Delete RefRef_dataset.py

RefRef_dataset.py

@@ -1,105 +0,0 @@
-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):
-        # Automatically find all JSON files matching the split patterns
-        return [
-            datasets.SplitGenerator(
-                name=f"{'cube' if cat == 'textured_cube_scene' else 'sphere' if cat == 'textured_sphere_scene' else 'env'}_{'smcvx' if self.config.name == 'single-convex' else 'smncvx' if self.config.name == 'single-non-convex' else 'mmncvx'}_{self.config.scene}",
-                gen_kwargs={
-                    "filepaths": os.path.join(f"https://huggingface.co/datasets/yinyue27/RefRef_dataset/resolve/main/image_data/{cat}/{self.config.name}/",
-                                              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),
-                    "split": f"{'cube' if cat == 'textured_cube_scene' else 'sphere' if cat == 'textured_sphere_scene' else 'env'}_{'smcvx' if self.config.name == 'single-convex' else 'smncvx' if self.config.name == 'single-non-convex' else 'mmncvx'}_{self.config.scene}",
-                },
-            ) for cat in ["textured_sphere_scene", "textured_cube_scene", "environment_map_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)
-                    }