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106 changes: 89 additions & 17 deletions src/__main__.py
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Original file line number Diff line number Diff line change
@@ -1,30 +1,102 @@
import glob
import os
import sys

try:
sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % (
sys.version_info.major,
sys.version_info.minor,
'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])
except IndexError:
pass

sys.path.append(r"D:\CARLA_0.9.8_2\WindowsNoEditor\PythonAPI\carla\dist\carla-0.9.8-py3.7-win-amd64.egg")

sys.path.append(r"D:\CARLA_Code\integrate two session\src")
from utils.carla_utils import draw_waypoints, filter_waypoints, TrajectoryToFollow, InfiniteLoopThread

import time

import carla

from src.simulator_handler import SimulatorHandler
from utils.vehicle_command import VehicleCommand
sys.path.append(r"D:\CARLA_Code\integrate two session\src")
sys.path.append(r"D:\CARLA_Code\integrate two session\utils")

from simulator_handler import SimulatorHandler
from path_following_handler import PathFollowingHandler
from vehicle_command import VehicleCommand

if __name__ == '__main__':
client = carla.Client("localhost", 2000)
client.set_timeout(8.0)

town_name = "Town05"
# spawn_index = 2

try:
print("Trying to communicate with the client...")
world = client.get_world()
if os.path.basename(world.get_map().name) != town_name:
world: carla.World = client.load_world(town_name)

blueprint_library = world.get_blueprint_library()
actor_list = []
print("Successfully connected to CARLA client")
except Exception as error:
raise Exception(f"Error while initializing the simulator: {error}")

simulator_handler = SimulatorHandler(client=client, actor_list=actor_list)

weather = [carla.WeatherParameters(cloudiness=20.0, sun_altitude_angle=90.0, fog_density=0.0), # day
carla.WeatherParameters(cloudiness=20.0, sun_altitude_angle=-90.0, fog_density=0.0), # night
carla.WeatherParameters(cloudiness=20.0, sun_altitude_angle=90.0, fog_density=60.0), # fog
carla.WeatherParameters(cloudiness=85.0, sun_altitude_angle=90.0, fog_density=0.0)] # cloud

world.set_weather(weather[3])

# weather = carla.WeatherParameters(cloudiness=100.0,sun_altitude_angle=165.0,fog_density=0.0)
# world.set_weather(weather)

# carla.WeatherParameters(cloudiness=20.0,
# sun_altitude_angle=100.0,fog_density=60.0)

path_following_handler = PathFollowingHandler(client=client, debug_mode=False)

vehicle_blueprint = blueprint_library.filter("model3")[0] # choosing the car
# spawn_point = world.get_map().get_spawn_points()[spawn_index]
# vehicle = world.spawn_actor(vehicle_blueprint, spawn_point)

ego_spawn_point = path_following_handler.ego_spawn_point

if __name__ == "__main__":
simulator_handler = SimulatorHandler(town_name="Town04")
simulator_handler.spawn_vehicle(spawn_index=13)
simulator_handler.set_weather(weather=carla.WeatherParameters.ClearNoon)
filtered_waypoints = filter_waypoints(path_following_handler.waypoints, road_id=ego_spawn_point["road_id"])
spawn_point = filtered_waypoints[ego_spawn_point["filtered_points_index"]].transform
spawn_point.location.z += 2
vehicle = client.get_world().spawn_actor(vehicle_blueprint, spawn_point)
actor_list.append(vehicle)

# potential weather choices are [ClearNoon, ClearSunset, CloudyNoon, CloudySunset,
# WetNoon, WetSunset, MidRainyNoon, MidRainSunset, HardRainNoon, HardRainSunset,
# SoftRainNoon, SoftRainSunset]
rgb_cam = simulator_handler.rgb_cam(vehicle)

# add sensors
rgb_cam = simulator_handler.rgb_cam()
gnss_sensor = simulator_handler.gnss()
imu_sensor = simulator_handler.imu()
# gnss_sensor = simulator_handler.gnss(vehicle)
# imu_sensor = simulator_handler.imu(vehicle)
# lidar = simulator_handler.lidar(vehicle)
# radar = simulator_handler.radar(vehicle)
# collision = simulator_handler.collision(vehicle)

# listen to sensor data
rgb_cam.listen(lambda image: simulator_handler.rgb_cam_callback(image))
imu_sensor.listen(lambda imu: simulator_handler.imu_callback(imu))
gnss_sensor.listen(lambda gnss: simulator_handler.gnss_callback(gnss))
VehicleCommand(throttle=1.0).send_control(simulator_handler.vehicle)
time.sleep(20.0)

# imu_sensor.listen(lambda imu: simulator_handler.imu_callback(imu))
# gnss_sensor.listen(lambda gnss: simulator_handler.gnss_callback(gnss))
# lidar.listen(lambda data: simulator_handler.lidar_callback(data))
# radar.listen(lambda data: simulator_handler.radar_callback(data))
# collision.listen(lambda event: simulator_handler.collision_callback(event))

if path_following_handler.debug_mode:
path_following_handler.start()
else:
ego_pid_controller = path_following_handler.pid_controller(vehicle,
path_following_handler.pid_values_lateral,
path_following_handler.pid_values_longitudinal)

path_following_handler.vehicle_and_controller_inputs(vehicle, ego_pid_controller)
path_following_handler.start()
35 changes: 31 additions & 4 deletions src/adverse_weather_classification/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,11 +9,17 @@
from tensorflow.keras.callbacks import ModelCheckpoint
from mock import Mock
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report, confusion_matrix

num_of_test_samples = 0
for root_dir, cur_dir, files in os.walk(r"D:\CARLA_Code\trainSet\test"):
num_of_test_samples += len(files)
print('num_of_test_samples count:', num_of_test_samples)


class TrainHyperParameters:
def __init__(self, input_shape: Tuple[int, int, int] = (256, 256, 3), number_of_classes: int = 2,
learning_rate: float = 0.001, batch_size: int = 32, number_of_epochs: int = 3) -> None:
def __init__(self, input_shape: Tuple[int, int, int] = (256, 256, 3), number_of_classes: int = 4,
learning_rate: float = 0.001, batch_size: int = 32, number_of_epochs: int = 5) -> None:
self.hyperparameters = Mock()
self.hyperparameters.input_shape = input_shape
self.hyperparameters.number_of_classes = number_of_classes
Expand Down Expand Up @@ -60,12 +66,23 @@ def model_builder(self):
self.model = keras.models.Sequential([
keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=self.hyperparameters.input_shape),
keras.layers.MaxPooling2D((2, 2)),
keras.layers.Dropout(0.2),
keras.layers.Conv2D(64, (3, 3), activation='relu'),
keras.layers.Conv2D(64, (3, 3), activation='relu'),
keras.layers.MaxPooling2D((2, 2)),
keras.layers.Dropout(0.2),
keras.layers.Conv2D(128, (3, 3), activation='relu'),
keras.layers.Conv2D(128, (3, 3), activation='relu'),
keras.layers.MaxPooling2D((2, 2)),
keras.layers.Dropout(0.2),
keras.layers.Conv2D(512, (3, 3), activation='relu'),
keras.layers.Conv2D(512, (3, 3), activation='relu'),
keras.layers.MaxPooling2D((2, 2)),
keras.layers.Dropout(0.2),
keras.layers.Flatten(),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(1024, activation='relu'),
keras.layers.Dropout(0.2),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dropout(0.5),
keras.layers.Dense(self.hyperparameters.number_of_classes, activation='softmax')
])
Expand Down Expand Up @@ -106,6 +123,15 @@ def train(self, train_generator, test_generator):
# plot loss and accuracy on train and validation set
self.plot_history(history)

Y_pred = self.model.predict_generator(test_generator,
num_of_test_samples // self.hyperparameters.batch_size + 1)
y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix')
print(confusion_matrix(test_generator.classes, y_pred))
print('Classification Report')
target_names = ['fog ', 'day', 'cloud', 'night']
print(classification_report(test_generator.classes, y_pred, target_names=target_names))

def plot_history(self, history):
matplotlib.use('Agg')
plt.figure(figsize=(10, 5))
Expand All @@ -128,6 +154,7 @@ def exec(self):


if __name__ == '__main__':
data_dir_ = '/home/ahv/PycharmProjects/Visual-Inertial-Odometry/simulation/CARLA/output/root_dir'
data_dir_ = r"D:\CARLA_Code\trainSet"
train_custom_cnn = TrainCustomCNN(data_dir_)
train_custom_cnn.exec()

6 changes: 3 additions & 3 deletions src/adverse_weather_classification/weather_classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@ def __init__(self, model_path, model_input_size: Tuple[int, int] = (256, 256)) -
self.model = None
self.model_path = model_path
self.model_input_size = model_input_size
self.class_labels = ['day', 'night']
self.class_labels = ['fog ', 'day','night','cloud']

def load(self):
start_time = time.time()
Expand Down Expand Up @@ -40,8 +40,8 @@ def exec(self, frame: np.ndarray) -> str:


if __name__ == "__main__":
img_dir = "/home/ahv/PycharmProjects/Visual-Inertial-Odometry/simulation/CARLA/output/root_dir/testing_imgs"
model_path_ = "/src/adverse_weather_classification/output/checkpoints/best_model.h5"
img_dir = r"D:\CARLA_Code\trainSet\finalTest"
model_path_ = r"D:\CARLA_Code\output\checkpoints\best_model.h5"
adverse_weather_classifier = AdverseWeatherClassifier(model_path_)
adverse_weather_classifier.load()
for root, dirs, files in os.walk(img_dir):
Expand Down
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