lableme制作语义分割数据集

首先用在anaconda的命令行输入labelme打开，然后开始标注，标注完成后，将原图和标注后的json文件放在同一个文件夹下，如命名为annocated，并制作文件夹labels，lables文件夹下面仅含有一个文件lables.txt，此文件下输入__ignore__，_background，后面每行再加上自己标注的类别

接着运行如下程序

#!/usr/bin/env python

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


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)
    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'))
    print('Creating dataset:', args.output_dir)

    class_names = []
    class_name_to_id = {}
    for i, line in enumerate(open(args.labels).readlines()):
        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("label_file:",label_file)
        print('Generating dataset from:', label_file)
        with open(label_file) as f:
            base = osp.splitext(osp.basename(label_file))[0]

            print("base",base)

            out_img_file = osp.join(
                args.output_dir, 'JPEGImages', base + '.jpg')
            out_lbl_file = osp.join(
                args.output_dir, 'SegmentationClass', base + '.npy')
            out_png_file = osp.join(
                args.output_dir, 'SegmentationClassPNG', base + '.png')
            out_viz_file = osp.join(
                args.output_dir,
                'SegmentationClassVisualization',
                base + '.jpg',
            )

            data = json.load(f)

            #img_file = osp.join(osp.dirname(label_file), data['imagePath'])

            img_file = osp.join("D://1225data//annocated",base + '.png')

            img = np.asarray(PIL.Image.open(img_file))
            PIL.Image.fromarray(img).save(out_img_file)

            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)


if __name__ == '__main__':
    main()

注意img_file这一行需要修改一下路径，命令行参数输入如下：

--input

"./annocated"

--output

"./随便命名一个"

--labels

"./labels/labels.txt"

如果报错就换绝对路径，注意每次运行程序之后可能会新建一个文件夹在--output下，需要删除再运行程序