def telemetry(sid, 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"]
speed = float(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)
# Add the preprocessing step
image_array = preprocess_image(image_array)
transformed_image_array = image_array[None, :, :, :]
# This model currently assumes that the features of the model are just the images. Feel free to change this.
steering_angle = float(model.predict(transformed_image_array,
batch_size=1))
throttle = 0.1
print(steering_angle, throttle)
send_control(steering_angle, throttle)
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)
# Custom Preprocessing
image_array = preprocess_image(image_array)
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 telemetry(sid, 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"]
speed = float(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)
# Add the preprocessing step
image_array = preprocess_image(image_array)
transformed_image_array = image_array[None, :, :, :]
# This model currently assumes that the features of the model are just the images. Feel free to change this.
steering_angle = float(model.predict(transformed_image_array,
batch_size=1))
# The driving model currently just outputs a constant throttle. Feel free to edit this.
# ADD PD control to make the car move at a constant speed.
TargetSpeed = 5
Error = (TargetSpeed - speed)
Difference = Error - PID.preError
throttle = Error * 0.2 + Difference * 10
if throttle < 0:
throttle = 0
print(steering_angle, throttle, speed, PID.preError)
send_control(steering_angle, throttle)
PID.preError = Error
def telemetry(sid, 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)
# Add the preprocessing step to match the process from training
image_array = preprocess_image(image_array)
transformed_image_array = image_array[None, :, :, :]
steering_angle = float(model.predict(transformed_image_array, batch_size=1))
# Try to maintain speed close to goal speed
goal_speed = 25.0
diff = goal_speed - float(speed)
# Proportional control
c_p = 0.6
throttle = max(diff * c_p, 0)
print(steering_angle, throttle)
send_control(steering_angle, throttle)
def telemetry(sid, data):
global current_throttle
global prev_speed
# 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)
image_array = preprocess_image(image_array)
transformed_image_array = image_array[None, :, :, :]
# This model currently assumes that the features of the model are just the images. Feel free to change this.
steering_angle = float(model.predict(transformed_image_array,
batch_size=1))
print(steering_angle)
# The driving model currently just outputs a constant throttle. Feel free to edit this.
speed = float(speed)
if speed < 15:
if speed < prev_speed + 0.5:
current_throttle += 0.03
else:
current_throttle = 0.2
else:
current_throttle = 0.0
prev_speed = speed
print(steering_angle, current_throttle)
send_control(steering_angle, current_throttle)
def telemetry(sid, 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"]
speed = float(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)
# Add the preprocessing step
image_array = preprocess_image(image_array)
transformed_image_array = image_array[None, :, :, :]
# This model currently assumes that the features of the model are just the images. Feel free to change this.
steering_angle = float(model.predict(transformed_image_array, batch_size=1))
# The driving model currently just outputs a constant throttle. Feel free to edit this.
throttle = (17.0 - speed)*0.5
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))
def plot_image_res(data_point):
image = get_image(data_point)
image_small = model.preprocess_image(image).astype(np.uint8)
plt.subplot(1, 2, 1)
plt.imshow(image)
plt.axis("off")
plt.title("original resolution")
plt.subplot(1, 2, 2)
plt.imshow(image_small)
plt.axis("off")
plt.title("reduced resolution")
plt.show()
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"]
speed = float(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)
# Add the preprocessing step
image_array = preprocess_image(image_array)
transformed_image_array = image_array[None, :, :, :]
# This model currently assumes that the features of the model are just the images. Feel free to change this.
steering_angle = float(
model.predict(transformed_image_array, batch_size=1))
# min_speed = 8
# max_speed = 10
# if float(speed) < min_speed:
# throttle = 1.0
# elif float(speed) > max_speed:
# throttle = -1.0
# else:
# throttle = 0.1
# Faster
throttle = (17.0 - speed) * 0.5
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. <--- BUT I WILL!
sio.emit('manual', data={}, skip_sid=True)
def preprocess():
if request.method == 'POST':
print("preprocess request recieved")
f = request.get_json(force=True)
filename = f['filename']
print(filename)
for python_filename in os.listdir(registerPath):
print(python_filename)
if python_filename == filename:
print('file found under register directory')
else:
print('file missing under register directory')
#File saved to location defined under upload folder
vector = preprocess_image(os.path.join(registerPath, python_filename))
print(vector)
return dumps(vector)
import pandas as pd
import os
from model import (preprocess_image)
dataset = []
for filename in os.listdir('data'): # loop through all the files and folders
print(filename)
if os.path.isdir(os.path.join(
os.path.abspath("data"),
filename)): # check whether the current object is a folder or not
path = 'data/' + filename
for img in os.listdir(path):
feature_vector = preprocess_image(path + '/' + img)
dataset.append(list(feature_vector) + [filename])
dataset_df = pd.DataFrame(dataset)
dataset_df.to_csv('data/training_data.csv')
def proprocessed_image(image):
return preprocess_image(image)