### 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}