""" 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 = """\ Dataset for the ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents. """ # Add a link to an official homepage for the dataset here _HOMEPAGE = "https://ds4sd.github.io/icdar23-doclaynet/" # Add the licence for the dataset here if you can find it _LICENSE = "apache-2.0" # 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 = { "dev": "https://ds4sd-icdar23-doclaynet-competition.s3.eu-de.cloud-object-storage.appdomain.cloud/dev-dataset-public.zip", "test": "https://ds4sd-icdar23-doclaynet-competition.s3.eu-de.cloud-object-storage.appdomain.cloud/competition-dataset-public.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="2023.01", splits=["dev", "test"]), ] DEFAULT_CONFIG_NAME = "2023.01" 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 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["dev"], "coco.json" ), "image_dir": os.path.join(archive_path["dev"], "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["test"], "coco.json" ), "image_dir": os.path.join(archive_path["test"], "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