Datasets:
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# 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.
"""Dataset of illustrated and non illustrated 19th Century newspaper ads."""
import ast
import os
import pandas as pd
import datasets
from PIL import Image
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@dataset{van_strien_daniel_2021_5838410,
author = {van Strien, Daniel},
title = {{19th Century United States Newspaper Advert images
with 'illustrated' or 'non illustrated' labels}},
month = oct,
year = 2021,
publisher = {Zenodo},
version = {0.0.1},
doi = {10.5281/zenodo.5838410},
url = {https://doi.org/10.5281/zenodo.5838410}}
"""
_DESCRIPTION = """\
The Dataset contains images derived from the Newspaper Navigator (news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection.
"""
_HOMEPAGE = "https://doi.org/10.5281/zenodo.5838410"
_LICENSE = "Public Domain"
_URLS = "https://zenodo.org/record/5838410/files/images.zip?download=1"
_DTYPES = {
"page_seq_num": "int64",
"edition_seq_num": "int64",
"batch": "string",
"lccn": "string",
"score": "float64",
"place_of_publication": "string",
"name": "string",
"publisher": "string",
"url": "string",
"page_url": "string",
}
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class IllustratedAds(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.1.0")
def _info(self):
features = datasets.Features(
{
"file": datasets.Value("string"),
"image": datasets.Image(),
"label": datasets.ClassLabel(names=["text-only", "illustrations"]),
"pub_date": datasets.Value("timestamp[ns]"),
"page_seq_num": datasets.Value("int64"),
"edition_seq_num": datasets.Value("int64"),
"batch": datasets.Value("string"),
"lccn": datasets.Value("string"),
"box": datasets.Sequence(datasets.Value("float32")),
"score": datasets.Value("float64"),
"ocr": datasets.Value("string"),
"place_of_publication": datasets.Value("string"),
"geographic_coverage": datasets.Value("string"),
"name": datasets.Value("string"),
"publisher": datasets.Value("string"),
"url": datasets.Value("string"),
"page_url": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
images = dl_manager.download_and_extract(_URLS)
annotations = dl_manager.download(
[
"https://zenodo.org/record/5838410/files/ads.csv?download=1",
"https://zenodo.org/record/5838410/files/sample.csv?download=1"
]
)
df_labels = pd.read_csv(
annotations[0], index_col=0
)
df_metadata = pd.read_csv(
annotations[1],
index_col=0,
dtype=_DTYPES,
)
df_metadata["file"] = df_metadata.filepath.str.replace("/", "_")
df_metadata = df_metadata.set_index("file", drop=True)
df = df_labels.join(df_metadata)
df = df.reset_index()
annotations = df.to_dict(orient="records")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images": images,
"annotations": annotations,
},
),
]
def _generate_examples(self, images, annotations):
for id_, row in enumerate(annotations):
box = ast.literal_eval(row["box"])
row["box"] = box
row.pop("filepath")
ocr = " ".join(ast.literal_eval(row["ocr"]))
row["ocr"] = ocr
image = row["file"]
row["image"] = os.path.join(images, image)
yield id_, row
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