image-classification-yenthienviet.py

"""TODO: Add a description here."""
import csv
import json
import os

import datasets
from datasets.tasks import ImageClassification

_DESCRIPTION = """\
This dataset contains all THIENVIET products images split in training,
    validation and testing
"""

_URLS = {
    "train": "https://huggingface.co/datasets/chanelcolgate/image-classification-yenthienviet/resolve/main/data/train.zip",
    "val": "https://huggingface.co/datasets/chanelcolgate/image-classification-yenthienviet/resolve/main/data/val.zip",
    "test": "https://huggingface.co/datasets/chanelcolgate/image-classification-yenthienviet/resolve/main/data/test.zip"
}

_CATEGORIES  = ['botkhi','thuytinh','ocvit','ban','contrung','kimloai','toc']

class YenthienvietConfig(datasets.BuilderConfig):
    """Builder Config for image-classification-yenthienviet"""
    def __init__(self, name, data_urls, **kwargs):
        """
        BuilderConfig for image-classification-yenthienviet.

        Args:
            data_urls: `dict`, name to url to download the zip file from.
            **kwargs: keyword arguments forwared to super.
        """
        super().__init__(version=datasets.Version("1.0.0", **kwargs))
        self.name
        self.data_urls = data_urls

# TODO: Name of the dataset usually matches the script name
class YenthienvietClassification(datasets.GeneratorBasedBuilder):
    """ Builder for image-classification-yenthienviet"""
    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIG_CLASS = YenthienvietConfig
    BUILDER_CONFIGS = [
        YenthienvietConfig(
            name="version-10/10",
            description="Version 10/10 of image-classification-yenthienviet dataset.",
            data_urls=_URLS,
        )
    ]

    def _info(self):
        features = datasets.Features(
            {
                "image_file_path": datasets.Value("string"),
                "image": datasets.Image(),
                "labels": datasets.features.ClassLabel(names=_CATEGORIES)
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=("image", "label"),
            task_templates=[ImageClassification(image_column="image", label_column="labels")]
        )

    def _split_generators(self, dl_manager):
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and exract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        data_files = dl_manager.download_and_extract(self.config.data_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["val"]]),
                },
            ),
            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", ".png", ".jpeg", ".bmp", ".tif", ".tiff")):
                yield i, {
                    "image_file_path": path,
                    "image": path,
                    "labels": os.path.basename(os.path.dirname(path)),
                }