I am using fisheye calibration to solve chessboard images using OpenCV. It fails for the calibration flag cv2.fisheye.CALIB_RECOMPUTE_EXTRINSIC, so I work around it. But once I get the parameters, the distortion is not looking good. I am attaching an image too.

From the Fisheye calibration

This is my code here:

chessboardsize = (13, 9)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1)
flags = ( cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_NORMALIZE_IMAGE + cv2.CALIB_CB_FILTER_QUADS)
# calibration_flags = cv2.fisheye.CALIB_RECOMPUTE_EXTRINSIC + cv2.fisheye.CALIB_CHECK_COND + cv2.fisheye.CALIB_FIX_SKEW
calibration_flags = cv2.fisheye.CALIB_CHECK_COND + cv2.fisheye.CALIB_FIX_SKEW

objp = np.zeros((1, chessboardsize[0]*chessboardsize[1], 3), np.float32)
objp[0, :, :2] = np.mgrid[0:chessboardsize[0], 0:chessboardsize[1]].T.reshape(-1, 2)
_img_shape = None

objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob('calibration_frames/*.jpg')  # Images

if len(images) == 0:
    raise ValueError("No images found in the specified directory.")

for fname in images:
    
    img = cv2.imread(fname)
    if img is None:
        print(f"Image {fname} cannot be read.")
        continue  # Skip if the image is not read properly
    if _img_shape == None:
        _img_shape = img.shape[:2]
    else:
        assert _img_shape == img.shape[:2], "All images must share the same size."

    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    # Find the chess board corners
    ret, corners = cv2.findChessboardCorners(gray, chessboardsize, flags)
    # If found, add object points, image points (after refining them)
    if ret == True:
        objpoints.append(objp)
        cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
        imgpoints.append(corners)
        
        cv2.drawChessboardCorners(img, chessboardsize, corners, ret)
        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        plt.figure(figsize=(10, 7))
        plt.imshow(img_rgb)
        plt.title(f'Chessboard Corners: {fname}')
        plt.axis('off')
        plt.show()
    else:
        print(f"Chessboard corners not found in {fname}")

if len(objpoints) == 0 or len(imgpoints) == 0:
    raise ValueError("No valid images found for calibration.")

N_OK = len(objpoints)
K = np.zeros((3, 3))
D = np.zeros((4, 1))
rvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(N_OK)]
tvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(N_OK)]

 # Perform fisheye calibration
try:
    rms, _, _, _, _ = cv2.fisheye.calibrate(
        objpoints,
        imgpoints,
        _img_shape[::-1],
        K,
        D,
        rvecs,
        tvecs,
        calibration_flags,  # cv2.fisheye.CALIB_RECOMPUTE_EXTRINSIC throws error
        (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-6)
    )

    print("Found " + str(N_OK) + " valid images for calibration")
    print("DIM=" + str(_img_shape[::-1]))
    print("K=np.array(" + str(K.tolist()) + ")")
    print("D=np.array(" + str(D.tolist()) + ")")

except cv2.error as e:
    print("Calibration failed:", e)

I have tried using normal camera calibrate too. It looks like this:

From the normal camera calibration. I am trying to make it as better as possible as I will be detecting stars and patterns later with the distortion parameters.