Camera Calibration using OpenCV

This project performs camera calibration using a single checkerboard image. The script detects checkerboard corners, calibrates the camera, and undistorts the image. Calibration parameters and results are saved for future use.

🔧 Features

• Detects checkerboard corners using OpenCV.
• Computes camera intrinsic matrix and distortion coefficients.
• Saves calibration parameters (camera_calibration.npz).
• Undistorts the original image and saves the result (undistorted_image.jpg).

🧠 Dependencies

• Python 3.x
• OpenCV (cv2)
• NumPy

📷 Checkerboard Setup

Ensure your checkerboard pattern has 8 inner corners per row and 6 per column (i.e., 9x7 total squares). Modify checkerboard_dims if your pattern differs.

🚀 How to Run

1. Clone the repository or download the script.

2. Place your checkerboard image in a known location and update the image_path variable in the script:

   image_path = r"D:\Path\To\Your\Image.jpg"

3. Install the required packages:

pip install -r requirements.txt

4. Run the script:

   python camera_calibration.py

🗂 Output

• camera_calibration.npz — Numpy archive containing:

  • Camera matrix
  • Distortion coefficients
  • Rotation and translation vectors

• undistorted_image.jpg — Corrected image without lens distortion.

Screenshot

📌 Notes

• If the checkerboard corners are not detected, verify the pattern, lighting, and image quality.
• You can use multiple images to improve calibration accuracy by looping through a directory.