Center of the ArucoGridBoard

Python
,
1

Hi

I would like to know if it is possible to obtain the POSE (RVEC and TVEC) in relation to the center of the Aruco GridBoard. Here is an example image:

centerArucoGridBoard
centerArucoGridBoard547×514 149 KB

here’s a part of my code:

# Check for camera calibration data
#escolher aqui o arqivo de calibracao para a resolucao desejada
calibration_file = "calibration_640_480.pckl"

if not os.path.exists('./calibration_files/'+str(calibration_file)):
    print("You need to calibrate the camera you'll be using. See calibration project directory for details.")
    exit()
else:
    f = open('calibration_files/'+str(calibration_file), 'rb')
    (cameraMatrix, distCoeffs, _, _) = pickle.load(f)
    f.close()
    if cameraMatrix is None or distCoeffs is None:
        print("Calibration issue. Remove ./calibration_files/"+str(calibration_file)+" and recalibrate your camera with CalibrateCamera.py.")
        exit()

# Constant parameters used in Aruco methods
ARUCO_PARAMETERS = aruco.DetectorParameters_create()
ARUCO_DICT = aruco.Dictionary_get(aruco.DICT_4X4_50)

# Create grid board object we're using in our stream
board = aruco.GridBoard_create(
        markersX=2,
        markersY=2,
        markerLength=0.0615,
        markerSeparation=0.0615,
        dictionary=ARUCO_DICT)

# Create vectors we'll be using for rotations and translations for postures
rvecs, tvecs = None, None

i = 0

while True:
    
    ret = True
    QueryImg = frame
    
    if ret == True:
        # grayscale image
        #gray = cv2.cvtColor(QueryImg, cv2.COLOR_BGR2GRAY)
    
        # Detect Aruco markers
        corners, ids, rejectedImgPoints = aruco.detectMarkers(frame, ARUCO_DICT, parameters=ARUCO_PARAMETERS)
  
        # Refine detected markers
        # Eliminates markers not part of our board, adds missing markers to the board
        corners, ids, rejectedImgPoints, recoveredIds = aruco.refineDetectedMarkers(
                image = frame,
                board = board,
                detectedCorners = corners,
                detectedIds = ids,
                rejectedCorners = rejectedImgPoints,
                cameraMatrix = cameraMatrix,
                distCoeffs = distCoeffs)   

        # Outline all of the markers detected in our image
        QueryImg = aruco.drawDetectedMarkers(QueryImg, corners, borderColor=(0, 0, 255))

        #cameraMatrix[0][2]),int(cameraMatrix[1][2] sao o ponto principal da camera e quase a posicao central
        QueryImg = cv2.circle(QueryImg,(int(cameraMatrix[0][2]),int(cameraMatrix[1][2])),5,(255,0,0),2)


        # Require 15 markers before drawing axis
        if ids is not None and len(ids) > 2:
            # Estimate the posture of the gridboard, which is a construction of 3D space based on the 2D video 
            pose, rvec, tvec = aruco.estimatePoseBoard(corners, ids, board, cameraMatrix, distCoeffs)
            if pose:
                # Draw the camera posture calculated from the gridboard
                QueryImg = aruco.drawAxis(QueryImg, cameraMatrix, distCoeffs, rvec, tvec, 0.3)
            i=i+1
            print('Contagem: ', i)
	    print('TVEC: ', tvec)
        # Display our image
        cv2.imshow('QueryImage', QueryImg)

    # Exit at the end of the video on the 'q' keypress
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
    
    #----------------------------------------------------------------------------------------------------------------------------------------#

cv2.destroyAllWindows()

thanks