#
# set it to False
# !!! YOUR CODE HERE
time_military_split = time.ctime().split()[3].split(':')
time_military = int(time_military_split[0] + time_military_split[1])
env = DuckietownEnv(seed=time_military,
map_name=args.map_name,
domain_rand=False)
# !!! ==============
# First we want to take a top-down "map" image of the entire map
# This serves as an example for how to implement the rest of this assignment,
# so PLEASE READ THIS CAREFFULLY!
env.reset()
env.mapmode = True
mapimg = env.render_obs(top_down=True)
np.save(f'data/maps/{args.map_name}.npy', mapimg)
env.mapmode = False
# Now, using a for-loop, take N samples of the raw camera view and the
# ground-truth semantic segmentation outputs from the simulator
#
# Loo at the code for taking the map image for guidance
#
# You must use the --num-samples option to control the number of samples taken
#
# !!! YOUR CODE HERE
for i in range(args.num_samples):
camera_view = env.render_obs()
np.save(f'data/inputs/{i}.npy', camera_view)
# set it to False
# !!! YOUR CODE HERE
env = DuckietownEnv(
seed=_,
map_name=_,
domain_rand=_)
# !!! ==============
# First we want to take a top-down "map" image of the entire map
# This serves as an example for how to implement the rest of this assignment,
# so PLEASE READ THIS CAREFFULLY!
env.reset()
env.mapmode = True
mapimg = env.render_obs(top_down=True)
np.save(f'data/maps/{args.map_name}.npy', mapimg)
env.mapmode = False
# Now, using a for-loop, take N samples of the raw camera view and the
# ground-truth semantic segmentation outputs from the simulator
#
# Loo at the code for taking the map image for guidance
#
# You must use the --num-samples option to control the number of samples taken
#
# !!! YOUR CODE HERE
for i in range(_):
camera_view = _
