Datasets:

biglam
/

nls_chapbook_illustrations

	# 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