43 lines
2.0 KiB
Python
43 lines
2.0 KiB
Python
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# Copyright (c) OpenMMLab. All rights reserved.
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from mmseg.datasets import BaseSegDataset
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from mmseg.registry import DATASETS
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classes_exp = ('unlabelled', 'road', 'road marks', 'vegetation',
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'painted metal', 'sky', 'concrete', 'pedestrian', 'water',
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'unpainted metal', 'glass')
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palette_exp = [[0, 0, 0], [77, 77, 77], [255, 255, 255], [0, 255, 0],
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[255, 0, 0], [0, 0, 255], [102, 51, 0], [255, 255, 0],
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[0, 207, 250], [255, 166, 0], [0, 204, 204]]
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@DATASETS.register_module()
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class HSIDrive20Dataset(BaseSegDataset):
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"""HSI-Drive v2.0 (https://ieeexplore.ieee.org/document/10371793), the
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updated version of HSI-Drive
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(https://ieeexplore.ieee.org/document/9575298), is a structured dataset for
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the research and development of automated driving systems (ADS) supported
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by hyperspectral imaging (HSI). It contains per-pixel manually annotated
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images selected from videos recorded in real driving conditions and has
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been organized according to four parameters: season, daytime, road type,
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and weather conditions.
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The video sequences have been captured with a small-size 25-band VNIR
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(Visible-NearlnfraRed) snapshot hyperspectral camera mounted on a driving
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automobile. As a consequence, you need to modify the in_channels parameter
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of your model from 3 (RGB images) to 25 (HSI images) as it is done in
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configs/unet/unet-s5-d16_fcn_4xb4-160k_hsidrive-192x384.py
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Apart from the abovementioned articles, additional information is provided
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in the website (https://ipaccess.ehu.eus/HSI-Drive/) from where you can
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download the dataset and also visualize some examples of segmented videos.
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"""
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METAINFO = dict(classes=classes_exp, palette=palette_exp)
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def __init__(self,
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img_suffix='.npy',
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seg_map_suffix='.png',
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**kwargs) -> None:
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super().__init__(
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img_suffix=img_suffix, seg_map_suffix=seg_map_suffix, **kwargs)
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