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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} |