One of the basic tasks in
Computer Stereo Vision is to calibrate the stereo camera in order to obtain the parameters that will allow you to calculate 3D information of the scene.
Now, I could tell you a lot of stuff about camera projection models, stereoscopy, lens distortion, etc... but there is a lot of information available about such topics out there. So, this post is for those who simply need to calibrate a stereo camera system and start calculating 3D stuff right away by using
OpenCV.
So... what do I need to calibrate my stereo camera? A chessboard like this:
Why a chessboard? Because its corners are very easy to find by using computer vision algorithms and its geometry is very simple. In order to find out the position of any corner you only need to know how many horizontal and vertical squares there are in the chessboard and the size of a square. The chessboard in the image is a 9x6 chessboard and if you print it in a paper of size A4 the size of the squares would be more or less 2.5cm.
OK, I've printed my chessboard and I have measured the real size of the squares, now what?
Now you just take multiple views of the chessboard in different positions and orientations with your stereo camera using your favorite software (maybe your own software, software provided by your camera manufacturer or some other free software like Coriander). The images should look like this:
 |
| (Yeah, that is me in Hawaiian shorts on a summer day :P) |
The more variety of positions and orientations of the checkerboard in the images the better.
Great, you have taken a lot of shots of the chessboard in different positions, now create a text file with the paths to the images. For example:
images/left01.ppm
images/right01.ppm
images/left02.ppm
images/right02.ppm
images/left03.ppm
images/right03.ppm
images/left04.ppm
images/right04.ppm
images/left05.ppm
images/right05.ppm
images/left06.ppm
images/right06.ppm
images/left07.ppm
images/right07.ppm
images/left08.ppm
images/right08.ppm
images/left09.ppm
images/right09.ppm
images/left10.ppm
images/right10.ppm
images/left11.ppm
images/right11.ppm
images/left12.ppm
images/right12.ppm
images/left13.ppm
images/right13.ppm
images/left14.ppm
images/right14.ppm
images/left15.ppm
images/right15.ppm
images/left16.ppm
images/right16.ppm
images/left17.ppm
images/right17.ppm
images/left18.ppm
images/right18.ppm
images/left19.ppm
images/right19.ppm
images/left20.ppm
images/right20.ppm
Now download this software and compile it.
It is just one of the examples of the book mentioned above that I modified to accept some configuration parameters and store the results of the calibration. The usage of the software is as follows:
USAGE: ./stereo_calibrate imageList nx ny squareSize
imageList : Filename of the image list (string). Example : list.txt
nx : Number of horizontal squares (int > 0). Example : 9
ny : Number of vertical squares (int > 0). Example : 6
squareSize : Size of a square (float > 0). Example : 2.5
So, in this example the call to the program stereo_calibrate would be:
./stereo_calibrate list.txt 9 6 2.5
The program will start showing the detected chessboards, calculate the calibration parameters and store them in a bunch of xml files:
D1.xml D2.xml
M1.xml M2.xml
mx1.xml mx2.xml
my1.xml my2.xml
P1.xml P2.xml
R1.xml R2.xml
Q.xml
Congratulations! You have calibrated your stereo camera!! Now you can load this parameters into any other program that uses that stereo camera and play with them:
CvMat *Q = (CvMat *)cvLoad("Q.xml",NULL,NULL,NULL);
CvMat *mx1 = (CvMat *)cvLoad("mx1.xml",NULL,NULL,NULL);
CvMat *my1 = (CvMat *)cvLoad("my1.xml",NULL,NULL,NULL);
CvMat *mx2 = (CvMat *)cvLoad("mx2.xml",NULL,NULL,NULL);
CvMat *my2 = (CvMat *)cvLoad("my2.xml",NULL,NULL,NULL);
Each of the files contains a matrix, if you would like to know the meaning of each matrix, please refer to the book at the beginning of this post. Right now, the useful stuff is contained on the files mx1.xml, my1.xml, mx2.xml, my2.xml and Q.xml.
The files m*.xml are the distortion models of the cameras. So we will need these matrices to undo the distortion of the images caused by the lens. Using the
cvRemap() function:
cvRemap(imgLeftOrig, imgLeftUndistorted, mx1, my1);
cvRemap(imgRightOrig, imgRightUndistoreted, mx2, my2);
The goal of all this is to be able to calculate the 3D position (in meters, cm, mm or whatever magnitude you chose) of a point given its position (in pixels) on the left image and its correspondent on the right image. We are almost there, but for that we need the matrix Q. Given the position an interest point in the left and right image, its 3D position can be calculated as follows:
d = pointRightImage.X - pointLeftImage.X;
X = pointLeftImage.X * Q[0, 0] + Q[0, 3];
Y = pointLeftImage.Y * Q[1, 1] + Q[1, 3];
Z = Q[2, 3];
W = d * Q[3, 2] + Q[3, 3];
X = X / W;
Y = Y / W;
Z = Z / W;
And thats pretty much it, now you know how to calculate 3D positions from 2 images using OpenCV. How to find interest points in one image and its correspondent on the other is an art that will be explained another day ;)
EDIT [2011/06/16]: Many people asked me about a good book to get started in OpenCV, so take a look at this:
OpenCV 2 Computer Vision Application Programming Cookbook
EDIT [2011/08/22]: What next? Check out the following post:
OpenCV: Stereo Matching
EDIT [2011/08/27]: I changed the Makefile of the software for one much simpler and without hard-coded stuff.
Also hosted the software in my own server (it was in megaupload, sorry about that), the link to the software in this post has been updated. Here it is, just in case: http://www.martinperis.com/stereocalibration/OpenCV-Stereo-Calibration.tgz
EDIT [2012/01/05]: My hosting bandwidth was not enough to handle the traffic so I had to host the software on googlecode, the link to the software in this post has been updated. Here it is, just in case:
http://opencv-stereo-calibration.googlecode.com/files/OpenCV-Stereo-Calibration-1.0.tgz
