转载：批量将.json文件转换成img.png；label.png；label_name.txt；label_viz.pgn；info.yaml文件

转载：https://blog.csdn.net/gaoyi135/article/details/103870646

labelme 安装：

conda create -n labelme python=3.6
source activate labelme
conda install pyqt
pip install labelme

转换为mask.png 格式：

直接修改labelme中的json_to_dataset.py文件

import argparse
import json
import os
import os.path as osp
import warnings
import copy
import numpy as np
import PIL.Image
from skimage import io
import yaml
from labelme import utils

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('json_file')   # 标注文件json所在的文件夹
    parser.add_argument('-o', '--out', default=None)
    args = parser.parse_args()

    json_file = args.json_file

    list = os.listdir(json_file)   # 获取json文件列表
    for i in range(0, len(list)):
        path = os.path.join(json_file, list[i])  # 获取每个json文件的绝对路径
        filename = list[i][:-5]       # 提取出.json前的字符作为文件名，以便后续保存Label图片的时候使用
        extension = list[i][-4:]
        if extension == 'json':
            if os.path.isfile(path):
                data = json.load(open(path))
                img = utils.image.img_b64_to_arr(data['imageData'])  # 根据'imageData'字段的字符可以得到原图像
                # lbl为label图片（标注的地方用类别名对应的数字来标，其他为0）lbl_names为label名和数字的对应关系字典
                lbl, lbl_names = utils.shape.labelme_shapes_to_label(img.shape, data['shapes'])   # data['shapes']是json文件中记录着标注的位置及label等信息的字段

                #captions = ['%d: %s' % (l, name) for l, name in enumerate(lbl_names)]
                #lbl_viz = utils.draw.draw_label(lbl, img, captions)
                out_dir = osp.basename(list[i])[:-5]+'_json'
                out_dir = osp.join(osp.dirname(list[i]), out_dir)
                if not osp.exists(out_dir):
                    os.mkdir(out_dir)

                PIL.Image.fromarray(img).save(osp.join(out_dir, '{}_source.png'.format(filename)))
                PIL.Image.fromarray(lbl).save(osp.join(out_dir, '{}_mask.png'.format(filename)))
                #PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, '{}_viz.jpg'.format(filename)))

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

                warnings.warn('info.yaml is being replaced by label_names.txt')
                info = dict(label_names=lbl_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()

## Labeleme png格式可视化：查看PennFudanPed数据集中的图片和mask等数据
from PIL import Image
mask = Image.open('label.png').convert('L')

mask.putpalette([0, 0, 0,     #　putpalette给对象加上调色板，相当于上色：背景为黑色，目标１为红色，目标2为黄色，目标3为橙色（如果你的图中有更多的目标，可以自行添加更多的调色值）
                 255, 0, 0,
                 255, 255, 0,
                 255, 153, 0])
mask.show()  # 查看mask上色后的效果

## 也可以直接代码中将labelme json 转成mask数据，不用单独转了
def __init__(self, root, transforms=None):
    self.root = root
    self.transforms = transforms

    self.imgs = list(sorted(os.listdir(os.path.join(root, 'PNGImages'))))
    self.jsons = list(sorted(os.listdir(os.path.join(root, 'JSONFiles'))))

def __getitem__(self, idx):
    img_path = os.path.join(self.root, 'PNGImages', self.imgs[idx])
    json_path = os.path.join(self.root, 'JSONFiles', self.jsons[idx])
        
    img = Image.open(img_path).convert('RGB')
    data = json.load(open(json_path))
    #mask = Image.open(mask_path).convert('L')  # 使用mask.mode可以知道读进来的mask图片格式是I，也就是32位整形数据，因此要转化为L格式
    # 改成直接从json文件中读取的形式
    mask, label_names = utils.shape.labelme_shapes_to_label(img.size, data['shapes'])

    obj_ids = np.unique(mask)  # 去除数组中重复的数字，并进行排序
    obj_ids = obj_ids[1:]  # 去除第一个索引，因为它是背景
    masks = mask == obj_ids[:, None, None]  # split the color-encoded mask into a set of binary masks

直接利用labelme中的函数：

import argparse
import json
import os
import os.path as osp
import warnings
import numpy as np
import PIL.Image
import yaml
from labelme import utils
def main():
    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 = os.listdir(json_file) 
    for i in range(0, len(list)):
        path = os.path.join(json_file, list[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(list[i]).replace('.', '_')
            out_dir = osp.join(osp.dirname(list[i]), out_dir)
            if not osp.exists(out_dir):
                os.mkdir(out_dir)
            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 lbl_names:
                    f.write(lbl_name + '
')
            warnings.warn('info.yaml is being replaced by label_names.txt')
            info = dict(label_names=lbl_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()