RefRef_test.py

import json
import os
import datasets

_CITATION = """\
@InProceedings{...},
title = {Your Dataset Title},
author={Your Name},
year={2023}
}
"""

_DESCRIPTION = """\
Dataset containing multi-view images with camera poses, depth maps, and masks for NeRF training.
"""

_LICENSE = "MIT"

class RefRef_test(datasets.GeneratorBasedBuilder):
    """A dataset loader for NeRF-style data with camera poses, depth maps, and masks."""

    VERSION = datasets.Version("1.0.0")
    
    # No multiple configs needed - using single configuration
    BUILDER_CONFIGS = [
        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):
        return [
            datasets.SplitGenerator(
                name=split,
                gen_kwargs={
                    "filepaths": os.path.join(f"https://huggingface.co/datasets/eztao/RefRef_test/resolve/main/{self.config.name}/", f"transforms_{split}.json"),
                    "split": split
                },
            ) for split in ["train", "val", "test"]
        ]

    # def _generate_examples(self, filepaths, split):
    #     # Iterate through all JSON files for this split
    #     for scene_idx, filepath in enumerate(filepaths):
    #         print(filepath)
    #         with open(filepath, "r", encoding="utf-8") as f:
    #             data = json.load(f)
    #             scene_name = os.path.basename(os.path.dirname(filepath))

    #             for frame_idx, frame in enumerate(data["frames"]):
    #                 # Build absolute paths relative to JSON file location
    #                 base_dir = os.path.dirname(filepath)
                    
    #                 # Generate unique key using scene and frame indices
    #                 unique_key = f"{scene_name}_{split}_{scene_idx}_{frame_idx}"
                    
    #                 yield unique_key, {
    #                     "image": os.path.join(base_dir, frame["file_path"]),
    #                     "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)
    #                 }
    def _generate_examples(self, filepaths, split):
        # for filepath in filepaths:
        # print(filepaths)
        # Add validation for JSON files
        # if not filepaths.endswith(".json") or os.path.isdir(filepaths):
        #     continue
            
        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)
                print(os.path.join(base_dir, frame["file_path"]+".png"))
                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)
                }