jeonghyo.k
3946ff6a25
- prediction/image/ FastAPI 서버 Docker 환경 구성 - Dockerfile: PyTorch 2.1 + CUDA 12.1 기반 GPU 이미지 - docker-compose.yml: GPU 할당 + 데이터 볼륨 마운트 - requirements.txt: 서버 의존성 목록 - .env.example: 환경변수 템플릿 - DOCKER_USAGE.md: 빌드/실행/API 사용법 문서 - Dockerfile에 .dockerignore 제외 폴더 mkdir -p 추가 - .gitignore: prediction/image 결과물 및 모델 가중치(.pth) 제외 추가 - dbInsert_csv.py, dbInsert_shp.py 삭제 (미사용 DB 로직) - api.py: dbInsert import 및 주석 처리된 DB 호출 코드 제거 - aerialRouter.ts: req.params 타입 오류 수정
112 lines
4.3 KiB
Python
112 lines
4.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os
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import os.path as osp
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import tempfile
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import zipfile
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import mmcv
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HRF_LEN = 15
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TRAINING_LEN = 5
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Convert HRF dataset to mmsegmentation format')
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parser.add_argument('healthy_path', help='the path of healthy.zip')
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parser.add_argument(
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'healthy_manualsegm_path', help='the path of healthy_manualsegm.zip')
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parser.add_argument('glaucoma_path', help='the path of glaucoma.zip')
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parser.add_argument(
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'glaucoma_manualsegm_path', help='the path of glaucoma_manualsegm.zip')
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parser.add_argument(
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'diabetic_retinopathy_path',
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help='the path of diabetic_retinopathy.zip')
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parser.add_argument(
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'diabetic_retinopathy_manualsegm_path',
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help='the path of diabetic_retinopathy_manualsegm.zip')
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parser.add_argument('--tmp_dir', help='path of the temporary directory')
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parser.add_argument('-o', '--out_dir', help='output path')
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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images_path = [
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args.healthy_path, args.glaucoma_path, args.diabetic_retinopathy_path
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]
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annotations_path = [
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args.healthy_manualsegm_path, args.glaucoma_manualsegm_path,
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args.diabetic_retinopathy_manualsegm_path
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]
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if args.out_dir is None:
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out_dir = osp.join('data', 'HRF')
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else:
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out_dir = args.out_dir
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print('Making directories...')
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mmcv.mkdir_or_exist(out_dir)
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mmcv.mkdir_or_exist(osp.join(out_dir, 'images'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'training'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'validation'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'training'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'validation'))
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print('Generating images...')
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for now_path in images_path:
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with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
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zip_file = zipfile.ZipFile(now_path)
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zip_file.extractall(tmp_dir)
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assert len(os.listdir(tmp_dir)) == HRF_LEN, \
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'len(os.listdir(tmp_dir)) != {}'.format(HRF_LEN)
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for filename in sorted(os.listdir(tmp_dir))[:TRAINING_LEN]:
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img = mmcv.imread(osp.join(tmp_dir, filename))
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mmcv.imwrite(
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img,
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osp.join(out_dir, 'images', 'training',
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osp.splitext(filename)[0] + '.png'))
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for filename in sorted(os.listdir(tmp_dir))[TRAINING_LEN:]:
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img = mmcv.imread(osp.join(tmp_dir, filename))
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mmcv.imwrite(
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img,
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osp.join(out_dir, 'images', 'validation',
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osp.splitext(filename)[0] + '.png'))
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print('Generating annotations...')
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for now_path in annotations_path:
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with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
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zip_file = zipfile.ZipFile(now_path)
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zip_file.extractall(tmp_dir)
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assert len(os.listdir(tmp_dir)) == HRF_LEN, \
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'len(os.listdir(tmp_dir)) != {}'.format(HRF_LEN)
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for filename in sorted(os.listdir(tmp_dir))[:TRAINING_LEN]:
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img = mmcv.imread(osp.join(tmp_dir, filename))
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# The annotation img should be divided by 128, because some of
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# the annotation imgs are not standard. We should set a
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# threshold to convert the nonstandard annotation imgs. The
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# value divided by 128 is equivalent to '1 if value >= 128
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# else 0'
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mmcv.imwrite(
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img[:, :, 0] // 128,
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osp.join(out_dir, 'annotations', 'training',
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osp.splitext(filename)[0] + '.png'))
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for filename in sorted(os.listdir(tmp_dir))[TRAINING_LEN:]:
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img = mmcv.imread(osp.join(tmp_dir, filename))
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mmcv.imwrite(
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img[:, :, 0] // 128,
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osp.join(out_dir, 'annotations', 'validation',
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osp.splitext(filename)[0] + '.png'))
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print('Done!')
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if __name__ == '__main__':
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main()
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