File size: 3,226 Bytes
760e6b0 a044009 760e6b0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 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": scene
},
) 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)
} |