realms_adventurers.py

# 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"),
                    },
                }