使用ROS节点进行多无人机的画面同步接收

由于需要在ubuntu18.04下基于python3进行图像处理，18.04ROS中默认使用python2的cv_bridge，为方便进行图像传输，本文直接将图片编码为字符串后传输，并在主机端进行解码显示。同时，在主机端对多个无人机画面同步显示。

无人机端

#!/usr/bin/env python3
# coding:utf-8
import rospy
from cv_bridge import CvBridge, CvBridgeError
import cv2
import numpy as np


from std_msgs.msg import String
import base64



def encode_image(img):
    _, buffer = cv2.imencode('.jpg', img)
    img_str = base64.b64encode(buffer).decode('utf-8')
    return img_str

if __name__=="__main__":
    
    cap = cv2.VideoCapture(0)
    
    # 检查摄像头是否成功打开
    if not cap.isOpened():
        print("无法打开摄像头0，尝试使用配置1")
        # 尝试打开另一个摄像头（配置1）
        cap = cv2.VideoCapture(1)

    rospy.init_node('yolo_detector_node', anonymous=True)
    bridge = CvBridge()

    image_pub = rospy.Publisher('/image_topic_7', String, queue_size=10)


    while not rospy.is_shutdown():
        ret, cv_image = cap.read()

        x_position_denied = drone_x + x_offset
        y_position_denied = drone_y + y_offset
        pos_text = f"uav:7 pos: {x_position_denied:.2f},{y_position_denied:.2f}"
        cv2.putText(cv_image, pos_text, (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 255), 4)    


        encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 50]

        _, buffer = cv2.imencode('.jpg', cv_image, encode_param)
        encoded_img_str = base64.b64encode(buffer).decode('utf-8')
        image_pub.publish(encoded_img_str)

        cv2.imshow("cv_image",cv_image)
        if cv2.waitKey(10) & 0xFF == ord("q"):
            break

    cap.release()
    output.release()
    cv2.destroyAllWindows()

主机端

#!/usr/bin/env python3
# coding:utf-8
import cv2
import base64
import numpy as np
import rospy
import time
from std_msgs.msg import String

# 初始化一个列表来存储每个图片
images = [None] * 8
# 初始化一个列表来存储每张图片的最后更新时间
last_update_time = [0] * 8
# 设置黑色占位图的大小为 320x240
placeholder_image = np.zeros((240, 320, 3), dtype=np.uint8)

# 用于指示是否有新的图像更新
new_image_received = False
# 设置超时时间（秒）
timeout_duration = 2.0  # 2秒内没有更新则置为黑色

def decode_image(img_str):
    img_bytes = base64.b64decode(img_str)
    img_np = np.frombuffer(img_bytes, dtype=np.uint8)
    img = cv2.imdecode(img_np, cv2.IMREAD_COLOR)
    img_resized = cv2.resize(img, (320, 240))
    return img_resized

def image_callback(msg, index):
    global images, last_update_time, new_image_received
    try:
        # 解码并存储图像
        images[index] = decode_image(msg.data)
        last_update_time[index] = time.time()  # 更新图像的接收时间
        new_image_received = True  # 标记有新图像更新
    except Exception as e:
        rospy.logwarn(f"Failed to decode image at index {index}: {e}")
        images[index] = None  # 如果解码失败，使用None表示

def update_display():
    # 检查每张图像是否超时
    current_time = time.time()
    for i in range(len(images)):
        if current_time - last_update_time[i] > timeout_duration:
            images[i] = placeholder_image  # 如果超时，将该图像置为黑色

    # 拼接图像，未接收到的图像用黑色占位图代替
    resized_images = [img if img is not None else placeholder_image for img in images]
    row1 = np.hstack(resized_images[:4])
    row2 = np.hstack(resized_images[4:])
    combined_image = np.vstack((row1, row2))

    # 显示组合图像
    cv2.imshow("Combined Image (with placeholders)", combined_image)
    cv2.waitKey(1)

def image_receiver():
    global new_image_received
    rospy.init_node('image_receiver', anonymous=True)

    # 创建8个订阅者，每个接收一个图片主题
    for i in range(8):
        rospy.Subscriber(f'/image_topic_{i}', String, image_callback, i)

    # 在接收节点运行时显示窗口
    cv2.namedWindow("Combined Image (with placeholders)", cv2.WINDOW_NORMAL)
    cv2.resizeWindow("Combined Image (with placeholders)", 1280, 480)

    # 循环刷新显示
    rate = rospy.Rate(10)  # 控制刷新率
    while not rospy.is_shutdown():
        # 只有在接收到新图像时才更新显示
        if new_image_received:
            update_display()
            new_image_received = False  # 重置标志
        rate.sleep()

    # 退出时关闭窗口
    cv2.destroyAllWindows()

if __name__ == '__main__':
    try:
        image_receiver()
    except rospy.ROSInterruptException:
        pass

从而实现多机画面实时显示在终端

待解决

基于ROS主节点传输过多消息导致通道堵塞，可能由于电台通信堵塞或机载板处理能力较差，导致画面越多延迟越大，可尝试绕过ROS进行点对点传输，或增强通信设备进行充分测试

原文地址：https://blog.csdn.net/m0_46182398/article/details/143742064

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