49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import json
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import os.path as osp
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from typing import List
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from mmseg.registry import DATASETS
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from .basesegdataset import BaseSegDataset
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@DATASETS.register_module()
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class AtlanticDataset(BaseSegDataset):
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METAINFO = dict(
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classes=('Background', 'Deforestation area'),
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palette=[[0,0,0], [255,255,255]]
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)
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def __init__(self,
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img_suffix='.tif',
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seg_map_suffix='.tif',
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reduce_zero_label=False,
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**kwargs) -> None:
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super().__init__(
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img_suffix=img_suffix,
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seg_map_suffix=seg_map_suffix,
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reduce_zero_label=reduce_zero_label,
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**kwargs)
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def load_data_list(self) -> List[dict]:
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"""Load annotation from directory or annotation file.
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Returns:
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list[dict]: All data info of dataset.
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"""
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data_list = []
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if not osp.isdir(self.ann_file) and self.ann_file:
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assert osp.isfile(self.ann_file), \
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f'Failed to load `ann_file` {self.ann_file}'
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lines = json.load(open(self.ann_file))
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for line in lines:
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# img_name = line.strip()
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data_info = dict(
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img_path=osp.join(self.data_root, line['s2_path']))
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if 'target_path' in line.keys():
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data_info['seg_map_path'] = osp.join(self.data_root, line['target_path'])
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data_info['label_map'] = self.label_map
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data_info['reduce_zero_label'] = self.reduce_zero_label
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data_info['seg_fields'] = []
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data_list.append(data_info)
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data_list = sorted(data_list, key=lambda x: x['img_path'])
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return data_list
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