Datasets:
Tasks:
Object Detection
Size Categories:
n<1K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
ArXiv:
License:
| # 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 'Object Detection for Chess Pieces' dataset.""" | |
| import os | |
| from glob import glob | |
| import datasets | |
| from PIL import Image | |
| _CITATION = """\ | |
| @dataset{clerice_thibault_2022_6814770, | |
| author = {Clérice, Thibault}, | |
| title = {{YALTAi: Segmonto Manuscript and Early Printed Book | |
| Dataset}}, | |
| month = jul, | |
| year = 2022, | |
| publisher = {Zenodo}, | |
| version = {1.0.0}, | |
| doi = {10.5281/zenodo.6814770}, | |
| url = {https://doi.org/10.5281/zenodo.6814770} | |
| """ | |
| _DESCRIPTION = """YALTAi: Segmonto Manuscript and Early Printed Book Dataset""" | |
| _HOMEPAGE = "https://doi.org/10.5281/zenodo.6814770" | |
| _LICENSE = "Creative Commons Attribution 4.0 International" | |
| _URL = "https://zenodo.org/record/6814770/files/yaltai-segmonto-dataset.zip?download=1" | |
| _CATEGORIES = [ | |
| "DamageZone", | |
| "DigitizationArtefactZone", | |
| "DropCapitalZone", | |
| "GraphicZone", | |
| "MainZone", | |
| "MarginTextZone", | |
| "MusicZone", | |
| "NumberingZone", | |
| "QuireMarksZone", | |
| "RunningTitleZone", | |
| "SealZone", | |
| "StampZone", | |
| "TableZone", | |
| "TitlePageZone", | |
| ] | |
| 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-segmonto-dataset", "train" | |
| ) | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "data_dir": os.path.join(data_dir, "yaltai-segmonto-dataset", "val") | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "data_dir": os.path.join( | |
| data_dir, "yaltai-segmonto-dataset", "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) | |
| ): | |
| image = Image.open(image_path) | |
| width, height = image.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, | |
| "objects": objects, | |
| } | |