illustrated_ads.py

# 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