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# coding=utf-8
import json
import os
from PIL import Image
import datasets
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]),
]
logger = datasets.logging.get_logger(__name__)
class ResumeDataConfig(datasets.BuilderConfig):
"""BuilderConfig for Resume NER"""
def __init__(self, **kwargs):
"""BuilderConfig for FUNSD.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(ResumeDataConfig, self).__init__(**kwargs)
class ResumeData(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
ResumeDataConfig(name="funsd",
version=datasets.Version("1.0.0"),
description="Resume Dataset"),
]
def _info(self):
return datasets.DatasetInfo(
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",
"B-ADDRESS",
"B-EMAIL",
"B-NAME",
"B-PHONE",
"B-SECTIONHEADER",
"E-ADDRESS",
"E-EMAIL",
"E-NAME",
"E-PHONE",
"E-SECTIONHEADER",
"I-ADDRESS",
"I-EMAIL",
"I-NAME",
"I-PHONE",
"I-SECTIONHEADER",
"S-ADDRESS",
"S-EMAIL",
"S-NAME",
"S-PHONE",
"S-SECTIONHEADER"
]
)
),
"image": datasets.features.Image(),
}
),
supervised_keys=None,
homepage="",
citation="",
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_file = dl_manager.download_and_extract("https://huggingface.co/datasets/Kunling/layoutlm_resume_data/resolve/main/person_resume_funsd_format_v5.zip")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/dataset/training/"}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/dataset/testing/"}
),
]
def get_line_bbox(self, bboxs):
x = [bboxs[i][j] for i in range(len(bboxs)) for j in range(0, len(bboxs[i]), 2)]
y = [bboxs[i][j] for i in range(len(bboxs)) for j in range(1, len(bboxs[i]), 2)]
x0, y0, x1, y1 = min(x), min(y), max(x), max(y)
assert x1 >= x0 and y1 >= y0
bbox = [[x0, y0, x1, y1] for _ in range(len(bboxs))]
return bbox
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", "jpeg")
image, size = load_image(image_path)
for item in data["form"]:
cur_line_bboxes = []
words, label = item["words"], item["label"]
words = [w for w in words if w["text"].strip() != ""]
if len(words) == 0:
continue
if label.lower() == "other":
for w in words:
tokens.append(w["text"])
ner_tags.append("O")
cur_line_bboxes.append(normalize_bbox(w["box"], size))
else:
tokens.append(words[0]["text"])
ner_tags.append("B-" + label.upper())
cur_line_bboxes.append(normalize_bbox(words[0]["box"], size))
for w in words[1:]:
tokens.append(w["text"])
ner_tags.append("I-" + label.upper())
cur_line_bboxes.append(normalize_bbox(w["box"], size))
cur_line_bboxes = self.get_line_bbox(cur_line_bboxes)
bboxes.extend(cur_line_bboxes)
yield guid, {"id": str(guid), "tokens": tokens, "bboxes": bboxes, "ner_tags": ner_tags,
"image": image} |