Camera() { _origin = Vector3f(0, 0, 0); _lower_left_corner = Vector3f(-2.0, -1.5, -1.0); _horizontal = Vector3f(4.0, 0.0, 0.0); _vetical = Vector3f(0.0, 3.0, 0.0); } Ray GenerateRay(float u, float v) { // center - (size*up)/2 - (size*horizontal)/2 -> center + (size*up)/2 + (size*horizontal)/2 return Ray(_origin - (size * _vetical) / 2 - (size * _horizontal) / 2, _origin + (size * _vetical) / 2 + (size * _horizontal) / 2);
时间: 2024-02-29 16:54:40 浏览: 23
这段代码看起来像是一个简单的相机模型。其中,Camera()函数初始化了相机的位置、方向和大小等参数。GenerateRay(float u, float v)函数用于生成经过像素(u,v)的射线,该射线的起点是相机的位置,方向是从相机位置到像素(u,v)的方向。其中,size是相机的大小。
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相关问题
void CheckpointDetector::CameraInfoCallback(const sensor_msgs::CameraInfoConstPtr& camera_info) { sensor_msgs::CameraInfo my_camera_info_ // my_camera_info_ = camera_info; my_camera_info_.header = camera_info->header; my_camera_info_.distortion_model = camera_info->distortion_model; my_camera_info_.binning_x = camera_info->binning_x; my_camera_info_.binning_y = camera_info->binning_y; my_camera_info_.width = camera_info->width; my_camera_info_.height = camera_info->height; my_camera_info_.D = camera_info->D; my_camera_info_.K = camera_info->K; my_camera_info_.P = camera_info->P; my_camera_info_.R= camera_info->R; } 帮我把以上代码移植到ros2
在ROS2中,需要对代码进行一些修改和调整来适应新的API和数据类型。以下是将代码移植到ROS2的示例:
```cpp
#include "rclcpp/rclcpp.hpp"
#include "sensor_msgs/msg/camera_info.hpp"
class CheckpointDetector : public rclcpp::Node
{
public:
CheckpointDetector() : Node("checkpoint_detector")
{
camera_info_subscription_ = create_subscription<sensor_msgs::msg::CameraInfo>(
"camera_info", 10, std::bind(&CheckpointDetector::cameraInfoCallback, this, std::placeholders::_1));
}
private:
void cameraInfoCallback(const sensor_msgs::msg::CameraInfo::SharedPtr camera_info)
{
sensor_msgs::msg::CameraInfo my_camera_info;
my_camera_info.header = camera_info->header;
my_camera_info.distortion_model = camera_info->distortion_model;
my_camera_info.binning_x = camera_info->binning_x;
my_camera_info.binning_y = camera_info->binning_y;
my_camera_info.width = camera_info->width;
my_camera_info.height = camera_info->height;
my_camera_info.d = camera_info->d;
my_camera_info.k = camera_info->k;
my_camera_info.p = camera_info->p;
my_camera_info.r = camera_info->r;
}
rclcpp::Subscription<sensor_msgs::msg::CameraInfo>::SharedPtr camera_info_subscription_;
};
int main(int argc, char** argv)
{
rclcpp::init(argc, argv);
rclcpp::spin(std::make_shared<CheckpointDetector>());
rclcpp::shutdown();
return 0;
}
```
请注意,ROS2中使用`rclcpp`库代替了ROS中的`ros::NodeHandle`和`ros::Subscriber`等。另外,`sensor_msgs::CameraInfo`的成员变量名称也有所不同。在ROS2中,它们被改为小写字母。
你可以将这个代码段放入你的ROS2工程中,并进行构建和运行。这样,当订阅到`camera_info`主题时,`cameraInfoCallback`函数将被调用,并将`camera_info`消息的内容赋值给`my_camera_info`变量。
for (int camera_index = 0; camera_index < this->m_safe_camera_list.size(); ++camera_index) { camera* cam = &(this->m_safe_camera_list[camera_index]); if (cam->m_is_exter_calib_check_mark == true) { // as a Internal reference K of the camera, the K-1 is : // 1/ax 0 -px/ax // 0 1/ay -py/ay // 0 0 1 Eigen::Matrix3f invk; invk.setIdentity(); invk(0, 0) = 1.0 / cam->m_inter_calib(0, 0); invk(0, 2) = -1.0 * cam->m_inter_calib(0, 2) / cam->m_inter_calib(0, 0); invk(1, 1) = 1.0 / cam->m_inter_calib(1, 1); invk(1, 2) = -1.0 * cam->m_inter_calib(1, 2) / cam->m_inter_calib(1, 1); Eigen::Vector3f tmp_t_verts = cam->m_exter_calib.topRightCorner(3, 1); Eigen::Matrix3f tmp_inv_r_mat= cam->m_exter_calib.topLeftCorner(3, 3).transpose(); Eigen::Vector3f tmp_root_point = -tmp_inv_r_mat * tmp_t_verts; for (int pose_index = 0; pose_index < cam->m_2D_pose_list.size(); ++pose_index) { Eigen::MatrixXf pose = cam->m_2D_pose_list[pose_index]; // check the pose's Confidence, if all the joints's confidiance is smaller than the gain, drop out float confidence = pose.row(2).maxCoeff(); if (confidence < this->m_joint_confidence_gian) { continue; }; my_radials tmpradials; tmpradials.m_2d_pose = pose; tmpradials.m_root_point = tmp_root_point; tmpradials.m_radials_points = (invk * pose.topRows(2).colwise().homogeneous()).colwise().normalized(); tmpradials.m_radials_points = tmp_inv_r_mat * tmpradials.m_radials_points; tmpradials.m_3d_pose_ID = -1; tmpradials.m_camera_index = camera_index; tmpradials.m_poes_index = pose_index; tmpradials.m_pose_confidence = pose.row(2).leftCols(7).sum(); this->m_3d_radials.push_back(tmpradials); } } }
这段代码是一个 for 循环,遍历了一个名为 m_safe_camera_list 的相机列表。对于列表中的每个相机,如果其 m_is_exter_calib_check_mark 属性为 true,则进行以下操作:
1. 计算相机的内参矩阵 K 的逆矩阵 invk,其中 K 的值被存储在相机的 m_inter_calib 属性中;
2. 计算相机的外参矩阵 m_exter_calib 的逆矩阵的转置矩阵 tmp_inv_r_mat;
3. 根据 tmp_inv_r_mat 和 m_exter_calib 中的平移向量计算相机在世界坐标系下的位置 tmp_root_point;
4. 遍历相机 m_2D_pose_list 中的姿态,对于每个姿态:
- 检查该姿态的置信度是否大于设定的阈值 m_joint_confidence_gian,如果不是则跳过;
- 构建一个名为 tmpradials 的结构体,存储该姿态的相关信息,包括姿态的 2D 坐标、3D 坐标、相机索引、姿态索引等;
- 将 tmpradials 添加到名为 m_3d_radials 的结构体列表中。
整个代码的作用是将相机的 2D 姿态转换为 3D 姿态,并将结果存储在 m_3d_radials 中。