| |
| """erukaLabels.ipynb |
| |
| Automatically generated by Colaboratory. |
| |
| Original file is located at |
| https://colab.research.google.com/drive/1p0VRh0b-OtHjNNLIcNUPb2BaoiE9Mh7O |
| """ |
|
|
|
|
|
|
| |
| import json |
| import os |
|
|
| import datasets |
|
|
| from PIL import Image |
| import numpy as np |
|
|
| 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[4] / size[0]), |
| int(1000 * bbox[5] / size[1]), |
| ] |
|
|
| logger = datasets.logging.get_logger(__name__) |
|
|
|
|
| _CITATION = """\ |
| @article{Jaume2019FUNSDAD, |
| title={FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents}, |
| author={Guillaume Jaume and H. K. Ekenel and J. Thiran}, |
| journal={2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)}, |
| year={2019}, |
| volume={2}, |
| pages={1-6} |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| https://guillaumejaume.github.io/FUNSD/ |
| """ |
|
|
|
|
| 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=["O", "R0C0", "R1C0", "R2C0", "R3C0", "R4C0", "R5C0", "R6C0", "R7C0", "R8C0", "R9C0", "R10C0", |
| "R0C1", "R1C1", "R2C1", "R3C1", "R4C1", "R5C1", "R6C1", "R7C1", "R8C1", "R9C1", "R10C1", |
| "R0C2", "R1C2", "R2C2", "R3C2", "R4C2", "R5C2", "R6C2", "R7C2", "R8C2", "R9C2", "R10C2", |
| "VALUATIONS", "LAND", "BUILDINGS", "TOTAL"] |
| ) |
| ), |
| "image": datasets.features.Image(), |
| } |
| ), |
| supervised_keys=None, |
| homepage="https://guillaumejaume.github.io/FUNSD/", |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| """Returns SplitGenerators.""" |
| downloaded_file = dl_manager.download_and_extract("dataset_eruka2.zip") |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/dataset_eruka/training_data/"} |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/dataset_eruka/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 = [] |
| if file == ".DS_Store": |
| continue |
|
|
| file_path = os.path.join(ann_dir, file) |
| print(file_path) |
| with open(file_path, "r", encoding="utf-8") as f: |
| data = json.load(f) |
| image_path = os.path.join(img_dir, file) |
|
|
| |
| image_path = image_path.replace("json", "jpg") |
| image, size = load_image(image_path) |
|
|
| |
| ddata_path = data["analyzeResult"]["pages"][0]["words"] |
|
|
| for item in ddata_path: |
| cur_line_bboxes = [] |
| words, label = [item["content"]], item["confidence"] |
| if len(words) == 0: |
| continue |
| tokens.append(words[0]) |
| print(label) |
| print(item) |
| if isinstance(label, float): |
| ner_tags.append("O") |
| else: |
| print(label) |
| ner_tags.append(label.upper()) |
| cur_line_bboxes.append(normalize_bbox(item["polygon"], 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} |
|
|
|
|
