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opencv_calibrate_camera.py

import numpy as np
import cv2 as cv
import glob
# termination criteria
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*7,3), np.float32)
objp[:,:2] = np.mgrid[0:7,0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob('*.jpg')
camera_mats = []
for fname in images:
	img = cv.imread(fname)
	gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
	# Find the chess board corners
	ret, corners = cv.findChessboardCorners(gray, (7,6), None)
	# If found, add object points, image points (after refining them)
	if ret == True:
		objpoints.append(objp)
		corners2 = cv.cornerSubPix(gray,corners, (11,11), (-1,-1), criteria)
		imgpoints.append(corners)
		# Draw and display the corners
		cv.drawChessboardCorners(img, (7,6), corners2, ret)
		cv.imshow('img', img)
		cv.waitKey(500)
		ret, mtx, dist, rvecs, tvecs = cv.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
		camera_mats.append(mtx)
		h,  w = img.shape[:2]
		newcameramtx, roi = cv.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
		dst = cv.undistort(img, mtx, dist, None, newcameramtx)
		x, y, w, h = roi
		dst = dst[y:y+h, x:x+w]
		cv.imshow('img', dst)
		cv.waitKey(500)
cv.destroyAllWindows()
print(np.mean(np.array(camera_mats), axis=0))