add HKU-IS dataset

docs/dataset_prepare.md

@@ -125,6 +125,11 @@ mmsegmentation
 │   │   │   ├── validation
 │   │   ├── annotations
 │   │   │   ├── validation
+|   ├── HKU-IS
+│   │   ├── images
+│   │   │   ├── validation
+│   │   ├── annotations
+│   │   │   ├── validation
 ```
 
 ### Cityscapes
@@ -304,3 +309,15 @@ To convert ECSSD dataset to MMSegmentation format, you should run the following
 ```shell
 python tools/convert_datasets/ecssd.py /path/to/images.zip /path/to/ground_truth_mask.zip
 ```
+
+### HKU-IS
+
+In salient object detection (SOD), HKU-IS is used for evaluation.
+
+First，download [HKU-IS.rar](https://sites.google.com/site/ligb86/mdfsaliency/).
+
+To convert HKU-IS dataset to MMSegmentation format, you should run the following command:
+
+```shell
+python tools/convert_datasets/hku_is.py /path/to/HKU-IS.rar
+```

docs_zh-CN/dataset_prepare.md

@@ -106,6 +106,11 @@ mmsegmentation
 │   │   │   ├── validation
 │   │   ├── annotations
 │   │   │   ├── validation
+|   ├── HKU-IS
+│   │   ├── images
+│   │   │   ├── validation
+│   │   ├── annotations
+│   │   │   ├── validation
 ```
 
 ### Cityscapes
@@ -246,3 +251,15 @@ python tools/convert_datasets/dut_omron.py /path/to/DUT-OMRON-image.zip /path/to
 ```shell
 python tools/convert_datasets/ecssd.py /path/to/images.zip /path/to/ground_truth_mask.zip
 ```
+
+### HKU-IS
+
+显著性检测（SOD）任务中 HKU-IS 仅作为测试集。
+
+首先，下载 [HKU-IS.rar](https://sites.google.com/site/ligb86/mdfsaliency/) 。
+
+为了将 HKU-IS 数据集转换成 MMSegmentation 格式，您需要运行如下命令：
+
+```shell
+python tools/convert_datasets/hku_is.py /path/to/HKU-IS.rar
+```

mmseg/datasets/__init__.py

@@ -11,6 +11,7 @@
 from .dut_omron import DUTOMRONDataset
 from .duts import DUTSDataset
 from .ecssd import ECSSDDataset
+from .hku_is import HKUISDataset
 from .hrf import HRFDataset
 from .night_driving import NightDrivingDataset
 from .pascal_context import PascalContextDataset, PascalContextDataset59
@@ -23,5 +24,6 @@
     'PascalVOCDataset', 'ADE20KDataset', 'PascalContextDataset',
     'PascalContextDataset59', 'ChaseDB1Dataset', 'DRIVEDataset', 'HRFDataset',
     'STAREDataset', 'DarkZurichDataset', 'NightDrivingDataset',
-    'COCOStuffDataset', 'DUTSDataset', 'DUTOMRONDataset', 'ECSSDDataset'
+    'COCOStuffDataset', 'DUTSDataset', 'DUTOMRONDataset', 'ECSSDDataset',
+    'HKUISDataset'
 ]

mmseg/datasets/hku_is.py

@@ -0,0 +1,27 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+
+from .builder import DATASETS
+from .custom import CustomDataset
+
+
+@DATASETS.register_module()
+class HKUISDataset(CustomDataset):
+    """HKU-IS dataset.
+
+    In saliency map annotation for HKU-IS, 0 stands for background.
+    ``reduce_zero_label`` is fixed to False. The ``img_suffix`` is fixed to
+    '.png' and ``seg_map_suffix`` is fixed to '.png'.
+    """
+
+    CLASSES = ('background', 'foreground')
+
+    PALETTE = [[120, 120, 120], [6, 230, 230]]
+
+    def __init__(self, **kwargs):
+        super(HKUISDataset, self).__init__(
+            img_suffix='.png',
+            seg_map_suffix='.png',
+            reduce_zero_label=False,
+            **kwargs)
+        assert osp.exists(self.img_dir)

setup.cfg

@@ -8,6 +8,6 @@ line_length = 79
 multi_line_output = 0
 known_standard_library = setuptools
 known_first_party = mmseg
-known_third_party = PIL,cityscapesscripts,cv2,detail,matplotlib,mmcv,numpy,onnxruntime,packaging,prettytable,pytest,pytorch_sphinx_theme,requests,scipy,seaborn,torch,ts
+known_third_party = PIL,cityscapesscripts,cv2,detail,matplotlib,mmcv,numpy,onnxruntime,packaging,prettytable,pytest,pytorch_sphinx_theme,rarfile,requests,scipy,seaborn,torch,ts
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY

tools/convert_datasets/hku_is.py

@@ -0,0 +1,67 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+import shutil
+import tempfile
+
+import mmcv
+import rarfile
+
+HKU_IS_LEN = 4447
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert HKU-IS dataset to mmsegmentation format')
+    parser.add_argument('hkuis_path', help='the path of HKU-IS.rar')
+    parser.add_argument('--tmp_dir', help='path of the temporary directory')
+    parser.add_argument('-o', '--out_dir', help='output path')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    if args.out_dir is None:
+        out_dir = osp.join('data', 'HKU-IS')
+    else:
+        out_dir = args.out_dir
+
+    print('Making directories...')
+    mmcv.mkdir_or_exist(out_dir)
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'images'))
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'validation'))
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations'))
+    mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'validation'))
+
+    print('Generating images...')
+    with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
+        rar_file = rarfile.RarFile(args.hkuis_path)
+        rar_file.extractall(tmp_dir)
+
+        image_dir = osp.join(tmp_dir, 'HKU-IS', 'imgs')
+        mask_dir = osp.join(tmp_dir, 'HKU-IS', 'gt')
+
+        assert len(os.listdir(image_dir)) == HKU_IS_LEN \
+               and len(os.listdir(mask_dir)) == \
+               HKU_IS_LEN, 'len(HKU-IS) != {}'.format(HKU_IS_LEN)
+
+        for filename in sorted(os.listdir(image_dir)):
+            shutil.copy(
+                osp.join(image_dir, filename),
+                osp.join(out_dir, 'images', 'validation',
+                         osp.splitext(filename)[0] + '.png'))
+
+        for filename in sorted(os.listdir(mask_dir)):
+            img = mmcv.imread(osp.join(mask_dir, filename))
+            mmcv.imwrite(
+                img[:, :, 0] // 128,
+                osp.join(out_dir, 'annotations', 'validation',
+                         osp.splitext(filename)[0] + '.png'))
+
+    print('Done!')
+
+
+if __name__ == '__main__':
+    main()