def view(self, cfg, model_path=None, tub=None):
reset_graph()
CNN_model = CNN(is_training=False)
if model_path:
print(model_path)
CNN_model.load(model_path)
sviewer = sViewr()
tubs = os.listdir(tub)
tubs = list(filter(lambda x: x.endswith('jpg'), tubs))
tubs.sort(key=lambda x: int(x[:-21]))
index = 8304
filename = 'data/data'
for etub in tubs:
path = tub + '/' + etub
print(tub, etub, path)
img_PIL = Image.open(path)
img_PIL.save(filename + '/' + str(index) + '_cam-image_array_.jpg')
# angle, throttle = CNN_model.run(img_PIL_Tensor)
# sviewer.run(path, angle, throttle)
string = ""
for i in path.split('/')[:-1]:
string += i + "/"
print(string)
snum = path.split('/')[-1].split('_')[0]
f = open(string + "record_" + snum + ".json")
jsonfile = json.load(f)
jsonpath = filename + '/' + 'record_' + str(index) + 'json'
json.dump(jsonfile, jsonpath, ensure_ascii=False)
index = index + 1
def view(self, cfg, model_path=None, tub=None):
reset_graph()
CNN_model = CNN(is_training=False)
if model_path:
print(model_path)
CNN_model.load(model_path)
sviewer = sViewr()
tubs = os.listdir(tub)
tubs = list(filter(lambda x: x.endswith('jpg'), tubs))
tubs.sort(key=lambda x: int(x[:-21]))
cam1 = np.zeros((144, 256, 3))
cam2 = np.zeros((144, 256, 3))
cam3 = np.zeros((144, 256, 3))
cam4 = np.zeros((144, 256, 3))
for etub in tubs:
path = tub + '/' + etub
print(tub, etub, path)
img_PIL = Image.open(path)
img_PIL_Tensor = np.array(img_PIL)
cam1 = cam2
cam2 = cam3
cam3 = cam4
cam4 = img_PIL_Tensor
cam = np.array([cam1, cam2, cam3, cam4])
angle, throttle = CNN_model.run(cam)
sviewer.run(path, angle, throttle)
def train(self, cfg, tub_names, new_model_path, base_model_path=None):
"""
use the specified data in tub_names to train an artifical neural network
saves the output trained model as model_name
"""
X_keys = ['cam/image_array']
y_keys = ['user/angle', 'user/throttle']
def train_record_transform(record):
""" convert categorical steering to linear and apply image augmentations """
record['user/angle'] = data.linear_bin(record['user/angle'])
# TODO add augmentation that doesn't use opencv
return record
def val_record_transform(record):
""" convert categorical steering to linear """
record['user/angle'] = data.linear_bin(record['user/angle'])
return record
if not tub_names:
tub_names = os.path.join("./data", '*')
tubgroup = TubGroup(tub_names)
X_train, Y_train, X_val, Y_val = tubgroup.get_train_val_gen(
X_keys,
y_keys,
record_transform=train_record_transform,
seq_len=cfg['TRAINING']['SEQUENCE_LENGTH'],
batch_size=cfg['TRAINING']['BATCH_SIZE'],
train_frac=cfg['TRAINING']['TRAIN_TEST_SPLIT'])
print('tub_names', tub_names)
total_records = len(tubgroup.df)
total_train = int(total_records * cfg['TRAINING']['TRAIN_TEST_SPLIT'])
total_val = total_records - total_train
print('train: %d, validation: %d' % (total_train, total_train))
steps_per_epoch = total_train // cfg['TRAINING']['BATCH_SIZE']
print('steps_per_epoch', steps_per_epoch)
new_model_path = os.path.expanduser(new_model_path)
reset_graph()
CNN_model = CNN(is_training=True, learning_rate=0.001)
if base_model_path is not None:
base_model_path = os.path.expanduser(base_model_path)
#tfcategorical.load_tensorflow(base_model_path)
CNN_model.train(X_train,
Y_train,
X_val,
Y_val,
saved_model=new_model_path,
epochs=50,
batch_size=cfg['TRAINING']['BATCH_SIZE'],
new_model=True)
CNN_model.close_sess()
def drive(self, cfg, model_path=None):
#Initialize car
V = Vehicle()
cam = Webcam(resolution=(cfg['CAMERA']['CAMERA_RESOLUTION']['HEIGHT'],
cfg['CAMERA']['CAMERA_RESOLUTION']['WIDTH']),
framerate=cfg['CAMERA']['CAMERA_FRAMERATE'])
V.add(cam, outputs=['cam/image_array'], threaded=True)
ctr = JoystickController(
max_throttle=cfg['JOYSTICK']['JOYSTICK_MAX_THROTTLE'],
steering_scale=cfg['JOYSTICK']['JOYSTICK_STEERING_SCALE'],
auto_record_on_throttle=cfg['JOYSTICK']['AUTO_RECORD_ON_THROTTLE'])
V.add(
ctr,
inputs=['cam/image_array'],
outputs=['user/angle', 'user/throttle', 'user/mode', 'recording'],
threaded=True)
#See if we should even run the pilot module.
#This is only needed because the part run_condition only accepts boolean
def pilot_condition(mode):
if mode == 'user':
return False
else:
return True
pilot_condition_part = Lambda(pilot_condition)
V.add(pilot_condition_part,
inputs=['user/mode'],
outputs=['run_pilot'])
seq = sequence(seq_num=16)
V.add(seq,
inputs=['cam/image_array'],
outputs=['cam/image_array'],
run_condition='run_pilot')
#Run the pilot if the mode is not user.
reset_graph()
CNN_model = CNN(is_training=False)
if model_path:
CNN_model.load(model_path)
V.add(CNN_model,
inputs=['cam/image_array'],
outputs=['pilot/angle', 'pilot/throttle'],
run_condition='run_pilot')
#Choose what inputs should change the car.
def drive_mode(mode, user_angle, user_throttle, pilot_angle,
pilot_throttle):
if mode == 'user':
return user_angle, user_throttle
elif mode == 'local_angle':
return pilot_angle, user_throttle
else:
return pilot_angle, pilot_throttle
drive_mode_part = Lambda(drive_mode)
V.add(drive_mode_part,
inputs=[
'user/mode', 'user/angle', 'user/throttle', 'pilot/angle',
'pilot/throttle'
],
outputs=['angle', 'throttle'])
steering_controller = PCA9685(cfg['STEERING']['STEERING_CHANNEL'])
steering = PWMSteering(
controller=steering_controller,
left_pulse=cfg['STEERING']['STEERING_LEFT_PWM'],
right_pulse=cfg['STEERING']['STEERING_RIGHT_PWM'])
throttle_controller = PCA9685(cfg['THROTTLE']['THROTTLE_CHANNEL'])
throttle = PWMThrottle(
controller=throttle_controller,
max_pulse=cfg['THROTTLE']['THROTTLE_FORWARD_PWM'],
zero_pulse=cfg['THROTTLE']['THROTTLE_STOPPED_PWM'],
min_pulse=cfg['THROTTLE']['THROTTLE_REVERSE_PWM'])
V.add(steering, inputs=['angle'])
V.add(throttle, inputs=['throttle'])
#add tub to save data
inputs = [
'cam/image_array', 'user/angle', 'user/throttle', 'user/mode'
]
types = ['image_array', 'float', 'float', 'str']
th = TubHandler(path="./data")
tub = th.new_tub_writer(inputs=inputs, types=types)
V.add(tub, inputs=inputs, run_condition='recording')
#run the vehicle
V.start(rate_hz=cfg['VEHICLE']['DRIVE_LOOP_HZ'],
max_loop_count=cfg['VEHICLE']['MAX_LOOPS'])
def drive(self, cfg, model_path=None, imglist_Path=None):
#Initialize car
V = Vehicle()
cam = ImageListCamera(imglist_Path + '/*.jpg')
V.add(cam, outputs=['cam/image_array', 'img_path'], threaded=True)
#sViewer
sviewer = sViewr()
V.add(sviewer, inputs=['img_path', 'pilot/angle'], threaded=False)
ctr = JoystickController(
max_throttle=cfg['JOYSTICK']['JOYSTICK_MAX_THROTTLE'],
steering_scale=cfg['JOYSTICK']['JOYSTICK_STEERING_SCALE'],
auto_record_on_throttle=cfg['JOYSTICK']['AUTO_RECORD_ON_THROTTLE'])
V.add(
ctr,
inputs=['cam/image_array'],
outputs=['user/angle', 'user/throttle', 'user/mode', 'recording'],
threaded=True)
# carLigth=Driver()
# V.add(carLigth,inputs=['user/angle','user/throttle','pilot/angle','pilot/throttle','user/mode'],threaded=True)
#See if we should even run the pilot module.
#This is only needed because the part run_condition only accepts boolean
def pilot_condition(mode):
if mode == 'user':
return False
else:
return True
pilot_condition_part = Lambda(pilot_condition)
V.add(pilot_condition_part,
inputs=['user/mode'],
outputs=['run_pilot'])
#Run the pilot if the mode is not user.
reset_graph()
CNN_model = CNN(is_training=False)
if model_path:
CNN_model.load(model_path)
V.add(CNN_model,
inputs=['cam/image_array'],
outputs=['pilot/angle', 'pilot/throttle'],
run_condition='run_pilot')
#Choose what inputs should change the car.
def drive_mode(mode, user_angle, user_throttle, pilot_angle,
pilot_throttle):
if mode == 'user':
return user_angle, user_throttle
elif mode == 'local_angle':
return pilot_angle, user_throttle
else:
return pilot_angle, pilot_throttle
drive_mode_part = Lambda(drive_mode)
V.add(drive_mode_part,
inputs=[
'user/mode', 'user/angle', 'user/throttle', 'pilot/angle',
'pilot/throttle'
],
outputs=['angle', 'throttle'])
#add tub to save data
inputs = [
'cam/image_array', 'user/angle', 'user/throttle', 'user/mode'
]
types = ['image_array', 'float', 'float', 'str']
th = TubHandler(path="./data")
tub = th.new_tub_writer(inputs=inputs, types=types)
V.add(tub, inputs=inputs, run_condition='recording')
#run the vehicle
V.start(rate_hz=cfg['VEHICLE']['DRIVE_LOOP_HZ'],
max_loop_count=cfg['VEHICLE']['MAX_LOOPS'])