lableme安装目录下  你安装的盘C:\Anaconda\Lib\site-packages\labelme\cli目录，可以看到json_to_dataset.py文件

替换下面程序后，在终端输入labelme_json_to_dataset + (.json文件的文件夹)

#!/usr/bin/python
# -*- coding: UTF-8 -*-
# !H:\Anaconda3\envs\new_labelme\python.exe
import argparse
import json
import os
import os.path as osp
import base64
import warnings

import PIL.Image
import yaml

from labelme import utils

import cv2
import numpy as np
from skimage import img_as_ubyte


# from sys import argv

def main():
    warnings.warn("This script is aimed to demonstrate how to convert the\n"
                  "JSON file to a single image dataset, and not to handle\n"
                  "multiple JSON files to generate a real-use dataset.")

    parser = argparse.ArgumentParser()
    parser.add_argument('json_file')
    parser.add_argument('-o', '--out', default=None)
    args = parser.parse_args()

    json_file = args.json_file

    # freedom
    list_path = os.listdir(json_file)
    print('freedom =', json_file)
    for i in range(0, len(list_path)):
        path = os.path.join(json_file, list_path[i])
        if os.path.isfile(path):

            data = json.load(open(path))
            img = utils.img_b64_to_arr(data['imageData'])
            lbl, lbl_names = utils.labelme_shapes_to_label(img.shape, data['shapes'])

            captions = ['%d: %s' % (l, name) for l, name in enumerate(lbl_names)]

            lbl_viz = utils.draw_label(lbl, img, captions)
            out_dir = osp.basename(path).replace('.', '_')
            save_file_name = out_dir
            out_dir = osp.join(osp.dirname(path), out_dir)

            if not osp.exists(json_file + '\\' + 'labelme_json'):
                os.mkdir(json_file + '\\' + 'labelme_json')
            labelme_json = json_file + '\\' + 'labelme_json'

            out_dir1 = labelme_json + '\\' + save_file_name
            if not osp.exists(out_dir1):
                os.mkdir(out_dir1)

            PIL.Image.fromarray(img).save(out_dir1 + '\\' + save_file_name + '_img.png')
            PIL.Image.fromarray(lbl).save(out_dir1 + '\\' + save_file_name + '_label.png')

            PIL.Image.fromarray(lbl_viz).save(out_dir1 + '\\' + save_file_name +
                                              '_label_viz.png')

            if not osp.exists(json_file + '\\' + 'mask_png'):
                os.mkdir(json_file + '\\' + 'mask_png')
            mask_save2png_path = json_file + '\\' + 'mask_png'
            ################################
            # mask_pic = cv2.imread(out_dir1+'\\'+save_file_name+'_label.png',)
            # print('pic1_deep:',mask_pic.dtype)

            mask_dst = img_as_ubyte(lbl)  # mask_pic
            print('pic2_deep:', mask_dst.dtype)
            cv2.imwrite(mask_save2png_path + '\\' + save_file_name + '_label.png', mask_dst)
            ##################################

            with open(osp.join(out_dir1, 'label_names.txt'), 'w') as f:
                for lbl_name in lbl_names:
                    f.write(lbl_name + '\n')

            warnings.warn('info.yaml is being replaced by label_names.txt')
            info = dict(label_names=lbl_names)
            with open(osp.join(out_dir1, 'info.yaml'), 'w') as f:
                yaml.safe_dump(info, f, default_flow_style=False)

            print('Saved to: %s' % out_dir1)


if __name__ == '__main__':
    # base64path = argv[1]
    main()

会生成mask_png文件与 labelme_json