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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import tempfile
import zipfile
import cv2
import mmcv
from mmengine.utils import mkdir_or_exist
def parse_args():
parser = argparse.ArgumentParser(
description='Convert DRIVE dataset to mmsegmentation format')
parser.add_argument(
'training_path', help='the training part of DRIVE dataset')
parser.add_argument(
'testing_path', help='the testing part of DRIVE dataset')
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()
training_path = args.training_path
testing_path = args.testing_path
if args.out_dir is None:
out_dir = osp.join('data', 'DRIVE')
else:
out_dir = args.out_dir
print('Making directories...')
mkdir_or_exist(out_dir)
mkdir_or_exist(osp.join(out_dir, 'images'))
mkdir_or_exist(osp.join(out_dir, 'images', 'training'))
mkdir_or_exist(osp.join(out_dir, 'images', 'validation'))
mkdir_or_exist(osp.join(out_dir, 'annotations'))
mkdir_or_exist(osp.join(out_dir, 'annotations', 'training'))
mkdir_or_exist(osp.join(out_dir, 'annotations', 'validation'))
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
print('Extracting training.zip...')
zip_file = zipfile.ZipFile(training_path)
zip_file.extractall(tmp_dir)
print('Generating training dataset...')
now_dir = osp.join(tmp_dir, 'training', 'images')
for img_name in os.listdir(now_dir):
img = mmcv.imread(osp.join(now_dir, img_name))
mmcv.imwrite(
img,
osp.join(
out_dir, 'images', 'training',
osp.splitext(img_name)[0].replace('_training', '') +
'.png'))
now_dir = osp.join(tmp_dir, 'training', '1st_manual')
for img_name in os.listdir(now_dir):
cap = cv2.VideoCapture(osp.join(now_dir, img_name))
ret, img = cap.read()
mmcv.imwrite(
img[:, :, 0] // 128,
osp.join(out_dir, 'annotations', 'training',
osp.splitext(img_name)[0] + '.png'))
print('Extracting test.zip...')
zip_file = zipfile.ZipFile(testing_path)
zip_file.extractall(tmp_dir)
print('Generating validation dataset...')
now_dir = osp.join(tmp_dir, 'test', 'images')
for img_name in os.listdir(now_dir):
img = mmcv.imread(osp.join(now_dir, img_name))
mmcv.imwrite(
img,
osp.join(
out_dir, 'images', 'validation',
osp.splitext(img_name)[0].replace('_test', '') + '.png'))
now_dir = osp.join(tmp_dir, 'test', '1st_manual')
if osp.exists(now_dir):
for img_name in os.listdir(now_dir):
cap = cv2.VideoCapture(osp.join(now_dir, img_name))
ret, img = cap.read()
# 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', 'validation',
osp.splitext(img_name)[0] + '.png'))
now_dir = osp.join(tmp_dir, 'test', '2nd_manual')
if osp.exists(now_dir):
for img_name in os.listdir(now_dir):
cap = cv2.VideoCapture(osp.join(now_dir, img_name))
ret, img = cap.read()
mmcv.imwrite(
img[:, :, 0] // 128,
osp.join(out_dir, 'annotations', 'validation',
osp.splitext(img_name)[0] + '.png'))
print('Removing the temporary files...')
print('Done!')
if __name__ == '__main__':
main()
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