X_SSL_Net/tmp/VMamba-main/segmentation/tools/dataset_converters/voc_aug.py

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
from functools import partial

import numpy as np
from mmengine.utils import mkdir_or_exist, scandir, track_parallel_progress
from PIL import Image
from scipy.io import loadmat

AUG_LEN = 10582


def convert_mat(mat_file, in_dir, out_dir):
    data = loadmat(osp.join(in_dir, mat_file))
    mask = data['GTcls'][0]['Segmentation'][0].astype(np.uint8)
    seg_filename = osp.join(out_dir, mat_file.replace('.mat', '.png'))
    Image.fromarray(mask).save(seg_filename, 'PNG')


def generate_aug_list(merged_list, excluded_list):
    return list(set(merged_list) - set(excluded_list))


def parse_args():
    parser = argparse.ArgumentParser(
        description='Convert PASCAL VOC annotations to mmsegmentation format')
    parser.add_argument('devkit_path', help='pascal voc devkit path')
    parser.add_argument('aug_path', help='pascal voc aug path')
    parser.add_argument('-o', '--out_dir', help='output path')
    parser.add_argument(
        '--nproc', default=1, type=int, help='number of process')
    args = parser.parse_args()
    return args


def main():
    args = parse_args()
    devkit_path = args.devkit_path
    aug_path = args.aug_path
    nproc = args.nproc
    if args.out_dir is None:
        out_dir = osp.join(devkit_path, 'VOC2012', 'SegmentationClassAug')
    else:
        out_dir = args.out_dir
    mkdir_or_exist(out_dir)
    in_dir = osp.join(aug_path, 'dataset', 'cls')

    track_parallel_progress(
        partial(convert_mat, in_dir=in_dir, out_dir=out_dir),
        list(scandir(in_dir, suffix='.mat')),
        nproc=nproc)

    full_aug_list = []
    with open(osp.join(aug_path, 'dataset', 'train.txt')) as f:
        full_aug_list += [line.strip() for line in f]
    with open(osp.join(aug_path, 'dataset', 'val.txt')) as f:
        full_aug_list += [line.strip() for line in f]

    with open(
            osp.join(devkit_path, 'VOC2012/ImageSets/Segmentation',
                     'train.txt')) as f:
        ori_train_list = [line.strip() for line in f]
    with open(
            osp.join(devkit_path, 'VOC2012/ImageSets/Segmentation',
                     'val.txt')) as f:
        val_list = [line.strip() for line in f]

    aug_train_list = generate_aug_list(ori_train_list + full_aug_list,
                                       val_list)
    assert len(aug_train_list) == AUG_LEN, 'len(aug_train_list) != {}'.format(
        AUG_LEN)

    with open(
            osp.join(devkit_path, 'VOC2012/ImageSets/Segmentation',
                     'trainaug.txt'), 'w') as f:
        f.writelines(line + '\n' for line in aug_train_list)

    aug_list = generate_aug_list(full_aug_list, ori_train_list + val_list)
    assert len(aug_list) == AUG_LEN - len(
        ori_train_list), 'len(aug_list) != {}'.format(AUG_LEN -
                                                      len(ori_train_list))
    with open(
            osp.join(devkit_path, 'VOC2012/ImageSets/Segmentation', 'aug.txt'),
            'w') as f:
        f.writelines(line + '\n' for line in aug_list)

    print('Done!')


if __name__ == '__main__':
    main()