def detector(self, *args, **kwargs):
visualize = kwargs.pop('visualize', False)
submethod = kwargs.pop('submethod')
parameters = kwargs.pop('parameters')
object_ids = parameters['object_ids']
object_db = ObjectDb(parameters['db'])
model_documents = Models(object_db, object_ids, self.type_name(), json_helper.dict_to_cpp_json_str(submethod))
model_file = parameters['extra'].get('model_file')
return object_recognition_by_parts_cells.Detector(model_file=model_file, visualize=visualize)
def detector(self, *args, **kwargs):
visualize = kwargs.pop('visualize', False)
submethod = kwargs.pop('subtype')
parameters = kwargs.pop('parameters')
object_ids = parameters['object_ids']
object_db = ObjectDb(parameters['db'])
model_documents = Models(object_db, object_ids, self.type_name(),
json_helper.dict_to_cpp_json_str(submethod))
return PBDDetector(object_db, object_ids, visualize, *args,
**parameters['extra'])
def detector(self, *args, **kwargs):
#visualize = args.get('visualize', False)
submethod = kwargs.get('submethod')
parameters = kwargs.get('parameters')
object_ids = parameters['object_ids']
object_db = ObjectDb(parameters['db'])
model_documents = Models(object_db, object_ids, self.type_name(),
json_helper.dict_to_cpp_json_str(submethod))
threshold = parameters.get('threshold', 90)
return ecto_linemod.Detector(model_documents=model_documents,
db=object_db,
threshold=threshold)
def detector(self, *args, **kwargs):
visualize = kwargs.pop('visualize', False)
submethod = kwargs.pop('submethod')
parameters = kwargs.pop('parameters')
object_ids = parameters['object_ids']
object_db = ObjectDb(parameters['db'])
model_documents = Models(object_db, object_ids, self.type_name(),
json_helper.dict_to_cpp_json_str(submethod))
registrationMaskFilename = parameters.get('registrationMaskFilename')
return transparent_objects_cells.Detector(
model_documents=model_documents,
object_db=object_db,
registrationMaskFilename=registrationMaskFilename,
visualize=visualize)
def handles_training(req):
parser = create_parser(do_training=True)
#args = parser.parse_args()
args = parser.parse_args(['-c', req.cmd])
args = parser.parse_args(['--config_file', req.path])
ork_params, _args = read_arguments(args)
for _pipeline_id, pipeline_param in ork_params.iteritems():
pipeline_class = find_class([ pipeline_param['module'] ], pipeline_param['type'] )
if not pipeline_class:
raise RuntimeError('Invalid pipeline name: %s\nMake sure that the pipeline type is defined by a '
'TrainingPipeline class, in the name class function.' % pipeline_param['type'])
db_params_raw = pipeline_param['parameters'].get('json_db', {})
db_params = db_params_raw
object_ids = interpret_object_ids(db_params, pipeline_param.get('parameters', {}).get('json_object_ids', []))
print('Training %d objects.' % len(object_ids))
for object_id in object_ids:
print('computing object_id: %s' % object_id)
object_id = object_id.encode('ascii')
ork_params_sub = {}
ork_params_sub[_pipeline_id] = pipeline_param
ork_params_sub[_pipeline_id]['parameters']['object_id'] = object_id
plasm = create_plasm(ork_params_sub)
plasm.execute()
# Make sure the views exist
object_db = ObjectDb(db_params)
db_params = ObjectDbParameters(db_params)
if db_params.type == ObjectDbTypes.COUCHDB:
import couchdb
import json
dic = json.loads(db_params_raw)
couch = couchdb.Server(dic['root'])
init_object_databases(couch)
return training_srvResponse()
options = parser.parse_args()
return options
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,
from object_recognition_core.db.tools import db_params_to_db
from object_recognition_core.db.object_db import core_db_types
print 'Existing core types: ' + str(core_db_types())
str_to_enum = {'CouchDB': ObjectDbTypes.COUCHDB, 'filesystem': ObjectDbTypes.FILESYSTEM, 'empty': ObjectDbTypes.EMPTY }
# test default parameters
for db_params_raw in [{'type': 'CouchDB', 'root': 'http://localhost:5984', 'collection': 'object_recognition'},
{'path': '/tmp', 'type': 'filesystem', 'collection': 'object_recognition'},
{'type': 'empty'}]:
type_str = db_params_raw['type']
print 'starting type ' + type_str
db_params = ObjectDbParameters(db_params_raw)
db = ObjectDb(db_params)
db_params_new = db.parameters().raw
for dic1, dic2 in [(db_params_raw, db_params_new), (db_params_new, db_params_raw)]:
for k, v in dic1.items():
if (k not in dic2) or (k in dic2 and dic2[k] != v):
raise RuntimeError('Key "%s" in %s but not in %s' % (str(k), str(dic1), str(dic2)))
if str_to_enum[type_str] != db_params.type:
raise RuntimeError('The "type" argument in db_params are wrong for db of type %s' % type_str)
print 'ending type ' + type_str
# test that we can convert a JSON string to an ObjectDbParameters type
db_params_to_db(db_params)