Datasets:
Sub-tasks:
instance-segmentation
Size Categories:
10K<n<100K
Annotations Creators:
crowdsourced
License:
File size: 8,343 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
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)
# This script is supposed to work with local (downloaded) COCO dataset.
_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):
# This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
feature_dict = {
"id": datasets.Value("int64"),
"height": datasets.Value("int64"),
"width": datasets.Value("int64"),
"file_name": datasets.Value("string"),
# 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
# "precedence": datasets.Value("int64"), # annotation order, non-zero in case of redundant double- or triple-annotation
}
features = datasets.Features(feature_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."""
# This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# data_dir = self.config.data_dir
# if not data_dir:
# raise ValueError(
# "This script is supposed to work with local (downloaded) COCO dataset. The argument `data_dir` in `load_dataset()` is required."
# )
# _DL_URLS = {
# "train": os.path.join(data_dir, "train2017.zip"),
# "val": os.path.join(data_dir, "val2017.zip"),
# "test": os.path.join(data_dir, "test2017.zip"),
# "annotations_trainval": os.path.join(data_dir, "annotations_trainval2017.zip"),
# "image_info_test": os.path.join(data_dir, "image_info_test2017.zip"),
# }
archive_path = dl_manager.download_and_extract(_URLs)
print("archive_path: ", archive_path)
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.
_features = ["image_id", "image_path", "doc_category", "collection", "height", "width", "file_name", "doc_name", "page_no", "id"]
features = list(_features)
with open(json_path, 'r', encoding='UTF-8') as fp:
data = json.load(fp)
# list of dict
images = data["images"]
entries = images
# build a dict of image_id -> image info dict
d = {image["id"]: image for image in images}
# list of dict
if split in ["train", "val"]:
annotations = data["annotations"]
# build a dict of image_id ->
for annotation in annotations:
_id = annotation["id"]
image_info = d[annotation["image_id"]]
annotation.update(image_info)
annotation["id"] = _id
entries = annotations
for id_, entry in enumerate(entries):
entry = {k: v for k, v in entry.items() if k in features}
if split == "test":
entry["image_id"] = entry["id"]
entry["id"] = -1
entry["image_path"] = os.path.join(image_dir, entry["file_name"])
entry = {k: entry[k] for k in _features if k in entry}
yield str((entry["image_id"], entry["id"])), entry
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