def processor(cls, *args, **kwargs):
object_db = kwargs['object_db']
object_id = kwargs.get('object_id', None)
subtype = kwargs['subtype']
# db_models = Models(db_params, [ object_id ], method, subtype)
if object_id:
# TODO these should be loaded from the database?
json_K = kwargs['pipeline_params']['K']
json_D = kwargs['pipeline_params']['D']
imageWidth = kwargs['pipeline_params']['imageWidth']
imageHeight = kwargs['pipeline_params']['imageHeight']
document_ids = find_model_for_object(db_params_to_db(
object_db.parameters()),
object_id,
model_type='mesh')
print document_ids
db_models = Documents(object_db, document_ids)
print 'Found %d meshes:' % len(db_models)
else:
# TODO these should be loaded from the database?
json_K = []
json_D = []
imageWidth = 640
imageHeight = 480
db_models = []
return TransparentObjectsProcessor(
json_subtype=obj_to_cpp_json_str(subtype),
json_K=obj_to_cpp_json_str(json_K),
json_D=obj_to_cpp_json_str(json_D),
imageWidth=imageWidth,
imageHeight=imageHeight,
db_models=db_models)
def processor(cls, *args, **kwargs):
object_db = kwargs['object_db']
object_id = kwargs.get('object_id', None)
subtype = kwargs['subtype']
# db_models = Models(db_params, [ object_id ], method, subtype)
if object_id:
# TODO these should be loaded from the database?
json_K = kwargs['pipeline_params']['K']
json_D = kwargs['pipeline_params']['D']
imageWidth = kwargs['pipeline_params']['imageWidth']
imageHeight = kwargs['pipeline_params']['imageHeight']
document_ids = find_model_for_object(db_params_to_db(object_db.parameters()), object_id, model_type='mesh')
print document_ids
db_models = Documents(object_db, document_ids)
print 'Found %d meshes:' % len(db_models)
else:
# TODO these should be loaded from the database?
json_K = []
json_D = []
imageWidth = 640
imageHeight = 480
db_models = []
return TransparentObjectsProcessor(json_subtype=obj_to_cpp_json_str(subtype), json_K=obj_to_cpp_json_str(json_K), json_D=obj_to_cpp_json_str(json_D), imageWidth=imageWidth, imageHeight=imageHeight, db_models=db_models)
def __init__(self, detection_class, *args, **kwargs):
self._detector = detection_class(*args, **kwargs)
self._info = PipelineInfo(parameters=obj_to_cpp_json_str(kwargs))
ecto.BlackBox.__init__(self, *args, **kwargs)
def parse_args():
import argparse
parser = argparse.ArgumentParser(description='Compute a LINE-MOD model and saves it to the DB.')
scheduler_options(parser.add_argument_group('Scheduler'))
options = parser.parse_args()
return options
if __name__ == '__main__':
options = parse_args()
plasm = ecto.Plasm()
json_db_params = obj_to_cpp_json_str({'type': 'CouchDB', 'root': 'http://bwl.willowgarage.com:5984',
'collection': 'object_recognition'})
#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)
object_ids = obj_to_cpp_json_str(['whoolite', 'tilex'])
detector = Detector(json_object_ids=object_ids, json_db=json_db_params, threshold=90, visualize=True)
#connect up the pose_est
plasm.connect(source['image'] >> detector['image'],
source['depth'] >> detector['depth']
)
plasm.connect(source['image'] >> imshow(name='source')[:])
if 0:
