bbo-calibcam 2.2.0

Calibrate intrinsic and extrinsic parameters of cameras with charuco boards

calibcam

A charuco based calibrator for camera setups (intrinsic and extrinsic parameters), including omnidirectional cameras.

First, OpenCV is used for single camera calibration., followed by an initial estimation of camera positions and orientations. Subsequently, all intrinsic and extrinsic parameters are optimised for reprojection error using Jax autograd.

See calibcamlib for a library for triangualtion, reprojection etc.

Installation

Windows

1. (If not already done:) Install Anaconda
2. Create conda env conda env create -f https://raw.githubusercontent.com/bbo-lab/calibcam/main/environment.yml
3. Switch to calibcam environment: conda activate calibcam

Usage

Windows

1. From [repository]/boards, copy the appropriate board into the calibration video directory and rename to board.npy
2. Open Anaconda prompt via Start Menu
3. Switch to calibcam environment: conda activate calibcam
4. Run the program with python -m calibcam --videos [LIST OF VIDEOS TO INCLUDE]

BBO internal MATLAB use only:

Use MATLAB function mcl = cameralib.helper.mcl_from_calibcam([PATH TO MAT FILE OUTPUT OF CALIBRATION]) from bboanlysis_m to generate an MCL file.

Format

Result

multicam_calibration.npy/mat holds a dictionary/struct with the calibration result. The filed "calibs" holds an array of calibration dictionarys/structs with entries

* 'rvec_cam': (3,) - Rotation vector of the respective cam (world->cam)
* 'tvec_cam': (3,) - Translation vector of the respective cam (world->cam)
* 'A': (3,3) - Camera matrix
* 'k': (5,) - Camera distortion coefficients

For further structure, refer to camcalibrator.build_result()

Board

Besides the videos, each calibration folder (folder of first video) needs to contain a file board.npy. For the boards at BBO, files are available in the boards directory of the repository. Else, files must be created, containing a dict with the following entries:

* boardWidth: int - number of checkerboard squares
* boardHeight: int - number of checkerboard squares
* square_size_real: float - Absolute edge length of checkerboard squares, unit determines unit of calibration
* marker_size: float - Relative marker size
* dictionary_type: int - Aruco dictionary type

These values are used to create the board in the following way:

board = cv2.aruco.CharucoBoard_create(board_params['boardWidth'],
                                          board_params['boardHeight'],
                                          board_params['square_size_real'],
                                          board_params['marker_size'] * board_params['square_size_real'],
                                          cv2.aruco.getPredefinedDictionary( 
                                              board_params['dictionary_type']))