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import os
import pandas as pd
import datasets
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {WikiArt},
author={Medellín AI.
},
year={2023}
}
"""
_DESCRIPTION = """\
Este dataset fue creado para el workshop de Medellin AI y Bancolombia con fines educativos.
"""
_HOMEPAGE = "https://www.meetup.com/medellin-ai/"
_LICENSE = "mit"
_URLS = {
"train": "https://workshophuggingface.blob.core.windows.net/wikiart/train.zip",
"test": "https://workshophuggingface.blob.core.windows.net/wikiart/test.zip"
}
_NAMES = ["Baroque", "Realism"]
class WikiArt(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
DEFAULT_WRITER_BATCH_SIZE = 200
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="All", version=VERSION, description="This contains the whole dataset"),
datasets.BuilderConfig(name="Baroque", version=VERSION, description="This part of the dataset contains only Baroque style"),
datasets.BuilderConfig(name="Realism", version=VERSION, description="This part of the dataset contains only Realism style"),
]
def _info(self):
features = datasets.Features(
{
"style": datasets.features.ClassLabel(names=_NAMES),
"artwork": datasets.Value("string"),
"image": datasets.Image(decode=True)
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=("image", "style"),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
data_dir = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"folderpath" : data_dir['train'],
"csv_file": 'wikiart_scraped_train.csv',
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"folderpath" : data_dir['test'],
"csv_file": 'wikiart_scraped_test.csv',
"split": "test"
},
)
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, folderpath, csv_file, split):
df_wiki_art = pd.read_csv(os.path.join(folderpath,split,csv_file), header=0)
if self.config.name != 'All':
df_wiki_art.query(f"Style == '{self.config.name}'", inplace=True)
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
for index, row in df_wiki_art.iterrows():
image_path = os.path.join(folderpath,split,row['Link'].split('/')[-1])
# Yields examples as (key, example) tuples
yield index, {
"style": row["Style"],
"artwork": row["Artwork"],
"image": image_path
}
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