Workspace introduction
workspace is a large directory that stores the entire project.
which contains:
src: source code.
build: Compile the file.
install: installation space, which stores the target files after successful compilation.
log: log.
We create a new workspace directory, which contains the src directory, git clone https://gitee.com/guyuehome/ros2_21_tutorials.gitinto the src directory.
Install dependencies (via rosdepc) in the working directory, compile the workspace, and set environment variables.
The code function package can be created through the pkg create function of ros. Execute under the src folder:
$ ros2 pkg create --build-type ament_cmake learning_pkg_c
$ ros2 pkg create --build-type ament_python learning_pkg_python
The C package contains package.xml and CMakeLists.txt. package.xml contains basic package information and required dependencies, and CMakeLists.txt indicates how to compile.
The Python function package contains package.xml and Setup.cfg/Setup.py, and Setup.py contains some program configurations, entry nodes, etc.
https://docs.ros.org/en/humble/Tutorials/Workspace/Creating-A-Workspace.html
https://docs.ros.org/en/humble/Tutorials/Creating-Your-First-ROS2-Package.html
node
The node is the basic unit of the robot, which independently performs specific tasks. They can be in different programming languages, run in different locations (cloud, local...)
Helloworld case
Goal: output helloworld every 0.5s.
In the case of learning_node/learning_node/node_helloworld.py you can see:
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@作者: 古月居(www.guyuehome.com)
@说明: ROS2节点示例-发布“Hello World”日志信息, 使用面向过程的实现方式
"""
import rclpy # ROS2 Python接口库
from rclpy.node import Node # ROS2 节点类
import time
def main(args=None): # ROS2节点主入口main函数
rclpy.init(args=args) # ROS2 Python接口初始化
node = Node("node_helloworld") # 创建ROS2节点对象并进行初始化
while rclpy.ok(): # ROS2系统是否正常运行
node.get_logger().info("Hello World") # ROS2日志输出
time.sleep(0.5) # 休眠控制循环时间
node.destroy_node() # 销毁节点对象
rclpy.shutdown() # 关闭ROS2 Python接口
rclpy: system.
node: node.
The previous parts are fixed, import the package, define the main function, and initialize the interface.
Then in learning_node/setup.py:
from setuptools import setup
package_name = 'learning_node'
setup(
name=package_name,
version='0.0.0',
packages=[package_name],
data_files=[
('share/ament_index/resource_index/packages',
['resource/' + package_name]),
('share/' + package_name, ['package.xml']),
],
install_requires=['setuptools'],
zip_safe=True,
maintainer='Hu Chunxu',
maintainer_email='[email protected]',
description='TODO: Package description',
license='TODO: License declaration',
tests_require=['pytest'],
entry_points={
'console_scripts': [
'node_helloworld = learning_node.node_helloworld:main',
'node_helloworld_class = learning_node.node_helloworld_class:main',
'node_object = learning_node.node_object:main',
'node_object_webcam = learning_node.node_object_webcam:main',
],
},
)
The entry_points entry point contains the corresponding files to be compiled, so that the learning_node function package and its programs and entry functions in the program can be found in ros run.
Another way of programming (more recommended) is node object-oriented programming. In node_helloworld_class.py:
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@作者: 古月居(www.guyuehome.com)
@说明: ROS2节点示例-发布“Hello World”日志信息, 使用面向对象的实现方式
"""
import rclpy # ROS2 Python接口库
from rclpy.node import Node # ROS2 节点类
import time
"""
创建一个HelloWorld节点, 初始化时输出“hello world”日志
"""
class HelloWorldNode(Node):
def __init__(self, name):
super().__init__(name) # ROS2节点父类初始化
while rclpy.ok(): # ROS2系统是否正常运行
self.get_logger().info("Hello World") # ROS2日志输出
time.sleep(0.5) # 休眠控制循环时间
def main(args=None): # ROS2节点主入口main函数
rclpy.init(args=args) # ROS2 Python接口初始化
node = HelloWorldNode("node_helloworld_class") # 创建ROS2节点对象并进行初始化
node.destroy_node() # 销毁节点对象
rclpy.shutdown() # 关闭ROS2 Python接口
item identification
Identify the red apple in the picture.
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@作者: 古月居(www.guyuehome.com)
@说明: ROS2节点示例-通过颜色识别检测图片中出现的苹果
"""
import rclpy # ROS2 Python接口库
from rclpy.node import Node # ROS2 节点类
import cv2 # OpenCV图像处理库
import numpy as np # Python数值计算库
lower_red = np.array([0, 90, 128]) # 红色的HSV阈值下限
upper_red = np.array([180, 255, 255]) # 红色的HSV阈值上限
def object_detect(image):
hsv_img = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 图像从BGR颜色模型转换为HSV模型
mask_red = cv2.inRange(hsv_img, lower_red, upper_red) # 图像二值化
contours, hierarchy = cv2.findContours(mask_red, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) # 图像中轮廓检测
for cnt in contours: # 去除一些轮廓面积太小的噪声
if cnt.shape[0] < 150:
continue
(x, y, w, h) = cv2.boundingRect(cnt) # 得到苹果所在轮廓的左上角xy像素坐标及轮廓范围的宽和高
cv2.drawContours(image, [cnt], -1, (0, 255, 0), 2) # 将苹果的轮廓勾勒出来
cv2.circle(image, (int(x+w/2), int(y+h/2)), 5, (0, 255, 0), -1) # 将苹果的图像中心点画出来
cv2.imshow("object", image) # 使用OpenCV显示处理后的图像效果
cv2.waitKey(0)
cv2.destroyAllWindows()
def main(args=None): # ROS2节点主入口main函数
rclpy.init(args=args) # ROS2 Python接口初始化
node = Node("node_object") # 创建ROS2节点对象并进行初始化
node.get_logger().info("ROS2节点示例：检测图片中的苹果")
image = cv2.imread('/home/jingqing3948/Develop/ros/dev_ws/src/ros2_21_tutorials/learning_node/learning_node/apple.jpg') # 读取图像
object_detect(image) # 苹果检测
rclpy.spin(node) # 循环等待ROS2退出
node.destroy_node() # 销毁节点对象
rclpy.shutdown() # 关闭ROS2 Python接口
You can also change the image to call the camera for dynamic identification.
cap = cv2.VideoCapture(0)
while rclpy.ok():
ret, image = cap.read() # 读取一帧图像
if ret == True:
object_detect(image) # 苹果检测
Of course, nodes are not isolated, such as controlling the game interface with a joystick.