A3 mortezahosseini 401624827 1

src/__main__.py

@@ -6,25 +6,32 @@
 from utils.vehicle_command import VehicleCommand
 
 if __name__ == "__main__":
-    simulator_handler = SimulatorHandler(town_name="Town04")
-    simulator_handler.spawn_vehicle(spawn_index=13)
+    simulator_handler = SimulatorHandler(town_name="Town03")
+    simulator_handler.spawn_vehicle(spawn_index=30)
     simulator_handler.set_weather(weather=carla.WeatherParameters.ClearNoon)
 
     # potential weather choices are [ClearNoon, ClearSunset, CloudyNoon, CloudySunset,
     # WetNoon, WetSunset, MidRainyNoon, MidRainSunset, HardRainNoon, HardRainSunset,
     # SoftRainNoon, SoftRainSunset]
 
     # add sensors
+    LIDAR_sensor = simulator_handler.LIDAR()
+    radar_sensor = simulator_handler.radar()
+    collision_sensor = simulator_handler.collision()
     rgb_cam = simulator_handler.rgb_cam()
     gnss_sensor = simulator_handler.gnss()
     imu_sensor = simulator_handler.imu()
 
     # listen to sensor data
+    LIDAR_sensor.listen(lambda LIDAR: simulator_handler.LIDAR_callback(LIDAR))
+    radar_sensor.listen(lambda radar: simulator_handler.radar_callback(radar))
+    collision_sensor.listen(lambda collision: simulator_handler.collision_callback(collision))
     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)
+
+   time.sleep(1000.0)
 
 
 

src/adverse_weather_classification/train.py

@@ -1,6 +1,5 @@
 import os
 from typing import Tuple
-
 import matplotlib
 import numpy as np
 import tensorflow as tf
@@ -10,7 +9,6 @@
 from mock import Mock
 import matplotlib.pyplot as plt
 
-
 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:

src/adverse_weather_classification/weather_classification.py

@@ -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 = ['ClearNoon', 'ClearSunset','fog','HardRainSunset']
 
     def load(self):
         start_time = time.time()
@@ -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:\Set\TestFinal"
+    model_path_ = r"D:\output\checkpoints\best_model.h5"
     adverse_weather_classifier = AdverseWeatherClassifier(model_path_)
     adverse_weather_classifier.load()
     for root, dirs, files in os.walk(img_dir):

src/path_following_handler.py

@@ -3,7 +3,8 @@
 import sys
 from abc import ABC
 from typing import Any, Union, Dict, List
-
+from datetime import datetime
+import numpy as np
 import carla
 from carla import World
 
@@ -26,23 +27,24 @@ def __init__(self, client: carla.Client, debug_mode: bool = False,
             {'road_id': road_id_list, 'filtered_points_index': filtered_point_index_list}
 
         self.world: World = self.client.get_world()
+        self.spectator = self.world.get_spectator()
         if os.path.basename(self.world.get_map().name) != carla_map:
             self.world: World = client.load_world(carla_map)
         self.waypoints: list = self.client.get_world().get_map().generate_waypoints(distance=1.0)
         self.debug_mode = debug_mode
         if self.debug_mode:
-            self.waypoints_to_visualize = {'road_id': [4], 'filtered_points_index': [0]}
+            self.waypoints_to_visualize = {'road_id': [1], 'filtered_points_index': [0]}
         self.pid_values_lateral: Union[Dict[str, float], Dict[str, float], Dict[str, float]] = \
-            {'K_P': 1,
-             'K_D': 0.07,
+            {'K_P': 2,
+             'K_D': 0,
              'K_I': 0.05}  # control steering
         self.pid_values_longitudinal: Union[Dict[str, float], Dict[str, float], Dict[str, float]] = \
-            {'K_P': 1,
-             'K_D': 0.07,
-             'K_I': 0.05}  # control speed
+            {'K_P': 1.5,
+             'K_D': 0.08,
+             'K_I': 0.07}  # control speed
         self.vehicle_to_target_distance_threshold: float = 2.5
 
-        self.desired_speed: int = 20  # meter per second
+        self.desired_speed: int = 30  # meter per second
         self.reached_destination: bool = False
         self.previous_waypoint: Union[carla.Waypoint, None] = None
 
@@ -117,6 +119,17 @@ def follow_trajectory(self, vehicle: Any, ego_pid_controller_: VehiclePIDControl
             self.__follow_target_waypoints__(vehicle, target_waypoint, ego_pid_controller_)
             self.previous_waypoint = target_waypoint
 
+      def infinite_trajectory_tracking(self, vehicle, ego_pid_controller_):
+        curr_waypoint_index = {'road_id': 13, 'filtered_points_index': 0}
+        curr_waypoint = filter_waypoints(self.waypoints,
+                                         road_id=curr_waypoint_index['road_id'])[curr_waypoint_index['filtered_points_index']]
+        print(curr_waypoint)
+        while True:
+            way_points = curr_waypoint.next_until_lane_end(2)
+            draw_waypoints(self.world, self.waypoints, road_id=curr_waypoint.road_id , life_time=30)
+            for target_waypoint in way_points:
+                self.__follow_target_waypoints__(vehicle, target_waypoint, ego_pid_controller_)
+            curr_waypoint = target_waypoint.next(3.5)[0]
     def vehicle_and_controller_inputs(self, ego_vehicle_, ego_pid_controller_):
         self.ego_vehicle = ego_vehicle_
         self.ego_pid_controller = ego_pid_controller_
@@ -130,7 +143,7 @@ def __step__(self):
         if not self.reached_destination:
             self.follow_trajectory(self.ego_vehicle, self.ego_pid_controller)
             self.reached_destination = True
-            print("Destination has been reached.")
+
         else:
             sys.exit(1)
 

src/simulator_handler.py

@@ -1,13 +1,17 @@
 import os
-
+import sys
+from src.path_following_handler import PathFollowingHandler
+from src.utils.carla_utils import draw_waypoints, filter_waypoints, TrajectoryToFollow, InfiniteLoopThread
+from typing import Any, Union, Dict, List
 import carla
 import cv2
 import numpy as np
 import pandas as pd
 from matplotlib import pyplot as plt
+import datetime
+import glob
 
-
-class SimulatorHandler:
+class SimulatorHandler():
     def __init__(self, town_name):
         self.spawn_point = None
         self.vehicle = None
@@ -23,9 +27,9 @@ def __init__(self, town_name):
 
         try:
             print("Trying to communicate with the client...")
-            client = carla.Client("localhost", 2000)
-            client.set_timeout(8.0)
-            self.world = client.get_world()
+            self.client = carla.Client("localhost", 2000)
+            self.client.set_timeout(8.0)
+            self.world = self.client.get_world()
             if os.path.basename(self.world.get_map().name) != town_name:
                 self.world: carla.World = client.load_world(town_name)
 
@@ -40,12 +44,27 @@ def __init__(self, town_name):
 
         self.imu_dataframe = pd.DataFrame({})
         self.gnss_dataframe = pd.DataFrame({})
-
-    def spawn_vehicle(self, spawn_index: int = 90):
-        self.vehicle_blueprint = self.blueprint_library.filter("model3")[0]  # choosing the car
-        self.spawn_point = self.world.get_map().get_spawn_points()[spawn_index]
-        self.vehicle = self.world.spawn_actor(self.vehicle_blueprint, self.spawn_point)
+        self.LIDAR_dataframe = pd.DataFrame({})
+        self.radar_dataframe = pd.DataFrame({})
+        self.collision_dataframe = pd.DataFrame({})
+        self.waypoints: list = self.client.get_world().get_map().generate_waypoints(distance=1.0)
+
+        for actor in self.world.get_actors().filter('*vehicle*'):
+            actor.destroy()
+        for actor in self.world.get_actors().filter('*sensor*'):
+            actor.destroy()
+
+    def spawn_ego_vehicles(self, road_id: int, filtered_points_index: int) -> Any:
+        print("spawning ego vehicle at road_id={} filtered_points_index={}".format(road_id,
+                                                                                   filtered_points_index))
+        vehicle_blueprint = \
+            self.client.get_world().get_blueprint_library().filter("model3")[0]
+        filtered_waypoints = filter_waypoints(self.waypoints, road_id=road_id)
+        spawn_point = filtered_waypoints[filtered_points_index].transform
+        spawn_point.location.z += 2
+        self.vehicle = self.client.get_world().spawn_actor(vehicle_blueprint, spawn_point)
         self.actor_list.append(self.vehicle)
+        return self.vehicle
 
     def set_weather(self, weather=carla.WeatherParameters.ClearNoon):
         self.world.set_weather(weather)
@@ -83,6 +102,7 @@ def imu(self):
                                          attachment_type=carla.AttachmentType.Rigid)
         self.actor_list.append(ego_imu)
         return ego_imu
+
 
     def rgb_cam_callback(self, image):
         image.save_to_disk("data/rgb_cam/%06d.jpg" % image.frame)
@@ -95,7 +115,41 @@ def rgb_cam_callback(self, image):
         plt.show()
         # cv2.imshow("rgb camera", rgb_image)  # FixMe: Replace with pygame visualization
         # cv2.waitKey(1)
-
+    def LIDAR(self):
+        LIDAR_sensor = self.blueprint_library.find("sensor.lidar.ray_cast")
+        LIDAR_sensor.set_attribute('range', '100.0')
+        LIDAR_sensor.set_attribute('upper_fov', '15.0')
+        LIDAR_sensor.set_attribute('lower_fov', '-25.0')
+        LIDAR_sensor.set_attribute('channels', '64.0')
+        LIDAR_sensor.set_attribute('rotation_frequency', '20.0')
+        LIDAR_sensor.set_attribute('points_per_second', '500000')
+
+        LIDAR_transform = carla.Transform(carla.Location(z=2))
+        ego_LIDAR = self.world.spawn_actor(LIDAR_sensor, LIDAR_transform, attach_to=self.vehicle,
+                                          attachment_type=carla.AttachmentType.Rigid)
+        self.actor_list.append(ego_LIDAR)
+        return ego_LIDAR
+
+    def radar(self):
+        radar_sensor = self.blueprint_library.find("sensor.other.radar")
+        radar_sensor.set_attribute('horizontal_fov', str(30))
+        radar_sensor.set_attribute('vertical_fov', str(30))
+        radar_sensor.set_attribute('points_per_second', '10000')
+        radar_transform = carla.Transform(carla.Location(z=2))
+        ego_radar = self.world.spawn_actor(radar_sensor, radar_transform, attach_to=self.vehicle,
+                                          attachment_type=carla.AttachmentType.Rigid)
+        self.actor_list.append(ego_radar)
+        return ego_radar
+
+    def collision(self):
+        collision_sensor = self.blueprint_library.find('sensor.other.collision')
+        collision_location = carla.Location(0, 0, 0)
+        collision_rotation = carla.Rotation(0, 0, 0)
+        collision_transform = carla.Transform(collision_location, collision_rotation)
+        ego_collision =self.world.spawn_actor(collision_sensor, collision_transform, attach_to=self.vehicle,
+                                          attachment_type=carla.AttachmentType.Rigid)
+        self.actor_list.append(ego_collision)
+        return ego_collision
     def imu_callback(self, imu):  # accelerometer is m/s^2 and gyroscope data is rad/sec
         imu_dict = {}
         imu_dict["timestamp"] = imu.timestamp
@@ -108,13 +162,48 @@ def imu_callback(self, imu):  # accelerometer is m/s^2 and gyroscope data is rad
         # create a pandas dataframe
         self.imu_dataframe = self.imu_dataframe.append(imu_dict, ignore_index=True)
         # save the dataframe to a csv file
-        self.imu_dataframe.to_csv(os.path.join(self.save_dir, "imu.csv"), index=False)
-
+        self.imu_dataframe.to_csv(os.path.join(self.save_dir, "imu.csv"), index=False, mode='a')
+    def collision_callback(self, collision):
+        collision_dict = {}
+        collision_dict["Collision Time"] = collision.timestamp
+        collision_dict["collision_actor"] = collision.other_actor  # The actor collided with
+        collision_dict["collision_impulse"] = collision.normal_impulse  # The impulse of the hit
+        self.collision_dataframe = self.collision_dataframe.append(collision_dict, ignore_index=True)
+        self.collision_dataframe.to_csv(os.path.join(self.save_dir, "collision.csv"), index=False)
+
+    def LIDAR_callback(self, LIDAR):
+        # LIDAR_dict = {}
+        points = np.frombuffer(LIDAR.raw_data, dtype=np.dtype('f4'))
+        points = np.reshape(points, (len(LIDAR), 3))
+        # LIDAR_dict["timestamp"] = LIDAR.timestamp
+        # LIDAR_dict["horizontal_angle"] = LIDAR.horizontal_angle
+        # LIDAR_dict["raw_data"] = LIDAR.raw_data
+        # LIDAR_dict["location"] = LIDAR.transform.location
+        # LIDAR_dict["rotation"] = LIDAR.transform.rotation
+        df = pd.DataFrame(points, columns=["X", "Y", "Z"])
+        self.LIDAR_dataframe = self.LIDAR_dataframe.append(df, ignore_index=True)
+        self.LIDAR_dataframe.to_csv(os.path.join(self.save_dir, "LIDAR.csv"), index=False)
+
+    def radar_callback(self,radar):
+        points = np.frombuffer(radar.raw_data, dtype=np.dtype('f4'))
+        points = np.reshape(points, (len(radar), 4))
+        df = pd.DataFrame(points, columns=["altitude","azimuth","depth","velocity"])
+        self.radar_dataframe = self.radar_dataframe.append(df, ignore_index=True)
+        self.radar_dataframe.to_csv(os.path.join(self.save_dir, "radar.csv"), index=False)
+
     def gnss_callback(self, gnss):
         gnss_dict = {}
         gnss_dict["timestamp"] = gnss.timestamp
         gnss_dict["latitude"] = gnss.latitude
         gnss_dict["longitude"] = gnss.longitude
         gnss_dict["altitude"] = gnss.altitude
         self.gnss_dataframe = self.gnss_dataframe.append(gnss_dict, ignore_index=True)
-        self.gnss_dataframe.to_csv(os.path.join(self.save_dir, "gnss.csv"), index=False)
+        self.gnss_dataframe.to_csv(os.path.join(self.save_dir, "gnss.csv"), index=False, mode='a')
+
+    def clearing(self):
+        for actor in self.world.get_actors().filter('*vehicle*'):
+            actor.destroy()
+        for actor in self.world.get_actors().filter('*sensor*'):
+            actor.destroy()
+
+