amazonian_fish_classifier.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.
"""TODO."""


import os
from pathlib import Path
import pandas as pd
import datasets

_CITATION = """TODO"""


_DESCRIPTION = """\
TODO
"""

_HOMEPAGE = "https://doi.org/10.25573/data.17314730.v1"

_LICENSE = "CC BY 4.0"


_URLS = {
    "images": "https://smithsonian.figshare.com/ndownloader/files/31975544",
}


class AmazonianFish(datasets.GeneratorBasedBuilder):
    """TODO"""

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        features = datasets.Features(
            {
                "image": datasets.Image(),
                "label": datasets.ClassLabel(
                    names=[
                        "Ancistrus",
                        "Apistogramma",
                        "Astyanax",
                        "Bario",
                        "Bryconops",
                        "Bujurquina",
                        "Bunocephalus",
                        "Characidium",
                        "Charax",
                        "Copella",
                        "Corydoras",
                        "Creagrutus",
                        "Curimata",
                        "Doras",
                        "Erythrinus",
                        "Gasteropelecus",
                        "Gymnotus",
                        "Hemigrammus",
                        "Hyphessobrycon",
                        "Knodus",
                        "Moenkhausia",
                        "Otocinclus",
                        "Oxyropsis",
                        "Phenacogaster",
                        "Pimelodella",
                        "Prochilodus",
                        "Pygocentrus",
                        "Pyrrhulina",
                        "Rineloricaria",
                        "Sorubim",
                        "Tatia",
                        "Tetragonopterus",
                        "Tyttocharax",
                    ]
                ),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        images = dl_manager.download_and_extract(_URLS["images"])
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": os.path.join(images,"training_images"),
                },
            ),
        ]

    def _generate_examples(self, images):
        id_ = 0
        for example in Path(images).rglob("*.jpg"):
            if example.name.startswith("._"):
                continue
            id_ += 1
            yield id_, {"image": str(example), "label": example.parent.name}