Datasets:
Sub-tasks:
instance-segmentation
Size Categories:
10K<n<100K
Annotations Creators:
crowdsourced
License:
| """ | |
| 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 | |