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depth-of-field

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Image Classification

Image Segmentation

Annotations Creators: Stavros Niafas

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depth-of-field / dof.py

Stavros Niafas

update data builder

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	# coding=utf-8
	# Copyright 2021 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.
	"""The depth-of-field dataset"""

	from PIL import Image
	import datasets
	from datasets.tasks import ImageClassification


	logger = datasets.logging.get_logger(__name__)

	_URL = (
	"https://drive.google.com/uc?export=download&id=1oTOOC6kF4KL5nj__x6vPjwn8yPEgG7Ou"
	)

	_HOMEPAGE = "https://github.com/sniafas/photography-style-analysis"

	_DESCRIPTION = "A set of annotated images in shallow and deep depth of field"

	_CITATION = """\
	@article{sniafas2021,
	title={DoF: An image dataset for depth of field classification},
	author={Niafas, Stavros},
	doi= {10.13140/RG.2.2.29880.62722},
	url= {https://www.researchgate.net/publication/364356051_DoF_depth_of_field_datase}
	year={2021}
	}
	"""


	class DoF(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"image": datasets.Image(),
	"label": datasets.features.ClassLabel(names=["0", "1"]),
	}
	),
	supervised_keys=("image", "label"),
	task_templates=[
	ImageClassification(image_column="image", label_column="label")
	],
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	images_path = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"images": dl_manager.iter_files(images_path),
	},
	)
	]

	def _generate_examples(self, images):
	"""Generate images and labels for splits."""
	for file_path in images:
	label = file_path.split("/")[-2:][0]
	yield file_path, {
	"image": file_path,
	"label": label,
	}