| """Dataset class for stained-glass dataset.""" | |
| import datasets | |
| import os | |
| import pandas as pd | |
| _DESCRIPTION = """Dataset consists of 21 high-resolution images of stained glass art, accompanied by corresponding captions""" | |
| _HOMEPAGE = """https://huggingface.co/datasets/abinthomasonline/stained-glass""" | |
| _ADJECTIVES = ["", "good", "cropped", "clean", "bright", "cool", "nice", "small", "large", "dark", "weird"] | |
| class StainedGlass(datasets.GeneratorBasedBuilder): | |
| """Stained Glass Images dataset""" | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "image": datasets.Image(), | |
| "caption": datasets.Value(dtype='string'), | |
| } | |
| ), | |
| supervised_keys=("image", "caption"), | |
| homepage=_HOMEPAGE, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| captions_path = dl_manager.download("captions.csv") | |
| df = pd.read_csv(captions_path, header=None) | |
| image_paths = {row[0]: os.path.join("images", row[0]) for _, row in df.iterrows()} | |
| image_paths = dl_manager.download(image_paths) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "captions_path": captions_path, | |
| "image_paths": image_paths, | |
| }, | |
| ) | |
| ] | |
| def _generate_examples(self, captions_path, image_paths): | |
| df = pd.read_csv(captions_path, header=None) | |
| captions = {row[0]: row[1].replace('"', '') for _, row in df.iterrows()} | |
| for adjective in _ADJECTIVES: | |
| for key, image_path in image_paths.items(): | |
| yield key + adjective, { | |
| "image": image_path, | |
| "caption": captions[key].format(token='{token}', adjective=adjective), | |
| } | |