[Feature] add HSI-Drive dataset (#3365)

## Motivation

The motivation is to add a hyperspectral dataset [HSI Drive
2.0](https://ipaccess.ehu.eus/HSI-Drive/) to the dataset registry which
would be, as far as I know, the first hyperspectral database of
mmsegmentation. This database has been presented in [HSI-Drive v2.0:
More Data for New Challenges in Scene Understanding for Autonomous
Driving](https://ieeexplore.ieee.org/document/10371793) and the initival
v1 was presented in [HSI-Drive: A Dataset for the Research of
Hyperspectral Image Processing Applied to Autonomous Driving
Systems](https://ieeexplore.ieee.org/document/9575298)

## Modification

I have created/modified the following aspects:
- READMEs: `README.md` and `README_zh-CN.md` (sorry if translation is
not accurate).
- Example project: `projects/hsidrive20_dataset` has been created and
filled for users to know how to work with this database.
- Documentation: `docs/en/user_guides/2_dataset_prepare.md` and
`docs/zh_cn/user_guides/2_dataset_prepare.md` (sorry if translation is
not accurate) have been updated for users to know how to download and
configure the dataset.
- Database related files: `mmseg/datasets/__init__.py`,
`mmseg/datasets/hsi_drive.py` and `configs/_base_/datasets/hsi_drive.py`
where the dataset is described and also prepared for
training/validation/test.
- Transforms related files:
`mmsegmentation/mmseg/datasets/transforms/loading.py` to *include
support for loading images from .npy files* such as the hyperspectral
images of this dataset.
- Training config with well-known neural network:
`configs/unet/unet-s5-d16_fcn_4xb4-160k_hsidrive-192x384.py` for people
to train a standard neural network with this dataset.
- Tests: added necessary files under
`tests/data/pseudo_hsidrive20_dataset`.

**Important:** I have also modified `.pre-commit-config.yaml` to ignore
HSI error in codespell.

## BC-breaking (Optional)

No.

## Use cases (Optional)

A train example has been added under `projects/hsidrive20_dataset` and
documentation has been updated as it is explained in Modification
section.

## Checklist

1. Pre-commit or other linting tools are used to fix the potential lint
issues.
2. The modification is covered by complete unit tests. If not, please
add more unit test to ensure the correctness.
3. If the modification has potential influence on downstream projects,
this PR should be tested with downstream projects, like MMDet or
MMDet3D.
4. The documentation has been modified accordingly, like docstring or
example tutorials.

Regarding 1. I don't know how to solve this problem. Could you help me,
please? This causes 2 checks not to be successful.

---------

Co-authored-by: xiexinch <xiexinch@outlook.com>

.pre-commit-config.yaml

@@ -37,6 +37,7 @@ repos:
     rev: v2.2.1
     hooks:
       - id: codespell
+        args: [--ignore-words-list=hsi]
   - repo: https://github.com/myint/docformatter
     rev: v1.3.1
     hooks:

README.md

@@ -339,6 +339,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
           <li><a href="https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#levir-cd">LEVIR-CD</a></li>
           <li><a href="https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#bdd100K">BDD100K</a></li>
           <li><a href="https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#nyu">NYU</a></li>
+          <li><a href="https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#hsi-drive-2.0">HSIDrive20</a></li>
         </ul>
       </td>
       <td>

README_zh-CN.md

@@ -328,6 +328,7 @@ MMSegmentation v1.x 在 0.x 版本的基础上有了显著的提升，提供了
           <li><a href="https://github.com/open-mmlab/mmsegmentation/blob/main/docs/zh_cn/user_guides/2_dataset_prepare.md#levir-cd">LEVIR-CD</a></li>
           <li><a href="https://github.com/open-mmlab/mmsegmentation/blob/main/docs/zh_cn/user_guides/2_dataset_prepare.md#bdd100K">BDD100K</a></li>
           <li><a href="https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#nyu">NYU</a></li>
+          <li><a href="https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#hsi-drive-2.0">HSIDrive20</a></li>
         </ul>
       </td>
       <td>

configs/_base_/datasets/hsi_drive.py

@@ -0,0 +1,53 @@
+train_pipeline = [
+    dict(type='LoadImageFromNpyFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='RandomCrop', crop_size=(192, 384)),
+    dict(type='PackSegInputs')
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromNpyFile'),
+    dict(type='RandomCrop', crop_size=(192, 384)),
+    dict(type='LoadAnnotations'),
+    dict(type='PackSegInputs')
+]
+
+train_dataloader = dict(
+    batch_size=4,
+    num_workers=1,
+    persistent_workers=True,
+    sampler=dict(type='InfiniteSampler', shuffle=True),
+    dataset=dict(
+        type='HSIDrive20Dataset',
+        data_root='data/HSIDrive20',
+        data_prefix=dict(
+            img_path='images/training', seg_map_path='annotations/training'),
+        pipeline=train_pipeline))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type='HSIDrive20Dataset',
+        data_root='data/HSIDrive20',
+        data_prefix=dict(
+            img_path='images/validation',
+            seg_map_path='annotations/validation'),
+        pipeline=test_pipeline))
+
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type='HSIDrive20Dataset',
+        data_root='data/HSIDrive20',
+        data_prefix=dict(
+            img_path='images/test', seg_map_path='annotations/test'),
+        pipeline=test_pipeline))
+
+val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'], ignore_index=0)
+test_evaluator = val_evaluator

configs/unet/unet-s5-d16_fcn_4xb4-160k_hsidrive-192x384.py

@@ -0,0 +1,36 @@
+_base_ = [
+    '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/hsi_drive.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+]
+crop_size = (192, 384)
+data_preprocessor = dict(
+    type='SegDataPreProcessor',
+    size=crop_size,
+    mean=None,
+    std=None,
+    bgr_to_rgb=None,
+    pad_val=0,
+    seg_pad_val=255)
+
+model = dict(
+    data_preprocessor=data_preprocessor,
+    backbone=dict(in_channels=25),
+    decode_head=dict(
+        ignore_index=0,
+        num_classes=11,
+        loss_decode=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=False,
+            loss_weight=1.0,
+            avg_non_ignore=True)),
+    auxiliary_head=dict(
+        ignore_index=0,
+        num_classes=11,
+        loss_decode=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=False,
+            loss_weight=1.0,
+            avg_non_ignore=True)),
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))

docs/en/user_guides/2_dataset_prepare.md

@@ -205,6 +205,15 @@ mmsegmentation
 │   │   ├── annotations
 │   │   │   ├── train
 │   │   │   ├── test
+│   ├── HSIDrive20
+│   │   ├── images
+│   │   │   ├── train
+│   │   │   ├── validation
+│   │   │   ├── test
+│   │   ├── annotations
+│   │   │   ├── train
+│   │   │   ├── validation
+│   │   │   ├── test
 ```
 
 ## Download dataset via MIM
@@ -752,3 +761,46 @@ mmsegmentation
   ```bash
   python tools/dataset_converters/nyu.py nyu.zip
   ```
+
+## HSI Drive 2.0
+
+- You could download HSI Drive 2.0 dataset from [here](https://ipaccess.ehu.eus/HSI-Drive/#download) after just sending an email to gded@ehu.eus with the subject "download HSI-Drive". You will receive a password to uncompress the files.
+
+- After download, unzip by the following instructions:
+
+  ```bash
+  7z x -p"password" ./HSI_Drive_v2_0_Phyton.zip
+
+  mv ./HSIDrive20 path_to_mmsegmentation/data
+  mv ./HSI_Drive_v2_0_release_notes_Python_version.md path_to_mmsegmentation/data
+  mv ./image_numbering.pdf path_to_mmsegmentation/data
+  ```
+
+- After unzip, you get
+
+```none
+mmsegmentation
+├── mmseg
+├── tools
+├── configs
+├── data
+│   ├── HSIDrive20
+│   │   ├── images
+│   │   │   ├── training
+│   │   │   ├── validation
+│   │   │   ├── test
+│   │   ├── annotations
+│   │   │   ├── training
+│   │   │   ├── validation
+│   │   │   ├── test
+│   │   ├── images_MF
+│   │   │   ├── training
+│   │   │   ├── validation
+│   │   │   ├── test
+│   │   ├── RGB
+│   │   ├── training_filenames.txt
+│   │   ├── validation_filenames.txt
+│   │   ├── test_filenames.txt
+│   ├── HSI_Drive_v2_0_release_notes_Python_version.md
+│   ├── image_numbering.pdf
+```

docs/zh_cn/user_guides/2_dataset_prepare.md

@@ -205,6 +205,15 @@ mmsegmentation
 │   │   ├── annotations
 │   │   │   ├── train
 │   │   │   ├── test
+│   ├── HSIDrive20
+│   │   ├── images
+│   │   │   ├── train
+│   │   │   ├── validation
+│   │   │   ├── test
+│   │   ├── annotations
+│   │   │   ├── train
+│   │   │   ├── validation
+│   │   │   ├── test
 ```
 
 ## 用 MIM 下载数据集
@@ -748,3 +757,46 @@ mmsegmentation
   ```bash
   python tools/dataset_converters/nyu.py nyu.zip
   ```
+
+## HSI Drive 2.0
+
+- 您可以从以下位置下载 HSI Drive 2.0 数据集 [here](https://ipaccess.ehu.eus/HSI-Drive/#download) 刚刚向 gded@ehu.eus 发送主题为“下载 HSI-Drive”的电子邮件后 您将收到解压缩文件的密码.
+
+- 下载后，按照以下说明解压：
+
+  ```bash
+  7z x -p"password" ./HSI_Drive_v2_0_Phyton.zip
+
+  mv ./HSIDrive20 path_to_mmsegmentation/data
+  mv ./HSI_Drive_v2_0_release_notes_Python_version.md path_to_mmsegmentation/data
+  mv ./image_numbering.pdf path_to_mmsegmentation/data
+  ```
+
+- 解压后得到:
+
+```none
+mmsegmentation
+├── mmseg
+├── tools
+├── configs
+├── data
+│   ├── HSIDrive20
+│   │   ├── images
+│   │   │   ├── training
+│   │   │   ├── validation
+│   │   │   ├── test
+│   │   ├── annotations
+│   │   │   ├── training
+│   │   │   ├── validation
+│   │   │   ├── test
+│   │   ├── images_MF
+│   │   │   ├── training
+│   │   │   ├── validation
+│   │   │   ├── test
+│   │   ├── RGB
+│   │   ├── training_filenames.txt
+│   │   ├── validation_filenames.txt
+│   │   ├── test_filenames.txt
+│   ├── HSI_Drive_v2_0_release_notes_Python_version.md
+│   ├── image_numbering.pdf
+```

mmseg/datasets/__init__.py

@@ -12,6 +12,7 @@
 from .drive import DRIVEDataset
 from .dsdl import DSDLSegDataset
 from .hrf import HRFDataset
+from .hsi_drive import HSIDrive20Dataset
 from .isaid import iSAIDDataset
 from .isprs import ISPRSDataset
 from .levir import LEVIRCDDataset
@@ -60,5 +61,5 @@
     'MapillaryDataset_v2', 'Albu', 'LEVIRCDDataset',
     'LoadMultipleRSImageFromFile', 'LoadSingleRSImageFromFile',
     'ConcatCDInput', 'BaseCDDataset', 'DSDLSegDataset', 'BDD100KDataset',
-    'NYUDataset'
+    'NYUDataset', 'HSIDrive20Dataset'
 ]

mmseg/datasets/hsi_drive.py

@@ -0,0 +1,42 @@
+# 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)

mmseg/datasets/transforms/loading.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import warnings
+from pathlib import Path
 from typing import Dict, Optional, Union
 
 import mmcv
@@ -702,3 +703,69 @@ def __repr__(self):
                     f'to_float32={self.to_float32}, '
                     f'backend_args={self.backend_args})')
         return repr_str
+
+
+@TRANSFORMS.register_module()
+class LoadImageFromNpyFile(LoadImageFromFile):
+    """Load an image from ``results['img_path']``.
+
+    Required Keys:
+
+    - img_path
+
+    Modified Keys:
+
+    - img
+    - img_shape
+    - ori_shape
+
+    Args:
+        to_float32 (bool): Whether to convert the loaded image to a float32
+            numpy array. If set to False, the loaded image is an uint8 array.
+            Defaults to False.
+    """
+
+    def transform(self, results: dict) -> Optional[dict]:
+        """Functions to load image.
+
+        Args:
+            results (dict): Result dict from
+                :class:`mmengine.dataset.BaseDataset`.
+
+        Returns:
+            dict: The dict contains loaded image and meta information.
+        """
+
+        filename = results['img_path']
+
+        try:
+            if Path(filename).suffix in ['.npy', '.npz']:
+                img = np.load(filename)
+            else:
+                if self.file_client_args is not None:
+                    file_client = fileio.FileClient.infer_client(
+                        self.file_client_args, filename)
+                    img_bytes = file_client.get(filename)
+                else:
+                    img_bytes = fileio.get(
+                        filename, backend_args=self.backend_args)
+                img = mmcv.imfrombytes(
+                    img_bytes,
+                    flag=self.color_type,
+                    backend=self.imdecode_backend)
+        except Exception as e:
+            if self.ignore_empty:
+                return None
+            else:
+                raise e
+
+        # in some cases, images are not read successfully, the img would be
+        # `None`, refer to https://github.com/open-mmlab/mmpretrain/issues/1427
+        assert img is not None, f'failed to load image: {filename}'
+        if self.to_float32:
+            img = img.astype(np.float32)
+
+        results['img'] = img
+        results['img_shape'] = img.shape[:2]
+        results['ori_shape'] = img.shape[:2]
+        return results