samples = []
for tub in tubs:
num_records = tub.get_num_records()
for iRec in range(0, num_records):
json_data = tub.get_json_record(iRec)
sample = json_data[record]
samples.append(float(sample))
plt.hist(samples, 50)
plt.xlabel(record)
plt.show()
if __name__ == '__main__':
args = docopt(__doc__)
cfg = dk.load_config()
if args['drive']:
drive(cfg, model_path = args['--model'], use_joystick=args['--js'], mode = args['--mode'])
elif args['calibrate']:
calibrate()
elif args['train']:
tub = args['--tub']
model = args['--model']
mode=args['--mode']
train(cfg, tub, model, mode)
elif args['check']:
tub = args['--tub']
print(
"You can now go to <your hostname.local>:%d to drive your car."
% cfg.WEB_CONTROL_PORT)
elif isinstance(ctr, JoystickController):
print("You can now move your joystick to drive your car.")
ctr.set_tub(tub_writer.tub)
ctr.print_controls()
#run the vehicle for 20 seconds
print(
'11111111111111111111111111111111111111111111111111111111111111111111111111111111111'
)
V.start(rate_hz=cfg.DRIVE_LOOP_HZ, max_loop_count=cfg.MAX_LOOPS)
if __name__ == '__main__':
args = docopt(__doc__)
cfg = dk.load_config(myconfig=args['--myconfig'])
if args['drive']:
model_type = args['--type']
camera_type = args['--camera']
drive(cfg,
model_path=args['--model'],
use_joystick=args['--js'],
model_type=model_type,
camera_type=camera_type,
meta=args['--meta'])
elif args['train']:
print('Use python train.py instead.\n')
import argparse
from vae_model import KerasVAE
from donkeycar.templates.train import collate_records, preprocessFileList
parser = argparse.ArgumentParser(
description='Generate training data for a WorldModels RNN.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('vae', help='Path to the weights of a trained VAE.')
parser.add_argument('file',
nargs='+',
help='Text file with a list of tubs to preprocess.')
args = parser.parse_args()
try:
cfg = dk.load_config()
except FileNotFoundError:
cfg = dk.load_config("config.py") # retry in the current directory
tub_names = preprocessFileList(args.file)
input_shape = (cfg.IMAGE_W, cfg.IMAGE_H, cfg.IMAGE_DEPTH)
z_dim, dropout, aux = model_meta(args.vae)
if aux is None:
aux = 0
kl = KerasVAE(input_shape=input_shape,
z_dim=z_dim,
aux=aux,
dropout=dropout)
kl.set_weights(args.vae)
kl.compile()
def run(self, args, parser):
'''
Load the images from a tub and create a movie from them.
Movie
'''
if args.tub is None:
print("ERR>> --tub argument missing.")
parser.print_help()
return
conf = os.path.expanduser(args.config)
if not os.path.exists(conf):
print("No config file at location: %s. Add --config to specify\
location or run from dir containing config.py." % conf)
return
self.cfg = dk.load_config(conf)
if args.type is None and args.model is not None:
args.type = self.cfg.DEFAULT_MODEL_TYPE
print(
"Model type not provided. Using default model type from config file"
)
if args.salient:
if args.model is None:
print(
"ERR>> salient visualization requires a model. Pass with the --model arg."
)
parser.print_help()
if args.type not in ['linear', 'categorical']:
print(
"Model type {} is not supported. Only linear or categorical is supported for salient visualization"
.format(args.type))
parser.print_help()
return
self.tub = Tub(args.tub)
self.index = self.tub.get_index(shuffled=False)
start = args.start
self.end = args.end if args.end != -1 else len(self.index)
if self.end >= len(self.index):
self.end = len(self.index) - 1
num_frames = self.end - start
self.iRec = start
self.scale = args.scale
self.keras_part = None
self.do_salient = False
if args.model is not None:
self.keras_part = get_model_by_type(args.type, cfg=self.cfg)
self.keras_part.load(args.model)
self.keras_part.compile()
if args.salient:
self.do_salient = self.init_salient(self.keras_part.model)
print('making movie', args.out, 'from', num_frames, 'images')
clip = mpy.VideoClip(self.make_frame,
duration=((num_frames - 1) /
self.cfg.DRIVE_LOOP_HZ))
clip.write_videofile(args.out, fps=self.cfg.DRIVE_LOOP_HZ)
def test_config():
path = default_template(d2_path(gettempdir()))
cfg = dk.load_config(os.path.join(path, 'config.py'))
assert (cfg != None)
"""
import os
import time
import math
from docopt import docopt
import donkeycar as dk
from donkeycar.parts.cv import CvImageView, ImgBGR2RGB, ImgRGB2BGR, ImageScale, ImgWriter
from donkeycar.parts.salient import SalientVis
from donkeycar.parts.network import ZMQValueSub, UDPValueSub, TCPClientValue
from donkeycar.parts.transform import Lambda
from donkeycar.parts.image import JpgToImgArr
V = dk.vehicle.Vehicle()
args = docopt(__doc__)
cfg = dk.load_config(args['--config'])
model_path = args['--model']
model_type = args['--type']
ip = args['--ip']
if model_type is None:
model_type = "categorical"
model = dk.utils.get_model_by_type(model_type, cfg)
model.load(model_path)
V.add(TCPClientValue(name="camera", host=ip), outputs=["packet"])
V.add(JpgToImgArr(), inputs=["packet"], outputs=["img"])
V.add(ImgBGR2RGB(), inputs=["img"], outputs=["img"])
V.add(SalientVis(model), inputs=["img"], outputs=["img"])
def MAIN():
cfg = dk.load_config()
CFG = cfg
parts = []
# 1. power train
steering_controller = PCA9685(cfg.STEERING_CHANNEL,
cfg.PCA9685_I2C_ADDR,
busnum=cfg.PCA9685_I2C_BUSNUM)
steering = PWMSteering(controller=steering_controller,
left_pulse=cfg.STEERING_LEFT_PWM,
right_pulse=cfg.STEERING_RIGHT_PWM)
throttle_controller = PCA9685(cfg.THROTTLE_CHANNEL,
cfg.PCA9685_I2C_ADDR,
busnum=cfg.PCA9685_I2C_BUSNUM)
throttle = PWMThrottle(controller=throttle_controller,
max_pulse=cfg.THROTTLE_FORWARD_PWM,
zero_pulse=cfg.THROTTLE_STOPPED_PWM,
min_pulse=cfg.THROTTLE_REVERSE_PWM)
# 2. driver
drivers = []
ctr = get_js_controller(cfg)
# 3. sensors
cam = PiCamera(image_w=cfg.IMAGE_W,
image_h=cfg.IMAGE_H,
image_d=cfg.IMAGE_DEPTH)
# 4. vehicle run configurations
run_params = {'DRIVE_LOOP_HZ': 20, 'MAX_LOOPS': None}
#### SHOULD END HERE
"""
V = dk.vehicle.Vehicle(
cfg
)
"""
parts = [
{
'part': throttle,
'inputs': ['throttle'],
'outputs': [],
'threaded': False
},
{
'part': steering,
'inputs': ['angle'],
'outputs': [],
'threaded': False
},
{
'part': cam,
'inputs': [],
'outputs': ['cam/image_array'],
'threaded': True
},
{
'part': ctr,
'inputs': [],
'outputs': ['angle', 'throttle', 'user/mode', 'recording'],
'threaded': True
},
]
"""
V.add(throttle, inputs=['throttle'], outputs=[])
V.add(steering, inputs=['angle'], outputs=[])
V.add(
ctr,
inputs=[],
outputs=['angle', 'throttle', 'user/mode', 'recording'],
threaded=True
)
"""
#V.add(cam, outputs=['cam/image_array'], threaded=True)
return parts, CFG
def run(self, args):
'''
Load the images from a tub and create a movie from them.
Movie
'''
import moviepy.editor as mpy
args, parser = self.parse_args(args)
if args.tub is None:
print("ERR>> --tub argument missing.")
parser.print_help()
return
if args.type is None and args.model is not None:
print(
"ERR>> --type argument missing. Required when providing a model."
)
parser.print_help()
return
if args.salient:
if args.model is None:
print(
"ERR>> salient visualization requires a model. Pass with the --model arg."
)
parser.print_help()
return
# imported like this, we make TF conditional on use of --salient
# and we keep the context maintained throughout our callbacks to
# compute the salient mask
from tensorflow.python.keras import backend as K
import tensorflow as tf
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
conf = os.path.expanduser(args.config)
if not os.path.exists(conf):
print("No config file at location: %s. Add --config to specify\
location or run from dir containing config.py." % conf)
return
self.cfg = dk.load_config(conf)
self.tub = Tub(args.tub)
self.index = self.tub.get_index(shuffled=False)
start = args.start
self.end = args.end if args.end != -1 else len(self.index)
if self.end >= len(self.index):
self.end = len(self.index) - 1
num_frames = self.end - start
self.iRec = start
self.scale = args.scale
self.keras_part = None
self.convolution_part = None
if args.model is not None:
self.keras_part = get_model_by_type(args.type, cfg=self.cfg)
self.keras_part.load(args.model)
self.keras_part.compile()
if args.salient:
self.init_salient(self.keras_part.model)
#This method nested in this way to take the conditional import of TF
#in a manner that extends to this callback. Done this way, we avoid
#importing in the below method, which triggers a new cuda device allocation
#each call.
def compute_visualisation_mask(img):
#from https://github.com/ermolenkodev/keras-salient-object-visualisation
activations = self.functor([np.array([img])])
activations = [
np.reshape(
img, (1, img.shape[0], img.shape[1], img.shape[2]))
] + activations
upscaled_activation = np.ones((3, 6))
for layer in [5, 4, 3, 2, 1]:
averaged_activation = np.mean(
activations[layer],
axis=3).squeeze(axis=0) * upscaled_activation
output_shape = (activations[layer - 1].shape[1],
activations[layer - 1].shape[2])
x = tf.constant(
np.reshape(averaged_activation,
(1, averaged_activation.shape[0],
averaged_activation.shape[1], 1)),
tf.float32)
conv = tf.nn.conv2d_transpose(
x,
self.layers_kernels[layer],
output_shape=(1, output_shape[0], output_shape[1],
1),
strides=self.layers_strides[layer],
padding='VALID')
with tf.Session() as session:
result = session.run(conv)
upscaled_activation = np.reshape(result, output_shape)
final_visualisation_mask = upscaled_activation
return (final_visualisation_mask -
np.min(final_visualisation_mask)) / (
np.max(final_visualisation_mask) -
np.min(final_visualisation_mask))
self.compute_visualisation_mask = compute_visualisation_mask
print('making movie', args.out, 'from', num_frames, 'images')
clip = mpy.VideoClip(self.make_frame,
duration=((num_frames - 1) /
self.cfg.DRIVE_LOOP_HZ))
clip.write_videofile(args.out, fps=self.cfg.DRIVE_LOOP_HZ)
def run(self, args, parser):
'''
Load the images from a tub and create a movie from them.
Movie
'''
global cfg
if args.tub is None:
print("ERR>> --tub argument missing.")
parser.print_help()
return
conf = os.path.expanduser(args.config)
if not os.path.exists(conf):
print("No config file at location: %s. Add --config to specify\
location or run from dir containing config.py." % conf)
return
cfg = dk.load_config(conf)
if args.type is None and args.model is not None:
args.type = cfg.DEFAULT_MODEL_TYPE
print(
"Model type not provided. Using default model type from config file"
)
if args.salient:
if args.model is None:
print(
"ERR>> salient visualization requires a model. Pass with the --model arg."
)
parser.print_help()
#if args.type not in ['linear', 'categorical']:
# print("Model type {} is not supported. Only linear or categorical is supported for salient visualization".format(args.type))
# parser.print_help()
# return
self.tub = Tub(args.tub)
start = args.start
self.end_index = args.end if args.end != -1 else len(self.tub)
num_frames = self.end_index - start
# Move to the correct offset
self.current = 0
self.iterator = self.tub.__iter__()
while self.current < start:
self.iterator.next()
self.current += 1
self.scale = args.scale
self.keras_part = None
self.do_salient = False
self.user = args.draw_user_input
self.pilot_angle = 0.0
self.pilot_throttle = 0.0
self.pilot_score = 1.0 # used for color intensity
self.user_angle = 0.0
self.user_throttle = 0.0
self.control_score = 0.25 # used for control size
self.flag_test_pilot_angle = 1
self.flag_test_pilot_throttle = 1
self.flag_test_user_angle = 1
self.flag_test_user_throttle = 1
self.throttle_circle_pilot_angle = 0
self.throttle_circle_user_angle = 0
self.is_test = False
self.last_pilot_throttle = 0.0 # used for color transparency
self.last_user_throttle = 0.0 # used for color transparency
self.pilot_throttle_trans = 1.0 # used for color transparency
self.user_throttle_trans = 1.0 # used for color transparency
self.pilot_throttle_trans_rate = 0.25 # used for color transparency
self.user_throttle_trans_rate = 0.25 # used for color transparency
if args.model is not None:
self.keras_part = get_model_by_type(args.type, cfg=cfg)
self.keras_part.load(args.model)
if args.salient:
self.do_salient = self.init_salient(
self.keras_part.interpreter.model)
print('making movie', args.out, 'from', num_frames, 'images')
clip = mpy.VideoClip(self.make_frame,
duration=((num_frames - 1) / cfg.DRIVE_LOOP_HZ))
clip.write_videofile(args.out, fps=cfg.DRIVE_LOOP_HZ)
steps_per_epoch = total_train // cfg.BATCH_SIZE
print('steps_per_epoch', steps_per_epoch)
kl.train(train_gen,
val_gen,
saved_model_path=model_path,
steps=steps_per_epoch,
train_split=cfg.TRAIN_TEST_SPLIT)
if __name__ == '__main__':
args = docopt(__doc__)
cfg = dk.load_config()
try:
if args['drive']:
drive(cfg, model_path = args['--model'], use_joystick=args['--js'])
elif args['train']:
tub = args['--tub']
model = args['--model']
cache = not args['--no_cache']
train(cfg, tub, model)
except (KeyboardInterrupt,SystemExit):
GPIO.clean_up()
raise
def default_categorical(input_shape=(120, 160, 3), roi_crop=(0, 0)):
opt = keras.optimizers.Adam()
drop = 0.2
cfg = dk.load_config()
#we now expect that cropping done elsewhere. we will adjust our expeected image size here:
input_shape = adjust_input_shape(input_shape, roi_crop)
img_in = Input(
shape=input_shape, name='img_in'
) # First layer, input layer, Shape comes from camera.py resolution, RGB
x = img_in
x = Convolution2D(
24, (5, 5), strides=(2, 2), activation='relu', name="conv2d_1"
)(
x
) # 24 features, 5 pixel x 5 pixel kernel (convolution, feauture) window, 2wx2h stride, relu activation
x = Dropout(drop)(
x
) # Randomly drop out (turn off) 10% of the neurons (Prevent overfitting)
x = Convolution2D(
32, (5, 5), strides=(2, 2), activation='relu', name="conv2d_2"
)(x) # 32 features, 5px5p kernel window, 2wx2h stride, relu activatiion
x = Dropout(drop)(
x
) # Randomly drop out (turn off) 10% of the neurons (Prevent overfitting)
if input_shape[0] > 32:
x = Convolution2D(
64, (5, 5), strides=(2, 2), activation='relu', name="conv2d_3")(
x) # 64 features, 5px5p kernal window, 2wx2h stride, relu
else:
x = Convolution2D(
64, (3, 3), strides=(1, 1), activation='relu', name="conv2d_3")(
x) # 64 features, 5px5p kernal window, 2wx2h stride, relu
if input_shape[0] > 64:
x = Convolution2D(
64, (3, 3), strides=(2, 2), activation='relu', name="conv2d_4")(
x) # 64 features, 3px3p kernal window, 2wx2h stride, relu
elif input_shape[0] > 32:
x = Convolution2D(
64, (3, 3), strides=(1, 1), activation='relu', name="conv2d_4")(
x) # 64 features, 3px3p kernal window, 2wx2h stride, relu
x = Dropout(drop)(
x
) # Randomly drop out (turn off) 10% of the neurons (Prevent overfitting)
x = Convolution2D(
64, (3, 3), strides=(1, 1), activation='relu', name="conv2d_5")(
x) # 64 features, 3px3p kernal window, 1wx1h stride, relu
x = Dropout(drop)(
x
) # Randomly drop out (turn off) 10% of the neurons (Prevent overfitting)
# Possibly add MaxPooling (will make it less sensitive to position in image). Camera angle fixed, so may not to be needed
# x = Flatten(name='flattened')(x)
a, b, c, d = x.shape # returns dimension
a = b * c * d
x = Permute([1, 2, 3])(x)
x = Reshape((int(a), ))(
x
) # convert dim -> int # Flatten to 1D (Fully connected)
x = Dense(100, activation='relu', name="fc_1")(
x) # Classify the data into 100 features, make all negatives 0
x = Dropout(drop)(
x
) # Randomly drop out (turn off) 10% of the neurons (Prevent overfitting)
x = Dense(50, activation='relu', name="fc_2")(
x) # Classify the data into 50 features, make all negatives 0
x = Dropout(drop)(
x) # Randomly drop out 10% of the neurons (Prevent overfitting)
#categorical output of the angle
if cfg.DEFAULT_MODEL_TYPE == 'copter':
angle_out = Dense(4, activation='softmax', name='angle_out')(x)
model = Model(inputs=[img_in], outputs=[angle_out])
else:
angle_out = Dense(15, activation='softmax', name='angle_out')(
x
) # Connect every input with every output and output 15 hidden units. Use Softmax to give percentage. 15 categories and find best one based off percentage 0.0-1.0
throttle_out = Dense(20, activation='softmax', name='throttle_out')(
x) # Reduce to 1 number, Positive number only
model = Model(inputs=[img_in], outputs=[angle_out, throttle_out])
return model
sample_vae(kl, dirs, count)
if __name__ == "__main__":
args = docopt(__doc__)
if args['--meta']:
z, dropout, aux = model_meta(args['<model>'])
print("Z_dim: {}".format(z))
print(" Aux: {}".format(aux))
print(" Drop: {}".format(dropout))
exit()
try:
# This will look in the directory containing this script, not necessarily the current dir
cfg = dk.load_config()
except FileNotFoundError:
cfg = dk.load_config("config.py")
tub = args['--tub']
model = args['<model>']
model_type = args['--type']
count = int(args['--count'])
z_dim = int(args['--z_dim'])
z, dropout, aux = model_meta(model)
if z is not None:
z_dim = z
if aux is None:
aux = 0
print("Model meta: {} auxiliary outputs, z_dim {}, dropout {}".format(
th = TubHandler(path=cfg.DATA_PATH)
tub = th.new_tub_writer(inputs=inputs, types=types, user_meta=meta)
V.add(tub, inputs=inputs, outputs=["tub/num_records"], run_condition='recording')
###########################################################3yy
# 8) V.start (Main Loop)
###########################################################
#V.start(rate_hz=cfg.DRIVE_LOOP_HZ, max_loop_count=cfg.MAX_LOOPS)
V.start(rate_hz=cfg.DRIVE_LOOP_HZ, max_loop_count=maxloop)
#
#
# main program
#
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model_path',help='.h5 nvidia model path')
parser.add_argument('--myconfig', default='myconfig.py', help='configuration file')
parser.add_argument('--speed', type=int, default=12, help='speed percentage')
parser.add_argument('--maxloop', type=int, default=200, help='max loop coount')
args = parser.parse_args()
print(args)
cfg = dk.load_config(myconfig=args.myconfig)
drive(cfg,args)
import donkeycar as dk from donkeycar.vehicle import Vehicle from lanedetection import LanesDetector cfg = dk.load_config(config_path='./config.py') print(cfg.LD_PARAMETERS["videofile_in"]) V = Vehicle() laneDetector = LanesDetector(cfg.CAMERA_ID) V.add(laneDetector) V.start()