def put(self, key=config.CONFIG_SINGLETON_KEY):
model_key = ndb.Key(config.Configuration, key)
old_model = model_key.get()
if not old_model:
raise NotFound('{} with key {} does not exist'.format('Configuration', key))
# the history mechanism doesn't work unless we make a copy. So a put is always a clone, never
# an actual update.
model = config.Configuration(**old_model.to_dict())
model.key = model_key
model.configuration = request.get_json(force=True)
self.validate(model)
date = None
if config.getSettingJson(config.ALLOW_NONPROD_REQUESTS, False):
date = request.headers.get('x-pretend-date', None)
if date is not None:
date = parse_date(date)
client_id = app_util.get_oauth_id()
config.store(model, date=date, client_id=client_id)
return model.configuration
plt.plot(accuracy_check_iterations, allAccuracyTrain, 'b-')
plt.plot(accuracy_check_iterations, allAccuracyTest, 'ro')
plt.plot(accuracy_check_iterations, allAccuracyTest, 'r-')
plt.plot(accuracy_check_iterations, sliding_window_graph, 'g-')
axes = plt.gca()
axes.set_ylim([0, 1000.05])
plt.title("training (blue), test (red), avg " +
str(round(sliding_window_graph[-1], 5)) + " / " +
str(len(sliding_window)))
plt.xlabel('iteration')
plt.ylabel('diff squared')
plt.savefig(os.path.join(output_path, "progress.png"))
# Save the model.
save_path = saver.save(sess, os.path.join(output_path, "model.ckpt"))
config.store('last_tf_model', save_path)
# put the print after writing everything so it indicates things have been written.
debug_iteration = format(iteration, '^10')
debug_loss = format(results_loss, '^16')
debug_regularizers = format(results_regularizers, '^16')
#Format sliding window
debug_sliding_window = generate_color_text(prev_sliding_window,
sliding_window_graph[-1])
debug_sliding_window = format(debug_sliding_window, '^25')
prev_sliding_window = sliding_window_graph[-1]
# Format steering regression accuracy
debug_sqr_acc = generate_color_text(prev_squared_diff,
results_squared_diff)
def post(self, key=config.CONFIG_SINGLETON_KEY):
model = config.Configuration(key=ndb.Key(config.Configuration, key),
configuration=request.get_json(force=True))
self.validate(model)
config.store(model)
return model.configuration
def main():
global last_odometer_reset
# Init some vars..
old_steering = 0
old_throttle = 0
old_aux1 = 0
steering = 90
throttle = 90
aux1 = 0
frame_count = 0
session_full_path = ''
last_switch = 0
button_arduino_out = 0
currently_running = False
override_autonomous_control = False
train_on_this_image = True
vel = 0.0
last_odo = 0
last_millis = 0.0
# Check for insomnia
if platform.system() == "Darwin":
check_for_insomnia()
# Setup ports.
port_in, port_out = setup_serial_and_reset_arduinos()
# Setup tensorflow
sess, net_model = setup_tensorflow()
# Start the clock.
drive_start_time = time.time()
print 'Awaiting switch flip..'
while True:
loop_start_time = time.time()
# Switch was just flipped.
if last_switch != button_arduino_out:
last_switch = button_arduino_out
# See if the car started up with the switch already flipped.
if time.time() - drive_start_time < 1:
print 'Error: start switch in the wrong position.'
sys.exit()
if button_arduino_out == 1:
currently_running = True
print '%s: Switch flipped.' % frame_count
last_odometer_reset = odometer_ticks
if we_are_recording and (not we_are_autonomous):
session_full_path = make_data_folder('~/training-images')
print 'STARTING TO RECORD.'
print 'Folder: %s' % session_full_path
config.store('last_record_dir', session_full_path)
elif we_are_recording and we_are_autonomous:
session_full_path = make_data_folder('~/tf-driving-images')
print 'DRIVING AUTONOMOUSLY and STARTING TO RECORD'
print 'Folder: %s' % session_full_path
else:
print("DRIVING AUTONOMOUSLY (not recording).")
else:
print("%s: Switch flipped. Recording stopped." % frame_count)
currently_running = False
# Read input data from arduinos.
new_steering, new_throttle, new_aux1, button_arduino_in, button_arduino_out = (
process_input(port_in, port_out))
if new_steering != None:
steering = new_steering
if new_throttle != None:
throttle = new_throttle
if new_aux1 != None:
aux1 = new_aux1
# Check to see if we should stop the car via the RC during TF control.
# But also provide a way to re-engage autonomous control after an override.
if we_are_autonomous and currently_running:
if (steering > 130 or steering < 50) and throttle > 130:
if not override_autonomous_control:
print '%s: Detected RC override: stopping.' % frame_count
override_autonomous_control = True
if abs(aux1 - old_aux1) > 400 and override_autonomous_control:
old_aux1 = aux1
print '%s: Detected RC input: re-engaging autonomous control.' % frame_count
override_autonomous_control = False
# Check to see if we should reset the odometer via aux1 during manual
# driving. This is Button E on the RC transmitter.
# The values will swing from ~1100 to ~1900.
if abs(aux1 - old_aux1) > 400:
old_aux1 = aux1
print '%s: Resetting the odometer.' % frame_count
last_odometer_reset = odometer_ticks
# Overwrite steering with neural net output in autonomous mode.
# This seems to take about 10ms.
if we_are_autonomous and currently_running:
# Calculate velocity from odometer. Gets weird when stopped.
if odometer_ticks != last_odo and milliseconds > last_millis:
vel = (float(odometer_ticks) - last_odo) / (milliseconds -
last_millis)
if last_millis == 0 and last_odo == 0:
vel = 0
if odometer_ticks - last_odo > 50 or last_odo >= odometer_ticks:
vel = 0
last_odo = odometer_ticks
last_millis = milliseconds
# Read a frame from the camera.
frame = camera_stream.read()
steering, throttle = do_tensorflow(
sess, net_model, frame, odometer_ticks - last_odometer_reset,
vel)
# steering, throttle = do_tensor_flow(frame, odometer_ticks - last_odometer_reset, vel)
if we_are_recording and currently_running:
# TODO(matt): also record vel in filename for tf?
# Read a frame from the camera.
frame = camera_stream.read()
# Save image with car data in filename.
cv2.imwrite(
"%s/" % session_full_path + "frame_" +
str(frame_count).zfill(5) + "_thr_" + str(throttle) + "_ste_" +
str(steering) + "_mil_" + str(milliseconds) + "_odo_" +
str(odometer_ticks - last_odometer_reset).zfill(5) + ".png",
frame)
else:
frame = camera_stream.read()
cv2.imwrite('/tmp/test.png', frame)
if override_autonomous_control:
# Full brake and neutral steering.
throttle, steering = 0, 90
# Send output data to arduinos.
process_output(old_steering, old_throttle, steering, throttle,
port_out)
old_steering = steering
old_throttle = throttle
# Attempt to go at 30 fps. In reality, we could go slower if something hiccups.
seconds = time.time() - loop_start_time
while seconds < 1 / 30.:
time.sleep(0.001)
seconds = time.time() - loop_start_time
frame_count += 1
# plt.plot(accuracy_check_iterations, allAccuracyTrain, 'bo')
# plt.plot(accuracy_check_iterations, allAccuracyTrain, 'b-')
# plt.plot(accuracy_check_iterations, allAccuracyTest, 'ro')
# plt.plot(accuracy_check_iterations, allAccuracyTest, 'r-')
# plt.plot(accuracy_check_iterations, sliding_window_graph, 'g-')
# axes = plt.gca()
# axes.set_ylim([0, 1000.05])
# plt.title("training (blue), test (red), avg " + str(round(sliding_window_graph[-1], 5)) + " / " + str(
# len(sliding_window)))
# plt.xlabel('iteration')
# plt.ylabel('diff squared')
# plt.savefig(os.path.join(output_path, "progress.png"))
# Save the model.
save_path = saver.save(sess, os.path.join(output_path, "model.ckpt"))
config.store('last_tf_model', save_path)
copyfile(os.path.join(output_path, "debug.html"), os.path.join(output_path, "debug_best.html"))
print("Saved: " + str(cool_score))
# put the print after writing everything so it indicates things have been written.
debug_iteration = format(iteration, '^10')
debug_loss = format(results_loss, '^16')
debug_regularizers = format(results_regularizers, '^16')
# Format sliding window
debug_sliding_window = Colorize(prev_sliding_window, sliding_window_graph[-1], '^24')
prev_sliding_window = sliding_window_graph[-1]
# Format steering regression accuracy
debug_sqr_acc = Colorize(prev_squared_diff, results_squared_diff, '^28')
prev_squared_diff = results_squared_diff
def switch_cams(self):
self._switch_cams()
self.config['switch_cameras_on_start'] = not self.config.get(
'switch_cameras_on_start', False)
config.store(self.config)
def main():
global last_odometer_reset
# Init some vars..
telemetry = []
old_steering = 0
old_throttle = 0
old_aux1 = 0
steering = 90
throttle = 90
aux1 = 0
frame_count = 0
session_full_path = ''
last_switch = 0
button_arduino_out = 0
currently_running = False
override_autonomous_control = False
train_on_this_image = True
vel = 0.0
last_odo_queue = [0] * (config.odo_delta + 1)
last_millis_queue = [0] * (config.odo_delta + 1)
# Check for insomnia
if platform.system() == "Darwin":
check_for_insomnia()
# Setup ports.
port_in, port_out, imu_port = setup_serial_and_reset_arduinos()
# Setup tensorflow
sess, net_model = setup_tensorflow()
# Start the clock.
drive_start_time = time.time()
print 'Awaiting switch flip..'
if we_are_autonomous:
print("Warning, we are intending to drive with tensorflow")
session_full_path = make_data_folder('~/training-images')
while True:
loop_start_time = time.time()
# Switch was just flipped.
if last_switch != button_arduino_out:
last_switch = button_arduino_out
# resets stuck_override status
last_time_when_nonzero_velocity = -1
stuck_override_start = -1
stuck_override_active = False
# See if the car started up with the switch already flipped.
# if time.time() - drive_start_time < 1:
# print 'Error: start switch in the wrong position.'
# sys.exit()
if button_arduino_out == 1:
currently_running = True
print '%s: Switch flipped.' % frame_count
last_odometer_reset = odometer_ticks
if we_are_recording and (not we_are_autonomous):
print 'STARTING TO RECORD.'
print 'Folder: %s' % session_full_path
config.store('last_record_dir', session_full_path)
elif we_are_recording and we_are_autonomous:
session_full_path = make_data_folder('~/tf-driving-images')
print 'DRIVING AUTONOMOUSLY and STARTING TO RECORD'
print 'Folder: %s' % session_full_path
else:
print("DRIVING AUTONOMOUSLY (not recording).")
else:
print("%s: Switch flipped. Recording stopped." % frame_count)
override_autonomous_control = False
currently_running = False
# Read input data from arduinos.
new_steering, new_throttle, new_aux1, button_arduino_in, button_arduino_out = (
process_input(port_in, port_out))
if new_steering != None:
steering = new_steering
if new_throttle != None:
throttle = new_throttle
if new_aux1 != None:
aux1 = new_aux1
#telemetry = process_imu(imu_port)
# print("Throttle: {}".format(new_throttle))
# Check to see if we should stop the car via the RC during TF control.
# But also provide a way to re-engage autonomous control after an override.
if we_are_autonomous and currently_running:
if (steering > 130 or steering < 50) and throttle > 130:
if not override_autonomous_control:
print '%s: Detected RC override: stopping.' % frame_count
override_autonomous_control = True
if abs(aux1 - old_aux1) > 400 and override_autonomous_control:
old_aux1 = aux1
print '%s: Detected RC input: re-engaging autonomous control.' % frame_count
center_esc(port_out)
override_autonomous_control = False
# Check to see if we should reset the odometer via aux1 during manual
# driving. This is Button E on the RC transmitter.
# The values will swing from ~1100 to ~1900.
if abs(aux1 - old_aux1) > 400:
old_aux1 = aux1
print '%s: Resetting the odometer.' % frame_count
last_odometer_reset = odometer_ticks
# Overwrite steering with neural net output in autonomous mode.
# This seems to take about 10ms.
if we_are_autonomous and currently_running:
# Calculate velocity from odometer. Gets weird when stopped.
last_odo = last_odo_queue[-config.odo_delta]
last_millis = last_millis_queue[-config.odo_delta]
vel = 0
if odometer_ticks != last_odo and milliseconds > last_millis:
if milliseconds - last_millis == 0: vel = 0
else: vel = (float(odometer_ticks) - last_odo) / (milliseconds - last_millis)
if last_millis == 0 and last_odo == 0: vel = 0
if last_odo >= odometer_ticks: vel = 0
# append to circular queue
last_odo_queue.append(odometer_ticks)
last_millis_queue.append(milliseconds)
if len(last_odo_queue) > config.odo_delta: last_odo_queue = last_odo_queue[1:]
if len(last_millis_queue) > config.odo_delta: last_millis_queue = last_millis_queue[1:]
# Read a frame from the camera.
frame = camera_stream.read()
steering, throttle = do_tensorflow(sess, net_model, frame, odometer_ticks - last_odometer_reset, vel)
if ((frame_count % 25) == 0) and (vel != 0):
# Simulate dropped radio frames from rc
#throttle = 0
pass
# Stuck: throttle override
# If the car is stuck for more than 2 s, override and full throttle.
if config.stuck_override: # TODO config.py
global last_time_when_nonzero_velocity, stuck_override_start, stuck_override_active
stt = time.time()
if vel > 0.0:
last_time_when_nonzero_velocity = stt
#print throttle,stuck_override_active,stt - stuck_override_start,stt - last_time_when_nonzero_velocity
if stuck_override_active:
throttle = 115
if stt - stuck_override_start > 1.0: # How long to force throttle for: 1.0s
stuck_override_active = False
else:
if stt - last_time_when_nonzero_velocity > 1.0: # this is true at the beginning.
stuck_override_active = True
stuck_override_start = stt
last_time_when_nonzero_velocity = stt
# steering, throttle = do_tensor_flow(frame, odometer_ticks - last_odometer_reset, vel)
global override_random_error
if we_are_recording and currently_running and config.use_override_random_error:
steering = override_random_error.tick(loop_start_time, steering)
if we_are_recording and currently_running and override_random_error.state != 'override':
# TODO(matt): also record vel in filename for tf?
# Read a frame from the camera.
frame = camera_stream.read()
# Save image with car data in filename.
cv2.imwrite("%s/" % session_full_path +
"frame_" + str(frame_count).zfill(5) +
"_thr_" + str(throttle) +
"_ste_" + str(steering) +
"_mil_" + str(milliseconds) +
"_odo_" + str(odometer_ticks - last_odometer_reset).zfill(5) +
".png", frame)
else:
frame = camera_stream.read()
cv2.imwrite('/tmp/test.png', frame)
#if telemetry is not None:
# frames = [str(frame_count).zfill(5)]
# telemetry = frames + telemetry + [throttle, steering]
# data_logger.log_data(telemetry)
if override_autonomous_control:
# Full brake and neutral steering.
throttle, steering = 0, 90
#print("Sending kill command to car")
stop_car(steering, throttle, port_out)
else:
# Send output data to arduinos.
process_output(old_steering, old_throttle, steering, throttle, port_out)
old_steering = steering
old_throttle = throttle
# Attempt to go at 30 fps. In reality, we could go slower if something hiccups.
seconds = time.time() - loop_start_time
while seconds < 1 / 30.:
time.sleep(0.001)
seconds = time.time() - loop_start_time
frame_count += 1
def set_brightness(self, value): value = min(max(value, 0), 8) config.brightness = value self.update_brightness() config.store()
def set_brightness(self, value): value = min(max(value, 0), 8) config.brightness = value self.update_brightness() config.store()
def main():
# Init some vars..
telemetry = []
old_steering = 0
old_throttle = 0
old_aux1 = 0
steering = 90
throttle = 90
aux1 = 0
frame_count = 0
session_full_path = ''
last_switch = 0 # set to 1 for auto-switch
button_arduino_out = 0
currently_running = False
override_autonomous_control = False
train_on_this_image = True
vel = 0.0
# Check for insomnia
#if platform.system() == "Darwin":
# check_for_insomnia()
# Setup ports.
port_in, port_out, imu_port = setup_serial_and_reset_arduinos()
# Setup tensorflow
sess, net_model = setup_tensorflow()
# Start the clock.
print 'Awaiting switch flip..'
if we_are_autonomous:
print("Warning, we are intending to drive with tensorflow")
session_full_path = make_data_folder('./training-images')
# This block is copied from below, and is a temporary hack to make recording auto-start
currently_running = True
print '%s: Switch flipped.' % frame_count
if we_are_recording and (not we_are_autonomous):
print 'STARTING TO RECORD.'
print 'Folder: %s' % session_full_path
config.store('last_record_dir', session_full_path)
elif we_are_recording and we_are_autonomous:
session_full_path = make_data_folder('~/tf-driving-images')
print 'DRIVING AUTONOMOUSLY and STARTING TO RECORD'
print 'Folder: %s' % session_full_path
else:
print("DRIVING AUTONOMOUSLY (not recording).")
# Endhack
while True:
loop_start_time = time.time()
# Switch was just flipped.
if last_switch != button_arduino_out:
last_switch = button_arduino_out
if button_arduino_out == 1:
currently_running = True
print '%s: Switch flipped.' % frame_count
if we_are_recording and (not we_are_autonomous):
print 'STARTING TO RECORD.'
print 'Folder: %s' % session_full_path
config.store('last_record_dir', session_full_path)
elif we_are_recording and we_are_autonomous:
session_full_path = make_data_folder('~/tf-driving-images')
print 'DRIVING AUTONOMOUSLY and STARTING TO RECORD'
print 'Folder: %s' % session_full_path
else:
print("DRIVING AUTONOMOUSLY (not recording).")
else:
print("%s: Switch flipped. Recording stopped." % frame_count)
override_autonomous_control = False
currently_running = False
# Read input data from arduinos.
# new_steering, new_throttle, new_aux1, button_arduino_in, button_arduino_out = (
# process_input(port_in, port_out))
# if new_steering != None:
# steering = new_steering
# if new_throttle != None:
# throttle = new_throttle
# if new_aux1 != None:
# aux1 = new_aux1
# Check to see if we should stop the car via the RC during TF control.
# But also provide a way to re-engage autonomous control after an override.
if we_are_autonomous and currently_running:
if (steering > 130 or steering < 50) and throttle > 130:
if not override_autonomous_control:
print '%s: Detected RC override: stopping.' % frame_count
override_autonomous_control = True
if abs(aux1 -
old_aux1) > 400 and override_autonomous_control:
old_aux1 = aux1
print '%s: Detected RC input: re-engaging autonomous control.' % frame_count
center_esc(port_out)
override_autonomous_control = False
# Overwrite steering with neural net output in autonomous mode.
# This seems to take about 10ms.
if we_are_autonomous and currently_running:
# Read a frame from the camera.
frame = camera_stream.read()
# Hard code odo_ticks for pinball purposes
odo_ticks = 0
steering, throttle = do_tensorflow(sess, net_model, frame,
odo_ticks, vel)
if ((frame_count % 25) == 0) and (vel != 0):
# Simulate dropped radio frames from rc
#throttle = 0
pass
if we_are_recording and currently_running:
# TODO(matt): also record vel in filename for tf?
# Read a frame from the camera.
frame = camera_stream.read()
# Save image with car data in filename.
cv2.imwrite(
"%s/" % session_full_path + "frame_" +
str(frame_count).zfill(5) + "_thr_" + str(throttle) + "_ste_" +
str(steering) + "_mil_" + str(milliseconds) + ".png", frame)
else:
frame = camera_stream.read()
cv2.imwrite('/tmp/test.png', frame)
if telemetry is not None:
frames = [str(frame_count).zfill(5)]
telemetry = frames + telemetry
data_logger.log_data(telemetry)
if override_autonomous_control:
# Full brake and neutral steering.
throttle, steering = 0, 90
#print("Sending kill command to car")
stop_car(steering, throttle, port_out)
else:
# Send output data to arduinos.
# process_output(old_steering, old_throttle, steering, throttle, port_out)
old_steering = steering
old_throttle = throttle
# Attempt to go at 30 fps. In reality, we could go slower if something hiccups.
seconds = time.time() - loop_start_time
while seconds < 1 / 30.:
time.sleep(0.001)
seconds = time.time() - loop_start_time
frame_count += 1