| """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) |
|
|
