OpenCV Calibrate.py

import numpy as np
import cv2
import glob

# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.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((7*7, 3), np.float32)
objp[:, :2] = np.mgrid[0:7, 0:7].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('images/calibration/rightsample/*.png')

for fname in images:
    img = cv2.imread(fname)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Find the chess board corners
    ret, corners = cv2.findChessboardCorners(gray, (7, 7), None)

    # If found, add object points, image points (after refining them)
    if ret:
        objpoints.append(objp)

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

        # Draw and display the corners
        img = cv2.drawChessboardCorners(img, (7, 7), corners2, ret)
        cv2.imshow('img', img)
        cv2.waitKey(500)

cv2.destroyAllWindows()

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

img = cv2.imread('images/calibration/rightsample/frame1.png')
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.imwrite('calibresult1left.png', dst)

# undistort
mapx, mapy = cv2.initUndistortRectifyMap(mtx, dist, None, newcameramtx, (w, h), 5)
dst = cv2.remap(img, mapx, mapy, cv2.INTER_LINEAR)

# crop the image
x, y, w, h = roi
dst = dst[y:y+h, x:x+w]
cv2.imwrite('calibresult2left.png', dst)

mean_error = 0
for i in range(len(objpoints)):
    imgpoints2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
    error = cv2.norm(imgpoints[i], imgpoints2, cv2.NORM_L2)/len(imgpoints2)
    mean_error += error

print("total error: ", 100*mean_error/len(objpoints))