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*8,3), np.float32)
objp[:,:2] = np.mgrid[0:8,0:6].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('C:\\Users\\26909\Desktop\camera_data\*.jpg')
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, (8,6), 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, (8,6), corners2, ret)
        cv.imshow('img', img)
        cv.waitKey(500)
cv.destroyAllWindows()

#参数
ret, mtx, dist, rvecs, tvecs = cv.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
print("ret:", ret)
print("mtx:\n", mtx) # 内参数矩阵
print("dist:\n", dist)  # 畸变系数   distortion cofficients = (k_1,k_2,p_1,p_2,k_3)
print("rvecs:\n", rvecs)  # 旋转向量  # 外参数
print("tvecs:\n", tvecs ) # 平移向量  # 外参数

#畸变矫正
img = cv.imread('C:\\Users\\26909\Desktop\camera_data\IMG12.jpg')
h,  w = img.shape[:2]
newcameramtx, roi = cv.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
# undistort
dst = cv.undistort(img, mtx, dist, None, newcameramtx)
# crop the image
x, y, w, h = roi
dst = dst[y:y+h, x:x+w]
cv.imwrite('calibresult.png', dst)

ret: 0.7404995966474621

mtx:

?[[274.4022963??? 0.???????? 309.41661229]

?[? 0.???????? 274.81034452 228.80419383]

?[? 0.?????????? 0.?????????? 1.??????? ]]

dist:

?[[-0.3454201?? 0.1457639? -0.00195636? 0.00171827 -0.02320787]]

rvecs:

?(array([[ 0.14050152],

?????? [-0.08172329],

?????? [ 1.55213876]]), array([[-0.50002852],

?????? [-0.20014157],

?????? [-1.38796715]]), array([[-0.20899323],

?????? [-0.3015702 ],

?????? [ 1.17631626]]), array([[ 0.14426414],

?????? [-0.48615287],

?????? [ 1.25333586]]), array([[-0.11196678],

?????? [ 0.13929802],

?????? [-1.51166718]]), array([[-0.20041373],

?????? [ 0.21822841],

?????? [-1.48091619]]), array([[-0.23701475],

?????? [ 0.18275228],

?????? [-0.84192608]]), array([[-0.1877632 ],

?????? [ 0.08558443],

?????? [-0.38675306]]), array([[-0.12538803],

?????? [ 0.23143798],

?????? [-1.36803536]]), array([[-0.25324129],

?????? [ 0.26484299],

?????? [-1.0249051 ]]), array([[-0.15181587],

?????? [ 0.17507901],

?????? [-1.19285926]]), array([[ 0.16808607],

????? ?[ 0.09056166],

?????? [-0.01890093]]), array([[0.00670207],

?????? [0.36090502],

?????? [1.46210405]]), array([[0.575493? ],

?????? [0.1586262 ],

?????? [1.02133379]]), array([[-0.06530318],

?????? [ 0.2759119 ],

?????? [-0.76715368]]), array([[-0.17849515],

?????? [ 0.1838762 ],

?????? [ 0.00634664]]), array([[-0.2013951 ],

?????? [ 0.09738451],

?????? [ 0.72377849]]), array([[-0.44136116],

?????? [ 0.37411479],

?????? [-0.53144601]]), array([[-0.5093139 ],

?????? [ 0.11895948],

?????? [-0.65299492]]), array([[-0.57225479],

?????? [-0.06450065],

?????? [-1.01460071]]))

tvecs:

?(array([[ 2.07154433],

?????? [-2.30660832],

?????? [ 7.4506053 ]]), array([[-3.95294077],

?????? [ 3.55032646],

?????? [ 8.64371851]]), array([[ 2.99558578],

?????? [-3.57085102],

?????? [11.73926593]]), array([[ 2.3984248 ],

?????? [-3.01994723],

?????? [10.69421613]]), array([[-0.31440216],

?????? [ 4.03730762],

?????? [11.24204555]]), array([[-0.96673367],

?????? [ 3.831028? ],

?????? [ 9.009737? ]]), array([[-3.63737237],

?????? [ 1.72964791],

?????? [10.5699932 ]]), array([[-4.74312794],

?????? [-0.28685914],

?????? [10.31412678]]), array([[-3.13384807],

?????? [ 3.48362812],

?????? [12.11070046]]), array([[-6.74105261],

?????? [ 1.96433573],

?????? [12.56616204]]), array([[-5.97698749],

?????? [ 2.40248059],

?????? [ 8.73901348]]), array([[-3.365379? ],

?????? [-2.08857099],

?????? [ 7.58537102]]), array([[ 4.60034986],

?????? [-1.63283684],

?????? [10.16924106]]), array([[ 0.96341843],

?????? [-2.83724946],

?????? [ 7.42342456]]), array([[-3.96962978],

?????? [ 1.8225874 ],

?????? [11.23708158]]), array([[-3.97587032],

?????? [-1.26762813],

?????? [11.19197982]]), array([[-2.10782797],

?????? [-4.64916982],

?????? [10.41076044]]), array([[-6.00656584],

?????? [-0.11113525],

?????? [11.82626777]]), array([[-8.59365244],

?????? [-1.02351119],

?????? [11.98704289]]), array([[-6.2890475 ],

?????? [ 1.47511978],

?????? [ 9.99562781]]))