mask']
for path_name in path_names:
path = os.path.join(object_id, path_name)
if not os.path.exists(path):
os.makedirs(path)
writer_image = ImageSaver(
filename_format=os.path.join(path, '%05d.png'))[:]
plasm.connect(db_reader[path_name] >>
(writer_image, imshow(name=path_name)['image']))
return args
args = parse_args()
db = dbtools.init_object_databases(couchdb.Server(args.db_root))
for object_id in args.objects:
#get a list of observation ids for a particular object id.
obs_ids = models.find_all_observations_for_object(db, object_id)
if not obs_ids:
print 'No observations found for object %s.' % object_id
continue
plasm = ecto.Plasm()
#the db_reader transforms observation id into a set of image,depth,mask,K,R,T
db_reader = capture.ObservationReader("db_reader", db_params=dbtools.args_to_db_params(args))
#this iterates over all of the observation ids.
observation_dealer = ecto.Dealer(tendril=db_reader.inputs.at('observation'), iterable=obs_ids)
plasm.connect(observation_dealer[:] >> db_reader['observation'])
path_names = [ 'image', 'depth', 'mask']
for path_name in path_names:
path = os.path.join(object_id, path_name)
if not os.path.exists(path):
os.makedirs(path)
writer_image = ImageSaver(filename_format=os.path.join(path, '%05d.png'))[:]
plasm.connect(db_reader[path_name] >> (writer_image, imshow(name=path_name)['image']))
sched = ecto.schedulers.Singlethreaded(plasm)
sched.execute()
