How to use the histomicstk.utils function in histomicstk

def sample_pixels(args):
    """Version of histomicstk.utils.sample_pixels that takes a Namespace
    and handles the special default values.
    """
    args = (args._asdict() if hasattr(args, '_asdict') else vars(args)).copy()
    for k in 'magnification', 'sample_fraction', 'sample_approximate_total':
        if args[k] == -1:
            del args[k]
    return htk_utils.sample_pixels(**args)

M x N eigenvector angle image for E(:,:,0) in radians
        see eigen.py. Oriented parallel to vessel structures.

    References
    ----------
    .. [#] Frangi, Alejandro F., et al. "Multiscale vessel enhancement
       filtering." Medical Image Computing and Computer-Assisted
       Interventation. MICCAI98. Springer Berlin Heidelberg,1998. 130-137.

    """

    # calculate hessian matrix
    H = sigma ** 2 * htk_utils.hessian(im_input, sigma)

    # calculate eigenvalue image
    E, V1, V2 = htk_utils.eigen(H)

    # compute blobness measures
    Deviation = E[:, :, 0]/(E[:, :, 1] + np.spacing(1))
    Frobenius = np.sqrt(E[:, :, 0]**2 + E[:, :, 1]**2)

    # calculate angles for 'Theta'
    Theta = np.arctan2(V1[:, :, 1], V1[:, :, 0])

    return Deviation, Frobenius, E, Theta

'width': args.analysis_roi[2],
            'height': args.analysis_roi[3],
            'units': 'base_pixels'
        }
    # =========================================================================
    if is_wsi:
        print('\n>> Computing foreground fraction of all tiles ...\n')

        start_time = time.time()

        num_tiles = ts.getSingleTile(**it_kwargs)['iterator_range']['position']

        print('Number of tiles = {}'.format(num_tiles))

        if process_whole_image:
            tile_fgnd_frac_list = htk_utils.compute_tile_foreground_fraction(
                args.inputImageFile, im_fgnd_mask_lres, fgnd_seg_scale,
                it_kwargs
            )

        else:

            tile_fgnd_frac_list = np.full(num_tiles, 1.0)

        num_fgnd_tiles = np.count_nonzero(
            tile_fgnd_frac_list >= args.min_fgnd_frac)

        percent_fgnd_tiles = 100.0 * num_fgnd_tiles / num_tiles

        fgnd_frac_comp_time = time.time() - start_time

        print('Number of foreground tiles = {:d} ({:2f}%)'.format(