File size: 6,108 Bytes
d3c7b2f | 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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | """HuggingFace `datasets` loading script for the Panorama Depth Benchmark.
The data lives in sibling folders of this script (`stanford2d3d/`, …).
Non-parquet subsets expose `rgb`, `depth`, and optional `mask` as raw
bytes with the correct file extension; `zuripano` is already parquet
and keeps its native schema.
Usage:
from datasets import load_dataset
ds = load_dataset("/path/to/benchmark", "stanford2d3d", split="test")
row = ds[0]
# row["rgb"] -> dict {"path": ..., "bytes": raw PNG/JPG}
# row["depth"] -> same (PNG u16 / RGB-encoded / EXR)
# row["mask"] -> same (JPG), or None if not available
# row["stem"] -> str
# row["depth_scale"]-> str recipe hint: "v / 512.0 -> meters", etc.
"""
from __future__ import annotations
from pathlib import Path
import datasets as hfds
_DESCRIPTION = (
"Unified evaluation suite for equirectangular (360°) depth estimation,"
" assembled from eight public panorama depth test splits."
)
_CITATION = """
If you use this benchmark please cite each original dataset's paper
(Stanford2D3D, Matterport3D, PanoSUNCG, Structured3D, Metropolis / DA360,
ZuriPano / PaGeR, Holo360D).
"""
# -- per-subset config ---------------------------------------------------
# depth_fmt : how depth maps to meters (string hint for consumers)
# has_mask : whether a per-sample mask file is present
# min/max : depth clamp range used for evaluation
# scene : indoor / outdoor / synthetic
_CONFIGS = {
"stanford2d3d": dict(fmt="png_u16", scale="v / 512.0", has_mask=False, min=0.001, max=5.0, scene="indoor", n=373),
"matterport3d": dict(fmt="png_u16", scale="v / 4000.0", has_mask=False, min=0.001, max=5.0, scene="indoor", n=2014),
"panosuncg": dict(fmt="png_u8_rgb", scale="v[...,0] * 10 / 255", has_mask=False, min=0.001, max=5.0, scene="synthetic", n=3944),
"structured3d": dict(fmt="png_u16", scale="v / 1000.0", has_mask=False, min=0.001, max=5.0, scene="synthetic", n=1691),
"metropolis": dict(fmt="png_u16", scale="v / 512.0", has_mask=False, min=0.2, max=128.0, scene="outdoor", n=3000),
"holo360d_indoor": dict(fmt="exr", scale="EXR channel 0 is metric", has_mask=True, min=0.01, max=10.0, scene="indoor", n=360),
"holo360d_outdoor":dict(fmt="exr", scale="EXR channel 0 is metric", has_mask=True, min=0.01, max=75.0, scene="outdoor", n=90),
"replica360_2k": dict(fmt="dpt", scale="Sintel .dpt float32 is metric", has_mask=False, min=0.001, max=10.0, scene="indoor", n=130),
"panoinfinigen_indoor": dict(fmt="npy", scale="raw float16 meters", has_mask=False, min=0.001, max=10.0, scene="indoor", n=4392),
"panoinfinigen_nature": dict(fmt="npy", scale="raw float16 meters", has_mask=False, min=0.001, max=100.0, scene="nature", n=848),
"panoinfinigen_urban": dict(fmt="npy", scale="raw float32 meters", has_mask=False, min=0.001, max=500.0, scene="urban", n=788),
}
# zuripano is handled via its own parquet shards; see the native dataset
# card for its schema. Its entry lives in _loader.py CONFIG (min=0.01,
# max=75.0) — keep the two in sync.
class PanoDepthBenchmarkConfig(hfds.BuilderConfig):
def __init__(self, scene: str = "", **kw):
super().__init__(**kw)
self.scene = scene
class PanoDepthBenchmark(hfds.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
PanoDepthBenchmarkConfig(name=k, version=hfds.Version("1.0.0"),
description=f"{k} test split", scene=v["scene"])
for k, v in _CONFIGS.items()
]
def _info(self):
cfg = _CONFIGS[self.config.name]
features = {
"stem": hfds.Value("string"),
"rgb": hfds.Image(decode=False),
"depth": hfds.Value("binary") if cfg["fmt"] in ("exr", "dpt", "npy")
else hfds.Image(decode=False),
"depth_scale": hfds.Value("string"),
"min_depth": hfds.Value("float32"),
"max_depth": hfds.Value("float32"),
"scene": hfds.Value("string"),
}
if cfg["has_mask"]:
features["mask"] = hfds.Image(decode=False)
return hfds.DatasetInfo(
description=_DESCRIPTION,
features=hfds.Features(features),
citation=_CITATION,
)
def _split_generators(self, dl_manager):
root = Path(self.base_path or ".") / self.config.name
return [hfds.SplitGenerator(name=hfds.Split.TEST,
gen_kwargs={"root": str(root)})]
def _generate_examples(self, root):
cfg = _CONFIGS[self.config.name]
root = Path(root)
split_file = root / "test.txt"
for idx, ln in enumerate(split_file.read_text().strip().splitlines()):
parts = ln.split()
if len(parts) < 2:
continue
rgb_rel, dep_rel = parts[0], parts[1]
mask_rel = parts[2] if len(parts) >= 3 and parts[2] else None
def _read_bytes(rel):
with open(root / rel, "rb") as fp:
return fp.read()
stem = Path(rgb_rel).stem
if cfg["fmt"] in ("exr", "dpt", "npy"):
depth_val = _read_bytes(dep_rel)
else:
depth_val = {"path": str(root / dep_rel), "bytes": _read_bytes(dep_rel)}
row = {
"stem": stem,
"rgb": {"path": str(root / rgb_rel), "bytes": _read_bytes(rgb_rel)},
"depth": depth_val,
"depth_scale": cfg["scale"],
"min_depth": float(cfg["min"]),
"max_depth": float(cfg["max"]),
"scene": cfg["scene"],
}
if cfg["has_mask"] and mask_rel:
row["mask"] = {"path": str(root / mask_rel), "bytes": _read_bytes(mask_rel)}
elif cfg["has_mask"]:
row["mask"] = None
yield idx, row
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