import os import datasets #from datasets import DownloadManager, DatasetInfo _DESCRIPTION = """\ A segmentation dataset for [TODO: complete...] """ _HOMEPAGE = "https://huggingface.co/datasets/alkzar90/cell_benchmark" _EXTENSION = [".jpg", ".png"] _URL_BASE = "https://huggingface.co/datasets/alkzar90/cell_benchmark/resolve/main/data/" _SPLIT_URLS = { "train": _URL_BASE + "train.zip", "val": _URL_BASE + "val.zip", "test": _URL_BASE + "test.zip", "masks": _URL_BASE + "masks.zip", } class Cellsegmentation(datasets.GeneratorBasedBuilder): def _info(self): features = datasets.Features({ "image": datasets.Image(), "masks": datasets.Image(), "path" : datasets.Value("string"), }) return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features(features), supervised_keys=("image", "masks"), homepage=_HOMEPAGE, citation="", ) def _split_generators(self, dl_manager): data_files = dl_manager.download_and_extract(_SPLIT_URLS) splits = [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "files" : dl_manager.iter_files([data_files["train"]]), "masks": data_files["masks"], "split": "training", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "files" : dl_manager.iter_files([data_files["val"]]), "masks": data_files["masks"], "split": "validation", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "files" : dl_manager.iter_files([data_files["test"]]), "masks": data_files["masks"], "split": "test", } ) ] return splits def _generate_examples(self, files, masks, split): mask_path = os.path.basename(masks) for i, path in enumereate(files): file_name = os.path.basename(path) yield i, { "image": path, "masks": mask_path + "mask_" + file_name.replace("jpg", "png"), "path": path, }