ros::Publisher tag_detections_publisher_; tag_detections_publisher_.getNumSubscribers() == 0 tag_detections_publisher_ = nh.advertise<AprilTagDetectionArray>(camera_name_ + "/tag_detections", 1); 帮我移植到ros2

在ROS2中，发布器（Publisher）的使用方式与ROS1有所不同。以下是将ROS1代码移植到ROS2的示例：

rclcpp::Publisher<apriltag_msgs::msg::AprilTagDetectionArray>::SharedPtr tag_detections_publisher_;
if (tag_detections_publisher_->get_subscription_count() == 0) {
    tag_detections_publisher_ = node->create_publisher<apriltag_msgs::msg::AprilTagDetectionArray>(
        camera_name_ + "/tag_detections", rclcpp::QoS(1));
}

需要注意的是，ROS2中使用`rclcpp::Publisher`代替了ROS1中的`ros::Publisher`，消息类型也发生了变化。在这个示例中，我假设你已经定义了`apriltag_msgs::msg::AprilTagDetectionArray`消息类型。

请确保在ROS2的构建系统（如`CMakeLists.txt`）中添加了正确的依赖项和包含路径，以便正确编译和链接代码。

希望这能帮助到你！如果你有任何其他问题，请随时提问。

相关问题

apriltag_ros python

Apriltag is a popular library for detecting and identifying visual fiducial markers called tags. The `apriltag_ros` package is a ROS wrapper for the Apriltag library, allowing you to use Apriltag functionalities within a ROS environment. It provides ROS nodes that can subscribe to image topics, detect Apriltags in the images, and publish the detected tag poses.

To use `apriltag_ros` in Python, you can follow these steps:

1. Install the `apriltag_ros` package:

sudo apt-get install ros-<distro>-apriltag-ros

Replace `<distro>` with your ROS distribution (e.g., melodic, noetic).

2. Create a ROS package (if you haven't already) where you'll write your Python code:

cd ~/catkin_ws/src
catkin_create_pkg my_apriltag_pkg rospy apriltag_ros

Replace `my_apriltag_pkg` with your desired package name.

3. In your Python script, import the necessary modules:

#!/usr/bin/env python

import rospy
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
from apriltag_ros.msg import AprilTagDetectionArray

4. Initialize the ROS node and create a subscriber to the image topic:

rospy.init_node('apriltag_detector')
bridge = CvBridge()

def image_callback(msg):
    cv_image = bridge.imgmsg_to_cv2(msg, desired_encoding='passthrough')
    # Perform Apriltag detection using cv_image

image_sub = rospy.Subscriber('/camera/image_raw', Image, image_callback)

Replace `/camera/image_raw` with the appropriate image topic for your setup.

5. Process the received image in the callback function and publish the detected tag poses:

def image_callback(msg):
    cv_image = bridge.imgmsg_to_cv2(msg, desired_encoding='passthrough')
    
    # Perform Apriltag detection using cv_image
    
    # Publish the detected tag poses
    detection_array = AprilTagDetectionArray()
    # Populate detection_array with detected tag poses
    
    pub = rospy.Publisher('/apriltag/detections', AprilTagDetectionArray, queue_size=10)
    pub.publish(detection_array)

Replace `/apriltag/detections` with the desired topic to publish the detected tag poses.

6. Finally, run your ROS node:

rosrun my_apriltag_pkg my_apriltag_node.py

Remember to replace `my_apriltag_pkg` and `my_apriltag_node.py` with your actual package and node names.

This is a basic example to get you started with `apriltag_ros` in Python. You can find more information about the package and its functionalities in the official ROS documentation and the `apriltag_ros` GitHub repository.

怎么把darknet_ros包调整成只识别person

要将Darknet_ros包调整为仅识别人，您可以按照以下步骤进行操作：

1. 从Darknet_ros包中下载yolov4.weights文件和yolov4.cfg文件。
2. 打开darknet_ros/config文件夹中的yolov4.yaml文件。
3. 在yolov4.yaml文件中，你可以看到以下内容：

yolo_dimensions:
  height: 480
  width: 640
  channels: 3
  input_type: uchar
net_definition:
  cfg_file: "yolo.cfg"
  weights_file: "yolo.weights"
  data_file: "coco.data"
  input_type: "image"
  publish_images: true
  publish_detections_image: false
  weights_url: "https://pjreddie.com/media/files/yolov3.weights"
  weights_md5: "4c703d61e34d80daac8edde9aad21b58"
  weights_num_bytes: 248007048
  cfg_url: "https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg"
  cfg_md5: "2ff0a639f3b3f276e5b6c59d7c2e14c9"
  weights_cache: true
  cfg_cache: true
  dont_show: false
  thresh: 0.5
  hierarchical: 0
  names: []
  colors: []
  labels:
    - person

4. 在“labels”字段中添加“person”标签。
5. 将“weights_file”字段的值更改为“yolov4.weights”文件的路径。
6. 将“cfg_file”字段的值更改为“yolov4.cfg”文件的路径。
7. 保存文件并重启darknet_ros节点。

这样，Darknet_ros节点将仅检测“person”标签并忽略其他标签。