labelme安装和使用教程

design by ht

一、安装教程

基于视频教程:https://www.bilibili.com/video/BV16b411G7kB?p=1修改

Step 1：安装Anaconda

教学视频：https://www.bilibili.com/video/av21676110?from=search&seid=9067865995586509468

Blog教学：https://blog.csdn.net/a838771230/article/details/80968049
Attention：下载最新版本、安装时添加环境变量

Step 2：安装Labelme

Blog教学：https://blog.csdn.net/DuanTuiXiaoALi/article/details/78295053

1.打开 Anaconda promt

2.查看python版本 输入 python -V

3.输入 conda create–name=labelme python=3.X（根据下载的python版本而定）

4.输入 conda activate labelme

5.输入 pip install pyqt5

6.输入 conda install pillow

7.输入 pip install labelme==3.16.2

step3 :修改参数

Blog教学：https://blog.csdn.net/yql_617540298/article/details/81110685
1.在lableme安装目录下有C:\Users\ht\anaconda3\envs\labelme\Lib\site-packages\labelme\cli目录，可以看到json_to_dataset.py文件

2.修改json_to_dataset.py

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

import PIL.Image
import yaml

from labelme import utils
import base64

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
    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)
     
    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))
            
            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)
            
            out_dir = osp.basename(count[i]).replace('.', '_')
            out_dir = osp.join(osp.dirname(count[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'))
            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()

二、使用教程

1.打开 Anaconda promt

2.输入 activate labelme

3.输入 labelme

4.开始标记

5.在 lableme安装目录下有C:\Users\ht\anaconda3\envs\labelme\Scripts目录，可以看到labelme_json_to_dataset.exe文件

6.输入 cd C:\Users\ht\anaconda3\envs\labelme\Scripts

7.标注好的所有json文件全放到文件夹F:\Labelme\json

8.输入 labelme_json_to_dataset.exe F:\Labelme\json

9.生成的文件在 C:\Users\ht\anaconda3\envs\labelme\Scripts