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.
"""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)
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