Datasets:

biglam
/

loc_beyond_words

	# 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.
	"""Beyond Words"""

	import collections
	import json
	import os
	from typing import Any, Dict, List
	import datasets
	from pathlib import Path

	_CITATION = "TODO"


	_DESCRIPTION = "TODO"


	_HOMEPAGE = "TODO"


	_LICENSE = "Public Domain Mark 1.0"


	class BeyondWords(datasets.GeneratorBasedBuilder):
	"""Beyond Words Dataset"""

	def _info(self):
	features = datasets.Features(
	{
	"image_id": datasets.Value("int64"),
	"image": datasets.Image(),
	"width": datasets.Value("int32"),
	"height": datasets.Value("int32"),
	}
	)

	object_dict = {
	"bw_id": datasets.Value("string"),
	"category_id": datasets.ClassLabel(
	names=[
	"Photograph",
	"Illustration",
	"Map",
	"Comics/Cartoon",
	"Editorial Cartoon",
	"Headline",
	"Advertisement",
	]
	),
	"image_id": datasets.Value("string"),
	"id": datasets.Value("int64"),
	"area": datasets.Value("int64"),
	"bbox": datasets.Sequence(datasets.Value("float32"), length=4),
	"iscrowd": datasets.Value(
	"bool"
	), # always False for stuff segmentation task
	}
	features["objects"] = [object_dict]

	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("data/images.zip")
	training = dl_manager.download("data/train_80_percent.json")
	validation = dl_manager.download("data/val_20_percent.json")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"annotations_file": Path(training),
	"image_dir": Path(images),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"annotations_file": Path(validation),
	"image_dir": Path(images),
	},
	),
	]

	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, "images", 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
	)
	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 annotation in annotations:
	objects.append(annotation)
	example["objects"] = objects
	yield (idx, example)