def main():
parser = argparse.ArgumentParser()
parser.add_argument('dataset')
parser.add_argument('graspable')
parser.add_argument('--grasp-dir')
args = parser.parse_args()
dataset = database.Dataset(args.dataset, CONFIG)
graspable = dataset[args.graspable]
grasps = dataset.load_grasps(args.graspable, args.grasp_dir)
visualize(grasps)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('dataset')
parser.add_argument('graspable')
parser.add_argument('--grasp_dir', default=None)
args = parser.parse_args()
dataset = database.Dataset(args.dataset, CONFIG)
graspable = dataset[args.graspable]
grasps = dataset.load_grasps(args.graspable, args.grasp_dir)
rotated_grasps = []
for g in grasps:
rotated_grasps.extend(g.transform(graspable.tf, THETA_RES))
visualize(graspable, rotated_grasps)
def new_dataset():
args = merge_http_request_arguments(True)
user = database.User.query.filter_by(id=g.user_id).one()
dataset_type = database.DatasetType.query.filter_by(id=int(args['dataset_type'])).one()
dataset = database.Dataset(name=args['name'], user_created=user)
dataset.short_notes = args['short_notes']
dataset.long_notes = rass_app.LONG_NOTES
dataset.user_modified = user
date_created = datetime.strptime(args['date_created'], '%d.%m.%Y')
dataset.date_created = date_created
dataset.type = dataset_type
database.db.session.add(dataset)
database.db.session.commit()
return render_template('datastore/dataset.html', uid=dataset.id, dataset=dataset)
def load_data(path, config):
precomputed = exists(path)
training = db.Dataset(config['dataset'], config)
all_grasps = []
all_features = []
for obj in training:
obj_grasps = training.load_grasps(obj.key)
all_grasps.extend(obj_grasps)
if not precomputed:
feature_loader = ff.GraspableFeatureLoader(obj, training.name, config)
obj_features = feature_loader.load_all_features(obj_grasps)
all_features.extend(obj_features)
# break
if precomputed:
logging.info('Loading from %s', path)
with h5py.File(path, 'r') as f:
design_matrix = f['projection_window'][()]
logging.info('Loaded.')
return all_grasps, design_matrix
num_grasps = len(all_grasps)
design_matrix = np.zeros((num_grasps, 2 * config['window_steps']**2))
i = 0
for grasp, feature in zip(all_grasps, all_features):
w1 = feature.extractors_[0]
w2 = feature.extractors_[1]
proj1 = w1.extractors_[0]
proj2 = w2.extractors_[0]
design_matrix[i, :] = np.concatenate([proj1.phi, proj2.phi])
i += 1
logging.info('Saving to %s', path)
with h5py.File(path, 'w') as f:
f['projection_window'] = design_matrix
logging.info('Saved.')
return all_grasps, design_matrix
logging.getLogger().setLevel(logging.INFO)
# read config file
config = ec.ExperimentConfig(args.config)
chunk = db.Chunk(config)
# make output directory
dest = os.path.join(args.output_dest, chunk.name)
try:
os.makedirs(dest)
except os.error:
pass
if 'priors_dataset' in config:
priors_dataset = db.Dataset(config['priors_dataset'], config)
else:
priors_dataset = None
# loop through objects, labelling each
results = []
for obj in chunk:
if obj.key in skip_keys:
continue
logging.info('Labelling object {}'.format(obj.key))
experiment_result = label_correlated(
obj,
chunk,
config,
priors_dataset=priors_dataset,