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6ed4a9c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | # Copyright (c) OpenMMLab. All rights reserved.
from mmseg.datasets import BaseSegDataset
from mmseg.registry import DATASETS
classes_exp = ('unlabelled', 'road', 'road marks', 'vegetation',
'painted metal', 'sky', 'concrete', 'pedestrian', 'water',
'unpainted metal', 'glass')
palette_exp = [[0, 0, 0], [77, 77, 77], [255, 255, 255], [0, 255, 0],
[255, 0, 0], [0, 0, 255], [102, 51, 0], [255, 255, 0],
[0, 207, 250], [255, 166, 0], [0, 204, 204]]
@DATASETS.register_module()
class HSIDrive20Dataset(BaseSegDataset):
"""HSI-Drive v2.0 (https://ieeexplore.ieee.org/document/10371793), the
updated version of HSI-Drive
(https://ieeexplore.ieee.org/document/9575298), is a structured dataset for
the research and development of automated driving systems (ADS) supported
by hyperspectral imaging (HSI). It contains per-pixel manually annotated
images selected from videos recorded in real driving conditions and has
been organized according to four parameters: season, daytime, road type,
and weather conditions.
The video sequences have been captured with a small-size 25-band VNIR
(Visible-NearlnfraRed) snapshot hyperspectral camera mounted on a driving
automobile. As a consequence, you need to modify the in_channels parameter
of your model from 3 (RGB images) to 25 (HSI images) as it is done in
configs/unet/unet-s5-d16_fcn_4xb4-160k_hsidrive-192x384.py
Apart from the abovementioned articles, additional information is provided
in the website (https://ipaccess.ehu.eus/HSI-Drive/) from where you can
download the dataset and also visualize some examples of segmented videos.
"""
METAINFO = dict(classes=classes_exp, palette=palette_exp)
def __init__(self,
img_suffix='.npy',
seg_map_suffix='.png',
**kwargs) -> None:
super().__init__(
img_suffix=img_suffix, seg_map_suffix=seg_map_suffix, **kwargs)
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