| |
| 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) |
|
|
