def __init__(self,
traj_dir,
house_id=args.house_id,
traj_id=None,
load_graph=True):
self.house_id = house_id
self.traj_dir = traj_dir
if house_id != None:
self.create_env(args.config_path, house_id, args.render_width,
args.render_height)
# Contains methods for calculataing distances, room location, etc
self.hp = HouseParse(dataDir=args.data_dir)
# Loads house graph and generates shortest paths
self.utils = House3DUtils(
self.env,
rotation_sensitivity=45,
target_obj_conn_map_dir=False,
# Changed load_graph method to use pickle directly and Graph(g) initialisation;
# self.graph.load(path) left the graph empty!
build_graph=load_graph,
graph_dir=args.graph_dir)
self.house = {}
self.current_room = None
self.env_coors = None
self.traj_id = None
if traj_id != None:
self.update_trajectory(traj_id)
parser.add_argument('--HouseApiDir', default='../pyutils/House3D', help='house3d api dir')
parser.add_argument('--cacheDir', default='../cache/question-gen-outputs', help='directory for saving generated questions')
parser.add_argument('--outputJson', default='questions_from_engine_v2.json', help='output json file')
parser.add_argument('--object_counts_by_room_file', default='env_lists/800env_object_counts_by_room.json', help='roomTp to objT to cnt')
parser.add_argument('--env_obj_colors_file', default='env_lists/env_obj_colors_v2.json', help='obj to color mapping')
args = parser.parse_args()
# load splits
splits = json.load(open(osp.join(args.dataDir, 'eqa_v1', args.dataJson), 'r'))['splits']
for split, hids in splits.items():
print('There are %s %s house_ids.' % (len(hids), split))
house_ids = [hid for split, hids in splits.items() for hid in hids]
print('There are in all %s house_ids.' % len(house_ids))
# HouseParse and QA-engine
Hp = HouseParse(dataDir=osp.join(args.dataDir, 'SUNCGdata'),
objrenderPath=osp.join(args.HouseApiDir, 'House3D'))
E = Engine(args.object_counts_by_room_file, args.env_obj_colors_file)
# # try one house
# hid = splits['train'][2]
# Hp.parse(hid); E.cacheHouse(Hp)
# qns = E.executeFn(E.template_defs['room_size_compare'])
# pprint(qns)
# SAVE QUESTIONS TO A JSON FILE
T = ['object_dist_compare', 'object_color_compare', 'object_size_compare', 'room_size_compare']
# T = E.template_defs.keys()
num_envs = len(house_ids)
idx, all_qns = 0, []
empty_envs = []
for i in tqdm(range(num_envs)):
default="data/working/questions.json")
parser.add_argument("-dataDir",
help="Directory that has the SUNCG dataset",
default="/path/to/suncg/")
parser.add_argument("-numEnvs",
help="No. of environments to read",
default=False)
args = parser.parse_args()
data = json.load(open(args.inputJson, 'r'))
house_ids = list(data.keys())
num_envs = len(house_ids)
if args.numEnvs != False:
num_envs = int(args.numEnvs)
Hp = HouseParse(dataDir=args.dataDir)
E = Engine()
# SAVE QUESTIONS TO A JSON FILE
#
# -- STARTS HERE
idx, all_qns = 1, []
empty_envs = []
for i in tqdm(range(num_envs)):
Hp.parse(house_ids[i])
num_qns_for_house = 0
for j in E.template_defs:
if j == 'global_object_count': continue
E.cacheHouse(Hp)
qns = E.executeFn(E.template_defs[j])