Create RefRef.py

RefRef.py

@@ -0,0 +1,94 @@
+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 RefRef(datasets.GeneratorBasedBuilder):
+    """A dataset loader for NeRF-style data with camera poses, depth maps, and masks."""
+
+    VERSION = datasets.Version("1.0.0")
+    
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="default",
+            version=VERSION,
+            description="Default configuration for NeRF dataset"
+        ),
+        datasets.BuilderConfig(
+            name="ball",
+            version=VERSION,
+            description="Default configuration for NeRF dataset"
+        ),
+        datasets.BuilderConfig(
+            name="ampoule",
+            version=VERSION,
+            description="Default configuration for NeRF 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=scene,
+                gen_kwargs={
+                    "filepaths": os.path.join(f"https://huggingface.co/datasets/yinyue27/RefRef/resolve/main/image_data/textured_cube_scene/single-convex/{scene}/"),
+                    "split": split
+                },
+            ) for scene in ["ball", "ball_coloured", "cube", "cube_coloured"]
+        ]
+
+    def _generate_examples(self, filepaths, split):
+        for split in ["train", "val", "test"]:
+            filepaths = os.path.join(filepaths, f"transforms_{split}.json")
+            with open(filepaths, "r", encoding="utf-8") as f:
+                try:
+                    data = json.load(f)
+                except json.JSONDecodeError:
+                    print("error")
+    
+                scene_name = os.path.basename(os.path.dirname(filepaths))
+                
+                for frame_idx, frame in enumerate(data.get("frames", [])):
+                    base_dir = os.path.dirname(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)
+                    }