1.anaconda 安装
anaconda下载地址如下：
官网
一直下一步，注意下面这个地方
同时手动配置环境变量，下面三个路径需添加
Anaconda安装路径
Anaconda安装路径\Scripts
Anaconda安装路径\Library\bin
最后测试一下
cmd
依次输入
conda --version
conda info
activate
python
均有对应版本显示，说明均安装成功
2.labelme
接下来一切在 anaconda 虚拟环境里操作
打开 anaconda prompt
输入
conda create -n labelme python=3.8
加载模块的过程中，中间一定要点 Y
运行结束后，输入
conda env list
查看当前已安装的虚拟环境；
之后进行激活，并安装相关的库
conda activate labelme
conda install pyqt
conda install pillow
安装 labelme
pip install labelme==3.16.2
中途也会需要按[Y/N]? Y
3.labelme 使用
每次在 anaconda prompt 中输入下面代码块进行激活
activate labelme
随后打开 labelme
labelme
弹出这个界面
选择多边形标注完之后，并设置标签名字，点击保存。
4. json文件转图片
修改两个地方即可使用
- 将 35 行的 default 设置为自己的输出文件夹；
- 将106行的 json_path 设置为 labelme 保存 json的文件夹；
# !/usr/bin/env python
# -*- coding:utf-8 -*-
# @Time : 2022.04
# @Author : 绿色羽毛
# @Email : lvseyumao@foxmail.com
# @Blog : https://blog.csdn.net/ViatorSun
# @Paper :
# @arXiv :
# @version: "1.0"
# @Note :
import argparse
import base64
import json
import os
import os.path as osp
import imgviz
import PIL.Image
from labelme.logger import logger
from labelme import utils
def main(json_file):
logger.warning( "This script is aimed to demonstrate how to convert the "
"JSON file to a single image dataset." )
logger.warning( "It won't handle multiple JSON files to generate a "
"real-use dataset." )
parser = argparse.ArgumentParser()
# parser.add_argument("json_file")
parser.add_argument("-o", "--out", default='/Users/viatorsun/Desktop/Demo/Tomato/')
args = parser.parse_args()
# json_file = args.json_file
# json_file = '/Users/viatorsun/Desktop/Demo/Tomato/TomatoJSON/HIMG_20211108_144919_1.json'
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
out_dir = osp.join(osp.dirname(json_file), out_dir)
if not osp.exists(out_dir):
os.mkdir(out_dir)
img_name = osp.basename(json_file)[:-5]
data = json.load(open(json_file))
imageData = data.get("imageData")
if not imageData:
imagePath = os.path.join(os.path.dirname(json_file), 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 sorted(data["shapes"], key=lambda x: x["label"]):
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
lbl, _ = utils.shapes_to_label( img.shape, data["shapes"], label_name_to_value )
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = imgviz.label2rgb( lbl, imgviz.asgray(img), label_names=label_names, loc="rb" )
# 原图保存
Images = osp.join(out_dir, 'PNGImages')
if not osp.exists(Images):
os.mkdir(Images)
PIL.Image.fromarray(img).save(osp.join(Images ,img_name + ".png"))
# 标签保存
Labels = osp.join(out_dir,'SegmentLabels')
if not osp.exists(Labels):
os.mkdir(Labels)
utils.lblsave(osp.join(Labels, img_name + ".png" ), lbl)
with open(osp.join(out_dir, 'SegmentLabels', img_name + ".txt"), "w") as f:
for lbl_name in label_names:
f.write(lbl_name + "\n")
# 合成图保存
Label_viz = osp.join(out_dir, "Label_viz")
if not osp.exists(Label_viz):
os.mkdir(Label_viz)
PIL.Image.fromarray(lbl_viz).save(osp.join(Label_viz, img_name + ".png"))
logger.info("Saved to: {}".format(out_dir))
if __name__ == "__main__":
json_path = '/Users/viatorsun/Desktop/Demo/Tomato/TomatoJSON'
for root , dirs , files in os.walk(json_path):
for file in files:
if file == '.DS_Store':
continue
json_path = os.path.join(root ,file)
print(json_path)
main(json_path)
参考博客
anaconda安装
labelme使用
json转换
