import json import datasets from datasets.tasks import QuestionAnsweringExtractive import re logger = datasets.logging.get_logger(__name__) _CITATION = """\ @article{tobedetermined, author = {Zihao}, title = "{Food Images}", journal = {Nah}, year = 2022, eid = {arXiv:Nah}, pages = {arXiv:Nah}, archivePrefix = {arXiv}, eprint = {Nah}, } """ _DESCRIPTION = """\ For finetunning stable diffuser with chinese food images """ _URL = "https://huggingface.co/datasets/zmao/chinese_food_caption/resolve/main/chinese_food_caption.tar.gz" class FoodCaption(datasets.GeneratorBasedBuilder): """Chinese_food image and captions Dataset. Version 1.1.""" def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "text": datasets.Value("string"), "image": datasets.Image(), } ), # No default supervised_keys (as we have to pass both question # and context as input). supervised_keys=None, homepage="https://huggingface.co/datasets/zmao/food_img_caption", citation=_CITATION, ) def _split_generators(self, dl_manager): path = dl_manager.download(_URL) image_iters = dl_manager.iter_archive(path) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "images": image_iters } ), ] def _generate_examples(self, images): """This function returns the examples in the raw (text) form.""" idx = 0 #iterate through images: for filepath, image in images: text = filepath[14:-4] text = text.replace('-',' ') text = re.sub("^\d+\s|\s\d+\s|\s\d+$", " ", text) text = text.strip() yield idx, { "image" : {"path": filepath, "bytes":image.read()}, "text": text } idx += 1