def build_class_dataset(context):
import concurrent.futures
import ai2thor.controller
import multiprocessing as mp
mp.set_start_method('spawn')
controller = ai2thor.controller.Controller()
executor = concurrent.futures.ProcessPoolExecutor(max_workers=4)
futures = []
for scene in controller.scene_names():
print("processing scene %s" % scene)
futures.append(executor.submit(class_dataset_images_for_scene, scene))
for f in concurrent.futures.as_completed(futures):
scene = f.result()
print("scene name complete: %s" % scene)
type=int,
default=200,
help='number of viewpoints to sample per scene')
parser.add_argument('--fov',
type=int,
default=60,
help='camera field of view')
args = parser.parse_args()
os.mkdir(args.data_dir)
os.mkdir(os.path.join(args.data_dir, 'train'))
os.mkdir(os.path.join(args.data_dir, 'val'))
controller = ai2thor.controller.Controller()
controller.start()
scene_names = controller.scene_names()
scene_names = [s.replace('_physics', '') for s in scene_names]
validation_scenes = [
'FloorPlan1', 'FloorPlan2', 'FloorPlan201', 'FloorPlan202',
'FloorPlan301', 'FloorPlan302', 'FloorPlan401', 'FloorPlan402'
]
for scene_name in scene_names:
if scene_name in validation_scenes:
save_dir = os.path.join(args.data_dir, 'val', scene_name)
else:
save_dir = os.path.join(args.data_dir, 'train', scene_name)
os.mkdir(save_dir)
navigable_points, centre = setup_scene(controller, scene_name)
locations, rotations, horizons = random_viewpoints(