# 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 contextlib from typing import Dict import requests import datasets from PIL import Image from pathlib import Path import xml.etree.ElementTree as ET from xml.etree.ElementTree import Element from typing import Any, List from pathlib import PosixPath _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.6984525" _LICENSE = "Creative Commons Attribution Non Commercial Share Alike 2.0 Generic" ZENODO_API_URL = "https://zenodo.org/api/records/6984525" _CATEGORIES = [ "zebra", "tree", "nude", "crucifixion", "scroll", "head", "swan", "shield", "lily", "mouse", "knight", "dragon", "horn", "dog", "palm", "tiara", "helmet", "sheep", "deer", "person", "sword", "rooster", "bear", "halo", "lion", "monkey", "prayer", "crown of thorns", "elephant", "zucchetto", "unicorn", "holy shroud", "cat", "apple", "banana", "chalice", "bird", "eagle", "pegasus", "crown", "camauro", "saturno", "arrow", "dove", "centaur", "horse", "hands", "skull", "orange", "monk", "trumpet", "key of heaven", "fish", "cow", "angel", "devil", "book", "stole", "butterfly", "serpent", "judith", "mitre", "banner", "donkey", "shepherd", "boat", "god the father", "crozier", "jug", "lance", ] _POSES = [ "stand", "sit", "partial", "Unspecified", "squats", "lie", "bend", "fall", "walk", "push", "pray", "undefined", "kneel", "unrecognize", "unknown", "other", "ride", ] logger = datasets.utils.logging.get_logger(__name__) def parse_annotation(annotations_object: Element) -> Dict[str, Any]: with contextlib.suppress(ValueError): name = annotations_object.find("name").text pose = annotations_object.find("pose").text diffult = int(annotations_object.find("difficult").text) bndbox = annotations_object.find("bndbox") xmin = float(bndbox.find("xmin").text) ymin = float(bndbox.find("ymin").text) xmax = float(bndbox.find("xmax").text) ymax = float(bndbox.find("ymax").text) return { "name": name, "pose": pose, "diffult": diffult, "xmin": xmin, "ymin": ymin, "xmax": xmax, "ymax": ymax, } def create_annotations_dict(xmls: List[PosixPath]) -> Dict[str, Any]: annotations = {} for xml in xmls: tree = ET.parse(xml) root = tree.getroot() filename = root.find("filename").text source = root.find("source/database").text size = root.find("size") width = int(size.find("width").text) height = int(size.find("height").text) depth = int(size.find("depth").text) segmented = root.find("segmented") segmented = int(segmented.text) if segmented else None annotation_objects = root.findall("object") annotation_objects = [ parse_annotation(annotation) for annotation in annotation_objects ] annotation_objects = [ annotation for annotation in annotation_objects if annotation is not None ] annotations[filename] = { "source": source, "width": width, "height": height, "dept": depth, "segmented": segmented, "objects": annotation_objects, } return annotations def get_coco_annotation_from_obj( image_id, label, xmin, ymin, xmax, ymax ): # adapted from https://github.com/yukkyo/voc2coco/blob/abd05bbfa0740a04bb483862eccea2032bc80e24/voc2coco.py#L60 category_id = label assert xmax > xmin and ymax > ymin, logger.warn( f"Box size error !: (xmin, ymin, xmax, ymax): {xmin, ymin, xmax, ymax}" ) o_width = xmax - xmin o_height = ymax - ymin ann = { "image_id": image_id, "area": o_width * o_height, "iscrowd": 0, "bbox": [xmin, ymin, o_width, o_height], "category_id": category_id, # "ignore": 0, "segmentation": [], } return ann common_features = features = datasets.Features( { # "image_id": datasets.Value("int64"), "image": datasets.Image(), "source": datasets.Value("string"), "width": datasets.Value("int16"), "height": datasets.Value("int16"), "dept": datasets.Value("int8"), "segmented": datasets.Value("int8"), } ) class DeartDatasetConfig(datasets.BuilderConfig): """BuilderConfig for YaltAiTabularDataset.""" def __init__(self, name, **kwargs): """BuilderConfig for YaltAiTabularDataset.""" super(DeartDatasetConfig, self).__init__( version=datasets.Version("1.0.0"), name=name, description=None, **kwargs ) class DeartDataset(datasets.GeneratorBasedBuilder): """Object Detection for historic manuscripts""" BUILDER_CONFIGS = [ DeartDatasetConfig("raw"), DeartDatasetConfig("coco"), ] def _info(self): if self.config.name == "coco": features = common_features features["image_id"] = datasets.Value("string") object_dict = { "category_id": datasets.ClassLabel(names=_CATEGORIES), "image_id": datasets.Value("string"), "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 == "raw": features = common_features object_dict = { "name": datasets.ClassLabel(names=_CATEGORIES), "pose": datasets.ClassLabel(names=_POSES), "diffult": datasets.Value("int32"), "xmin": datasets.Value("float64"), "ymin": datasets.Value("float64"), "xmax": datasets.Value("float64"), "ymax": datasets.Value("float64"), } features["objects"] = [object_dict] return datasets.DatasetInfo( features=features, supervised_keys=None, description=_DESCRIPTION, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): zenodo_record = requests.get(ZENODO_API_URL).json() urls = sorted( [ file["links"]["self"] for file in zenodo_record["files"] if file["type"] == "zip" ] ) annotation_data = urls.pop(0) annotation_data = dl_manager.download_and_extract(annotation_data) image_data = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "annotations_data": Path(annotation_data), "image_data": image_data, }, ), ] def _generate_examples(self, annotations_data, image_data): xmls = list(annotations_data.rglob("*.xml")) annotations_data = create_annotations_dict(xmls) count = 0 for directory in image_data: for file in Path(directory).glob("*.jpg"): with Image.open(file) as image: try: if self.config.name == "raw": example = annotations_data[file.name] example["image"] = image count += 1 yield count, example if self.config.name == "coco": updated_annotations = [] example = annotations_data[file.name] annotations = example["objects"] for annotation in annotations: label = annotation["name"] xmin, ymin = annotation["xmin"], annotation["ymin"] xmax, ymax = annotation["xmax"], annotation["ymax"] updated_annotations.append( get_coco_annotation_from_obj( count, label, xmin, ymin, xmax, ymax ), ) example["image"] = image example["objects"] = updated_annotations example["image_id"] = str(count) count += 1 yield count, example except Exception: logger.warn(file.name) continue