def test_random_augment(self):
d = model.MyDriver()
(X_train, y_train) = d.load_train_samples("../data/driving_log.csv")
assert (X_train[0] == "../data/IMG/center_2016_12_01_13_30_48_287.jpg")
assert (X_train[1] == "../data/IMG/left_2016_12_01_13_30_48_287.jpg")
assert (X_train[2] == "../data/IMG/right_2016_12_01_13_30_48_287.jpg")
assert (os.path.isfile(X_train[0]))
assert (os.path.isfile(X_train[1]))
assert (os.path.isfile(X_train[2]))
plt.figure(2)
frow = 4
fcol = 4
index = 1
plt.subplot(fcol, frow, index)
index += 1
org_image = d.load_image(X_train[1])
steer = 0.0
plt.imshow(org_image)
plt.title("org")
for i in range(0, frow * fcol - 1):
plt.subplot(fcol, frow, index)
index += 1
print(org_image)
# 130, 163, 208
(image, steer) = d.augment_and_preprocess((org_image, steer))
plt.imshow(np.copy(image))
plt.show()
return
def _test_train_sample(self):
d = model.MyDriver()
(X_train, y_train) = d.load_train_samples("../data/driving_log.csv")
assert (
X_train[0] == "../data/IMAGE/center_2016_12_01_13_30_48_287.jpg")
assert (X_train[1] == "../data/IMAGE/left_2016_12_01_13_30_48_287.jpg")
assert (
X_train[2] == "../data/IMAGE/right_2016_12_01_13_30_48_287.jpg")
assert (y_train[0] == 0.0)
assert (y_train[1] == 0.2)
assert (y_train[2] == -0.2)
return
def test_train_generator_call(self):
d = model.MyDriver()
plt.figure(3)
frow = 4
fcol = 4
index = 1
gen = d.train_generator("../data/driving_log.csv", 1)
for i in range(0, frow * fcol - 1):
plt.subplot(fcol, frow, index)
index += 1
(image, steer) = gen.next()
plt.imshow(np.copy(image[0]))
plt.title("steer=%f" % steer)
plt.show()
return
def _test_train_generator(self):
d = model.MyDriver()
(X_train, y_train) = d.load_example_training_data(
examples=10, width=8,
height=8) # //d.load_traing_data("../data/driving_log.csv"))
model = d.simple_network(8, 8)
history_object = model.fit_generator(d.train_generator_example(
zip(X_train, y_train), 10),
steps_per_epoch=10,
epochs=10,
verbose=1)
if 0:
model.save('test_train.md5')
model = keras.models.load_model('test_train.md5')
y = model.predict(X_train)
y_index = np.argmax(y, 1)
print(y_index)
for i in range(0, 10):
assert (y_index[i] == i)
return
def _test_train(self):
d = model.MyDriver()
(X_train, y_train) = d.load_example_training_data(
examples=10, width=8,
height=8) # //d.load_traing_data("../data/driving_log.csv"))
model = d.simple_network(8, 8)
model.fit(X_train,
y_train,
validation_split=0.0,
shuffle=True,
nb_epoch=100,
verbose=1)
if 0:
model.save('test_train.md5')
model = keras.models.load_model('test_train.md5')
y = model.predict(X_train)
y_index = np.argmax(y, 1)
print(y_index)
for i in range(0, 10):
assert (y_index[i] == i)
return
def telemetry(sid, data):
if data:
# The current steering angle of the car
steering_angle = data["steering_angle"]
# The current throttle of the car
throttle = data["throttle"]
# The current speed of the car
speed = data["speed"]
# The current image from the center camera of the car
imgString = data["image"]
image = Image.open(BytesIO(base64.b64decode(imgString)))
image_array = np.asarray(image)
# added tony
driver = model.MyDriver()
image_array = driver.crop(image_array, driver.UP_CROP,
driver.DOWN_CROP)
image_array = driver.resize(image_array, driver.IMAGE_WIDTH,
driver.IMAGE_HEIGHT)
steering_angle = float(
model.predict(image_array[None, :, :, :], batch_size=1))
throttle = controller.update(float(speed))
print(steering_angle, throttle)
send_control(steering_angle, throttle)
# save frame
if args.image_folder != '':
timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
image_filename = os.path.join(args.image_folder, timestamp)
image.save('{}.jpg'.format(image_filename))
else:
# NOTE: DON'T EDIT THIS.
sio.emit('manual', data={}, skip_sid=True)
def test_augment(self):
d = model.MyDriver()
(X_train, y_train) = d.load_train_samples("../data/driving_log.csv")
assert (X_train[0] == "../data/IMG/center_2016_12_01_13_30_48_287.jpg")
assert (X_train[1] == "../data/IMG/left_2016_12_01_13_30_48_287.jpg")
assert (X_train[2] == "../data/IMG/right_2016_12_01_13_30_48_287.jpg")
assert (os.path.isfile(X_train[0]))
assert (os.path.isfile(X_train[1]))
assert (os.path.isfile(X_train[2]))
image = d.load_image(X_train[0])
plt.figure(1)
frow = 3
fcol = 3
index = 1
plt.subplot(fcol, frow, index)
index += 1
steer = 0.0
plt.imshow(image)
plt.title("org")
plt.subplot(fcol, frow, index)
index += 1
image = d.crop(image, 50, 20)
plt.imshow(image)
plt.title("crop")
plt.subplot(fcol, frow, index)
index += 1
(image, steer) = d.augment_flip((image, steer), 1)
plt.imshow(image)
plt.title("flip")
plt.subplot(fcol, frow, index)
index += 1
(image, steer) = d.augment_translate((image, steer), 1.0, 1.0)
plt.imshow(image)
plt.title("trans")
# plt.subplot(fcol,frow,index) ; index+=1
# (image, steer) = d.augment_rotate_and_scale( (image, steer), 1, 1.0)
# plt.imshow(image)
# plt.title("rotate_and_scale")
plt.subplot(fcol, frow, index)
index += 1
image = d.resize(image, 100, 30)
plt.imshow(image)
plt.title("resize")
plt.subplot(fcol, frow, index)
index += 1
(image, steer) = d.augment_bright((image, steer), 1.0)
plt.imshow(image)
plt.title("augment_bright")
plt.subplot(fcol, frow, index)
index += 1
(image, steer) = d.augment_bright((image, steer), 0.0)
plt.imshow(image)
plt.title("augment_bright")
plt.show()
return