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I used OpenCV's findChessboardCorners on a few checkerboard images (40~) and about 27 seemed to find the corners accurately. How does one proceed from here? Do I calculate the reprojection error over just the correctly identified images? Is this normal to expect?

Some of the correctly identified images -

enter image description here

Some of the incorrectly identified images - enter image description here

How I'm calibrating my images -

def calibrate():

    criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001)

    objp = np.zeros((6*9,3),np.float32)
    objp[:,:2] = np.mgrid[0:9, 0:6].T.reshape(-1,2)

    objpoints = []
    imgpoints = []

    images = glob.glob('*.png')

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

        ret, corners = cv.findChessboardCorners(gray, (9,6), None)

        if ret == True:
            print('Hello')

            objpoints.append(objp)

            corners2 = cv.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria)
            cv.drawChessboardCorners(img, (9,6), corners, ret)
            imgpoints.append(corners2)

            cv.imshow('img',img)
            cv.waitKey(500)
    cv.destroyAllWindows()

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

    return objpoints, imgpoints, ret, mtx, dist, rvecs, tvecs
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