from pathlib import Path from typing import List import datasets logger = datasets.logging.get_logger(__name__) _DESCRIPTION = "A generic image folder" try: from datasets.tasks import ImageClassification has_img_classification_task = True except ImportError as e: has_img_classification_task = False class ImageFolder(datasets.GeneratorBasedBuilder): def _info(self): folder=None if isinstance(self.config.data_files, str): folder = self.config.data_files elif isinstance(self.config.data_files, dict): folder = self.config.data_files.get('train', None) if folder is None: raise RuntimeError() classes = sorted([x.name.lower() for x in Path(folder).glob('*/**')]) if has_img_classification_task: task_templates = [datasets.tasks.ImageClassification(image_file_path_column="file", label_column="labels", labels=classes)] else: task_templates = None return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "file": datasets.Value("string"), "labels": datasets.features.ClassLabel(names=classes) } ), task_templates=task_templates, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: data_files = self.config.data_files if isinstance(data_files, str): return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'archive_path': data_files})] splits = [] for split_name, folder in data_files.items(): splits.append(datasets.SplitGenerator(name=split_name, gen_kwargs={'archive_path': folder})) return splits def _generate_examples(self, archive_path): labels = self.info.features['labels'] logger.info("generating examples from = %s", archive_path) extensions = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'} for i, path in enumerate(Path(archive_path).glob('**/*')): if path.suffix in extensions: yield i, {'file': path.as_posix(), 'labels': labels.encode_example(path.parent.name.lower())}