options = parser.parse_args()
return options
if __name__ == '__main__':
options = parse_args()
plasm = ecto.Plasm()
#setup the input source, grayscale conversion
trainer = Trainer(path='/wg/stor2a/vrabaud/workspace/recognition_kitchen_groovy/build/buildspace/lib/object_recognition_reconstruction')
model_filler = ModelFiller()
#connect up the pose_est
plasm.connect(trainer['detector'] >> model_filler['detector'])
object_id = 'whoolite'
writer = ModelWriter(session_ids=list_to_cpp_json_str([]),
object_id=object_id, db_params=ObjectDbParameters({'type': 'CouchDB',
'root': 'http://bwl.willowgarage.com:5984',
'collection': 'object_recognition'}),
method=LinemodDetectionPipeline.type_name(),
json_submethod=dict_to_cpp_json_str({}),
json_params=dict_to_cpp_json_str({}))
plasm.connect(model_filler["db_document"] >> writer["db_document"])
options.niter = 1
run_plasm(options, plasm, locals=vars())
db_params = ObjectDbParameters({
'type': 'CouchDB',
'root': 'http://bwl.willowgarage.com:5984',
'collection': 'object_recognition'
})
object_db = ObjectDb(db_params)
#setup the input source, grayscale conversion
from ecto_openni import VGA_RES, FPS_30
source = create_source('image_pipeline',
'OpenNISource',
image_mode=VGA_RES,
image_fps=FPS_30)
print LinemodDetectionPipeline.type_name()
object_ids = ['whoolite', 'tilex']
model_documents = Models(object_db, object_ids,
LinemodDetectionPipeline.type_name(),
json_helper.dict_to_cpp_json_str({}))
print len(model_documents)
detector = Detector(model_documents=model_documents,
db=object_db,
threshold=90)
#connect up the pose_est
plasm.connect(source['image'] >> detector['image'],
source['depth'] >> detector['depth'])
plasm.connect(source['image'] >> imshow(name='source')[:])
if 0:
if __name__ == '__main__':
options = parse_args()
plasm = ecto.Plasm()
db_params = ObjectDbParameters({'type': 'CouchDB', 'root': 'http://bwl.willowgarage.com:5984',
'collection': 'object_recognition'})
object_db = ObjectDb(db_params)
#setup the input source, grayscale conversion
from ecto_openni import VGA_RES, FPS_30
source = create_source('image_pipeline','OpenNISource',image_mode=VGA_RES,image_fps=FPS_30)
print LinemodDetectionPipeline.type_name()
object_ids = ['whoolite', 'tilex']
model_documents = Models(object_db, object_ids, LinemodDetectionPipeline.type_name(), json_helper.dict_to_cpp_json_str({}))
print len(model_documents)
detector = Detector(model_documents=model_documents, db=object_db, threshold=90)
#connect up the pose_est
plasm.connect(source['image'] >> detector['image'],
source['depth'] >> detector['depth']
)
plasm.connect(source['image'] >> imshow(name='source')[:])
if 0:
import ecto_ros
import ecto_ros.ecto_sensor_msgs
import sys
#setup the input source, grayscale conversion
trainer = Trainer(
path=
'/wg/stor2a/vrabaud/workspace/recognition_kitchen_groovy/build/buildspace/lib/object_recognition_reconstruction'
)
model_filler = ModelFiller()
#connect up the pose_est
plasm.connect(trainer['detector'] >> model_filler['detector'])
object_id = 'whoolite'
writer = ModelWriter(session_ids=list_to_cpp_json_str([]),
object_id=object_id,
db_params=ObjectDbParameters({
'type':
'CouchDB',
'root':
'http://bwl.willowgarage.com:5984',
'collection':
'object_recognition'
}),
method=LinemodDetectionPipeline.type_name(),
json_submethod=dict_to_cpp_json_str({}),
json_params=dict_to_cpp_json_str({}))
plasm.connect(model_filler["db_document"] >> writer["db_document"])
options.niter = 1
run_plasm(options, plasm, locals=vars())