This repository provides software for an automatic camera calibration of a DVS128. It uses the calibration framework of Jean-Yves Bouguet (http://www.vision.caltech.edu/bouguetj/calib_doc/index.html) together with a recently proposed feature detections method for low-resolution cameras (https://github.com/RobVisLab/camera_calibration). For showing a fullscreen picture in MATLAB, the repository contains the code from https://de.mathworks.com/matlabcentral/fileexchange/11112-fullscreen .
The main idea of this framework is to use a LCD monitor as calibration target. By recording a flickering target, it is easy to reconstruct images by simple addition of all observed events.
The software consists of two parts, a camera grabber written in C++ and the main calibration routine in MATLAB.
For your convenience, the required libraries that are on Github are added as
submodules. So clone this repository with --recursive or do a
git submodule update --init --recursive
after cloning.
This software requires:
- libcaer >=2.0 (https://github.com/inilabs/libcaer)
- camera_calibration (https://github.com/RobVisLab/camera_calibration)
- DVS128 camera
To compile, first build and install libcaer, then the grabber:
cd libcaer
cmake .
make
(sudo) make install
cd ../grabber
mkdir build
cd build
cmake ..
make
Per default, the application will compile to support the iniLabs DVS128.
Run the do_calibration.m file in MATLAB. It will capture 20 images using a flickering target and then start the calibration routine. Adapt resolution=[1920,1080]; to the resolution of your second screen (fullscreen.m only works reliably on the non-primary monitor).
In case you get an error about a broken .mexa64 file on Linux systems, you will have to override the libstdc++.so.6 that comes with MATLAB by executing export LD_PRELOAD=$LD_PRELOAD:/usr/lib/x86_64-linux-gnu/libstdc++.so.6:/usr/lib/x86_64-linux-gnu/libprotobuf.so.9 before starting MATLAB.
During the calibration, the target will flicker for approximately 2 seconds, after that you have 3 seconds to reposition the camera before the next picture is taken. Make sure to take pictures from different angles and distances to the target. Use a tripod in order to minimize camera movement!
A few images are already included in the data/ folder to test the method without a camera.
The result of the calibration is stored in the following variables:
Calibration results after optimization (with uncertainties):
Focal Length: fc = [ 158.85961 158.52668 ] +/- [ 2.81702 2.86259 ]
Principal point: cc = [ 63.50000 63.50000 ] +/- [ 0.00000 0.00000 ]
Skew: alpha_c = [ 0.00000 ] +/- [ 0.00000 ] => angle of pixel axes = 90.00000 +/- 0.00000 degrees
Distortion: kc = [ -0.19702 0.00000 0.00000 0.00000 0.00000 ] +/- [ 0.01750 0.00000 0.00000 0.00000 0.00000 ]
Pixel error: err = [ 0.05699 0.05704 ]