"""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)), }