import os import pathlib from typing import List, TypedDict, Union, cast import datasets as ds from datasets.utils.logging import get_logger from PIL import Image from PIL.Image import Image as PilImage logger = get_logger(__name__) _DESCRIPTION = ( "A New Dataset and Benchmark for Content-aware Visual-Textual Presentation Layout" ) _CITATION = """\ @inproceedings{hsu2023posterlayout, title={PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout}, author={Hsu, Hsiao Yuan and He, Xiangteng and Peng, Yuxin and Kong, Hao and Zhang, Qing}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={6018--6026}, year={2023} } """ _HOMEPAGE = "http://59.108.48.34/tiki/PosterLayout/" _LICENSE = "Images in PKU PosterLayout are distributed under the CC BY-SA 4.0 license." class TrainPoster(TypedDict): original: str inpainted: str class TestPoster(TypedDict): canvas: str class SaliencyMaps(TypedDict): pfpn: str basnet: str class TrainDataset(TypedDict): poster: TrainPoster saliency_maps: SaliencyMaps class TestDataset(TypedDict): poster: TestPoster saliency_maps: SaliencyMaps class DatasetUrls(TypedDict): train: TrainDataset test: TestDataset # The author of this loading script has uploaded the poster image and saliency maps to the HuggingFace's private repository to facilitate testing. # If you are using this loading script, please download the annotations from the appropriate channels, such as the OneDrive link provided by the Magazine dataset's author. # (To the author of Magazine dataset, if there are any issues regarding this matter, please contact us. We will address it promptly.) _URLS: DatasetUrls = { "train": { "poster": { "original": "https://huggingface.co./datasets/shunk031/PKU-PosterLayout-private/resolve/main/train/original_poster.zip", "inpainted": "https://huggingface.co./datasets/shunk031/PKU-PosterLayout-private/resolve/main/train/inpainted_poster.zip", }, "saliency_maps": { "pfpn": "https://huggingface.co./datasets/shunk031/PKU-PosterLayout-private/resolve/main/train/saliencymaps_pfpn.zip", "basnet": "https://huggingface.co./datasets/shunk031/PKU-PosterLayout-private/resolve/main/train/saliencymaps_basnet.zip", }, }, "test": { "poster": { "canvas": "https://huggingface.co./datasets/shunk031/PKU-PosterLayout-private/resolve/main/test/image_canvas.zip", }, "saliency_maps": { "pfpn": "https://huggingface.co./datasets/shunk031/PKU-PosterLayout-private/resolve/main/test/saliencymaps_pfpn.zip", "basnet": "https://huggingface.co./datasets/shunk031/PKU-PosterLayout-private/resolve/main/test/saliencymaps_basnet.zip", }, }, } def file_sorter(f: pathlib.Path) -> int: idx, *_ = f.stem.split("_") return int(idx) def load_image(file_path: pathlib.Path) -> PilImage: logger.info(f"Load from {file_path}") return Image.open(file_path) def get_original_poster_files(base_dir: str) -> List[pathlib.Path]: poster_dir = pathlib.Path(base_dir) / "original_poster" return sorted(poster_dir.iterdir(), key=lambda f: int(f.stem)) def get_inpainted_poster_files(base_dir: str) -> List[pathlib.Path]: inpainted_dir = pathlib.Path(base_dir) / "inpainted_poster" return sorted(inpainted_dir.iterdir(), key=file_sorter) def get_basnet_map_files(base_dir: str) -> List[pathlib.Path]: basnet_map_dir = pathlib.Path(base_dir) / "saliencymaps_basnet" return sorted(basnet_map_dir.iterdir(), key=file_sorter) def get_pfpn_map_files(base_dir: str) -> List[pathlib.Path]: pfpn_map_dir = pathlib.Path(base_dir) / "saliencymaps_pfpn" return sorted(pfpn_map_dir.iterdir(), key=file_sorter) def get_canvas_files(base_dir: str) -> List[pathlib.Path]: canvas_dir = pathlib.Path(base_dir) / "image_canvas" return sorted(canvas_dir.iterdir(), key=lambda f: int(f.stem)) class PosterLayoutDataset(ds.GeneratorBasedBuilder): VERSION = ds.Version("1.0.0") BUILDER_CONFIGS = [ds.BuilderConfig(version=VERSION)] def _info(self) -> ds.DatasetInfo: features = ds.Features( { "original_poster": ds.Image(), "inpainted_poster": ds.Image(), "basnet_saliency_map": ds.Image(), "pfpn_saliency_map": ds.Image(), "canvas": ds.Image(), } ) return ds.DatasetInfo( description=_DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, license=_LICENSE, features=features, ) @property def _manual_download_instructions(self) -> str: return ( "To use PKU-PosterLayout dataset, you need to download the poster image " "and saliency maps via [PKU Netdisk](https://disk.pku.edu.cn/link/999C6E97BB354DF8AD0F9E1F9003BE05) " "or [Google Drive](https://drive.google.com/drive/folders/1Gk202RVs9Qy2zbJUNeurC1CaQYNU-Vuv?usp=share_link)." ) def _download_from_hf(self, dl_manager: ds.DownloadManager) -> DatasetUrls: return dl_manager.download_and_extract(_URLS) def _download_from_local(self, dl_manager: ds.DownloadManager) -> DatasetUrls: assert dl_manager.manual_dir is not None, dl_manager.manual_dir dir_path = os.path.expanduser(dl_manager.manual_dir) tng_dir_path = os.path.join(dir_path, "train") tst_dir_path = os.path.join(dir_path, "test") if not os.path.exists(dir_path): raise FileNotFoundError( "Make sure you have downloaded and placed the PKU-PosterLayout dataset correctly. " 'Furthermore, you shoud check that a manual dir via `datasets.load_dataset("shunk031/PKU-PosterLayout", data_dir=...)` ' "that include zip files from the downloaded files. " f"Manual downloaded instructions: {self._manual_download_instructions}" ) return dl_manager.extract( path_or_paths={ "train": { "poster": { "original": os.path.join(tng_dir_path, "inpainted_poster.zip"), "inpainted": os.path.join(tng_dir_path, "inpainted_poster.zip"), }, "saliency_maps": { "pfpn": os.path.join(tng_dir_path, "saliencymaps_pfpn.zip"), "basnet": os.path.join(tng_dir_path, "saliencymaps_basnet.zip"), }, }, "test": { "poster": { "canvas": os.path.join(tst_dir_path, "image_canvas.zip"), }, "saliency_maps": { "pfpn": os.path.join(tst_dir_path, "salieycmaps_pfpn.zip"), "basnet": os.path.join(tst_dir_path, "saliencymaps_basnet.zip"), }, }, } ) def _split_generators(self, dl_manager: ds.DownloadManager): file_paths = ( self._download_from_hf(dl_manager) if dl_manager.download_config.token else self._download_from_local(dl_manager) ) tng_files = file_paths["train"] tst_files = file_paths["test"] return [ ds.SplitGenerator( name=ds.Split.TRAIN, gen_kwargs={ "poster": tng_files["poster"], "saliency_maps": tng_files["saliency_maps"], }, ), ds.SplitGenerator( name=ds.Split.TEST, gen_kwargs={ "poster": tst_files["poster"], "saliency_maps": tst_files["saliency_maps"], }, ), ] def _generate_train_examples( self, poster: TrainPoster, saliency_maps: SaliencyMaps ): poster_files = get_original_poster_files(base_dir=poster["original"]) inpainted_files = get_inpainted_poster_files(base_dir=poster["inpainted"]) basnet_map_files = get_basnet_map_files(base_dir=saliency_maps["basnet"]) pfpn_map_files = get_pfpn_map_files(base_dir=saliency_maps["pfpn"]) assert ( len(poster_files) == len(inpainted_files) == len(basnet_map_files) == len(pfpn_map_files) ) it = zip(poster_files, inpainted_files, basnet_map_files, pfpn_map_files) for i, ( original_poster_path, inpainted_poster_path, basnet_map_path, pfpn_map_path, ) in enumerate(it): yield i, { "original_poster": load_image(original_poster_path), "inpainted_poster": load_image(inpainted_poster_path), "basnet_saliency_map": load_image(basnet_map_path), "pfpn_saliency_map": load_image(pfpn_map_path), "canvas": None, } def _generate_test_examples(self, poster: TestPoster, saliency_maps: SaliencyMaps): canvas_files = get_canvas_files(base_dir=poster["canvas"]) basnet_map_files = get_basnet_map_files(base_dir=saliency_maps["basnet"]) pfpn_map_files = get_pfpn_map_files(base_dir=saliency_maps["pfpn"]) assert len(canvas_files) == len(basnet_map_files) == len(pfpn_map_files) it = zip(canvas_files, basnet_map_files, pfpn_map_files) for i, (canvas_path, basnet_map_path, pfpn_map_path) in enumerate(it): yield i, { "original_poster": None, "inpainted_poster": None, "basnet_saliency_map": load_image(basnet_map_path), "pfpn_saliency_map": load_image(pfpn_map_path), "canvas": load_image(canvas_path), } def _generate_examples( self, poster: Union[TrainPoster, TestPoster], saliency_maps: SaliencyMaps ): if "original" in poster and "inpainted" in poster: yield from self._generate_train_examples( poster=cast(TrainPoster, poster), saliency_maps=saliency_maps ) elif "canvas" in poster: yield from self._generate_test_examples( poster=cast(TestPoster, poster), saliency_maps=saliency_maps ) else: raise ValueError("Invalid dataset")