Datasets:
Sub-tasks:
instance-segmentation
Size Categories:
10K<n<100K
Annotations Creators:
crowdsourced
License:
File size: 8,438 Bytes
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"""
Inspired from
https://huggingface.co/datasets/ydshieh/coco_dataset_script/blob/main/coco_dataset_script.py
"""
import json
import os
import datasets
import collections
class COCOBuilderConfig(datasets.BuilderConfig):
def __init__(self, name, splits, **kwargs):
super().__init__(name, **kwargs)
self.splits = splits
# Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},
doi = {10.1145/3534678.353904},
url = {https://arxiv.org/abs/2206.01062},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022}
}
"""
# Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
DocLayNet is a human-annotated document layout segmentation dataset from a broad variety of document sources.
"""
# Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://developer.ibm.com/exchanges/data/all/doclaynet/"
# Add the licence for the dataset here if you can find it
_LICENSE = "CDLA-Permissive-1.0"
# Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLs = {
"core": "https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_core.zip",
}
# Name of the dataset usually match the script name with CamelCase instead of snake_case
class COCODataset(datasets.GeneratorBasedBuilder):
"""An example dataset script to work with the local (downloaded) COCO dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIG_CLASS = COCOBuilderConfig
BUILDER_CONFIGS = [
COCOBuilderConfig(name="2022.08", splits=["train", "val", "test"]),
]
DEFAULT_CONFIG_NAME = "2022.08"
def _info(self):
features = datasets.Features(
{
"image_id": datasets.Value("int64"),
"image": datasets.Image(),
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
# Custom fields
"doc_category": datasets.Value(
"string"
), # high-level document category
"collection": datasets.Value("string"), # sub-collection name
"doc_name": datasets.Value("string"), # original document filename
"page_no": datasets.Value("int64"), # page number in original document
}
)
object_dict = {
"category_id": datasets.ClassLabel(
names=[
"Caption",
"Footnote",
"Formula",
"List-item",
"Page-footer",
"Page-header",
"Picture",
"Section-header",
"Table",
"Text",
"Title",
]
),
"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"),
"precedence": datasets.Value("int32"),
}
features["objects"] = [object_dict]
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
archive_path = dl_manager.download_and_extract(_URLs)
splits = []
for split in self.config.splits:
if split == "train":
dataset = datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"json_path": os.path.join(
archive_path["core"], "COCO", "train.json"
),
"image_dir": os.path.join(archive_path["core"], "PNG"),
"split": "train",
},
)
elif split in ["val", "valid", "validation", "dev"]:
dataset = datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"json_path": os.path.join(
archive_path["core"], "COCO", "val.json"
),
"image_dir": os.path.join(archive_path["core"], "PNG"),
"split": "val",
},
)
elif split == "test":
dataset = datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"json_path": os.path.join(
archive_path["core"], "COCO", "test.json"
),
"image_dir": os.path.join(archive_path["core"], "PNG"),
"split": "test",
},
)
else:
continue
splits.append(dataset)
return splits
def _generate_examples(
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
self,
json_path,
image_dir,
split,
):
"""Yields examples as (key, example) tuples."""
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is here for legacy reason (tfds) and is not important in itself.
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"],
"doc_category": image_info["doc_category"],
"collection": image_info["collection"],
"doc_name": image_info["doc_name"],
"page_no": image_info["page_no"],
}
with open(json_path, encoding="utf8") as f:
annotation_data = json.load(f)
images = annotation_data["images"]
annotations = annotation_data["annotations"]
image_id_to_annotations = collections.defaultdict(list)
for annotation in annotations:
image_id_to_annotations[annotation["image_id"]].append(annotation)
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:
category_id = annotation["category_id"] # Zero based counting
if category_id != -1:
category_id = category_id - 1
annotation["category_id"] = category_id
objects.append(annotation)
example["objects"] = objects
yield idx, example
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