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## Support `Mapillary Vistas Dataset` ## Motivation Support **`Mapillary Vistas Dataset`** Dataset Paper link : https://ieeexplore.ieee.org/document/9878466/ Download and more information view https://www.mapillary.com/dataset/vistas ``` @InProceedings{Neuhold_2017_ICCV, author = {Neuhold, Gerhard and Ollmann, Tobias and Rota Bulo, Samuel and Kontschieder, Peter}, title = {The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes}, booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)}, month = {Oct}, year = {2017} } ``` ## Modification Add `Mapillary_dataset` in `mmsegmentation/projects` Add `configs/_base_/mapillary_v1_2.py` and `configs/_base_/mapillary_v2_0.py` Add `configs/deeplabv3plus_r18-d8_4xb2-80k_mapillay-512x1024.py` to test training and testing on Mapillary datasets Add `docs/en/user_guides/2_dataset_prepare.md` , add Mapillary Vistas Dataset Preparing and Structure. Add `tools/dataset_converters/mapillary.py` to convert RGB labels to Mask labels. Co-authored-by: 谢昕辰 <xiexinch@outlook.com>
66 lines
3.4 KiB
Python
66 lines
3.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from mmseg.datasets.basesegdataset import BaseSegDataset
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from mmseg.registry import DATASETS
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@DATASETS.register_module()
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class MapillaryDataset_v1_2(BaseSegDataset):
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"""Mapillary Vistas Dataset.
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Dataset paper link:
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http://ieeexplore.ieee.org/document/8237796/
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v1.2 contain 66 object classes.
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(37 instance-specific)
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v2.0 contain 124 object classes.
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(70 instance-specific, 46 stuff, 8 void or crowd).
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The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is
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fixed to '.png' for Mapillary Vistas Dataset.
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"""
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METAINFO = dict(
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classes=('Bird', 'Ground Animal', 'Curb', 'Fence', 'Guard Rail',
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'Barrier', 'Wall', 'Bike Lane', 'Crosswalk - Plain',
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'Curb Cut', 'Parking', 'Pedestrian Area', 'Rail Track',
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'Road', 'Service Lane', 'Sidewalk', 'Bridge', 'Building',
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'Tunnel', 'Person', 'Bicyclist', 'Motorcyclist',
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'Other Rider', 'Lane Marking - Crosswalk',
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'Lane Marking - General', 'Mountain', 'Sand', 'Sky', 'Snow',
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'Terrain', 'Vegetation', 'Water', 'Banner', 'Bench',
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'Bike Rack', 'Billboard', 'Catch Basin', 'CCTV Camera',
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'Fire Hydrant', 'Junction Box', 'Mailbox', 'Manhole',
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'Phone Booth', 'Pothole', 'Street Light', 'Pole',
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'Traffic Sign Frame', 'Utility Pole', 'Traffic Light',
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'Traffic Sign (Back)', 'Traffic Sign (Front)', 'Trash Can',
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'Bicycle', 'Boat', 'Bus', 'Car', 'Caravan', 'Motorcycle',
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'On Rails', 'Other Vehicle', 'Trailer', 'Truck',
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'Wheeled Slow', 'Car Mount', 'Ego Vehicle', 'Unlabeled'),
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palette=[[165, 42, 42], [0, 192, 0], [196, 196, 196], [190, 153, 153],
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[180, 165, 180], [90, 120, 150], [102, 102, 156],
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[128, 64, 255], [140, 140, 200], [170, 170, 170],
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[250, 170, 160], [96, 96, 96],
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[230, 150, 140], [128, 64, 128], [110, 110, 110],
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[244, 35, 232], [150, 100, 100], [70, 70, 70], [150, 120, 90],
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[220, 20, 60], [255, 0, 0], [255, 0, 100], [255, 0, 200],
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[200, 128, 128], [255, 255, 255], [64, 170,
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64], [230, 160, 50],
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[70, 130, 180], [190, 255, 255], [152, 251, 152],
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[107, 142, 35], [0, 170, 30], [255, 255, 128], [250, 0, 30],
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[100, 140, 180], [220, 220, 220], [220, 128, 128],
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[222, 40, 40], [100, 170, 30], [40, 40, 40], [33, 33, 33],
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[100, 128, 160], [142, 0, 0], [70, 100, 150], [210, 170, 100],
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[153, 153, 153], [128, 128, 128], [0, 0, 80], [250, 170, 30],
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[192, 192, 192], [220, 220, 0], [140, 140, 20], [119, 11, 32],
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[150, 0, 255], [0, 60, 100], [0, 0, 142], [0, 0, 90],
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[0, 0, 230], [0, 80, 100], [128, 64, 64], [0, 0, 110],
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[0, 0, 70], [0, 0, 192], [32, 32, 32], [120, 10,
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10], [0, 0, 0]])
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def __init__(self,
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img_suffix='.jpg',
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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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