| import json | |
| import os | |
| from PIL import Image | |
| import datasets | |
| from datasets import load_dataset | |
| 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__) | |
| _CITATION = """\ | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| """ | |
| class FunsdConfig(datasets.BuilderConfig): | |
| """BuilderConfig for FUNSD""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for FUNSD. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(FunsdConfig, self).__init__(**kwargs) | |
| class Funsd(datasets.GeneratorBasedBuilder): | |
| """Conll2003 dataset.""" | |
| BUILDER_CONFIGS = [ | |
| FunsdConfig(name="funsd", version=datasets.Version("1.0.0"), description="FUNSD dataset"), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_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 = [ "S-ANSWER_EXP", | |
| "S-ANSWER_FECHA_SERVICIO", | |
| "S-ANSWER_HORA_SERVICIO", | |
| "S-ANSWER_SALA", | |
| "B-ANSWER_NOMBRE1", | |
| "I-ANSWER_NOMBRE1", | |
| "E-ANSWER_NOMBRE1", | |
| "B-ANSWER_DIRECCION", | |
| "I-ANSWER_DIRECCION", | |
| "E-ANSWER_DIRECCION", | |
| "B-ANSWER_POBLACION", | |
| "I-ANSWER_POBLACION", | |
| "E-ANSWER_POBLACION", | |
| "S-ANSWER_DNI", | |
| "S-ANSWER_TELEFONO", | |
| "S-ANSWER_EDAD", | |
| "S-ANSWER_NACIMIENTO_DIF", | |
| "S-ANSWER_ESTADO_CIVIL_DIF", | |
| "S-ANSWER_FECHA_DEF", | |
| "B-ANSWER_LUGAR_DEF", | |
| "I-ANSWER_LUGAR_DEF", | |
| "E-ANSWER_LUGAR_DEF", | |
| "S-ANSWER_NATURAL_DE_DIF", | |
| "B-ANSWER_PADRES_DIF", | |
| "I-ANSWER_PADRES_DIF", | |
| "E-ANSWER_PADRES_DIF", | |
| "B-ANSWER_NOMBRE_TITULAR", | |
| "I-ANSWER_NOMBRE_TITULAR", | |
| "E-ANSWER_NOMBRE_TITULAR", | |
| "S-ANSWER_AUT_DNI_TITULAR", | |
| "B-ANSWER_DIRECCION_TITULAR", | |
| "I-ANSWER_DIRECCION_TITULAR", | |
| "E-ANSWER_DIRECCION_TITULAR", | |
| "B-ANSWER_POBLACION_TITULAR", | |
| "I-ANSWER_POBLACION_TITULAR", | |
| "E-ANSWER_POBLACION_TITULAR", | |
| "B-ANSWER_AUTORIZACION_TITULAR", | |
| "I-ANSWER_AUTORIZACION_TITULAR", | |
| "E-ANSWER_AUTORIZACION_TITULAR", | |
| "S-ANSWER_DNI_TITULAR", | |
| "S-ANSWER_HORA_DEFUNCION", | |
| "B-ANSWER_DESCRIPCION", | |
| "I-ANSWER_DESCRIPCION", | |
| "E-ANSWER_DESCRIPCION", | |
| "B-ANSWER_NOMBRE", | |
| "I-ANSWER_NOMBRE", | |
| "E-ANSWER_NOMBRE", | |
| "S-ANSWER_CANTIDAD", | |
| "S-ANSWER_IMPORTE"] | |
| ) | |
| ), | |
| "image": datasets.features.Image(), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage="", | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| downloaded_file = dl_manager.download_and_extract("https://huggingface.co/datasets/LauraExp/LILT2/resolve/main/Data.zip") | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/Data/training_data/"} | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/Data/testing_data/"} | |
| ), | |
| ] | |
| 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", "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: | |
| tokens.append(w["text"]) | |
| ner_tags.append("O") | |
| bboxes.append(normalize_bbox(w["box"], size)) | |
| else: | |
| if len(words_example) == 1: | |
| tokens.append(words_example[0]["text"]) | |
| ner_tags.append("S-" + label.upper()) | |
| bboxes.append(normalize_bbox(words_example[0]["box"], size)) | |
| else: | |
| tokens.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:]: | |
| tokens.append(w["text"]) | |
| ner_tags.append("I-" + label.upper()) | |
| bboxes.append(normalize_bbox(w["box"], size)) | |
| tokens.append(words_example[-1]["text"]) | |
| ner_tags.append("E-" + label.upper()) | |
| bboxes.append(normalize_bbox(words_example[-1]["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} | |