We captured a 3d Image using Kinect with OpenNI Library and got the rgb and depth images in the form of OpenCV Mat using this code.
main()
{
OpenNI::
To improve the answer of antarctician, to display the image in 3D you need to create your point cloud first... The RGB and Depth images give you the necessary data to create an organized colored pointcloud. To do so, you need to calculate the x,y,z values for each point. The z value comes from the depth pixel, but the x and y must be calculated.
to do it you can do something like this:
void Viewer::get_pcl(cv::Mat& color_mat, cv::Mat& depth_mat, pcl::PointCloud<pcl::PointXYZRGBA>& cloud ){
float x,y,z;
for (int j = 0; j< depth_mat.rows; j ++){
for(int i = 0; i < depth_mat.cols; i++){
// the RGB data is created
PCD_BGRA pcd_BGRA;
pcd_BGRA.B = color_mat.at<cv::Vec3b>(j,i)[0];
pcd_BGRA.R = color_mat.at<cv::Vec3b>(j,i)[2];
pcd_BGRA.G = color_mat.at<cv::Vec3b>(j,i)[1];
pcd_BGRA.A = 0;
pcl::PointXYZRGBA vertex;
int depth_value = (int) depth_mat.at<unsigned short>(j,i);
// find the world coordinates
openni::CoordinateConverter::convertDepthToWorld(depth, i, j, (openni::DepthPixel) depth_mat.at<unsigned short>(j,i), &x, &y,&z );
// the point is created with depth and color data
if ( limitx_min <= i && limitx_max >=i && limity_min <= j && limity_max >= j && depth_value != 0 && depth_value <= limitz_max && depth_value >= limitz_min){
vertex.x = (float) x;
vertex.y = (float) y;
vertex.z = (float) depth_value;
} else {
// if the data is outside the boundaries
vertex.x = bad_point;
vertex.y = bad_point;
vertex.z = bad_point;
}
vertex.rgb = pcd_BGRA.RGB_float;
// the point is pushed back in the cloud
cloud.points.push_back( vertex );
}
}
}
and PCD_BGRA is
union PCD_BGRA
{
struct
{
uchar B; // LSB
uchar G; // ---
uchar R; // MSB
uchar A; //
};
float RGB_float;
uint RGB_uint;
};
Of course, this is for the case you want to use PCL, but it is more or less the calculations of the x,y,z values stands. This relies on openni::CoordinateConverter::convertDepthToWorld
to find the position of the point in 3D. You may also do this manually
const float invfocalLength = 1.f / 525.f;
const float centerX = 319.5f;
const float centerY = 239.5f;
const float factor = 1.f / 1000.f;
float dist = factor * (float)(*depthdata);
p.x = (x-centerX) * dist * invfocalLength;
p.y = (y-centerY) * dist * invfocalLength;
p.z = dist;
Where centerX, centerY, and focallength are the intrinsic calibration of the camera (this one is for Kinect). and the factor it is if you need the distance in meters or millimeters... this value depends on your program
For the questions:
I haven't done this with OpenNI and OpenCV but I hope I can help you. So first of all to answer your first two questions:
If you only want to visualize a point cloud such as the "3D View" of the Kinect Studio, you wouldn't need PCL as it would be too much for this simple job.
The basic idea of doing this task is to create 3D quads as the same number of pixels you have on your images. For example if you have a 640x480 resolution, you would need 640*480 quads. Each quad would have the color of the corresponding pixel depending on the pixel values from the color image. You would then move these quads back and forth on the Z axis, depending on the values from the depth image. This can be done with modern OpenGL or if you feel more comfortable with C++, OpenSceneGraph(which is also based on OpenGL).
You would have to be careful about two things:
If you decide to do this with OpenGL, I would suggest reading about the GPU pipeline if you aren't familiar with it. This would help you to save a lot of time when working with the vertex shaders.