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