nls_chapbook_illustrations.py

# Copyright 2022 Daniel van Strien.
#
# 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.
"""NLS Chapbook Images"""

import collections
import json
import os
from typing import Any, Dict, List

import datasets

_CITATION = "TODO"


_DESCRIPTION = "TODO"


_HOMEPAGE = "TODO"


_LICENSE = "Public Domain Mark 1.0"  # TODO confirm licence terms for annotations


_IMAGES_URL = "https://nlsfoundry.s3.amazonaws.com/data/nls-data-chapbooks.zip"

# TODO update url if this is merged upstream
_ANNOTATIONS_URL = "https://gitlab.com/davanstrien/nls-chapbooks-illustrations/-/raw/master/data/annotations/step5-manual-verification-image-0-47329_train_coco.json"

logger = datasets.utils.logging.get_logger(__name__)


class NationalLibraryScotlandChapBooksConfig(datasets.BuilderConfig):
    """BuilderConfig for National Library of Scotland Chapbooks dataset."""

    def __init__(self, name, **kwargs):
        super(NationalLibraryScotlandChapBooksConfig, self).__init__(
            version=datasets.Version("1.0.0"),
            name=name,
            description="TODO",
            **kwargs,
        )


class NationalLibraryScotlandChapBooks(datasets.GeneratorBasedBuilder):
    """National Library of Scotland Chapbooks dataset."""

    BUILDER_CONFIGS = [
        NationalLibraryScotlandChapBooksConfig("illustration-detection"),
        NationalLibraryScotlandChapBooksConfig("image-classification"),
    ]

    def _info(self):
        if self.config.name == "illustration-detection":
            features = datasets.Features(
                {
                    "image_id": datasets.Value("int64"),
                    "image": datasets.Image(),
                    "width": datasets.Value("int32"),
                    "height": datasets.Value("int32"),
                }
            )
            object_dict = {
                "category_id": datasets.ClassLabel(
                    names=["early_printed_illustration"]
                ),
                "image_id": datasets.Value("string"),
                "id": datasets.Value("int64"),
                "area": datasets.Value("int64"),
                "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
                "segmentation": [[datasets.Value("float32")]],
                "iscrowd": datasets.Value("bool"),
            }
            features["objects"] = [object_dict]
        if self.config.name == "image-classification":
            features = datasets.Features(
                {
                    "image": datasets.Image(),
                    "label": datasets.ClassLabel(
                        num_classes=2, names=["not-illustrated", "illustrated"]
                    ),
                }
            )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        images = dl_manager.download_and_extract(_IMAGES_URL)
        annotations = dl_manager.download(_ANNOTATIONS_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "annotations_file": os.path.join(annotations),
                    "image_dir": os.path.join(images, "nls-data-chapbooks"),
                },
            )
        ]

    def _get_image_id_to_annotations_mapping(
        self, annotations: List[Dict]
    ) -> Dict[int, List[Dict[Any, Any]]]:
        """
        A helper function to build a mapping from image ids to annotations.
        """
        image_id_to_annotations = collections.defaultdict(list)
        for annotation in annotations:
            image_id_to_annotations[annotation["image_id"]].append(annotation)
        return image_id_to_annotations

    def _generate_examples(self, annotations_file, image_dir):
        def _image_info_to_example(image_info, image_dir):
            image = image_info["file_name"]
            return {
                "image_id": image_info["id"],
                "image": os.path.join(image_dir, image),
                "width": image_info["width"],
                "height": image_info["height"],
            }

        with open(annotations_file, encoding="utf8") as f:
            annotation_data = json.load(f)
            images = annotation_data["images"]
            annotations = annotation_data["annotations"]

            image_id_to_annotations = self._get_image_id_to_annotations_mapping(
                annotations
            )
            if self.config.name == "illustration-detection":
                for idx, image_info in enumerate(images):
                    example = _image_info_to_example(
                        image_info,
                        image_dir,
                    )
                    annotations = image_id_to_annotations[image_info["id"]]
                    objects = []
                    for annot in annotations:
                        category_id = annot["category_id"]
                        if category_id == 1:
                            annot["category_id"] = 0
                        objects.append(annot)
                    example["objects"] = objects
                    yield idx, example
            if self.config.name == "image-classification":
                for idx, image_info in enumerate(images):
                    annotations = image_id_to_annotations[image_info["id"]]
                    if len(annotations) < 1:
                        label = 0
                    else:
                        label = 1
                    example = {
                        "image": os.path.join(image_dir, image_info["file_name"]),
                        "label": label,
                    }
                    yield idx, example