> For the complete documentation index, see [llms.txt](https://ykkim.gitbook.io/dlip/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ykkim.gitbook.io/dlip/image-processing/tutorial/tutorial-camera-calibration.md).

# Tutorial: Camera Calibration

DLIP Tutorial for Camera Calibration using GML Program or MATLAB

## Tutorial(Ver. 1) - Calibration Using GML Camera Calibration Program

1. Download ***GML Camera Calibraion Program*** in link.

[calibration resources](https://github.com/ykkimhgu/DLIP_doc/files/14879534/calibration.resources.zip)

2. GML Program Guide

![](https://github.com/user-attachments/assets/e0e4f7d0-db02-4591-8923-8d2be5d5ba01)

![](https://github.com/user-attachments/assets/8562246c-c02e-46ab-b553-5c4429c36abf)

3. Create a new C++ project in Visual Studio Community
   * Project Name: `DLIP_Tutorial_Camera_Calibration`
   * Project Folder: `C:\Users\yourID\source\repos\DLIP\Tutorial\`
4. Load the file in the path: `calibration.resources\sourceCode\` into the project folder
   * Source Code: `ShowUndistorted.cpp`, `ShowUndistorted_tiny.cpp`
   * header file: `tinyxml2.cpp`, `tinyxml2.h`
   * xml file: `calibTest.xml`

## Tutorial(Ver. 2) - Calibration using MATLAB Toolbox

**Using \_"Computer Vision Toolbox"**\_\*\* Application in MATLAB\*\*

1. Download ***Computer Vision Toolbox*** in MATLAB.

![img3](https://user-images.githubusercontent.com/84509483/226327538-cb410359-6337-4030-b6fd-83042b1db028.PNG)

2. Open the ***Camera Calibrator*** application.

![img2](https://user-images.githubusercontent.com/84509483/226327602-6d01d8c2-bf21-4fb0-812c-c6438fec07ba.PNG)

3. Download images for camera calibration. [(link)](https://github.com/ykkimhgu/DLIP-src/blob/main/Tutorial_Calibration/camera_calibration_images.zip)
4. Load calibration images to the camera calibrator app.

![img](https://user-images.githubusercontent.com/84509483/226327653-216ad6ed-34ea-4fab-bd60-98499c6e18c7.PNG)

5. Configure Image and Pattern Properties as

* Pattern Selection: Checkerboard
* Size of checkerboard square: 25 mm
* Image distortion: Low

![img4](https://user-images.githubusercontent.com/84509483/226327686-7ee6cf2d-e079-4b28-9e30-1db0482f04a9.PNG)

6. Click ***Calibrate*** button.

![img5](https://user-images.githubusercontent.com/84509483/226327718-35316e83-78bc-4d68-aa43-ee61d96d16ac.PNG)

7. Export Parameters to workspace
8. Save the Workspace ***cameraParams*** as **"cameraParams.mat"**

![img6](https://user-images.githubusercontent.com/84509483/226327732-b066f4a1-fc5e-4d07-8d66-f5ddfafb2acb.PNG)

8. Download test code([link](https://github.com/ykkimhgu/DLIP-src/blob/main/Tutorial_Calibration/DLIP_Tutorial_Calibration_GetUndistortedImg.m)) and Run the code

![img7](https://user-images.githubusercontent.com/84509483/226327756-702956a0-f1d7-4098-a7fb-2b149f31df37.PNG)

9. Apply the camera parameter values from ***cameraParams*** to the cpp test code ([link](https://github.com/ykkimhgu/DLIP-src/blob/main/Tutorial_Calibration/ShowUndistorted.cpp))

![img8](https://user-images.githubusercontent.com/84509483/226327795-2cf5e1fc-e856-4a53-8c23-625d71ad43ff.PNG)

10. (Option) Create a simple function that returns undistort output image from the input raw image

### Other Calibration Tutorial

1. Calibration with OpenCV C++

{% embed url="<https://docs.opencv.org/4.x/d4/d94/tutorial_camera_calibration.html>" %}

2. Calibration with OpenCV-Python

{% embed url="<https://docs.opencv.org/4.x/dc/dbb/tutorial_py_calibration.html>" %}