Datasets:

foduucom
/

table-detection-yolo

	import collections
	import json
	import os

	import datasets



	_CATEGORIES = ['bordered', 'borderless']
	_ANNOTATION_FILENAME = "_annotations.coco.json"


	class TABLEEXTRACTIONConfig(datasets.BuilderConfig):
	"""Builder Config for table-extraction"""

	def __init__(self, data_urls, **kwargs):
	"""
	BuilderConfig for table-extraction.
	Args:
	data_urls: `dict`, name to url to download the zip file from.
	**kwargs: keyword arguments forwarded to super.
	"""
	super(TABLEEXTRACTIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
	self.data_urls = data_urls


	class TABLEEXTRACTION(datasets.GeneratorBasedBuilder):
	"""table-extraction object detection dataset"""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	TABLEEXTRACTIONConfig(
	name="full",
	description="Full version of table-extraction dataset.",
	data_urls={
	"train": "https://huggingface.co/datasets/foduucom/table-detection-yolo/resolve/main/data/train.zip",
	"validation": "https://huggingface.co/datasets/foduucom/table-detection-yolo/resolve/main/data/valid.zip",
	"test": "https://huggingface.co/datasets/foduucom/table-detection-yolo/resolve/main/data/test.zip",
	},
	)
	]

	def _info(self):
	features = datasets.Features(
	{
	"image_id": datasets.Value("int64"),
	"image": datasets.Image(),
	"width": datasets.Value("int32"),
	"height": datasets.Value("int32"),
	"objects": datasets.Sequence(
	{
	"id": datasets.Value("int64"),
	"area": datasets.Value("int64"),
	"bbox": datasets.Sequence(datasets.Value("float32"), length=4),
	"category": datasets.ClassLabel(names=_CATEGORIES),
	}
	),
	}
	)
	return datasets.DatasetInfo(
	features=features
	)

	def _split_generators(self, dl_manager):
	data_files = dl_manager.download_and_extract(self.config.data_urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"folder_dir": data_files["train"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"folder_dir": data_files["validation"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"folder_dir": data_files["test"],
	},
	),
	]

	def _generate_examples(self, folder_dir):
	def process_annot(annot, category_id_to_category):
	return {
	"id": annot["id"],
	"area": annot["area"],
	"bbox": annot["bbox"],
	"category": category_id_to_category[annot["category_id"]],
	}

	image_id_to_image = {}
	idx = 0

	annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
	with open(annotation_filepath, "r") as f:
	annotations = json.load(f)
	category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
	image_id_to_annotations = collections.defaultdict(list)
	for annot in annotations["annotations"]:
	image_id_to_annotations[annot["image_id"]].append(annot)
	filename_to_image = {image["file_name"]: image for image in annotations["images"]}

	for filename in os.listdir(folder_dir):
	filepath = os.path.join(folder_dir, filename)
	if filename in filename_to_image:
	image = filename_to_image[filename]
	objects = [
	process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
	]
	with open(filepath, "rb") as f:
	image_bytes = f.read()
	yield idx, {
	"image_id": image["id"],
	"image": {"path": filepath, "bytes": image_bytes},
	"width": image["width"],
	"height": image["height"],
	"objects": objects,
	}
	idx += 1