OpenCV Camera Calibration source code

refer to code:

.

import numpy as np

import cv2

import glob

import yaml

from icecream import ic

import os

def calibrate_camera(images, chess_box_scale_mm):

# Termination criteria for refining the detected corners

criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)

# Prepare object points: (0,0,0), (1,0,0), (2,0,0), ..., (9,6,0)

objp = np.zeros((9*6,3), np.float32)

objp[:,:2] = np.mgrid[0:6, 0:9].T.reshape(-1,2) * chess_box_scale_mm# Scale by 7.5mm or 17.5mm or 25mm

# Arrays to store object and image points from all the images

objpoints = [] # 3d point in real world space

imgpoints = [] # 2d points in image plane

if not images:

raise Exception("No images found in the calibration directory.")

for fname in images:

ic(fname)

img = cv2.imread(fname)

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Find the chessboard corners

ret, corners = cv2.findChessboardCorners(gray, (6,9), None)

ic(ret)

# If found, add object points and image points

if ret == True:

objpoints.append(objp)

corners2 = cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria)

imgpoints.append(corners2)

# Draw and display the corners

cv2.drawChessboardCorners(img, (6,9), corners2, ret)

# Save the image with corners in the same directory but with a .png extension

base_name = os.path.basename(fname)

file_root, file_ext = os.path.splitext(base_name)

save_path = os.path.join(os.path.dirname(fname), f"{file_root}.png")

cv2.imwrite(save_path, img)

cv2.imshow('img', img)

cv2.waitKey(500)

cv2.destroyAllWindows()

if not objpoints or not imgpoints:

raise Exception("Chessboard corners not found in any images.")

# Calibrate the camera using the last value of gray from the loop

ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)

return ret, mtx, dist, rvecs, tvecs

def main():

path = "./path/to/images/"

images = glob.glob(f'{path}/*.JPG')

ret, mtx, dist, rvecs, tvecs = calibrate_camera(images, 7.5) #chess_box_scale_mm is 7.5 mm

print("Camera matrix: \n", mtx)

print("Distortion coefficients: \n", dist)

# Save to YAML file

data = {'camera_matrix': np.asarray(mtx).tolist(), 'dist_coeff': np.asarray(dist).tolist()}

with open(f"{path}/calibration.yaml", "w") as f:

yaml.dump(data, f)

# Display one of the images after undistortion

img = cv2.imread(images[0]) # Replace with an image from your calibration set

h, w = img.shape[:2]

newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))

# Undistort

dst = cv2.undistort(img, mtx, dist, None, newcameramtx)

# Crop the image

x, y, w, h = roi

dst = dst[y:y+h, x:x+w]

cv2.imshow('origin Image', img)

cv2.imshow('Undistorted Image', dst)

cv2.waitKey(0)

cv2.destroyAllWindows()

if __name__ == "__main__":

main()

.. 

Here is chess board which has 10x7.

png files in same folder are that images succeed for finding pattern.

yaml file will be generated in image folder for camera intrinsic params.

Thank you.

www.marearts.com

🙇🏻‍♂️