# datasets-cli test pokemon-pixel-art.py --save_info --all_configs # # Copyright 2020 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. # TODO: Address all TODOs and remove all explanatory comments """TODO: Add a description here.""" import csv import json import os import pandas as pd import datasets from PIL import Image from io import BytesIO # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @InProceedings{huggingface:dataset, title = {A pixel art Pokemon sprites dataset}, author={InmortalKaktus / aleoli. }, year={2022} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ This new dataset is designed to solve this great NLP task and is crafted with a lot of care. """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" _URL = "https://huggingface.co/datasets/inmortalkaktus/pokemon-pixel-art/resolve/main/data/train-00000-of-00001.parquet" class PokemonPixelArt(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("1.1.0") def _info(self): features = datasets.Features( { "image": datasets.Image(decode=True), "text": datasets.Value("string") } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): data_dir = dl_manager.download(_URL) print('data_dir:', data_dir) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir, "split": "train", }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split): file = pd.read_parquet(filepath) for index, row in file.iterrows(): byte_img = BytesIO(row['image']['bytes']) img = Image.open(byte_img) img = img.convert('RGB') yield index, { "image": img, "text": row['text'] }