在此，对于labelme安装就不再赘述了，初学者可参考github地址https://github.com/wkentaro/labelme

这里，我修改了一下json_to_dataset.py文件，就可以批量生成了，执行 python <文件夹名称>即可：

import argparse
import base64
import json
import os
import os.path as osp
import warnings

import PIL.Image
import yaml

from labelme import utils


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
    
    list_file = os.listdir(json_file)
    
#     if args.out is None:
#         out_dir = osp.basename(json_file).replace('.', '_')
#         out_dir = osp.join(osp.dirname(json_file), out_dir)
#     else:
#         out_dir = args.out
#     if not osp.exists(out_dir):
#         os.mkdir(out_dir)
        
    for i in range(0,len(list_file)):
        if args.out is None:
            out_dir = osp.basename(list_file[i]).replace('.', '_')
            out_dir = osp.join(osp.dirname(list_file[i]), out_dir)
        else:
            out_dir = args.out
        if not osp.exists(out_dir):
            os.mkdir(out_dir)
        
        path = os.path.join(json_file, list_file[i])
        data = json.load(open(path))

        if data['imageData']:
            imageData = data['imageData']
        else:
            imagePath = os.path.join(os.path.dirname(json_file), data['imagePath'])
            with open(imagePath, 'rb') as f:
                imageData = f.read()
                imageData = base64.b64encode(imageData).decode('utf-8')
        img = utils.img_b64_to_arr(imageData)

        label_name_to_value = {'_background_': 0}
        for shape in sorted(data['shapes'], key=lambda x: x['label']):
            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():
            label_names[value] = name
        lbl_viz = utils.draw_label(lbl, img, label_names)

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

        warnings.warn('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)

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


if __name__ == '__main__':
    main()

如此处理完成之后，每一张图片都将生成一个以_json结尾的文件夹，其中对应5个文件。然而要取出所有图像的label.png文件每次一张一张图片的复制太过于繁琐，在此我们用代码去进行批量处理，将每一张图片的label.png复制到新的文件夹中。

import cv2 as cv  
import random  
import glob  
import os
from PIL import Image
import shutil

def get_samples(foldername,savePath):
    print('savePath:',savePath)
    if os.path.exists(savePath) is False:
        os.makedirs(savePath)
   
    filenames = os.listdir(foldername)
            
    for filename in filenames:  
        full_path = os.path.join(foldername, filename)
        new_name = filename[:-5]+'.png'
        label_png = os.listdir(full_path)[2]
        #os.rename(os.path.join(filename, label_png),os.path.join(filename, name))
        shutil.copy(os.path.join(full_path, label_png), os.path.join(savePath, label_png))
        os.rename(os.path.join(savePath, label_png),os.path.join(savePath, new_name))
        #print(os.listdir(filename))
        
savePath = 'E:\\label\\yejian\\'  
get_samples('E:\\水尺2\\yejian',savePath )       

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