鱼眼镜头立体声校准OpenCV，Python

我使用本教程link成功校准了我的相机作为参考 . 这是代码：

CHESSBOARD_SIZE = (9,6)
img_dir_left = "/home/dir_left"
img_dir_right = "/home/dir_right"


criteria = (cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1)
calib_flags = cv2.fisheye.CALIB_RECOMPUTE_EXTRINSIC+cv2.fisheye.CALIB_CHECK_COND+cv2.fisheye.CALIB_FIX_SKEW

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

def calculate(imgDir):
    imgPath = glob.glob(imgDir+'/*jpg')
    img_size = None 
    objpoints = []
    imgpoints = [] 

    for image in sorted(imgPath):
        img = cv2.imread(image)
        if img_size == None:
            img_size = img.shape[:2]
        else:
            assert img_size == img.shape[:2], "All images must share the same size."

         gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
         ret, corners = cv2.findChessboardCorners(gray, CHESSBOARD_SIZE, cv2.CALIB_CB_ADAPTIVE_THRESH+cv2.CALIB_CB_FAST_CHECK+cv2.CALIB_CB_NORMALIZE_IMAGE)

        if ret:
            objpoints.append(objp)
            cornersM = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
            imgpoints.append(cornersM)

        cv2.drawChessboardCorners(img, CHESSBOARD_SIZE, cornersM, ret)
        cv2.imshow(imgDir, img)
        cv2.waitKey(1)

    return objpoints, imgpoints, img_size, gray

def calibrate(objpoints, imgpoints, img_size, gray):
    img_number = len(objpoints) #number of the pictures
    K = np.zeros((3, 3))
    D = np.zeros((4, 1))
    rvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(img_number)]
    tvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(img_number)]

    print("rvecs", rvecs)

    rms, _, _, _, _ = cv2.fisheye.calibrate(objpoints,imgpoints,gray.shape[::-1],K,D,rvecs,tvecs,calib_flags,
     (cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-6))

    print("RMS", rms)
    print("Found " + str(img_number) + " valid images for calibration")
    print("DIM=" + str(img_size[::-1]))
    print("K=np.array(" + str(K.tolist()) + ")", )
    print("D=np.array(" + str(D.tolist()) + ")\n", )

    dimension = img_size[::-1]
    k = K.tolist()
    d = D.tolist()

    return  dimension , k , d

我同时拍摄了13张图像 . 图像从原始10位状态转换为8位 . 摄像机安装在7厘米基线的同一线路上 . 我的RMS_left是0.509，RMS_right是0.341 .

这是我用来从相机中取消分配帧的功能 .

def undistorted(frame, data):
    data_in = data
    img = frame
    K, D, DIM = data_in['K'], data_in['D'], data_in['DIM']
    K = np.array(K)
    D = np.array(D)

    map1, map2 = cv2.fisheye.initUndistortRectifyMap(K, D, np.eye(3), K, DIM, cv2.CV_16SC2)#cv2.CV_16SC2   
    undistorted_frame = cv2.remap(img, map1, map2,   interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_TRANSPARENT,  borderValue=29)

   return undistorted_frame

当我尝试进行鱼眼立体声校准时，我得到了与此github link中描述的相同的错误 . 但是所有错误都有一个解决方法 .

K_left = np.zeros((3, 3))
D_left = np.zeros((4, 1))

K_right = np.zeros((3, 3))
D_right = np.zeros((4, 1))

R = np.zeros((1, 1, 3), dtype=np.float64)
T = np.zeros((1, 1, 3), dtype=np.float64)

N_OK = len(imgpoints_left)

objp = np.zeros( (CHECKERBOARD[0]*CHECKERBOARD[1], 1, 3) , np.float64)
objp[:,0, :2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2)

objpoints = np.array([objp]*len(imgpoints_left),dtype=np.float64)
imgpoints_left = np.asarray(imgpoints_left, dtype=np.float64)
imgpoints_right = np.asarray(imgpoints_right, dtype=np.float64)

objpoints = np.reshape(objpoints, (N_OK, 1, CHECKERBOARD[0]*CHECKERBOARD[1], 3))
imgpoints_left = np.reshape(imgpoints_left, (N_OK, 1, CHECKERBOARD[0]*CHECKERBOARD[1], 2))
imgpoints_right = np.reshape(imgpoints_right, (N_OK, 1, CHECKERBOARD[0]*CHECKERBOARD[1], 2))    

(rms, K_left, D_left, K_right, D_right, R, T) = \
    cv2.fisheye.stereoCalibrate(
        objpoints,
        imgpoints_left,
        imgpoints_right,
        K_left,
        D_left,
        K_right,
        D_right,
        img_size,
        R,
        T,
        calibration_flags
    )

有人可以帮我澄清这个解决方法，我是OpenCV的新手，可以完全理解它 .