利用labelme生成的json文件批量转化为图片

代码如下

import argparse
import json
import os
import os.path as osp
import warnings
 
import PIL.Image
import yaml
 
from labelme import utils
import base64

import numpy as np
from skimage import img_as_ubyte
 
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
 
    count = os.listdir(json_file) 
    for i in range(0, len(count)):
        path = os.path.join(json_file, count[i])
        if os.path.isfile(path):
            data = json.load(open(path))

			##############################
			#save_diretory
            out_dir1 = osp.basename(path).replace('.', '_')
            save_file_name = out_dir1
            out_dir1 = osp.join(osp.dirname(path), out_dir1)

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

            out_dir2 = labelme_json + '\\' + save_file_name
            if not osp.exists(out_dir2):
                os.mkdir(out_dir2)

			#########################

            if data['imageData']:
                imageData = data['imageData']
            else:
                imagePath = os.path.join(os.path.dirname(path), 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 data['shapes']:
                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
            
            # 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(out_dir2+'\\'+save_file_name+'_img.png')
            #PIL.Image.fromarray(lbl).save(osp.join(out_dir2, 'label.png'))
            utils.lblsave(osp.join(out_dir2, save_file_name+'_label.png'), lbl)
            PIL.Image.fromarray(lbl_viz).save(out_dir2+'\\'+save_file_name+
            '_label_viz.png')
 
            with open(osp.join(out_dir2, '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_dir2, 'info.yaml'), 'w') as f:
                yaml.safe_dump(info, f, default_flow_style=False)
				
				
			#save png to another directory
            if not osp.exists(json_file + '\\' + 'mask_png'):
                os.mkdir(json_file + '\\' + 'mask_png')
            mask_save2png_path = json_file + '\\' + 'mask_png'

            utils.lblsave(osp.join(mask_save2png_path, save_file_name+'_label.png'), lbl)
 
            print('Saved to: %s' % out_dir2)
            
if __name__ == '__main__':
    main()

使用方法：
在命令行下，进入该.py文件所在目录，输入像这样的指令：python json_to_dataset2.py F:\deeplearning_cv\train_object_segmentation_bottle\dataset\Annotations

生成文件如下所示，有两个：