https://github.com/ccomkhj/charuco_stereo_calibrator

Charuco Camera Calibrator for Stereo vision and raspberry pi camera

https://github.com/ccomkhj/charuco_stereo_calibrator

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Keywords

calibration calibration-toolbox charuco computer-vision depth stereo-vision
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Repository

Charuco Camera Calibrator for Stereo vision and raspberry pi camera

Basic Info
  • Host: GitHub
  • Owner: ccomkhj
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 156 KB
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Topics
calibration calibration-toolbox charuco computer-vision depth stereo-vision
Created over 1 year ago · Last pushed 10 months ago
Metadata Files
Readme License

README.md

Overview

The CharucoStereoCalibrator, StereoCalibrator and Rectifier are Python tools for stereo camera calibration and rectification. They compute intrinsic and stereo parameters, rectify stereo images, and visualize epipolar geometry, essential for 3D vision tasks like depth estimation and 3D reconstruction.


Features

StereoCalibrator

  • Automatic detection of chessboard corners in stereo images.
  • Computes intrinsic parameters (camera matrix, distortion coefficients).
  • Computes stereo parameters (rotation, translation, rectification, projection, and Q matrix).
  • Saves calibration data to stereoMap.xml.
  • Visualizes calibration errors and highlights outliers.

CharucoStereoCalibrator

  • Support same feature with StereoCalibrator but using Charucoboard.

CharucoCalibrator

  • One camera calibrator using Charucoboard.
  • Feel free to use generate_charuco.py to generate your own Charucoboard. It's designed for A4 printing.

Rectifier

  • Loads stereo calibration data from stereoMap.xml.
  • Rectifies stereo image pairs for horizontal alignment.
  • Computes the Fundamental Matrix (F).
  • Visualizes epipolar geometry with epilines and matched points.
  • Saves rectified images and visualization outputs.

Quick Start

0. Installation

conda create -n csc python=3.12 -y conda activate csc pip install -e .

CharucoCalibrator is sensitive to opencv version, because there's big update since 4.6

1. Calibration

```python from csc.charucostereocalibrator import CharucoStereoCalibrator import glob

Specify image paths

imagesleft = glob.glob("input/charuco/left/*.jpg") imagesright = glob.glob("input/charuco/right/*.jpg")

Specify number of column, row of checkerboard

chessboard_size = (11, 8)

Specify image sizes

framesizeh = 1296 framesizew = 2304

[Optional] if you don't know camera spec, then algorithm figure this out.

finmm = 4.74 # or None pixelsizemm = 1.4e-3 # or None debug = False # or False

stereocalibrator = CharucoStereoCalibrator( chessboardsize=chessboardsize, framesizeh=framesizeh, framesizew=framesizew, finmm=finmm, pixelsizemm=pixelsize_mm, debug=debug, )

Specify samples to show epipolar geometry qualitatively to evaluate calibration

leftshow = "demo/samples/leftsample.jpg" rightshow = "demo/samples/rightsample.jpg"

stereocalibrator.performcalibration(imagesleft, imagesright) stereocalibrator.saverectifiedimages(imagesleft, imagesright) stereocalibrator.visualizeepipolar(leftshow, rightshow, save=debug) stereocalibrator.printresults() stereocalibrator.measure_outlier() ```

Note: To explore additional examples, please refer to the __main__ section within each function.


Outputs

  • Calibration Data: stereoMap.xml (camera matrices, rectification maps, etc.).
  • Rectified Images: Horizontally aligned stereo pairs.
  • Epipolar Visualizations: Saved epipolar images highlighting epilines and point matches.

Key Methods

  • StereoCalibrator.perform_calibration() - Calibrate cameras.
  • Rectifier.rectify_image() - Rectify stereo image pairs.
  • Rectifier.visualize_epipolar() - Draw epilines and visualize epipolar geometry.

Notes

  • Ensure chessboard size matches the setup (chessboard_size).
  • Use visualize_epipolar() to verify and debug calibration.
  • Unlike Checker board, Charuco board is strict for the order of col, row
    • Checker board considers the number of conjunctions.
    • Charuco board considers the number of rows and columns of blocks.

License

This project is licensed under the MIT License.

Owner

  • Name: Huijo
  • Login: ccomkhj
  • Kind: user
  • Location: Germany
  • Company: @hexafarms

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Dependencies

requirements.txt pypi
  • matplotlib *
  • numpy *
  • opencv-python ==4.10.0.84
  • pandas *
setup.py pypi
  • Dependencies *
  • line.strip *