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# 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.
import json
import datasets
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Graffiti},
author={UR
},
year={2023}
}
"""
_DESCRIPTION = """\
Graffiti dataset taken from https://www.graffiti.org/ and https://www.graffiti-database.com/.
"""
_HOMEPAGE = "https://huggingface.co/datasets/artificialhoney/graffiti"
_LICENSE = "Apache License 2.0"
_VERSION = "0.1.0"
_SOURCES = [
"graffiti.org",
"graffiti-database.com"
]
class GraffitiConfig(datasets.BuilderConfig):
"""BuilderConfig for Graffiti."""
def __init__(self, **kwargs):
"""BuilderConfig for Graffiti.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(GraffitiConfig, self).__init__(**kwargs)
class Graffiti(datasets.GeneratorBasedBuilder):
"""Graffiti dataset taken from https://www.graffiti.org/ and https://www.graffiti-database.com/."""
BUILDER_CONFIG_CLASS = GraffitiConfig
BUILDER_CONFIGS = [
GraffitiConfig(
name="default",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image": datasets.Image(),
"conditioning_image": datasets.Image(),
"text": datasets.Value("string")
}
),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
version=_VERSION,
task_templates=[],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
images = []
metadata = []
conditioning = []
for source in _SOURCES:
images.append(dl_manager.iter_archive(dl_manager.download("./data/{0}/images.tar.gz".format(source))))
conditioning.append(dl_manager.iter_archive(dl_manager.download("./data/{0}/conditioning.tar.gz".format(source))))
metadata.append(dl_manager.download("./data/{0}/metadata.jsonl".format(source)))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"images": images,
"metadata": metadata,
"conditioning": conditioning
},
)
]
def _generate_examples(self, metadata, images, conditioning):
idx = 0
for index, meta in enumerate(metadata):
m = []
with open(meta, encoding="utf-8") as f:
for row in f:
m.append(json.loads(row))
c = iter(conditioning[index])
for file_path, file_obj in images[index]:
data = [x for x in m if file_path.endswith(x["file"])][0]
conditioning_file = next(c)
conditioning_file_path = conditioning_file[0]
conditioning_file_obj = conditioning_file[1]
text = data["caption"]
if data["palette"] != None:
colors = []
for color in data["palette"]:
if color[2] in colors or "grey" in color[2]:
continue
colors.append(color[2])
if len(colors) > 0:
text += ", in the colors "
text += " and ".join(colors)
if data["artist"] != None:
# text += ", with text " + data["artist"]
text += ", by " + data["artist"]
if data["city"] != None:
text += ", located in " + data["city"]
yield idx, {
"image": {"path": file_path, "bytes": file_obj.read()},
"conditioning_image": {"path": conditioning_file_path, "bytes": conditioning_file_obj.read()},
"text": text,
}
idx+=1 |