What could make undistortion code not work for several chessboard images having the same dimensions?

I am beginner in OpenCV-Python. I would like to know what could make my undistortion code work for this chessboard picture

and not work for this one

knowing that the too chessboards have the same dimension.

The code i am talking about is as below

import cv2
import numpy as np
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
cbrow = 7
cbcol = 9

objp = np.zeros((cbrow * cbcol, 3), np.float32)
objp[:, :2] = np.mgrid[0:cbcol, 0:cbrow].T.reshape(-1,2)

objpoints = []  
imgpoints = []

CHECKERBOARD =  (7,9)

img = cv2.imread("ChessUOledVGA.jpg")

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

ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD, cv2.CALIB_CB_ADAPTIVE_THRESH+cv2.CALIB_CB_FAST_CHECK+cv2.CALIB_CB_NORMALIZE_IMAGE)
print(corners)

if ret == True:
        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, (cbcol, cbrow), corners2, ret)
        cv2.imshow('img', img)
        cv2.waitKey(0) 

 ret, mtx, dist , rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
        print('mtx', mtx)
        print('dist',dist)
        img = cv2.imread('ChessUOledVGA.jpg', 1)
        h, w = img.shape[:2]  # get the size and the shape of the image h,w
        newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h))
        print('newcameramtx', newcameramtx)
        dst = cv2.undistort(img, mtx, dist, None, newcameramtx)
        cv2.imwrite('result.jpg', dst)

Could someone enlighten me please. Thanks

Your checkboard dimensions must be (no of corners per row, no of corners per col) ->(9,7). And, you should threshold your image before passing into the find corners function as there is shades in four corners of the problematic image. Try this, it works.

import cv2
import numpy as np
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
cbrow = 7
cbcol = 9

objp = np.zeros((cbrow * cbcol, 3), np.float32)
objp[:, :2] = np.mgrid[0:cbcol, 0:cbrow].T.reshape(-1,2)

objpoints = []  
imgpoints = []

CHECKERBOARD =  (9,7)

img = cv2.imread("ChessUOledVGA.jpg")

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
_, out = cv2.threshold(gray, 40, 255, cv2.THRESH_BINARY_INV)

ret, corners = cv2.findChessboardCorners(out, CHECKERBOARD, cv2.CALIB_CB_ADAPTIVE_THRESH+cv2.CALIB_CB_FAST_CHECK+cv2.CALIB_CB_NORMALIZE_IMAGE)
print(corners, ret)

if ret == True:
    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, (cbcol, cbrow), corners2, ret)
    cv2.imshow('img', img)
    cv2.waitKey(0) 

if 0:
    ret, mtx, dist , rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
    print('mtx', mtx)
    print('dist',dist)
    img = cv2.imread('ChessUOledVGA.jpg', 1)
    h, w = img.shape[:2]  # get the size and the shape of the image h,w
    newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h))
    print('newcameramtx', newcameramtx)
    dst = cv2.undistort(img, mtx, dist, None, newcameramtx)
    cv2.imwrite('result.jpg', dst)