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https://github.com/lerman25/matlabcameracalibrationwithpython

Using Matlab's Python engine for stereo calibration (For those who are unfamiliar with Matlab but prefers it over OpenCV)
https://github.com/lerman25/matlabcameracalibrationwithpython

camera-calibration matlab opencv stereo-camera-calibration

Last synced: 25 days ago
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Using Matlab's Python engine for stereo calibration (For those who are unfamiliar with Matlab but prefers it over OpenCV)

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README 

        
# MatlabCameraCalibrationWithPython

%%% The project is still in works (the entire code is written, but not yet organized) 



Using [Matlab's Python engine](https://www.mathworks.com/help/matlab/matlab-engine-for-python.html) for stereo/camera calibration (For those who are unfamiliar with Matlab but prefers its camera calibration algorithm over OpenCV's), using chessboard.

Can be used for videos or images.



OpenCV is used in this project for:

1. Chessboard detection (checkChessboard())

2. Split video to frames if needed



Originally made for 3 video cameras in a triangle setting: 



![image](https://github.com/user-attachments/assets/a878b724-a359-406f-bc97-7636730acdd4)



Will calibrate Cam1&Cam3, Cam2&Cam3 and will try to calibrate Cam1&Cam2 if possible.



## Process explanation: 



### When calibrating from videos frames, one might encouter 3 problems:

  1. Many frames *without* a chessboard visible

  2. Many frames *with* chessboard visible

  3. Too many frames to manually select the best ones, accounting for both FOV (field of view) coverage and reprojection error

### Solutions: 

  1) Matlab chessboard detection is slower then OpenCV's, and OpenCV's is just as good.

     For that reason, the code iterates over each frame/images and uses OpenCV's chessboard detection to check if a chessboard is visible in that image.

  2) When too many frames/images contains a chessboard visible, the code random samples an appropiate amount of images to use.

  3) To select automaticly from a large set of images, does an initial camera calibration of the entire set (a slow process, but helps for a better result).

     The initial calibration gives reprojection errors for the large set of images.

     To select the best ones accouting for both FOV coverage and reprojection error, the code uses K-means algorithm

     to create sub-sets of images based on their chessboard location, that would be each mean given to image (thus accounting for FOV).

     From each mean it selects the images with the lowest reprojection error (thus accounting for reprojection error).

     

## Pipeline for single camera:

1. Split video of physical checkerboard calibration  into frames.

2. Detect in which frames the chessboard is visible, factoring in reprojection error and coverage of FOV.

3. Calibrate the camera from valid frames to create initial camera parameteres.

4. Select the best frames accounting for FOV and reprojection error (as described in [Process explanation](##-Process-explanation)).

5. Re-calibrate using the best frames.

6. Convert to OpenCV format if required.

## Pipeline for stereo:

1. Split videos of physical checkerboard calibration  into frames (The videos must be synced - starting and ending in the same "real" time).

2. Detect in which frames the chessboard is visible, factoring in reprojection error and coverage of FOV.

3. Calibrate each camera individually from valid frames to create camera parameteres.

4. Detect in which frames the chessboard is visible in both cameras, factoring in the reprojection error and coverage of FOV.

5. Stereo calibrate each pair of cameras  using camera parameters.

6. Convert to OpenCV format if required.