Complete Demo for Computing Camera Pose in CCTag Coordinate Frame Using ROS C++

can you generate a complete demo that compute the camera pose in the CCTag coordinate frame in ROS C++? In this demo, includes the section of configurating camera parameters from a config file (e.g. camera_info.yaml). The example can test with a camera (e.g. webcam) and the CCTag has ID no. 5.

Tell me which CCTag library version you are using

Sure, I'll provide a complete demo for computing the camera pose in the CCTag coordinate frame using ROS and C++. This demo will include reading camera parameters from a configuration file and using a webcam for testing. We'll assume you are using the CCTag library version 3.0.0.

Prerequisites

1. ROS: Make sure you have ROS installed and set up.
2. CCTag Library: Install the CCTag library version 3.0.0.
3. OpenCV: Ensure you have OpenCV installed for image processing.

Directory Structure

catkin_ws/
├── src/
│   ├── camera_pose_demo/
│   │   ├── CMakeLists.txt
│   │   ├── package.xml
│   │   ├── config/
│   │   │   └── camera_info.yaml
│   │   └── src/
│   │       └── camera_pose_demo.cpp

Step-by-Step Guide

1. Create a ROS Package

Navigate to your src directory in your catkin workspace and create a new package:

cd ~/catkin_ws/src
catkin_create_pkg camera_pose_demo roscpp std_msgs sensor_msgs cv_bridge image_transport

2. Add Dependencies

Edit the CMakeLists.txt to include necessary dependencies:

cmake_minimum_required(VERSION 3.0.2)
project(camera_pose_demo)

find_package(catkin REQUIRED COMPONENTS
  roscpp
  std_msgs
  sensor_msgs
  cv_bridge
  image_transport
)

find_package(OpenCV REQUIRED)

catkin_package()

include_directories(
  ${catkin_INCLUDE_DIRS}
  ${OpenCV_INCLUDE_DIRS}
)

add_executable(camera_pose_demo src/camera_pose_demo.cpp)
target_link_libraries(camera_pose_demo ${catkin_LIBRARIES} ${OpenCV_LIBRARIES})

3. Create Configuration File

Create a camera_info.yaml file in the config directory:

camera_matrix:
  rows: 3
  cols: 3
  data: [fx, 0, cx, 0, fy, cy, 0, 0, 1]

distortion_coefficients:
  rows: 1
  cols: 5
  data: [k1, k2, p1, p2, k3]

Replace fx, fy, cx, cy, k1, k2, p1, p2, k3 with your camera's intrinsic parameters.

4. Implement the C++ Node

Create a camera_pose_demo.cpp file in the src directory:

#include <ros/ros.h>
#include <sensor_msgs/Image.h>
#include <cv_bridge/cv_bridge.h>
#include <image_transport/image_transport.h>
#include <opencv2/opencv.hpp>
#include <yaml-cpp/yaml.h>
#include <fstream>
#include <cctag/CCTag.hpp>

cv::Mat cameraMatrix, distCoeffs;

void loadCameraParameters(const std::string& filename) {
    YAML::Node config = YAML::LoadFile(filename);
    std::vector<double> camera_matrix = config["camera_matrix"]["data"].as<std::vector<double>>();
    std::vector<double> distortion_coefficients = config["distortion_coefficients"]["data"].as<std::vector<double>>();

    cameraMatrix = cv::Mat(3, 3, CV_64F, camera_matrix.data()).clone();
    distCoeffs = cv::Mat(1, 5, CV_64F, distortion_coefficients.data()).clone();
}

void imageCallback(const sensor_msgs::ImageConstPtr& msg) {
    cv_bridge::CvImagePtr cv_ptr;
    try {
        cv_ptr = cv_bridge::toCvCopy(msg, sensor_msgs::image_encodings::BGR8);
    } catch (cv_bridge::Exception& e) {
        ROS_ERROR("cv_bridge exception: %s", e.what());
        return;
    }

    cv::Mat image = cv_ptr->image;

    // Detect CCTag
    cctag::CCTag cctag;
    std::vector<cctag::CCTag::Detection> detections = cctag.detect(image);

    for (const auto& detection : detections) {
        if (detection.id == 5) {
            std::vector<cv::Point2f> imagePoints = detection.corners;
            std::vector<cv::Point3f> objectPoints = {
                {0, 0, 0}, {1, 0, 0}, {1, 1, 0}, {0, 1, 0}
            };

            cv::Mat rvec, tvec;
            cv::solvePnP(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec);

            cv::Mat R;
            cv::Rodrigues(rvec, R);

            ROS_INFO_STREAM("Rotation Matrix: " << R);
            ROS_INFO_STREAM("Translation Vector: " << tvec);
        }
    }
}

int main(int argc, char** argv) {
    ros::init(argc, argv, "camera_pose_demo");
    ros::NodeHandle nh;

    std::string config_file;
    nh.param<std::string>("config_file", config_file, "config/camera_info.yaml");
    loadCameraParameters(config_file);

    image_transport::ImageTransport it(nh);
    image_transport::Subscriber sub = it.subscribe("camera/image", 1, imageCallback);

    ros::spin();
    return 0;
}

5. Update package.xml

Ensure your package.xml includes the necessary dependencies:

<package format="2">
  <name>camera_pose_demo</name>
  <version>0.0.0</version>
  <description>The camera_pose_demo package</description>

  <maintainer email="[email protected]">user</maintainer>
  <license>TODO</license>

  <buildtool_depend>catkin</buildtool_depend>
  <build_depend>roscpp</build_depend>
  <build_depend>std_msgs</build_depend>
  <build_depend>sensor_msgs</build_depend>
  <build_depend>cv_bridge</build_depend>
  <build_depend>image_transport</build_depend>
  <build_depend>OpenCV</build_depend>

  <exec_depend>roscpp</exec_depend>
  <exec_depend>std_msgs</exec_depend>
  <exec_depend>sensor_msgs</exec_depend>
  <exec_depend>cv_bridge</exec_depend>
  <exec_depend>image_transport</exec_depend>
  <exec_depend>OpenCV</exec_depend>
</package>

6. Build the Package

Navigate to your catkin workspace and build the package:

cd ~/catkin_ws
catkin_make
source devel/setup.bash

7. Run the Node

Launch the node with the appropriate parameters:

rosrun camera_pose_demo camera_pose_demo _config_file:=/path/to/your/camera_info.yaml

Make sure to replace /path/to/your/camera_info.yaml with the actual path to your camera_info.yaml file.

Testing

Ensure your webcam is publishing images to the camera/image topic. You can use usb_cam or any other ROS package to publish webcam images.

This demo should now detect the CCTag with ID 5 in the webcam feed and compute the camera pose relative to the CCTag coordinate frame.