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
83 lines
3.2 KiB
Python
83 lines
3.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import mmcv
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from mmcv.utils import print_log
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from ..utils import get_root_logger
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from .builder import DATASETS
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from .custom import CustomDataset
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@DATASETS.register_module()
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class iSAIDDataset(CustomDataset):
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""" iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
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In segmentation map annotation for iSAID dataset, which is included
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in 16 categories. ``reduce_zero_label`` is fixed to False. The
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``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to
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'_manual1.png'.
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"""
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CLASSES = ('background', 'ship', 'store_tank', 'baseball_diamond',
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'tennis_court', 'basketball_court', 'Ground_Track_Field',
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'Bridge', 'Large_Vehicle', 'Small_Vehicle', 'Helicopter',
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'Swimming_pool', 'Roundabout', 'Soccer_ball_field', 'plane',
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'Harbor')
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PALETTE = [[0, 0, 0], [0, 0, 63], [0, 63, 63], [0, 63, 0], [0, 63, 127],
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[0, 63, 191], [0, 63, 255], [0, 127, 63], [0, 127, 127],
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[0, 0, 127], [0, 0, 191], [0, 0, 255], [0, 191, 127],
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[0, 127, 191], [0, 127, 255], [0, 100, 155]]
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def __init__(self, **kwargs):
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super(iSAIDDataset, self).__init__(
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img_suffix='.png',
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seg_map_suffix='.png',
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ignore_index=255,
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**kwargs)
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assert self.file_client.exists(self.img_dir)
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def load_annotations(self,
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img_dir,
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img_suffix,
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ann_dir,
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seg_map_suffix=None,
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split=None):
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"""Load annotation from directory.
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Args:
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img_dir (str): Path to image directory
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img_suffix (str): Suffix of images.
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ann_dir (str|None): Path to annotation directory.
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seg_map_suffix (str|None): Suffix of segmentation maps.
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split (str|None): Split txt file. If split is specified, only file
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with suffix in the splits will be loaded. Otherwise, all images
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in img_dir/ann_dir will be loaded. Default: None
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Returns:
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list[dict]: All image info of dataset.
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"""
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img_infos = []
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if split is not None:
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with open(split) as f:
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for line in f:
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name = line.strip()
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img_info = dict(filename=name + img_suffix)
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if ann_dir is not None:
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ann_name = name + '_instance_color_RGB'
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seg_map = ann_name + seg_map_suffix
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img_info['ann'] = dict(seg_map=seg_map)
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img_infos.append(img_info)
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else:
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for img in mmcv.scandir(img_dir, img_suffix, recursive=True):
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img_info = dict(filename=img)
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if ann_dir is not None:
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seg_img = img
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seg_map = seg_img.replace(
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img_suffix, '_instance_color_RGB' + seg_map_suffix)
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img_info['ann'] = dict(seg_map=seg_map)
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img_infos.append(img_info)
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print_log(f'Loaded {len(img_infos)} images', logger=get_root_logger())
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return img_infos
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