movingthings.py

import os
import datasets
from datasets.tasks import ImageClassification

"""MovingThings: The moving things dataset."""


_HOMEPAGE = "https://github.com/Rivoks"

_CITATION = """\
@ONLINE {beansdata,
    author="Rivoks",
    title="Moving things dataset",
    month="August",
    year="2023",
    url="https://github.com/Rivoks"
}
"""

_DESCRIPTION = """\
MovingThings is a dataset of images of moving things programmaticaly generated with Stable Diffusion (v1.5).
It consists of 5 self movement class: roll, flow, zigzag, walk and linear.
Data was annoted by the script that generates the images with the prompt passed to Stable Diffusion.
"""

_URLS = {
    "train": "coming soon...",
    "validation": "coming soon...",
    "test": "coming soon...",
}

_NAMES = ["roll", "flow", "zigzag", "walk", "linear"]


class Beans(datasets.GeneratorBasedBuilder):
    """Beans plant leaf images dataset."""

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

    def _split_generators(self, dl_manager):
        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.VALIDATION,
                gen_kwargs={
                    "files": dl_manager.iter_files([data_files["validation"]]),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "files": dl_manager.iter_files([data_files["test"]]),
                },
            ),
        ]

    def _generate_examples(self, files):
        for i, path in enumerate(files):
            file_name = os.path.basename(path)
            if file_name.endswith(".jpg"):
                yield i, {
                    "text": path,
                    "image": path,
                    "labels": os.path.basename(os.path.dirname(path)).lower(),
                }