Datasets:
Tasks:
Image-to-Image
Languages:
English
Size:
1M<n<10M
Tags:
image-restoration
super-resolution
image-denoising
image-deblurring
low-light-enhancement
dehazing
License:
| import os | |
| import json | |
| from datasets import DatasetInfo, GeneratorBasedBuilder, Features, Image, ClassLabel, Split | |
| from huggingface_hub import hf_hub_download | |
| class UIR25MReference(GeneratorBasedBuilder): | |
| """UIR-2.5M Reference Dataset Loader with metadata mapping""" | |
| VERSION = "1.0.0" | |
| # Directory for pretrained models | |
| PRETRAINED_DIR = os.path.expanduser("./pretrained_models") | |
| os.makedirs(PRETRAINED_DIR, exist_ok=True) | |
| # Pretrained models info | |
| PRETRAINED_MODELS = [ | |
| "nafnet_maskdcpt_12d.pth", | |
| "nafnet_maskdcpt_5d.pth", | |
| "nafnet_maskdcpt_mixed.pth", | |
| "promptir_maskdcpt_5d.pth", | |
| "restormer_maskdcpt_5d.pth", | |
| "swinir_maskdcpt_5d.pth" | |
| ] | |
| PRETRAINED_REPO = "Jiakui/MaskDCPT" | |
| def download_pretrained_models(self): | |
| """Download all pretrained models to local directory.""" | |
| downloaded_files = [] | |
| for filename in self.PRETRAINED_MODELS: | |
| target_path = os.path.join(self.PRETRAINED_DIR, filename) | |
| if not os.path.exists(target_path): | |
| print(f"Downloading pretrained model: {filename}") | |
| hf_hub_download( | |
| repo_id=self.PRETRAINED_REPO, | |
| filename=f"pretrained_models/{filename}", | |
| local_dir=self.PRETRAINED_DIR | |
| ) | |
| else: | |
| print(f"{filename} already exists, skipping download.") | |
| downloaded_files.append(target_path) | |
| return downloaded_files | |
| def _info(self): | |
| return DatasetInfo( | |
| description="UIR-2.5M Reference: Universal Image Restoration paired dataset schema.", | |
| homepage="https://huggingface.co/datasets/Legitking4pf/UIR-2.5M-Reference", | |
| license="mit", | |
| features=Features({ | |
| "low_quality": Image(), | |
| "high_quality": Image(), | |
| "degradation_type": ClassLabel(names=[ | |
| "noise", "blur", "compression", "haze", "low_light", | |
| "degradation_5", "degradation_6", "degradation_7", | |
| "degradation_8", "degradation_9", | |
| "degradation_10", "degradation_11", "degradation_12", | |
| "degradation_13", "degradation_14", "degradation_15", | |
| "degradation_16", "degradation_17", "degradation_18", "degradation_19" | |
| ]) | |
| }), | |
| task_categories=["image-to-image"], | |
| language=["en"], | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """ | |
| Automatic split detection: | |
| Expects directories named train/test/validation | |
| """ | |
| data_dir = dl_manager.download_and_extract("https://github.com/MILab-PKU/MaskDCPT.git") | |
| splits = [] | |
| for split_name in ["train", "test", "validation"]: | |
| split_path = os.path.join(data_dir, split_name) | |
| if os.path.exists(split_path): | |
| splits.append( | |
| self.SplitGenerator( | |
| name=getattr(Split, split_name.upper()), | |
| gen_kwargs={"images_dir": split_path} | |
| ) | |
| ) | |
| return splits | |
| def _generate_examples(self, images_dir): | |
| """Yield examples using metadata JSON mapping""" | |
| low_dir = os.path.join(images_dir, "low_quality") | |
| high_dir = os.path.join(images_dir, "high_quality") | |
| metadata_file = os.path.join(images_dir, "metadata.json") | |
| if not os.path.exists(metadata_file): | |
| raise FileNotFoundError(f"Metadata file not found: {metadata_file}") | |
| with open(metadata_file, "r") as f: | |
| metadata = json.load(f) | |
| for idx, sample in enumerate(metadata): | |
| low_fname = sample["low_quality"] | |
| high_fname = sample["high_quality"] | |
| degradation_type = sample["degradation_type"] | |
| low_path = os.path.join(low_dir, low_fname) | |
| high_path = os.path.join(high_dir, high_fname) | |
| if os.path.exists(low_path) and os.path.exists(high_path): | |
| yield idx, { | |
| "low_quality": low_path, | |
| "high_quality": high_path, | |
| "degradation_type": degradation_type | |
| } | |
| else: | |
| print(f"Skipping missing files: {low_fname} or {high_fname}") |