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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.
"""This dataset contains interior images created using DALL-E.
The dataset contains 512x512 images split into 5 classes:
* bathroom: 1000 images
* bedroom: 1000 images
* dining_room: 1000 images
* kitchen: 1000 images
* living_room: 1000 images
"""
import datasets
from datasets.download.download_manager import DownloadManager
from datasets.tasks import ImageClassification
from pathlib import Path
from typing import List, Iterator
_ALLOWED_IMG_EXT = {".png", ".jpg"}
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Computer Generated interior images},
author={Padilla, Rafael},
year={2023}
}
"""
_DESCRIPTION = """\
This new dataset contains CG interior images representing interior of houses in 5 classes, with \
1000 images per class.
"""
_HOMEPAGE = "https://huggingface.co/datasets/rafaelpadilla/interior-cgi/"
_LICENSE = ""
_URLS = {
"test": "https://huggingface.co/datasets/rafaelpadilla/interior-cgi/resolve/main/data/test.zip",
"train": "https://huggingface.co/datasets/rafaelpadilla/interior-cgi/resolve/main/data/train.zip",
}
_NAMES = ["bathroom", "bedroom", "dining_room", "kitchen", "living_room"]
class CGInteriorDataset(datasets.GeneratorBasedBuilder):
"""CGInterior: Computer Generated Interior images dataset"""
VERSION = datasets.Version("1.1.0")
def _info(self) -> datasets.DatasetInfo:
"""
Returns the dataset metadata and features.
Returns:
DatasetInfo: Metadata and features of the dataset.
"""
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image": datasets.Image(),
"label_id": datasets.features.ClassLabel(names=_NAMES),
"label_name": datasets.Value("string"),
}
),
supervised_keys=("image", "label_id"),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
task_templates=[
ImageClassification(image_column="image", label_column="label_id")
],
)
def _split_generators(
self, dl_manager: DownloadManager
) -> List[datasets.SplitGenerator]:
"""
Provides the split information and downloads the data.
Args:
dl_manager (DownloadManager): The DownloadManager to use for downloading and extracting data.
Returns:
List[SplitGenerator]: List of SplitGenerator objects representing the data splits.
"""
data_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"files": dl_manager.iter_files(data_files["train"]),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"files": dl_manager.iter_files(data_files["test"]),
},
),
]
def _generate_examples(self, files: List[str]) -> Iterator:
"""
Generates examples for the dataset.
Args:
files (List[str]): List of image paths.
Yields:
Dict[str, Union[str, Image]]: A dictionary containing the generated examples.
"""
for idx, img_path in enumerate(files):
path = Path(img_path)
if path.suffix in _ALLOWED_IMG_EXT:
yield idx, {
"image": img_path,
"label_id": path.parent.name,
"label_name": path.parent.name,
}
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