def update_cfg():
import argparse
import warnings
parser = argparse.ArgumentParser(description='Testing Complex-YOLO.')
parser.add_argument("--use_cuda",
action="store_true",
default=False,
help="Whether to use CUDA. Default is `False`.")
args = parser.parse_args()
kwargs = {}
if torch.cuda.is_available():
if not args.use_cuda:
warnings.warn("CUDA devices are available. " +
"It is recommended to run with `--use_cuda`.")
elif args.use_cuda:
warnings.warn(
"CUDA devices are excepted but not available. CPU will be used.")
args.use_cuda = False
kwargs["device"] = torch.device("cuda" if args.use_cuda else "cpu")
kwargs["split"] = "val"
cfg.update(vars(args), kwargs)
def filter_dataset():
'''Filter dataset according to active class.
A new split file named `active_train.txt` will be generated.
'''
active_split_dir = os.path.join(cfg.get_datasets_cache_root(), "Kitti")
if not os.path.exists(os.path.dirname(active_split_dir)):
os.makedirs(active_split_dir)
active_split_path = os.path.join(active_split_dir, "active_train.txt")
dataset = BEVKitti()
ids = dataset.get_ids()
active_ids = []
active_clss = cfg.get_active_cls_str()
for idx in ids:
objs = dataset.get_objs(idx)
for obj in objs:
if obj.get_cls_str() in active_clss:
active_ids.append("{:0>6}\n".format(idx))
break
active_ids[-1] = active_ids[-1][:-1]
with open(active_split_path, 'w') as f:
f.writelines(active_ids)
kwargs = {"split": "active_train"}
cfg.update(kwargs)
# def show_bev(bev):
# fig, ax = plt.subplots(ncols=3, nrows=1)
# height_map = bev[0]
# density_map = bev[1]
# intensity_map = bev[2]
# ax[0].imshow(height_map, cmap=plt.cm.gray)
# ax[1].imshow(density_map, cmap=plt.cm.gray)
# ax[2].imshow(intensity_map, cmap=plt.cm.gray)
# plt.show()
if __name__ == "__main__":
datasets_cache_root = cfg.get_datasets_cache_root()
kwargs = {"split": "trainval"}
cfg.update(kwargs)
dataset = BasicKitti()
ids = dataset.get_ids()
for idx in ids:
# idx = ids[0]
print("Generating BEV maps {:0>6} ...".format(idx))
pts_lidar = dataset.get_valid_pts_lidar(idx)
bev = generate_BEV(pts_lidar)
# show_bev(bev)
# break
BEVKitti.save_bev(bev, idx)
print("Successfully save BEV to " + datasets_cache_root + "/Kitti/bev/")