dof.py

# coding=utf-8
# Copyright 2021 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.
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"""The depth-of-field dataset"""

from PIL import Image
import datasets
from datasets.tasks import ImageClassification


logger = datasets.logging.get_logger(__name__)

_URL = (
    "https://drive.google.com/uc?export=download&id=1baxZd7X_pSZ7e9_15s5weFY5AH_sPSNi"
)

_HOMEPAGE = "https://github.com/sniafas/photography-style-analysis"

_DESCRIPTION = "A set of annotated images in shallow and deep depth of field"

_CITATION = """\
@article{sniafas2021,
  title={DoF: An image dataset for depth of field classification},
  author={Niafas, Stavros},
  doi= {10.13140/RG.2.2.29880.62722},
  url= {https://www.researchgate.net/publication/364356051_DoF_depth_of_field_datase}
  year={2021}
}
"""


class DoF(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "label": datasets.features.ClassLabel(names=["0", "1"]),
                }
            ),
            supervised_keys=("image", "label"),
            task_templates=[
                ImageClassification(image_column="image", label_column="label")
            ],
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        images_path = dl_manager.download_and_extract(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": dl_manager.iter_files(images_path),
                },
            )
        ]

    def _generate_examples(self, images):
        """Generate images and labels for splits."""
        for file_path in images:
            label = file_path.split("/")[-2:][0]
            yield file_path, {
                "image": {"path": file_path, "image": Image.open(file_path)},
                "label": label,
            }