I’m calibrating a camera in opencv. I was able to perform both calibration and pose estimation and now I should find the board_corners of the chessboard in python. To find board corners, I need board center, l and w (dimention of the chessboard) and the orientation of the board in the plane. I know the board lays on x-y plane in world coordinates, but it’s not parallel to x or y axis.
Right now I have the board_center in world coordinates but I’m not quite sure how to find the corners of the board:

board_center = np.mean(objp, axis=0) 

How can I do this in python?
Also I’m not sure how to pass those points in the camera frame with the rotation and the tralsation vector obtainded through solvePnP.

This is my code for now for pose estimation, but I’m not quite sure how to find the orientation of the plane

import numpy as np
import cv2 as cv
import glob


# Load previously saved data
with np.load('CameraParmas.npz') as file:
    mtx, dist, _, _ = [file[i] for i in ('cameraMatrix','dist','rvecs','tvecs')]


def draw(img, corners, imgpts):
    def tupleOfInts(arr):
        return tuple(int(x) for x in arr)

    corner = tupleOfInts(corners[0].ravel())
    cv.line(img, (corner), tupleOfInts(imgpts[0].ravel()), (255,0,0), 5)
    cv.line(img, (corner), tupleOfInts(imgpts[1].ravel()), (0,255,0), 5)
    cv.line(img, (corner), tupleOfInts(imgpts[2].ravel()), (0,0,255), 5)
    return img




def drawBoxes(img, corners, imgpts):

    imgpts = np.int32(imgpts).reshape(-1,2)

    # draw ground floor in green
    img = cv.drawContours(img, [imgpts[:4]],-1,(0,255,0),-3)

    # draw pillars in blue color
    for i,j in zip(range(4),range(4,8)):
        img = cv.line(img, tuple(imgpts[i]), tuple(imgpts[j]),(255),3)

    # draw top layer in red color
    img = cv.drawContours(img, [imgpts[4:]],-1,(0,0,255),3)

    return img



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

objp = np.zeros((8*6,3), np.float32)
objp[:,:2] = np.mgrid[0:8,0:6].T.reshape(-1,2)

size_of_chessboard_squares_mm = 30
objp = objp * size_of_chessboard_squares_mm

axis = np.float32([[50,0,0], [0,50,0], [0,0,-50]]).reshape(-1,3)
axisBoxes = np.float32([[0,0,0], [0,50,0], [50,50,0], [50,0,0],
                   [0,0,-50],[0,50,-50],[50,50,-50],[50,0,-50] ])

images = glob.glob('right*.png')
for image in images:

    img = cv.imread(image)
    gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
    ret, corners = cv.findChessboardCorners(gray, (8,6),None)
    if ret == True:

        corners2 = cv.cornerSubPix(gray,corners,(11,11),(-1,-1), criteria)
        
        
       
        
        # Find the rotation and translation vectors.
        ret, rvecs, tvecs = cv.solvePnP(objp, corners2, mtx, None)
        rotM, _ = cv.Rodrigues(rvecs)
       
        
        # Project 3D points to image plane
        imgpts, jac = cv.projectPoints(axis, rvecs, tvecs, mtx, None)
        
        img = draw(img,corners2,imgpts)
        cv.imshow('img',img)

        k = cv.waitKey(1000)
        cv.imwrite('pose'+image, img)
        
#Board Center 
board_center = np.mean(objp, axis=0)
print("nThe Board Center is:n",board_center)
print("nobjptsn", objp)
#print("nobjpn",objp)