I see some 3D Facial devices that using 3 Camera and find the 3D picture of face.
IS there any specific angle these camera should be fixed for this
calculation?
Is there any SDK, or tools in this domain that could simplify producing 3D image
from these fixed camera?
The less angle you have, the less information about depth you will get from the cameras. So an angle is important, but i cannot say it will need x° degrees.
Related
I know the classical tools for camera calibration use a planar board that we know its dimension.
My problem is slightly different. Instead of having a planar board, I do have some 3D points that I know their coordinates (6 dof) with respect to the camera. Those 3D points can be detected in the image, so we know their pixel coordinates.
Is there any tools that can calibration the camera using 3D points?
I try to construct 3D point cloud and measure real sizes or distances of objects using stereo camera. The cameras are stereo calibrated, and I find 3D points using reprojection matrix Q and disparity.
My problem is the calculated sizes are changing depending the distance from cameras. I calculate the distances between two 3D points, it has to be constant, but when object gets closer to the camera, distance increasing.
Am i missing something? The 3D coordinates have to be in camera coordinates, not in pixel coordinates. So it seems inaccurate to me. Any idea?
You didn't mention how far apart your cameras are - the baseline. If they are very close together compared with the distance of the point that you are measuring, a slight inaccuracy in your measurement can lead to a big difference in the computed distance.
One way you can check if this is the problem is by testing with only lateral movement of the camera.
I am tracking a moving vehicle with a stereo camera system. In both images I use background segmentation to get only the moving parts in the pictures, then put a rectangle around the biggest object.
Now I want to get the 3D coordinates of the center of the rectangle. The identified centers in the two 2D pictures are almost correlating points (I know not exactly). I did a stereo calibration with MATLAB, so I have the intrinsic parameters of both cameras and the extrinsic parameters of the stereo system.
OpenCV doesn't provide any function for doing this as far as I know and to be honest reading Zisserman didn't really help me, but maybe I am just blind to the obvious.
This should work:
1. For both camera's, compute a ray from your camera origin through the rectangle's center.
2. Convert the rays to world coordinates.
3. Compute the intersection between the two rays (or the closest point, in case they do not exactly intersect)
Suppose I've got two images taken by the same camera. I know the 3d position of the camera and the 3d angle of the camera when each picture was taken. I want to extract some 3d data from the images on the portion of them that overlaps. It seems that OpenCV could help me solve this problem, but I can't seem to find where my camera position and angle would be used in their method stack. Help? Is there some other C library that would be more helpful? I don't even know what keywords to search for on the web. What's the technical term for overlapping image content?
You need to learn a little more about camera geometry, and stereo rig geometry. Unless your camera was mounted on a special rig, it's rather doubtful that its pose at each image can be specified with just an angle and a point. Rather, you'd need three angles (e.g. roll, pitch, yaw). Plus, if you want your reconstruction to be metrical accurate, you need to calibrate accurately the focal length of the camera (at a minimum).
I am using OpenCV, a newbie to the entire thing.
I have a scenario, I am projecting on a wall, I am building a kind of a robot which has a camera. I wanted to know how can I process the image so that I could get the real-world values of the co-ordinates of the blobs tracked by my camera?
First of all, you need to calibrate the intrinsic of the camera. Use checkerboard-patterns printed on cardboard to do this, OpenCV has methods for this although there are finished tools for this as well.
To get an idea, I have written some python code to calibrate from a live video stream, move the cardboard along the camera in some different angles and distances. Take a look here: http://svn.ioctl.eu/pub/opencv/py-camera_intrinsic/
Then you need to calibrate the extrinsic of the camera, that is the position of the camera wrt. your world coordinates. You can place some markers on the wall, define the 3D-position of those markers and let OpenCV calibrate the extrinsic for this (cvFindExtrinsicCameraParams2).
In my sample code, I calculate the extrinsic wrt. the checkerboard so I can render a Teapot in the correct perspective of the camera. You have to adjust this to your needs.
I assume you project only onto a flat surface. You have to know the geometry to get the 3D coordinates of your detected blob. You can then find the blobs in your camera image and knowing intrinsic, extrinsic and the geometry, you can cast rays for each blob from the camera according to your intrinsic/extrinsic and calculate the intersection of each such ray with your known geometry. The intersection then is your 3D point in world space where the blob is projected to.