File size: 5,728 Bytes
94abe3a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
import collections
import json
import os
import datasets
_HOMEPAGE = "https://universe.roboflow.com/tables-cfun2/pdf-exporter/dataset/8"
_LICENSE = "CC BY 4.0"
_CITATION = """\
@misc{
pdf-exporter_dataset,
title = { PDF Exporter Dataset },
type = { Open Source Dataset },
author = { Tables },
howpublished = { \\url{ https://universe.roboflow.com/tables-cfun2/pdf-exporter } },
url = { https://universe.roboflow.com/tables-cfun2/pdf-exporter },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { apr },
note = { visited on 2025-04-16 },
}
"""
_CATEGORIES = ['table', 'table column', 'table row', 'table spanning cell']
_ANNOTATION_FILENAME = "_annotations.coco.json"
class [TR]FINTABLEConfig(datasets.BuilderConfig):
"""Builder Config for [TR]-FinTable"""
def __init__(self, data_urls, **kwargs):
"""
BuilderConfig for [TR]-FinTable.
Args:
data_urls: `dict`, name to url to download the zip file from.
**kwargs: keyword arguments forwarded to super.
"""
super([TR]FINTABLEConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
self.data_urls = data_urls
class [TR]FINTABLE(datasets.GeneratorBasedBuilder):
"""[TR]-FinTable object detection dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
[TR]FINTABLEConfig(
name="full",
description="Full version of [TR]-FinTable dataset.",
data_urls={
"train": "https://huggingface.co/datasets/Hoixi/[TR]-FinTable/resolve/main/data/train.zip",
"validation": "https://huggingface.co/datasets/Hoixi/[TR]-FinTable/resolve/main/data/valid.zip",
},
),
[TR]FINTABLEConfig(
name="mini",
description="Mini version of [TR]-FinTable dataset.",
data_urls={
"train": "https://huggingface.co/datasets/Hoixi/[TR]-FinTable/resolve/main/data/valid-mini.zip",
"validation": "https://huggingface.co/datasets/Hoixi/[TR]-FinTable/resolve/main/data/valid-mini.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,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
)
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.TEST,
gen_kwargs={
"folder_dir": data_files["validation"],
},
),
]
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
|