將labelme生成的json文件轉換成png圖

將labelme生成的json文件轉換成png圖

我圖片的每個標記只有一類，所以轉換成png圖後，png只有0和1像素，因爲單通道圖的範圍是0~255，0和1的區別很小，下面的程序可以對你標記的mask做可視化，但是生成的png還是像素值爲0和1的，如果自己有需要可以改像素值。

首先要把所有你需要轉換的json文件放在一個文件夾裏，然後把這個文件夾的路徑填入到下面的json_file就可以了。程序運行完會在文件下生成兩個文件夾：mask和mask_viz

mask中有png圖，和兩個標籤文件

mask_viz中是可視化的mask圖

程序：

#!/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.")

    json_file = "/home/jfw/needlabel/6_json/"

    # 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('.', '_')
            out_dir = osp.basename(path).split('.json')[0]
            save_file_name = out_dir
            # out_dir = osp.join(osp.dirname(path), out_dir)

            if not osp.exists(json_file + 'mask'):
                os.mkdir(json_file + 'mask')
            maskdir = json_file + 'mask'

            if not osp.exists(json_file + 'mask_viz'):
                os.mkdir(json_file + 'mask_viz')
            maskvizdir = json_file + 'mask_viz'

            out_dir1 = maskdir
            # 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 + '.png')

            PIL.Image.fromarray(lbl_viz).save(maskvizdir + '/' + 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()