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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,
}
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