| """FreiHAND v1.1 helpers. Media remain unchanged; numerical fields use bundles.""" |
| from pathlib import Path |
| import codecs, collections, hashlib, io, json, pickle, subprocess |
|
|
| import shutil |
| import numpy as np |
| import torch |
| from bundle_io import PROFILE, exact_tensor |
| from migrate_freihand import LAYOUT |
|
|
| SCRIPT_DIR = Path(__file__).resolve().parent |
| RELEASE_ROOT = SCRIPT_DIR.parent |
| DATASET_ROOT = RELEASE_ROOT.parent |
| RAW_ROOT = DATASET_ROOT / "raw" / "data" |
| TMP_ROOT = DATASET_ROOT / "tmp" / "freihand-tools" |
| FFMPEG = shutil.which("ffmpeg") |
| FF = FFMPEG |
|
|
| def load(path): |
| return json.loads(Path(path).read_text(encoding="utf-8")) |
|
|
| def save(path, value): |
| path = Path(path); path.parent.mkdir(parents=True, exist_ok=True) |
| tmp = path.with_suffix(path.suffix + ".tmp") |
| tmp.write_text(json.dumps(value, ensure_ascii=False, indent=2, allow_nan=False), encoding="utf-8") |
| tmp.replace(path) |
|
|
| def write_jsonl(path, rows): |
| path = Path(path); path.parent.mkdir(parents=True, exist_ok=True) |
| path.write_text("".join(json.dumps(x, ensure_ascii=False, allow_nan=False) + "\n" for x in rows), encoding="utf-8") |
|
|
| def save_tensor(path, value): |
| path = Path(path); path.parent.mkdir(parents=True, exist_ok=True) |
| torch.save(torch.as_tensor(np.asarray(value).copy()), path) |
|
|
| lines = write_jsonl |
| tensor = save_tensor |
|
|
| def ffmpeg(args, data=None): |
| result = subprocess.run([FFMPEG, "-v", "error", "-y", *map(str, args)], input=data, stdout=subprocess.PIPE, stderr=subprocess.PIPE) |
| if result.returncode: |
| raise RuntimeError(result.stderr.decode(errors="replace")) |
| return result.stdout |
|
|
| def storage(profile, codec, pixel_format, size, source_lossy, parameters): |
| version = ffmpeg(["-version"]).decode().splitlines()[0] |
| return {"profile": profile, "codec": codec, "pixel_format": pixel_format, |
| "encoded_size": size, "source_lossy": source_lossy, |
| "decoder": version, "encoder_parameters": parameters} |
|
|
| class RestrictedUnpickler(pickle.Unpickler): |
| def find_class(self, module, name): |
| allowed = { |
| ("numpy.core.multiarray", "_reconstruct"): np.core.multiarray._reconstruct, |
| ("numpy._core.multiarray", "_reconstruct"): np.core.multiarray._reconstruct, |
| ("numpy", "ndarray"): np.ndarray, ("numpy", "dtype"): np.dtype, |
| ("numpy.core.numeric", "_frombuffer"): np.core.numeric._frombuffer, |
| ("numpy._core.numeric", "_frombuffer"): np.core.numeric._frombuffer, |
| ("_codecs", "encode"): codecs.encode, |
| ("collections", "OrderedDict"): collections.OrderedDict, |
| } |
| if (module, name) not in allowed: |
| raise pickle.UnpicklingError(f"Unsupported class: {module}.{name}") |
| return allowed[(module, name)] |
|
|
| class View: |
| def __init__(self, dataset, sequence, camera, frame_ids, size, source, kind="image", split="unknown"): |
| assert dataset == "FreiHAND" and camera == "camera" and kind == "image" |
| self.root = RELEASE_ROOT |
| self.path = RELEASE_ROOT / "data" / sequence / camera |
| self.path.mkdir(parents=True, exist_ok=True) |
| self.count = len(frame_ids) |
| archive = Path(source["archive"]).name |
| self.source = dict(source) |
| self.checks = [] |
| self.meta = { |
| "schema_version": "hand-warehouse/1.1", "dataset_id": "FreiHAND", |
| "release_id": "shared-v1.1", "sequence_id": sequence, "camera_id": "camera", |
| "sample_type": "image", "num_frames": self.count, |
| "hand_slots": ["left", "right"], "split": split, |
| "split_scheme": "FreiHAND_official_train_evaluation", |
| "split_source_locator": {"archive": "raw/data/" + archive, "member_prefix": split + "/"}, |
| "sampling": {"type": "independent", "source_fps": None, "frames_ref": "camera/frames.jsonl", |
| "shard_max_images": 4096, |
| "selection": "all released RGB images, sorted by source RGB index; all background variants retained"}, |
| "camera": {"model": "unknown", "schema_id": None, "image_size": size}, |
| "components": {}, "storage_revision": "lossless-video-2026-09-18"} |
| self.frames = [{"frame_index": i, "source_frame_id": str(fid), "timestamp_ns": None, |
| "clock_domain": None, "source_locator": {**self.source, "source_frame_id": str(fid)}} |
| for i, fid in enumerate(frame_ids)] |
|
|
| @property |
| def p(self): return self.path |
| @property |
| def T(self): return self.count |
| @property |
| def m(self): return self.meta |
|
|
| def add(self, name, ref, schema, axes, valid, method="source values retained", **metadata): |
| valid_ref = Path(ref).with_name(Path(ref).stem + "_valid.pt").as_posix() |
| save_tensor(self.path / valid_ref, valid) |
| self.meta["components"][name] = { |
| "available": True, "schema_id": schema, "data_ref": ref, "axes": axes, |
| "valid_ref": valid_ref, "valid_axes": axes[:np.asarray(valid).ndim], |
| "provenance": {"kind": "format_conversion", "method": method}, |
| "source_locator": dict(self.source), **metadata} |
|
|
| def array(self, name, ref, schema, value, valid, axes, **metadata): |
| value = np.asarray(value, np.float32).copy(); valid = np.asarray(valid, bool) |
| assert value.shape[:valid.ndim] == valid.shape and value.shape[0] == self.count |
| assert np.isfinite(value[valid]).all() |
| value[~valid] = np.nan |
| bundle_ref, entry = LAYOUT[name] |
| path = self.path / bundle_ref; path.parent.mkdir(parents=True, exist_ok=True) |
| data_tensor = torch.from_numpy(value.copy()); valid_tensor = torch.from_numpy(valid.copy()) |
| torch.save({entry: {"data": data_tensor, "valid": valid_tensor}}, path) |
| self.meta["components"][name] = { |
| "available": True, "schema_id": schema, "data_ref": bundle_ref, "data_key": [entry, "data"], |
| "axes": axes, "valid_ref": bundle_ref, "valid_key": [entry, "valid"], "valid_axes": axes[:valid.ndim], |
| "storage": {"profile": PROFILE}, "valid_storage": {"profile": PROFILE}, |
| "provenance": {"kind": "format_conversion", "method": metadata.pop("method", "source values retained")}, |
| "source_locator": dict(self.source), **metadata} |
| restored = torch.load(path, map_location="cpu", weights_only=True)[entry] |
| assert exact_tensor(restored["data"], data_tensor) and exact_tensor(restored["valid"], valid_tensor) |
| self.checks.append({"test": "tensor_serialization_exact", "file": bundle_ref, |
| "shape": list(value.shape), "valid_count": int(valid.sum())}) |
|
|
| def intrinsic(self, value): |
| self.meta["camera"].update(model="pinhole", schema_id="camera/pinhole/v1") |
| self.array("intrinsic", "camera/intrinsic.pt", "camera/pinhole/v1", value, |
| np.ones(self.count, bool), ["T", "row", "column"]) |
|
|
| def finish(self): |
| write_jsonl(self.path / "camera/frames.jsonl", self.frames) |
| save(self.path / "metainfo.json", self.meta) |
| save(TMP_ROOT / "checks" / f'{self.meta["sequence_id"]}__camera.checks.json', |
| {"checks": self.checks, "frames": self.count, "input": self.source}) |
|
|
| def sha256(path): |
| h = hashlib.sha256() |
| with Path(path).open("rb") as stream: |
| for block in iter(lambda: stream.read(8 << 20), b""): h.update(block) |
| return h.hexdigest() |
|
|
|
|
