[Feature] add HSI-Drive dataset

.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 [email protected] 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 [email protected] 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