File size: 2,678 Bytes
16f95cb | 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 | """Reader for the training export (numpy, PIL and the stdlib only).
from sam3dvid.export_reader import iter_samples, load_voxels
for clip_id, rgb, visible, info in iter_samples("export/train/train-000000.tar"):
# rgb (32,512,512,3) uint8, visible (32,512,512) uint8 (0 = background, k = object k)
for obj in info["objects"]:
R_co = np.array(obj["R_co"]) # (32,3,3), camera-frame pose per frame, our convention
t_co, s_co = np.array(obj["t_co"]), np.array(obj["s_co"]) # (32,3), (32,3)
voxels = load_voxels("export/train/voxels_train.npz", obj["asset_id"]) # (64,64,64) bool
Samples come out in tar order. Pass `load_voxels` an open `np.load` result to avoid reopening
the file per object.
"""
from __future__ import annotations
import io
import json
import tarfile
from collections.abc import Iterator
from pathlib import Path
import numpy as np
from PIL import Image
VOXEL_RES = 64
def _decode(group: dict[str, bytes]) -> tuple[str, np.ndarray, np.ndarray, dict]:
rgb, visible = {}, {}
for ext, data in group.items():
kind, _, rest = ext.partition(".")
if kind == "rgb":
rgb[int(rest.split(".")[0])] = np.asarray(Image.open(io.BytesIO(data)).convert("RGB"))
elif kind == "visible":
visible[int(rest.split(".")[0])] = np.asarray(Image.open(io.BytesIO(data)))
info = json.loads(group["json"])
return (
info["clip_id"],
np.stack([rgb[t] for t in sorted(rgb)]),
np.stack([visible[t] for t in sorted(visible)]),
info,
)
def iter_samples(shard: str | Path) -> Iterator[tuple[str, np.ndarray, np.ndarray, dict]]:
"""Yield (clip_id, rgb, visible, info) for every clip in a shard."""
key, group = None, {}
with tarfile.open(shard, "r|") as tar:
for member in tar:
if not member.isfile():
continue
name = member.name.rsplit("/", 1)[-1]
member_key, _, ext = name.partition(".")
if member_key != key and group:
yield _decode(group)
group = {}
key = member_key
group[ext] = tar.extractfile(member).read()
if group:
yield _decode(group)
def load_voxels(voxels_npz, asset_id: str) -> np.ndarray:
"""Unpack one asset's bool (64,64,64) grid from `voxels_<split>.npz` (a path or np.load result)."""
if isinstance(voxels_npz, (str, Path)):
with np.load(voxels_npz) as f:
return load_voxels(f, asset_id)
bits = np.unpackbits(voxels_npz[asset_id], count=VOXEL_RES**3)
return bits.reshape(VOXEL_RES, VOXEL_RES, VOXEL_RES).astype(bool)
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