Commit 477df5d2 authored by Sacha Pronost's avatar Sacha Pronost
Browse files

Clean

parent 5611e60d

carla/Dataset_Carla.ipynb

0 → 100644
+259 −0
Changes for carla/Dataset_Carla.ipynb: 259 added lines, 0 removed lines.
Original line number Diff line number Diff line 
%% Cell type:code id:a5a81071 tags:



``` python

import carla

import time

import numpy as np

import keyboard

import cv2

import random





# Connect to the CARLA simulator

client = carla.Client("localhost", 2000)

client.set_timeout(10.0)

world = client.get_world()



```



%% Cell type:code id: tags:



``` python

carla = client.load_world('Town01')

```



%% Cell type:code id: tags:



``` python

# Obtention des coordonnées de la caméra

spectator = world.get_spectator()

camera = world.get_spectator()

camera_transform = camera.get_transform()

camera_location = camera_transform.location

camera_rotation = camera_transform.rotation

print(f"Camera location: {camera_location}")

print(f"Camera rotation: {camera_rotation}")

```



%% Output



    ---------------------------------------------------------------------------

    RuntimeError                              Traceback (most recent call last)

    ~\AppData\Local\Temp\ipykernel_3172\3509461821.py in <module>

    ----> 1 camera = world.get_spectator()

          2 camera_transform = camera.get_transform()

          3 camera_location = camera_transform.location

          4 camera_rotation = camera_transform.rotation

          5 print(f"Camera location: {camera_location}")

    RuntimeError: internal error: unable to find spectator



%% Cell type:code id: tags:



``` python

# Tentative de faire créer un long trajet pour faire un dataset valide

# Concluion : Très long à faire pour le peu d'image obtenu

def generation_trajet():

    blueprint = world.get_blueprint_library().find("vehicle.tesla.model3")

    spawn_point = carla.Transform(carla.Location(x=330.845581, y=195.527863, z=3), carla.Rotation(pitch=0.000000, yaw=180.000000, roll=0.000000))

    vehicle = world.spawn_actor(blueprint, spawn_point)



    blueprint = world.get_blueprint_library().find("vehicle.audi.a2")

    spawn_point = carla.Transform(carla.Location(x=309.853577, y=199.227951, z=3))

    vehicle2 = world.spawn_actor(blueprint, spawn_point)



    blueprint = random.choice(world.get_blueprint_library().filter('vehicle'))

    spawn_point = carla.Transform(carla.Location(x=270.685883, y=198.910965, z=3))

    vehicle3 = world.spawn_actor(blueprint, spawn_point)



    blueprint = random.choice(world.get_blueprint_library().filter('vehicle'))

    spawn_point = carla.Transform(carla.Location(x=245.644699, y=199.270432, z=3))

    vehicle4 = world.spawn_actor(blueprint, spawn_point)



    blueprint = random.choice(world.get_blueprint_library().filter('vehicle'))

    spawn_point = carla.Transform(carla.Location(x=197.804962, y=199.209579, z=3))

    vehicle5 = world.spawn_actor(blueprint, spawn_point)



    blueprint = random.choice(world.get_blueprint_library().filter('walker'))

    spawn_point = carla.Transform(carla.Location(x=306.125458, y=191.453323, z=3))

    pieton1 = world.spawn_actor(blueprint, spawn_point)



    blueprint = random.choice(world.get_blueprint_library().filter('walker'))

    spawn_point = carla.Transform(carla.Location(x=285.016296, y=191.565521, z=3))

    pieton2 = world.spawn_actor(blueprint, spawn_point)







generation_trajet()

```



%% Cell type:code id: tags:



``` python

for v in world.get_actors():

    v.destroy()

```



%% Cell type:code id:0c29a8da tags:



``` python

# Fonction créer qui permet de changer de météo afin d'améliorer notre modèle de réseau de neurones dans le futur

weatherList = np.array([carla.WeatherParameters.WetCloudySunset,carla.WeatherParameters.CloudyNoon,carla.WeatherParameters.WetNoon,carla.WeatherParameters.WetCloudyNoon,carla.WeatherParameters.MidRainyNoon,carla.WeatherParameters.HardRainNoon,carla.WeatherParameters.SoftRainNoon,carla.WeatherParameters.ClearSunset,carla.WeatherParameters.CloudySunset,carla.WeatherParameters.WetSunset,carla.WeatherParameters.MidRainSunset,carla.WeatherParameters.HardRainSunset,carla.WeatherParameters.SoftRainSunset])

np.random.shuffle(weatherList)

world.set_weather(weatherList[0])

```



%% Cell type:code id:ff384592 tags:



``` python

weather_parameters = {

    'ClearNoon': carla.WeatherParameters.ClearNoon,

    'WetCloudySunset': carla.WeatherParameters.WetCloudySunset,

    'SoftRainNoon': carla.WeatherParameters.SoftRainNoon,

    'MidRainSunset': carla.WeatherParameters.MidRainSunset,

    'HardRainSunset': carla.WeatherParameters.HardRainSunset,

    'ClearSunset': carla.WeatherParameters.ClearSunset,

    'WetSunset': carla.WeatherParameters.WetSunset,

    'SoftRainSunset': carla.WeatherParameters.SoftRainSunset

}

random_key = random.choice(list(weather_parameters.keys()))

random_value = weather_parameters[random_key]

world.set_weather(random_value)

```



%% Cell type:code id:94260e0d tags:



``` python

# Charger le véhicule principal

def charger_vehicle():

    bp_lib = world.get_blueprint_library()

    spawn_points = world.get_map().get_spawn_points()

    vehicule_bp = bp_lib.find('vehicle.lincoln.mkz_2020')

    vehicle =world.try_spawn_actor(vehicule_bp,random.choice(spawn_points))

    return vehicle

```



%% Cell type:code id:680a1e1a tags:



``` python

# Faire spawner plusieurs voiture dans le monde

def spanwnVehicle(n):

    for i in range (n):

        vehicle_bp = random.choice(bp_lib.filter('vehicle'))

        npc = world.try_spawn_actor(vehicle_bp,random.choice(spawn_points))

```



%% Cell type:code id: tags:



``` python

# Faire supprimer tous les véhicules du monde

def destruction():

    for v in world.get_actors().filter('*vehicle*'):

        v.destroy()

```



%% Cell type:code id:33170479 tags:



``` python

def isCirculation(value):

    for v in world.get_actors().filter('*vehicle*'):

        v.set_autopilot(value)

```



%% Cell type:code id:bae6d847 tags:



``` python

def attach_cam(vehicle):

    # Génération d'une caméra RGB

    camera = world.get_blueprint_library().find("sensor.camera.rgb")

    camera.set_attribute("image_size_x", str(800))

    camera.set_attribute("image_size_y", str(600))



    # Génération d'une caméra sémantique

    cameraS = world.get_blueprint_library().find("sensor.camera.semantic_segmentation")

    cameraS.set_attribute("image_size_x", str(800))

    cameraS.set_attribute("image_size_y", str(600))



    # Attacher les caméras au véhicule

    spawn_point = carla.Transform(carla.Location(z=2))

    sensor = world.spawn_actor(camera, spawn_point, attach_to=vehicle)

    sensorS = world.spawn_actor(cameraS, spawn_point, attach_to=vehicle)

    return sensor,sensorS

```



%% Cell type:code id:47110331 tags:



``` python

# Fonction qui permet de récupérer les images des caméras

def process_img(image,typeCam,weather):

    time.sleep(2)

    # Si on souhaite avoir des images de segmentation selon la palette de couleurs de CityScapes ( évite d'avoir une image noir car l'image contient les numéros des classes )

   # if(typeCam == "seg"):

   #      image.convert(carla.ColorConverter.CityScapesPalette)

    # Convert the image to a numpy array

    np_image = np.array(image.raw_data)

    np_image = np_image.reshape((image.height, image.width, 4))



    # Compteurs globaux pour les images RGB et Segmentation

    global counterRGB

    counterRGB += 1



    global counterSEG

    counterSEG += 1



    #Enregistrement des images RGB et Segmentation

    if(typeCam == "rgb" ):

        filename = "dataset/"+weather+"/rgb/image{}.png".format(counterRGB)

        filenameAll = "dataset/All_Image/rgb/image{}.png".format(counterRGB)

        cv2.imwrite(filename, np_image)

        cv2.imwrite(filenameAll,np_image)

    elif(typeCam == "seg"):

        filename = "dataset/"+weather+"/segmentation/image{}.png".format(counterSEG)

        filenameAll = "dataset/All_Image/segmentation/image{}.png".format(counterSEG)

        cv2.imwrite(filename, np_image)

        cv2.imwrite(filenameAll,np_image)

```



%% Cell type:code id:f268b919 tags:



``` python

def dataset():

    weather_parameters = {

    'ClearNoon': carla.WeatherParameters.ClearNoon,

    'WetCloudySunset': carla.WeatherParameters.WetCloudySunset,

    'SoftRainNoon': carla.WeatherParameters.SoftRainNoon,

    'MidRainSunset': carla.WeatherParameters.MidRainSunset,

    'HardRainSunset': carla.WeatherParameters.HardRainSunset,

    'ClearSunset': carla.WeatherParameters.ClearSunset,

    'WetSunset': carla.WeatherParameters.WetSunset,

    'SoftRainSunset': carla.WeatherParameters.SoftRainSunset

    }

    list_weather = list(weather_parameters.keys())

    for weather in list_weather :

        params = weather_parameters[weather]

        world.set_weather(params)

        vehicle = charger_vehicle()

        spanwnVehicle(15)

        isCirculation(True)

        sensor,sensorS = attach_cam(vehicle)



        # Enregistrement des images

        sensor.listen(lambda im:process_img(im,"rgb",weather))

        sensorS.listen(lambda im:process_img(im,"seg",weather))



        # Lance la simulation pendant 60 secondes

        time.sleep(60)

        # Arrête la simulation, arrête l'enregistrement des images et détruit les véhicules

        sensor.stop()

        sensorS.stop()



        destruction()





bp_lib = world.get_blueprint_library()

spawn_points = world.get_map().get_spawn_points()

# Initialisation des compteurs

counterRGB = 0

counterSEG = 0



dataset()

```

carla/Scenarios.ipynb

0 → 100644
+350 −0

File added.

Preview size limit exceeded, changes collapsed.

simulator/Camera_sensors.ipynb

deleted100644 → 0
+0 −159
Changes for simulator/Camera_sensors.ipynb: 0 added lines, 159 removed lines.
Original line number Diff line number Diff line 
%% Cell type:code id:bfaff6d1 tags:



``` python

# Fichier qui vient d'une vidéo youtube Carla qui permet de voir comment gérer les capteurs Carla

# https://www.youtube.com/watch?v=om8klsBj4rc

```



%% Cell type:code id:156fe94b tags:



``` python

# Ce fichier a été modifier pour répondre à notre problème (choix des caméras utilisées, possibilité de changer de météo...)

```



%% Cell type:code id:6e6b6dec-c0f6-4496-9b75-d74bcba07183 tags:



``` python

import carla

import math

import random

import time

import numpy as np

import cv2



# Connect the client and set up bp library and spawn points

client = carla.Client('localhost', 2000)

world = client.get_world()

bp_lib = world.get_blueprint_library()

spawn_points = world.get_map().get_spawn_points()

```



%% Cell type:code id:ce48bb74 tags:



``` python

# Fonction créer qui permet de changer de météo afin d'améliorer notre modèle de réseau de neurones dans le futur

weatherList = np.array([carla.WeatherParameters.WetCloudySunset,carla.WeatherParameters.CloudyNoon,carla.WeatherParameters.WetNoon,carla.WeatherParameters.WetCloudyNoon,carla.WeatherParameters.MidRainyNoon,carla.WeatherParameters.HardRainNoon,carla.WeatherParameters.SoftRainNoon,carla.WeatherParameters.ClearSunset,carla.WeatherParameters.CloudySunset,carla.WeatherParameters.WetSunset,carla.WeatherParameters.MidRainSunset,carla.WeatherParameters.HardRainSunset,carla.WeatherParameters.SoftRainSunset])

np.random.shuffle(weatherList)

world.set_weather(weatherList[0])

```



%% Cell type:code id:c9675857 tags:



``` python

# Add the ego vehicle

vehicle_bp = bp_lib.find('vehicle.lincoln.mkz_2020')

vehicle = world.try_spawn_actor(vehicle_bp, spawn_points[79])

```



%% Cell type:code id:93c94c6a-4c3e-487b-8de9-823e440f3c43 tags:



``` python



# Move the spectator behind the vehicle to view it

spectator = world.get_spectator()

transform = carla.Transform(vehicle.get_transform().transform(carla.Location(x=-4,z=2.5)),vehicle.get_transform().rotation)

spectator.set_transform(transform)



```



%% Cell type:code id:b500fb9b tags:



``` python

# Add traffic

for i in range(50):

    vehicle_bp = random.choice(bp_lib.filter('vehicle'))

    npc = world.try_spawn_actor(vehicle_bp, random.choice(spawn_points))

```



%% Cell type:code id:85f22a80-52d1-4706-9231-5835bfc27a2e tags:



``` python

# Set traffic in motion

for v in world.get_actors().filter('*vehicle*'):

    v.set_autopilot(True)

```



%% Cell type:code id:2db29235-7d72-43e3-9b34-89a92ff9fbf2 tags:



``` python

# Set initial camera translation

camera_init_trans = carla.Transform(carla.Location(z=2))



# On choisit de types de caméras (caméra rgb et caméra de detection sémantique)

camera_bp = bp_lib.find('sensor.camera.rgb')

camera_bp.set_attribute('image_size_x', '500')

camera_bp.set_attribute('image_size_y', '500')

camera = world.spawn_actor(camera_bp, camera_init_trans, attach_to=vehicle)

sem_camera_bp = bp_lib.find('sensor.camera.semantic_segmentation')

sem_camera_bp.set_attribute('image_size_x', '500')

sem_camera_bp.set_attribute('image_size_y', '500')

sem_camera = world.spawn_actor(sem_camera_bp, camera_init_trans, attach_to=vehicle)

```



%% Cell type:code id:6bb223b5-c5a0-4797-9055-79afafd24349 tags:



``` python

# Define respective callbacks

def rgb_callback(image, data_dict):

    data_dict['rgb_image'] = np.reshape(np.copy(image.raw_data), (image.height, image.width, 4))



def sem_callback(image, data_dict):

    image.convert(carla.ColorConverter.CityScapesPalette)

    data_dict['sem_image'] = np.reshape(np.copy(image.raw_data), (image.height, image.width, 4))

```



%% Cell type:code id:74ea1188-96b4-452a-9932-c8ab42241588 tags:



``` python

# Initialise parameters and data

image_w = camera_bp.get_attribute("image_size_x").as_int()

image_h = camera_bp.get_attribute("image_size_y").as_int()





sensor_data = {'rgb_image': np.zeros((image_h, image_w, 4)),

               'sem_image': np.zeros((image_h, image_w, 4))}



# OpenCV named window for display

cv2.namedWindow('All cameras', cv2.WINDOW_AUTOSIZE)



# Tile all data in one array

top_row = np.concatenate((sensor_data['rgb_image'], sensor_data['sem_image']),axis=1)



# Display with imshow

cv2.imshow('All cameras',top_row)

cv2.waitKey(1)



# Set sensors recording

camera.listen(lambda image: rgb_callback(image, sensor_data))

sem_camera.listen(lambda image: sem_callback(image, sensor_data))



# Indefinite while loop

while True:



    # Tile camera images into one array

    top_row = np.concatenate((sensor_data['rgb_image'], sensor_data['sem_image']),axis=1)



    # Dispaly with imshow

    cv2.imshow('All cameras',top_row)



    # Break loop if user presses q

    if cv2.waitKey(1) == ord('q'):

        break



# Stop sensors and destroy OpenCV window

camera.stop()

sem_camera.stop()

cv2.destroyAllWindows()

```



%% Cell type:code id:c8c88800 tags:



``` python

for v in world.get_actors().filter('*vehicle*'):

    v.destroy()

```



%% Cell type:code id:4a2bcb8c tags:



``` python

```

simulator/fondamental.ipynb

deleted100644 → 0
+0 −316

File deleted.

Preview size limit exceeded, changes collapsed.

test_dir/image.png

deleted100644 → 0
−593 KiB
Loading image diff...