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depth_image_proc/include/robot_depth_image_proc/depth_conversions.h
2026-06-24 17:42:12 +07:00

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#ifndef DEPTH_IMAGE_PROC_DEPTH_CONVERSIONS
#define DEPTH_IMAGE_PROC_DEPTH_CONVERSIONS
#include <robot_sensor_msgs/Image.h>
#include <robot_sensor_msgs/CameraInfo.h>
#include <robot_sensor_msgs/point_cloud2_iterator.h>
#include <robot_image_geometry/pinhole_camera_model.h>
#include <robot_depth_image_proc/depth_traits.h>
#include <limits>
namespace depth_image_proc {
typedef robot_sensor_msgs::PointCloud2 PointCloud;
// Handles float or uint16 depths
template<typename T>
void convert(
const robot_sensor_msgs::Image& depth_msg,
PointCloud& cloud_msg,
const image_geometry::PinholeCameraModel& model,
double range_max = 0.0)
{
// Use correct principal point from calibration
float center_x = model.cx();
float center_y = model.cy();
// Combine unit conversion (if necessary) with scaling by focal length for computing (X,Y)
double unit_scaling = DepthTraits<T>::toMeters( T(1) );
float constant_x = unit_scaling / model.fx();
float constant_y = unit_scaling / model.fy();
float bad_point = std::numeric_limits<float>::quiet_NaN();
robot_sensor_msgs::PointCloud2Iterator<float> iter_x(cloud_msg, "x");
robot_sensor_msgs::PointCloud2Iterator<float> iter_y(cloud_msg, "y");
robot_sensor_msgs::PointCloud2Iterator<float> iter_z(cloud_msg, "z");
const T* depth_row = reinterpret_cast<const T*>(&depth_msg.data[0]);
int row_step = depth_msg.step / sizeof(T);
for (int v = 0; v < (int)cloud_msg.height; ++v, depth_row += row_step)
{
for (int u = 0; u < (int)cloud_msg.width; ++u, ++iter_x, ++iter_y, ++iter_z)
{
T depth = depth_row[u];
// Missing points denoted by NaNs
if (!DepthTraits<T>::valid(depth))
{
if (range_max != 0.0)
{
depth = DepthTraits<T>::fromMeters(range_max);
}
else
{
*iter_x = *iter_y = *iter_z = bad_point;
continue;
}
}
// Fill in XYZ
*iter_x = (u - center_x) * depth * constant_x;
*iter_y = (v - center_y) * depth * constant_y;
*iter_z = DepthTraits<T>::toMeters(depth);
}
}
}
} // namespace depth_image_proc
#endif