From c7d311c59f002dd026ba515da12d6c3b71e60d5b Mon Sep 17 00:00:00 2001 From: Pengfei Xiao <136677635+jiandaoshou-aidehua@users.noreply.github.com> Date: Sun, 7 Jul 2024 18:05:01 +0800 Subject: [PATCH] course (#22) * course * course --------- Co-authored-by: zoudai <2184819232> --- docs/course/locate_moving.md | 139 ++++++++++++++++++ docs/course/microscopic_Intersection_demo.md | 20 ++- docs/course/multi-view_ camera.md | 10 +- docs/course/navigation.md | 40 +++-- docs/course/object_detection.md | 4 +- docs/course/traffic_indicators.md | 4 +- docs/course/trajectory_tracking.md | 4 +- docs/img/traffic_course_img/5.png | Bin 0 -> 2407694 bytes docs/img/traffic_course_img/locate_moving.gif | Bin 0 -> 309485 bytes src/course/locate_moving.py | 107 ++++++++++++++ .../navigation/automatic_control_revised.py | 34 +++-- 11 files changed, 319 insertions(+), 43 deletions(-) create mode 100644 docs/course/locate_moving.md create mode 100644 docs/img/traffic_course_img/5.png create mode 100644 docs/img/traffic_course_img/locate_moving.gif create mode 100644 src/course/locate_moving.py diff --git a/docs/course/locate_moving.md b/docs/course/locate_moving.md new file mode 100644 index 00000000..9709d49e --- /dev/null +++ b/docs/course/locate_moving.md @@ -0,0 +1,139 @@ +# 车辆按地图选点进行移动 + +​ 此示例支持在 **pygame** 窗口对显示区域进行车辆生成并进行移动。 + +## **环境依赖** + +- **python 3.7.9** +- **carla 0.9.15** +- **[湖工商场景](https://pan.baidu.com/s/15T1hGoWJ70tVmsTX7-zcSw?pwd=hutb )(WindowsNoEditor)** +- **[运行 locate_moving.py](../../src/course/locate_moving.py)** + + + +## CarlaSimulator 类 + +​ 该类主要完成设置相机、处理相机图像、将Pygame坐标转换为CARLA世界坐标。 + + + +### 函数初始化__init__ + +​ 初始化函数,设置模拟器窗口的宽度和高度,连接CARLA服务器,并获取世界和蓝图库。 + +``` +def __init__(self, width, height): + self.width = width + self.height = height + self.client = carla.Client('localhost', 2000) + self.client.set_timeout(10.0) + self.world = self.client.get_world() + self.blueprint_library = self.world.get_blueprint_library() + self.vehicle_bp = self.blueprint_library.filter('vehicle.*')[0] + self.camera = None + self.vehicles = [] + self.fov = VIEW_FOV + +``` + +### setup_camera函数 + +​ 设置相机,定义相机的蓝图、分辨率、视场角(FOV),并将相机生成在指定的位置和角度。 + +``` +def setup_camera(self): + camera_bp = self.blueprint_library.find('sensor.camera.rgb') + camera_bp.set_attribute('image_size_x', str(self.width)) + camera_bp.set_attribute('image_size_y', str(self.height)) + camera_bp.set_attribute('fov', str(self.fov)) + camera_transform = carla.Transform(carla.Location(x=0, y=0, z=50), carla.Rotation(yaw=270, pitch=-90)) + self.camera = self.world.spawn_actor(camera_bp, camera_transform) + +``` + +### process_image函数 + +​ 处理相机捕获的图像,将其转换为Pygame可显示的格式。 + +``` +def process_image(self, image): + array = np.frombuffer(image.raw_data, dtype=np.uint8) + array = array.reshape((self.height, self.width, 4)) + array = array[:, :, :3] # Remove alpha channel + array = array[:, :, ::-1] # Convert BGR to RGB + surface = pygame.surfarray.make_surface(array.swapaxes(0, 1)) + return surface + +``` + +### convert_pygame_to_world函数 + +​ 将Pygame窗口中的坐标转换为CARLA世界坐标。主要通过相机的视角和Pygame窗口的大小进行计算。 + +``` +def convert_pygame_to_world(self, x, y): + cam_transform = self.camera.get_transform() + cam_loc = cam_transform.location + + aspect_ratio = self.width / self.height + camera_fov_rad = np.radians(self.fov) + camera_fov_y = 2 * np.arctan(np.tan(camera_fov_rad / 2) / aspect_ratio) + + world_units_per_pixel_x = (2 * cam_loc.z * np.tan(camera_fov_rad / 2)) / self.width + world_units_per_pixel_y = (2 * cam_loc.z * np.tan(camera_fov_y / 2)) / self.height + + relative_x = (x - self.width / 2) * world_units_per_pixel_x + relative_y = (y - self.height / 2) * world_units_per_pixel_y + + world_x = cam_loc.x + relative_x + world_y = cam_loc.y - relative_y + + return world_x, world_y + +``` + +### run函数 + +​ 运行模拟器,初始化Pygame窗口,设置相机监听器,并处理Pygame事件,如点击事件生成车辆。 + +``` +def run(self): + pygame.init() + display = pygame.display.set_mode((self.width, self.height), pygame.HWSURFACE | pygame.DOUBLEBUF) + clock = pygame.time.Clock() + + self.setup_camera() + self.camera.listen(lambda image: display.blit(self.process_image(image), (0, 0))) + + running = True + while running: + for event in pygame.event.get(): + if event.type == pygame.QUIT: + running = False + elif event.type == pygame.MOUSEBUTTONDOWN: + x, y = event.pos + world_x, world_y = self.convert_pygame_to_world(x, y) + + spawn_point = carla.Transform(carla.Location(x=world_x, y=-world_y, z=1), carla.Rotation()) + vehicle = self.world.spawn_actor(self.vehicle_bp, spawn_point) + vehicle.set_autopilot() + self.vehicles.append(vehicle) + + pygame.display.flip() + clock.tick(30) + + for vehicle in self.vehicles: + vehicle.destroy() + self.camera.destroy() + pygame.quit() + +``` + + + +![](../img/traffic_course_img/locate_moving.gif) + + + + + diff --git a/docs/course/microscopic_Intersection_demo.md b/docs/course/microscopic_Intersection_demo.md index e1f846c4..fd88edb4 100644 --- a/docs/course/microscopic_Intersection_demo.md +++ b/docs/course/microscopic_Intersection_demo.md @@ -4,13 +4,19 @@ 此脚本连接到CARLA仿真环境,并定期切换湖工商附近的四个预设路口视角。通过不断改变观众(Spectator)的视角,用户可以观察到不同路口的交通状况。 -#### **环境要求** - -- Python 3.6 及以上版本 - -- CARLA 仿真环境 - -- 安装必要的Python库: +- #### **环境要求** + + - Python 3.6 及以上版本 + + - CARLA 仿真环境 + + - 安装必要的Python库: + + - **[spectator.py](https://github.com/OpenHUTB/carla_doc/blob/master/src/course/spectator/spectator.py)** + + - [**湖工商场景**](https://pan.baidu.com/s/15T1hGoWJ70tVmsTX7-zcSw?pwd=hutb )**(WindowsNoEditor)**,并运行**[generate_traffic.py](https://github.com/OpenHUTB/carla_doc/blob/master/src/examples/generate_traffic.py)** + +- 安装必要的python库 ``` pip install carla diff --git a/docs/course/multi-view_ camera.md b/docs/course/multi-view_ camera.md index 415a82fa..3820b859 100644 --- a/docs/course/multi-view_ camera.md +++ b/docs/course/multi-view_ camera.md @@ -4,11 +4,13 @@ 此脚本连接到CARLA仿真环境,在车辆的左右两侧设置相机,并使用Pygame显示相机捕获的实时图像。系统采用多进程架构,确保左右相机图像分别在不同窗口中实时显示。 -#### **环境要求** +- #### **环境要求** -- Python 3.6 及以上版本 -- CARLA 仿真环境 -- 安装必要的Python库: + - Python 3.6 及以上版本 + - CARLA 仿真环境 + - 安装必要的Python库: + - [**camera.py**](https://github.com/OpenHUTB/carla_doc/blob/master/src/course/camera.py) + - [**湖工商场景**](https://pan.baidu.com/s/15T1hGoWJ70tVmsTX7-zcSw?pwd=hutb )**(WindowsNoEditor)** ``` pip install carla pygame numpy opencv-python diff --git a/docs/course/navigation.md b/docs/course/navigation.md index fc6fd024..5fb5042e 100644 --- a/docs/course/navigation.md +++ b/docs/course/navigation.md @@ -2,11 +2,13 @@ ​ 车辆运动支持定义车辆运动起点和目标位置,为每辆车指定随机速度值,使其沿轨迹移动时不会发生碰撞或模拟碰撞,看到车辆沿指定轨迹行驶以到达其目标位置。 -​ 本示例设计了一个**automatic_control_revised.py**脚本进行实现 +​ 本示例设计了一个 [**automatic_control_revised.py**](https://github.com/OpenHUTB/carla_doc/blob/master/src/course/navigation/automatic_control_revised.py) 脚本进行实现 ![](../img/traffic_course_img/4.gif) -**注意** 需要将**agents**文件夹位置添加到代码环境变量中,或者复制该文件夹使其与本示例脚本同级。 +**注意** 需要将 [**agents**](https://github.com/OpenHUTB/carla_doc/tree/master/src/carla_agent) 文件夹位置添加到代码环境变量中,或者复制该文件夹使其与本示例脚本同级。 + + [**湖工商场景**](https://pan.baidu.com/s/15T1hGoWJ70tVmsTX7-zcSw?pwd=hutb )**(WindowsNoEditor)** @@ -28,18 +30,20 @@ diming = { ​ 定义命令行参数 ```python -argparser = argparse.ArgumentParser( - description=__doc__) -argparser.add_argument( - '--s', - default="北门", - type=str) -argparser.add_argument( - '--e', - default="西门", - type=str) -args = argparser.parse_args() - + argparser = argparse.ArgumentParser( + description=__doc__) + argparser.add_argument( + '--s', + default="北门", + type=str) + argparser.add_argument( + '--e', + default="西门", + type=str) + argparser.add_argument( + '--speed', + default=5, + type=int) ``` ​ 获取仿真时间对象及设置 @@ -76,9 +80,11 @@ vehicle = world.spawn_actor(ego_vehicle_bp, spawn_point) world.tick() ``` -​ 生成终点,给起点和终点生成一个车辆运行轨迹 +​ 生成终点,给起点和终点生成一个车辆运行轨迹,定义初始速度 ```python + # 设置初始速度 + vehicle.set_target_velocity(carla.Vector3D(args.speed, 0, 0)) # 创建代理 agent = BehaviorAgent(vehicle, behavior='normal') destination = carla.Location(locations[args.e]) @@ -167,4 +173,6 @@ while True: # agent.get_local_planner().set_speed(50) control = agent.run_step(debug=True) vehicle.apply_control(control) -``` \ No newline at end of file +``` + +![](../img/traffic_course_img/5.png) \ No newline at end of file diff --git a/docs/course/object_detection.md b/docs/course/object_detection.md index d40b84a2..874f1a57 100644 --- a/docs/course/object_detection.md +++ b/docs/course/object_detection.md @@ -1,6 +1,8 @@ # 基于Carla仿真环境的目标检测 -​ **object_dection.py** 脚本主要是在 **Carla** 中使用 **YOLOv3** 检测车辆。[相关链接](https://openhutb.github.io/carla_doc/algorithms/perception/) +​ [**object_dection.py**](https://github.com/OpenHUTB/carla_doc/blob/master/src/course/object_detection/object_detection.py) 脚本主要是在 **Carla** 中使用 **YOLOv3** 检测车辆。[相关链接](https://openhutb.github.io/carla_doc/algorithms/perception/) + +​ [**湖工商场景**](https://pan.baidu.com/s/15T1hGoWJ70tVmsTX7-zcSw?pwd=hutb )**(WindowsNoEditor)**,并运行**[generate_traffic.py](https://github.com/OpenHUTB/carla_doc/blob/master/src/examples/generate_traffic.py)** ![](../img/traffic_course_img/3.gif) diff --git a/docs/course/traffic_indicators.md b/docs/course/traffic_indicators.md index edb879c8..9f5922c7 100644 --- a/docs/course/traffic_indicators.md +++ b/docs/course/traffic_indicators.md @@ -1,8 +1,8 @@ # 路口评价指标分析计算(HutbCarlaCity) -​ 为支持高保正的十字路口三维建模,对路口交通流量、路口车均延误、路口饱和、排队长度四种路口真实性评价指标分析。 +​ 为支持 [**湖工商场景**](https://pan.baidu.com/s/15T1hGoWJ70tVmsTX7-zcSw?pwd=hutb ) **(WindowsNoEditor)** 高保正的十字路口三维建模,设计 **[traffic_indicators.py](https://github.com/OpenHUTB/carla_doc/blob/master/src/course/traffic%20indicators.py)** 脚本对路口交通流量、路口车均延误、路口饱和、排队长度四种路口真实性评价指标分析。 -**首先需要对场景中添加车辆,运行generate_traffic.py脚本,生成足够的车辆** +**首先需要对场景中添加车辆,运行[generate_traffic.py](https://github.com/OpenHUTB/carla_doc/blob/master/src/examples/generate_traffic.py)脚本,生成足够的车辆** diff --git a/docs/course/trajectory_tracking.md b/docs/course/trajectory_tracking.md index 7c63af16..94ed313b 100644 --- a/docs/course/trajectory_tracking.md +++ b/docs/course/trajectory_tracking.md @@ -1,8 +1,8 @@ # 轨迹跟踪 Drive.py -​ 本示例使用 **Carla** 模拟器来执行轨迹跟踪任务,主要功能包括连接CARLA服务器、设置模拟环境、生成车辆、读取和处理路径点数据、控制车辆运动、记录和保存仿真结果下面对于主要函数进行介绍。 - +​ 本示例使用 **Carla** 模拟器来执行轨迹跟踪任务, [**Drive.py**](https://github.com/OpenHUTB/carla_doc/blob/master/src/course/trajectory_tracking/Drive.py) 主要功能包括连接CARLA服务器、设置模拟环境、生成车辆、读取和处理路径点数据、控制车辆运动、记录和保存仿真结果下面对于主要函数进行介绍。 + 基于 [**湖工商场景**](https://pan.baidu.com/s/15T1hGoWJ70tVmsTX7-zcSw?pwd=hutb )**(WindowsNoEditor)**,首先需要运行[generate_traffic.py](https://github.com/OpenHUTB/carla_doc/blob/master/src/examples/generate_traffic.py) ## 定义CARLA设置 diff --git a/docs/img/traffic_course_img/5.png b/docs/img/traffic_course_img/5.png new file mode 100644 index 0000000000000000000000000000000000000000..e5abd7176d41029cb2576552e6aa6a57b717b7c9 GIT binary patch literal 2407694 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zhp3#&+8Nl&?|vaUe%fMu=){;l3b>-gfN>7uUjcvYbfkh(V&`~734BS4u}SE1Cy&lZEuTQOPKGefTlb6MK4DDUXs{Wje)dtrVq{8MiO>j4t>VaLr zY_F5dn6xS|w>3MA%gUWnuZ2}m31RVW(=5{Ln#AgLE>>CakYqzl_74+%s?Hn^{=A_D z+Dc)QiU7k}7Y*;hJdnK1vn(Mq2B>u)Pb_ub1!W9Z7C6q0m7omvPC^ZAIenoFqUtnd xWlx!v(YeBEdM>A?pezM7<(BFNy{3%`Dr9q-x(yw=cllT+r56feBmdA<{}<0QF^K>G literal 0 HcmV?d00001 diff --git a/src/course/locate_moving.py b/src/course/locate_moving.py new file mode 100644 index 00000000..c01a07af --- /dev/null +++ b/src/course/locate_moving.py @@ -0,0 +1,107 @@ +import carla +import pygame +import numpy as np +import random + +# 设置Pygame窗口大小 +VIEW_WIDTH = 1920 // 2 +VIEW_HEIGHT = 1080 // 2 +VIEW_FOV = 90 + + +class CarlaSimulator: + def __init__(self, width, height): + self.width = width + self.height = height + self.client = carla.Client('localhost', 2000) + self.client.set_timeout(10.0) + self.world = self.client.get_world() + self.blueprint_library = self.world.get_blueprint_library() + self.vehicle_bp = self.blueprint_library.filter('vehicle.*')[0] + self.camera = None + self.vehicles = [] + self.fov = VIEW_FOV + + def setup_camera(self): + camera_bp = self.blueprint_library.find('sensor.camera.rgb') + camera_bp.set_attribute('image_size_x', str(self.width)) + camera_bp.set_attribute('image_size_y', str(self.height)) + camera_bp.set_attribute('fov', str(self.fov)) + camera_transform = carla.Transform(carla.Location(x=0, y=0, z=50), carla.Rotation(yaw=270, pitch=-90)) + self.camera = self.world.spawn_actor(camera_bp, camera_transform) + + def process_image(self, image): + array = np.frombuffer(image.raw_data, dtype=np.uint8) + array = array.reshape((self.height, self.width, 4)) + array = array[:, :, :3] # Remove alpha channel + array = array[:, :, ::-1] # Convert BGR to RGB + surface = pygame.surfarray.make_surface(array.swapaxes(0, 1)) + return surface + + def convert_pygame_to_world(self, x, y): + # 获取相机位置和旋转 + cam_transform = self.camera.get_transform() + cam_loc = cam_transform.location + + # 计算相机的水平和垂直视角 + aspect_ratio = self.width / self.height + camera_fov_rad = np.radians(self.fov) + camera_fov_y = 2 * np.arctan(np.tan(camera_fov_rad / 2) / aspect_ratio) + + # 计算每个像素在实际世界中的对应距离 + world_units_per_pixel_x = (2 * cam_loc.z * np.tan(camera_fov_rad / 2)) / self.width + world_units_per_pixel_y = (2 * cam_loc.z * np.tan(camera_fov_y / 2)) / self.height + + # 将Pygame坐标转换为相对于图像中心的坐标 + relative_x = (x - self.width / 2) * world_units_per_pixel_x + relative_y = (y - self.height / 2) * world_units_per_pixel_y + + # 转换为世界坐标 + world_x = cam_loc.x + relative_x + world_y = cam_loc.y - relative_y # 注意这里是减去,因为y轴方向是相反的 + + return world_x, world_y + + def run(self): + pygame.init() + display = pygame.display.set_mode((self.width, self.height), pygame.HWSURFACE | pygame.DOUBLEBUF) + clock = pygame.time.Clock() + + self.setup_camera() + self.camera.listen(lambda image: display.blit(self.process_image(image), (0, 0))) + + spectator = self.world.get_spectator() + + running = True + while running: + for event in pygame.event.get(): + if event.type == pygame.QUIT: + running = False + elif event.type == pygame.MOUSEBUTTONDOWN: + x, y = event.pos + # 将Pygame坐标转换为CARLA世界坐标 + world_x, world_y = self.convert_pygame_to_world(x, y) + # print(f'World coordinates: ({world_x}, {world_y})') + + # self.world.debug.draw_string(carla.Location(x=world_x, y=-world_y, z=1), 'ss', life_time=1000, + # color=carla.Color(255, 0, 0)) + spawn_point = carla.Transform(carla.Location(x=world_x, y=-world_y, z=1), carla.Rotation()) + vehicle = self.world.spawn_actor(self.vehicle_bp, spawn_point) + vehicle.set_autopilot() + # if self.vehicles is not None: + # last_vehicle_transform = self.vehicles[-1].get_transform() + # spectator.set_transform(last_vehicle_transform) + self.vehicles.append(vehicle) + + pygame.display.flip() + clock.tick(30) + + for vehicle in self.vehicles: + vehicle.destroy() + self.camera.destroy() + pygame.quit() + + +if __name__ == '__main__': + simulator = CarlaSimulator(VIEW_WIDTH, VIEW_HEIGHT) + simulator.run() diff --git a/src/course/navigation/automatic_control_revised.py b/src/course/navigation/automatic_control_revised.py index beba5ded..d7cf4974 100644 --- a/src/course/navigation/automatic_control_revised.py +++ b/src/course/navigation/automatic_control_revised.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- import carla import time import argparse @@ -7,7 +6,6 @@ import glob spectator_obj_list = [] - from agents.navigation.behavior_agent import BehaviorAgent diming = { @@ -21,7 +19,6 @@ "科技楼": {"x": -97.4, "y": -217, "z": 15}, } - def main(): argparser = argparse.ArgumentParser( description=__doc__) @@ -33,14 +30,18 @@ def main(): '--e', default="西门", type=str) + argparser.add_argument( + '--speed', + default=5, + type=int) args = argparser.parse_args() + # 连接到carla服务器 + client = carla.Client('localhost', 2000) + client.set_timeout(10) + world = client.get_world() + origin_settings = world.get_settings() + vehicle = None try: - # 连接到carla服务器 - client = carla.Client('localhost', 2000) - client.set_timeout(10) - world = client.get_world() - origin_settings = world.get_settings() - # 设置 settings = world.get_settings() settings.synchronous_mode = True @@ -63,6 +64,9 @@ def main(): # we need to tick the world once to let the client update the spawn position world.tick() + # 设置初始速度 + vehicle.set_target_velocity(carla.Vector3D(args.speed, 0, 0)) + # 创建代理 agent = BehaviorAgent(vehicle, behavior='normal') destination = carla.Location(locations[args.e]) @@ -71,7 +75,6 @@ def main(): agent.set_destination(destination) # print(len(agent._local_planner._waypoints_queue)) - # 获得车辆的中心点,并将点定位到右上角,便于运算 bound_x = 0.5 + vehicle.bounding_box.extent.x bound_y = 0.5 + vehicle.bounding_box.extent.y @@ -112,7 +115,7 @@ def main(): # 设置Vehicle_transform_list[0]为初始视图(上帝视图); spectator_obj = Vehicle_transform_list[0] c = 1 - + trajectory = [] while True: agent._update_information() @@ -150,6 +153,15 @@ def main(): # agent.get_local_planner().set_speed(50) control = agent.run_step(debug=True) vehicle.apply_control(control) + # 记录车辆当前位置 + trajectory.append(transform.location) + + # 在地图上绘制车辆轨迹 + if len(trajectory) > 1: + step = 10 + for i in range(0, len(trajectory) - step, step): + world.debug.draw_arrow(trajectory[i], trajectory[i + step], thickness=0.5, color=carla.Color(255, 0, 0), + life_time=1000) finally: world.apply_settings(origin_settings)

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