【语义分割】用labelme制作VOC数据集

使用pip安装labelme；
使用cmd启动labelme标注界面；
labelme
labels.txt存放分类数据。

自己修改下labelme2voc.py

python labelme2voc.py --labels=labels.txt input_folder data_dataset_voc

# -*- coding: utf-8 -*-
#!/usr/bin/env python
"""
Created on Thu Sep 19 15:46:16 2019

@author: Andrea
"""



from __future__ import print_function

import argparse
import glob
import json
import os
import os.path as osp
import sys

import numpy as np
import PIL.Image

import labelme
from sklearn.model_selection import train_test_split


def main():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument('input_dir', help='input annotated directory')
    parser.add_argument('output_dir', help='output dataset directory')
    parser.add_argument('--labels', help='labels file', required=True)
    args = parser.parse_args()


    if osp.exists(args.output_dir):
        print('Output directory already exists:', args.output_dir)
#        sys.exit(1)
    else:
        os.makedirs(args.output_dir)
        os.makedirs(osp.join(args.output_dir, 'JPEGImages'))
        os.makedirs(osp.join(args.output_dir, 'SegmentationClass'))
    #    os.makedirs(osp.join(args.output_dir, 'SegmentationClassPNG'))
        os.makedirs(osp.join(args.output_dir, 'SegmentationClassVisualization'))
    saved_path = args.output_dir
    if not os.path.exists(os.path.join(saved_path , 'ImageSets','Segmentation')):
        os.makedirs(os.path.join(saved_path , 'ImageSets','Segmentation'))
    print('Creating dataset:', args.output_dir)

    class_names = []
    class_name_to_id = {}
    for i, line in enumerate(open(args.labels).readlines()):
        print(i)
        class_id = i+1 # starts with -1
        class_name = line.strip()
        class_name_to_id[class_name] = class_id
        if class_id == -1:
            assert class_name == '__ignore__'
            continue
        elif class_id == 0:
            assert class_name == '_background_'
        class_names.append(class_name)
    class_names = tuple(class_names)
    print('class_names:', class_names)
    out_class_names_file = osp.join(args.output_dir, 'class_names.txt')
    with open(out_class_names_file, 'w') as f:
        f.writelines('\n'.join(class_names))
    print('Saved class_names:', out_class_names_file)

    colormap = labelme.utils.label_colormap(255)

    for label_file in glob.glob(osp.join(args.input_dir, '*.json')):
        print('Generating dataset from:', label_file)
        try:
            with open(label_file) as f:
                base = osp.splitext(osp.basename(label_file))[0]
                out_img_file = osp.join(
                    args.output_dir, 'JPEGImages', base + '.jpg')
    #            out_lbl_file = osp.join(
    #                args.output_dir, 'SegmentationClass', base + '.npy')
    #                args.output_dir, 'SegmentationClass', base + '.npy')
                out_png_file = osp.join(
                    args.output_dir, 'SegmentationClass', base + '.png')
                out_viz_file = osp.join(
                    args.output_dir,
                    'SegmentationClassVisualization',
                    base + '.jpg',
                )
    
                data = json.load(f)
    
                img_file = osp.join(label_file.split('.json')[0]+'.jpg')
                img = np.asarray(PIL.Image.open(img_file))
                PIL.Image.fromarray(img).save(out_img_file)
    
    
                print('class_name_to_id:',class_name_to_id)
                lbl = labelme.utils.shapes_to_label(
                    img_shape=img.shape,
                    shapes=data['shapes'],
                    label_name_to_value=class_name_to_id,
                )
                labelme.utils.lblsave(out_png_file, lbl)
    
    #            np.save(out_lbl_file, lbl)
    
                viz = labelme.utils.draw_label(
                    lbl, img, class_names, colormap=colormap)
                PIL.Image.fromarray(viz).save(out_viz_file)
        except:
            with open('wrongdata.txt','w') as f:
                f.write(label_file+'\n')
            print('这张图像有错误')
            continue
            
    #6.split files for txt
    txtsavepath = os.path.join(saved_path , 'ImageSets','Segmentation')
    ftrainval = open(os.path.join(txtsavepath,'trainval.txt'), 'w')
    ftest = open(os.path.join(txtsavepath,'test.txt'), 'w')
    ftrain = open(os.path.join(txtsavepath,'train.txt'), 'w')
    fval = open(os.path.join(txtsavepath,'val.txt'), 'w')
    total_files = os.listdir(osp.join(args.output_dir, 'SegmentationClass'))
    total_files = [i.split("/")[-1].split(".png")[0] for i in total_files]
    #test_filepath = ""
    for file in total_files:
        ftrainval.write(file + "\n")
    #test
    #for file in os.listdir(test_filepath):
    #    ftest.write(file.split(".jpg")[0] + "\n")
    #split
    train_files,val_files = train_test_split(total_files,test_size=0.15,random_state=42)
    #train
    for file in train_files:
        ftrain.write(file + "\n")
    #val
    for file in val_files:
        fval.write(file + "\n")
    
    ftrainval.close()
    ftrain.close()
    fval.close()
    ftest.close()



if __name__ == '__main__':
    main()

运行完毕生成VOC数据集。

如果需要制作【目标检测】数据集：labelme 标注矩形检测数据格式转 VOC 数据集格式。

可参考：http://spytensor.com/index.php/archives/35/?hutoto=th9jn1&labule=dmrxn1