本文主要是介绍rospy节点一边接收topic，一边将topic数据可视化，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
如果想要matplotlib动态更新画图，只能将matplotlib放在主线程中，如果放进子线程，就会报这个错
ValueError: set_wakeup_fd only works in main thread但是如果要订阅ros的topic，主线程会被阻塞，matplotlib无法动态更新。
因此，通过将matplotlib嵌入tkinter中，实现一边订阅topic，一边动态更新画图
import math
import rospy
import message_filters
import tkinter as tk
from matplotlib.figure import Figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from my_pkg.msg import MyArrayMessageclass ROS_Tkinter_App:def __init__(self, master):self.master = mastermaster.title("ROS Tkinter App")# Matplotlib setup for scatter plotself.figure = Figure(figsize=(10, 8), dpi=100)self.scatter_plot = self.figure.add_subplot(111)self.scatter_plot.set_title('Scatter Plot')self.canvas = FigureCanvasTkAgg(self.figure, master=master)self.canvas.get_tk_widget().pack()# ROS Initializationrospy.init_node('ros_tkinter_app', anonymous=True)# Define subscribers for the two topicssub_topic1 = message_filters.Subscriber("/global_path", MyArrayMessage)sub_topic2 = message_filters.Subscriber("/vehicle_position", MyArrayMessage)# Synchronize the subscribers using ApproximateTimeSynchronizerts = message_filters.ApproximateTimeSynchronizer([sub_topic1, sub_topic2], queue_size=10, slop=0.1)ts.registerCallback(self.callback)# Bind the update_plot_size function to the <Configure> event# master.bind("<Configure>", self.update_plot_size)def callback(self, data1, data2):# This method will be called when a new message is received on the /ros_topicrospy.loginfo("ROS Topic Data")# 这里处理topic内的数据# Update scatter plot with new dataself.update_scatter_plot(Target_Angle, Center_y, Center_x, waypoints_y, waypoints_x) # Assuming the data is a float, update accordinglydef update_scatter_plot(self, angle_radians, Center_x, Center_y, waypoints_x, waypoints_y):# Update the scatter plot with new dataself.scatter_plot.clear()self.scatter_plot.set_title('Scatter Plot')self.scatter_plot.scatter(Center_x, Center_y, color='red', marker='o', label='Center Point')self.scatter_plot.plot([Center_x, end_x], [Center_y, end_y], color='blue', linestyle='--', label='Line Segment')self.scatter_plot.scatter(waypoints_x, waypoints_y, color='green', marker='o', label='Waypoint')# 设置x轴和y轴的显示范围self.scatter_plot.set_xlim(-100, 100) # 设置x轴的范围self.scatter_plot.set_ylim(-100, 100) # 设置y轴的范围# 设置 x 轴和 y 轴的相同比例尺self.scatter_plot.set_aspect('equal')# 设置坐标轴标签self.scatter_plot.set_xlabel('CARLA Y Axis')self.scatter_plot.set_ylabel('CARLA X Axis')self.canvas.draw()def update_plot_size(self, event):# Update the plot size based on the new window sizenew_width = event.width / self.canvas.figure.get_dpi()new_height = event.height / self.canvas.figure.get_dpi()self.figure.set_size_inches(new_width, new_height)self.canvas.draw()def main():root = tk.Tk()app = ROS_Tkinter_App(root)root.mainloop()if __name__ == '__main__':main()
这篇关于rospy节点一边接收topic，一边将topic数据可视化的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
