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# coding=utf-8
import json
import os
import datasets
from PIL import Image
import numpy as np
logger = datasets.logging.get_logger(__name__)
_CITATION = """\\n@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 = """\\nhttps://guillaumejaume.github.io/FUNSD/
"""
def load_image(image_path):
image = Image.open(image_path).convert("RGB")
w, h = image.size
# resize image to 224x224
image = image.resize((224, 224))
image = np.asarray(image)
image = image[:, :, ::-1] # flip color channels from RGB to BGR
image = image.transpose(2, 0, 1) # move channels to first dimension
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]),
]
def custom_download(url, dst_path):
import boto3
print(dst_path)
s3 = boto3.client('s3')
s3.download_file('modeldocuments', url, dst_path)
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"),
"tokens": datasets.Sequence(datasets.Value("string")),
"bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=["O", 'S-HOSPITAL-NAME', 'S-MRN',
'S-PAID-AMOUNT', 'I-HOSPITAL-NAME', 'S-PATIENT-NAME',
'S-PATIENT-NRIC', 'S-RECEIPT-DATE', 'S-RECEIPT-NO', 'B-MRN',
'S-TOTAL', 'S-TREATING-DOCTOR', 'S-TREATMENT-DATE',
'B-HOSPITAL-NAME', 'I-MRN', 'B-PATIENT-NRIC',
'I-PATIENT-NRIC', 'B-PAID-AMOUNT', 'I-PAID-AMOUNT',
'B-PATIENT-NAME', 'I-PATIENT-NAME', 'B-RECEIPT-DATE',
'I-RECEIPT-DATE', 'B-TOTAL', 'I-TOTAL', 'B-TREATING-DOCTOR',
'I-TREATING-DOCTOR', 'B-TREATMENT-DATE', 'B-RECEIPT-NO',
'I-RECEIPT-NO', 'I-TREATMENT-DATE']
)
),
#"image": datasets.Array3D(shape=(3, 224, 224), dtype="uint8"),
"image_path": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://guillaumejaume.github.io/FUNSD/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
url = 'https://transfer.sh/h1YqN8/datafiles.zip'
#url = 'datafiles.zip'
downloaded_file = dl_manager.download_and_extract(url)
#downloaded_file = dl_manager.download_custom(url, custom_download)
#downloaded_file = dl_manager.extract(downloaded_file)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/data/training_data/"}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/data/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))):
tokens = []
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, label = item["words"], item["label"]
words = [w for w in words if w["text"].strip() != ""]
if len(words) == 0:
continue
if label == "others":
for w in words:
tokens.append(w["text"])
ner_tags.append("O")
bboxes.append(normalize_bbox(w["box"], size))
else:
tokens.append(words[0]["text"])
ner_tags.append("B-" + label.upper())
bboxes.append(normalize_bbox(words[0]["box"], size))
for w in words[1:]:
tokens.append(w["text"])
ner_tags.append("I-" + label.upper())
bboxes.append(normalize_bbox(w["box"], size))
yield guid, {"id": str(guid), "tokens": tokens,
"bboxes": bboxes, "ner_tags": ner_tags,
"image_path": image_path} |