Datasets:

rvorias
/

realms_adventurers

	# 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 os

	import datasets
	from huggingface_hub import hf_hub_url


	_CITATION = ""
	_DESCRIPTION = """This is the public dataset for the realms adventurer generator.
	It contains images of characters and annotations to form structured captions."""

	_HOMEPAGE = ""

	_LICENSE = "https://docs.midjourney.com/docs/terms-of-service"

	_URLS = {
	"images": hf_hub_url(
	"rvorias/realms_adventurers",
	filename="images_001.zip",
	subfolder="data",
	repo_type="dataset",
	),
	"metadata": hf_hub_url(
	"rvorias/realms_adventurers",
	filename=f"metadata.json",
	repo_type="dataset",
	),
	}


	class RealmsAdventurersDataset(datasets.GeneratorBasedBuilder):
	"""Public dataset for the realms adventurer generator.
	Containts images + structured captions."""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	features = datasets.Features(
	{
	"image": datasets.Image(),
	"caption": datasets.Value("string"),
	"components": {
	"sex": datasets.Value("string"),
	"race": datasets.Value("string"),
	"class": datasets.Value("string"),
	"inherent_features": datasets.Value("string"),
	"clothing": datasets.Value("string"),
	"accessories": datasets.Value("string"),
	"background": datasets.Value("string"),
	"shot": datasets.Value("string"),
	"view": 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
	supervised_keys=("image", "caption"),
	# 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):
	images_url = _URLS["images"]
	images_dir = dl_manager.download_and_extract(images_url)

	annotations_url = _URLS["metadata"]
	annotations_path = dl_manager.download(annotations_url)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"root_dir": images_dir,
	"metadata_path": annotations_path,
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, root_dir, metadata_path):
	with open(metadata_path, encoding="utf-8") as f:
	data = json.load(f)
	for sample in data:
	image_path = os.path.join(root_dir, sample["file_name"])
	if "caption" in sample:
	caption = sample["caption"]
	elif "discord_prompt" in sample:
	caption = sample["discord_prompt"]
	else:
	continue
	with open(image_path, "rb") as file_obj:
	yield image_path, {
	"image": {"path": image_path, "bytes": file_obj.read()},
	"caption": caption,
	"components": {
	"sex": sample.get("sex"),
	"race": sample.get("race"),
	"class": sample.get("class"),
	"inherent_features": sample.get("inherent_features"),
	"clothing": sample.get("clothing"),
	"accessories": sample.get("accessories"),
	"background": sample.get("background"),
	"shot": sample.get("shot"),
	"view": sample.get("view"),
	},
	}