File size: 7,741 Bytes
435e59c bbe15cd c504717 bbe15cd c504717 bbe15cd db25197 bbe15cd 4d92144 bbe15cd 5a4d8f1 1479bb7 8784b66 29ed392 cf60671 3e6bd1e 435e59c c504717 5a4d8f1 c504717 3461cbf c504717 bbe15cd 435e59c bbe15cd dd3e40b bbe15cd 92464de bbe15cd 6297149 6a25200 2681be9 bbe15cd c504717 ddc19d2 bbe15cd 92464de bbe15cd e85bf76 bbe15cd c504717 5a4d8f1 f688a6e 523fa94 4dcc370 34672f2 c504717 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 |
### Data loader script uploaded to huggingface hub
import json
import os
from pathlib import Path
import uuid
import datasets
import imgaug.augmenters as iaa
from imgaug.augmentables.bbs import BoundingBox, BoundingBoxesOnImage
import imageio
from PIL import Image
import re
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
Created for IntellectAI Hackathon!
"""
def load_image(image_path):
image = Image.open(image_path).convert("RGB")
w, h = image.size
return image, (w, h)
def _get_drive_url(url):
base_url = 'https://drive.google.com/uc?id='
split_url = url.split('/')
return base_url + split_url[5]
def quad_to_box(quad):
x1, y1 = quad[0].values()
x3, y3 = quad[2].values()
box = [x1, y1, x3, y3]
if box[3] < box[1]:
bbox = list(box)
tmp = bbox[3]
bbox[3] = bbox[1]
bbox[1] = tmp
box = tuple(bbox)
if box[2] < box[0]:
bbox = list(box)
tmp = bbox[2]
bbox[2] = bbox[0]
bbox[0] = tmp
box = tuple(bbox)
return box
def augment_image(file_path, file, bboxes):
aug = iaa.SomeOf(2,[
iaa.ElasticTransformation(alpha=(0, 2.0), sigma=0.25),
# iaa.imgcorruptlike.GaussianBlur(severity=1),
iaa.imgcorruptlike.Pixelate(severity=2),
iaa.imgcorruptlike.Contrast(severity=2),
# iaa.PerspectiveTransform(scale=(0.01, 0.15)),
iaa.imgcorruptlike.Brightness(severity=1),
])
image = imageio.imread(os.path.join(file_path, file))
bbs = BoundingBoxesOnImage.from_xyxy_array(bboxes, shape=image.shape)
image_aug, bbs_aug = aug(image=image, bounding_boxes=bbs)
bbs_aug = bbs_aug.remove_out_of_image()
bbs_aug = bbs_aug.clip_out_of_image()
if re.findall('Image...', str(bbs_aug)) == ['Image([]']:
return None, None
else:
aug_bboxes = bbs_aug.to_xyxy_array()
return Image.fromarray(image_aug, 'L').convert("RGB"), aug_bboxes
_URLS = {
"image_files": _get_drive_url("https://drive.google.com/file/d/1bVc6xIAYO22RpehEkmuihaGsZ_VoOH2E"), #URL to zip file containing images
"metadata_file": _get_drive_url("https://drive.google.com/file/d/1dgH6LiEPc2xuj0y7NcUyp6IDCELdwuqe") #URL to metadata.json from UBIAI annotation
}
class IntellectConfig(datasets.BuilderConfig):
"""BuilderConfig for IntellectAI"""
def __init__(self, **kwargs):
"""BuilderConfig for IntellectAI.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(IntellectConfig, self).__init__(**kwargs)
class IntellectAI(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
IntellectConfig(name="intellectai", version=datasets.Version("1.0.0"), description="IntellectAI Hackathon 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=[
'O',
'B-BILL_TO_NAME',
'B-BILL_TO_ADDRESS',
'B-SHIP_TO_NAME',
'B-SHIP_TO_ADDRESS',
'B-INVOICE_NUMBER',
'B-INVOICE_DATE',
'B-PAYMENT_INFO',
'B-DUE_DATE',
'B-TOTAL_TAX_AMOUNT',
'B-TOTAL_AMOUNT',
'I-BILL_TO_NAME',
'I-BILL_TO_ADDRESS',
'I-SHIP_TO_NAME',
'I-SHIP_TO_ADDRESS',
'I-INVOICE_NUMBER',
'I-INVOICE_DATE',
'I-PAYMENT_INFO',
'I-DUE_DATE',
'I-TOTAL_TAX_AMOUNT',
'I-TOTAL_AMOUNT',
]
)
),
"image": datasets.features.Image(),
}
),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
"""Uses local files located with data_dir"""
self.metadata_file = dl_manager.download(_URLS["metadata_file"])
downloaded_file = dl_manager.download_and_extract(_URLS["image_files"])
print(downloaded_file)
print(os.listdir(downloaded_file))
dest = Path(downloaded_file)/"invoice_data"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": dest/"inv_train"}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dest/"inv_dev"}
)
]
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):
with open(self.metadata_file, "r", encoding="utf8") as f:
metadata = json.load(f)
logger.info("Generating examples from = %s", filepath)
for guid, file in enumerate(sorted(os.listdir(filepath))):
words = []
bboxes = []
ner_tags = []
image_path = os.path.join(filepath, file)
image, size = load_image(image_path)
data = [obj for obj in metadata if obj["documentName"]==file][0]
for item in data["annotation"]:
cur_line_bboxes = []
line_words, label = item["boundingBoxes"], item["label"]
line_words = [w for w in line_words if w["word"].strip() != ""]
if len(line_words) == 0:
continue
if label == "OTHER":
for w in line_words:
words.append(w["word"])
ner_tags.append("O")
cur_line_bboxes.append(quad_to_box(w["normalizedVertices"]))
else:
words.append(line_words[0]["word"])
ner_tags.append("B-" + label.upper())
cur_line_bboxes.append(quad_to_box(line_words[0]["normalizedVertices"]))
for w in line_words[1:]:
words.append(w["word"])
ner_tags.append("I-" + label.upper())
cur_line_bboxes.append(quad_to_box(w["normalizedVertices"]))
cur_line_bboxes = self.get_line_bbox(cur_line_bboxes)
bboxes.extend(cur_line_bboxes)
image_variants = [(image, bboxes)]
for _ in range(4):
aug_image, aug_bboxes = augment_image(filepath, file, bboxes)
if aug_image is not None and aug_bboxes is not None:
image_variants.append((aug_image, aug_bboxes))
for img, bbs in image_variants:
yield str(uuid.uuid4()), {"id": str(uuid.uuid4()), "words": words, "bboxes": bbs, "ner_tags": ner_tags,
"image": img}
|