File size: 4,514 Bytes
38b72d4 |
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 |
# coding=utf-8
import json
import os
import datasets
from PIL import Image
import numpy as np
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@article{Jaume2019FUNSDAD,
title={FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents},
author={Guillaume Jaume and H. K. Ekenel and J. Thiran},
journal={2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)},
year={2019},
volume={2},
pages={1-6}
}
"""
_DESCRIPTION = """\
https://guillaumejaume.github.io/FUNSD/
"""
def load_image(image_path):
image = Image.open(image_path).convert("RGB")
w, h = image.size
return image, (w, h)
def normalize_bbox(bbox, size):
return [
int(1000 * bbox[0] / size[0]),
int(1000 * bbox[1] / size[1]),
int(1000 * bbox[2] / size[0]),
int(1000 * bbox[3] / size[1]),
]
class FunsdConfig(datasets.BuilderConfig):
"""BuilderConfig for FUNSD"""
def __init__(self, **kwargs):
"""BuilderConfig for FUNSD.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(FunsdConfig, self).__init__(**kwargs)
class Funsd(datasets.GeneratorBasedBuilder):
"""FUNSD dataset."""
BUILDER_CONFIGS = [
FunsdConfig(name="funsd", version=datasets.Version("1.0.0"), description="FUNSD dataset"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"words": datasets.Sequence(datasets.Value("string")),
"bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=["header", "name", "initial", "range", "price", "other"]
)
),
"image_path": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://guillaumejaume.github.io/FUNSD/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_file = dl_manager.download_and_extract("https://filebin.net/haec8sl3m9a0muop/PPOCR_SER.zip")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/PPOCR_SER/training_data/"}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/PPOCR_SER/testing_data/"}
),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
ann_dir = os.path.join(filepath, "annotations")
img_dir = os.path.join(filepath, "images")
for guid, file in enumerate(sorted(os.listdir(ann_dir))):
words = []
bboxes = []
ner_tags = []
file_path = os.path.join(ann_dir, file)
with open(file_path, "r", encoding="utf8") as f:
data = json.load(f)
image_path = os.path.join(img_dir, file)
image_path = image_path.replace("json", "png")
image, size = load_image(image_path)
for item in data["form"]:
words_example, label = item["words"], item["label"]
words_example = [w for w in words_example if w["text"].strip() != ""]
if len(words_example) == 0:
continue
if label == "other":
for w in words_example:
words.append(w["text"])
ner_tags.append("O")
bboxes.append(normalize_bbox(w["box"], size))
else:
words.append(words_example[0]["text"])
ner_tags.append("B-" + label.upper())
bboxes.append(normalize_bbox(words_example[0]["box"], size))
for w in words_example[1:]:
words.append(w["text"])
ner_tags.append("I-" + label.upper())
bboxes.append(normalize_bbox(w["box"], size))
yield guid, {"id": str(guid), "words": words, "bboxes": bboxes, "ner_tags": ner_tags, "image_path": image_path} |