73 lines
3.3 KiB
Python
73 lines
3.3 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 Callable, Dict, List, Optional, Sequence, Union
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from mmseg.registry import DATASETS
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from .basesegdataset import BaseSegDataset
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from mmengine.logging import print_log
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import pandas as pd
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# LEGEND = [
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# 255 255 255; % Background
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# 0 0 0; % Roads
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# 100 100 100; % Buildings
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# 0 125 0; % Trees
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# 0 255 0; % Grass
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# 150 80 0; % Bare Soil
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# 0 0 150; % Water
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# 255 255 0; % Railways
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# 150 150 255]; % Swimming Pools
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@DATASETS.register_module()
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class GermanyCropDataset(BaseSegDataset):
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"""Zurich 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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# {0: "unknown", 1: "sugar_beet", 2: "summer_oat", 3: "meadow", 5: "rape", 8: "hop",
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# 9: "winter_spelt", 12: "winter_triticale", 13: "beans", 15: "peas", 16: "potatoes",
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# 17: "soybeans", 19: "asparagus", 22: "winter_wheat", 23: "winter_barley", 24: "winter_rye",
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# 25: "summer_barley", 26: "maize"}
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METAINFO = dict(
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classes=('sugar_beet', 'summer_oat', 'meadow', 'rape', 'hop', 'winter_spelt', 'winter_triticale', 'beans', 'peas',\
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'potatoes', 'soybeans', 'asparagus', 'winter_wheat', 'winter_barley', 'winter_rye', 'summer_barley', 'maize'),
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palette=[(255, 255, 255), (255, 255, 170), (255, 255, 85), (255, 170, 255), (255, 170, 170), (255, 170, 85), \
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(255, 85, 255), (255, 85, 170), (255, 85, 85), (170, 255, 255), (170, 255, 170), (170, 255, 85), (170, 170, 255), \
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(170, 170, 170), (170, 170, 85), (170, 85, 255), (170, 85, 170)])
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def __init__(self,
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img_suffix='.pickle',
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seg_map_suffix='.pickle',
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reduce_zero_label=True,
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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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print_log(f'dataset count: {len(lines)}')
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for line in lines:
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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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