OpenCV - How to get real world distance from a 2D image using a chessboard as reference

After check several pieces of codes, I took several shots, found the chessboard corners and use them to get the camera matrix, distortion coefficients, rotation and translation vectors. Now, can someone tell me which python opencv function do I need to calculate the distance in real world from the 2D image? project points? For example, using a chessboard as reference (see picture), if the tile size is 5cm, the distance for 4 tiles should be 20 cm. I saw some functions like projectPoints,findHomography, solvePnP but I am not sure which one do I need to solve my problem and get the transformation matrix between the camera world and the chessboard world. 1 single camera, same position of the camera for all cases but not exactly over the chessboard, and chessboard is placed over a planar object (table)

# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
    objp = np.zeros((nx * ny, 3), np.float32)
    objp[:, :2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2)

    # Arrays to store object points and image points from all the images.
    objpoints = []  # 3d points in real world space
    imgpoints = []  # 2d points in image plane.

    # Make a list of calibration images
    images = glob.glob(path.join(calib_images_dir, 'calibration*.jpg'))
    print(images)
    # Step through the list and search for chessboard corners
    for filename in images:

        img = cv2.imread(filename)

        imgScale = 0.5
        newX,newY = img.shape[1]*imgScale, img.shape[0]*imgScale
        res = cv2.resize(img,(int(newX),int(newY)))

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

        # Find the chessboard corners
        pattern_found, corners = cv2.findChessboardCorners(gray, (nx,ny), None)

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

            # Increase accuracy using subpixel corner refinement
            cv2.cornerSubPix(gray,corners,(5,5),(-1,-1),(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1 ))
            imgpoints.append(corners)

            if verbose:
                # Draw and display the corners
                draw = cv2.drawChessboardCorners(res, (nx, ny), corners, pattern_found)
                cv2.imshow('img',draw)
                cv2.waitKey(500)

    if verbose:
        cv2.destroyAllWindows()

    #Now we have our object points and image points, we are ready to go for calibration
    # Get the camera matrix, distortion coefficients, rotation and translation vectors
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
    print(mtx)
    print(dist)
    print('rvecs:', type(rvecs),' ',len(rvecs),' ',rvecs)
    print('tvecs:', type(tvecs),' ',len(tvecs),' ',tvecs)

    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: ", mean_error/len(objpoints))


    imagePoints,jacobian = cv2.projectPoints(objpoints[0], rvecs[0], tvecs[0], mtx, dist)
    print('Image points: ',imagePoints)

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