def __init__(self,
env,
max_episodes,
max_steps,
log_file=None,
downscale=False,
playback=True,
filter_bad_data=False):
if not log_file:
log_file = f"dataset.log"
self.env = env
self.env.reset()
self.datagen = Logger(self.env, log_file=log_file)
self.episode = 1
self.max_episodes = max_episodes
self.filter_bad_data = filter_bad_data
#! Temporary Variable Setup:
self.last_reward = 0
self.playback_buffer = []
#! Enter main event loop
pyglet.clock.schedule_interval(self.update,
1.0 / self.env.unwrapped.frame_rate,
self.env)
#! Get Joystick
self.joysticks = pyglet.input.get_joysticks()
assert self.joysticks, 'No joystick device is connected'
self.joystick = self.joysticks[0]
self.joystick.open()
self.joystick.push_handlers(self.on_joybutton_press)
pyglet.app.run()
#! Log and exit
self.datagen.close()
self.env.close()
def __init__(self,
env,
max_episodes,
max_steps,
log_file=None,
downscale=False):
if not log_file:
log_file = f"dataset.log"
self.env = env
self.env.reset()
self.logger = Logger(self.env, log_file=log_file)
self.episode = 1
self.max_episodes = max_episodes
self.downscale = downscale
#! Enter main event loop
print("Starting data generation")
pyglet.clock.schedule_interval(self.update,
1.0 / self.env.unwrapped.frame_rate,
self.env)
pyglet.app.run()
print("App exited, closing file descriptors")
self.logger.close()
self.env.close()
class HumanDriver:
def __init__(self,
env,
max_episodes,
max_steps,
log_file=None,
downscale=False,
playback=True,
filter_bad_data=False):
if not log_file:
log_file = f"dataset.log"
self.env = env
self.env.reset()
self.datagen = Logger(self.env, log_file=log_file)
self.episode = 1
self.max_episodes = max_episodes
self.filter_bad_data = filter_bad_data
#! Temporary Variable Setup:
self.last_reward = 0
self.playback_buffer = []
#! Enter main event loop
pyglet.clock.schedule_interval(self.update,
1.0 / self.env.unwrapped.frame_rate,
self.env)
#! Get Joystick
self.joysticks = pyglet.input.get_joysticks()
assert self.joysticks, 'No joystick device is connected'
self.joystick = self.joysticks[0]
self.joystick.open()
self.joystick.push_handlers(self.on_joybutton_press)
pyglet.app.run()
#! Log and exit
self.datagen.close()
self.env.close()
def sleep_after_reset(self, seconds):
for remaining in range(seconds, 0, -1):
sys.stdout.write("\r")
sys.stdout.write("{:2d} seconds remaining.".format(remaining))
sys.stdout.flush()
time.sleep(1)
sys.stdout.write("\rGO! \n")
return
def playback(self):
#! Render Image
if args.playback:
for entry in self.playback_buffer:
(recorded, action, reward) = entry
x = action[0]
z = action[1]
canvas = cv2.cvtColor(recorded, cv2.COLOR_BGR2RGB)
#! Speed bar indicator
cv2.rectangle(canvas, (20, 240), (50, int(240 - 220 * x)),
(76, 84, 255), cv2.FILLED)
cv2.rectangle(canvas, (320, 430), (int(320 - 150 * z), 460),
(76, 84, 255), cv2.FILLED)
cv2.imshow('Playback', canvas)
cv2.waitKey(20)
#! User interaction for log commitment
qa = input('1 to commit, 2 to abort: ')
while not (qa == '1' or qa == '2'):
qa = input('1 to commit, 2 to abort: ')
if qa == '2':
self.datagen.reset_episode()
print('Reset log. Discard current...')
else:
print("Comitting Episode")
self.datagen.on_episode_done()
self.playback_buffer = [] # reset playback buffer
return
def image_resize(self,
image,
width=None,
height=None,
inter=cv2.INTER_AREA):
"""
Resize an image with a given width or a given height
and preserve the aspect ratio.
"""
dim = None
(h, w) = image.shape[:2]
if width is None and height is None:
return image
if width is None:
r = height / float(h)
dim = (int(w * r), height)
else:
r = width / float(w)
dim = (width, int(h * r))
resized = cv2.resize(image, dim, interpolation=inter)
return resized
def on_key_press(self, symbol, modifiers):
"""
This handler processes keyboard commands that
control the simulation
"""
if symbol == key.BACKSPACE or symbol == key.SLASH:
print('RESET')
self.playback()
self.env.reset()
self.env.render()
self.sleep_after_reset(5)
elif symbol == key.PAGEUP:
self.env.unwrapped.cam_angle[0] = 0
self.env.render()
elif symbol == key.ESCAPE or symbol == key.Q:
self.env.close()
sys.exit(0)
def on_joybutton_press(self, joystick, button):
"""
Event Handler for Controller Button Inputs
Relevant Button Definitions:
3 - Y - Resets Env.
"""
# Y Button
if button == 3:
print('RESET')
self.playback()
self.env.reset()
self.env.render()
self.sleep_after_reset(5)
def update(self, dt, env):
"""
This function is called at every frame to handle
movement/stepping and redrawing
"""
#! Joystick no action do not record
if round(self.joystick.z, 2) == 0.0 and round(self.joystick.y,
2) == 0.0:
return
#! Nominal Joystick Interpretation
x = round(self.joystick.y,
2) * 0.9 # To ensure maximum trun/velocity ratio
z = round(self.joystick.z, 2) * 3.0
#! Joystick deadband
# if (abs(round(joystick.y, 2)) < 0.01):
# z = 0.0
# if (abs(round(joystick.z, 2)) < 0.01):
# x = 0.0
#! DRS enable for straight line
if self.joystick.buttons[6]:
x = -1.0
z = 0.0
action = np.array([-x, -z])
#! GO! and get next
# * Observation is 640x480 pixels
(obs, reward, done, info) = self.env.step(action)
if reward != -1000:
print('Current Command: ', action, ' speed. Score: ', reward)
if ((reward > self.last_reward - 0.02)
or not self.filter_bad_data):
print('log')
#! resize to Nvidia standard:
obs_distorted_DS = self.image_resize(obs, width=200)
#! Image pre-processing
height, width = obs_distorted_DS.shape[:2]
cropped = obs_distorted_DS[0:150, 0:200]
# NOTICE: OpenCV changes the order of the channels !!!
cropped_final = cv2.cvtColor(cropped, cv2.COLOR_RGB2YUV)
self.playback_buffer.append((obs, action, reward))
step = Step(cropped_final, reward, action, done)
self.datagen.log(step, info)
self.last_reward = reward
else:
print('Bad Training Data! Discarding...')
self.last_reward = reward
else:
print('!!!OUT OF BOUND!!!')
if done:
self.playback()
self.env.reset()
self.env.render()
self.sleep_after_reset(5)
return
self.env.render()
import collections
import rosbag
import cv_bridge
from copy import copy
from extract_data_functions import image_preprocessing, synchronize_data
from log_util import Logger
from log_schema import Episode, Step
import cv2
VEHICLE_NAME = 'avlduck2'
# A collection of ros messages coming from a single topic.
MessageCollection = collections.namedtuple(
"MessageCollection", ["topic", "type", "messages"])
frank_logger = Logger(log_file='converted/dataset.log')
def extract_messages(path, requested_topics):
# check if path is string and requested_topics a list
assert isinstance(path, str)
assert isinstance(requested_topics, list)
bag = rosbag.Bag(path)
_, available_topics = bag.get_type_and_topic_info()
# print(available_topics)
# check if the requested topics exist in bag's topics and if yes extract the messages only for them
class DataGenerator:
def __init__(self,
env,
max_episodes,
max_steps,
log_file=None,
downscale=False):
if not log_file:
log_file = f"dataset.log"
self.env = env
self.env.reset()
self.logger = Logger(self.env, log_file=log_file)
self.episode = 1
self.max_episodes = max_episodes
self.downscale = downscale
#! Enter main event loop
print("Starting data generation")
pyglet.clock.schedule_interval(self.update,
1.0 / self.env.unwrapped.frame_rate,
self.env)
pyglet.app.run()
print("App exited, closing file descriptors")
self.logger.close()
self.env.close()
def image_resize(self,
image,
width=None,
height=None,
inter=cv2.INTER_AREA):
# initialize the dimensions of the image to be resized and
# grab the image size
dim = None
(h, w) = image.shape[:2]
# if both the width and height are None, then return the
# original image
if width is None and height is None:
return image
# check to see if the width is None
if width is None:
# calculate the ratio of the height and construct the
# dimensions
r = height / float(h)
dim = (int(w * r), height)
# otherwise, the height is None
else:
# calculate the ratio of the width and construct the
# dimensions
r = width / float(w)
dim = (width, int(h * r))
# resize the image
resized = cv2.resize(image, dim, interpolation=inter)
# return the resized image
return resized
def pure_pursuite(self, env) -> List[float]:
"""
Implement pure-pursuit & PID using ground truth
Returns [velocity, steering]
"""
# Find the curve point closest to the agent, and the tangent at that point
closest_point, closest_tangent = env.closest_curve_point(
env.cur_pos, env.cur_angle)
iterations = 0
lookup_distance = 0.5
max_iterations = 1000
gain = 4.0 # 2.0
velocity = 0.35
curve_point = None
while iterations < max_iterations:
# Project a point ahead along the curve tangent,
# then find the closest point to to that
follow_point = closest_point + closest_tangent * lookup_distance
curve_point, _ = env.closest_curve_point(follow_point,
env.cur_angle)
# If we have a valid point on the curve, stop
if curve_point is not None:
break
iterations += 1
lookup_distance *= 0.5
# Compute a normalized vector to the curve point
point_vec = curve_point - env.cur_pos
point_vec /= np.linalg.norm(point_vec)
right_vec = [math.sin(env.cur_angle), 0, math.cos(env.cur_angle)]
dot = np.dot(right_vec, point_vec)
steering = gain * -dot
return [velocity, steering]
def update(self, dt, env):
"""
This function is called at every frame to handle
movement/stepping and redrawing
"""
action = self.pure_pursuite(env)
#! GO! and get next
# * Observation is 640x480 pixels
obs, reward, done, info = env.step(action)
if reward == REWARD_INVALID_POSE:
print("Out of bound")
else:
output_img = obs
if self.downscale:
# Resize to (150x200)
#! resize to Nvidia standard:
obs_distorted_DS = self.image_resize(obs, width=200)
#! ADD IMAGE-PREPROCESSING HERE!!!!!
height, width = obs_distorted_DS.shape[:2]
# print('Distorted return image Height: ', height,' Width: ',width)
cropped = obs_distorted_DS[0:150, 0:200]
# NOTICE: OpenCV changes the order of the channels !!!
output_img = cv2.cvtColor(cropped, cv2.COLOR_RGB2YUV)
# print(f"Recorded shape: {obs.shape}")
# print(f"Saved image shape: {cropped.shape}")
step = Step(output_img, reward, action, done)
self.logger.log(step, info)
# rawlog.log(obs, action, reward, done, info)
# last_reward = reward
if done:
self.logger.on_episode_done()
print(f"episode {self.episode}/{self.max_episodes}")
self.episode += 1
env.reset()
if self.logger.episode_count >= args.nb_episodes:
print("Training completed !")
sys.exit()
time.sleep(1)
return
import rosbag
import cv_bridge
from copy import copy
from extract_data_functions import image_preprocessing, synchronize_data
from log_util import Logger
from log_schema import Episode, Step
import cv2
# Change this based on the bag files being used.
VEHICLE_NAME = 'maserati'
# A collection of ros messages coming from a single topic.
MessageCollection = collections.namedtuple(
"MessageCollection", ["topic", "type", "messages"])
frank_logger = Logger(log_file=f'converted/{VEHICLE_NAME}')
def extract_messages(path, requested_topics):
# check if path is string and requested_topics a list
assert isinstance(path, str)
assert isinstance(requested_topics, list)
bag = rosbag.Bag(path)
_, available_topics = bag.get_type_and_topic_info()
# check if the requested topics exist in bag's topics and if yes extract the messages only for them
extracted_messages = {}
for topic in requested_topics:
if topic in available_topics:
