import os import datasets from datasets.tasks import ImageClassification _HOMEPAGE = "https://universe.roboflow.com/art-dataset/wiki-art/dataset/1" _LICENSE = "CC BY 4.0" _CITATION = """\ @misc{ wiki-art_dataset, title = { wiki art Dataset }, type = { Open Source Dataset }, author = { Art Dataset }, howpublished = { \\url{ https://universe.roboflow.com/art-dataset/wiki-art } }, url = { https://universe.roboflow.com/art-dataset/wiki-art }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { mar }, note = { visited on 2023-01-18 }, } """ _CATEGORIES = ['Realism', 'Art_Nouveau_Modern', 'Analytical_Cubism', 'Cubism', 'Expressionism', 'Action_painting', 'Synthetic_Cubism', 'Symbolism', 'Ukiyo_e', 'Naive_Art_Primitivism', 'Post_Impressionism', 'Impressionism', 'Fauvism', 'Rococo', 'Minimalism', 'Mannerism_Late_Renaissance', 'Color_Field_Painting', 'High_Renaissance', 'Romanticism', 'Pop_Art', 'Contemporary_Realism', 'Baroque', 'New_Realism', 'Pointillism', 'Northern_Renaissance', 'Early_Renaissance', 'Abstract_Expressionism'] class PAINTINGSTYLECLASSIFICATIONConfig(datasets.BuilderConfig): """Builder Config for painting-style-classification""" def __init__(self, data_urls, **kwargs): """ BuilderConfig for painting-style-classification. Args: data_urls: `dict`, name to url to download the zip file from. **kwargs: keyword arguments forwarded to super. """ super(PAINTINGSTYLECLASSIFICATIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs) self.data_urls = data_urls class PAINTINGSTYLECLASSIFICATION(datasets.GeneratorBasedBuilder): """painting-style-classification image classification dataset""" VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ PAINTINGSTYLECLASSIFICATIONConfig( name="full", description="Full version of painting-style-classification dataset.", data_urls={ "train": "https://huggingface.co/datasets/keremberke/painting-style-classification/resolve/main/data/train.zip", "validation": "https://huggingface.co/datasets/keremberke/painting-style-classification/resolve/main/data/valid.zip", "test": "https://huggingface.co/datasets/keremberke/painting-style-classification/resolve/main/data/test.zip", } , ), PAINTINGSTYLECLASSIFICATIONConfig( name="mini", description="Mini version of painting-style-classification dataset.", data_urls={ "train": "https://huggingface.co/datasets/keremberke/painting-style-classification/resolve/main/data/valid-mini.zip", "validation": "https://huggingface.co/datasets/keremberke/painting-style-classification/resolve/main/data/valid-mini.zip", "test": "https://huggingface.co/datasets/keremberke/painting-style-classification/resolve/main/data/valid-mini.zip", }, ) ] def _info(self): return datasets.DatasetInfo( features=datasets.Features( { "image_file_path": datasets.Value("string"), "image": datasets.Image(), "labels": datasets.features.ClassLabel(names=_CATEGORIES), } ), supervised_keys=("image", "labels"), homepage=_HOMEPAGE, citation=_CITATION, license=_LICENSE, task_templates=[ImageClassification(image_column="image", label_column="labels")], ) def _split_generators(self, dl_manager): 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["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", ".png", ".jpeg", ".bmp", ".tif", ".tiff")): yield i, { "image_file_path": path, "image": path, "labels": os.path.basename(os.path.dirname(path)), }