observation_big, reward, done, info = env.step(action)
# we can resize the image here
observation = cv2.resize(observation_big, (80, 60))
# NOTICE: OpenCV changes the order of the channels !!!
observation = cv2.cvtColor(observation, cv2.COLOR_BGR2RGB)
#observation = cv2.cvtColor(observation, cv2.COLOR_BGR2GRAY)
flip_action = (action[0], -action[1])
flip_observation = cv2.flip(observation, 1)
except:
break
# we may use this to debug our expert.
if DEBUG:
cv2.imshow('debug', cv2.resize(observation, (0, 0), fx=3,
fy=3))
cv2.waitKey(1)
logger.log(observation, action, reward, done, info)
logger.log(flip_observation, flip_action, reward, done, info)
# [optional] env.render() to watch the expert interaction with the environment
# we log here
logger.on_episode_done() # speed up logging by flushing the file
env.reset()
# we flush everything and close the file, it should be ~ 120mb
# NOTICE: we make the log file read-only, this prevent us from erasing all collected data by mistake
# believe me, this is an important issue... can you imagine loosing 2 GB of data? No? We do...
logger.close()
env.close()
closest_point, _ = env.closest_curve_point(env.cur_pos,
env.cur_angle)
if closest_point is None:
done = True
break
# Cut the horizon: obs.shape = (480,640,3) --> (300,640,3)
observation = observation[150:450, :]
# we can resize the image here
observation = cv2.resize(observation, (120, 60))
# NOTICE: OpenCV changes the order of the channels !!!
observation = cv2.cvtColor(observation, cv2.COLOR_BGR2RGB)
# we may use this to debug our expert.
if DEBUG:
cv2.imshow('debug', observation)
cv2.waitKey(1)
logger.log(observation, action, reward, done, info)
logger.on_episode_done() # speed up logging by flushing the file
env.reset()
logger.close()
env.close()
end_time = time.time()
print(
f"Process finished. It took {(end_time - start_time) / (60*60):.2f} hours!"
)