Camera Calibration for monocular and stereo camera setup using OpenCV

Info : The provided code has been tested only under Visual Studio 2015, x64, Windows 10.

Method 1 : Single Camera calibration using live camera stream

The program uses the connected camera it finds first when using the live-feed option.

start program with the following parameters (respectively for chessboard or asymmetric circles)

singlecamcalibration.exe -w 9 -h 6 -pt chessboard -n 15 -d 2000 -o mycam -op -oe
singlecamcalibration.exe -w 4 -h 11 -pt acircles -n 15 -d 2000 -o mycam -op -oe

In case you are using the Sony Eye 3 camera using the dedicated driver for this camera may work better. This driver can be chosen by the flag -useSonyEye

singlecamcalibration.exe -useSonyEye -w 9 -h 6 -pt chessboard -n 15 -d 2000 -o mycam -op -oe
singlecamcalibration.exe -useSonyEye -w 4 -h 11 -pt acircles -n 15 -d 2000 -o mycam -op -oe

increase the number of images (-n) to increase calibration quality increase the duration between image capture (-d) in milliseconds to have more time repositioning the camera/checkerboard change the output name of the calibration file (-o) if required skip writing of the feature points (-op) or extrinsic camera parameters (-oe) for the captured images if not required When starting the program you can adjust the camera angle to a central view before hitting 'g' to start the capture process.

Then move the camera from angle to angle. Stop movement while the program captures an image (image flashes) to reduce image motion blur and rolling shutter effects as much as possible.

After the defined number of images has been captures the program computes a camera calibration and writes the data into the defined file. The resulting average pixel error when - based on the derived camera matrix - projecting the detected features points back into the captured images is written into the console (mostly about 0.1 and 0.3 pixels for a successful calibration). In the calibrated state you can hit 'u' to toggle between the lens distortion corrected version and the original camera image. Distortion correction is performed using a build-in OpenCV function.

Method 2 : Single Camera calibration using image list

capture images for calibration manually create xml containing image file names by using image list generator from OpenCV or by using the Matlab script provided below run calibration using the following parameters

singlecamcalibration.exe files.xml -w 9 -h 6 -pt chessboard -o gopro3 -oe -su
singlecamcalibration.exe files.xml -w 4 -h 11 -pt acircles -o gopro3 -oe -su

Option : Manual Calibration Optimization

The automatic calibration pattern reconstruction is not perfect for difficult lighting situations. I therefore added a guided calibration routine which can be compiled by deactivating the define CALIBRATION_DEFAULT and activating the define CALIBRATION_GUIDED.

Then run the program as normal using a camera or a file list. You can adjust the pattern detection now by the provided GUI. If you are happy with the quality of the respective camera angle and detection quality press the 'C' to proceed with the next image. If you want to skip an image use the 'S' key.

Short summary for manual calibration mode

use #define CALIBRATION_GUIDED when compiling the program run the program as normal use GUI to adjust pattern recognition quality press C key to process the current frame (proceed in file list mode) press S key to skip an image (only for file list mode) use parameter -su to show calibration result (file list mode) use U key to show undisortion result (camera mode)

Example image list (files.xml) :

<?xml version="1.0"?>
<opencv_storage>
<images>
./frames/frame_00001.png
./frames/frame_00059.png
./frames/frame_00126.png
./frames/frame_00181.png
./frames/frame_00353.png
./frames/frame_00439.png
./frames/frame_00478.png
./frames/frame_00560.png
./frames/frame_00589.png
./frames/frame_00646.png
./frames/frame_00695.png
./frames/frame_00959.png
./frames/frame_01062.png
./frames/frame_01153.png
./frames/frame_01461.png
./frames/frame_01516.png
./frames/frame_01672.png
./frames/frame_01759.png
./frames/frame_01822.png
./frames/frame_01868.png
./frames/frame_01917.png
./frames/frame_02115.png
./frames/frame_02199.png
./frames/frame_02292.png
./frames/frame_02731.png
./frames/frame_02875.png
</images>
</opencv_storage>

Output : Example camera calibration for GoPro 3 Black (Superview Mode)

%YAML:1.0
calibration_time: "Tue May  9 19:23:56 2017"
image_width: 1920
image_height: 1080
board_width: 9
board_height: 6
square_size: 1.
flags: 0
camera_matrix: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 8.7984333348949622e+02, 0., 9.6701979597278034e+02, 0.,
       8.8574368328130231e+02, 4.9043015925119653e+02, 0., 0., 1. ]
distortion_coefficients: !!opencv-matrix
   rows: 5
   cols: 1
   dt: d
   data: [ -2.3470770830549290e-01, 6.1921637942502571e-02,
       1.1228509374665811e-04, 1.6030680031137417e-04,
       -7.6287817408728937e-03 ]
avg_reprojection_error: 9.0238007052623903e-01