# Copyright (c) OpenMMLab. All rights reserved.

import argparse

import os

import os.path as osp

import tempfile

import zipfile

import mmcv

HRF_LEN = 15

TRAINING_LEN = 5

def parse_args():

    parser = argparse.ArgumentParser(

        description='Convert HRF dataset to mmsegmentation format')

    parser.add_argument('healthy_path', help='the path of healthy.zip')

    parser.add_argument(

        'healthy_manualsegm_path', help='the path of healthy_manualsegm.zip')

    parser.add_argument('glaucoma_path', help='the path of glaucoma.zip')

    parser.add_argument(

        'glaucoma_manualsegm_path', help='the path of glaucoma_manualsegm.zip')

    parser.add_argument(

        'diabetic_retinopathy_path',

        help='the path of diabetic_retinopathy.zip')

    parser.add_argument(

        'diabetic_retinopathy_manualsegm_path',

        help='the path of diabetic_retinopathy_manualsegm.zip')

    parser.add_argument('--tmp_dir', help='path of the temporary directory')

    parser.add_argument('-o', '--out_dir', help='output path')

    args = parser.parse_args()

    return args

def main():

    args = parse_args()

    images_path = [

        args.healthy_path, args.glaucoma_path, args.diabetic_retinopathy_path

    ]

    annotations_path = [

        args.healthy_manualsegm_path, args.glaucoma_manualsegm_path,

        args.diabetic_retinopathy_manualsegm_path

    ]

    if args.out_dir is None:

        out_dir = osp.join('data', 'HRF')

    else:

        out_dir = args.out_dir

    print('Making directories...')

    mmcv.mkdir_or_exist(out_dir)

    mmcv.mkdir_or_exist(osp.join(out_dir, 'images'))

    mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'training'))

    mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'validation'))

    mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations'))

    mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'training'))

    mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'validation'))

    print('Generating images...')

    for now_path in images_path:

        with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:

            zip_file = zipfile.ZipFile(now_path)

            zip_file.extractall(tmp_dir)

            assert len(os.listdir(tmp_dir)) == HRF_LEN, \

                'len(os.listdir(tmp_dir)) != {}'.format(HRF_LEN)

            for filename in sorted(os.listdir(tmp_dir))[:TRAINING_LEN]:

                img = mmcv.imread(osp.join(tmp_dir, filename))

                mmcv.imwrite(

                    img,

                    osp.join(out_dir, 'images', 'training',

                             osp.splitext(filename)[0] + '.png'))

            for filename in sorted(os.listdir(tmp_dir))[TRAINING_LEN:]:

                img = mmcv.imread(osp.join(tmp_dir, filename))

                mmcv.imwrite(

                    img,

                    osp.join(out_dir, 'images', 'validation',

                             osp.splitext(filename)[0] + '.png'))

    print('Generating annotations...')

    for now_path in annotations_path:

        with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:

            zip_file = zipfile.ZipFile(now_path)

            zip_file.extractall(tmp_dir)

            assert len(os.listdir(tmp_dir)) == HRF_LEN, \

                'len(os.listdir(tmp_dir)) != {}'.format(HRF_LEN)

            for filename in sorted(os.listdir(tmp_dir))[:TRAINING_LEN]:

                img = mmcv.imread(osp.join(tmp_dir, filename))

                # The annotation img should be divided by 128, because some of

                # the annotation imgs are not standard. We should set a

                # threshold to convert the nonstandard annotation imgs. The

                # value divided by 128 is equivalent to '1 if value >= 128

                # else 0'

                mmcv.imwrite(

                    img[:, :, 0] // 128,

                    osp.join(out_dir, 'annotations', 'training',

                             osp.splitext(filename)[0] + '.png'))

            for filename in sorted(os.listdir(tmp_dir))[TRAINING_LEN:]:

                img = mmcv.imread(osp.join(tmp_dir, filename))

                mmcv.imwrite(

                    img[:, :, 0] // 128,

                    osp.join(out_dir, 'annotations', 'validation',

                             osp.splitext(filename)[0] + '.png'))

    print('Done!')

if __name__ == '__main__':

    main()