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
93 lines
3.3 KiB
Python
93 lines
3.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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import mmcv
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import numpy as np
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from PIL import Image
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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 LoveDADataset(CustomDataset):
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"""LoveDA dataset.
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In segmentation map annotation for LoveDA, 0 is the ignore index.
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``reduce_zero_label`` should be set to True. The ``img_suffix`` and
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``seg_map_suffix`` are both fixed to '.png'.
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"""
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CLASSES = ('background', 'building', 'road', 'water', 'barren', 'forest',
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'agricultural')
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PALETTE = [[255, 255, 255], [255, 0, 0], [255, 255, 0], [0, 0, 255],
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[159, 129, 183], [0, 255, 0], [255, 195, 128]]
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def __init__(self, **kwargs):
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super(LoveDADataset, self).__init__(
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img_suffix='.png',
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seg_map_suffix='.png',
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reduce_zero_label=True,
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**kwargs)
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def results2img(self, results, imgfile_prefix, indices=None):
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"""Write the segmentation results to images.
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Args:
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results (list[ndarray]): Testing results of the
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dataset.
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imgfile_prefix (str): The filename prefix of the png files.
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If the prefix is "somepath/xxx",
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the png files will be named "somepath/xxx.png".
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indices (list[int], optional): Indices of input results, if not
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set, all the indices of the dataset will be used.
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Default: None.
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Returns:
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list[str: str]: result txt files which contains corresponding
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semantic segmentation images.
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"""
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mmcv.mkdir_or_exist(imgfile_prefix)
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result_files = []
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for result, idx in zip(results, indices):
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filename = self.img_infos[idx]['filename']
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basename = osp.splitext(osp.basename(filename))[0]
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png_filename = osp.join(imgfile_prefix, f'{basename}.png')
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# The index range of official requirement is from 0 to 6.
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output = Image.fromarray(result.astype(np.uint8))
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output.save(png_filename)
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result_files.append(png_filename)
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return result_files
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def format_results(self, results, imgfile_prefix, indices=None):
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"""Format the results into dir (standard format for LoveDA evaluation).
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Args:
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results (list): Testing results of the dataset.
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imgfile_prefix (str): The prefix of images files. It
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includes the file path and the prefix of filename, e.g.,
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"a/b/prefix".
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indices (list[int], optional): Indices of input results,
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if not set, all the indices of the dataset will be used.
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Default: None.
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Returns:
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tuple: (result_files, tmp_dir), result_files is a list containing
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the image paths, tmp_dir is the temporal directory created
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for saving json/png files when img_prefix is not specified.
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"""
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if indices is None:
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indices = list(range(len(self)))
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assert isinstance(results, list), 'results must be a list.'
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assert isinstance(indices, list), 'indices must be a list.'
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result_files = self.results2img(results, imgfile_prefix, indices)
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return result_files
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