Datasets:

eugenesiow
/

BSD100

	# coding=utf-8
	# 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.
	"""BSD100 dataset: An evaluation dataset for the image super resolution task"""


	import datasets
	from pathlib import Path


	_CITATION = """
	@inproceedings{martin2001database,
	title={A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics},
	author={Martin, David and Fowlkes, Charless and Tal, Doron and Malik, Jitendra},
	booktitle={Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001},
	volume={2},
	pages={416--423},
	year={2001},
	organization={IEEE}
	}
	"""

	_DESCRIPTION = """
	BSD is a dataset used frequently for image denoising and super-resolution.
	BSD100 is the testing set of the Berkeley segmentation dataset BSD300.
	"""

	_HOMEPAGE = "https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/"

	_LICENSE = "UNK"

	_DL_URL = "https://huggingface.co/datasets/eugenesiow/BSD100/resolve/main/data/"

	_DEFAULT_CONFIG = "bicubic_x2"

	_DATA_OPTIONS = {
	"bicubic_x2": {
	"hr": _DL_URL + "BSD100_HR.tar.gz",
	"lr": _DL_URL + "BSD100_LR_x2.tar.gz",
	},
	"bicubic_x3": {
	"hr": _DL_URL + "BSD100_HR.tar.gz",
	"lr": _DL_URL + "BSD100_LR_x3.tar.gz",
	},
	"bicubic_x4": {
	"hr": _DL_URL + "BSD100_HR.tar.gz",
	"lr": _DL_URL + "BSD100_LR_x4.tar.gz",
	}
	}


	class Bsd100Config(datasets.BuilderConfig):
	"""BuilderConfig for BSD100."""

	def __init__(
	self,
	name,
	hr_url,
	lr_url,
	**kwargs,
	):
	if name not in _DATA_OPTIONS:
	raise ValueError("data must be one of %s" % _DATA_OPTIONS)
	super(Bsd100Config, self).__init__(name=name, version=datasets.Version("1.0.0"), **kwargs)
	self.hr_url = hr_url
	self.lr_url = lr_url


	class Bsd100(datasets.GeneratorBasedBuilder):
	"""BSD100 dataset for single image super resolution evaluation."""

	BUILDER_CONFIGS = [
	Bsd100Config(
	name=key,
	hr_url=values['hr'],
	lr_url=values['lr']
	) for key, values in _DATA_OPTIONS.items()
	]

	DEFAULT_CONFIG_NAME = _DEFAULT_CONFIG

	def _info(self):
	features = datasets.Features(
	{
	"hr": datasets.Value("string"),
	"lr": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	hr_data_dir = dl_manager.download_and_extract(self.config.hr_url)
	lr_data_dir = dl_manager.download_and_extract(self.config.lr_url)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"lr_path": lr_data_dir,
	"hr_path": str(Path(hr_data_dir) / 'BSD100_HR')
	},
	)
	]

	def _generate_examples(
	self, hr_path, lr_path
	):
	""" Yields examples as (key, example) tuples. """
	# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is here for legacy reason (tfds) and is not important in itself.
	extensions = {'.png'}
	for file_path in sorted(Path(lr_path).glob("*/")):
	if file_path.suffix in extensions:
	file_path_str = str(file_path.as_posix())
	yield file_path_str, {
	'lr': file_path_str,
	'hr': str((Path(hr_path) / file_path.name).as_posix())
	}