Monocular Camera Calibration using Chessboard and OpenCV

This repository contains the code for calibrating a monocular camera using a chessboard pattern. The code is written in Python and uses OpenCV for camera calibration.

Table of Contents

• Overview
• Installation
• Usage
• References
• Acknowledgements
• License

Overview

A camera is a fundamental tool used across various domains, including robotics, surveillance, and entertainment. To leverage a camera effectively as a visual sensor, understanding its parameters through camera calibration is crucial.

What is Camera Calibration?

Camera calibration is the process of estimating a camera's intrinsic and extrinsic parameters to establish a precise relationship between real-world 3D points and their corresponding 2D projections (pixels) in captured images.

Parameters Estimated in Camera Calibration

1. Internal Parameters: represented in the form of the intrinsic matrix (K), which includes:

   • Focal length
   • Optical center
   • Radial distortion coefficients

2. External Parameters:

   • Rotation matrix (R)
   • Translation vector (t)

Camera Calibration Workflow

The calibration workflow typically involves:

1. Capturing images of a known calibration pattern (e.g., checkerboard) from various viewpoints.
2. Detecting and refining the corners of the calibration pattern in the images.
3. Using the detected 2D-3D correspondences to estimate the camera's intrinsic matrix (K), rotation matrix (R), and translation vector (t).

Installation

1. Clone the repository
2. Create a virtual environment using conda:

conda create -n camera_calibration python=3.8

3. Activate the virtual environment:

conda activate camera_calibration

4. Install the required packages:

pip install -r requirements.txt

Usage

1. Capture images of the chessboard pattern from different angles and distances. Feel free to use the images provided in the data/images folder.
2. Run the following command to calibrate the camera:

   python src/main.py --chessboard_size "24,17" --square_size 0.015 

3. this will result in the following outputs:
   • The camera matrix and distortion coefficients will be saved in the data/params folder.
   • The undistorted images will be saved in the data/undistorted folder.
   • The chessboard corners will be drawn on the images and saved in the data/corners folder.

References

• OpenCV Camera Calibration Tutorial

Acknowledgements

The utilized images are taken from the this repository. I would like to thank the author for providing the images.

License

This project is licensed under the MIT License - see the LICENSE file for details.