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''' |
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Reference: https://huggingface.co/datasets/nielsr/funsd/blob/main/funsd.py |
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''' |
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import json |
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import os |
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from PIL import Image |
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import datasets |
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def load_image(image_path): |
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image = Image.open(image_path).convert("RGB") |
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w, h = image.size |
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return image, (w, h) |
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def normalize_bbox(bbox, size): |
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return [ |
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int(1000 * bbox[0] / size[0]), |
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int(1000 * bbox[1] / size[1]), |
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int(1000 * bbox[2] / size[0]), |
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int(1000 * bbox[3] / size[1]), |
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] |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@article{Jaume2019FUNSDAD, |
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title={FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents}, |
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author={Guillaume Jaume and H. K. Ekenel and J. Thiran}, |
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journal={2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)}, |
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year={2019}, |
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volume={2}, |
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pages={1-6} |
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} |
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""" |
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_DESCRIPTION = """\ |
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https://guillaumejaume.github.io/FUNSD/ |
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""" |
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class FunsdConfig(datasets.BuilderConfig): |
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"""BuilderConfig for FUNSD""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for FUNSD. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(FunsdConfig, self).__init__(**kwargs) |
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class Funsd(datasets.GeneratorBasedBuilder): |
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"""Conll2003 dataset.""" |
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BUILDER_CONFIGS = [ |
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FunsdConfig(name="funsd", version=datasets.Version("1.0.0"), description="FUNSD dataset"), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"tokens": datasets.Sequence(datasets.Value("string")), |
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"bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))), |
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"ner_tags": datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=["O", "B-HEADER", "I-HEADER", "B-QUESTION", "I-QUESTION", "B-ANSWER", "I-ANSWER"] |
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) |
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), |
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"image": datasets.features.Image(), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://guillaumejaume.github.io/FUNSD/", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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downloaded_file = dl_manager.download_and_extract("https://guillaumejaume.github.io/FUNSD/dataset.zip") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/dataset/training_data/"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/dataset/testing_data/"} |
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), |
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] |
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def get_line_bbox(self, bboxs): |
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x = [bboxs[i][j] for i in range(len(bboxs)) for j in range(0, len(bboxs[i]), 2)] |
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y = [bboxs[i][j] for i in range(len(bboxs)) for j in range(1, len(bboxs[i]), 2)] |
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x0, y0, x1, y1 = min(x), min(y), max(x), max(y) |
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assert x1 >= x0 and y1 >= y0 |
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bbox = [[x0, y0, x1, y1] for _ in range(len(bboxs))] |
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return bbox |
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def _generate_examples(self, filepath): |
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logger.info("⏳ Generating examples from = %s", filepath) |
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ann_dir = os.path.join(filepath, "annotations") |
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img_dir = os.path.join(filepath, "images") |
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for guid, file in enumerate(sorted(os.listdir(ann_dir))): |
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tokens = [] |
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bboxes = [] |
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ner_tags = [] |
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file_path = os.path.join(ann_dir, file) |
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with open(file_path, "r", encoding="utf8") as f: |
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data = json.load(f) |
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image_path = os.path.join(img_dir, file) |
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image_path = image_path.replace("json", "png") |
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image, size = load_image(image_path) |
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for item in data["form"]: |
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cur_line_bboxes = [] |
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words, label = item["words"], item["label"] |
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words = [w for w in words if w["text"].strip() != ""] |
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if len(words) == 0: |
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continue |
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if label == "other": |
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for w in words: |
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tokens.append(w["text"]) |
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ner_tags.append("O") |
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cur_line_bboxes.append(normalize_bbox(w["box"], size)) |
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else: |
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tokens.append(words[0]["text"]) |
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ner_tags.append("B-" + label.upper()) |
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cur_line_bboxes.append(normalize_bbox(words[0]["box"], size)) |
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for w in words[1:]: |
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tokens.append(w["text"]) |
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ner_tags.append("I-" + label.upper()) |
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cur_line_bboxes.append(normalize_bbox(w["box"], size)) |
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cur_line_bboxes = self.get_line_bbox(cur_line_bboxes) |
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bboxes.extend(cur_line_bboxes) |
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yield guid, {"id": str(guid), "tokens": tokens, "bboxes": bboxes, "ner_tags": ner_tags, |
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"image": image} |
