Datasets:
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# 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
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