| | import json |
| | import os |
| | import datasets |
| |
|
| | _CITATION = """\ |
| | @InProceedings{...}, |
| | title = {Your Dataset Title}, |
| | author={Your Name}, |
| | year={2025} |
| | } |
| | """ |
| |
|
| | _DESCRIPTION = """\ |
| | Dataset containing multi-view images with camera poses, depth maps, and masks for NeRF training. |
| | """ |
| |
|
| | _LICENSE = "MIT" |
| |
|
| | class RefRefConfig(datasets.BuilderConfig): |
| | """BuilderConfig for RefRef dataset.""" |
| |
|
| | def __init__(self, scene=None, **kwargs): |
| | """BuilderConfig for RefRef dataset. |
| | |
| | Args: |
| | **kwargs: keyword arguments forwarded to super. |
| | """ |
| | super().__init__(**kwargs) |
| | self.scene = scene |
| |
|
| | class RefRef(datasets.GeneratorBasedBuilder): |
| | """A dataset loader for NeRF-style data with camera poses, depth maps, and masks.""" |
| |
|
| | VERSION = datasets.Version("1.0.0") |
| | BUILDER_CONFIG_CLASS = RefRefConfig |
| | BUILDER_CONFIGS = [ |
| | RefRefConfig( |
| | name="single-non-convex", |
| | description="Single non-convex scene configuration for RefRef dataset.", |
| | ), |
| | RefRefConfig( |
| | name="multiple-non-convex", |
| | description="Multiple non-convex scene configuration for RefRef dataset.", |
| | ), |
| | RefRefConfig( |
| | name="single-convex", |
| | description="Single convex scene configuration for RefRef dataset.", |
| | ) |
| | ] |
| |
|
| | def _info(self): |
| | features = datasets.Features({ |
| | "image": datasets.Image(), |
| | "depth": datasets.Image(), |
| | "mask": datasets.Image(), |
| | "transform_matrix": datasets.Sequence( |
| | datasets.Sequence(datasets.Value("float64"), length=4), |
| | length=4 |
| | ), |
| | "rotation": datasets.Value("float32") |
| | }) |
| | |
| | return datasets.DatasetInfo( |
| | description=_DESCRIPTION, |
| | features=features, |
| | homepage="", |
| | license=_LICENSE, |
| | citation=_CITATION |
| | ) |
| |
|
| | def _split_generators(self, dl_manager): |
| | |
| | return [ |
| | datasets.SplitGenerator( |
| | name=f"{'cubeBg' if cat == 'textured_cube_scene' else 'sphereBg' if cat == 'textured_sphere_scene' else 'envMapBg'}_{'singleMatConvex' if self.config.name == 'single-convex' else 'singleMatNonConvex' if self.config.name == 'single-non-convex' else 'multiMatNonConvex'}_{self.config.scene}", |
| | gen_kwargs={ |
| | "filepaths": os.path.join(f"https://huggingface.co/datasets/yinyue27/RefRef/resolve/main/image_data/{cat}/{self.config.name}/", |
| | f"{self.config.scene}_sphere" if cat == "textured_sphere_scene" else self.config.scene), |
| | "split": f"{'cubeBg' if cat == 'textured_cube_scene' else 'sphereBg' if cat == 'textured_sphere_scene' else 'envMapBg'}_{'singleMatConvex' if self.config.name == 'single-convex' else 'singleMatNonConvex' if self.config.name == 'single-non-convex' else 'multiMatNonConvex'}_{self.config.scene}", |
| | }, |
| | ) for cat in ["textured_sphere_scene", "textured_cube_scene"] |
| | ] |
| |
|
| | def _generate_examples(self, filepaths, split): |
| | for split in ["train", "val", "test"]: |
| | split_filepaths = os.path.join(filepaths, f"transforms_{split}.json") |
| | with open(split_filepaths, "r", encoding="utf-8") as f: |
| | try: |
| | data = json.load(f) |
| | except json.JSONDecodeError: |
| | print("Error opening " + split_filepaths) |
| | continue |
| | |
| | scene_name = os.path.basename(os.path.dirname(split_filepaths)) |
| | |
| | for frame_idx, frame in enumerate(data.get("frames", [])): |
| | base_dir = os.path.dirname(split_filepaths) |
| | |
| | yield f"{scene_name}_{frame_idx}", { |
| | "image": os.path.join(base_dir, frame["file_path"]+".png"), |
| | "depth": os.path.join(base_dir, frame["depth_file_path"]), |
| | "mask": os.path.join(base_dir, frame["mask_file_path"]), |
| | "transform_matrix": frame["transform_matrix"], |
| | "rotation": frame.get("rotation", 0.0) |
| | } |
