| | |
| | 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) |
| |
|
