![](https://img.haomeiwen.com/i15042741/0030fc771462d0bd.png)
2、安装pyqt,搜索框输入pyqt,查询出来的列表,选中pyqt,如图
![](https://img.haomeiwen.com/i15042741/653d1601882a1738.png)
点击右下角apply,会出现下图
![](https://img.haomeiwen.com/i15042741/cead740dd3cdb67d.png)
继续点击apply,等待安装,安装成功截图,安装完成后,选中的框变成对勾
![](https://img.haomeiwen.com/i15042741/fc9285f3aa106ae8.png)
3、安装pyside2
![](https://img.haomeiwen.com/i15042741/22f4a35c7d8ea0cc.png)
同2步骤,点击右下角apply,再次点击apply,等待安装
截图示例:
![](https://img.haomeiwen.com/i15042741/eee9fa2132ae554f.png)
4、安装labelme
![](https://img.haomeiwen.com/i15042741/69a8386429a2768c.png)
同上,继续点击apply,等待安装。
![](https://img.haomeiwen.com/i15042741/e732334b6ded7cd8.png)
5、测试是否安装成功,在labelme点击
![](https://img.haomeiwen.com/i15042741/db1bf29f846db166.png)
弹出的命令框中输入 labelme ,回车
![](https://img.haomeiwen.com/i15042741/47b9a73abf9bdd7b.png)
弹出如下图,即安装成功
![](https://img.haomeiwen.com/i15042741/faa68be38571f954.png)
随意选取一张图片,进行选取
![](https://img.haomeiwen.com/i15042741/f798dd208da7ca6a.png)
保存后,会生成你选择图片的json数据。
使用命令,生成训练文件
C:\soft\Anaconda3\envs\labelme\Scripts\labelme_json_to_dataset.exe C:\01\source\01.json
生成成功后会生成一个文件夹,文件夹中就是需要训练的数据文件
![](https://img.haomeiwen.com/i15042741/6930909ddd5f2788.png)
因版本问题,未生成info.yaml
注: 生成info.yaml的解决方法。
修改的文件路径:
\Anaconda3安装路径\envs\labelme\Lib\site-packages\labelme\cli
文件名字:
json_to_dataset.py
新增的代码
import yaml
![](https://img.haomeiwen.com/i15042741/c2bded1bd036da32.png)
logger.warning('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)
![](https://img.haomeiwen.com/i15042741/916f21a55410ef13.png)
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