# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Script for reading 'You Actually Look Twice At it (YALTAi)' dataset.""" import os from glob import glob import datasets from PIL import Image _CITATION = """\ @dataset{clerice_thibault_2022_6827706, author = {Clérice, Thibault}, title = {YALTAi: Tabular Dataset}, month = jul, year = 2022, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.6827706}, url = {https://doi.org/10.5281/zenodo.6827706} } """ _DESCRIPTION = """Yalt AI Tabular Dataset""" _HOMEPAGE = "https://doi.org/10.5281/zenodo.6827706" _LICENSE = "Creative Commons Attribution 4.0 International" _URL = "https://zenodo.org/record/6827706/files/yaltai-table.zip?download=1" _CATEGORIES = ["Header", "Col", "Marginal", "text"] class YaltAiTabularDatasetConfig(datasets.BuilderConfig): """BuilderConfig for YaltAiTabularDataset.""" def __init__(self, name, **kwargs): """BuilderConfig for YaltAiTabularDataset.""" super(YaltAiTabularDatasetConfig, self).__init__( version=datasets.Version("1.0.0"), name=name, description=None, **kwargs ) class YaltAiTabularDataset(datasets.GeneratorBasedBuilder): """Object Detection for historic manuscripts""" BUILDER_CONFIGS = [ YaltAiTabularDatasetConfig("YOLO"), YaltAiTabularDatasetConfig("COCO"), ] def _info(self): if self.config.name == "COCO": features = datasets.Features( { "image_id": datasets.Value("int64"), "image": datasets.Image(), "width": datasets.Value("int32"), "height": datasets.Value("int32"), } ) object_dict = { "category_id": datasets.ClassLabel(names=_CATEGORIES), "image_id": datasets.Value("string"), "id": datasets.Value("int64"), "area": datasets.Value("int64"), "bbox": datasets.Sequence(datasets.Value("float32"), length=4), "segmentation": [[datasets.Value("float32")]], "iscrowd": datasets.Value("bool"), } features["objects"] = [object_dict] if self.config.name == "YOLO": features = datasets.Features( { "image": datasets.Image(), "objects": datasets.Sequence( { "label": datasets.ClassLabel(names=_CATEGORIES), "bbox": datasets.Sequence( datasets.Value("int32"), length=4 ), } ), } ) return datasets.DatasetInfo( features=features, supervised_keys=None, description=_DESCRIPTION, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): data_dir = dl_manager.download_and_extract(_URL) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "data_dir": os.path.join(data_dir, "yaltai-table/", "train") }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"data_dir": os.path.join(data_dir, "yaltai-table/", "val")}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "data_dir": os.path.join(data_dir, "yaltai-table/", "test") }, ), ] def _generate_examples(self, data_dir): def create_annotation_from_yolo_format( min_x, min_y, width, height, image_id, category_id, annotation_id, segmentation=False, ): bbox = (float(min_x), float(min_y), float(width), float(height)) area = width * height max_x = min_x + width max_y = min_y + height if segmentation: seg = [[min_x, min_y, max_x, min_y, max_x, max_y, min_x, max_y]] else: seg = [] return { "id": annotation_id, "image_id": image_id, "bbox": bbox, "area": area, "iscrowd": 0, "category_id": category_id, "segmentation": seg, } image_dir = os.path.join(data_dir, "images") label_dir = os.path.join(data_dir, "labels") image_paths = sorted(glob(f"{image_dir}/*.jpg")) label_paths = sorted(glob(f"{label_dir}/*.txt")) if self.config.name == "COCO": for idx, (image_path, label_path) in enumerate( zip(image_paths, label_paths) ): image_id = idx annotations = [] image = Image.open(image_path) # Possibly conver to RGB? w, h = image.size with open(label_path, "r") as f: lines = f.readlines() for line in lines: line = line.strip().split() category_id = line[0] x_center = float(line[1]) y_center = float(line[2]) width = float(line[3]) height = float(line[4]) float_x_center = w * x_center float_y_center = h * y_center float_width = w * width float_height = h * height min_x = int(float_x_center - float_width / 2) min_y = int(float_y_center - float_height / 2) width = int(float_width) height = int(float_height) annotation = create_annotation_from_yolo_format( min_x, min_y, width, height, image_id, category_id, image_id, ) annotations.append(annotation) example = { "image_id": image_id, "image": image, "width": w, "height": h, "objects": annotations, } yield idx, example if self.config.name == "YOLO": for idx, (image_path, label_path) in enumerate( zip(image_paths, label_paths) ): im = Image.open(image_path) width, height = im.size image_id = idx annotations = [] with open(label_path, "r") as f: lines = f.readlines() objects = [] for line in lines: line = line.strip().split() bbox_class = int(line[0]) bbox_xcenter = int(float(line[1]) * width) bbox_ycenter = int(float(line[2]) * height) bbox_width = int(float(line[3]) * width) bbox_height = int(float(line[4]) * height) objects.append( { "label": bbox_class, "bbox": [ bbox_xcenter, bbox_ycenter, bbox_width, bbox_height, ], } ) yield idx, { "image": image_path, "objects": objects, }