Datasets:
Tasks:
Image Segmentation
License:
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset | |
# script contributor. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
"""Semantic Segmentation of Underwater IMagery (SUIM) dataset""" | |
import os | |
import datasets | |
_CITATION = """\ | |
@inproceedings{islam2020suim, | |
title={{Semantic Segmentation of Underwater Imagery: Dataset and Benchmark}}, | |
author={Islam, Md Jahidul and Edge, Chelsey and Xiao, Yuyang and Luo, Peigen and Mehtaz, | |
Muntaqim and Morse, Christopher and Enan, Sadman Sakib and Sattar, Junaed}, | |
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, | |
year={2020}, | |
organization={IEEE/RSJ} | |
} | |
""" | |
_DESCRIPTION = """\ | |
The SUIM dataset is a dataset for semantic segmentation of underwater imagery. | |
The dataset consists of 1525 annotated images for training/validation and | |
110 samples for testing. | |
| Object category | Symbol | RGB color code | | |
|----------------------------------|--------|----------------| | |
| Background (waterbody) | BW | 000 (black) | | |
| Human divers | HD | 001 (blue) | | |
| Aquatic plants and sea-grass | PF | 010 (green) | | |
| Wrecks and ruins | WR | 011 (sky) | | |
| Robots (AUVs/ROVs/instruments) | RO | 100 (red) | | |
| Reefs and invertebrates | RI | 101 (pink) | | |
| Fish and vertebrates | FV | 110 (yellow) | | |
| Sea-floor and rocks | SR | 111 (white) | | |
For more information about the original SUIM dataset, | |
please visit the official dataset page: | |
https://irvlab.cs.umn.edu/resources/suim-dataset | |
Please refer to the original dataset source for any additional details, | |
citations, or specific usage guidelines provided by the dataset creators. | |
""" | |
_HOMEPAGE = "https://irvlab.cs.umn.edu/resources/suim-dataset" | |
_LICENSE = "mit" | |
class ExDark(datasets.GeneratorBasedBuilder): | |
"""Semantic Segmentation of Underwater IMagery (SUIM) dataset""" | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig( | |
name="suim", | |
version=VERSION, | |
description="Semantic Segmentation of Underwater IMagery (SUIM) dataset", | |
), | |
] | |
DEFAULT_CONFIG_NAME = "suim" | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"img": datasets.Image(), | |
"mask": datasets.Image(), | |
} | |
), | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
data_dir = dl_manager.download_and_extract("SUIM.zip") | |
train_dir = os.path.join(data_dir, "SUIM", "train_val") | |
test_dir = os.path.join(data_dir, "SUIM", "TEST") | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"data_dir": train_dir, | |
"split": "train", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"data_dir": test_dir, | |
"split": "test", | |
}, | |
), | |
] | |
def _generate_examples(self, data_dir, split): | |
img_dir = os.path.join(data_dir, "images") | |
masks_dir = os.path.join(data_dir, "masks") | |
img_files = os.listdir(img_dir) | |
for idx, img_file in enumerate(img_files): | |
img_path = os.path.join(img_dir, img_file) | |
mask_path = os.path.join( | |
masks_dir, | |
img_file.replace(".jpg", ".bmp"), | |
) | |
record = { | |
"img": img_path, | |
"mask": mask_path, | |
} | |
yield idx, record | |