Datasets:
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# Copyright 2022 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.
"""Brill Iconclass AI Test Set data."""
import json
import os
from PIL import Image
import datasets
_CITATION = """\
@MISC{iconclass,
title = {Brill Iconclass AI Test Set},
author={Etienne Posthumus},
year={2020}
}
"""
_DESCRIPTION = """\
A dataset for applying machine learning to collections described with the Iconclass classification system.
"""
_HOMEPAGE = "https://labs.brill.com/ictestset/"
_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
_URL = "https://labs.brill.com/ictestset/779ba2ca9e977c58d818e3823a676973.zip"
class BrillIconclass(datasets.GeneratorBasedBuilder):
"""Brill IconClass AI dataset"""
VERSION = datasets.Version("1.1.0")
def _info(self):
features = datasets.Features(
{
"image": datasets.Image(),
"label": [datasets.Value("string")]
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"data_json": os.path.join(data_dir, "data.json"), "data_dir": data_dir},
),
]
def _generate_examples(self, data_json, data_dir):
with open(data_json, encoding="utf-8") as f:
data = json.load(f)
for row, item in enumerate(data.items()):
filepath, labels = item
image = Image.open(os.path.join(data_dir, filepath))
yield row, {"image": image, "label": labels}
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