labelme的安装：
在Pycharm终端中输入：

pip install labelme==3.16.2 -i https://pypi.tuna.tsinghua.edu.cn/simple

同理，缺什么包就安装什么包。
在main声明里面输入自己的路径，运行就行了。

from __future__ import print_function

import argparse
import glob
import json
import os
import os.path as osp
import sys
import numpy as np
import PIL.Image
import base64
import labelme
from labelme import utils
from sklearn.model_selection import train_test_split


def main(args):
    os.makedirs(args.output_dir, exist_ok=True)
    os.makedirs(osp.join(args.output_dir, "JPEGImages"), exist_ok=True)
    os.makedirs(osp.join(args.output_dir, "SegmentationClass"), exist_ok=True)
    os.makedirs(osp.join(args.output_dir, "SegmentationClassPNG"), exist_ok=True)
    os.makedirs(osp.join(args.output_dir, "SegmentationClassVisualization"), exist_ok=True)

    saved_path = args.output_dir
    os.makedirs(osp.join(saved_path, "ImageSets", "Segmentation"), exist_ok=True)
    print('Creating dataset:', args.output_dir)

    class_names = []
    class_name_to_id = {}
    for i, line in enumerate(open(args.labels).readlines()):
        print(i)
        class_id = i + 1  # starts with -1
        class_name = line.strip()
        class_name_to_id[class_name] = class_id
        if class_id == -1:
            assert class_name == '__ignore__'
            continue
        elif class_id == 0:
            assert class_name == '_background_'
        class_names.append(class_name)
    class_names = tuple(class_names)
    print('class_names:', class_names)
    out_class_names_file = osp.join(args.output_dir, 'class_names.txt')
    with open(out_class_names_file, 'w') as f:
        f.writelines('\n'.join(class_names))
    print('Saved class_names:', out_class_names_file)

    colormap = labelme.utils.label_colormap(255)  # pip install labelme==3.16.2 -i https://pypi.tuna.tsinghua.edu.cn/simple

    for label_file in glob.glob(osp.join(args.input_dir, '*.json')):
        print('Generating dataset from:', label_file)
        try:
            with open(label_file) as f:
                base = osp.splitext(osp.basename(label_file))[0]
                out_img_file = osp.join(args.output_dir, 'JPEGImages', base + '.jpg')
                #            out_lbl_file = osp.join(
                #                args.output_dir, 'SegmentationClass', base + '.npy')
                #                args.output_dir, 'SegmentationClass', base + '.npy')
                out_png_file = osp.join(args.output_dir, 'SegmentationClass', base + '.png')
                out_viz_file = osp.join(args.output_dir, 'SegmentationClassVisualization', base + '.jpg', )

                data = json.load(f)
                if data['imageData']:
                    imagedata = data['imageData']
                    img = utils.img_b64_to_arr(imagedata)
                    img_file = osp.join(label_file.split('.json')[0] + '.jpg')
                    PIL.Image.fromarray(img).save(out_img_file)

                print('class_name_to_id:', class_name_to_id)
                print(img.shape)
                print(data['shapes'])

                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)
        except:
            # with open('wrongdata.txt', 'w') as f:
            #     f.write(label_file + '\n')
            print('这张图像有错误')
            continue

    # 6.split files for txt
    txtsavepath = os.path.join(saved_path, 'ImageSets', 'Segmentation')
    ftrainval = open(os.path.join(txtsavepath, 'trainval.txt'), 'w')
    ftest = open(os.path.join(txtsavepath, 'test.txt'), 'w')
    ftrain = open(os.path.join(txtsavepath, 'train.txt'), 'w')
    fval = open(os.path.join(txtsavepath, 'val.txt'), 'w')
    total_files = os.listdir(osp.join(args.output_dir, 'SegmentationClass'))
    total_files = [i.split("/")[-1].split(".png")[0] for i in total_files]
    # test_filepath = ""
    for file in total_files:
        ftrainval.write(file + "\n")
    # test
    # for file in os.listdir(test_filepath):
    #    ftest.write(file.split(".jpg")[0] + "\n")
    # split
    train_files, val_files = train_test_split(total_files, test_size=0.15, random_state=42)
    # train
    for file in train_files:
        ftrain.write(file + "\n")
    # val
    for file in val_files:
        fval.write(file + "\n")

    ftrainval.close()
    ftrain.close()
    fval.close()
    ftest.close()


if __name__ == '__main__':
    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('--input_dir', type=str, help='input annotated directory', required=False, default=r'E:\labelmeSet')
    parser.add_argument('--output_dir', type=str, help='output dataset directory', required=False, default=r'E:\labelmeSet_VOC')
    parser.add_argument('--labels', type=str, help='labels file', required=False, default=r'E:\labelmeSet\labels.txt')
    argsInput = parser.parse_args()
    main(argsInput)