pythonopencvcomputer-visiondistortion

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

enter image description here

and not work for this one

enter image description here

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


Solution

  • 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)