How to use the labelme.utils.draw_label function in labelme

def test_draw_label():
    img, lbl, label_names = _get_img_and_lbl()

    viz = labelme.utils.draw_label(lbl, img, label_names=label_names)
    assert viz.shape[:2] == img.shape[:2] == lbl.shape[:2]
    assert viz.dtype == np.uint8

label_name_to_value = {'_background_': 0}
    for shape in sorted(region['objects'], key=lambda x: x['name']):
        label_name = shape['name']

        if label_name in label_name_to_value:
            label_value = label_name_to_value[label_name]
        else:
            label_value = len(label_name_to_value)
            label_name_to_value[label_name] = label_value

    lbl = shapes_to_label(img.shape, region['objects'], label_name_to_value)
    label_names = [None] * (max(label_name_to_value.values()) + 1)
    for name, value in label_name_to_value.items():
        label_names[value] = name

    lbl_viz = labelme.utils.draw_label(lbl, img, label_names)
    PIL.Image.fromarray(lbl_viz).save(out_colorize_file)

    # save voc annotation to xml file
    with open(out_xml_file, 'wb') as f:
        f.write(lxml.etree.tostring(xml, pretty_print=True))

    if region['seg_data'] is not None:
        out_png_file = osp.join(voc_dir, 'SegmentationClassPNG', region['base'] + '.png')
        region['seg_data'].save(out_png_file)

else:
            label_value = len(label_name_to_value)
            label_name_to_value[label_name] = label_value

    # label_values must be dense
    label_values, label_names = [], []
    for ln, lv in sorted(label_name_to_value.items(), key=lambda x: x[1]):
        label_values.append(lv)
        label_names.append(ln)
    assert label_values == list(range(len(label_values)))

    lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)

    captions = ['{}: {}'.format(lv, ln)
                for ln, lv in label_name_to_value.items()]
    lbl_viz = utils.draw_label(lbl, img, captions)

    PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
    PIL.Image.fromarray(lbl).save(osp.join(out_dir, 'label.png'))
    PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))

    with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
        for lbl_name in label_names:
            f.write(lbl_name + '\n')

    # print('\n  info.yaml is being replaced by label_names.txt')
    info = dict(label_names=label_names)
    with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
        yaml.safe_dump(info, f, default_flow_style=False)

img = utils.img_b64_to_arr(imageData)
            else:
                img_file = osp.join(osp.dirname(label_file), data['imagePath'])
                img = np.asarray(PIL.Image.open(img_file))
            PIL.Image.fromarray(img).save(out_img_file)

            lbl = labelme.utils.shapes_to_label(
                img_shape=img.shape,
                shapes=data['shapes'],
                label_name_to_value=class_name_to_id,
            )
            labelme.utils.lblsave(out_png_file, lbl)

            np.save(out_lbl_file, lbl)

            viz = labelme.utils.draw_label(
                lbl, img, class_names, colormap=colormap)
            PIL.Image.fromarray(viz).save(out_viz_file)

label_name = shape['label']

            if label_name in label_name_to_value:
                label_value = label_name_to_value[label_name]
            else:
                label_value = len(label_name_to_value)
                label_name_to_value[label_name] = label_value

        lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)
        label_names = [None] * (max(label_name_to_value.values()) + 1)
        for name, value in label_name_to_value.items():
            if args.label_dict is not None:
                name = fst2snd_dict[name]
            label_names[value] = name

        lbl_viz = utils.draw_label(lbl, img, label_names)
        PIL.Image.fromarray(lbl_viz).save(out_colorize_file)

        # save voc annotation to xml file
        with open(out_xml_file, 'wb') as f:
            f.write(lxml.etree.tostring(xml, pretty_print=True))

def main():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('label_png', help='label PNG file')
    args = parser.parse_args()

    lbl = np.asarray(PIL.Image.open(args.label_png))

    logger.info('label shape: {}'.format(lbl.shape))
    logger.info('unique label values: {}'.format(np.unique(lbl)))

    lbl_viz = utils.draw_label(lbl)
    plt.imshow(lbl_viz)
    plt.show()