File size: 4,543 Bytes
8b1a711 5beb7db 8b1a711 8110ce0 8b1a711 |
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 |
# coding=utf-8
import json
import os
import datasets
from PIL import Image
import numpy as np
logger = datasets.logging.get_logger(__name__)
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 TTFormLMMConfig(datasets.BuilderConfig):
"""BuilderConfig for TTForm"""
def __init__(self, **kwargs):
"""BuilderConfig for TTForm.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(TTFormLMMConfig, self).__init__(**kwargs)
class TTFormLMM(datasets.GeneratorBasedBuilder):
"""TTForm dataset."""
BUILDER_CONFIGS = [
TTFormLMMConfig(name="ttform", version=datasets.Version("1.0.0"), description="TTFormLMM dataset"),
]
def _info(self):
return datasets.DatasetInfo(
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=["O", "B-swift_code", "I-swift_code", "B-swift_code_ans", "I-swift_code_ans", "B-clearing_code", "I-clearing_code", "B-clearing_code_ans", "I-clearing_code_ans", "B-bank_name", "I-bank_name", "B-bank_name_ans", "I-bank_name_ans", "B-address_line_1", "I-address_line_1", "B-address_line_1_ans", "I-address_line_1_ans", "B-address_line_2", "I-address_line_2","B-address_line_3", "I-address_line_3", "B-country", "I-country", "B-country_ans", "I-country_ans","B-account", "I-account", "B-account_ans", "I-account_ans", "B-name", "I-name","B-name_ans", "I-name_ans"]
)
),
"image_path": datasets.Value("string"),
}
),
supervised_keys=None
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_file = dl_manager.download_and_extract("https://drive.google.com/uc?export=download&id=1EdRtnBjDVnaCGQ5P31Mwp1uQTwg_A7_L")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/dataset/training_data/"}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/dataset/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}
|