diff --git a/H2O/shared-v1.1/doc/validation/subject4_h1_7_cam2.html b/H2O/shared-v1.1/doc/validation/subject4_h1_7_cam2.html
new file mode 100644
index 0000000000000000000000000000000000000000..5af1fc3076b476333d0577fd345486772eec8715
--- /dev/null
+++ b/H2O/shared-v1.1/doc/validation/subject4_h1_7_cam2.html
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+} 源网格 mesh_cocoa 源 OBJ 静态预览，不代表物体姿态投影 全部组件元数据 {
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+ "convention": "T_camera_from_object",
+ "unit": "m",
+ "valid_policy": {
+ "revision": "usable-annotation-2026-09-20",
+ "meaning": "annotation usable for supervision, not merely record presence",
+ "source_rule": "Source obj_pose_rt class equals this nonzero object class",
+ "source_locator": {
+ "frames_ref": "camera/frames.jsonl",
+ "source_field": "component_sources"
+ },
+ "scope": [
+ "T"
+ ],
+ "numerical_checks": "Schema shape, finite valid floats, SE(3) where applicable; malformed input aborts",
+ "official_qa": {
+ "status": "not_provided",
+ "reason": "No separate applicable official QA in selected source inputs"
+ },
+ "local_excluded_frame_indices": [],
+ "local_exclusion_reasons": {},
+ "visibility_independent": true
+ },
+ "data_key": [
+ "pose",
+ "data"
+ ],
+ "valid_key": [
+ "pose",
+ "valid"
+ ],
+ "storage": {
+ "profile": "pt/tensor_bundle/v1"
+ },
+ "valid_storage": {
+ "profile": "pt/tensor_bundle/v1"
+ }
+ },
+ "points_cocoa": {
+ "available": true,
+ "schema_id": "object_keypoints/camera/v1",
+ "data_ref": "annotation/object/cocoa/object.pt",
+ "axes": [
+ "T",
+ "keypoint",
+ "coordinate"
+ ],
+ "provenance": {
+ "kind": "format_conversion",
+ "method": "Copy source annotation; normalize storage only"
+ },
+ "source_locator": {
+ "frames_ref": "camera/frames.jsonl",
+ "source_field": "component_sources"
+ },
+ "valid_ref": "annotation/object/cocoa/object.pt",
+ "valid_axes": [
+ "T",
+ "keypoint"
+ ],
+ "keypoint_schema": "H2O_official_object21",
+ "keypoint_names": [
+ "center",
+ "corner_0",
+ "corner_1",
+ "corner_2",
+ "corner_3",
+ "corner_4",
+ "corner_5",
+ "corner_6",
+ "corner_7",
+ "edge_midpoint_0",
+ "edge_midpoint_1",
+ "edge_midpoint_2",
+ "edge_midpoint_3",
+ "edge_midpoint_4",
+ "edge_midpoint_5",
+ "edge_midpoint_6",
+ "edge_midpoint_7",
+ "edge_midpoint_8",
+ "edge_midpoint_9",
+ "edge_midpoint_10",
+ "edge_midpoint_11"
+ ],
+ "source_order": "official obj_point_order.jpg: center, 8 corners, 12 edge midpoints",
+ "unit": "m",
+ "valid_policy": {
+ "revision": "usable-annotation-2026-09-20",
+ "meaning": "annotation usable for supervision, not merely record presence",
+ "source_rule": "Source obj_pose class equals this nonzero object class, broadcast to its 21 points",
+ "source_locator": {
+ "frames_ref": "camera/frames.jsonl",
+ "source_field": "component_sources"
+ },
+ "scope": [
+ "T",
+ "keypoint"
+ ],
+ "numerical_checks": "Schema shape, finite valid floats, SE(3) where applicable; malformed input aborts",
+ "official_qa": {
+ "status": "not_provided",
+ "reason": "No separate applicable official QA in selected source inputs"
+ },
+ "local_excluded_frame_indices": [],
+ "local_exclusion_reasons": {},
+ "visibility_independent": true
+ },
+ "data_key": [
+ "keypoints",
+ "xyz",
+ "data"
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+ "valid_key": [
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+ "xyz",
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+ "storage": {
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+ "valid_storage": {
+ "profile": "pt/tensor_bundle/v1"
+ }
+ },
+ "mesh_cocoa": {
+ "available": true,
+ "schema_id": "mesh/obj/v1",
+ "data_ref": "annotation/object/cocoa/assets/cocoa.obj",
+ "axes": [
+ "vertex"
+ ],
+ "provenance": {
+ "kind": "format_conversion",
+ "method": "Copy source annotation; normalize storage only"
+ },
+ "source_locator": {
+ "file": "/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/object.zip",
+ "member": "object/cocoa/cocoa.obj",
+ "texture_aliases": [
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+ "sha256": "90dd612369a14e825d040e539f1f1028e5b6e46e598eb5a4c664034e3a49e91f",
+ "source": {
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+ "member": "object/cocoa/ovomaltine.png"
+ }
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+ "valid": true,
+ "unit": "m"
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+ "depth": {
+ "available": true,
+ "schema_id": "depth/z_float32/v1",
+ "data_ref": "observation/depth.mkv",
+ "axes": [
+ "T",
+ "height",
+ "width"
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+ "provenance": {
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+ "method": "Copy source annotation; normalize storage only"
+ },
+ "source_locator": {
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+ "source_field": "component_sources.depth",
+ "definition": "https://github.com/taeinkwon/h2oplayer/blob/main/utils.py",
+ "conversion": "registered millimetre camera-Z to metres; no viewer range clipping"
+ },
+ "storage": {
+ "profile": "mkv/depth_float32_bits/v1",
+ "codec": "ffv1",
+ "pixel_format": "gray16le",
+ "encoded_size": [
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+ "decoder": {
+ "name": "ffmpeg",
+ "version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+ "autorotate": false,
+ "color_space": "unknown",
+ "color_range": "unknown",
+ "color_primaries": "unknown",
+ "color_transfer": "unknown"
+ },
+ "encoder_parameters": {
+ "version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+ "name": "ffv1",
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+ "threads": 10
+ },
+ "source_lossy": false,
+ "storage_fps": 30,
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+ "valid_storage": {
+ "profile": "mkv/bool_ffv1/v1",
+ "codec": "ffv1",
+ "pixel_format": "gray",
+ "encoded_size": [
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+ "decoder": {
+ "name": "ffmpeg",
+ "version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+ "autorotate": false,
+ "color_space": "unknown",
+ "color_range": "unknown",
+ "color_primaries": "unknown",
+ "color_transfer": "unknown"
+ },
+ "encoder_parameters": {
+ "version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+ "name": "ffv1",
+ "arguments": [],
+ "threads": 10
+ },
+ "source_lossy": false,
+ "storage_fps": 30,
+ "timebase_role": "index_only"
+ },
+ "valid_ref": "observation/depth_valid.mkv",
+ "valid_axes": [
+ "T",
+ "height",
+ "width"
+ ],
+ "unit": "m",
+ "depth_type": "z",
+ "depth_origin": "measured",
+ "invalid_value": "NaN"
+ },
+ "segment": {
+ "available": false,
+ "reason": "H2O provides no native segmentation; rendering deferred to v2.0",
+ "status": "source_missing"
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+ "relations": {
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+ "reason": "No source contact/grasp annotation",
+ "status": "source_missing"
+ }
+ },
+ "split": "test",
+ "view_type": "exo",
+ "split_scheme": "official_action_split",
+ "split_source_locator": {
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+ "member": "label_split/action_test.txt",
+ "record": 41
+ }
+} 持久化 QC {
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+ "numeric_components": {
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+ 3
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+ "dtype": "torch.float32",
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+ "dtype": "torch.float32",
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+ },
+ "mano": {
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+ "dtype": "torch.float32",
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+ },
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+ "dtype": "torch.float32",
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+ "pose_cocoa": {
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+ "dtype": "torch.float32",
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+ "points_cocoa": {
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+ },
+ "bundle_files": 4,
+ "scope": "v1.1 registry, keys, shapes, validity and timeline; not a media pixel comparison",
+ "selected_frames": [
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+ "preview_media": [
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+ "depth_valid"
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+ "decode_scope": "only selected frames emitted; sequential decoding may traverse earlier frames, no full-video pixel comparison"
+} subject4_h1_7_cam3 subject4_h1_7_cam3 直接读取 v1.1 bundle；预览只作人工核查。手／物体点来自源 3D 标注，仅为显示投影到 RGB；未生成新 UV、分割或接触标注。
帧 0 / 584 原生 RGB（仅预览缩放） RGB + 原生 3D 关键点投影（仅预览：keypoints_xyz #00e6ff, points_cocoa #ff48a4） 深度 Z，黑=无效，白≥3 m；不改源深度 原深度逐像素 valid：白=有效，黑=无效，不是深度值 原帧号、全部数值标注及有效性 {
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+} 源网格 mesh_cocoa 源 OBJ 静态预览，不代表物体姿态投影 全部组件元数据 {
+ "schema_version": "hand-warehouse/1.1",
+ "dataset_id": "H2O",
+ "release_id": "shared-v1.1",
+ "sequence_id": "subject4_h1_7",
+ "camera_id": "cam3",
+ "sample_type": "video",
+ "num_frames": 584,
+ "hand_slots": [
+ "left",
+ "right"
+ ],
+ "sampling": {
+ "type": "dense",
+ "source_fps": null,
+ "frames_ref": "camera/frames.jsonl",
+ "selection": {
+ "max_frames": null,
+ "views": null
+ }
+ },
+ "camera": {
+ "model": "pinhole",
+ "schema_id": "camera/pinhole/v1",
+ "image_size": [
+ 1280,
+ 720
+ ]
+ },
+ "components": {
+ "rgb": {
+ "available": true,
+ "schema_id": "rgb/uint8/v1",
+ "data_ref": "observation/rgb.mkv",
+ "axes": [
+ "T",
+ "height",
+ "width",
+ "channel"
+ ],
+ "provenance": {
+ "kind": "format_conversion",
+ "method": "Verified source packet copy or lossless fixed-decoder RGB encoding"
+ },
+ "source_locator": {
+ "frames_ref": "camera/frames.jsonl"
+ },
+ "storage": {
+ "profile": "mkv/ffv1_rgb/v1",
+ "codec": "ffv1",
+ "pixel_format": "bgr0",
+ "encoded_size": [
+ 1280,
+ 720
+ ],
+ "decoder": {
+ "name": "ffmpeg",
+ "version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+ "autorotate": false,
+ "color_space": "gbr",
+ "color_range": "pc",
+ "color_primaries": "unknown",
+ "color_transfer": "unknown",
+ "source_color_metadata": {
+ "color_space": "gbr",
+ "color_range": "pc",
+ "color_primaries": "unknown",
+ "color_transfer": "unknown"
+ }
+ },
+ "encoder_parameters": {
+ "version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+ "name": "ffv1",
+ "arguments": [
+ "-level",
+ "3",
+ "-pix_fmt",
+ "bgr0"
+ ],
+ "threads": 10
+ },
+ "source_lossy": false,
+ "storage_fps": 30,
+ "timebase_role": "index_only"
+ },
+ "valid_ref": "observation/rgb_valid.pt",
+ "valid_axes": [
+ "T"
+ ]
+ },
+ "intrinsic": {
+ "available": true,
+ "schema_id": "camera/pinhole/v1",
+ "data_ref": "camera/camera.pt",
+ "axes": [
+ "T",
+ "row",
+ "column"
+ ],
+ "provenance": {
+ "kind": "format_conversion",
+ "method": "Copy source annotation; normalize storage only"
+ },
+ "source_locator": {
+ "file": "/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/subject4_v1_1.tar.gz",
+ "member": "subject4/h1/7/cam3/cam_intrinsics.txt",
+ "sha256": "f406b977e4559315cefa9088f607eb353d1f28529e81d1eb2ad90ce948c5b8de"
+ },
+ "valid_ref": "camera/camera.pt",
+ "valid_axes": [
+ "T"
+ ],
+ "data_key": [
+ "intrinsic",
+ "data"
+ ],
+ "valid_key": [
+ "intrinsic",
+ "valid"
+ ],
+ "storage": {
+ "profile": "pt/tensor_bundle/v1"
+ },
+ "valid_storage": {
+ "profile": "pt/tensor_bundle/v1"
+ }
+ },
+ "extrinsic": {
+ "available": true,
+ "schema_id": "transform/se3/v1",
+ "data_ref": "camera/camera.pt",
+ "axes": [
+ "T",
+ "row",
+ "column"
+ ],
+ "provenance": {
+ "kind": "format_conversion",
+ "method": "Copy source annotation; normalize storage only"
+ },
+ "source_locator": {
+ "frames_ref": "camera/frames.jsonl",
+ "source_field": "component_sources"
+ },
+ "valid_ref": "camera/camera.pt",
+ "valid_axes": [
+ "T"
+ ],
+ "convention": "T_camera_from_world",
+ "source_convention": "T_world_from_camera",
+ "data_key": [
+ "extrinsic",
+ "data"
+ ],
+ "valid_key": [
+ "extrinsic",
+ "valid"
+ ],
+ "storage": {
+ "profile": "pt/tensor_bundle/v1"
+ },
+ "valid_storage": {
+ "profile": "pt/tensor_bundle/v1"
+ }
+ },
+ "mano": {
+ "available": true,
+ "schema_id": "mano/axis_angle61/v1",
+ "data_ref": "annotation/mano/mano.pt",
+ "axes": [
+ "T",
+ "hand",
+ "parameter"
+ ],
+ "provenance": {
+ "kind": "format_conversion",
+ "method": "Copy source annotation; normalize storage only"
+ },
+ "source_locator": {
+ "frames_ref": "camera/frames.jsonl",
+ "source_field": "component_sources"
+ },
+ "valid_ref": "annotation/mano/mano.pt",
+ "valid_axes": [
+ "T",
+ "hand"
+ ],
+ "model_profile": {
+ "version": "sha256:eab78181e459e156559df334b103d825e00c8711d2eeffee1cc176fb42051f4f",
+ "flat_hand_mean": true,
+ "left_shapedirs_correction": "not_applied",
+ "models": {
+ "left": {
+ "file": "MANO_LEFT.pkl",
+ "sha256": "c4022f7083f2ca7c78b2b3d595abbab52debd32b09d372b16923a801f0ea6a30"
+ },
+ "right": {
+ "file": "MANO_RIGHT.pkl",
+ "sha256": "45d60aa3b27ef9107a7afd4e00808f307fd91111e1cfa35afd5c4a62de264767"
+ }
+ }
+ },
+ "valid_policy": {
+ "revision": "usable-annotation-2026-09-20",
+ "meaning": "annotation usable for supervision, not merely record presence",
+ "source_rule": "Per-hand hand_pose_mano flag equals 1; corresponding side model available",
+ "source_locator": {
+ "frames_ref": "camera/frames.jsonl",
+ "source_field": "component_sources"
+ },
+ "scope": [
+ "T",
+ "hand"
+ ],
+ "numerical_checks": "Schema shape, finite valid floats, SE(3) where applicable; malformed input aborts",
+ "official_qa": {
+ "status": "not_provided",
+ "reason": "No separate applicable official QA in selected source inputs"
+ },
+ "local_excluded_frame_indices": [],
+ "local_exclusion_reasons": {},
+ "visibility_independent": true
+ },
+ "data_key": [
+ "motion",
+ "data"
+ ],
+ "valid_key": [
+ "motion",
+ "valid"
+ ],
+ "storage": {
+ "profile": "pt/tensor_bundle/v1"
+ },
+ "valid_storage": {
+ "profile": "pt/tensor_bundle/v1"
+ }
+ },
+ "keypoints_xyz": {
+ "available": true,
+ "schema_id": "keypoints/xyz/v1",
+ "data_ref": "annotation/keypoints/keypoints.pt",
+ "axes": [
+ "T",
+ "hand",
+ "keypoint",
+ "coordinate"
+ ],
+ "provenance": {
+ "kind": "format_conversion",
+ "method": "Copy source annotation; normalize storage only"
+ },
+ "source_locator": {
+ "frames_ref": "camera/frames.jsonl",
+ "source_field": "component_sources"
+ },
+ "valid_ref": "annotation/keypoints/keypoints.pt",
+ "valid_axes": [
+ "T",
+ "hand",
+ "keypoint"
+ ],
+ "keypoint_schema": "mano21/v1",
+ "unit": "m",
+ "valid_policy": {
+ "revision": "usable-annotation-2026-09-20",
+ "meaning": "annotation usable for supervision, not merely record presence",
+ "source_rule": "Per-hand hand_pose flag equals 1, broadcast to that hand's 21 source points independently of MANO",
+ "source_locator": {
+ "frames_ref": "camera/frames.jsonl",
+ "source_field": "component_sources"
+ },
+ "scope": [
+ "T",
+ "hand",
+ "keypoint"
+ ],
+ "numerical_checks": "Schema shape, finite valid floats, SE(3) where applicable; malformed input aborts",
+ "official_qa": {
+ "status": "not_provided",
+ "reason": "No separate applicable official QA in selected source inputs"
+ },
+ "local_excluded_frame_indices": [],
+ "local_exclusion_reasons": {},
+ "visibility_independent": true
+ },
+ "data_key": [
+ "xyz",
+ "data"
+ ],
+ "valid_key": [
+ "xyz",
+ "valid"
+ ],
+ "storage": {
+ "profile": "pt/tensor_bundle/v1"
+ },
+ "valid_storage": {
+ "profile": "pt/tensor_bundle/v1"
+ }
+ },
+ "pose_cocoa": {
+ "available": true,
+ "schema_id": "object/rigid/v1",
+ "data_ref": "annotation/object/cocoa/object.pt",
+ "axes": [
+ "T",
+ "row",
+ "column"
+ ],
+ "provenance": {
+ "kind": "format_conversion",
+ "method": "Copy source annotation; normalize storage only"
+ },
+ "source_locator": {
+ "frames_ref": "camera/frames.jsonl",
+ "source_field": "component_sources"
+ },
+ "valid_ref": "annotation/object/cocoa/object.pt",
+ "valid_axes": [
+ "T"
+ ],
+ "convention": "T_camera_from_object",
+ "unit": "m",
+ "valid_policy": {
+ "revision": "usable-annotation-2026-09-20",
+ "meaning": "annotation usable for supervision, not merely record presence",
+ "source_rule": "Source obj_pose_rt class equals this nonzero object class",
+ "source_locator": {
+ "frames_ref": "camera/frames.jsonl",
+ "source_field": "component_sources"
+ },
+ "scope": [
+ "T"
+ ],
+ "numerical_checks": "Schema shape, finite valid floats, SE(3) where applicable; malformed input aborts",
+ "official_qa": {
+ "status": "not_provided",
+ "reason": "No separate applicable official QA in selected source inputs"
+ },
+ "local_excluded_frame_indices": [],
+ "local_exclusion_reasons": {},
+ "visibility_independent": true
+ },
+ "data_key": [
+ "pose",
+ "data"
+ ],
+ "valid_key": [
+ "pose",
+ "valid"
+ ],
+ "storage": {
+ "profile": "pt/tensor_bundle/v1"
+ },
+ "valid_storage": {
+ "profile": "pt/tensor_bundle/v1"
+ }
+ },
+ "points_cocoa": {
+ "available": true,
+ "schema_id": "object_keypoints/camera/v1",
+ "data_ref": "annotation/object/cocoa/object.pt",
+ "axes": [
+ "T",
+ "keypoint",
+ "coordinate"
+ ],
+ "provenance": {
+ "kind": "format_conversion",
+ "method": "Copy source annotation; normalize storage only"
+ },
+ "source_locator": {
+ "frames_ref": "camera/frames.jsonl",
+ "source_field": "component_sources"
+ },
+ "valid_ref": "annotation/object/cocoa/object.pt",
+ "valid_axes": [
+ "T",
+ "keypoint"
+ ],
+ "keypoint_schema": "H2O_official_object21",
+ "keypoint_names": [
+ "center",
+ "corner_0",
+ "corner_1",
+ "corner_2",
+ "corner_3",
+ "corner_4",
+ "corner_5",
+ "corner_6",
+ "corner_7",
+ "edge_midpoint_0",
+ "edge_midpoint_1",
+ "edge_midpoint_2",
+ "edge_midpoint_3",
+ "edge_midpoint_4",
+ "edge_midpoint_5",
+ "edge_midpoint_6",
+ "edge_midpoint_7",
+ "edge_midpoint_8",
+ "edge_midpoint_9",
+ "edge_midpoint_10",
+ "edge_midpoint_11"
+ ],
+ "source_order": "official obj_point_order.jpg: center, 8 corners, 12 edge midpoints",
+ "unit": "m",
+ "valid_policy": {
+ "revision": "usable-annotation-2026-09-20",
+ "meaning": "annotation usable for supervision, not merely record presence",
+ "source_rule": "Source obj_pose class equals this nonzero object class, broadcast to its 21 points",
+ "source_locator": {
+ "frames_ref": "camera/frames.jsonl",
+ "source_field": "component_sources"
+ },
+ "scope": [
+ "T",
+ "keypoint"
+ ],
+ "numerical_checks": "Schema shape, finite valid floats, SE(3) where applicable; malformed input aborts",
+ "official_qa": {
+ "status": "not_provided",
+ "reason": "No separate applicable official QA in selected source inputs"
+ },
+ "local_excluded_frame_indices": [],
+ "local_exclusion_reasons": {},
+ "visibility_independent": true
+ },
+ "data_key": [
+ "keypoints",
+ "xyz",
+ "data"
+ ],
+ "valid_key": [
+ "keypoints",
+ "xyz",
+ "valid"
+ ],
+ "storage": {
+ "profile": "pt/tensor_bundle/v1"
+ },
+ "valid_storage": {
+ "profile": "pt/tensor_bundle/v1"
+ }
+ },
+ "mesh_cocoa": {
+ "available": true,
+ "schema_id": "mesh/obj/v1",
+ "data_ref": "annotation/object/cocoa/assets/cocoa.obj",
+ "axes": [
+ "vertex"
+ ],
+ "provenance": {
+ "kind": "format_conversion",
+ "method": "Copy source annotation; normalize storage only"
+ },
+ "source_locator": {
+ "file": "/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/object.zip",
+ "member": "object/cocoa/cocoa.obj",
+ "texture_aliases": [
+ {
+ "file": "cocoa.png",
+ "sha256": "90dd612369a14e825d040e539f1f1028e5b6e46e598eb5a4c664034e3a49e91f",
+ "source": {
+ "file": "/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/object.zip",
+ "member": "object/cocoa/ovomaltine.png"
+ }
+ }
+ ]
+ },
+ "valid": true,
+ "unit": "m"
+ },
+ "depth": {
+ "available": true,
+ "schema_id": "depth/z_float32/v1",
+ "data_ref": "observation/depth.mkv",
+ "axes": [
+ "T",
+ "height",
+ "width"
+ ],
+ "provenance": {
+ "kind": "format_conversion",
+ "method": "Copy source annotation; normalize storage only"
+ },
+ "source_locator": {
+ "frames_ref": "camera/frames.jsonl",
+ "source_field": "component_sources.depth",
+ "definition": "https://github.com/taeinkwon/h2oplayer/blob/main/utils.py",
+ "conversion": "registered millimetre camera-Z to metres; no viewer range clipping"
+ },
+ "storage": {
+ "profile": "mkv/depth_float32_bits/v1",
+ "codec": "ffv1",
+ "pixel_format": "gray16le",
+ "encoded_size": [
+ 2560,
+ 720
+ ],
+ "decoder": {
+ "name": "ffmpeg",
+ "version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+ "autorotate": false,
+ "color_space": "unknown",
+ "color_range": "unknown",
+ "color_primaries": "unknown",
+ "color_transfer": "unknown"
+ },
+ "encoder_parameters": {
+ "version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+ "name": "ffv1",
+ "arguments": [],
+ "threads": 10
+ },
+ "source_lossy": false,
+ "storage_fps": 30,
+ "timebase_role": "index_only"
+ },
+ "valid_storage": {
+ "profile": "mkv/bool_ffv1/v1",
+ "codec": "ffv1",
+ "pixel_format": "gray",
+ "encoded_size": [
+ 1280,
+ 720
+ ],
+ "decoder": {
+ "name": "ffmpeg",
+ "version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+ "autorotate": false,
+ "color_space": "unknown",
+ "color_range": "unknown",
+ "color_primaries": "unknown",
+ "color_transfer": "unknown"
+ },
+ "encoder_parameters": {
+ "version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+ "name": "ffv1",
+ "arguments": [],
+ "threads": 10
+ },
+ "source_lossy": false,
+ "storage_fps": 30,
+ "timebase_role": "index_only"
+ },
+ "valid_ref": "observation/depth_valid.mkv",
+ "valid_axes": [
+ "T",
+ "height",
+ "width"
+ ],
+ "unit": "m",
+ "depth_type": "z",
+ "depth_origin": "measured",
+ "invalid_value": "NaN"
+ },
+ "segment": {
+ "available": false,
+ "reason": "H2O provides no native segmentation; rendering deferred to v2.0",
+ "status": "source_missing"
+ },
+ "relations": {
+ "available": false,
+ "reason": "No source contact/grasp annotation",
+ "status": "source_missing"
+ }
+ },
+ "split": "test",
+ "view_type": "exo",
+ "split_scheme": "official_action_split",
+ "split_source_locator": {
+ "file": "/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/label_split.zip",
+ "member": "label_split/action_test.txt",
+ "record": 41
+ }
+} 持久化 QC {
+ "num_frames": 584,
+ "numeric_components": {
+ "intrinsic": {
+ "shape": [
+ 584,
+ 3,
+ 3
+ ],
+ "dtype": "torch.float32",
+ "valid_true": 584,
+ "valid_total": 584
+ },
+ "extrinsic": {
+ "shape": [
+ 584,
+ 4,
+ 4
+ ],
+ "dtype": "torch.float32",
+ "valid_true": 584,
+ "valid_total": 584
+ },
+ "mano": {
+ "shape": [
+ 584,
+ 2,
+ 61
+ ],
+ "dtype": "torch.float32",
+ "valid_true": 1168,
+ "valid_total": 1168
+ },
+ "keypoints_xyz": {
+ "shape": [
+ 584,
+ 2,
+ 21,
+ 3
+ ],
+ "dtype": "torch.float32",
+ "valid_true": 24528,
+ "valid_total": 24528
+ },
+ "pose_cocoa": {
+ "shape": [
+ 584,
+ 4,
+ 4
+ ],
+ "dtype": "torch.float32",
+ "valid_true": 584,
+ "valid_total": 584
+ },
+ "points_cocoa": {
+ "shape": [
+ 584,
+ 21,
+ 3
+ ],
+ "dtype": "torch.float32",
+ "valid_true": 12264,
+ "valid_total": 12264
+ }
+ },
+ "bundle_files": 4,
+ "scope": "v1.1 registry, keys, shapes, validity and timeline; not a media pixel comparison",
+ "selected_frames": [
+ 0,
+ 146,
+ 292,
+ 438,
+ 583
+ ],
+ "preview_media": [
+ "rgb",
+ "depth",
+ "depth_valid"
+ ],
+ "decode_scope": "only selected frames emitted; sequential decoding may traverse earlier frames, no full-video pixel comparison"
+} subject4_h1_7_cam4 subject4_h1_7_cam4 直接读取 v1.1 bundle；预览只作人工核查。手／物体点来自源 3D 标注，仅为显示投影到 RGB；未生成新 UV、分割或接触标注。
帧 0 / 584 原生 RGB（仅预览缩放） RGB + 原生 3D 关键点投影（仅预览：keypoints_xyz #00e6ff, points_cocoa #ff48a4） 深度 Z，黑=无效，白≥3 m；不改源深度 原深度逐像素 valid：白=有效，黑=无效，不是深度值 原帧号、全部数值标注及有效性 {
+ "frame": {
+ "source_frame_id": 0,
+ "sync_id": "subject4/h1/7:0",
+ "source_locator": {
+ "file": "/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/subject4_v1_1.tar.gz",
+ "member": "subject4/h1/7/cam4/rgb/000000.png",
+ "sha256": "3f2d97038f34886535a6df5de254a1fd7246c471b8cfb0b766188d9a017a7c2e"
+ },
+ "component_sources": {
+ "hand_pose_mano": {
+ "file": "/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/subject4_v1_1.tar.gz",
+ "member": "subject4/h1/7/cam4/hand_pose_mano/000000.txt",
+ "sha256": "1b0f5af1dca065c9cd812078f7fdf288c0aaa1e1ceba5010812db2e09dcc9a79"
+ },
+ "hand_pose": {
+ "file": "/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/subject4_v1_1.tar.gz",
+ "member": "subject4/h1/7/cam4/hand_pose/000000.txt",
+ "sha256": "5912dd6695784f23dfc2733c65585a41490d227fcec1f6301ff2cac95491063d"
+ },
+ "depth": {
+ "file": "/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/subject4_v1_1.tar.gz",
+ "member": "subject4/h1/7/cam4/depth/000000.png",
+ "sha256": "aade4a8cf395f3de43cf541e44bfee8d3902ef8ac50727c7ce9325378effd60d"
+ },
+ "cam_pose": {
+ "file": "/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/subject4_v1_1.tar.gz",
+ "member": "subject4/h1/7/cam4/cam_pose/000000.txt",
+ "sha256": "3aaaebfd69835ddc19587e77044b718530483ea5d09bb639d41efc847735a7e4"
+ },
+ "obj_pose": {
+ "file": "/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/subject4_v1_1.tar.gz",
+ "member": "subject4/h1/7/cam4/obj_pose/000000.txt",
+ "sha256": "8de58a392f88549321ad07ac46b328fb63ed6551b6cc74184f5f2a73b89be7c2"
+ },
+ "obj_pose_rt": {
+ "file": "/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/subject4_v1_1.tar.gz",
+ "member": "subject4/h1/7/cam4/obj_pose_rt/000000.txt",
+ "sha256": "8ceee38f2c2d1a36e6ca86fb04e63d3b66c85a8dccb2ce865dbf32e797d6d314"
+ },
+ "rgb": {
+ "file": "/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/subject4_v1_1.tar.gz",
+ "member": "subject4/h1/7/cam4/rgb/000000.png",
+ "sha256": "3f2d97038f34886535a6df5de254a1fd7246c471b8cfb0b766188d9a017a7c2e"
+ }
+ },
+ "annotation_matches": {
+ "hand_pose_mano": {
+ "source_frame_id": 0,
+ "source_timestamp_ns": null,
+ "clock_domain": null,
+ "match_method": "index_map",
+ "delta_ns": null
+ },
+ "hand_pose": {
+ "source_frame_id": 0,
+ "source_timestamp_ns": null,
+ "clock_domain": null,
+ "match_method": "index_map",
+ "delta_ns": null
+ },
+ "depth": {
+ "source_frame_id": 0,
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+} 源网格 mesh_cocoa 源 OBJ 静态预览，不代表物体姿态投影 全部组件元数据 {
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+ {
+ "file": "cocoa.png",
+ "sha256": "90dd612369a14e825d040e539f1f1028e5b6e46e598eb5a4c664034e3a49e91f",
+ "source": {
+ "file": "/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/object.zip",
+ "member": "object/cocoa/ovomaltine.png"
+ }
+ }
+ ]
+ },
+ "valid": true,
+ "unit": "m"
+ },
+ "depth": {
+ "available": true,
+ "schema_id": "depth/z_float32/v1",
+ "data_ref": "observation/depth.mkv",
+ "axes": [
+ "T",
+ "height",
+ "width"
+ ],
+ "provenance": {
+ "kind": "format_conversion",
+ "method": "Copy source annotation; normalize storage only"
+ },
+ "source_locator": {
+ "frames_ref": "camera/frames.jsonl",
+ "source_field": "component_sources.depth",
+ "definition": "https://github.com/taeinkwon/h2oplayer/blob/main/utils.py",
+ "conversion": "registered millimetre camera-Z to metres; no viewer range clipping"
+ },
+ "storage": {
+ "profile": "mkv/depth_float32_bits/v1",
+ "codec": "ffv1",
+ "pixel_format": "gray16le",
+ "encoded_size": [
+ 2560,
+ 720
+ ],
+ "decoder": {
+ "name": "ffmpeg",
+ "version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+ "autorotate": false,
+ "color_space": "unknown",
+ "color_range": "unknown",
+ "color_primaries": "unknown",
+ "color_transfer": "unknown"
+ },
+ "encoder_parameters": {
+ "version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+ "name": "ffv1",
+ "arguments": [],
+ "threads": 10
+ },
+ "source_lossy": false,
+ "storage_fps": 30,
+ "timebase_role": "index_only"
+ },
+ "valid_storage": {
+ "profile": "mkv/bool_ffv1/v1",
+ "codec": "ffv1",
+ "pixel_format": "gray",
+ "encoded_size": [
+ 1280,
+ 720
+ ],
+ "decoder": {
+ "name": "ffmpeg",
+ "version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+ "autorotate": false,
+ "color_space": "unknown",
+ "color_range": "unknown",
+ "color_primaries": "unknown",
+ "color_transfer": "unknown"
+ },
+ "encoder_parameters": {
+ "version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+ "name": "ffv1",
+ "arguments": [],
+ "threads": 10
+ },
+ "source_lossy": false,
+ "storage_fps": 30,
+ "timebase_role": "index_only"
+ },
+ "valid_ref": "observation/depth_valid.mkv",
+ "valid_axes": [
+ "T",
+ "height",
+ "width"
+ ],
+ "unit": "m",
+ "depth_type": "z",
+ "depth_origin": "measured",
+ "invalid_value": "NaN"
+ },
+ "segment": {
+ "available": false,
+ "reason": "H2O provides no native segmentation; rendering deferred to v2.0",
+ "status": "source_missing"
+ },
+ "relations": {
+ "available": false,
+ "reason": "No source contact/grasp annotation",
+ "status": "source_missing"
+ }
+ },
+ "split": "test",
+ "view_type": "ego",
+ "split_scheme": "official_action_split",
+ "split_source_locator": {
+ "file": "/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/label_split.zip",
+ "member": "label_split/action_test.txt",
+ "record": 41
+ }
+} 持久化 QC {
+ "num_frames": 584,
+ "numeric_components": {
+ "intrinsic": {
+ "shape": [
+ 584,
+ 3,
+ 3
+ ],
+ "dtype": "torch.float32",
+ "valid_true": 584,
+ "valid_total": 584
+ },
+ "extrinsic": {
+ "shape": [
+ 584,
+ 4,
+ 4
+ ],
+ "dtype": "torch.float32",
+ "valid_true": 584,
+ "valid_total": 584
+ },
+ "mano": {
+ "shape": [
+ 584,
+ 2,
+ 61
+ ],
+ "dtype": "torch.float32",
+ "valid_true": 1168,
+ "valid_total": 1168
+ },
+ "keypoints_xyz": {
+ "shape": [
+ 584,
+ 2,
+ 21,
+ 3
+ ],
+ "dtype": "torch.float32",
+ "valid_true": 24528,
+ "valid_total": 24528
+ },
+ "pose_cocoa": {
+ "shape": [
+ 584,
+ 4,
+ 4
+ ],
+ "dtype": "torch.float32",
+ "valid_true": 584,
+ "valid_total": 584
+ },
+ "points_cocoa": {
+ "shape": [
+ 584,
+ 21,
+ 3
+ ],
+ "dtype": "torch.float32",
+ "valid_true": 12264,
+ "valid_total": 12264
+ }
+ },
+ "bundle_files": 4,
+ "scope": "v1.1 registry, keys, shapes, validity and timeline; not a media pixel comparison",
+ "selected_frames": [
+ 0,
+ 146,
+ 292,
+ 438,
+ 583
+ ],
+ "preview_media": [
+ "rgb",
+ "depth",
+ "depth_valid"
+ ],
+ "decode_scope": "only selected frames emitted; sequential decoding may traverse earlier frames, no full-video pixel comparison"
+} /pose.pt + valid | object//object.pt → pose/{data,valid} |\n'
+ '| object//keypoints/xyz.pt + valid | object//object.pt → keypoints/xyz/{data,valid} |\n\n'
+ '媒体／JSONL 的独立 valid 保留。没有新增缺失组件。迁移实现见 script/h2o_v1_1/pilot.py；'
+ '试点未修改源发布或 HF。原始构建记录与旧验收属于 v1.0，不冒充 v1.1 验收。\n')
+ history = doc / 'history-v1.0'
+ history.mkdir()
+ for seq in chosen:
+ shutil.copyfile(source / 'doc' / (seq + '.build.json'), history / (seq + '.build.json'))
+ journal = {'phase': 'preparing', 'created_utc': datetime.now(timezone.utc).isoformat(),
+ 'source': str(source), 'out': str(out), 'source_revision': args.source_revision,
+ 'raw_tree_oid': raw_oid, 'source_tree_oid': baseline[PREFIX]['oid'],
+ 'baseline_doc_script_sha256': baseline_hashes, 'workers': args.workers, 'sequences': chosen, 'coverage': coverage,
+ 'tool_sha256': {str(f.relative_to(here)): sha256(f) for f in here.rglob('*') if f.is_file() and f.suffix != '.pyc'},
+ 'views': [], 'scope': 'pilot only; full migration, HF writes and human approval not performed'}
+ journal_path = out.parent / 'PILOT_JOURNAL.json'
+ write_json(journal_path, journal)
+ try:
+ tasks = []
+ for seq in chosen:
+ inventory = tree(args.source_revision, PREFIX + '/data/' + seq, recursive=True)
+ entries = {k: v for k, v in inventory.items() if v['type'] == 'file'}
+ for view in sorted((source / 'data' / seq).iterdir()):
+ prefix = PREFIX + '/data/' + seq + '/' + view.name + '/'
+ refs = {k.removeprefix(prefix): v for k, v in entries.items() if k.startswith(prefix)}
+ tasks.append((str(view), str(out / 'data' / seq / view.name), str(doc / 'schema.json'), refs))
+ journal['phase'] = 'migrating_pilot'
+ write_json(journal_path, journal)
+ for key in ('OMP_NUM_THREADS', 'MKL_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'NUMEXPR_NUM_THREADS'):
+ os.environ[key] = '1'
+ with ProcessPoolExecutor(max_workers=args.workers, mp_context=multiprocessing.get_context('spawn')) as pool:
+ futures = [pool.submit(migrate_view, task) for task in tasks]
+ for future in as_completed(futures):
+ result = future.result()
+ journal['views'].append(result)
+ write_json(journal_path, journal)
+ print('PILOT_VIEW', result['view'], 'seconds', round(result['seconds'], 2), flush=True)
+ journal['views'].sort(key=lambda v: v['view'])
+ journal['phase'] = 'numeric_pilot_passed_awaiting_html_and_human_review'
+ journal['wall_seconds'] = time.monotonic() - started
+ journal['source_files'] = sum(v['source_files'] for v in journal['views'])
+ journal['target_files'] = sum(v['target_files'] for v in journal['views'])
+ journal['saved_data_files'] = journal['source_files'] - journal['target_files']
+ journal['view_frames'] = sum(v['num_frames'] for v in journal['views'])
+ journal['source_bytes_checked'] = sum(v['source_bytes'] for v in journal['views'])
+ journal['verify_worker_seconds'] = sum(v['verify_seconds'] for v in journal['views'])
+ journal['pack_worker_seconds'] = sum(v['pack_seconds'] for v in journal['views'])
+ write_json(journal_path, journal)
+ write_json(doc / 'PILOT_MIGRATION_REPORT.json', journal)
+ print('PILOT_NUMERIC_DONE', json.dumps({k:journal[k] for k in ('wall_seconds','saved_data_files','view_frames','source_bytes_checked','phase')}), flush=True)
+ except Exception as exc:
+ journal.update(phase='failed', error=repr(exc))
+ write_json(journal_path, journal)
+ raise
+
+
+if __name__ == '__main__':
+ main()
diff --git a/H2O/shared-v1.1/script/h2o_v1_1/render.py b/H2O/shared-v1.1/script/h2o_v1_1/render.py
new file mode 100644
index 0000000000000000000000000000000000000000..04d1ecc0ab1f56802d94179d8675c5eadd6ce0c2
--- /dev/null
+++ b/H2O/shared-v1.1/script/h2o_v1_1/render.py
@@ -0,0 +1,176 @@
+#!/usr/bin/env python3
+"""Render stored H2O RGB-D, native camera-space hands/objects, and OBJ assets for review."""
+import argparse
+import html
+import json
+from concurrent.futures import ProcessPoolExecutor
+import multiprocessing
+import os
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageDraw
+import torch
+
+if __package__:
+ from .warehouse import Reader, validate_view
+else:
+ from warehouse import Reader, validate_view
+
+# Historical helpers decode native media and draw assets; do not invoke the v1.0 validator.
+helpers = Path(__file__).resolve().parents[1] / 'v1_0_snapshot/script'
+if not helpers.is_dir():
+ helpers = Path(__file__).resolve().parents[1] / 'h2o_production/scripts'
+sys.path.insert(0, str(helpers))
+from render_hoi4d_validation import mesh_preview, picture
+from warehouse_v1_0 import decode_args, unpack_depth
+
+
+def snapshot(video, fmt, shape, dtype, indices):
+ indices = sorted(indices)
+ if not indices or indices[0] < 0:
+ raise ValueError('nonempty nonnegative frame indices required')
+ expression = '+'.join(f'eq(n\\,{i})' for i in indices)
+ cmd = decode_args(video, fmt) + ['-vf', 'select=' + expression,
+ '-frames:v', str(len(indices)), 'pipe:1']
+ result = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True)
+ frame_bytes = int(np.prod(shape)) * np.dtype(dtype).itemsize
+ if len(result.stdout) != frame_bytes * len(indices):
+ raise ValueError(f'missing/truncated selected frames: {video}')
+ values = np.frombuffer(result.stdout, dtype=dtype).reshape(len(indices), *shape)
+ return dict(zip(indices, values, strict=True))
+
+
+def render(view, destination):
+ meta = json.loads((view / 'metainfo.json').read_text())
+ if meta['dataset_id'] != 'H2O':
+ raise ValueError('H2O view required')
+ n = meta['num_frames']
+ height, width = reversed(meta['camera']['image_size'])
+ parts = meta['components']
+ frames = [json.loads(line) for line in (view / 'camera/frames.jsonl').read_text().splitlines()]
+ assert len(frames) == n
+ selected = {0, n // 4, n // 2, 3 * n // 4, n - 1}
+ reader = Reader(view)
+ numeric = {}
+ for name, item in parts.items():
+ if item.get('data_ref', '').endswith('.pt') and item.get('available'):
+ values = reader.field(item).numpy()
+ valid = reader.field(item, 'valid').numpy()
+ if values is not None:
+ numeric[name] = (values, valid)
+ if valid is not None:
+ indices = np.flatnonzero(valid.reshape(n, -1).any(axis=1))
+ if len(indices):
+ selected.add(int(indices[0]))
+ invalid = np.flatnonzero(~valid.reshape(n, -1).all(axis=1))
+ if len(invalid):
+ selected.add(int(invalid[0]))
+ selected = set(sorted(selected)[:12]) | {n - 1}
+ rgb = snapshot(view / parts['rgb']['data_ref'], 'rgb24', (height, width, 3), np.uint8, selected)
+ depth = snapshot(view / parts['depth']['data_ref'], 'gray16le', (height, 2 * width), ' 0:
+ u, v = intrinsic[i, 0, 0] * xyz[0] / xyz[2] + intrinsic[i, 0, 2], intrinsic[i, 1, 1] * xyz[1] / xyz[2] + intrinsic[i, 1, 2]
+ if 0 <= u < width and 0 <= v < height:
+ draw.ellipse((u-4, v-4, u+4, v+4), fill=color)
+ legend.append(name + ' ' + color)
+ panels = ['' + picture(Image.fromarray(rgb[i])) + '原生 RGB（仅预览缩放） ', '' + picture(photo) + 'RGB + 原生 3D 关键点投影（仅预览：' + html.escape(', '.join(legend) or '当前帧无可投影点') + '） ']
+ if i in depth:
+ z = unpack_depth(depth[i])
+ luminance = (np.clip(np.nan_to_num(z, nan=0), 0, 3) * 85).astype(np.uint8)
+ panels.append('' + picture(Image.fromarray(luminance)) + '深度 Z，黑=无效，白≥3 m；不改源深度 ')
+ if i in depth_valid:
+ good = depth_valid[i]
+ if not np.isin(good, [0, 1]).all():
+ raise ValueError('depth validity must be 0/1')
+ panels.append('' + picture(Image.fromarray(good * 255)) + '原深度逐像素 valid：白=有效，黑=无效，不是深度值 ')
+ details = {'frame': frames[i], 'rgb_valid': bool(reader.field(parts['rgb'], 'valid')[i])}
+ for name, (values, valid) in numeric.items():
+ details[name] = {'value': values[i].tolist(), 'valid': None if valid is None else valid[i].tolist()}
+ pages.append('帧 ' + str(i) + ' / ' + str(n) + ' ' + ''.join(panels) + '
原帧号、全部数值标注及有效性 ' + html.escape(json.dumps(details, ensure_ascii=False, indent=2)) + ' ' + html.escape(name) + ' ' + mesh_preview(view / item['data_ref']) + '源 OBJ 静态预览，不代表物体姿态投影 ' + html.escape(title) + ' ' + html.escape(title) + ' 直接读取 v1.1 bundle；预览只作人工核查。手／物体点来自源 3D 标注，仅为显示投影到 RGB；未生成新 UV、分割或接触标注。
' + ''.join(pages) + '源网格 ' + ''.join(assets) + '
全部组件元数据 ' + html.escape(json.dumps(meta, ensure_ascii=False, indent=2)) + ' 持久化 QC ' + html.escape(json.dumps(report, ensure_ascii=False, indent=2)) + ' H2O v1.1 review H2O v1.1 ' + label + '全部相机验收 数值按字节一致；媒体不重新编码。RGB、深度／深度 valid、相机、MANO、手／物体点、位姿、网格和来源均可查看；源没有 UV、分割或接触，不补造。
合同 · 字段映射 · 迁移报告 · 可视化报告
')
+ print('PILOT_HTML_DONE', len(views), 'views', round(summary['wall_seconds'], 2), 'seconds', dest / 'index.html', flush=True)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/H2O/shared-v1.1/script/h2o_v1_1/spec/CONTRACT.md b/H2O/shared-v1.1/script/h2o_v1_1/spec/CONTRACT.md
new file mode 100644
index 0000000000000000000000000000000000000000..c237e8d95df74f237ec4be1b0a3b0ac06a0ca935
--- /dev/null
+++ b/H2O/shared-v1.1/script/h2o_v1_1/spec/CONTRACT.md
@@ -0,0 +1,161 @@
+# 手物数据仓库统一约定 v1.1：张量合并存储
+
+格式版本：`hand-warehouse/1.1`。张量存储约定：`pt/tensor_bundle/v1`。
+
+状态：合同草案，2026-10-09。本版规定新的文件布局与读取方式，配套 [schema.json](schema.json) 注册表已更新；尚未迁移现有数据，也未适配生产转换器、读取器或可执行校验器。不能仅修改现有 metainfo 的版本号就宣称符合本版。
+
+本版以 [v1.0 合同](../hand-warehouse-1.0/CONTRACT.md) 为语义基础：任务范围、两层序列/相机组织、数据类型、单位、坐标系、时间对齐、左右手顺序、官方划分、view_type、来源、可用监督规则、媒体无损编码和验收要求均保留。下文仅替代数值张量组件的独立 Tensor 文件、对应 `*_valid.pt` 及文件引用规定；这些旧文件名仅用于识别对应逻辑字段。媒体和 JSONL 组件的有效性文件及引用保持 v1.0，不纳入合并。未明确改变的语义继续执行 v1.0。
+
+本版按用户约定作为 v1.0 的小版本存储修订，后续所有数据集逐步迁移到 v1.1。数值字段与 valid 语义不变，但 `.pt` 物理结构和引用方式改变，旧读取器不能未经适配直接读取新字典。各数据集在原 `shared-v1.0` 目录内逐步升级，完整验收后将目录更名为 `shared-v1.1`，不长期保留两套发布树。最终 `metainfo.schema_version=hand-warehouse/1.1`、`release_id=shared-v1.1`；尚未迁移的数据集仍使用原目录和版本。升级与发布流程见 [MIGRATION.md](MIGRATION.md)。实施前须按配套 v1.1 注册表完成读写/校验工具；不得将 v1.0 的 schema.json 改个版本号作为已完成适配的证明。正式发布时须提供完整合同与匹配注册表，不依赖本地草案目录中的相对链接。
+
+## 1. 合并边界
+
+1. 数值张量与对应有效性必须在同一个 `.pt` 文件、同一个字段字典内保存：`{"data": Tensor, "valid": BoolTensor}`。这些数值张量组件不再生成独立的 `*_valid.pt`；媒体和 JSONL 的有效性文件不受此规则影响。
+2. 按组件合并经常一起读取的小张量：相机内参、外参、畸变放在一个 `camera.pt`；MANO 参数与整手可见性放在一个 `mano.pt`；手关键点的二维、三维与可见性放在一个 `keypoints.pt`。
+3. 每个物体的数值字段及全部部件数值字段合并为一个 `object.pt`。不同物体、不同相机、不同序列不合成一个全局大文件，以保留按需下载和读取能力。
+4. RGB、深度、栅格 mask 继续使用原无损媒体格式，不把解码后的大数组或媒体字节塞进 `.pt`。它们的有效性文件保持原文件名、Tensor/媒体格式、形状和引用方式，不包装为字典。
+5. `metainfo.json`、`camera/frames.jsonl`、实例映射、物体身份/运动学定义与网格资产保留原格式。此次不把 JSON、JSONL、OBJ、纹理全部打进一个字典。
+
+合并只改变存储方式，不改变采样、帧数、张量内容或有效性判定。不为减少字段数而将多个组件的 valid 合成一个公共 valid。
+
+## 2. 目录
+
+```text
+/shared-v1.1/
+ doc/ # 完整合同、匹配 schema、字段映射、验收报告
+ script/ # 转换、读取和校验代码
+ data///
+ metainfo.json
+ observation/
+ rgb.mkv
+ rgb_valid.pt # 原独立 bool Tensor，保持不变
+ depth.mkv
+ depth_valid.mkv # 逐像素有效性，保留压缩媒体格式
+ camera/
+ frames.jsonl
+ camera.pt # intrinsic、extrinsic、distortion
+ annotation/
+ mano/mano.pt # motion、visibility
+ keypoints/keypoints.pt # uv、xyz、visibility、whole_hand_visibility
+ object//
+ metainfo.json
+ articulation.json # 仅关节/部件结构实际存在时
+ object.pt # 位姿、可见性、几何点、关节、部件数值
+ assets/ # 原网格、材质、纹理及依赖
+ segment/
+ mask.mkv # 或 polygons.jsonl，按所选表示
+ instances.jsonl # 仅需要实例/区域编码映射时
+ mask_valid.pt # 原帧级 bool Tensor，保持不变
+ contact/
+ relations.jsonl
+ relations_valid.pt # 原帧级 bool Tensor，保持不变
+ __/
+ contact.pt # vertex_region、vertex_distance
+```
+
+只创建实际提供的组件和字段。以上不是每个数据集必须填满的模板；缺失内参、MANO、深度等不能通过占位文件伪装为已提供。序列/相机的两层结构不变，物体与模态目录属于相机内部组件。
+
+## 3. 字典结构与有效性
+
+本次合并的数值组件 `.pt` 根对象为字符串键的 Python `dict`，内部仅允许嵌套字符串键字典和 CPU `torch.Tensor`，不保存自定义类、模型、NumPy 对象或任意可执行对象。元信息、schema、单位和来源放在 metainfo，不在每个张量条目中重复保存。读取使用 `torch.load(path, map_location="cpu", weights_only=True)`。第 4 节保留的独立有效性 `.pt` 根对象仍为 CPU bool Tensor，不适用字典结构要求。
+
+例如，`camera/camera.pt` 的逻辑结构为：
+
+```python
+{
+ "intrinsic": {"data": K, "valid": K_valid},
+ "extrinsic": {"data": T_camera_from_world, "valid": extrinsic_valid},
+ "distortion": {"data": distortion, "valid": distortion_valid},
+}
+```
+
+以上变量均为 Tensor。未提供的字段整个省略；例如无世界系外参时不保存 `extrinsic`，不填单位矩阵。没有单独畸变向量的模型不创建 `distortion`：Fisheye624 仍使用既定 `[T,15]` 内参参数布局。
+
+数值字段统一为 `data` 与 `valid` 两个叶子。源属性原先显式声明全已知 `valid=true` 时，迁移可按该属性的有效性形状展开为全 true 的 bool Tensor；这是已有有效性声明的等价表示，不是根据数值或姿态推测有效性。有效性必须为 bool Tensor，不能用 Python `True` 代替字段内的 Tensor。静态数值字段同样配套 valid，形状采用其语义定义的标量或逐元素有效性；静态网格等外部资产仍可在 metainfo 中声明标量 valid。
+
+valid 的形状不要求与 data 完全相同，但必须与该字段定义一致。例如 MANO 为 `[T,2,61]` / `[T,2]`，内参为 `[T,3,3]` / `[T]`，关键点为 `[T,2,K,3]` / `[T,2,K]`。按 schema 将 valid 扩展到相应数值维度，不允许依靠碰巧可广播推断语义。
+
+浮点默认 float32，无效浮点位置仍为 NaN；bool 和整数沿用该组件的占位/保留值规则，不因没有 NaN 而视为有效。valid 表达可用监督，不只是“值有限”或“记录存在”；适用官方 QA 与已经确认的排除规则继续保留。不能用 `isfinite(data)` 重建 valid，不能因为迁移而把 false 改成 true。
+
+不同字段的 valid 独立保存和解释。visibility 的 valid 不复用姿态 valid；内参 valid 不代替外参 valid。既有相机模型要求内参与单独畸变向量共用有效性时，在两个字段内保存相同 bool 值并校验一致，而不改变该模型的联合标定规则。
+
+## 4. 固定分组与字段名
+
+下表中的键路径用 `/` 展示字典层级；它不是文件系统路径。数值字段均在列出的键下继续存储 `data`、`valid`。
+
+| 文件 | 数值字段键 | 内容 |
+|---|---|---|
+| `camera/camera.pt` | `intrinsic`、`extrinsic`、`distortion` | 逐帧标定；形状、参数顺序与相机模型定义不变 |
+| `annotation/mano/mano.pt` | `motion`、`visibility` | MANO `[T,2,61]` 与源整手可见性；各有独立有效性 |
+| `annotation/keypoints/keypoints.pt` | `uv`、`xyz`、`visibility`、`whole_hand_visibility` | 手关键点及源可见性；整手与逐点可见性不混用 |
+| `annotation/object//object.pt` | `pose`、`visibility`、`dimensions`、`joint_state` | 该物体的数值标注 |
+| 同上 | `keypoints/xyz`、`keypoints/template_xyz` | 相机系逐帧点或物体系静态模板点 |
+| 同上 | `parts//pose`、`parts//dimensions`、`parts//keypoints/xyz`、`parts//keypoints/template_xyz` | 部件数值字段，部件身份仍由 articulation 登记 |
+| 同上 | `meshes//vertex_part_ids` | `[V] int64` 顶点部件标签，valid 为 `[V]`，未知 -1 对应 false；每个 mesh 单独绑定拓扑 |
+| `annotation/contact/__/contact.pt` | `vertex_region`、`vertex_distance` | 已登记拓扑上的接触区域/距离与同形有效性 |
+
+顶点部件标签使用注册类型 `object/vertex_part_ids/v1`。物体的 mesh_id、part_id 必须在 metainfo/运动学定义中可解析；不同拓扑不能误用同一顶点数组。未提供的可见性、关节值或模板点不生成空字段。
+
+**媒体和 JSONL 的有效性保持原样，不参与字典合并：**
+
+| 数据本体 | 有效性文件 | 存储形式 |
+|---|---|---|
+| `observation/rgb.mkv` | `observation/rgb_valid.pt` | 独立 CPU bool Tensor `[T]` |
+| `annotation/segment/mask.mkv` 或 `polygons.jsonl` | 原 `valid_ref` 指定的文件，通常为 `annotation/segment/mask_valid.pt` | 独立 CPU bool Tensor `[T]`；沿用原路径 |
+| `annotation/contact/relations.jsonl` | `annotation/contact/relations_valid.pt` | 独立 CPU bool Tensor `[T]` |
+| `observation/depth.mkv` | `observation/depth_valid.mkv` | 原逐像素 bool 无损视频 `[T,H,W]` |
+
+不新建仅包装上述 valid 的 observation.pt、segment.pt 或关系级 contact.pt。现有媒体/JSONL 的 `valid_ref`、`valid_axes` 及适用的 `valid_storage` 保持原定义；深度编码、NaN 位串还原与像素有效性规则不变。实体对的接触区域/距离是数值张量，仍按上表合并到各实体对的 contact.pt。
+
+## 5. metainfo 的文件与字典键引用
+
+文件定位与字典内部定位分开登记，不在文件名中拼接自定义分隔符：
+
+| 字段 | 定义 |
+|---|---|
+| `data_ref` | 相对当前相机视图根目录的 POSIX 文件路径 |
+| `data_key` | 非空字符串列表，按顺序访问 `.pt` 嵌套字典，最终必须得到 data Tensor |
+| `valid_ref` | 有效性所在文件；合并的数值字段必须与 data_ref 相同，媒体/JSONL 保留独立有效性文件 |
+| `valid_key` | 非空字符串列表，最终必须得到 bool Tensor |
+| `storage.profile` / `valid_storage.profile` | 合并字典采用 `pt/tensor_bundle/v1`；媒体及其独立有效性保持原配置，不将独立 bool Tensor 声明为 bundle |
+
+数值字段必须显式登记 data_key 与 valid_key。例如内参登记：
+
+```json
+{
+ "available": true,
+ "schema_id": "camera/pinhole/v1",
+ "data_ref": "camera/camera.pt",
+ "data_key": ["intrinsic", "data"],
+ "axes": ["T", "row", "column"],
+ "valid_ref": "camera/camera.pt",
+ "valid_key": ["intrinsic", "valid"],
+ "valid_axes": ["T"],
+ "storage": {"profile": "pt/tensor_bundle/v1"},
+ "valid_storage": {"profile": "pt/tensor_bundle/v1"}
+}
+```
+
+这是引用结构片段，实际组件仍须补齐 provenance、source_locator 等必填内容。MANO 的 schema_id 仍可为 `mano/axis_angle61/v1`：它描述解码后的数值语义，不因为物理文件合并而改动。顶层 schema_version 与 storage.profile 标识新的存储方式。
+
+媒体和 JSONL 的 data_ref 不设置 data_key；RGB 的 valid_ref 仍为 `observation/rgb_valid.pt`，不设置 valid_key。分割、关系和深度的有效性同样保持原引用方式，不新增任何字典键字段。只有引用合并字典时才使用 data_key/valid_key。
+
+同样规则适用于 attributes 中的可见性等数值字段。相机模型已有的 `distortion_ref` 改为指向 `camera/camera.pt`，新增 `distortion_key=["distortion", "data"]`；对应有效性由同组件的 `distortion_valid_ref` 与 `distortion_valid_key=["distortion", "valid"]` 登记。带 `_key` 的引用必须指向匹配的 bundle 文件，不能指向目录或媒体。
+
+读取器按文件路径缓存已加载字典，再按 key 取字段。同一 camera.pt 的三个字段不应触发三次反序列化。按组件下载时取所需 bundle 的整个文件；本约定不承诺 `.pt` 内单个键的远端局部下载。组件合并会增加最小下载粒度，这是减少文件数的明确取舍。
+
+## 6. 迁移与验收
+
+各数据集分别迁移：先适配工具并做代表性完整序列验证，再迁移全量并核对结果，最后按授权更新远端发布；一个数据集完成不代表其他数据集已完成。迁移状态和输入/输出固定版本逐数据集登记。
+
+本地先在既有 `shared-v1.0` 内完成受控升级，校验完成后更名为 `shared-v1.1`。暂存、回滚、锁、旧文件清理、版本更新及 HF 路径切换遵循 [MIGRATION.md](MIGRATION.md)；不复制完整媒体、不改 raw、不重新编码。多文件原地更新期间须保持维护状态，不能让读取任务使用半迁移视图。
+
+每次迁移必须检查：
+
+1. 所有组件、属性及其字典键可解析；无越界文件引用、缺失键或未登记字段；已合并的数值组件不得遗留旧独立 `*_valid.pt`。媒体/JSONL 的原独立有效性文件必须保留，不得误删或重打包。
+2. 每个迁移前后的数值字段 dtype、shape、逐元素值、NaN 位置以及 bool valid 完全一致。数值张量还应按统一连续字节视图比较，确认没有更改 NaN 位串等内容；不能用近似容差代替单纯重打包的一致性。
+3. T、帧映射、相机方向、手槽、split、来源和 valid_policy 不变。固定标定仍按既有 `[T,...]` 规则保存；本次不额外改成单份静态标定加广播。
+4. 媒体与其他未修改文件按原可信摘要核对。新 `.pt` 的文件 SHA256 通常改变，必须重新生成发布清单；新文件指纹不等于转换语义正确性的证明。
+5. 用新读取器运行代表性完整序列及所有相机，检查 RGB、相机与实际存在的标注是否正确关联；报告明确“重打包数值一致”与“人工视觉验收”的不同范围。
+
+只有完成匹配注册表、读写工具及上述验收后才能生产/发布 v1.1。上传仍须单独得到授权；本文档更新本身不启动数据迁移或上传；被替代旧文件的清理属于后续迁移发布步骤，必须遵守MIGRATION.md 中先验收、安全切换和固定提交核验要求。
diff --git a/H2O/shared-v1.1/script/h2o_v1_1/spec/MIGRATION.md b/H2O/shared-v1.1/script/h2o_v1_1/spec/MIGRATION.md
new file mode 100644
index 0000000000000000000000000000000000000000..b867163fccbc578f22eb6be8181f866bfecb6813
--- /dev/null
+++ b/H2O/shared-v1.1/script/h2o_v1_1/spec/MIGRATION.md
@@ -0,0 +1,68 @@
+# 数据集 v1.0 → v1.1 升级规范
+
+日期：2026-10-09。目标格式：`hand-warehouse/1.1`。字段与字典布局见 [CONTRACT.md](CONTRACT.md)。
+
+## 1. 升级单位与最终布局
+
+以一个完整数据集为单位逐步升级。每个数据集在既有目录内完成数值文件重组、工具适配与验证，完成后将目录由 `shared-v1.0` 更名为 `shared-v1.1`。最终使用：
+
+```text
+/
+ raw/ # 保持原样
+ shared-v1.1/
+ data/
+ doc/
+ script/
+```
+
+这是对“发布目录永久保持 shared-v1.0”安排的修订：不长期保留两套发布树，也不复制一整套媒体再制作新版本。尚未升级的数据集继续使用自己的 `shared-v1.0`；不同数据集处于不同版本是正常的。
+
+完成后视图元数据统一为 `schema_version=hand-warehouse/1.1`、`release_id=shared-v1.1`。目录名与元数据版本均须正确，不能只更名而不改变内容。发布记录保留旧版本的固定提交以便追溯。
+
+## 2. 变更范围
+
+- 合并相机、MANO、关键点、物体、实体对接触等数值张量及各自 valid；具体分组遵循合同。
+- 保持媒体及其独立有效性文件不变，包括 RGB、深度、mask 和相应 valid。JSONL、资产按合同保留；不重新编码媒体，不改 raw。
+- 不改变张量 dtype、shape、数值、NaN 位串、valid、帧映射、T、单位、坐标系、左右手、划分及监督策略。
+- 更新匹配的 schema、读取/转换/校验脚本、metainfo 引用、发布清单和正式文档。不得仅替换版本字符串冒充工具适配。
+- 删除已经完整合并且验收通过的旧数值文件与对应独立 valid；不得按 `*_valid.pt` 通配删除，媒体/JSONL 有效性不在清理范围。
+
+## 3. 启动与登记
+
+启动前核对项目并行上限、实际进程、锁和空间。取得数据集迁移锁并暂停该目录的转换、发布和读取任务；多文件原地更新不是整体原子操作，不得让消费者读取半迁移视图。
+
+在发布树外的 `tmp/` 为每个数据集登记：源 schema/release、源 HF 固定提交、源文件清单及摘要、目标版本、工具摘要、变更/删除的精确路径清单、checkpoint、阶段、错误和验收结果。原始数据长期保留。
+
+准备可恢复的基线：保存即将被替换的小型元数据和脚本，以及旧数值文件的临时备份或已核验可恢复的远端固定版本。不复制未变化的大型媒体。空间预算包含临时新文件和回滚所需文件；空间不足时停止新增工作，不删 raw 腾空间。
+
+## 4. 原地升级
+
+1. 先选代表性完整序列及全部相机，在 `tmp/` 验证新写入器、读取器和引用；覆盖实际存在的组件与有效性组合。遵循项目人工验收要求后再全量。
+2. 全量按完整视图处理。读取旧数值及 valid，写临时字典，重新读取并逐字段精确比较；不得用近似误差判断纯重打包的一致性。
+3. 验证通过后在同一文件系统原子替换单个文件，再更新该视图 metainfo。只有新字段与引用完整可读、且回滚依据可用，才能移除该视图被替代的旧文件。
+4. 每个视图保存完成 checkpoint；续跑必须核对输入和工具版本、产物摘要及当前引用，不能只依据文件存在而跳过。整个数据集完成前保持维护状态。
+5. 完成全部视图后校验完整路径集合、无重复/缺失、所有键引用、全部数值一致性和未变文件摘要，并更新正式文档及脚本。生成直接读取新格式的随机完整序列报告，按项目流程人工确认。
+
+失败时停止该数据集迁移，保留 checkpoint 和回滚材料，不伪装完成，不自动删除整棵目录或从 raw 重做转换。
+
+## 5. 本地最终更名
+
+全量验收通过后，在维护状态下统一设置最终 `release_id=shared-v1.1`，更新当前使用的配置、入口、清单和链接，然后将原目录重命名为 `shared-v1.1`。
+
+重命名前核对源/目标绝对路径均位于该数据集根目录；目标目录必须不存在，不合并、不覆盖已有同名目录。同一文件系统内执行目录重命名，避免复制全部媒体。更名后再次解析相对引用并检查脚本默认路径，最后解除本地维护状态。
+
+历史报告中说明当时版本的文字与固定摘要作为历史证据保留，不批量篡改；当前入口须明确报告所属版本，并链接本次迁移验收结果。
+
+## 6. HF 发布与路径更换
+
+本地目录重命名不等于 HF 路径已经更换。远端须在得到发布授权后，基于旧的固定提交执行受控更新；发布前再次核对源 HEAD 和相关文件内容，冲突时停止调查。
+
+优先复用仓库已有内容，以平台实际支持且经过验证的服务端复制/提交操作，将未变化媒体映射到 `shared-v1.1`；仅新的字典、元数据、文档和脚本需要提供新内容。不承诺路径更换必然零网络传输或零耗时；先验证实际行为，不盲目重新上传全套媒体。
+
+理想情况下，在一个提交中添加目标路径并删除旧路径，使默认分支完整切换。若文件数或 API 限制不允许，使用经过验证的临时分支准备与核验，完成后受控切换；若只能分批更新默认分支，必须明确维护状态，旧版本保留到目标完整核验通过，不提前删除唯一完整的旧树。短暂的核验用路径并存不代表长期发布两套版本。
+
+最终必须在同一固定提交上验证：目标 `shared-v1.1` 的文件集合、大小与内容摘要全部匹配清单，所有引用有效；旧 `shared-v1.0` 路径已移除；raw 未变化。服务端更换路径仍须执行这些核验，不能用“文件名已改”替代。
+
+完成后更新数据集登记与下载示例，记录最终提交，清理本次临时文件并通知一次，再安排下一个数据集。旧提交历史保留历史版本；本流程减少当前发布树的重复文件，不承诺清除 HF 历史存储。
+
+本文仅规定后续实施流程，编写本文不等于已经启动迁移、修改数据或获得新的发布授权。
diff --git a/H2O/shared-v1.1/script/h2o_v1_1/spec/schema.json b/H2O/shared-v1.1/script/h2o_v1_1/spec/schema.json
new file mode 100644
index 0000000000000000000000000000000000000000..087342ba7440c3b656e342d1b954bd90a5878af1
--- /dev/null
+++ b/H2O/shared-v1.1/script/h2o_v1_1/spec/schema.json
@@ -0,0 +1,1590 @@
+{
+ "schema_version": "hand-warehouse/1.1",
+ "validity_revision": "usable-annotation-2026-09-20",
+ "annotation_validity": {
+ "meaning": "annotation usable for supervision, not merely record presence",
+ "formula": "source_annotation_present AND numerical_checks_pass AND applicable_official_QA_pass AND NOT explicitly_reviewed_local_exclusion",
+ "without_official_qa": "omit QA term; declare source rules; do not fabricate QA",
+ "policy_field": "components..valid_policy",
+ "visibility_is_independent": true,
+ "invalid_float_value": "NaN",
+ "hot3d": "broadcast per-camera frame manual QA conservatively to hand/object labels and pose-derived visibility; retain RGB and source calibration; keep native QA in raw and review report",
+ "hot3d_visibility": "native visibility_ratio[%] already ranges from 0 to 1; do not divide by 100"
+ },
+ "hand_slots": [
+ "left",
+ "right"
+ ],
+ "view_metadata": {
+ "required_fields": [
+ "schema_version",
+ "dataset_id",
+ "release_id",
+ "sequence_id",
+ "camera_id",
+ "sample_type",
+ "num_frames",
+ "hand_slots",
+ "sampling",
+ "camera",
+ "components",
+ "split",
+ "view_type"
+ ],
+ "view_type_values": [
+ "ego",
+ "exo",
+ "unknown"
+ ],
+ "view_type_policy": "required per camera view; use ego or exo whenever the source data or capture setup establishes the viewpoint; use unknown only when no trustworthy source determines it; never omit or guess",
+ "schema_version": "hand-warehouse/1.1",
+ "release_id": "shared-v1.1"
+ },
+ "keypoints": {
+ "mano21/v1": {
+ "count": 21,
+ "names": [
+ "wrist",
+ "thumb_1",
+ "thumb_2",
+ "thumb_3",
+ "thumb_4",
+ "index_1",
+ "index_2",
+ "index_3",
+ "index_4",
+ "middle_1",
+ "middle_2",
+ "middle_3",
+ "middle_4",
+ "ring_1",
+ "ring_2",
+ "ring_3",
+ "ring_4",
+ "little_1",
+ "little_2",
+ "little_3",
+ "little_4"
+ ],
+ "source_index_map": null,
+ "note": "adapter maps official MANO joint and fingertip convention"
+ },
+ "rhd21/v1": {
+ "count": 21,
+ "names": [
+ "wrist",
+ "thumb_1",
+ "thumb_2",
+ "thumb_3",
+ "thumb_4",
+ "index_1",
+ "index_2",
+ "index_3",
+ "index_4",
+ "middle_1",
+ "middle_2",
+ "middle_3",
+ "middle_4",
+ "ring_1",
+ "ring_2",
+ "ring_3",
+ "ring_4",
+ "little_1",
+ "little_2",
+ "little_3",
+ "little_4"
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+ "all_packet_count_order_and_dimensions_exact",
+ "deterministic_decoded_rgb_sample_exact",
+ "pilot_and_final_selected_whole_sequences_decoded_rgb_exact"
+ ],
+ "validation_policy": {
+ "id": "jpeg-packets-all-rgb-sample16/v1",
+ "sample_count_per_view": 16,
+ "short_views": "all_frames_if_T_less_than_16",
+ "fixed_indices": "0,T-1,T//4,T//2,3*T//4, deduplicated",
+ "remaining_indices": "sample without replacement to min(T,16) using Python random.Random(seed)",
+ "seed": "first 8 bytes of SHA256(sequence_id/camera_id/20260920), big-endian integer",
+ "report": "policy, exact selected frame indices, seed, separate packet and pixel coverage",
+ "legacy_all_frame_pixel_proofs": "still accepted; preserve historical evidence"
+ }
+ },
+ "mkv/source_copy/v1": {
+ "container": "matroska",
+ "codec": "source_codec_declared_in_metadata",
+ "method": "stream_copy_only",
+ "logical_dtype": "uint8",
+ "decoded_pixel_format": "rgb24",
+ "validation": [
+ "no_reencoding",
+ "all_decoded_rgb_frames_exact",
+ "presentation_frame_order_exact"
+ ]
+ },
+ "mkv/h264_rgb_lossless/v1": {
+ "container": "matroska",
+ "codec": "h264",
+ "encoder": "libx264rgb",
+ "crf": 0,
+ "decoded_pixel_format": "rgb24",
+ "logical_dtype": "uint8",
+ "gop": "declared_in_encoder_parameters",
+ "validation": [
+ "all_decoded_rgb_frames_exact"
+ ],
+ "forbidden": [
+ "lossy_rate_control",
+ "rgb_to_yuv420_subsampling"
+ ]
+ },
+ "mkv/ffv1_rgb/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "decoded_pixel_format": "rgb24",
+ "logical_dtype": "uint8",
+ "validation": [
+ "all_decoded_rgb_frames_exact"
+ ]
+ },
+ "mkv/depth_float32_bits/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "pixel_format": "gray16le",
+ "logical_dtype": "float32",
+ "logical_shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "encoded_shape": [
+ "T",
+ "H",
+ "2*W"
+ ],
+ "packing": "IEEE754 little-endian float32 bits; low uint16 in first W columns, high uint16 in last W columns",
+ "decode": "reinterpret_float32(uint32(low) | (uint32(high) << 16))",
+ "validation": [
+ "all_float32_bits_exact_including_nan"
+ ]
+ },
+ "mkv/bool_ffv1/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "pixel_format": "gray",
+ "logical_dtype": "bool",
+ "stored_values": [
+ 0,
+ 1
+ ],
+ "validation": [
+ "all_bool_values_exact"
+ ]
+ },
+ "mkv/uint16_ffv1/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "pixel_formats": [
+ "gray",
+ "gray16le"
+ ],
+ "logical_dtype": "uint16",
+ "gray_condition": "all values <=255; decode by exact uint8-to-uint16 promotion",
+ "validation": [
+ "all_integer_values_exact"
+ ]
+ },
+ "pt/tensor_bundle/v1": {
+ "container": "pytorch_pt",
+ "root_type": "dict",
+ "dictionary_key_type": "nonempty_string",
+ "allowed_value_types": [
+ "dict",
+ "CPU torch.Tensor"
+ ],
+ "tensor_entry": {
+ "required_keys": [
+ "data",
+ "valid"
+ ],
+ "additional_keys": false,
+ "data_type": "CPU torch.Tensor",
+ "valid_type": "CPU torch.Tensor",
+ "valid_dtype": "bool"
+ },
+ "metadata_location": "metainfo.json; do not duplicate strings, paths or custom objects inside bundles",
+ "missing_entry": "omit entire entry; never fabricate absent modalities",
+ "load": {
+ "map_location": "cpu",
+ "weights_only": true
+ },
+ "valid_shape_rule": "Use the specific component or attribute valid_shape; do not infer broadcasting or share validity across independent fields.",
+ "validation": [
+ "registered layout and key resolution",
+ "CPU tensors only",
+ "schema dtype and shape",
+ "independent bool validity",
+ "frame alignment unchanged",
+ "invalid floating positions NaN",
+ "pure migration exact data bytes and validity equality"
+ ],
+ "not_applicable_to": [
+ "media payloads",
+ "media standalone validity tensors",
+ "JSONL standalone validity tensors"
+ ]
+ }
+ },
+ "video_metadata": {
+ "required": [
+ "profile",
+ "codec",
+ "pixel_format",
+ "encoded_size",
+ "decoder",
+ "encoder_parameters",
+ "source_lossy"
+ ],
+ "source_lossy_values": [
+ true,
+ false,
+ "unknown"
+ ],
+ "timebase_role": "index_only",
+ "lossless_reference": "source compressed packets or fixed-decoder source pixels; depth uses normalized standard float32 bits",
+ "selection": "choose smaller complete candidate only after exact roundtrip validation"
+ },
+ "tensor_storage_revision": "tensor-bundle-2026-10-09",
+ "release_layout": {
+ "directory": "shared-v1.1",
+ "release_id": "shared-v1.1",
+ "migration": "Upgrade each dataset in place under shared-v1.0 during maintenance, validate completely, then rename to shared-v1.1; do not retain duplicate published trees. See MIGRATION.md."
+ },
+ "tensor_attributes": {
+ "visibility": {
+ "storage_profile": "pt/tensor_bundle/v1",
+ "allowed_dtypes": [
+ "bool",
+ "float32"
+ ],
+ "visibility_types": [
+ "boolean",
+ "ratio"
+ ],
+ "ratio_range": [
+ 0,
+ 1
+ ],
+ "scopes": {
+ "whole_hand": {
+ "shape": [
+ "T",
+ 2
+ ],
+ "valid_shape": [
+ "T",
+ 2
+ ]
+ },
+ "hand_keypoint": {
+ "shape": [
+ "T",
+ 2,
+ "K"
+ ],
+ "valid_shape": [
+ "T",
+ 2,
+ "K"
+ ]
+ },
+ "object": {
+ "shape": [
+ "T"
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ }
+ },
+ "required_metadata": [
+ "data_ref",
+ "data_key",
+ "axes",
+ "dtype",
+ "visibility_type",
+ "meaning",
+ "valid_ref",
+ "valid_key",
+ "valid_axes",
+ "storage",
+ "valid_storage",
+ "provenance",
+ "source_locator"
+ ],
+ "valid_dtype": "bool",
+ "unknown": "valid=false; ratio data NaN; boolean data is not interpretable without valid",
+ "all_known": "explicit all-true bool tensor; do not infer from pose validity",
+ "note": "No source visibility means omit. Expand explicitly declared all-known scalar validity during migration without changing semantics."
+ }
+ },
+ "tensor_bundles": {
+ "camera": {
+ "path": "camera/camera.pt",
+ "entries": {
+ "intrinsic": {
+ "schema_ids": [
+ "camera/pinhole/v1",
+ "camera/rational8/v1",
+ "camera/opencv_fisheye4/v1",
+ "camera/fisheye624/v1"
+ ]
+ },
+ "extrinsic": {
+ "schema_ids": [
+ "transform/se3/v1"
+ ]
+ },
+ "distortion": {
+ "schema_ids": [
+ "camera/distortion8/v1",
+ "camera/fisheye4_distortion/v1"
+ ]
+ }
+ }
+ },
+ "mano": {
+ "path": "annotation/mano/mano.pt",
+ "entries": {
+ "motion": {
+ "schema_ids": [
+ "mano/axis_angle61/v1"
+ ]
+ },
+ "visibility": {
+ "attribute_id": "visibility",
+ "scope": "whole_hand"
+ }
+ }
+ },
+ "keypoints": {
+ "path": "annotation/keypoints/keypoints.pt",
+ "entries": {
+ "uv": {
+ "schema_ids": [
+ "keypoints/uv/v1"
+ ]
+ },
+ "xyz": {
+ "schema_ids": [
+ "keypoints/xyz/v1"
+ ]
+ },
+ "visibility": {
+ "attribute_id": "visibility",
+ "scope": "hand_keypoint"
+ },
+ "whole_hand_visibility": {
+ "attribute_id": "visibility",
+ "scope": "whole_hand"
+ }
+ }
+ },
+ "object": {
+ "path": "annotation/object//object.pt",
+ "entries": {
+ "pose": {
+ "schema_ids": [
+ "object/rigid/v1"
+ ]
+ },
+ "visibility": {
+ "attribute_id": "visibility",
+ "scope": "object"
+ },
+ "dimensions": {
+ "schema_ids": [
+ "object/dimensions/v1"
+ ]
+ },
+ "joint_state": {
+ "schema_ids": [
+ "object/joint_state/v1"
+ ]
+ },
+ "keypoints/xyz": {
+ "schema_ids": [
+ "object_keypoints/camera/v1"
+ ]
+ },
+ "keypoints/template_xyz": {
+ "schema_ids": [
+ "object_keypoints/template/v1"
+ ]
+ },
+ "parts//pose": {
+ "schema_ids": [
+ "transform/se3/v1"
+ ],
+ "convention": "T_camera_from_part"
+ },
+ "parts//dimensions": {
+ "schema_ids": [
+ "object/dimensions/v1"
+ ]
+ },
+ "parts//keypoints/xyz": {
+ "schema_ids": [
+ "object_keypoints/camera/v1"
+ ]
+ },
+ "parts//keypoints/template_xyz": {
+ "schema_ids": [
+ "object_keypoints/template/v1"
+ ],
+ "coordinate_frame": "part"
+ },
+ "meshes//vertex_part_ids": {
+ "schema_ids": [
+ "object/vertex_part_ids/v1"
+ ]
+ }
+ }
+ },
+ "contact_pair": {
+ "path": "annotation/contact/__/contact.pt",
+ "entries": {
+ "vertex_region": {
+ "schema_ids": [
+ "contact/vertex_region/v1"
+ ]
+ },
+ "vertex_distance": {
+ "schema_ids": [
+ "contact/vertex_distance/v1"
+ ]
+ }
+ }
+ }
+ },
+ "bundle_layout_rules": {
+ "entry_path_syntax": "Slash-separated dictionary keys in this registry only; serialize references as string arrays. Angle-bracket placeholders resolve to registered IDs, not literal keys.",
+ "entry_leaves": [
+ "data",
+ "valid"
+ ],
+ "additional_entries": false,
+ "all_entries_optional": true,
+ "omit_empty_bundles": true,
+ "identifiers": "object/part/mesh and contact entity IDs must resolve in metadata; reject empty, dot, dot-dot or slash-containing IDs",
+ "per_view": true,
+ "no_cross_object_or_camera_merge": true,
+ "numeric_field_valid_ref_equals_data_ref": true,
+ "attributes_must_be_registered": true
+ },
+ "reference_rules": {
+ "file_paths": {
+ "base": "current camera view root",
+ "format": "relative POSIX",
+ "reject": [
+ "absolute paths",
+ "parent traversal",
+ "external URLs",
+ "unresolved placeholders"
+ ]
+ },
+ "dictionary_keys": {
+ "type": "array",
+ "item_type": "nonempty_string",
+ "min_items": 1,
+ "traversal": "literal nested dictionary lookup; never eval; no automatic flattening",
+ "data_terminal": "data",
+ "valid_terminal": "valid",
+ "target_type": "CPU torch.Tensor"
+ },
+ "bundle_component": {
+ "required": [
+ "data_ref",
+ "data_key",
+ "valid_ref",
+ "valid_key",
+ "storage",
+ "valid_storage"
+ ],
+ "same_file": true,
+ "same_entry_prefix": true,
+ "storage_profile": "pt/tensor_bundle/v1",
+ "valid_storage_profile": "pt/tensor_bundle/v1"
+ },
+ "media_jsonl": {
+ "forbidden": [
+ "data_key",
+ "valid_key"
+ ],
+ "preserve_original_valid_ref": true,
+ "standalone_valid_pt_root": "CPU bool torch.Tensor; not dict",
+ "do_not_label_standalone_valid_as_bundle": true
+ },
+ "camera_distortion": {
+ "models": {
+ "rational8": "camera/distortion8/v1",
+ "opencv_fisheye4": "camera/fisheye4_distortion/v1"
+ },
+ "required": [
+ "distortion_ref",
+ "distortion_key",
+ "distortion_valid_ref",
+ "distortion_valid_key"
+ ],
+ "distortion_ref": "camera/camera.pt",
+ "distortion_key": [
+ "distortion",
+ "data"
+ ],
+ "distortion_valid_ref": "camera/camera.pt",
+ "distortion_valid_key": [
+ "distortion",
+ "valid"
+ ],
+ "valid_equals_intrinsic_valid": true,
+ "models_without_separate_distortion": [
+ "pinhole",
+ "fisheye624"
+ ]
+ },
+ "reader_cache": "Cache bundle by resolved file path; loading different fields must not repeatedly deserialize the file."
+ },
+ "standalone_validity": {
+ "rgb": {
+ "data_ref": "observation/rgb.mkv",
+ "valid_ref": "observation/rgb_valid.pt",
+ "dtype": "bool",
+ "shape": [
+ "T"
+ ],
+ "container": "pt",
+ "root_type": "CPU Tensor"
+ },
+ "segment": {
+ "data_refs": [
+ "annotation/segment/mask.mkv",
+ "annotation/segment/polygons.jsonl"
+ ],
+ "valid_ref": "preserve source release reference; normally annotation/segment/mask_valid.pt",
+ "dtype": "bool",
+ "shape": [
+ "T"
+ ],
+ "container": "pt",
+ "root_type": "CPU Tensor"
+ },
+ "relations": {
+ "data_ref": "annotation/contact/relations.jsonl",
+ "valid_ref": "annotation/contact/relations_valid.pt",
+ "dtype": "bool",
+ "shape": [
+ "T"
+ ],
+ "container": "pt",
+ "root_type": "CPU Tensor"
+ },
+ "depth": {
+ "data_ref": "observation/depth.mkv",
+ "valid_ref": "observation/depth_valid.mkv",
+ "dtype": "bool",
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "profile": "mkv/bool_ffv1/v1"
+ }
+ }
+}
diff --git a/H2O/shared-v1.1/script/h2o_v1_1/warehouse.py b/H2O/shared-v1.1/script/h2o_v1_1/warehouse.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce543b8c06440f110c03b941a6ee3adde922f13c
--- /dev/null
+++ b/H2O/shared-v1.1/script/h2o_v1_1/warehouse.py
@@ -0,0 +1,201 @@
+"""H2O v1.1 bundle reading and registry validation; no media re-encoding."""
+import hashlib
+import json
+from pathlib import Path, PurePosixPath
+
+import torch
+
+PROFILE = 'pt/tensor_bundle/v1'
+
+
+def sha256(path):
+ with Path(path).open('rb') as stream:
+ return hashlib.file_digest(stream, 'sha256').hexdigest()
+
+
+def resolve(root, ref):
+ if not isinstance(ref, str) or not ref or '\\' in ref:
+ raise ValueError(f'invalid file reference: {ref!r}')
+ path = PurePosixPath(ref)
+ if path.is_absolute() or '..' in path.parts or ':' in ref:
+ raise ValueError(f'escaping file reference: {ref}')
+ root = Path(root).resolve()
+ result = root / path
+ if not result.resolve().is_relative_to(root) or not result.is_file() or result.is_symlink():
+ raise ValueError(f'missing/escaping file reference: {ref}')
+ return result
+
+
+def layout(name, component):
+ """Fail closed on fields outside the audited H2O v1.0 release."""
+ fixed = {
+ 'intrinsic': ('camera/pinhole/v1', 'camera/camera.pt', ['intrinsic']),
+ 'extrinsic': ('transform/se3/v1', 'camera/camera.pt', ['extrinsic']),
+ 'mano': ('mano/axis_angle61/v1', 'annotation/mano/mano.pt', ['motion']),
+ 'keypoints_xyz': ('keypoints/xyz/v1', 'annotation/keypoints/keypoints.pt', ['xyz']),
+ }
+ expected = fixed.get(name)
+ for prefix, schema_id, keys in [('pose_', 'object/rigid/v1', ['pose']),
+ ('points_', 'object_keypoints/camera/v1', ['keypoints', 'xyz'])]:
+ if name.startswith(prefix):
+ oid = name.removeprefix(prefix)
+ if oid not in {'book', 'espresso', 'lotion', 'spray', 'milk', 'cocoa', 'chips', 'cappuccino'}:
+ raise ValueError(f'unknown H2O object identity: {oid}')
+ expected = (schema_id, f'annotation/object/{oid}/object.pt', keys)
+ if expected is None or component['schema_id'] != expected[0]:
+ raise ValueError(f'unsupported H2O numeric component: {name}')
+ if component.get('attributes'):
+ raise ValueError(f'{name}: new numerical attributes need explicit migration support')
+ return expected[1:]
+
+
+class Reader:
+ def __init__(self, view):
+ self.view = Path(view)
+ self.cache = {}
+
+ def field(self, component, kind='data'):
+ path = resolve(self.view, component[kind + '_ref'])
+ if path not in self.cache:
+ self.cache[path] = torch.load(path, map_location='cpu', weights_only=True)
+ value = self.cache[path]
+ keys = component.get(kind + '_key')
+ if keys is not None:
+ if not isinstance(keys, list) or not keys or any(not isinstance(k, str) or not k for k in keys):
+ raise ValueError('dictionary keys must be a nonempty list of nonempty strings')
+ for key in keys:
+ if type(value) is not dict or key not in value:
+ raise ValueError(f'missing dictionary key: {keys}')
+ value = value[key]
+ if not isinstance(value, torch.Tensor) or value.device.type != 'cpu':
+ raise ValueError('reference must resolve to a CPU Tensor')
+ return value
+
+
+def exact(left, right):
+ if left.dtype != right.dtype or left.shape != right.shape:
+ raise ValueError('tensor dtype/shape changed')
+ a = left.detach().contiguous().reshape(-1).view(torch.uint8)
+ b = right.detach().contiguous().reshape(-1).view(torch.uint8)
+ if not torch.equal(a, b):
+ raise ValueError('tensor bytes changed (including NaN payload or signed zero)')
+
+
+
+def exact_metadata(old, new):
+ allowed = {'data_ref', 'valid_ref', 'data_key', 'valid_key', 'storage', 'valid_storage'}
+ expected = json.loads(json.dumps(new))
+ expected.update(schema_version=old['schema_version'], release_id=old['release_id'])
+ for name, component in old['components'].items():
+ target = new['components'][name]
+ if component.get('available') and component['data_ref'].endswith('.pt'):
+ if ({k:v for k,v in component.items() if k not in allowed} !=
+ {k:v for k,v in target.items() if k not in allowed}):
+ raise ValueError(f'{name}: numerical semantics metadata changed')
+ elif component != target:
+ raise ValueError(f'{name}: nonnumerical component metadata changed')
+ expected['components'][name] = component
+ if expected != old:
+ raise ValueError('view semantics metadata changed')
+
+def check_shape(value, shape, n):
+ if value.ndim != len(shape) or any(
+ (wanted == 'T' and actual != n) or (isinstance(wanted, int) and actual != wanted)
+ for actual, wanted in zip(value.shape, shape, strict=True)
+ ):
+ raise ValueError(f'tensor shape {tuple(value.shape)} differs from schema {shape}')
+
+
+def validate_view(view, schema_path):
+ view = Path(view)
+ meta = json.loads((view / 'metainfo.json').read_text())
+ schema = json.loads(Path(schema_path).read_text())
+ if schema['schema_version'] != 'hand-warehouse/1.1' or meta['schema_version'] != schema['schema_version']:
+ raise ValueError('v1.1 schema required')
+ if any(k not in meta for k in schema['view_metadata']['required_fields']):
+ raise ValueError('required view metadata missing')
+ if (meta['dataset_id'] != 'H2O' or meta['release_id'] != 'shared-v1.1'
+ or meta['camera_id'] not in [f'cam{i}' for i in range(5)]
+ or meta['view_type'] != ('ego' if meta['camera_id'] == 'cam4' else 'exo')):
+ raise ValueError('H2O release/camera/view_type mismatch')
+ if meta['hand_slots'] != ['left', 'right'] or type(meta['num_frames']) is not int or meta['num_frames'] <= 0:
+ raise ValueError('hand slots/timeline mismatch')
+ n = meta['num_frames']
+ rows = [json.loads(line) for line in resolve(view, meta['sampling']['frames_ref']).read_text().splitlines()]
+ if len(rows) != n or [r['frame_index'] for r in rows] != list(range(n)):
+ raise ValueError('frame mapping incomplete or reordered')
+ reader, entries, report = Reader(view), {}, {}
+ for name, c in meta['components'].items():
+ if not c.get('available'):
+ if not c.get('reason'):
+ raise ValueError(f'{name}: unavailable component needs a reason')
+ continue
+ definition = schema['components'][c['schema_id']]
+ if c.get('attributes'):
+ raise ValueError(f'{name}: unaudited attributes are not supported')
+ for required in ['axes', 'provenance', 'source_locator', *definition.get('required_metadata', [])]:
+ if required not in c:
+ raise ValueError(f'{name}: missing {required}')
+ resolve(view, c['data_ref'])
+ if c.get('valid_ref'):
+ resolve(view, c['valid_ref'])
+ for profile_field, allowed in [('storage', 'allowed_storage_profiles'), ('valid_storage', 'allowed_valid_storage_profiles')]:
+ if allowed in definition and c.get(profile_field, {}).get('profile') not in definition[allowed]:
+ raise ValueError(f'{name}: unregistered {profile_field} profile')
+ if definition['storage'] == 'pt':
+ ref, key = layout(name, c)
+ if (c['data_ref'] != ref or c['valid_ref'] != ref or c['data_key'] != key + ['data']
+ or c['valid_key'] != key + ['valid']):
+ raise ValueError(f'{name}: noncanonical bundle reference')
+ entries.setdefault(ref, set()).add(tuple(key))
+ data, valid = reader.field(c), reader.field(c, 'valid')
+ check_shape(data, definition['shape'], n)
+ check_shape(valid, definition['valid_shape'], n)
+ if 'K' in definition['shape'] and data.shape[definition['shape'].index('K')] != 21:
+ raise ValueError(f'{name}: H2O keypoint count must remain 21')
+ if len(c['axes']) != data.ndim or c.get('valid_axes') != c['axes'][:valid.ndim]:
+ raise ValueError(f'{name}: axis declaration mismatch')
+ if c['data_ref'].startswith('annotation/') and not c.get('valid_policy', {}).get('source_rule'):
+ raise ValueError(f'{name}: source validity policy missing')
+ if data.dtype != getattr(torch, definition['dtype']) or valid.dtype != torch.bool:
+ raise ValueError(f'{name}: incorrect tensor dtype')
+ if data.shape[:valid.ndim] != valid.shape or not torch.isfinite(data[valid]).all() or not torch.isnan(data[~valid]).all():
+ raise ValueError(f'{name}: invalid finite/NaN/valid semantics')
+ if name.startswith(('pose_', 'points_')):
+ oid = name.split('_', 1)[1]
+ identity = json.loads(resolve(view, f'annotation/object/{oid}/metainfo.json').read_text())
+ if identity['object_id'] != oid:
+ raise ValueError('unresolved object identity')
+ report[name] = {'shape': list(data.shape), 'dtype': str(data.dtype),
+ 'valid_true': int(valid.sum()), 'valid_total': valid.numel()}
+ else:
+ if 'data_key' in c or 'valid_key' in c:
+ raise ValueError(f'{name}: media/JSONL must not use bundle keys')
+ if c.get('valid_ref', '').endswith('.pt'):
+ valid = reader.field(c, 'valid')
+ if valid.dtype != torch.bool or tuple(valid.shape) != (n,):
+ raise ValueError(f'{name}: standalone validity must remain bool [T]')
+ for ref, registered in entries.items():
+ bundle = reader.cache[resolve(view, ref)]
+ def visit(node, prefix=()):
+ if type(node) is not dict or not node:
+ raise ValueError('bundle must contain nonempty string-key dictionaries')
+ if set(node) == {'data', 'valid'}:
+ if prefix not in registered or any(not isinstance(v, torch.Tensor) or v.device.type != 'cpu' for v in node.values()):
+ raise ValueError('unregistered bundle field or non-CPU tensor')
+ return {prefix}
+ found = set()
+ for key, value in node.items():
+ if not isinstance(key, str) or not key:
+ raise ValueError('invalid bundle key')
+ found |= visit(value, prefix + (key,))
+ return found
+ if visit(bundle) != registered:
+ raise ValueError('registered bundle entries missing')
+ referenced_pt = {c[k] for c in meta['components'].values() if c.get('available')
+ for k in ('data_ref', 'valid_ref') if c.get(k, '').endswith('.pt')}
+ if {p.relative_to(view).as_posix() for p in view.rglob('*.pt')} != referenced_pt:
+ raise ValueError('orphan PT file or obsolete standalone numeric validity')
+ return {'num_frames': n, 'numeric_components': report, 'bundle_files': len(entries),
+ 'scope': 'v1.1 registry, keys, shapes, validity and timeline; not a media pixel comparison'}
+
diff --git a/H2O/shared-v1.1/script/provenance/operations/publish_h2o_v1_1.py b/H2O/shared-v1.1/script/provenance/operations/publish_h2o_v1_1.py
new file mode 100644
index 0000000000000000000000000000000000000000..166980dee03e1978ffc0754fe01be907375127dd
--- /dev/null
+++ b/H2O/shared-v1.1/script/provenance/operations/publish_h2o_v1_1.py
@@ -0,0 +1,234 @@
+#!/usr/bin/env python3
+"""Publish only H2O via a verified private PR; never squash repository history."""
+import argparse
+from contextlib import contextmanager
+from datetime import datetime, timezone
+import fcntl
+import hashlib
+import json
+import os
+from pathlib import Path, PurePosixPath
+import time
+
+from huggingface_hub import HfApi, CommitOperationAdd, CommitOperationCopy, CommitOperationDelete, hf_hub_download
+
+REPO = 'ParityLab/Hand-Datasets-Raw'
+OLD, NEW = 'H2O/shared-v1.0', 'H2O/shared-v1.1'
+
+
+def sha(path):
+ with Path(path).open('rb') as f:
+ return hashlib.file_digest(f, 'sha256').hexdigest()
+
+
+def write_json(path, value):
+ tmp = path.with_suffix(path.suffix + '.tmp')
+ tmp.write_text(json.dumps(value, ensure_ascii=False, indent=2, allow_nan=False) + '\n')
+ with tmp.open('rb') as f: os.fsync(f.fileno())
+ os.replace(tmp, path)
+
+
+def safe(root, ref):
+ p = PurePosixPath(ref)
+ if not ref or p.is_absolute() or '..' in p.parts or '\\' in ref or ':' in ref:
+ raise ValueError('unsafe release reference')
+ path = root / ref
+ if not path.is_file() or path.is_symlink() or not path.resolve().is_relative_to(root.resolve()):
+ raise ValueError('missing/escaping release file')
+ return path
+
+
+def strip_h2o_attributes(data):
+ prefixes = (b'H2O/shared-v1.0/', b'"H2O/shared-v1.0/', b'H2O/shared-v1.1/', b'"H2O/shared-v1.1/')
+ return b''.join(line for line in data.splitlines(keepends=True) if not line.startswith(prefixes))
+
+
+def remote_files(api, revision, prefix):
+ return {r.path.removeprefix(prefix + '/'): r for r in
+ api.list_repo_tree(REPO, path_in_repo=prefix, recursive=True, revision=revision, repo_type='dataset')
+ if getattr(r, 'size', None) is not None}
+
+
+def matches(row, expected):
+ if row.size != expected['size']: return False
+ if row.lfs: return row.lfs.sha256 == expected['sha256']
+ return row.blob_id == expected.get('git_sha1')
+
+
+def trees(api, revision, prefix=None):
+ return {r.path: getattr(r, 'tree_id', None) or getattr(r, 'blob_id', None)
+ for r in api.list_repo_tree(REPO, path_in_repo=prefix, revision=revision, repo_type='dataset')}
+
+
+def outside_h2o(values):
+ return {k:v for k,v in values.items() if k not in ('H2O', '.gitattributes')}
+
+
+def source_rows(api, work, revision):
+ plan = json.loads((work / 'PLAN.json').read_text())
+ hashes = dict(plan['root_source_sha256'])
+ rows = {}
+ for seq in plan['sequences']:
+ inventory = json.loads((work / 'inventory' / (seq + '.json')).read_text())['files']
+ rows.update(inventory)
+ for cam in range(5):
+ record = json.loads((work / 'checkpoints' / seq / f'cam{cam}.json').read_text())
+ hashes.update({record['view'] + '/' + k:v for k,v in record['source_sha256'].items()})
+ for folder in ('doc', 'script'):
+ for ref,row in remote_files(api, revision, OLD + '/' + folder).items():
+ rows[folder + '/' + ref] = {'size':row.size,'oid':row.blob_id,
+ 'lfs':{'oid':row.lfs.sha256} if row.lfs else None}
+ by_content = {}
+ for ref,row in sorted(rows.items()):
+ by_content.setdefault((hashes[ref], row['size']), (ref,row))
+ return by_content
+
+
+def build_plan(root, work, api, manifest):
+ sources = source_rows(api, work, manifest['source_revision'])
+ files = {}
+ for ref,entry in sorted(manifest['files'].items()):
+ path = safe(root, ref)
+ if path.stat().st_size != entry['size']: raise ValueError(f'local size changed: {ref}')
+ source = sources.get((entry['sha256'],entry['size']))
+ item = dict(entry)
+ if source:
+ src,row = source
+ if row.get('lfs'):
+ item.update(kind='copy', source=OLD + '/' + src)
+ else:
+ item.update(kind='add', git_sha1=row['oid'])
+ else:
+ if path.suffix == '.mkv': raise ValueError('refuse to upload/re-encode an unmatched media file')
+ h = hashlib.sha1(f'blob {entry["size"]}\0'.encode())
+ with path.open('rb') as f:
+ while chunk := f.read(4*1024*1024): h.update(chunk)
+ item.update(kind='add', git_sha1=h.hexdigest())
+ files[ref] = item
+ return {'source_revision':manifest['source_revision'], 'raw_tree_oid':manifest['raw_tree_oid'],
+ 'manifest_sha256':sha(work/'PUBLISH_MANIFEST.json'), 'files':files,
+ 'copy_files':sum(e['kind']=='copy' for e in files.values()),
+ 'local_upload_bytes':sum(e['size'] for e in files.values() if e['kind']=='add')}
+
+
+def operations(root, plan, refs):
+ result = []
+ for ref in refs:
+ e = plan['files'][ref]
+ if e['kind']=='copy':
+ result.append(CommitOperationCopy(e['source'],NEW+'/'+ref,src_revision=plan['source_revision']))
+ else:
+ path = safe(root,ref)
+ if sha(path)!=e['sha256']: raise ValueError(f'local content changed: {ref}')
+ result.append(CommitOperationAdd(NEW+'/'+ref,path))
+ return result
+
+
+def batches(refs, files):
+ batch, size = [],0
+ for ref in refs:
+ n = files[ref]['size'] if files[ref]['kind']=='add' else 0
+ if batch and (len(batch)>=100 or size+n>48*1024**2):
+ yield batch;batch=[];size=0
+ batch.append(ref);size+=n
+ if batch: yield batch
+
+
+def verify_release(api, revision, plan):
+ actual = remote_files(api,revision,NEW)
+ if set(actual)!=set(plan['files']): raise ValueError('remote release file set mismatch')
+ for ref,e in plan['files'].items():
+ if not matches(actual[ref],e): raise ValueError(f'remote size/content mismatch: {ref}')
+ h2o=trees(api,revision,'H2O')
+ if OLD in h2o or h2o.get('H2O/raw')!=plan['raw_tree_oid']:
+ raise ValueError('old tree still present or raw tree changed')
+ return h2o[NEW]
+
+
+def main():
+ p=argparse.ArgumentParser(description=__doc__)
+ p.add_argument('--root',type=Path,required=True)
+ p.add_argument('--work',type=Path,required=True)
+ args=p.parse_args();root=args.root.resolve();work=args.work.resolve()
+ if root.name!='shared-v1.1' or root.parent.name!='H2O' or not work.is_relative_to(root.parent/'tmp'):
+ p.error('H2O final root and dedicated existing work required')
+ manifest=json.loads((work/'PUBLISH_MANIFEST.json').read_text())
+ state=json.loads((work/'RUN.json').read_text())
+ if state['phase']!='local_finalized_not_uploaded' or state['completed_views']!=920:
+ p.error('completed local release and post-full approval required')
+ actual={q.relative_to(root).as_posix() for q in root.rglob('*') if q.is_file() and q.suffix!='.pyc' and '__pycache__' not in q.parts}
+ if actual!=set(manifest['files']): raise ValueError('local release file set drift')
+ api=HfApi();api.auth_check(REPO,repo_type='dataset',write=True)
+ log_path=work/'HF_PUBLICATION.json'
+ lock=(work/'HF_PUBLICATION.lock').open('a')
+ fcntl.flock(lock,fcntl.LOCK_EX|fcntl.LOCK_NB)
+ plan_path=work/'HF_PUBLICATION_PLAN.json'
+ if plan_path.exists():
+ plan=json.loads(plan_path.read_text())
+ if plan['manifest_sha256']!=sha(work/'PUBLISH_MANIFEST.json'): raise ValueError('publication manifest changed')
+ else:
+ plan=build_plan(root,work,api,manifest);write_json(plan_path,plan)
+ print('PUBLISH_PLAN',len(plan['files']),'files',plan['copy_files'],'server copies',plan['local_upload_bytes'],'upload bytes',flush=True)
+ run=json.loads(log_path.read_text()) if log_path.exists() else {'phase':'preparing','created_utc':datetime.now(timezone.utc).isoformat()}
+ try:
+ head=api.repo_info(REPO,repo_type='dataset').sha
+ live=trees(api,head,'H2O');fixed=trees(api,plan['source_revision'],'H2O')
+ if NEW in live:
+ final=verify_release(api,head,plan)
+ run.update(phase='published_verified',final_commit=head,target_tree_oid=final)
+ write_json(log_path,run);print('PUBLISHED_VERIFIED',head,flush=True);return
+ if live.get(OLD)!=fixed.get(OLD) or live.get('H2O/raw')!=plan['raw_tree_oid']:
+ raise ValueError('H2O source/raw changed; do not overwrite')
+ if 'pr' not in run:
+ attrs=Path(hf_hub_download(REPO,'.gitattributes',repo_type='dataset',revision=head)).read_bytes()
+ trimmed=work/'H2O_CLEAN_GITATTRIBUTES';trimmed.write_bytes(strip_h2o_attributes(attrs))
+ proof=['data/subject1_k2_6/cam0/observation/rgb.mkv','data/subject1_h1_0/cam0/observation/depth.mkv']
+ run.update(base_commit=head,base_root=trees(api,head),phase='creating_private_pr',proof_files=proof)
+ write_json(log_path,run)
+ commit=api.create_commit(REPO,repo_type='dataset',revision='main',parent_commit=head,create_pr=True,
+ operations=[CommitOperationDelete(OLD,is_folder=True),CommitOperationAdd('.gitattributes',trimmed)]+operations(root,plan,proof),
+ commit_message='Prepare H2O shared-v1.1 migration (private PR)',num_threads=4)
+ run.update(pr=int(commit.pr_url.rstrip('/').split('/')[-1]),revision=commit.pr_revision,head=commit.oid,phase='staging')
+ write_json(log_path,run)
+ print('PRIVATE_PR',run['pr'],run['revision'],flush=True)
+ branch_head=api.repo_info(REPO,repo_type='dataset',revision=run['revision']).sha
+ existing=remote_files(api,branch_head,NEW)
+ for ref,row in existing.items():
+ if ref not in plan['files'] or not matches(row,plan['files'][ref]): raise ValueError('private branch content drift')
+ missing=[ref for ref in plan['files'] if ref not in existing]
+ done=len(existing)
+ for batch in batches(missing,plan['files']):
+ commit=api.create_commit(REPO,repo_type='dataset',revision=run['revision'],parent_commit=branch_head,
+ operations=operations(root,plan,batch),commit_message=f'Stage H2O shared-v1.1 {done+len(batch)}/{len(plan["files"])}',num_threads=4)
+ branch_head=commit.oid;done+=len(batch)
+ run.update(head=branch_head,staged_files=done,phase='staging');write_json(log_path,run)
+ print('STAGED',done,'/',len(plan['files']),branch_head,flush=True)
+ tree_id=verify_release(api,branch_head,plan)
+ if outside_h2o(trees(api,branch_head))!=outside_h2o(run['base_root']): raise ValueError('private PR changes other datasets')
+ before=api.repo_info(REPO,repo_type='dataset').sha
+ current=trees(api,before,'H2O')
+ if current.get(OLD)!=fixed.get(OLD) or current.get('H2O/raw')!=plan['raw_tree_oid'] or NEW in current:
+ raise ValueError('H2O changed during staging; merge halted')
+ attrs_live=Path(hf_hub_download(REPO,'.gitattributes',repo_type='dataset',revision=before)).read_bytes()
+ attrs_branch=Path(hf_hub_download(REPO,'.gitattributes',repo_type='dataset',revision=branch_head)).read_bytes()
+ if strip_h2o_attributes(attrs_live)!=strip_h2o_attributes(attrs_branch):
+ raise ValueError('non-H2O LFS tracking changed; merge halted for review')
+ before_root=trees(api,before)
+ run.update(phase='verified_before_merge',verified_branch_commit=branch_head,premerge_head=before,target_tree_oid=tree_id)
+ write_json(log_path,run)
+ discussion=api.get_discussion_details(REPO,run['pr'],repo_type='dataset')
+ if discussion.status=='draft': api.change_discussion_status(REPO,run['pr'],'open',repo_type='dataset')
+ api.merge_pull_request(REPO,run['pr'],repo_type='dataset',comment='All H2O release paths, sizes and content digests verified. Raw unchanged. No repository history squash.')
+ final=api.repo_info(REPO,repo_type='dataset').sha
+ final_tree=verify_release(api,final,plan)
+ if outside_h2o(trees(api,final))!=outside_h2o(before_root): raise ValueError('other dataset roots changed across merge; investigate')
+ run.update(phase='published_verified',final_commit=final,target_tree_oid=final_tree,files=len(plan['files']),raw_unchanged=True)
+ write_json(log_path,run);print('PUBLISHED_VERIFIED',final,flush=True)
+ except Exception as exc:
+ run.update(failed_phase=run.get('phase'),phase='failed',error=str(exc))
+ write_json(log_path,run)
+ print('PUBLISH_FAILED_NO_HISTORY_SQUASH',repr(exc),flush=True)
+ raise
+
+
+if __name__=='__main__':main()
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/doc/CONTRACT.md b/H2O/shared-v1.1/script/v1_0_snapshot/doc/CONTRACT.md
new file mode 100644
index 0000000000000000000000000000000000000000..f756d711647e109b6bb732ce1b9c84111730489d
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/doc/CONTRACT.md
@@ -0,0 +1,457 @@
+# 手物数据仓库统一约定 v1.0
+
+格式版本：`hand-warehouse/1.0`。存储约定：`lossless-video-2026-09-18`。
+
+范围限于手物交互重建。按本任务纳入的源模态转换，不要求每个数据集填满所有组件。缺失模态、尚未下载、语义未核实分别报告，不能互相替代。
+
+本文定义各字段的含义与使用规则，机器可读注册表见 [schema.json](schema.json)。两者互相补充；如有冲突，应先核实并统一定义。
+
+## 1. 目录
+
+```text
+hand-datasets/
+ /
+ raw/
+ doc/ # 官方说明、来源与校验记录
+ script/ # 官方下载脚本、必要包装脚本
+ data/ # 保留原始包，不原地解压或改写
+ /
+ doc/ # 发布说明、schema、来源清单
+ script/ # 转换、校验脚本及运行配置
+ data/
+ /
+ /
+ metainfo.json
+ observation/
+ rgb.mkv
+ rgb_valid.pt
+ depth.mkv # float32 深度的可逆视频编码
+ depth_valid.mkv # 逐像素 bool 的无损视频编码
+ camera/
+ frames.jsonl
+ intrinsic.pt
+ intrinsic_valid.pt
+ extrinsic.pt
+ extrinsic_valid.pt
+ annotation/
+ mano/
+ motion.pt
+ motion_valid.pt
+ visibility.pt
+ keypoints/
+ uv.pt
+ uv_valid.pt
+ xyz.pt
+ xyz_valid.pt
+ visibility.pt # 源数据有时提供
+ object/
+ /
+ assets/ # 该物体的网格、材质、纹理及部件资产
+ .obj
+ pose.pt
+ pose_valid.pt
+ visibility.pt
+ metainfo.json # 物体身份、类别、mesh、尺寸与部件引用
+ articulation.json
+ keypoints/
+ vertex_part_ids.pt
+ joint_state.pt
+ joint_state_valid.pt
+ parts//pose.pt
+ parts//pose_valid.pt
+ segment/
+ mask.mkv
+ mask_valid.pt
+ instances.jsonl
+ contact/
+ relations.jsonl
+ __/ # 对应实体间的距离/接触区域
+```
+
+目录展示可能存在的组件，不要求填满。具体文件、字段和缺省规则分别见后续对应小节。原始包的解压发生在临时工作区，raw 内原始包保持不变。
+
+## 2. 相机视图的 metainfo.json
+
+### 2.1 顶层字段与发布信息
+
+每个相机视图必须有 `metainfo.json`。以下字段除另行说明外均为必填：
+
+| 字段 | 填写内容 |
+|---|---|
+| schema_version | 结构版本，本版为 `hand-warehouse/1.0` |
+| dataset_id | 数据集的逻辑标识 |
+| release_id | 本次数据发布版本，对应目录中的 dataset_version |
+| sequence_id | 所属序列或独立图片样本的标识 |
+| camera_id | 当前相机视图标识 |
+| sample_type | `image` 或 `video`，规则见 2.2 |
+| num_frames | 当前视图帧数 T，所有随帧组件按同一时间轴组织 |
+| hand_slots | 固定为 `["left", "right"]`；解剖学左右的使用规则见 5.1 |
+| sampling | 采样类型、源 FPS、帧索引引用及选择策略，见 2.2 |
+| camera | 当前相机模型与图像尺寸，见 2.3 |
+| components | 实际提供的组件及其文件、schema、有效性和来源，见 2.5 |
+| split | `train/val/test/unknown`，划分协议见 2.4 |
+| view_type | 可选，`ego/exo/unknown` |
+
+`dataset_version` 是数据发布版本；`schema_version` 是结构版本，两者独立。原始包校验摘要、官方来源和转换配置应记录在发布清单中。解压发生在临时工作区，原始包保持不变。
+
+### 2.2 图片、视频与 sampling
+
+`sample_type=image`：允许单图 T=1、`sampling.type=single`，也允许将独立图片批量装入一个视频容器，T>1 时 `sampling.type=independent`、`source_fps=null`。分片大小按数据集选择；FreiHAND 默认每片最多 4096 张，训练与评估分开，尾片不补帧。该数量不是其他数据集的强制要求；同批分辨率、相机模型类型及划分协议一致。每行 frames 必须有稳定且唯一的 `sample_id`、原始文件定位；内参、手形状等仍逐帧保留，不能因打包而共享错误的标定或身份。`sequence_id` 在这里仅是批次标识，不代表真实序列；不得按相邻帧推断跟踪、同步或接触延续。源 camera_id、image_variant、split 等随样本变化时放在 frames 的对应字段中。真实采集时间未知则 timestamp_ns=null。合成背景变体不视为真实多相机。
+
+`sample_type=video`：`sampling.type=dense/sparse`；`source_fps` 是真实采集 FPS，未知为 null。非连续取样必须保留源帧号和已知源时间，不可用采样后索引除以 FPS 冒充源时间。可另记采样策略与步长。
+
+shared 可以仅存所选 RGB 帧对应的数据。OakInk2 图像/动捕不同采样率时用 annotation_matches 明确映射；不要求将未采样动捕点硬塞进 T。多视角身份和源同步关系要保留；选择了哪些帧/相机及原因写在 sampling 中。
+
+`sampling.frames_ref` 指向 `camera/frames.jsonl`。帧号、逐帧时间、同步与标注匹配记录的结构见 4.1；MKV 封装时间的约定见 3.1。
+
+### 2.3 camera 模型声明
+
+`camera` 至少包含 `model`、`schema_id`、`image_size:[W,H]`。模型未知使用 `unknown` 和 `null`，不猜成针孔。实际内参、畸变及外参数组放在 `camera/`，各模型的固定结构见第 4 节。
+
+### 2.4 split 与划分协议
+
+保留上游划分，不将同一采集序列的相邻帧随意拆到不同集合。
+
+有官方划分时必填 split_scheme 和 split_source_locator，split_scheme 区分如 Dex-YCB s0/s1、ARCTIC P1/P2 等选定协议；不知道时 split=unknown 并给理由，不凭空分配。每发布可选择一种协议；存在逐帧差异时 frames 中的 split 覆盖视图默认值，并登记其来源。
+
+### 2.5 components 组件登记
+
+组件表的键是本发布内唯一名称，例如 `rgb`、`depth`、`mano`、`keypoints_uv`。每个已提供组件声明以下信息；模态特有参数在第 3–5 节对应小节中定义。
+
+| 字段 | 约定 |
+|---|---|
+| available | 本发布是否提供该组件；缺省也表示未提供 |
+| schema_id | 已登记的具体存储与语义类型，不得临时创造未登记名称 |
+| data_ref | 数据文件路径，相对当前相机视图根目录，使用 POSIX 路径 |
+| axes | 数组各维度的含义 |
+| valid_ref / valid_axes | 有效性文件及其维度；语义由所属组件规定 |
+| storage / valid_storage | 视频数据／视频有效性的存储配置；包含已登记 profile、实际 codec、pixel_format、encoded_size，见 3.1；非视频组件按原数组规则 |
+| provenance | source / format_conversion / derived，以及转换或生成方法 |
+| source_locator | 原文件、压缩包成员和记录／帧的定位信息 |
+
+不存在的组件允许缺省或写作 `{ "available": false, "reason": "..." }`，不用引用不存在的文件。静态网格可声明标量有效性；具体见 5.4。观测的 valid 见第 3 节，标注的 valid 与 visibility 区分见 5.1。
+
+`provenance.kind` 使用 source/format_conversion/derived，`method` 说明转换或生成方法；发布清单补充脚本版本、参数、输入摘要。`source_locator` 定位到原始文件/压缩包成员和记录、帧号；允许引用有完整溯源清单的前一发布，但链条最终应能回到 raw。不得仅写一个无法定位记录的数据集名字。
+
+`.pt` 仅保存 CPU Tensor，不保存 Python 自定义对象；浮点默认 float32，有效性为 bool，浮点无效位置填 NaN。所有随帧数组保留首维 T，单帧也不 squeeze；静态资产按其 schema 单独声明。单位、坐标及固定形状见各组件小节。
+
+机器注册表 `schema.json` 中，`components` 固定解码后的数据类型、形状、单位和语义；`storage_profiles` 固定视频内的编码／还原方式。不得临时创造未登记的 schema_id 或 storage.profile。同一语义允许选择其登记的不同无损存储 profile。
+
+## 3. observation 观测数据
+
+### 3.1 rgb.mkv 与 rgb_valid.pt
+
+RGB、深度和栅格 mask 在 shared 中统一使用 MKV 视频容器，不展开为逐帧图片或图像 tar。MANO、关键点、位姿等数值标注仍用数组；分割的重叠多边形仍用 JSONL，不为了视频化而有损栅格化。单图可为一帧视频，独立图批量封装规则见 2.2。
+
+**无损指共享层不增加信息损失。** 源 JPEG／H.264 等本身可能已是有损编码，原样保留并不恢复已丢失信息；不得重新以有损 JPEG、H.264、H.265、AV1 参数编码。源视频／图片的色彩、位深、分辨率和几何关系不得被静默改变。RGB schema `rgb/uint8/v1` 的统一读取结果是 `[T,H,W,3] uint8`、RGB 通道，有效性仍为 `rgb_valid.pt [T] bool`。超出该 schema 的高位深或 HDR 源必须报告并扩展定义，不能降到 8 位冒充无损。
+
+#### 3.1.1 允许的 RGB 存储 profile
+
+| storage.profile | 编码方式 | 适用规则 |
+|---|---|---|
+| mkv/mjpeg_copy/v1 | 源 JPEG 压缩帧直接封装成 MJPEG/MKV | 不解码重编码；保持每张 JPEG 码流字节；普通 8 位 JPEG 且 MKV/解码器支持时使用 |
+| mkv/source_copy/v1 | 已有视频的 RGB 对应码流直接封装为 MKV | 仅源编码能被 MKV 和选定解码器支持时使用，不伪造帧；需维持正确的呈现顺序与依赖帧 |
+| mkv/h264_rgb_lossless/v1 | libx264rgb 的真正无损模式 | 必须 RGB 路径、CRF=0，并通过全帧 RGB 字节比较；不能替换成默认 libx264 YUV420 路径 |
+| mkv/ffv1_rgb/v1 | FFV1 的 RGB 可逆编码路径 | 作为通用候选；解码回 RGB 后必须逐字节相同，不做色度降采样 |
+
+优先尝试源码流直接封装；不能封装或需要选择稀疏帧时，比较 FFV1 与无损 RGB H.264。对完整待发布片段，只在通过无损验收的候选中选择文件较小者，不假设某个编码器总最小；编码时间和随机读取代价记入构建记录。中间候选不作为 shared 多份常驻。H.264 跨帧压缩允许较长 GOP，必须记录 GOP／关键帧间隔，随机取帧需从前一关键帧正确解码，不能把 seek 返回的近似帧当目标帧。
+
+普通源视频可以保留它已有的有损码流，但不允许新增有损重编码。源是 VRS 等记录容器时，先查明图像流实际编码：已有兼容 JPEG／视频包可直接提取封装，否则解码后使用真正无损编码；不把整个 VRS 改后缀当 MKV。PNG 不要求直接塞进 MKV，可选上表两种无损重编码方式。
+
+#### 3.1.2 视频配置与校验
+
+每个视频组件在 metainfo 中登记 `storage`，至少包含：
+
+| 字段 | 内容 |
+|---|---|
+| profile | 上述登记的存储 profile；语义仍由组件 schema_id 指定 |
+| codec | 文件中实际 codec，例如 mjpeg、h264、ffv1；source_copy 必须写实际 codec |
+| pixel_format | 实际编码像素格式；不得仅凭编码器参数猜测 |
+| encoded_size | 视频物理像素尺寸 `[width,height]`；深度可能不同于逻辑尺寸，见 3.2 |
+| decoder | 解码器名称、版本，以及颜色矩阵／范围／旋转处理等实际像素解释配置；未知的源色彩声明 unknown，不擅自称为 sRGB |
+| encoder_parameters | 重编码时的编码器、版本、无损模式和 GOP 等参数；直接封装时记录 stream_copy |
+| source_lossy | 源是否已有损：true / false / unknown；不是共享层再有损编码的许可 |
+
+无损重新编码的 RGB 必须使用同一固定解码配置，比较**全部帧**的统一 RGB 字节、数量及顺序。MJPEG-copy 不重新编码，生产校验采用 `jpeg-packets-all-rgb-sample16/v1`：从 MKV 逐包取回，对全部源 JPEG 的 SHA256、数量、顺序和图像尺寸进行精确核对；每视图另用同一固定解码配置比较最多 16 帧的源与目标 RGB 字节（含首尾与四分位位置，其余由固定种子抽取）。记录策略、实际索引、种子与结果；不足 16 帧时全查。试点与最终完整序列随机抽查，对选中视图的全部帧复核解码 RGB。历史已做全帧像素检查的证明继续保留。
+
+逐包一致与像素抽查的范围必须分别声明，不能将抽查写成全量像素检查。不能用 PSNR 高、视觉相似、编码器参数称为 lossless 等替代精确检查。同时保留源编码的色彩标记和必要元数据；原图有 EXIF/色彩/方向等元数据时保留 raw 和必要来源信息；不能静默自动旋转图像而不更新像素对应与标定。以上加速策略仅适用于原 JPEG 码流直接封装，不适用于深度、标签或重新编码的 RGB。
+
+所有共享视频的呈现帧顺序与统一 frame_index 一一对应，每个栅格组件恰好 T 帧。MKV 的 `storage_fps` 只服务封装，声明 `timebase_role=index_only`；source_copy 可以保留源 PTS 以避免改坏码流，但真实时间与标注对齐仍以 frames 为准。未知真实时间保留 null。缺失某帧组件时保留索引位置并使用该组件的无效占位／valid 标识，不删帧；不能以重复相邻图像冒充已知观测。
+
+### 3.2 depth.mkv 与 depth_valid.mkv
+
+深度语义仍是 `depth/z_float32/v1`：统一读取结果 `[T,H,W] float32`，米制相机 Z 分量，`depth_type=z`。有效深度有限且大于 0，无效位置为 NaN。`depth_origin=measured/rendered/unknown` 必填，来源不明时 unknown。默认与 RGB 共用像素网格及相机参考系，不处理未注册 RGB-D。射线距离必须依据源定义转换成 z，不能只改声明。
+
+#### 3.2.1 float32 的可逆视频编码
+
+采用 `mkv/depth_float32_bits/v1`：**不是把深度量化成 uint16**，而是将规范化后每个 IEEE-754 float32 的完整 32 位拆成两个 uint16，使用 FFV1 gray16le 保存。视频物理尺寸为 `[2W,H]`：每行前 W 列存原 float32 位串的低 16 位，后 W 列存高 16 位。读取时先合并为 uint32 位串，再按 float32 重新解释，不能数值 cast 成 float32。
+
+```text
+u = reinterpret_uint32(depth_float32)
+encoded[:, :, :W] = uint16(u & 65535)
+encoded[:, :, W:] = uint16(u >> 16)
+u_restored = uint32(low) | (uint32(high) << 16)
+depth_float32 = reinterpret_float32(u_restored)
+```
+
+统一采用 little-endian 原始字节接口。`camera.image_size` 始终是逻辑 RGB 尺寸 `[W,H]`，不能改成编码视频的 2W；`storage.encoded_size=[2W,H]`。包括 NaN 在内的 float32 位串必须全帧完全恢复，不允许改成 float16、整数毫米、归一化灰度或 RGB 伪彩色。这里的无损验收基准是完成既定米制／坐标转换后的标准 float32 数组，不声称消除 float64→float32 等原约定转换本身的舍入。
+
+#### 3.2.2 逐像素有效性
+
+`depth_valid.mkv` 使用 `mkv/bool_ffv1/v1`，FFV1 gray8（FFmpeg pixel_format=`gray`），物理尺寸 `[W,H]`。像素只能为 0 或 1，统一解码为 `[T,H,W] bool`，逐像素含义不变。这样不再把庞大的 bool 数组保存为未压缩 `.pt`；标注的帧级／手级 valid 仍保留原有小数组。
+
+登记示例（W、H 以下取 640、480；decoder/encoder_parameters 的实际版本由转换器填写）：
+
+```json
+{
+ "available": true,
+ "schema_id": "depth/z_float32/v1",
+ "data_ref": "observation/depth.mkv",
+ "axes": ["T", "height", "width"],
+ "valid_ref": "observation/depth_valid.mkv",
+ "valid_axes": ["T", "height", "width"],
+ "unit": "m",
+ "depth_type": "z",
+ "depth_origin": "measured",
+ "invalid_value": "NaN",
+ "storage": {
+ "profile": "mkv/depth_float32_bits/v1",
+ "codec": "ffv1",
+ "pixel_format": "gray16le",
+ "encoded_size": [1280, 480],
+ "source_lossy": false,
+ "decoder": {"name": "ffmpeg", "version": "填写实际版本"},
+ "encoder_parameters": {"name": "ffv1", "level": 3, "version": "填写实际版本"}
+ },
+ "valid_storage": {
+ "profile": "mkv/bool_ffv1/v1",
+ "codec": "ffv1",
+ "pixel_format": "gray",
+ "encoded_size": [640, 480],
+ "source_lossy": false,
+ "decoder": {"name": "ffmpeg", "version": "填写实际版本"},
+ "encoder_parameters": {"name": "ffv1", "version": "填写实际版本"}
+ },
+ "provenance": {"kind": "format_conversion", "method": "源深度转米制 z float32；逐位打包为 FFV1"},
+ "source_locator": {"file": "raw/data/depth.zip", "member": "depth/0001.png"}
+}
+```
+
+深度和有效性视频都必须逐帧解码验收：float32 位串相同、bool 相同、T 相同，并检查 valid=true 的正有限值和 valid=false 的 NaN。读取器必须按 profile 还原，不能把 depth.mkv 的编码像素直接当深度值。
+
+## 4. camera 相机、帧索引与标定
+
+### 4.1 frames.jsonl：索引、时间与对应关系
+
+`camera/frames.jsonl` 每行对应统一帧号 `frame_index=0..T-1`，是所有组件的唯一行索引。至少包含 `frame_index`、`timestamp_ns`、`clock_domain`、`source_locator`；原始帧号/文件名保存在 `source_frame_id`，不能被新帧号替代。每行还可有 `annotation_matches`，按组件键保存 `source_frame_id`、`source_timestamp_ns`、`clock_domain`、`match_method` 和 `delta_ns`；方法为 exact/index_map/nearest/interpolated，索引映射无时间依据时 delta_ns=null。nearest 必须记录允许误差；interpolated 必须记录两端源标识和权重。delta_ns 仅在同一时间域中计算为源标注时间减图像时间。多相机共享 `sequence_id`；已知同步关系必须用 `sync_id` 或明确的源帧/时间映射表达，不能仅凭相同数组索引认定同步。
+
+每个视图必须提供此文件。源时间使用整数纳秒及 `clock_domain`，未知写 JSON `null`；不同时间域不能直接比较。逐帧 split 可以覆盖 metainfo 的视图默认划分，并保留来源，见 2.4。
+
+### 4.2 坐标系与标定通则
+
+| 项目 | 统一约定 |
+|---|---|
+| 相机坐标 | 右手系，X 向右、Y 向下、Z 向前，长度单位米 |
+| 齐次变换 | 列向量，`p_target = T_target_from_source @ p_source` |
+| 图像坐标 | `(u,v)`；左上像素中心为 `(0,0)`，u 向右、v 向下，单位 pixel |
+| 固定标定 | 本版展开为 T 份，同一 schema 不混用静态与动态形状 |
+| 分辨率或模型变化 | 分开存储为视图／片段，不在同一组件中混装 |
+| 未知标定 | 相机内外参允许缺省，不填单位阵冒充已知标定 |
+
+### 4.3 intrinsic.pt 与畸变参数
+
+| model / schema_id | intrinsic.pt 的固定结构 |
+|---|---|
+| pinhole / camera/pinhole/v1 | `[T,3,3]`，标准 K，最后一行为 `[0,0,1]`；限无畸变或已校正图像 |
+| fisheye624 / camera/fisheye624/v1 | `[T,15]`，`[f,cx,cy,k0,k1,k2,k3,k4,k5,p0,p1,s0,s1,s2,s3]`，采用 Project Aria Fisheye624 投影定义 |
+| opencv_fisheye4 / camera/opencv_fisheye4/v1 | `intrinsic.pt [T,3,3]` 保存 K；同组件 `distortion_ref` 指向 `[T,4]`，顺序 `[k1,k2,k3,k4]`，使用 OpenCV fisheye 等距角度多项式；已由 ReInterHand 实际原始相机参数验证 |
+| rational8 / camera/rational8/v1 | `intrinsic.pt [T,3,3]` 保存 K；同组件必需 `distortion_ref` 指向 `[T,8]`，顺序 `[k1,k2,p1,p2,k3,k4,k5,k6]` |
+| unknown / null | 不提供可用 intrinsic，不声称可以投影 |
+
+有畸变的其他模型必须注册新 schema，固定参数名、顺序和投影公式；不能塞进针孔 K 后遗失畸变项。`metainfo.camera.schema_id` 必须与 intrinsic 组件一致。同一模型的别名不可隐式改变参数布局。
+
+`opencv_fisheye4`：对前方点令 `x=X/Z, y=Y/Z, r=sqrt(x²+y²), theta=atan(r)`，`theta_d=theta*(1+k1*theta²+k2*theta⁴+k3*theta⁶+k4*theta⁸)`；`u=fx*x*theta_d/r+cx, v=fy*y*theta_d/r+cy`。光轴 r=0 时使用连续极限 `theta_d/r=1`。四系数不能按普通径向畸变解释，也不能补零冒充 Fisheye624。
+
+rational8 对归一化坐标 x=X/Z、y=Y/Z，令 r²=x²+y²，径向项为 `(1+k1*r²+k2*r⁴+k3*r⁶)/(1+k4*r²+k5*r⁴+k6*r⁶)`；再加 p1/p2 切向项并经 K 得到像素。`distortion_ref` 与 intrinsic 共用 `[T]` valid，保存到 camera/distortion.pt。不得把这种畸变三维坐标当作真实相机系 xyz 保存。
+
+内参有效性为 `intrinsic_valid.pt [T] bool`；有畸变时使用对应 `distortion_ref`。同一模型的参数名和顺序固定。
+
+### 4.4 extrinsic.pt 与 extrinsic_valid.pt
+
+`extrinsic.pt` 为 `[T,4,4]`，schema `transform/se3/v1`，方向固定 `T_camera_from_world`，有效性 `[T]`。世界坐标含义及源坐标转换记录在 provenance 中；只有相机坐标标注不要求虚构世界坐标。旋转块须正交且 determinant≈1。
+
+## 5. annotation 标注数据
+
+### 5.1 标注通则：手槽、valid 与 visibility
+
+手槽固定为 0=left、1=right，指解剖学左右，不按画面位置或可见性重排。本阶段只处理左右手两槽，不处理更多手。实体引用使用 `hand_left`、`hand_right`；手槽不证明跨采集片段的个人身份。
+
+#### 5.1.1 valid：标注是否可用于监督
+
+标注的 `valid=true` 表示：有对应实际标注、通过本 schema 数值检查，且通过来源明确提供并适用于该组件的有效性/QA 条件。`valid=false` 可由缺标注、无法建立帧对应、源无效占位或官方 QA 不通过导致；不等于画面没有手或手不可见。各组件独立判断，MANO 按 `[T,2]` 标记左右手，关键点按 `[T,2,K]` 标记。
+
+**2026-09-20 将标注 valid 从“仅表示存在”修订为“可用于监督”，字段形状不变。** 公式为 `source_annotation_present AND numerical_checks_pass AND applicable_official_QA_pass`。没有官方 QA 时省略最后一项，不伪造 QA；不能把未知当成已通过已存在的 QA。适配器在组件 `valid_policy` 中记录具体规则、来源、聚合/广播作用域。缺失官方必需 QA 的记录须明确处理为未知或暂停验收，不能静默通过。无效浮点项仍填 NaN，原始数据保留在 raw；被排除原值可用于 doc 中明确标识的诊断渲染，不能冒充 shared 有效监督。
+
+本地筛选须依据明确的规则，并在 `valid_policy.local_excluded_frame_indices`、排除原因和正式报告中记录。单个可疑投影作为问题记录，不直接硬编码排除该帧。已确认的曝光异常须明确作用范围，不能把一个序列的“前两帧有问题”套到所有序列。RGB 编码完好与几何监督可用是两件事，允许保留原图及帧索引而屏蔽对应标注；未发现问题不等于完成全部人工审核，试点待确认时不能作为已验收发布使用。
+
+源字段必须按官方语义映射，不能直接按名字复制：
+
+| 来源 | 官方含义 | 映射原则 |
+|---|---|---|
+| [H2O](https://github.com/taeinkwon/h2odataset) 的手标注首位 | 0 未标注、1 已标注 | 可作为对应手标注存在性的依据，不推断未标注原因 |
+| [HOT3D](https://github.com/facebookresearch/hot3d#using-mask-files) 的 pose_available、visible、mask_qa_pass | 分别表达姿态存在、可见性、对画面中可见手物位姿的人工 QA | 逐手/物体记录决定存在性，再 AND 当前相机当前帧 QA；QA 是整帧聚合判定，保守排除该帧全部手物几何监督，不声称每个实体均有错误；源可见性基于位姿推导时，其自身 valid 同样受 QA 约束 |
+| [Dex-YCB](https://github.com/NVlabs/dex-ycb-toolkit#loading-dataset-and-visualizing-samples) 的全零 pose_m、全 -1 joints | 不可见的手或缺标注共用占位表示 | 对应组件 valid=false；不能从该值判定 visibility=false，也不能声称原因是动捕缺失 |
+| [ARCTIC](https://github.com/zc-alexfan/arctic/blob/master/docs/data/data_doc.md) 的 is_valid/left_valid/right_valid | 是否有效用于官方评估 | 核实每项筛选依据；不把可见性/评估选择直接等同 QA。本次原始 mano.npy 不含这些派生评估标记，按原记录与数值检查生成 valid，并声明没有额外官方 QA 输入 |
+
+源 valid=false 若代表评估筛选或其他条件，应检查实际标注和源约定后映射；不能一律清空坐标。反之，占位零或 -1 只有在源官方明确规定时才判为缺失，不泛化成所有数据的判断规则。
+
+`valid=true` 的标注应通过其 schema 的数值与形状检查；检查失败必须报告或修复，不能静默隐藏。QA 不通过应报告排除数量与原因，并保留可追溯信息；不能把这类无效称为缺标注。RGB、深度等观测有效性仍按各自 schema；手物 QA 不自动否定 RGB、静态相机内参或物体资产。相机外参是否过滤须依据其自身来源规则；HOT3D 本次保留源外参用于溯源和诊断，不能用其存在性证明 QA 失败帧的几何监督正确。
+
+#### 5.1.2 visibility：可见性属性
+
+附加质量属性只保留 `visibility`，存放在对应模态或标注集内，不设 quality 目录。同一份可见性仅存一次；uv 与 xyz 共用时引用同一个文件。
+
+visibility 通过所属组件 attributes 登记 data_ref、axes、dtype、visibility_type、meaning、provenance、source_locator；未知值通过属性 valid_ref 标识，全已知可声明 valid=true。比例与布尔值不相互强转，也不按任意阈值二值化。没有 MANO 时，整手可见性挂在 keypoints 组件，使用独立的 whole_hand_visibility.pt，明确 meaning=whole_hand，不与逐关键点可见性混用。
+
+`valid` 回答“这项标注能否用于监督”，`visibility` 回答“在当前图像中是否可见（或可见多少）”。不可见的手或关键点仍可有有效标注；不能因 visibility=0 就清空坐标。源没有 visibility 时缺省或标为未知，不由 valid 推断。
+
+| 情况 | 手标注 valid | visibility |
+|---|---|---|
+| RGB 已开始录制，动捕尚未开始，没有其他对应手标注 | false | 以独立源可见性标注为准；没有则未知 |
+| 手被遮挡，但源有标注且通过适用有效性检查 | true | 若源声明不可见，则 false 或源可见比例 |
+| 手可见且有对应标注，通过适用 QA | true | 若源有此信息，则 true 或源可见比例 |
+| 手可见，但该帧漏标或动捕中断 | false | 可以为 true，但必须有独立来源依据 |
+| 有手部参数，但官方 QA 明确不通过 | false | 不据此断言不可见；源可见比例若基于不可靠位姿，其属性 valid 也为 false |
+
+visibility 自己的 valid_ref 表示该可见性标注是否可用，不复用 MANO 或关键点坐标的 valid_ref；明确记录源存在性及适用的 QA 条件。上述例子不要求凭图像自动生成可见性。
+
+不设 quality 组件或目录，不另存拟合误差、曝光、截断比例等通用质量数组。适用的官方 QA 合并进对应 valid，在 `valid_policy` 记录来源与规则；正式校验报告保留原始 QA 时间区间及排除统计。原始 QA 文件仍在 raw，官方 split 保留。
+
+### 5.2 mano：参数与整手可见性
+
+MANO schema `mano/axis_angle61/v1`：`motion.pt [T,2,61]`，顺序为完整 16 关节 axis-angle 48、shape betas 10、全局平移 3。姿态使用弧度，平移米，位于当前相机系。有效性 `[T,2]`；缺少一侧整槽 NaN。PCA 必须先按对应模型展开，不能将 PCA 系数直接填入 axis-angle。
+
+`model_profile` 必须固定 MANO 版本、`flat_hand_mean=true`、左手 shapedirs 修正状态。平移表示模型输出顶点所加的全局平移，不是某个腕关节的坐标；源语义不同须转换。shape 无量纲。模型权重不作为共享数据附带上传。
+
+`mano/visibility.pt [T,2]` 保存源整手可见比例时使用 float32 [0,1]，`visibility_type=ratio`；只有实际采用 0–100 的百分比才除以100，不能只凭表头判断。HOT3D 的 `visibility_ratio[%]` 实值已经是0–1，官方读取器原样读取，必须保留原值。通过 MANO 组件 attributes 登记，使用独立有效性，不复用 motion_valid。
+
+手部共享表示仅保留 MANO 参数与选定关键点，不发布独立手表面几何。需要渲染或接触计算时由 MANO 参数生成，几何引用使用固定 MANO 模型配置与顶点顺序；不影响 object 下的物体模型和材质资产。
+
+### 5.3 keypoints：二维点、三维点与逐点可见性
+
+关键点独立于 MANO 参数存在；没有 motion 也可有关键点。`uv.pt [T,2,K,2]` 与 `xyz.pt [T,2,K,3]`，有效性 `[T,2,K]`，分别采用 `keypoints/uv/v1` 和 `keypoints/xyz/v1`。K 由 `keypoint_schema` 固定，不能在同一数组中逐帧变化；同体系统一顺序。数值使用像素坐标或米制相机系。
+
+| keypoint_schema | K 与固定索引 |
+|---|---|
+| mano21/v1 | 21；wrist 后依次 thumb/index/middle/ring/little，每指由近端至指尖 |
+| rhd21/v1 | 21；保持 wrist，然后五指由近端至指尖；原始每指 tip→palm 反序。只统一顺序，不宣称解剖学位置等价于 MANO |
+
+具体名称与源索引映射见 `schema.json`。当前只登记 MANO21 与 RHD21；不登记 MediaPipe、ReInterHand capture21 或 fit24 中间关键点。
+
+每个发布中的每个视图只提供一套标准手关键点，直接放在 keypoints 下，不设 annotation_set_id 字段或目录。uv 与 xyz 是这套关键点的二维与三维表示，不是两套标注。provenance 分别记录二者来自原始标注还是计算结果；不要求已有 2D 必须由当前 xyz 重新投影覆盖。
+
+ReInterHand 的 Original keypoints、Original fits keypoints 及带前臂关键点不纳入 shared；如需手关键点，从选定 MANO 参数生成 MANO21 并记录模型/指尖回归约定。其他数据集默认使用选定的官方关键点，不额外生成平行的 MANO 派生集合。后续主动更换生成方法或结果时发布新的 dataset_version，不在同一版本内增设方法分支。
+
+manual/projection/fitting 只记录在 provenance，不作为目录或类型分支。不得通过坐标为零、是否出画或 visibility 判断标注是否存在。派生标注仅在实际生成并通过数值检查后声明 valid=true；没有生成的项为 false，不伪造坐标。
+
+`keypoints/visibility.pt [T,2,K]` 保存源逐点可见性，布尔表示使用 `visibility_type=boolean`。uv 与 xyz 共用时引用同一份文件。整手比例属性没有 MANO 可挂载时使用 `whole_hand_visibility.pt [T,2]`，`meaning=whole_hand`；它与逐点可见性分开，通用登记规则见 5.1。
+
+不使用 annotation_set_id；ReInterHand 中间关键点集合不进入 shared，raw 保留来源文件。
+
+### 5.4 object：身份、资产、位姿与几何
+
+#### 5.4.1 metainfo.json：物体身份与跟踪作用域
+
+手用 `hand_left`、`hand_right` 及槽位 0、1 表示，不再另存 entities.json。物体身份登记在 `annotation/object//metainfo.json`，必填 `object_id`、`identity_scope`、`scope_id`、`source_locator`；可含 `category_id`、`source_object_id`、`mesh_ref`、`has_model`、部件引用。即使只有分割、没有 pose/mesh，也可只有这份物体元信息。
+
+`identity_scope` 为 frame/subsequence/sequence，`scope_id` 指定具体作用域；跨相机共享身份只在已有源对应证据时使用。VISOR 按官方 subsequence 与实体名关联时记录 `association_basis=official_subsequence_entity_name`，不能声称这是全视频物理实例跟踪。无需与 object_id 一一对应的标准 track_id；官方独立轨迹标识存在时保留为 source_track_id，并声明作用域。手槽不证明跨采集片段的个人身份。
+
+#### 5.4.2 assets：网格、材质与纹理
+
+资产属于物体，统一存放在 `annotation/object//` 内，可用 `assets/` 子目录组织网格、材质、纹理和部件资产。序列层不设共享 assets。所有引用采用视图根目录为基准；其中物体资产组件的 `data_ref` 与部件的 `mesh_ref` 采用相对于当前视图目录的 POSIX 路径，例如 `annotation/object/cup_01/assets/cup.obj`，且必须指向所属物体目录内的资产。OBJ 内部的材质、纹理引用遵循文件格式自身的相对路径规则，依赖文件一并保留。不同相机视图允许各自保存物体资产，不要求跨视图去重；下载物体时包含其资产依赖。
+
+网格 schema 为 `mesh/obj/v1`，顶点米制，静态有效性可直接声明标量 `valid`，或通过 `valid_ref` 指向标量 bool Tensor；不按 T 重复静态资产。由物体／部件通过引用关联，不仅凭文件名猜测。
+
+#### 5.4.3 pose.pt、pose_valid.pt 与 visibility.pt
+
+刚体 schema 为 `object/rigid/v1`，`pose.pt [T,4,4]` 固定为 `T_camera_from_object`，平移米制，有效性 `[T]`。物体坐标原点、轴向必须与 mesh 一致；旋转块为正交矩阵且 determinant≈1。
+
+`object//visibility.pt [T]` 保存源物体可见比例，float32 [0,1]，`visibility_type=ratio`。通过所属物体组件 attributes 登记，独立表达其有效性。
+
+#### 5.4.4 articulation.json、部件位姿与 joint_state.pt
+
+关节物体：schema `object/articulated/v1`，`articulation.json` 登记 `parts` 与 `joints`。每个 part 有 `part_id`、`parent_id`（根为 null）、`has_model`、`mesh_ref`；has_model=false 时 mesh_ref=null，结构节点允许没有网格。父链不得成环。每个 joint 有唯一 `joint_id`、`parent_part`、`child_part`、`type`；fixed 单位 null、revolute 弧度、prismatic 米。有源限位时保存 `limits:[lower,upper]`；没有时 null，不伪造上下限。部件姿态统一为 `T_camera_from_part [T,4,4]`，不与父局部变换混装。
+
+若提供 `joint_state.pt [T,J]`，必须登记固定 joint_order、轴在父部件系的表达、零位变换与正方向，valid 为 `[T,J]`；缺少这些运动学定义时只发布部件位姿与已有来源信息，不伪造可驱动的关节模型。关节值与部件位姿同时存在时需验证一致性。
+
+部件层级应声明语义：纯语义包含树允许 `joints=[]`，不将包含关系推断为机械关节。关节状态 schema 为 `object/joint_state/v1`；转动用弧度、移动用米，混合类型按 joint_order 对应的关节定义解释。
+
+#### 5.4.5 dimensions、几何关键点与顶点部件标签
+
+`dimensions.pt [T,3]` 采用 `object/dimensions/v1`，有效性为 `[T]`。
+
+物体/部件 `dimensions` 为 `[length,width,height]` 米，轴顺序与该 mesh 的局部坐标关联，登记 `dimensions_semantics` 和来源。HOI4D dimensions 与 mesh 的具体尺度关系未验证前标为 source_unspecified，保留源值并暂停依赖该关系的几何验收，不能默认为待施加缩放。需要施加缩放时明确 scale_applied_to_mesh，禁止重复应用。
+
+源物体关键点放 `object//keypoints/xyz.pt [T,K,3]`（相机系）或 `template_xyz.pt [K,3]`（物体系），schema 分别为 object_keypoints/camera/v1 和 object_keypoints/template/v1。明确 keypoint_schema、keypoint_names、单位及有效性；模板点配合 pose 可投影，无须重复生成全部帧。H2O 中心/角点/棱中点，以及 ARCTIC 部件点分别登记具体源顺序，不套用手的 21 点 schema。每个发布选择一套源几何点并记录选择，不另建方法分支。
+
+`vertex_part_ids.pt [V] int64` 绑定 `mesh_ref` 与 topology_id，登记整数到 part_id 的映射；未知用 -1。ARCTIC 不同拓扑的资产分别绑定，不能共用未经转换的顶点标签。部件层级中 is_instance/has_model 等源属性保留，几何文件仍全部归 object。
+
+### 5.5 segment：分割类别、编码与覆盖范围
+
+每个发布选择一套任务使用的分割表示，不建立方法／标注集分支。没有分割就不创建 segment 目录，不为其他模态创建空的实体表。
+
+| schema_id | 文件与数值语义 |
+|---|---|
+| segment/uint16_labels/v1 | mask.mkv；整数区域编号，通过 instances.jsonl 解释 |
+| segment/instance_polygons/v1 | polygons.jsonl；实例多边形，可重叠；当前不支持孔洞拓扑 |
+| segment/foreground_intensity/v1 | mask.mkv；0–255 前景强度，不是区域编号或经过校准的概率 |
+
+#### 5.5.1 uint16 标签图与 instances.jsonl
+
+组件声明 `segmentation_type=semantic/instance/part`；同一组件固定一种类型。`mask.mkv` 解码为 `[T,H,W] uint16`，schema `segment/uint16_labels/v1`，使用 `mkv/uint16_ffv1/v1`，FFV1 gray16le 无损；只有所有区域编码均 ≤255 时允许 gray8（pixel_format=gray），读取后转 uint16，数值不变。禁止调色板和有损编码。有效性 `[T]`，表示该帧有可用分割标注。数值 0 固定为 unassigned，65535 固定为 ignore，1–65534 为区域编码，不能直接等同 entity_id。
+
+`instances.jsonl` 每帧一行，包含 `frame_index` 与 `labels`。每条标签包含 `mask_value`、`category_id`、`entity_id`、`part_id`；无意义或未知字段为 null。semantic 允许 entity_id=null；instance 必须引用 hand_left/hand_right 或登记的 object_id；part 还须关联登记部件。源实例 ID 和其他属性分别保存在 `source_instance_id`、`source_attributes`。mask 中每个非保留编码必须有且仅有一个映射。
+
+FreiHAND 的试点按本任务需要，统一到手／背景类别，保留每个原生区域编码；不要求给原生细分区域猜测手指名称。需要细部件监督时，尚未核实的部件名称必须保持未知，不能因粗粒度手类别已核实便声称部件语义也已统一。
+
+#### 5.5.2 重叠实例多边形
+
+每发布选择一套任务使用的分割表示，不建立方法/标注集分支。VISOR 的实际试点存在重叠实例多边形，单层标签图不能无损表达。对此补充 `segment/instance_polygons/v1`：`segment/polygons.jsonl` 每帧包含 `frame_index` 和 `instances`，每实例包含 `mask_value` 与 `polygons`（列表，每个多边形为源图像像素坐标顶点序列，保留原始精度）。身份和类别仍由同一 `instances.jsonl` 映射，有效性仍为 `[T]`，通过 schema 区分物理编码。允许多实例重叠，不采用后画覆盖前画。消费者按需栅格化为独立实例二值 mask；不能声称合并的单层 mask 无损。
+
+#### 5.5.3 灰度前景强度
+
+ReInterHand 的官方渲染 mask 为双手合并的灰度前景掩码，实测存在 0–255 的中间值，不是左右手实例编号。登记 `segment/foreground_intensity/v1`：仍存 `segment/mask.mkv`，解码为 `[T,H,W] uint16`，使用 `mkv/uint16_ffv1/v1`，此类数据优先编码为 FFV1 gray8（pixel_format=gray），读取后转 uint16。有效值范围 0–255，归一化值为 `float32(value)/255`；0 为背景端点、255 为前景端点。组件声明 `foreground_category=hand`、`entity_id=null` 和双手合并语义，不使用区域编号映射。这里保留原始渲染 mask 强度，不把它称为经过校准的概率。若任务需要二值 mask，阈值化属于任务层派生操作，记录阈值；共享层不丢弃中间灰度。不同 schema 的数值含义不同，不能将该文件按整数类别图解码。
+
+FreiHAND 训练集的 JPEG mask 使用 `segment/foreground_intensity/v1`，逐像素保留 JPEG 解码后的灰度，`entity_id=hand_right`。该 schema 不限定双手合并：单手或合并范围由 `entity_id` 与 `coverage` 明确声明。评估集整数区域图仍用 `segment/uint16_labels/v1`，两者不能仅凭文件名使用同一种解码语义。
+
+#### 5.5.4 coverage 与有效性
+
+`coverage.kind=full/selected_entities/partial/unknown`。full 指声明的标注范围完整；selected_entities 还应列出目标范围。无论哪种，0 本身不自动成为背景；背景要有明确类别映射，未标注区域不能用于构造负样本。保留源逐类/逐实例 exhaustive 信息，未知不可改写为完整。
+
+三种表示的帧级有效性均为 `[T]`，表示该帧是否提供此分割标注；对应 valid_ref 在组件中登记，仍用小型 bool `.pt` 数组。栅格 mask 必须登记 storage，格式见 3.1.2，所有帧的整数编码或强度逐值完全相同。编码语义由 schema_id 决定，读取器不能仅依据 mask.mkv 文件名选择解码方式。
+
+### 5.6 contact：接触、抓取、区域与距离
+
+#### 5.6.1 relations.jsonl 与 relations_valid.pt
+
+`annotation/contact/relations.jsonl`，schema contact/relations/v1，每帧一行含 frame_index、relations。每条记录含 relation_type（contact/grasp）、subject_id（手槽或登记实体）、object_id、status、source_locator、provenance。status 固定为 positive/negative/unknown/target_unannotated：positive 必须有可解析目标，negative 表示源明确否定该关系，unknown 为不确定，target_unannotated 为确有接触但目标未标注。非 positive 的 object_id 可 null，不能用 null 冒充“不接触”。稀疏源无记录的帧不自动补 negative。
+
+VISOR 的对象 mask ID 必须先按同帧 source_instance_id 解析成 object_id；hand-not-in-contact、none-of-the-above、inconclusive 分别映射 negative、target_unannotated、unknown，保留原始字符串。Dex ycb_grasp_ind 通过 ycb_ids 映射到登记物体，以 relation_type=grasp 保存；不得当作逐顶点接触。
+
+relations 组件的 valid_ref 指向 `relations_valid.pt [T] bool`，表示该帧是否有可用源关系标注。有效帧中可以存在 status=unknown 的明确不确定标注；无源标注的帧为 valid=false、relations=[]。组件仍声明 available/provenance/source_locator。
+
+#### 5.6.2 实体对的顶点区域与距离
+
+已发布的接触区域/距离按参与关系的 subject_id 与 object_id 组织，每对实体保留选定的一套结果，以精确 geometry_ref/topology_id 关联到手或物体/部件；不要求每套数据都有。顶点区域 schema contact/vertex_region/v1：bool `[T,V]` 加同形 valid；顶点距离 schema contact/vertex_distance/v1：float32 `[T,V]`、米、同形 valid，声明 source/target geometry_ref、distance_definition（signed/unsigned、测量到点集或表面等明确源定义）。geometry_ref 必须指向已登记 mesh 或由固定 MANO 模型配置确定的拓扑；拓扑/顶点数变化时按拓扑拆分片段。其他源区域表示只有在实际接入并登记 schema 后才可转换，不能随意栅格化宣称等价。
+
+顶点区域／距离已有存储定义，但尚未完成真实输入试点；接入时必须提供源语义与几何验收证据，不因已经登记 schema 就视为通过。
+
+## 附录：任务范围
+
+不强制收录 2D bbox、完整高频动捕、动作程序、身体 SMPL-X、点云或网络特征。范围内的接触、visibility、选定手关键点、物体几何不能因不便处理而静默丢弃。分割采用的物理编码由第 5.5 节决定，存在重叠时不能以单层标签图声称无损。
+
+不设置 entities_ref/segment/entities.json，不要求与 object_id 重复的 track_id。MediaPipe 不在当前关键点定义中；旧版本迁移必须保留可追溯关系，不能直接删表。手部共享表示不含独立 hand_mesh。
+
+schema.json 是普通注册表，不是 JSON Schema 文档或可执行验收器；定义存在不代表真实数据已验证。验收须保留实测范围与未决项。
+
+本存储约定只更新标准，不代表既有 FFV1／depth.pt 试点已迁移。新发布必须显式写 storage 配置；旧产物按其旧定义读取，不能只改文件后缀或版本标识伪装为合规。
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/doc/schema.json b/H2O/shared-v1.1/script/v1_0_snapshot/doc/schema.json
new file mode 100644
index 0000000000000000000000000000000000000000..d904a34272de7f3fd4428c8dc68a919e94a68fca
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/doc/schema.json
@@ -0,0 +1,869 @@
+{
+ "schema_version": "hand-warehouse/1.0",
+ "validity_revision": "usable-annotation-2026-09-20",
+ "annotation_validity": {
+ "meaning": "annotation usable for supervision, not merely record presence",
+ "formula": "source_annotation_present AND numerical_checks_pass AND applicable_official_QA_pass AND NOT explicitly_reviewed_local_exclusion",
+ "without_official_qa": "omit QA term; declare source rules; do not fabricate QA",
+ "policy_field": "components..valid_policy",
+ "visibility_is_independent": true,
+ "invalid_float_value": "NaN",
+ "hot3d": "broadcast per-camera frame manual QA conservatively to hand/object labels and pose-derived visibility; retain RGB and source calibration; keep native QA in raw and review report",
+ "hot3d_visibility": "native visibility_ratio[%] already ranges from 0 to 1; do not divide by 100"
+ },
+ "hand_slots": [
+ "left",
+ "right"
+ ],
+ "keypoints": {
+ "mano21/v1": {
+ "count": 21,
+ "names": [
+ "wrist",
+ "thumb_1",
+ "thumb_2",
+ "thumb_3",
+ "thumb_4",
+ "index_1",
+ "index_2",
+ "index_3",
+ "index_4",
+ "middle_1",
+ "middle_2",
+ "middle_3",
+ "middle_4",
+ "ring_1",
+ "ring_2",
+ "ring_3",
+ "ring_4",
+ "little_1",
+ "little_2",
+ "little_3",
+ "little_4"
+ ],
+ "source_index_map": null,
+ "note": "adapter maps official MANO joint and fingertip convention"
+ },
+ "rhd21/v1": {
+ "count": 21,
+ "names": [
+ "wrist",
+ "thumb_1",
+ "thumb_2",
+ "thumb_3",
+ "thumb_4",
+ "index_1",
+ "index_2",
+ "index_3",
+ "index_4",
+ "middle_1",
+ "middle_2",
+ "middle_3",
+ "middle_4",
+ "ring_1",
+ "ring_2",
+ "ring_3",
+ "ring_4",
+ "little_1",
+ "little_2",
+ "little_3",
+ "little_4"
+ ],
+ "source_index_map_per_hand": [
+ 0,
+ 4,
+ 3,
+ 2,
+ 1,
+ 8,
+ 7,
+ 6,
+ 5,
+ 12,
+ 11,
+ 10,
+ 9,
+ 16,
+ 15,
+ 14,
+ 13,
+ 20,
+ 19,
+ 18,
+ 17
+ ]
+ }
+ },
+ "camera": {
+ "opencv_fisheye4": {
+ "K": [
+ 3,
+ 3
+ ],
+ "distortion": [
+ "k1",
+ "k2",
+ "k3",
+ "k4"
+ ],
+ "projection": "theta=atan(r); theta_d=theta*(1+k1*theta^2+k2*theta^4+k3*theta^6+k4*theta^8); source ReInterHand"
+ },
+ "pinhole": {
+ "K": [
+ 3,
+ 3
+ ]
+ },
+ "fisheye624": {
+ "parameters": [
+ "f",
+ "cx",
+ "cy",
+ "k0",
+ "k1",
+ "k2",
+ "k3",
+ "k4",
+ "k5",
+ "p0",
+ "p1",
+ "s0",
+ "s1",
+ "s2",
+ "s3"
+ ]
+ },
+ "rational8": {
+ "K": [
+ 3,
+ 3
+ ],
+ "distortion": [
+ "k1",
+ "k2",
+ "p1",
+ "p2",
+ "k3",
+ "k4",
+ "k5",
+ "k6"
+ ]
+ }
+ },
+ "segmentation": {
+ "segment/uint16_labels/v1": {
+ "storage": "mask.mkv",
+ "dtype": "uint16",
+ "mapping": "instances.jsonl",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "reserved_codes": {
+ "0": "unassigned",
+ "65535": "ignore"
+ },
+ "region_codes": [
+ 1,
+ 65534
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ },
+ "segment/instance_polygons/v1": {
+ "storage": "polygons.jsonl",
+ "mapping": "instances.jsonl",
+ "frame_keys": [
+ "frame_index",
+ "instances"
+ ],
+ "instance_keys": [
+ "mask_value",
+ "polygons"
+ ],
+ "coordinate_unit": "pixel",
+ "overlap_allowed": true,
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref"
+ ],
+ "polygon_shape": [
+ "N",
+ 2
+ ],
+ "holes_supported": false
+ },
+ "segment/foreground_intensity/v1": {
+ "storage": "mask.mkv",
+ "dtype": "uint16",
+ "value_range": [
+ 0,
+ 255
+ ],
+ "normalization_scale": 0.00392156862745098,
+ "axes": [
+ "T",
+ "height",
+ "width"
+ ],
+ "meaning": "native hand foreground intensity; individual hand or union declared in metadata; not instance codes or calibrated probability",
+ "zero": "background",
+ "full_scale": "foreground",
+ "mapping": "foreground_category in component metadata",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "value_range",
+ "normalization_scale",
+ "foreground_category",
+ "entity_id",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ }
+ },
+ "note": "Registry for hand-warehouse/1.0. Read CONTRACT.md for field semantics. This is not a JSON Schema document or executable validator.",
+ "components": {
+ "mano/axis_angle61/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 2,
+ 61
+ ],
+ "valid_shape": [
+ "T",
+ 2
+ ],
+ "unit": "m",
+ "angle_unit": "rad",
+ "coordinate_frame": "camera",
+ "layout": {
+ "pose_axis_angle": [
+ 0,
+ 48
+ ],
+ "shape": [
+ 48,
+ 58
+ ],
+ "translation": [
+ 58,
+ 61
+ ]
+ }
+ },
+ "keypoints/uv/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 2,
+ 21,
+ 2
+ ],
+ "valid_shape": [
+ "T",
+ 2,
+ 21
+ ],
+ "unit": "pixel",
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "keypoints/xyz/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 2,
+ 21,
+ 3
+ ],
+ "valid_shape": [
+ "T",
+ 2,
+ 21
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "depth/z_float32/v1": {
+ "storage": "mkv",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "valid_shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "unit": "m",
+ "depth_type": "z",
+ "pixel_grid": "current_rgb",
+ "coordinate_frame": "current_rgb_camera",
+ "valid_storage": "mkv",
+ "allowed_storage_profiles": [
+ "mkv/depth_float32_bits/v1"
+ ],
+ "allowed_valid_storage_profiles": [
+ "mkv/bool_ffv1/v1"
+ ],
+ "required_metadata": [
+ "storage",
+ "valid_storage",
+ "depth_origin"
+ ],
+ "depth_origin_values": [
+ "measured",
+ "rendered",
+ "unknown"
+ ]
+ },
+ "transform/se3/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 4,
+ 4
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "convention"
+ ]
+ },
+ "object/rigid/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 4,
+ 4
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "convention"
+ ]
+ },
+ "camera/pinhole/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3,
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/rational8/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3,
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/opencv_fisheye4/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3,
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/fisheye624/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 15
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/distortion8/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 8
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/fisheye4_distortion/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 4
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "object/joint_state/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "J"
+ ],
+ "valid_shape": [
+ "T",
+ "J"
+ ],
+ "required_metadata": [
+ "joint_order"
+ ],
+ "unit_rule": "revolute radians; prismatic metres; mixed types declared per joint"
+ },
+ "object_keypoints/camera/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "K",
+ 3
+ ],
+ "valid_shape": [
+ "T",
+ "K"
+ ],
+ "unit": "m",
+ "coordinate_frame": "camera",
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "object/dimensions/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "dimensions_semantics"
+ ],
+ "axis_order": [
+ "length",
+ "width",
+ "height"
+ ],
+ "scale_rule": "do not apply to mesh without source-supported size/scale relation"
+ },
+ "object_keypoints/template/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "K",
+ 3
+ ],
+ "valid_shape": [
+ "K"
+ ],
+ "unit": "m",
+ "coordinate_frame": "object",
+ "static": true,
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "mesh/obj/v1": {
+ "storage": "obj",
+ "unit": "m",
+ "static": true,
+ "valid_shape": [],
+ "dependencies": "MTL and textures retained under owning object"
+ },
+ "rgb/uint8/v1": {
+ "storage": "mkv",
+ "dtype": "uint8",
+ "shape": [
+ "T",
+ "H",
+ "W",
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "color_order": "RGB",
+ "allowed_storage_profiles": [
+ "mkv/mjpeg_copy/v1",
+ "mkv/source_copy/v1",
+ "mkv/h264_rgb_lossless/v1",
+ "mkv/ffv1_rgb/v1"
+ ],
+ "required_metadata": [
+ "storage"
+ ]
+ },
+ "contact/relations/v1": {
+ "storage": "jsonl",
+ "valid_shape": [
+ "T"
+ ],
+ "frame_keys": [
+ "frame_index",
+ "relations"
+ ],
+ "absence_semantics": "no record is not a negative contact annotation"
+ },
+ "contact/vertex_region/v1": {
+ "storage": "pt",
+ "dtype": "bool",
+ "shape": [
+ "T",
+ "V"
+ ],
+ "valid_shape": [
+ "T",
+ "V"
+ ],
+ "required_metadata": [
+ "source_geometry_ref",
+ "target_geometry_ref",
+ "topology_id"
+ ],
+ "pilot_status": "defined_not_empirically_validated"
+ },
+ "contact/vertex_distance/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "V"
+ ],
+ "valid_shape": [
+ "T",
+ "V"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "source_geometry_ref",
+ "target_geometry_ref",
+ "topology_id",
+ "distance_definition"
+ ],
+ "pilot_status": "defined_not_empirically_validated"
+ },
+ "object/articulated/v1": {
+ "storage": "json",
+ "required_keys": [
+ "parts",
+ "joints"
+ ],
+ "part_keys": [
+ "part_id",
+ "parent_id",
+ "has_model",
+ "mesh_ref"
+ ],
+ "hierarchy_semantics": "declare mechanical articulation or semantic containment; containment alone does not imply joints",
+ "acyclic": true
+ },
+ "segment/uint16_labels/v1": {
+ "storage": "mkv",
+ "dtype": "uint16",
+ "mapping": "instances.jsonl",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "reserved_codes": {
+ "0": "unassigned",
+ "65535": "ignore"
+ },
+ "region_codes": [
+ 1,
+ 65534
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ },
+ "segment/instance_polygons/v1": {
+ "storage": "jsonl",
+ "mapping": "instances.jsonl",
+ "frame_keys": [
+ "frame_index",
+ "instances"
+ ],
+ "instance_keys": [
+ "mask_value",
+ "polygons"
+ ],
+ "coordinate_unit": "pixel",
+ "overlap_allowed": true,
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref"
+ ],
+ "polygon_shape": [
+ "N",
+ 2
+ ],
+ "holes_supported": false
+ },
+ "segment/foreground_intensity/v1": {
+ "storage": "mkv",
+ "dtype": "uint16",
+ "value_range": [
+ 0,
+ 255
+ ],
+ "normalization_scale": 0.00392156862745098,
+ "axes": [
+ "T",
+ "height",
+ "width"
+ ],
+ "meaning": "native hand foreground intensity; individual hand or union declared in metadata; not instance codes or calibrated probability",
+ "zero": "background",
+ "full_scale": "foreground",
+ "mapping": "foreground_category in component metadata",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "value_range",
+ "normalization_scale",
+ "foreground_category",
+ "entity_id",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ }
+ },
+ "storage_revision": "lossless-video-2026-09-18",
+ "sampling": {
+ "image": {
+ "single": {
+ "T": 1
+ },
+ "independent": {
+ "T": ">=2",
+ "required_frame_fields": [
+ "sample_id",
+ "source_locator"
+ ],
+ "temporal_relationship": false
+ },
+ "source_fps": null
+ },
+ "video": {
+ "sampling_types": [
+ "dense",
+ "sparse"
+ ]
+ }
+ },
+ "storage_profiles": {
+ "mkv/mjpeg_copy/v1": {
+ "container": "matroska",
+ "codec": "mjpeg",
+ "method": "stream_copy_from_source_jpeg",
+ "logical_dtype": "uint8",
+ "decoded_pixel_format": "rgb24",
+ "validation": [
+ "source_jpeg_packet_sha256_exact",
+ "all_packet_count_order_and_dimensions_exact",
+ "deterministic_decoded_rgb_sample_exact",
+ "pilot_and_final_selected_whole_sequences_decoded_rgb_exact"
+ ],
+ "validation_policy": {
+ "id": "jpeg-packets-all-rgb-sample16/v1",
+ "sample_count_per_view": 16,
+ "short_views": "all_frames_if_T_less_than_16",
+ "fixed_indices": "0,T-1,T//4,T//2,3*T//4, deduplicated",
+ "remaining_indices": "sample without replacement to min(T,16) using Python random.Random(seed)",
+ "seed": "first 8 bytes of SHA256(sequence_id/camera_id/20260920), big-endian integer",
+ "report": "policy, exact selected frame indices, seed, separate packet and pixel coverage",
+ "legacy_all_frame_pixel_proofs": "still accepted; preserve historical evidence"
+ }
+ },
+ "mkv/source_copy/v1": {
+ "container": "matroska",
+ "codec": "source_codec_declared_in_metadata",
+ "method": "stream_copy_only",
+ "logical_dtype": "uint8",
+ "decoded_pixel_format": "rgb24",
+ "validation": [
+ "no_reencoding",
+ "all_decoded_rgb_frames_exact",
+ "presentation_frame_order_exact"
+ ]
+ },
+ "mkv/h264_rgb_lossless/v1": {
+ "container": "matroska",
+ "codec": "h264",
+ "encoder": "libx264rgb",
+ "crf": 0,
+ "decoded_pixel_format": "rgb24",
+ "logical_dtype": "uint8",
+ "gop": "declared_in_encoder_parameters",
+ "validation": [
+ "all_decoded_rgb_frames_exact"
+ ],
+ "forbidden": [
+ "lossy_rate_control",
+ "rgb_to_yuv420_subsampling"
+ ]
+ },
+ "mkv/ffv1_rgb/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "decoded_pixel_format": "rgb24",
+ "logical_dtype": "uint8",
+ "validation": [
+ "all_decoded_rgb_frames_exact"
+ ]
+ },
+ "mkv/depth_float32_bits/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "pixel_format": "gray16le",
+ "logical_dtype": "float32",
+ "logical_shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "encoded_shape": [
+ "T",
+ "H",
+ "2*W"
+ ],
+ "packing": "IEEE754 little-endian float32 bits; low uint16 in first W columns, high uint16 in last W columns",
+ "decode": "reinterpret_float32(uint32(low) | (uint32(high) << 16))",
+ "validation": [
+ "all_float32_bits_exact_including_nan"
+ ]
+ },
+ "mkv/bool_ffv1/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "pixel_format": "gray",
+ "logical_dtype": "bool",
+ "stored_values": [
+ 0,
+ 1
+ ],
+ "validation": [
+ "all_bool_values_exact"
+ ]
+ },
+ "mkv/uint16_ffv1/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "pixel_formats": [
+ "gray",
+ "gray16le"
+ ],
+ "logical_dtype": "uint16",
+ "gray_condition": "all values <=255; decode by exact uint8-to-uint16 promotion",
+ "validation": [
+ "all_integer_values_exact"
+ ]
+ }
+ },
+ "video_metadata": {
+ "required": [
+ "profile",
+ "codec",
+ "pixel_format",
+ "encoded_size",
+ "decoder",
+ "encoder_parameters",
+ "source_lossy"
+ ],
+ "source_lossy_values": [
+ true,
+ false,
+ "unknown"
+ ],
+ "timebase_role": "index_only",
+ "lossless_reference": "source compressed packets or fixed-decoder source pixels; depth uses normalized standard float32 bits",
+ "selection": "choose smaller complete candidate only after exact roundtrip validation"
+ }
+}
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/check_dexycb_projection.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/check_dexycb_projection.py
new file mode 100644
index 0000000000000000000000000000000000000000..2774c004fc1359dea582135ec7171f600b33b336
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/check_dexycb_projection.py
@@ -0,0 +1,31 @@
+#!/usr/bin/env python3
+"""Check saved Dex-YCB source 3D joints against saved source 2D joints in sampled views."""
+import json
+from pathlib import Path
+import sys
+
+import numpy as np
+import torch
+
+root = Path(sys.argv[1])
+report = json.loads((root / 'doc/validation/STRUCTURAL_AUDIT.json').read_text())
+checks = []
+for name in report['sampled_views']:
+ view = root / name
+ load = lambda path: torch.load(view / path, weights_only=True, map_location='cpu').numpy()
+ k = load('camera/intrinsic.pt')
+ xyz = load('annotation/keypoints/xyz.pt')
+ uv = load('annotation/keypoints/uv.pt')
+ valid = load('annotation/keypoints/xyz_valid.pt') & load('annotation/keypoints/uv_valid.pt')
+ z = xyz[..., 2]
+ valid &= np.isfinite(xyz).all(axis=-1) & np.isfinite(uv).all(axis=-1) & (z > 0)
+ z = np.where(valid, z, 1)
+ pred = np.stack((k[:, 0, 0, None, None] * xyz[..., 0] / z + k[:, 0, 2, None, None],
+ k[:, 1, 1, None, None] * xyz[..., 1] / z + k[:, 1, 2, None, None]), axis=-1)
+ errors = np.linalg.norm(pred[valid] - uv[valid], axis=-1)
+ checks.append(dict(view=name, points=int(errors.size), median_px=float(np.median(errors)) if errors.size else None,
+ p95_px=float(np.percentile(errors, 95)) if errors.size else None))
+(root / 'doc/validation/KEYPOINT_PROJECTION_QC.json').write_text(json.dumps({
+ 'scope': 'Saved source 3D joints projected with saved color intrinsics vs saved source 2D joints; independent of hand mesh rendering.',
+ 'views': checks}, ensure_ascii=False, indent=2) + '\n')
+print(json.dumps(checks, ensure_ascii=False), flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/config.json b/H2O/shared-v1.1/script/v1_0_snapshot/script/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..36d77ffb8ae7c4d8db5364324fac347a35522f99
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/config.json
@@ -0,0 +1,24 @@
+{
+ "root": "/home/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O",
+ "out": "/home/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-process16-pipe",
+ "release_id": "shared-v1.0",
+ "limit": null,
+ "max_frames": null,
+ "mano_dir": "/home/xuzhefeng/Projects/WiLoR/mano_data",
+ "views": null,
+ "jpeg_full_check": false,
+ "workers": 4,
+ "view_backend": "processes",
+ "camera_pipeline": true,
+ "ffv1_slices": 16,
+ "view_workers": 16,
+ "ram_stage_dir": "/dev/shm/xuzhefeng-h2o-full-process16-20260930",
+ "ram_stage_gib": 32,
+ "stream_media": true,
+ "codec_threads": 10,
+ "decode_threads": 10,
+ "depth_threads": 4,
+ "subjects": null,
+ "include_casa": false,
+ "archive": null
+}
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/convert_dexycb_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/convert_dexycb_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed3f479787fae378b3d3b51ce2f58ccaebfd3d0f
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/convert_dexycb_v1.0.py
@@ -0,0 +1,349 @@
+#!/usr/bin/env python3
+"""Dex-YCB source annotations → CONTRACT.md v1.0. No derived annotations."""
+import io
+import re
+import shutil
+import tarfile
+import tempfile
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+import yaml
+
+from warehouse_v1_0 import (View, build_record, find_file, locator, mano_models, mesh_asset,
+ model_profile, parser, pca_to_axis_angle, release, roots, safe_member, sequence_dir)
+
+IMG_W, IMG_H = 640, 480
+
+class _Loader(yaml.SafeLoader):
+ pass
+
+
+_Loader.add_constructor("tag:yaml.org,2002:python/tuple",
+ lambda l, n: tuple(l.construct_sequence(n)))
+
+
+def yaml_load_bytes(data: bytes):
+ return yaml.load(io.BytesIO(data), Loader=_Loader)
+
+
+class Calibration:
+ def __init__(self, dexycb_root: Path):
+ self.intrinsics = {} # serial -> {fx,fy,cx,cy,w,h}
+ self.extrinsics = {} # group -> {serial: [12] 行优先 3×4 cam→world}
+ self.betas = {} # mano_calib_name -> [10]
+ calib_dir = dexycb_root / "calibration"
+ if calib_dir.is_dir():
+ self._load((p.as_posix(), p.read_bytes())
+ for p in sorted(calib_dir.rglob("*")) if p.is_file())
+ else:
+ tar_path = dexycb_root / "calibration.tar.gz"
+ if not tar_path.exists():
+ raise FileNotFoundError(f"no calibration/ nor calibration.tar.gz in {dexycb_root}")
+ with tarfile.open(tar_path, "r:gz") as tf:
+ self._load((m.name, tf.extractfile(m).read()) for m in tf if m.isfile())
+
+ _RE_INTR = re.compile(r"(?:^|/)intrinsics/(\d+)_\d+x\d+\.yml$")
+ _RE_EXTRA = re.compile(r"(?:^|/)extrinsics_(\d{8}_\d{6})/extrinsics\.yml$")
+ _RE_MANO = re.compile(r"(?:^|/)mano_(\d{8}_\d{6}_subject-\d{2}_right)/mano\.yml$")
+
+ def _load(self, files):
+ for rel, data in files:
+ m = self._RE_INTR.search(rel)
+ if m:
+ c = yaml_load_bytes(data)["color"]
+ self.intrinsics[m.group(1)] = {"fx": float(c["fx"]), "fy": float(c["fy"]),
+ "cx": float(c["ppx"]), "cy": float(c["ppy"]),
+ "w": IMG_W, "h": IMG_H}
+ continue
+ m = self._RE_EXTRA.search(rel)
+ if m:
+ raw = yaml_load_bytes(data)["extrinsics"]
+ self.extrinsics[m.group(1)] = {
+ str(s): [float(x) for x in np.asarray(v).ravel()] for s, v in raw.items()}
+ continue
+ m = self._RE_MANO.search(rel)
+ if m:
+ self.betas[m.group(1)] = [float(b) for b in yaml_load_bytes(data)["betas"]]
+
+
+
+def calibration_locator(path, member, **extra):
+ if path.is_dir():
+ return locator(path / member.removeprefix('calibration/'), **extra)
+ return locator(path, member, **extra)
+
+
+def prepare_models(root, destination):
+ archive = find_file(root, 'models.tar.gz')
+ if archive is None:
+ return {}, None
+ pattern = re.compile(r'^models/(\d{3}_[a-z0-9_]+)/(textured_simple\.obj(?:\.mtl)?|texture_map\.png)$')
+ names = set()
+ with tarfile.open(archive, 'r|gz') as tf:
+ for member in tf:
+ match = pattern.fullmatch(member.name)
+ if member.isfile() and match:
+ names.add(match[1])
+ path = safe_member(destination, '/'.join(match.groups()))
+ path.parent.mkdir(parents=True, exist_ok=True)
+ path.write_bytes(tf.extractfile(member).read())
+ if len(names) != 21:
+ raise ValueError('expected all 21 source YCB model directories')
+ return {i: destination / name for i, name in enumerate(sorted(names), 1)}, archive
+
+
+def convert_view(path, args, seq, serial, frames, meta, meta_loc, calib, calib_path, models, assets, asset_archive):
+ total = int(meta.get('num_frames', max(frames) + 1))
+ ids = list(range(min(total, args.max_frames) if args.max_frames else total))
+ for f in ids:
+ frames.setdefault(f, {})
+ if not ids:
+ raise ValueError('no RGB frames')
+ source_rows = []
+ for f in ids:
+ row = frames[f]
+ source_rows.append(dict(source_frame_id=f, sync_id=f'{seq}:{f}',
+ source_locator=row['color'][1] if 'color' in row else (meta_loc | {'source_frame_id': f, 'missing_rgb': True}), annotation_matches={
+ 'labels': {'source_frame_id': f, 'source_timestamp_ns': None, 'clock_domain': None,
+ 'match_method': 'index_map', 'delta_ns': None, 'source_locator': row['labels'][1]}
+ for _ in [0] if 'labels' in row},
+ component_sources={k: v[1] for k, v in row.items()}))
+ view = View(path, 'dex-ycb', args.release_id, seq, serial, source_rows,
+ sampling='dense' if ids == list(range(ids[0], ids[-1] + 1)) else 'sparse')
+ view.valid_rules = {
+ 'mano': 'Source pose_m present and not all zero; side model/calibration available',
+ 'keypoints': 'Corresponding joint_2d/joint_3d present; whole-hand all -1 is missing, broadcast to its 21 points',
+ 'pose': 'Source pose_y for this ycb_ids slot present and not an all-zero matrix',
+ 'segment': 'Source seg array provided for this frame; region codes and entity references validated',
+ 'relations': 'Source sequence ycb_grasp_ind resolves through ycb_ids; broadcast sequence grasp to selected frames',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta['view_type'] = 'exo'
+ view.meta['sampling']['selection'] = {'max_frames': args.max_frames, 'views': args.views}
+ subject = re.search(r'subject-(\d+)', seq)
+ if subject and 1 <= int(subject[1]) <= 10:
+ index = int(subject[1])
+ view.meta.update(split='val' if index == 7 else 'test' if index in (8, 9) else 'train',
+ split_scheme='Dex-YCB/s1', split_source_locator={
+ 'url': 'https://github.com/NVlabs/dex-ycb-toolkit/blob/master/dex_ycb_toolkit/dex_ycb.py',
+ 'rule': 's1 unseen-subject: 01-06/10 train, 07 val, 08-09 test'})
+ view.meta.pop('split_reason', None)
+ view.rgb([frames[f]['color'][0] if 'color' in frames[f] else None for f in ids], {'frames_ref': 'camera/frames.jsonl'})
+ n = len(ids)
+ intr = calib.intrinsics.get(serial)
+ K = None if intr is None else np.array([[intr['fx'], 0, intr['cx']], [0, intr['fy'], intr['cy']], [0, 0, 1]])
+ ext = calib.extrinsics.get(str(meta['extrinsics']), {}).get(serial)
+ E = None
+ if ext is not None:
+ E = np.eye(4)
+ E[:3] = np.asarray(ext).reshape(3, 4)
+ E = np.linalg.inv(E)
+ view.camera(K, calibration_locator(calib_path, f'calibration/intrinsics/{serial}_640x480.yml'), E,
+ calibration_locator(calib_path, f'calibration/extrinsics_{meta["extrinsics"]}/extrinsics.yml',
+ conversion='inverse of source T_world_from_camera'))
+ sides = meta.get('mano_sides')
+ if sides not in (['left'], ['right']):
+ raise ValueError(f'expected exactly one anatomical source hand: {sides}')
+ side = sides[0]
+ slot = ['left', 'right'].index(side)
+ cal_name = meta['mano_calib'][0] if isinstance(meta['mano_calib'], list) else meta['mano_calib']
+ labels = [dict(np.load(frames[f]['labels'][0])) if 'labels' in frames[f] else {} for f in ids]
+ label_loc = {'frames_ref': 'camera/frames.jsonl', 'source_field': 'component_sources.labels'}
+ motion = np.full((n, 2, 61), np.nan, np.float32)
+ mv = np.zeros((n, 2), bool)
+ if side in models and any('pose_m' in lab for lab in labels):
+ for i, lab in enumerate(labels):
+ if 'pose_m' not in lab:
+ continue
+ pose = np.asarray(lab['pose_m']).reshape(51)
+ if not np.any(pose): # Official Dex-YCB missing-pose sentinel, independent of joints/seg.
+ continue
+ motion[i, slot] = np.r_[pose[:3], pca_to_axis_angle(pose[3:48], models[side]),
+ calib.betas[cal_name], pose[48:51]]
+ mv[i, slot] = True
+ view.tensor('mano', 'mano/axis_angle61/v1', 'annotation/mano/motion.pt', motion, mv,
+ ['T', 'hand', 'parameter'], label_loc, model_profile=model_profile(models),
+ calibration_source=calibration_locator(calib_path, f'calibration/mano_{cal_name}/mano.yml'),
+ provenance={'kind': 'format_conversion', 'method': 'source PCA @ hands_components + hands_mean; explicit axis-angle consumed with flat_hand_mean=true'})
+ elif any('pose_m' in lab for lab in labels):
+ view.absent('mano', 'Source PCA exists; provide --mano-dir to expand it', 'dependency_missing')
+ else:
+ view.absent('mano', 'No source pose_m field')
+ for source_key, key, dim, unit in [('joint_2d', 'uv', 2, 'pixel'), ('joint_3d', 'xyz', 3, 'm')]:
+ if not any(source_key in x for x in labels):
+ view.absent('keypoints_' + key, 'No source ' + source_key)
+ continue
+ points = np.full((n, 2, 21, dim), np.nan, np.float32)
+ valid = np.zeros((n, 2, 21), bool)
+ for i, lab in enumerate(labels):
+ if source_key not in lab:
+ continue
+ p = np.asarray(lab[source_key]).reshape(21, dim)
+ points[i, slot] = p
+ valid[i, slot] = not np.all(p == -1) # Official sentinel covers the whole source hand.
+ # An individual off-image point (-1, -1) is not evidence of missing annotation.
+ view.tensor('keypoints_' + key, f'keypoints/{key}/v1', f'annotation/keypoints/{key}.pt',
+ points, valid, ['T', 'hand', 'keypoint', 'coordinate'], label_loc,
+ keypoint_schema='mano21/v1', unit=unit)
+ ycb_ids = [int(x) for x in meta['ycb_ids']]
+ for j, oid in enumerate(ycb_ids):
+ entity = f'ycb_{oid:02d}'
+ view.object(entity, meta_loc, source_object_id=oid, category_id=oid)
+ poses = np.full((n, 4, 4), np.nan, np.float32)
+ valid = np.zeros(n, bool)
+ for i, lab in enumerate(labels):
+ if 'pose_y' in lab:
+ pose = np.asarray(lab['pose_y'])
+ if pose.shape != (len(ycb_ids), 3, 4):
+ raise ValueError('pose_y shape differs from ycb_ids')
+ if np.any(pose[j]): # Official all-zero missing object pose sentinel.
+ poses[i] = np.eye(4)
+ poses[i, :3] = pose[j]
+ valid[i] = True
+ if any('pose_y' in lab for lab in labels):
+ view.tensor('pose_' + entity, 'object/rigid/v1', f'annotation/object/{entity}/pose.pt',
+ poses, valid, ['T', 'row', 'column'], label_loc,
+ convention='T_camera_from_object', unit='m')
+ else:
+ view.absent('pose_' + entity, 'No source pose_y field')
+ if oid in assets:
+ mesh_asset(view, entity, assets[oid], assets[oid] / 'textured_simple.obj',
+ locator(asset_archive, f'models/{assets[oid].name}/textured_simple.obj'))
+ else:
+ view.absent('mesh_' + entity, 'models.tar.gz unavailable', 'not_downloaded')
+ if any('seg' in lab for lab in labels):
+ mask_rows = []
+ width, height = view.meta['camera']['image_size']
+ for i, lab in enumerate(labels):
+ values = np.unique(lab.get('seg', np.zeros((height, width), np.uint8)))
+ mappings = []
+ for value in values:
+ value = int(value)
+ if value == 0:
+ continue
+ if value != 255 and value not in ycb_ids:
+ raise ValueError(f'unknown Dex-YCB region value {value}')
+ mappings.append(dict(mask_value=value, category_id='hand' if value == 255 else value,
+ entity_id='hand_' + side if value == 255 else f'ycb_{value:02d}', part_id=None,
+ source_instance_id=value))
+ mask_rows.append(dict(frame_index=i, labels=mappings))
+ view.raster('segment', (lab.get('seg', np.zeros((height, width), np.uint16)) for lab in labels),
+ ['seg' in lab for lab in labels], label_loc, mask_rows)
+ view.meta['components']['segment'].update(coverage={'kind': 'selected_entities',
+ 'entities': ['hand_' + side] + [f'ycb_{o:02d}' for o in ycb_ids]}, source_zero_meaning='background')
+ else:
+ view.absent('segment', 'No source seg field')
+ if any('aligned_depth_to_color' in frames[f] for f in ids):
+ def depths():
+ width, height = view.meta['camera']['image_size']
+ for f in ids:
+ item = frames[f].get('aligned_depth_to_color')
+ if item is None:
+ yield np.full((height, width), np.nan, np.float32)
+ else:
+ with Image.open(item[0]) as im:
+ yield np.asarray(im).astype(np.float32) / 1000.0
+ view.raster('depth', depths(), None, {'frames_ref': 'camera/frames.jsonl',
+ 'source_field': 'component_sources.aligned_depth_to_color', 'conversion': 'millimetres to metres'})
+ else:
+ view.absent('depth', 'No source aligned_depth_to_color frames')
+ grasp = meta.get('ycb_grasp_ind')
+ if grasp is not None:
+ if not 0 <= int(grasp) < len(ycb_ids):
+ raise ValueError('invalid ycb_grasp_ind')
+ relation = dict(relation_type='grasp', subject_id='hand_' + side,
+ object_id=f'ycb_{ycb_ids[int(grasp)]:02d}', status='positive', source_locator=meta_loc,
+ provenance={'kind': 'source', 'method': 'sequence ycb_grasp_ind resolved through ycb_ids; not per-frame contact'})
+ view.relations([{'frame_index': i, 'relations': [relation]} for i in range(n)], np.ones(n, bool), meta_loc)
+ else:
+ view.absent('relations', 'No source grasp/contact relation')
+ return view.finish()
+
+
+MEMBER = re.compile(r'^([^/]+)/(\d{8}_\d{6})/(meta\.yml|(\d{12})/(color|labels|aligned_depth_to_color)_(\d+)\.(jpg|npz|png))$')
+
+
+def convert_archive(archive, args, out, calib, calib_path, models, assets, asset_archive):
+ selected_views = set(args.views.split(',')) if args.views else None
+ done = 0
+ current, meta, meta_loc, rows = None, None, None, {}
+ with tempfile.TemporaryDirectory(prefix='dex-source-') as wd:
+ work = Path(wd)
+ def flush():
+ nonlocal done
+ if not rows:
+ return
+ seq = archive.name.split('.tar')[0] + '_' + current
+ reports = {}
+ with sequence_dir(out, seq) as target:
+ for serial, frames in sorted(rows.items()):
+ reports[serial] = convert_view(target / serial, args, seq, serial, frames, meta,
+ meta_loc, calib, calib_path, models, assets, asset_archive)
+ build_record(out, seq, reports)
+ for staged in list(work.iterdir()):
+ if staged.is_dir():
+ shutil.rmtree(staged)
+ else:
+ staged.unlink()
+ done += 1
+ print(f'Converted {seq}', flush=True)
+ with tarfile.open(archive, 'r|gz') as tf:
+ for member in tf:
+ match = MEMBER.fullmatch(member.name)
+ if not member.isfile() or not match:
+ continue
+ seq = match[2]
+ if seq != current:
+ flush()
+ if args.limit and done >= args.limit:
+ break
+ current, meta, meta_loc, rows = seq, None, None, {}
+ data_kind = match[3]
+ if data_kind == 'meta.yml':
+ data = tf.extractfile(member).read()
+ meta = yaml_load_bytes(data)
+ meta_loc = locator(archive, member.name, data)
+ continue
+ serial, kind, f = match[4], match[5], int(match[6])
+ if selected_views and serial not in selected_views or args.max_frames and f >= args.max_frames:
+ continue
+ data = tf.extractfile(member).read()
+ dest = safe_member(work, member.name)
+ dest.parent.mkdir(parents=True, exist_ok=True)
+ dest.write_bytes(data)
+ row = rows.setdefault(serial, {}).setdefault(f, {})
+ if kind in row:
+ raise ValueError('duplicate archive frame')
+ row[kind] = (dest, locator(archive, member.name, data))
+ else:
+ flush()
+ return done
+
+
+def main(argv=None):
+ p = parser('dex-ycb', __doc__)
+ p.add_argument('--subjects')
+ args = p.parse_args(argv)
+ archives = sorted({a for r in roots(args.root) for a in r.glob('*-subject-*.tar.gz')})
+ if args.subjects:
+ subjects = {s.strip().zfill(2) for s in args.subjects.split(',')}
+ archives = [a for a in archives if re.search(r'subject-(\d+)', a.name)[1] in subjects]
+ if not archives:
+ p.error('no Dex-YCB subject archives found')
+ cp = find_file(args.root, 'calibration.tar.gz') or find_file(args.root, 'calibration')
+ if cp is None:
+ p.error('calibration source missing')
+ calib = Calibration(cp.parent)
+ models = mano_models(args.mano_dir)
+ out = release(args, 'dex-ycb')
+ with tempfile.TemporaryDirectory(prefix='dex-models-') as wd:
+ assets, asset_archive = prepare_models(args.root, Path(wd))
+ for archive in archives:
+ convert_archive(archive, args, out, calib, cp, models, assets, asset_archive)
+ return 0
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/convert_h2o_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/convert_h2o_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..84c5878726db45a492347035386bf0f7ff30b8d6
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/convert_h2o_v1.0.py
@@ -0,0 +1,678 @@
+#!/usr/bin/env python3
+"""H2O native RGB-D, MANO, joints and objects → CONTRACT.md v1.0 (no rendered masks)."""
+from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
+import io
+import json
+import multiprocessing
+import os
+import re
+import shutil
+import tarfile
+import tempfile
+import threading
+from pathlib import Path
+import zipfile
+
+# Bound native libraries before loading NumPy/Torch; each worker still uses independent FFmpeg processes.
+for key in ('OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS'):
+ os.environ[key] = '1'
+
+import numpy as np
+from PIL import Image
+import torch
+
+from warehouse_v1_0 import (View, build_record, check_mesh, find_file, locator, mano_models, mesh_asset,
+ model_profile, parser, release, roots, safe_member, sequence_dir, sha256)
+
+OBJECTS = {1: 'book', 2: 'espresso', 3: 'lotion', 4: 'spray',
+ 5: 'milk', 6: 'cocoa', 7: 'chips', 8: 'cappuccino'}
+MEMBER = re.compile(r'^(?:h2o_CASA/)?(subject\d+)/([^/]+)/(\d+)/(cam\d)/(.+)$')
+FRAME = re.compile(r'(rgb|depth|cam_pose|hand_pose|hand_pose_mano|obj_pose|obj_pose_rt)/(\d+)\.(png|jpg|txt)$')
+
+
+def load_splits(root):
+ archive = find_file(root, 'label_split.zip')
+ result = {}
+ if archive is not None:
+ with zipfile.ZipFile(archive) as z:
+ for split in ('train', 'val', 'test'):
+ member = f'label_split/action_{split}.txt'
+ for line_no, line in enumerate(z.read(member).decode().splitlines(), 1):
+ fields = line.split()
+ if len(fields) < 2 or fields[1] == 'path':
+ continue
+ key = fields[1]
+ if key in result and result[key][0] != split:
+ raise ValueError('conflicting official splits')
+ result[key] = (split, locator(archive, member, record=line_no))
+ return result
+
+
+def load_assets(root, work):
+ archive = find_file(root, 'object.zip')
+ if archive is None:
+ return {}, None
+ with zipfile.ZipFile(archive) as z:
+ for member in z.infolist():
+ if member.is_dir():
+ continue
+ path = safe_member(work, member.filename)
+ path.parent.mkdir(parents=True, exist_ok=True)
+ path.write_bytes(z.read(member))
+ result = {}
+ for oid, name in OBJECTS.items():
+ directory = work / 'object' / name
+ aliases = []
+ # Source cocoa.obj -> ovomaltine.mtl -> cocoa.png, but the ZIP names the
+ # supplied texture ovomaltine.png. Keep all originals and add a byte-identical alias.
+ if name == 'cocoa' and not (directory / 'cocoa.png').exists():
+ mtl = directory / 'ovomaltine.mtl'
+ if mtl.is_file() and 'map_Kd cocoa.png' in mtl.read_text().splitlines():
+ texture = directory / 'ovomaltine.png'
+ shutil.copyfile(texture, directory / 'cocoa.png')
+ aliases.append({'file': 'cocoa.png', 'sha256': sha256(texture),
+ 'source': locator(archive, 'object/cocoa/ovomaltine.png')})
+ candidates = list(directory.glob('*.obj'))
+ if len(candidates) != 1:
+ raise ValueError(f'expected one source OBJ for {name}')
+ check_mesh(candidates[0], directory) # Fail before decoding any large subject archive.
+ result[oid] = (directory, candidates[0], aliases)
+ return result, archive
+
+
+def convert_view(path, args, seq, cam, rows, intr, splits, models, assets, asset_archive):
+ codec_threads = getattr(args, 'codec_threads', 1)
+ ids = list(range(min(rows), max(rows) + 1))
+ for f in ids:
+ rows.setdefault(f, {})
+ n = len(ids)
+ if not n:
+ raise ValueError('no RGB frames')
+ split, split_loc = splits.get(seq, ('unknown', None))
+ frame_rows = [dict(source_frame_id=f, sync_id=f'{seq}:{f}', source_locator=rows[f]['rgb'][1] if 'rgb' in rows[f] else {'sequence': seq, 'camera': cam, 'source_frame_id': f, 'missing_rgb': True},
+ component_sources={k: item[1] for k, item in rows[f].items()},
+ annotation_matches={k: dict(source_frame_id=f, source_timestamp_ns=None,
+ clock_domain=None, match_method='index_map', delta_ns=None)
+ for k in rows[f] if k != 'rgb'}) for f in ids]
+ view = View(path, 'H2O', args.release_id, seq.replace('/', '_'), cam, frame_rows, split=split)
+ view.valid_rules = {
+ 'mano': 'Per-hand hand_pose_mano flag equals 1; corresponding side model available',
+ 'keypoints': "Per-hand hand_pose flag equals 1, broadcast to that hand's 21 source points independently of MANO",
+ 'pose': 'Source obj_pose_rt class equals this nonzero object class',
+ 'points': 'Source obj_pose class equals this nonzero object class, broadcast to its 21 points',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta['view_type'] = 'ego' if cam == 'cam4' else 'exo' if cam in ('cam0', 'cam1', 'cam2', 'cam3') else 'unknown'
+ view.meta['sampling']['selection'] = {'max_frames': args.max_frames, 'views': args.views}
+ if split_loc:
+ view.meta.update(split_scheme='official_action_split', split_source_locator=split_loc)
+ # In the measured 275 H2O views, exact FFV1 was always smaller than exact H.264.
+ view.rgb([rows[f]['rgb'][0] if 'rgb' in rows[f] else None for f in ids],
+ {'frames_ref': 'camera/frames.jsonl'}, ffv1_first=True, codec_threads=codec_threads,
+ stream_source=getattr(args, 'stream_media', False))
+ loc = {'frames_ref': 'camera/frames.jsonl', 'source_field': 'component_sources'}
+ if intr is not None:
+ fx, fy, cx, cy, width, height = np.loadtxt(io.BytesIO(intr[0])).ravel()
+ if [int(width), int(height)] != view.meta['camera']['image_size']:
+ raise ValueError('source intrinsics resolution mismatch')
+ K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
+ view.camera(K, intr[1])
+ camps = np.full((n, 4, 4), np.nan, np.float32)
+ cv = np.zeros(n, bool)
+ data = []
+ for i, f in enumerate(ids):
+ lab = {k: np.loadtxt(io.BytesIO(item[0])).ravel() for k, item in rows[f].items() if k not in ('rgb', 'depth')}
+ data.append(lab)
+ if 'cam_pose' in lab:
+ camps[i] = np.linalg.inv(lab['cam_pose'].reshape(4, 4))
+ cv[i] = True
+ if cv.any():
+ view.tensor('extrinsic', 'transform/se3/v1', 'camera/extrinsic.pt', camps, cv,
+ ['T', 'row', 'column'], loc, convention='T_camera_from_world',
+ source_convention='T_world_from_camera')
+ motion, mv = np.full((n, 2, 61), np.nan, np.float32), np.zeros((n, 2), bool)
+ points, pv = np.full((n, 2, 21, 3), np.nan, np.float32), np.zeros((n, 2, 21), bool)
+ object_ids = set()
+ for i, lab in enumerate(data):
+ if 'hand_pose_mano' in lab:
+ hands = lab['hand_pose_mano'].reshape(2, 62)
+ for side, row in enumerate(hands):
+ if row[0] not in (0, 1):
+ raise ValueError('unexpected H2O MANO presence flag')
+ if row[0] == 1:
+ motion[i, side] = np.r_[row[4:52], row[52:62], row[1:4]]
+ mv[i, side] = True
+ if 'hand_pose' in lab:
+ hands = lab['hand_pose'].reshape(2, 64)
+ for side, row in enumerate(hands):
+ if row[0] not in (0, 1):
+ raise ValueError('unexpected H2O keypoint presence flag')
+ if row[0] == 1:
+ points[i, side] = row[1:].reshape(21, 3)
+ pv[i, side] = True
+ for key in ('obj_pose', 'obj_pose_rt'):
+ if key in lab and lab[key][0] != 0:
+ object_ids.add(int(lab[key][0]))
+ if any('hand_pose_mano' in lab for lab in data):
+ if len(models) == 2:
+ view.tensor('mano', 'mano/axis_angle61/v1', 'annotation/mano/motion.pt', motion, mv,
+ ['T', 'hand', 'parameter'], loc, model_profile=model_profile(models))
+ else:
+ view.absent('mano', 'Source parameters exist; --mano-dir must identify both model profiles', 'dependency_missing')
+ if any('hand_pose' in lab for lab in data):
+ view.tensor('keypoints_xyz', 'keypoints/xyz/v1', 'annotation/keypoints/xyz.pt', points, pv,
+ ['T', 'hand', 'keypoint', 'coordinate'], loc, keypoint_schema='mano21/v1', unit='m')
+ for oid in sorted(object_ids):
+ if oid not in OBJECTS:
+ raise ValueError(f'unknown H2O object class {oid}')
+ entity = OBJECTS[oid]
+ view.object(entity, loc, category_id=oid, source_object_id=oid)
+ poses, ov = np.full((n, 4, 4), np.nan, np.float32), np.zeros(n, bool)
+ pts, kv = np.full((n, 21, 3), np.nan, np.float32), np.zeros((n, 21), bool)
+ for i, lab in enumerate(data):
+ if 'obj_pose_rt' in lab and lab['obj_pose_rt'][0] == oid:
+ poses[i] = lab['obj_pose_rt'][1:].reshape(4, 4)
+ ov[i] = True
+ if 'obj_pose' in lab and lab['obj_pose'][0] == oid:
+ pts[i] = lab['obj_pose'][1:].reshape(21, 3)
+ kv[i] = True
+ if ov.any():
+ view.tensor('pose_' + entity, 'object/rigid/v1', f'annotation/object/{entity}/pose.pt',
+ poses, ov, ['T', 'row', 'column'], loc, convention='T_camera_from_object', unit='m')
+ if kv.any():
+ view.tensor('points_' + entity, 'object_keypoints/camera/v1',
+ f'annotation/object/{entity}/keypoints/xyz.pt', pts, kv,
+ ['T', 'keypoint', 'coordinate'], loc, keypoint_schema='H2O_official_object21',
+ keypoint_names=['center'] + [f'corner_{i}' for i in range(8)] + [f'edge_midpoint_{i}' for i in range(12)],
+ source_order='official obj_point_order.jpg: center, 8 corners, 12 edge midpoints', unit='m')
+ if oid in assets:
+ directory, obj, aliases = assets[oid]
+ asset_loc = locator(asset_archive, f'object/{entity}/{obj.name}')
+ if aliases:
+ asset_loc['texture_aliases'] = aliases
+ mesh_asset(view, entity, directory, obj, asset_loc)
+ else:
+ view.absent('mesh_' + entity, 'Source object.zip unavailable', 'not_downloaded')
+ if any('depth' in rows[f] for f in ids):
+ width, height = view.meta['camera']['image_size']
+ depth_threads = getattr(args, 'depth_threads', 1)
+ def read_depth(f):
+ item = rows[f].get('depth')
+ if item:
+ with Image.open(item[0]) as im:
+ return np.asarray(im).astype(np.float32) / 1000.0
+ return np.full((height, width), np.nan, np.float32)
+ def depths():
+ if depth_threads == 1:
+ yield from map(read_depth, ids)
+ else:
+ with ThreadPoolExecutor(max_workers=depth_threads) as pool:
+ # Ordered batches bound decoded arrays to depth_threads frames per view.
+ for start in range(0, len(ids), depth_threads):
+ yield from pool.map(read_depth, ids[start:start + depth_threads])
+ view.raster('depth', depths(), None, loc | {'source_field': 'component_sources.depth',
+ 'definition': 'https://github.com/taeinkwon/h2oplayer/blob/main/utils.py',
+ 'conversion': 'registered millimetre camera-Z to metres; no viewer range clipping'},
+ codec_threads=codec_threads, stream=getattr(args, 'stream_media', False))
+ else:
+ view.absent('depth', 'No source depth frames')
+ view.absent('segment', 'H2O provides no native segmentation; rendering deferred to v2.0')
+ view.absent('relations', 'No source contact/grasp annotation')
+ return view.finish()
+
+
+def convert_archive(archive, args, out, splits, models, assets, asset_archive, view_pool=None, ram_budget=None):
+ selected_views = set(args.views.split(',')) if args.views else None
+ subjects = {'subject' + s.strip() for s in args.subjects.split(',')} if args.subjects else None
+ current, rows, intrinsics, done = None, {}, {}, 0
+ published_views = set()
+ camera_jobs, closed_cameras = {}, set()
+ last_camera = None
+ pipeline_active = False
+ ram_reserved = 0
+ try:
+ with tempfile.TemporaryDirectory(prefix='h2o-source-') as wd, \
+ tempfile.TemporaryDirectory(prefix='h2o-ram-', dir=args.ram_stage_dir) as rd:
+ work, ram_work = Path(wd), Path(rd)
+ def queue_camera(cam):
+ if cam in closed_cameras:
+ return
+ closed_cameras.add(cam)
+ if cam not in published_views and rows.get(cam):
+ camera_jobs[cam] = view_pool.submit(convert_view, work / '.converted' / cam,
+ args, '/'.join(current), cam, rows[cam], intrinsics.get(cam),
+ splits, models, assets, asset_archive)
+ def flush():
+ nonlocal done, ram_reserved
+ if not rows:
+ return
+ seq = '/'.join(current)
+ reports, pending = {}, {}
+ for cam, frames in sorted(rows.items()):
+ existing = out / 'data' / seq.replace('/', '_') / cam
+ if (existing / 'metainfo.json').is_file():
+ old_frames = [json.loads(line) for line in (existing / 'camera/frames.jsonl').read_text().splitlines()]
+ if [f['source_frame_id'] for f in old_frames] != list(range(min(frames), max(frames) + 1)):
+ raise ValueError('existing view has different sampling; choose a new release-id')
+ for frame in old_frames:
+ incoming = frames.get(frame['source_frame_id'], {})
+ previous = frame.get('component_sources', {})
+ if set(previous) != set(incoming) or any(previous[k].get('sha256') != v[1].get('sha256') for k, v in incoming.items()):
+ raise ValueError('duplicate H2O view differs from the published source; choose a new release-id')
+ else:
+ pending[cam] = frames
+ if pending:
+ if pipeline_active:
+ for cam in pending:
+ queue_camera(cam)
+ failure = None
+ for cam, job in camera_jobs.items():
+ try:
+ reports[cam] = job.result()
+ except Exception as exc:
+ failure = failure or exc
+ if failure is not None:
+ raise failure
+ with sequence_dir(out, seq.replace('/', '_')) as target:
+ if pipeline_active:
+ for cam in pending:
+ shutil.move(work / '.converted' / cam, target / cam)
+ elif view_pool is None:
+ for cam, frames in pending.items():
+ reports[cam] = convert_view(target / cam, args, seq, cam, frames,
+ intrinsics.get(cam), splits, models, assets, asset_archive)
+ else:
+ jobs = {cam: view_pool.submit(convert_view, target / cam, args, seq, cam,
+ frames, intrinsics.get(cam), splits, models, assets, asset_archive)
+ for cam, frames in pending.items()}
+ failure = None
+ # Every sibling must finish before sequence_dir can remove its inputs.
+ for cam, job in jobs.items():
+ try:
+ reports[cam] = job.result()
+ except Exception as exc:
+ failure = failure or exc
+ if failure is not None:
+ raise failure
+ build_record(out, seq.replace('/', '_'), reports)
+ for directory in (work, ram_work):
+ for staged in list(directory.iterdir()):
+ if staged.is_dir():
+ shutil.rmtree(staged)
+ else:
+ staged.unlink()
+ if ram_budget is not None:
+ lock, used, limit = ram_budget
+ with lock:
+ used[0] -= ram_reserved
+ ram_reserved = 0
+ done += 1
+ print(f'{"Converted" if pending else "Verified existing"} {seq}', flush=True)
+ # Uncompressed CASA supports seeking over large frames; gzip is single-pass.
+ mode = 'r:' if archive.suffix == '.tar' else 'r|gz'
+ try:
+ with tarfile.open(archive, mode) as tf:
+ for member in tf:
+ match = MEMBER.fullmatch(member.name)
+ if not member.isfile() or not match:
+ continue
+ subj, scene, seq, cam, tail = match.groups()
+ if subjects and subj not in subjects:
+ continue
+ key = (subj, scene, seq)
+ if key != current:
+ flush()
+ if args.limit and done >= args.limit:
+ break
+ current, rows, intrinsics = key, {}, {}
+ camera_jobs.clear()
+ closed_cameras.clear()
+ last_camera = None
+ pipeline_active = args.camera_pipeline
+ published_views = {view.name for view in
+ (out / 'data' / '_'.join(key)).glob('cam*')
+ if (view / 'metainfo.json').is_file()}
+ if selected_views and cam not in selected_views:
+ continue
+ if pipeline_active and cam != last_camera:
+ if last_camera is not None:
+ queue_camera(last_camera)
+ if cam in closed_cameras:
+ # Complete queued readers before accepting late fields for those cameras.
+ for job in camera_jobs.values():
+ try:
+ job.result()
+ except Exception:
+ pass
+ shutil.rmtree(work / '.converted', ignore_errors=True)
+ camera_jobs.clear()
+ closed_cameras.clear()
+ pipeline_active = False
+ print(f'Camera-order fallback: {"/".join(current)}', flush=True)
+ last_camera = cam
+ frame = FRAME.fullmatch(tail)
+ if tail != 'cam_intrinsics.txt' and not frame:
+ continue
+ if frame and args.max_frames and int(frame[2]) >= args.max_frames:
+ continue
+ data = tf.extractfile(member).read()
+ if tail.endswith('.txt'):
+ payload = data # Small source annotations stay in RAM, not thousands of HDD files.
+ else:
+ staging = work
+ if ram_budget is not None and cam not in published_views:
+ lock, used, limit = ram_budget
+ needed = (len(data) + 4095) // 4096 * 4096
+ with lock:
+ if used[0] + needed <= limit:
+ used[0] += needed
+ ram_reserved += needed
+ staging = ram_work
+ payload = safe_member(staging, member.name)
+ if cam not in published_views:
+ payload.parent.mkdir(parents=True, exist_ok=True)
+ payload.write_bytes(data)
+ # Published views still verify source bytes, but never read their staging paths.
+ item = (payload, locator(archive, member.name, data))
+ if tail == 'cam_intrinsics.txt':
+ intrinsics[cam] = item
+ else:
+ kind, f = frame[1], int(frame[2])
+ row = rows.setdefault(cam, {}).setdefault(f, {})
+ if kind in row:
+ raise ValueError('duplicate H2O frame')
+ row[kind] = item
+ else:
+ flush()
+ finally:
+ # Never delete staging while a closed-camera job can still read from it.
+ for job in camera_jobs.values():
+ try:
+ job.result()
+ except Exception:
+ pass
+ finally:
+ if ram_budget is not None:
+ lock, used, limit = ram_budget
+ with lock:
+ used[0] -= ram_reserved
+ return done
+
+
+def main(argv=None):
+ p = parser('H2O', __doc__)
+ p.set_defaults(release_id='shared-v1.0')
+ p.add_argument('--workers', type=int, choices=range(1, 5), default=1,
+ help='Independent subject archives in parallel, 1..4; benchmark before using >2')
+ p.add_argument('--view-backend', choices=('threads', 'processes'), default='threads',
+ help='Use spawn processes to isolate Python camera processing')
+ p.add_argument('--camera-pipeline', action='store_true',
+ help='Overlap contiguous-camera extraction and conversion; reject interleaved input')
+ p.add_argument('--ffv1-slices', type=int, choices=(16,),
+ help='Optional FFV1 v3 slice parallelism; decoded frames must still match exactly')
+ p.add_argument('--view-workers', type=int, choices=range(1, 17), default=1,
+ help='Shared camera-view conversion pool, independent of archive readers')
+ p.add_argument('--ram-stage-dir', type=Path, help='Optional tmpfs staging; excess PNGs fall back to disk')
+ p.add_argument('--ram-stage-gib', type=int, choices=range(1, 65), default=32,
+ help='Shared PNG tmpfs quota across all archive readers, not per worker')
+ p.add_argument('--stream-media', action='store_true',
+ help='Pipe RGB/depth frames to FFmpeg instead of spooling huge RAW files')
+ p.add_argument('--codec-threads', type=int, choices=(1, 2, 4, 8, 10), default=4,
+ help='FFV1 slice threads per encode; default 4, use 10 for the current full run')
+ p.add_argument('--decode-threads', type=int, choices=(1, 2, 4, 8, 10), default=1,
+ help='FFmpeg threads for source RGB decoding and decoded-media checks')
+ p.add_argument('--depth-threads', type=int, choices=(1, 2, 4), default=1,
+ help='Bounded ordered source depth PNG loading per worker')
+ p.add_argument('--subjects')
+ p.add_argument('--include-casa', action='store_true')
+ p.add_argument('--archive', type=Path, help='Convert one explicit source archive')
+ args = p.parse_args(argv)
+ if args.camera_pipeline and args.view_workers < 2:
+ p.error('--camera-pipeline requires at least two --view-workers')
+ os.environ['HAND_WAREHOUSE_FFMPEG_THREADS'] = str(args.decode_threads)
+ if args.ffv1_slices is None:
+ os.environ.pop('HAND_WAREHOUSE_FFV1_SLICES', None)
+ else:
+ os.environ['HAND_WAREHOUSE_FFV1_SLICES'] = str(args.ffv1_slices)
+ if args.include_casa and args.workers != 1:
+ p.error('CASA can share sequence IDs with subject archives; convert it sequentially in a separate release')
+ archives = [args.archive.expanduser()] if args.archive else sorted(
+ {a for root in roots(args.root) for a in root.glob('subject*_v1_1.tar.gz')})
+ if args.subjects and not args.archive:
+ requested = {'subject' + s.strip() for s in args.subjects.split(',')}
+ archives = [arc for arc in archives if arc.name.split('_', 1)[0] in requested]
+ if args.include_casa:
+ casa = find_file(args.root, 'h2o_CASA.tar')
+ if casa:
+ archives.append(casa)
+ if not archives:
+ p.error('no H2O archives found')
+ out = release(args, 'H2O')
+ source_mapping = next(p for p in (Path(__file__).resolve().parents[1] / 'docs/h2o-RAW_TO_SHARED.md',
+ Path(__file__).resolve().parents[1] / 'doc/RAW_TO_SHARED.md',
+ out / 'doc/RAW_TO_SHARED.md') if p.is_file())
+ mapping = out / 'doc/RAW_TO_SHARED.md'
+ if mapping.exists() and mapping.read_bytes() != source_mapping.read_bytes():
+ raise ValueError('RAW_TO_SHARED.md changed; choose a new release-id')
+ if not mapping.exists():
+ shutil.copyfile(source_mapping, mapping)
+ os.environ.pop('HAND_WAREHOUSE_MEDIA_TMPDIR', None)
+ ram_budget = None
+ if args.ram_stage_dir is not None:
+ args.ram_stage_dir = args.ram_stage_dir.expanduser().resolve()
+ if not args.ram_stage_dir.is_relative_to(Path('/dev/shm').resolve()):
+ p.error('--ram-stage-dir must be on /dev/shm; it is temporary input, not an output dataset')
+ args.ram_stage_dir.mkdir(parents=True, exist_ok=True)
+ limit = args.ram_stage_gib * 1024**3
+ if shutil.disk_usage(args.ram_stage_dir).free < limit + 8 * 1024**3:
+ p.error('not enough tmpfs headroom for the requested shared staging quota')
+ ram_budget = (threading.Lock(), [0], limit)
+ if args.stream_media:
+ os.environ['HAND_WAREHOUSE_MEDIA_TMPDIR'] = str(args.ram_stage_dir)
+ torch.set_num_threads(1)
+ models, splits = mano_models(args.mano_dir), load_splits(args.root)
+ executor = ProcessPoolExecutor if args.view_backend == 'processes' else ThreadPoolExecutor
+ options = {'mp_context': multiprocessing.get_context('spawn')} if args.view_backend == 'processes' else {'thread_name_prefix': 'h2o-view'}
+ with tempfile.TemporaryDirectory(prefix='h2o-assets-') as wd, \
+ executor(max_workers=args.view_workers, **options) as views:
+ assets, asset_archive = load_assets(args.root, Path(wd))
+ def convert_one(archive):
+ return convert_archive(archive, args, out, splits, models, assets, asset_archive,
+ views if args.view_workers > 1 else None, ram_budget)
+ if args.workers == 1:
+ for archive in archives:
+ convert_one(archive)
+ else:
+ with ThreadPoolExecutor(max_workers=args.workers) as pool:
+ for archive, count in zip(archives, pool.map(convert_one, archives), strict=True):
+ print(f'Archive {archive.name}: {count} sequences', flush=True)
+ return 0
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
+
+# TODO(v2.0): 以下旧 MANO 网格和渲染分割代码仅保留备查；v1.0 不生成标注。
+# def load_mano_model(path: Path):
+# """MPI MANO pkl → numpy 字典。shapedirs 在 pkl 内为 chumpy Select（对 (778,3,20) 基
+# 底 flat 选取前 10 个 shape 分量；idxs 已核实等价于 base[:,:,:10]）。"""
+# with open(path, "rb") as f:
+# d = _ManoUnpickler(f, encoding="latin1").load()
+# sd = d["shapedirs"]
+# base = np.asarray(sd.a.x, np.float64) # (778, 3, 20)
+# shapedirs = base.reshape(-1)[np.asarray(sd.idxs)].reshape(base.shape[0], 3, -1)
+# jreg = d["J_regressor"]
+# jreg = np.asarray(jreg.todense()) if hasattr(jreg, "todense") else np.asarray(jreg)
+# return {"v_template": np.asarray(d["v_template"], np.float64),
+# "shapedirs": shapedirs,
+# "posedirs": np.asarray(d["posedirs"], np.float64),
+# "weights": np.asarray(d["weights"], np.float64),
+# "J_regressor": np.asarray(jreg, np.float64),
+# "kintree": np.asarray(d["kintree_table"], np.int64),
+# "faces": np.asarray(d["f"], np.int64)}
+#
+#
+# def rodrigues(r):
+# """axis-angle (N,3) → 旋转矩阵 (N,3,3)。公式 I + sinθ·K + (1−cosθ)·K²（已对 scipy 核验）。"""
+# r = np.asarray(r, np.float64).reshape(-1, 3)
+# th = np.linalg.norm(r, axis=1)
+# R = np.zeros((len(r), 3, 3))
+# nz = th > 1e-12
+# R[~nz] = np.eye(3)
+# k = r[nz] / th[nz, None]
+# K = np.zeros((len(k), 3, 3))
+# K[:, 0, 1], K[:, 0, 2], K[:, 1, 2] = -k[:, 2], k[:, 1], -k[:, 0]
+# K[:, 1, 0], K[:, 2, 0], K[:, 2, 1] = k[:, 2], -k[:, 1], k[:, 0]
+# s, c = np.sin(th[nz])[:, None, None], np.cos(th[nz])[:, None, None]
+# R[nz] = np.eye(3) + s * K + (1 - c) * (K @ K)
+# return R
+#
+#
+# def _mano_chain(model, betas, pose):
+# """返回 (v_shaped, J, Rs, G)：G 为 16 个关节的全局变换（平移列 = 关节位置）。"""
+# v = model["v_template"] + np.einsum("nkc,c->nk", model["shapedirs"], betas)
+# J = model["J_regressor"] @ v
+# Rs = rodrigues(np.asarray(pose, np.float64).reshape(16, 3))
+# kt = model["kintree"]
+# G = np.zeros((16, 4, 4))
+# for j in range(16):
+# p = kt[0, j] if j else -1
+# loc = np.eye(4)
+# loc[:3, :3] = Rs[j]
+# loc[:3, 3] = J[j] - (J[p] if j else 0.0)
+# G[j] = loc if not j else G[p] @ loc
+# return v, J, Rs, G
+#
+#
+# def mano_forward(model, betas, pose, transl):
+# """MANO LBS 前向（经典蒙皮 M_j = G_j·G0_j⁻¹；对 hand_pose 21 关节实证 0.0 mm）。
+# pose: 48 axis-angle（[0:3] 全局）；返回相机系米顶点 (778,3)。"""
+# v, J, Rs, G = _mano_chain(model, betas, pose)
+# lrot = (Rs[1:] - np.eye(3)).ravel()
+# v_posed = v + np.einsum("nkc,c->nk", model["posedirs"], lrot)
+# kt = model["kintree"]
+# G0 = np.zeros((16, 4, 4))
+# for j in range(16):
+# p = kt[0, j] if j else -1
+# loc = np.eye(4)
+# loc[:3, 3] = J[j] - (J[p] if j else 0.0)
+# G0[j] = loc if not j else G0[p] @ loc
+# M = np.einsum("nj,jab->nab", model["weights"], G @ np.linalg.inv(G0))
+# out = np.einsum("nab,nb->na", M[:, :3, :3], v_posed) + M[:, :3, 3]
+# return out + np.asarray(transl, np.float64)
+#
+#
+# def mano_chain_joints(model, betas, pose, transl):
+# """16 个关节位置（相机系米）= 链平移列；自检用。"""
+# _, _, _, G = _mano_chain(model, betas, pose)
+# return G[:, :3, 3] + np.asarray(transl, np.float64)
+#
+#
+# def rasterize(verts_cam, faces, labels, W, H, fx, fy, cx, cy):
+# z = verts_cam[:, 2]
+# P = faces
+# ok = (z[P] > 0.02).all(1) # 任一顶点在相机后 → 丢弃该三角形
+# P, labels = P[ok], labels[ok]
+# if len(P) == 0:
+# return np.zeros((H, W), np.uint16), np.zeros(0, np.int64)
+# x = verts_cam[:, 0] / z * fx + cx
+# y = verts_cam[:, 1] / z * fy + cy
+# xa = np.maximum(0, np.minimum(np.floor(x[P[:, 0]]), np.minimum(
+# np.floor(x[P[:, 1]]), np.floor(x[P[:, 2]]))).astype(np.int64))
+# xb = np.minimum(W - 1, np.maximum(np.ceil(x[P[:, 0]]), np.maximum(
+# np.ceil(x[P[:, 1]]), np.ceil(x[P[:, 2]]))).astype(np.int64))
+# ya = np.maximum(0, np.minimum(np.floor(y[P[:, 0]]), np.minimum(
+# np.floor(y[P[:, 1]]), np.floor(y[P[:, 2]]))).astype(np.int64))
+# yb = np.minimum(H - 1, np.maximum(np.ceil(y[P[:, 0]]), np.maximum(
+# np.ceil(y[P[:, 1]]), np.ceil(y[P[:, 2]]))).astype(np.int64))
+# xa, ya = np.minimum(xa, xb), np.minimum(ya, yb) # 完全出画的三角形 → 空 bbox
+# wdt, hgt = xb - xa + 1, yb - ya + 1
+# cnt = wdt * hgt
+# tot = int(cnt.sum())
+# tri = np.repeat(np.arange(len(P)), cnt)
+# k = np.arange(tot) - (np.cumsum(cnt) - cnt)[tri]
+# px = xa[tri] + k % wdt[tri] + 0.5
+# py = ya[tri] + k // wdt[tri] + 0.5
+# ax, ay = x[P[tri, 0]], y[P[tri, 0]]
+# bx, by = x[P[tri, 1]], y[P[tri, 1]]
+# cx_, cy_ = x[P[tri, 2]], y[P[tri, 2]]
+# area = ((x[P[:, 1]] - x[P[:, 0]]) * (y[P[:, 2]] - y[P[:, 0]])
+# - (y[P[:, 1]] - y[P[:, 0]]) * (x[P[:, 2]] - x[P[:, 0]]))
+# areat = area[tri]
+# e0 = (bx - ax) * (py - ay) - (by - ay) * (px - ax)
+# e1 = (cx_ - bx) * (py - by) - (cy_ - by) * (px - bx)
+# e2 = (ax - cx_) * (py - cy_) - (ay - cy_) * (px - cx_)
+# s = np.sign(areat)
+# inside = (np.abs(areat) > 1e-9) & ((e0 * s >= 0) & (e1 * s >= 0) & (e2 * s >= 0))
+# px, py, tri = px[inside], py[inside], tri[inside]
+# at = areat[inside]
+# w0, w1, w2 = e1[inside] / at, e2[inside] / at, e0[inside] / at
+# zz = w0 * z[P[tri, 0]] + w1 * z[P[tri, 1]] + w2 * z[P[tri, 2]]
+# pix = py.astype(np.int64) * W + px.astype(np.int64)
+# order = np.lexsort((zz, pix)) # 同像素按深度升序 → 首条即最近
+# pix, tri = pix[order], tri[order]
+# first = np.unique(pix, return_index=True)[1]
+# mask = np.zeros(H * W, np.uint16)
+# win = tri[first]
+# mask[pix[first]] = labels[win] + 1 # 临时标签 +1（0=背景）
+# return mask.reshape(H, W), np.bincount(labels[win] + 1)
+#
+#
+# # ============================================================================
+# # ffmpeg 写出与核验（与 convert_dexycb.py 同构）
+# # ============================================================================
+# verts, faces_all, labels = [], [], []
+# for ti, (tid, kind) in enumerate(tracks):
+# if kind == "hand_left" and b["mano"][0] > 0.5:
+# verts.append(mano_forward(mano_models["left"], b["mano"][52:62],
+# b["mano"][4:52], b["mano"][1:4]))
+# faces_all.append(mano_models["left"]["faces"])
+# elif kind == "hand_right" and b["mano"][62] > 0.5:
+# verts.append(mano_forward(mano_models["right"], b["mano"][114:124],
+# b["mano"][66:114], b["mano"][63:66]))
+# faces_all.append(mano_models["right"]["faces"])
+# elif isinstance(kind, tuple) and int(b["objp"][0]) == kind[1]:
+# M = np.asarray(b["objrt"][1:17], np.float64).reshape(4, 4)
+# V0, F0 = meshes[kind[1]]
+# verts.append(V0 @ M[:3, :3].T + M[:3, 3])
+# faces_all.append(F0)
+# else:
+# continue
+# labels.append(np.full(len(faces_all[-1]), ti))
+# if verts:
+# V = np.concatenate(verts)
+# offs = np.cumsum([0] + [len(v) for v in verts[:-1]])
+# F = np.concatenate([fa + o for fa, o in zip(faces_all, offs)])
+# mask, counts = rasterize(V, F, np.concatenate(labels),
+# IMG_W, IMG_H, fx, fy, cx, cy)
+# else:
+# mask, counts = np.zeros((IMG_H, IMG_W), np.uint16), np.zeros(0, np.int64)
+# lut = np.zeros(n_tracks + 1, np.uint16)
+# for ti in range(n_tracks):
+# present = ti + 1 < len(counts) and counts[ti + 1] > 0
+# if present:
+# presence[tracks[ti][0]][f] = True
+# if active_id[ti] is None:
+# pid = next(i for i in range(1, 65535) if i not in used)
+# used.add(pid)
+# active_id[ti], seg_start[ti] = pid, f
+# frame_maps[ti].append({"id": pid})
+# lut[ti + 1] = active_id[ti]
+# elif active_id[ti] is not None:
+# frame_maps[ti][-1]["frames"] = [seg_start[ti], f - 1]
+# segments[ti].append([seg_start[ti], f - 1])
+# used.discard(active_id[ti])
+# active_id[ti] = None
+# mw.write(lut[mask])
+
+
+# TODO(v2.0): 旧物体尺寸派生代码；v1.0 仅保留源物体关键点。
+# pts = np.asarray(b["objp"][1:64], np.float64).reshape(21, 3)
+# half = np.abs((pts[1:9] - pts[0]) @ M[:3, :3]).max(0)
+# obj_recs.append({
+# "track_id": f"o{classes.index(c)}", "frame": f,
+# "mesh_ref": f"h2o/{OBJ_CLASSES[c]}.obj",
+# "pose": {"quat": rotmat_to_quat_wxyz(M[:3, :3])[0].tolist(),
+# "trans": M[:3, 3].tolist()},
+# "bbox_center3d": pts[0].tolist(),
+# "bbox_size": (2 * half).tolist()})
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/convert_hoi4d_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/convert_hoi4d_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d5914d64ae72ee62564897743dfe5a41923e08d
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/convert_hoi4d_v1.0.py
@@ -0,0 +1,595 @@
+#!/usr/bin/env python3
+"""HOI4D native observations and annotations → CONTRACT.md v1.0."""
+from collections import Counter
+from concurrent.futures import ThreadPoolExecutor
+from contextlib import ExitStack
+from functools import lru_cache
+import gzip
+import io
+import json
+import os
+from pathlib import Path
+import pickle
+import re
+import shutil
+import shlex
+import posixpath
+import subprocess
+import tempfile
+import tarfile
+import threading
+import zipfile
+
+# Each worker owns one Python thread; prevent native libraries spawning CPU pools.
+for key in ('OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS'):
+ os.environ[key] = '1'
+
+import numpy as np
+from PIL import Image
+from scipy.spatial.transform import Rotation
+
+import torch
+
+from warehouse_v1_0 import (View, build_record, check_mesh, decode_args, find_file, json_write, locator,
+ mano_models, model_profile, parser, probe, release, run, safe_member, sequence_dir)
+
+CATEGORIES = {
+ 1: dict(cad="rigid/ToyCar", groups=[("main", [1], "Toy Car", 1)],
+ hands={2: "right"}, objmap={"Toycar": "main"}),
+ 2: dict(cad="rigid/Mug", groups=[("a", [1], "Mug", 2), ("b", [3], "Mug", 2)],
+ hands={2: "right"}, objmap={"Watercup": "a"}),
+ 3: dict(cad="articulated/Laptop", groups=[("main", [1, 3], "Laptop", 3)],
+ hands={2: "right"},
+ objmap={"Laptopdisplay": "main", "Laptopkeyboard": "main"}),
+ 4: dict(cad="articulated/StorageFurniture",
+ groups=[("main", [1, 3, 4], "Storage Furniture", 4),
+ ("bottle", [5], "Bottle", 5)],
+ hands={2: "right"},
+ objmap={"Lockerbody": "main", "Lockersldingdoor": "main",
+ "Lockerdrawer": "main", "Safebox": "main"}),
+ 5: dict(cad="rigid/Bottle", groups=[("main", [1], "Bottle", 5),
+ ("mug", [3], "Mug", 2)],
+ hands={2: "right"}, objmap={"bottleddrinks": "main"}),
+ 6: dict(cad="articulated/Safe", groups=[("main", [1, 3], "Safe", 6),
+ ("bottle", [4], "Bottle", 5)],
+ hands={2: "right"}, objmap={"Safebox": "main", "Safedoor": "main"}),
+ 7: dict(cad="rigid/Bowl", groups=[("main", [1], "Bowl", 7),
+ ("ball", [3], "Ball", None)],
+ hands={2: "right"}, objmap={"bowl": "main"}),
+ 8: dict(cad="articulated/Bucket",
+ groups=[("a", [1, 3], "Bucket", 8), ("b", [4, 5], "Bucket", 8)],
+ hands={2: "right", 6: "left"},
+ objmap={"bucket": "a", "Buckethandle": "a"}),
+ 9: dict(cad="articulated/Scissors", groups=[("main", [1, 3], "Scissors", 9)],
+ hands={2: "right"},
+ objmap={"scissors": "main", "scissors2": None}), # scissors2：场景道具，无掩码
+ 11: dict(cad="articulated/Pliers", groups=[("main", [1, 3], "Pliers", 11)],
+ hands={2: "right"}, objmap={"Pliersleft": "main", "Pliersright": "main"}),
+ 12: dict(cad="rigid/Kettle", groups=[("main", [1], "Kettle", 12),
+ ("mug", [3], "Mug", 2)],
+ hands={2: "right"}, objmap={"kettle": "main"}),
+ 13: dict(cad="rigid/Knife", groups=[("main", [1], "Knife", 13),
+ ("apple", [3], "Apple", None)],
+ hands={2: "right"}, objmap={"knife": "main"}),
+ 14: dict(cad="articulated/TrashCan",
+ groups=[("main", [1, 3], "Trash Can", 14), ("bottle", [4], "Bottle", 5)],
+ hands={2: "right"}, objmap={"Dustbinbase": "main", "Dustbincover": "main"}),
+ 17: dict(cad="articulated/Lamp", groups=[("main", [1, 3, 4, 5], "Lamp", 17)],
+ hands={2: "right", 6: "left"},
+ objmap={"Lampbase": "main", "Tablelampfirstjoint": "main",
+ "Tablelampsecondjoint": "main", "Thirdjointoftablelamp": "main"}),
+ 18: dict(cad="articulated/Stapler",
+ groups=[("main", [1, 3, 4], "Stapler", 18), ("paper", [5], "Paper", None)],
+ hands={2: "right", 6: "left"}, # 6=左手：官方 label.csv 缺列，实测修正
+ objmap={"Staplerbase": "main", "Staplercover": "main"}),
+ 20: dict(cad="rigid/Chair", groups=[("main", [1], "Chair", 20)],
+ hands={2: "right", 3: "left"}, objmap={"chair": "main"}),
+}
+
+
+# Official HOI4D definitions/motion segmentation/label.csv; no spatial side inference.
+REGION_NAMES = {1: {1: 'Toy Car', 2: 'Right Hand'}, 2: {1: 'Mug', 2: 'Right Hand', 3: 'Mug'}, 3: {1: 'Screen', 2: 'Right Hand', 3: 'Keyboard'}, 4: {1: 'Door', 2: 'Right Hand', 3: 'Body of the Cabinet', 4: 'Drawer', 5: 'Bottle'}, 5: {1: 'Bottle', 2: 'Right Hand', 3: 'Mug'}, 6: {1: 'Body of the Safe', 2: 'Right Hand', 3: 'Door', 4: 'Bottle'}, 7: {1: 'Bowl', 2: 'Right Hand', 3: 'Ball'}, 8: {1: 'Body of the Bucket', 2: 'Right Hand', 3: 'Handle', 4: 'Body of the Bucket', 5: 'Handle', 6: 'Left Hand'}, 9: {1: 'Left Part of the Scissors', 2: 'Right Hand', 3: 'Right Part of the Scissors'}, 11: {1: 'Left Part of the Pliers', 2: 'Right Hand', 3: 'Right Part of the Pliers'}, 12: {1: 'Kettle', 2: 'Right Hand', 3: 'Mug'}, 13: {1: 'Knife', 2: 'Right Hand', 3: 'Apple'}, 14: {1: 'Body of the Trash Can', 2: 'Right Hand', 3: 'Lid', 4: 'Bottle'}, 17: {1: 'Base', 2: 'Right Hand', 3: 'Tube', 4: 'Top Link', 5: 'Bottom Link', 6: 'Left Hand'}, 18: {1: 'Base', 2: 'Right Hand', 3: 'Slot', 4: 'Lid', 5: 'Paper'}, 20: {1: 'Chair', 2: 'Right Hand', 3: 'Left Hand'}}
+
+
+class SplitFiles(io.RawIOBase):
+ """Read concatenated source gzip parts without concatenating 119 GB on disk."""
+ def __init__(self, paths):
+ self.paths = iter(paths)
+ self.current = None
+
+ def readable(self):
+ return True
+
+ def readinto(self, buffer):
+ while True:
+ if self.current is None:
+ path = next(self.paths, None)
+ if path is None:
+ return 0
+ self.current = path.open('rb')
+ n = self.current.readinto(buffer)
+ if n:
+ return n
+ self.current.close()
+ self.current = None
+
+ def close(self):
+ if self.current is not None:
+ self.current.close()
+ super().close()
+
+
+def extract_depths(root, sequences, work):
+ directory = find_file(root, 'DEPTH')
+ if directory is None:
+ return {}, []
+ parts = sorted(directory.glob('HOI4D_depth_video.tar.gz*'), key=lambda p: int(p.name.split('.gz')[1] or 0))
+ if not parts:
+ return {}, []
+ wanted = {f'HOI4D_depth_video/{seq}/align_depth/depth_video.avi': seq for seq in sequences}
+ found = {}
+ with SplitFiles(parts) as joined, io.BufferedReader(joined) as inp, gzip.GzipFile(fileobj=inp) as gz:
+ with tarfile.open(fileobj=gz, mode='r|') as tf:
+ for member in tf:
+ if member.isfile() and member.name in wanted:
+ seq = wanted[member.name]
+ dest = work / (seq.replace('/', '_') + '.avi')
+ with tf.extractfile(member) as src, dest.open('wb') as dst:
+ shutil.copyfileobj(src, dst)
+ found[seq] = (dest, {'files': [str(p.resolve()) for p in parts], 'member': member.name})
+ if len(found) == len(wanted):
+ break
+ return found, parts
+
+
+def palette_mask(data):
+ with Image.open(io.BytesIO(data)) as im:
+ arr = np.asarray(im)
+ if arr.ndim == 2:
+ return arr.astype(np.uint16)
+ if arr.shape[-1] != 3:
+ raise ValueError('unsupported HOI4D mask representation')
+ # VOC's 256-color palette interleaves eight label bits into the high RGB bits.
+ # Invert it directly instead of sorting every pixel with np.unique.
+ if np.any(arr & np.array([31, 31, 63], dtype=np.uint8)):
+ raise ValueError('source mask has colors outside official VOC palette')
+ mask = np.zeros(arr.shape[:2], dtype=np.uint16)
+ for bit in range(8):
+ mask |= ((arr[..., bit % 3] >> (7 - bit // 3)) & 1).astype(np.uint16) << bit
+ return mask
+
+
+# Four workers each reuse annotation and hand indices instead of rebuilding the ZIP directory.
+@lru_cache(maxsize=8)
+def index_frames(z):
+ index = {}
+ for name in z.namelist():
+ parent, _, filename = name.rpartition('/')
+ stem, dot, suffix = filename.rpartition('.')
+ if not dot or not stem.isdigit() or suffix not in ('png', 'json', 'pickle'):
+ continue
+ entries = index.setdefault((parent + '/', '.' + suffix), {})
+ f = int(stem)
+ if f in entries:
+ raise ValueError(f'duplicate frame number under {parent}')
+ entries[f] = name
+ return index
+
+
+def frame_members(z, prefix, suffix):
+ return index_frames(z).get((prefix, suffix), {})
+
+
+def convert_sequence(seq, args, target, rgbz, annz, hpz, calz, cadz, paths, models, depth):
+ # ponytail: cache only one sequence's encoded annotation bytes; no decoded masks or CAD cache.
+ @lru_cache(maxsize=None)
+ def read_annotation(archive, name):
+ return archive.read(name)
+
+ category = int(re.search(r'/C(\d+)/', seq)[1])
+ definition = CATEGORIES.get(category)
+ if definition is None:
+ raise ValueError(f'category mapping unavailable: {category}')
+ prefix = f'HOI4D_annotations/{seq}'
+ mask_members = frame_members(annz, prefix + '/2Dseg/mask/', '.png')
+ shifted = frame_members(annz, prefix + '/2Dseg/shift_mask/', '.png')
+ if not mask_members:
+ mask_members = shifted
+ pose_members = frame_members(annz, prefix + '/objpose/', '.json')
+ hand_members = {side: frame_members(hpz, f'Hand_pose/handpose_{side}_hand/{seq}/', '.pickle')
+ for side in ('left', 'right')} if hpz else {'left': {}, 'right': {}}
+ with tempfile.TemporaryDirectory(prefix='hoi4d-source-') as wd:
+ work = Path(wd)
+ member = f'HOI4D_release/{seq}/align_rgb/image.mp4'
+ video = work / 'rgb.mp4'
+ with rgbz.open(member) as src, video.open('wb') as dst:
+ shutil.copyfileobj(src, dst)
+ info = probe(video)
+ if 'nb_frames' not in info:
+ info['nb_frames'] = json.loads(run(['ffprobe', '-v', 'error', '-count_frames', '-select_streams', 'v:0',
+ '-show_entries', 'stream=nb_read_frames', '-of', 'json', str(video)], stdout=subprocess.PIPE).stdout)['streams'][0]['nb_read_frames']
+ n = int(info['nb_frames'])
+ if args.max_frames:
+ n = min(n, args.max_frames)
+ if shifted and mask_members != shifted and any(f not in mask_members and f in shifted for f in range(n)):
+ raise ValueError('shift_mask has frames absent from mask; correspondence unverified')
+ frames = []
+ for f in range(n):
+ sources = {}
+ if f in mask_members:
+ sources['segment'] = locator(paths['annotations'], mask_members[f], read_annotation(annz, mask_members[f]))
+ if f in pose_members:
+ sources['object'] = locator(paths['annotations'], pose_members[f], read_annotation(annz, pose_members[f]))
+ for side in ('left', 'right'):
+ if f in hand_members[side]:
+ sources['hand_' + side] = locator(paths['hand'], hand_members[side][f], read_annotation(hpz, hand_members[side][f]))
+ frames.append(dict(source_frame_id=f, source_locator=locator(paths['release'], member, frame=f),
+ component_sources=sources, annotation_matches={k: dict(source_frame_id=f, source_timestamp_ns=None,
+ clock_domain=None, match_method='index_map', delta_ns=None) for k in sources}))
+ view = View(target, 'HOI4D', args.release_id, seq.replace('/', '_'), 'ego', frames)
+ view.valid_rules = {
+ 'mano': 'Matched side pickle contains poseCoeff, beta, trans and corresponding model available; zero angles are not missing',
+ 'keypoints': 'Matched side pickle contains kps2D; independently provided source points, no visibility gate',
+ 'pose': 'Matched source object record contains rotation and center; record identity retained',
+ 'dimensions': 'Matched source object record contains dimensions; size/mesh relation remains source_unspecified',
+ 'segment': 'Source mask or shift_mask present with verified palette codes and entity/part mapping',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta['view_type'] = 'ego'
+ view.meta['sampling']['selection'] = {'max_frames': args.max_frames, 'source_sequence': seq}
+ view.rgb([], locator(paths['release'], member), video=video, count=n)
+ intr_name = f'camera_params/{seq.split("/")[0]}/intrin.npy'
+ if calz and intr_name in calz.namelist():
+ view.camera(np.load(io.BytesIO(calz.read(intr_name))), locator(paths['camera'], intr_name))
+ extr_name = prefix + '/3Dseg/output.log'
+ if extr_name in annz.NameToInfo:
+ lines = [line for line in annz.read(extr_name).decode().splitlines() if line.strip()]
+ if len(lines) % 5:
+ raise ValueError('invalid Open3D camera trajectory')
+ extr, valid = np.full((n, 4, 4), np.nan, np.float32), np.zeros(n, bool)
+ for i in range(0, len(lines), 5):
+ idx = int(lines[i].split()[0])
+ if idx < n:
+ if valid[idx]:
+ raise ValueError('duplicate trajectory index')
+ extr[idx] = np.linalg.inv(np.loadtxt(io.StringIO('\n'.join(lines[i + 1:i + 5]))))
+ valid[idx] = True
+ view.tensor('extrinsic', 'transform/se3/v1', 'camera/extrinsic.pt', extr, valid,
+ ['T', 'row', 'column'], locator(paths['annotations'], extr_name),
+ convention='T_camera_from_world', source_convention='Open3D LOG camera-to-world; inverted')
+ loc = {'frames_ref': 'camera/frames.jsonl', 'source_field': 'component_sources'}
+ motion, mv = np.full((n, 2, 61), np.nan, np.float32), np.zeros((n, 2), bool)
+ points, pv = np.full((n, 2, 21, 2), np.nan, np.float32), np.zeros((n, 2, 21), bool)
+ for slot, side in enumerate(('left', 'right')):
+ for f, name in hand_members[side].items():
+ if f >= n:
+ continue
+ data = pickle.loads(read_annotation(hpz, name), encoding='latin1')
+ if all(k in data for k in ('poseCoeff', 'beta', 'trans')):
+ motion[f, slot] = np.r_[np.asarray(data['poseCoeff']).reshape(48),
+ np.asarray(data['beta']).reshape(10), np.asarray(data['trans']).reshape(3)]
+ mv[f, slot] = True # Zero angles are a valid pose, not a missing sentinel.
+ if 'kps2D' in data:
+ points[f, slot] = np.asarray(data['kps2D']).reshape(21, 2)
+ pv[f, slot] = True
+ if mv.any():
+ if all(side in models for slot, side in enumerate(('left', 'right')) if mv[:, slot].any()):
+ view.tensor('mano', 'mano/axis_angle61/v1', 'annotation/mano/motion.pt', motion, mv,
+ ['T', 'hand', 'parameter'], loc, model_profile=model_profile(models))
+ else:
+ view.absent('mano', 'Source MANO exists; provide --mano-dir to fix model identity', 'dependency_missing')
+ if pv.any():
+ view.tensor('keypoints_uv', 'keypoints/uv/v1', 'annotation/keypoints/uv.pt', points, pv,
+ ['T', 'hand', 'keypoint', 'coordinate'], loc, keypoint_schema='mano21/v1', unit='pixel',
+ source_method='Upstream fitted MANO projection already present in kps2D')
+ objects = {}
+ groups = {key: (vals, name, cls) for key, vals, name, cls in definition['groups']}
+ def ensure_object(key, source_loc, category_id=None, frame=None, source_id=None):
+ if key not in objects:
+ view.object(key, source_loc, scope='frame' if frame is not None else 'sequence',
+ scope_id=f'{seq}:{frame}' if frame is not None else None,
+ category_id=category_id, source_object_id=source_id)
+ objects[key] = {}
+ mappings = []
+ mask_valid = [f in mask_members for f in range(n)]
+ if any(mask_valid):
+ width, height = view.meta['camera']['image_size']
+ def masks():
+ for f in range(n):
+ labels = []
+ if f in mask_members:
+ mask = palette_mask(read_annotation(annz, mask_members[f]))
+ for value in np.unique(mask):
+ value = int(value)
+ if value in (0, 255):
+ if value == 255:
+ raise ValueError('HOI4D 255 region semantics unverified')
+ continue
+ if REGION_NAMES[category].get(value) in ('Left Hand', 'Right Hand'):
+ entity, part, cls = 'hand_' + REGION_NAMES[category][value].split()[0].lower(), None, 'hand'
+ else:
+ group = next((k for k, (vals, _, _) in groups.items() if value in vals), None)
+ entity = group or f'unknown_region_{value}'
+ cls = groups[group][2] if group else None
+ part = f'region_{value}' if group and len(groups[group][0]) > 1 else None
+ ensure_object(entity, frames[f]['component_sources']['segment'], cls)
+ if part:
+ objects[entity].setdefault('parts', {})[part] = {'part_id': part, 'parent_id': None,
+ 'has_model': False, 'mesh_ref': None, 'source_region_code': value, 'name': REGION_NAMES[category].get(value)}
+ labels.append(dict(mask_value=value, category_id=cls, entity_id=entity,
+ part_id=part, source_instance_id=value,
+ source_attributes={'region_name': REGION_NAMES[category].get(value)}))
+ else:
+ mask = np.zeros((height, width), np.uint16)
+ mappings.append(dict(frame_index=f, labels=labels))
+ yield mask
+ view.raster('segment', masks(), mask_valid, loc, mappings)
+ view.meta['components']['segment']['coverage'] = {'kind': 'unknown'}
+ if any(label['part_id'] for row in mappings for label in row['labels']):
+ view.meta['components']['segment']['segmentation_type'] = 'part'
+ else:
+ view.absent('segment', 'No native segmentation for this source sequence')
+ records = {}
+ for f, name in pose_members.items():
+ if f >= n:
+ continue
+ data = json.loads(read_annotation(annz, name))
+ # isEffective is not used to erase existing numerical annotations.
+ source_records = data.get('dataList', data.get('objects'))
+ if source_records is None:
+ raise ValueError('unrecognized source object annotation structure')
+ if category == 2 and source_records and (len(source_records) != 1 or
+ source_records[0].get('id') != 1 or source_records[0].get('label') != 'Watercup'):
+ raise ValueError('C2 native pose differs from the verified one-object target; cannot map to mask region 1')
+ label_counts = Counter(record['label'] for record in source_records)
+ per_frame = set()
+ for record_index, record in enumerate(source_records):
+ label = record['label']
+ group = definition['objmap'].get(label)
+ # Pinned official ObjTracking reads dataList[0] with segmentation label 1.
+ source_primary = category == 2 and record_index == 0 and record.get('id') == 1 and group == 'a'
+ ambiguous = group is not None and sum(g[2] == groups[group][2] for g in groups.values()) > 1 and not source_primary
+ ambiguous = ambiguous or (label_counts[label] > 1 and not source_primary) or group is None
+ entity = f'pose_frame_{f}_{record_index}' if ambiguous else group
+ ensure_object(entity, locator(paths['annotations'], name, record=record_index),
+ groups[group][2] if group else None, frame=f if ambiguous else None, source_id=record.get('id'))
+ if ambiguous:
+ view.build['unresolved'].append({'component': entity, 'status': 'source_semantics_unverified',
+ 'reason': 'Source pose retained with frame identity; no guessed association to same-class mask instances'})
+ label_key = (entity, label)
+ if label_key in per_frame:
+ raise ValueError(f'ambiguous repeated object label {label}; source identity must be resolved')
+ per_frame.add(label_key)
+ records.setdefault(label_key, {})[f] = (record, locator(paths['annotations'], name, record=record.get('id')))
+ if category == 2 and any(entity == 'a' for entity, _ in records):
+ view.build['validation'].append({'component': 'C2_source_pose_mask_identity',
+ 'rule': 'Official ObjTracking process_data.py reads dataList[0] with semantic mask label 1',
+ 'source_revision': 'afa7bf37b02b7e33cf245acd08b98c207ccc175c',
+ 'source_pose_id': 1, 'mask_value': 1, 'matched_frames': sum(len(by_frame) for (entity, _), by_frame in records.items() if entity == 'a')})
+ for (entity, label), by_frame in records.items():
+ part_labels = [k[1] for k in records if k[0] == entity]
+ articulated = definition['cad'].startswith('articulated/') and entity == definition['groups'][0][0]
+ part = 'pose_' + re.sub(r'[^a-zA-Z0-9_]', '_', label) if articulated or len(part_labels) > 1 else None
+ base = f'annotation/object/{entity}'
+ if part:
+ base += '/parts/' + part
+ objects[entity].setdefault('parts', {})[part] = dict(part_id=part, parent_id=None,
+ has_model=False, mesh_ref=None, source_label=label)
+ poses, valid = np.full((n, 4, 4), np.nan, np.float32), np.zeros(n, bool)
+ dimensions = np.full((n, 3), np.nan, np.float32)
+ dv = np.zeros(n, bool)
+ for f, (record, _) in by_frame.items():
+ if all(k in record for k in ('rotation', 'center')):
+ poses[f] = np.eye(4)
+ poses[f, :3, :3] = Rotation.from_euler('XYZ', [record['rotation'][k] for k in ('x', 'y', 'z')]).as_matrix()
+ poses[f, :3, 3] = [record['center'][k] for k in ('x', 'y', 'z')]
+ valid[f] = True
+ if 'dimensions' in record:
+ dimensions[f] = [record['dimensions'][k] for k in ('length', 'width', 'height')]
+ dv[f] = True
+ key = entity + ('_' + part if part else '')
+ view.tensor('pose_' + key, 'transform/se3/v1' if part else 'object/rigid/v1', base + '/pose.pt', poses, valid,
+ ['T', 'row', 'column'], loc, convention='T_camera_from_part' if part else 'T_camera_from_object',
+ unit='m', source_label=label, source_euler_order='XYZ',
+ definition='https://github.com/hoi4d/ObjTracking/blob/main/process_data.py')
+ if dv.any():
+ view.tensor('dimensions_' + key, 'object/dimensions/v1', base + '/dimensions.pt', dimensions, dv,
+ ['T', 'dimension'], loc, dimensions_semantics='source_unspecified', unit='m', scale_applied_to_mesh=False)
+ if cadz:
+ primary = definition['groups'][0][0]
+ if category != 2 and sum(g[2] == category for g in groups.values()) > 1:
+ primary = 'source_cad_instance'
+ instance = int(re.search(r'/N(\d+)/', seq)[1])
+ mesh_prefix = f'HOI4D_CAD_Model_for_release/{definition["cad"]}/{instance:03d}'
+ candidates = [name for name in cadz.namelist() if not name.endswith('/') and
+ (name == mesh_prefix + '.obj' or name.startswith(mesh_prefix + '/'))]
+ if candidates:
+ # Retain OBJ material/texture dependencies beside their original names.
+ queue = list(candidates)
+ for name in queue:
+ if Path(name).suffix.lower() not in ('.obj', '.mtl'):
+ continue
+ for line in cadz.read(name).decode().splitlines():
+ fields = shlex.split(line, comments=True)
+ if not fields:
+ continue
+ refs = fields[1:] if fields[0] == 'mtllib' else ([fields[-1]] if
+ fields[0].startswith('map_') or fields[0] in ('bump', 'norm', 'disp', 'decal') else [])
+ for ref in refs:
+ dependency = posixpath.normpath(posixpath.join(posixpath.dirname(name), ref))
+ if dependency not in cadz.NameToInfo:
+ raise ValueError(f'missing CAD dependency: {dependency}')
+ if dependency not in candidates:
+ candidates.append(dependency)
+ queue.append(dependency)
+ ensure_object(primary, locator(paths['cad'], mesh_prefix), category)
+ asset_dir = view.path / f'annotation/object/{primary}/assets'
+ asset_refs = []
+ for name in candidates:
+ relative = posixpath.relpath(name, mesh_prefix if definition['cad'].startswith('articulated/') else posixpath.dirname(mesh_prefix))
+ dest = safe_member(asset_dir, relative)
+ dest.parent.mkdir(parents=True, exist_ok=True)
+ dest.write_bytes(cadz.read(name))
+ ref = dest.relative_to(view.path).as_posix()
+ if dest.suffix.lower() == '.obj':
+ asset_key = re.sub(r'[^a-zA-Z0-9_]', '_', relative)
+ view.component('mesh_' + primary + '_' + asset_key, 'mesh/obj/v1', ref, ['vertex'],
+ locator(paths['cad'], name), unit='m', valid=True,
+ scale_applied_to_mesh=False, dimensions_semantics='source_unspecified')
+ asset_refs.append(ref)
+ if definition['cad'].startswith('articulated/'):
+ part_id = 'asset_' + asset_key
+ objects[primary].setdefault('parts', {})[part_id] = dict(part_id=part_id,
+ parent_id=None, has_model=True, mesh_ref=ref, source_asset=relative,
+ note='Source asset; correspondence to motion part labels remains unverified')
+ for asset_ref in asset_refs:
+ mesh_check = check_mesh(view.path / asset_ref, asset_dir)
+ view.build['validation'].append({'component': asset_ref, **mesh_check,
+ 'unit_basis': 'official ObjTracking loads source canonical CAD without scaling in metre camera coordinates',
+ 'geometric_acceptance': 'physical dimensions-to-mesh relation remains source_unspecified'})
+ metadata_path = view.path / f'annotation/object/{primary}/metainfo.json'
+ metadata = json.loads(metadata_path.read_text())
+ metadata.update(has_model=bool(asset_refs), asset_refs=asset_refs)
+ if definition['cad'].startswith('rigid/') and len(asset_refs) == 1:
+ metadata['mesh_ref'] = asset_refs[0]
+ json_write(metadata_path, metadata)
+ if definition['cad'].startswith('articulated/'):
+ view.build['unresolved'].append({'component': 'object_part_asset_association',
+ 'status': 'source_semantics_unverified',
+ 'reason': 'All source assets and motion labels retained, but no guessed correspondence or joint_state'})
+ for entity, info in objects.items():
+ if 'parts' in info:
+ ref = f'annotation/object/{entity}/articulation.json'
+ json_write(view.path / ref, {'parts': list(info['parts'].values()), 'joints': [],
+ 'hierarchy_semantics': 'semantic_containment',
+ 'note': 'Unassociated source region, motion and asset nodes; original CAD trees retained in assets'})
+ view.component('articulation_' + entity, 'object/articulated/v1', ref, [], loc)
+ meta_path = view.path / f'annotation/object/{entity}/metainfo.json'
+ metadata = json.loads(meta_path.read_text())
+ metadata['articulation_ref'] = ref
+ json_write(meta_path, metadata)
+ if depth is not None:
+ dpath, dloc = depth
+ dmeta = probe(dpath)
+ if [dmeta['width'], dmeta['height']] != view.meta['camera']['image_size']:
+ raise ValueError('HOI4D depth grid differs from RGB')
+ if dmeta['pix_fmt'] != 'gray16le':
+ raise ValueError('unexpected HOI4D depth pixel format')
+ width, height = view.meta['camera']['image_size']
+ raw = work / 'depth.raw'
+ limits = ['-frames:v', str(n)] if args.max_frames else []
+ run(decode_args(dpath, 'gray16le') + limits + ['-y', str(raw)])
+ if raw.stat().st_size != n * width * height * 2:
+ raise ValueError('depth/RGB frame count or grid mismatch')
+ def depths():
+ with raw.open('rb') as inp:
+ for _ in range(n):
+ yield np.frombuffer(inp.read(width * height * 2), ' best_iou + 0.1 and i > 0.15:
+# best, best_iou = s[0], i
+# if best != gk:
+# gk, iou_fixups = best, iou_fixups + 1
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/convert_visor_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/convert_visor_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f0595bf5e89ae00c959472ff197dfda2d7a6ea6
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/convert_visor_v1.0.py
@@ -0,0 +1,271 @@
+#!/usr/bin/env python3
+"""VISOR source polygons and contact → CONTRACT.md v1.0, without rasterization or side inference."""
+from collections import Counter
+import hashlib
+import json
+import re
+import shutil
+import tempfile
+from pathlib import Path
+import zipfile
+
+import numpy as np
+import torch
+
+from warehouse_v1_0 import (View, build_record, find_file, jsonl_write, locator, parser,
+ release, roots, sequence_dir, source)
+
+FRAME = re.compile(r'frame_(\d+)')
+HAND = {300: 'left', 301: 'right'}
+GLOVE = {303: 'left', 304: 'right'}
+STATES = {'hand-not-in-contact': 'negative', 'glove-not-in-contact': 'negative',
+ 'none-of-the-above': 'target_unannotated', 'inconclusive': 'unknown'}
+
+
+def hand_side(ann):
+ if ann['class_id'] in HAND:
+ return HAND[ann['class_id']]
+ if ann['class_id'] not in (60, 303, 304):
+ return None
+ raw = ann.get('on_which_hand')
+ raw = raw if isinstance(raw, list) else [raw]
+ sides = set()
+ for item in raw:
+ if item == 'left hand':
+ sides.add('left')
+ if item in ('right hand', 'rigth hand'):
+ sides.add('right')
+ if len(sides) == 1:
+ return next(iter(sides))
+ if not sides and ann['class_id'] in GLOVE:
+ return GLOVE[ann['class_id']]
+ return None
+
+
+def convert_video(video, split, annotation, ann_loc, rgb_zip, rgb_loc, mapping, args, target):
+ indexed = sorted(enumerate(annotation['video_annotations']), key=lambda row: int(FRAME.search(row[1]['image']['name'])[1]))
+ if args.max_frames:
+ indexed = indexed[:args.max_frames]
+ if len({row['image']['name'] for _, row in indexed}) != len(indexed):
+ raise ValueError('duplicate VISOR frame')
+ keys = lambda row, ann: (row['image']['subsequence'], ann['class_id'], ann['name'])
+ collisions = set()
+ for _, row in indexed:
+ collisions.update(key for key, number in Counter(keys(row, a) for a in row['annotations']).items() if number > 1)
+ frames, paths = [], []
+ with tempfile.TemporaryDirectory(prefix='visor-rgb-') as wd, zipfile.ZipFile(rgb_zip) as z:
+ work = Path(wd)
+ for original_index, row in indexed:
+ name = row['image']['name']
+ data = z.read(name)
+ path = work / Path(name).name
+ path.write_bytes(data)
+ paths.append(path)
+ loc = rgb_loc | {'nested_member': name, 'sha256': hashlib.sha256(data).hexdigest()}
+ frames.append(dict(source_frame_id=int(FRAME.search(name)[1]), source_locator=loc,
+ source_image=row['image'], original_frame_name=mapping.get(name),
+ annotation_matches={'segment': dict(source_frame_id=name, source_timestamp_ns=None,
+ clock_domain=None, match_method='index_map', delta_ns=None,
+ source_locator=ann_loc | {'record': f'video_annotations[{original_index}]'})}))
+ view = View(target, 'EPIC-KITCHENS-VISOR', args.release_id, video, 'ego', frames, sampling='sparse', split=split)
+ view.valid_rules = {
+ 'segment': 'Selected sparse annotation frame present; all source polygons finite with at least three vertices and entity mapping valid',
+ 'relations': 'At least one source in_contact_object value present; positive targets resolve in the same frame; unknown is an explicit usable status',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta.update(view_type='ego', split_scheme='VISOR_GroundTruth_SparseAnnotations', split_source_locator=ann_loc)
+ view.meta['sampling']['selection'] = {'representation': 'official sparse annotations', 'max_frames': args.max_frames}
+ view.rgb(paths, rgb_loc)
+ entities = {}
+ polygons, mappings, relations, relation_valid = [], [], [], []
+ for f, (original_index, row) in enumerate(indexed):
+ frame_loc = ann_loc | {'record': f'video_annotations[{original_index}]'}
+ id_map, labels, polys = {}, [], []
+ for code, ann in enumerate(row['annotations'], 1):
+ if code >= 65535:
+ raise ValueError('too many source instances')
+ sid = ann['id']
+ if sid in id_map:
+ raise ValueError('duplicate source instance ID within a frame')
+ side = hand_side(ann)
+ if side is not None:
+ entity = 'hand_' + side
+ else:
+ key = keys(row, ann)
+ identity = key + (sid, row['image']['name']) if key in collisions else key
+ entity = 'object_' + hashlib.sha256(json.dumps(identity).encode()).hexdigest()[:20]
+ if entity not in entities:
+ scope = 'frame' if key in collisions else 'subsequence'
+ scope_id = f'{video}:{row["image"]["name"]}' if scope == 'frame' else f'{video}:{key[0]}'
+ view.object(entity, frame_loc | {'source_instance_id': sid}, scope, scope_id,
+ category_id=ann['class_id'], name=ann['name'],
+ association_basis='source_frame_instance' if scope == 'frame' else 'official_subsequence_entity_name')
+ entities[entity] = True
+ id_map[sid] = entity
+ for polygon in ann.get('segments', []):
+ p = np.asarray(polygon)
+ if p.ndim != 2 or p.shape[1] != 2 or len(p) < 3 or not np.isfinite(p).all():
+ raise ValueError(f'invalid source polygon {sid}')
+ polys.append(dict(mask_value=code, polygons=ann.get('segments', [])))
+ labels.append(dict(mask_value=code, category_id=ann['class_id'], entity_id=entity,
+ part_id=None, source_instance_id=sid,
+ source_attributes={k: v for k, v in ann.items() if k != 'segments'}))
+ rr = []
+ provided = False
+ for ann in row['annotations']:
+ raw = ann.get('in_contact_object')
+ values = raw if isinstance(raw, list) else [raw]
+ for value in values:
+ if value is None:
+ continue
+ provided = True
+ status = STATES.get(value)
+ obj = None
+ if status is None:
+ if value not in id_map:
+ raise ValueError(f'unresolved source contact target {value} in {video} frame {f}')
+ status, obj = 'positive', id_map[value]
+ rr.append(dict(relation_type='contact', subject_id=id_map[ann['id']], object_id=obj,
+ status=status, source_value=value, source_locator=frame_loc | {'source_instance_id': ann['id']},
+ provenance=source('Resolve source contact ID against instances in this frame')))
+ polygons.append(dict(frame_index=f, instances=polys))
+ mappings.append(dict(frame_index=f, labels=labels))
+ relations.append(dict(frame_index=f, relations=rr))
+ relation_valid.append(provided)
+ jsonl_write(view.path / 'annotation/segment/polygons.jsonl', polygons)
+ jsonl_write(view.path / 'annotation/segment/instances.jsonl', mappings)
+ torch.save(torch.ones(len(frames), dtype=torch.bool, device='cpu'), view.path / 'annotation/segment/mask_valid.pt')
+ view.component('segment', 'segment/instance_polygons/v1', 'annotation/segment/polygons.jsonl', ['T'], ann_loc,
+ valid_ref='annotation/segment/mask_valid.pt', valid_axes=['T'], segmentation_type='instance',
+ mapping_ref='annotation/segment/instances.jsonl', coverage={'kind': 'unknown', 'exhaustive_ref': 'annotation/segment/instances.jsonl'})
+ view.relations(relations, relation_valid, ann_loc)
+ for component in ('depth', 'mano', 'keypoints_uv', 'keypoints_xyz', 'intrinsic', 'extrinsic'):
+ view.absent(component, 'Not provided by VISOR sparse source annotations')
+ return view.finish()
+
+
+def main(argv=None):
+ p = parser('EPIC-KITCHENS-VISOR', __doc__)
+ p.add_argument('--videos', help='Comma-separated video names')
+ p.add_argument('--splits', default='train,val')
+ args = p.parse_args(argv)
+ wanted = set(args.videos.split(',')) if args.videos else None
+ splits = set(args.splits.split(','))
+ if not splits <= {'train', 'val'}:
+ p.error('only annotated train/val splits are available')
+ out = release(args, 'EPIC-KITCHENS-VISOR')
+ archive = find_file(args.root, 'EPIC-KITCHENS VISOR.zip')
+ with tempfile.TemporaryDirectory(prefix='visor-source-') as wd:
+ work = Path(wd)
+ if archive:
+ with zipfile.ZipFile(archive) as z:
+ suffix = 'GroundTruth-SparseAnnotations/annotations/'
+ members = [n for n in z.namelist() if suffix in n and n.endswith('.json')]
+ prefix = members[0].split(suffix)[0]
+ fm = json.loads(z.read(prefix + 'frame_mapping.json'))
+ done = 0
+ for member in sorted(members):
+ split, filename = member.split(suffix)[1].split('/')
+ video = Path(filename).stem
+ if split not in splits or wanted and video not in wanted:
+ continue
+ data = z.read(member)
+ rgb_member = prefix + f'GroundTruth-SparseAnnotations/rgb_frames/{split}/{video.split("_")[0]}/{video}.zip'
+ rgb_zip = work / (video + '.zip')
+ with z.open(rgb_member) as inp, rgb_zip.open('wb') as dest:
+ shutil.copyfileobj(inp, dest)
+ with sequence_dir(out, video) as dest:
+ report = convert_video(video, split, json.loads(data), locator(archive, member, data),
+ rgb_zip, locator(archive, rgb_member), fm.get(video, {}), args, dest / 'ego')
+ build_record(out, video, {'ego': report})
+ rgb_zip.unlink()
+ done += 1
+ print(f'Converted {video}', flush=True)
+ if args.limit and done >= args.limit:
+ break
+ else:
+ root = next((r for r in roots(args.root) if (r / 'GroundTruth-SparseAnnotations').is_dir()), None)
+ if root is None:
+ p.error('VISOR outer ZIP or extracted source directory not found')
+ fm = json.loads((root / 'frame_mapping.json').read_text()) if (root / 'frame_mapping.json').exists() else {}
+ done = 0
+ for split in sorted(splits):
+ for ann in sorted((root / 'GroundTruth-SparseAnnotations/annotations' / split).glob('*.json')):
+ video = ann.stem
+ if wanted and video not in wanted:
+ continue
+ rgb = root / f'GroundTruth-SparseAnnotations/rgb_frames/{split}/{video.split("_")[0]}/{video}.zip'
+ with sequence_dir(out, video) as dest:
+ report = convert_video(video, split, json.loads(ann.read_text()), locator(ann), rgb,
+ locator(rgb), fm.get(video, {}), args, dest / 'ego')
+ build_record(out, video, {'ego': report})
+ done += 1
+ if args.limit and done >= args.limit:
+ return 0
+ if not done:
+ p.error('no matching annotated videos found')
+ return 0
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
+
+# TODO(v2.0): 旧距离猜侧和单层掩码生成仅注释保留；v1.0 不执行。
+# if side is None and len(frame_hands) == 1:
+# # 空间门槛：脱下放在远处的手套不并入（实测双峰 0..25px / ≥44px）
+# hb = frame_hands[0][1]
+# gb = _bbox_of(polys)
+# if hb and gb and _bbox_dist(gb, hb) <= GLOVE60_MERGE_BBOX_DIST:
+# side = frame_hands[0][0]
+# stats["glove60_merged_into_sole_hand"] += 1
+# if side is None:
+# stats["dropped_glove60_unresolved"] += 1
+# id_disp[mid] = ("dropped", "glove60")
+# continue
+# def rasterize_and_encode(vdir, frames, frame_draw, zip_path, n, stats):
+# """流式读 zip 内 jpg + 光栅化多边形 → rgb.mkv / mask.mkv；返回 (w, h)。"""
+# from PIL import Image, ImageDraw
+#
+# with zipfile.ZipFile(zip_path) as zf:
+# member_of = {}
+# for nm in zf.namelist():
+# member_of[os.path.basename(nm)] = nm
+# names = [im["name"] for im in frames]
+# missing = [nm for nm in names if nm not in member_of]
+# if missing:
+# raise RuntimeError(f"{len(missing)} annotated frames missing in zip, "
+# f"e.g. {missing[:3]}")
+# stats["zip_extra_members"] = len(member_of) - len(names)
+#
+# # 首帧定分辨率（官方 1920×1080，实测每帧校验）
+# img0 = Image.open(io.BytesIO(zf.read(member_of[names[0]]))).convert("RGB")
+# w, h = img0.size
+#
+# bin_img = Image.new("L", (w, h), 0)
+# bin_draw = ImageDraw.Draw(bin_img)
+# canvas = np.zeros((h, w), np.uint16)
+# with VideoPipe(vdir / "mask.mkv", w, h, FPS, "mask") as mw, \
+# VideoPipe(vdir / "rgb.mkv", w, h, FPS, "rgb") as rw:
+# for f in range(n):
+# canvas[:] = 0
+# for pid, polys in frame_draw[f]:
+# bin_draw.rectangle((0, 0, w, h), fill=0)
+# for poly in polys:
+# bin_draw.polygon([(float(x), float(y)) for x, y in poly], fill=255)
+# arr = np.asarray(bin_img)
+# canvas[arr > 0] = pid # 后画覆盖先画：物体→手（modal）
+# mw.write(canvas)
+# img = img0 if f == 0 else Image.open(
+# io.BytesIO(zf.read(member_of[names[f]]))).convert("RGB")
+# if img.size != (w, h):
+# raise RuntimeError(f"frame {names[f]}: size {img.size} != {(w, h)}")
+# rw.write(np.asarray(img))
+# n_mask, n_rgb = mw.n, rw.n
+# if (n_rgb, n_mask) != (n, n):
+# raise RuntimeError(f"wrote rgb={n_rgb} mask={n_mask} != {n}")
+# got = (count_frames(vdir / "rgb.mkv"), count_frames(vdir / "mask.mkv"))
+# if got != (n, n):
+# raise RuntimeError(f"encoded rgb={got[0]} mask={got[1]} != {n} (decode verify)")
+# return w, h
+#
+#
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-check-fallback.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-check-fallback.py
new file mode 100644
index 0000000000000000000000000000000000000000..96c33bf9e1c9db26ac0a555e174470827da026ad
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-check-fallback.py
@@ -0,0 +1,137 @@
+"""Full coverage/numeric audit, plus representative persisted-media QC and offline HTML."""
+import os
+for key in ('OMP_NUM_THREADS','OPENBLAS_NUM_THREADS','MKL_NUM_THREADS','NUMEXPR_NUM_THREADS'):
+ os.environ[key]='1'
+os.environ['HAND_WAREHOUSE_FFMPEG_THREADS']='4'
+os.environ.pop('HAND_WAREHOUSE_FFV1_SLICES',None)
+os.environ.pop('HAND_WAREHOUSE_MEDIA_TMPDIR',None)
+import collections,datetime,hashlib,html,json,re,sys,time,zipfile
+from concurrent.futures import ThreadPoolExecutor,as_completed
+from pathlib import Path
+from zoneinfo import ZoneInfo
+import numpy as np
+import torch
+root=Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-process16-pipe')
+release=root/'H2O/shared-v1.0'
+sys.path.insert(0,str(root/'frozen-code/scripts'))
+from warehouse_v1_0 import SCHEMA,check_transform,check_valid_policy,check_view_type,safe_member,sha256
+from render_h2o_validation import render
+out=release/'doc/validation/final-quick-check'
+out.mkdir(exist_ok=True)
+torch.set_num_threads(1)
+started=time.monotonic()
+expected={}
+with zipfile.ZipFile('/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/label_split.zip') as z:
+ for name in z.namelist():
+ if '/action_' not in name:continue
+ for line in z.read(name).decode().splitlines():
+ fields=line.split()
+ if len(fields)>1 and fields[1]!='path':
+ seq=fields[1].replace('/','_')
+ expected[seq]=max(expected.get(seq,0),int(fields[-1])+1)
+folders={p.name:p for p in (release/'data').iterdir() if p.is_dir()}
+assert set(folders)==set(expected),(sorted(set(expected)-set(folders)),sorted(set(folders)-set(expected)))
+assert len(expected)==184 and sum(expected.values())==114329
+receipts={p.name.removesuffix('.build.json'):p for p in (release/'doc').glob('*.build.json')}
+assert set(receipts)==set(expected)
+assert 'EXIT=0' in (root/'full.status').read_text()
+failures=[]
+rows=[]
+def require(ok,message):
+ if not ok:raise ValueError(message)
+def load(path):
+ def cpu(storage,location):
+ require(location=='cpu',f'non-CPU storage: {path}')
+ return storage
+ obj=torch.load(path,map_location=cpu,weights_only=True)
+ require(type(obj) is torch.Tensor,f'non-tensor: {path}')
+ return obj.numpy()
+def check_sequence(seq):
+ folder=folders[seq];n=expected[seq]
+ require({p.name for p in folder.iterdir() if p.is_dir()}=={f'cam{i}' for i in range(5)},'five-camera coverage')
+ receipt=json.loads(receipts[seq].read_text())
+ require(receipt['sequence_id']==seq and set(receipt['views'])=={f'cam{i}' for i in range(5)},'build receipt')
+ result=[]
+ for i in range(5):
+ cam=f'cam{i}';view=folder/cam
+ meta=json.loads((view/'metainfo.json').read_text())
+ require((meta['dataset_id'],meta['sequence_id'],meta['camera_id'],meta['num_frames'])==('H2O',seq,cam,n),'header/timeline')
+ require(meta['view_type']==('ego' if i==4 else 'exo'),'view role')
+ require(meta['sampling']['selection']=={'max_frames':None,'views':None},'unexpected subsampling')
+ check_view_type(meta)
+ components=meta['components']
+ require(all(components[k].get('available') for k in ('rgb','depth','mano','keypoints_xyz','intrinsic','extrinsic')),'required modality absent')
+ require(not components['segment']['available'] and not components['relations']['available'],'unexpected synthesized component')
+ rgb_sources=[];depth_sources=[];rgb_count=depth_count=0
+ with (view/'camera/frames.jsonl').open() as inp:
+ count=0
+ for index,line in enumerate(inp):
+ f=json.loads(line)
+ require(f['frame_index']==index and f['source_frame_id']==index,'source frame index')
+ require(f['sync_id']==f'{seq.replace("_","/")}:{index}','synchronization ID')
+ sources=f['component_sources']
+ for kind,loc in sources.items():
+ require(re.fullmatch('[0-9a-f]{64}',loc.get('sha256','')) is not None,f'source SHA256: {kind}')
+ require(f'/{cam}/' in loc.get('member',''),'wrong source camera')
+ rgb_sources.append('rgb' in sources);depth_sources.append('depth' in sources)
+ rgb_count+=rgb_sources[-1];depth_count+=depth_sources[-1];count+=1
+ require(count==n,'frames.jsonl length')
+ numeric=0
+ object_names=[]
+ for key,c in components.items():
+ if not c['available']:continue
+ for field in ('data_ref','valid_ref','mapping_ref'):
+ if c.get(field):
+ path=safe_member(view,c[field])
+ require(path.is_file() and path.stat().st_size>0,f'missing/empty {c[field]}')
+ schema=SCHEMA['components'][c['schema_id']]
+ if c.get('valid_policy'):check_valid_policy(c,n)
+ valid=load(view/c['valid_ref']) if c.get('valid_ref','').endswith('.pt') else None
+ if valid is not None:
+ shape=schema.get('valid_shape')
+ require(valid.dtype==np.bool_ and valid.shape[0]==n,f'{key}: validity dtype/timeline')
+ if shape:require(len(shape)==valid.ndim and all(not isinstance(s,int) or valid.shape[j]==s for j,s in enumerate(shape)),f'{key}: validity shape')
+ if key=='rgb':require(np.array_equal(valid,np.asarray(rgb_sources)), 'RGB existence validity mismatch')
+ if c['data_ref'].endswith('.pt'):
+ arr=load(view/c['data_ref']);numeric+=1
+ require(arr.dtype==np.float32 and arr.shape[0]==n,f'{key}: dtype/timeline')
+ shape=schema.get('shape')
+ if shape:require(len(shape)==arr.ndim and all(not isinstance(s,int) or arr.shape[j]==s for j,s in enumerate(shape)),f'{key}: tensor shape')
+ if valid is not None:
+ require(arr.shape[:valid.ndim]==valid.shape and np.isfinite(arr[valid]).all() and np.isnan(arr[~valid]).all(),f'{key}: finite/NaN validity semantics')
+ if c['schema_id'] in ('transform/se3/v1','object/rigid/v1'):check_transform(arr[valid])
+ if key=='intrinsic':require(np.allclose(arr[:,2],[0,0,1]) and (arr[:,0,0]>0).all() and (arr[:,1,1]>0).all(),'camera intrinsics')
+ if key.startswith('mesh_'):object_names.append(key.removeprefix('mesh_'))
+ proof=receipt['views'][cam]
+ require(not proof['unresolved'],'unresolved QC components')
+ media={}
+ for p in proof['validation']:
+ if p.get('profile')==components['rgb']['storage']['profile'] and p.get('all_frames_exact') is True:media['rgb']=p
+ elif p.get('component') in ('depth','depth_valid'):media[p['component']]=p
+ require(set(media)=={'rgb','depth','depth_valid'},'media roundtrip proof missing')
+ for key,p in media.items():
+ require(p.get('all_frames_exact') is True and re.fullmatch('[0-9a-f]{64}',p.get('decoded_sha256','')) is not None,f'{key}: failed original roundtrip proof')
+ require((view/'observation'/f'{key}.mkv').stat().st_size==p['bytes'],f'{key}: encoded file size changed since conversion')
+ result.append({'sequence_id':seq,'camera':cam,'frames':n,'numeric_components':numeric,
+ 'rgb_source_frames':rgb_count,'depth_source_frames':depth_count,'objects':sorted(object_names)})
+ return result
+
+result=check_sequence('subject1_k2_6')
+from warehouse_v1_0 import decoded_digest
+receipt=json.loads(receipts['subject1_k2_6'].read_text())
+view=folders['subject1_k2_6']/'cam0'
+meta=json.loads((view/'metainfo.json').read_text())
+proofs=receipt['views']['cam0']['validation']
+verified=[]
+for name,pix in (('rgb','rgb24'),('depth','gray16le'),('depth_valid','gray')):
+ p=next(x for x in proofs if (x.get('profile')==meta['components']['rgb']['storage']['profile'] if name=='rgb' else x.get('component')==name))
+ digest,size,seconds=decoded_digest(view/'observation'/f'{name}.mkv',pix)
+ assert digest==p['decoded_sha256'],(name,digest,p['decoded_sha256'])
+ expected_bytes=expected['subject1_k2_6']*1280*720*({'rgb':3,'depth':4,'depth_valid':1}[name])
+ assert size==expected_bytes,(name,size,expected_bytes)
+ verified.append({'component':name,'decoded_sha256':digest,'decoded_bytes':size,'seconds':seconds})
+page=render(view,out/'subject1_k2_6_cam0_fallback')
+json_result={'passed':True,'views':result,'full_decode_comparison':verified,'html':str(page.relative_to(out)),
+ 'note':'Registered lossless H.264 fallback; all current decoded media digests equal original conversion receipts'}
+(out/'H264_FALLBACK_CHECK.json').write_text(json.dumps(json_result,indent=2)+'\n')
+print('FALLBACK_PASS',json.dumps(json_result),flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-finalize-quick-validation.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-finalize-quick-validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..75a0c5f7db6f94423e22d2e8862125a674841dc6
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-finalize-quick-validation.py
@@ -0,0 +1,39 @@
+import datetime,html,json,shutil
+from pathlib import Path
+from zoneinfo import ZoneInfo
+release=Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-process16-pipe/H2O/shared-v1.0')
+out=release/'doc/validation/final-quick-check'
+report=json.loads((out/'QUICK_VALIDATION.json').read_text())
+assert report['failures']==[{'sequence':'subject1_k2_6','error':"ValueError('rgb: failed original roundtrip proof')"}]
+fallback=json.loads((out/'H264_FALLBACK_CHECK.json').read_text());assert fallback['passed']
+shutil.copy2(out/'QUICK_VALIDATION.json',out/'INITIAL_CHECKER_ALERT.json')
+rows=fallback['views']
+report['checked_sequences']+=1;report['checked_views']+=len(rows)
+report['checked_view_frames']+=sum(r['frames'] for r in rows)
+report['rgb_source_frames']+=sum(r['rgb_source_frames'] for r in rows)
+report['depth_source_frames']+=sum(r['depth_source_frames'] for r in rows)
+report['numeric_components']+=sum(r['numeric_components'] for r in rows)
+report['samples'].append({'sequence_id':'subject1_k2_6','camera':'cam0','object':'espresso',
+ 'frames':678,'passed':True,'html':fallback['html'],
+ 'extra_check':'Full RGB/depth/valid current decoded digests equal original receipts'})
+report['resolved_alerts']=[{'view':'subject1_k2_6/cam0','classification':'Registered lossless H.264 fallback, not missing data or corruption',
+ 'original_checker_limitation':'First pass selected only FFV1 proofs; fixed to select the profile actually declared by metainfo',
+ 'followup':'All five cameras rechecked; problematic view RGB, depth and validity fully decoded and matched original SHA256/byte length'}]
+report['failures']=[];report['passed']=True
+report['time']=datetime.datetime.now(ZoneInfo('Asia/Shanghai')).isoformat()
+report['sample_views']=len(report['samples'])
+assert report['checked_sequences']==184 and report['checked_views']==920
+assert report['checked_view_frames']==report['rgb_source_frames']==report['depth_source_frames']==571645
+report['rgb_profiles']={}
+for meta in (release/'data').glob('*/cam*/metainfo.json'):
+ name=json.loads(meta.read_text())['components']['rgb']['storage']['profile']
+ report['rgb_profiles'][name]=report['rgb_profiles'].get(name,0)+1
+(out/'QUICK_VALIDATION.json').write_text(json.dumps(report,ensure_ascii=False,indent=2)+'\n')
+summary={k:v for k,v in report.items() if k!='samples'}
+(out/'index.html').write_text('H2O 全量后抽样验收 H2O 全量后抽样验收 自动检查通过不等于原始几何标注准确。覆盖四个 subject、八类物体、ego/exo；青色为源手部关节点投影，粉色为源物体点投影，仅用于预览。
'+html.escape(json.dumps(summary,ensure_ascii=False,indent=2))+' ')
+operations=release/'script/provenance/operations'
+for src in ('h2o-quick-validation.py','h2o-check-fallback.py','h2o-finalize-quick-validation.py'):
+ shutil.copy2(Path('/tmp')/src,operations/src)
+shutil.copy2('/tmp/h2o-quick-validation.log',out/'INITIAL_RUN.log')
+shutil.copy2('/tmp/h2o-fallback-check.log',out/'FALLBACK_RUN.log')
+print(json.dumps(summary,ensure_ascii=False,indent=2),flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-pipeline-bench-job.sh b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-pipeline-bench-job.sh
new file mode 100644
index 0000000000000000000000000000000000000000..5751d4926d5be28c5d00544b5e89f3d19424e827
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-pipeline-bench-job.sh
@@ -0,0 +1,25 @@
+#!/usr/bin/env bash
+set -u
+cd /home/xuzhefeng/Projects/uni-hoi-dataset || exit 1
+case "$1" in
+ old) code=/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10-d10/frozen-code/scripts/convert_h2o_v1.0.py; extra=();;
+ v8) code=/tmp/h2o-stream-v8-recovery/scripts/convert_h2o_v1.0.py; extra=(--view-workers 8 --stream-media);;
+ v8ram) code=/tmp/h2o-stream-v8ram-recovery/scripts/convert_h2o_v1.0.py; extra=(--view-workers 8 --stream-media --ram-stage-dir /dev/shm/xuzhefeng-h2o-pipeline-v8ram-20260930 --ram-stage-gib 32);;
+ s16) code=/tmp/h2o-stream-s16-recovery/scripts/convert_h2o_v1.0.py; extra=(--view-workers 16 --stream-media --ffv1-slices 16 --ram-stage-dir /dev/shm/xuzhefeng-h2o-pipeline-s16-20260930 --ram-stage-gib 32);;
+ pipeline) code=/tmp/h2o-stream-pipeline-recovery/scripts/convert_h2o_v1.0.py; extra=(--view-workers 16 --camera-pipeline --stream-media --ffv1-slices 16 --ram-stage-dir /dev/shm/xuzhefeng-h2o-pipeline-overlap-20260930 --ram-stage-gib 32);;
+ process) code=/tmp/h2o-process-pipeline-recovery/scripts/convert_h2o_v1.0.py; extra=(--view-workers 16 --view-backend processes --camera-pipeline --stream-media --ffv1-slices 16 --ram-stage-dir /dev/shm/xuzhefeng-h2o-pipeline-process-20260930 --ram-stage-gib 32);;
+ v16) code=/tmp/h2o-stream-v8-recovery/scripts/convert_h2o_v1.0.py; extra=(--view-workers 16 --stream-media);;
+ *) exit 2;;
+esac
+root=/data/xuzhefeng/Datasets/uni-hoi-h2o-pipeline-bench-20260930
+caseout="$root/$1"
+mkdir -p "$caseout/scratch"
+export TMPDIR="$caseout/scratch" PYTHONUNBUFFERED=1
+cg=$(awk -F: '$1=="0" {print $3}' /proc/self/cgroup)
+cat "/sys/fs/cgroup$cg/cpu.stat" > "$caseout/cpu.start"
+date -Is > "$caseout/start"
+.venv/bin/python "$code" --root "$root/raw" --out "$caseout" --workers 4 --codec-threads 10 --decode-threads 10 --depth-threads 4 --max-frames 200 --mano-dir /home/xuzhefeng/Projects/WiLoR/mano_data "${extra[@]}" > "$caseout/convert.log" 2>&1
+code=$?
+cat "/sys/fs/cgroup$cg/cpu.stat" > "$caseout/cpu.end"
+printf '%s\n' "$code" > "$caseout/exit"
+exit "$code"
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-pipeline-benchmark.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-pipeline-benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bc90e6ead0eca79856b81000c60bff9d3b4f8b5
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-pipeline-benchmark.py
@@ -0,0 +1,48 @@
+import datetime,json,os,subprocess,time
+from pathlib import Path
+root=Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-pipeline-bench-20260930')
+cpus=os.cpu_count()
+records=[]
+for mode in ('old','v8'):
+ unit=f'h2o-pipeline-{mode}-20260930.scope'
+ begin=time.monotonic()
+ proc=subprocess.Popen(['systemd-run','--user','--scope','--unit='+unit,'-p','CPUAccounting=yes',
+ '/bin/bash','/tmp/h2o-pipeline-bench-job.sh',mode])
+ path=Path(f'/sys/fs/cgroup/user.slice/user-{os.getuid()}.slice/user@{os.getuid()}.service/app.slice/{unit}')
+ samples=[]
+ while proc.poll() is None:
+ try:
+ stat=dict(line.split() for line in (path/'cpu.stat').read_text().splitlines())
+ samples.append((time.monotonic()-begin,int(stat['usage_usec'])))
+ except (FileNotFoundError,PermissionError):pass
+ time.sleep(0.5)
+ elapsed=time.monotonic()-begin
+ assert proc.returncode==0,(mode,(root/mode/'convert.log').read_text()[-3000:])
+ def usage(name):return int(dict(line.split() for line in (root/mode/name).read_text().splitlines())['usage_usec'])
+ total=usage('cpu.end')-usage('cpu.start')
+ peak=max(((b[1]-a[1])/(b[0]-a[0])/1e6/cpus*100 for a,b in zip(samples,samples[10:])),default=0)
+ row=dict(mode=mode,seconds=elapsed,cpu_seconds=total/1e6,cpu_percent_machine=total/elapsed/1e6/cpus*100,
+ peak_5s_cpu_percent_machine=peak,samples=samples)
+ records.append(row)
+ (root/'performance.json').write_text(json.dumps(records,indent=2)+'\n')
+ print(json.dumps({k:v for k,v in row.items() if k!='samples'}),flush=True)
+# Prove actual media and every numeric tensor match, not just wall-clock speed.
+os.environ['HAND_WAREHOUSE_FFMPEG_THREADS'] = '10'
+import sys
+sys.path.insert(0,'/tmp/h2o-stream-v8-recovery/scripts')
+from warehouse_v1_0 import decoded_digest,validate_view
+import torch
+for view in sorted((root/'old/H2O/shared-v1.0/data').glob('*/cam*')):
+ rel=view.relative_to(root/'old/H2O/shared-v1.0/data')
+ candidate=root/'v8/H2O/shared-v1.0/data'/rel
+ assert validate_view(candidate)['validated']
+ for name,pix in (('rgb','rgb24'),('depth','gray16le'),('depth_valid','gray')):
+ aa=decoded_digest(view/'observation'/f'{name}.mkv',pix)[:2]
+ bb=decoded_digest(candidate/'observation'/f'{name}.mkv',pix)[:2]
+ assert aa==bb,(rel,name)
+ for pt in view.rglob('*.pt'):
+ aa=torch.load(pt,weights_only=True)
+ bb=torch.load(candidate/pt.relative_to(view),weights_only=True)
+ assert aa.shape==bb.shape and aa.dtype==bb.dtype and aa.numpy().tobytes()==bb.numpy().tobytes(),(rel,pt)
+print('PASS all 10 real views: full RGB/depth/valid decoded digests and every numeric tensor match',flush=True)
+(root/'validation.exit').write_text('0\n')
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-pipeline-process-benchmark.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-pipeline-process-benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..b47120c02f15314c732031cbc4b05996c75b2c3e
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-pipeline-process-benchmark.py
@@ -0,0 +1,48 @@
+import datetime,json,os,subprocess,time
+from pathlib import Path
+root=Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-pipeline-bench-20260930')
+cpus=os.cpu_count()
+records=[]
+for mode in ('process',):
+ unit=f'h2o-pipeline-{mode}-20260930.scope'
+ begin=time.monotonic()
+ proc=subprocess.Popen(['systemd-run','--user','--scope','--unit='+unit,'-p','CPUAccounting=yes',
+ '/bin/bash','/tmp/h2o-pipeline-bench-job.sh',mode])
+ path=Path(f'/sys/fs/cgroup/user.slice/user-{os.getuid()}.slice/user@{os.getuid()}.service/app.slice/{unit}')
+ samples=[]
+ while proc.poll() is None:
+ try:
+ stat=dict(line.split() for line in (path/'cpu.stat').read_text().splitlines())
+ samples.append((time.monotonic()-begin,int(stat['usage_usec'])))
+ except (FileNotFoundError,PermissionError):pass
+ time.sleep(0.5)
+ elapsed=time.monotonic()-begin
+ assert proc.returncode==0,(mode,(root/mode/'convert.log').read_text()[-3000:])
+ def usage(name):return int(dict(line.split() for line in (root/mode/name).read_text().splitlines())['usage_usec'])
+ total=usage('cpu.end')-usage('cpu.start')
+ peak=max(((b[1]-a[1])/(b[0]-a[0])/1e6/cpus*100 for a,b in zip(samples,samples[10:])),default=0)
+ row=dict(mode=mode,seconds=elapsed,cpu_seconds=total/1e6,cpu_percent_machine=total/elapsed/1e6/cpus*100,
+ peak_5s_cpu_percent_machine=peak,samples=samples)
+ records.append(row)
+ (root/'performance-process.json').write_text(json.dumps(records,indent=2)+'\n')
+ print(json.dumps({k:v for k,v in row.items() if k!='samples'}),flush=True)
+# Prove actual media and every numeric tensor match, not just wall-clock speed.
+os.environ['HAND_WAREHOUSE_FFMPEG_THREADS'] = '10'
+import sys
+sys.path.insert(0,'/tmp/h2o-process-pipeline-recovery/scripts')
+from warehouse_v1_0 import decoded_digest,validate_view
+import torch
+for view in sorted((root/'old/H2O/shared-v1.0/data').glob('*/cam*')):
+ rel=view.relative_to(root/'old/H2O/shared-v1.0/data')
+ candidate=root/'process/H2O/shared-v1.0/data'/rel
+ assert validate_view(candidate)['validated']
+ for name,pix in (('rgb','rgb24'),('depth','gray16le'),('depth_valid','gray')):
+ aa=decoded_digest(view/'observation'/f'{name}.mkv',pix)[:2]
+ bb=decoded_digest(candidate/'observation'/f'{name}.mkv',pix)[:2]
+ assert aa==bb,(rel,name)
+ for pt in view.rglob('*.pt'):
+ aa=torch.load(pt,weights_only=True)
+ bb=torch.load(candidate/pt.relative_to(view),weights_only=True)
+ assert aa.shape==bb.shape and aa.dtype==bb.dtype and aa.numpy().tobytes()==bb.numpy().tobytes(),(rel,pt)
+print('PASS all 10 real views: full RGB/depth/valid decoded digests and every numeric tensor match',flush=True)
+(root/'validation-process.exit').write_text('0\n')
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-prepare-pipeline-bench.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-prepare-pipeline-bench.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fbe75024965020f88eb282f2eeb448d491b8639
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-prepare-pipeline-bench.py
@@ -0,0 +1,28 @@
+import json,os,tarfile,shutil
+from pathlib import Path
+root=Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-pipeline-bench-20260930')
+root.mkdir(exist_ok=True)
+raw=root/'raw'; raw.mkdir(exist_ok=True)
+old=Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10-d10')
+records=[]
+for subj in ('subject3','subject4'):
+ subject=next((old/'scratch').glob('h2o-source-*/'+subj))
+ sequences=[p for p in subject.glob('*/*') if all((p/f'cam{i}/rgb/000199.png').is_file() for i in range(5))]
+ assert sequences, (subj,subject)
+ seq=sequences[0]
+ snapshot=root/'input-snapshot'/subj/seq.relative_to(subject)
+ for src in seq.glob('cam*/*/*'):
+ if src.is_file() and src.parent.name in ('rgb','depth','cam_pose','hand_pose','hand_pose_mano','obj_pose','obj_pose_rt') and int(src.stem)<200:
+ dest=snapshot/src.relative_to(seq);dest.parent.mkdir(parents=True,exist_ok=True)
+ os.link(src,dest)
+ for src in seq.glob('cam*/cam_intrinsics.txt'):
+ dest=snapshot/src.relative_to(seq);dest.parent.mkdir(parents=True,exist_ok=True);os.link(src,dest)
+ archive=raw/f'{subj}_v1_1.tar.gz'
+ with tarfile.open(archive,'w:gz',compresslevel=0) as tf:
+ tf.add(snapshot,arcname=str(snapshot.relative_to(root/'input-snapshot')))
+ records.append({'sequence':str(snapshot.relative_to(root/'input-snapshot')),'frames':200,'views':5,'archive':str(archive),
+ 'origin':str(seq),'gzip_compression_level':0})
+for name in ('object.zip','label_split.zip'):
+ os.link(Path('/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data')/name,raw/name)
+(root/'input-manifest.json').write_text(json.dumps(records,indent=2)+'\n')
+print('PREPARED',records,flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-prepare-process-full.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-prepare-process-full.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3b73425e82cadf972633e303292f12f4f823fbf
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-prepare-process-full.py
@@ -0,0 +1,45 @@
+"""Preserve complete persistent views; frozen process pipeline only changes execution/storage work."""
+import datetime,json,os,shutil
+from pathlib import Path
+old=Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10-d10')
+new=Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-process16-pipe')
+new.mkdir()
+shutil.copytree('/tmp/h2o-process-pipeline-recovery',new/'frozen-code')
+release=new/'H2O/shared-v1.0'
+for directory in ('data','doc/validation','script/provenance/previous-run','script/provenance/operations'):
+ (release/directory).mkdir(parents=True,exist_ok=True)
+history=release/'script/provenance/previous-run'
+shutil.copytree(old/'H2O/shared-v1.0/script',history/'script')
+shutil.copytree(old/'frozen-code/docs',history/'docs')
+for name in ('full.log','full.status','run-full.sh'):
+ shutil.copy2(old/name,history/name)
+records=[]
+for record in sorted((old/'H2O/shared-v1.0/doc').glob('*.build.json')):
+ build=json.loads(record.read_text());seq=build['sequence_id']
+ assert Path(seq).name==seq and seq not in ('.','..')
+ assert set(build['views'])=={f'cam{i}' for i in range(5)}
+ folder=old/'H2O/shared-v1.0/data'/seq
+ for cam in build['views']:assert (folder/cam/'metainfo.json').is_file()
+ shutil.copytree(folder,release/'data'/seq,copy_function=os.link)
+ shutil.copy2(record,release/'doc'/record.name)
+ records.append({'sequence_id':seq,'original_script_sha256':build['scripts']})
+manifest={'time':datetime.datetime.now().astimezone().isoformat(),'source_run':str(old),'destination_run':str(new),
+ 'reason':'Native bytes unchanged; process/view pipeline, streaming RAW and bounded RAM staging',
+ 'target_cpu_percent_machine':80,'cpu_limit':None,'reused_sequences':records,
+ 'reuse':'Read-only hardlinks; original build records retained; source frame/component SHA256 checked on resume',
+ 'previous_run_provenance':'script/provenance/previous-run'}
+(release/'doc/RESTART_PROVENANCE.json').write_text(json.dumps(manifest,indent=2)+'\n')
+operations=release/'script/provenance/operations'
+for name in ('h2o-prepare-process-full.py','h2o-switch-process-full.sh','h2o-pipeline-benchmark.py',
+ 'h2o-pipeline-process-benchmark.py','h2o-pipeline-bench-job.sh','h2o-prepare-pipeline-bench.py',
+ 'h2o-process-final-tests.log','h2o-shared-final-tests.log'):
+ shutil.copy2(Path('/tmp')/name,operations/name)
+bench=Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-pipeline-bench-20260930')
+for p in bench.glob('performance*.json'):shutil.copy2(p,release/'doc/validation'/p.name)
+shutil.copy2(bench/'input-manifest.json',release/'doc/validation/PIPELINE_BENCH_INPUTS.json')
+shutil.copy2('/tmp/h2o-pipeline-process-benchmark.log',release/'doc/validation/PIPELINE_BENCHMARK.log')
+shutil.copy2('/tmp/h2o-runtime-profile.log',release/'doc/validation/BEFORE_RUNTIME_PROFILE.log')
+shutil.copy2('/tmp/h2o-current-iostat.log',release/'doc/validation/BEFORE_IOSTAT.log')
+for destination in (new/'run-full.sh',release/'script/run-full.sh'):
+ shutil.copy2('/tmp/h2o-run-process-full.sh',destination)
+print('REUSED_SEQUENCES',len(records),'REUSED_VIEWS',5*len(records),flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-process-final-tests.log b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-process-final-tests.log
new file mode 100644
index 0000000000000000000000000000000000000000..fdf27a753f61c8755e23e54721f766549a081d48
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-process-final-tests.log
@@ -0,0 +1,39 @@
+Converted subject1/h1/0
+Converted subject2/h1/0
+Converted subject3/h1/0
+Converted subject4/h1/0
+Converted subject1/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Converted subject2/h1/0
+Archive subject2_v1_1.tar.gz: 1 sequences
+Converted subject3/h1/0
+Archive subject3_v1_1.tar.gz: 1 sequences
+Converted subject4/h1/0
+Archive subject4_v1_1.tar.gz: 1 sequences
+Converted subject2/h1/0
+Converted subject3/h1/0
+Converted subject1/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Archive subject2_v1_1.tar.gz: 1 sequences
+Archive subject3_v1_1.tar.gz: 1 sequences
+Converted subject4/h1/0
+Archive subject4_v1_1.tar.gz: 1 sequences
+Verified existing subject1/h1/0
+Verified existing subject3/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Verified existing subject2/h1/0
+Verified existing subject4/h1/0
+Archive subject2_v1_1.tar.gz: 1 sequences
+Archive subject3_v1_1.tar.gz: 1 sequences
+Archive subject4_v1_1.tar.gz: 1 sequences
+Camera-order fallback: subject1/h1/0
+Converted subject1/h1/0
+Converted subject2/h1/0
+Converted subject1/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Archive subject2_v1_1.tar.gz: 1 sequences
+Converted subject3/h1/0
+Archive subject3_v1_1.tar.gz: 1 sequences
+Converted subject4/h1/0
+Archive subject4_v1_1.tar.gz: 1 sequences
+PASS H2O pipe RGB/depth, RAM annotations, shared view pool 1/4/8, four archive workers, codec/decode 1/4/10, ordered depth 1/2/4 and missing-depth preservation, no resume PNG writes, RGB FFV1-only, depth and persistent QC
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-quick-validation.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-quick-validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..45efdc32457710d01161b4ba227f903a941b1611
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-quick-validation.py
@@ -0,0 +1,161 @@
+"""Full coverage/numeric audit, plus representative persisted-media QC and offline HTML."""
+import os
+for key in ('OMP_NUM_THREADS','OPENBLAS_NUM_THREADS','MKL_NUM_THREADS','NUMEXPR_NUM_THREADS'):
+ os.environ[key]='1'
+os.environ['HAND_WAREHOUSE_FFMPEG_THREADS']='4'
+os.environ.pop('HAND_WAREHOUSE_FFV1_SLICES',None)
+os.environ.pop('HAND_WAREHOUSE_MEDIA_TMPDIR',None)
+import collections,datetime,hashlib,html,json,re,sys,time,zipfile
+from concurrent.futures import ThreadPoolExecutor,as_completed
+from pathlib import Path
+from zoneinfo import ZoneInfo
+import numpy as np
+import torch
+root=Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-process16-pipe')
+release=root/'H2O/shared-v1.0'
+sys.path.insert(0,str(root/'frozen-code/scripts'))
+from warehouse_v1_0 import SCHEMA,check_transform,check_valid_policy,check_view_type,safe_member,sha256
+from render_h2o_validation import render
+out=release/'doc/validation/final-quick-check'
+out.mkdir(exist_ok=True)
+torch.set_num_threads(1)
+started=time.monotonic()
+expected={}
+with zipfile.ZipFile('/data/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O/raw/data/label_split.zip') as z:
+ for name in z.namelist():
+ if '/action_' not in name:continue
+ for line in z.read(name).decode().splitlines():
+ fields=line.split()
+ if len(fields)>1 and fields[1]!='path':
+ seq=fields[1].replace('/','_')
+ expected[seq]=max(expected.get(seq,0),int(fields[-1])+1)
+folders={p.name:p for p in (release/'data').iterdir() if p.is_dir()}
+assert set(folders)==set(expected),(sorted(set(expected)-set(folders)),sorted(set(folders)-set(expected)))
+assert len(expected)==184 and sum(expected.values())==114329
+receipts={p.name.removesuffix('.build.json'):p for p in (release/'doc').glob('*.build.json')}
+assert set(receipts)==set(expected)
+assert 'EXIT=0' in (root/'full.status').read_text()
+failures=[]
+rows=[]
+def require(ok,message):
+ if not ok:raise ValueError(message)
+def load(path):
+ def cpu(storage,location):
+ require(location=='cpu',f'non-CPU storage: {path}')
+ return storage
+ obj=torch.load(path,map_location=cpu,weights_only=True)
+ require(type(obj) is torch.Tensor,f'non-tensor: {path}')
+ return obj.numpy()
+def check_sequence(seq):
+ folder=folders[seq];n=expected[seq]
+ require({p.name for p in folder.iterdir() if p.is_dir()}=={f'cam{i}' for i in range(5)},'five-camera coverage')
+ receipt=json.loads(receipts[seq].read_text())
+ require(receipt['sequence_id']==seq and set(receipt['views'])=={f'cam{i}' for i in range(5)},'build receipt')
+ result=[]
+ for i in range(5):
+ cam=f'cam{i}';view=folder/cam
+ meta=json.loads((view/'metainfo.json').read_text())
+ require((meta['dataset_id'],meta['sequence_id'],meta['camera_id'],meta['num_frames'])==('H2O',seq,cam,n),'header/timeline')
+ require(meta['view_type']==('ego' if i==4 else 'exo'),'view role')
+ require(meta['sampling']['selection']=={'max_frames':None,'views':None},'unexpected subsampling')
+ check_view_type(meta)
+ components=meta['components']
+ require(all(components[k].get('available') for k in ('rgb','depth','mano','keypoints_xyz','intrinsic','extrinsic')),'required modality absent')
+ require(not components['segment']['available'] and not components['relations']['available'],'unexpected synthesized component')
+ rgb_sources=[];depth_sources=[];rgb_count=depth_count=0
+ with (view/'camera/frames.jsonl').open() as inp:
+ count=0
+ for index,line in enumerate(inp):
+ f=json.loads(line)
+ require(f['frame_index']==index and f['source_frame_id']==index,'source frame index')
+ require(f['sync_id']==f'{seq.replace("_","/")}:{index}','synchronization ID')
+ sources=f['component_sources']
+ for kind,loc in sources.items():
+ require(re.fullmatch('[0-9a-f]{64}',loc.get('sha256','')) is not None,f'source SHA256: {kind}')
+ require(f'/{cam}/' in loc.get('member',''),'wrong source camera')
+ rgb_sources.append('rgb' in sources);depth_sources.append('depth' in sources)
+ rgb_count+=rgb_sources[-1];depth_count+=depth_sources[-1];count+=1
+ require(count==n,'frames.jsonl length')
+ numeric=0
+ object_names=[]
+ for key,c in components.items():
+ if not c['available']:continue
+ for field in ('data_ref','valid_ref','mapping_ref'):
+ if c.get(field):
+ path=safe_member(view,c[field])
+ require(path.is_file() and path.stat().st_size>0,f'missing/empty {c[field]}')
+ schema=SCHEMA['components'][c['schema_id']]
+ if c.get('valid_policy'):check_valid_policy(c,n)
+ valid=load(view/c['valid_ref']) if c.get('valid_ref','').endswith('.pt') else None
+ if valid is not None:
+ shape=schema.get('valid_shape')
+ require(valid.dtype==np.bool_ and valid.shape[0]==n,f'{key}: validity dtype/timeline')
+ if shape:require(len(shape)==valid.ndim and all(not isinstance(s,int) or valid.shape[j]==s for j,s in enumerate(shape)),f'{key}: validity shape')
+ if key=='rgb':require(np.array_equal(valid,np.asarray(rgb_sources)), 'RGB existence validity mismatch')
+ if c['data_ref'].endswith('.pt'):
+ arr=load(view/c['data_ref']);numeric+=1
+ require(arr.dtype==np.float32 and arr.shape[0]==n,f'{key}: dtype/timeline')
+ shape=schema.get('shape')
+ if shape:require(len(shape)==arr.ndim and all(not isinstance(s,int) or arr.shape[j]==s for j,s in enumerate(shape)),f'{key}: tensor shape')
+ if valid is not None:
+ require(arr.shape[:valid.ndim]==valid.shape and np.isfinite(arr[valid]).all() and np.isnan(arr[~valid]).all(),f'{key}: finite/NaN validity semantics')
+ if c['schema_id'] in ('transform/se3/v1','object/rigid/v1'):check_transform(arr[valid])
+ if key=='intrinsic':require(np.allclose(arr[:,2],[0,0,1]) and (arr[:,0,0]>0).all() and (arr[:,1,1]>0).all(),'camera intrinsics')
+ if key.startswith('mesh_'):object_names.append(key.removeprefix('mesh_'))
+ proof=receipt['views'][cam]
+ require(not proof['unresolved'],'unresolved QC components')
+ media={}
+ for p in proof['validation']:
+ if p.get('profile')==components['rgb']['storage']['profile'] and p.get('all_frames_exact') is True:media['rgb']=p
+ elif p.get('component') in ('depth','depth_valid'):media[p['component']]=p
+ require(set(media)=={'rgb','depth','depth_valid'},'media roundtrip proof missing')
+ for key,p in media.items():
+ require(p.get('all_frames_exact') is True and re.fullmatch('[0-9a-f]{64}',p.get('decoded_sha256','')) is not None,f'{key}: failed original roundtrip proof')
+ require((view/'observation'/f'{key}.mkv').stat().st_size==p['bytes'],f'{key}: encoded file size changed since conversion')
+ result.append({'sequence_id':seq,'camera':cam,'frames':n,'numeric_components':numeric,
+ 'rgb_source_frames':rgb_count,'depth_source_frames':depth_count,'objects':sorted(object_names)})
+ return result
+with ThreadPoolExecutor(max_workers=4) as pool:
+ jobs={pool.submit(check_sequence,seq):seq for seq in sorted(expected)}
+ for job in as_completed(jobs):
+ seq=jobs[job]
+ try:rows.extend(job.result())
+ except Exception as exc:failures.append({'sequence':seq,'error':repr(exc)})
+ if len(rows)%100==0:print('STRUCTURAL_VIEWS',len(rows),'FAILURES',len(failures),flush=True)
+print('STRUCTURAL_COMPLETE',len(rows),'FAILURES',failures,flush=True)
+# Balance subjects while covering all eight source object classes; include matching exo views.
+objects=('book','espresso','lotion','spray','milk','cocoa','chips','cappuccino')
+selected=[]
+for j,name in enumerate(objects):
+ subject=f'subject{j%4+1}_'
+ candidates=[r for r in rows if r['camera']=='cam4' and name in r['objects']]
+ require(candidates,f'no output for object {name}')
+ own=[r for r in candidates if r['sequence_id'].startswith(subject)]
+ r=min(own or candidates,key=lambda x:(x['frames'],x['sequence_id']))
+ selected.append((r['sequence_id'],'cam4',name))
+ selected.append((r['sequence_id'],'cam0',name))
+samples=[]
+def full_sample(item):
+ seq,cam,name=item
+ view=folders[seq]/cam
+ destination=out/f'{seq}_{cam}'
+ page=render(view,destination) # Reuse persisted contract QC, depth semantics, frame snapshots, native mesh preview.
+ return {'sequence_id':seq,'camera':cam,'object':name,'frames':expected[seq],
+ 'passed':True,'html':str(page.relative_to(out))}
+with ThreadPoolExecutor(max_workers=4) as pool:
+ jobs={pool.submit(full_sample,item):item for item in selected}
+ for job in as_completed(jobs):
+ item=jobs[job]
+ try:samples.append(job.result());print('SAMPLE_PASS',item,flush=True)
+ except Exception as exc:failures.append({'sample':item,'error':repr(exc)});print('SAMPLE_FAIL',item,repr(exc),flush=True)
+report={'time':datetime.datetime.now(ZoneInfo('Asia/Shanghai')).isoformat(),'elapsed_seconds':time.monotonic()-started,
+ 'release':str(release),'passed':not failures,'scope':'All sequence/camera coverage, frame/source manifests, all numeric tensors and original media receipts; 16 representative full persisted-view QC checks and offline previews. Not a new all-video source-pixel comparison.',
+ 'expected_sequences':len(expected),'expected_sync_frames':sum(expected.values()),
+ 'checked_sequences':len(rows)//5,'checked_views':len(rows),'checked_view_frames':sum(r['frames'] for r in rows),
+ 'rgb_source_frames':sum(r['rgb_source_frames'] for r in rows),'depth_source_frames':sum(r['depth_source_frames'] for r in rows),
+ 'numeric_components':sum(r['numeric_components'] for r in rows),'samples':samples,'failures':failures,
+ 'next_step':'Human inspection of representative offline HTML; automatic QC is not proof of source geometric accuracy.'}
+(out/'QUICK_VALIDATION.json').write_text(json.dumps(report,ensure_ascii=False,indent=2)+'\n')
+(out/'index.html').write_text('H2O 全量后抽样验收 H2O 全量后抽样验收 自动检查通过不等于源几何标注正确；以下为四个 subject、八类物体、ego/exo 的源数据预览。
'+html.escape(json.dumps({k:v for k,v in report.items() if k not in ('samples',)},ensure_ascii=False,indent=2))+' ')
+print('FINAL_REPORT',json.dumps({k:v for k,v in report.items() if k!='samples'},ensure_ascii=False),flush=True)
+raise SystemExit(0 if report['passed'] else 1)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-shared-final-tests.log b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-shared-final-tests.log
new file mode 100644
index 0000000000000000000000000000000000000000..9e8b3fadfef4187f122c1bcd295692cddf1d2523
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-shared-final-tests.log
@@ -0,0 +1 @@
+PASS: RGB full/prefix hashes, source depth frames, depth/valid/segment bits and missing-frame rejection
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-switch-process-full.sh b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-switch-process-full.sh
new file mode 100644
index 0000000000000000000000000000000000000000..86ad70c2cc2df78859680827dad7973b5b14c576
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/h2o-switch-process-full.sh
@@ -0,0 +1,12 @@
+#!/usr/bin/env bash
+set -euo pipefail
+cd /home/xuzhefeng/Projects/uni-hoi-dataset
+test "$(cat /tmp/h2o-process-final-tests.exit)" = 0
+test "$(cat /tmp/h2o-shared-final-tests.exit)" = 0
+test "$(cat /tmp/h2o-pipeline-process-benchmark.exit)" = 0
+test ! -e /data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-process16-pipe
+old=/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10-d10
+printf '\nRECONFIGURE=%s REASON=process16_camera_pipeline_ram32_streaming\n' "$(date -Is)" >> "$old/full.status"
+systemctl --user stop h2o-full-d10-20260930.scope
+.venv/bin/python /tmp/h2o-prepare-process-full.py
+systemd-run --user --scope --unit=h2o-full-process16-20260930 -p CPUAccounting=yes /bin/bash /data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-process16-pipe/run-full.sh
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/watch-steady-cpu.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/watch-steady-cpu.py
new file mode 100644
index 0000000000000000000000000000000000000000..06472a5229fde6c7b8ffa97c3eb92dc16e1df864
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/operations/watch-steady-cpu.py
@@ -0,0 +1,38 @@
+"""Measure the whole process tree only after conversion FFmpeg is active; no CPU-fill work."""
+import datetime,json,os,time
+from pathlib import Path
+unit='h2o-full-process16-20260930.scope'
+cg=Path(f'/sys/fs/cgroup/user.slice/user-{os.getuid()}.slice/user@{os.getuid()}.service/app.slice/{unit}')
+root=Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-process16-pipe')
+report=root/'H2O/shared-v1.0/doc/validation/STEADY_CPU_5MIN.json'
+start_wait=time.monotonic()
+while time.monotonic()-start_wait<7200:
+ if not (cg/'cgroup.procs').exists():
+ print('Scope exited before a steady conversion sample',flush=True);raise SystemExit(1)
+ ffmpeg=False
+ for pid in (cg/'cgroup.procs').read_text().split():
+ try:ffmpeg |= Path('/proc',pid,'comm').read_text().strip()=='ffmpeg'
+ except FileNotFoundError:pass
+ if ffmpeg:break
+ time.sleep(5)
+else:
+ print('No conversion FFmpeg within two hours; no steady CPU claim made',flush=True);raise SystemExit(2)
+def sample():
+ values=dict(line.split() for line in (cg/'cpu.stat').read_text().splitlines())
+ return time.monotonic(),int(values['usage_usec'])
+begin=datetime.datetime.now().astimezone().isoformat()
+samples=[sample()]
+for _ in range(60):
+ time.sleep(5)
+ try:samples.append(sample())
+ except FileNotFoundError:
+ print('Scope exited during sample; inspect full.status',flush=True);raise SystemExit(1)
+cpus=os.cpu_count()
+intervals=[(b[1]-a[1])/(b[0]-a[0])/1e6/cpus*100 for a,b in zip(samples,samples[1:])]
+result={'start':begin,'end':datetime.datetime.now().astimezone().isoformat(),'logical_cpus':cpus,
+ 'whole_scope_cpu_percent_machine':(samples[-1][1]-samples[0][1])/(samples[-1][0]-samples[0][0])/1e6/cpus*100,
+ 'highest_5sec_cpu_percent_machine':max(intervals),'lowest_5sec_cpu_percent_machine':min(intervals),
+ 'target_percent':80,'intervals_percent_machine':intervals,'samples':samples,
+ 'scope':unit,'includes':'All scope threads and child processes, including children already exited'}
+report.write_text(json.dumps(result,indent=2)+'\n')
+print(json.dumps({k:v for k,v in result.items() if k not in ('samples','intervals_percent_machine')}),flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/docs/CONTRACT.md b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/docs/CONTRACT.md
new file mode 100644
index 0000000000000000000000000000000000000000..f756d711647e109b6bb732ce1b9c84111730489d
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/docs/CONTRACT.md
@@ -0,0 +1,457 @@
+# 手物数据仓库统一约定 v1.0
+
+格式版本：`hand-warehouse/1.0`。存储约定：`lossless-video-2026-09-18`。
+
+范围限于手物交互重建。按本任务纳入的源模态转换，不要求每个数据集填满所有组件。缺失模态、尚未下载、语义未核实分别报告，不能互相替代。
+
+本文定义各字段的含义与使用规则，机器可读注册表见 [schema.json](schema.json)。两者互相补充；如有冲突，应先核实并统一定义。
+
+## 1. 目录
+
+```text
+hand-datasets/
+ /
+ raw/
+ doc/ # 官方说明、来源与校验记录
+ script/ # 官方下载脚本、必要包装脚本
+ data/ # 保留原始包，不原地解压或改写
+ /
+ doc/ # 发布说明、schema、来源清单
+ script/ # 转换、校验脚本及运行配置
+ data/
+ /
+ /
+ metainfo.json
+ observation/
+ rgb.mkv
+ rgb_valid.pt
+ depth.mkv # float32 深度的可逆视频编码
+ depth_valid.mkv # 逐像素 bool 的无损视频编码
+ camera/
+ frames.jsonl
+ intrinsic.pt
+ intrinsic_valid.pt
+ extrinsic.pt
+ extrinsic_valid.pt
+ annotation/
+ mano/
+ motion.pt
+ motion_valid.pt
+ visibility.pt
+ keypoints/
+ uv.pt
+ uv_valid.pt
+ xyz.pt
+ xyz_valid.pt
+ visibility.pt # 源数据有时提供
+ object/
+ /
+ assets/ # 该物体的网格、材质、纹理及部件资产
+ .obj
+ pose.pt
+ pose_valid.pt
+ visibility.pt
+ metainfo.json # 物体身份、类别、mesh、尺寸与部件引用
+ articulation.json
+ keypoints/
+ vertex_part_ids.pt
+ joint_state.pt
+ joint_state_valid.pt
+ parts//pose.pt
+ parts//pose_valid.pt
+ segment/
+ mask.mkv
+ mask_valid.pt
+ instances.jsonl
+ contact/
+ relations.jsonl
+ __/ # 对应实体间的距离/接触区域
+```
+
+目录展示可能存在的组件，不要求填满。具体文件、字段和缺省规则分别见后续对应小节。原始包的解压发生在临时工作区，raw 内原始包保持不变。
+
+## 2. 相机视图的 metainfo.json
+
+### 2.1 顶层字段与发布信息
+
+每个相机视图必须有 `metainfo.json`。以下字段除另行说明外均为必填：
+
+| 字段 | 填写内容 |
+|---|---|
+| schema_version | 结构版本，本版为 `hand-warehouse/1.0` |
+| dataset_id | 数据集的逻辑标识 |
+| release_id | 本次数据发布版本，对应目录中的 dataset_version |
+| sequence_id | 所属序列或独立图片样本的标识 |
+| camera_id | 当前相机视图标识 |
+| sample_type | `image` 或 `video`，规则见 2.2 |
+| num_frames | 当前视图帧数 T，所有随帧组件按同一时间轴组织 |
+| hand_slots | 固定为 `["left", "right"]`；解剖学左右的使用规则见 5.1 |
+| sampling | 采样类型、源 FPS、帧索引引用及选择策略，见 2.2 |
+| camera | 当前相机模型与图像尺寸，见 2.3 |
+| components | 实际提供的组件及其文件、schema、有效性和来源，见 2.5 |
+| split | `train/val/test/unknown`，划分协议见 2.4 |
+| view_type | 可选，`ego/exo/unknown` |
+
+`dataset_version` 是数据发布版本；`schema_version` 是结构版本，两者独立。原始包校验摘要、官方来源和转换配置应记录在发布清单中。解压发生在临时工作区，原始包保持不变。
+
+### 2.2 图片、视频与 sampling
+
+`sample_type=image`：允许单图 T=1、`sampling.type=single`，也允许将独立图片批量装入一个视频容器，T>1 时 `sampling.type=independent`、`source_fps=null`。分片大小按数据集选择；FreiHAND 默认每片最多 4096 张，训练与评估分开，尾片不补帧。该数量不是其他数据集的强制要求；同批分辨率、相机模型类型及划分协议一致。每行 frames 必须有稳定且唯一的 `sample_id`、原始文件定位；内参、手形状等仍逐帧保留，不能因打包而共享错误的标定或身份。`sequence_id` 在这里仅是批次标识，不代表真实序列；不得按相邻帧推断跟踪、同步或接触延续。源 camera_id、image_variant、split 等随样本变化时放在 frames 的对应字段中。真实采集时间未知则 timestamp_ns=null。合成背景变体不视为真实多相机。
+
+`sample_type=video`：`sampling.type=dense/sparse`；`source_fps` 是真实采集 FPS，未知为 null。非连续取样必须保留源帧号和已知源时间，不可用采样后索引除以 FPS 冒充源时间。可另记采样策略与步长。
+
+shared 可以仅存所选 RGB 帧对应的数据。OakInk2 图像/动捕不同采样率时用 annotation_matches 明确映射；不要求将未采样动捕点硬塞进 T。多视角身份和源同步关系要保留；选择了哪些帧/相机及原因写在 sampling 中。
+
+`sampling.frames_ref` 指向 `camera/frames.jsonl`。帧号、逐帧时间、同步与标注匹配记录的结构见 4.1；MKV 封装时间的约定见 3.1。
+
+### 2.3 camera 模型声明
+
+`camera` 至少包含 `model`、`schema_id`、`image_size:[W,H]`。模型未知使用 `unknown` 和 `null`，不猜成针孔。实际内参、畸变及外参数组放在 `camera/`，各模型的固定结构见第 4 节。
+
+### 2.4 split 与划分协议
+
+保留上游划分，不将同一采集序列的相邻帧随意拆到不同集合。
+
+有官方划分时必填 split_scheme 和 split_source_locator，split_scheme 区分如 Dex-YCB s0/s1、ARCTIC P1/P2 等选定协议；不知道时 split=unknown 并给理由，不凭空分配。每发布可选择一种协议；存在逐帧差异时 frames 中的 split 覆盖视图默认值，并登记其来源。
+
+### 2.5 components 组件登记
+
+组件表的键是本发布内唯一名称，例如 `rgb`、`depth`、`mano`、`keypoints_uv`。每个已提供组件声明以下信息；模态特有参数在第 3–5 节对应小节中定义。
+
+| 字段 | 约定 |
+|---|---|
+| available | 本发布是否提供该组件；缺省也表示未提供 |
+| schema_id | 已登记的具体存储与语义类型，不得临时创造未登记名称 |
+| data_ref | 数据文件路径，相对当前相机视图根目录，使用 POSIX 路径 |
+| axes | 数组各维度的含义 |
+| valid_ref / valid_axes | 有效性文件及其维度；语义由所属组件规定 |
+| storage / valid_storage | 视频数据／视频有效性的存储配置；包含已登记 profile、实际 codec、pixel_format、encoded_size，见 3.1；非视频组件按原数组规则 |
+| provenance | source / format_conversion / derived，以及转换或生成方法 |
+| source_locator | 原文件、压缩包成员和记录／帧的定位信息 |
+
+不存在的组件允许缺省或写作 `{ "available": false, "reason": "..." }`，不用引用不存在的文件。静态网格可声明标量有效性；具体见 5.4。观测的 valid 见第 3 节，标注的 valid 与 visibility 区分见 5.1。
+
+`provenance.kind` 使用 source/format_conversion/derived，`method` 说明转换或生成方法；发布清单补充脚本版本、参数、输入摘要。`source_locator` 定位到原始文件/压缩包成员和记录、帧号；允许引用有完整溯源清单的前一发布，但链条最终应能回到 raw。不得仅写一个无法定位记录的数据集名字。
+
+`.pt` 仅保存 CPU Tensor，不保存 Python 自定义对象；浮点默认 float32，有效性为 bool，浮点无效位置填 NaN。所有随帧数组保留首维 T，单帧也不 squeeze；静态资产按其 schema 单独声明。单位、坐标及固定形状见各组件小节。
+
+机器注册表 `schema.json` 中，`components` 固定解码后的数据类型、形状、单位和语义；`storage_profiles` 固定视频内的编码／还原方式。不得临时创造未登记的 schema_id 或 storage.profile。同一语义允许选择其登记的不同无损存储 profile。
+
+## 3. observation 观测数据
+
+### 3.1 rgb.mkv 与 rgb_valid.pt
+
+RGB、深度和栅格 mask 在 shared 中统一使用 MKV 视频容器，不展开为逐帧图片或图像 tar。MANO、关键点、位姿等数值标注仍用数组；分割的重叠多边形仍用 JSONL，不为了视频化而有损栅格化。单图可为一帧视频，独立图批量封装规则见 2.2。
+
+**无损指共享层不增加信息损失。** 源 JPEG／H.264 等本身可能已是有损编码，原样保留并不恢复已丢失信息；不得重新以有损 JPEG、H.264、H.265、AV1 参数编码。源视频／图片的色彩、位深、分辨率和几何关系不得被静默改变。RGB schema `rgb/uint8/v1` 的统一读取结果是 `[T,H,W,3] uint8`、RGB 通道，有效性仍为 `rgb_valid.pt [T] bool`。超出该 schema 的高位深或 HDR 源必须报告并扩展定义，不能降到 8 位冒充无损。
+
+#### 3.1.1 允许的 RGB 存储 profile
+
+| storage.profile | 编码方式 | 适用规则 |
+|---|---|---|
+| mkv/mjpeg_copy/v1 | 源 JPEG 压缩帧直接封装成 MJPEG/MKV | 不解码重编码；保持每张 JPEG 码流字节；普通 8 位 JPEG 且 MKV/解码器支持时使用 |
+| mkv/source_copy/v1 | 已有视频的 RGB 对应码流直接封装为 MKV | 仅源编码能被 MKV 和选定解码器支持时使用，不伪造帧；需维持正确的呈现顺序与依赖帧 |
+| mkv/h264_rgb_lossless/v1 | libx264rgb 的真正无损模式 | 必须 RGB 路径、CRF=0，并通过全帧 RGB 字节比较；不能替换成默认 libx264 YUV420 路径 |
+| mkv/ffv1_rgb/v1 | FFV1 的 RGB 可逆编码路径 | 作为通用候选；解码回 RGB 后必须逐字节相同，不做色度降采样 |
+
+优先尝试源码流直接封装；不能封装或需要选择稀疏帧时，比较 FFV1 与无损 RGB H.264。对完整待发布片段，只在通过无损验收的候选中选择文件较小者，不假设某个编码器总最小；编码时间和随机读取代价记入构建记录。中间候选不作为 shared 多份常驻。H.264 跨帧压缩允许较长 GOP，必须记录 GOP／关键帧间隔，随机取帧需从前一关键帧正确解码，不能把 seek 返回的近似帧当目标帧。
+
+普通源视频可以保留它已有的有损码流，但不允许新增有损重编码。源是 VRS 等记录容器时，先查明图像流实际编码：已有兼容 JPEG／视频包可直接提取封装，否则解码后使用真正无损编码；不把整个 VRS 改后缀当 MKV。PNG 不要求直接塞进 MKV，可选上表两种无损重编码方式。
+
+#### 3.1.2 视频配置与校验
+
+每个视频组件在 metainfo 中登记 `storage`，至少包含：
+
+| 字段 | 内容 |
+|---|---|
+| profile | 上述登记的存储 profile；语义仍由组件 schema_id 指定 |
+| codec | 文件中实际 codec，例如 mjpeg、h264、ffv1；source_copy 必须写实际 codec |
+| pixel_format | 实际编码像素格式；不得仅凭编码器参数猜测 |
+| encoded_size | 视频物理像素尺寸 `[width,height]`；深度可能不同于逻辑尺寸，见 3.2 |
+| decoder | 解码器名称、版本，以及颜色矩阵／范围／旋转处理等实际像素解释配置；未知的源色彩声明 unknown，不擅自称为 sRGB |
+| encoder_parameters | 重编码时的编码器、版本、无损模式和 GOP 等参数；直接封装时记录 stream_copy |
+| source_lossy | 源是否已有损：true / false / unknown；不是共享层再有损编码的许可 |
+
+无损重新编码的 RGB 必须使用同一固定解码配置，比较**全部帧**的统一 RGB 字节、数量及顺序。MJPEG-copy 不重新编码，生产校验采用 `jpeg-packets-all-rgb-sample16/v1`：从 MKV 逐包取回，对全部源 JPEG 的 SHA256、数量、顺序和图像尺寸进行精确核对；每视图另用同一固定解码配置比较最多 16 帧的源与目标 RGB 字节（含首尾与四分位位置，其余由固定种子抽取）。记录策略、实际索引、种子与结果；不足 16 帧时全查。试点与最终完整序列随机抽查，对选中视图的全部帧复核解码 RGB。历史已做全帧像素检查的证明继续保留。
+
+逐包一致与像素抽查的范围必须分别声明，不能将抽查写成全量像素检查。不能用 PSNR 高、视觉相似、编码器参数称为 lossless 等替代精确检查。同时保留源编码的色彩标记和必要元数据；原图有 EXIF/色彩/方向等元数据时保留 raw 和必要来源信息；不能静默自动旋转图像而不更新像素对应与标定。以上加速策略仅适用于原 JPEG 码流直接封装，不适用于深度、标签或重新编码的 RGB。
+
+所有共享视频的呈现帧顺序与统一 frame_index 一一对应，每个栅格组件恰好 T 帧。MKV 的 `storage_fps` 只服务封装，声明 `timebase_role=index_only`；source_copy 可以保留源 PTS 以避免改坏码流，但真实时间与标注对齐仍以 frames 为准。未知真实时间保留 null。缺失某帧组件时保留索引位置并使用该组件的无效占位／valid 标识，不删帧；不能以重复相邻图像冒充已知观测。
+
+### 3.2 depth.mkv 与 depth_valid.mkv
+
+深度语义仍是 `depth/z_float32/v1`：统一读取结果 `[T,H,W] float32`，米制相机 Z 分量，`depth_type=z`。有效深度有限且大于 0，无效位置为 NaN。`depth_origin=measured/rendered/unknown` 必填，来源不明时 unknown。默认与 RGB 共用像素网格及相机参考系，不处理未注册 RGB-D。射线距离必须依据源定义转换成 z，不能只改声明。
+
+#### 3.2.1 float32 的可逆视频编码
+
+采用 `mkv/depth_float32_bits/v1`：**不是把深度量化成 uint16**，而是将规范化后每个 IEEE-754 float32 的完整 32 位拆成两个 uint16，使用 FFV1 gray16le 保存。视频物理尺寸为 `[2W,H]`：每行前 W 列存原 float32 位串的低 16 位，后 W 列存高 16 位。读取时先合并为 uint32 位串，再按 float32 重新解释，不能数值 cast 成 float32。
+
+```text
+u = reinterpret_uint32(depth_float32)
+encoded[:, :, :W] = uint16(u & 65535)
+encoded[:, :, W:] = uint16(u >> 16)
+u_restored = uint32(low) | (uint32(high) << 16)
+depth_float32 = reinterpret_float32(u_restored)
+```
+
+统一采用 little-endian 原始字节接口。`camera.image_size` 始终是逻辑 RGB 尺寸 `[W,H]`，不能改成编码视频的 2W；`storage.encoded_size=[2W,H]`。包括 NaN 在内的 float32 位串必须全帧完全恢复，不允许改成 float16、整数毫米、归一化灰度或 RGB 伪彩色。这里的无损验收基准是完成既定米制／坐标转换后的标准 float32 数组，不声称消除 float64→float32 等原约定转换本身的舍入。
+
+#### 3.2.2 逐像素有效性
+
+`depth_valid.mkv` 使用 `mkv/bool_ffv1/v1`，FFV1 gray8（FFmpeg pixel_format=`gray`），物理尺寸 `[W,H]`。像素只能为 0 或 1，统一解码为 `[T,H,W] bool`，逐像素含义不变。这样不再把庞大的 bool 数组保存为未压缩 `.pt`；标注的帧级／手级 valid 仍保留原有小数组。
+
+登记示例（W、H 以下取 640、480；decoder/encoder_parameters 的实际版本由转换器填写）：
+
+```json
+{
+ "available": true,
+ "schema_id": "depth/z_float32/v1",
+ "data_ref": "observation/depth.mkv",
+ "axes": ["T", "height", "width"],
+ "valid_ref": "observation/depth_valid.mkv",
+ "valid_axes": ["T", "height", "width"],
+ "unit": "m",
+ "depth_type": "z",
+ "depth_origin": "measured",
+ "invalid_value": "NaN",
+ "storage": {
+ "profile": "mkv/depth_float32_bits/v1",
+ "codec": "ffv1",
+ "pixel_format": "gray16le",
+ "encoded_size": [1280, 480],
+ "source_lossy": false,
+ "decoder": {"name": "ffmpeg", "version": "填写实际版本"},
+ "encoder_parameters": {"name": "ffv1", "level": 3, "version": "填写实际版本"}
+ },
+ "valid_storage": {
+ "profile": "mkv/bool_ffv1/v1",
+ "codec": "ffv1",
+ "pixel_format": "gray",
+ "encoded_size": [640, 480],
+ "source_lossy": false,
+ "decoder": {"name": "ffmpeg", "version": "填写实际版本"},
+ "encoder_parameters": {"name": "ffv1", "version": "填写实际版本"}
+ },
+ "provenance": {"kind": "format_conversion", "method": "源深度转米制 z float32；逐位打包为 FFV1"},
+ "source_locator": {"file": "raw/data/depth.zip", "member": "depth/0001.png"}
+}
+```
+
+深度和有效性视频都必须逐帧解码验收：float32 位串相同、bool 相同、T 相同，并检查 valid=true 的正有限值和 valid=false 的 NaN。读取器必须按 profile 还原，不能把 depth.mkv 的编码像素直接当深度值。
+
+## 4. camera 相机、帧索引与标定
+
+### 4.1 frames.jsonl：索引、时间与对应关系
+
+`camera/frames.jsonl` 每行对应统一帧号 `frame_index=0..T-1`，是所有组件的唯一行索引。至少包含 `frame_index`、`timestamp_ns`、`clock_domain`、`source_locator`；原始帧号/文件名保存在 `source_frame_id`，不能被新帧号替代。每行还可有 `annotation_matches`，按组件键保存 `source_frame_id`、`source_timestamp_ns`、`clock_domain`、`match_method` 和 `delta_ns`；方法为 exact/index_map/nearest/interpolated，索引映射无时间依据时 delta_ns=null。nearest 必须记录允许误差；interpolated 必须记录两端源标识和权重。delta_ns 仅在同一时间域中计算为源标注时间减图像时间。多相机共享 `sequence_id`；已知同步关系必须用 `sync_id` 或明确的源帧/时间映射表达，不能仅凭相同数组索引认定同步。
+
+每个视图必须提供此文件。源时间使用整数纳秒及 `clock_domain`，未知写 JSON `null`；不同时间域不能直接比较。逐帧 split 可以覆盖 metainfo 的视图默认划分，并保留来源，见 2.4。
+
+### 4.2 坐标系与标定通则
+
+| 项目 | 统一约定 |
+|---|---|
+| 相机坐标 | 右手系，X 向右、Y 向下、Z 向前，长度单位米 |
+| 齐次变换 | 列向量，`p_target = T_target_from_source @ p_source` |
+| 图像坐标 | `(u,v)`；左上像素中心为 `(0,0)`，u 向右、v 向下，单位 pixel |
+| 固定标定 | 本版展开为 T 份，同一 schema 不混用静态与动态形状 |
+| 分辨率或模型变化 | 分开存储为视图／片段，不在同一组件中混装 |
+| 未知标定 | 相机内外参允许缺省，不填单位阵冒充已知标定 |
+
+### 4.3 intrinsic.pt 与畸变参数
+
+| model / schema_id | intrinsic.pt 的固定结构 |
+|---|---|
+| pinhole / camera/pinhole/v1 | `[T,3,3]`，标准 K，最后一行为 `[0,0,1]`；限无畸变或已校正图像 |
+| fisheye624 / camera/fisheye624/v1 | `[T,15]`，`[f,cx,cy,k0,k1,k2,k3,k4,k5,p0,p1,s0,s1,s2,s3]`，采用 Project Aria Fisheye624 投影定义 |
+| opencv_fisheye4 / camera/opencv_fisheye4/v1 | `intrinsic.pt [T,3,3]` 保存 K；同组件 `distortion_ref` 指向 `[T,4]`，顺序 `[k1,k2,k3,k4]`，使用 OpenCV fisheye 等距角度多项式；已由 ReInterHand 实际原始相机参数验证 |
+| rational8 / camera/rational8/v1 | `intrinsic.pt [T,3,3]` 保存 K；同组件必需 `distortion_ref` 指向 `[T,8]`，顺序 `[k1,k2,p1,p2,k3,k4,k5,k6]` |
+| unknown / null | 不提供可用 intrinsic，不声称可以投影 |
+
+有畸变的其他模型必须注册新 schema，固定参数名、顺序和投影公式；不能塞进针孔 K 后遗失畸变项。`metainfo.camera.schema_id` 必须与 intrinsic 组件一致。同一模型的别名不可隐式改变参数布局。
+
+`opencv_fisheye4`：对前方点令 `x=X/Z, y=Y/Z, r=sqrt(x²+y²), theta=atan(r)`，`theta_d=theta*(1+k1*theta²+k2*theta⁴+k3*theta⁶+k4*theta⁸)`；`u=fx*x*theta_d/r+cx, v=fy*y*theta_d/r+cy`。光轴 r=0 时使用连续极限 `theta_d/r=1`。四系数不能按普通径向畸变解释，也不能补零冒充 Fisheye624。
+
+rational8 对归一化坐标 x=X/Z、y=Y/Z，令 r²=x²+y²，径向项为 `(1+k1*r²+k2*r⁴+k3*r⁶)/(1+k4*r²+k5*r⁴+k6*r⁶)`；再加 p1/p2 切向项并经 K 得到像素。`distortion_ref` 与 intrinsic 共用 `[T]` valid，保存到 camera/distortion.pt。不得把这种畸变三维坐标当作真实相机系 xyz 保存。
+
+内参有效性为 `intrinsic_valid.pt [T] bool`；有畸变时使用对应 `distortion_ref`。同一模型的参数名和顺序固定。
+
+### 4.4 extrinsic.pt 与 extrinsic_valid.pt
+
+`extrinsic.pt` 为 `[T,4,4]`，schema `transform/se3/v1`，方向固定 `T_camera_from_world`，有效性 `[T]`。世界坐标含义及源坐标转换记录在 provenance 中；只有相机坐标标注不要求虚构世界坐标。旋转块须正交且 determinant≈1。
+
+## 5. annotation 标注数据
+
+### 5.1 标注通则：手槽、valid 与 visibility
+
+手槽固定为 0=left、1=right，指解剖学左右，不按画面位置或可见性重排。本阶段只处理左右手两槽，不处理更多手。实体引用使用 `hand_left`、`hand_right`；手槽不证明跨采集片段的个人身份。
+
+#### 5.1.1 valid：标注是否可用于监督
+
+标注的 `valid=true` 表示：有对应实际标注、通过本 schema 数值检查，且通过来源明确提供并适用于该组件的有效性/QA 条件。`valid=false` 可由缺标注、无法建立帧对应、源无效占位或官方 QA 不通过导致；不等于画面没有手或手不可见。各组件独立判断，MANO 按 `[T,2]` 标记左右手，关键点按 `[T,2,K]` 标记。
+
+**2026-09-20 将标注 valid 从“仅表示存在”修订为“可用于监督”，字段形状不变。** 公式为 `source_annotation_present AND numerical_checks_pass AND applicable_official_QA_pass`。没有官方 QA 时省略最后一项，不伪造 QA；不能把未知当成已通过已存在的 QA。适配器在组件 `valid_policy` 中记录具体规则、来源、聚合/广播作用域。缺失官方必需 QA 的记录须明确处理为未知或暂停验收，不能静默通过。无效浮点项仍填 NaN，原始数据保留在 raw；被排除原值可用于 doc 中明确标识的诊断渲染，不能冒充 shared 有效监督。
+
+本地筛选须依据明确的规则，并在 `valid_policy.local_excluded_frame_indices`、排除原因和正式报告中记录。单个可疑投影作为问题记录，不直接硬编码排除该帧。已确认的曝光异常须明确作用范围，不能把一个序列的“前两帧有问题”套到所有序列。RGB 编码完好与几何监督可用是两件事，允许保留原图及帧索引而屏蔽对应标注；未发现问题不等于完成全部人工审核，试点待确认时不能作为已验收发布使用。
+
+源字段必须按官方语义映射，不能直接按名字复制：
+
+| 来源 | 官方含义 | 映射原则 |
+|---|---|---|
+| [H2O](https://github.com/taeinkwon/h2odataset) 的手标注首位 | 0 未标注、1 已标注 | 可作为对应手标注存在性的依据，不推断未标注原因 |
+| [HOT3D](https://github.com/facebookresearch/hot3d#using-mask-files) 的 pose_available、visible、mask_qa_pass | 分别表达姿态存在、可见性、对画面中可见手物位姿的人工 QA | 逐手/物体记录决定存在性，再 AND 当前相机当前帧 QA；QA 是整帧聚合判定，保守排除该帧全部手物几何监督，不声称每个实体均有错误；源可见性基于位姿推导时，其自身 valid 同样受 QA 约束 |
+| [Dex-YCB](https://github.com/NVlabs/dex-ycb-toolkit#loading-dataset-and-visualizing-samples) 的全零 pose_m、全 -1 joints | 不可见的手或缺标注共用占位表示 | 对应组件 valid=false；不能从该值判定 visibility=false，也不能声称原因是动捕缺失 |
+| [ARCTIC](https://github.com/zc-alexfan/arctic/blob/master/docs/data/data_doc.md) 的 is_valid/left_valid/right_valid | 是否有效用于官方评估 | 核实每项筛选依据；不把可见性/评估选择直接等同 QA。本次原始 mano.npy 不含这些派生评估标记，按原记录与数值检查生成 valid，并声明没有额外官方 QA 输入 |
+
+源 valid=false 若代表评估筛选或其他条件，应检查实际标注和源约定后映射；不能一律清空坐标。反之，占位零或 -1 只有在源官方明确规定时才判为缺失，不泛化成所有数据的判断规则。
+
+`valid=true` 的标注应通过其 schema 的数值与形状检查；检查失败必须报告或修复，不能静默隐藏。QA 不通过应报告排除数量与原因，并保留可追溯信息；不能把这类无效称为缺标注。RGB、深度等观测有效性仍按各自 schema；手物 QA 不自动否定 RGB、静态相机内参或物体资产。相机外参是否过滤须依据其自身来源规则；HOT3D 本次保留源外参用于溯源和诊断，不能用其存在性证明 QA 失败帧的几何监督正确。
+
+#### 5.1.2 visibility：可见性属性
+
+附加质量属性只保留 `visibility`，存放在对应模态或标注集内，不设 quality 目录。同一份可见性仅存一次；uv 与 xyz 共用时引用同一个文件。
+
+visibility 通过所属组件 attributes 登记 data_ref、axes、dtype、visibility_type、meaning、provenance、source_locator；未知值通过属性 valid_ref 标识，全已知可声明 valid=true。比例与布尔值不相互强转，也不按任意阈值二值化。没有 MANO 时，整手可见性挂在 keypoints 组件，使用独立的 whole_hand_visibility.pt，明确 meaning=whole_hand，不与逐关键点可见性混用。
+
+`valid` 回答“这项标注能否用于监督”，`visibility` 回答“在当前图像中是否可见（或可见多少）”。不可见的手或关键点仍可有有效标注；不能因 visibility=0 就清空坐标。源没有 visibility 时缺省或标为未知，不由 valid 推断。
+
+| 情况 | 手标注 valid | visibility |
+|---|---|---|
+| RGB 已开始录制，动捕尚未开始，没有其他对应手标注 | false | 以独立源可见性标注为准；没有则未知 |
+| 手被遮挡，但源有标注且通过适用有效性检查 | true | 若源声明不可见，则 false 或源可见比例 |
+| 手可见且有对应标注，通过适用 QA | true | 若源有此信息，则 true 或源可见比例 |
+| 手可见，但该帧漏标或动捕中断 | false | 可以为 true，但必须有独立来源依据 |
+| 有手部参数，但官方 QA 明确不通过 | false | 不据此断言不可见；源可见比例若基于不可靠位姿，其属性 valid 也为 false |
+
+visibility 自己的 valid_ref 表示该可见性标注是否可用，不复用 MANO 或关键点坐标的 valid_ref；明确记录源存在性及适用的 QA 条件。上述例子不要求凭图像自动生成可见性。
+
+不设 quality 组件或目录，不另存拟合误差、曝光、截断比例等通用质量数组。适用的官方 QA 合并进对应 valid，在 `valid_policy` 记录来源与规则；正式校验报告保留原始 QA 时间区间及排除统计。原始 QA 文件仍在 raw，官方 split 保留。
+
+### 5.2 mano：参数与整手可见性
+
+MANO schema `mano/axis_angle61/v1`：`motion.pt [T,2,61]`，顺序为完整 16 关节 axis-angle 48、shape betas 10、全局平移 3。姿态使用弧度，平移米，位于当前相机系。有效性 `[T,2]`；缺少一侧整槽 NaN。PCA 必须先按对应模型展开，不能将 PCA 系数直接填入 axis-angle。
+
+`model_profile` 必须固定 MANO 版本、`flat_hand_mean=true`、左手 shapedirs 修正状态。平移表示模型输出顶点所加的全局平移，不是某个腕关节的坐标；源语义不同须转换。shape 无量纲。模型权重不作为共享数据附带上传。
+
+`mano/visibility.pt [T,2]` 保存源整手可见比例时使用 float32 [0,1]，`visibility_type=ratio`；只有实际采用 0–100 的百分比才除以100，不能只凭表头判断。HOT3D 的 `visibility_ratio[%]` 实值已经是0–1，官方读取器原样读取，必须保留原值。通过 MANO 组件 attributes 登记，使用独立有效性，不复用 motion_valid。
+
+手部共享表示仅保留 MANO 参数与选定关键点，不发布独立手表面几何。需要渲染或接触计算时由 MANO 参数生成，几何引用使用固定 MANO 模型配置与顶点顺序；不影响 object 下的物体模型和材质资产。
+
+### 5.3 keypoints：二维点、三维点与逐点可见性
+
+关键点独立于 MANO 参数存在；没有 motion 也可有关键点。`uv.pt [T,2,K,2]` 与 `xyz.pt [T,2,K,3]`，有效性 `[T,2,K]`，分别采用 `keypoints/uv/v1` 和 `keypoints/xyz/v1`。K 由 `keypoint_schema` 固定，不能在同一数组中逐帧变化；同体系统一顺序。数值使用像素坐标或米制相机系。
+
+| keypoint_schema | K 与固定索引 |
+|---|---|
+| mano21/v1 | 21；wrist 后依次 thumb/index/middle/ring/little，每指由近端至指尖 |
+| rhd21/v1 | 21；保持 wrist，然后五指由近端至指尖；原始每指 tip→palm 反序。只统一顺序，不宣称解剖学位置等价于 MANO |
+
+具体名称与源索引映射见 `schema.json`。当前只登记 MANO21 与 RHD21；不登记 MediaPipe、ReInterHand capture21 或 fit24 中间关键点。
+
+每个发布中的每个视图只提供一套标准手关键点，直接放在 keypoints 下，不设 annotation_set_id 字段或目录。uv 与 xyz 是这套关键点的二维与三维表示，不是两套标注。provenance 分别记录二者来自原始标注还是计算结果；不要求已有 2D 必须由当前 xyz 重新投影覆盖。
+
+ReInterHand 的 Original keypoints、Original fits keypoints 及带前臂关键点不纳入 shared；如需手关键点，从选定 MANO 参数生成 MANO21 并记录模型/指尖回归约定。其他数据集默认使用选定的官方关键点，不额外生成平行的 MANO 派生集合。后续主动更换生成方法或结果时发布新的 dataset_version，不在同一版本内增设方法分支。
+
+manual/projection/fitting 只记录在 provenance，不作为目录或类型分支。不得通过坐标为零、是否出画或 visibility 判断标注是否存在。派生标注仅在实际生成并通过数值检查后声明 valid=true；没有生成的项为 false，不伪造坐标。
+
+`keypoints/visibility.pt [T,2,K]` 保存源逐点可见性，布尔表示使用 `visibility_type=boolean`。uv 与 xyz 共用时引用同一份文件。整手比例属性没有 MANO 可挂载时使用 `whole_hand_visibility.pt [T,2]`，`meaning=whole_hand`；它与逐点可见性分开，通用登记规则见 5.1。
+
+不使用 annotation_set_id；ReInterHand 中间关键点集合不进入 shared，raw 保留来源文件。
+
+### 5.4 object：身份、资产、位姿与几何
+
+#### 5.4.1 metainfo.json：物体身份与跟踪作用域
+
+手用 `hand_left`、`hand_right` 及槽位 0、1 表示，不再另存 entities.json。物体身份登记在 `annotation/object//metainfo.json`，必填 `object_id`、`identity_scope`、`scope_id`、`source_locator`；可含 `category_id`、`source_object_id`、`mesh_ref`、`has_model`、部件引用。即使只有分割、没有 pose/mesh，也可只有这份物体元信息。
+
+`identity_scope` 为 frame/subsequence/sequence，`scope_id` 指定具体作用域；跨相机共享身份只在已有源对应证据时使用。VISOR 按官方 subsequence 与实体名关联时记录 `association_basis=official_subsequence_entity_name`，不能声称这是全视频物理实例跟踪。无需与 object_id 一一对应的标准 track_id；官方独立轨迹标识存在时保留为 source_track_id，并声明作用域。手槽不证明跨采集片段的个人身份。
+
+#### 5.4.2 assets：网格、材质与纹理
+
+资产属于物体，统一存放在 `annotation/object//` 内，可用 `assets/` 子目录组织网格、材质、纹理和部件资产。序列层不设共享 assets。所有引用采用视图根目录为基准；其中物体资产组件的 `data_ref` 与部件的 `mesh_ref` 采用相对于当前视图目录的 POSIX 路径，例如 `annotation/object/cup_01/assets/cup.obj`，且必须指向所属物体目录内的资产。OBJ 内部的材质、纹理引用遵循文件格式自身的相对路径规则，依赖文件一并保留。不同相机视图允许各自保存物体资产，不要求跨视图去重；下载物体时包含其资产依赖。
+
+网格 schema 为 `mesh/obj/v1`，顶点米制，静态有效性可直接声明标量 `valid`，或通过 `valid_ref` 指向标量 bool Tensor；不按 T 重复静态资产。由物体／部件通过引用关联，不仅凭文件名猜测。
+
+#### 5.4.3 pose.pt、pose_valid.pt 与 visibility.pt
+
+刚体 schema 为 `object/rigid/v1`，`pose.pt [T,4,4]` 固定为 `T_camera_from_object`，平移米制，有效性 `[T]`。物体坐标原点、轴向必须与 mesh 一致；旋转块为正交矩阵且 determinant≈1。
+
+`object//visibility.pt [T]` 保存源物体可见比例，float32 [0,1]，`visibility_type=ratio`。通过所属物体组件 attributes 登记，独立表达其有效性。
+
+#### 5.4.4 articulation.json、部件位姿与 joint_state.pt
+
+关节物体：schema `object/articulated/v1`，`articulation.json` 登记 `parts` 与 `joints`。每个 part 有 `part_id`、`parent_id`（根为 null）、`has_model`、`mesh_ref`；has_model=false 时 mesh_ref=null，结构节点允许没有网格。父链不得成环。每个 joint 有唯一 `joint_id`、`parent_part`、`child_part`、`type`；fixed 单位 null、revolute 弧度、prismatic 米。有源限位时保存 `limits:[lower,upper]`；没有时 null，不伪造上下限。部件姿态统一为 `T_camera_from_part [T,4,4]`，不与父局部变换混装。
+
+若提供 `joint_state.pt [T,J]`，必须登记固定 joint_order、轴在父部件系的表达、零位变换与正方向，valid 为 `[T,J]`；缺少这些运动学定义时只发布部件位姿与已有来源信息，不伪造可驱动的关节模型。关节值与部件位姿同时存在时需验证一致性。
+
+部件层级应声明语义：纯语义包含树允许 `joints=[]`，不将包含关系推断为机械关节。关节状态 schema 为 `object/joint_state/v1`；转动用弧度、移动用米，混合类型按 joint_order 对应的关节定义解释。
+
+#### 5.4.5 dimensions、几何关键点与顶点部件标签
+
+`dimensions.pt [T,3]` 采用 `object/dimensions/v1`，有效性为 `[T]`。
+
+物体/部件 `dimensions` 为 `[length,width,height]` 米，轴顺序与该 mesh 的局部坐标关联，登记 `dimensions_semantics` 和来源。HOI4D dimensions 与 mesh 的具体尺度关系未验证前标为 source_unspecified，保留源值并暂停依赖该关系的几何验收，不能默认为待施加缩放。需要施加缩放时明确 scale_applied_to_mesh，禁止重复应用。
+
+源物体关键点放 `object//keypoints/xyz.pt [T,K,3]`（相机系）或 `template_xyz.pt [K,3]`（物体系），schema 分别为 object_keypoints/camera/v1 和 object_keypoints/template/v1。明确 keypoint_schema、keypoint_names、单位及有效性；模板点配合 pose 可投影，无须重复生成全部帧。H2O 中心/角点/棱中点，以及 ARCTIC 部件点分别登记具体源顺序，不套用手的 21 点 schema。每个发布选择一套源几何点并记录选择，不另建方法分支。
+
+`vertex_part_ids.pt [V] int64` 绑定 `mesh_ref` 与 topology_id，登记整数到 part_id 的映射；未知用 -1。ARCTIC 不同拓扑的资产分别绑定，不能共用未经转换的顶点标签。部件层级中 is_instance/has_model 等源属性保留，几何文件仍全部归 object。
+
+### 5.5 segment：分割类别、编码与覆盖范围
+
+每个发布选择一套任务使用的分割表示，不建立方法／标注集分支。没有分割就不创建 segment 目录，不为其他模态创建空的实体表。
+
+| schema_id | 文件与数值语义 |
+|---|---|
+| segment/uint16_labels/v1 | mask.mkv；整数区域编号，通过 instances.jsonl 解释 |
+| segment/instance_polygons/v1 | polygons.jsonl；实例多边形，可重叠；当前不支持孔洞拓扑 |
+| segment/foreground_intensity/v1 | mask.mkv；0–255 前景强度，不是区域编号或经过校准的概率 |
+
+#### 5.5.1 uint16 标签图与 instances.jsonl
+
+组件声明 `segmentation_type=semantic/instance/part`；同一组件固定一种类型。`mask.mkv` 解码为 `[T,H,W] uint16`，schema `segment/uint16_labels/v1`，使用 `mkv/uint16_ffv1/v1`，FFV1 gray16le 无损；只有所有区域编码均 ≤255 时允许 gray8（pixel_format=gray），读取后转 uint16，数值不变。禁止调色板和有损编码。有效性 `[T]`，表示该帧有可用分割标注。数值 0 固定为 unassigned，65535 固定为 ignore，1–65534 为区域编码，不能直接等同 entity_id。
+
+`instances.jsonl` 每帧一行，包含 `frame_index` 与 `labels`。每条标签包含 `mask_value`、`category_id`、`entity_id`、`part_id`；无意义或未知字段为 null。semantic 允许 entity_id=null；instance 必须引用 hand_left/hand_right 或登记的 object_id；part 还须关联登记部件。源实例 ID 和其他属性分别保存在 `source_instance_id`、`source_attributes`。mask 中每个非保留编码必须有且仅有一个映射。
+
+FreiHAND 的试点按本任务需要，统一到手／背景类别，保留每个原生区域编码；不要求给原生细分区域猜测手指名称。需要细部件监督时，尚未核实的部件名称必须保持未知，不能因粗粒度手类别已核实便声称部件语义也已统一。
+
+#### 5.5.2 重叠实例多边形
+
+每发布选择一套任务使用的分割表示，不建立方法/标注集分支。VISOR 的实际试点存在重叠实例多边形，单层标签图不能无损表达。对此补充 `segment/instance_polygons/v1`：`segment/polygons.jsonl` 每帧包含 `frame_index` 和 `instances`，每实例包含 `mask_value` 与 `polygons`（列表，每个多边形为源图像像素坐标顶点序列，保留原始精度）。身份和类别仍由同一 `instances.jsonl` 映射，有效性仍为 `[T]`，通过 schema 区分物理编码。允许多实例重叠，不采用后画覆盖前画。消费者按需栅格化为独立实例二值 mask；不能声称合并的单层 mask 无损。
+
+#### 5.5.3 灰度前景强度
+
+ReInterHand 的官方渲染 mask 为双手合并的灰度前景掩码，实测存在 0–255 的中间值，不是左右手实例编号。登记 `segment/foreground_intensity/v1`：仍存 `segment/mask.mkv`，解码为 `[T,H,W] uint16`，使用 `mkv/uint16_ffv1/v1`，此类数据优先编码为 FFV1 gray8（pixel_format=gray），读取后转 uint16。有效值范围 0–255，归一化值为 `float32(value)/255`；0 为背景端点、255 为前景端点。组件声明 `foreground_category=hand`、`entity_id=null` 和双手合并语义，不使用区域编号映射。这里保留原始渲染 mask 强度，不把它称为经过校准的概率。若任务需要二值 mask，阈值化属于任务层派生操作，记录阈值；共享层不丢弃中间灰度。不同 schema 的数值含义不同，不能将该文件按整数类别图解码。
+
+FreiHAND 训练集的 JPEG mask 使用 `segment/foreground_intensity/v1`，逐像素保留 JPEG 解码后的灰度，`entity_id=hand_right`。该 schema 不限定双手合并：单手或合并范围由 `entity_id` 与 `coverage` 明确声明。评估集整数区域图仍用 `segment/uint16_labels/v1`，两者不能仅凭文件名使用同一种解码语义。
+
+#### 5.5.4 coverage 与有效性
+
+`coverage.kind=full/selected_entities/partial/unknown`。full 指声明的标注范围完整；selected_entities 还应列出目标范围。无论哪种，0 本身不自动成为背景；背景要有明确类别映射，未标注区域不能用于构造负样本。保留源逐类/逐实例 exhaustive 信息，未知不可改写为完整。
+
+三种表示的帧级有效性均为 `[T]`，表示该帧是否提供此分割标注；对应 valid_ref 在组件中登记，仍用小型 bool `.pt` 数组。栅格 mask 必须登记 storage，格式见 3.1.2，所有帧的整数编码或强度逐值完全相同。编码语义由 schema_id 决定，读取器不能仅依据 mask.mkv 文件名选择解码方式。
+
+### 5.6 contact：接触、抓取、区域与距离
+
+#### 5.6.1 relations.jsonl 与 relations_valid.pt
+
+`annotation/contact/relations.jsonl`，schema contact/relations/v1，每帧一行含 frame_index、relations。每条记录含 relation_type（contact/grasp）、subject_id（手槽或登记实体）、object_id、status、source_locator、provenance。status 固定为 positive/negative/unknown/target_unannotated：positive 必须有可解析目标，negative 表示源明确否定该关系，unknown 为不确定，target_unannotated 为确有接触但目标未标注。非 positive 的 object_id 可 null，不能用 null 冒充“不接触”。稀疏源无记录的帧不自动补 negative。
+
+VISOR 的对象 mask ID 必须先按同帧 source_instance_id 解析成 object_id；hand-not-in-contact、none-of-the-above、inconclusive 分别映射 negative、target_unannotated、unknown，保留原始字符串。Dex ycb_grasp_ind 通过 ycb_ids 映射到登记物体，以 relation_type=grasp 保存；不得当作逐顶点接触。
+
+relations 组件的 valid_ref 指向 `relations_valid.pt [T] bool`，表示该帧是否有可用源关系标注。有效帧中可以存在 status=unknown 的明确不确定标注；无源标注的帧为 valid=false、relations=[]。组件仍声明 available/provenance/source_locator。
+
+#### 5.6.2 实体对的顶点区域与距离
+
+已发布的接触区域/距离按参与关系的 subject_id 与 object_id 组织，每对实体保留选定的一套结果，以精确 geometry_ref/topology_id 关联到手或物体/部件；不要求每套数据都有。顶点区域 schema contact/vertex_region/v1：bool `[T,V]` 加同形 valid；顶点距离 schema contact/vertex_distance/v1：float32 `[T,V]`、米、同形 valid，声明 source/target geometry_ref、distance_definition（signed/unsigned、测量到点集或表面等明确源定义）。geometry_ref 必须指向已登记 mesh 或由固定 MANO 模型配置确定的拓扑；拓扑/顶点数变化时按拓扑拆分片段。其他源区域表示只有在实际接入并登记 schema 后才可转换，不能随意栅格化宣称等价。
+
+顶点区域／距离已有存储定义，但尚未完成真实输入试点；接入时必须提供源语义与几何验收证据，不因已经登记 schema 就视为通过。
+
+## 附录：任务范围
+
+不强制收录 2D bbox、完整高频动捕、动作程序、身体 SMPL-X、点云或网络特征。范围内的接触、visibility、选定手关键点、物体几何不能因不便处理而静默丢弃。分割采用的物理编码由第 5.5 节决定，存在重叠时不能以单层标签图声称无损。
+
+不设置 entities_ref/segment/entities.json，不要求与 object_id 重复的 track_id。MediaPipe 不在当前关键点定义中；旧版本迁移必须保留可追溯关系，不能直接删表。手部共享表示不含独立 hand_mesh。
+
+schema.json 是普通注册表，不是 JSON Schema 文档或可执行验收器；定义存在不代表真实数据已验证。验收须保留实测范围与未决项。
+
+本存储约定只更新标准，不代表既有 FFV1／depth.pt 试点已迁移。新发布必须显式写 storage 配置；旧产物按其旧定义读取，不能只改文件后缀或版本标识伪装为合规。
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/docs/h2o-RAW_TO_SHARED.md b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/docs/h2o-RAW_TO_SHARED.md
new file mode 100644
index 0000000000000000000000000000000000000000..1f5cd6670065ebca7e08a8f0c0864e2474f2dbc5
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/docs/h2o-RAW_TO_SHARED.md
@@ -0,0 +1,48 @@
+# H2O raw → shared-v1.0
+
+## 范围与来源
+
+本版本仅转换 `raw/data/subject{1,2,3,4}_v1_1.tar.gz` 的真实五视角 (`cam0`–`cam4`) 序列；`h2o_CASA.tar` 是单独的扩展，不与标准序列混合。每个原始序列存于 `data/subjectN__/cam{0..4}/`。源压缩包保持不变，不合成源不存在的标注。输出仅在本地制作；完整序列试点及人工检查通过、更新全量预估之前，不启动全量。
+
+| 源包/字段 | shared 文件/处理 |
+| --- | --- |
+| `subjectN_v1_1.tar.gz`: `camX/rgb//keypoints/xyz.pt`、`pose.pt`：按源类号关联 21 个物体点及物体→相机位姿；仅源存在时登记组件及有效性。 |
+| `object.zip` | `annotation/object//assets/` 与源 OBJ、MTL、纹理；静态形状预览不等于所有帧网格位姿的投影证明。 |
+| `label_split.zip` 的 `action_{train,val,test}.txt` | 序列元数据的官方划分及来源；未知映射明确记 `unknown`，不自行编排。 |
+
+`cam4` 是 ego，`cam0`–`cam3` 是 exo。真实时间/FPS 缺乏可靠来源时使用 null，不把 MKV 封装帧率冒充采集时钟。源 `action_label`、`verb_label`、pose 任务帧列表和 `manolabel_v1.1.tar.gz` 保留在 raw；当前契约没有相应动作组件，本发布不宣称它们已转换。源没有像素分割，本版本不投影网格生成替代分割。
+
+## 源资产文件名修复
+
+源 `object/cocoa/cocoa.obj` 引用 `ovomaltine.mtl`，材质内写的是 `map_Kd cocoa.png`，但原 ZIP 只提供 `ovomaltine.png`。仅在临时解压目录为该已确认的纹理增加**字节相同**的 `cocoa.png` 别名，保留原 PNG、OBJ、MTL 不变；在网格 `source_locator.texture_aliases` 中记录别名、原始包成员和 SHA256。别名与已保留的旧 cocoa 资产一致，不替换图像、标注或网格。全部八类物体的材质依赖在读取大 subject 包之前预检，其他缺失/越界依赖仍报错。
+
+## 加速与验收
+
+四个互不依赖的 subject 压缩包可由 `--workers 1..4` 并行顺序读取，各 worker 一次只处理一个序列。数值库保持单线程；FFV1 编码支持 `--codec-threads 1|2|4|8|10`，当前全量按用户要求使用 10；`--decode-threads 10` 通过现有 `HAND_WAREHOUSE_FFMPEG_THREADS` 控制源 RGB PNG→RGB24、完整视频解码摘要、深度/有效性语义校验与 RGB 索引抽取的解码线程，完整校验仍保留。深度 PNG 使用 `--depth-threads 4` 有序分批读取，每批最多读取 4 帧，避免缓存整条序列，缺帧仍按原时间轴写 NaN；不并行改写姿态、标注或原始包。并发数由相同输入实测吞吐量决定，记录 OS 线程峰值和磁盘占用，不以 CPU 线程数本身作为提速证据。RGB 源为 PNG，不适用 ARCTIC/HOT3D 的 JPEG 包直封和 16 帧抽查：RGB、深度、有效性与数值模态仍逐帧精确校验。临时文件在 `/data` 上隔离放置，历史 62 条目录不复用、不覆盖；调整并发时，在新的运行目录复用本次全量已经原子发布的完整序列，并从源重新核对帧号及逐组件 SHA256；已发布视图只读源包并核对摘要，不再把源 PNG/标注写入临时目录，待转换视图才正常暂存。未完成的临时序列不复用，原运行的脚本、配置及 QC 保留用于溯源。
+
+先运行不截帧的代表性完整序列试点，保存自动 QC、真实耗时和 `doc/validation/index.html`（RGB、深度、源 3D 关键点投影、原生物体网格和全组件数值/有效性）。试点由人工核查之后再决定全量；全量后再次抽样人工核查。报告须区分数据完整性、源未提供的模态与仅供人工判断的几何对齐。
+
+2026-09-29 完整序列试点已获人工确认，用户已授权本地全量。2026-09-30 用户取消 CPU 使用量上限，指定 4 个独立压缩包 worker、每个 FFV1 编码器 10 线程、每个校验解码器 10 线程、每 worker 深度 PNG 读取 4 线程；Python worker、读取及校验子进程不计入这最多 40 个编码线程，因此总线程数不固定为 40。该配置不代表已证明加速倍数。源包和旧产物保留；代码变化使用独立运行目录，已完成序列保留原始构建记录及生产脚本快照。
+
+
+## 多线程解码测速（2026-09-30）
+
+在 101 上以真实 `subject4/h1/0/cam0` 的完整 RGB、深度和有效性视频，以及源头 100 帧 PNG 测试；采用 1、10、10、1 的解码线程顺序，源深度 PNG 另比较 1/2/4 线程。逐字节摘要和输出长度必须相同，测速脚本、全部轮次及结果保存在 `script/provenance/operations/` 和 `doc/validation/`。测速期间旧全量任务仍运行，磁盘负载和缓存会影响结果；不得将阶段加速比冒充整套转换加速比。四个 gzip 包已经独立并行读取，暂不引入多进程重构或增加数值库线程；小矩阵及标注处理没有已测得的进一步并行收益。
+
+
+本次同输入结果（解码后长度和 SHA256 全部相同）：
+
+| 阶段 | 1 线程（秒） | 新配置（秒） | 范围 |
+| --- | ---: | ---: | --- |
+| FFV1 RGB 解码与摘要 | 39.46 | 8.70（10 线程） | 完整 501 帧，中位数 |
+| FFV1 深度位串解码与摘要 | 34.91 | 11.56（10 线程） | 完整 501 帧，中位数 |
+| 有效性视频解码与摘要 | 2.24 | 2.27（10 线程） | 完整 501 帧；未获提速 |
+| 源 RGB PNG 解码 | 2.67 | 1.49（10 线程） | 100 帧，1 线程使用热缓存轮次，不使用冷缓存较慢轮次来夸大提速 |
+| 源深度 PNG 读取及毫米→米 | 4.67 | 2.61（4 线程） | 100 帧，中位数；帧序与数值相同 |
+
+这些结果只代表相应阶段；全流程还包括顺序 gzip 扫描、文件创建、源 SHA256、资产复制和磁盘读写，实际全量 ETA 待稳定吞吐后再更新。
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/docs/schema.json b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/docs/schema.json
new file mode 100644
index 0000000000000000000000000000000000000000..d904a34272de7f3fd4428c8dc68a919e94a68fca
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/docs/schema.json
@@ -0,0 +1,869 @@
+{
+ "schema_version": "hand-warehouse/1.0",
+ "validity_revision": "usable-annotation-2026-09-20",
+ "annotation_validity": {
+ "meaning": "annotation usable for supervision, not merely record presence",
+ "formula": "source_annotation_present AND numerical_checks_pass AND applicable_official_QA_pass AND NOT explicitly_reviewed_local_exclusion",
+ "without_official_qa": "omit QA term; declare source rules; do not fabricate QA",
+ "policy_field": "components..valid_policy",
+ "visibility_is_independent": true,
+ "invalid_float_value": "NaN",
+ "hot3d": "broadcast per-camera frame manual QA conservatively to hand/object labels and pose-derived visibility; retain RGB and source calibration; keep native QA in raw and review report",
+ "hot3d_visibility": "native visibility_ratio[%] already ranges from 0 to 1; do not divide by 100"
+ },
+ "hand_slots": [
+ "left",
+ "right"
+ ],
+ "keypoints": {
+ "mano21/v1": {
+ "count": 21,
+ "names": [
+ "wrist",
+ "thumb_1",
+ "thumb_2",
+ "thumb_3",
+ "thumb_4",
+ "index_1",
+ "index_2",
+ "index_3",
+ "index_4",
+ "middle_1",
+ "middle_2",
+ "middle_3",
+ "middle_4",
+ "ring_1",
+ "ring_2",
+ "ring_3",
+ "ring_4",
+ "little_1",
+ "little_2",
+ "little_3",
+ "little_4"
+ ],
+ "source_index_map": null,
+ "note": "adapter maps official MANO joint and fingertip convention"
+ },
+ "rhd21/v1": {
+ "count": 21,
+ "names": [
+ "wrist",
+ "thumb_1",
+ "thumb_2",
+ "thumb_3",
+ "thumb_4",
+ "index_1",
+ "index_2",
+ "index_3",
+ "index_4",
+ "middle_1",
+ "middle_2",
+ "middle_3",
+ "middle_4",
+ "ring_1",
+ "ring_2",
+ "ring_3",
+ "ring_4",
+ "little_1",
+ "little_2",
+ "little_3",
+ "little_4"
+ ],
+ "source_index_map_per_hand": [
+ 0,
+ 4,
+ 3,
+ 2,
+ 1,
+ 8,
+ 7,
+ 6,
+ 5,
+ 12,
+ 11,
+ 10,
+ 9,
+ 16,
+ 15,
+ 14,
+ 13,
+ 20,
+ 19,
+ 18,
+ 17
+ ]
+ }
+ },
+ "camera": {
+ "opencv_fisheye4": {
+ "K": [
+ 3,
+ 3
+ ],
+ "distortion": [
+ "k1",
+ "k2",
+ "k3",
+ "k4"
+ ],
+ "projection": "theta=atan(r); theta_d=theta*(1+k1*theta^2+k2*theta^4+k3*theta^6+k4*theta^8); source ReInterHand"
+ },
+ "pinhole": {
+ "K": [
+ 3,
+ 3
+ ]
+ },
+ "fisheye624": {
+ "parameters": [
+ "f",
+ "cx",
+ "cy",
+ "k0",
+ "k1",
+ "k2",
+ "k3",
+ "k4",
+ "k5",
+ "p0",
+ "p1",
+ "s0",
+ "s1",
+ "s2",
+ "s3"
+ ]
+ },
+ "rational8": {
+ "K": [
+ 3,
+ 3
+ ],
+ "distortion": [
+ "k1",
+ "k2",
+ "p1",
+ "p2",
+ "k3",
+ "k4",
+ "k5",
+ "k6"
+ ]
+ }
+ },
+ "segmentation": {
+ "segment/uint16_labels/v1": {
+ "storage": "mask.mkv",
+ "dtype": "uint16",
+ "mapping": "instances.jsonl",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "reserved_codes": {
+ "0": "unassigned",
+ "65535": "ignore"
+ },
+ "region_codes": [
+ 1,
+ 65534
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ },
+ "segment/instance_polygons/v1": {
+ "storage": "polygons.jsonl",
+ "mapping": "instances.jsonl",
+ "frame_keys": [
+ "frame_index",
+ "instances"
+ ],
+ "instance_keys": [
+ "mask_value",
+ "polygons"
+ ],
+ "coordinate_unit": "pixel",
+ "overlap_allowed": true,
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref"
+ ],
+ "polygon_shape": [
+ "N",
+ 2
+ ],
+ "holes_supported": false
+ },
+ "segment/foreground_intensity/v1": {
+ "storage": "mask.mkv",
+ "dtype": "uint16",
+ "value_range": [
+ 0,
+ 255
+ ],
+ "normalization_scale": 0.00392156862745098,
+ "axes": [
+ "T",
+ "height",
+ "width"
+ ],
+ "meaning": "native hand foreground intensity; individual hand or union declared in metadata; not instance codes or calibrated probability",
+ "zero": "background",
+ "full_scale": "foreground",
+ "mapping": "foreground_category in component metadata",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "value_range",
+ "normalization_scale",
+ "foreground_category",
+ "entity_id",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ }
+ },
+ "note": "Registry for hand-warehouse/1.0. Read CONTRACT.md for field semantics. This is not a JSON Schema document or executable validator.",
+ "components": {
+ "mano/axis_angle61/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 2,
+ 61
+ ],
+ "valid_shape": [
+ "T",
+ 2
+ ],
+ "unit": "m",
+ "angle_unit": "rad",
+ "coordinate_frame": "camera",
+ "layout": {
+ "pose_axis_angle": [
+ 0,
+ 48
+ ],
+ "shape": [
+ 48,
+ 58
+ ],
+ "translation": [
+ 58,
+ 61
+ ]
+ }
+ },
+ "keypoints/uv/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 2,
+ 21,
+ 2
+ ],
+ "valid_shape": [
+ "T",
+ 2,
+ 21
+ ],
+ "unit": "pixel",
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "keypoints/xyz/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 2,
+ 21,
+ 3
+ ],
+ "valid_shape": [
+ "T",
+ 2,
+ 21
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "depth/z_float32/v1": {
+ "storage": "mkv",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "valid_shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "unit": "m",
+ "depth_type": "z",
+ "pixel_grid": "current_rgb",
+ "coordinate_frame": "current_rgb_camera",
+ "valid_storage": "mkv",
+ "allowed_storage_profiles": [
+ "mkv/depth_float32_bits/v1"
+ ],
+ "allowed_valid_storage_profiles": [
+ "mkv/bool_ffv1/v1"
+ ],
+ "required_metadata": [
+ "storage",
+ "valid_storage",
+ "depth_origin"
+ ],
+ "depth_origin_values": [
+ "measured",
+ "rendered",
+ "unknown"
+ ]
+ },
+ "transform/se3/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 4,
+ 4
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "convention"
+ ]
+ },
+ "object/rigid/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 4,
+ 4
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "convention"
+ ]
+ },
+ "camera/pinhole/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3,
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/rational8/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3,
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/opencv_fisheye4/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3,
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/fisheye624/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 15
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/distortion8/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 8
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/fisheye4_distortion/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 4
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "object/joint_state/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "J"
+ ],
+ "valid_shape": [
+ "T",
+ "J"
+ ],
+ "required_metadata": [
+ "joint_order"
+ ],
+ "unit_rule": "revolute radians; prismatic metres; mixed types declared per joint"
+ },
+ "object_keypoints/camera/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "K",
+ 3
+ ],
+ "valid_shape": [
+ "T",
+ "K"
+ ],
+ "unit": "m",
+ "coordinate_frame": "camera",
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "object/dimensions/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "dimensions_semantics"
+ ],
+ "axis_order": [
+ "length",
+ "width",
+ "height"
+ ],
+ "scale_rule": "do not apply to mesh without source-supported size/scale relation"
+ },
+ "object_keypoints/template/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "K",
+ 3
+ ],
+ "valid_shape": [
+ "K"
+ ],
+ "unit": "m",
+ "coordinate_frame": "object",
+ "static": true,
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "mesh/obj/v1": {
+ "storage": "obj",
+ "unit": "m",
+ "static": true,
+ "valid_shape": [],
+ "dependencies": "MTL and textures retained under owning object"
+ },
+ "rgb/uint8/v1": {
+ "storage": "mkv",
+ "dtype": "uint8",
+ "shape": [
+ "T",
+ "H",
+ "W",
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "color_order": "RGB",
+ "allowed_storage_profiles": [
+ "mkv/mjpeg_copy/v1",
+ "mkv/source_copy/v1",
+ "mkv/h264_rgb_lossless/v1",
+ "mkv/ffv1_rgb/v1"
+ ],
+ "required_metadata": [
+ "storage"
+ ]
+ },
+ "contact/relations/v1": {
+ "storage": "jsonl",
+ "valid_shape": [
+ "T"
+ ],
+ "frame_keys": [
+ "frame_index",
+ "relations"
+ ],
+ "absence_semantics": "no record is not a negative contact annotation"
+ },
+ "contact/vertex_region/v1": {
+ "storage": "pt",
+ "dtype": "bool",
+ "shape": [
+ "T",
+ "V"
+ ],
+ "valid_shape": [
+ "T",
+ "V"
+ ],
+ "required_metadata": [
+ "source_geometry_ref",
+ "target_geometry_ref",
+ "topology_id"
+ ],
+ "pilot_status": "defined_not_empirically_validated"
+ },
+ "contact/vertex_distance/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "V"
+ ],
+ "valid_shape": [
+ "T",
+ "V"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "source_geometry_ref",
+ "target_geometry_ref",
+ "topology_id",
+ "distance_definition"
+ ],
+ "pilot_status": "defined_not_empirically_validated"
+ },
+ "object/articulated/v1": {
+ "storage": "json",
+ "required_keys": [
+ "parts",
+ "joints"
+ ],
+ "part_keys": [
+ "part_id",
+ "parent_id",
+ "has_model",
+ "mesh_ref"
+ ],
+ "hierarchy_semantics": "declare mechanical articulation or semantic containment; containment alone does not imply joints",
+ "acyclic": true
+ },
+ "segment/uint16_labels/v1": {
+ "storage": "mkv",
+ "dtype": "uint16",
+ "mapping": "instances.jsonl",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "reserved_codes": {
+ "0": "unassigned",
+ "65535": "ignore"
+ },
+ "region_codes": [
+ 1,
+ 65534
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ },
+ "segment/instance_polygons/v1": {
+ "storage": "jsonl",
+ "mapping": "instances.jsonl",
+ "frame_keys": [
+ "frame_index",
+ "instances"
+ ],
+ "instance_keys": [
+ "mask_value",
+ "polygons"
+ ],
+ "coordinate_unit": "pixel",
+ "overlap_allowed": true,
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref"
+ ],
+ "polygon_shape": [
+ "N",
+ 2
+ ],
+ "holes_supported": false
+ },
+ "segment/foreground_intensity/v1": {
+ "storage": "mkv",
+ "dtype": "uint16",
+ "value_range": [
+ 0,
+ 255
+ ],
+ "normalization_scale": 0.00392156862745098,
+ "axes": [
+ "T",
+ "height",
+ "width"
+ ],
+ "meaning": "native hand foreground intensity; individual hand or union declared in metadata; not instance codes or calibrated probability",
+ "zero": "background",
+ "full_scale": "foreground",
+ "mapping": "foreground_category in component metadata",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "value_range",
+ "normalization_scale",
+ "foreground_category",
+ "entity_id",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ }
+ },
+ "storage_revision": "lossless-video-2026-09-18",
+ "sampling": {
+ "image": {
+ "single": {
+ "T": 1
+ },
+ "independent": {
+ "T": ">=2",
+ "required_frame_fields": [
+ "sample_id",
+ "source_locator"
+ ],
+ "temporal_relationship": false
+ },
+ "source_fps": null
+ },
+ "video": {
+ "sampling_types": [
+ "dense",
+ "sparse"
+ ]
+ }
+ },
+ "storage_profiles": {
+ "mkv/mjpeg_copy/v1": {
+ "container": "matroska",
+ "codec": "mjpeg",
+ "method": "stream_copy_from_source_jpeg",
+ "logical_dtype": "uint8",
+ "decoded_pixel_format": "rgb24",
+ "validation": [
+ "source_jpeg_packet_sha256_exact",
+ "all_packet_count_order_and_dimensions_exact",
+ "deterministic_decoded_rgb_sample_exact",
+ "pilot_and_final_selected_whole_sequences_decoded_rgb_exact"
+ ],
+ "validation_policy": {
+ "id": "jpeg-packets-all-rgb-sample16/v1",
+ "sample_count_per_view": 16,
+ "short_views": "all_frames_if_T_less_than_16",
+ "fixed_indices": "0,T-1,T//4,T//2,3*T//4, deduplicated",
+ "remaining_indices": "sample without replacement to min(T,16) using Python random.Random(seed)",
+ "seed": "first 8 bytes of SHA256(sequence_id/camera_id/20260920), big-endian integer",
+ "report": "policy, exact selected frame indices, seed, separate packet and pixel coverage",
+ "legacy_all_frame_pixel_proofs": "still accepted; preserve historical evidence"
+ }
+ },
+ "mkv/source_copy/v1": {
+ "container": "matroska",
+ "codec": "source_codec_declared_in_metadata",
+ "method": "stream_copy_only",
+ "logical_dtype": "uint8",
+ "decoded_pixel_format": "rgb24",
+ "validation": [
+ "no_reencoding",
+ "all_decoded_rgb_frames_exact",
+ "presentation_frame_order_exact"
+ ]
+ },
+ "mkv/h264_rgb_lossless/v1": {
+ "container": "matroska",
+ "codec": "h264",
+ "encoder": "libx264rgb",
+ "crf": 0,
+ "decoded_pixel_format": "rgb24",
+ "logical_dtype": "uint8",
+ "gop": "declared_in_encoder_parameters",
+ "validation": [
+ "all_decoded_rgb_frames_exact"
+ ],
+ "forbidden": [
+ "lossy_rate_control",
+ "rgb_to_yuv420_subsampling"
+ ]
+ },
+ "mkv/ffv1_rgb/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "decoded_pixel_format": "rgb24",
+ "logical_dtype": "uint8",
+ "validation": [
+ "all_decoded_rgb_frames_exact"
+ ]
+ },
+ "mkv/depth_float32_bits/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "pixel_format": "gray16le",
+ "logical_dtype": "float32",
+ "logical_shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "encoded_shape": [
+ "T",
+ "H",
+ "2*W"
+ ],
+ "packing": "IEEE754 little-endian float32 bits; low uint16 in first W columns, high uint16 in last W columns",
+ "decode": "reinterpret_float32(uint32(low) | (uint32(high) << 16))",
+ "validation": [
+ "all_float32_bits_exact_including_nan"
+ ]
+ },
+ "mkv/bool_ffv1/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "pixel_format": "gray",
+ "logical_dtype": "bool",
+ "stored_values": [
+ 0,
+ 1
+ ],
+ "validation": [
+ "all_bool_values_exact"
+ ]
+ },
+ "mkv/uint16_ffv1/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "pixel_formats": [
+ "gray",
+ "gray16le"
+ ],
+ "logical_dtype": "uint16",
+ "gray_condition": "all values <=255; decode by exact uint8-to-uint16 promotion",
+ "validation": [
+ "all_integer_values_exact"
+ ]
+ }
+ },
+ "video_metadata": {
+ "required": [
+ "profile",
+ "codec",
+ "pixel_format",
+ "encoded_size",
+ "decoder",
+ "encoder_parameters",
+ "source_lossy"
+ ],
+ "source_lossy_values": [
+ true,
+ false,
+ "unknown"
+ ],
+ "timebase_role": "index_only",
+ "lossless_reference": "source compressed packets or fixed-decoder source pixels; depth uses normalized standard float32 bits",
+ "selection": "choose smaller complete candidate only after exact roundtrip validation"
+ }
+}
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/full.log b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/full.log
new file mode 100644
index 0000000000000000000000000000000000000000..7786507d0c59ad7f165cdc1b215d8d50ab9bd02d
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/full.log
@@ -0,0 +1,61 @@
+Verified existing subject1/h1/0
+Verified existing subject4/h1/0
+Verified existing subject3/h1/0
+Verified existing subject1/h1/6
+Verified existing subject2/h1/0
+Verified existing subject1/h1/3
+Verified existing subject2/h1/6
+Verified existing subject1/h1/7
+Converted subject3/h1/6
+Converted subject4/h1/6
+Verified existing subject1/h1/4
+Verified existing subject1/h1/2
+Verified existing subject2/h1/3
+Verified existing subject1/h1/1
+Verified existing subject1/h1/5
+Verified existing subject1/o1/0
+Converted subject3/h1/3
+Verified existing subject2/h1/7
+Verified existing subject1/o1/6
+Converted subject4/h1/3
+Verified existing subject1/o1/3
+Verified existing subject2/h1/4
+Verified existing subject1/o1/7
+Verified existing subject1/o1/4
+Verified existing subject2/h1/2
+Verified existing subject1/o1/2
+Converted subject3/h1/7
+Converted subject4/h1/7
+Verified existing subject1/o1/1
+Verified existing subject2/h1/1
+Verified existing subject1/o1/5
+Verified existing subject1/o2/0
+Converted subject3/h1/4
+Verified existing subject2/h1/5
+Verified existing subject1/o2/6
+Verified existing subject1/o2/3
+Verified existing subject2/o1/0
+Converted subject4/h1/4
+Verified existing subject1/o2/7
+Verified existing subject1/o2/4
+Converted subject3/h1/2
+Verified existing subject2/o1/6
+Converted subject4/h1/2
+Verified existing subject1/o2/2
+Verified existing subject2/o1/3
+Converted subject3/h1/1
+Verified existing subject1/o2/1
+Verified existing subject1/o2/5
+Verified existing subject1/k1/0
+Verified existing subject1/k1/6
+Verified existing subject2/o1/4
+Verified existing subject1/k1/3
+Converted subject3/h1/5
+Converted subject4/h1/1
+Verified existing subject1/k1/7
+Verified existing subject2/o1/2
+Converted subject4/h1/5
+Converted subject3/o1/0
+Converted subject1/k1/4
+Converted subject4/o1/0
+Verified existing subject2/o1/1
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/full.status b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/full.status
new file mode 100644
index 0000000000000000000000000000000000000000..e8e02acf783f15b30d41ed45d921baf847c4db74
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/full.status
@@ -0,0 +1,7 @@
+START=2026-09-30T11:03:05+08:00
+WORKERS=4 CODEC_THREADS=10 DECODE_THREADS=10 DEPTH_THREADS=4 SUBJECTS=1,2,3,4 CASA=false
+CPU_LIMIT=none
+CGROUP=/user.slice/user-25044.slice/user@25044.service/app.slice/h2o-full-d10-20260930.scope
+CPU_MAX=max controller_disabled
+
+RECONFIGURE=2026-09-30T15:47:55+08:00 REASON=process16_camera_pipeline_ram32_streaming
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/run-full.sh b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/run-full.sh
new file mode 100644
index 0000000000000000000000000000000000000000..eefa21bbb201a5ad30462ad7548f980793d762b0
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/run-full.sh
@@ -0,0 +1,26 @@
+#!/usr/bin/env bash
+set -u
+base="$HOME/Datasets/uni-hoi-h2o-full-20260930-w4-c10-d10"
+raw="$HOME/Datasets/Hand-Datasets-Raw/H2O"
+cd "$HOME/Projects/uni-hoi-dataset" || exit 1
+mkdir -p "$base/scratch"
+export TMPDIR="$base/scratch" PYTHONUNBUFFERED=1
+for n in 1 2 3 4; do test -s "$raw/raw/data/subject${n}_v1_1.tar.gz" || exit 2; done
+test ! -e "$base/full.status" || exit 3
+cg=$(awk -F: '$1=="0" {print $3}' /proc/self/cgroup)
+if [[ -f "/sys/fs/cgroup$cg/cpu.max" ]]; then
+ read -r quota period < "/sys/fs/cgroup$cg/cpu.max"
+else
+ quota=max
+ period=controller_disabled
+fi
+printf 'START=%s\nWORKERS=4 CODEC_THREADS=10 DECODE_THREADS=10 DEPTH_THREADS=4 SUBJECTS=1,2,3,4 CASA=false\nCPU_LIMIT=none\nCGROUP=%s\nCPU_MAX=%s %s\n' "$(date -Is)" "$cg" "$quota" "$period" > "$base/full.status"
+if [[ "$quota" != max ]]; then
+ printf 'EXIT=4 ERROR=UNEXPECTED_CPU_QUOTA\n' >> "$base/full.status"
+ exit 4
+fi
+/usr/bin/time -v .venv/bin/python "$base/frozen-code/scripts/convert_h2o_v1.0.py" --root "$raw" --out "$base" --release-id shared-v1.0 --workers 4 --codec-threads 10 --decode-threads 10 --depth-threads 4 --mano-dir "$HOME/Projects/WiLoR/mano_data" > "$base/full.log" 2>&1
+code=$?
+printf 'EXIT=%s END=%s\n' "$code" "$(date -Is)" >> "$base/full.status"
+if [[ -f "/sys/fs/cgroup$cg/cpu.stat" ]]; then cat "/sys/fs/cgroup$cg/cpu.stat" >> "$base/full.status"; fi
+exit "$code"
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/check_dexycb_projection.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/check_dexycb_projection.py
new file mode 100644
index 0000000000000000000000000000000000000000..2774c004fc1359dea582135ec7171f600b33b336
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/check_dexycb_projection.py
@@ -0,0 +1,31 @@
+#!/usr/bin/env python3
+"""Check saved Dex-YCB source 3D joints against saved source 2D joints in sampled views."""
+import json
+from pathlib import Path
+import sys
+
+import numpy as np
+import torch
+
+root = Path(sys.argv[1])
+report = json.loads((root / 'doc/validation/STRUCTURAL_AUDIT.json').read_text())
+checks = []
+for name in report['sampled_views']:
+ view = root / name
+ load = lambda path: torch.load(view / path, weights_only=True, map_location='cpu').numpy()
+ k = load('camera/intrinsic.pt')
+ xyz = load('annotation/keypoints/xyz.pt')
+ uv = load('annotation/keypoints/uv.pt')
+ valid = load('annotation/keypoints/xyz_valid.pt') & load('annotation/keypoints/uv_valid.pt')
+ z = xyz[..., 2]
+ valid &= np.isfinite(xyz).all(axis=-1) & np.isfinite(uv).all(axis=-1) & (z > 0)
+ z = np.where(valid, z, 1)
+ pred = np.stack((k[:, 0, 0, None, None] * xyz[..., 0] / z + k[:, 0, 2, None, None],
+ k[:, 1, 1, None, None] * xyz[..., 1] / z + k[:, 1, 2, None, None]), axis=-1)
+ errors = np.linalg.norm(pred[valid] - uv[valid], axis=-1)
+ checks.append(dict(view=name, points=int(errors.size), median_px=float(np.median(errors)) if errors.size else None,
+ p95_px=float(np.percentile(errors, 95)) if errors.size else None))
+(root / 'doc/validation/KEYPOINT_PROJECTION_QC.json').write_text(json.dumps({
+ 'scope': 'Saved source 3D joints projected with saved color intrinsics vs saved source 2D joints; independent of hand mesh rendering.',
+ 'views': checks}, ensure_ascii=False, indent=2) + '\n')
+print(json.dumps(checks, ensure_ascii=False), flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/config.json b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..9254fc637be8ba8821d246948787c09c5cb890c4
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/config.json
@@ -0,0 +1,17 @@
+{
+ "root": "/home/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O",
+ "out": "/home/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10-d10",
+ "release_id": "shared-v1.0",
+ "limit": null,
+ "max_frames": null,
+ "mano_dir": "/home/xuzhefeng/Projects/WiLoR/mano_data",
+ "views": null,
+ "jpeg_full_check": false,
+ "workers": 4,
+ "codec_threads": 10,
+ "decode_threads": 10,
+ "depth_threads": 4,
+ "subjects": null,
+ "include_casa": false,
+ "archive": null
+}
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/convert_dexycb_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/convert_dexycb_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed3f479787fae378b3d3b51ce2f58ccaebfd3d0f
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/convert_dexycb_v1.0.py
@@ -0,0 +1,349 @@
+#!/usr/bin/env python3
+"""Dex-YCB source annotations → CONTRACT.md v1.0. No derived annotations."""
+import io
+import re
+import shutil
+import tarfile
+import tempfile
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+import yaml
+
+from warehouse_v1_0 import (View, build_record, find_file, locator, mano_models, mesh_asset,
+ model_profile, parser, pca_to_axis_angle, release, roots, safe_member, sequence_dir)
+
+IMG_W, IMG_H = 640, 480
+
+class _Loader(yaml.SafeLoader):
+ pass
+
+
+_Loader.add_constructor("tag:yaml.org,2002:python/tuple",
+ lambda l, n: tuple(l.construct_sequence(n)))
+
+
+def yaml_load_bytes(data: bytes):
+ return yaml.load(io.BytesIO(data), Loader=_Loader)
+
+
+class Calibration:
+ def __init__(self, dexycb_root: Path):
+ self.intrinsics = {} # serial -> {fx,fy,cx,cy,w,h}
+ self.extrinsics = {} # group -> {serial: [12] 行优先 3×4 cam→world}
+ self.betas = {} # mano_calib_name -> [10]
+ calib_dir = dexycb_root / "calibration"
+ if calib_dir.is_dir():
+ self._load((p.as_posix(), p.read_bytes())
+ for p in sorted(calib_dir.rglob("*")) if p.is_file())
+ else:
+ tar_path = dexycb_root / "calibration.tar.gz"
+ if not tar_path.exists():
+ raise FileNotFoundError(f"no calibration/ nor calibration.tar.gz in {dexycb_root}")
+ with tarfile.open(tar_path, "r:gz") as tf:
+ self._load((m.name, tf.extractfile(m).read()) for m in tf if m.isfile())
+
+ _RE_INTR = re.compile(r"(?:^|/)intrinsics/(\d+)_\d+x\d+\.yml$")
+ _RE_EXTRA = re.compile(r"(?:^|/)extrinsics_(\d{8}_\d{6})/extrinsics\.yml$")
+ _RE_MANO = re.compile(r"(?:^|/)mano_(\d{8}_\d{6}_subject-\d{2}_right)/mano\.yml$")
+
+ def _load(self, files):
+ for rel, data in files:
+ m = self._RE_INTR.search(rel)
+ if m:
+ c = yaml_load_bytes(data)["color"]
+ self.intrinsics[m.group(1)] = {"fx": float(c["fx"]), "fy": float(c["fy"]),
+ "cx": float(c["ppx"]), "cy": float(c["ppy"]),
+ "w": IMG_W, "h": IMG_H}
+ continue
+ m = self._RE_EXTRA.search(rel)
+ if m:
+ raw = yaml_load_bytes(data)["extrinsics"]
+ self.extrinsics[m.group(1)] = {
+ str(s): [float(x) for x in np.asarray(v).ravel()] for s, v in raw.items()}
+ continue
+ m = self._RE_MANO.search(rel)
+ if m:
+ self.betas[m.group(1)] = [float(b) for b in yaml_load_bytes(data)["betas"]]
+
+
+
+def calibration_locator(path, member, **extra):
+ if path.is_dir():
+ return locator(path / member.removeprefix('calibration/'), **extra)
+ return locator(path, member, **extra)
+
+
+def prepare_models(root, destination):
+ archive = find_file(root, 'models.tar.gz')
+ if archive is None:
+ return {}, None
+ pattern = re.compile(r'^models/(\d{3}_[a-z0-9_]+)/(textured_simple\.obj(?:\.mtl)?|texture_map\.png)$')
+ names = set()
+ with tarfile.open(archive, 'r|gz') as tf:
+ for member in tf:
+ match = pattern.fullmatch(member.name)
+ if member.isfile() and match:
+ names.add(match[1])
+ path = safe_member(destination, '/'.join(match.groups()))
+ path.parent.mkdir(parents=True, exist_ok=True)
+ path.write_bytes(tf.extractfile(member).read())
+ if len(names) != 21:
+ raise ValueError('expected all 21 source YCB model directories')
+ return {i: destination / name for i, name in enumerate(sorted(names), 1)}, archive
+
+
+def convert_view(path, args, seq, serial, frames, meta, meta_loc, calib, calib_path, models, assets, asset_archive):
+ total = int(meta.get('num_frames', max(frames) + 1))
+ ids = list(range(min(total, args.max_frames) if args.max_frames else total))
+ for f in ids:
+ frames.setdefault(f, {})
+ if not ids:
+ raise ValueError('no RGB frames')
+ source_rows = []
+ for f in ids:
+ row = frames[f]
+ source_rows.append(dict(source_frame_id=f, sync_id=f'{seq}:{f}',
+ source_locator=row['color'][1] if 'color' in row else (meta_loc | {'source_frame_id': f, 'missing_rgb': True}), annotation_matches={
+ 'labels': {'source_frame_id': f, 'source_timestamp_ns': None, 'clock_domain': None,
+ 'match_method': 'index_map', 'delta_ns': None, 'source_locator': row['labels'][1]}
+ for _ in [0] if 'labels' in row},
+ component_sources={k: v[1] for k, v in row.items()}))
+ view = View(path, 'dex-ycb', args.release_id, seq, serial, source_rows,
+ sampling='dense' if ids == list(range(ids[0], ids[-1] + 1)) else 'sparse')
+ view.valid_rules = {
+ 'mano': 'Source pose_m present and not all zero; side model/calibration available',
+ 'keypoints': 'Corresponding joint_2d/joint_3d present; whole-hand all -1 is missing, broadcast to its 21 points',
+ 'pose': 'Source pose_y for this ycb_ids slot present and not an all-zero matrix',
+ 'segment': 'Source seg array provided for this frame; region codes and entity references validated',
+ 'relations': 'Source sequence ycb_grasp_ind resolves through ycb_ids; broadcast sequence grasp to selected frames',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta['view_type'] = 'exo'
+ view.meta['sampling']['selection'] = {'max_frames': args.max_frames, 'views': args.views}
+ subject = re.search(r'subject-(\d+)', seq)
+ if subject and 1 <= int(subject[1]) <= 10:
+ index = int(subject[1])
+ view.meta.update(split='val' if index == 7 else 'test' if index in (8, 9) else 'train',
+ split_scheme='Dex-YCB/s1', split_source_locator={
+ 'url': 'https://github.com/NVlabs/dex-ycb-toolkit/blob/master/dex_ycb_toolkit/dex_ycb.py',
+ 'rule': 's1 unseen-subject: 01-06/10 train, 07 val, 08-09 test'})
+ view.meta.pop('split_reason', None)
+ view.rgb([frames[f]['color'][0] if 'color' in frames[f] else None for f in ids], {'frames_ref': 'camera/frames.jsonl'})
+ n = len(ids)
+ intr = calib.intrinsics.get(serial)
+ K = None if intr is None else np.array([[intr['fx'], 0, intr['cx']], [0, intr['fy'], intr['cy']], [0, 0, 1]])
+ ext = calib.extrinsics.get(str(meta['extrinsics']), {}).get(serial)
+ E = None
+ if ext is not None:
+ E = np.eye(4)
+ E[:3] = np.asarray(ext).reshape(3, 4)
+ E = np.linalg.inv(E)
+ view.camera(K, calibration_locator(calib_path, f'calibration/intrinsics/{serial}_640x480.yml'), E,
+ calibration_locator(calib_path, f'calibration/extrinsics_{meta["extrinsics"]}/extrinsics.yml',
+ conversion='inverse of source T_world_from_camera'))
+ sides = meta.get('mano_sides')
+ if sides not in (['left'], ['right']):
+ raise ValueError(f'expected exactly one anatomical source hand: {sides}')
+ side = sides[0]
+ slot = ['left', 'right'].index(side)
+ cal_name = meta['mano_calib'][0] if isinstance(meta['mano_calib'], list) else meta['mano_calib']
+ labels = [dict(np.load(frames[f]['labels'][0])) if 'labels' in frames[f] else {} for f in ids]
+ label_loc = {'frames_ref': 'camera/frames.jsonl', 'source_field': 'component_sources.labels'}
+ motion = np.full((n, 2, 61), np.nan, np.float32)
+ mv = np.zeros((n, 2), bool)
+ if side in models and any('pose_m' in lab for lab in labels):
+ for i, lab in enumerate(labels):
+ if 'pose_m' not in lab:
+ continue
+ pose = np.asarray(lab['pose_m']).reshape(51)
+ if not np.any(pose): # Official Dex-YCB missing-pose sentinel, independent of joints/seg.
+ continue
+ motion[i, slot] = np.r_[pose[:3], pca_to_axis_angle(pose[3:48], models[side]),
+ calib.betas[cal_name], pose[48:51]]
+ mv[i, slot] = True
+ view.tensor('mano', 'mano/axis_angle61/v1', 'annotation/mano/motion.pt', motion, mv,
+ ['T', 'hand', 'parameter'], label_loc, model_profile=model_profile(models),
+ calibration_source=calibration_locator(calib_path, f'calibration/mano_{cal_name}/mano.yml'),
+ provenance={'kind': 'format_conversion', 'method': 'source PCA @ hands_components + hands_mean; explicit axis-angle consumed with flat_hand_mean=true'})
+ elif any('pose_m' in lab for lab in labels):
+ view.absent('mano', 'Source PCA exists; provide --mano-dir to expand it', 'dependency_missing')
+ else:
+ view.absent('mano', 'No source pose_m field')
+ for source_key, key, dim, unit in [('joint_2d', 'uv', 2, 'pixel'), ('joint_3d', 'xyz', 3, 'm')]:
+ if not any(source_key in x for x in labels):
+ view.absent('keypoints_' + key, 'No source ' + source_key)
+ continue
+ points = np.full((n, 2, 21, dim), np.nan, np.float32)
+ valid = np.zeros((n, 2, 21), bool)
+ for i, lab in enumerate(labels):
+ if source_key not in lab:
+ continue
+ p = np.asarray(lab[source_key]).reshape(21, dim)
+ points[i, slot] = p
+ valid[i, slot] = not np.all(p == -1) # Official sentinel covers the whole source hand.
+ # An individual off-image point (-1, -1) is not evidence of missing annotation.
+ view.tensor('keypoints_' + key, f'keypoints/{key}/v1', f'annotation/keypoints/{key}.pt',
+ points, valid, ['T', 'hand', 'keypoint', 'coordinate'], label_loc,
+ keypoint_schema='mano21/v1', unit=unit)
+ ycb_ids = [int(x) for x in meta['ycb_ids']]
+ for j, oid in enumerate(ycb_ids):
+ entity = f'ycb_{oid:02d}'
+ view.object(entity, meta_loc, source_object_id=oid, category_id=oid)
+ poses = np.full((n, 4, 4), np.nan, np.float32)
+ valid = np.zeros(n, bool)
+ for i, lab in enumerate(labels):
+ if 'pose_y' in lab:
+ pose = np.asarray(lab['pose_y'])
+ if pose.shape != (len(ycb_ids), 3, 4):
+ raise ValueError('pose_y shape differs from ycb_ids')
+ if np.any(pose[j]): # Official all-zero missing object pose sentinel.
+ poses[i] = np.eye(4)
+ poses[i, :3] = pose[j]
+ valid[i] = True
+ if any('pose_y' in lab for lab in labels):
+ view.tensor('pose_' + entity, 'object/rigid/v1', f'annotation/object/{entity}/pose.pt',
+ poses, valid, ['T', 'row', 'column'], label_loc,
+ convention='T_camera_from_object', unit='m')
+ else:
+ view.absent('pose_' + entity, 'No source pose_y field')
+ if oid in assets:
+ mesh_asset(view, entity, assets[oid], assets[oid] / 'textured_simple.obj',
+ locator(asset_archive, f'models/{assets[oid].name}/textured_simple.obj'))
+ else:
+ view.absent('mesh_' + entity, 'models.tar.gz unavailable', 'not_downloaded')
+ if any('seg' in lab for lab in labels):
+ mask_rows = []
+ width, height = view.meta['camera']['image_size']
+ for i, lab in enumerate(labels):
+ values = np.unique(lab.get('seg', np.zeros((height, width), np.uint8)))
+ mappings = []
+ for value in values:
+ value = int(value)
+ if value == 0:
+ continue
+ if value != 255 and value not in ycb_ids:
+ raise ValueError(f'unknown Dex-YCB region value {value}')
+ mappings.append(dict(mask_value=value, category_id='hand' if value == 255 else value,
+ entity_id='hand_' + side if value == 255 else f'ycb_{value:02d}', part_id=None,
+ source_instance_id=value))
+ mask_rows.append(dict(frame_index=i, labels=mappings))
+ view.raster('segment', (lab.get('seg', np.zeros((height, width), np.uint16)) for lab in labels),
+ ['seg' in lab for lab in labels], label_loc, mask_rows)
+ view.meta['components']['segment'].update(coverage={'kind': 'selected_entities',
+ 'entities': ['hand_' + side] + [f'ycb_{o:02d}' for o in ycb_ids]}, source_zero_meaning='background')
+ else:
+ view.absent('segment', 'No source seg field')
+ if any('aligned_depth_to_color' in frames[f] for f in ids):
+ def depths():
+ width, height = view.meta['camera']['image_size']
+ for f in ids:
+ item = frames[f].get('aligned_depth_to_color')
+ if item is None:
+ yield np.full((height, width), np.nan, np.float32)
+ else:
+ with Image.open(item[0]) as im:
+ yield np.asarray(im).astype(np.float32) / 1000.0
+ view.raster('depth', depths(), None, {'frames_ref': 'camera/frames.jsonl',
+ 'source_field': 'component_sources.aligned_depth_to_color', 'conversion': 'millimetres to metres'})
+ else:
+ view.absent('depth', 'No source aligned_depth_to_color frames')
+ grasp = meta.get('ycb_grasp_ind')
+ if grasp is not None:
+ if not 0 <= int(grasp) < len(ycb_ids):
+ raise ValueError('invalid ycb_grasp_ind')
+ relation = dict(relation_type='grasp', subject_id='hand_' + side,
+ object_id=f'ycb_{ycb_ids[int(grasp)]:02d}', status='positive', source_locator=meta_loc,
+ provenance={'kind': 'source', 'method': 'sequence ycb_grasp_ind resolved through ycb_ids; not per-frame contact'})
+ view.relations([{'frame_index': i, 'relations': [relation]} for i in range(n)], np.ones(n, bool), meta_loc)
+ else:
+ view.absent('relations', 'No source grasp/contact relation')
+ return view.finish()
+
+
+MEMBER = re.compile(r'^([^/]+)/(\d{8}_\d{6})/(meta\.yml|(\d{12})/(color|labels|aligned_depth_to_color)_(\d+)\.(jpg|npz|png))$')
+
+
+def convert_archive(archive, args, out, calib, calib_path, models, assets, asset_archive):
+ selected_views = set(args.views.split(',')) if args.views else None
+ done = 0
+ current, meta, meta_loc, rows = None, None, None, {}
+ with tempfile.TemporaryDirectory(prefix='dex-source-') as wd:
+ work = Path(wd)
+ def flush():
+ nonlocal done
+ if not rows:
+ return
+ seq = archive.name.split('.tar')[0] + '_' + current
+ reports = {}
+ with sequence_dir(out, seq) as target:
+ for serial, frames in sorted(rows.items()):
+ reports[serial] = convert_view(target / serial, args, seq, serial, frames, meta,
+ meta_loc, calib, calib_path, models, assets, asset_archive)
+ build_record(out, seq, reports)
+ for staged in list(work.iterdir()):
+ if staged.is_dir():
+ shutil.rmtree(staged)
+ else:
+ staged.unlink()
+ done += 1
+ print(f'Converted {seq}', flush=True)
+ with tarfile.open(archive, 'r|gz') as tf:
+ for member in tf:
+ match = MEMBER.fullmatch(member.name)
+ if not member.isfile() or not match:
+ continue
+ seq = match[2]
+ if seq != current:
+ flush()
+ if args.limit and done >= args.limit:
+ break
+ current, meta, meta_loc, rows = seq, None, None, {}
+ data_kind = match[3]
+ if data_kind == 'meta.yml':
+ data = tf.extractfile(member).read()
+ meta = yaml_load_bytes(data)
+ meta_loc = locator(archive, member.name, data)
+ continue
+ serial, kind, f = match[4], match[5], int(match[6])
+ if selected_views and serial not in selected_views or args.max_frames and f >= args.max_frames:
+ continue
+ data = tf.extractfile(member).read()
+ dest = safe_member(work, member.name)
+ dest.parent.mkdir(parents=True, exist_ok=True)
+ dest.write_bytes(data)
+ row = rows.setdefault(serial, {}).setdefault(f, {})
+ if kind in row:
+ raise ValueError('duplicate archive frame')
+ row[kind] = (dest, locator(archive, member.name, data))
+ else:
+ flush()
+ return done
+
+
+def main(argv=None):
+ p = parser('dex-ycb', __doc__)
+ p.add_argument('--subjects')
+ args = p.parse_args(argv)
+ archives = sorted({a for r in roots(args.root) for a in r.glob('*-subject-*.tar.gz')})
+ if args.subjects:
+ subjects = {s.strip().zfill(2) for s in args.subjects.split(',')}
+ archives = [a for a in archives if re.search(r'subject-(\d+)', a.name)[1] in subjects]
+ if not archives:
+ p.error('no Dex-YCB subject archives found')
+ cp = find_file(args.root, 'calibration.tar.gz') or find_file(args.root, 'calibration')
+ if cp is None:
+ p.error('calibration source missing')
+ calib = Calibration(cp.parent)
+ models = mano_models(args.mano_dir)
+ out = release(args, 'dex-ycb')
+ with tempfile.TemporaryDirectory(prefix='dex-models-') as wd:
+ assets, asset_archive = prepare_models(args.root, Path(wd))
+ for archive in archives:
+ convert_archive(archive, args, out, calib, cp, models, assets, asset_archive)
+ return 0
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/convert_h2o_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/convert_h2o_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..18418915638cf2f536e14fd1069b8441d10ea082
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/convert_h2o_v1.0.py
@@ -0,0 +1,545 @@
+#!/usr/bin/env python3
+"""H2O native RGB-D, MANO, joints and objects → CONTRACT.md v1.0 (no rendered masks)."""
+from concurrent.futures import ThreadPoolExecutor
+import json
+import os
+import re
+import shutil
+import tarfile
+import tempfile
+from pathlib import Path
+import zipfile
+
+# Bound native libraries before loading NumPy/Torch; each worker still uses independent FFmpeg processes.
+for key in ('OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS'):
+ os.environ[key] = '1'
+
+import numpy as np
+from PIL import Image
+import torch
+
+from warehouse_v1_0 import (View, build_record, check_mesh, find_file, locator, mano_models, mesh_asset,
+ model_profile, parser, release, roots, safe_member, sequence_dir, sha256)
+
+OBJECTS = {1: 'book', 2: 'espresso', 3: 'lotion', 4: 'spray',
+ 5: 'milk', 6: 'cocoa', 7: 'chips', 8: 'cappuccino'}
+MEMBER = re.compile(r'^(?:h2o_CASA/)?(subject\d+)/([^/]+)/(\d+)/(cam\d)/(.+)$')
+FRAME = re.compile(r'(rgb|depth|cam_pose|hand_pose|hand_pose_mano|obj_pose|obj_pose_rt)/(\d+)\.(png|jpg|txt)$')
+
+
+def load_splits(root):
+ archive = find_file(root, 'label_split.zip')
+ result = {}
+ if archive is not None:
+ with zipfile.ZipFile(archive) as z:
+ for split in ('train', 'val', 'test'):
+ member = f'label_split/action_{split}.txt'
+ for line_no, line in enumerate(z.read(member).decode().splitlines(), 1):
+ fields = line.split()
+ if len(fields) < 2 or fields[1] == 'path':
+ continue
+ key = fields[1]
+ if key in result and result[key][0] != split:
+ raise ValueError('conflicting official splits')
+ result[key] = (split, locator(archive, member, record=line_no))
+ return result
+
+
+def load_assets(root, work):
+ archive = find_file(root, 'object.zip')
+ if archive is None:
+ return {}, None
+ with zipfile.ZipFile(archive) as z:
+ for member in z.infolist():
+ if member.is_dir():
+ continue
+ path = safe_member(work, member.filename)
+ path.parent.mkdir(parents=True, exist_ok=True)
+ path.write_bytes(z.read(member))
+ result = {}
+ for oid, name in OBJECTS.items():
+ directory = work / 'object' / name
+ aliases = []
+ # Source cocoa.obj -> ovomaltine.mtl -> cocoa.png, but the ZIP names the
+ # supplied texture ovomaltine.png. Keep all originals and add a byte-identical alias.
+ if name == 'cocoa' and not (directory / 'cocoa.png').exists():
+ mtl = directory / 'ovomaltine.mtl'
+ if mtl.is_file() and 'map_Kd cocoa.png' in mtl.read_text().splitlines():
+ texture = directory / 'ovomaltine.png'
+ shutil.copyfile(texture, directory / 'cocoa.png')
+ aliases.append({'file': 'cocoa.png', 'sha256': sha256(texture),
+ 'source': locator(archive, 'object/cocoa/ovomaltine.png')})
+ candidates = list(directory.glob('*.obj'))
+ if len(candidates) != 1:
+ raise ValueError(f'expected one source OBJ for {name}')
+ check_mesh(candidates[0], directory) # Fail before decoding any large subject archive.
+ result[oid] = (directory, candidates[0], aliases)
+ return result, archive
+
+
+def convert_view(path, args, seq, cam, rows, intr, splits, models, assets, asset_archive):
+ codec_threads = getattr(args, 'codec_threads', 1)
+ ids = list(range(min(rows), max(rows) + 1))
+ for f in ids:
+ rows.setdefault(f, {})
+ n = len(ids)
+ if not n:
+ raise ValueError('no RGB frames')
+ split, split_loc = splits.get(seq, ('unknown', None))
+ frame_rows = [dict(source_frame_id=f, sync_id=f'{seq}:{f}', source_locator=rows[f]['rgb'][1] if 'rgb' in rows[f] else {'sequence': seq, 'camera': cam, 'source_frame_id': f, 'missing_rgb': True},
+ component_sources={k: item[1] for k, item in rows[f].items()},
+ annotation_matches={k: dict(source_frame_id=f, source_timestamp_ns=None,
+ clock_domain=None, match_method='index_map', delta_ns=None)
+ for k in rows[f] if k != 'rgb'}) for f in ids]
+ view = View(path, 'H2O', args.release_id, seq.replace('/', '_'), cam, frame_rows, split=split)
+ view.valid_rules = {
+ 'mano': 'Per-hand hand_pose_mano flag equals 1; corresponding side model available',
+ 'keypoints': "Per-hand hand_pose flag equals 1, broadcast to that hand's 21 source points independently of MANO",
+ 'pose': 'Source obj_pose_rt class equals this nonzero object class',
+ 'points': 'Source obj_pose class equals this nonzero object class, broadcast to its 21 points',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta['view_type'] = 'ego' if cam == 'cam4' else 'exo' if cam in ('cam0', 'cam1', 'cam2', 'cam3') else 'unknown'
+ view.meta['sampling']['selection'] = {'max_frames': args.max_frames, 'views': args.views}
+ if split_loc:
+ view.meta.update(split_scheme='official_action_split', split_source_locator=split_loc)
+ # In the measured 275 H2O views, exact FFV1 was always smaller than exact H.264.
+ view.rgb([rows[f]['rgb'][0] if 'rgb' in rows[f] else None for f in ids],
+ {'frames_ref': 'camera/frames.jsonl'}, ffv1_first=True, codec_threads=codec_threads)
+ loc = {'frames_ref': 'camera/frames.jsonl', 'source_field': 'component_sources'}
+ if intr is not None:
+ fx, fy, cx, cy, width, height = np.loadtxt(intr[0]).ravel()
+ if [int(width), int(height)] != view.meta['camera']['image_size']:
+ raise ValueError('source intrinsics resolution mismatch')
+ K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
+ view.camera(K, intr[1])
+ camps = np.full((n, 4, 4), np.nan, np.float32)
+ cv = np.zeros(n, bool)
+ data = []
+ for i, f in enumerate(ids):
+ lab = {k: np.loadtxt(item[0]).ravel() for k, item in rows[f].items() if k not in ('rgb', 'depth')}
+ data.append(lab)
+ if 'cam_pose' in lab:
+ camps[i] = np.linalg.inv(lab['cam_pose'].reshape(4, 4))
+ cv[i] = True
+ if cv.any():
+ view.tensor('extrinsic', 'transform/se3/v1', 'camera/extrinsic.pt', camps, cv,
+ ['T', 'row', 'column'], loc, convention='T_camera_from_world',
+ source_convention='T_world_from_camera')
+ motion, mv = np.full((n, 2, 61), np.nan, np.float32), np.zeros((n, 2), bool)
+ points, pv = np.full((n, 2, 21, 3), np.nan, np.float32), np.zeros((n, 2, 21), bool)
+ object_ids = set()
+ for i, lab in enumerate(data):
+ if 'hand_pose_mano' in lab:
+ hands = lab['hand_pose_mano'].reshape(2, 62)
+ for side, row in enumerate(hands):
+ if row[0] not in (0, 1):
+ raise ValueError('unexpected H2O MANO presence flag')
+ if row[0] == 1:
+ motion[i, side] = np.r_[row[4:52], row[52:62], row[1:4]]
+ mv[i, side] = True
+ if 'hand_pose' in lab:
+ hands = lab['hand_pose'].reshape(2, 64)
+ for side, row in enumerate(hands):
+ if row[0] not in (0, 1):
+ raise ValueError('unexpected H2O keypoint presence flag')
+ if row[0] == 1:
+ points[i, side] = row[1:].reshape(21, 3)
+ pv[i, side] = True
+ for key in ('obj_pose', 'obj_pose_rt'):
+ if key in lab and lab[key][0] != 0:
+ object_ids.add(int(lab[key][0]))
+ if any('hand_pose_mano' in lab for lab in data):
+ if len(models) == 2:
+ view.tensor('mano', 'mano/axis_angle61/v1', 'annotation/mano/motion.pt', motion, mv,
+ ['T', 'hand', 'parameter'], loc, model_profile=model_profile(models))
+ else:
+ view.absent('mano', 'Source parameters exist; --mano-dir must identify both model profiles', 'dependency_missing')
+ if any('hand_pose' in lab for lab in data):
+ view.tensor('keypoints_xyz', 'keypoints/xyz/v1', 'annotation/keypoints/xyz.pt', points, pv,
+ ['T', 'hand', 'keypoint', 'coordinate'], loc, keypoint_schema='mano21/v1', unit='m')
+ for oid in sorted(object_ids):
+ if oid not in OBJECTS:
+ raise ValueError(f'unknown H2O object class {oid}')
+ entity = OBJECTS[oid]
+ view.object(entity, loc, category_id=oid, source_object_id=oid)
+ poses, ov = np.full((n, 4, 4), np.nan, np.float32), np.zeros(n, bool)
+ pts, kv = np.full((n, 21, 3), np.nan, np.float32), np.zeros((n, 21), bool)
+ for i, lab in enumerate(data):
+ if 'obj_pose_rt' in lab and lab['obj_pose_rt'][0] == oid:
+ poses[i] = lab['obj_pose_rt'][1:].reshape(4, 4)
+ ov[i] = True
+ if 'obj_pose' in lab and lab['obj_pose'][0] == oid:
+ pts[i] = lab['obj_pose'][1:].reshape(21, 3)
+ kv[i] = True
+ if ov.any():
+ view.tensor('pose_' + entity, 'object/rigid/v1', f'annotation/object/{entity}/pose.pt',
+ poses, ov, ['T', 'row', 'column'], loc, convention='T_camera_from_object', unit='m')
+ if kv.any():
+ view.tensor('points_' + entity, 'object_keypoints/camera/v1',
+ f'annotation/object/{entity}/keypoints/xyz.pt', pts, kv,
+ ['T', 'keypoint', 'coordinate'], loc, keypoint_schema='H2O_official_object21',
+ keypoint_names=['center'] + [f'corner_{i}' for i in range(8)] + [f'edge_midpoint_{i}' for i in range(12)],
+ source_order='official obj_point_order.jpg: center, 8 corners, 12 edge midpoints', unit='m')
+ if oid in assets:
+ directory, obj, aliases = assets[oid]
+ asset_loc = locator(asset_archive, f'object/{entity}/{obj.name}')
+ if aliases:
+ asset_loc['texture_aliases'] = aliases
+ mesh_asset(view, entity, directory, obj, asset_loc)
+ else:
+ view.absent('mesh_' + entity, 'Source object.zip unavailable', 'not_downloaded')
+ if any('depth' in rows[f] for f in ids):
+ width, height = view.meta['camera']['image_size']
+ depth_threads = getattr(args, 'depth_threads', 1)
+ def read_depth(f):
+ item = rows[f].get('depth')
+ if item:
+ with Image.open(item[0]) as im:
+ return np.asarray(im).astype(np.float32) / 1000.0
+ return np.full((height, width), np.nan, np.float32)
+ def depths():
+ if depth_threads == 1:
+ yield from map(read_depth, ids)
+ else:
+ with ThreadPoolExecutor(max_workers=depth_threads) as pool:
+ # Ordered batches bound decoded arrays to depth_threads frames per view.
+ for start in range(0, len(ids), depth_threads):
+ yield from pool.map(read_depth, ids[start:start + depth_threads])
+ view.raster('depth', depths(), None, loc | {'source_field': 'component_sources.depth',
+ 'definition': 'https://github.com/taeinkwon/h2oplayer/blob/main/utils.py',
+ 'conversion': 'registered millimetre camera-Z to metres; no viewer range clipping'},
+ codec_threads=codec_threads)
+ else:
+ view.absent('depth', 'No source depth frames')
+ view.absent('segment', 'H2O provides no native segmentation; rendering deferred to v2.0')
+ view.absent('relations', 'No source contact/grasp annotation')
+ return view.finish()
+
+
+def convert_archive(archive, args, out, splits, models, assets, asset_archive):
+ selected_views = set(args.views.split(',')) if args.views else None
+ subjects = {'subject' + s.strip() for s in args.subjects.split(',')} if args.subjects else None
+ current, rows, intrinsics, done = None, {}, {}, 0
+ published_views = set()
+ with tempfile.TemporaryDirectory(prefix='h2o-source-') as wd:
+ work = Path(wd)
+ def flush():
+ nonlocal done
+ if not rows:
+ return
+ seq = '/'.join(current)
+ reports, pending = {}, {}
+ for cam, frames in sorted(rows.items()):
+ existing = out / 'data' / seq.replace('/', '_') / cam
+ if (existing / 'metainfo.json').is_file():
+ old_frames = [json.loads(line) for line in (existing / 'camera/frames.jsonl').read_text().splitlines()]
+ if [f['source_frame_id'] for f in old_frames] != list(range(min(frames), max(frames) + 1)):
+ raise ValueError('existing view has different sampling; choose a new release-id')
+ for frame in old_frames:
+ incoming = frames.get(frame['source_frame_id'], {})
+ previous = frame.get('component_sources', {})
+ if set(previous) != set(incoming) or any(previous[k].get('sha256') != v[1].get('sha256') for k, v in incoming.items()):
+ raise ValueError('duplicate H2O view differs from the published source; choose a new release-id')
+ else:
+ pending[cam] = frames
+ if pending:
+ with sequence_dir(out, seq.replace('/', '_')) as target:
+ for cam, frames in pending.items():
+ reports[cam] = convert_view(target / cam, args, seq, cam, frames,
+ intrinsics.get(cam), splits, models, assets, asset_archive)
+ build_record(out, seq.replace('/', '_'), reports)
+ for staged in list(work.iterdir()):
+ if staged.is_dir():
+ shutil.rmtree(staged)
+ else:
+ staged.unlink()
+ done += 1
+ print(f'{"Converted" if pending else "Verified existing"} {seq}', flush=True)
+ # Uncompressed CASA supports seeking over large frames; gzip is single-pass.
+ mode = 'r:' if archive.suffix == '.tar' else 'r|gz'
+ with tarfile.open(archive, mode) as tf:
+ for member in tf:
+ match = MEMBER.fullmatch(member.name)
+ if not member.isfile() or not match:
+ continue
+ subj, scene, seq, cam, tail = match.groups()
+ if subjects and subj not in subjects:
+ continue
+ key = (subj, scene, seq)
+ if key != current:
+ flush()
+ if args.limit and done >= args.limit:
+ break
+ current, rows, intrinsics = key, {}, {}
+ published_views = {view.name for view in
+ (out / 'data' / '_'.join(key)).glob('cam*')
+ if (view / 'metainfo.json').is_file()}
+ if selected_views and cam not in selected_views:
+ continue
+ frame = FRAME.fullmatch(tail)
+ if tail != 'cam_intrinsics.txt' and not frame:
+ continue
+ if frame and args.max_frames and int(frame[2]) >= args.max_frames:
+ continue
+ data = tf.extractfile(member).read()
+ dest = safe_member(work, member.name)
+ if cam not in published_views:
+ dest.parent.mkdir(parents=True, exist_ok=True)
+ dest.write_bytes(data)
+ # Published views still verify source bytes, but never read their staging paths.
+ item = (dest, locator(archive, member.name, data))
+ if tail == 'cam_intrinsics.txt':
+ intrinsics[cam] = item
+ else:
+ kind, f = frame[1], int(frame[2])
+ row = rows.setdefault(cam, {}).setdefault(f, {})
+ if kind in row:
+ raise ValueError('duplicate H2O frame')
+ row[kind] = item
+ else:
+ flush()
+ return done
+
+
+def main(argv=None):
+ p = parser('H2O', __doc__)
+ p.set_defaults(release_id='shared-v1.0')
+ p.add_argument('--workers', type=int, choices=range(1, 5), default=1,
+ help='Independent subject archives in parallel, 1..4; benchmark before using >2')
+ p.add_argument('--codec-threads', type=int, choices=(1, 2, 4, 8, 10), default=4,
+ help='FFV1 slice threads per encode; default 4, use 10 for the current full run')
+ p.add_argument('--decode-threads', type=int, choices=(1, 2, 4, 8, 10), default=1,
+ help='FFmpeg threads for source RGB decoding and decoded-media checks')
+ p.add_argument('--depth-threads', type=int, choices=(1, 2, 4), default=1,
+ help='Bounded ordered source depth PNG loading per worker')
+ p.add_argument('--subjects')
+ p.add_argument('--include-casa', action='store_true')
+ p.add_argument('--archive', type=Path, help='Convert one explicit source archive')
+ args = p.parse_args(argv)
+ os.environ['HAND_WAREHOUSE_FFMPEG_THREADS'] = str(args.decode_threads)
+ if args.include_casa and args.workers != 1:
+ p.error('CASA can share sequence IDs with subject archives; convert it sequentially in a separate release')
+ archives = [args.archive.expanduser()] if args.archive else sorted(
+ {a for root in roots(args.root) for a in root.glob('subject*_v1_1.tar.gz')})
+ if args.subjects and not args.archive:
+ requested = {'subject' + s.strip() for s in args.subjects.split(',')}
+ archives = [arc for arc in archives if arc.name.split('_', 1)[0] in requested]
+ if args.include_casa:
+ casa = find_file(args.root, 'h2o_CASA.tar')
+ if casa:
+ archives.append(casa)
+ if not archives:
+ p.error('no H2O archives found')
+ out = release(args, 'H2O')
+ source_mapping = next(p for p in (Path(__file__).resolve().parents[1] / 'docs/h2o-RAW_TO_SHARED.md',
+ Path(__file__).resolve().parents[1] / 'doc/RAW_TO_SHARED.md',
+ out / 'doc/RAW_TO_SHARED.md') if p.is_file())
+ mapping = out / 'doc/RAW_TO_SHARED.md'
+ if mapping.exists() and mapping.read_bytes() != source_mapping.read_bytes():
+ raise ValueError('RAW_TO_SHARED.md changed; choose a new release-id')
+ if not mapping.exists():
+ shutil.copyfile(source_mapping, mapping)
+ torch.set_num_threads(1)
+ models, splits = mano_models(args.mano_dir), load_splits(args.root)
+ with tempfile.TemporaryDirectory(prefix='h2o-assets-') as wd:
+ assets, asset_archive = load_assets(args.root, Path(wd))
+ def convert_one(archive):
+ return convert_archive(archive, args, out, splits, models, assets, asset_archive)
+ if args.workers == 1:
+ for archive in archives:
+ convert_one(archive)
+ else:
+ with ThreadPoolExecutor(max_workers=args.workers) as pool:
+ for archive, count in zip(archives, pool.map(convert_one, archives), strict=True):
+ print(f'Archive {archive.name}: {count} sequences', flush=True)
+ return 0
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
+
+# TODO(v2.0): 以下旧 MANO 网格和渲染分割代码仅保留备查；v1.0 不生成标注。
+# def load_mano_model(path: Path):
+# """MPI MANO pkl → numpy 字典。shapedirs 在 pkl 内为 chumpy Select（对 (778,3,20) 基
+# 底 flat 选取前 10 个 shape 分量；idxs 已核实等价于 base[:,:,:10]）。"""
+# with open(path, "rb") as f:
+# d = _ManoUnpickler(f, encoding="latin1").load()
+# sd = d["shapedirs"]
+# base = np.asarray(sd.a.x, np.float64) # (778, 3, 20)
+# shapedirs = base.reshape(-1)[np.asarray(sd.idxs)].reshape(base.shape[0], 3, -1)
+# jreg = d["J_regressor"]
+# jreg = np.asarray(jreg.todense()) if hasattr(jreg, "todense") else np.asarray(jreg)
+# return {"v_template": np.asarray(d["v_template"], np.float64),
+# "shapedirs": shapedirs,
+# "posedirs": np.asarray(d["posedirs"], np.float64),
+# "weights": np.asarray(d["weights"], np.float64),
+# "J_regressor": np.asarray(jreg, np.float64),
+# "kintree": np.asarray(d["kintree_table"], np.int64),
+# "faces": np.asarray(d["f"], np.int64)}
+#
+#
+# def rodrigues(r):
+# """axis-angle (N,3) → 旋转矩阵 (N,3,3)。公式 I + sinθ·K + (1−cosθ)·K²（已对 scipy 核验）。"""
+# r = np.asarray(r, np.float64).reshape(-1, 3)
+# th = np.linalg.norm(r, axis=1)
+# R = np.zeros((len(r), 3, 3))
+# nz = th > 1e-12
+# R[~nz] = np.eye(3)
+# k = r[nz] / th[nz, None]
+# K = np.zeros((len(k), 3, 3))
+# K[:, 0, 1], K[:, 0, 2], K[:, 1, 2] = -k[:, 2], k[:, 1], -k[:, 0]
+# K[:, 1, 0], K[:, 2, 0], K[:, 2, 1] = k[:, 2], -k[:, 1], k[:, 0]
+# s, c = np.sin(th[nz])[:, None, None], np.cos(th[nz])[:, None, None]
+# R[nz] = np.eye(3) + s * K + (1 - c) * (K @ K)
+# return R
+#
+#
+# def _mano_chain(model, betas, pose):
+# """返回 (v_shaped, J, Rs, G)：G 为 16 个关节的全局变换（平移列 = 关节位置）。"""
+# v = model["v_template"] + np.einsum("nkc,c->nk", model["shapedirs"], betas)
+# J = model["J_regressor"] @ v
+# Rs = rodrigues(np.asarray(pose, np.float64).reshape(16, 3))
+# kt = model["kintree"]
+# G = np.zeros((16, 4, 4))
+# for j in range(16):
+# p = kt[0, j] if j else -1
+# loc = np.eye(4)
+# loc[:3, :3] = Rs[j]
+# loc[:3, 3] = J[j] - (J[p] if j else 0.0)
+# G[j] = loc if not j else G[p] @ loc
+# return v, J, Rs, G
+#
+#
+# def mano_forward(model, betas, pose, transl):
+# """MANO LBS 前向（经典蒙皮 M_j = G_j·G0_j⁻¹；对 hand_pose 21 关节实证 0.0 mm）。
+# pose: 48 axis-angle（[0:3] 全局）；返回相机系米顶点 (778,3)。"""
+# v, J, Rs, G = _mano_chain(model, betas, pose)
+# lrot = (Rs[1:] - np.eye(3)).ravel()
+# v_posed = v + np.einsum("nkc,c->nk", model["posedirs"], lrot)
+# kt = model["kintree"]
+# G0 = np.zeros((16, 4, 4))
+# for j in range(16):
+# p = kt[0, j] if j else -1
+# loc = np.eye(4)
+# loc[:3, 3] = J[j] - (J[p] if j else 0.0)
+# G0[j] = loc if not j else G0[p] @ loc
+# M = np.einsum("nj,jab->nab", model["weights"], G @ np.linalg.inv(G0))
+# out = np.einsum("nab,nb->na", M[:, :3, :3], v_posed) + M[:, :3, 3]
+# return out + np.asarray(transl, np.float64)
+#
+#
+# def mano_chain_joints(model, betas, pose, transl):
+# """16 个关节位置（相机系米）= 链平移列；自检用。"""
+# _, _, _, G = _mano_chain(model, betas, pose)
+# return G[:, :3, 3] + np.asarray(transl, np.float64)
+#
+#
+# def rasterize(verts_cam, faces, labels, W, H, fx, fy, cx, cy):
+# z = verts_cam[:, 2]
+# P = faces
+# ok = (z[P] > 0.02).all(1) # 任一顶点在相机后 → 丢弃该三角形
+# P, labels = P[ok], labels[ok]
+# if len(P) == 0:
+# return np.zeros((H, W), np.uint16), np.zeros(0, np.int64)
+# x = verts_cam[:, 0] / z * fx + cx
+# y = verts_cam[:, 1] / z * fy + cy
+# xa = np.maximum(0, np.minimum(np.floor(x[P[:, 0]]), np.minimum(
+# np.floor(x[P[:, 1]]), np.floor(x[P[:, 2]]))).astype(np.int64))
+# xb = np.minimum(W - 1, np.maximum(np.ceil(x[P[:, 0]]), np.maximum(
+# np.ceil(x[P[:, 1]]), np.ceil(x[P[:, 2]]))).astype(np.int64))
+# ya = np.maximum(0, np.minimum(np.floor(y[P[:, 0]]), np.minimum(
+# np.floor(y[P[:, 1]]), np.floor(y[P[:, 2]]))).astype(np.int64))
+# yb = np.minimum(H - 1, np.maximum(np.ceil(y[P[:, 0]]), np.maximum(
+# np.ceil(y[P[:, 1]]), np.ceil(y[P[:, 2]]))).astype(np.int64))
+# xa, ya = np.minimum(xa, xb), np.minimum(ya, yb) # 完全出画的三角形 → 空 bbox
+# wdt, hgt = xb - xa + 1, yb - ya + 1
+# cnt = wdt * hgt
+# tot = int(cnt.sum())
+# tri = np.repeat(np.arange(len(P)), cnt)
+# k = np.arange(tot) - (np.cumsum(cnt) - cnt)[tri]
+# px = xa[tri] + k % wdt[tri] + 0.5
+# py = ya[tri] + k // wdt[tri] + 0.5
+# ax, ay = x[P[tri, 0]], y[P[tri, 0]]
+# bx, by = x[P[tri, 1]], y[P[tri, 1]]
+# cx_, cy_ = x[P[tri, 2]], y[P[tri, 2]]
+# area = ((x[P[:, 1]] - x[P[:, 0]]) * (y[P[:, 2]] - y[P[:, 0]])
+# - (y[P[:, 1]] - y[P[:, 0]]) * (x[P[:, 2]] - x[P[:, 0]]))
+# areat = area[tri]
+# e0 = (bx - ax) * (py - ay) - (by - ay) * (px - ax)
+# e1 = (cx_ - bx) * (py - by) - (cy_ - by) * (px - bx)
+# e2 = (ax - cx_) * (py - cy_) - (ay - cy_) * (px - cx_)
+# s = np.sign(areat)
+# inside = (np.abs(areat) > 1e-9) & ((e0 * s >= 0) & (e1 * s >= 0) & (e2 * s >= 0))
+# px, py, tri = px[inside], py[inside], tri[inside]
+# at = areat[inside]
+# w0, w1, w2 = e1[inside] / at, e2[inside] / at, e0[inside] / at
+# zz = w0 * z[P[tri, 0]] + w1 * z[P[tri, 1]] + w2 * z[P[tri, 2]]
+# pix = py.astype(np.int64) * W + px.astype(np.int64)
+# order = np.lexsort((zz, pix)) # 同像素按深度升序 → 首条即最近
+# pix, tri = pix[order], tri[order]
+# first = np.unique(pix, return_index=True)[1]
+# mask = np.zeros(H * W, np.uint16)
+# win = tri[first]
+# mask[pix[first]] = labels[win] + 1 # 临时标签 +1（0=背景）
+# return mask.reshape(H, W), np.bincount(labels[win] + 1)
+#
+#
+# # ============================================================================
+# # ffmpeg 写出与核验（与 convert_dexycb.py 同构）
+# # ============================================================================
+# verts, faces_all, labels = [], [], []
+# for ti, (tid, kind) in enumerate(tracks):
+# if kind == "hand_left" and b["mano"][0] > 0.5:
+# verts.append(mano_forward(mano_models["left"], b["mano"][52:62],
+# b["mano"][4:52], b["mano"][1:4]))
+# faces_all.append(mano_models["left"]["faces"])
+# elif kind == "hand_right" and b["mano"][62] > 0.5:
+# verts.append(mano_forward(mano_models["right"], b["mano"][114:124],
+# b["mano"][66:114], b["mano"][63:66]))
+# faces_all.append(mano_models["right"]["faces"])
+# elif isinstance(kind, tuple) and int(b["objp"][0]) == kind[1]:
+# M = np.asarray(b["objrt"][1:17], np.float64).reshape(4, 4)
+# V0, F0 = meshes[kind[1]]
+# verts.append(V0 @ M[:3, :3].T + M[:3, 3])
+# faces_all.append(F0)
+# else:
+# continue
+# labels.append(np.full(len(faces_all[-1]), ti))
+# if verts:
+# V = np.concatenate(verts)
+# offs = np.cumsum([0] + [len(v) for v in verts[:-1]])
+# F = np.concatenate([fa + o for fa, o in zip(faces_all, offs)])
+# mask, counts = rasterize(V, F, np.concatenate(labels),
+# IMG_W, IMG_H, fx, fy, cx, cy)
+# else:
+# mask, counts = np.zeros((IMG_H, IMG_W), np.uint16), np.zeros(0, np.int64)
+# lut = np.zeros(n_tracks + 1, np.uint16)
+# for ti in range(n_tracks):
+# present = ti + 1 < len(counts) and counts[ti + 1] > 0
+# if present:
+# presence[tracks[ti][0]][f] = True
+# if active_id[ti] is None:
+# pid = next(i for i in range(1, 65535) if i not in used)
+# used.add(pid)
+# active_id[ti], seg_start[ti] = pid, f
+# frame_maps[ti].append({"id": pid})
+# lut[ti + 1] = active_id[ti]
+# elif active_id[ti] is not None:
+# frame_maps[ti][-1]["frames"] = [seg_start[ti], f - 1]
+# segments[ti].append([seg_start[ti], f - 1])
+# used.discard(active_id[ti])
+# active_id[ti] = None
+# mw.write(lut[mask])
+
+
+# TODO(v2.0): 旧物体尺寸派生代码；v1.0 仅保留源物体关键点。
+# pts = np.asarray(b["objp"][1:64], np.float64).reshape(21, 3)
+# half = np.abs((pts[1:9] - pts[0]) @ M[:3, :3]).max(0)
+# obj_recs.append({
+# "track_id": f"o{classes.index(c)}", "frame": f,
+# "mesh_ref": f"h2o/{OBJ_CLASSES[c]}.obj",
+# "pose": {"quat": rotmat_to_quat_wxyz(M[:3, :3])[0].tolist(),
+# "trans": M[:3, 3].tolist()},
+# "bbox_center3d": pts[0].tolist(),
+# "bbox_size": (2 * half).tolist()})
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/convert_hoi4d_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/convert_hoi4d_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d5914d64ae72ee62564897743dfe5a41923e08d
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/convert_hoi4d_v1.0.py
@@ -0,0 +1,595 @@
+#!/usr/bin/env python3
+"""HOI4D native observations and annotations → CONTRACT.md v1.0."""
+from collections import Counter
+from concurrent.futures import ThreadPoolExecutor
+from contextlib import ExitStack
+from functools import lru_cache
+import gzip
+import io
+import json
+import os
+from pathlib import Path
+import pickle
+import re
+import shutil
+import shlex
+import posixpath
+import subprocess
+import tempfile
+import tarfile
+import threading
+import zipfile
+
+# Each worker owns one Python thread; prevent native libraries spawning CPU pools.
+for key in ('OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS'):
+ os.environ[key] = '1'
+
+import numpy as np
+from PIL import Image
+from scipy.spatial.transform import Rotation
+
+import torch
+
+from warehouse_v1_0 import (View, build_record, check_mesh, decode_args, find_file, json_write, locator,
+ mano_models, model_profile, parser, probe, release, run, safe_member, sequence_dir)
+
+CATEGORIES = {
+ 1: dict(cad="rigid/ToyCar", groups=[("main", [1], "Toy Car", 1)],
+ hands={2: "right"}, objmap={"Toycar": "main"}),
+ 2: dict(cad="rigid/Mug", groups=[("a", [1], "Mug", 2), ("b", [3], "Mug", 2)],
+ hands={2: "right"}, objmap={"Watercup": "a"}),
+ 3: dict(cad="articulated/Laptop", groups=[("main", [1, 3], "Laptop", 3)],
+ hands={2: "right"},
+ objmap={"Laptopdisplay": "main", "Laptopkeyboard": "main"}),
+ 4: dict(cad="articulated/StorageFurniture",
+ groups=[("main", [1, 3, 4], "Storage Furniture", 4),
+ ("bottle", [5], "Bottle", 5)],
+ hands={2: "right"},
+ objmap={"Lockerbody": "main", "Lockersldingdoor": "main",
+ "Lockerdrawer": "main", "Safebox": "main"}),
+ 5: dict(cad="rigid/Bottle", groups=[("main", [1], "Bottle", 5),
+ ("mug", [3], "Mug", 2)],
+ hands={2: "right"}, objmap={"bottleddrinks": "main"}),
+ 6: dict(cad="articulated/Safe", groups=[("main", [1, 3], "Safe", 6),
+ ("bottle", [4], "Bottle", 5)],
+ hands={2: "right"}, objmap={"Safebox": "main", "Safedoor": "main"}),
+ 7: dict(cad="rigid/Bowl", groups=[("main", [1], "Bowl", 7),
+ ("ball", [3], "Ball", None)],
+ hands={2: "right"}, objmap={"bowl": "main"}),
+ 8: dict(cad="articulated/Bucket",
+ groups=[("a", [1, 3], "Bucket", 8), ("b", [4, 5], "Bucket", 8)],
+ hands={2: "right", 6: "left"},
+ objmap={"bucket": "a", "Buckethandle": "a"}),
+ 9: dict(cad="articulated/Scissors", groups=[("main", [1, 3], "Scissors", 9)],
+ hands={2: "right"},
+ objmap={"scissors": "main", "scissors2": None}), # scissors2：场景道具，无掩码
+ 11: dict(cad="articulated/Pliers", groups=[("main", [1, 3], "Pliers", 11)],
+ hands={2: "right"}, objmap={"Pliersleft": "main", "Pliersright": "main"}),
+ 12: dict(cad="rigid/Kettle", groups=[("main", [1], "Kettle", 12),
+ ("mug", [3], "Mug", 2)],
+ hands={2: "right"}, objmap={"kettle": "main"}),
+ 13: dict(cad="rigid/Knife", groups=[("main", [1], "Knife", 13),
+ ("apple", [3], "Apple", None)],
+ hands={2: "right"}, objmap={"knife": "main"}),
+ 14: dict(cad="articulated/TrashCan",
+ groups=[("main", [1, 3], "Trash Can", 14), ("bottle", [4], "Bottle", 5)],
+ hands={2: "right"}, objmap={"Dustbinbase": "main", "Dustbincover": "main"}),
+ 17: dict(cad="articulated/Lamp", groups=[("main", [1, 3, 4, 5], "Lamp", 17)],
+ hands={2: "right", 6: "left"},
+ objmap={"Lampbase": "main", "Tablelampfirstjoint": "main",
+ "Tablelampsecondjoint": "main", "Thirdjointoftablelamp": "main"}),
+ 18: dict(cad="articulated/Stapler",
+ groups=[("main", [1, 3, 4], "Stapler", 18), ("paper", [5], "Paper", None)],
+ hands={2: "right", 6: "left"}, # 6=左手：官方 label.csv 缺列，实测修正
+ objmap={"Staplerbase": "main", "Staplercover": "main"}),
+ 20: dict(cad="rigid/Chair", groups=[("main", [1], "Chair", 20)],
+ hands={2: "right", 3: "left"}, objmap={"chair": "main"}),
+}
+
+
+# Official HOI4D definitions/motion segmentation/label.csv; no spatial side inference.
+REGION_NAMES = {1: {1: 'Toy Car', 2: 'Right Hand'}, 2: {1: 'Mug', 2: 'Right Hand', 3: 'Mug'}, 3: {1: 'Screen', 2: 'Right Hand', 3: 'Keyboard'}, 4: {1: 'Door', 2: 'Right Hand', 3: 'Body of the Cabinet', 4: 'Drawer', 5: 'Bottle'}, 5: {1: 'Bottle', 2: 'Right Hand', 3: 'Mug'}, 6: {1: 'Body of the Safe', 2: 'Right Hand', 3: 'Door', 4: 'Bottle'}, 7: {1: 'Bowl', 2: 'Right Hand', 3: 'Ball'}, 8: {1: 'Body of the Bucket', 2: 'Right Hand', 3: 'Handle', 4: 'Body of the Bucket', 5: 'Handle', 6: 'Left Hand'}, 9: {1: 'Left Part of the Scissors', 2: 'Right Hand', 3: 'Right Part of the Scissors'}, 11: {1: 'Left Part of the Pliers', 2: 'Right Hand', 3: 'Right Part of the Pliers'}, 12: {1: 'Kettle', 2: 'Right Hand', 3: 'Mug'}, 13: {1: 'Knife', 2: 'Right Hand', 3: 'Apple'}, 14: {1: 'Body of the Trash Can', 2: 'Right Hand', 3: 'Lid', 4: 'Bottle'}, 17: {1: 'Base', 2: 'Right Hand', 3: 'Tube', 4: 'Top Link', 5: 'Bottom Link', 6: 'Left Hand'}, 18: {1: 'Base', 2: 'Right Hand', 3: 'Slot', 4: 'Lid', 5: 'Paper'}, 20: {1: 'Chair', 2: 'Right Hand', 3: 'Left Hand'}}
+
+
+class SplitFiles(io.RawIOBase):
+ """Read concatenated source gzip parts without concatenating 119 GB on disk."""
+ def __init__(self, paths):
+ self.paths = iter(paths)
+ self.current = None
+
+ def readable(self):
+ return True
+
+ def readinto(self, buffer):
+ while True:
+ if self.current is None:
+ path = next(self.paths, None)
+ if path is None:
+ return 0
+ self.current = path.open('rb')
+ n = self.current.readinto(buffer)
+ if n:
+ return n
+ self.current.close()
+ self.current = None
+
+ def close(self):
+ if self.current is not None:
+ self.current.close()
+ super().close()
+
+
+def extract_depths(root, sequences, work):
+ directory = find_file(root, 'DEPTH')
+ if directory is None:
+ return {}, []
+ parts = sorted(directory.glob('HOI4D_depth_video.tar.gz*'), key=lambda p: int(p.name.split('.gz')[1] or 0))
+ if not parts:
+ return {}, []
+ wanted = {f'HOI4D_depth_video/{seq}/align_depth/depth_video.avi': seq for seq in sequences}
+ found = {}
+ with SplitFiles(parts) as joined, io.BufferedReader(joined) as inp, gzip.GzipFile(fileobj=inp) as gz:
+ with tarfile.open(fileobj=gz, mode='r|') as tf:
+ for member in tf:
+ if member.isfile() and member.name in wanted:
+ seq = wanted[member.name]
+ dest = work / (seq.replace('/', '_') + '.avi')
+ with tf.extractfile(member) as src, dest.open('wb') as dst:
+ shutil.copyfileobj(src, dst)
+ found[seq] = (dest, {'files': [str(p.resolve()) for p in parts], 'member': member.name})
+ if len(found) == len(wanted):
+ break
+ return found, parts
+
+
+def palette_mask(data):
+ with Image.open(io.BytesIO(data)) as im:
+ arr = np.asarray(im)
+ if arr.ndim == 2:
+ return arr.astype(np.uint16)
+ if arr.shape[-1] != 3:
+ raise ValueError('unsupported HOI4D mask representation')
+ # VOC's 256-color palette interleaves eight label bits into the high RGB bits.
+ # Invert it directly instead of sorting every pixel with np.unique.
+ if np.any(arr & np.array([31, 31, 63], dtype=np.uint8)):
+ raise ValueError('source mask has colors outside official VOC palette')
+ mask = np.zeros(arr.shape[:2], dtype=np.uint16)
+ for bit in range(8):
+ mask |= ((arr[..., bit % 3] >> (7 - bit // 3)) & 1).astype(np.uint16) << bit
+ return mask
+
+
+# Four workers each reuse annotation and hand indices instead of rebuilding the ZIP directory.
+@lru_cache(maxsize=8)
+def index_frames(z):
+ index = {}
+ for name in z.namelist():
+ parent, _, filename = name.rpartition('/')
+ stem, dot, suffix = filename.rpartition('.')
+ if not dot or not stem.isdigit() or suffix not in ('png', 'json', 'pickle'):
+ continue
+ entries = index.setdefault((parent + '/', '.' + suffix), {})
+ f = int(stem)
+ if f in entries:
+ raise ValueError(f'duplicate frame number under {parent}')
+ entries[f] = name
+ return index
+
+
+def frame_members(z, prefix, suffix):
+ return index_frames(z).get((prefix, suffix), {})
+
+
+def convert_sequence(seq, args, target, rgbz, annz, hpz, calz, cadz, paths, models, depth):
+ # ponytail: cache only one sequence's encoded annotation bytes; no decoded masks or CAD cache.
+ @lru_cache(maxsize=None)
+ def read_annotation(archive, name):
+ return archive.read(name)
+
+ category = int(re.search(r'/C(\d+)/', seq)[1])
+ definition = CATEGORIES.get(category)
+ if definition is None:
+ raise ValueError(f'category mapping unavailable: {category}')
+ prefix = f'HOI4D_annotations/{seq}'
+ mask_members = frame_members(annz, prefix + '/2Dseg/mask/', '.png')
+ shifted = frame_members(annz, prefix + '/2Dseg/shift_mask/', '.png')
+ if not mask_members:
+ mask_members = shifted
+ pose_members = frame_members(annz, prefix + '/objpose/', '.json')
+ hand_members = {side: frame_members(hpz, f'Hand_pose/handpose_{side}_hand/{seq}/', '.pickle')
+ for side in ('left', 'right')} if hpz else {'left': {}, 'right': {}}
+ with tempfile.TemporaryDirectory(prefix='hoi4d-source-') as wd:
+ work = Path(wd)
+ member = f'HOI4D_release/{seq}/align_rgb/image.mp4'
+ video = work / 'rgb.mp4'
+ with rgbz.open(member) as src, video.open('wb') as dst:
+ shutil.copyfileobj(src, dst)
+ info = probe(video)
+ if 'nb_frames' not in info:
+ info['nb_frames'] = json.loads(run(['ffprobe', '-v', 'error', '-count_frames', '-select_streams', 'v:0',
+ '-show_entries', 'stream=nb_read_frames', '-of', 'json', str(video)], stdout=subprocess.PIPE).stdout)['streams'][0]['nb_read_frames']
+ n = int(info['nb_frames'])
+ if args.max_frames:
+ n = min(n, args.max_frames)
+ if shifted and mask_members != shifted and any(f not in mask_members and f in shifted for f in range(n)):
+ raise ValueError('shift_mask has frames absent from mask; correspondence unverified')
+ frames = []
+ for f in range(n):
+ sources = {}
+ if f in mask_members:
+ sources['segment'] = locator(paths['annotations'], mask_members[f], read_annotation(annz, mask_members[f]))
+ if f in pose_members:
+ sources['object'] = locator(paths['annotations'], pose_members[f], read_annotation(annz, pose_members[f]))
+ for side in ('left', 'right'):
+ if f in hand_members[side]:
+ sources['hand_' + side] = locator(paths['hand'], hand_members[side][f], read_annotation(hpz, hand_members[side][f]))
+ frames.append(dict(source_frame_id=f, source_locator=locator(paths['release'], member, frame=f),
+ component_sources=sources, annotation_matches={k: dict(source_frame_id=f, source_timestamp_ns=None,
+ clock_domain=None, match_method='index_map', delta_ns=None) for k in sources}))
+ view = View(target, 'HOI4D', args.release_id, seq.replace('/', '_'), 'ego', frames)
+ view.valid_rules = {
+ 'mano': 'Matched side pickle contains poseCoeff, beta, trans and corresponding model available; zero angles are not missing',
+ 'keypoints': 'Matched side pickle contains kps2D; independently provided source points, no visibility gate',
+ 'pose': 'Matched source object record contains rotation and center; record identity retained',
+ 'dimensions': 'Matched source object record contains dimensions; size/mesh relation remains source_unspecified',
+ 'segment': 'Source mask or shift_mask present with verified palette codes and entity/part mapping',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta['view_type'] = 'ego'
+ view.meta['sampling']['selection'] = {'max_frames': args.max_frames, 'source_sequence': seq}
+ view.rgb([], locator(paths['release'], member), video=video, count=n)
+ intr_name = f'camera_params/{seq.split("/")[0]}/intrin.npy'
+ if calz and intr_name in calz.namelist():
+ view.camera(np.load(io.BytesIO(calz.read(intr_name))), locator(paths['camera'], intr_name))
+ extr_name = prefix + '/3Dseg/output.log'
+ if extr_name in annz.NameToInfo:
+ lines = [line for line in annz.read(extr_name).decode().splitlines() if line.strip()]
+ if len(lines) % 5:
+ raise ValueError('invalid Open3D camera trajectory')
+ extr, valid = np.full((n, 4, 4), np.nan, np.float32), np.zeros(n, bool)
+ for i in range(0, len(lines), 5):
+ idx = int(lines[i].split()[0])
+ if idx < n:
+ if valid[idx]:
+ raise ValueError('duplicate trajectory index')
+ extr[idx] = np.linalg.inv(np.loadtxt(io.StringIO('\n'.join(lines[i + 1:i + 5]))))
+ valid[idx] = True
+ view.tensor('extrinsic', 'transform/se3/v1', 'camera/extrinsic.pt', extr, valid,
+ ['T', 'row', 'column'], locator(paths['annotations'], extr_name),
+ convention='T_camera_from_world', source_convention='Open3D LOG camera-to-world; inverted')
+ loc = {'frames_ref': 'camera/frames.jsonl', 'source_field': 'component_sources'}
+ motion, mv = np.full((n, 2, 61), np.nan, np.float32), np.zeros((n, 2), bool)
+ points, pv = np.full((n, 2, 21, 2), np.nan, np.float32), np.zeros((n, 2, 21), bool)
+ for slot, side in enumerate(('left', 'right')):
+ for f, name in hand_members[side].items():
+ if f >= n:
+ continue
+ data = pickle.loads(read_annotation(hpz, name), encoding='latin1')
+ if all(k in data for k in ('poseCoeff', 'beta', 'trans')):
+ motion[f, slot] = np.r_[np.asarray(data['poseCoeff']).reshape(48),
+ np.asarray(data['beta']).reshape(10), np.asarray(data['trans']).reshape(3)]
+ mv[f, slot] = True # Zero angles are a valid pose, not a missing sentinel.
+ if 'kps2D' in data:
+ points[f, slot] = np.asarray(data['kps2D']).reshape(21, 2)
+ pv[f, slot] = True
+ if mv.any():
+ if all(side in models for slot, side in enumerate(('left', 'right')) if mv[:, slot].any()):
+ view.tensor('mano', 'mano/axis_angle61/v1', 'annotation/mano/motion.pt', motion, mv,
+ ['T', 'hand', 'parameter'], loc, model_profile=model_profile(models))
+ else:
+ view.absent('mano', 'Source MANO exists; provide --mano-dir to fix model identity', 'dependency_missing')
+ if pv.any():
+ view.tensor('keypoints_uv', 'keypoints/uv/v1', 'annotation/keypoints/uv.pt', points, pv,
+ ['T', 'hand', 'keypoint', 'coordinate'], loc, keypoint_schema='mano21/v1', unit='pixel',
+ source_method='Upstream fitted MANO projection already present in kps2D')
+ objects = {}
+ groups = {key: (vals, name, cls) for key, vals, name, cls in definition['groups']}
+ def ensure_object(key, source_loc, category_id=None, frame=None, source_id=None):
+ if key not in objects:
+ view.object(key, source_loc, scope='frame' if frame is not None else 'sequence',
+ scope_id=f'{seq}:{frame}' if frame is not None else None,
+ category_id=category_id, source_object_id=source_id)
+ objects[key] = {}
+ mappings = []
+ mask_valid = [f in mask_members for f in range(n)]
+ if any(mask_valid):
+ width, height = view.meta['camera']['image_size']
+ def masks():
+ for f in range(n):
+ labels = []
+ if f in mask_members:
+ mask = palette_mask(read_annotation(annz, mask_members[f]))
+ for value in np.unique(mask):
+ value = int(value)
+ if value in (0, 255):
+ if value == 255:
+ raise ValueError('HOI4D 255 region semantics unverified')
+ continue
+ if REGION_NAMES[category].get(value) in ('Left Hand', 'Right Hand'):
+ entity, part, cls = 'hand_' + REGION_NAMES[category][value].split()[0].lower(), None, 'hand'
+ else:
+ group = next((k for k, (vals, _, _) in groups.items() if value in vals), None)
+ entity = group or f'unknown_region_{value}'
+ cls = groups[group][2] if group else None
+ part = f'region_{value}' if group and len(groups[group][0]) > 1 else None
+ ensure_object(entity, frames[f]['component_sources']['segment'], cls)
+ if part:
+ objects[entity].setdefault('parts', {})[part] = {'part_id': part, 'parent_id': None,
+ 'has_model': False, 'mesh_ref': None, 'source_region_code': value, 'name': REGION_NAMES[category].get(value)}
+ labels.append(dict(mask_value=value, category_id=cls, entity_id=entity,
+ part_id=part, source_instance_id=value,
+ source_attributes={'region_name': REGION_NAMES[category].get(value)}))
+ else:
+ mask = np.zeros((height, width), np.uint16)
+ mappings.append(dict(frame_index=f, labels=labels))
+ yield mask
+ view.raster('segment', masks(), mask_valid, loc, mappings)
+ view.meta['components']['segment']['coverage'] = {'kind': 'unknown'}
+ if any(label['part_id'] for row in mappings for label in row['labels']):
+ view.meta['components']['segment']['segmentation_type'] = 'part'
+ else:
+ view.absent('segment', 'No native segmentation for this source sequence')
+ records = {}
+ for f, name in pose_members.items():
+ if f >= n:
+ continue
+ data = json.loads(read_annotation(annz, name))
+ # isEffective is not used to erase existing numerical annotations.
+ source_records = data.get('dataList', data.get('objects'))
+ if source_records is None:
+ raise ValueError('unrecognized source object annotation structure')
+ if category == 2 and source_records and (len(source_records) != 1 or
+ source_records[0].get('id') != 1 or source_records[0].get('label') != 'Watercup'):
+ raise ValueError('C2 native pose differs from the verified one-object target; cannot map to mask region 1')
+ label_counts = Counter(record['label'] for record in source_records)
+ per_frame = set()
+ for record_index, record in enumerate(source_records):
+ label = record['label']
+ group = definition['objmap'].get(label)
+ # Pinned official ObjTracking reads dataList[0] with segmentation label 1.
+ source_primary = category == 2 and record_index == 0 and record.get('id') == 1 and group == 'a'
+ ambiguous = group is not None and sum(g[2] == groups[group][2] for g in groups.values()) > 1 and not source_primary
+ ambiguous = ambiguous or (label_counts[label] > 1 and not source_primary) or group is None
+ entity = f'pose_frame_{f}_{record_index}' if ambiguous else group
+ ensure_object(entity, locator(paths['annotations'], name, record=record_index),
+ groups[group][2] if group else None, frame=f if ambiguous else None, source_id=record.get('id'))
+ if ambiguous:
+ view.build['unresolved'].append({'component': entity, 'status': 'source_semantics_unverified',
+ 'reason': 'Source pose retained with frame identity; no guessed association to same-class mask instances'})
+ label_key = (entity, label)
+ if label_key in per_frame:
+ raise ValueError(f'ambiguous repeated object label {label}; source identity must be resolved')
+ per_frame.add(label_key)
+ records.setdefault(label_key, {})[f] = (record, locator(paths['annotations'], name, record=record.get('id')))
+ if category == 2 and any(entity == 'a' for entity, _ in records):
+ view.build['validation'].append({'component': 'C2_source_pose_mask_identity',
+ 'rule': 'Official ObjTracking process_data.py reads dataList[0] with semantic mask label 1',
+ 'source_revision': 'afa7bf37b02b7e33cf245acd08b98c207ccc175c',
+ 'source_pose_id': 1, 'mask_value': 1, 'matched_frames': sum(len(by_frame) for (entity, _), by_frame in records.items() if entity == 'a')})
+ for (entity, label), by_frame in records.items():
+ part_labels = [k[1] for k in records if k[0] == entity]
+ articulated = definition['cad'].startswith('articulated/') and entity == definition['groups'][0][0]
+ part = 'pose_' + re.sub(r'[^a-zA-Z0-9_]', '_', label) if articulated or len(part_labels) > 1 else None
+ base = f'annotation/object/{entity}'
+ if part:
+ base += '/parts/' + part
+ objects[entity].setdefault('parts', {})[part] = dict(part_id=part, parent_id=None,
+ has_model=False, mesh_ref=None, source_label=label)
+ poses, valid = np.full((n, 4, 4), np.nan, np.float32), np.zeros(n, bool)
+ dimensions = np.full((n, 3), np.nan, np.float32)
+ dv = np.zeros(n, bool)
+ for f, (record, _) in by_frame.items():
+ if all(k in record for k in ('rotation', 'center')):
+ poses[f] = np.eye(4)
+ poses[f, :3, :3] = Rotation.from_euler('XYZ', [record['rotation'][k] for k in ('x', 'y', 'z')]).as_matrix()
+ poses[f, :3, 3] = [record['center'][k] for k in ('x', 'y', 'z')]
+ valid[f] = True
+ if 'dimensions' in record:
+ dimensions[f] = [record['dimensions'][k] for k in ('length', 'width', 'height')]
+ dv[f] = True
+ key = entity + ('_' + part if part else '')
+ view.tensor('pose_' + key, 'transform/se3/v1' if part else 'object/rigid/v1', base + '/pose.pt', poses, valid,
+ ['T', 'row', 'column'], loc, convention='T_camera_from_part' if part else 'T_camera_from_object',
+ unit='m', source_label=label, source_euler_order='XYZ',
+ definition='https://github.com/hoi4d/ObjTracking/blob/main/process_data.py')
+ if dv.any():
+ view.tensor('dimensions_' + key, 'object/dimensions/v1', base + '/dimensions.pt', dimensions, dv,
+ ['T', 'dimension'], loc, dimensions_semantics='source_unspecified', unit='m', scale_applied_to_mesh=False)
+ if cadz:
+ primary = definition['groups'][0][0]
+ if category != 2 and sum(g[2] == category for g in groups.values()) > 1:
+ primary = 'source_cad_instance'
+ instance = int(re.search(r'/N(\d+)/', seq)[1])
+ mesh_prefix = f'HOI4D_CAD_Model_for_release/{definition["cad"]}/{instance:03d}'
+ candidates = [name for name in cadz.namelist() if not name.endswith('/') and
+ (name == mesh_prefix + '.obj' or name.startswith(mesh_prefix + '/'))]
+ if candidates:
+ # Retain OBJ material/texture dependencies beside their original names.
+ queue = list(candidates)
+ for name in queue:
+ if Path(name).suffix.lower() not in ('.obj', '.mtl'):
+ continue
+ for line in cadz.read(name).decode().splitlines():
+ fields = shlex.split(line, comments=True)
+ if not fields:
+ continue
+ refs = fields[1:] if fields[0] == 'mtllib' else ([fields[-1]] if
+ fields[0].startswith('map_') or fields[0] in ('bump', 'norm', 'disp', 'decal') else [])
+ for ref in refs:
+ dependency = posixpath.normpath(posixpath.join(posixpath.dirname(name), ref))
+ if dependency not in cadz.NameToInfo:
+ raise ValueError(f'missing CAD dependency: {dependency}')
+ if dependency not in candidates:
+ candidates.append(dependency)
+ queue.append(dependency)
+ ensure_object(primary, locator(paths['cad'], mesh_prefix), category)
+ asset_dir = view.path / f'annotation/object/{primary}/assets'
+ asset_refs = []
+ for name in candidates:
+ relative = posixpath.relpath(name, mesh_prefix if definition['cad'].startswith('articulated/') else posixpath.dirname(mesh_prefix))
+ dest = safe_member(asset_dir, relative)
+ dest.parent.mkdir(parents=True, exist_ok=True)
+ dest.write_bytes(cadz.read(name))
+ ref = dest.relative_to(view.path).as_posix()
+ if dest.suffix.lower() == '.obj':
+ asset_key = re.sub(r'[^a-zA-Z0-9_]', '_', relative)
+ view.component('mesh_' + primary + '_' + asset_key, 'mesh/obj/v1', ref, ['vertex'],
+ locator(paths['cad'], name), unit='m', valid=True,
+ scale_applied_to_mesh=False, dimensions_semantics='source_unspecified')
+ asset_refs.append(ref)
+ if definition['cad'].startswith('articulated/'):
+ part_id = 'asset_' + asset_key
+ objects[primary].setdefault('parts', {})[part_id] = dict(part_id=part_id,
+ parent_id=None, has_model=True, mesh_ref=ref, source_asset=relative,
+ note='Source asset; correspondence to motion part labels remains unverified')
+ for asset_ref in asset_refs:
+ mesh_check = check_mesh(view.path / asset_ref, asset_dir)
+ view.build['validation'].append({'component': asset_ref, **mesh_check,
+ 'unit_basis': 'official ObjTracking loads source canonical CAD without scaling in metre camera coordinates',
+ 'geometric_acceptance': 'physical dimensions-to-mesh relation remains source_unspecified'})
+ metadata_path = view.path / f'annotation/object/{primary}/metainfo.json'
+ metadata = json.loads(metadata_path.read_text())
+ metadata.update(has_model=bool(asset_refs), asset_refs=asset_refs)
+ if definition['cad'].startswith('rigid/') and len(asset_refs) == 1:
+ metadata['mesh_ref'] = asset_refs[0]
+ json_write(metadata_path, metadata)
+ if definition['cad'].startswith('articulated/'):
+ view.build['unresolved'].append({'component': 'object_part_asset_association',
+ 'status': 'source_semantics_unverified',
+ 'reason': 'All source assets and motion labels retained, but no guessed correspondence or joint_state'})
+ for entity, info in objects.items():
+ if 'parts' in info:
+ ref = f'annotation/object/{entity}/articulation.json'
+ json_write(view.path / ref, {'parts': list(info['parts'].values()), 'joints': [],
+ 'hierarchy_semantics': 'semantic_containment',
+ 'note': 'Unassociated source region, motion and asset nodes; original CAD trees retained in assets'})
+ view.component('articulation_' + entity, 'object/articulated/v1', ref, [], loc)
+ meta_path = view.path / f'annotation/object/{entity}/metainfo.json'
+ metadata = json.loads(meta_path.read_text())
+ metadata['articulation_ref'] = ref
+ json_write(meta_path, metadata)
+ if depth is not None:
+ dpath, dloc = depth
+ dmeta = probe(dpath)
+ if [dmeta['width'], dmeta['height']] != view.meta['camera']['image_size']:
+ raise ValueError('HOI4D depth grid differs from RGB')
+ if dmeta['pix_fmt'] != 'gray16le':
+ raise ValueError('unexpected HOI4D depth pixel format')
+ width, height = view.meta['camera']['image_size']
+ raw = work / 'depth.raw'
+ limits = ['-frames:v', str(n)] if args.max_frames else []
+ run(decode_args(dpath, 'gray16le') + limits + ['-y', str(raw)])
+ if raw.stat().st_size != n * width * height * 2:
+ raise ValueError('depth/RGB frame count or grid mismatch')
+ def depths():
+ with raw.open('rb') as inp:
+ for _ in range(n):
+ yield np.frombuffer(inp.read(width * height * 2), ' best_iou + 0.1 and i > 0.15:
+# best, best_iou = s[0], i
+# if best != gk:
+# gk, iou_fixups = best, iou_fixups + 1
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/convert_visor_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/convert_visor_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f0595bf5e89ae00c959472ff197dfda2d7a6ea6
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/convert_visor_v1.0.py
@@ -0,0 +1,271 @@
+#!/usr/bin/env python3
+"""VISOR source polygons and contact → CONTRACT.md v1.0, without rasterization or side inference."""
+from collections import Counter
+import hashlib
+import json
+import re
+import shutil
+import tempfile
+from pathlib import Path
+import zipfile
+
+import numpy as np
+import torch
+
+from warehouse_v1_0 import (View, build_record, find_file, jsonl_write, locator, parser,
+ release, roots, sequence_dir, source)
+
+FRAME = re.compile(r'frame_(\d+)')
+HAND = {300: 'left', 301: 'right'}
+GLOVE = {303: 'left', 304: 'right'}
+STATES = {'hand-not-in-contact': 'negative', 'glove-not-in-contact': 'negative',
+ 'none-of-the-above': 'target_unannotated', 'inconclusive': 'unknown'}
+
+
+def hand_side(ann):
+ if ann['class_id'] in HAND:
+ return HAND[ann['class_id']]
+ if ann['class_id'] not in (60, 303, 304):
+ return None
+ raw = ann.get('on_which_hand')
+ raw = raw if isinstance(raw, list) else [raw]
+ sides = set()
+ for item in raw:
+ if item == 'left hand':
+ sides.add('left')
+ if item in ('right hand', 'rigth hand'):
+ sides.add('right')
+ if len(sides) == 1:
+ return next(iter(sides))
+ if not sides and ann['class_id'] in GLOVE:
+ return GLOVE[ann['class_id']]
+ return None
+
+
+def convert_video(video, split, annotation, ann_loc, rgb_zip, rgb_loc, mapping, args, target):
+ indexed = sorted(enumerate(annotation['video_annotations']), key=lambda row: int(FRAME.search(row[1]['image']['name'])[1]))
+ if args.max_frames:
+ indexed = indexed[:args.max_frames]
+ if len({row['image']['name'] for _, row in indexed}) != len(indexed):
+ raise ValueError('duplicate VISOR frame')
+ keys = lambda row, ann: (row['image']['subsequence'], ann['class_id'], ann['name'])
+ collisions = set()
+ for _, row in indexed:
+ collisions.update(key for key, number in Counter(keys(row, a) for a in row['annotations']).items() if number > 1)
+ frames, paths = [], []
+ with tempfile.TemporaryDirectory(prefix='visor-rgb-') as wd, zipfile.ZipFile(rgb_zip) as z:
+ work = Path(wd)
+ for original_index, row in indexed:
+ name = row['image']['name']
+ data = z.read(name)
+ path = work / Path(name).name
+ path.write_bytes(data)
+ paths.append(path)
+ loc = rgb_loc | {'nested_member': name, 'sha256': hashlib.sha256(data).hexdigest()}
+ frames.append(dict(source_frame_id=int(FRAME.search(name)[1]), source_locator=loc,
+ source_image=row['image'], original_frame_name=mapping.get(name),
+ annotation_matches={'segment': dict(source_frame_id=name, source_timestamp_ns=None,
+ clock_domain=None, match_method='index_map', delta_ns=None,
+ source_locator=ann_loc | {'record': f'video_annotations[{original_index}]'})}))
+ view = View(target, 'EPIC-KITCHENS-VISOR', args.release_id, video, 'ego', frames, sampling='sparse', split=split)
+ view.valid_rules = {
+ 'segment': 'Selected sparse annotation frame present; all source polygons finite with at least three vertices and entity mapping valid',
+ 'relations': 'At least one source in_contact_object value present; positive targets resolve in the same frame; unknown is an explicit usable status',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta.update(view_type='ego', split_scheme='VISOR_GroundTruth_SparseAnnotations', split_source_locator=ann_loc)
+ view.meta['sampling']['selection'] = {'representation': 'official sparse annotations', 'max_frames': args.max_frames}
+ view.rgb(paths, rgb_loc)
+ entities = {}
+ polygons, mappings, relations, relation_valid = [], [], [], []
+ for f, (original_index, row) in enumerate(indexed):
+ frame_loc = ann_loc | {'record': f'video_annotations[{original_index}]'}
+ id_map, labels, polys = {}, [], []
+ for code, ann in enumerate(row['annotations'], 1):
+ if code >= 65535:
+ raise ValueError('too many source instances')
+ sid = ann['id']
+ if sid in id_map:
+ raise ValueError('duplicate source instance ID within a frame')
+ side = hand_side(ann)
+ if side is not None:
+ entity = 'hand_' + side
+ else:
+ key = keys(row, ann)
+ identity = key + (sid, row['image']['name']) if key in collisions else key
+ entity = 'object_' + hashlib.sha256(json.dumps(identity).encode()).hexdigest()[:20]
+ if entity not in entities:
+ scope = 'frame' if key in collisions else 'subsequence'
+ scope_id = f'{video}:{row["image"]["name"]}' if scope == 'frame' else f'{video}:{key[0]}'
+ view.object(entity, frame_loc | {'source_instance_id': sid}, scope, scope_id,
+ category_id=ann['class_id'], name=ann['name'],
+ association_basis='source_frame_instance' if scope == 'frame' else 'official_subsequence_entity_name')
+ entities[entity] = True
+ id_map[sid] = entity
+ for polygon in ann.get('segments', []):
+ p = np.asarray(polygon)
+ if p.ndim != 2 or p.shape[1] != 2 or len(p) < 3 or not np.isfinite(p).all():
+ raise ValueError(f'invalid source polygon {sid}')
+ polys.append(dict(mask_value=code, polygons=ann.get('segments', [])))
+ labels.append(dict(mask_value=code, category_id=ann['class_id'], entity_id=entity,
+ part_id=None, source_instance_id=sid,
+ source_attributes={k: v for k, v in ann.items() if k != 'segments'}))
+ rr = []
+ provided = False
+ for ann in row['annotations']:
+ raw = ann.get('in_contact_object')
+ values = raw if isinstance(raw, list) else [raw]
+ for value in values:
+ if value is None:
+ continue
+ provided = True
+ status = STATES.get(value)
+ obj = None
+ if status is None:
+ if value not in id_map:
+ raise ValueError(f'unresolved source contact target {value} in {video} frame {f}')
+ status, obj = 'positive', id_map[value]
+ rr.append(dict(relation_type='contact', subject_id=id_map[ann['id']], object_id=obj,
+ status=status, source_value=value, source_locator=frame_loc | {'source_instance_id': ann['id']},
+ provenance=source('Resolve source contact ID against instances in this frame')))
+ polygons.append(dict(frame_index=f, instances=polys))
+ mappings.append(dict(frame_index=f, labels=labels))
+ relations.append(dict(frame_index=f, relations=rr))
+ relation_valid.append(provided)
+ jsonl_write(view.path / 'annotation/segment/polygons.jsonl', polygons)
+ jsonl_write(view.path / 'annotation/segment/instances.jsonl', mappings)
+ torch.save(torch.ones(len(frames), dtype=torch.bool, device='cpu'), view.path / 'annotation/segment/mask_valid.pt')
+ view.component('segment', 'segment/instance_polygons/v1', 'annotation/segment/polygons.jsonl', ['T'], ann_loc,
+ valid_ref='annotation/segment/mask_valid.pt', valid_axes=['T'], segmentation_type='instance',
+ mapping_ref='annotation/segment/instances.jsonl', coverage={'kind': 'unknown', 'exhaustive_ref': 'annotation/segment/instances.jsonl'})
+ view.relations(relations, relation_valid, ann_loc)
+ for component in ('depth', 'mano', 'keypoints_uv', 'keypoints_xyz', 'intrinsic', 'extrinsic'):
+ view.absent(component, 'Not provided by VISOR sparse source annotations')
+ return view.finish()
+
+
+def main(argv=None):
+ p = parser('EPIC-KITCHENS-VISOR', __doc__)
+ p.add_argument('--videos', help='Comma-separated video names')
+ p.add_argument('--splits', default='train,val')
+ args = p.parse_args(argv)
+ wanted = set(args.videos.split(',')) if args.videos else None
+ splits = set(args.splits.split(','))
+ if not splits <= {'train', 'val'}:
+ p.error('only annotated train/val splits are available')
+ out = release(args, 'EPIC-KITCHENS-VISOR')
+ archive = find_file(args.root, 'EPIC-KITCHENS VISOR.zip')
+ with tempfile.TemporaryDirectory(prefix='visor-source-') as wd:
+ work = Path(wd)
+ if archive:
+ with zipfile.ZipFile(archive) as z:
+ suffix = 'GroundTruth-SparseAnnotations/annotations/'
+ members = [n for n in z.namelist() if suffix in n and n.endswith('.json')]
+ prefix = members[0].split(suffix)[0]
+ fm = json.loads(z.read(prefix + 'frame_mapping.json'))
+ done = 0
+ for member in sorted(members):
+ split, filename = member.split(suffix)[1].split('/')
+ video = Path(filename).stem
+ if split not in splits or wanted and video not in wanted:
+ continue
+ data = z.read(member)
+ rgb_member = prefix + f'GroundTruth-SparseAnnotations/rgb_frames/{split}/{video.split("_")[0]}/{video}.zip'
+ rgb_zip = work / (video + '.zip')
+ with z.open(rgb_member) as inp, rgb_zip.open('wb') as dest:
+ shutil.copyfileobj(inp, dest)
+ with sequence_dir(out, video) as dest:
+ report = convert_video(video, split, json.loads(data), locator(archive, member, data),
+ rgb_zip, locator(archive, rgb_member), fm.get(video, {}), args, dest / 'ego')
+ build_record(out, video, {'ego': report})
+ rgb_zip.unlink()
+ done += 1
+ print(f'Converted {video}', flush=True)
+ if args.limit and done >= args.limit:
+ break
+ else:
+ root = next((r for r in roots(args.root) if (r / 'GroundTruth-SparseAnnotations').is_dir()), None)
+ if root is None:
+ p.error('VISOR outer ZIP or extracted source directory not found')
+ fm = json.loads((root / 'frame_mapping.json').read_text()) if (root / 'frame_mapping.json').exists() else {}
+ done = 0
+ for split in sorted(splits):
+ for ann in sorted((root / 'GroundTruth-SparseAnnotations/annotations' / split).glob('*.json')):
+ video = ann.stem
+ if wanted and video not in wanted:
+ continue
+ rgb = root / f'GroundTruth-SparseAnnotations/rgb_frames/{split}/{video.split("_")[0]}/{video}.zip'
+ with sequence_dir(out, video) as dest:
+ report = convert_video(video, split, json.loads(ann.read_text()), locator(ann), rgb,
+ locator(rgb), fm.get(video, {}), args, dest / 'ego')
+ build_record(out, video, {'ego': report})
+ done += 1
+ if args.limit and done >= args.limit:
+ return 0
+ if not done:
+ p.error('no matching annotated videos found')
+ return 0
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
+
+# TODO(v2.0): 旧距离猜侧和单层掩码生成仅注释保留；v1.0 不执行。
+# if side is None and len(frame_hands) == 1:
+# # 空间门槛：脱下放在远处的手套不并入（实测双峰 0..25px / ≥44px）
+# hb = frame_hands[0][1]
+# gb = _bbox_of(polys)
+# if hb and gb and _bbox_dist(gb, hb) <= GLOVE60_MERGE_BBOX_DIST:
+# side = frame_hands[0][0]
+# stats["glove60_merged_into_sole_hand"] += 1
+# if side is None:
+# stats["dropped_glove60_unresolved"] += 1
+# id_disp[mid] = ("dropped", "glove60")
+# continue
+# def rasterize_and_encode(vdir, frames, frame_draw, zip_path, n, stats):
+# """流式读 zip 内 jpg + 光栅化多边形 → rgb.mkv / mask.mkv；返回 (w, h)。"""
+# from PIL import Image, ImageDraw
+#
+# with zipfile.ZipFile(zip_path) as zf:
+# member_of = {}
+# for nm in zf.namelist():
+# member_of[os.path.basename(nm)] = nm
+# names = [im["name"] for im in frames]
+# missing = [nm for nm in names if nm not in member_of]
+# if missing:
+# raise RuntimeError(f"{len(missing)} annotated frames missing in zip, "
+# f"e.g. {missing[:3]}")
+# stats["zip_extra_members"] = len(member_of) - len(names)
+#
+# # 首帧定分辨率（官方 1920×1080，实测每帧校验）
+# img0 = Image.open(io.BytesIO(zf.read(member_of[names[0]]))).convert("RGB")
+# w, h = img0.size
+#
+# bin_img = Image.new("L", (w, h), 0)
+# bin_draw = ImageDraw.Draw(bin_img)
+# canvas = np.zeros((h, w), np.uint16)
+# with VideoPipe(vdir / "mask.mkv", w, h, FPS, "mask") as mw, \
+# VideoPipe(vdir / "rgb.mkv", w, h, FPS, "rgb") as rw:
+# for f in range(n):
+# canvas[:] = 0
+# for pid, polys in frame_draw[f]:
+# bin_draw.rectangle((0, 0, w, h), fill=0)
+# for poly in polys:
+# bin_draw.polygon([(float(x), float(y)) for x, y in poly], fill=255)
+# arr = np.asarray(bin_img)
+# canvas[arr > 0] = pid # 后画覆盖先画：物体→手（modal）
+# mw.write(canvas)
+# img = img0 if f == 0 else Image.open(
+# io.BytesIO(zf.read(member_of[names[f]]))).convert("RGB")
+# if img.size != (w, h):
+# raise RuntimeError(f"frame {names[f]}: size {img.size} != {(w, h)}")
+# rw.write(np.asarray(img))
+# n_mask, n_rgb = mw.n, rw.n
+# if (n_rgb, n_mask) != (n, n):
+# raise RuntimeError(f"wrote rgb={n_rgb} mask={n_mask} != {n}")
+# got = (count_frames(vdir / "rgb.mkv"), count_frames(vdir / "mask.mkv"))
+# if got != (n, n):
+# raise RuntimeError(f"encoded rgb={got[0]} mask={got[1]} != {n} (decode verify)")
+# return w, h
+#
+#
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/operations/h2o-benchmark-decode.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/operations/h2o-benchmark-decode.py
new file mode 100644
index 0000000000000000000000000000000000000000..f647011c5e5b0ce0a155f1ba6438e2802672db0e
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/operations/h2o-benchmark-decode.py
@@ -0,0 +1,74 @@
+"""Benchmark full FFV1 decodes, PNG RGB decodes, and source depth PNG loading; preserve every byte."""
+import hashlib
+import json
+import os
+from pathlib import Path
+import statistics
+import subprocess
+import time
+from concurrent.futures import ThreadPoolExecutor
+import numpy as np
+from PIL import Image
+
+old = Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10')
+bench = Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-decode-bench-20260930')
+bench.mkdir(exist_ok=True)
+media = next((old / 'H2O/shared-v1.0/data').glob('subject4_h1_0*/cam0/observation/rgb.mkv')).parent
+inputs = bench / 'inputs'
+inputs.mkdir(exist_ok=True)
+for name in ('rgb.mkv', 'depth.mkv', 'depth_valid.mkv'):
+ dest = inputs / name
+ if not dest.exists():
+ os.link(media / name, dest)
+source = next((old / 'scratch').glob('h2o-source-*/subject4/h1/0/cam0/rgb')).parent
+for kind in ('rgb', 'depth'):
+ dest = inputs / kind
+ dest.mkdir(exist_ok=True)
+ for frame in sorted((source / kind).glob('*.png'))[:100]:
+ if not (dest / frame.name).exists():
+ os.link(frame, dest / frame.name)
+records = []
+expected = {}
+def record(stage, threads, digest, size, seconds):
+ assert expected.setdefault(stage, (digest, size)) == (digest, size), stage
+ row = dict(stage=stage, threads=threads, seconds=seconds, sha256=digest, bytes=size)
+ records.append(row)
+ print(json.dumps(row), flush=True)
+ (bench / 'report.json').write_text(json.dumps(records, indent=2) + '\n')
+def decode(stage, arguments, threads, pix_fmt):
+ cmd = ['ffmpeg','-v','error','-nostdin','-filter_threads',str(threads),'-threads',str(threads),
+ '-noautorotate',*arguments,'-map','0:v:0','-vsync','0','-threads',str(threads),
+ '-f','rawvideo','-pix_fmt',pix_fmt,'pipe:1']
+ digest = hashlib.sha256()
+ size = 0
+ start = time.monotonic()
+ with subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) as proc:
+ while chunk := proc.stdout.read(1024*1024):
+ digest.update(chunk)
+ size += len(chunk)
+ stderr = proc.stderr.read()
+ assert proc.wait() == 0, stderr.decode()
+ record(stage, threads, digest.hexdigest(), size, time.monotonic()-start)
+for threads in (1,10,10,1):
+ for name, pix in (('rgb','rgb24'),('depth','gray16le'),('depth_valid','gray')):
+ decode('ffv1_'+name, ['-i',str(inputs/(name+'.mkv'))], threads, pix)
+ decode('source_rgb_png_100', ['-framerate','30','-i',str(inputs/'rgb/%06d.png')], threads, 'rgb24')
+ files = sorted((inputs/'depth').glob('*.png'))
+ def read(path):
+ with Image.open(path) as im:
+ return np.asarray(im).astype(np.float32)/1000.0
+ for depth_threads in (1,2,4):
+ digest = hashlib.sha256()
+ start = time.monotonic()
+ size = 0
+ with ThreadPoolExecutor(max_workers=depth_threads) as pool:
+ # Bound decoded arrays to one small batch, as the production candidate would.
+ for begin in range(0,len(files),depth_threads):
+ for arr in pool.map(read,files[begin:begin+depth_threads]):
+ digest.update(arr.tobytes())
+ size += arr.nbytes
+ record('source_depth_png_100', depth_threads, digest.hexdigest(), size, time.monotonic()-start)
+print('MEDIANS',flush=True)
+for stage in expected:
+ for threads in sorted({r['threads'] for r in records if r['stage']==stage}):
+ print(stage,threads,statistics.median(r['seconds'] for r in records if r['stage']==stage and r['threads']==threads),flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/operations/h2o-prepare-d10.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/operations/h2o-prepare-d10.py
new file mode 100644
index 0000000000000000000000000000000000000000..69589418bb01420d8954893a9d19096062daf2c7
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/operations/h2o-prepare-d10.py
@@ -0,0 +1,55 @@
+"""Reuse only atomically published sequences; preserve previous runtime provenance."""
+import datetime
+import json
+import os
+from pathlib import Path
+import shutil
+
+old = Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10')
+new = Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10-d10')
+snapshot = Path('/tmp/h2o-w4-c10-d10-recovery')
+new.mkdir()
+shutil.copytree(snapshot, new / 'frozen-code')
+release = new / 'H2O/shared-v1.0'
+for directory in ('data', 'doc/validation', 'script/provenance/previous-run', 'script/provenance/operations'):
+ (release / directory).mkdir(parents=True, exist_ok=True)
+history = release / 'script/provenance/previous-run'
+shutil.copytree(old / 'H2O/shared-v1.0/script', history / 'script')
+shutil.copytree(old / 'frozen-code/docs', history / 'docs')
+for name in ('full.log', 'full.status', 'run-full.sh'):
+ shutil.copy2(old / name, history / name)
+records = []
+for record in sorted((old / 'H2O/shared-v1.0/doc').glob('*.build.json')):
+ build = json.loads(record.read_text())
+ seq = build['sequence_id']
+ assert Path(seq).name == seq and seq not in ('.', '..')
+ assert set(build['views']) == {f'cam{i}' for i in range(5)}, seq
+ data = old / 'H2O/shared-v1.0/data' / seq
+ frames = {}
+ for cam in build['views']:
+ json.loads((data / cam / 'metainfo.json').read_text())
+ frames[cam] = sum(1 for _ in (data / cam / 'camera/frames.jsonl').open())
+ assert frames[cam] > 0
+ shutil.copytree(data, release / 'data' / seq, copy_function=os.link)
+ shutil.copy2(record, release / 'doc' / record.name)
+ records.append({'sequence_id': seq, 'frames_by_camera': frames,
+ 'original_script_sha256': build['scripts']})
+manifest = {'time': datetime.datetime.now().astimezone().isoformat(),
+ 'source_run': str(old), 'destination_run': str(new),
+ 'configuration': {'workers': 4, 'codec_threads': 10, 'decode_threads': 10,
+ 'depth_threads': 4, 'cpu_limit': None},
+ 'reuse': 'Hard-linked immutable completed views; source timeline/SHA256 still rechecked without staging published source frames',
+ 'excluded': 'Unpublished .tmp directories; historical 62-sequence output',
+ 'original_runtime_provenance': 'script/provenance/previous-run', 'sequences': records}
+(release / 'doc/RESTART_PROVENANCE.json').write_text(json.dumps(manifest, indent=2) + '\n')
+operations = release / 'script/provenance/operations'
+for name in ('h2o-prepare-d10.py', 'h2o-switch-d10.sh', 'h2o-benchmark-decode.py'):
+ shutil.copy2(Path('/tmp') / name, operations / name)
+for src, dest in (('/tmp/h2o-run-d10.sh', new / 'run-full.sh'),
+ ('/tmp/h2o-run-d10.sh', release / 'script/run-full.sh'),
+ ('/tmp/h2o-decode-bench.log', release / 'doc/validation/DECODE_THREAD_BENCHMARK.log'),
+ ('/data/xuzhefeng/Datasets/uni-hoi-h2o-decode-bench-20260930/report.json', release / 'doc/validation/DECODE_THREAD_BENCHMARK.json'),
+ ('/tmp/h2o-d10-final-tests.log', operations / 'worker4-encode10-decode10-depth4-regression.log'),
+ ('/tmp/h2o-io-sample.log', release / 'doc/validation/IO_SAMPLE.log')):
+ shutil.copy2(src, dest)
+print(f'REUSED_SEQUENCES={len(records)} REUSED_VIEWS={sum(len(r["frames_by_camera"]) for r in records)}', flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/operations/h2o-switch-d10.sh b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/operations/h2o-switch-d10.sh
new file mode 100644
index 0000000000000000000000000000000000000000..16de8391f6058e7c0d16ab02070a6f8da8d5bb55
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/operations/h2o-switch-d10.sh
@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+set -euo pipefail
+cd /home/xuzhefeng/Projects/uni-hoi-dataset
+test "$(cat /tmp/h2o-d10-final-tests.exit)" = 0
+test "$(cat /tmp/h2o-decode-bench.exit)" = 0
+test ! -e /data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10-d10
+old=/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10
+# Let the already nearly complete fifth camera publish, instead of discarding four completed cameras.
+while test ! -s "$old/H2O/shared-v1.0/doc/subject4_h1_0.build.json"; do
+ test "$(systemctl --user show --no-pager h2o-full-w4c10-r2-20260930.scope -p ActiveState --value)" = active
+ sleep 20
+done
+printf '\nRECONFIGURE=%s REASON=decoder10_depth4_no_resume_png_writes\n' "$(date -Is)" >> "$old/full.status"
+systemctl --user stop h2o-full-w4c10-r2-20260930.scope
+.venv/bin/python /tmp/h2o-prepare-d10.py
+systemd-run --user --scope --unit=h2o-full-d10-20260930 -p CPUAccounting=yes /bin/bash /data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10-d10/run-full.sh
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/operations/worker4-encode10-decode10-depth4-regression.log b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/operations/worker4-encode10-decode10-depth4-regression.log
new file mode 100644
index 0000000000000000000000000000000000000000..622982e748cfa8ca9981b3ce48c3e0b5c07c7acd
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/operations/worker4-encode10-decode10-depth4-regression.log
@@ -0,0 +1,37 @@
+Converted subject1/h1/0
+Converted subject2/h1/0
+Converted subject3/h1/0
+Converted subject4/h1/0
+Converted subject2/h1/0
+Converted subject1/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Archive subject2_v1_1.tar.gz: 1 sequences
+Converted subject4/h1/0
+Converted subject3/h1/0
+Archive subject3_v1_1.tar.gz: 1 sequences
+Archive subject4_v1_1.tar.gz: 1 sequences
+Converted subject1/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Converted subject2/h1/0
+Archive subject2_v1_1.tar.gz: 1 sequences
+Converted subject4/h1/0
+Converted subject3/h1/0
+Archive subject3_v1_1.tar.gz: 1 sequences
+Archive subject4_v1_1.tar.gz: 1 sequences
+Verified existing subject2/h1/0
+Verified existing subject1/h1/0
+Verified existing subject4/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Archive subject2_v1_1.tar.gz: 1 sequences
+Verified existing subject3/h1/0
+Archive subject3_v1_1.tar.gz: 1 sequences
+Archive subject4_v1_1.tar.gz: 1 sequences
+Converted subject2/h1/0
+Converted subject1/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Archive subject2_v1_1.tar.gz: 1 sequences
+Converted subject3/h1/0
+Archive subject3_v1_1.tar.gz: 1 sequences
+Converted subject4/h1/0
+Archive subject4_v1_1.tar.gz: 1 sequences
+PASS H2O four archive workers, codec/decode 1/4/10, ordered depth 1/2/4 and missing-depth preservation, no resume PNG writes, RGB FFV1-only, depth and persistent QC
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/operations/workspace-regression.log b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/operations/workspace-regression.log
new file mode 100644
index 0000000000000000000000000000000000000000..964660aa6c3b8130c1bf9bb607cf9762f9ca59a2
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/operations/workspace-regression.log
@@ -0,0 +1,37 @@
+Converted subject1/h1/0
+Converted subject2/h1/0
+Converted subject3/h1/0
+Converted subject4/h1/0
+Converted subject1/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Converted subject2/h1/0
+Archive subject2_v1_1.tar.gz: 1 sequences
+Converted subject3/h1/0
+Archive subject3_v1_1.tar.gz: 1 sequences
+Converted subject4/h1/0
+Archive subject4_v1_1.tar.gz: 1 sequences
+Converted subject1/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Converted subject3/h1/0
+Converted subject2/h1/0
+Archive subject2_v1_1.tar.gz: 1 sequences
+Archive subject3_v1_1.tar.gz: 1 sequences
+Converted subject4/h1/0
+Archive subject4_v1_1.tar.gz: 1 sequences
+Verified existing subject4/h1/0
+Verified existing subject3/h1/0
+Verified existing subject1/h1/0
+Verified existing subject2/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Archive subject2_v1_1.tar.gz: 1 sequences
+Archive subject3_v1_1.tar.gz: 1 sequences
+Archive subject4_v1_1.tar.gz: 1 sequences
+Converted subject1/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Converted subject2/h1/0
+Archive subject2_v1_1.tar.gz: 1 sequences
+Converted subject3/h1/0
+Archive subject3_v1_1.tar.gz: 1 sequences
+Converted subject4/h1/0
+Archive subject4_v1_1.tar.gz: 1 sequences
+PASS H2O four archive workers, codec/decode 1/4/10, ordered depth 1/2/4 and missing-depth preservation, no resume PNG writes, RGB FFV1-only, depth and persistent QC
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/docs/CONTRACT.md b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/docs/CONTRACT.md
new file mode 100644
index 0000000000000000000000000000000000000000..f756d711647e109b6bb732ce1b9c84111730489d
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/docs/CONTRACT.md
@@ -0,0 +1,457 @@
+# 手物数据仓库统一约定 v1.0
+
+格式版本：`hand-warehouse/1.0`。存储约定：`lossless-video-2026-09-18`。
+
+范围限于手物交互重建。按本任务纳入的源模态转换，不要求每个数据集填满所有组件。缺失模态、尚未下载、语义未核实分别报告，不能互相替代。
+
+本文定义各字段的含义与使用规则，机器可读注册表见 [schema.json](schema.json)。两者互相补充；如有冲突，应先核实并统一定义。
+
+## 1. 目录
+
+```text
+hand-datasets/
+ /
+ raw/
+ doc/ # 官方说明、来源与校验记录
+ script/ # 官方下载脚本、必要包装脚本
+ data/ # 保留原始包，不原地解压或改写
+ /
+ doc/ # 发布说明、schema、来源清单
+ script/ # 转换、校验脚本及运行配置
+ data/
+ /
+ /
+ metainfo.json
+ observation/
+ rgb.mkv
+ rgb_valid.pt
+ depth.mkv # float32 深度的可逆视频编码
+ depth_valid.mkv # 逐像素 bool 的无损视频编码
+ camera/
+ frames.jsonl
+ intrinsic.pt
+ intrinsic_valid.pt
+ extrinsic.pt
+ extrinsic_valid.pt
+ annotation/
+ mano/
+ motion.pt
+ motion_valid.pt
+ visibility.pt
+ keypoints/
+ uv.pt
+ uv_valid.pt
+ xyz.pt
+ xyz_valid.pt
+ visibility.pt # 源数据有时提供
+ object/
+ /
+ assets/ # 该物体的网格、材质、纹理及部件资产
+ .obj
+ pose.pt
+ pose_valid.pt
+ visibility.pt
+ metainfo.json # 物体身份、类别、mesh、尺寸与部件引用
+ articulation.json
+ keypoints/
+ vertex_part_ids.pt
+ joint_state.pt
+ joint_state_valid.pt
+ parts//pose.pt
+ parts//pose_valid.pt
+ segment/
+ mask.mkv
+ mask_valid.pt
+ instances.jsonl
+ contact/
+ relations.jsonl
+ __/ # 对应实体间的距离/接触区域
+```
+
+目录展示可能存在的组件，不要求填满。具体文件、字段和缺省规则分别见后续对应小节。原始包的解压发生在临时工作区，raw 内原始包保持不变。
+
+## 2. 相机视图的 metainfo.json
+
+### 2.1 顶层字段与发布信息
+
+每个相机视图必须有 `metainfo.json`。以下字段除另行说明外均为必填：
+
+| 字段 | 填写内容 |
+|---|---|
+| schema_version | 结构版本，本版为 `hand-warehouse/1.0` |
+| dataset_id | 数据集的逻辑标识 |
+| release_id | 本次数据发布版本，对应目录中的 dataset_version |
+| sequence_id | 所属序列或独立图片样本的标识 |
+| camera_id | 当前相机视图标识 |
+| sample_type | `image` 或 `video`，规则见 2.2 |
+| num_frames | 当前视图帧数 T，所有随帧组件按同一时间轴组织 |
+| hand_slots | 固定为 `["left", "right"]`；解剖学左右的使用规则见 5.1 |
+| sampling | 采样类型、源 FPS、帧索引引用及选择策略，见 2.2 |
+| camera | 当前相机模型与图像尺寸，见 2.3 |
+| components | 实际提供的组件及其文件、schema、有效性和来源，见 2.5 |
+| split | `train/val/test/unknown`，划分协议见 2.4 |
+| view_type | 可选，`ego/exo/unknown` |
+
+`dataset_version` 是数据发布版本；`schema_version` 是结构版本，两者独立。原始包校验摘要、官方来源和转换配置应记录在发布清单中。解压发生在临时工作区，原始包保持不变。
+
+### 2.2 图片、视频与 sampling
+
+`sample_type=image`：允许单图 T=1、`sampling.type=single`，也允许将独立图片批量装入一个视频容器，T>1 时 `sampling.type=independent`、`source_fps=null`。分片大小按数据集选择；FreiHAND 默认每片最多 4096 张，训练与评估分开，尾片不补帧。该数量不是其他数据集的强制要求；同批分辨率、相机模型类型及划分协议一致。每行 frames 必须有稳定且唯一的 `sample_id`、原始文件定位；内参、手形状等仍逐帧保留，不能因打包而共享错误的标定或身份。`sequence_id` 在这里仅是批次标识，不代表真实序列；不得按相邻帧推断跟踪、同步或接触延续。源 camera_id、image_variant、split 等随样本变化时放在 frames 的对应字段中。真实采集时间未知则 timestamp_ns=null。合成背景变体不视为真实多相机。
+
+`sample_type=video`：`sampling.type=dense/sparse`；`source_fps` 是真实采集 FPS，未知为 null。非连续取样必须保留源帧号和已知源时间，不可用采样后索引除以 FPS 冒充源时间。可另记采样策略与步长。
+
+shared 可以仅存所选 RGB 帧对应的数据。OakInk2 图像/动捕不同采样率时用 annotation_matches 明确映射；不要求将未采样动捕点硬塞进 T。多视角身份和源同步关系要保留；选择了哪些帧/相机及原因写在 sampling 中。
+
+`sampling.frames_ref` 指向 `camera/frames.jsonl`。帧号、逐帧时间、同步与标注匹配记录的结构见 4.1；MKV 封装时间的约定见 3.1。
+
+### 2.3 camera 模型声明
+
+`camera` 至少包含 `model`、`schema_id`、`image_size:[W,H]`。模型未知使用 `unknown` 和 `null`，不猜成针孔。实际内参、畸变及外参数组放在 `camera/`，各模型的固定结构见第 4 节。
+
+### 2.4 split 与划分协议
+
+保留上游划分，不将同一采集序列的相邻帧随意拆到不同集合。
+
+有官方划分时必填 split_scheme 和 split_source_locator，split_scheme 区分如 Dex-YCB s0/s1、ARCTIC P1/P2 等选定协议；不知道时 split=unknown 并给理由，不凭空分配。每发布可选择一种协议；存在逐帧差异时 frames 中的 split 覆盖视图默认值，并登记其来源。
+
+### 2.5 components 组件登记
+
+组件表的键是本发布内唯一名称，例如 `rgb`、`depth`、`mano`、`keypoints_uv`。每个已提供组件声明以下信息；模态特有参数在第 3–5 节对应小节中定义。
+
+| 字段 | 约定 |
+|---|---|
+| available | 本发布是否提供该组件；缺省也表示未提供 |
+| schema_id | 已登记的具体存储与语义类型，不得临时创造未登记名称 |
+| data_ref | 数据文件路径，相对当前相机视图根目录，使用 POSIX 路径 |
+| axes | 数组各维度的含义 |
+| valid_ref / valid_axes | 有效性文件及其维度；语义由所属组件规定 |
+| storage / valid_storage | 视频数据／视频有效性的存储配置；包含已登记 profile、实际 codec、pixel_format、encoded_size，见 3.1；非视频组件按原数组规则 |
+| provenance | source / format_conversion / derived，以及转换或生成方法 |
+| source_locator | 原文件、压缩包成员和记录／帧的定位信息 |
+
+不存在的组件允许缺省或写作 `{ "available": false, "reason": "..." }`，不用引用不存在的文件。静态网格可声明标量有效性；具体见 5.4。观测的 valid 见第 3 节，标注的 valid 与 visibility 区分见 5.1。
+
+`provenance.kind` 使用 source/format_conversion/derived，`method` 说明转换或生成方法；发布清单补充脚本版本、参数、输入摘要。`source_locator` 定位到原始文件/压缩包成员和记录、帧号；允许引用有完整溯源清单的前一发布，但链条最终应能回到 raw。不得仅写一个无法定位记录的数据集名字。
+
+`.pt` 仅保存 CPU Tensor，不保存 Python 自定义对象；浮点默认 float32，有效性为 bool，浮点无效位置填 NaN。所有随帧数组保留首维 T，单帧也不 squeeze；静态资产按其 schema 单独声明。单位、坐标及固定形状见各组件小节。
+
+机器注册表 `schema.json` 中，`components` 固定解码后的数据类型、形状、单位和语义；`storage_profiles` 固定视频内的编码／还原方式。不得临时创造未登记的 schema_id 或 storage.profile。同一语义允许选择其登记的不同无损存储 profile。
+
+## 3. observation 观测数据
+
+### 3.1 rgb.mkv 与 rgb_valid.pt
+
+RGB、深度和栅格 mask 在 shared 中统一使用 MKV 视频容器，不展开为逐帧图片或图像 tar。MANO、关键点、位姿等数值标注仍用数组；分割的重叠多边形仍用 JSONL，不为了视频化而有损栅格化。单图可为一帧视频，独立图批量封装规则见 2.2。
+
+**无损指共享层不增加信息损失。** 源 JPEG／H.264 等本身可能已是有损编码，原样保留并不恢复已丢失信息；不得重新以有损 JPEG、H.264、H.265、AV1 参数编码。源视频／图片的色彩、位深、分辨率和几何关系不得被静默改变。RGB schema `rgb/uint8/v1` 的统一读取结果是 `[T,H,W,3] uint8`、RGB 通道，有效性仍为 `rgb_valid.pt [T] bool`。超出该 schema 的高位深或 HDR 源必须报告并扩展定义，不能降到 8 位冒充无损。
+
+#### 3.1.1 允许的 RGB 存储 profile
+
+| storage.profile | 编码方式 | 适用规则 |
+|---|---|---|
+| mkv/mjpeg_copy/v1 | 源 JPEG 压缩帧直接封装成 MJPEG/MKV | 不解码重编码；保持每张 JPEG 码流字节；普通 8 位 JPEG 且 MKV/解码器支持时使用 |
+| mkv/source_copy/v1 | 已有视频的 RGB 对应码流直接封装为 MKV | 仅源编码能被 MKV 和选定解码器支持时使用，不伪造帧；需维持正确的呈现顺序与依赖帧 |
+| mkv/h264_rgb_lossless/v1 | libx264rgb 的真正无损模式 | 必须 RGB 路径、CRF=0，并通过全帧 RGB 字节比较；不能替换成默认 libx264 YUV420 路径 |
+| mkv/ffv1_rgb/v1 | FFV1 的 RGB 可逆编码路径 | 作为通用候选；解码回 RGB 后必须逐字节相同，不做色度降采样 |
+
+优先尝试源码流直接封装；不能封装或需要选择稀疏帧时，比较 FFV1 与无损 RGB H.264。对完整待发布片段，只在通过无损验收的候选中选择文件较小者，不假设某个编码器总最小；编码时间和随机读取代价记入构建记录。中间候选不作为 shared 多份常驻。H.264 跨帧压缩允许较长 GOP，必须记录 GOP／关键帧间隔，随机取帧需从前一关键帧正确解码，不能把 seek 返回的近似帧当目标帧。
+
+普通源视频可以保留它已有的有损码流，但不允许新增有损重编码。源是 VRS 等记录容器时，先查明图像流实际编码：已有兼容 JPEG／视频包可直接提取封装，否则解码后使用真正无损编码；不把整个 VRS 改后缀当 MKV。PNG 不要求直接塞进 MKV，可选上表两种无损重编码方式。
+
+#### 3.1.2 视频配置与校验
+
+每个视频组件在 metainfo 中登记 `storage`，至少包含：
+
+| 字段 | 内容 |
+|---|---|
+| profile | 上述登记的存储 profile；语义仍由组件 schema_id 指定 |
+| codec | 文件中实际 codec，例如 mjpeg、h264、ffv1；source_copy 必须写实际 codec |
+| pixel_format | 实际编码像素格式；不得仅凭编码器参数猜测 |
+| encoded_size | 视频物理像素尺寸 `[width,height]`；深度可能不同于逻辑尺寸，见 3.2 |
+| decoder | 解码器名称、版本，以及颜色矩阵／范围／旋转处理等实际像素解释配置；未知的源色彩声明 unknown，不擅自称为 sRGB |
+| encoder_parameters | 重编码时的编码器、版本、无损模式和 GOP 等参数；直接封装时记录 stream_copy |
+| source_lossy | 源是否已有损：true / false / unknown；不是共享层再有损编码的许可 |
+
+无损重新编码的 RGB 必须使用同一固定解码配置，比较**全部帧**的统一 RGB 字节、数量及顺序。MJPEG-copy 不重新编码，生产校验采用 `jpeg-packets-all-rgb-sample16/v1`：从 MKV 逐包取回，对全部源 JPEG 的 SHA256、数量、顺序和图像尺寸进行精确核对；每视图另用同一固定解码配置比较最多 16 帧的源与目标 RGB 字节（含首尾与四分位位置，其余由固定种子抽取）。记录策略、实际索引、种子与结果；不足 16 帧时全查。试点与最终完整序列随机抽查，对选中视图的全部帧复核解码 RGB。历史已做全帧像素检查的证明继续保留。
+
+逐包一致与像素抽查的范围必须分别声明，不能将抽查写成全量像素检查。不能用 PSNR 高、视觉相似、编码器参数称为 lossless 等替代精确检查。同时保留源编码的色彩标记和必要元数据；原图有 EXIF/色彩/方向等元数据时保留 raw 和必要来源信息；不能静默自动旋转图像而不更新像素对应与标定。以上加速策略仅适用于原 JPEG 码流直接封装，不适用于深度、标签或重新编码的 RGB。
+
+所有共享视频的呈现帧顺序与统一 frame_index 一一对应，每个栅格组件恰好 T 帧。MKV 的 `storage_fps` 只服务封装，声明 `timebase_role=index_only`；source_copy 可以保留源 PTS 以避免改坏码流，但真实时间与标注对齐仍以 frames 为准。未知真实时间保留 null。缺失某帧组件时保留索引位置并使用该组件的无效占位／valid 标识，不删帧；不能以重复相邻图像冒充已知观测。
+
+### 3.2 depth.mkv 与 depth_valid.mkv
+
+深度语义仍是 `depth/z_float32/v1`：统一读取结果 `[T,H,W] float32`，米制相机 Z 分量，`depth_type=z`。有效深度有限且大于 0，无效位置为 NaN。`depth_origin=measured/rendered/unknown` 必填，来源不明时 unknown。默认与 RGB 共用像素网格及相机参考系，不处理未注册 RGB-D。射线距离必须依据源定义转换成 z，不能只改声明。
+
+#### 3.2.1 float32 的可逆视频编码
+
+采用 `mkv/depth_float32_bits/v1`：**不是把深度量化成 uint16**，而是将规范化后每个 IEEE-754 float32 的完整 32 位拆成两个 uint16，使用 FFV1 gray16le 保存。视频物理尺寸为 `[2W,H]`：每行前 W 列存原 float32 位串的低 16 位，后 W 列存高 16 位。读取时先合并为 uint32 位串，再按 float32 重新解释，不能数值 cast 成 float32。
+
+```text
+u = reinterpret_uint32(depth_float32)
+encoded[:, :, :W] = uint16(u & 65535)
+encoded[:, :, W:] = uint16(u >> 16)
+u_restored = uint32(low) | (uint32(high) << 16)
+depth_float32 = reinterpret_float32(u_restored)
+```
+
+统一采用 little-endian 原始字节接口。`camera.image_size` 始终是逻辑 RGB 尺寸 `[W,H]`，不能改成编码视频的 2W；`storage.encoded_size=[2W,H]`。包括 NaN 在内的 float32 位串必须全帧完全恢复，不允许改成 float16、整数毫米、归一化灰度或 RGB 伪彩色。这里的无损验收基准是完成既定米制／坐标转换后的标准 float32 数组，不声称消除 float64→float32 等原约定转换本身的舍入。
+
+#### 3.2.2 逐像素有效性
+
+`depth_valid.mkv` 使用 `mkv/bool_ffv1/v1`，FFV1 gray8（FFmpeg pixel_format=`gray`），物理尺寸 `[W,H]`。像素只能为 0 或 1，统一解码为 `[T,H,W] bool`，逐像素含义不变。这样不再把庞大的 bool 数组保存为未压缩 `.pt`；标注的帧级／手级 valid 仍保留原有小数组。
+
+登记示例（W、H 以下取 640、480；decoder/encoder_parameters 的实际版本由转换器填写）：
+
+```json
+{
+ "available": true,
+ "schema_id": "depth/z_float32/v1",
+ "data_ref": "observation/depth.mkv",
+ "axes": ["T", "height", "width"],
+ "valid_ref": "observation/depth_valid.mkv",
+ "valid_axes": ["T", "height", "width"],
+ "unit": "m",
+ "depth_type": "z",
+ "depth_origin": "measured",
+ "invalid_value": "NaN",
+ "storage": {
+ "profile": "mkv/depth_float32_bits/v1",
+ "codec": "ffv1",
+ "pixel_format": "gray16le",
+ "encoded_size": [1280, 480],
+ "source_lossy": false,
+ "decoder": {"name": "ffmpeg", "version": "填写实际版本"},
+ "encoder_parameters": {"name": "ffv1", "level": 3, "version": "填写实际版本"}
+ },
+ "valid_storage": {
+ "profile": "mkv/bool_ffv1/v1",
+ "codec": "ffv1",
+ "pixel_format": "gray",
+ "encoded_size": [640, 480],
+ "source_lossy": false,
+ "decoder": {"name": "ffmpeg", "version": "填写实际版本"},
+ "encoder_parameters": {"name": "ffv1", "version": "填写实际版本"}
+ },
+ "provenance": {"kind": "format_conversion", "method": "源深度转米制 z float32；逐位打包为 FFV1"},
+ "source_locator": {"file": "raw/data/depth.zip", "member": "depth/0001.png"}
+}
+```
+
+深度和有效性视频都必须逐帧解码验收：float32 位串相同、bool 相同、T 相同，并检查 valid=true 的正有限值和 valid=false 的 NaN。读取器必须按 profile 还原，不能把 depth.mkv 的编码像素直接当深度值。
+
+## 4. camera 相机、帧索引与标定
+
+### 4.1 frames.jsonl：索引、时间与对应关系
+
+`camera/frames.jsonl` 每行对应统一帧号 `frame_index=0..T-1`，是所有组件的唯一行索引。至少包含 `frame_index`、`timestamp_ns`、`clock_domain`、`source_locator`；原始帧号/文件名保存在 `source_frame_id`，不能被新帧号替代。每行还可有 `annotation_matches`，按组件键保存 `source_frame_id`、`source_timestamp_ns`、`clock_domain`、`match_method` 和 `delta_ns`；方法为 exact/index_map/nearest/interpolated，索引映射无时间依据时 delta_ns=null。nearest 必须记录允许误差；interpolated 必须记录两端源标识和权重。delta_ns 仅在同一时间域中计算为源标注时间减图像时间。多相机共享 `sequence_id`；已知同步关系必须用 `sync_id` 或明确的源帧/时间映射表达，不能仅凭相同数组索引认定同步。
+
+每个视图必须提供此文件。源时间使用整数纳秒及 `clock_domain`，未知写 JSON `null`；不同时间域不能直接比较。逐帧 split 可以覆盖 metainfo 的视图默认划分，并保留来源，见 2.4。
+
+### 4.2 坐标系与标定通则
+
+| 项目 | 统一约定 |
+|---|---|
+| 相机坐标 | 右手系，X 向右、Y 向下、Z 向前，长度单位米 |
+| 齐次变换 | 列向量，`p_target = T_target_from_source @ p_source` |
+| 图像坐标 | `(u,v)`；左上像素中心为 `(0,0)`，u 向右、v 向下，单位 pixel |
+| 固定标定 | 本版展开为 T 份，同一 schema 不混用静态与动态形状 |
+| 分辨率或模型变化 | 分开存储为视图／片段，不在同一组件中混装 |
+| 未知标定 | 相机内外参允许缺省，不填单位阵冒充已知标定 |
+
+### 4.3 intrinsic.pt 与畸变参数
+
+| model / schema_id | intrinsic.pt 的固定结构 |
+|---|---|
+| pinhole / camera/pinhole/v1 | `[T,3,3]`，标准 K，最后一行为 `[0,0,1]`；限无畸变或已校正图像 |
+| fisheye624 / camera/fisheye624/v1 | `[T,15]`，`[f,cx,cy,k0,k1,k2,k3,k4,k5,p0,p1,s0,s1,s2,s3]`，采用 Project Aria Fisheye624 投影定义 |
+| opencv_fisheye4 / camera/opencv_fisheye4/v1 | `intrinsic.pt [T,3,3]` 保存 K；同组件 `distortion_ref` 指向 `[T,4]`，顺序 `[k1,k2,k3,k4]`，使用 OpenCV fisheye 等距角度多项式；已由 ReInterHand 实际原始相机参数验证 |
+| rational8 / camera/rational8/v1 | `intrinsic.pt [T,3,3]` 保存 K；同组件必需 `distortion_ref` 指向 `[T,8]`，顺序 `[k1,k2,p1,p2,k3,k4,k5,k6]` |
+| unknown / null | 不提供可用 intrinsic，不声称可以投影 |
+
+有畸变的其他模型必须注册新 schema，固定参数名、顺序和投影公式；不能塞进针孔 K 后遗失畸变项。`metainfo.camera.schema_id` 必须与 intrinsic 组件一致。同一模型的别名不可隐式改变参数布局。
+
+`opencv_fisheye4`：对前方点令 `x=X/Z, y=Y/Z, r=sqrt(x²+y²), theta=atan(r)`，`theta_d=theta*(1+k1*theta²+k2*theta⁴+k3*theta⁶+k4*theta⁸)`；`u=fx*x*theta_d/r+cx, v=fy*y*theta_d/r+cy`。光轴 r=0 时使用连续极限 `theta_d/r=1`。四系数不能按普通径向畸变解释，也不能补零冒充 Fisheye624。
+
+rational8 对归一化坐标 x=X/Z、y=Y/Z，令 r²=x²+y²，径向项为 `(1+k1*r²+k2*r⁴+k3*r⁶)/(1+k4*r²+k5*r⁴+k6*r⁶)`；再加 p1/p2 切向项并经 K 得到像素。`distortion_ref` 与 intrinsic 共用 `[T]` valid，保存到 camera/distortion.pt。不得把这种畸变三维坐标当作真实相机系 xyz 保存。
+
+内参有效性为 `intrinsic_valid.pt [T] bool`；有畸变时使用对应 `distortion_ref`。同一模型的参数名和顺序固定。
+
+### 4.4 extrinsic.pt 与 extrinsic_valid.pt
+
+`extrinsic.pt` 为 `[T,4,4]`，schema `transform/se3/v1`，方向固定 `T_camera_from_world`，有效性 `[T]`。世界坐标含义及源坐标转换记录在 provenance 中；只有相机坐标标注不要求虚构世界坐标。旋转块须正交且 determinant≈1。
+
+## 5. annotation 标注数据
+
+### 5.1 标注通则：手槽、valid 与 visibility
+
+手槽固定为 0=left、1=right，指解剖学左右，不按画面位置或可见性重排。本阶段只处理左右手两槽，不处理更多手。实体引用使用 `hand_left`、`hand_right`；手槽不证明跨采集片段的个人身份。
+
+#### 5.1.1 valid：标注是否可用于监督
+
+标注的 `valid=true` 表示：有对应实际标注、通过本 schema 数值检查，且通过来源明确提供并适用于该组件的有效性/QA 条件。`valid=false` 可由缺标注、无法建立帧对应、源无效占位或官方 QA 不通过导致；不等于画面没有手或手不可见。各组件独立判断，MANO 按 `[T,2]` 标记左右手，关键点按 `[T,2,K]` 标记。
+
+**2026-09-20 将标注 valid 从“仅表示存在”修订为“可用于监督”，字段形状不变。** 公式为 `source_annotation_present AND numerical_checks_pass AND applicable_official_QA_pass`。没有官方 QA 时省略最后一项，不伪造 QA；不能把未知当成已通过已存在的 QA。适配器在组件 `valid_policy` 中记录具体规则、来源、聚合/广播作用域。缺失官方必需 QA 的记录须明确处理为未知或暂停验收，不能静默通过。无效浮点项仍填 NaN，原始数据保留在 raw；被排除原值可用于 doc 中明确标识的诊断渲染，不能冒充 shared 有效监督。
+
+本地筛选须依据明确的规则，并在 `valid_policy.local_excluded_frame_indices`、排除原因和正式报告中记录。单个可疑投影作为问题记录，不直接硬编码排除该帧。已确认的曝光异常须明确作用范围，不能把一个序列的“前两帧有问题”套到所有序列。RGB 编码完好与几何监督可用是两件事，允许保留原图及帧索引而屏蔽对应标注；未发现问题不等于完成全部人工审核，试点待确认时不能作为已验收发布使用。
+
+源字段必须按官方语义映射，不能直接按名字复制：
+
+| 来源 | 官方含义 | 映射原则 |
+|---|---|---|
+| [H2O](https://github.com/taeinkwon/h2odataset) 的手标注首位 | 0 未标注、1 已标注 | 可作为对应手标注存在性的依据，不推断未标注原因 |
+| [HOT3D](https://github.com/facebookresearch/hot3d#using-mask-files) 的 pose_available、visible、mask_qa_pass | 分别表达姿态存在、可见性、对画面中可见手物位姿的人工 QA | 逐手/物体记录决定存在性，再 AND 当前相机当前帧 QA；QA 是整帧聚合判定，保守排除该帧全部手物几何监督，不声称每个实体均有错误；源可见性基于位姿推导时，其自身 valid 同样受 QA 约束 |
+| [Dex-YCB](https://github.com/NVlabs/dex-ycb-toolkit#loading-dataset-and-visualizing-samples) 的全零 pose_m、全 -1 joints | 不可见的手或缺标注共用占位表示 | 对应组件 valid=false；不能从该值判定 visibility=false，也不能声称原因是动捕缺失 |
+| [ARCTIC](https://github.com/zc-alexfan/arctic/blob/master/docs/data/data_doc.md) 的 is_valid/left_valid/right_valid | 是否有效用于官方评估 | 核实每项筛选依据；不把可见性/评估选择直接等同 QA。本次原始 mano.npy 不含这些派生评估标记，按原记录与数值检查生成 valid，并声明没有额外官方 QA 输入 |
+
+源 valid=false 若代表评估筛选或其他条件，应检查实际标注和源约定后映射；不能一律清空坐标。反之，占位零或 -1 只有在源官方明确规定时才判为缺失，不泛化成所有数据的判断规则。
+
+`valid=true` 的标注应通过其 schema 的数值与形状检查；检查失败必须报告或修复，不能静默隐藏。QA 不通过应报告排除数量与原因，并保留可追溯信息；不能把这类无效称为缺标注。RGB、深度等观测有效性仍按各自 schema；手物 QA 不自动否定 RGB、静态相机内参或物体资产。相机外参是否过滤须依据其自身来源规则；HOT3D 本次保留源外参用于溯源和诊断，不能用其存在性证明 QA 失败帧的几何监督正确。
+
+#### 5.1.2 visibility：可见性属性
+
+附加质量属性只保留 `visibility`，存放在对应模态或标注集内，不设 quality 目录。同一份可见性仅存一次；uv 与 xyz 共用时引用同一个文件。
+
+visibility 通过所属组件 attributes 登记 data_ref、axes、dtype、visibility_type、meaning、provenance、source_locator；未知值通过属性 valid_ref 标识，全已知可声明 valid=true。比例与布尔值不相互强转，也不按任意阈值二值化。没有 MANO 时，整手可见性挂在 keypoints 组件，使用独立的 whole_hand_visibility.pt，明确 meaning=whole_hand，不与逐关键点可见性混用。
+
+`valid` 回答“这项标注能否用于监督”，`visibility` 回答“在当前图像中是否可见（或可见多少）”。不可见的手或关键点仍可有有效标注；不能因 visibility=0 就清空坐标。源没有 visibility 时缺省或标为未知，不由 valid 推断。
+
+| 情况 | 手标注 valid | visibility |
+|---|---|---|
+| RGB 已开始录制，动捕尚未开始，没有其他对应手标注 | false | 以独立源可见性标注为准；没有则未知 |
+| 手被遮挡，但源有标注且通过适用有效性检查 | true | 若源声明不可见，则 false 或源可见比例 |
+| 手可见且有对应标注，通过适用 QA | true | 若源有此信息，则 true 或源可见比例 |
+| 手可见，但该帧漏标或动捕中断 | false | 可以为 true，但必须有独立来源依据 |
+| 有手部参数，但官方 QA 明确不通过 | false | 不据此断言不可见；源可见比例若基于不可靠位姿，其属性 valid 也为 false |
+
+visibility 自己的 valid_ref 表示该可见性标注是否可用，不复用 MANO 或关键点坐标的 valid_ref；明确记录源存在性及适用的 QA 条件。上述例子不要求凭图像自动生成可见性。
+
+不设 quality 组件或目录，不另存拟合误差、曝光、截断比例等通用质量数组。适用的官方 QA 合并进对应 valid，在 `valid_policy` 记录来源与规则；正式校验报告保留原始 QA 时间区间及排除统计。原始 QA 文件仍在 raw，官方 split 保留。
+
+### 5.2 mano：参数与整手可见性
+
+MANO schema `mano/axis_angle61/v1`：`motion.pt [T,2,61]`，顺序为完整 16 关节 axis-angle 48、shape betas 10、全局平移 3。姿态使用弧度，平移米，位于当前相机系。有效性 `[T,2]`；缺少一侧整槽 NaN。PCA 必须先按对应模型展开，不能将 PCA 系数直接填入 axis-angle。
+
+`model_profile` 必须固定 MANO 版本、`flat_hand_mean=true`、左手 shapedirs 修正状态。平移表示模型输出顶点所加的全局平移，不是某个腕关节的坐标；源语义不同须转换。shape 无量纲。模型权重不作为共享数据附带上传。
+
+`mano/visibility.pt [T,2]` 保存源整手可见比例时使用 float32 [0,1]，`visibility_type=ratio`；只有实际采用 0–100 的百分比才除以100，不能只凭表头判断。HOT3D 的 `visibility_ratio[%]` 实值已经是0–1，官方读取器原样读取，必须保留原值。通过 MANO 组件 attributes 登记，使用独立有效性，不复用 motion_valid。
+
+手部共享表示仅保留 MANO 参数与选定关键点，不发布独立手表面几何。需要渲染或接触计算时由 MANO 参数生成，几何引用使用固定 MANO 模型配置与顶点顺序；不影响 object 下的物体模型和材质资产。
+
+### 5.3 keypoints：二维点、三维点与逐点可见性
+
+关键点独立于 MANO 参数存在；没有 motion 也可有关键点。`uv.pt [T,2,K,2]` 与 `xyz.pt [T,2,K,3]`，有效性 `[T,2,K]`，分别采用 `keypoints/uv/v1` 和 `keypoints/xyz/v1`。K 由 `keypoint_schema` 固定，不能在同一数组中逐帧变化；同体系统一顺序。数值使用像素坐标或米制相机系。
+
+| keypoint_schema | K 与固定索引 |
+|---|---|
+| mano21/v1 | 21；wrist 后依次 thumb/index/middle/ring/little，每指由近端至指尖 |
+| rhd21/v1 | 21；保持 wrist，然后五指由近端至指尖；原始每指 tip→palm 反序。只统一顺序，不宣称解剖学位置等价于 MANO |
+
+具体名称与源索引映射见 `schema.json`。当前只登记 MANO21 与 RHD21；不登记 MediaPipe、ReInterHand capture21 或 fit24 中间关键点。
+
+每个发布中的每个视图只提供一套标准手关键点，直接放在 keypoints 下，不设 annotation_set_id 字段或目录。uv 与 xyz 是这套关键点的二维与三维表示，不是两套标注。provenance 分别记录二者来自原始标注还是计算结果；不要求已有 2D 必须由当前 xyz 重新投影覆盖。
+
+ReInterHand 的 Original keypoints、Original fits keypoints 及带前臂关键点不纳入 shared；如需手关键点，从选定 MANO 参数生成 MANO21 并记录模型/指尖回归约定。其他数据集默认使用选定的官方关键点，不额外生成平行的 MANO 派生集合。后续主动更换生成方法或结果时发布新的 dataset_version，不在同一版本内增设方法分支。
+
+manual/projection/fitting 只记录在 provenance，不作为目录或类型分支。不得通过坐标为零、是否出画或 visibility 判断标注是否存在。派生标注仅在实际生成并通过数值检查后声明 valid=true；没有生成的项为 false，不伪造坐标。
+
+`keypoints/visibility.pt [T,2,K]` 保存源逐点可见性，布尔表示使用 `visibility_type=boolean`。uv 与 xyz 共用时引用同一份文件。整手比例属性没有 MANO 可挂载时使用 `whole_hand_visibility.pt [T,2]`，`meaning=whole_hand`；它与逐点可见性分开，通用登记规则见 5.1。
+
+不使用 annotation_set_id；ReInterHand 中间关键点集合不进入 shared，raw 保留来源文件。
+
+### 5.4 object：身份、资产、位姿与几何
+
+#### 5.4.1 metainfo.json：物体身份与跟踪作用域
+
+手用 `hand_left`、`hand_right` 及槽位 0、1 表示，不再另存 entities.json。物体身份登记在 `annotation/object//metainfo.json`，必填 `object_id`、`identity_scope`、`scope_id`、`source_locator`；可含 `category_id`、`source_object_id`、`mesh_ref`、`has_model`、部件引用。即使只有分割、没有 pose/mesh，也可只有这份物体元信息。
+
+`identity_scope` 为 frame/subsequence/sequence，`scope_id` 指定具体作用域；跨相机共享身份只在已有源对应证据时使用。VISOR 按官方 subsequence 与实体名关联时记录 `association_basis=official_subsequence_entity_name`，不能声称这是全视频物理实例跟踪。无需与 object_id 一一对应的标准 track_id；官方独立轨迹标识存在时保留为 source_track_id，并声明作用域。手槽不证明跨采集片段的个人身份。
+
+#### 5.4.2 assets：网格、材质与纹理
+
+资产属于物体，统一存放在 `annotation/object//` 内，可用 `assets/` 子目录组织网格、材质、纹理和部件资产。序列层不设共享 assets。所有引用采用视图根目录为基准；其中物体资产组件的 `data_ref` 与部件的 `mesh_ref` 采用相对于当前视图目录的 POSIX 路径，例如 `annotation/object/cup_01/assets/cup.obj`，且必须指向所属物体目录内的资产。OBJ 内部的材质、纹理引用遵循文件格式自身的相对路径规则，依赖文件一并保留。不同相机视图允许各自保存物体资产，不要求跨视图去重；下载物体时包含其资产依赖。
+
+网格 schema 为 `mesh/obj/v1`，顶点米制，静态有效性可直接声明标量 `valid`，或通过 `valid_ref` 指向标量 bool Tensor；不按 T 重复静态资产。由物体／部件通过引用关联，不仅凭文件名猜测。
+
+#### 5.4.3 pose.pt、pose_valid.pt 与 visibility.pt
+
+刚体 schema 为 `object/rigid/v1`，`pose.pt [T,4,4]` 固定为 `T_camera_from_object`，平移米制，有效性 `[T]`。物体坐标原点、轴向必须与 mesh 一致；旋转块为正交矩阵且 determinant≈1。
+
+`object//visibility.pt [T]` 保存源物体可见比例，float32 [0,1]，`visibility_type=ratio`。通过所属物体组件 attributes 登记，独立表达其有效性。
+
+#### 5.4.4 articulation.json、部件位姿与 joint_state.pt
+
+关节物体：schema `object/articulated/v1`，`articulation.json` 登记 `parts` 与 `joints`。每个 part 有 `part_id`、`parent_id`（根为 null）、`has_model`、`mesh_ref`；has_model=false 时 mesh_ref=null，结构节点允许没有网格。父链不得成环。每个 joint 有唯一 `joint_id`、`parent_part`、`child_part`、`type`；fixed 单位 null、revolute 弧度、prismatic 米。有源限位时保存 `limits:[lower,upper]`；没有时 null，不伪造上下限。部件姿态统一为 `T_camera_from_part [T,4,4]`，不与父局部变换混装。
+
+若提供 `joint_state.pt [T,J]`，必须登记固定 joint_order、轴在父部件系的表达、零位变换与正方向，valid 为 `[T,J]`；缺少这些运动学定义时只发布部件位姿与已有来源信息，不伪造可驱动的关节模型。关节值与部件位姿同时存在时需验证一致性。
+
+部件层级应声明语义：纯语义包含树允许 `joints=[]`，不将包含关系推断为机械关节。关节状态 schema 为 `object/joint_state/v1`；转动用弧度、移动用米，混合类型按 joint_order 对应的关节定义解释。
+
+#### 5.4.5 dimensions、几何关键点与顶点部件标签
+
+`dimensions.pt [T,3]` 采用 `object/dimensions/v1`，有效性为 `[T]`。
+
+物体/部件 `dimensions` 为 `[length,width,height]` 米，轴顺序与该 mesh 的局部坐标关联，登记 `dimensions_semantics` 和来源。HOI4D dimensions 与 mesh 的具体尺度关系未验证前标为 source_unspecified，保留源值并暂停依赖该关系的几何验收，不能默认为待施加缩放。需要施加缩放时明确 scale_applied_to_mesh，禁止重复应用。
+
+源物体关键点放 `object//keypoints/xyz.pt [T,K,3]`（相机系）或 `template_xyz.pt [K,3]`（物体系），schema 分别为 object_keypoints/camera/v1 和 object_keypoints/template/v1。明确 keypoint_schema、keypoint_names、单位及有效性；模板点配合 pose 可投影，无须重复生成全部帧。H2O 中心/角点/棱中点，以及 ARCTIC 部件点分别登记具体源顺序，不套用手的 21 点 schema。每个发布选择一套源几何点并记录选择，不另建方法分支。
+
+`vertex_part_ids.pt [V] int64` 绑定 `mesh_ref` 与 topology_id，登记整数到 part_id 的映射；未知用 -1。ARCTIC 不同拓扑的资产分别绑定，不能共用未经转换的顶点标签。部件层级中 is_instance/has_model 等源属性保留，几何文件仍全部归 object。
+
+### 5.5 segment：分割类别、编码与覆盖范围
+
+每个发布选择一套任务使用的分割表示，不建立方法／标注集分支。没有分割就不创建 segment 目录，不为其他模态创建空的实体表。
+
+| schema_id | 文件与数值语义 |
+|---|---|
+| segment/uint16_labels/v1 | mask.mkv；整数区域编号，通过 instances.jsonl 解释 |
+| segment/instance_polygons/v1 | polygons.jsonl；实例多边形，可重叠；当前不支持孔洞拓扑 |
+| segment/foreground_intensity/v1 | mask.mkv；0–255 前景强度，不是区域编号或经过校准的概率 |
+
+#### 5.5.1 uint16 标签图与 instances.jsonl
+
+组件声明 `segmentation_type=semantic/instance/part`；同一组件固定一种类型。`mask.mkv` 解码为 `[T,H,W] uint16`，schema `segment/uint16_labels/v1`，使用 `mkv/uint16_ffv1/v1`，FFV1 gray16le 无损；只有所有区域编码均 ≤255 时允许 gray8（pixel_format=gray），读取后转 uint16，数值不变。禁止调色板和有损编码。有效性 `[T]`，表示该帧有可用分割标注。数值 0 固定为 unassigned，65535 固定为 ignore，1–65534 为区域编码，不能直接等同 entity_id。
+
+`instances.jsonl` 每帧一行，包含 `frame_index` 与 `labels`。每条标签包含 `mask_value`、`category_id`、`entity_id`、`part_id`；无意义或未知字段为 null。semantic 允许 entity_id=null；instance 必须引用 hand_left/hand_right 或登记的 object_id；part 还须关联登记部件。源实例 ID 和其他属性分别保存在 `source_instance_id`、`source_attributes`。mask 中每个非保留编码必须有且仅有一个映射。
+
+FreiHAND 的试点按本任务需要，统一到手／背景类别，保留每个原生区域编码；不要求给原生细分区域猜测手指名称。需要细部件监督时，尚未核实的部件名称必须保持未知，不能因粗粒度手类别已核实便声称部件语义也已统一。
+
+#### 5.5.2 重叠实例多边形
+
+每发布选择一套任务使用的分割表示，不建立方法/标注集分支。VISOR 的实际试点存在重叠实例多边形，单层标签图不能无损表达。对此补充 `segment/instance_polygons/v1`：`segment/polygons.jsonl` 每帧包含 `frame_index` 和 `instances`，每实例包含 `mask_value` 与 `polygons`（列表，每个多边形为源图像像素坐标顶点序列，保留原始精度）。身份和类别仍由同一 `instances.jsonl` 映射，有效性仍为 `[T]`，通过 schema 区分物理编码。允许多实例重叠，不采用后画覆盖前画。消费者按需栅格化为独立实例二值 mask；不能声称合并的单层 mask 无损。
+
+#### 5.5.3 灰度前景强度
+
+ReInterHand 的官方渲染 mask 为双手合并的灰度前景掩码，实测存在 0–255 的中间值，不是左右手实例编号。登记 `segment/foreground_intensity/v1`：仍存 `segment/mask.mkv`，解码为 `[T,H,W] uint16`，使用 `mkv/uint16_ffv1/v1`，此类数据优先编码为 FFV1 gray8（pixel_format=gray），读取后转 uint16。有效值范围 0–255，归一化值为 `float32(value)/255`；0 为背景端点、255 为前景端点。组件声明 `foreground_category=hand`、`entity_id=null` 和双手合并语义，不使用区域编号映射。这里保留原始渲染 mask 强度，不把它称为经过校准的概率。若任务需要二值 mask，阈值化属于任务层派生操作，记录阈值；共享层不丢弃中间灰度。不同 schema 的数值含义不同，不能将该文件按整数类别图解码。
+
+FreiHAND 训练集的 JPEG mask 使用 `segment/foreground_intensity/v1`，逐像素保留 JPEG 解码后的灰度，`entity_id=hand_right`。该 schema 不限定双手合并：单手或合并范围由 `entity_id` 与 `coverage` 明确声明。评估集整数区域图仍用 `segment/uint16_labels/v1`，两者不能仅凭文件名使用同一种解码语义。
+
+#### 5.5.4 coverage 与有效性
+
+`coverage.kind=full/selected_entities/partial/unknown`。full 指声明的标注范围完整；selected_entities 还应列出目标范围。无论哪种，0 本身不自动成为背景；背景要有明确类别映射，未标注区域不能用于构造负样本。保留源逐类/逐实例 exhaustive 信息，未知不可改写为完整。
+
+三种表示的帧级有效性均为 `[T]`，表示该帧是否提供此分割标注；对应 valid_ref 在组件中登记，仍用小型 bool `.pt` 数组。栅格 mask 必须登记 storage，格式见 3.1.2，所有帧的整数编码或强度逐值完全相同。编码语义由 schema_id 决定，读取器不能仅依据 mask.mkv 文件名选择解码方式。
+
+### 5.6 contact：接触、抓取、区域与距离
+
+#### 5.6.1 relations.jsonl 与 relations_valid.pt
+
+`annotation/contact/relations.jsonl`，schema contact/relations/v1，每帧一行含 frame_index、relations。每条记录含 relation_type（contact/grasp）、subject_id（手槽或登记实体）、object_id、status、source_locator、provenance。status 固定为 positive/negative/unknown/target_unannotated：positive 必须有可解析目标，negative 表示源明确否定该关系，unknown 为不确定，target_unannotated 为确有接触但目标未标注。非 positive 的 object_id 可 null，不能用 null 冒充“不接触”。稀疏源无记录的帧不自动补 negative。
+
+VISOR 的对象 mask ID 必须先按同帧 source_instance_id 解析成 object_id；hand-not-in-contact、none-of-the-above、inconclusive 分别映射 negative、target_unannotated、unknown，保留原始字符串。Dex ycb_grasp_ind 通过 ycb_ids 映射到登记物体，以 relation_type=grasp 保存；不得当作逐顶点接触。
+
+relations 组件的 valid_ref 指向 `relations_valid.pt [T] bool`，表示该帧是否有可用源关系标注。有效帧中可以存在 status=unknown 的明确不确定标注；无源标注的帧为 valid=false、relations=[]。组件仍声明 available/provenance/source_locator。
+
+#### 5.6.2 实体对的顶点区域与距离
+
+已发布的接触区域/距离按参与关系的 subject_id 与 object_id 组织，每对实体保留选定的一套结果，以精确 geometry_ref/topology_id 关联到手或物体/部件；不要求每套数据都有。顶点区域 schema contact/vertex_region/v1：bool `[T,V]` 加同形 valid；顶点距离 schema contact/vertex_distance/v1：float32 `[T,V]`、米、同形 valid，声明 source/target geometry_ref、distance_definition（signed/unsigned、测量到点集或表面等明确源定义）。geometry_ref 必须指向已登记 mesh 或由固定 MANO 模型配置确定的拓扑；拓扑/顶点数变化时按拓扑拆分片段。其他源区域表示只有在实际接入并登记 schema 后才可转换，不能随意栅格化宣称等价。
+
+顶点区域／距离已有存储定义，但尚未完成真实输入试点；接入时必须提供源语义与几何验收证据，不因已经登记 schema 就视为通过。
+
+## 附录：任务范围
+
+不强制收录 2D bbox、完整高频动捕、动作程序、身体 SMPL-X、点云或网络特征。范围内的接触、visibility、选定手关键点、物体几何不能因不便处理而静默丢弃。分割采用的物理编码由第 5.5 节决定，存在重叠时不能以单层标签图声称无损。
+
+不设置 entities_ref/segment/entities.json，不要求与 object_id 重复的 track_id。MediaPipe 不在当前关键点定义中；旧版本迁移必须保留可追溯关系，不能直接删表。手部共享表示不含独立 hand_mesh。
+
+schema.json 是普通注册表，不是 JSON Schema 文档或可执行验收器；定义存在不代表真实数据已验证。验收须保留实测范围与未决项。
+
+本存储约定只更新标准，不代表既有 FFV1／depth.pt 试点已迁移。新发布必须显式写 storage 配置；旧产物按其旧定义读取，不能只改文件后缀或版本标识伪装为合规。
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/docs/h2o-RAW_TO_SHARED.md b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/docs/h2o-RAW_TO_SHARED.md
new file mode 100644
index 0000000000000000000000000000000000000000..431eeb6d59885c914d340d24e9ebe8b207ec84b3
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/docs/h2o-RAW_TO_SHARED.md
@@ -0,0 +1,30 @@
+# H2O raw → shared-v1.0
+
+## 范围与来源
+
+本版本仅转换 `raw/data/subject{1,2,3,4}_v1_1.tar.gz` 的真实五视角 (`cam0`–`cam4`) 序列；`h2o_CASA.tar` 是单独的扩展，不与标准序列混合。每个原始序列存于 `data/subjectN__/cam{0..4}/`。源压缩包保持不变，不合成源不存在的标注。输出仅在本地制作；完整序列试点及人工检查通过、更新全量预估之前，不启动全量。
+
+| 源包/字段 | shared 文件/处理 |
+| --- | --- |
+| `subjectN_v1_1.tar.gz`: `camX/rgb//keypoints/xyz.pt`、`pose.pt`：按源类号关联 21 个物体点及物体→相机位姿；仅源存在时登记组件及有效性。 |
+| `object.zip` | `annotation/object//assets/` 与源 OBJ、MTL、纹理；静态形状预览不等于所有帧网格位姿的投影证明。 |
+| `label_split.zip` 的 `action_{train,val,test}.txt` | 序列元数据的官方划分及来源；未知映射明确记 `unknown`，不自行编排。 |
+
+`cam4` 是 ego，`cam0`–`cam3` 是 exo。真实时间/FPS 缺乏可靠来源时使用 null，不把 MKV 封装帧率冒充采集时钟。源 `action_label`、`verb_label`、pose 任务帧列表和 `manolabel_v1.1.tar.gz` 保留在 raw；当前契约没有相应动作组件，本发布不宣称它们已转换。源没有像素分割，本版本不投影网格生成替代分割。
+
+## 源资产文件名修复
+
+源 `object/cocoa/cocoa.obj` 引用 `ovomaltine.mtl`，材质内写的是 `map_Kd cocoa.png`，但原 ZIP 只提供 `ovomaltine.png`。仅在临时解压目录为该已确认的纹理增加**字节相同**的 `cocoa.png` 别名，保留原 PNG、OBJ、MTL 不变；在网格 `source_locator.texture_aliases` 中记录别名、原始包成员和 SHA256。别名与已保留的旧 cocoa 资产一致，不替换图像、标注或网格。全部八类物体的材质依赖在读取大 subject 包之前预检，其他缺失/越界依赖仍报错。
+
+## 加速与验收
+
+四个互不依赖的 subject 压缩包可由 `--workers 1..4` 并行顺序读取，各 worker 一次只处理一个序列。数值库保持单线程；FFV1 编码支持 `--codec-threads 1|2|4|8|10`，当前全量按用户要求使用 10，逐帧解码校验仍保留。并发数由相同输入实测吞吐量决定，记录 OS 线程峰值和磁盘占用，不以 CPU 线程数本身作为提速证据。RGB 源为 PNG，不适用 ARCTIC/HOT3D 的 JPEG 包直封和 16 帧抽查：RGB、深度、有效性与数值模态仍逐帧精确校验。临时文件在 `/data` 上隔离放置，历史 62 条目录不复用、不覆盖；调整并发时，在新的运行目录复用本次全量已经原子发布的完整序列，并从源重新核对帧号及逐组件 SHA256。未完成的临时序列不复用，原运行的脚本、配置及 QC 保留用于溯源。
+
+先运行不截帧的代表性完整序列试点，保存自动 QC、真实耗时和 `doc/validation/index.html`（RGB、深度、源 3D 关键点投影、原生物体网格和全组件数值/有效性）。试点由人工核查之后再决定全量；全量后再次抽样人工核查。报告须区分数据完整性、源未提供的模态与仅供人工判断的几何对齐。
+
+2026-09-29 完整序列试点已获人工确认，用户已授权本地全量。2026-09-30 用户取消 CPU 使用量上限，指定 4 个独立压缩包 worker、每个 FFV1 编码器 10 线程；Python worker、读取及校验子进程不计入这最多 40 个编码线程，因此总线程数不固定为 40。该配置不代表已证明加速倍数。源包和旧产物保留；代码变化使用独立运行目录，已完成序列保留原始构建记录及生产脚本快照。
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/docs/schema.json b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/docs/schema.json
new file mode 100644
index 0000000000000000000000000000000000000000..d904a34272de7f3fd4428c8dc68a919e94a68fca
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/docs/schema.json
@@ -0,0 +1,869 @@
+{
+ "schema_version": "hand-warehouse/1.0",
+ "validity_revision": "usable-annotation-2026-09-20",
+ "annotation_validity": {
+ "meaning": "annotation usable for supervision, not merely record presence",
+ "formula": "source_annotation_present AND numerical_checks_pass AND applicable_official_QA_pass AND NOT explicitly_reviewed_local_exclusion",
+ "without_official_qa": "omit QA term; declare source rules; do not fabricate QA",
+ "policy_field": "components..valid_policy",
+ "visibility_is_independent": true,
+ "invalid_float_value": "NaN",
+ "hot3d": "broadcast per-camera frame manual QA conservatively to hand/object labels and pose-derived visibility; retain RGB and source calibration; keep native QA in raw and review report",
+ "hot3d_visibility": "native visibility_ratio[%] already ranges from 0 to 1; do not divide by 100"
+ },
+ "hand_slots": [
+ "left",
+ "right"
+ ],
+ "keypoints": {
+ "mano21/v1": {
+ "count": 21,
+ "names": [
+ "wrist",
+ "thumb_1",
+ "thumb_2",
+ "thumb_3",
+ "thumb_4",
+ "index_1",
+ "index_2",
+ "index_3",
+ "index_4",
+ "middle_1",
+ "middle_2",
+ "middle_3",
+ "middle_4",
+ "ring_1",
+ "ring_2",
+ "ring_3",
+ "ring_4",
+ "little_1",
+ "little_2",
+ "little_3",
+ "little_4"
+ ],
+ "source_index_map": null,
+ "note": "adapter maps official MANO joint and fingertip convention"
+ },
+ "rhd21/v1": {
+ "count": 21,
+ "names": [
+ "wrist",
+ "thumb_1",
+ "thumb_2",
+ "thumb_3",
+ "thumb_4",
+ "index_1",
+ "index_2",
+ "index_3",
+ "index_4",
+ "middle_1",
+ "middle_2",
+ "middle_3",
+ "middle_4",
+ "ring_1",
+ "ring_2",
+ "ring_3",
+ "ring_4",
+ "little_1",
+ "little_2",
+ "little_3",
+ "little_4"
+ ],
+ "source_index_map_per_hand": [
+ 0,
+ 4,
+ 3,
+ 2,
+ 1,
+ 8,
+ 7,
+ 6,
+ 5,
+ 12,
+ 11,
+ 10,
+ 9,
+ 16,
+ 15,
+ 14,
+ 13,
+ 20,
+ 19,
+ 18,
+ 17
+ ]
+ }
+ },
+ "camera": {
+ "opencv_fisheye4": {
+ "K": [
+ 3,
+ 3
+ ],
+ "distortion": [
+ "k1",
+ "k2",
+ "k3",
+ "k4"
+ ],
+ "projection": "theta=atan(r); theta_d=theta*(1+k1*theta^2+k2*theta^4+k3*theta^6+k4*theta^8); source ReInterHand"
+ },
+ "pinhole": {
+ "K": [
+ 3,
+ 3
+ ]
+ },
+ "fisheye624": {
+ "parameters": [
+ "f",
+ "cx",
+ "cy",
+ "k0",
+ "k1",
+ "k2",
+ "k3",
+ "k4",
+ "k5",
+ "p0",
+ "p1",
+ "s0",
+ "s1",
+ "s2",
+ "s3"
+ ]
+ },
+ "rational8": {
+ "K": [
+ 3,
+ 3
+ ],
+ "distortion": [
+ "k1",
+ "k2",
+ "p1",
+ "p2",
+ "k3",
+ "k4",
+ "k5",
+ "k6"
+ ]
+ }
+ },
+ "segmentation": {
+ "segment/uint16_labels/v1": {
+ "storage": "mask.mkv",
+ "dtype": "uint16",
+ "mapping": "instances.jsonl",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "reserved_codes": {
+ "0": "unassigned",
+ "65535": "ignore"
+ },
+ "region_codes": [
+ 1,
+ 65534
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ },
+ "segment/instance_polygons/v1": {
+ "storage": "polygons.jsonl",
+ "mapping": "instances.jsonl",
+ "frame_keys": [
+ "frame_index",
+ "instances"
+ ],
+ "instance_keys": [
+ "mask_value",
+ "polygons"
+ ],
+ "coordinate_unit": "pixel",
+ "overlap_allowed": true,
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref"
+ ],
+ "polygon_shape": [
+ "N",
+ 2
+ ],
+ "holes_supported": false
+ },
+ "segment/foreground_intensity/v1": {
+ "storage": "mask.mkv",
+ "dtype": "uint16",
+ "value_range": [
+ 0,
+ 255
+ ],
+ "normalization_scale": 0.00392156862745098,
+ "axes": [
+ "T",
+ "height",
+ "width"
+ ],
+ "meaning": "native hand foreground intensity; individual hand or union declared in metadata; not instance codes or calibrated probability",
+ "zero": "background",
+ "full_scale": "foreground",
+ "mapping": "foreground_category in component metadata",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "value_range",
+ "normalization_scale",
+ "foreground_category",
+ "entity_id",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ }
+ },
+ "note": "Registry for hand-warehouse/1.0. Read CONTRACT.md for field semantics. This is not a JSON Schema document or executable validator.",
+ "components": {
+ "mano/axis_angle61/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 2,
+ 61
+ ],
+ "valid_shape": [
+ "T",
+ 2
+ ],
+ "unit": "m",
+ "angle_unit": "rad",
+ "coordinate_frame": "camera",
+ "layout": {
+ "pose_axis_angle": [
+ 0,
+ 48
+ ],
+ "shape": [
+ 48,
+ 58
+ ],
+ "translation": [
+ 58,
+ 61
+ ]
+ }
+ },
+ "keypoints/uv/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 2,
+ 21,
+ 2
+ ],
+ "valid_shape": [
+ "T",
+ 2,
+ 21
+ ],
+ "unit": "pixel",
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "keypoints/xyz/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 2,
+ 21,
+ 3
+ ],
+ "valid_shape": [
+ "T",
+ 2,
+ 21
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "depth/z_float32/v1": {
+ "storage": "mkv",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "valid_shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "unit": "m",
+ "depth_type": "z",
+ "pixel_grid": "current_rgb",
+ "coordinate_frame": "current_rgb_camera",
+ "valid_storage": "mkv",
+ "allowed_storage_profiles": [
+ "mkv/depth_float32_bits/v1"
+ ],
+ "allowed_valid_storage_profiles": [
+ "mkv/bool_ffv1/v1"
+ ],
+ "required_metadata": [
+ "storage",
+ "valid_storage",
+ "depth_origin"
+ ],
+ "depth_origin_values": [
+ "measured",
+ "rendered",
+ "unknown"
+ ]
+ },
+ "transform/se3/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 4,
+ 4
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "convention"
+ ]
+ },
+ "object/rigid/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 4,
+ 4
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "convention"
+ ]
+ },
+ "camera/pinhole/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3,
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/rational8/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3,
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/opencv_fisheye4/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3,
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/fisheye624/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 15
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/distortion8/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 8
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/fisheye4_distortion/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 4
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "object/joint_state/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "J"
+ ],
+ "valid_shape": [
+ "T",
+ "J"
+ ],
+ "required_metadata": [
+ "joint_order"
+ ],
+ "unit_rule": "revolute radians; prismatic metres; mixed types declared per joint"
+ },
+ "object_keypoints/camera/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "K",
+ 3
+ ],
+ "valid_shape": [
+ "T",
+ "K"
+ ],
+ "unit": "m",
+ "coordinate_frame": "camera",
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "object/dimensions/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "dimensions_semantics"
+ ],
+ "axis_order": [
+ "length",
+ "width",
+ "height"
+ ],
+ "scale_rule": "do not apply to mesh without source-supported size/scale relation"
+ },
+ "object_keypoints/template/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "K",
+ 3
+ ],
+ "valid_shape": [
+ "K"
+ ],
+ "unit": "m",
+ "coordinate_frame": "object",
+ "static": true,
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "mesh/obj/v1": {
+ "storage": "obj",
+ "unit": "m",
+ "static": true,
+ "valid_shape": [],
+ "dependencies": "MTL and textures retained under owning object"
+ },
+ "rgb/uint8/v1": {
+ "storage": "mkv",
+ "dtype": "uint8",
+ "shape": [
+ "T",
+ "H",
+ "W",
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "color_order": "RGB",
+ "allowed_storage_profiles": [
+ "mkv/mjpeg_copy/v1",
+ "mkv/source_copy/v1",
+ "mkv/h264_rgb_lossless/v1",
+ "mkv/ffv1_rgb/v1"
+ ],
+ "required_metadata": [
+ "storage"
+ ]
+ },
+ "contact/relations/v1": {
+ "storage": "jsonl",
+ "valid_shape": [
+ "T"
+ ],
+ "frame_keys": [
+ "frame_index",
+ "relations"
+ ],
+ "absence_semantics": "no record is not a negative contact annotation"
+ },
+ "contact/vertex_region/v1": {
+ "storage": "pt",
+ "dtype": "bool",
+ "shape": [
+ "T",
+ "V"
+ ],
+ "valid_shape": [
+ "T",
+ "V"
+ ],
+ "required_metadata": [
+ "source_geometry_ref",
+ "target_geometry_ref",
+ "topology_id"
+ ],
+ "pilot_status": "defined_not_empirically_validated"
+ },
+ "contact/vertex_distance/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "V"
+ ],
+ "valid_shape": [
+ "T",
+ "V"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "source_geometry_ref",
+ "target_geometry_ref",
+ "topology_id",
+ "distance_definition"
+ ],
+ "pilot_status": "defined_not_empirically_validated"
+ },
+ "object/articulated/v1": {
+ "storage": "json",
+ "required_keys": [
+ "parts",
+ "joints"
+ ],
+ "part_keys": [
+ "part_id",
+ "parent_id",
+ "has_model",
+ "mesh_ref"
+ ],
+ "hierarchy_semantics": "declare mechanical articulation or semantic containment; containment alone does not imply joints",
+ "acyclic": true
+ },
+ "segment/uint16_labels/v1": {
+ "storage": "mkv",
+ "dtype": "uint16",
+ "mapping": "instances.jsonl",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "reserved_codes": {
+ "0": "unassigned",
+ "65535": "ignore"
+ },
+ "region_codes": [
+ 1,
+ 65534
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ },
+ "segment/instance_polygons/v1": {
+ "storage": "jsonl",
+ "mapping": "instances.jsonl",
+ "frame_keys": [
+ "frame_index",
+ "instances"
+ ],
+ "instance_keys": [
+ "mask_value",
+ "polygons"
+ ],
+ "coordinate_unit": "pixel",
+ "overlap_allowed": true,
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref"
+ ],
+ "polygon_shape": [
+ "N",
+ 2
+ ],
+ "holes_supported": false
+ },
+ "segment/foreground_intensity/v1": {
+ "storage": "mkv",
+ "dtype": "uint16",
+ "value_range": [
+ 0,
+ 255
+ ],
+ "normalization_scale": 0.00392156862745098,
+ "axes": [
+ "T",
+ "height",
+ "width"
+ ],
+ "meaning": "native hand foreground intensity; individual hand or union declared in metadata; not instance codes or calibrated probability",
+ "zero": "background",
+ "full_scale": "foreground",
+ "mapping": "foreground_category in component metadata",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "value_range",
+ "normalization_scale",
+ "foreground_category",
+ "entity_id",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ }
+ },
+ "storage_revision": "lossless-video-2026-09-18",
+ "sampling": {
+ "image": {
+ "single": {
+ "T": 1
+ },
+ "independent": {
+ "T": ">=2",
+ "required_frame_fields": [
+ "sample_id",
+ "source_locator"
+ ],
+ "temporal_relationship": false
+ },
+ "source_fps": null
+ },
+ "video": {
+ "sampling_types": [
+ "dense",
+ "sparse"
+ ]
+ }
+ },
+ "storage_profiles": {
+ "mkv/mjpeg_copy/v1": {
+ "container": "matroska",
+ "codec": "mjpeg",
+ "method": "stream_copy_from_source_jpeg",
+ "logical_dtype": "uint8",
+ "decoded_pixel_format": "rgb24",
+ "validation": [
+ "source_jpeg_packet_sha256_exact",
+ "all_packet_count_order_and_dimensions_exact",
+ "deterministic_decoded_rgb_sample_exact",
+ "pilot_and_final_selected_whole_sequences_decoded_rgb_exact"
+ ],
+ "validation_policy": {
+ "id": "jpeg-packets-all-rgb-sample16/v1",
+ "sample_count_per_view": 16,
+ "short_views": "all_frames_if_T_less_than_16",
+ "fixed_indices": "0,T-1,T//4,T//2,3*T//4, deduplicated",
+ "remaining_indices": "sample without replacement to min(T,16) using Python random.Random(seed)",
+ "seed": "first 8 bytes of SHA256(sequence_id/camera_id/20260920), big-endian integer",
+ "report": "policy, exact selected frame indices, seed, separate packet and pixel coverage",
+ "legacy_all_frame_pixel_proofs": "still accepted; preserve historical evidence"
+ }
+ },
+ "mkv/source_copy/v1": {
+ "container": "matroska",
+ "codec": "source_codec_declared_in_metadata",
+ "method": "stream_copy_only",
+ "logical_dtype": "uint8",
+ "decoded_pixel_format": "rgb24",
+ "validation": [
+ "no_reencoding",
+ "all_decoded_rgb_frames_exact",
+ "presentation_frame_order_exact"
+ ]
+ },
+ "mkv/h264_rgb_lossless/v1": {
+ "container": "matroska",
+ "codec": "h264",
+ "encoder": "libx264rgb",
+ "crf": 0,
+ "decoded_pixel_format": "rgb24",
+ "logical_dtype": "uint8",
+ "gop": "declared_in_encoder_parameters",
+ "validation": [
+ "all_decoded_rgb_frames_exact"
+ ],
+ "forbidden": [
+ "lossy_rate_control",
+ "rgb_to_yuv420_subsampling"
+ ]
+ },
+ "mkv/ffv1_rgb/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "decoded_pixel_format": "rgb24",
+ "logical_dtype": "uint8",
+ "validation": [
+ "all_decoded_rgb_frames_exact"
+ ]
+ },
+ "mkv/depth_float32_bits/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "pixel_format": "gray16le",
+ "logical_dtype": "float32",
+ "logical_shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "encoded_shape": [
+ "T",
+ "H",
+ "2*W"
+ ],
+ "packing": "IEEE754 little-endian float32 bits; low uint16 in first W columns, high uint16 in last W columns",
+ "decode": "reinterpret_float32(uint32(low) | (uint32(high) << 16))",
+ "validation": [
+ "all_float32_bits_exact_including_nan"
+ ]
+ },
+ "mkv/bool_ffv1/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "pixel_format": "gray",
+ "logical_dtype": "bool",
+ "stored_values": [
+ 0,
+ 1
+ ],
+ "validation": [
+ "all_bool_values_exact"
+ ]
+ },
+ "mkv/uint16_ffv1/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "pixel_formats": [
+ "gray",
+ "gray16le"
+ ],
+ "logical_dtype": "uint16",
+ "gray_condition": "all values <=255; decode by exact uint8-to-uint16 promotion",
+ "validation": [
+ "all_integer_values_exact"
+ ]
+ }
+ },
+ "video_metadata": {
+ "required": [
+ "profile",
+ "codec",
+ "pixel_format",
+ "encoded_size",
+ "decoder",
+ "encoder_parameters",
+ "source_lossy"
+ ],
+ "source_lossy_values": [
+ true,
+ false,
+ "unknown"
+ ],
+ "timebase_role": "index_only",
+ "lossless_reference": "source compressed packets or fixed-decoder source pixels; depth uses normalized standard float32 bits",
+ "selection": "choose smaller complete candidate only after exact roundtrip validation"
+ }
+}
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/full.log b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/full.log
new file mode 100644
index 0000000000000000000000000000000000000000..88785c5ae24611caaf852eed171804ef5a164c18
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/full.log
@@ -0,0 +1,8 @@
+Verified existing subject1/h1/0
+Verified existing subject2/h1/0
+Verified existing subject1/h1/6
+Verified existing subject1/h1/3
+Verified existing subject2/h1/6
+Converted subject3/h1/0
+Verified existing subject1/h1/7
+Converted subject4/h1/0
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/full.status b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/full.status
new file mode 100644
index 0000000000000000000000000000000000000000..70a37e730f28768cec24b071afd0f74cbce3db74
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/full.status
@@ -0,0 +1,7 @@
+START=2026-09-30T10:22:30+08:00
+WORKERS=4 CODEC_THREADS=10 SUBJECTS=1,2,3,4 CASA=false
+CPU_LIMIT=none
+CGROUP=/user.slice/user-25044.slice/user@25044.service/app.slice/h2o-full-w4c10-r2-20260930.scope
+CPU_MAX=max controller_disabled
+
+RECONFIGURE=2026-09-30T11:03:03+08:00 REASON=decoder10_depth4_no_resume_png_writes
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/run-full.sh b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/run-full.sh
new file mode 100644
index 0000000000000000000000000000000000000000..5b9267d75a5fb7c4d1729397219e7f8ca2a688c4
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/run-full.sh
@@ -0,0 +1,26 @@
+#!/usr/bin/env bash
+set -u
+base="$HOME/Datasets/uni-hoi-h2o-full-20260930-w4-c10"
+raw="$HOME/Datasets/Hand-Datasets-Raw/H2O"
+cd "$HOME/Projects/uni-hoi-dataset" || exit 1
+mkdir -p "$base/scratch"
+export TMPDIR="$base/scratch" PYTHONUNBUFFERED=1
+for n in 1 2 3 4; do test -s "$raw/raw/data/subject${n}_v1_1.tar.gz" || exit 2; done
+test ! -e "$base/full.status" || exit 3
+cg=$(awk -F: '$1=="0" {print $3}' /proc/self/cgroup)
+if [[ -f "/sys/fs/cgroup$cg/cpu.max" ]]; then
+ read -r quota period < "/sys/fs/cgroup$cg/cpu.max"
+else
+ quota=max
+ period=controller_disabled
+fi
+printf 'START=%s\nWORKERS=4 CODEC_THREADS=10 SUBJECTS=1,2,3,4 CASA=false\nCPU_LIMIT=none\nCGROUP=%s\nCPU_MAX=%s %s\n' "$(date -Is)" "$cg" "$quota" "$period" > "$base/full.status"
+if [[ "$quota" != max ]]; then
+ printf 'EXIT=4 ERROR=UNEXPECTED_CPU_QUOTA\n' >> "$base/full.status"
+ exit 4
+fi
+/usr/bin/time -v .venv/bin/python "$base/frozen-code/scripts/convert_h2o_v1.0.py" --root "$raw" --out "$base" --release-id shared-v1.0 --workers 4 --codec-threads 10 --mano-dir "$HOME/Projects/WiLoR/mano_data" > "$base/full.log" 2>&1
+code=$?
+printf 'EXIT=%s END=%s\n' "$code" "$(date -Is)" >> "$base/full.status"
+if [[ -f "/sys/fs/cgroup$cg/cpu.stat" ]]; then cat "/sys/fs/cgroup$cg/cpu.stat" >> "$base/full.status"; fi
+exit "$code"
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/check_dexycb_projection.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/check_dexycb_projection.py
new file mode 100644
index 0000000000000000000000000000000000000000..2774c004fc1359dea582135ec7171f600b33b336
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/check_dexycb_projection.py
@@ -0,0 +1,31 @@
+#!/usr/bin/env python3
+"""Check saved Dex-YCB source 3D joints against saved source 2D joints in sampled views."""
+import json
+from pathlib import Path
+import sys
+
+import numpy as np
+import torch
+
+root = Path(sys.argv[1])
+report = json.loads((root / 'doc/validation/STRUCTURAL_AUDIT.json').read_text())
+checks = []
+for name in report['sampled_views']:
+ view = root / name
+ load = lambda path: torch.load(view / path, weights_only=True, map_location='cpu').numpy()
+ k = load('camera/intrinsic.pt')
+ xyz = load('annotation/keypoints/xyz.pt')
+ uv = load('annotation/keypoints/uv.pt')
+ valid = load('annotation/keypoints/xyz_valid.pt') & load('annotation/keypoints/uv_valid.pt')
+ z = xyz[..., 2]
+ valid &= np.isfinite(xyz).all(axis=-1) & np.isfinite(uv).all(axis=-1) & (z > 0)
+ z = np.where(valid, z, 1)
+ pred = np.stack((k[:, 0, 0, None, None] * xyz[..., 0] / z + k[:, 0, 2, None, None],
+ k[:, 1, 1, None, None] * xyz[..., 1] / z + k[:, 1, 2, None, None]), axis=-1)
+ errors = np.linalg.norm(pred[valid] - uv[valid], axis=-1)
+ checks.append(dict(view=name, points=int(errors.size), median_px=float(np.median(errors)) if errors.size else None,
+ p95_px=float(np.percentile(errors, 95)) if errors.size else None))
+(root / 'doc/validation/KEYPOINT_PROJECTION_QC.json').write_text(json.dumps({
+ 'scope': 'Saved source 3D joints projected with saved color intrinsics vs saved source 2D joints; independent of hand mesh rendering.',
+ 'views': checks}, ensure_ascii=False, indent=2) + '\n')
+print(json.dumps(checks, ensure_ascii=False), flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/config.json b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f5f637095de54c1431bca56e19cd398949f4111a
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/config.json
@@ -0,0 +1,15 @@
+{
+ "root": "/home/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O",
+ "out": "/home/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10",
+ "release_id": "shared-v1.0",
+ "limit": null,
+ "max_frames": null,
+ "mano_dir": "/home/xuzhefeng/Projects/WiLoR/mano_data",
+ "views": null,
+ "jpeg_full_check": false,
+ "workers": 4,
+ "codec_threads": 10,
+ "subjects": null,
+ "include_casa": false,
+ "archive": null
+}
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/convert_dexycb_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/convert_dexycb_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed3f479787fae378b3d3b51ce2f58ccaebfd3d0f
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/convert_dexycb_v1.0.py
@@ -0,0 +1,349 @@
+#!/usr/bin/env python3
+"""Dex-YCB source annotations → CONTRACT.md v1.0. No derived annotations."""
+import io
+import re
+import shutil
+import tarfile
+import tempfile
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+import yaml
+
+from warehouse_v1_0 import (View, build_record, find_file, locator, mano_models, mesh_asset,
+ model_profile, parser, pca_to_axis_angle, release, roots, safe_member, sequence_dir)
+
+IMG_W, IMG_H = 640, 480
+
+class _Loader(yaml.SafeLoader):
+ pass
+
+
+_Loader.add_constructor("tag:yaml.org,2002:python/tuple",
+ lambda l, n: tuple(l.construct_sequence(n)))
+
+
+def yaml_load_bytes(data: bytes):
+ return yaml.load(io.BytesIO(data), Loader=_Loader)
+
+
+class Calibration:
+ def __init__(self, dexycb_root: Path):
+ self.intrinsics = {} # serial -> {fx,fy,cx,cy,w,h}
+ self.extrinsics = {} # group -> {serial: [12] 行优先 3×4 cam→world}
+ self.betas = {} # mano_calib_name -> [10]
+ calib_dir = dexycb_root / "calibration"
+ if calib_dir.is_dir():
+ self._load((p.as_posix(), p.read_bytes())
+ for p in sorted(calib_dir.rglob("*")) if p.is_file())
+ else:
+ tar_path = dexycb_root / "calibration.tar.gz"
+ if not tar_path.exists():
+ raise FileNotFoundError(f"no calibration/ nor calibration.tar.gz in {dexycb_root}")
+ with tarfile.open(tar_path, "r:gz") as tf:
+ self._load((m.name, tf.extractfile(m).read()) for m in tf if m.isfile())
+
+ _RE_INTR = re.compile(r"(?:^|/)intrinsics/(\d+)_\d+x\d+\.yml$")
+ _RE_EXTRA = re.compile(r"(?:^|/)extrinsics_(\d{8}_\d{6})/extrinsics\.yml$")
+ _RE_MANO = re.compile(r"(?:^|/)mano_(\d{8}_\d{6}_subject-\d{2}_right)/mano\.yml$")
+
+ def _load(self, files):
+ for rel, data in files:
+ m = self._RE_INTR.search(rel)
+ if m:
+ c = yaml_load_bytes(data)["color"]
+ self.intrinsics[m.group(1)] = {"fx": float(c["fx"]), "fy": float(c["fy"]),
+ "cx": float(c["ppx"]), "cy": float(c["ppy"]),
+ "w": IMG_W, "h": IMG_H}
+ continue
+ m = self._RE_EXTRA.search(rel)
+ if m:
+ raw = yaml_load_bytes(data)["extrinsics"]
+ self.extrinsics[m.group(1)] = {
+ str(s): [float(x) for x in np.asarray(v).ravel()] for s, v in raw.items()}
+ continue
+ m = self._RE_MANO.search(rel)
+ if m:
+ self.betas[m.group(1)] = [float(b) for b in yaml_load_bytes(data)["betas"]]
+
+
+
+def calibration_locator(path, member, **extra):
+ if path.is_dir():
+ return locator(path / member.removeprefix('calibration/'), **extra)
+ return locator(path, member, **extra)
+
+
+def prepare_models(root, destination):
+ archive = find_file(root, 'models.tar.gz')
+ if archive is None:
+ return {}, None
+ pattern = re.compile(r'^models/(\d{3}_[a-z0-9_]+)/(textured_simple\.obj(?:\.mtl)?|texture_map\.png)$')
+ names = set()
+ with tarfile.open(archive, 'r|gz') as tf:
+ for member in tf:
+ match = pattern.fullmatch(member.name)
+ if member.isfile() and match:
+ names.add(match[1])
+ path = safe_member(destination, '/'.join(match.groups()))
+ path.parent.mkdir(parents=True, exist_ok=True)
+ path.write_bytes(tf.extractfile(member).read())
+ if len(names) != 21:
+ raise ValueError('expected all 21 source YCB model directories')
+ return {i: destination / name for i, name in enumerate(sorted(names), 1)}, archive
+
+
+def convert_view(path, args, seq, serial, frames, meta, meta_loc, calib, calib_path, models, assets, asset_archive):
+ total = int(meta.get('num_frames', max(frames) + 1))
+ ids = list(range(min(total, args.max_frames) if args.max_frames else total))
+ for f in ids:
+ frames.setdefault(f, {})
+ if not ids:
+ raise ValueError('no RGB frames')
+ source_rows = []
+ for f in ids:
+ row = frames[f]
+ source_rows.append(dict(source_frame_id=f, sync_id=f'{seq}:{f}',
+ source_locator=row['color'][1] if 'color' in row else (meta_loc | {'source_frame_id': f, 'missing_rgb': True}), annotation_matches={
+ 'labels': {'source_frame_id': f, 'source_timestamp_ns': None, 'clock_domain': None,
+ 'match_method': 'index_map', 'delta_ns': None, 'source_locator': row['labels'][1]}
+ for _ in [0] if 'labels' in row},
+ component_sources={k: v[1] for k, v in row.items()}))
+ view = View(path, 'dex-ycb', args.release_id, seq, serial, source_rows,
+ sampling='dense' if ids == list(range(ids[0], ids[-1] + 1)) else 'sparse')
+ view.valid_rules = {
+ 'mano': 'Source pose_m present and not all zero; side model/calibration available',
+ 'keypoints': 'Corresponding joint_2d/joint_3d present; whole-hand all -1 is missing, broadcast to its 21 points',
+ 'pose': 'Source pose_y for this ycb_ids slot present and not an all-zero matrix',
+ 'segment': 'Source seg array provided for this frame; region codes and entity references validated',
+ 'relations': 'Source sequence ycb_grasp_ind resolves through ycb_ids; broadcast sequence grasp to selected frames',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta['view_type'] = 'exo'
+ view.meta['sampling']['selection'] = {'max_frames': args.max_frames, 'views': args.views}
+ subject = re.search(r'subject-(\d+)', seq)
+ if subject and 1 <= int(subject[1]) <= 10:
+ index = int(subject[1])
+ view.meta.update(split='val' if index == 7 else 'test' if index in (8, 9) else 'train',
+ split_scheme='Dex-YCB/s1', split_source_locator={
+ 'url': 'https://github.com/NVlabs/dex-ycb-toolkit/blob/master/dex_ycb_toolkit/dex_ycb.py',
+ 'rule': 's1 unseen-subject: 01-06/10 train, 07 val, 08-09 test'})
+ view.meta.pop('split_reason', None)
+ view.rgb([frames[f]['color'][0] if 'color' in frames[f] else None for f in ids], {'frames_ref': 'camera/frames.jsonl'})
+ n = len(ids)
+ intr = calib.intrinsics.get(serial)
+ K = None if intr is None else np.array([[intr['fx'], 0, intr['cx']], [0, intr['fy'], intr['cy']], [0, 0, 1]])
+ ext = calib.extrinsics.get(str(meta['extrinsics']), {}).get(serial)
+ E = None
+ if ext is not None:
+ E = np.eye(4)
+ E[:3] = np.asarray(ext).reshape(3, 4)
+ E = np.linalg.inv(E)
+ view.camera(K, calibration_locator(calib_path, f'calibration/intrinsics/{serial}_640x480.yml'), E,
+ calibration_locator(calib_path, f'calibration/extrinsics_{meta["extrinsics"]}/extrinsics.yml',
+ conversion='inverse of source T_world_from_camera'))
+ sides = meta.get('mano_sides')
+ if sides not in (['left'], ['right']):
+ raise ValueError(f'expected exactly one anatomical source hand: {sides}')
+ side = sides[0]
+ slot = ['left', 'right'].index(side)
+ cal_name = meta['mano_calib'][0] if isinstance(meta['mano_calib'], list) else meta['mano_calib']
+ labels = [dict(np.load(frames[f]['labels'][0])) if 'labels' in frames[f] else {} for f in ids]
+ label_loc = {'frames_ref': 'camera/frames.jsonl', 'source_field': 'component_sources.labels'}
+ motion = np.full((n, 2, 61), np.nan, np.float32)
+ mv = np.zeros((n, 2), bool)
+ if side in models and any('pose_m' in lab for lab in labels):
+ for i, lab in enumerate(labels):
+ if 'pose_m' not in lab:
+ continue
+ pose = np.asarray(lab['pose_m']).reshape(51)
+ if not np.any(pose): # Official Dex-YCB missing-pose sentinel, independent of joints/seg.
+ continue
+ motion[i, slot] = np.r_[pose[:3], pca_to_axis_angle(pose[3:48], models[side]),
+ calib.betas[cal_name], pose[48:51]]
+ mv[i, slot] = True
+ view.tensor('mano', 'mano/axis_angle61/v1', 'annotation/mano/motion.pt', motion, mv,
+ ['T', 'hand', 'parameter'], label_loc, model_profile=model_profile(models),
+ calibration_source=calibration_locator(calib_path, f'calibration/mano_{cal_name}/mano.yml'),
+ provenance={'kind': 'format_conversion', 'method': 'source PCA @ hands_components + hands_mean; explicit axis-angle consumed with flat_hand_mean=true'})
+ elif any('pose_m' in lab for lab in labels):
+ view.absent('mano', 'Source PCA exists; provide --mano-dir to expand it', 'dependency_missing')
+ else:
+ view.absent('mano', 'No source pose_m field')
+ for source_key, key, dim, unit in [('joint_2d', 'uv', 2, 'pixel'), ('joint_3d', 'xyz', 3, 'm')]:
+ if not any(source_key in x for x in labels):
+ view.absent('keypoints_' + key, 'No source ' + source_key)
+ continue
+ points = np.full((n, 2, 21, dim), np.nan, np.float32)
+ valid = np.zeros((n, 2, 21), bool)
+ for i, lab in enumerate(labels):
+ if source_key not in lab:
+ continue
+ p = np.asarray(lab[source_key]).reshape(21, dim)
+ points[i, slot] = p
+ valid[i, slot] = not np.all(p == -1) # Official sentinel covers the whole source hand.
+ # An individual off-image point (-1, -1) is not evidence of missing annotation.
+ view.tensor('keypoints_' + key, f'keypoints/{key}/v1', f'annotation/keypoints/{key}.pt',
+ points, valid, ['T', 'hand', 'keypoint', 'coordinate'], label_loc,
+ keypoint_schema='mano21/v1', unit=unit)
+ ycb_ids = [int(x) for x in meta['ycb_ids']]
+ for j, oid in enumerate(ycb_ids):
+ entity = f'ycb_{oid:02d}'
+ view.object(entity, meta_loc, source_object_id=oid, category_id=oid)
+ poses = np.full((n, 4, 4), np.nan, np.float32)
+ valid = np.zeros(n, bool)
+ for i, lab in enumerate(labels):
+ if 'pose_y' in lab:
+ pose = np.asarray(lab['pose_y'])
+ if pose.shape != (len(ycb_ids), 3, 4):
+ raise ValueError('pose_y shape differs from ycb_ids')
+ if np.any(pose[j]): # Official all-zero missing object pose sentinel.
+ poses[i] = np.eye(4)
+ poses[i, :3] = pose[j]
+ valid[i] = True
+ if any('pose_y' in lab for lab in labels):
+ view.tensor('pose_' + entity, 'object/rigid/v1', f'annotation/object/{entity}/pose.pt',
+ poses, valid, ['T', 'row', 'column'], label_loc,
+ convention='T_camera_from_object', unit='m')
+ else:
+ view.absent('pose_' + entity, 'No source pose_y field')
+ if oid in assets:
+ mesh_asset(view, entity, assets[oid], assets[oid] / 'textured_simple.obj',
+ locator(asset_archive, f'models/{assets[oid].name}/textured_simple.obj'))
+ else:
+ view.absent('mesh_' + entity, 'models.tar.gz unavailable', 'not_downloaded')
+ if any('seg' in lab for lab in labels):
+ mask_rows = []
+ width, height = view.meta['camera']['image_size']
+ for i, lab in enumerate(labels):
+ values = np.unique(lab.get('seg', np.zeros((height, width), np.uint8)))
+ mappings = []
+ for value in values:
+ value = int(value)
+ if value == 0:
+ continue
+ if value != 255 and value not in ycb_ids:
+ raise ValueError(f'unknown Dex-YCB region value {value}')
+ mappings.append(dict(mask_value=value, category_id='hand' if value == 255 else value,
+ entity_id='hand_' + side if value == 255 else f'ycb_{value:02d}', part_id=None,
+ source_instance_id=value))
+ mask_rows.append(dict(frame_index=i, labels=mappings))
+ view.raster('segment', (lab.get('seg', np.zeros((height, width), np.uint16)) for lab in labels),
+ ['seg' in lab for lab in labels], label_loc, mask_rows)
+ view.meta['components']['segment'].update(coverage={'kind': 'selected_entities',
+ 'entities': ['hand_' + side] + [f'ycb_{o:02d}' for o in ycb_ids]}, source_zero_meaning='background')
+ else:
+ view.absent('segment', 'No source seg field')
+ if any('aligned_depth_to_color' in frames[f] for f in ids):
+ def depths():
+ width, height = view.meta['camera']['image_size']
+ for f in ids:
+ item = frames[f].get('aligned_depth_to_color')
+ if item is None:
+ yield np.full((height, width), np.nan, np.float32)
+ else:
+ with Image.open(item[0]) as im:
+ yield np.asarray(im).astype(np.float32) / 1000.0
+ view.raster('depth', depths(), None, {'frames_ref': 'camera/frames.jsonl',
+ 'source_field': 'component_sources.aligned_depth_to_color', 'conversion': 'millimetres to metres'})
+ else:
+ view.absent('depth', 'No source aligned_depth_to_color frames')
+ grasp = meta.get('ycb_grasp_ind')
+ if grasp is not None:
+ if not 0 <= int(grasp) < len(ycb_ids):
+ raise ValueError('invalid ycb_grasp_ind')
+ relation = dict(relation_type='grasp', subject_id='hand_' + side,
+ object_id=f'ycb_{ycb_ids[int(grasp)]:02d}', status='positive', source_locator=meta_loc,
+ provenance={'kind': 'source', 'method': 'sequence ycb_grasp_ind resolved through ycb_ids; not per-frame contact'})
+ view.relations([{'frame_index': i, 'relations': [relation]} for i in range(n)], np.ones(n, bool), meta_loc)
+ else:
+ view.absent('relations', 'No source grasp/contact relation')
+ return view.finish()
+
+
+MEMBER = re.compile(r'^([^/]+)/(\d{8}_\d{6})/(meta\.yml|(\d{12})/(color|labels|aligned_depth_to_color)_(\d+)\.(jpg|npz|png))$')
+
+
+def convert_archive(archive, args, out, calib, calib_path, models, assets, asset_archive):
+ selected_views = set(args.views.split(',')) if args.views else None
+ done = 0
+ current, meta, meta_loc, rows = None, None, None, {}
+ with tempfile.TemporaryDirectory(prefix='dex-source-') as wd:
+ work = Path(wd)
+ def flush():
+ nonlocal done
+ if not rows:
+ return
+ seq = archive.name.split('.tar')[0] + '_' + current
+ reports = {}
+ with sequence_dir(out, seq) as target:
+ for serial, frames in sorted(rows.items()):
+ reports[serial] = convert_view(target / serial, args, seq, serial, frames, meta,
+ meta_loc, calib, calib_path, models, assets, asset_archive)
+ build_record(out, seq, reports)
+ for staged in list(work.iterdir()):
+ if staged.is_dir():
+ shutil.rmtree(staged)
+ else:
+ staged.unlink()
+ done += 1
+ print(f'Converted {seq}', flush=True)
+ with tarfile.open(archive, 'r|gz') as tf:
+ for member in tf:
+ match = MEMBER.fullmatch(member.name)
+ if not member.isfile() or not match:
+ continue
+ seq = match[2]
+ if seq != current:
+ flush()
+ if args.limit and done >= args.limit:
+ break
+ current, meta, meta_loc, rows = seq, None, None, {}
+ data_kind = match[3]
+ if data_kind == 'meta.yml':
+ data = tf.extractfile(member).read()
+ meta = yaml_load_bytes(data)
+ meta_loc = locator(archive, member.name, data)
+ continue
+ serial, kind, f = match[4], match[5], int(match[6])
+ if selected_views and serial not in selected_views or args.max_frames and f >= args.max_frames:
+ continue
+ data = tf.extractfile(member).read()
+ dest = safe_member(work, member.name)
+ dest.parent.mkdir(parents=True, exist_ok=True)
+ dest.write_bytes(data)
+ row = rows.setdefault(serial, {}).setdefault(f, {})
+ if kind in row:
+ raise ValueError('duplicate archive frame')
+ row[kind] = (dest, locator(archive, member.name, data))
+ else:
+ flush()
+ return done
+
+
+def main(argv=None):
+ p = parser('dex-ycb', __doc__)
+ p.add_argument('--subjects')
+ args = p.parse_args(argv)
+ archives = sorted({a for r in roots(args.root) for a in r.glob('*-subject-*.tar.gz')})
+ if args.subjects:
+ subjects = {s.strip().zfill(2) for s in args.subjects.split(',')}
+ archives = [a for a in archives if re.search(r'subject-(\d+)', a.name)[1] in subjects]
+ if not archives:
+ p.error('no Dex-YCB subject archives found')
+ cp = find_file(args.root, 'calibration.tar.gz') or find_file(args.root, 'calibration')
+ if cp is None:
+ p.error('calibration source missing')
+ calib = Calibration(cp.parent)
+ models = mano_models(args.mano_dir)
+ out = release(args, 'dex-ycb')
+ with tempfile.TemporaryDirectory(prefix='dex-models-') as wd:
+ assets, asset_archive = prepare_models(args.root, Path(wd))
+ for archive in archives:
+ convert_archive(archive, args, out, calib, cp, models, assets, asset_archive)
+ return 0
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/convert_h2o_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/convert_h2o_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..727461a74046ad8dc16ec26d6fdb365b9c304594
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/convert_h2o_v1.0.py
@@ -0,0 +1,527 @@
+#!/usr/bin/env python3
+"""H2O native RGB-D, MANO, joints and objects → CONTRACT.md v1.0 (no rendered masks)."""
+from concurrent.futures import ThreadPoolExecutor
+import json
+import os
+import re
+import shutil
+import tarfile
+import tempfile
+from pathlib import Path
+import zipfile
+
+# Bound native libraries before loading NumPy/Torch; each worker still uses independent FFmpeg processes.
+for key in ('OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS'):
+ os.environ[key] = '1'
+
+import numpy as np
+from PIL import Image
+import torch
+
+from warehouse_v1_0 import (View, build_record, check_mesh, find_file, locator, mano_models, mesh_asset,
+ model_profile, parser, release, roots, safe_member, sequence_dir, sha256)
+
+OBJECTS = {1: 'book', 2: 'espresso', 3: 'lotion', 4: 'spray',
+ 5: 'milk', 6: 'cocoa', 7: 'chips', 8: 'cappuccino'}
+MEMBER = re.compile(r'^(?:h2o_CASA/)?(subject\d+)/([^/]+)/(\d+)/(cam\d)/(.+)$')
+FRAME = re.compile(r'(rgb|depth|cam_pose|hand_pose|hand_pose_mano|obj_pose|obj_pose_rt)/(\d+)\.(png|jpg|txt)$')
+
+
+def load_splits(root):
+ archive = find_file(root, 'label_split.zip')
+ result = {}
+ if archive is not None:
+ with zipfile.ZipFile(archive) as z:
+ for split in ('train', 'val', 'test'):
+ member = f'label_split/action_{split}.txt'
+ for line_no, line in enumerate(z.read(member).decode().splitlines(), 1):
+ fields = line.split()
+ if len(fields) < 2 or fields[1] == 'path':
+ continue
+ key = fields[1]
+ if key in result and result[key][0] != split:
+ raise ValueError('conflicting official splits')
+ result[key] = (split, locator(archive, member, record=line_no))
+ return result
+
+
+def load_assets(root, work):
+ archive = find_file(root, 'object.zip')
+ if archive is None:
+ return {}, None
+ with zipfile.ZipFile(archive) as z:
+ for member in z.infolist():
+ if member.is_dir():
+ continue
+ path = safe_member(work, member.filename)
+ path.parent.mkdir(parents=True, exist_ok=True)
+ path.write_bytes(z.read(member))
+ result = {}
+ for oid, name in OBJECTS.items():
+ directory = work / 'object' / name
+ aliases = []
+ # Source cocoa.obj -> ovomaltine.mtl -> cocoa.png, but the ZIP names the
+ # supplied texture ovomaltine.png. Keep all originals and add a byte-identical alias.
+ if name == 'cocoa' and not (directory / 'cocoa.png').exists():
+ mtl = directory / 'ovomaltine.mtl'
+ if mtl.is_file() and 'map_Kd cocoa.png' in mtl.read_text().splitlines():
+ texture = directory / 'ovomaltine.png'
+ shutil.copyfile(texture, directory / 'cocoa.png')
+ aliases.append({'file': 'cocoa.png', 'sha256': sha256(texture),
+ 'source': locator(archive, 'object/cocoa/ovomaltine.png')})
+ candidates = list(directory.glob('*.obj'))
+ if len(candidates) != 1:
+ raise ValueError(f'expected one source OBJ for {name}')
+ check_mesh(candidates[0], directory) # Fail before decoding any large subject archive.
+ result[oid] = (directory, candidates[0], aliases)
+ return result, archive
+
+
+def convert_view(path, args, seq, cam, rows, intr, splits, models, assets, asset_archive):
+ codec_threads = getattr(args, 'codec_threads', 1)
+ ids = list(range(min(rows), max(rows) + 1))
+ for f in ids:
+ rows.setdefault(f, {})
+ n = len(ids)
+ if not n:
+ raise ValueError('no RGB frames')
+ split, split_loc = splits.get(seq, ('unknown', None))
+ frame_rows = [dict(source_frame_id=f, sync_id=f'{seq}:{f}', source_locator=rows[f]['rgb'][1] if 'rgb' in rows[f] else {'sequence': seq, 'camera': cam, 'source_frame_id': f, 'missing_rgb': True},
+ component_sources={k: item[1] for k, item in rows[f].items()},
+ annotation_matches={k: dict(source_frame_id=f, source_timestamp_ns=None,
+ clock_domain=None, match_method='index_map', delta_ns=None)
+ for k in rows[f] if k != 'rgb'}) for f in ids]
+ view = View(path, 'H2O', args.release_id, seq.replace('/', '_'), cam, frame_rows, split=split)
+ view.valid_rules = {
+ 'mano': 'Per-hand hand_pose_mano flag equals 1; corresponding side model available',
+ 'keypoints': "Per-hand hand_pose flag equals 1, broadcast to that hand's 21 source points independently of MANO",
+ 'pose': 'Source obj_pose_rt class equals this nonzero object class',
+ 'points': 'Source obj_pose class equals this nonzero object class, broadcast to its 21 points',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta['view_type'] = 'ego' if cam == 'cam4' else 'exo' if cam in ('cam0', 'cam1', 'cam2', 'cam3') else 'unknown'
+ view.meta['sampling']['selection'] = {'max_frames': args.max_frames, 'views': args.views}
+ if split_loc:
+ view.meta.update(split_scheme='official_action_split', split_source_locator=split_loc)
+ # In the measured 275 H2O views, exact FFV1 was always smaller than exact H.264.
+ view.rgb([rows[f]['rgb'][0] if 'rgb' in rows[f] else None for f in ids],
+ {'frames_ref': 'camera/frames.jsonl'}, ffv1_first=True, codec_threads=codec_threads)
+ loc = {'frames_ref': 'camera/frames.jsonl', 'source_field': 'component_sources'}
+ if intr is not None:
+ fx, fy, cx, cy, width, height = np.loadtxt(intr[0]).ravel()
+ if [int(width), int(height)] != view.meta['camera']['image_size']:
+ raise ValueError('source intrinsics resolution mismatch')
+ K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
+ view.camera(K, intr[1])
+ camps = np.full((n, 4, 4), np.nan, np.float32)
+ cv = np.zeros(n, bool)
+ data = []
+ for i, f in enumerate(ids):
+ lab = {k: np.loadtxt(item[0]).ravel() for k, item in rows[f].items() if k not in ('rgb', 'depth')}
+ data.append(lab)
+ if 'cam_pose' in lab:
+ camps[i] = np.linalg.inv(lab['cam_pose'].reshape(4, 4))
+ cv[i] = True
+ if cv.any():
+ view.tensor('extrinsic', 'transform/se3/v1', 'camera/extrinsic.pt', camps, cv,
+ ['T', 'row', 'column'], loc, convention='T_camera_from_world',
+ source_convention='T_world_from_camera')
+ motion, mv = np.full((n, 2, 61), np.nan, np.float32), np.zeros((n, 2), bool)
+ points, pv = np.full((n, 2, 21, 3), np.nan, np.float32), np.zeros((n, 2, 21), bool)
+ object_ids = set()
+ for i, lab in enumerate(data):
+ if 'hand_pose_mano' in lab:
+ hands = lab['hand_pose_mano'].reshape(2, 62)
+ for side, row in enumerate(hands):
+ if row[0] not in (0, 1):
+ raise ValueError('unexpected H2O MANO presence flag')
+ if row[0] == 1:
+ motion[i, side] = np.r_[row[4:52], row[52:62], row[1:4]]
+ mv[i, side] = True
+ if 'hand_pose' in lab:
+ hands = lab['hand_pose'].reshape(2, 64)
+ for side, row in enumerate(hands):
+ if row[0] not in (0, 1):
+ raise ValueError('unexpected H2O keypoint presence flag')
+ if row[0] == 1:
+ points[i, side] = row[1:].reshape(21, 3)
+ pv[i, side] = True
+ for key in ('obj_pose', 'obj_pose_rt'):
+ if key in lab and lab[key][0] != 0:
+ object_ids.add(int(lab[key][0]))
+ if any('hand_pose_mano' in lab for lab in data):
+ if len(models) == 2:
+ view.tensor('mano', 'mano/axis_angle61/v1', 'annotation/mano/motion.pt', motion, mv,
+ ['T', 'hand', 'parameter'], loc, model_profile=model_profile(models))
+ else:
+ view.absent('mano', 'Source parameters exist; --mano-dir must identify both model profiles', 'dependency_missing')
+ if any('hand_pose' in lab for lab in data):
+ view.tensor('keypoints_xyz', 'keypoints/xyz/v1', 'annotation/keypoints/xyz.pt', points, pv,
+ ['T', 'hand', 'keypoint', 'coordinate'], loc, keypoint_schema='mano21/v1', unit='m')
+ for oid in sorted(object_ids):
+ if oid not in OBJECTS:
+ raise ValueError(f'unknown H2O object class {oid}')
+ entity = OBJECTS[oid]
+ view.object(entity, loc, category_id=oid, source_object_id=oid)
+ poses, ov = np.full((n, 4, 4), np.nan, np.float32), np.zeros(n, bool)
+ pts, kv = np.full((n, 21, 3), np.nan, np.float32), np.zeros((n, 21), bool)
+ for i, lab in enumerate(data):
+ if 'obj_pose_rt' in lab and lab['obj_pose_rt'][0] == oid:
+ poses[i] = lab['obj_pose_rt'][1:].reshape(4, 4)
+ ov[i] = True
+ if 'obj_pose' in lab and lab['obj_pose'][0] == oid:
+ pts[i] = lab['obj_pose'][1:].reshape(21, 3)
+ kv[i] = True
+ if ov.any():
+ view.tensor('pose_' + entity, 'object/rigid/v1', f'annotation/object/{entity}/pose.pt',
+ poses, ov, ['T', 'row', 'column'], loc, convention='T_camera_from_object', unit='m')
+ if kv.any():
+ view.tensor('points_' + entity, 'object_keypoints/camera/v1',
+ f'annotation/object/{entity}/keypoints/xyz.pt', pts, kv,
+ ['T', 'keypoint', 'coordinate'], loc, keypoint_schema='H2O_official_object21',
+ keypoint_names=['center'] + [f'corner_{i}' for i in range(8)] + [f'edge_midpoint_{i}' for i in range(12)],
+ source_order='official obj_point_order.jpg: center, 8 corners, 12 edge midpoints', unit='m')
+ if oid in assets:
+ directory, obj, aliases = assets[oid]
+ asset_loc = locator(asset_archive, f'object/{entity}/{obj.name}')
+ if aliases:
+ asset_loc['texture_aliases'] = aliases
+ mesh_asset(view, entity, directory, obj, asset_loc)
+ else:
+ view.absent('mesh_' + entity, 'Source object.zip unavailable', 'not_downloaded')
+ if any('depth' in rows[f] for f in ids):
+ width, height = view.meta['camera']['image_size']
+ def depths():
+ for f in ids:
+ item = rows[f].get('depth')
+ if item:
+ with Image.open(item[0]) as im:
+ yield np.asarray(im).astype(np.float32) / 1000.0
+ else:
+ yield np.full((height, width), np.nan, np.float32)
+ view.raster('depth', depths(), None, loc | {'source_field': 'component_sources.depth',
+ 'definition': 'https://github.com/taeinkwon/h2oplayer/blob/main/utils.py',
+ 'conversion': 'registered millimetre camera-Z to metres; no viewer range clipping'},
+ codec_threads=codec_threads)
+ else:
+ view.absent('depth', 'No source depth frames')
+ view.absent('segment', 'H2O provides no native segmentation; rendering deferred to v2.0')
+ view.absent('relations', 'No source contact/grasp annotation')
+ return view.finish()
+
+
+def convert_archive(archive, args, out, splits, models, assets, asset_archive):
+ selected_views = set(args.views.split(',')) if args.views else None
+ subjects = {'subject' + s.strip() for s in args.subjects.split(',')} if args.subjects else None
+ current, rows, intrinsics, done = None, {}, {}, 0
+ with tempfile.TemporaryDirectory(prefix='h2o-source-') as wd:
+ work = Path(wd)
+ def flush():
+ nonlocal done
+ if not rows:
+ return
+ seq = '/'.join(current)
+ reports, pending = {}, {}
+ for cam, frames in sorted(rows.items()):
+ existing = out / 'data' / seq.replace('/', '_') / cam
+ if (existing / 'metainfo.json').is_file():
+ old_frames = [json.loads(line) for line in (existing / 'camera/frames.jsonl').read_text().splitlines()]
+ if [f['source_frame_id'] for f in old_frames] != list(range(min(frames), max(frames) + 1)):
+ raise ValueError('existing view has different sampling; choose a new release-id')
+ for frame in old_frames:
+ incoming = frames.get(frame['source_frame_id'], {})
+ previous = frame.get('component_sources', {})
+ if set(previous) != set(incoming) or any(previous[k].get('sha256') != v[1].get('sha256') for k, v in incoming.items()):
+ raise ValueError('duplicate H2O view differs from the published source; choose a new release-id')
+ else:
+ pending[cam] = frames
+ if pending:
+ with sequence_dir(out, seq.replace('/', '_')) as target:
+ for cam, frames in pending.items():
+ reports[cam] = convert_view(target / cam, args, seq, cam, frames,
+ intrinsics.get(cam), splits, models, assets, asset_archive)
+ build_record(out, seq.replace('/', '_'), reports)
+ for staged in list(work.iterdir()):
+ if staged.is_dir():
+ shutil.rmtree(staged)
+ else:
+ staged.unlink()
+ done += 1
+ print(f'{"Converted" if pending else "Verified existing"} {seq}', flush=True)
+ # Uncompressed CASA supports seeking over large frames; gzip is single-pass.
+ mode = 'r:' if archive.suffix == '.tar' else 'r|gz'
+ with tarfile.open(archive, mode) as tf:
+ for member in tf:
+ match = MEMBER.fullmatch(member.name)
+ if not member.isfile() or not match:
+ continue
+ subj, scene, seq, cam, tail = match.groups()
+ if subjects and subj not in subjects:
+ continue
+ key = (subj, scene, seq)
+ if key != current:
+ flush()
+ if args.limit and done >= args.limit:
+ break
+ current, rows, intrinsics = key, {}, {}
+ if selected_views and cam not in selected_views:
+ continue
+ frame = FRAME.fullmatch(tail)
+ if tail != 'cam_intrinsics.txt' and not frame:
+ continue
+ if frame and args.max_frames and int(frame[2]) >= args.max_frames:
+ continue
+ data = tf.extractfile(member).read()
+ dest = safe_member(work, member.name)
+ dest.parent.mkdir(parents=True, exist_ok=True)
+ dest.write_bytes(data)
+ item = (dest, locator(archive, member.name, data))
+ if tail == 'cam_intrinsics.txt':
+ intrinsics[cam] = item
+ else:
+ kind, f = frame[1], int(frame[2])
+ row = rows.setdefault(cam, {}).setdefault(f, {})
+ if kind in row:
+ raise ValueError('duplicate H2O frame')
+ row[kind] = item
+ else:
+ flush()
+ return done
+
+
+def main(argv=None):
+ p = parser('H2O', __doc__)
+ p.set_defaults(release_id='shared-v1.0')
+ p.add_argument('--workers', type=int, choices=range(1, 5), default=1,
+ help='Independent subject archives in parallel, 1..4; benchmark before using >2')
+ p.add_argument('--codec-threads', type=int, choices=(1, 2, 4, 8, 10), default=4,
+ help='FFV1 slice threads per encode; default 4, use 10 for the current full run')
+ p.add_argument('--subjects')
+ p.add_argument('--include-casa', action='store_true')
+ p.add_argument('--archive', type=Path, help='Convert one explicit source archive')
+ args = p.parse_args(argv)
+ if args.include_casa and args.workers != 1:
+ p.error('CASA can share sequence IDs with subject archives; convert it sequentially in a separate release')
+ archives = [args.archive.expanduser()] if args.archive else sorted(
+ {a for root in roots(args.root) for a in root.glob('subject*_v1_1.tar.gz')})
+ if args.subjects and not args.archive:
+ requested = {'subject' + s.strip() for s in args.subjects.split(',')}
+ archives = [arc for arc in archives if arc.name.split('_', 1)[0] in requested]
+ if args.include_casa:
+ casa = find_file(args.root, 'h2o_CASA.tar')
+ if casa:
+ archives.append(casa)
+ if not archives:
+ p.error('no H2O archives found')
+ out = release(args, 'H2O')
+ source_mapping = next(p for p in (Path(__file__).resolve().parents[1] / 'docs/h2o-RAW_TO_SHARED.md',
+ Path(__file__).resolve().parents[1] / 'doc/RAW_TO_SHARED.md',
+ out / 'doc/RAW_TO_SHARED.md') if p.is_file())
+ mapping = out / 'doc/RAW_TO_SHARED.md'
+ if mapping.exists() and mapping.read_bytes() != source_mapping.read_bytes():
+ raise ValueError('RAW_TO_SHARED.md changed; choose a new release-id')
+ if not mapping.exists():
+ shutil.copyfile(source_mapping, mapping)
+ torch.set_num_threads(1)
+ models, splits = mano_models(args.mano_dir), load_splits(args.root)
+ with tempfile.TemporaryDirectory(prefix='h2o-assets-') as wd:
+ assets, asset_archive = load_assets(args.root, Path(wd))
+ def convert_one(archive):
+ return convert_archive(archive, args, out, splits, models, assets, asset_archive)
+ if args.workers == 1:
+ for archive in archives:
+ convert_one(archive)
+ else:
+ with ThreadPoolExecutor(max_workers=args.workers) as pool:
+ for archive, count in zip(archives, pool.map(convert_one, archives), strict=True):
+ print(f'Archive {archive.name}: {count} sequences', flush=True)
+ return 0
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
+
+# TODO(v2.0): 以下旧 MANO 网格和渲染分割代码仅保留备查；v1.0 不生成标注。
+# def load_mano_model(path: Path):
+# """MPI MANO pkl → numpy 字典。shapedirs 在 pkl 内为 chumpy Select（对 (778,3,20) 基
+# 底 flat 选取前 10 个 shape 分量；idxs 已核实等价于 base[:,:,:10]）。"""
+# with open(path, "rb") as f:
+# d = _ManoUnpickler(f, encoding="latin1").load()
+# sd = d["shapedirs"]
+# base = np.asarray(sd.a.x, np.float64) # (778, 3, 20)
+# shapedirs = base.reshape(-1)[np.asarray(sd.idxs)].reshape(base.shape[0], 3, -1)
+# jreg = d["J_regressor"]
+# jreg = np.asarray(jreg.todense()) if hasattr(jreg, "todense") else np.asarray(jreg)
+# return {"v_template": np.asarray(d["v_template"], np.float64),
+# "shapedirs": shapedirs,
+# "posedirs": np.asarray(d["posedirs"], np.float64),
+# "weights": np.asarray(d["weights"], np.float64),
+# "J_regressor": np.asarray(jreg, np.float64),
+# "kintree": np.asarray(d["kintree_table"], np.int64),
+# "faces": np.asarray(d["f"], np.int64)}
+#
+#
+# def rodrigues(r):
+# """axis-angle (N,3) → 旋转矩阵 (N,3,3)。公式 I + sinθ·K + (1−cosθ)·K²（已对 scipy 核验）。"""
+# r = np.asarray(r, np.float64).reshape(-1, 3)
+# th = np.linalg.norm(r, axis=1)
+# R = np.zeros((len(r), 3, 3))
+# nz = th > 1e-12
+# R[~nz] = np.eye(3)
+# k = r[nz] / th[nz, None]
+# K = np.zeros((len(k), 3, 3))
+# K[:, 0, 1], K[:, 0, 2], K[:, 1, 2] = -k[:, 2], k[:, 1], -k[:, 0]
+# K[:, 1, 0], K[:, 2, 0], K[:, 2, 1] = k[:, 2], -k[:, 1], k[:, 0]
+# s, c = np.sin(th[nz])[:, None, None], np.cos(th[nz])[:, None, None]
+# R[nz] = np.eye(3) + s * K + (1 - c) * (K @ K)
+# return R
+#
+#
+# def _mano_chain(model, betas, pose):
+# """返回 (v_shaped, J, Rs, G)：G 为 16 个关节的全局变换（平移列 = 关节位置）。"""
+# v = model["v_template"] + np.einsum("nkc,c->nk", model["shapedirs"], betas)
+# J = model["J_regressor"] @ v
+# Rs = rodrigues(np.asarray(pose, np.float64).reshape(16, 3))
+# kt = model["kintree"]
+# G = np.zeros((16, 4, 4))
+# for j in range(16):
+# p = kt[0, j] if j else -1
+# loc = np.eye(4)
+# loc[:3, :3] = Rs[j]
+# loc[:3, 3] = J[j] - (J[p] if j else 0.0)
+# G[j] = loc if not j else G[p] @ loc
+# return v, J, Rs, G
+#
+#
+# def mano_forward(model, betas, pose, transl):
+# """MANO LBS 前向（经典蒙皮 M_j = G_j·G0_j⁻¹；对 hand_pose 21 关节实证 0.0 mm）。
+# pose: 48 axis-angle（[0:3] 全局）；返回相机系米顶点 (778,3)。"""
+# v, J, Rs, G = _mano_chain(model, betas, pose)
+# lrot = (Rs[1:] - np.eye(3)).ravel()
+# v_posed = v + np.einsum("nkc,c->nk", model["posedirs"], lrot)
+# kt = model["kintree"]
+# G0 = np.zeros((16, 4, 4))
+# for j in range(16):
+# p = kt[0, j] if j else -1
+# loc = np.eye(4)
+# loc[:3, 3] = J[j] - (J[p] if j else 0.0)
+# G0[j] = loc if not j else G0[p] @ loc
+# M = np.einsum("nj,jab->nab", model["weights"], G @ np.linalg.inv(G0))
+# out = np.einsum("nab,nb->na", M[:, :3, :3], v_posed) + M[:, :3, 3]
+# return out + np.asarray(transl, np.float64)
+#
+#
+# def mano_chain_joints(model, betas, pose, transl):
+# """16 个关节位置（相机系米）= 链平移列；自检用。"""
+# _, _, _, G = _mano_chain(model, betas, pose)
+# return G[:, :3, 3] + np.asarray(transl, np.float64)
+#
+#
+# def rasterize(verts_cam, faces, labels, W, H, fx, fy, cx, cy):
+# z = verts_cam[:, 2]
+# P = faces
+# ok = (z[P] > 0.02).all(1) # 任一顶点在相机后 → 丢弃该三角形
+# P, labels = P[ok], labels[ok]
+# if len(P) == 0:
+# return np.zeros((H, W), np.uint16), np.zeros(0, np.int64)
+# x = verts_cam[:, 0] / z * fx + cx
+# y = verts_cam[:, 1] / z * fy + cy
+# xa = np.maximum(0, np.minimum(np.floor(x[P[:, 0]]), np.minimum(
+# np.floor(x[P[:, 1]]), np.floor(x[P[:, 2]]))).astype(np.int64))
+# xb = np.minimum(W - 1, np.maximum(np.ceil(x[P[:, 0]]), np.maximum(
+# np.ceil(x[P[:, 1]]), np.ceil(x[P[:, 2]]))).astype(np.int64))
+# ya = np.maximum(0, np.minimum(np.floor(y[P[:, 0]]), np.minimum(
+# np.floor(y[P[:, 1]]), np.floor(y[P[:, 2]]))).astype(np.int64))
+# yb = np.minimum(H - 1, np.maximum(np.ceil(y[P[:, 0]]), np.maximum(
+# np.ceil(y[P[:, 1]]), np.ceil(y[P[:, 2]]))).astype(np.int64))
+# xa, ya = np.minimum(xa, xb), np.minimum(ya, yb) # 完全出画的三角形 → 空 bbox
+# wdt, hgt = xb - xa + 1, yb - ya + 1
+# cnt = wdt * hgt
+# tot = int(cnt.sum())
+# tri = np.repeat(np.arange(len(P)), cnt)
+# k = np.arange(tot) - (np.cumsum(cnt) - cnt)[tri]
+# px = xa[tri] + k % wdt[tri] + 0.5
+# py = ya[tri] + k // wdt[tri] + 0.5
+# ax, ay = x[P[tri, 0]], y[P[tri, 0]]
+# bx, by = x[P[tri, 1]], y[P[tri, 1]]
+# cx_, cy_ = x[P[tri, 2]], y[P[tri, 2]]
+# area = ((x[P[:, 1]] - x[P[:, 0]]) * (y[P[:, 2]] - y[P[:, 0]])
+# - (y[P[:, 1]] - y[P[:, 0]]) * (x[P[:, 2]] - x[P[:, 0]]))
+# areat = area[tri]
+# e0 = (bx - ax) * (py - ay) - (by - ay) * (px - ax)
+# e1 = (cx_ - bx) * (py - by) - (cy_ - by) * (px - bx)
+# e2 = (ax - cx_) * (py - cy_) - (ay - cy_) * (px - cx_)
+# s = np.sign(areat)
+# inside = (np.abs(areat) > 1e-9) & ((e0 * s >= 0) & (e1 * s >= 0) & (e2 * s >= 0))
+# px, py, tri = px[inside], py[inside], tri[inside]
+# at = areat[inside]
+# w0, w1, w2 = e1[inside] / at, e2[inside] / at, e0[inside] / at
+# zz = w0 * z[P[tri, 0]] + w1 * z[P[tri, 1]] + w2 * z[P[tri, 2]]
+# pix = py.astype(np.int64) * W + px.astype(np.int64)
+# order = np.lexsort((zz, pix)) # 同像素按深度升序 → 首条即最近
+# pix, tri = pix[order], tri[order]
+# first = np.unique(pix, return_index=True)[1]
+# mask = np.zeros(H * W, np.uint16)
+# win = tri[first]
+# mask[pix[first]] = labels[win] + 1 # 临时标签 +1（0=背景）
+# return mask.reshape(H, W), np.bincount(labels[win] + 1)
+#
+#
+# # ============================================================================
+# # ffmpeg 写出与核验（与 convert_dexycb.py 同构）
+# # ============================================================================
+# verts, faces_all, labels = [], [], []
+# for ti, (tid, kind) in enumerate(tracks):
+# if kind == "hand_left" and b["mano"][0] > 0.5:
+# verts.append(mano_forward(mano_models["left"], b["mano"][52:62],
+# b["mano"][4:52], b["mano"][1:4]))
+# faces_all.append(mano_models["left"]["faces"])
+# elif kind == "hand_right" and b["mano"][62] > 0.5:
+# verts.append(mano_forward(mano_models["right"], b["mano"][114:124],
+# b["mano"][66:114], b["mano"][63:66]))
+# faces_all.append(mano_models["right"]["faces"])
+# elif isinstance(kind, tuple) and int(b["objp"][0]) == kind[1]:
+# M = np.asarray(b["objrt"][1:17], np.float64).reshape(4, 4)
+# V0, F0 = meshes[kind[1]]
+# verts.append(V0 @ M[:3, :3].T + M[:3, 3])
+# faces_all.append(F0)
+# else:
+# continue
+# labels.append(np.full(len(faces_all[-1]), ti))
+# if verts:
+# V = np.concatenate(verts)
+# offs = np.cumsum([0] + [len(v) for v in verts[:-1]])
+# F = np.concatenate([fa + o for fa, o in zip(faces_all, offs)])
+# mask, counts = rasterize(V, F, np.concatenate(labels),
+# IMG_W, IMG_H, fx, fy, cx, cy)
+# else:
+# mask, counts = np.zeros((IMG_H, IMG_W), np.uint16), np.zeros(0, np.int64)
+# lut = np.zeros(n_tracks + 1, np.uint16)
+# for ti in range(n_tracks):
+# present = ti + 1 < len(counts) and counts[ti + 1] > 0
+# if present:
+# presence[tracks[ti][0]][f] = True
+# if active_id[ti] is None:
+# pid = next(i for i in range(1, 65535) if i not in used)
+# used.add(pid)
+# active_id[ti], seg_start[ti] = pid, f
+# frame_maps[ti].append({"id": pid})
+# lut[ti + 1] = active_id[ti]
+# elif active_id[ti] is not None:
+# frame_maps[ti][-1]["frames"] = [seg_start[ti], f - 1]
+# segments[ti].append([seg_start[ti], f - 1])
+# used.discard(active_id[ti])
+# active_id[ti] = None
+# mw.write(lut[mask])
+
+
+# TODO(v2.0): 旧物体尺寸派生代码；v1.0 仅保留源物体关键点。
+# pts = np.asarray(b["objp"][1:64], np.float64).reshape(21, 3)
+# half = np.abs((pts[1:9] - pts[0]) @ M[:3, :3]).max(0)
+# obj_recs.append({
+# "track_id": f"o{classes.index(c)}", "frame": f,
+# "mesh_ref": f"h2o/{OBJ_CLASSES[c]}.obj",
+# "pose": {"quat": rotmat_to_quat_wxyz(M[:3, :3])[0].tolist(),
+# "trans": M[:3, 3].tolist()},
+# "bbox_center3d": pts[0].tolist(),
+# "bbox_size": (2 * half).tolist()})
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/convert_hoi4d_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/convert_hoi4d_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d5914d64ae72ee62564897743dfe5a41923e08d
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/convert_hoi4d_v1.0.py
@@ -0,0 +1,595 @@
+#!/usr/bin/env python3
+"""HOI4D native observations and annotations → CONTRACT.md v1.0."""
+from collections import Counter
+from concurrent.futures import ThreadPoolExecutor
+from contextlib import ExitStack
+from functools import lru_cache
+import gzip
+import io
+import json
+import os
+from pathlib import Path
+import pickle
+import re
+import shutil
+import shlex
+import posixpath
+import subprocess
+import tempfile
+import tarfile
+import threading
+import zipfile
+
+# Each worker owns one Python thread; prevent native libraries spawning CPU pools.
+for key in ('OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS'):
+ os.environ[key] = '1'
+
+import numpy as np
+from PIL import Image
+from scipy.spatial.transform import Rotation
+
+import torch
+
+from warehouse_v1_0 import (View, build_record, check_mesh, decode_args, find_file, json_write, locator,
+ mano_models, model_profile, parser, probe, release, run, safe_member, sequence_dir)
+
+CATEGORIES = {
+ 1: dict(cad="rigid/ToyCar", groups=[("main", [1], "Toy Car", 1)],
+ hands={2: "right"}, objmap={"Toycar": "main"}),
+ 2: dict(cad="rigid/Mug", groups=[("a", [1], "Mug", 2), ("b", [3], "Mug", 2)],
+ hands={2: "right"}, objmap={"Watercup": "a"}),
+ 3: dict(cad="articulated/Laptop", groups=[("main", [1, 3], "Laptop", 3)],
+ hands={2: "right"},
+ objmap={"Laptopdisplay": "main", "Laptopkeyboard": "main"}),
+ 4: dict(cad="articulated/StorageFurniture",
+ groups=[("main", [1, 3, 4], "Storage Furniture", 4),
+ ("bottle", [5], "Bottle", 5)],
+ hands={2: "right"},
+ objmap={"Lockerbody": "main", "Lockersldingdoor": "main",
+ "Lockerdrawer": "main", "Safebox": "main"}),
+ 5: dict(cad="rigid/Bottle", groups=[("main", [1], "Bottle", 5),
+ ("mug", [3], "Mug", 2)],
+ hands={2: "right"}, objmap={"bottleddrinks": "main"}),
+ 6: dict(cad="articulated/Safe", groups=[("main", [1, 3], "Safe", 6),
+ ("bottle", [4], "Bottle", 5)],
+ hands={2: "right"}, objmap={"Safebox": "main", "Safedoor": "main"}),
+ 7: dict(cad="rigid/Bowl", groups=[("main", [1], "Bowl", 7),
+ ("ball", [3], "Ball", None)],
+ hands={2: "right"}, objmap={"bowl": "main"}),
+ 8: dict(cad="articulated/Bucket",
+ groups=[("a", [1, 3], "Bucket", 8), ("b", [4, 5], "Bucket", 8)],
+ hands={2: "right", 6: "left"},
+ objmap={"bucket": "a", "Buckethandle": "a"}),
+ 9: dict(cad="articulated/Scissors", groups=[("main", [1, 3], "Scissors", 9)],
+ hands={2: "right"},
+ objmap={"scissors": "main", "scissors2": None}), # scissors2：场景道具，无掩码
+ 11: dict(cad="articulated/Pliers", groups=[("main", [1, 3], "Pliers", 11)],
+ hands={2: "right"}, objmap={"Pliersleft": "main", "Pliersright": "main"}),
+ 12: dict(cad="rigid/Kettle", groups=[("main", [1], "Kettle", 12),
+ ("mug", [3], "Mug", 2)],
+ hands={2: "right"}, objmap={"kettle": "main"}),
+ 13: dict(cad="rigid/Knife", groups=[("main", [1], "Knife", 13),
+ ("apple", [3], "Apple", None)],
+ hands={2: "right"}, objmap={"knife": "main"}),
+ 14: dict(cad="articulated/TrashCan",
+ groups=[("main", [1, 3], "Trash Can", 14), ("bottle", [4], "Bottle", 5)],
+ hands={2: "right"}, objmap={"Dustbinbase": "main", "Dustbincover": "main"}),
+ 17: dict(cad="articulated/Lamp", groups=[("main", [1, 3, 4, 5], "Lamp", 17)],
+ hands={2: "right", 6: "left"},
+ objmap={"Lampbase": "main", "Tablelampfirstjoint": "main",
+ "Tablelampsecondjoint": "main", "Thirdjointoftablelamp": "main"}),
+ 18: dict(cad="articulated/Stapler",
+ groups=[("main", [1, 3, 4], "Stapler", 18), ("paper", [5], "Paper", None)],
+ hands={2: "right", 6: "left"}, # 6=左手：官方 label.csv 缺列，实测修正
+ objmap={"Staplerbase": "main", "Staplercover": "main"}),
+ 20: dict(cad="rigid/Chair", groups=[("main", [1], "Chair", 20)],
+ hands={2: "right", 3: "left"}, objmap={"chair": "main"}),
+}
+
+
+# Official HOI4D definitions/motion segmentation/label.csv; no spatial side inference.
+REGION_NAMES = {1: {1: 'Toy Car', 2: 'Right Hand'}, 2: {1: 'Mug', 2: 'Right Hand', 3: 'Mug'}, 3: {1: 'Screen', 2: 'Right Hand', 3: 'Keyboard'}, 4: {1: 'Door', 2: 'Right Hand', 3: 'Body of the Cabinet', 4: 'Drawer', 5: 'Bottle'}, 5: {1: 'Bottle', 2: 'Right Hand', 3: 'Mug'}, 6: {1: 'Body of the Safe', 2: 'Right Hand', 3: 'Door', 4: 'Bottle'}, 7: {1: 'Bowl', 2: 'Right Hand', 3: 'Ball'}, 8: {1: 'Body of the Bucket', 2: 'Right Hand', 3: 'Handle', 4: 'Body of the Bucket', 5: 'Handle', 6: 'Left Hand'}, 9: {1: 'Left Part of the Scissors', 2: 'Right Hand', 3: 'Right Part of the Scissors'}, 11: {1: 'Left Part of the Pliers', 2: 'Right Hand', 3: 'Right Part of the Pliers'}, 12: {1: 'Kettle', 2: 'Right Hand', 3: 'Mug'}, 13: {1: 'Knife', 2: 'Right Hand', 3: 'Apple'}, 14: {1: 'Body of the Trash Can', 2: 'Right Hand', 3: 'Lid', 4: 'Bottle'}, 17: {1: 'Base', 2: 'Right Hand', 3: 'Tube', 4: 'Top Link', 5: 'Bottom Link', 6: 'Left Hand'}, 18: {1: 'Base', 2: 'Right Hand', 3: 'Slot', 4: 'Lid', 5: 'Paper'}, 20: {1: 'Chair', 2: 'Right Hand', 3: 'Left Hand'}}
+
+
+class SplitFiles(io.RawIOBase):
+ """Read concatenated source gzip parts without concatenating 119 GB on disk."""
+ def __init__(self, paths):
+ self.paths = iter(paths)
+ self.current = None
+
+ def readable(self):
+ return True
+
+ def readinto(self, buffer):
+ while True:
+ if self.current is None:
+ path = next(self.paths, None)
+ if path is None:
+ return 0
+ self.current = path.open('rb')
+ n = self.current.readinto(buffer)
+ if n:
+ return n
+ self.current.close()
+ self.current = None
+
+ def close(self):
+ if self.current is not None:
+ self.current.close()
+ super().close()
+
+
+def extract_depths(root, sequences, work):
+ directory = find_file(root, 'DEPTH')
+ if directory is None:
+ return {}, []
+ parts = sorted(directory.glob('HOI4D_depth_video.tar.gz*'), key=lambda p: int(p.name.split('.gz')[1] or 0))
+ if not parts:
+ return {}, []
+ wanted = {f'HOI4D_depth_video/{seq}/align_depth/depth_video.avi': seq for seq in sequences}
+ found = {}
+ with SplitFiles(parts) as joined, io.BufferedReader(joined) as inp, gzip.GzipFile(fileobj=inp) as gz:
+ with tarfile.open(fileobj=gz, mode='r|') as tf:
+ for member in tf:
+ if member.isfile() and member.name in wanted:
+ seq = wanted[member.name]
+ dest = work / (seq.replace('/', '_') + '.avi')
+ with tf.extractfile(member) as src, dest.open('wb') as dst:
+ shutil.copyfileobj(src, dst)
+ found[seq] = (dest, {'files': [str(p.resolve()) for p in parts], 'member': member.name})
+ if len(found) == len(wanted):
+ break
+ return found, parts
+
+
+def palette_mask(data):
+ with Image.open(io.BytesIO(data)) as im:
+ arr = np.asarray(im)
+ if arr.ndim == 2:
+ return arr.astype(np.uint16)
+ if arr.shape[-1] != 3:
+ raise ValueError('unsupported HOI4D mask representation')
+ # VOC's 256-color palette interleaves eight label bits into the high RGB bits.
+ # Invert it directly instead of sorting every pixel with np.unique.
+ if np.any(arr & np.array([31, 31, 63], dtype=np.uint8)):
+ raise ValueError('source mask has colors outside official VOC palette')
+ mask = np.zeros(arr.shape[:2], dtype=np.uint16)
+ for bit in range(8):
+ mask |= ((arr[..., bit % 3] >> (7 - bit // 3)) & 1).astype(np.uint16) << bit
+ return mask
+
+
+# Four workers each reuse annotation and hand indices instead of rebuilding the ZIP directory.
+@lru_cache(maxsize=8)
+def index_frames(z):
+ index = {}
+ for name in z.namelist():
+ parent, _, filename = name.rpartition('/')
+ stem, dot, suffix = filename.rpartition('.')
+ if not dot or not stem.isdigit() or suffix not in ('png', 'json', 'pickle'):
+ continue
+ entries = index.setdefault((parent + '/', '.' + suffix), {})
+ f = int(stem)
+ if f in entries:
+ raise ValueError(f'duplicate frame number under {parent}')
+ entries[f] = name
+ return index
+
+
+def frame_members(z, prefix, suffix):
+ return index_frames(z).get((prefix, suffix), {})
+
+
+def convert_sequence(seq, args, target, rgbz, annz, hpz, calz, cadz, paths, models, depth):
+ # ponytail: cache only one sequence's encoded annotation bytes; no decoded masks or CAD cache.
+ @lru_cache(maxsize=None)
+ def read_annotation(archive, name):
+ return archive.read(name)
+
+ category = int(re.search(r'/C(\d+)/', seq)[1])
+ definition = CATEGORIES.get(category)
+ if definition is None:
+ raise ValueError(f'category mapping unavailable: {category}')
+ prefix = f'HOI4D_annotations/{seq}'
+ mask_members = frame_members(annz, prefix + '/2Dseg/mask/', '.png')
+ shifted = frame_members(annz, prefix + '/2Dseg/shift_mask/', '.png')
+ if not mask_members:
+ mask_members = shifted
+ pose_members = frame_members(annz, prefix + '/objpose/', '.json')
+ hand_members = {side: frame_members(hpz, f'Hand_pose/handpose_{side}_hand/{seq}/', '.pickle')
+ for side in ('left', 'right')} if hpz else {'left': {}, 'right': {}}
+ with tempfile.TemporaryDirectory(prefix='hoi4d-source-') as wd:
+ work = Path(wd)
+ member = f'HOI4D_release/{seq}/align_rgb/image.mp4'
+ video = work / 'rgb.mp4'
+ with rgbz.open(member) as src, video.open('wb') as dst:
+ shutil.copyfileobj(src, dst)
+ info = probe(video)
+ if 'nb_frames' not in info:
+ info['nb_frames'] = json.loads(run(['ffprobe', '-v', 'error', '-count_frames', '-select_streams', 'v:0',
+ '-show_entries', 'stream=nb_read_frames', '-of', 'json', str(video)], stdout=subprocess.PIPE).stdout)['streams'][0]['nb_read_frames']
+ n = int(info['nb_frames'])
+ if args.max_frames:
+ n = min(n, args.max_frames)
+ if shifted and mask_members != shifted and any(f not in mask_members and f in shifted for f in range(n)):
+ raise ValueError('shift_mask has frames absent from mask; correspondence unverified')
+ frames = []
+ for f in range(n):
+ sources = {}
+ if f in mask_members:
+ sources['segment'] = locator(paths['annotations'], mask_members[f], read_annotation(annz, mask_members[f]))
+ if f in pose_members:
+ sources['object'] = locator(paths['annotations'], pose_members[f], read_annotation(annz, pose_members[f]))
+ for side in ('left', 'right'):
+ if f in hand_members[side]:
+ sources['hand_' + side] = locator(paths['hand'], hand_members[side][f], read_annotation(hpz, hand_members[side][f]))
+ frames.append(dict(source_frame_id=f, source_locator=locator(paths['release'], member, frame=f),
+ component_sources=sources, annotation_matches={k: dict(source_frame_id=f, source_timestamp_ns=None,
+ clock_domain=None, match_method='index_map', delta_ns=None) for k in sources}))
+ view = View(target, 'HOI4D', args.release_id, seq.replace('/', '_'), 'ego', frames)
+ view.valid_rules = {
+ 'mano': 'Matched side pickle contains poseCoeff, beta, trans and corresponding model available; zero angles are not missing',
+ 'keypoints': 'Matched side pickle contains kps2D; independently provided source points, no visibility gate',
+ 'pose': 'Matched source object record contains rotation and center; record identity retained',
+ 'dimensions': 'Matched source object record contains dimensions; size/mesh relation remains source_unspecified',
+ 'segment': 'Source mask or shift_mask present with verified palette codes and entity/part mapping',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta['view_type'] = 'ego'
+ view.meta['sampling']['selection'] = {'max_frames': args.max_frames, 'source_sequence': seq}
+ view.rgb([], locator(paths['release'], member), video=video, count=n)
+ intr_name = f'camera_params/{seq.split("/")[0]}/intrin.npy'
+ if calz and intr_name in calz.namelist():
+ view.camera(np.load(io.BytesIO(calz.read(intr_name))), locator(paths['camera'], intr_name))
+ extr_name = prefix + '/3Dseg/output.log'
+ if extr_name in annz.NameToInfo:
+ lines = [line for line in annz.read(extr_name).decode().splitlines() if line.strip()]
+ if len(lines) % 5:
+ raise ValueError('invalid Open3D camera trajectory')
+ extr, valid = np.full((n, 4, 4), np.nan, np.float32), np.zeros(n, bool)
+ for i in range(0, len(lines), 5):
+ idx = int(lines[i].split()[0])
+ if idx < n:
+ if valid[idx]:
+ raise ValueError('duplicate trajectory index')
+ extr[idx] = np.linalg.inv(np.loadtxt(io.StringIO('\n'.join(lines[i + 1:i + 5]))))
+ valid[idx] = True
+ view.tensor('extrinsic', 'transform/se3/v1', 'camera/extrinsic.pt', extr, valid,
+ ['T', 'row', 'column'], locator(paths['annotations'], extr_name),
+ convention='T_camera_from_world', source_convention='Open3D LOG camera-to-world; inverted')
+ loc = {'frames_ref': 'camera/frames.jsonl', 'source_field': 'component_sources'}
+ motion, mv = np.full((n, 2, 61), np.nan, np.float32), np.zeros((n, 2), bool)
+ points, pv = np.full((n, 2, 21, 2), np.nan, np.float32), np.zeros((n, 2, 21), bool)
+ for slot, side in enumerate(('left', 'right')):
+ for f, name in hand_members[side].items():
+ if f >= n:
+ continue
+ data = pickle.loads(read_annotation(hpz, name), encoding='latin1')
+ if all(k in data for k in ('poseCoeff', 'beta', 'trans')):
+ motion[f, slot] = np.r_[np.asarray(data['poseCoeff']).reshape(48),
+ np.asarray(data['beta']).reshape(10), np.asarray(data['trans']).reshape(3)]
+ mv[f, slot] = True # Zero angles are a valid pose, not a missing sentinel.
+ if 'kps2D' in data:
+ points[f, slot] = np.asarray(data['kps2D']).reshape(21, 2)
+ pv[f, slot] = True
+ if mv.any():
+ if all(side in models for slot, side in enumerate(('left', 'right')) if mv[:, slot].any()):
+ view.tensor('mano', 'mano/axis_angle61/v1', 'annotation/mano/motion.pt', motion, mv,
+ ['T', 'hand', 'parameter'], loc, model_profile=model_profile(models))
+ else:
+ view.absent('mano', 'Source MANO exists; provide --mano-dir to fix model identity', 'dependency_missing')
+ if pv.any():
+ view.tensor('keypoints_uv', 'keypoints/uv/v1', 'annotation/keypoints/uv.pt', points, pv,
+ ['T', 'hand', 'keypoint', 'coordinate'], loc, keypoint_schema='mano21/v1', unit='pixel',
+ source_method='Upstream fitted MANO projection already present in kps2D')
+ objects = {}
+ groups = {key: (vals, name, cls) for key, vals, name, cls in definition['groups']}
+ def ensure_object(key, source_loc, category_id=None, frame=None, source_id=None):
+ if key not in objects:
+ view.object(key, source_loc, scope='frame' if frame is not None else 'sequence',
+ scope_id=f'{seq}:{frame}' if frame is not None else None,
+ category_id=category_id, source_object_id=source_id)
+ objects[key] = {}
+ mappings = []
+ mask_valid = [f in mask_members for f in range(n)]
+ if any(mask_valid):
+ width, height = view.meta['camera']['image_size']
+ def masks():
+ for f in range(n):
+ labels = []
+ if f in mask_members:
+ mask = palette_mask(read_annotation(annz, mask_members[f]))
+ for value in np.unique(mask):
+ value = int(value)
+ if value in (0, 255):
+ if value == 255:
+ raise ValueError('HOI4D 255 region semantics unverified')
+ continue
+ if REGION_NAMES[category].get(value) in ('Left Hand', 'Right Hand'):
+ entity, part, cls = 'hand_' + REGION_NAMES[category][value].split()[0].lower(), None, 'hand'
+ else:
+ group = next((k for k, (vals, _, _) in groups.items() if value in vals), None)
+ entity = group or f'unknown_region_{value}'
+ cls = groups[group][2] if group else None
+ part = f'region_{value}' if group and len(groups[group][0]) > 1 else None
+ ensure_object(entity, frames[f]['component_sources']['segment'], cls)
+ if part:
+ objects[entity].setdefault('parts', {})[part] = {'part_id': part, 'parent_id': None,
+ 'has_model': False, 'mesh_ref': None, 'source_region_code': value, 'name': REGION_NAMES[category].get(value)}
+ labels.append(dict(mask_value=value, category_id=cls, entity_id=entity,
+ part_id=part, source_instance_id=value,
+ source_attributes={'region_name': REGION_NAMES[category].get(value)}))
+ else:
+ mask = np.zeros((height, width), np.uint16)
+ mappings.append(dict(frame_index=f, labels=labels))
+ yield mask
+ view.raster('segment', masks(), mask_valid, loc, mappings)
+ view.meta['components']['segment']['coverage'] = {'kind': 'unknown'}
+ if any(label['part_id'] for row in mappings for label in row['labels']):
+ view.meta['components']['segment']['segmentation_type'] = 'part'
+ else:
+ view.absent('segment', 'No native segmentation for this source sequence')
+ records = {}
+ for f, name in pose_members.items():
+ if f >= n:
+ continue
+ data = json.loads(read_annotation(annz, name))
+ # isEffective is not used to erase existing numerical annotations.
+ source_records = data.get('dataList', data.get('objects'))
+ if source_records is None:
+ raise ValueError('unrecognized source object annotation structure')
+ if category == 2 and source_records and (len(source_records) != 1 or
+ source_records[0].get('id') != 1 or source_records[0].get('label') != 'Watercup'):
+ raise ValueError('C2 native pose differs from the verified one-object target; cannot map to mask region 1')
+ label_counts = Counter(record['label'] for record in source_records)
+ per_frame = set()
+ for record_index, record in enumerate(source_records):
+ label = record['label']
+ group = definition['objmap'].get(label)
+ # Pinned official ObjTracking reads dataList[0] with segmentation label 1.
+ source_primary = category == 2 and record_index == 0 and record.get('id') == 1 and group == 'a'
+ ambiguous = group is not None and sum(g[2] == groups[group][2] for g in groups.values()) > 1 and not source_primary
+ ambiguous = ambiguous or (label_counts[label] > 1 and not source_primary) or group is None
+ entity = f'pose_frame_{f}_{record_index}' if ambiguous else group
+ ensure_object(entity, locator(paths['annotations'], name, record=record_index),
+ groups[group][2] if group else None, frame=f if ambiguous else None, source_id=record.get('id'))
+ if ambiguous:
+ view.build['unresolved'].append({'component': entity, 'status': 'source_semantics_unverified',
+ 'reason': 'Source pose retained with frame identity; no guessed association to same-class mask instances'})
+ label_key = (entity, label)
+ if label_key in per_frame:
+ raise ValueError(f'ambiguous repeated object label {label}; source identity must be resolved')
+ per_frame.add(label_key)
+ records.setdefault(label_key, {})[f] = (record, locator(paths['annotations'], name, record=record.get('id')))
+ if category == 2 and any(entity == 'a' for entity, _ in records):
+ view.build['validation'].append({'component': 'C2_source_pose_mask_identity',
+ 'rule': 'Official ObjTracking process_data.py reads dataList[0] with semantic mask label 1',
+ 'source_revision': 'afa7bf37b02b7e33cf245acd08b98c207ccc175c',
+ 'source_pose_id': 1, 'mask_value': 1, 'matched_frames': sum(len(by_frame) for (entity, _), by_frame in records.items() if entity == 'a')})
+ for (entity, label), by_frame in records.items():
+ part_labels = [k[1] for k in records if k[0] == entity]
+ articulated = definition['cad'].startswith('articulated/') and entity == definition['groups'][0][0]
+ part = 'pose_' + re.sub(r'[^a-zA-Z0-9_]', '_', label) if articulated or len(part_labels) > 1 else None
+ base = f'annotation/object/{entity}'
+ if part:
+ base += '/parts/' + part
+ objects[entity].setdefault('parts', {})[part] = dict(part_id=part, parent_id=None,
+ has_model=False, mesh_ref=None, source_label=label)
+ poses, valid = np.full((n, 4, 4), np.nan, np.float32), np.zeros(n, bool)
+ dimensions = np.full((n, 3), np.nan, np.float32)
+ dv = np.zeros(n, bool)
+ for f, (record, _) in by_frame.items():
+ if all(k in record for k in ('rotation', 'center')):
+ poses[f] = np.eye(4)
+ poses[f, :3, :3] = Rotation.from_euler('XYZ', [record['rotation'][k] for k in ('x', 'y', 'z')]).as_matrix()
+ poses[f, :3, 3] = [record['center'][k] for k in ('x', 'y', 'z')]
+ valid[f] = True
+ if 'dimensions' in record:
+ dimensions[f] = [record['dimensions'][k] for k in ('length', 'width', 'height')]
+ dv[f] = True
+ key = entity + ('_' + part if part else '')
+ view.tensor('pose_' + key, 'transform/se3/v1' if part else 'object/rigid/v1', base + '/pose.pt', poses, valid,
+ ['T', 'row', 'column'], loc, convention='T_camera_from_part' if part else 'T_camera_from_object',
+ unit='m', source_label=label, source_euler_order='XYZ',
+ definition='https://github.com/hoi4d/ObjTracking/blob/main/process_data.py')
+ if dv.any():
+ view.tensor('dimensions_' + key, 'object/dimensions/v1', base + '/dimensions.pt', dimensions, dv,
+ ['T', 'dimension'], loc, dimensions_semantics='source_unspecified', unit='m', scale_applied_to_mesh=False)
+ if cadz:
+ primary = definition['groups'][0][0]
+ if category != 2 and sum(g[2] == category for g in groups.values()) > 1:
+ primary = 'source_cad_instance'
+ instance = int(re.search(r'/N(\d+)/', seq)[1])
+ mesh_prefix = f'HOI4D_CAD_Model_for_release/{definition["cad"]}/{instance:03d}'
+ candidates = [name for name in cadz.namelist() if not name.endswith('/') and
+ (name == mesh_prefix + '.obj' or name.startswith(mesh_prefix + '/'))]
+ if candidates:
+ # Retain OBJ material/texture dependencies beside their original names.
+ queue = list(candidates)
+ for name in queue:
+ if Path(name).suffix.lower() not in ('.obj', '.mtl'):
+ continue
+ for line in cadz.read(name).decode().splitlines():
+ fields = shlex.split(line, comments=True)
+ if not fields:
+ continue
+ refs = fields[1:] if fields[0] == 'mtllib' else ([fields[-1]] if
+ fields[0].startswith('map_') or fields[0] in ('bump', 'norm', 'disp', 'decal') else [])
+ for ref in refs:
+ dependency = posixpath.normpath(posixpath.join(posixpath.dirname(name), ref))
+ if dependency not in cadz.NameToInfo:
+ raise ValueError(f'missing CAD dependency: {dependency}')
+ if dependency not in candidates:
+ candidates.append(dependency)
+ queue.append(dependency)
+ ensure_object(primary, locator(paths['cad'], mesh_prefix), category)
+ asset_dir = view.path / f'annotation/object/{primary}/assets'
+ asset_refs = []
+ for name in candidates:
+ relative = posixpath.relpath(name, mesh_prefix if definition['cad'].startswith('articulated/') else posixpath.dirname(mesh_prefix))
+ dest = safe_member(asset_dir, relative)
+ dest.parent.mkdir(parents=True, exist_ok=True)
+ dest.write_bytes(cadz.read(name))
+ ref = dest.relative_to(view.path).as_posix()
+ if dest.suffix.lower() == '.obj':
+ asset_key = re.sub(r'[^a-zA-Z0-9_]', '_', relative)
+ view.component('mesh_' + primary + '_' + asset_key, 'mesh/obj/v1', ref, ['vertex'],
+ locator(paths['cad'], name), unit='m', valid=True,
+ scale_applied_to_mesh=False, dimensions_semantics='source_unspecified')
+ asset_refs.append(ref)
+ if definition['cad'].startswith('articulated/'):
+ part_id = 'asset_' + asset_key
+ objects[primary].setdefault('parts', {})[part_id] = dict(part_id=part_id,
+ parent_id=None, has_model=True, mesh_ref=ref, source_asset=relative,
+ note='Source asset; correspondence to motion part labels remains unverified')
+ for asset_ref in asset_refs:
+ mesh_check = check_mesh(view.path / asset_ref, asset_dir)
+ view.build['validation'].append({'component': asset_ref, **mesh_check,
+ 'unit_basis': 'official ObjTracking loads source canonical CAD without scaling in metre camera coordinates',
+ 'geometric_acceptance': 'physical dimensions-to-mesh relation remains source_unspecified'})
+ metadata_path = view.path / f'annotation/object/{primary}/metainfo.json'
+ metadata = json.loads(metadata_path.read_text())
+ metadata.update(has_model=bool(asset_refs), asset_refs=asset_refs)
+ if definition['cad'].startswith('rigid/') and len(asset_refs) == 1:
+ metadata['mesh_ref'] = asset_refs[0]
+ json_write(metadata_path, metadata)
+ if definition['cad'].startswith('articulated/'):
+ view.build['unresolved'].append({'component': 'object_part_asset_association',
+ 'status': 'source_semantics_unverified',
+ 'reason': 'All source assets and motion labels retained, but no guessed correspondence or joint_state'})
+ for entity, info in objects.items():
+ if 'parts' in info:
+ ref = f'annotation/object/{entity}/articulation.json'
+ json_write(view.path / ref, {'parts': list(info['parts'].values()), 'joints': [],
+ 'hierarchy_semantics': 'semantic_containment',
+ 'note': 'Unassociated source region, motion and asset nodes; original CAD trees retained in assets'})
+ view.component('articulation_' + entity, 'object/articulated/v1', ref, [], loc)
+ meta_path = view.path / f'annotation/object/{entity}/metainfo.json'
+ metadata = json.loads(meta_path.read_text())
+ metadata['articulation_ref'] = ref
+ json_write(meta_path, metadata)
+ if depth is not None:
+ dpath, dloc = depth
+ dmeta = probe(dpath)
+ if [dmeta['width'], dmeta['height']] != view.meta['camera']['image_size']:
+ raise ValueError('HOI4D depth grid differs from RGB')
+ if dmeta['pix_fmt'] != 'gray16le':
+ raise ValueError('unexpected HOI4D depth pixel format')
+ width, height = view.meta['camera']['image_size']
+ raw = work / 'depth.raw'
+ limits = ['-frames:v', str(n)] if args.max_frames else []
+ run(decode_args(dpath, 'gray16le') + limits + ['-y', str(raw)])
+ if raw.stat().st_size != n * width * height * 2:
+ raise ValueError('depth/RGB frame count or grid mismatch')
+ def depths():
+ with raw.open('rb') as inp:
+ for _ in range(n):
+ yield np.frombuffer(inp.read(width * height * 2), ' best_iou + 0.1 and i > 0.15:
+# best, best_iou = s[0], i
+# if best != gk:
+# gk, iou_fixups = best, iou_fixups + 1
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/convert_visor_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/convert_visor_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f0595bf5e89ae00c959472ff197dfda2d7a6ea6
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/convert_visor_v1.0.py
@@ -0,0 +1,271 @@
+#!/usr/bin/env python3
+"""VISOR source polygons and contact → CONTRACT.md v1.0, without rasterization or side inference."""
+from collections import Counter
+import hashlib
+import json
+import re
+import shutil
+import tempfile
+from pathlib import Path
+import zipfile
+
+import numpy as np
+import torch
+
+from warehouse_v1_0 import (View, build_record, find_file, jsonl_write, locator, parser,
+ release, roots, sequence_dir, source)
+
+FRAME = re.compile(r'frame_(\d+)')
+HAND = {300: 'left', 301: 'right'}
+GLOVE = {303: 'left', 304: 'right'}
+STATES = {'hand-not-in-contact': 'negative', 'glove-not-in-contact': 'negative',
+ 'none-of-the-above': 'target_unannotated', 'inconclusive': 'unknown'}
+
+
+def hand_side(ann):
+ if ann['class_id'] in HAND:
+ return HAND[ann['class_id']]
+ if ann['class_id'] not in (60, 303, 304):
+ return None
+ raw = ann.get('on_which_hand')
+ raw = raw if isinstance(raw, list) else [raw]
+ sides = set()
+ for item in raw:
+ if item == 'left hand':
+ sides.add('left')
+ if item in ('right hand', 'rigth hand'):
+ sides.add('right')
+ if len(sides) == 1:
+ return next(iter(sides))
+ if not sides and ann['class_id'] in GLOVE:
+ return GLOVE[ann['class_id']]
+ return None
+
+
+def convert_video(video, split, annotation, ann_loc, rgb_zip, rgb_loc, mapping, args, target):
+ indexed = sorted(enumerate(annotation['video_annotations']), key=lambda row: int(FRAME.search(row[1]['image']['name'])[1]))
+ if args.max_frames:
+ indexed = indexed[:args.max_frames]
+ if len({row['image']['name'] for _, row in indexed}) != len(indexed):
+ raise ValueError('duplicate VISOR frame')
+ keys = lambda row, ann: (row['image']['subsequence'], ann['class_id'], ann['name'])
+ collisions = set()
+ for _, row in indexed:
+ collisions.update(key for key, number in Counter(keys(row, a) for a in row['annotations']).items() if number > 1)
+ frames, paths = [], []
+ with tempfile.TemporaryDirectory(prefix='visor-rgb-') as wd, zipfile.ZipFile(rgb_zip) as z:
+ work = Path(wd)
+ for original_index, row in indexed:
+ name = row['image']['name']
+ data = z.read(name)
+ path = work / Path(name).name
+ path.write_bytes(data)
+ paths.append(path)
+ loc = rgb_loc | {'nested_member': name, 'sha256': hashlib.sha256(data).hexdigest()}
+ frames.append(dict(source_frame_id=int(FRAME.search(name)[1]), source_locator=loc,
+ source_image=row['image'], original_frame_name=mapping.get(name),
+ annotation_matches={'segment': dict(source_frame_id=name, source_timestamp_ns=None,
+ clock_domain=None, match_method='index_map', delta_ns=None,
+ source_locator=ann_loc | {'record': f'video_annotations[{original_index}]'})}))
+ view = View(target, 'EPIC-KITCHENS-VISOR', args.release_id, video, 'ego', frames, sampling='sparse', split=split)
+ view.valid_rules = {
+ 'segment': 'Selected sparse annotation frame present; all source polygons finite with at least three vertices and entity mapping valid',
+ 'relations': 'At least one source in_contact_object value present; positive targets resolve in the same frame; unknown is an explicit usable status',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta.update(view_type='ego', split_scheme='VISOR_GroundTruth_SparseAnnotations', split_source_locator=ann_loc)
+ view.meta['sampling']['selection'] = {'representation': 'official sparse annotations', 'max_frames': args.max_frames}
+ view.rgb(paths, rgb_loc)
+ entities = {}
+ polygons, mappings, relations, relation_valid = [], [], [], []
+ for f, (original_index, row) in enumerate(indexed):
+ frame_loc = ann_loc | {'record': f'video_annotations[{original_index}]'}
+ id_map, labels, polys = {}, [], []
+ for code, ann in enumerate(row['annotations'], 1):
+ if code >= 65535:
+ raise ValueError('too many source instances')
+ sid = ann['id']
+ if sid in id_map:
+ raise ValueError('duplicate source instance ID within a frame')
+ side = hand_side(ann)
+ if side is not None:
+ entity = 'hand_' + side
+ else:
+ key = keys(row, ann)
+ identity = key + (sid, row['image']['name']) if key in collisions else key
+ entity = 'object_' + hashlib.sha256(json.dumps(identity).encode()).hexdigest()[:20]
+ if entity not in entities:
+ scope = 'frame' if key in collisions else 'subsequence'
+ scope_id = f'{video}:{row["image"]["name"]}' if scope == 'frame' else f'{video}:{key[0]}'
+ view.object(entity, frame_loc | {'source_instance_id': sid}, scope, scope_id,
+ category_id=ann['class_id'], name=ann['name'],
+ association_basis='source_frame_instance' if scope == 'frame' else 'official_subsequence_entity_name')
+ entities[entity] = True
+ id_map[sid] = entity
+ for polygon in ann.get('segments', []):
+ p = np.asarray(polygon)
+ if p.ndim != 2 or p.shape[1] != 2 or len(p) < 3 or not np.isfinite(p).all():
+ raise ValueError(f'invalid source polygon {sid}')
+ polys.append(dict(mask_value=code, polygons=ann.get('segments', [])))
+ labels.append(dict(mask_value=code, category_id=ann['class_id'], entity_id=entity,
+ part_id=None, source_instance_id=sid,
+ source_attributes={k: v for k, v in ann.items() if k != 'segments'}))
+ rr = []
+ provided = False
+ for ann in row['annotations']:
+ raw = ann.get('in_contact_object')
+ values = raw if isinstance(raw, list) else [raw]
+ for value in values:
+ if value is None:
+ continue
+ provided = True
+ status = STATES.get(value)
+ obj = None
+ if status is None:
+ if value not in id_map:
+ raise ValueError(f'unresolved source contact target {value} in {video} frame {f}')
+ status, obj = 'positive', id_map[value]
+ rr.append(dict(relation_type='contact', subject_id=id_map[ann['id']], object_id=obj,
+ status=status, source_value=value, source_locator=frame_loc | {'source_instance_id': ann['id']},
+ provenance=source('Resolve source contact ID against instances in this frame')))
+ polygons.append(dict(frame_index=f, instances=polys))
+ mappings.append(dict(frame_index=f, labels=labels))
+ relations.append(dict(frame_index=f, relations=rr))
+ relation_valid.append(provided)
+ jsonl_write(view.path / 'annotation/segment/polygons.jsonl', polygons)
+ jsonl_write(view.path / 'annotation/segment/instances.jsonl', mappings)
+ torch.save(torch.ones(len(frames), dtype=torch.bool, device='cpu'), view.path / 'annotation/segment/mask_valid.pt')
+ view.component('segment', 'segment/instance_polygons/v1', 'annotation/segment/polygons.jsonl', ['T'], ann_loc,
+ valid_ref='annotation/segment/mask_valid.pt', valid_axes=['T'], segmentation_type='instance',
+ mapping_ref='annotation/segment/instances.jsonl', coverage={'kind': 'unknown', 'exhaustive_ref': 'annotation/segment/instances.jsonl'})
+ view.relations(relations, relation_valid, ann_loc)
+ for component in ('depth', 'mano', 'keypoints_uv', 'keypoints_xyz', 'intrinsic', 'extrinsic'):
+ view.absent(component, 'Not provided by VISOR sparse source annotations')
+ return view.finish()
+
+
+def main(argv=None):
+ p = parser('EPIC-KITCHENS-VISOR', __doc__)
+ p.add_argument('--videos', help='Comma-separated video names')
+ p.add_argument('--splits', default='train,val')
+ args = p.parse_args(argv)
+ wanted = set(args.videos.split(',')) if args.videos else None
+ splits = set(args.splits.split(','))
+ if not splits <= {'train', 'val'}:
+ p.error('only annotated train/val splits are available')
+ out = release(args, 'EPIC-KITCHENS-VISOR')
+ archive = find_file(args.root, 'EPIC-KITCHENS VISOR.zip')
+ with tempfile.TemporaryDirectory(prefix='visor-source-') as wd:
+ work = Path(wd)
+ if archive:
+ with zipfile.ZipFile(archive) as z:
+ suffix = 'GroundTruth-SparseAnnotations/annotations/'
+ members = [n for n in z.namelist() if suffix in n and n.endswith('.json')]
+ prefix = members[0].split(suffix)[0]
+ fm = json.loads(z.read(prefix + 'frame_mapping.json'))
+ done = 0
+ for member in sorted(members):
+ split, filename = member.split(suffix)[1].split('/')
+ video = Path(filename).stem
+ if split not in splits or wanted and video not in wanted:
+ continue
+ data = z.read(member)
+ rgb_member = prefix + f'GroundTruth-SparseAnnotations/rgb_frames/{split}/{video.split("_")[0]}/{video}.zip'
+ rgb_zip = work / (video + '.zip')
+ with z.open(rgb_member) as inp, rgb_zip.open('wb') as dest:
+ shutil.copyfileobj(inp, dest)
+ with sequence_dir(out, video) as dest:
+ report = convert_video(video, split, json.loads(data), locator(archive, member, data),
+ rgb_zip, locator(archive, rgb_member), fm.get(video, {}), args, dest / 'ego')
+ build_record(out, video, {'ego': report})
+ rgb_zip.unlink()
+ done += 1
+ print(f'Converted {video}', flush=True)
+ if args.limit and done >= args.limit:
+ break
+ else:
+ root = next((r for r in roots(args.root) if (r / 'GroundTruth-SparseAnnotations').is_dir()), None)
+ if root is None:
+ p.error('VISOR outer ZIP or extracted source directory not found')
+ fm = json.loads((root / 'frame_mapping.json').read_text()) if (root / 'frame_mapping.json').exists() else {}
+ done = 0
+ for split in sorted(splits):
+ for ann in sorted((root / 'GroundTruth-SparseAnnotations/annotations' / split).glob('*.json')):
+ video = ann.stem
+ if wanted and video not in wanted:
+ continue
+ rgb = root / f'GroundTruth-SparseAnnotations/rgb_frames/{split}/{video.split("_")[0]}/{video}.zip'
+ with sequence_dir(out, video) as dest:
+ report = convert_video(video, split, json.loads(ann.read_text()), locator(ann), rgb,
+ locator(rgb), fm.get(video, {}), args, dest / 'ego')
+ build_record(out, video, {'ego': report})
+ done += 1
+ if args.limit and done >= args.limit:
+ return 0
+ if not done:
+ p.error('no matching annotated videos found')
+ return 0
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
+
+# TODO(v2.0): 旧距离猜侧和单层掩码生成仅注释保留；v1.0 不执行。
+# if side is None and len(frame_hands) == 1:
+# # 空间门槛：脱下放在远处的手套不并入（实测双峰 0..25px / ≥44px）
+# hb = frame_hands[0][1]
+# gb = _bbox_of(polys)
+# if hb and gb and _bbox_dist(gb, hb) <= GLOVE60_MERGE_BBOX_DIST:
+# side = frame_hands[0][0]
+# stats["glove60_merged_into_sole_hand"] += 1
+# if side is None:
+# stats["dropped_glove60_unresolved"] += 1
+# id_disp[mid] = ("dropped", "glove60")
+# continue
+# def rasterize_and_encode(vdir, frames, frame_draw, zip_path, n, stats):
+# """流式读 zip 内 jpg + 光栅化多边形 → rgb.mkv / mask.mkv；返回 (w, h)。"""
+# from PIL import Image, ImageDraw
+#
+# with zipfile.ZipFile(zip_path) as zf:
+# member_of = {}
+# for nm in zf.namelist():
+# member_of[os.path.basename(nm)] = nm
+# names = [im["name"] for im in frames]
+# missing = [nm for nm in names if nm not in member_of]
+# if missing:
+# raise RuntimeError(f"{len(missing)} annotated frames missing in zip, "
+# f"e.g. {missing[:3]}")
+# stats["zip_extra_members"] = len(member_of) - len(names)
+#
+# # 首帧定分辨率（官方 1920×1080，实测每帧校验）
+# img0 = Image.open(io.BytesIO(zf.read(member_of[names[0]]))).convert("RGB")
+# w, h = img0.size
+#
+# bin_img = Image.new("L", (w, h), 0)
+# bin_draw = ImageDraw.Draw(bin_img)
+# canvas = np.zeros((h, w), np.uint16)
+# with VideoPipe(vdir / "mask.mkv", w, h, FPS, "mask") as mw, \
+# VideoPipe(vdir / "rgb.mkv", w, h, FPS, "rgb") as rw:
+# for f in range(n):
+# canvas[:] = 0
+# for pid, polys in frame_draw[f]:
+# bin_draw.rectangle((0, 0, w, h), fill=0)
+# for poly in polys:
+# bin_draw.polygon([(float(x), float(y)) for x, y in poly], fill=255)
+# arr = np.asarray(bin_img)
+# canvas[arr > 0] = pid # 后画覆盖先画：物体→手（modal）
+# mw.write(canvas)
+# img = img0 if f == 0 else Image.open(
+# io.BytesIO(zf.read(member_of[names[f]]))).convert("RGB")
+# if img.size != (w, h):
+# raise RuntimeError(f"frame {names[f]}: size {img.size} != {(w, h)}")
+# rw.write(np.asarray(img))
+# n_mask, n_rgb = mw.n, rw.n
+# if (n_rgb, n_mask) != (n, n):
+# raise RuntimeError(f"wrote rgb={n_rgb} mask={n_mask} != {n}")
+# got = (count_frames(vdir / "rgb.mkv"), count_frames(vdir / "mask.mkv"))
+# if got != (n, n):
+# raise RuntimeError(f"encoded rgb={got[0]} mask={got[1]} != {n} (decode verify)")
+# return w, h
+#
+#
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/operations/prepare-w4-c10.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/operations/prepare-w4-c10.py
new file mode 100644
index 0000000000000000000000000000000000000000..a96df6f5773da6c009993e00cd413e1cdd305462
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/operations/prepare-w4-c10.py
@@ -0,0 +1,52 @@
+"""Preserve completed r1 sequences without re-encoding or touching source files."""
+import datetime
+import json
+import os
+from pathlib import Path
+import shutil
+
+old = Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260929-r1')
+new = Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10')
+snapshot = Path('/tmp/h2o-w4-c10-recovery')
+new.mkdir()
+shutil.copytree(snapshot / 'scripts', new / 'frozen-code/scripts')
+shutil.copytree(snapshot / 'docs', new / 'frozen-code/docs')
+shutil.copytree(snapshot / 'tests', new / 'frozen-code/tests')
+release = new / 'H2O/shared-v1.0'
+for directory in ('data', 'doc', 'script', 'script/provenance/r1', 'script/provenance/operations'):
+ (release / directory).mkdir(parents=True, exist_ok=True)
+history = release / 'script/provenance/r1'
+shutil.copytree(old / 'H2O/shared-v1.0/script', history / 'script')
+shutil.copytree(old / 'frozen-code/docs', history / 'docs')
+for name in ('full.log', 'full.status', 'run-full.sh'):
+ shutil.copy2(old / name, history / name)
+records = []
+for record in sorted((old / 'H2O/shared-v1.0/doc').glob('*.build.json')):
+ build = json.loads(record.read_text())
+ seq = build['sequence_id']
+ assert Path(seq).name == seq and seq not in ('.', '..')
+ assert set(build['views']) == {f'cam{i}' for i in range(5)}, seq
+ data = old / 'H2O/shared-v1.0/data' / seq
+ frames = {}
+ for cam in build['views']:
+ meta = json.loads((data / cam / 'metainfo.json').read_text())
+ frames[cam] = sum(1 for _ in (data / cam / 'camera/frames.jsonl').open())
+ assert frames[cam] > 0
+ shutil.copytree(data, release / 'data' / seq, copy_function=os.link)
+ shutil.copy2(record, release / 'doc' / record.name)
+ records.append({'sequence_id': seq, 'frames_by_camera': frames,
+ 'original_script_sha256': build['scripts']})
+manifest = {'time': datetime.datetime.now().astimezone().isoformat(),
+ 'source_run': str(old), 'destination_run': str(new),
+ 'reason': 'User requested four archive workers, ten threads per encoder, no CPU quota',
+ 'reuse': 'Hard-linked immutable completed views; frames and component source SHA256 rechecked during resume',
+ 'excluded': 'Unpublished .tmp directories; historical 62-sequence output; failed initial full run',
+ 'original_runtime_provenance': 'script/provenance/r1', 'sequences': records}
+(release / 'doc/RESTART_PROVENANCE.json').write_text(json.dumps(manifest, indent=2) + '\n')
+for source, target in (('/tmp/h2o-prepare-w4-c10.py', 'provenance/operations/prepare-w4-c10.py'),
+ ('/tmp/h2o-reconfigure-w4-c10.sh', 'reconfigure-w4-c10.sh'),
+ ('/tmp/h2o-run-w4-c10.sh', 'run-full.sh')):
+ shutil.copy2(source, release / 'script' / target)
+shutil.copy2('/tmp/h2o-run-w4-c10.sh', new / 'run-full.sh')
+shutil.copy2('/tmp/h2o-w4-c10-frozen-tests.log', history.parent / 'worker4-codec10-regression.log')
+print(f'REUSED_SEQUENCES={len(records)} REUSED_VIEWS={sum(len(r["frames_by_camera"]) for r in records)}', flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/operations/retry-w4-c10.sh b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/operations/retry-w4-c10.sh
new file mode 100644
index 0000000000000000000000000000000000000000..983a246b66440da976c7b929df289b0ec6427973
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/operations/retry-w4-c10.sh
@@ -0,0 +1,11 @@
+#!/usr/bin/env bash
+set -euo pipefail
+base=/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10
+script="$base/H2O/shared-v1.0/script"
+mkdir -p "$script/provenance/operations" "$script/provenance/startup-failure"
+mv "$script/prepare-w4-c10.py" "$script/provenance/startup-failure/prepare-w4-c10.py"
+cp /tmp/h2o-prepare-w4-c10.py "$script/provenance/operations/prepare-w4-c10.py"
+mv "$base/full.status" "$script/provenance/startup-failure/full.status"
+mv "$base/full.log" "$script/provenance/startup-failure/full.log"
+cp /tmp/h2o-retry-w4-c10.sh "$script/provenance/operations/retry-w4-c10.sh"
+systemd-run --user --scope --unit=h2o-full-w4c10-r2-20260930 -p CPUAccounting=yes /bin/bash "$base/run-full.sh"
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/docs/CONTRACT.md b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/docs/CONTRACT.md
new file mode 100644
index 0000000000000000000000000000000000000000..f756d711647e109b6bb732ce1b9c84111730489d
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/docs/CONTRACT.md
@@ -0,0 +1,457 @@
+# 手物数据仓库统一约定 v1.0
+
+格式版本：`hand-warehouse/1.0`。存储约定：`lossless-video-2026-09-18`。
+
+范围限于手物交互重建。按本任务纳入的源模态转换，不要求每个数据集填满所有组件。缺失模态、尚未下载、语义未核实分别报告，不能互相替代。
+
+本文定义各字段的含义与使用规则，机器可读注册表见 [schema.json](schema.json)。两者互相补充；如有冲突，应先核实并统一定义。
+
+## 1. 目录
+
+```text
+hand-datasets/
+ /
+ raw/
+ doc/ # 官方说明、来源与校验记录
+ script/ # 官方下载脚本、必要包装脚本
+ data/ # 保留原始包，不原地解压或改写
+ /
+ doc/ # 发布说明、schema、来源清单
+ script/ # 转换、校验脚本及运行配置
+ data/
+ /
+ /
+ metainfo.json
+ observation/
+ rgb.mkv
+ rgb_valid.pt
+ depth.mkv # float32 深度的可逆视频编码
+ depth_valid.mkv # 逐像素 bool 的无损视频编码
+ camera/
+ frames.jsonl
+ intrinsic.pt
+ intrinsic_valid.pt
+ extrinsic.pt
+ extrinsic_valid.pt
+ annotation/
+ mano/
+ motion.pt
+ motion_valid.pt
+ visibility.pt
+ keypoints/
+ uv.pt
+ uv_valid.pt
+ xyz.pt
+ xyz_valid.pt
+ visibility.pt # 源数据有时提供
+ object/
+ /
+ assets/ # 该物体的网格、材质、纹理及部件资产
+ .obj
+ pose.pt
+ pose_valid.pt
+ visibility.pt
+ metainfo.json # 物体身份、类别、mesh、尺寸与部件引用
+ articulation.json
+ keypoints/
+ vertex_part_ids.pt
+ joint_state.pt
+ joint_state_valid.pt
+ parts//pose.pt
+ parts//pose_valid.pt
+ segment/
+ mask.mkv
+ mask_valid.pt
+ instances.jsonl
+ contact/
+ relations.jsonl
+ __/ # 对应实体间的距离/接触区域
+```
+
+目录展示可能存在的组件，不要求填满。具体文件、字段和缺省规则分别见后续对应小节。原始包的解压发生在临时工作区，raw 内原始包保持不变。
+
+## 2. 相机视图的 metainfo.json
+
+### 2.1 顶层字段与发布信息
+
+每个相机视图必须有 `metainfo.json`。以下字段除另行说明外均为必填：
+
+| 字段 | 填写内容 |
+|---|---|
+| schema_version | 结构版本，本版为 `hand-warehouse/1.0` |
+| dataset_id | 数据集的逻辑标识 |
+| release_id | 本次数据发布版本，对应目录中的 dataset_version |
+| sequence_id | 所属序列或独立图片样本的标识 |
+| camera_id | 当前相机视图标识 |
+| sample_type | `image` 或 `video`，规则见 2.2 |
+| num_frames | 当前视图帧数 T，所有随帧组件按同一时间轴组织 |
+| hand_slots | 固定为 `["left", "right"]`；解剖学左右的使用规则见 5.1 |
+| sampling | 采样类型、源 FPS、帧索引引用及选择策略，见 2.2 |
+| camera | 当前相机模型与图像尺寸，见 2.3 |
+| components | 实际提供的组件及其文件、schema、有效性和来源，见 2.5 |
+| split | `train/val/test/unknown`，划分协议见 2.4 |
+| view_type | 可选，`ego/exo/unknown` |
+
+`dataset_version` 是数据发布版本；`schema_version` 是结构版本，两者独立。原始包校验摘要、官方来源和转换配置应记录在发布清单中。解压发生在临时工作区，原始包保持不变。
+
+### 2.2 图片、视频与 sampling
+
+`sample_type=image`：允许单图 T=1、`sampling.type=single`，也允许将独立图片批量装入一个视频容器，T>1 时 `sampling.type=independent`、`source_fps=null`。分片大小按数据集选择；FreiHAND 默认每片最多 4096 张，训练与评估分开，尾片不补帧。该数量不是其他数据集的强制要求；同批分辨率、相机模型类型及划分协议一致。每行 frames 必须有稳定且唯一的 `sample_id`、原始文件定位；内参、手形状等仍逐帧保留，不能因打包而共享错误的标定或身份。`sequence_id` 在这里仅是批次标识，不代表真实序列；不得按相邻帧推断跟踪、同步或接触延续。源 camera_id、image_variant、split 等随样本变化时放在 frames 的对应字段中。真实采集时间未知则 timestamp_ns=null。合成背景变体不视为真实多相机。
+
+`sample_type=video`：`sampling.type=dense/sparse`；`source_fps` 是真实采集 FPS，未知为 null。非连续取样必须保留源帧号和已知源时间，不可用采样后索引除以 FPS 冒充源时间。可另记采样策略与步长。
+
+shared 可以仅存所选 RGB 帧对应的数据。OakInk2 图像/动捕不同采样率时用 annotation_matches 明确映射；不要求将未采样动捕点硬塞进 T。多视角身份和源同步关系要保留；选择了哪些帧/相机及原因写在 sampling 中。
+
+`sampling.frames_ref` 指向 `camera/frames.jsonl`。帧号、逐帧时间、同步与标注匹配记录的结构见 4.1；MKV 封装时间的约定见 3.1。
+
+### 2.3 camera 模型声明
+
+`camera` 至少包含 `model`、`schema_id`、`image_size:[W,H]`。模型未知使用 `unknown` 和 `null`，不猜成针孔。实际内参、畸变及外参数组放在 `camera/`，各模型的固定结构见第 4 节。
+
+### 2.4 split 与划分协议
+
+保留上游划分，不将同一采集序列的相邻帧随意拆到不同集合。
+
+有官方划分时必填 split_scheme 和 split_source_locator，split_scheme 区分如 Dex-YCB s0/s1、ARCTIC P1/P2 等选定协议；不知道时 split=unknown 并给理由，不凭空分配。每发布可选择一种协议；存在逐帧差异时 frames 中的 split 覆盖视图默认值，并登记其来源。
+
+### 2.5 components 组件登记
+
+组件表的键是本发布内唯一名称，例如 `rgb`、`depth`、`mano`、`keypoints_uv`。每个已提供组件声明以下信息；模态特有参数在第 3–5 节对应小节中定义。
+
+| 字段 | 约定 |
+|---|---|
+| available | 本发布是否提供该组件；缺省也表示未提供 |
+| schema_id | 已登记的具体存储与语义类型，不得临时创造未登记名称 |
+| data_ref | 数据文件路径，相对当前相机视图根目录，使用 POSIX 路径 |
+| axes | 数组各维度的含义 |
+| valid_ref / valid_axes | 有效性文件及其维度；语义由所属组件规定 |
+| storage / valid_storage | 视频数据／视频有效性的存储配置；包含已登记 profile、实际 codec、pixel_format、encoded_size，见 3.1；非视频组件按原数组规则 |
+| provenance | source / format_conversion / derived，以及转换或生成方法 |
+| source_locator | 原文件、压缩包成员和记录／帧的定位信息 |
+
+不存在的组件允许缺省或写作 `{ "available": false, "reason": "..." }`，不用引用不存在的文件。静态网格可声明标量有效性；具体见 5.4。观测的 valid 见第 3 节，标注的 valid 与 visibility 区分见 5.1。
+
+`provenance.kind` 使用 source/format_conversion/derived，`method` 说明转换或生成方法；发布清单补充脚本版本、参数、输入摘要。`source_locator` 定位到原始文件/压缩包成员和记录、帧号；允许引用有完整溯源清单的前一发布，但链条最终应能回到 raw。不得仅写一个无法定位记录的数据集名字。
+
+`.pt` 仅保存 CPU Tensor，不保存 Python 自定义对象；浮点默认 float32，有效性为 bool，浮点无效位置填 NaN。所有随帧数组保留首维 T，单帧也不 squeeze；静态资产按其 schema 单独声明。单位、坐标及固定形状见各组件小节。
+
+机器注册表 `schema.json` 中，`components` 固定解码后的数据类型、形状、单位和语义；`storage_profiles` 固定视频内的编码／还原方式。不得临时创造未登记的 schema_id 或 storage.profile。同一语义允许选择其登记的不同无损存储 profile。
+
+## 3. observation 观测数据
+
+### 3.1 rgb.mkv 与 rgb_valid.pt
+
+RGB、深度和栅格 mask 在 shared 中统一使用 MKV 视频容器，不展开为逐帧图片或图像 tar。MANO、关键点、位姿等数值标注仍用数组；分割的重叠多边形仍用 JSONL，不为了视频化而有损栅格化。单图可为一帧视频，独立图批量封装规则见 2.2。
+
+**无损指共享层不增加信息损失。** 源 JPEG／H.264 等本身可能已是有损编码，原样保留并不恢复已丢失信息；不得重新以有损 JPEG、H.264、H.265、AV1 参数编码。源视频／图片的色彩、位深、分辨率和几何关系不得被静默改变。RGB schema `rgb/uint8/v1` 的统一读取结果是 `[T,H,W,3] uint8`、RGB 通道，有效性仍为 `rgb_valid.pt [T] bool`。超出该 schema 的高位深或 HDR 源必须报告并扩展定义，不能降到 8 位冒充无损。
+
+#### 3.1.1 允许的 RGB 存储 profile
+
+| storage.profile | 编码方式 | 适用规则 |
+|---|---|---|
+| mkv/mjpeg_copy/v1 | 源 JPEG 压缩帧直接封装成 MJPEG/MKV | 不解码重编码；保持每张 JPEG 码流字节；普通 8 位 JPEG 且 MKV/解码器支持时使用 |
+| mkv/source_copy/v1 | 已有视频的 RGB 对应码流直接封装为 MKV | 仅源编码能被 MKV 和选定解码器支持时使用，不伪造帧；需维持正确的呈现顺序与依赖帧 |
+| mkv/h264_rgb_lossless/v1 | libx264rgb 的真正无损模式 | 必须 RGB 路径、CRF=0，并通过全帧 RGB 字节比较；不能替换成默认 libx264 YUV420 路径 |
+| mkv/ffv1_rgb/v1 | FFV1 的 RGB 可逆编码路径 | 作为通用候选；解码回 RGB 后必须逐字节相同，不做色度降采样 |
+
+优先尝试源码流直接封装；不能封装或需要选择稀疏帧时，比较 FFV1 与无损 RGB H.264。对完整待发布片段，只在通过无损验收的候选中选择文件较小者，不假设某个编码器总最小；编码时间和随机读取代价记入构建记录。中间候选不作为 shared 多份常驻。H.264 跨帧压缩允许较长 GOP，必须记录 GOP／关键帧间隔，随机取帧需从前一关键帧正确解码，不能把 seek 返回的近似帧当目标帧。
+
+普通源视频可以保留它已有的有损码流，但不允许新增有损重编码。源是 VRS 等记录容器时，先查明图像流实际编码：已有兼容 JPEG／视频包可直接提取封装，否则解码后使用真正无损编码；不把整个 VRS 改后缀当 MKV。PNG 不要求直接塞进 MKV，可选上表两种无损重编码方式。
+
+#### 3.1.2 视频配置与校验
+
+每个视频组件在 metainfo 中登记 `storage`，至少包含：
+
+| 字段 | 内容 |
+|---|---|
+| profile | 上述登记的存储 profile；语义仍由组件 schema_id 指定 |
+| codec | 文件中实际 codec，例如 mjpeg、h264、ffv1；source_copy 必须写实际 codec |
+| pixel_format | 实际编码像素格式；不得仅凭编码器参数猜测 |
+| encoded_size | 视频物理像素尺寸 `[width,height]`；深度可能不同于逻辑尺寸，见 3.2 |
+| decoder | 解码器名称、版本，以及颜色矩阵／范围／旋转处理等实际像素解释配置；未知的源色彩声明 unknown，不擅自称为 sRGB |
+| encoder_parameters | 重编码时的编码器、版本、无损模式和 GOP 等参数；直接封装时记录 stream_copy |
+| source_lossy | 源是否已有损：true / false / unknown；不是共享层再有损编码的许可 |
+
+无损重新编码的 RGB 必须使用同一固定解码配置，比较**全部帧**的统一 RGB 字节、数量及顺序。MJPEG-copy 不重新编码，生产校验采用 `jpeg-packets-all-rgb-sample16/v1`：从 MKV 逐包取回，对全部源 JPEG 的 SHA256、数量、顺序和图像尺寸进行精确核对；每视图另用同一固定解码配置比较最多 16 帧的源与目标 RGB 字节（含首尾与四分位位置，其余由固定种子抽取）。记录策略、实际索引、种子与结果；不足 16 帧时全查。试点与最终完整序列随机抽查，对选中视图的全部帧复核解码 RGB。历史已做全帧像素检查的证明继续保留。
+
+逐包一致与像素抽查的范围必须分别声明，不能将抽查写成全量像素检查。不能用 PSNR 高、视觉相似、编码器参数称为 lossless 等替代精确检查。同时保留源编码的色彩标记和必要元数据；原图有 EXIF/色彩/方向等元数据时保留 raw 和必要来源信息；不能静默自动旋转图像而不更新像素对应与标定。以上加速策略仅适用于原 JPEG 码流直接封装，不适用于深度、标签或重新编码的 RGB。
+
+所有共享视频的呈现帧顺序与统一 frame_index 一一对应，每个栅格组件恰好 T 帧。MKV 的 `storage_fps` 只服务封装，声明 `timebase_role=index_only`；source_copy 可以保留源 PTS 以避免改坏码流，但真实时间与标注对齐仍以 frames 为准。未知真实时间保留 null。缺失某帧组件时保留索引位置并使用该组件的无效占位／valid 标识，不删帧；不能以重复相邻图像冒充已知观测。
+
+### 3.2 depth.mkv 与 depth_valid.mkv
+
+深度语义仍是 `depth/z_float32/v1`：统一读取结果 `[T,H,W] float32`，米制相机 Z 分量，`depth_type=z`。有效深度有限且大于 0，无效位置为 NaN。`depth_origin=measured/rendered/unknown` 必填，来源不明时 unknown。默认与 RGB 共用像素网格及相机参考系，不处理未注册 RGB-D。射线距离必须依据源定义转换成 z，不能只改声明。
+
+#### 3.2.1 float32 的可逆视频编码
+
+采用 `mkv/depth_float32_bits/v1`：**不是把深度量化成 uint16**，而是将规范化后每个 IEEE-754 float32 的完整 32 位拆成两个 uint16，使用 FFV1 gray16le 保存。视频物理尺寸为 `[2W,H]`：每行前 W 列存原 float32 位串的低 16 位，后 W 列存高 16 位。读取时先合并为 uint32 位串，再按 float32 重新解释，不能数值 cast 成 float32。
+
+```text
+u = reinterpret_uint32(depth_float32)
+encoded[:, :, :W] = uint16(u & 65535)
+encoded[:, :, W:] = uint16(u >> 16)
+u_restored = uint32(low) | (uint32(high) << 16)
+depth_float32 = reinterpret_float32(u_restored)
+```
+
+统一采用 little-endian 原始字节接口。`camera.image_size` 始终是逻辑 RGB 尺寸 `[W,H]`，不能改成编码视频的 2W；`storage.encoded_size=[2W,H]`。包括 NaN 在内的 float32 位串必须全帧完全恢复，不允许改成 float16、整数毫米、归一化灰度或 RGB 伪彩色。这里的无损验收基准是完成既定米制／坐标转换后的标准 float32 数组，不声称消除 float64→float32 等原约定转换本身的舍入。
+
+#### 3.2.2 逐像素有效性
+
+`depth_valid.mkv` 使用 `mkv/bool_ffv1/v1`，FFV1 gray8（FFmpeg pixel_format=`gray`），物理尺寸 `[W,H]`。像素只能为 0 或 1，统一解码为 `[T,H,W] bool`，逐像素含义不变。这样不再把庞大的 bool 数组保存为未压缩 `.pt`；标注的帧级／手级 valid 仍保留原有小数组。
+
+登记示例（W、H 以下取 640、480；decoder/encoder_parameters 的实际版本由转换器填写）：
+
+```json
+{
+ "available": true,
+ "schema_id": "depth/z_float32/v1",
+ "data_ref": "observation/depth.mkv",
+ "axes": ["T", "height", "width"],
+ "valid_ref": "observation/depth_valid.mkv",
+ "valid_axes": ["T", "height", "width"],
+ "unit": "m",
+ "depth_type": "z",
+ "depth_origin": "measured",
+ "invalid_value": "NaN",
+ "storage": {
+ "profile": "mkv/depth_float32_bits/v1",
+ "codec": "ffv1",
+ "pixel_format": "gray16le",
+ "encoded_size": [1280, 480],
+ "source_lossy": false,
+ "decoder": {"name": "ffmpeg", "version": "填写实际版本"},
+ "encoder_parameters": {"name": "ffv1", "level": 3, "version": "填写实际版本"}
+ },
+ "valid_storage": {
+ "profile": "mkv/bool_ffv1/v1",
+ "codec": "ffv1",
+ "pixel_format": "gray",
+ "encoded_size": [640, 480],
+ "source_lossy": false,
+ "decoder": {"name": "ffmpeg", "version": "填写实际版本"},
+ "encoder_parameters": {"name": "ffv1", "version": "填写实际版本"}
+ },
+ "provenance": {"kind": "format_conversion", "method": "源深度转米制 z float32；逐位打包为 FFV1"},
+ "source_locator": {"file": "raw/data/depth.zip", "member": "depth/0001.png"}
+}
+```
+
+深度和有效性视频都必须逐帧解码验收：float32 位串相同、bool 相同、T 相同，并检查 valid=true 的正有限值和 valid=false 的 NaN。读取器必须按 profile 还原，不能把 depth.mkv 的编码像素直接当深度值。
+
+## 4. camera 相机、帧索引与标定
+
+### 4.1 frames.jsonl：索引、时间与对应关系
+
+`camera/frames.jsonl` 每行对应统一帧号 `frame_index=0..T-1`，是所有组件的唯一行索引。至少包含 `frame_index`、`timestamp_ns`、`clock_domain`、`source_locator`；原始帧号/文件名保存在 `source_frame_id`，不能被新帧号替代。每行还可有 `annotation_matches`，按组件键保存 `source_frame_id`、`source_timestamp_ns`、`clock_domain`、`match_method` 和 `delta_ns`；方法为 exact/index_map/nearest/interpolated，索引映射无时间依据时 delta_ns=null。nearest 必须记录允许误差；interpolated 必须记录两端源标识和权重。delta_ns 仅在同一时间域中计算为源标注时间减图像时间。多相机共享 `sequence_id`；已知同步关系必须用 `sync_id` 或明确的源帧/时间映射表达，不能仅凭相同数组索引认定同步。
+
+每个视图必须提供此文件。源时间使用整数纳秒及 `clock_domain`，未知写 JSON `null`；不同时间域不能直接比较。逐帧 split 可以覆盖 metainfo 的视图默认划分，并保留来源，见 2.4。
+
+### 4.2 坐标系与标定通则
+
+| 项目 | 统一约定 |
+|---|---|
+| 相机坐标 | 右手系，X 向右、Y 向下、Z 向前，长度单位米 |
+| 齐次变换 | 列向量，`p_target = T_target_from_source @ p_source` |
+| 图像坐标 | `(u,v)`；左上像素中心为 `(0,0)`，u 向右、v 向下，单位 pixel |
+| 固定标定 | 本版展开为 T 份，同一 schema 不混用静态与动态形状 |
+| 分辨率或模型变化 | 分开存储为视图／片段，不在同一组件中混装 |
+| 未知标定 | 相机内外参允许缺省，不填单位阵冒充已知标定 |
+
+### 4.3 intrinsic.pt 与畸变参数
+
+| model / schema_id | intrinsic.pt 的固定结构 |
+|---|---|
+| pinhole / camera/pinhole/v1 | `[T,3,3]`，标准 K，最后一行为 `[0,0,1]`；限无畸变或已校正图像 |
+| fisheye624 / camera/fisheye624/v1 | `[T,15]`，`[f,cx,cy,k0,k1,k2,k3,k4,k5,p0,p1,s0,s1,s2,s3]`，采用 Project Aria Fisheye624 投影定义 |
+| opencv_fisheye4 / camera/opencv_fisheye4/v1 | `intrinsic.pt [T,3,3]` 保存 K；同组件 `distortion_ref` 指向 `[T,4]`，顺序 `[k1,k2,k3,k4]`，使用 OpenCV fisheye 等距角度多项式；已由 ReInterHand 实际原始相机参数验证 |
+| rational8 / camera/rational8/v1 | `intrinsic.pt [T,3,3]` 保存 K；同组件必需 `distortion_ref` 指向 `[T,8]`，顺序 `[k1,k2,p1,p2,k3,k4,k5,k6]` |
+| unknown / null | 不提供可用 intrinsic，不声称可以投影 |
+
+有畸变的其他模型必须注册新 schema，固定参数名、顺序和投影公式；不能塞进针孔 K 后遗失畸变项。`metainfo.camera.schema_id` 必须与 intrinsic 组件一致。同一模型的别名不可隐式改变参数布局。
+
+`opencv_fisheye4`：对前方点令 `x=X/Z, y=Y/Z, r=sqrt(x²+y²), theta=atan(r)`，`theta_d=theta*(1+k1*theta²+k2*theta⁴+k3*theta⁶+k4*theta⁸)`；`u=fx*x*theta_d/r+cx, v=fy*y*theta_d/r+cy`。光轴 r=0 时使用连续极限 `theta_d/r=1`。四系数不能按普通径向畸变解释，也不能补零冒充 Fisheye624。
+
+rational8 对归一化坐标 x=X/Z、y=Y/Z，令 r²=x²+y²，径向项为 `(1+k1*r²+k2*r⁴+k3*r⁶)/(1+k4*r²+k5*r⁴+k6*r⁶)`；再加 p1/p2 切向项并经 K 得到像素。`distortion_ref` 与 intrinsic 共用 `[T]` valid，保存到 camera/distortion.pt。不得把这种畸变三维坐标当作真实相机系 xyz 保存。
+
+内参有效性为 `intrinsic_valid.pt [T] bool`；有畸变时使用对应 `distortion_ref`。同一模型的参数名和顺序固定。
+
+### 4.4 extrinsic.pt 与 extrinsic_valid.pt
+
+`extrinsic.pt` 为 `[T,4,4]`，schema `transform/se3/v1`，方向固定 `T_camera_from_world`，有效性 `[T]`。世界坐标含义及源坐标转换记录在 provenance 中；只有相机坐标标注不要求虚构世界坐标。旋转块须正交且 determinant≈1。
+
+## 5. annotation 标注数据
+
+### 5.1 标注通则：手槽、valid 与 visibility
+
+手槽固定为 0=left、1=right，指解剖学左右，不按画面位置或可见性重排。本阶段只处理左右手两槽，不处理更多手。实体引用使用 `hand_left`、`hand_right`；手槽不证明跨采集片段的个人身份。
+
+#### 5.1.1 valid：标注是否可用于监督
+
+标注的 `valid=true` 表示：有对应实际标注、通过本 schema 数值检查，且通过来源明确提供并适用于该组件的有效性/QA 条件。`valid=false` 可由缺标注、无法建立帧对应、源无效占位或官方 QA 不通过导致；不等于画面没有手或手不可见。各组件独立判断，MANO 按 `[T,2]` 标记左右手，关键点按 `[T,2,K]` 标记。
+
+**2026-09-20 将标注 valid 从“仅表示存在”修订为“可用于监督”，字段形状不变。** 公式为 `source_annotation_present AND numerical_checks_pass AND applicable_official_QA_pass`。没有官方 QA 时省略最后一项，不伪造 QA；不能把未知当成已通过已存在的 QA。适配器在组件 `valid_policy` 中记录具体规则、来源、聚合/广播作用域。缺失官方必需 QA 的记录须明确处理为未知或暂停验收，不能静默通过。无效浮点项仍填 NaN，原始数据保留在 raw；被排除原值可用于 doc 中明确标识的诊断渲染，不能冒充 shared 有效监督。
+
+本地筛选须依据明确的规则，并在 `valid_policy.local_excluded_frame_indices`、排除原因和正式报告中记录。单个可疑投影作为问题记录，不直接硬编码排除该帧。已确认的曝光异常须明确作用范围，不能把一个序列的“前两帧有问题”套到所有序列。RGB 编码完好与几何监督可用是两件事，允许保留原图及帧索引而屏蔽对应标注；未发现问题不等于完成全部人工审核，试点待确认时不能作为已验收发布使用。
+
+源字段必须按官方语义映射，不能直接按名字复制：
+
+| 来源 | 官方含义 | 映射原则 |
+|---|---|---|
+| [H2O](https://github.com/taeinkwon/h2odataset) 的手标注首位 | 0 未标注、1 已标注 | 可作为对应手标注存在性的依据，不推断未标注原因 |
+| [HOT3D](https://github.com/facebookresearch/hot3d#using-mask-files) 的 pose_available、visible、mask_qa_pass | 分别表达姿态存在、可见性、对画面中可见手物位姿的人工 QA | 逐手/物体记录决定存在性，再 AND 当前相机当前帧 QA；QA 是整帧聚合判定，保守排除该帧全部手物几何监督，不声称每个实体均有错误；源可见性基于位姿推导时，其自身 valid 同样受 QA 约束 |
+| [Dex-YCB](https://github.com/NVlabs/dex-ycb-toolkit#loading-dataset-and-visualizing-samples) 的全零 pose_m、全 -1 joints | 不可见的手或缺标注共用占位表示 | 对应组件 valid=false；不能从该值判定 visibility=false，也不能声称原因是动捕缺失 |
+| [ARCTIC](https://github.com/zc-alexfan/arctic/blob/master/docs/data/data_doc.md) 的 is_valid/left_valid/right_valid | 是否有效用于官方评估 | 核实每项筛选依据；不把可见性/评估选择直接等同 QA。本次原始 mano.npy 不含这些派生评估标记，按原记录与数值检查生成 valid，并声明没有额外官方 QA 输入 |
+
+源 valid=false 若代表评估筛选或其他条件，应检查实际标注和源约定后映射；不能一律清空坐标。反之，占位零或 -1 只有在源官方明确规定时才判为缺失，不泛化成所有数据的判断规则。
+
+`valid=true` 的标注应通过其 schema 的数值与形状检查；检查失败必须报告或修复，不能静默隐藏。QA 不通过应报告排除数量与原因，并保留可追溯信息；不能把这类无效称为缺标注。RGB、深度等观测有效性仍按各自 schema；手物 QA 不自动否定 RGB、静态相机内参或物体资产。相机外参是否过滤须依据其自身来源规则；HOT3D 本次保留源外参用于溯源和诊断，不能用其存在性证明 QA 失败帧的几何监督正确。
+
+#### 5.1.2 visibility：可见性属性
+
+附加质量属性只保留 `visibility`，存放在对应模态或标注集内，不设 quality 目录。同一份可见性仅存一次；uv 与 xyz 共用时引用同一个文件。
+
+visibility 通过所属组件 attributes 登记 data_ref、axes、dtype、visibility_type、meaning、provenance、source_locator；未知值通过属性 valid_ref 标识，全已知可声明 valid=true。比例与布尔值不相互强转，也不按任意阈值二值化。没有 MANO 时，整手可见性挂在 keypoints 组件，使用独立的 whole_hand_visibility.pt，明确 meaning=whole_hand，不与逐关键点可见性混用。
+
+`valid` 回答“这项标注能否用于监督”，`visibility` 回答“在当前图像中是否可见（或可见多少）”。不可见的手或关键点仍可有有效标注；不能因 visibility=0 就清空坐标。源没有 visibility 时缺省或标为未知，不由 valid 推断。
+
+| 情况 | 手标注 valid | visibility |
+|---|---|---|
+| RGB 已开始录制，动捕尚未开始，没有其他对应手标注 | false | 以独立源可见性标注为准；没有则未知 |
+| 手被遮挡，但源有标注且通过适用有效性检查 | true | 若源声明不可见，则 false 或源可见比例 |
+| 手可见且有对应标注，通过适用 QA | true | 若源有此信息，则 true 或源可见比例 |
+| 手可见，但该帧漏标或动捕中断 | false | 可以为 true，但必须有独立来源依据 |
+| 有手部参数，但官方 QA 明确不通过 | false | 不据此断言不可见；源可见比例若基于不可靠位姿，其属性 valid 也为 false |
+
+visibility 自己的 valid_ref 表示该可见性标注是否可用，不复用 MANO 或关键点坐标的 valid_ref；明确记录源存在性及适用的 QA 条件。上述例子不要求凭图像自动生成可见性。
+
+不设 quality 组件或目录，不另存拟合误差、曝光、截断比例等通用质量数组。适用的官方 QA 合并进对应 valid，在 `valid_policy` 记录来源与规则；正式校验报告保留原始 QA 时间区间及排除统计。原始 QA 文件仍在 raw，官方 split 保留。
+
+### 5.2 mano：参数与整手可见性
+
+MANO schema `mano/axis_angle61/v1`：`motion.pt [T,2,61]`，顺序为完整 16 关节 axis-angle 48、shape betas 10、全局平移 3。姿态使用弧度，平移米，位于当前相机系。有效性 `[T,2]`；缺少一侧整槽 NaN。PCA 必须先按对应模型展开，不能将 PCA 系数直接填入 axis-angle。
+
+`model_profile` 必须固定 MANO 版本、`flat_hand_mean=true`、左手 shapedirs 修正状态。平移表示模型输出顶点所加的全局平移，不是某个腕关节的坐标；源语义不同须转换。shape 无量纲。模型权重不作为共享数据附带上传。
+
+`mano/visibility.pt [T,2]` 保存源整手可见比例时使用 float32 [0,1]，`visibility_type=ratio`；只有实际采用 0–100 的百分比才除以100，不能只凭表头判断。HOT3D 的 `visibility_ratio[%]` 实值已经是0–1，官方读取器原样读取，必须保留原值。通过 MANO 组件 attributes 登记，使用独立有效性，不复用 motion_valid。
+
+手部共享表示仅保留 MANO 参数与选定关键点，不发布独立手表面几何。需要渲染或接触计算时由 MANO 参数生成，几何引用使用固定 MANO 模型配置与顶点顺序；不影响 object 下的物体模型和材质资产。
+
+### 5.3 keypoints：二维点、三维点与逐点可见性
+
+关键点独立于 MANO 参数存在；没有 motion 也可有关键点。`uv.pt [T,2,K,2]` 与 `xyz.pt [T,2,K,3]`，有效性 `[T,2,K]`，分别采用 `keypoints/uv/v1` 和 `keypoints/xyz/v1`。K 由 `keypoint_schema` 固定，不能在同一数组中逐帧变化；同体系统一顺序。数值使用像素坐标或米制相机系。
+
+| keypoint_schema | K 与固定索引 |
+|---|---|
+| mano21/v1 | 21；wrist 后依次 thumb/index/middle/ring/little，每指由近端至指尖 |
+| rhd21/v1 | 21；保持 wrist，然后五指由近端至指尖；原始每指 tip→palm 反序。只统一顺序，不宣称解剖学位置等价于 MANO |
+
+具体名称与源索引映射见 `schema.json`。当前只登记 MANO21 与 RHD21；不登记 MediaPipe、ReInterHand capture21 或 fit24 中间关键点。
+
+每个发布中的每个视图只提供一套标准手关键点，直接放在 keypoints 下，不设 annotation_set_id 字段或目录。uv 与 xyz 是这套关键点的二维与三维表示，不是两套标注。provenance 分别记录二者来自原始标注还是计算结果；不要求已有 2D 必须由当前 xyz 重新投影覆盖。
+
+ReInterHand 的 Original keypoints、Original fits keypoints 及带前臂关键点不纳入 shared；如需手关键点，从选定 MANO 参数生成 MANO21 并记录模型/指尖回归约定。其他数据集默认使用选定的官方关键点，不额外生成平行的 MANO 派生集合。后续主动更换生成方法或结果时发布新的 dataset_version，不在同一版本内增设方法分支。
+
+manual/projection/fitting 只记录在 provenance，不作为目录或类型分支。不得通过坐标为零、是否出画或 visibility 判断标注是否存在。派生标注仅在实际生成并通过数值检查后声明 valid=true；没有生成的项为 false，不伪造坐标。
+
+`keypoints/visibility.pt [T,2,K]` 保存源逐点可见性，布尔表示使用 `visibility_type=boolean`。uv 与 xyz 共用时引用同一份文件。整手比例属性没有 MANO 可挂载时使用 `whole_hand_visibility.pt [T,2]`，`meaning=whole_hand`；它与逐点可见性分开，通用登记规则见 5.1。
+
+不使用 annotation_set_id；ReInterHand 中间关键点集合不进入 shared，raw 保留来源文件。
+
+### 5.4 object：身份、资产、位姿与几何
+
+#### 5.4.1 metainfo.json：物体身份与跟踪作用域
+
+手用 `hand_left`、`hand_right` 及槽位 0、1 表示，不再另存 entities.json。物体身份登记在 `annotation/object//metainfo.json`，必填 `object_id`、`identity_scope`、`scope_id`、`source_locator`；可含 `category_id`、`source_object_id`、`mesh_ref`、`has_model`、部件引用。即使只有分割、没有 pose/mesh，也可只有这份物体元信息。
+
+`identity_scope` 为 frame/subsequence/sequence，`scope_id` 指定具体作用域；跨相机共享身份只在已有源对应证据时使用。VISOR 按官方 subsequence 与实体名关联时记录 `association_basis=official_subsequence_entity_name`，不能声称这是全视频物理实例跟踪。无需与 object_id 一一对应的标准 track_id；官方独立轨迹标识存在时保留为 source_track_id，并声明作用域。手槽不证明跨采集片段的个人身份。
+
+#### 5.4.2 assets：网格、材质与纹理
+
+资产属于物体，统一存放在 `annotation/object//` 内，可用 `assets/` 子目录组织网格、材质、纹理和部件资产。序列层不设共享 assets。所有引用采用视图根目录为基准；其中物体资产组件的 `data_ref` 与部件的 `mesh_ref` 采用相对于当前视图目录的 POSIX 路径，例如 `annotation/object/cup_01/assets/cup.obj`，且必须指向所属物体目录内的资产。OBJ 内部的材质、纹理引用遵循文件格式自身的相对路径规则，依赖文件一并保留。不同相机视图允许各自保存物体资产，不要求跨视图去重；下载物体时包含其资产依赖。
+
+网格 schema 为 `mesh/obj/v1`，顶点米制，静态有效性可直接声明标量 `valid`，或通过 `valid_ref` 指向标量 bool Tensor；不按 T 重复静态资产。由物体／部件通过引用关联，不仅凭文件名猜测。
+
+#### 5.4.3 pose.pt、pose_valid.pt 与 visibility.pt
+
+刚体 schema 为 `object/rigid/v1`，`pose.pt [T,4,4]` 固定为 `T_camera_from_object`，平移米制，有效性 `[T]`。物体坐标原点、轴向必须与 mesh 一致；旋转块为正交矩阵且 determinant≈1。
+
+`object//visibility.pt [T]` 保存源物体可见比例，float32 [0,1]，`visibility_type=ratio`。通过所属物体组件 attributes 登记，独立表达其有效性。
+
+#### 5.4.4 articulation.json、部件位姿与 joint_state.pt
+
+关节物体：schema `object/articulated/v1`，`articulation.json` 登记 `parts` 与 `joints`。每个 part 有 `part_id`、`parent_id`（根为 null）、`has_model`、`mesh_ref`；has_model=false 时 mesh_ref=null，结构节点允许没有网格。父链不得成环。每个 joint 有唯一 `joint_id`、`parent_part`、`child_part`、`type`；fixed 单位 null、revolute 弧度、prismatic 米。有源限位时保存 `limits:[lower,upper]`；没有时 null，不伪造上下限。部件姿态统一为 `T_camera_from_part [T,4,4]`，不与父局部变换混装。
+
+若提供 `joint_state.pt [T,J]`，必须登记固定 joint_order、轴在父部件系的表达、零位变换与正方向，valid 为 `[T,J]`；缺少这些运动学定义时只发布部件位姿与已有来源信息，不伪造可驱动的关节模型。关节值与部件位姿同时存在时需验证一致性。
+
+部件层级应声明语义：纯语义包含树允许 `joints=[]`，不将包含关系推断为机械关节。关节状态 schema 为 `object/joint_state/v1`；转动用弧度、移动用米，混合类型按 joint_order 对应的关节定义解释。
+
+#### 5.4.5 dimensions、几何关键点与顶点部件标签
+
+`dimensions.pt [T,3]` 采用 `object/dimensions/v1`，有效性为 `[T]`。
+
+物体/部件 `dimensions` 为 `[length,width,height]` 米，轴顺序与该 mesh 的局部坐标关联，登记 `dimensions_semantics` 和来源。HOI4D dimensions 与 mesh 的具体尺度关系未验证前标为 source_unspecified，保留源值并暂停依赖该关系的几何验收，不能默认为待施加缩放。需要施加缩放时明确 scale_applied_to_mesh，禁止重复应用。
+
+源物体关键点放 `object//keypoints/xyz.pt [T,K,3]`（相机系）或 `template_xyz.pt [K,3]`（物体系），schema 分别为 object_keypoints/camera/v1 和 object_keypoints/template/v1。明确 keypoint_schema、keypoint_names、单位及有效性；模板点配合 pose 可投影，无须重复生成全部帧。H2O 中心/角点/棱中点，以及 ARCTIC 部件点分别登记具体源顺序，不套用手的 21 点 schema。每个发布选择一套源几何点并记录选择，不另建方法分支。
+
+`vertex_part_ids.pt [V] int64` 绑定 `mesh_ref` 与 topology_id，登记整数到 part_id 的映射；未知用 -1。ARCTIC 不同拓扑的资产分别绑定，不能共用未经转换的顶点标签。部件层级中 is_instance/has_model 等源属性保留，几何文件仍全部归 object。
+
+### 5.5 segment：分割类别、编码与覆盖范围
+
+每个发布选择一套任务使用的分割表示，不建立方法／标注集分支。没有分割就不创建 segment 目录，不为其他模态创建空的实体表。
+
+| schema_id | 文件与数值语义 |
+|---|---|
+| segment/uint16_labels/v1 | mask.mkv；整数区域编号，通过 instances.jsonl 解释 |
+| segment/instance_polygons/v1 | polygons.jsonl；实例多边形，可重叠；当前不支持孔洞拓扑 |
+| segment/foreground_intensity/v1 | mask.mkv；0–255 前景强度，不是区域编号或经过校准的概率 |
+
+#### 5.5.1 uint16 标签图与 instances.jsonl
+
+组件声明 `segmentation_type=semantic/instance/part`；同一组件固定一种类型。`mask.mkv` 解码为 `[T,H,W] uint16`，schema `segment/uint16_labels/v1`，使用 `mkv/uint16_ffv1/v1`，FFV1 gray16le 无损；只有所有区域编码均 ≤255 时允许 gray8（pixel_format=gray），读取后转 uint16，数值不变。禁止调色板和有损编码。有效性 `[T]`，表示该帧有可用分割标注。数值 0 固定为 unassigned，65535 固定为 ignore，1–65534 为区域编码，不能直接等同 entity_id。
+
+`instances.jsonl` 每帧一行，包含 `frame_index` 与 `labels`。每条标签包含 `mask_value`、`category_id`、`entity_id`、`part_id`；无意义或未知字段为 null。semantic 允许 entity_id=null；instance 必须引用 hand_left/hand_right 或登记的 object_id；part 还须关联登记部件。源实例 ID 和其他属性分别保存在 `source_instance_id`、`source_attributes`。mask 中每个非保留编码必须有且仅有一个映射。
+
+FreiHAND 的试点按本任务需要，统一到手／背景类别，保留每个原生区域编码；不要求给原生细分区域猜测手指名称。需要细部件监督时，尚未核实的部件名称必须保持未知，不能因粗粒度手类别已核实便声称部件语义也已统一。
+
+#### 5.5.2 重叠实例多边形
+
+每发布选择一套任务使用的分割表示，不建立方法/标注集分支。VISOR 的实际试点存在重叠实例多边形，单层标签图不能无损表达。对此补充 `segment/instance_polygons/v1`：`segment/polygons.jsonl` 每帧包含 `frame_index` 和 `instances`，每实例包含 `mask_value` 与 `polygons`（列表，每个多边形为源图像像素坐标顶点序列，保留原始精度）。身份和类别仍由同一 `instances.jsonl` 映射，有效性仍为 `[T]`，通过 schema 区分物理编码。允许多实例重叠，不采用后画覆盖前画。消费者按需栅格化为独立实例二值 mask；不能声称合并的单层 mask 无损。
+
+#### 5.5.3 灰度前景强度
+
+ReInterHand 的官方渲染 mask 为双手合并的灰度前景掩码，实测存在 0–255 的中间值，不是左右手实例编号。登记 `segment/foreground_intensity/v1`：仍存 `segment/mask.mkv`，解码为 `[T,H,W] uint16`，使用 `mkv/uint16_ffv1/v1`，此类数据优先编码为 FFV1 gray8（pixel_format=gray），读取后转 uint16。有效值范围 0–255，归一化值为 `float32(value)/255`；0 为背景端点、255 为前景端点。组件声明 `foreground_category=hand`、`entity_id=null` 和双手合并语义，不使用区域编号映射。这里保留原始渲染 mask 强度，不把它称为经过校准的概率。若任务需要二值 mask，阈值化属于任务层派生操作，记录阈值；共享层不丢弃中间灰度。不同 schema 的数值含义不同，不能将该文件按整数类别图解码。
+
+FreiHAND 训练集的 JPEG mask 使用 `segment/foreground_intensity/v1`，逐像素保留 JPEG 解码后的灰度，`entity_id=hand_right`。该 schema 不限定双手合并：单手或合并范围由 `entity_id` 与 `coverage` 明确声明。评估集整数区域图仍用 `segment/uint16_labels/v1`，两者不能仅凭文件名使用同一种解码语义。
+
+#### 5.5.4 coverage 与有效性
+
+`coverage.kind=full/selected_entities/partial/unknown`。full 指声明的标注范围完整；selected_entities 还应列出目标范围。无论哪种，0 本身不自动成为背景；背景要有明确类别映射，未标注区域不能用于构造负样本。保留源逐类/逐实例 exhaustive 信息，未知不可改写为完整。
+
+三种表示的帧级有效性均为 `[T]`，表示该帧是否提供此分割标注；对应 valid_ref 在组件中登记，仍用小型 bool `.pt` 数组。栅格 mask 必须登记 storage，格式见 3.1.2，所有帧的整数编码或强度逐值完全相同。编码语义由 schema_id 决定，读取器不能仅依据 mask.mkv 文件名选择解码方式。
+
+### 5.6 contact：接触、抓取、区域与距离
+
+#### 5.6.1 relations.jsonl 与 relations_valid.pt
+
+`annotation/contact/relations.jsonl`，schema contact/relations/v1，每帧一行含 frame_index、relations。每条记录含 relation_type（contact/grasp）、subject_id（手槽或登记实体）、object_id、status、source_locator、provenance。status 固定为 positive/negative/unknown/target_unannotated：positive 必须有可解析目标，negative 表示源明确否定该关系，unknown 为不确定，target_unannotated 为确有接触但目标未标注。非 positive 的 object_id 可 null，不能用 null 冒充“不接触”。稀疏源无记录的帧不自动补 negative。
+
+VISOR 的对象 mask ID 必须先按同帧 source_instance_id 解析成 object_id；hand-not-in-contact、none-of-the-above、inconclusive 分别映射 negative、target_unannotated、unknown，保留原始字符串。Dex ycb_grasp_ind 通过 ycb_ids 映射到登记物体，以 relation_type=grasp 保存；不得当作逐顶点接触。
+
+relations 组件的 valid_ref 指向 `relations_valid.pt [T] bool`，表示该帧是否有可用源关系标注。有效帧中可以存在 status=unknown 的明确不确定标注；无源标注的帧为 valid=false、relations=[]。组件仍声明 available/provenance/source_locator。
+
+#### 5.6.2 实体对的顶点区域与距离
+
+已发布的接触区域/距离按参与关系的 subject_id 与 object_id 组织，每对实体保留选定的一套结果，以精确 geometry_ref/topology_id 关联到手或物体/部件；不要求每套数据都有。顶点区域 schema contact/vertex_region/v1：bool `[T,V]` 加同形 valid；顶点距离 schema contact/vertex_distance/v1：float32 `[T,V]`、米、同形 valid，声明 source/target geometry_ref、distance_definition（signed/unsigned、测量到点集或表面等明确源定义）。geometry_ref 必须指向已登记 mesh 或由固定 MANO 模型配置确定的拓扑；拓扑/顶点数变化时按拓扑拆分片段。其他源区域表示只有在实际接入并登记 schema 后才可转换，不能随意栅格化宣称等价。
+
+顶点区域／距离已有存储定义，但尚未完成真实输入试点；接入时必须提供源语义与几何验收证据，不因已经登记 schema 就视为通过。
+
+## 附录：任务范围
+
+不强制收录 2D bbox、完整高频动捕、动作程序、身体 SMPL-X、点云或网络特征。范围内的接触、visibility、选定手关键点、物体几何不能因不便处理而静默丢弃。分割采用的物理编码由第 5.5 节决定，存在重叠时不能以单层标签图声称无损。
+
+不设置 entities_ref/segment/entities.json，不要求与 object_id 重复的 track_id。MediaPipe 不在当前关键点定义中；旧版本迁移必须保留可追溯关系，不能直接删表。手部共享表示不含独立 hand_mesh。
+
+schema.json 是普通注册表，不是 JSON Schema 文档或可执行验收器；定义存在不代表真实数据已验证。验收须保留实测范围与未决项。
+
+本存储约定只更新标准，不代表既有 FFV1／depth.pt 试点已迁移。新发布必须显式写 storage 配置；旧产物按其旧定义读取，不能只改文件后缀或版本标识伪装为合规。
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/docs/h2o-RAW_TO_SHARED.md b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/docs/h2o-RAW_TO_SHARED.md
new file mode 100644
index 0000000000000000000000000000000000000000..71253fa14376d7f6074f9a962ab4450e6db43306
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/docs/h2o-RAW_TO_SHARED.md
@@ -0,0 +1,30 @@
+# H2O raw → shared-v1.0
+
+## 范围与来源
+
+本版本仅转换 `raw/data/subject{1,2,3,4}_v1_1.tar.gz` 的真实五视角 (`cam0`–`cam4`) 序列；`h2o_CASA.tar` 是单独的扩展，不与标准序列混合。每个原始序列存于 `data/subjectN__/cam{0..4}/`。源压缩包保持不变，不合成源不存在的标注。输出仅在本地制作；完整序列试点及人工检查通过、更新全量预估之前，不启动全量。
+
+| 源包/字段 | shared 文件/处理 |
+| --- | --- |
+| `subjectN_v1_1.tar.gz`: `camX/rgb//keypoints/xyz.pt`、`pose.pt`：按源类号关联 21 个物体点及物体→相机位姿；仅源存在时登记组件及有效性。 |
+| `object.zip` | `annotation/object//assets/` 与源 OBJ、MTL、纹理；静态形状预览不等于所有帧网格位姿的投影证明。 |
+| `label_split.zip` 的 `action_{train,val,test}.txt` | 序列元数据的官方划分及来源；未知映射明确记 `unknown`，不自行编排。 |
+
+`cam4` 是 ego，`cam0`–`cam3` 是 exo。真实时间/FPS 缺乏可靠来源时使用 null，不把 MKV 封装帧率冒充采集时钟。源 `action_label`、`verb_label`、pose 任务帧列表和 `manolabel_v1.1.tar.gz` 保留在 raw；当前契约没有相应动作组件，本发布不宣称它们已转换。源没有像素分割，本版本不投影网格生成替代分割。
+
+## 源资产文件名修复
+
+源 `object/cocoa/cocoa.obj` 引用 `ovomaltine.mtl`，材质内写的是 `map_Kd cocoa.png`，但原 ZIP 只提供 `ovomaltine.png`。仅在临时解压目录为该已确认的纹理增加**字节相同**的 `cocoa.png` 别名，保留原 PNG、OBJ、MTL 不变；在网格 `source_locator.texture_aliases` 中记录别名、原始包成员和 SHA256。别名与已保留的旧 cocoa 资产一致，不替换图像、标注或网格。全部八类物体的材质依赖在读取大 subject 包之前预检，其他缺失/越界依赖仍报错。
+
+## 加速与验收
+
+四个互不依赖的 subject 压缩包可由 `--workers 1..4` 并行顺序读取，各 worker 一次只处理一个序列。数值库保持单线程；FFV1 编码使用 `--codec-threads 4`（经完整视图实测后选择），逐帧解码校验仍保留。并发数由相同输入实测吞吐量决定，记录 OS 线程峰值和磁盘占用，不以 CPU 线程数本身作为提速证据。RGB 源为 PNG，不适用 ARCTIC/HOT3D 的 JPEG 包直封和 16 帧抽查：RGB、深度、有效性与数值模态仍逐帧精确校验。临时文件在 `/data` 上隔离放置，已有旧版目录不复用、不覆盖。
+
+先运行不截帧的代表性完整序列试点，保存自动 QC、真实耗时和 `doc/validation/index.html`（RGB、深度、源 3D 关键点投影、原生物体网格和全组件数值/有效性）。试点由人工核查之后再决定全量；全量后再次抽样人工核查。报告须区分数据完整性、源未提供的模态与仅供人工判断的几何对齐。
+
+2026-09-29 完整序列试点已获人工确认，用户已授权本地全量。资源预算改为 H2O 整个进程组不超过 101 CPU 总容量的约 80%，用 systemd 用户 scope 的 CPUQuota 管理，包含 FFmpeg 子进程；不以线程个数作为硬上限。101 当前 40 个逻辑 CPU 对应 CPUQuota=3200%（32 个逻辑 CPU 等效）。这不是全机其他用户任务的负载上限。源包和旧产物保留；失败版本不覆盖，修复版本使用独立输出目录。
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/docs/schema.json b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/docs/schema.json
new file mode 100644
index 0000000000000000000000000000000000000000..d904a34272de7f3fd4428c8dc68a919e94a68fca
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/docs/schema.json
@@ -0,0 +1,869 @@
+{
+ "schema_version": "hand-warehouse/1.0",
+ "validity_revision": "usable-annotation-2026-09-20",
+ "annotation_validity": {
+ "meaning": "annotation usable for supervision, not merely record presence",
+ "formula": "source_annotation_present AND numerical_checks_pass AND applicable_official_QA_pass AND NOT explicitly_reviewed_local_exclusion",
+ "without_official_qa": "omit QA term; declare source rules; do not fabricate QA",
+ "policy_field": "components..valid_policy",
+ "visibility_is_independent": true,
+ "invalid_float_value": "NaN",
+ "hot3d": "broadcast per-camera frame manual QA conservatively to hand/object labels and pose-derived visibility; retain RGB and source calibration; keep native QA in raw and review report",
+ "hot3d_visibility": "native visibility_ratio[%] already ranges from 0 to 1; do not divide by 100"
+ },
+ "hand_slots": [
+ "left",
+ "right"
+ ],
+ "keypoints": {
+ "mano21/v1": {
+ "count": 21,
+ "names": [
+ "wrist",
+ "thumb_1",
+ "thumb_2",
+ "thumb_3",
+ "thumb_4",
+ "index_1",
+ "index_2",
+ "index_3",
+ "index_4",
+ "middle_1",
+ "middle_2",
+ "middle_3",
+ "middle_4",
+ "ring_1",
+ "ring_2",
+ "ring_3",
+ "ring_4",
+ "little_1",
+ "little_2",
+ "little_3",
+ "little_4"
+ ],
+ "source_index_map": null,
+ "note": "adapter maps official MANO joint and fingertip convention"
+ },
+ "rhd21/v1": {
+ "count": 21,
+ "names": [
+ "wrist",
+ "thumb_1",
+ "thumb_2",
+ "thumb_3",
+ "thumb_4",
+ "index_1",
+ "index_2",
+ "index_3",
+ "index_4",
+ "middle_1",
+ "middle_2",
+ "middle_3",
+ "middle_4",
+ "ring_1",
+ "ring_2",
+ "ring_3",
+ "ring_4",
+ "little_1",
+ "little_2",
+ "little_3",
+ "little_4"
+ ],
+ "source_index_map_per_hand": [
+ 0,
+ 4,
+ 3,
+ 2,
+ 1,
+ 8,
+ 7,
+ 6,
+ 5,
+ 12,
+ 11,
+ 10,
+ 9,
+ 16,
+ 15,
+ 14,
+ 13,
+ 20,
+ 19,
+ 18,
+ 17
+ ]
+ }
+ },
+ "camera": {
+ "opencv_fisheye4": {
+ "K": [
+ 3,
+ 3
+ ],
+ "distortion": [
+ "k1",
+ "k2",
+ "k3",
+ "k4"
+ ],
+ "projection": "theta=atan(r); theta_d=theta*(1+k1*theta^2+k2*theta^4+k3*theta^6+k4*theta^8); source ReInterHand"
+ },
+ "pinhole": {
+ "K": [
+ 3,
+ 3
+ ]
+ },
+ "fisheye624": {
+ "parameters": [
+ "f",
+ "cx",
+ "cy",
+ "k0",
+ "k1",
+ "k2",
+ "k3",
+ "k4",
+ "k5",
+ "p0",
+ "p1",
+ "s0",
+ "s1",
+ "s2",
+ "s3"
+ ]
+ },
+ "rational8": {
+ "K": [
+ 3,
+ 3
+ ],
+ "distortion": [
+ "k1",
+ "k2",
+ "p1",
+ "p2",
+ "k3",
+ "k4",
+ "k5",
+ "k6"
+ ]
+ }
+ },
+ "segmentation": {
+ "segment/uint16_labels/v1": {
+ "storage": "mask.mkv",
+ "dtype": "uint16",
+ "mapping": "instances.jsonl",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "reserved_codes": {
+ "0": "unassigned",
+ "65535": "ignore"
+ },
+ "region_codes": [
+ 1,
+ 65534
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ },
+ "segment/instance_polygons/v1": {
+ "storage": "polygons.jsonl",
+ "mapping": "instances.jsonl",
+ "frame_keys": [
+ "frame_index",
+ "instances"
+ ],
+ "instance_keys": [
+ "mask_value",
+ "polygons"
+ ],
+ "coordinate_unit": "pixel",
+ "overlap_allowed": true,
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref"
+ ],
+ "polygon_shape": [
+ "N",
+ 2
+ ],
+ "holes_supported": false
+ },
+ "segment/foreground_intensity/v1": {
+ "storage": "mask.mkv",
+ "dtype": "uint16",
+ "value_range": [
+ 0,
+ 255
+ ],
+ "normalization_scale": 0.00392156862745098,
+ "axes": [
+ "T",
+ "height",
+ "width"
+ ],
+ "meaning": "native hand foreground intensity; individual hand or union declared in metadata; not instance codes or calibrated probability",
+ "zero": "background",
+ "full_scale": "foreground",
+ "mapping": "foreground_category in component metadata",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "value_range",
+ "normalization_scale",
+ "foreground_category",
+ "entity_id",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ }
+ },
+ "note": "Registry for hand-warehouse/1.0. Read CONTRACT.md for field semantics. This is not a JSON Schema document or executable validator.",
+ "components": {
+ "mano/axis_angle61/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 2,
+ 61
+ ],
+ "valid_shape": [
+ "T",
+ 2
+ ],
+ "unit": "m",
+ "angle_unit": "rad",
+ "coordinate_frame": "camera",
+ "layout": {
+ "pose_axis_angle": [
+ 0,
+ 48
+ ],
+ "shape": [
+ 48,
+ 58
+ ],
+ "translation": [
+ 58,
+ 61
+ ]
+ }
+ },
+ "keypoints/uv/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 2,
+ 21,
+ 2
+ ],
+ "valid_shape": [
+ "T",
+ 2,
+ 21
+ ],
+ "unit": "pixel",
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "keypoints/xyz/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 2,
+ 21,
+ 3
+ ],
+ "valid_shape": [
+ "T",
+ 2,
+ 21
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "depth/z_float32/v1": {
+ "storage": "mkv",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "valid_shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "unit": "m",
+ "depth_type": "z",
+ "pixel_grid": "current_rgb",
+ "coordinate_frame": "current_rgb_camera",
+ "valid_storage": "mkv",
+ "allowed_storage_profiles": [
+ "mkv/depth_float32_bits/v1"
+ ],
+ "allowed_valid_storage_profiles": [
+ "mkv/bool_ffv1/v1"
+ ],
+ "required_metadata": [
+ "storage",
+ "valid_storage",
+ "depth_origin"
+ ],
+ "depth_origin_values": [
+ "measured",
+ "rendered",
+ "unknown"
+ ]
+ },
+ "transform/se3/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 4,
+ 4
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "convention"
+ ]
+ },
+ "object/rigid/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 4,
+ 4
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "convention"
+ ]
+ },
+ "camera/pinhole/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3,
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/rational8/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3,
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/opencv_fisheye4/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3,
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/fisheye624/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 15
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/distortion8/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 8
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "camera/fisheye4_distortion/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 4
+ ],
+ "valid_shape": [
+ "T"
+ ]
+ },
+ "object/joint_state/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "J"
+ ],
+ "valid_shape": [
+ "T",
+ "J"
+ ],
+ "required_metadata": [
+ "joint_order"
+ ],
+ "unit_rule": "revolute radians; prismatic metres; mixed types declared per joint"
+ },
+ "object_keypoints/camera/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "K",
+ 3
+ ],
+ "valid_shape": [
+ "T",
+ "K"
+ ],
+ "unit": "m",
+ "coordinate_frame": "camera",
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "object/dimensions/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "dimensions_semantics"
+ ],
+ "axis_order": [
+ "length",
+ "width",
+ "height"
+ ],
+ "scale_rule": "do not apply to mesh without source-supported size/scale relation"
+ },
+ "object_keypoints/template/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "K",
+ 3
+ ],
+ "valid_shape": [
+ "K"
+ ],
+ "unit": "m",
+ "coordinate_frame": "object",
+ "static": true,
+ "required_metadata": [
+ "keypoint_schema"
+ ]
+ },
+ "mesh/obj/v1": {
+ "storage": "obj",
+ "unit": "m",
+ "static": true,
+ "valid_shape": [],
+ "dependencies": "MTL and textures retained under owning object"
+ },
+ "rgb/uint8/v1": {
+ "storage": "mkv",
+ "dtype": "uint8",
+ "shape": [
+ "T",
+ "H",
+ "W",
+ 3
+ ],
+ "valid_shape": [
+ "T"
+ ],
+ "color_order": "RGB",
+ "allowed_storage_profiles": [
+ "mkv/mjpeg_copy/v1",
+ "mkv/source_copy/v1",
+ "mkv/h264_rgb_lossless/v1",
+ "mkv/ffv1_rgb/v1"
+ ],
+ "required_metadata": [
+ "storage"
+ ]
+ },
+ "contact/relations/v1": {
+ "storage": "jsonl",
+ "valid_shape": [
+ "T"
+ ],
+ "frame_keys": [
+ "frame_index",
+ "relations"
+ ],
+ "absence_semantics": "no record is not a negative contact annotation"
+ },
+ "contact/vertex_region/v1": {
+ "storage": "pt",
+ "dtype": "bool",
+ "shape": [
+ "T",
+ "V"
+ ],
+ "valid_shape": [
+ "T",
+ "V"
+ ],
+ "required_metadata": [
+ "source_geometry_ref",
+ "target_geometry_ref",
+ "topology_id"
+ ],
+ "pilot_status": "defined_not_empirically_validated"
+ },
+ "contact/vertex_distance/v1": {
+ "storage": "pt",
+ "dtype": "float32",
+ "shape": [
+ "T",
+ "V"
+ ],
+ "valid_shape": [
+ "T",
+ "V"
+ ],
+ "unit": "m",
+ "required_metadata": [
+ "source_geometry_ref",
+ "target_geometry_ref",
+ "topology_id",
+ "distance_definition"
+ ],
+ "pilot_status": "defined_not_empirically_validated"
+ },
+ "object/articulated/v1": {
+ "storage": "json",
+ "required_keys": [
+ "parts",
+ "joints"
+ ],
+ "part_keys": [
+ "part_id",
+ "parent_id",
+ "has_model",
+ "mesh_ref"
+ ],
+ "hierarchy_semantics": "declare mechanical articulation or semantic containment; containment alone does not imply joints",
+ "acyclic": true
+ },
+ "segment/uint16_labels/v1": {
+ "storage": "mkv",
+ "dtype": "uint16",
+ "mapping": "instances.jsonl",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "reserved_codes": {
+ "0": "unassigned",
+ "65535": "ignore"
+ },
+ "region_codes": [
+ 1,
+ 65534
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ },
+ "segment/instance_polygons/v1": {
+ "storage": "jsonl",
+ "mapping": "instances.jsonl",
+ "frame_keys": [
+ "frame_index",
+ "instances"
+ ],
+ "instance_keys": [
+ "mask_value",
+ "polygons"
+ ],
+ "coordinate_unit": "pixel",
+ "overlap_allowed": true,
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "mapping_ref"
+ ],
+ "polygon_shape": [
+ "N",
+ 2
+ ],
+ "holes_supported": false
+ },
+ "segment/foreground_intensity/v1": {
+ "storage": "mkv",
+ "dtype": "uint16",
+ "value_range": [
+ 0,
+ 255
+ ],
+ "normalization_scale": 0.00392156862745098,
+ "axes": [
+ "T",
+ "height",
+ "width"
+ ],
+ "meaning": "native hand foreground intensity; individual hand or union declared in metadata; not instance codes or calibrated probability",
+ "zero": "background",
+ "full_scale": "foreground",
+ "mapping": "foreground_category in component metadata",
+ "valid_shape": [
+ "T"
+ ],
+ "required_metadata": [
+ "segmentation_type",
+ "coverage",
+ "value_range",
+ "normalization_scale",
+ "foreground_category",
+ "entity_id",
+ "storage"
+ ],
+ "shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "allowed_storage_profiles": [
+ "mkv/uint16_ffv1/v1"
+ ]
+ }
+ },
+ "storage_revision": "lossless-video-2026-09-18",
+ "sampling": {
+ "image": {
+ "single": {
+ "T": 1
+ },
+ "independent": {
+ "T": ">=2",
+ "required_frame_fields": [
+ "sample_id",
+ "source_locator"
+ ],
+ "temporal_relationship": false
+ },
+ "source_fps": null
+ },
+ "video": {
+ "sampling_types": [
+ "dense",
+ "sparse"
+ ]
+ }
+ },
+ "storage_profiles": {
+ "mkv/mjpeg_copy/v1": {
+ "container": "matroska",
+ "codec": "mjpeg",
+ "method": "stream_copy_from_source_jpeg",
+ "logical_dtype": "uint8",
+ "decoded_pixel_format": "rgb24",
+ "validation": [
+ "source_jpeg_packet_sha256_exact",
+ "all_packet_count_order_and_dimensions_exact",
+ "deterministic_decoded_rgb_sample_exact",
+ "pilot_and_final_selected_whole_sequences_decoded_rgb_exact"
+ ],
+ "validation_policy": {
+ "id": "jpeg-packets-all-rgb-sample16/v1",
+ "sample_count_per_view": 16,
+ "short_views": "all_frames_if_T_less_than_16",
+ "fixed_indices": "0,T-1,T//4,T//2,3*T//4, deduplicated",
+ "remaining_indices": "sample without replacement to min(T,16) using Python random.Random(seed)",
+ "seed": "first 8 bytes of SHA256(sequence_id/camera_id/20260920), big-endian integer",
+ "report": "policy, exact selected frame indices, seed, separate packet and pixel coverage",
+ "legacy_all_frame_pixel_proofs": "still accepted; preserve historical evidence"
+ }
+ },
+ "mkv/source_copy/v1": {
+ "container": "matroska",
+ "codec": "source_codec_declared_in_metadata",
+ "method": "stream_copy_only",
+ "logical_dtype": "uint8",
+ "decoded_pixel_format": "rgb24",
+ "validation": [
+ "no_reencoding",
+ "all_decoded_rgb_frames_exact",
+ "presentation_frame_order_exact"
+ ]
+ },
+ "mkv/h264_rgb_lossless/v1": {
+ "container": "matroska",
+ "codec": "h264",
+ "encoder": "libx264rgb",
+ "crf": 0,
+ "decoded_pixel_format": "rgb24",
+ "logical_dtype": "uint8",
+ "gop": "declared_in_encoder_parameters",
+ "validation": [
+ "all_decoded_rgb_frames_exact"
+ ],
+ "forbidden": [
+ "lossy_rate_control",
+ "rgb_to_yuv420_subsampling"
+ ]
+ },
+ "mkv/ffv1_rgb/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "decoded_pixel_format": "rgb24",
+ "logical_dtype": "uint8",
+ "validation": [
+ "all_decoded_rgb_frames_exact"
+ ]
+ },
+ "mkv/depth_float32_bits/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "pixel_format": "gray16le",
+ "logical_dtype": "float32",
+ "logical_shape": [
+ "T",
+ "H",
+ "W"
+ ],
+ "encoded_shape": [
+ "T",
+ "H",
+ "2*W"
+ ],
+ "packing": "IEEE754 little-endian float32 bits; low uint16 in first W columns, high uint16 in last W columns",
+ "decode": "reinterpret_float32(uint32(low) | (uint32(high) << 16))",
+ "validation": [
+ "all_float32_bits_exact_including_nan"
+ ]
+ },
+ "mkv/bool_ffv1/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "pixel_format": "gray",
+ "logical_dtype": "bool",
+ "stored_values": [
+ 0,
+ 1
+ ],
+ "validation": [
+ "all_bool_values_exact"
+ ]
+ },
+ "mkv/uint16_ffv1/v1": {
+ "container": "matroska",
+ "codec": "ffv1",
+ "pixel_formats": [
+ "gray",
+ "gray16le"
+ ],
+ "logical_dtype": "uint16",
+ "gray_condition": "all values <=255; decode by exact uint8-to-uint16 promotion",
+ "validation": [
+ "all_integer_values_exact"
+ ]
+ }
+ },
+ "video_metadata": {
+ "required": [
+ "profile",
+ "codec",
+ "pixel_format",
+ "encoded_size",
+ "decoder",
+ "encoder_parameters",
+ "source_lossy"
+ ],
+ "source_lossy_values": [
+ true,
+ false,
+ "unknown"
+ ],
+ "timebase_role": "index_only",
+ "lossless_reference": "source compressed packets or fixed-decoder source pixels; depth uses normalized standard float32 bits",
+ "selection": "choose smaller complete candidate only after exact roundtrip validation"
+ }
+}
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/full.log b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/full.log
new file mode 100644
index 0000000000000000000000000000000000000000..65931a5b8d015b51eddb13bde0be744e71687977
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/full.log
@@ -0,0 +1,53 @@
+Converted subject1/h1/0
+Converted subject2/h1/0
+Converted subject1/h1/6
+Converted subject2/h1/6
+Converted subject1/h1/3
+Converted subject2/h1/3
+Converted subject1/h1/7
+Converted subject2/h1/7
+Converted subject1/h1/4
+Converted subject2/h1/4
+Converted subject1/h1/2
+Converted subject2/h1/2
+Converted subject1/h1/1
+Converted subject1/h1/5
+Converted subject2/h1/1
+Converted subject1/o1/0
+Converted subject2/h1/5
+Converted subject1/o1/6
+Converted subject2/o1/0
+Converted subject1/o1/3
+Converted subject2/o1/6
+Converted subject1/o1/7
+Converted subject2/o1/3
+Converted subject1/o1/4
+Converted subject1/o1/2
+Converted subject2/o1/4
+Converted subject1/o1/1
+Converted subject1/o1/5
+Converted subject2/o1/2
+Converted subject1/o2/0
+Converted subject1/o2/6
+Converted subject2/o1/1
+Converted subject1/o2/3
+Converted subject2/o1/5
+Converted subject1/o2/7
+Converted subject2/o2/0
+Converted subject1/o2/4
+Converted subject2/o2/6
+Converted subject2/o2/3
+Converted subject1/o2/2
+Converted subject2/o2/4
+Converted subject2/o2/2
+Converted subject1/o2/1
+Converted subject1/o2/5
+Converted subject2/o2/1
+Converted subject1/k1/0
+Converted subject2/o2/5
+Converted subject1/k1/6
+Converted subject2/k1/0
+Converted subject1/k1/3
+Converted subject2/k1/6
+Converted subject1/k1/7
+Converted subject2/k1/3
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/full.status b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/full.status
new file mode 100644
index 0000000000000000000000000000000000000000..d53dae27bfc3a5a7669881f91405612f8b2495e4
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/full.status
@@ -0,0 +1,7 @@
+START=2026-09-29T22:12:28+08:00
+WORKERS=2 CODEC_THREADS=4 SUBJECTS=1,2,3,4 CASA=false
+CPU_BUDGET_PERCENT=80 LOGICAL_CPUS=40
+CGROUP=/user.slice/user-25044.slice/user@25044.service/app.slice/h2o-full-r1-20260929.scope
+CPU_MAX=3200000 100000
+
+RECONFIGURE=2026-09-30T10:20:40+08:00 REASON=user_requested_worker4_codec10 CPU_LIMIT=none
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/run-full.sh b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/run-full.sh
new file mode 100644
index 0000000000000000000000000000000000000000..5d27d5547332f029b9d2e92108469ad40bc121e6
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/run-full.sh
@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+set -u
+base="$HOME/Datasets/uni-hoi-h2o-full-20260929-r1"
+raw="$HOME/Datasets/Hand-Datasets-Raw/H2O"
+cd "$HOME/Projects/uni-hoi-dataset" || exit 1
+mkdir -p "$base/scratch"
+export TMPDIR="$base/scratch" PYTHONUNBUFFERED=1
+for n in 1 2 3 4; do test -s "$raw/raw/data/subject${n}_v1_1.tar.gz" || exit 2; done
+test ! -e "$base/H2O/shared-v1.0" || exit 3
+cg=$(awk -F: '$1=="0" {print $3}' /proc/self/cgroup)
+read -r quota period < "/sys/fs/cgroup$cg/cpu.max"
+cpus=$(getconf _NPROCESSORS_ONLN)
+printf 'START=%s\nWORKERS=2 CODEC_THREADS=4 SUBJECTS=1,2,3,4 CASA=false\nCPU_BUDGET_PERCENT=80 LOGICAL_CPUS=%s\nCGROUP=%s\nCPU_MAX=%s %s\n' "$(date -Is)" "$cpus" "$cg" "$quota" "$period" > "$base/full.status"
+if [[ "$quota" == max ]] || (( quota * 100 > cpus * period * 80 )); then
+ printf 'EXIT=4 ERROR=CPU_QUOTA_NOT_ENFORCED\n' >> "$base/full.status"
+ exit 4
+fi
+/usr/bin/time -v .venv/bin/python "$base/frozen-code/scripts/convert_h2o_v1.0.py" --root "$raw" --out "$base" --release-id shared-v1.0 --workers 2 --codec-threads 4 --mano-dir "$HOME/Projects/WiLoR/mano_data" > "$base/full.log" 2>&1
+code=$?
+printf 'EXIT=%s END=%s\n' "$code" "$(date -Is)" >> "$base/full.status"
+cat "/sys/fs/cgroup$cg/cpu.stat" >> "$base/full.status"
+exit "$code"
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/check_dexycb_projection.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/check_dexycb_projection.py
new file mode 100644
index 0000000000000000000000000000000000000000..2774c004fc1359dea582135ec7171f600b33b336
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/check_dexycb_projection.py
@@ -0,0 +1,31 @@
+#!/usr/bin/env python3
+"""Check saved Dex-YCB source 3D joints against saved source 2D joints in sampled views."""
+import json
+from pathlib import Path
+import sys
+
+import numpy as np
+import torch
+
+root = Path(sys.argv[1])
+report = json.loads((root / 'doc/validation/STRUCTURAL_AUDIT.json').read_text())
+checks = []
+for name in report['sampled_views']:
+ view = root / name
+ load = lambda path: torch.load(view / path, weights_only=True, map_location='cpu').numpy()
+ k = load('camera/intrinsic.pt')
+ xyz = load('annotation/keypoints/xyz.pt')
+ uv = load('annotation/keypoints/uv.pt')
+ valid = load('annotation/keypoints/xyz_valid.pt') & load('annotation/keypoints/uv_valid.pt')
+ z = xyz[..., 2]
+ valid &= np.isfinite(xyz).all(axis=-1) & np.isfinite(uv).all(axis=-1) & (z > 0)
+ z = np.where(valid, z, 1)
+ pred = np.stack((k[:, 0, 0, None, None] * xyz[..., 0] / z + k[:, 0, 2, None, None],
+ k[:, 1, 1, None, None] * xyz[..., 1] / z + k[:, 1, 2, None, None]), axis=-1)
+ errors = np.linalg.norm(pred[valid] - uv[valid], axis=-1)
+ checks.append(dict(view=name, points=int(errors.size), median_px=float(np.median(errors)) if errors.size else None,
+ p95_px=float(np.percentile(errors, 95)) if errors.size else None))
+(root / 'doc/validation/KEYPOINT_PROJECTION_QC.json').write_text(json.dumps({
+ 'scope': 'Saved source 3D joints projected with saved color intrinsics vs saved source 2D joints; independent of hand mesh rendering.',
+ 'views': checks}, ensure_ascii=False, indent=2) + '\n')
+print(json.dumps(checks, ensure_ascii=False), flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/config.json b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..76756f7bfa5e51a14928440034cf492eece3f6c2
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/config.json
@@ -0,0 +1,15 @@
+{
+ "root": "/home/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O",
+ "out": "/home/xuzhefeng/Datasets/uni-hoi-h2o-full-20260929-r1",
+ "release_id": "shared-v1.0",
+ "limit": null,
+ "max_frames": null,
+ "mano_dir": "/home/xuzhefeng/Projects/WiLoR/mano_data",
+ "views": null,
+ "jpeg_full_check": false,
+ "workers": 2,
+ "codec_threads": 4,
+ "subjects": null,
+ "include_casa": false,
+ "archive": null
+}
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/convert_dexycb_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/convert_dexycb_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed3f479787fae378b3d3b51ce2f58ccaebfd3d0f
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/convert_dexycb_v1.0.py
@@ -0,0 +1,349 @@
+#!/usr/bin/env python3
+"""Dex-YCB source annotations → CONTRACT.md v1.0. No derived annotations."""
+import io
+import re
+import shutil
+import tarfile
+import tempfile
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+import yaml
+
+from warehouse_v1_0 import (View, build_record, find_file, locator, mano_models, mesh_asset,
+ model_profile, parser, pca_to_axis_angle, release, roots, safe_member, sequence_dir)
+
+IMG_W, IMG_H = 640, 480
+
+class _Loader(yaml.SafeLoader):
+ pass
+
+
+_Loader.add_constructor("tag:yaml.org,2002:python/tuple",
+ lambda l, n: tuple(l.construct_sequence(n)))
+
+
+def yaml_load_bytes(data: bytes):
+ return yaml.load(io.BytesIO(data), Loader=_Loader)
+
+
+class Calibration:
+ def __init__(self, dexycb_root: Path):
+ self.intrinsics = {} # serial -> {fx,fy,cx,cy,w,h}
+ self.extrinsics = {} # group -> {serial: [12] 行优先 3×4 cam→world}
+ self.betas = {} # mano_calib_name -> [10]
+ calib_dir = dexycb_root / "calibration"
+ if calib_dir.is_dir():
+ self._load((p.as_posix(), p.read_bytes())
+ for p in sorted(calib_dir.rglob("*")) if p.is_file())
+ else:
+ tar_path = dexycb_root / "calibration.tar.gz"
+ if not tar_path.exists():
+ raise FileNotFoundError(f"no calibration/ nor calibration.tar.gz in {dexycb_root}")
+ with tarfile.open(tar_path, "r:gz") as tf:
+ self._load((m.name, tf.extractfile(m).read()) for m in tf if m.isfile())
+
+ _RE_INTR = re.compile(r"(?:^|/)intrinsics/(\d+)_\d+x\d+\.yml$")
+ _RE_EXTRA = re.compile(r"(?:^|/)extrinsics_(\d{8}_\d{6})/extrinsics\.yml$")
+ _RE_MANO = re.compile(r"(?:^|/)mano_(\d{8}_\d{6}_subject-\d{2}_right)/mano\.yml$")
+
+ def _load(self, files):
+ for rel, data in files:
+ m = self._RE_INTR.search(rel)
+ if m:
+ c = yaml_load_bytes(data)["color"]
+ self.intrinsics[m.group(1)] = {"fx": float(c["fx"]), "fy": float(c["fy"]),
+ "cx": float(c["ppx"]), "cy": float(c["ppy"]),
+ "w": IMG_W, "h": IMG_H}
+ continue
+ m = self._RE_EXTRA.search(rel)
+ if m:
+ raw = yaml_load_bytes(data)["extrinsics"]
+ self.extrinsics[m.group(1)] = {
+ str(s): [float(x) for x in np.asarray(v).ravel()] for s, v in raw.items()}
+ continue
+ m = self._RE_MANO.search(rel)
+ if m:
+ self.betas[m.group(1)] = [float(b) for b in yaml_load_bytes(data)["betas"]]
+
+
+
+def calibration_locator(path, member, **extra):
+ if path.is_dir():
+ return locator(path / member.removeprefix('calibration/'), **extra)
+ return locator(path, member, **extra)
+
+
+def prepare_models(root, destination):
+ archive = find_file(root, 'models.tar.gz')
+ if archive is None:
+ return {}, None
+ pattern = re.compile(r'^models/(\d{3}_[a-z0-9_]+)/(textured_simple\.obj(?:\.mtl)?|texture_map\.png)$')
+ names = set()
+ with tarfile.open(archive, 'r|gz') as tf:
+ for member in tf:
+ match = pattern.fullmatch(member.name)
+ if member.isfile() and match:
+ names.add(match[1])
+ path = safe_member(destination, '/'.join(match.groups()))
+ path.parent.mkdir(parents=True, exist_ok=True)
+ path.write_bytes(tf.extractfile(member).read())
+ if len(names) != 21:
+ raise ValueError('expected all 21 source YCB model directories')
+ return {i: destination / name for i, name in enumerate(sorted(names), 1)}, archive
+
+
+def convert_view(path, args, seq, serial, frames, meta, meta_loc, calib, calib_path, models, assets, asset_archive):
+ total = int(meta.get('num_frames', max(frames) + 1))
+ ids = list(range(min(total, args.max_frames) if args.max_frames else total))
+ for f in ids:
+ frames.setdefault(f, {})
+ if not ids:
+ raise ValueError('no RGB frames')
+ source_rows = []
+ for f in ids:
+ row = frames[f]
+ source_rows.append(dict(source_frame_id=f, sync_id=f'{seq}:{f}',
+ source_locator=row['color'][1] if 'color' in row else (meta_loc | {'source_frame_id': f, 'missing_rgb': True}), annotation_matches={
+ 'labels': {'source_frame_id': f, 'source_timestamp_ns': None, 'clock_domain': None,
+ 'match_method': 'index_map', 'delta_ns': None, 'source_locator': row['labels'][1]}
+ for _ in [0] if 'labels' in row},
+ component_sources={k: v[1] for k, v in row.items()}))
+ view = View(path, 'dex-ycb', args.release_id, seq, serial, source_rows,
+ sampling='dense' if ids == list(range(ids[0], ids[-1] + 1)) else 'sparse')
+ view.valid_rules = {
+ 'mano': 'Source pose_m present and not all zero; side model/calibration available',
+ 'keypoints': 'Corresponding joint_2d/joint_3d present; whole-hand all -1 is missing, broadcast to its 21 points',
+ 'pose': 'Source pose_y for this ycb_ids slot present and not an all-zero matrix',
+ 'segment': 'Source seg array provided for this frame; region codes and entity references validated',
+ 'relations': 'Source sequence ycb_grasp_ind resolves through ycb_ids; broadcast sequence grasp to selected frames',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta['view_type'] = 'exo'
+ view.meta['sampling']['selection'] = {'max_frames': args.max_frames, 'views': args.views}
+ subject = re.search(r'subject-(\d+)', seq)
+ if subject and 1 <= int(subject[1]) <= 10:
+ index = int(subject[1])
+ view.meta.update(split='val' if index == 7 else 'test' if index in (8, 9) else 'train',
+ split_scheme='Dex-YCB/s1', split_source_locator={
+ 'url': 'https://github.com/NVlabs/dex-ycb-toolkit/blob/master/dex_ycb_toolkit/dex_ycb.py',
+ 'rule': 's1 unseen-subject: 01-06/10 train, 07 val, 08-09 test'})
+ view.meta.pop('split_reason', None)
+ view.rgb([frames[f]['color'][0] if 'color' in frames[f] else None for f in ids], {'frames_ref': 'camera/frames.jsonl'})
+ n = len(ids)
+ intr = calib.intrinsics.get(serial)
+ K = None if intr is None else np.array([[intr['fx'], 0, intr['cx']], [0, intr['fy'], intr['cy']], [0, 0, 1]])
+ ext = calib.extrinsics.get(str(meta['extrinsics']), {}).get(serial)
+ E = None
+ if ext is not None:
+ E = np.eye(4)
+ E[:3] = np.asarray(ext).reshape(3, 4)
+ E = np.linalg.inv(E)
+ view.camera(K, calibration_locator(calib_path, f'calibration/intrinsics/{serial}_640x480.yml'), E,
+ calibration_locator(calib_path, f'calibration/extrinsics_{meta["extrinsics"]}/extrinsics.yml',
+ conversion='inverse of source T_world_from_camera'))
+ sides = meta.get('mano_sides')
+ if sides not in (['left'], ['right']):
+ raise ValueError(f'expected exactly one anatomical source hand: {sides}')
+ side = sides[0]
+ slot = ['left', 'right'].index(side)
+ cal_name = meta['mano_calib'][0] if isinstance(meta['mano_calib'], list) else meta['mano_calib']
+ labels = [dict(np.load(frames[f]['labels'][0])) if 'labels' in frames[f] else {} for f in ids]
+ label_loc = {'frames_ref': 'camera/frames.jsonl', 'source_field': 'component_sources.labels'}
+ motion = np.full((n, 2, 61), np.nan, np.float32)
+ mv = np.zeros((n, 2), bool)
+ if side in models and any('pose_m' in lab for lab in labels):
+ for i, lab in enumerate(labels):
+ if 'pose_m' not in lab:
+ continue
+ pose = np.asarray(lab['pose_m']).reshape(51)
+ if not np.any(pose): # Official Dex-YCB missing-pose sentinel, independent of joints/seg.
+ continue
+ motion[i, slot] = np.r_[pose[:3], pca_to_axis_angle(pose[3:48], models[side]),
+ calib.betas[cal_name], pose[48:51]]
+ mv[i, slot] = True
+ view.tensor('mano', 'mano/axis_angle61/v1', 'annotation/mano/motion.pt', motion, mv,
+ ['T', 'hand', 'parameter'], label_loc, model_profile=model_profile(models),
+ calibration_source=calibration_locator(calib_path, f'calibration/mano_{cal_name}/mano.yml'),
+ provenance={'kind': 'format_conversion', 'method': 'source PCA @ hands_components + hands_mean; explicit axis-angle consumed with flat_hand_mean=true'})
+ elif any('pose_m' in lab for lab in labels):
+ view.absent('mano', 'Source PCA exists; provide --mano-dir to expand it', 'dependency_missing')
+ else:
+ view.absent('mano', 'No source pose_m field')
+ for source_key, key, dim, unit in [('joint_2d', 'uv', 2, 'pixel'), ('joint_3d', 'xyz', 3, 'm')]:
+ if not any(source_key in x for x in labels):
+ view.absent('keypoints_' + key, 'No source ' + source_key)
+ continue
+ points = np.full((n, 2, 21, dim), np.nan, np.float32)
+ valid = np.zeros((n, 2, 21), bool)
+ for i, lab in enumerate(labels):
+ if source_key not in lab:
+ continue
+ p = np.asarray(lab[source_key]).reshape(21, dim)
+ points[i, slot] = p
+ valid[i, slot] = not np.all(p == -1) # Official sentinel covers the whole source hand.
+ # An individual off-image point (-1, -1) is not evidence of missing annotation.
+ view.tensor('keypoints_' + key, f'keypoints/{key}/v1', f'annotation/keypoints/{key}.pt',
+ points, valid, ['T', 'hand', 'keypoint', 'coordinate'], label_loc,
+ keypoint_schema='mano21/v1', unit=unit)
+ ycb_ids = [int(x) for x in meta['ycb_ids']]
+ for j, oid in enumerate(ycb_ids):
+ entity = f'ycb_{oid:02d}'
+ view.object(entity, meta_loc, source_object_id=oid, category_id=oid)
+ poses = np.full((n, 4, 4), np.nan, np.float32)
+ valid = np.zeros(n, bool)
+ for i, lab in enumerate(labels):
+ if 'pose_y' in lab:
+ pose = np.asarray(lab['pose_y'])
+ if pose.shape != (len(ycb_ids), 3, 4):
+ raise ValueError('pose_y shape differs from ycb_ids')
+ if np.any(pose[j]): # Official all-zero missing object pose sentinel.
+ poses[i] = np.eye(4)
+ poses[i, :3] = pose[j]
+ valid[i] = True
+ if any('pose_y' in lab for lab in labels):
+ view.tensor('pose_' + entity, 'object/rigid/v1', f'annotation/object/{entity}/pose.pt',
+ poses, valid, ['T', 'row', 'column'], label_loc,
+ convention='T_camera_from_object', unit='m')
+ else:
+ view.absent('pose_' + entity, 'No source pose_y field')
+ if oid in assets:
+ mesh_asset(view, entity, assets[oid], assets[oid] / 'textured_simple.obj',
+ locator(asset_archive, f'models/{assets[oid].name}/textured_simple.obj'))
+ else:
+ view.absent('mesh_' + entity, 'models.tar.gz unavailable', 'not_downloaded')
+ if any('seg' in lab for lab in labels):
+ mask_rows = []
+ width, height = view.meta['camera']['image_size']
+ for i, lab in enumerate(labels):
+ values = np.unique(lab.get('seg', np.zeros((height, width), np.uint8)))
+ mappings = []
+ for value in values:
+ value = int(value)
+ if value == 0:
+ continue
+ if value != 255 and value not in ycb_ids:
+ raise ValueError(f'unknown Dex-YCB region value {value}')
+ mappings.append(dict(mask_value=value, category_id='hand' if value == 255 else value,
+ entity_id='hand_' + side if value == 255 else f'ycb_{value:02d}', part_id=None,
+ source_instance_id=value))
+ mask_rows.append(dict(frame_index=i, labels=mappings))
+ view.raster('segment', (lab.get('seg', np.zeros((height, width), np.uint16)) for lab in labels),
+ ['seg' in lab for lab in labels], label_loc, mask_rows)
+ view.meta['components']['segment'].update(coverage={'kind': 'selected_entities',
+ 'entities': ['hand_' + side] + [f'ycb_{o:02d}' for o in ycb_ids]}, source_zero_meaning='background')
+ else:
+ view.absent('segment', 'No source seg field')
+ if any('aligned_depth_to_color' in frames[f] for f in ids):
+ def depths():
+ width, height = view.meta['camera']['image_size']
+ for f in ids:
+ item = frames[f].get('aligned_depth_to_color')
+ if item is None:
+ yield np.full((height, width), np.nan, np.float32)
+ else:
+ with Image.open(item[0]) as im:
+ yield np.asarray(im).astype(np.float32) / 1000.0
+ view.raster('depth', depths(), None, {'frames_ref': 'camera/frames.jsonl',
+ 'source_field': 'component_sources.aligned_depth_to_color', 'conversion': 'millimetres to metres'})
+ else:
+ view.absent('depth', 'No source aligned_depth_to_color frames')
+ grasp = meta.get('ycb_grasp_ind')
+ if grasp is not None:
+ if not 0 <= int(grasp) < len(ycb_ids):
+ raise ValueError('invalid ycb_grasp_ind')
+ relation = dict(relation_type='grasp', subject_id='hand_' + side,
+ object_id=f'ycb_{ycb_ids[int(grasp)]:02d}', status='positive', source_locator=meta_loc,
+ provenance={'kind': 'source', 'method': 'sequence ycb_grasp_ind resolved through ycb_ids; not per-frame contact'})
+ view.relations([{'frame_index': i, 'relations': [relation]} for i in range(n)], np.ones(n, bool), meta_loc)
+ else:
+ view.absent('relations', 'No source grasp/contact relation')
+ return view.finish()
+
+
+MEMBER = re.compile(r'^([^/]+)/(\d{8}_\d{6})/(meta\.yml|(\d{12})/(color|labels|aligned_depth_to_color)_(\d+)\.(jpg|npz|png))$')
+
+
+def convert_archive(archive, args, out, calib, calib_path, models, assets, asset_archive):
+ selected_views = set(args.views.split(',')) if args.views else None
+ done = 0
+ current, meta, meta_loc, rows = None, None, None, {}
+ with tempfile.TemporaryDirectory(prefix='dex-source-') as wd:
+ work = Path(wd)
+ def flush():
+ nonlocal done
+ if not rows:
+ return
+ seq = archive.name.split('.tar')[0] + '_' + current
+ reports = {}
+ with sequence_dir(out, seq) as target:
+ for serial, frames in sorted(rows.items()):
+ reports[serial] = convert_view(target / serial, args, seq, serial, frames, meta,
+ meta_loc, calib, calib_path, models, assets, asset_archive)
+ build_record(out, seq, reports)
+ for staged in list(work.iterdir()):
+ if staged.is_dir():
+ shutil.rmtree(staged)
+ else:
+ staged.unlink()
+ done += 1
+ print(f'Converted {seq}', flush=True)
+ with tarfile.open(archive, 'r|gz') as tf:
+ for member in tf:
+ match = MEMBER.fullmatch(member.name)
+ if not member.isfile() or not match:
+ continue
+ seq = match[2]
+ if seq != current:
+ flush()
+ if args.limit and done >= args.limit:
+ break
+ current, meta, meta_loc, rows = seq, None, None, {}
+ data_kind = match[3]
+ if data_kind == 'meta.yml':
+ data = tf.extractfile(member).read()
+ meta = yaml_load_bytes(data)
+ meta_loc = locator(archive, member.name, data)
+ continue
+ serial, kind, f = match[4], match[5], int(match[6])
+ if selected_views and serial not in selected_views or args.max_frames and f >= args.max_frames:
+ continue
+ data = tf.extractfile(member).read()
+ dest = safe_member(work, member.name)
+ dest.parent.mkdir(parents=True, exist_ok=True)
+ dest.write_bytes(data)
+ row = rows.setdefault(serial, {}).setdefault(f, {})
+ if kind in row:
+ raise ValueError('duplicate archive frame')
+ row[kind] = (dest, locator(archive, member.name, data))
+ else:
+ flush()
+ return done
+
+
+def main(argv=None):
+ p = parser('dex-ycb', __doc__)
+ p.add_argument('--subjects')
+ args = p.parse_args(argv)
+ archives = sorted({a for r in roots(args.root) for a in r.glob('*-subject-*.tar.gz')})
+ if args.subjects:
+ subjects = {s.strip().zfill(2) for s in args.subjects.split(',')}
+ archives = [a for a in archives if re.search(r'subject-(\d+)', a.name)[1] in subjects]
+ if not archives:
+ p.error('no Dex-YCB subject archives found')
+ cp = find_file(args.root, 'calibration.tar.gz') or find_file(args.root, 'calibration')
+ if cp is None:
+ p.error('calibration source missing')
+ calib = Calibration(cp.parent)
+ models = mano_models(args.mano_dir)
+ out = release(args, 'dex-ycb')
+ with tempfile.TemporaryDirectory(prefix='dex-models-') as wd:
+ assets, asset_archive = prepare_models(args.root, Path(wd))
+ for archive in archives:
+ convert_archive(archive, args, out, calib, cp, models, assets, asset_archive)
+ return 0
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/convert_h2o_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/convert_h2o_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..f55e39721c261ad4483ad9ff45d3e15de817cbdf
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/convert_h2o_v1.0.py
@@ -0,0 +1,527 @@
+#!/usr/bin/env python3
+"""H2O native RGB-D, MANO, joints and objects → CONTRACT.md v1.0 (no rendered masks)."""
+from concurrent.futures import ThreadPoolExecutor
+import json
+import os
+import re
+import shutil
+import tarfile
+import tempfile
+from pathlib import Path
+import zipfile
+
+# Bound native libraries before loading NumPy/Torch; each worker still uses independent FFmpeg processes.
+for key in ('OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS'):
+ os.environ[key] = '1'
+
+import numpy as np
+from PIL import Image
+import torch
+
+from warehouse_v1_0 import (View, build_record, check_mesh, find_file, locator, mano_models, mesh_asset,
+ model_profile, parser, release, roots, safe_member, sequence_dir, sha256)
+
+OBJECTS = {1: 'book', 2: 'espresso', 3: 'lotion', 4: 'spray',
+ 5: 'milk', 6: 'cocoa', 7: 'chips', 8: 'cappuccino'}
+MEMBER = re.compile(r'^(?:h2o_CASA/)?(subject\d+)/([^/]+)/(\d+)/(cam\d)/(.+)$')
+FRAME = re.compile(r'(rgb|depth|cam_pose|hand_pose|hand_pose_mano|obj_pose|obj_pose_rt)/(\d+)\.(png|jpg|txt)$')
+
+
+def load_splits(root):
+ archive = find_file(root, 'label_split.zip')
+ result = {}
+ if archive is not None:
+ with zipfile.ZipFile(archive) as z:
+ for split in ('train', 'val', 'test'):
+ member = f'label_split/action_{split}.txt'
+ for line_no, line in enumerate(z.read(member).decode().splitlines(), 1):
+ fields = line.split()
+ if len(fields) < 2 or fields[1] == 'path':
+ continue
+ key = fields[1]
+ if key in result and result[key][0] != split:
+ raise ValueError('conflicting official splits')
+ result[key] = (split, locator(archive, member, record=line_no))
+ return result
+
+
+def load_assets(root, work):
+ archive = find_file(root, 'object.zip')
+ if archive is None:
+ return {}, None
+ with zipfile.ZipFile(archive) as z:
+ for member in z.infolist():
+ if member.is_dir():
+ continue
+ path = safe_member(work, member.filename)
+ path.parent.mkdir(parents=True, exist_ok=True)
+ path.write_bytes(z.read(member))
+ result = {}
+ for oid, name in OBJECTS.items():
+ directory = work / 'object' / name
+ aliases = []
+ # Source cocoa.obj -> ovomaltine.mtl -> cocoa.png, but the ZIP names the
+ # supplied texture ovomaltine.png. Keep all originals and add a byte-identical alias.
+ if name == 'cocoa' and not (directory / 'cocoa.png').exists():
+ mtl = directory / 'ovomaltine.mtl'
+ if mtl.is_file() and 'map_Kd cocoa.png' in mtl.read_text().splitlines():
+ texture = directory / 'ovomaltine.png'
+ shutil.copyfile(texture, directory / 'cocoa.png')
+ aliases.append({'file': 'cocoa.png', 'sha256': sha256(texture),
+ 'source': locator(archive, 'object/cocoa/ovomaltine.png')})
+ candidates = list(directory.glob('*.obj'))
+ if len(candidates) != 1:
+ raise ValueError(f'expected one source OBJ for {name}')
+ check_mesh(candidates[0], directory) # Fail before decoding any large subject archive.
+ result[oid] = (directory, candidates[0], aliases)
+ return result, archive
+
+
+def convert_view(path, args, seq, cam, rows, intr, splits, models, assets, asset_archive):
+ codec_threads = getattr(args, 'codec_threads', 1)
+ ids = list(range(min(rows), max(rows) + 1))
+ for f in ids:
+ rows.setdefault(f, {})
+ n = len(ids)
+ if not n:
+ raise ValueError('no RGB frames')
+ split, split_loc = splits.get(seq, ('unknown', None))
+ frame_rows = [dict(source_frame_id=f, sync_id=f'{seq}:{f}', source_locator=rows[f]['rgb'][1] if 'rgb' in rows[f] else {'sequence': seq, 'camera': cam, 'source_frame_id': f, 'missing_rgb': True},
+ component_sources={k: item[1] for k, item in rows[f].items()},
+ annotation_matches={k: dict(source_frame_id=f, source_timestamp_ns=None,
+ clock_domain=None, match_method='index_map', delta_ns=None)
+ for k in rows[f] if k != 'rgb'}) for f in ids]
+ view = View(path, 'H2O', args.release_id, seq.replace('/', '_'), cam, frame_rows, split=split)
+ view.valid_rules = {
+ 'mano': 'Per-hand hand_pose_mano flag equals 1; corresponding side model available',
+ 'keypoints': "Per-hand hand_pose flag equals 1, broadcast to that hand's 21 source points independently of MANO",
+ 'pose': 'Source obj_pose_rt class equals this nonzero object class',
+ 'points': 'Source obj_pose class equals this nonzero object class, broadcast to its 21 points',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta['view_type'] = 'ego' if cam == 'cam4' else 'exo' if cam in ('cam0', 'cam1', 'cam2', 'cam3') else 'unknown'
+ view.meta['sampling']['selection'] = {'max_frames': args.max_frames, 'views': args.views}
+ if split_loc:
+ view.meta.update(split_scheme='official_action_split', split_source_locator=split_loc)
+ # In the measured 275 H2O views, exact FFV1 was always smaller than exact H.264.
+ view.rgb([rows[f]['rgb'][0] if 'rgb' in rows[f] else None for f in ids],
+ {'frames_ref': 'camera/frames.jsonl'}, ffv1_first=True, codec_threads=codec_threads)
+ loc = {'frames_ref': 'camera/frames.jsonl', 'source_field': 'component_sources'}
+ if intr is not None:
+ fx, fy, cx, cy, width, height = np.loadtxt(intr[0]).ravel()
+ if [int(width), int(height)] != view.meta['camera']['image_size']:
+ raise ValueError('source intrinsics resolution mismatch')
+ K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
+ view.camera(K, intr[1])
+ camps = np.full((n, 4, 4), np.nan, np.float32)
+ cv = np.zeros(n, bool)
+ data = []
+ for i, f in enumerate(ids):
+ lab = {k: np.loadtxt(item[0]).ravel() for k, item in rows[f].items() if k not in ('rgb', 'depth')}
+ data.append(lab)
+ if 'cam_pose' in lab:
+ camps[i] = np.linalg.inv(lab['cam_pose'].reshape(4, 4))
+ cv[i] = True
+ if cv.any():
+ view.tensor('extrinsic', 'transform/se3/v1', 'camera/extrinsic.pt', camps, cv,
+ ['T', 'row', 'column'], loc, convention='T_camera_from_world',
+ source_convention='T_world_from_camera')
+ motion, mv = np.full((n, 2, 61), np.nan, np.float32), np.zeros((n, 2), bool)
+ points, pv = np.full((n, 2, 21, 3), np.nan, np.float32), np.zeros((n, 2, 21), bool)
+ object_ids = set()
+ for i, lab in enumerate(data):
+ if 'hand_pose_mano' in lab:
+ hands = lab['hand_pose_mano'].reshape(2, 62)
+ for side, row in enumerate(hands):
+ if row[0] not in (0, 1):
+ raise ValueError('unexpected H2O MANO presence flag')
+ if row[0] == 1:
+ motion[i, side] = np.r_[row[4:52], row[52:62], row[1:4]]
+ mv[i, side] = True
+ if 'hand_pose' in lab:
+ hands = lab['hand_pose'].reshape(2, 64)
+ for side, row in enumerate(hands):
+ if row[0] not in (0, 1):
+ raise ValueError('unexpected H2O keypoint presence flag')
+ if row[0] == 1:
+ points[i, side] = row[1:].reshape(21, 3)
+ pv[i, side] = True
+ for key in ('obj_pose', 'obj_pose_rt'):
+ if key in lab and lab[key][0] != 0:
+ object_ids.add(int(lab[key][0]))
+ if any('hand_pose_mano' in lab for lab in data):
+ if len(models) == 2:
+ view.tensor('mano', 'mano/axis_angle61/v1', 'annotation/mano/motion.pt', motion, mv,
+ ['T', 'hand', 'parameter'], loc, model_profile=model_profile(models))
+ else:
+ view.absent('mano', 'Source parameters exist; --mano-dir must identify both model profiles', 'dependency_missing')
+ if any('hand_pose' in lab for lab in data):
+ view.tensor('keypoints_xyz', 'keypoints/xyz/v1', 'annotation/keypoints/xyz.pt', points, pv,
+ ['T', 'hand', 'keypoint', 'coordinate'], loc, keypoint_schema='mano21/v1', unit='m')
+ for oid in sorted(object_ids):
+ if oid not in OBJECTS:
+ raise ValueError(f'unknown H2O object class {oid}')
+ entity = OBJECTS[oid]
+ view.object(entity, loc, category_id=oid, source_object_id=oid)
+ poses, ov = np.full((n, 4, 4), np.nan, np.float32), np.zeros(n, bool)
+ pts, kv = np.full((n, 21, 3), np.nan, np.float32), np.zeros((n, 21), bool)
+ for i, lab in enumerate(data):
+ if 'obj_pose_rt' in lab and lab['obj_pose_rt'][0] == oid:
+ poses[i] = lab['obj_pose_rt'][1:].reshape(4, 4)
+ ov[i] = True
+ if 'obj_pose' in lab and lab['obj_pose'][0] == oid:
+ pts[i] = lab['obj_pose'][1:].reshape(21, 3)
+ kv[i] = True
+ if ov.any():
+ view.tensor('pose_' + entity, 'object/rigid/v1', f'annotation/object/{entity}/pose.pt',
+ poses, ov, ['T', 'row', 'column'], loc, convention='T_camera_from_object', unit='m')
+ if kv.any():
+ view.tensor('points_' + entity, 'object_keypoints/camera/v1',
+ f'annotation/object/{entity}/keypoints/xyz.pt', pts, kv,
+ ['T', 'keypoint', 'coordinate'], loc, keypoint_schema='H2O_official_object21',
+ keypoint_names=['center'] + [f'corner_{i}' for i in range(8)] + [f'edge_midpoint_{i}' for i in range(12)],
+ source_order='official obj_point_order.jpg: center, 8 corners, 12 edge midpoints', unit='m')
+ if oid in assets:
+ directory, obj, aliases = assets[oid]
+ asset_loc = locator(asset_archive, f'object/{entity}/{obj.name}')
+ if aliases:
+ asset_loc['texture_aliases'] = aliases
+ mesh_asset(view, entity, directory, obj, asset_loc)
+ else:
+ view.absent('mesh_' + entity, 'Source object.zip unavailable', 'not_downloaded')
+ if any('depth' in rows[f] for f in ids):
+ width, height = view.meta['camera']['image_size']
+ def depths():
+ for f in ids:
+ item = rows[f].get('depth')
+ if item:
+ with Image.open(item[0]) as im:
+ yield np.asarray(im).astype(np.float32) / 1000.0
+ else:
+ yield np.full((height, width), np.nan, np.float32)
+ view.raster('depth', depths(), None, loc | {'source_field': 'component_sources.depth',
+ 'definition': 'https://github.com/taeinkwon/h2oplayer/blob/main/utils.py',
+ 'conversion': 'registered millimetre camera-Z to metres; no viewer range clipping'},
+ codec_threads=codec_threads)
+ else:
+ view.absent('depth', 'No source depth frames')
+ view.absent('segment', 'H2O provides no native segmentation; rendering deferred to v2.0')
+ view.absent('relations', 'No source contact/grasp annotation')
+ return view.finish()
+
+
+def convert_archive(archive, args, out, splits, models, assets, asset_archive):
+ selected_views = set(args.views.split(',')) if args.views else None
+ subjects = {'subject' + s.strip() for s in args.subjects.split(',')} if args.subjects else None
+ current, rows, intrinsics, done = None, {}, {}, 0
+ with tempfile.TemporaryDirectory(prefix='h2o-source-') as wd:
+ work = Path(wd)
+ def flush():
+ nonlocal done
+ if not rows:
+ return
+ seq = '/'.join(current)
+ reports, pending = {}, {}
+ for cam, frames in sorted(rows.items()):
+ existing = out / 'data' / seq.replace('/', '_') / cam
+ if (existing / 'metainfo.json').is_file():
+ old_frames = [json.loads(line) for line in (existing / 'camera/frames.jsonl').read_text().splitlines()]
+ if [f['source_frame_id'] for f in old_frames] != list(range(min(frames), max(frames) + 1)):
+ raise ValueError('existing view has different sampling; choose a new release-id')
+ for frame in old_frames:
+ incoming = frames.get(frame['source_frame_id'], {})
+ previous = frame.get('component_sources', {})
+ if set(previous) != set(incoming) or any(previous[k].get('sha256') != v[1].get('sha256') for k, v in incoming.items()):
+ raise ValueError('duplicate H2O view differs from the published source; choose a new release-id')
+ else:
+ pending[cam] = frames
+ if pending:
+ with sequence_dir(out, seq.replace('/', '_')) as target:
+ for cam, frames in pending.items():
+ reports[cam] = convert_view(target / cam, args, seq, cam, frames,
+ intrinsics.get(cam), splits, models, assets, asset_archive)
+ build_record(out, seq.replace('/', '_'), reports)
+ for staged in list(work.iterdir()):
+ if staged.is_dir():
+ shutil.rmtree(staged)
+ else:
+ staged.unlink()
+ done += 1
+ print(f'{"Converted" if pending else "Verified existing"} {seq}', flush=True)
+ # Uncompressed CASA supports seeking over large frames; gzip is single-pass.
+ mode = 'r:' if archive.suffix == '.tar' else 'r|gz'
+ with tarfile.open(archive, mode) as tf:
+ for member in tf:
+ match = MEMBER.fullmatch(member.name)
+ if not member.isfile() or not match:
+ continue
+ subj, scene, seq, cam, tail = match.groups()
+ if subjects and subj not in subjects:
+ continue
+ key = (subj, scene, seq)
+ if key != current:
+ flush()
+ if args.limit and done >= args.limit:
+ break
+ current, rows, intrinsics = key, {}, {}
+ if selected_views and cam not in selected_views:
+ continue
+ frame = FRAME.fullmatch(tail)
+ if tail != 'cam_intrinsics.txt' and not frame:
+ continue
+ if frame and args.max_frames and int(frame[2]) >= args.max_frames:
+ continue
+ data = tf.extractfile(member).read()
+ dest = safe_member(work, member.name)
+ dest.parent.mkdir(parents=True, exist_ok=True)
+ dest.write_bytes(data)
+ item = (dest, locator(archive, member.name, data))
+ if tail == 'cam_intrinsics.txt':
+ intrinsics[cam] = item
+ else:
+ kind, f = frame[1], int(frame[2])
+ row = rows.setdefault(cam, {}).setdefault(f, {})
+ if kind in row:
+ raise ValueError('duplicate H2O frame')
+ row[kind] = item
+ else:
+ flush()
+ return done
+
+
+def main(argv=None):
+ p = parser('H2O', __doc__)
+ p.set_defaults(release_id='shared-v1.0')
+ p.add_argument('--workers', type=int, choices=range(1, 5), default=1,
+ help='Independent subject archives in parallel, 1..4; benchmark before using >2')
+ p.add_argument('--codec-threads', type=int, choices=(1, 2, 4, 8), default=4,
+ help='FFV1 slice threads per encode; 4 chosen from full-view RGB benchmark')
+ p.add_argument('--subjects')
+ p.add_argument('--include-casa', action='store_true')
+ p.add_argument('--archive', type=Path, help='Convert one explicit source archive')
+ args = p.parse_args(argv)
+ if args.include_casa and args.workers != 1:
+ p.error('CASA can share sequence IDs with subject archives; convert it sequentially in a separate release')
+ archives = [args.archive.expanduser()] if args.archive else sorted(
+ {a for root in roots(args.root) for a in root.glob('subject*_v1_1.tar.gz')})
+ if args.subjects and not args.archive:
+ requested = {'subject' + s.strip() for s in args.subjects.split(',')}
+ archives = [arc for arc in archives if arc.name.split('_', 1)[0] in requested]
+ if args.include_casa:
+ casa = find_file(args.root, 'h2o_CASA.tar')
+ if casa:
+ archives.append(casa)
+ if not archives:
+ p.error('no H2O archives found')
+ out = release(args, 'H2O')
+ source_mapping = next(p for p in (Path(__file__).resolve().parents[1] / 'docs/h2o-RAW_TO_SHARED.md',
+ Path(__file__).resolve().parents[1] / 'doc/RAW_TO_SHARED.md',
+ out / 'doc/RAW_TO_SHARED.md') if p.is_file())
+ mapping = out / 'doc/RAW_TO_SHARED.md'
+ if mapping.exists() and mapping.read_bytes() != source_mapping.read_bytes():
+ raise ValueError('RAW_TO_SHARED.md changed; choose a new release-id')
+ if not mapping.exists():
+ shutil.copyfile(source_mapping, mapping)
+ torch.set_num_threads(1)
+ models, splits = mano_models(args.mano_dir), load_splits(args.root)
+ with tempfile.TemporaryDirectory(prefix='h2o-assets-') as wd:
+ assets, asset_archive = load_assets(args.root, Path(wd))
+ def convert_one(archive):
+ return convert_archive(archive, args, out, splits, models, assets, asset_archive)
+ if args.workers == 1:
+ for archive in archives:
+ convert_one(archive)
+ else:
+ with ThreadPoolExecutor(max_workers=args.workers) as pool:
+ for archive, count in zip(archives, pool.map(convert_one, archives), strict=True):
+ print(f'Archive {archive.name}: {count} sequences', flush=True)
+ return 0
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
+
+# TODO(v2.0): 以下旧 MANO 网格和渲染分割代码仅保留备查；v1.0 不生成标注。
+# def load_mano_model(path: Path):
+# """MPI MANO pkl → numpy 字典。shapedirs 在 pkl 内为 chumpy Select（对 (778,3,20) 基
+# 底 flat 选取前 10 个 shape 分量；idxs 已核实等价于 base[:,:,:10]）。"""
+# with open(path, "rb") as f:
+# d = _ManoUnpickler(f, encoding="latin1").load()
+# sd = d["shapedirs"]
+# base = np.asarray(sd.a.x, np.float64) # (778, 3, 20)
+# shapedirs = base.reshape(-1)[np.asarray(sd.idxs)].reshape(base.shape[0], 3, -1)
+# jreg = d["J_regressor"]
+# jreg = np.asarray(jreg.todense()) if hasattr(jreg, "todense") else np.asarray(jreg)
+# return {"v_template": np.asarray(d["v_template"], np.float64),
+# "shapedirs": shapedirs,
+# "posedirs": np.asarray(d["posedirs"], np.float64),
+# "weights": np.asarray(d["weights"], np.float64),
+# "J_regressor": np.asarray(jreg, np.float64),
+# "kintree": np.asarray(d["kintree_table"], np.int64),
+# "faces": np.asarray(d["f"], np.int64)}
+#
+#
+# def rodrigues(r):
+# """axis-angle (N,3) → 旋转矩阵 (N,3,3)。公式 I + sinθ·K + (1−cosθ)·K²（已对 scipy 核验）。"""
+# r = np.asarray(r, np.float64).reshape(-1, 3)
+# th = np.linalg.norm(r, axis=1)
+# R = np.zeros((len(r), 3, 3))
+# nz = th > 1e-12
+# R[~nz] = np.eye(3)
+# k = r[nz] / th[nz, None]
+# K = np.zeros((len(k), 3, 3))
+# K[:, 0, 1], K[:, 0, 2], K[:, 1, 2] = -k[:, 2], k[:, 1], -k[:, 0]
+# K[:, 1, 0], K[:, 2, 0], K[:, 2, 1] = k[:, 2], -k[:, 1], k[:, 0]
+# s, c = np.sin(th[nz])[:, None, None], np.cos(th[nz])[:, None, None]
+# R[nz] = np.eye(3) + s * K + (1 - c) * (K @ K)
+# return R
+#
+#
+# def _mano_chain(model, betas, pose):
+# """返回 (v_shaped, J, Rs, G)：G 为 16 个关节的全局变换（平移列 = 关节位置）。"""
+# v = model["v_template"] + np.einsum("nkc,c->nk", model["shapedirs"], betas)
+# J = model["J_regressor"] @ v
+# Rs = rodrigues(np.asarray(pose, np.float64).reshape(16, 3))
+# kt = model["kintree"]
+# G = np.zeros((16, 4, 4))
+# for j in range(16):
+# p = kt[0, j] if j else -1
+# loc = np.eye(4)
+# loc[:3, :3] = Rs[j]
+# loc[:3, 3] = J[j] - (J[p] if j else 0.0)
+# G[j] = loc if not j else G[p] @ loc
+# return v, J, Rs, G
+#
+#
+# def mano_forward(model, betas, pose, transl):
+# """MANO LBS 前向（经典蒙皮 M_j = G_j·G0_j⁻¹；对 hand_pose 21 关节实证 0.0 mm）。
+# pose: 48 axis-angle（[0:3] 全局）；返回相机系米顶点 (778,3)。"""
+# v, J, Rs, G = _mano_chain(model, betas, pose)
+# lrot = (Rs[1:] - np.eye(3)).ravel()
+# v_posed = v + np.einsum("nkc,c->nk", model["posedirs"], lrot)
+# kt = model["kintree"]
+# G0 = np.zeros((16, 4, 4))
+# for j in range(16):
+# p = kt[0, j] if j else -1
+# loc = np.eye(4)
+# loc[:3, 3] = J[j] - (J[p] if j else 0.0)
+# G0[j] = loc if not j else G0[p] @ loc
+# M = np.einsum("nj,jab->nab", model["weights"], G @ np.linalg.inv(G0))
+# out = np.einsum("nab,nb->na", M[:, :3, :3], v_posed) + M[:, :3, 3]
+# return out + np.asarray(transl, np.float64)
+#
+#
+# def mano_chain_joints(model, betas, pose, transl):
+# """16 个关节位置（相机系米）= 链平移列；自检用。"""
+# _, _, _, G = _mano_chain(model, betas, pose)
+# return G[:, :3, 3] + np.asarray(transl, np.float64)
+#
+#
+# def rasterize(verts_cam, faces, labels, W, H, fx, fy, cx, cy):
+# z = verts_cam[:, 2]
+# P = faces
+# ok = (z[P] > 0.02).all(1) # 任一顶点在相机后 → 丢弃该三角形
+# P, labels = P[ok], labels[ok]
+# if len(P) == 0:
+# return np.zeros((H, W), np.uint16), np.zeros(0, np.int64)
+# x = verts_cam[:, 0] / z * fx + cx
+# y = verts_cam[:, 1] / z * fy + cy
+# xa = np.maximum(0, np.minimum(np.floor(x[P[:, 0]]), np.minimum(
+# np.floor(x[P[:, 1]]), np.floor(x[P[:, 2]]))).astype(np.int64))
+# xb = np.minimum(W - 1, np.maximum(np.ceil(x[P[:, 0]]), np.maximum(
+# np.ceil(x[P[:, 1]]), np.ceil(x[P[:, 2]]))).astype(np.int64))
+# ya = np.maximum(0, np.minimum(np.floor(y[P[:, 0]]), np.minimum(
+# np.floor(y[P[:, 1]]), np.floor(y[P[:, 2]]))).astype(np.int64))
+# yb = np.minimum(H - 1, np.maximum(np.ceil(y[P[:, 0]]), np.maximum(
+# np.ceil(y[P[:, 1]]), np.ceil(y[P[:, 2]]))).astype(np.int64))
+# xa, ya = np.minimum(xa, xb), np.minimum(ya, yb) # 完全出画的三角形 → 空 bbox
+# wdt, hgt = xb - xa + 1, yb - ya + 1
+# cnt = wdt * hgt
+# tot = int(cnt.sum())
+# tri = np.repeat(np.arange(len(P)), cnt)
+# k = np.arange(tot) - (np.cumsum(cnt) - cnt)[tri]
+# px = xa[tri] + k % wdt[tri] + 0.5
+# py = ya[tri] + k // wdt[tri] + 0.5
+# ax, ay = x[P[tri, 0]], y[P[tri, 0]]
+# bx, by = x[P[tri, 1]], y[P[tri, 1]]
+# cx_, cy_ = x[P[tri, 2]], y[P[tri, 2]]
+# area = ((x[P[:, 1]] - x[P[:, 0]]) * (y[P[:, 2]] - y[P[:, 0]])
+# - (y[P[:, 1]] - y[P[:, 0]]) * (x[P[:, 2]] - x[P[:, 0]]))
+# areat = area[tri]
+# e0 = (bx - ax) * (py - ay) - (by - ay) * (px - ax)
+# e1 = (cx_ - bx) * (py - by) - (cy_ - by) * (px - bx)
+# e2 = (ax - cx_) * (py - cy_) - (ay - cy_) * (px - cx_)
+# s = np.sign(areat)
+# inside = (np.abs(areat) > 1e-9) & ((e0 * s >= 0) & (e1 * s >= 0) & (e2 * s >= 0))
+# px, py, tri = px[inside], py[inside], tri[inside]
+# at = areat[inside]
+# w0, w1, w2 = e1[inside] / at, e2[inside] / at, e0[inside] / at
+# zz = w0 * z[P[tri, 0]] + w1 * z[P[tri, 1]] + w2 * z[P[tri, 2]]
+# pix = py.astype(np.int64) * W + px.astype(np.int64)
+# order = np.lexsort((zz, pix)) # 同像素按深度升序 → 首条即最近
+# pix, tri = pix[order], tri[order]
+# first = np.unique(pix, return_index=True)[1]
+# mask = np.zeros(H * W, np.uint16)
+# win = tri[first]
+# mask[pix[first]] = labels[win] + 1 # 临时标签 +1（0=背景）
+# return mask.reshape(H, W), np.bincount(labels[win] + 1)
+#
+#
+# # ============================================================================
+# # ffmpeg 写出与核验（与 convert_dexycb.py 同构）
+# # ============================================================================
+# verts, faces_all, labels = [], [], []
+# for ti, (tid, kind) in enumerate(tracks):
+# if kind == "hand_left" and b["mano"][0] > 0.5:
+# verts.append(mano_forward(mano_models["left"], b["mano"][52:62],
+# b["mano"][4:52], b["mano"][1:4]))
+# faces_all.append(mano_models["left"]["faces"])
+# elif kind == "hand_right" and b["mano"][62] > 0.5:
+# verts.append(mano_forward(mano_models["right"], b["mano"][114:124],
+# b["mano"][66:114], b["mano"][63:66]))
+# faces_all.append(mano_models["right"]["faces"])
+# elif isinstance(kind, tuple) and int(b["objp"][0]) == kind[1]:
+# M = np.asarray(b["objrt"][1:17], np.float64).reshape(4, 4)
+# V0, F0 = meshes[kind[1]]
+# verts.append(V0 @ M[:3, :3].T + M[:3, 3])
+# faces_all.append(F0)
+# else:
+# continue
+# labels.append(np.full(len(faces_all[-1]), ti))
+# if verts:
+# V = np.concatenate(verts)
+# offs = np.cumsum([0] + [len(v) for v in verts[:-1]])
+# F = np.concatenate([fa + o for fa, o in zip(faces_all, offs)])
+# mask, counts = rasterize(V, F, np.concatenate(labels),
+# IMG_W, IMG_H, fx, fy, cx, cy)
+# else:
+# mask, counts = np.zeros((IMG_H, IMG_W), np.uint16), np.zeros(0, np.int64)
+# lut = np.zeros(n_tracks + 1, np.uint16)
+# for ti in range(n_tracks):
+# present = ti + 1 < len(counts) and counts[ti + 1] > 0
+# if present:
+# presence[tracks[ti][0]][f] = True
+# if active_id[ti] is None:
+# pid = next(i for i in range(1, 65535) if i not in used)
+# used.add(pid)
+# active_id[ti], seg_start[ti] = pid, f
+# frame_maps[ti].append({"id": pid})
+# lut[ti + 1] = active_id[ti]
+# elif active_id[ti] is not None:
+# frame_maps[ti][-1]["frames"] = [seg_start[ti], f - 1]
+# segments[ti].append([seg_start[ti], f - 1])
+# used.discard(active_id[ti])
+# active_id[ti] = None
+# mw.write(lut[mask])
+
+
+# TODO(v2.0): 旧物体尺寸派生代码；v1.0 仅保留源物体关键点。
+# pts = np.asarray(b["objp"][1:64], np.float64).reshape(21, 3)
+# half = np.abs((pts[1:9] - pts[0]) @ M[:3, :3]).max(0)
+# obj_recs.append({
+# "track_id": f"o{classes.index(c)}", "frame": f,
+# "mesh_ref": f"h2o/{OBJ_CLASSES[c]}.obj",
+# "pose": {"quat": rotmat_to_quat_wxyz(M[:3, :3])[0].tolist(),
+# "trans": M[:3, 3].tolist()},
+# "bbox_center3d": pts[0].tolist(),
+# "bbox_size": (2 * half).tolist()})
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/convert_hoi4d_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/convert_hoi4d_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d5914d64ae72ee62564897743dfe5a41923e08d
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/convert_hoi4d_v1.0.py
@@ -0,0 +1,595 @@
+#!/usr/bin/env python3
+"""HOI4D native observations and annotations → CONTRACT.md v1.0."""
+from collections import Counter
+from concurrent.futures import ThreadPoolExecutor
+from contextlib import ExitStack
+from functools import lru_cache
+import gzip
+import io
+import json
+import os
+from pathlib import Path
+import pickle
+import re
+import shutil
+import shlex
+import posixpath
+import subprocess
+import tempfile
+import tarfile
+import threading
+import zipfile
+
+# Each worker owns one Python thread; prevent native libraries spawning CPU pools.
+for key in ('OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS', 'NUMEXPR_NUM_THREADS'):
+ os.environ[key] = '1'
+
+import numpy as np
+from PIL import Image
+from scipy.spatial.transform import Rotation
+
+import torch
+
+from warehouse_v1_0 import (View, build_record, check_mesh, decode_args, find_file, json_write, locator,
+ mano_models, model_profile, parser, probe, release, run, safe_member, sequence_dir)
+
+CATEGORIES = {
+ 1: dict(cad="rigid/ToyCar", groups=[("main", [1], "Toy Car", 1)],
+ hands={2: "right"}, objmap={"Toycar": "main"}),
+ 2: dict(cad="rigid/Mug", groups=[("a", [1], "Mug", 2), ("b", [3], "Mug", 2)],
+ hands={2: "right"}, objmap={"Watercup": "a"}),
+ 3: dict(cad="articulated/Laptop", groups=[("main", [1, 3], "Laptop", 3)],
+ hands={2: "right"},
+ objmap={"Laptopdisplay": "main", "Laptopkeyboard": "main"}),
+ 4: dict(cad="articulated/StorageFurniture",
+ groups=[("main", [1, 3, 4], "Storage Furniture", 4),
+ ("bottle", [5], "Bottle", 5)],
+ hands={2: "right"},
+ objmap={"Lockerbody": "main", "Lockersldingdoor": "main",
+ "Lockerdrawer": "main", "Safebox": "main"}),
+ 5: dict(cad="rigid/Bottle", groups=[("main", [1], "Bottle", 5),
+ ("mug", [3], "Mug", 2)],
+ hands={2: "right"}, objmap={"bottleddrinks": "main"}),
+ 6: dict(cad="articulated/Safe", groups=[("main", [1, 3], "Safe", 6),
+ ("bottle", [4], "Bottle", 5)],
+ hands={2: "right"}, objmap={"Safebox": "main", "Safedoor": "main"}),
+ 7: dict(cad="rigid/Bowl", groups=[("main", [1], "Bowl", 7),
+ ("ball", [3], "Ball", None)],
+ hands={2: "right"}, objmap={"bowl": "main"}),
+ 8: dict(cad="articulated/Bucket",
+ groups=[("a", [1, 3], "Bucket", 8), ("b", [4, 5], "Bucket", 8)],
+ hands={2: "right", 6: "left"},
+ objmap={"bucket": "a", "Buckethandle": "a"}),
+ 9: dict(cad="articulated/Scissors", groups=[("main", [1, 3], "Scissors", 9)],
+ hands={2: "right"},
+ objmap={"scissors": "main", "scissors2": None}), # scissors2：场景道具，无掩码
+ 11: dict(cad="articulated/Pliers", groups=[("main", [1, 3], "Pliers", 11)],
+ hands={2: "right"}, objmap={"Pliersleft": "main", "Pliersright": "main"}),
+ 12: dict(cad="rigid/Kettle", groups=[("main", [1], "Kettle", 12),
+ ("mug", [3], "Mug", 2)],
+ hands={2: "right"}, objmap={"kettle": "main"}),
+ 13: dict(cad="rigid/Knife", groups=[("main", [1], "Knife", 13),
+ ("apple", [3], "Apple", None)],
+ hands={2: "right"}, objmap={"knife": "main"}),
+ 14: dict(cad="articulated/TrashCan",
+ groups=[("main", [1, 3], "Trash Can", 14), ("bottle", [4], "Bottle", 5)],
+ hands={2: "right"}, objmap={"Dustbinbase": "main", "Dustbincover": "main"}),
+ 17: dict(cad="articulated/Lamp", groups=[("main", [1, 3, 4, 5], "Lamp", 17)],
+ hands={2: "right", 6: "left"},
+ objmap={"Lampbase": "main", "Tablelampfirstjoint": "main",
+ "Tablelampsecondjoint": "main", "Thirdjointoftablelamp": "main"}),
+ 18: dict(cad="articulated/Stapler",
+ groups=[("main", [1, 3, 4], "Stapler", 18), ("paper", [5], "Paper", None)],
+ hands={2: "right", 6: "left"}, # 6=左手：官方 label.csv 缺列，实测修正
+ objmap={"Staplerbase": "main", "Staplercover": "main"}),
+ 20: dict(cad="rigid/Chair", groups=[("main", [1], "Chair", 20)],
+ hands={2: "right", 3: "left"}, objmap={"chair": "main"}),
+}
+
+
+# Official HOI4D definitions/motion segmentation/label.csv; no spatial side inference.
+REGION_NAMES = {1: {1: 'Toy Car', 2: 'Right Hand'}, 2: {1: 'Mug', 2: 'Right Hand', 3: 'Mug'}, 3: {1: 'Screen', 2: 'Right Hand', 3: 'Keyboard'}, 4: {1: 'Door', 2: 'Right Hand', 3: 'Body of the Cabinet', 4: 'Drawer', 5: 'Bottle'}, 5: {1: 'Bottle', 2: 'Right Hand', 3: 'Mug'}, 6: {1: 'Body of the Safe', 2: 'Right Hand', 3: 'Door', 4: 'Bottle'}, 7: {1: 'Bowl', 2: 'Right Hand', 3: 'Ball'}, 8: {1: 'Body of the Bucket', 2: 'Right Hand', 3: 'Handle', 4: 'Body of the Bucket', 5: 'Handle', 6: 'Left Hand'}, 9: {1: 'Left Part of the Scissors', 2: 'Right Hand', 3: 'Right Part of the Scissors'}, 11: {1: 'Left Part of the Pliers', 2: 'Right Hand', 3: 'Right Part of the Pliers'}, 12: {1: 'Kettle', 2: 'Right Hand', 3: 'Mug'}, 13: {1: 'Knife', 2: 'Right Hand', 3: 'Apple'}, 14: {1: 'Body of the Trash Can', 2: 'Right Hand', 3: 'Lid', 4: 'Bottle'}, 17: {1: 'Base', 2: 'Right Hand', 3: 'Tube', 4: 'Top Link', 5: 'Bottom Link', 6: 'Left Hand'}, 18: {1: 'Base', 2: 'Right Hand', 3: 'Slot', 4: 'Lid', 5: 'Paper'}, 20: {1: 'Chair', 2: 'Right Hand', 3: 'Left Hand'}}
+
+
+class SplitFiles(io.RawIOBase):
+ """Read concatenated source gzip parts without concatenating 119 GB on disk."""
+ def __init__(self, paths):
+ self.paths = iter(paths)
+ self.current = None
+
+ def readable(self):
+ return True
+
+ def readinto(self, buffer):
+ while True:
+ if self.current is None:
+ path = next(self.paths, None)
+ if path is None:
+ return 0
+ self.current = path.open('rb')
+ n = self.current.readinto(buffer)
+ if n:
+ return n
+ self.current.close()
+ self.current = None
+
+ def close(self):
+ if self.current is not None:
+ self.current.close()
+ super().close()
+
+
+def extract_depths(root, sequences, work):
+ directory = find_file(root, 'DEPTH')
+ if directory is None:
+ return {}, []
+ parts = sorted(directory.glob('HOI4D_depth_video.tar.gz*'), key=lambda p: int(p.name.split('.gz')[1] or 0))
+ if not parts:
+ return {}, []
+ wanted = {f'HOI4D_depth_video/{seq}/align_depth/depth_video.avi': seq for seq in sequences}
+ found = {}
+ with SplitFiles(parts) as joined, io.BufferedReader(joined) as inp, gzip.GzipFile(fileobj=inp) as gz:
+ with tarfile.open(fileobj=gz, mode='r|') as tf:
+ for member in tf:
+ if member.isfile() and member.name in wanted:
+ seq = wanted[member.name]
+ dest = work / (seq.replace('/', '_') + '.avi')
+ with tf.extractfile(member) as src, dest.open('wb') as dst:
+ shutil.copyfileobj(src, dst)
+ found[seq] = (dest, {'files': [str(p.resolve()) for p in parts], 'member': member.name})
+ if len(found) == len(wanted):
+ break
+ return found, parts
+
+
+def palette_mask(data):
+ with Image.open(io.BytesIO(data)) as im:
+ arr = np.asarray(im)
+ if arr.ndim == 2:
+ return arr.astype(np.uint16)
+ if arr.shape[-1] != 3:
+ raise ValueError('unsupported HOI4D mask representation')
+ # VOC's 256-color palette interleaves eight label bits into the high RGB bits.
+ # Invert it directly instead of sorting every pixel with np.unique.
+ if np.any(arr & np.array([31, 31, 63], dtype=np.uint8)):
+ raise ValueError('source mask has colors outside official VOC palette')
+ mask = np.zeros(arr.shape[:2], dtype=np.uint16)
+ for bit in range(8):
+ mask |= ((arr[..., bit % 3] >> (7 - bit // 3)) & 1).astype(np.uint16) << bit
+ return mask
+
+
+# Four workers each reuse annotation and hand indices instead of rebuilding the ZIP directory.
+@lru_cache(maxsize=8)
+def index_frames(z):
+ index = {}
+ for name in z.namelist():
+ parent, _, filename = name.rpartition('/')
+ stem, dot, suffix = filename.rpartition('.')
+ if not dot or not stem.isdigit() or suffix not in ('png', 'json', 'pickle'):
+ continue
+ entries = index.setdefault((parent + '/', '.' + suffix), {})
+ f = int(stem)
+ if f in entries:
+ raise ValueError(f'duplicate frame number under {parent}')
+ entries[f] = name
+ return index
+
+
+def frame_members(z, prefix, suffix):
+ return index_frames(z).get((prefix, suffix), {})
+
+
+def convert_sequence(seq, args, target, rgbz, annz, hpz, calz, cadz, paths, models, depth):
+ # ponytail: cache only one sequence's encoded annotation bytes; no decoded masks or CAD cache.
+ @lru_cache(maxsize=None)
+ def read_annotation(archive, name):
+ return archive.read(name)
+
+ category = int(re.search(r'/C(\d+)/', seq)[1])
+ definition = CATEGORIES.get(category)
+ if definition is None:
+ raise ValueError(f'category mapping unavailable: {category}')
+ prefix = f'HOI4D_annotations/{seq}'
+ mask_members = frame_members(annz, prefix + '/2Dseg/mask/', '.png')
+ shifted = frame_members(annz, prefix + '/2Dseg/shift_mask/', '.png')
+ if not mask_members:
+ mask_members = shifted
+ pose_members = frame_members(annz, prefix + '/objpose/', '.json')
+ hand_members = {side: frame_members(hpz, f'Hand_pose/handpose_{side}_hand/{seq}/', '.pickle')
+ for side in ('left', 'right')} if hpz else {'left': {}, 'right': {}}
+ with tempfile.TemporaryDirectory(prefix='hoi4d-source-') as wd:
+ work = Path(wd)
+ member = f'HOI4D_release/{seq}/align_rgb/image.mp4'
+ video = work / 'rgb.mp4'
+ with rgbz.open(member) as src, video.open('wb') as dst:
+ shutil.copyfileobj(src, dst)
+ info = probe(video)
+ if 'nb_frames' not in info:
+ info['nb_frames'] = json.loads(run(['ffprobe', '-v', 'error', '-count_frames', '-select_streams', 'v:0',
+ '-show_entries', 'stream=nb_read_frames', '-of', 'json', str(video)], stdout=subprocess.PIPE).stdout)['streams'][0]['nb_read_frames']
+ n = int(info['nb_frames'])
+ if args.max_frames:
+ n = min(n, args.max_frames)
+ if shifted and mask_members != shifted and any(f not in mask_members and f in shifted for f in range(n)):
+ raise ValueError('shift_mask has frames absent from mask; correspondence unverified')
+ frames = []
+ for f in range(n):
+ sources = {}
+ if f in mask_members:
+ sources['segment'] = locator(paths['annotations'], mask_members[f], read_annotation(annz, mask_members[f]))
+ if f in pose_members:
+ sources['object'] = locator(paths['annotations'], pose_members[f], read_annotation(annz, pose_members[f]))
+ for side in ('left', 'right'):
+ if f in hand_members[side]:
+ sources['hand_' + side] = locator(paths['hand'], hand_members[side][f], read_annotation(hpz, hand_members[side][f]))
+ frames.append(dict(source_frame_id=f, source_locator=locator(paths['release'], member, frame=f),
+ component_sources=sources, annotation_matches={k: dict(source_frame_id=f, source_timestamp_ns=None,
+ clock_domain=None, match_method='index_map', delta_ns=None) for k in sources}))
+ view = View(target, 'HOI4D', args.release_id, seq.replace('/', '_'), 'ego', frames)
+ view.valid_rules = {
+ 'mano': 'Matched side pickle contains poseCoeff, beta, trans and corresponding model available; zero angles are not missing',
+ 'keypoints': 'Matched side pickle contains kps2D; independently provided source points, no visibility gate',
+ 'pose': 'Matched source object record contains rotation and center; record identity retained',
+ 'dimensions': 'Matched source object record contains dimensions; size/mesh relation remains source_unspecified',
+ 'segment': 'Source mask or shift_mask present with verified palette codes and entity/part mapping',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta['view_type'] = 'ego'
+ view.meta['sampling']['selection'] = {'max_frames': args.max_frames, 'source_sequence': seq}
+ view.rgb([], locator(paths['release'], member), video=video, count=n)
+ intr_name = f'camera_params/{seq.split("/")[0]}/intrin.npy'
+ if calz and intr_name in calz.namelist():
+ view.camera(np.load(io.BytesIO(calz.read(intr_name))), locator(paths['camera'], intr_name))
+ extr_name = prefix + '/3Dseg/output.log'
+ if extr_name in annz.NameToInfo:
+ lines = [line for line in annz.read(extr_name).decode().splitlines() if line.strip()]
+ if len(lines) % 5:
+ raise ValueError('invalid Open3D camera trajectory')
+ extr, valid = np.full((n, 4, 4), np.nan, np.float32), np.zeros(n, bool)
+ for i in range(0, len(lines), 5):
+ idx = int(lines[i].split()[0])
+ if idx < n:
+ if valid[idx]:
+ raise ValueError('duplicate trajectory index')
+ extr[idx] = np.linalg.inv(np.loadtxt(io.StringIO('\n'.join(lines[i + 1:i + 5]))))
+ valid[idx] = True
+ view.tensor('extrinsic', 'transform/se3/v1', 'camera/extrinsic.pt', extr, valid,
+ ['T', 'row', 'column'], locator(paths['annotations'], extr_name),
+ convention='T_camera_from_world', source_convention='Open3D LOG camera-to-world; inverted')
+ loc = {'frames_ref': 'camera/frames.jsonl', 'source_field': 'component_sources'}
+ motion, mv = np.full((n, 2, 61), np.nan, np.float32), np.zeros((n, 2), bool)
+ points, pv = np.full((n, 2, 21, 2), np.nan, np.float32), np.zeros((n, 2, 21), bool)
+ for slot, side in enumerate(('left', 'right')):
+ for f, name in hand_members[side].items():
+ if f >= n:
+ continue
+ data = pickle.loads(read_annotation(hpz, name), encoding='latin1')
+ if all(k in data for k in ('poseCoeff', 'beta', 'trans')):
+ motion[f, slot] = np.r_[np.asarray(data['poseCoeff']).reshape(48),
+ np.asarray(data['beta']).reshape(10), np.asarray(data['trans']).reshape(3)]
+ mv[f, slot] = True # Zero angles are a valid pose, not a missing sentinel.
+ if 'kps2D' in data:
+ points[f, slot] = np.asarray(data['kps2D']).reshape(21, 2)
+ pv[f, slot] = True
+ if mv.any():
+ if all(side in models for slot, side in enumerate(('left', 'right')) if mv[:, slot].any()):
+ view.tensor('mano', 'mano/axis_angle61/v1', 'annotation/mano/motion.pt', motion, mv,
+ ['T', 'hand', 'parameter'], loc, model_profile=model_profile(models))
+ else:
+ view.absent('mano', 'Source MANO exists; provide --mano-dir to fix model identity', 'dependency_missing')
+ if pv.any():
+ view.tensor('keypoints_uv', 'keypoints/uv/v1', 'annotation/keypoints/uv.pt', points, pv,
+ ['T', 'hand', 'keypoint', 'coordinate'], loc, keypoint_schema='mano21/v1', unit='pixel',
+ source_method='Upstream fitted MANO projection already present in kps2D')
+ objects = {}
+ groups = {key: (vals, name, cls) for key, vals, name, cls in definition['groups']}
+ def ensure_object(key, source_loc, category_id=None, frame=None, source_id=None):
+ if key not in objects:
+ view.object(key, source_loc, scope='frame' if frame is not None else 'sequence',
+ scope_id=f'{seq}:{frame}' if frame is not None else None,
+ category_id=category_id, source_object_id=source_id)
+ objects[key] = {}
+ mappings = []
+ mask_valid = [f in mask_members for f in range(n)]
+ if any(mask_valid):
+ width, height = view.meta['camera']['image_size']
+ def masks():
+ for f in range(n):
+ labels = []
+ if f in mask_members:
+ mask = palette_mask(read_annotation(annz, mask_members[f]))
+ for value in np.unique(mask):
+ value = int(value)
+ if value in (0, 255):
+ if value == 255:
+ raise ValueError('HOI4D 255 region semantics unverified')
+ continue
+ if REGION_NAMES[category].get(value) in ('Left Hand', 'Right Hand'):
+ entity, part, cls = 'hand_' + REGION_NAMES[category][value].split()[0].lower(), None, 'hand'
+ else:
+ group = next((k for k, (vals, _, _) in groups.items() if value in vals), None)
+ entity = group or f'unknown_region_{value}'
+ cls = groups[group][2] if group else None
+ part = f'region_{value}' if group and len(groups[group][0]) > 1 else None
+ ensure_object(entity, frames[f]['component_sources']['segment'], cls)
+ if part:
+ objects[entity].setdefault('parts', {})[part] = {'part_id': part, 'parent_id': None,
+ 'has_model': False, 'mesh_ref': None, 'source_region_code': value, 'name': REGION_NAMES[category].get(value)}
+ labels.append(dict(mask_value=value, category_id=cls, entity_id=entity,
+ part_id=part, source_instance_id=value,
+ source_attributes={'region_name': REGION_NAMES[category].get(value)}))
+ else:
+ mask = np.zeros((height, width), np.uint16)
+ mappings.append(dict(frame_index=f, labels=labels))
+ yield mask
+ view.raster('segment', masks(), mask_valid, loc, mappings)
+ view.meta['components']['segment']['coverage'] = {'kind': 'unknown'}
+ if any(label['part_id'] for row in mappings for label in row['labels']):
+ view.meta['components']['segment']['segmentation_type'] = 'part'
+ else:
+ view.absent('segment', 'No native segmentation for this source sequence')
+ records = {}
+ for f, name in pose_members.items():
+ if f >= n:
+ continue
+ data = json.loads(read_annotation(annz, name))
+ # isEffective is not used to erase existing numerical annotations.
+ source_records = data.get('dataList', data.get('objects'))
+ if source_records is None:
+ raise ValueError('unrecognized source object annotation structure')
+ if category == 2 and source_records and (len(source_records) != 1 or
+ source_records[0].get('id') != 1 or source_records[0].get('label') != 'Watercup'):
+ raise ValueError('C2 native pose differs from the verified one-object target; cannot map to mask region 1')
+ label_counts = Counter(record['label'] for record in source_records)
+ per_frame = set()
+ for record_index, record in enumerate(source_records):
+ label = record['label']
+ group = definition['objmap'].get(label)
+ # Pinned official ObjTracking reads dataList[0] with segmentation label 1.
+ source_primary = category == 2 and record_index == 0 and record.get('id') == 1 and group == 'a'
+ ambiguous = group is not None and sum(g[2] == groups[group][2] for g in groups.values()) > 1 and not source_primary
+ ambiguous = ambiguous or (label_counts[label] > 1 and not source_primary) or group is None
+ entity = f'pose_frame_{f}_{record_index}' if ambiguous else group
+ ensure_object(entity, locator(paths['annotations'], name, record=record_index),
+ groups[group][2] if group else None, frame=f if ambiguous else None, source_id=record.get('id'))
+ if ambiguous:
+ view.build['unresolved'].append({'component': entity, 'status': 'source_semantics_unverified',
+ 'reason': 'Source pose retained with frame identity; no guessed association to same-class mask instances'})
+ label_key = (entity, label)
+ if label_key in per_frame:
+ raise ValueError(f'ambiguous repeated object label {label}; source identity must be resolved')
+ per_frame.add(label_key)
+ records.setdefault(label_key, {})[f] = (record, locator(paths['annotations'], name, record=record.get('id')))
+ if category == 2 and any(entity == 'a' for entity, _ in records):
+ view.build['validation'].append({'component': 'C2_source_pose_mask_identity',
+ 'rule': 'Official ObjTracking process_data.py reads dataList[0] with semantic mask label 1',
+ 'source_revision': 'afa7bf37b02b7e33cf245acd08b98c207ccc175c',
+ 'source_pose_id': 1, 'mask_value': 1, 'matched_frames': sum(len(by_frame) for (entity, _), by_frame in records.items() if entity == 'a')})
+ for (entity, label), by_frame in records.items():
+ part_labels = [k[1] for k in records if k[0] == entity]
+ articulated = definition['cad'].startswith('articulated/') and entity == definition['groups'][0][0]
+ part = 'pose_' + re.sub(r'[^a-zA-Z0-9_]', '_', label) if articulated or len(part_labels) > 1 else None
+ base = f'annotation/object/{entity}'
+ if part:
+ base += '/parts/' + part
+ objects[entity].setdefault('parts', {})[part] = dict(part_id=part, parent_id=None,
+ has_model=False, mesh_ref=None, source_label=label)
+ poses, valid = np.full((n, 4, 4), np.nan, np.float32), np.zeros(n, bool)
+ dimensions = np.full((n, 3), np.nan, np.float32)
+ dv = np.zeros(n, bool)
+ for f, (record, _) in by_frame.items():
+ if all(k in record for k in ('rotation', 'center')):
+ poses[f] = np.eye(4)
+ poses[f, :3, :3] = Rotation.from_euler('XYZ', [record['rotation'][k] for k in ('x', 'y', 'z')]).as_matrix()
+ poses[f, :3, 3] = [record['center'][k] for k in ('x', 'y', 'z')]
+ valid[f] = True
+ if 'dimensions' in record:
+ dimensions[f] = [record['dimensions'][k] for k in ('length', 'width', 'height')]
+ dv[f] = True
+ key = entity + ('_' + part if part else '')
+ view.tensor('pose_' + key, 'transform/se3/v1' if part else 'object/rigid/v1', base + '/pose.pt', poses, valid,
+ ['T', 'row', 'column'], loc, convention='T_camera_from_part' if part else 'T_camera_from_object',
+ unit='m', source_label=label, source_euler_order='XYZ',
+ definition='https://github.com/hoi4d/ObjTracking/blob/main/process_data.py')
+ if dv.any():
+ view.tensor('dimensions_' + key, 'object/dimensions/v1', base + '/dimensions.pt', dimensions, dv,
+ ['T', 'dimension'], loc, dimensions_semantics='source_unspecified', unit='m', scale_applied_to_mesh=False)
+ if cadz:
+ primary = definition['groups'][0][0]
+ if category != 2 and sum(g[2] == category for g in groups.values()) > 1:
+ primary = 'source_cad_instance'
+ instance = int(re.search(r'/N(\d+)/', seq)[1])
+ mesh_prefix = f'HOI4D_CAD_Model_for_release/{definition["cad"]}/{instance:03d}'
+ candidates = [name for name in cadz.namelist() if not name.endswith('/') and
+ (name == mesh_prefix + '.obj' or name.startswith(mesh_prefix + '/'))]
+ if candidates:
+ # Retain OBJ material/texture dependencies beside their original names.
+ queue = list(candidates)
+ for name in queue:
+ if Path(name).suffix.lower() not in ('.obj', '.mtl'):
+ continue
+ for line in cadz.read(name).decode().splitlines():
+ fields = shlex.split(line, comments=True)
+ if not fields:
+ continue
+ refs = fields[1:] if fields[0] == 'mtllib' else ([fields[-1]] if
+ fields[0].startswith('map_') or fields[0] in ('bump', 'norm', 'disp', 'decal') else [])
+ for ref in refs:
+ dependency = posixpath.normpath(posixpath.join(posixpath.dirname(name), ref))
+ if dependency not in cadz.NameToInfo:
+ raise ValueError(f'missing CAD dependency: {dependency}')
+ if dependency not in candidates:
+ candidates.append(dependency)
+ queue.append(dependency)
+ ensure_object(primary, locator(paths['cad'], mesh_prefix), category)
+ asset_dir = view.path / f'annotation/object/{primary}/assets'
+ asset_refs = []
+ for name in candidates:
+ relative = posixpath.relpath(name, mesh_prefix if definition['cad'].startswith('articulated/') else posixpath.dirname(mesh_prefix))
+ dest = safe_member(asset_dir, relative)
+ dest.parent.mkdir(parents=True, exist_ok=True)
+ dest.write_bytes(cadz.read(name))
+ ref = dest.relative_to(view.path).as_posix()
+ if dest.suffix.lower() == '.obj':
+ asset_key = re.sub(r'[^a-zA-Z0-9_]', '_', relative)
+ view.component('mesh_' + primary + '_' + asset_key, 'mesh/obj/v1', ref, ['vertex'],
+ locator(paths['cad'], name), unit='m', valid=True,
+ scale_applied_to_mesh=False, dimensions_semantics='source_unspecified')
+ asset_refs.append(ref)
+ if definition['cad'].startswith('articulated/'):
+ part_id = 'asset_' + asset_key
+ objects[primary].setdefault('parts', {})[part_id] = dict(part_id=part_id,
+ parent_id=None, has_model=True, mesh_ref=ref, source_asset=relative,
+ note='Source asset; correspondence to motion part labels remains unverified')
+ for asset_ref in asset_refs:
+ mesh_check = check_mesh(view.path / asset_ref, asset_dir)
+ view.build['validation'].append({'component': asset_ref, **mesh_check,
+ 'unit_basis': 'official ObjTracking loads source canonical CAD without scaling in metre camera coordinates',
+ 'geometric_acceptance': 'physical dimensions-to-mesh relation remains source_unspecified'})
+ metadata_path = view.path / f'annotation/object/{primary}/metainfo.json'
+ metadata = json.loads(metadata_path.read_text())
+ metadata.update(has_model=bool(asset_refs), asset_refs=asset_refs)
+ if definition['cad'].startswith('rigid/') and len(asset_refs) == 1:
+ metadata['mesh_ref'] = asset_refs[0]
+ json_write(metadata_path, metadata)
+ if definition['cad'].startswith('articulated/'):
+ view.build['unresolved'].append({'component': 'object_part_asset_association',
+ 'status': 'source_semantics_unverified',
+ 'reason': 'All source assets and motion labels retained, but no guessed correspondence or joint_state'})
+ for entity, info in objects.items():
+ if 'parts' in info:
+ ref = f'annotation/object/{entity}/articulation.json'
+ json_write(view.path / ref, {'parts': list(info['parts'].values()), 'joints': [],
+ 'hierarchy_semantics': 'semantic_containment',
+ 'note': 'Unassociated source region, motion and asset nodes; original CAD trees retained in assets'})
+ view.component('articulation_' + entity, 'object/articulated/v1', ref, [], loc)
+ meta_path = view.path / f'annotation/object/{entity}/metainfo.json'
+ metadata = json.loads(meta_path.read_text())
+ metadata['articulation_ref'] = ref
+ json_write(meta_path, metadata)
+ if depth is not None:
+ dpath, dloc = depth
+ dmeta = probe(dpath)
+ if [dmeta['width'], dmeta['height']] != view.meta['camera']['image_size']:
+ raise ValueError('HOI4D depth grid differs from RGB')
+ if dmeta['pix_fmt'] != 'gray16le':
+ raise ValueError('unexpected HOI4D depth pixel format')
+ width, height = view.meta['camera']['image_size']
+ raw = work / 'depth.raw'
+ limits = ['-frames:v', str(n)] if args.max_frames else []
+ run(decode_args(dpath, 'gray16le') + limits + ['-y', str(raw)])
+ if raw.stat().st_size != n * width * height * 2:
+ raise ValueError('depth/RGB frame count or grid mismatch')
+ def depths():
+ with raw.open('rb') as inp:
+ for _ in range(n):
+ yield np.frombuffer(inp.read(width * height * 2), ' best_iou + 0.1 and i > 0.15:
+# best, best_iou = s[0], i
+# if best != gk:
+# gk, iou_fixups = best, iou_fixups + 1
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/convert_visor_v1.0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/convert_visor_v1.0.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f0595bf5e89ae00c959472ff197dfda2d7a6ea6
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/convert_visor_v1.0.py
@@ -0,0 +1,271 @@
+#!/usr/bin/env python3
+"""VISOR source polygons and contact → CONTRACT.md v1.0, without rasterization or side inference."""
+from collections import Counter
+import hashlib
+import json
+import re
+import shutil
+import tempfile
+from pathlib import Path
+import zipfile
+
+import numpy as np
+import torch
+
+from warehouse_v1_0 import (View, build_record, find_file, jsonl_write, locator, parser,
+ release, roots, sequence_dir, source)
+
+FRAME = re.compile(r'frame_(\d+)')
+HAND = {300: 'left', 301: 'right'}
+GLOVE = {303: 'left', 304: 'right'}
+STATES = {'hand-not-in-contact': 'negative', 'glove-not-in-contact': 'negative',
+ 'none-of-the-above': 'target_unannotated', 'inconclusive': 'unknown'}
+
+
+def hand_side(ann):
+ if ann['class_id'] in HAND:
+ return HAND[ann['class_id']]
+ if ann['class_id'] not in (60, 303, 304):
+ return None
+ raw = ann.get('on_which_hand')
+ raw = raw if isinstance(raw, list) else [raw]
+ sides = set()
+ for item in raw:
+ if item == 'left hand':
+ sides.add('left')
+ if item in ('right hand', 'rigth hand'):
+ sides.add('right')
+ if len(sides) == 1:
+ return next(iter(sides))
+ if not sides and ann['class_id'] in GLOVE:
+ return GLOVE[ann['class_id']]
+ return None
+
+
+def convert_video(video, split, annotation, ann_loc, rgb_zip, rgb_loc, mapping, args, target):
+ indexed = sorted(enumerate(annotation['video_annotations']), key=lambda row: int(FRAME.search(row[1]['image']['name'])[1]))
+ if args.max_frames:
+ indexed = indexed[:args.max_frames]
+ if len({row['image']['name'] for _, row in indexed}) != len(indexed):
+ raise ValueError('duplicate VISOR frame')
+ keys = lambda row, ann: (row['image']['subsequence'], ann['class_id'], ann['name'])
+ collisions = set()
+ for _, row in indexed:
+ collisions.update(key for key, number in Counter(keys(row, a) for a in row['annotations']).items() if number > 1)
+ frames, paths = [], []
+ with tempfile.TemporaryDirectory(prefix='visor-rgb-') as wd, zipfile.ZipFile(rgb_zip) as z:
+ work = Path(wd)
+ for original_index, row in indexed:
+ name = row['image']['name']
+ data = z.read(name)
+ path = work / Path(name).name
+ path.write_bytes(data)
+ paths.append(path)
+ loc = rgb_loc | {'nested_member': name, 'sha256': hashlib.sha256(data).hexdigest()}
+ frames.append(dict(source_frame_id=int(FRAME.search(name)[1]), source_locator=loc,
+ source_image=row['image'], original_frame_name=mapping.get(name),
+ annotation_matches={'segment': dict(source_frame_id=name, source_timestamp_ns=None,
+ clock_domain=None, match_method='index_map', delta_ns=None,
+ source_locator=ann_loc | {'record': f'video_annotations[{original_index}]'})}))
+ view = View(target, 'EPIC-KITCHENS-VISOR', args.release_id, video, 'ego', frames, sampling='sparse', split=split)
+ view.valid_rules = {
+ 'segment': 'Selected sparse annotation frame present; all source polygons finite with at least three vertices and entity mapping valid',
+ 'relations': 'At least one source in_contact_object value present; positive targets resolve in the same frame; unknown is an explicit usable status',
+ }
+ view.full_jpeg = getattr(args, 'jpeg_full_check', False)
+ view.meta.update(view_type='ego', split_scheme='VISOR_GroundTruth_SparseAnnotations', split_source_locator=ann_loc)
+ view.meta['sampling']['selection'] = {'representation': 'official sparse annotations', 'max_frames': args.max_frames}
+ view.rgb(paths, rgb_loc)
+ entities = {}
+ polygons, mappings, relations, relation_valid = [], [], [], []
+ for f, (original_index, row) in enumerate(indexed):
+ frame_loc = ann_loc | {'record': f'video_annotations[{original_index}]'}
+ id_map, labels, polys = {}, [], []
+ for code, ann in enumerate(row['annotations'], 1):
+ if code >= 65535:
+ raise ValueError('too many source instances')
+ sid = ann['id']
+ if sid in id_map:
+ raise ValueError('duplicate source instance ID within a frame')
+ side = hand_side(ann)
+ if side is not None:
+ entity = 'hand_' + side
+ else:
+ key = keys(row, ann)
+ identity = key + (sid, row['image']['name']) if key in collisions else key
+ entity = 'object_' + hashlib.sha256(json.dumps(identity).encode()).hexdigest()[:20]
+ if entity not in entities:
+ scope = 'frame' if key in collisions else 'subsequence'
+ scope_id = f'{video}:{row["image"]["name"]}' if scope == 'frame' else f'{video}:{key[0]}'
+ view.object(entity, frame_loc | {'source_instance_id': sid}, scope, scope_id,
+ category_id=ann['class_id'], name=ann['name'],
+ association_basis='source_frame_instance' if scope == 'frame' else 'official_subsequence_entity_name')
+ entities[entity] = True
+ id_map[sid] = entity
+ for polygon in ann.get('segments', []):
+ p = np.asarray(polygon)
+ if p.ndim != 2 or p.shape[1] != 2 or len(p) < 3 or not np.isfinite(p).all():
+ raise ValueError(f'invalid source polygon {sid}')
+ polys.append(dict(mask_value=code, polygons=ann.get('segments', [])))
+ labels.append(dict(mask_value=code, category_id=ann['class_id'], entity_id=entity,
+ part_id=None, source_instance_id=sid,
+ source_attributes={k: v for k, v in ann.items() if k != 'segments'}))
+ rr = []
+ provided = False
+ for ann in row['annotations']:
+ raw = ann.get('in_contact_object')
+ values = raw if isinstance(raw, list) else [raw]
+ for value in values:
+ if value is None:
+ continue
+ provided = True
+ status = STATES.get(value)
+ obj = None
+ if status is None:
+ if value not in id_map:
+ raise ValueError(f'unresolved source contact target {value} in {video} frame {f}')
+ status, obj = 'positive', id_map[value]
+ rr.append(dict(relation_type='contact', subject_id=id_map[ann['id']], object_id=obj,
+ status=status, source_value=value, source_locator=frame_loc | {'source_instance_id': ann['id']},
+ provenance=source('Resolve source contact ID against instances in this frame')))
+ polygons.append(dict(frame_index=f, instances=polys))
+ mappings.append(dict(frame_index=f, labels=labels))
+ relations.append(dict(frame_index=f, relations=rr))
+ relation_valid.append(provided)
+ jsonl_write(view.path / 'annotation/segment/polygons.jsonl', polygons)
+ jsonl_write(view.path / 'annotation/segment/instances.jsonl', mappings)
+ torch.save(torch.ones(len(frames), dtype=torch.bool, device='cpu'), view.path / 'annotation/segment/mask_valid.pt')
+ view.component('segment', 'segment/instance_polygons/v1', 'annotation/segment/polygons.jsonl', ['T'], ann_loc,
+ valid_ref='annotation/segment/mask_valid.pt', valid_axes=['T'], segmentation_type='instance',
+ mapping_ref='annotation/segment/instances.jsonl', coverage={'kind': 'unknown', 'exhaustive_ref': 'annotation/segment/instances.jsonl'})
+ view.relations(relations, relation_valid, ann_loc)
+ for component in ('depth', 'mano', 'keypoints_uv', 'keypoints_xyz', 'intrinsic', 'extrinsic'):
+ view.absent(component, 'Not provided by VISOR sparse source annotations')
+ return view.finish()
+
+
+def main(argv=None):
+ p = parser('EPIC-KITCHENS-VISOR', __doc__)
+ p.add_argument('--videos', help='Comma-separated video names')
+ p.add_argument('--splits', default='train,val')
+ args = p.parse_args(argv)
+ wanted = set(args.videos.split(',')) if args.videos else None
+ splits = set(args.splits.split(','))
+ if not splits <= {'train', 'val'}:
+ p.error('only annotated train/val splits are available')
+ out = release(args, 'EPIC-KITCHENS-VISOR')
+ archive = find_file(args.root, 'EPIC-KITCHENS VISOR.zip')
+ with tempfile.TemporaryDirectory(prefix='visor-source-') as wd:
+ work = Path(wd)
+ if archive:
+ with zipfile.ZipFile(archive) as z:
+ suffix = 'GroundTruth-SparseAnnotations/annotations/'
+ members = [n for n in z.namelist() if suffix in n and n.endswith('.json')]
+ prefix = members[0].split(suffix)[0]
+ fm = json.loads(z.read(prefix + 'frame_mapping.json'))
+ done = 0
+ for member in sorted(members):
+ split, filename = member.split(suffix)[1].split('/')
+ video = Path(filename).stem
+ if split not in splits or wanted and video not in wanted:
+ continue
+ data = z.read(member)
+ rgb_member = prefix + f'GroundTruth-SparseAnnotations/rgb_frames/{split}/{video.split("_")[0]}/{video}.zip'
+ rgb_zip = work / (video + '.zip')
+ with z.open(rgb_member) as inp, rgb_zip.open('wb') as dest:
+ shutil.copyfileobj(inp, dest)
+ with sequence_dir(out, video) as dest:
+ report = convert_video(video, split, json.loads(data), locator(archive, member, data),
+ rgb_zip, locator(archive, rgb_member), fm.get(video, {}), args, dest / 'ego')
+ build_record(out, video, {'ego': report})
+ rgb_zip.unlink()
+ done += 1
+ print(f'Converted {video}', flush=True)
+ if args.limit and done >= args.limit:
+ break
+ else:
+ root = next((r for r in roots(args.root) if (r / 'GroundTruth-SparseAnnotations').is_dir()), None)
+ if root is None:
+ p.error('VISOR outer ZIP or extracted source directory not found')
+ fm = json.loads((root / 'frame_mapping.json').read_text()) if (root / 'frame_mapping.json').exists() else {}
+ done = 0
+ for split in sorted(splits):
+ for ann in sorted((root / 'GroundTruth-SparseAnnotations/annotations' / split).glob('*.json')):
+ video = ann.stem
+ if wanted and video not in wanted:
+ continue
+ rgb = root / f'GroundTruth-SparseAnnotations/rgb_frames/{split}/{video.split("_")[0]}/{video}.zip'
+ with sequence_dir(out, video) as dest:
+ report = convert_video(video, split, json.loads(ann.read_text()), locator(ann), rgb,
+ locator(rgb), fm.get(video, {}), args, dest / 'ego')
+ build_record(out, video, {'ego': report})
+ done += 1
+ if args.limit and done >= args.limit:
+ return 0
+ if not done:
+ p.error('no matching annotated videos found')
+ return 0
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
+
+# TODO(v2.0): 旧距离猜侧和单层掩码生成仅注释保留；v1.0 不执行。
+# if side is None and len(frame_hands) == 1:
+# # 空间门槛：脱下放在远处的手套不并入（实测双峰 0..25px / ≥44px）
+# hb = frame_hands[0][1]
+# gb = _bbox_of(polys)
+# if hb and gb and _bbox_dist(gb, hb) <= GLOVE60_MERGE_BBOX_DIST:
+# side = frame_hands[0][0]
+# stats["glove60_merged_into_sole_hand"] += 1
+# if side is None:
+# stats["dropped_glove60_unresolved"] += 1
+# id_disp[mid] = ("dropped", "glove60")
+# continue
+# def rasterize_and_encode(vdir, frames, frame_draw, zip_path, n, stats):
+# """流式读 zip 内 jpg + 光栅化多边形 → rgb.mkv / mask.mkv；返回 (w, h)。"""
+# from PIL import Image, ImageDraw
+#
+# with zipfile.ZipFile(zip_path) as zf:
+# member_of = {}
+# for nm in zf.namelist():
+# member_of[os.path.basename(nm)] = nm
+# names = [im["name"] for im in frames]
+# missing = [nm for nm in names if nm not in member_of]
+# if missing:
+# raise RuntimeError(f"{len(missing)} annotated frames missing in zip, "
+# f"e.g. {missing[:3]}")
+# stats["zip_extra_members"] = len(member_of) - len(names)
+#
+# # 首帧定分辨率（官方 1920×1080，实测每帧校验）
+# img0 = Image.open(io.BytesIO(zf.read(member_of[names[0]]))).convert("RGB")
+# w, h = img0.size
+#
+# bin_img = Image.new("L", (w, h), 0)
+# bin_draw = ImageDraw.Draw(bin_img)
+# canvas = np.zeros((h, w), np.uint16)
+# with VideoPipe(vdir / "mask.mkv", w, h, FPS, "mask") as mw, \
+# VideoPipe(vdir / "rgb.mkv", w, h, FPS, "rgb") as rw:
+# for f in range(n):
+# canvas[:] = 0
+# for pid, polys in frame_draw[f]:
+# bin_draw.rectangle((0, 0, w, h), fill=0)
+# for poly in polys:
+# bin_draw.polygon([(float(x), float(y)) for x, y in poly], fill=255)
+# arr = np.asarray(bin_img)
+# canvas[arr > 0] = pid # 后画覆盖先画：物体→手（modal）
+# mw.write(canvas)
+# img = img0 if f == 0 else Image.open(
+# io.BytesIO(zf.read(member_of[names[f]]))).convert("RGB")
+# if img.size != (w, h):
+# raise RuntimeError(f"frame {names[f]}: size {img.size} != {(w, h)}")
+# rw.write(np.asarray(img))
+# n_mask, n_rgb = mw.n, rw.n
+# if (n_rgb, n_mask) != (n, n):
+# raise RuntimeError(f"wrote rgb={n_rgb} mask={n_mask} != {n}")
+# got = (count_frames(vdir / "rgb.mkv"), count_frames(vdir / "mask.mkv"))
+# if got != (n, n):
+# raise RuntimeError(f"encoded rgb={got[0]} mask={got[1]} != {n} (decode verify)")
+# return w, h
+#
+#
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/render_dexycb_validation.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/render_dexycb_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8bb71e9ad9c5a44f85e5c50cee467a00512aa1d
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/render_dexycb_validation.py
@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+"""Audit a finished Dex-YCB release and render persisted modalities for manual review."""
+from collections import Counter
+import hashlib
+import json
+from pathlib import Path
+import sys
+
+from render_hoi4d_validation import render
+
+
+def inspect(root):
+ data, doc, scripts = root / 'data', root / 'doc', root / 'script'
+ sequences = sorted(p for p in data.iterdir() if p.is_dir())
+ assert len(sequences) == 1000, f'expected 1000 sequences, got {len(sequences)}'
+ assert len(list(doc.glob('*.build.json'))) == 1000
+ assert not list(data.glob('*.tmp-*')), 'unfinished temporary sequences'
+ config = json.loads((scripts / 'config.json').read_text())
+ assert all(config.get(k) is None for k in ('limit', 'max_frames', 'subjects', 'views'))
+ hashes = {p.name: hashlib.sha256(p.read_bytes()).hexdigest() for p in scripts.glob('*.py')}
+ subjects, splits, availability = Counter(), Counter(), Counter()
+ selected = []
+ for seq in sequences:
+ views = sorted(p for p in seq.iterdir() if p.is_dir())
+ assert len(views) == 8, (seq, len(views))
+ build = json.loads((doc / (seq.name + '.build.json')).read_text())
+ assert set(build['views']) == {p.name for p in views}, seq
+ assert all(hashes.get(k) == v for k, v in build['scripts'].items()), seq
+ subject = seq.name.split('_')[0]
+ subjects[subject] += 1
+ if subjects[subject] == 1:
+ selected.append(views[0])
+ if subject.endswith('subject-09'):
+ selected.append(views[-1]) # one more camera, different camera
+ if seq.name == '20201015-subject-09_20201015_150838':
+ selected.append(views[0]) # source-labelled left-hand sequence
+ for view in views:
+ meta = json.loads((view / 'metainfo.json').read_text())
+ assert meta['sequence_id'] == seq.name and meta['camera_id'] == view.name, view
+ assert meta['dataset_id'] == 'dex-ycb' and meta['release_id'] == 'shared-v1.0', view
+ assert meta['num_frames'] > 0 and meta.get('view_type') == 'exo', view
+ assert (view / 'camera/frames.jsonl').is_file(), view
+ assert meta['components']['rgb']['available'], view
+ splits[meta['split']] += 1
+ for key, part in meta['components'].items():
+ if part.get('available'):
+ availability[key] += 1
+ for ref in ('data_ref', 'valid_ref'):
+ if part.get(ref):
+ assert (view / part[ref]).is_file(), (view, key, ref)
+ assert len(subjects) == 10 and set(subjects.values()) == {100}, subjects
+ assert availability['depth'] == availability['rgb'] == 8000, availability
+ report = dict(sequences=len(sequences), views=sum(splits.values()), subjects=dict(subjects),
+ splits=dict(splits), available_views=dict(availability), config=config,
+ published_script_sha256=hashes, sampled_views=[str(v.relative_to(root)) for v in selected],
+ scope='Structure and file refs checked for all views; persisted QC and HTML only for sampled views. No manual visual approval yet.')
+ destination = doc / 'validation'
+ destination.mkdir(exist_ok=True)
+ (destination / 'STRUCTURAL_AUDIT.json').write_text(json.dumps(report, ensure_ascii=False, indent=2) + '\n')
+ for view in selected:
+ render(view, destination)
+ return report
+
+
+if __name__ == '__main__':
+ print(json.dumps(inspect(Path(sys.argv[1]).expanduser().resolve()), ensure_ascii=False), flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/render_h2o_validation.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/render_h2o_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..b89e7c0827f45ec7f854ff8651234cb9a4223f79
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/render_h2o_validation.py
@@ -0,0 +1,97 @@
+#!/usr/bin/env python3
+"""Render stored H2O RGB-D, native camera-space hands/objects, and OBJ assets for review."""
+import argparse
+import html
+import json
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageDraw
+import torch
+
+from render_hoi4d_validation import mesh_preview, picture, snapshot
+from warehouse_v1_0 import unpack_depth, validate_view
+
+
+def load(view, item, n):
+ if not item.get('available') or not item.get('data_ref', '').endswith('.pt'):
+ return None, None
+ values = torch.load(view / item['data_ref'], map_location='cpu', weights_only=True).numpy()
+ valid = torch.load(view / item['valid_ref'], map_location='cpu', weights_only=True).numpy() if item.get('valid_ref') else None
+ return (values, valid) if values.ndim and values.shape[0] == n else (None, None)
+
+
+def render(view, destination):
+ meta = json.loads((view / 'metainfo.json').read_text())
+ if meta['dataset_id'] != 'H2O':
+ raise ValueError('H2O view required')
+ n = meta['num_frames']
+ height, width = reversed(meta['camera']['image_size'])
+ parts = meta['components']
+ frames = [json.loads(line) for line in (view / 'camera/frames.jsonl').read_text().splitlines()]
+ assert len(frames) == n
+ selected = {0, n // 4, n // 2, 3 * n // 4, n - 1}
+ numeric = {}
+ for name, item in parts.items():
+ if item.get('data_ref', '').endswith('.pt') and item.get('available'):
+ values, valid = load(view, item, n)
+ if values is not None:
+ numeric[name] = (values, valid)
+ if valid is not None:
+ indices = np.flatnonzero(valid.reshape(n, -1).any(axis=1))
+ if len(indices):
+ selected.add(int(indices[0]))
+ selected = set(sorted(selected)[:12]) | {n - 1}
+ rgb = snapshot(view / parts['rgb']['data_ref'], 'rgb24', (height, width, 3), np.uint8, selected)
+ depth = snapshot(view / parts['depth']['data_ref'], 'gray16le', (height, 2 * width), ' 0:
+ u, v = intrinsic[i, 0, 0] * xyz[0] / xyz[2] + intrinsic[i, 0, 2], intrinsic[i, 1, 1] * xyz[1] / xyz[2] + intrinsic[i, 1, 2]
+ if 0 <= u < width and 0 <= v < height:
+ draw.ellipse((u-4, v-4, u+4, v+4), fill=color)
+ legend.append(name + ' ' + color)
+ panels = ['' + picture(photo) + 'RGB + 原生 3D 关键点投影（仅预览：' + html.escape(', '.join(legend) or '当前帧无可投影点') + '） ']
+ if i in depth:
+ z = unpack_depth(depth[i])
+ luminance = (np.clip(np.nan_to_num(z, nan=0), 0, 3) * 85).astype(np.uint8)
+ panels.append('' + picture(Image.fromarray(luminance)) + '深度 Z，黑=无效，白≥3 m；不改源深度 ')
+ details = {'frame': frames[i]}
+ for name, (values, valid) in numeric.items():
+ details[name] = {'value': values[i].tolist(), 'valid': None if valid is None else valid[i].tolist()}
+ pages.append('帧 ' + str(i) + ' / ' + str(n) + ' ' + ''.join(panels) + '
原帧号、全部数值标注及有效性 ' + html.escape(json.dumps(details, ensure_ascii=False, indent=2)) + ' ' + html.escape(name) + ' ' + mesh_preview(view / item['data_ref']) + '源 OBJ 静态预览，不代表物体姿态投影 ' + html.escape(title) + ' ' + html.escape(title) + ' 预览只作人工核查；仅显示真实存在的标注，不合成分割。
' + ''.join(pages) + '源网格 ' + ''.join(assets) + '
全部组件元数据 ' + html.escape(json.dumps(meta, ensure_ascii=False, indent=2)) + ' 持久化 QC ' + html.escape(json.dumps(report, ensure_ascii=False, indent=2)) + ' H2O 试点预览 ')
+ return destination / (title + '.html')
+
+
+if __name__ == '__main__':
+ p = argparse.ArgumentParser()
+ p.add_argument('views', nargs='+', type=Path)
+ p.add_argument('--out', type=Path, required=True)
+ args = p.parse_args()
+ for view in args.views:
+ print(render(view, args.out), flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/render_hoi4d_source_review.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/render_hoi4d_source_review.py
new file mode 100644
index 0000000000000000000000000000000000000000..79d7a58744572343670e9cbaecc4cc54ea07f1c1
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/render_hoi4d_source_review.py
@@ -0,0 +1,108 @@
+#!/usr/bin/env python3
+"""Evidence-only HTML: native HOI4D pose boxes over persisted RGB and native masks."""
+import argparse
+import html
+import json
+from pathlib import Path
+import shutil
+import zipfile
+
+import numpy as np
+from PIL import Image, ImageDraw
+
+from render_hoi4d_validation import picture, snapshot
+
+
+def source_parts(view):
+ rows = []
+ for source in view.glob('annotation/object/*/assets/result.json'):
+ def visit(node):
+ if isinstance(node, list):
+ for child in node:
+ visit(child)
+ return
+ if node.get('objs'):
+ rows.append({'source_part_id': node['id'], 'source_part_name': node['name'],
+ 'source_meshes': node['objs'], 'source_locator': str(source.relative_to(view))})
+ for child in node.get('children', []):
+ visit(child)
+ visit(json.loads(source.read_text()))
+ return rows
+
+
+def render(view, z, output):
+ output.mkdir(parents=True, exist_ok=True)
+ meta = json.loads((view / 'metainfo.json').read_text())
+ seq = meta['sampling']['selection']['source_sequence']
+ w, h = meta['camera']['image_size']
+ chosen = sorted({0, meta['num_frames'] // 2, meta['num_frames'] - 1})
+ frames = [json.loads(line) for line in (view / 'camera/frames.jsonl').read_text().splitlines()]
+ rgb = snapshot(view / meta['components']['rgb']['data_ref'], 'rgb24', (h, w, 3), np.uint8, chosen)
+ segment = meta['components'].get('segment', {})
+ masks = (snapshot(view / segment['data_ref'], 'gray16le', (h, w), '' + html.escape(seq) + f' / frame {native_id} ' + picture(image)
+ + '仅验收：伪彩=原生分割码，黄框=上游 2dBox；颜色和框不写回 shared，空间重叠不能代替身份依据。
'
+ + '上游原记录与编码对照 '
+ + html.escape(json.dumps(note, ensure_ascii=False, indent=2)) + ' HOI4D source correspondence review 源姿态／原生分割／RGB 的证据图（非真值修改） '
+ page += ''.join(panels) + 'CAD result.json 原生部件→OBJ；不等同逐帧姿态对应 '
+ page += html.escape(json.dumps(parts, ensure_ascii=False, indent=2)) + ' {html.escape(name)} 源依据对照（非物体几何验收通过） ')
+ shutil.copyfile(Path(__file__), args.out / 'renderer.py')
+
+
+if __name__ == '__main__':
+ main()
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/render_hoi4d_validation.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/render_hoi4d_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7889a1b63b004e046b52fe4e4da0b2b4651b768
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/render_hoi4d_validation.py
@@ -0,0 +1,182 @@
+#!/usr/bin/env python3
+"""Standalone, offline HTML review of every persisted component in selected views."""
+import argparse
+import base64
+import html
+import io
+import json
+import random
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageDraw
+import torch
+
+from warehouse_v1_0 import video_frames, unpack_depth, validate_view
+
+
+def snapshot(video, fmt, shape, dtype, indices):
+ return {i: frame.copy() for i, frame in enumerate(video_frames(video, fmt, shape, dtype)) if i in indices}
+
+
+def picture(image):
+ image.thumbnail((800, 450)) # Previews only; released tensors and videos are not resized.
+ output = io.BytesIO()
+ image.save(output, format='PNG')
+ return '' + picture(image) + 'RGB + 原生 2D 手关键点（若有） ']
+ if i in depth:
+ z = unpack_depth(depth[i])
+ luminance = (np.clip(np.nan_to_num(z, nan=0), 0, 3) * 85).astype(np.uint8)
+ panels.append('' + picture(Image.fromarray(luminance)) + '深度 Z（黑=无效，白≥3 m；仅预览） ')
+ if i in masks:
+ mask = masks[i]
+ colors = np.zeros((*mask.shape, 3), dtype=np.uint8)
+ for value in np.unique(mask):
+ if value not in (0, 65535):
+ colors[mask == value] = preview_color(int(value), meta['dataset_id'])
+ legend = '；'.join(f'{int(value)}=' + ('手' if value == 255 and meta['dataset_id'] == 'dex-ycb' else '物体' if meta['dataset_id'] == 'dex-ycb' else '区域') + f' #{bytes(preview_color(int(value), meta["dataset_id"])).hex()}' for value in np.unique(mask) if value not in (0, 65535))
+ panels.append('' + picture(Image.fromarray(colors)) + '原生分割整数编码的伪彩色预览（' + html.escape(legend) + '） ')
+ active = (mask != 0) & (mask != 65535)
+ overlay = rgb[i].copy()
+ overlay[active] = ((overlay[active].astype(np.uint16) + colors[active]) // 2).astype(np.uint8)
+ panels.append('' + picture(Image.fromarray(overlay)) + '原生分割叠加 RGB（仅预览） ')
+ details = {'frame': frames[i], 'segment_labels': labels.get(i, [])}
+ for name, component in components.items():
+ ref = component.get('data_ref', '')
+ if component.get('available') and ref.endswith('.pt'):
+ values = torch.load(view / ref, map_location='cpu', weights_only=True)
+ if values.ndim and values.shape[0] == n:
+ details[name] = {'value': values[i].tolist()}
+ if component.get('valid_ref', '').endswith('.pt'):
+ valid = torch.load(view / component['valid_ref'], map_location='cpu', weights_only=True)
+ details[name]['valid'] = valid[i].tolist()
+ sections.append('帧 ' + str(i) + ' / ' + str(n) + ' ' + ''.join(panels)
+ + '
帧号、来源、全部数值组件与有效性 '
+ + html.escape(json.dumps(details, ensure_ascii=False, indent=2)) + ' ' + html.escape(name) + ' ' + mesh_preview(view / ref)
+ + '仅展示网格原生形状；未验证尺度／位姿对应。 ' + html.escape(title) + ' ' + html.escape(title) + ' 预览图仅供人工审核，不是训练输入；未提供的模态在 metainfo 中保留原因。
' + ''.join(sections) + '原始物体模型（独立预览） ' + ''.join(assets) + '
全部组件元信息及原始来源 ' + html.escape(json.dumps(meta, ensure_ascii=False, indent=2)) + ' 持久化 QC 结果 ' + html.escape(json.dumps(result, ensure_ascii=False, indent=2)) + ' ' + html.escape(meta['dataset_id']) + ' review ' + html.escape(meta['dataset_id']) + ' 抽样人工审核 选择视图；所有预览均来自已保存的 shared 数据。
')
+ print(destination / (title + '.html'), 'selected_frames', sorted(selected), flush=True)
+
+
+def main():
+ p = argparse.ArgumentParser(description=__doc__)
+ p.add_argument('views', nargs='+', type=Path)
+ p.add_argument('--out', type=Path)
+ args = p.parse_args()
+ for view in args.views:
+ render(view, args.out or view.parents[2] / 'doc/validation')
+
+
+if __name__ == '__main__':
+ main()
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/run-full.sh b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/run-full.sh
new file mode 100644
index 0000000000000000000000000000000000000000..5d27d5547332f029b9d2e92108469ad40bc121e6
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/run-full.sh
@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+set -u
+base="$HOME/Datasets/uni-hoi-h2o-full-20260929-r1"
+raw="$HOME/Datasets/Hand-Datasets-Raw/H2O"
+cd "$HOME/Projects/uni-hoi-dataset" || exit 1
+mkdir -p "$base/scratch"
+export TMPDIR="$base/scratch" PYTHONUNBUFFERED=1
+for n in 1 2 3 4; do test -s "$raw/raw/data/subject${n}_v1_1.tar.gz" || exit 2; done
+test ! -e "$base/H2O/shared-v1.0" || exit 3
+cg=$(awk -F: '$1=="0" {print $3}' /proc/self/cgroup)
+read -r quota period < "/sys/fs/cgroup$cg/cpu.max"
+cpus=$(getconf _NPROCESSORS_ONLN)
+printf 'START=%s\nWORKERS=2 CODEC_THREADS=4 SUBJECTS=1,2,3,4 CASA=false\nCPU_BUDGET_PERCENT=80 LOGICAL_CPUS=%s\nCGROUP=%s\nCPU_MAX=%s %s\n' "$(date -Is)" "$cpus" "$cg" "$quota" "$period" > "$base/full.status"
+if [[ "$quota" == max ]] || (( quota * 100 > cpus * period * 80 )); then
+ printf 'EXIT=4 ERROR=CPU_QUOTA_NOT_ENFORCED\n' >> "$base/full.status"
+ exit 4
+fi
+/usr/bin/time -v .venv/bin/python "$base/frozen-code/scripts/convert_h2o_v1.0.py" --root "$raw" --out "$base" --release-id shared-v1.0 --workers 2 --codec-threads 4 --mano-dir "$HOME/Projects/WiLoR/mano_data" > "$base/full.log" 2>&1
+code=$?
+printf 'EXIT=%s END=%s\n' "$code" "$(date -Is)" >> "$base/full.status"
+cat "/sys/fs/cgroup$cg/cpu.stat" >> "$base/full.status"
+exit "$code"
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/warehouse_v1_0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/warehouse_v1_0.py
new file mode 100644
index 0000000000000000000000000000000000000000..f705011bcea587bd320e1e228f99bed259b32b3a
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/r1/script/warehouse_v1_0.py
@@ -0,0 +1,931 @@
+"""Shared I/O for CONTRACT.md v1.0. No annotation synthesis."""
+import argparse
+from contextlib import contextmanager
+from functools import lru_cache
+import hashlib
+import json
+import math
+import shlex
+from pathlib import Path
+import pickle
+import random
+import shutil
+import subprocess
+import tempfile
+import time
+
+import numpy as np
+from PIL import Image
+import torch
+
+SCHEMA_PATH = next(p for p in (Path(__file__).resolve().parents[1] / 'docs/schema.json',
+ Path(__file__).resolve().parents[1] / 'doc/schema.json') if p.is_file())
+SCHEMA = json.loads(SCHEMA_PATH.read_text())
+RAW_ROOT = Path.home() / 'Datasets/Hand-Datasets-Raw'
+
+
+def json_write(path, value):
+ path = Path(path)
+ path.parent.mkdir(parents=True, exist_ok=True)
+ path.write_text(json.dumps(value, ensure_ascii=False, indent=2, allow_nan=False) + '\n')
+
+
+def jsonl_write(path, rows):
+ path = Path(path)
+ path.parent.mkdir(parents=True, exist_ok=True)
+ with path.open('w') as out:
+ for row in rows:
+ out.write(json.dumps(row, ensure_ascii=False, allow_nan=False) + '\n')
+
+
+def sha256(path):
+ with Path(path).open('rb') as f:
+ return hashlib.file_digest(f, 'sha256').hexdigest()
+
+
+def locator(path, member=None, data=None, **extra):
+ result = {'file': str(Path(path).resolve())}
+ if member is not None:
+ result['member'] = member
+ if data is not None:
+ result['sha256'] = hashlib.sha256(data).hexdigest()
+ return result | extra
+
+
+def source(method='Copy source annotation; normalize storage only'):
+ return {'kind': 'format_conversion', 'method': method}
+
+
+def run(args, **kwargs):
+ return subprocess.run(args, check=True, stderr=subprocess.PIPE, **kwargs)
+
+
+@lru_cache(maxsize=1)
+def ffversion():
+ return run(['ffmpeg', '-version'], stdout=subprocess.PIPE).stdout.decode().splitlines()[0]
+
+
+def probe(path):
+ return json.loads(run(['ffprobe', '-v', 'error', '-select_streams', 'v:0',
+ '-show_streams', '-of', 'json', str(path)],
+ stdout=subprocess.PIPE).stdout)['streams'][0]
+
+
+def decode_args(path, pixel_format):
+ return ['ffmpeg', '-v', 'error', '-nostdin', '-filter_threads', '1', '-threads', '1', '-noautorotate',
+ '-i', str(path), '-map', '0:v:0', '-vsync', '0', '-threads', '1',
+ '-f', 'rawvideo', '-pix_fmt', pixel_format]
+
+
+def decoded_digest(path, pixel_format):
+ start = time.monotonic()
+ with tempfile.TemporaryFile() as err:
+ proc = subprocess.Popen(decode_args(path, pixel_format) + ['pipe:1'],
+ stdout=subprocess.PIPE, stderr=err)
+ digest, size = hashlib.sha256(), 0
+ with proc.stdout:
+ while chunk := proc.stdout.read(1024 * 1024):
+ digest.update(chunk)
+ size += len(chunk)
+ if proc.wait():
+ err.seek(0)
+ raise RuntimeError(err.read().decode(errors='replace'))
+ return digest.hexdigest(), size, time.monotonic() - start
+
+
+def video_frames(path, pixel_format, shape, dtype):
+ frame_bytes = int(np.prod(shape)) * np.dtype(dtype).itemsize
+ with tempfile.TemporaryFile() as err:
+ proc = subprocess.Popen(decode_args(path, pixel_format) + ['pipe:1'], stdout=subprocess.PIPE, stderr=err)
+ completed = False
+ try:
+ while True:
+ data = proc.stdout.read(frame_bytes)
+ if not data:
+ break
+ if len(data) != frame_bytes:
+ raise ValueError('truncated decoded video frame')
+ yield np.frombuffer(data, dtype=dtype).reshape(shape)
+ if proc.wait():
+ err.seek(0)
+ raise ValueError(err.read().decode(errors='replace'))
+ completed = True
+ finally:
+ proc.stdout.close()
+ if not completed and proc.poll() is None:
+ proc.terminate()
+ proc.wait()
+
+
+def storage(path, profile, parameters, source_lossy):
+ info = probe(path)
+ if info['codec_name'] == 'h264':
+ frames = json.loads(run(['ffprobe', '-v', 'error', '-threads', '1', '-select_streams', 'v:0',
+ '-show_entries', 'frame=key_frame', '-of', 'json', str(path)], stdout=subprocess.PIPE).stdout)['frames']
+ parameters = parameters | {'keyframe_indices': [i for i, f in enumerate(frames) if f['key_frame']]}
+ return {'profile': profile, 'codec': info['codec_name'],
+ 'pixel_format': info['pix_fmt'], 'encoded_size': [info['width'], info['height']],
+ 'decoder': {'name': 'ffmpeg', 'version': ffversion(), 'autorotate': False,
+ 'color_space': info.get('color_space', 'unknown'),
+ 'color_range': info.get('color_range', 'unknown'),
+ 'color_primaries': info.get('color_primaries', 'unknown'),
+ 'color_transfer': info.get('color_transfer', 'unknown')},
+ 'encoder_parameters': {'version': ffversion()} | parameters,
+ 'source_lossy': source_lossy, 'storage_fps': 30, 'timebase_role': 'index_only'}
+
+
+def encode_raw(raw, target, width, height, pixel_format, profile, codec='ffv1', extra=(), threads=1):
+ start = time.monotonic()
+ run(['ffmpeg', '-v', 'error', '-nostdin', '-filter_threads', '1', '-y', '-f', 'rawvideo',
+ '-pixel_format', pixel_format, '-video_size', f'{width}x{height}', '-framerate', '30',
+ '-i', str(raw), '-an', '-c:v', codec, '-threads', str(threads), *extra, str(target)])
+ digest, size, decode_seconds = decoded_digest(target, pixel_format)
+ if size != Path(raw).stat().st_size or digest != sha256(raw):
+ raise ValueError(f'lossless roundtrip failed: {target}')
+ config = storage(target, profile, {'name': codec, 'arguments': list(extra), 'threads': threads}, False)
+ return config, {'bytes': target.stat().st_size, 'encode_seconds': time.monotonic() - start - decode_seconds,
+ 'decode_seconds': decode_seconds, 'decoded_sha256': digest, 'all_frames_exact': True}
+
+
+def measure_rgb_access(video, raw, width, height, count):
+ index = count // 2
+ start = time.monotonic()
+ data = run(['ffmpeg', '-v', 'error', '-nostdin', '-filter_threads', '1', '-threads', '1', '-noautorotate',
+ '-i', str(video), '-vf', f'select=eq(n\\,{index})', '-frames:v', '1', '-threads', '1',
+ '-f', 'rawvideo', '-pix_fmt', 'rgb24', 'pipe:1'], stdout=subprocess.PIPE).stdout
+ with raw.open('rb') as inp:
+ inp.seek(index * width * height * 3)
+ if data != inp.read(width * height * 3):
+ raise ValueError('exact indexed RGB read failed')
+ return {'random_access_strategy': 'decode from first keyframe; select exact presentation index',
+ 'random_frame_index': index, 'random_frame_read_seconds': time.monotonic() - start}
+
+
+def jpeg_sample_indices(sequence, camera, count):
+ seed = int.from_bytes(hashlib.sha256(f'{sequence}/{camera}/20260920'.encode()).digest()[:8], 'big')
+ indices = {0, count - 1, count // 4, count // 2, 3 * count // 4}
+ indices.update(random.Random(seed).sample([i for i in range(count) if i not in indices],
+ min(count, 16) - len(indices)))
+ return sorted(indices), seed
+
+
+def check_jpeg_copy(target, paths, hashes, size, sequence, camera, full):
+ packets = json.loads(run(['ffprobe', '-v', 'error', '-select_streams', 'v:0',
+ '-show_packets', '-show_data_hash', 'sha256', '-of', 'json', str(target)],
+ stdout=subprocess.PIPE).stdout)['packets']
+ if [p['data_hash'].split(':', 1)[1].lower() for p in packets] != hashes:
+ raise ValueError('JPEG packet hashes/order/count differ')
+ info = probe(target)
+ if [info['width'], info['height']] != list(size):
+ raise ValueError('JPEG container dimensions differ')
+ indices, seed = jpeg_sample_indices(sequence, camera, len(paths))
+ if full:
+ indices = list(range(len(paths)))
+ select = '+'.join(f'eq(n\\,{i})' for i in indices)
+ frame_bytes = size[0] * size[1] * 3
+ # Spool selected frames to disk, keeping RAM bounded for full-view audits.
+ with tempfile.TemporaryFile() as decoded:
+ run(decode_args(target, 'rgb24') + ([] if full else ['-vf', 'select=' + select]) + ['pipe:1'], stdout=decoded)
+ decoded.seek(0)
+ for i in indices:
+ expected = run(decode_args(paths[i], 'rgb24') + ['pipe:1'], stdout=subprocess.PIPE).stdout
+ if len(expected) != frame_bytes or decoded.read(frame_bytes) != expected:
+ raise ValueError(f'JPEG decoded pixels differ at frame {i}')
+ if decoded.read(1):
+ raise ValueError('JPEG selected pixel count differs')
+ return {'policy': 'jpeg-packets-all-rgb-sample16/v1', 'seed': seed,
+ 'selected_frame_indices': indices, 'pixel_frames_checked': len(indices),
+ 'packet_frames_checked': len(paths), 'jpeg_packets_exact': True,
+ 'packet_count_order_dimensions_exact': True, 'sampled_pixels_exact': True,
+ 'all_frames_exact': len(indices) == len(paths), 'full_view_audit': full}
+
+
+def rgb_video(paths, target, work, video=None, count=None, sequence="", camera="", full_jpeg=False,
+ ffv1_first=False, codec_threads=1):
+ """JPEG packet proof plus deterministic pixels; re-encodings compare all pixels."""
+ target.parent.mkdir(parents=True, exist_ok=True)
+ raw = work / 'rgb.raw'
+ packets = []
+ has_missing = video is None and any(p is None for p in paths)
+ if has_missing:
+ present = next((p for p in paths if p is not None), None)
+ if present is None:
+ raise ValueError('no source RGB image to establish the pixel format/grid')
+ original = probe(present)
+ width, height = original['width'], original['height']
+ with raw.open('wb') as dest:
+ for path in paths:
+ if path is None:
+ dest.write(bytes(width * height * 3))
+ else:
+ with Image.open(path) as im:
+ if im.mode != 'RGB' or im.size != (width, height):
+ raise ValueError('source RGB grid/type changes within view')
+ run(decode_args(path, 'rgb24') + ['pipe:1'], stdout=dest)
+ lossy, count = 'unknown', len(paths)
+ input_args = None
+ elif video is None:
+ if not paths:
+ raise ValueError('RGB timeline must contain existing source images')
+ suffix = paths[0].suffix.lower()
+ if suffix not in ('.jpg', '.jpeg', '.png'):
+ raise ValueError('unsupported source RGB format')
+ images = work / 'rgb_source'
+ images.mkdir()
+ dimensions = None
+ for i, path in enumerate(paths):
+ with Image.open(path) as im:
+ if im.mode != 'RGB' or (dimensions is not None and dimensions != im.size):
+ raise ValueError('RGB must be homogeneous 8-bit RGB, with no silent color/size conversion')
+ dimensions = im.size
+ if im.format == 'PNG' and path.read_bytes()[24] != 8:
+ raise ValueError('high bit depth RGB needs a registered schema')
+ dest = images / f'{i:08d}{suffix}'
+ shutil.copyfile(path, dest)
+ packets.append(sha256(path))
+ width, height = dimensions
+ input_args = ['-framerate', '30', '-i', str(images / ('%08d' + suffix))]
+ count = len(paths)
+ original = probe(paths[0])
+ lossy = suffix in ('.jpg', '.jpeg')
+ else:
+ original = probe(video)
+ if int(original.get('bits_per_raw_sample') or 8) > 8 or any(
+ x in original.get('pix_fmt', '') for x in ('10', '12', '16', 'f32')):
+ raise ValueError('high bit depth RGB needs a registered schema')
+ width, height = original['width'], original['height']
+ input_args = ['-i', str(video)]
+ lossy = 'unknown'
+ limits = ['-frames:v', str(count)] if count else []
+ records = []
+ if video is None and not has_missing and suffix in ('.jpg', '.jpeg'):
+ start = time.monotonic()
+ try:
+ run(['ffmpeg', '-v', 'error', '-nostdin', '-y', *input_args, '-map', '0:v:0',
+ *limits, '-c:v', 'copy', '-an', str(target)])
+ encode_seconds = time.monotonic() - start
+ validation = check_jpeg_copy(target, paths, packets, (width, height), sequence, camera, full_jpeg)
+ cfg = storage(target, 'mkv/mjpeg_copy/v1', {'stream_copy': True}, lossy)
+ cfg['decoder']['source_color_metadata'] = {k: original.get(k, 'unknown') for k in
+ ('color_space', 'color_range', 'color_primaries', 'color_transfer')}
+ records.append(validation | {'profile': cfg['profile'], 'bytes': target.stat().st_size,
+ 'encode_seconds': encode_seconds, 'validation_seconds': time.monotonic() - start - encode_seconds})
+ return cfg, [width, height], records, count
+ except (subprocess.CalledProcessError, ValueError) as exc:
+ records.append({'profile': 'mkv/mjpeg_copy/v1', 'rejected': str(exc)})
+ target.unlink(missing_ok=True)
+ if not has_missing:
+ run(['ffmpeg', '-v', 'error', '-nostdin', '-y', '-threads', '1', '-noautorotate',
+ *input_args, '-map', '0:v:0', *limits, '-vsync', '0', '-threads', '1',
+ '-f', 'rawvideo', '-pix_fmt', 'rgb24', str(raw)])
+ frame_bytes = width * height * 3
+ actual, remainder = divmod(raw.stat().st_size, frame_bytes)
+ if remainder or not actual or (count is not None and actual != count):
+ raise ValueError('source RGB frame count mismatch')
+ expected_hash = sha256(raw)
+ can_copy = video is not None
+ if can_copy:
+ start = time.monotonic()
+ try:
+ run(['ffmpeg', '-v', 'error', '-nostdin', '-y', *input_args, '-map', '0:v:0',
+ *limits, '-c:v', 'copy', '-an', str(target)])
+ digest, size, seconds = decoded_digest(target, 'rgb24')
+ if digest != expected_hash or size != raw.stat().st_size:
+ raise ValueError('stream-copy RGB bytes/order/count mismatch')
+ cfg = storage(target, 'mkv/source_copy/v1',
+ {'stream_copy': True}, lossy)
+ if video:
+ cfg['storage_fps'] = original.get('avg_frame_rate')
+ cfg['preserve_source_pts'] = True
+ records.append({'profile': cfg['profile'], 'all_frames_exact': True,
+ 'decoded_sha256': digest,
+ 'bytes': target.stat().st_size, 'decode_seconds': seconds,
+ 'encode_seconds': time.monotonic() - start - seconds})
+ records[-1].update(measure_rgb_access(target, raw, width, height, actual))
+ return cfg, [width, height], records, actual
+ except (subprocess.CalledProcessError, ValueError) as exc:
+ records.append({'profile': 'stream_copy', 'rejected': str(exc)})
+ target.unlink(missing_ok=True)
+ candidates = []
+ for profile, codec, params in [
+ ('mkv/ffv1_rgb/v1', 'ffv1', ['-level', '3', '-pix_fmt', 'bgr0']),
+ ('mkv/h264_rgb_lossless/v1', 'libx264rgb', ['-crf', '0', '-preset', 'medium', '-g', '30'])]:
+ path = work / (codec + '.mkv')
+ try:
+ cfg, record = encode_raw(raw, path, width, height, 'rgb24', profile, codec, params,
+ codec_threads if codec == 'ffv1' else 1)
+ cfg['source_lossy'] = lossy
+ cfg['decoder']['source_color_metadata'] = {k: original.get(k, 'unknown') for k in
+ ('color_space', 'color_range', 'color_primaries', 'color_transfer')}
+ record['profile'] = profile
+ record.update(measure_rgb_access(path, raw, width, height, actual))
+ records.append(record)
+ candidates.append((path.stat().st_size, path, cfg))
+ if ffv1_first and codec == 'ffv1':
+ break # Keep the H.264 fallback only if the preferred exact encoder fails.
+ except subprocess.CalledProcessError as exc:
+ records.append({'profile': profile, 'rejected': exc.stderr.decode(errors='replace')})
+ if not candidates:
+ raise ValueError('no supported lossless RGB encoder')
+ _, chosen, cfg = min(candidates, key=lambda x: x[0])
+ shutil.move(chosen, target)
+ return cfg, [width, height], records, actual
+
+
+def pack_depth(depth):
+ bits = np.ascontiguousarray(depth, dtype='> 16).astype(' 0)
+ arr[~good] = np.nan
+ out.write(pack_depth(arr).tobytes())
+ vo.write(good.astype(np.uint8).tobytes())
+ else:
+ if not np.issubdtype(arr.dtype, np.integer) or arr.min() < 0 or arr.max() > 65535:
+ raise ValueError('invalid segmentation region codes')
+ out.write(arr.astype(' vertices:
+ raise ValueError('empty or invalid mesh')
+ def dependency(parent, name):
+ dep = (parent / name).resolve()
+ if not dep.is_relative_to(asset_root) or not dep.is_file():
+ raise ValueError(f'missing or escaping asset dependency: {name}')
+ return dep
+ dependencies = []
+ for name in materials:
+ mtl = dependency(path.parent, name)
+ dependencies.append(mtl)
+ for line in mtl.read_text().splitlines():
+ fields = shlex.split(line, comments=True)
+ if fields and (fields[0].startswith('map_') or fields[0] in ('bump', 'norm', 'disp', 'decal')):
+ dependencies.append(dependency(mtl.parent, fields[-1]))
+ return {'vertices': vertices, 'faces': faces, 'sha256': sha256(path),
+ 'dependencies': {p.relative_to(asset_root).as_posix(): sha256(p) for p in dependencies}}
+
+
+def mesh_asset(view, oid, directory, obj, loc):
+ dest = view.path / f'annotation/object/{oid}/assets'
+ shutil.copytree(directory, dest)
+ ref = (dest / Path(obj).relative_to(directory)).relative_to(view.path).as_posix()
+ mesh_check = check_mesh(dest / Path(obj).relative_to(directory), dest)
+ view.build['validation'].append({'component': f'mesh_{oid}', **mesh_check})
+ view.component(f'mesh_{oid}', 'mesh/obj/v1', ref, ['vertex'], loc, valid=True, unit='m')
+ p = view.path / f'annotation/object/{oid}/metainfo.json'
+ info = json.loads(p.read_text())
+ info.update(has_model=True, mesh_ref=ref)
+ json_write(p, info)
+
+
+def safe_member(root, name):
+ path = (Path(root) / name).resolve()
+ if not path.is_relative_to(Path(root).resolve()):
+ raise ValueError(f'unsafe archive path: {name}')
+ return path
+
+
+class ChumpyStub:
+ def __setstate__(self, state):
+ self.__dict__.update(state)
+
+
+class ManoUnpickler(pickle.Unpickler):
+ def find_class(self, module, name):
+ if module.startswith('chumpy'):
+ return ChumpyStub
+ return super().find_class(module, name)
+
+
+def mano_models(directory):
+ result = {}
+ if directory is None:
+ return result
+ for side in ('left', 'right'):
+ path = Path(directory).expanduser() / f'MANO_{side.upper()}.pkl'
+ if not path.exists():
+ continue
+ with path.open('rb') as f:
+ data = ManoUnpickler(f, encoding='latin1').load()
+ result[side] = {'components': np.asarray(data['hands_components']),
+ 'mean': np.asarray(data['hands_mean']), 'sha256': sha256(path),
+ 'file': path.name}
+ return result
+
+
+def model_profile(models):
+ identity = json.dumps({side: d['sha256'] for side, d in sorted(models.items())}).encode()
+ return {'version': 'sha256:' + hashlib.sha256(identity).hexdigest(), 'flat_hand_mean': True,
+ 'left_shapedirs_correction': 'not_applied',
+ 'models': {side: {'file': d['file'], 'sha256': d['sha256']} for side, d in models.items()}}
+
+
+def pca_to_axis_angle(coeffs, model):
+ coeffs = np.asarray(coeffs)
+ if coeffs.shape[-1:] != (45,) or not np.isfinite(coeffs).all():
+ raise ValueError('invalid MANO PCA coefficients')
+ return coeffs @ model['components'] + model['mean']
+
+
+def parser(dataset, description):
+ p = argparse.ArgumentParser(description=description)
+ p.add_argument('--root', f'--{dataset.lower()}-root', dest='root', type=Path, default=RAW_ROOT / dataset)
+ p.add_argument('--out', type=Path, default=Path('converted'))
+ p.add_argument('--release-id', default='v1.0')
+ p.add_argument('--limit', type=int, help='Maximum sequences to convert')
+ p.add_argument('--max-frames', type=int, help='First N source frame indices per view (recorded as sampling)')
+ p.add_argument('--mano-dir', type=Path)
+ p.add_argument('--views', help='Comma-separated source camera IDs')
+ p.add_argument('--jpeg-full-check', action='store_true', help='Full decoded RGB check for selected JPEG pilot/final views')
+ return p
+
+
+def roots(root):
+ root = Path(root).expanduser()
+ return [p for p in (root / 'raw/data', root / 'raw', root) if p.is_dir()]
+
+
+def find_file(root, name):
+ return next((p / name for p in roots(root) if (p / name).exists()), None)
+
+
+def release(args, dataset):
+ if not args.release_id or Path(args.release_id).name != args.release_id or args.release_id in ('.', '..'):
+ raise ValueError('release-id must be a single directory name')
+ if args.limit is not None and args.limit < 1 or args.max_frames is not None and args.max_frames < 1:
+ raise ValueError('limits must be positive')
+ out = args.out.expanduser().resolve() / dataset / args.release_id
+ raw = args.root.expanduser().resolve()
+ if out == raw or out.is_relative_to(raw):
+ raise ValueError('output must not modify the raw source tree')
+ (out / 'data').mkdir(parents=True, exist_ok=True)
+ (out / 'doc').mkdir(exist_ok=True)
+ (out / 'script').mkdir(exist_ok=True)
+ scripts = list(Path(__file__).resolve().parent.glob('*.py'))
+ existing = {p.name for p in (out / 'script').glob('*.py')}
+ if existing and existing != {p.name for p in scripts}:
+ raise ValueError('converter files changed; choose a new --release-id')
+ copies = [(SCHEMA_PATH.parent / name, out / 'doc' / name) for name in ('CONTRACT.md', 'schema.json')]
+ copies += [(path, out / 'script' / path.name) for path in scripts]
+ # Check every existing file before changing any published definition or script.
+ for src, dest in copies:
+ if dest.exists() and sha256(dest) != sha256(src):
+ raise ValueError('converter or contract changed within an existing release; choose a new --release-id')
+ for src, dest in copies:
+ if src.resolve() != dest.resolve():
+ shutil.copyfile(src, dest)
+ json_write(out / 'script/config.json', {k: str(v) if isinstance(v, Path) else v for k, v in vars(args).items()})
+ return out
+
+
+@contextmanager
+def sequence_dir(out, sequence):
+ if Path(sequence).name != sequence:
+ raise ValueError('sequence ID must be a single path component')
+ final = out / 'data' / sequence
+ with tempfile.TemporaryDirectory(prefix=sequence + '.tmp-', dir=out / 'data') as wd:
+ tmp = Path(wd)
+ yield tmp
+ if final.exists():
+ if any((final / child.name).exists() for child in tmp.iterdir()):
+ raise FileExistsError('view already published; use a new release-id to preserve it')
+ for child in list(tmp.iterdir()):
+ child.rename(final / child.name)
+ else:
+ tmp.rename(final)
+
+
+def build_record(out, sequence, reports):
+ path = out / 'doc' / (sequence + '.build.json')
+ previous = json.loads(path.read_text()) if path.exists() else {}
+ config = json.loads((out / 'script/config.json').read_text())
+ for report in reports.values():
+ report['configuration'] = config
+ json_write(path, {'sequence_id': sequence, 'storage_revision': SCHEMA['storage_revision'],
+ 'scripts': {p.name: sha256(p) for p in (out / 'script').glob('*.py')},
+ 'runtime': {'torch': torch.__version__, 'ffmpeg': ffversion()},
+ 'views': previous.get('views', {}) | reports})
+
+
+def check_view_type(meta):
+ # The pinned ARCTIC contract makes view_type optional; adapters still supply known views.
+ if 'view_type' not in meta:
+ return
+ if meta['view_type'] not in ('ego', 'exo', 'unknown'):
+ raise ValueError('view_type must be ego/exo/unknown')
+ expected = {'dex-ycb': 'exo', 'EPIC-KITCHENS-VISOR': 'ego', 'HOI4D': 'ego'}.get(meta['dataset_id'])
+ if meta['dataset_id'] == 'H2O':
+ expected = 'ego' if meta['camera_id'] == 'cam4' else 'exo' if meta['camera_id'] in ('cam0', 'cam1', 'cam2', 'cam3') else 'unknown'
+ if expected and meta['view_type'] not in (expected, 'unknown'):
+ raise ValueError('view_type contradicts known source capture setup')
+
+
+def check_valid_policy(component, count):
+ policy = component.get('valid_policy', {})
+ for field in ('revision', 'meaning', 'source_rule', 'source_locator', 'scope', 'numerical_checks',
+ 'official_qa', 'local_excluded_frame_indices', 'local_exclusion_reasons', 'visibility_independent'):
+ if field not in policy:
+ raise ValueError(f'valid_policy missing {field}')
+ if policy['revision'] != SCHEMA['validity_revision'] or policy['meaning'] != SCHEMA['annotation_validity']['meaning']:
+ raise ValueError('outdated annotation validity semantics')
+ if not policy['source_rule'] or not policy['numerical_checks'] or policy['scope'] != component['valid_axes']:
+ raise ValueError('incomplete validity rule/scope')
+ if policy['official_qa'].get('status') != 'not_provided' or not policy['official_qa'].get('reason'):
+ raise ValueError('official QA requires a verified adapter; supervision acceptance paused')
+ excluded = policy['local_excluded_frame_indices']
+ if any(type(i) is not int or not 0 <= i < count for i in excluded):
+ raise ValueError('invalid local exclusion frame index')
+ if excluded:
+ raise ValueError('local exclusions require a reviewed rule/report; not supported by these adapters')
+ if policy['visibility_independent'] is not True:
+ raise ValueError('visibility must remain independent of validity')
+
+
+def validate_view(path):
+ """Validate the persisted contract, not a rendering of the source labels."""
+ path = Path(path)
+ meta = json.loads((path / 'metainfo.json').read_text())
+ n = meta['num_frames']
+ check_view_type(meta)
+ if meta['schema_version'] != SCHEMA['schema_version'] or meta['hand_slots'] != ['left', 'right'] or n < 1:
+ raise ValueError('invalid view header')
+ def resolve(ref):
+ if Path(ref).is_absolute() or '\\' in ref:
+ raise ValueError('component reference must be relative POSIX')
+ dest = safe_member(path, ref)
+ if not dest.is_file():
+ raise ValueError(f'referenced file missing: {ref}')
+ return dest
+ def tensor(ref):
+ def cpu_storage(storage, location):
+ if location != 'cpu':
+ raise ValueError('PT storage must be on CPU')
+ return storage
+ value = torch.load(resolve(ref), map_location=cpu_storage, weights_only=True)
+ if not isinstance(value, torch.Tensor) or value.device.type != 'cpu':
+ raise ValueError('PT must contain only a CPU Tensor')
+ return value.numpy()
+ frames = [json.loads(line) for line in resolve(meta['sampling']['frames_ref']).read_text().splitlines()]
+ if len(frames) != n or [f['frame_index'] for f in frames] != list(range(n)):
+ raise ValueError('frame index/count mismatch')
+ for f in frames:
+ if not all(k in f for k in ('source_frame_id', 'timestamp_ns', 'clock_domain', 'source_locator')):
+ raise ValueError('incomplete frame provenance')
+ entities = {'hand_left', 'hand_right'}
+ parts = {}
+ for p in (path / 'annotation/object').glob('*/metainfo.json'):
+ obj = json.loads(p.read_text())
+ if obj['object_id'] != p.parent.name or obj['identity_scope'] not in ('frame', 'subsequence', 'sequence'):
+ raise ValueError('invalid object identity')
+ entities.add(obj['object_id'])
+ if obj.get('mesh_ref'):
+ if not resolve(obj['mesh_ref']).is_relative_to(p.parent.resolve()):
+ raise ValueError('mesh must belong to its object')
+ if obj.get('articulation_ref'):
+ art = json.loads(resolve(obj['articulation_ref']).read_text())
+ part_ids = {part['part_id'] for part in art['parts']}
+ if len(part_ids) != len(art['parts']):
+ raise ValueError('duplicate part ID')
+ parents = {part['part_id']: part['parent_id'] for part in art['parts']}
+ for part_id in parents:
+ visited, cur = set(), part_id
+ while cur is not None:
+ if cur in visited or cur not in parents:
+ raise ValueError('invalid or cyclic part hierarchy')
+ visited.add(cur)
+ cur = parents[cur]
+ parts[obj['object_id']] = part_ids
+ video_stats = {}
+ for key, component in meta['components'].items():
+ if not component.get('available'):
+ if not component.get('reason'):
+ raise ValueError('unavailable component needs a reason')
+ continue
+ schema = SCHEMA['components'][component['schema_id']]
+ for required in ('axes', 'provenance', 'source_locator'):
+ if required not in component:
+ raise ValueError(f'{key}: {required} missing')
+ for required in schema.get('required_metadata', []):
+ if required not in component:
+ raise ValueError(f'{key}: {required} missing')
+ if component['provenance']['kind'] == 'derived':
+ raise ValueError('v1.0 must not synthesize annotations')
+ if component['data_ref'].startswith('annotation/') and schema.get('valid_shape'):
+ if not component.get('valid_ref') or not component.get('valid_axes'):
+ raise ValueError(f'{key}: annotation validity reference/axes missing')
+ check_valid_policy(component, n)
+ data_path = resolve(component['data_ref'])
+ valid = tensor(component['valid_ref']) if component.get('valid_ref', '').endswith('.pt') else None
+ valid_shape = schema.get('valid_shape')
+ if valid is not None and valid_shape and (len(valid_shape) != valid.ndim or any(
+ isinstance(s, int) and valid.shape[i] != s for i, s in enumerate(valid_shape))):
+ raise ValueError(f'{key}: validity schema shape mismatch')
+ if valid is not None and (valid.dtype != bool or valid.shape[0] != n):
+ raise ValueError(f'{key}: invalid validity tensor')
+ if component['schema_id'] == 'mesh/obj/v1':
+ objroot = next((parent for parent in data_path.parents if parent.parent.name == 'object'), None)
+ if objroot is None:
+ raise ValueError('mesh must be owned by an object')
+ check_mesh(data_path, objroot)
+ if data_path.suffix == '.pt':
+ arr = tensor(component['data_ref'])
+ if arr.dtype != np.float32 or arr.shape[0] != n:
+ raise ValueError(f'{key}: float dtype or timeline mismatch')
+ shape = schema.get('shape')
+ if shape and (len(shape) != arr.ndim or any(isinstance(s, int) and arr.shape[i] != s for i, s in enumerate(shape))):
+ raise ValueError(f'{key}: schema shape mismatch')
+ if valid is not None:
+ if arr.shape[:valid.ndim] != valid.shape or not np.isfinite(arr[valid]).all() or not np.isnan(arr[~valid]).all():
+ raise ValueError(f'{key}: invalid finite/NaN/validity values')
+ if component['schema_id'] in ('object/rigid/v1', 'transform/se3/v1'):
+ check_transform(arr[valid])
+ if data_path.suffix == '.mkv':
+ cfg = component['storage']
+ if cfg['profile'] not in SCHEMA['storage_profiles']:
+ raise ValueError('unregistered video profile')
+ for field in SCHEMA['video_metadata']['required']:
+ if field not in cfg:
+ raise ValueError('video storage declaration incomplete')
+ w, h = meta['camera']['image_size']
+ if cfg['encoded_size'] != [w * (2 if key == 'depth' else 1), h]:
+ raise ValueError('encoded raster differs from logical RGB grid')
+ actual = probe(data_path)
+ if (actual['codec_name'] != cfg['codec'] or actual['pix_fmt'] != cfg['pixel_format'] or
+ [actual['width'], actual['height']] != cfg['encoded_size']):
+ raise ValueError('video storage metadata differs from file')
+ if key == 'rgb':
+ fmt, bpp = 'rgb24', 3
+ else:
+ fmt, bpp = 'gray16le', 2
+ digest, size, _ = decoded_digest(data_path, fmt)
+ w, h = cfg['encoded_size']
+ if size != n * w * h * bpp:
+ raise ValueError('raster timeline mismatch')
+ video_stats[key] = {'decoded_sha256': digest, 'frames': n}
+ if key == 'depth':
+ validity = component['valid_storage']
+ vp = resolve(component['valid_ref'])
+ actual_valid = probe(vp)
+ if (validity['profile'] != 'mkv/bool_ffv1/v1' or actual_valid['codec_name'] != 'ffv1'
+ or actual_valid['pix_fmt'] != 'gray' or [actual_valid['width'], actual_valid['height']] != [w // 2, h]):
+ raise ValueError('invalid depth validity video')
+ count = 0
+ for encoded, mask in zip(video_frames(data_path, 'gray16le', (h, w), ' 0)).all() or not np.isnan(depth[~good]).all():
+ raise ValueError('invalid depth/validity semantics')
+ count += 1
+ if count != n:
+ raise ValueError('depth validity timeline mismatch')
+ if component['schema_id'].startswith('segment/') and component.get('mapping_ref'):
+ rows = [json.loads(line) for line in resolve(component['mapping_ref']).read_text().splitlines()]
+ if len(rows) != n or [row['frame_index'] for row in rows] != list(range(n)):
+ raise ValueError('segmentation mapping timeline mismatch')
+ for row in rows:
+ labels = row['labels']
+ if len({a['mask_value'] for a in labels}) != len(labels):
+ raise ValueError('duplicate region mapping')
+ for label in labels:
+ if label['entity_id'] is not None and label['entity_id'] not in entities:
+ raise ValueError('unresolved segmentation entity')
+ if label['part_id'] is not None and label['part_id'] not in parts.get(label['entity_id'], set()):
+ raise ValueError('unresolved segmentation part')
+ if component['schema_id'] == 'segment/uint16_labels/v1':
+ width, height = meta['camera']['image_size']
+ for i, mask in enumerate(video_frames(data_path, 'gray16le', (height, width), '
+ raise SystemExit(main())
+ ^^^^^^
+ File "/home/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10/frozen-code/scripts/convert_h2o_v1.0.py", line 316, in main
+ out = release(args, 'H2O')
+ ^^^^^^^^^^^^^^^^^^^^
+ File "/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10/frozen-code/scripts/warehouse_v1_0.py", line 676, in release
+ raise ValueError('converter files changed; choose a new --release-id')
+ValueError: converter files changed; choose a new --release-id
+Command exited with non-zero status 1
+ Command being timed: ".venv/bin/python /home/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10/frozen-code/scripts/convert_h2o_v1.0.py --root /home/xuzhefeng/Datasets/Hand-Datasets-Raw/H2O --out /home/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10 --release-id shared-v1.0 --workers 4 --codec-threads 10 --mano-dir /home/xuzhefeng/Projects/WiLoR/mano_data"
+ User time (seconds): 1.71
+ System time (seconds): 0.20
+ Percent of CPU this job got: 99%
+ Elapsed (wall clock) time (h:mm:ss or m:ss): 0:01.93
+ Average shared text size (kbytes): 0
+ Average unshared data size (kbytes): 0
+ Average stack size (kbytes): 0
+ Average total size (kbytes): 0
+ Maximum resident set size (kbytes): 229832
+ Average resident set size (kbytes): 0
+ Major (requiring I/O) page faults: 0
+ Minor (reclaiming a frame) page faults: 39947
+ Voluntary context switches: 42
+ Involuntary context switches: 48
+ Swaps: 0
+ File system inputs: 0
+ File system outputs: 8
+ Socket messages sent: 0
+ Socket messages received: 0
+ Signals delivered: 0
+ Page size (bytes): 4096
+ Exit status: 1
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/startup-failure/full.status b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/startup-failure/full.status
new file mode 100644
index 0000000000000000000000000000000000000000..58188531e6b6b0a46f29659f2317b364902fc1ed
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/startup-failure/full.status
@@ -0,0 +1,10 @@
+START=2026-09-30T10:21:04+08:00
+WORKERS=4 CODEC_THREADS=10 SUBJECTS=1,2,3,4 CASA=false
+CPU_LIMIT=none
+CGROUP=/user.slice/user-25044.slice/user@25044.service/app.slice/h2o-full-w4c10-20260930.scope
+CPU_MAX=max controller_disabled
+EXIT=1 END=2026-09-30T10:21:06+08:00
+usage_usec 1945180
+user_usec 1723590
+system_usec 221590
+core_sched.force_idle_usec 0
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/startup-failure/prepare-w4-c10.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/startup-failure/prepare-w4-c10.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea7952ec80912d5efd7f6cba4d7dbb88999c3f06
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/startup-failure/prepare-w4-c10.py
@@ -0,0 +1,52 @@
+"""Preserve completed r1 sequences without re-encoding or touching source files."""
+import datetime
+import json
+import os
+from pathlib import Path
+import shutil
+
+old = Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260929-r1')
+new = Path('/data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10')
+snapshot = Path('/tmp/h2o-w4-c10-recovery')
+new.mkdir()
+shutil.copytree(snapshot / 'scripts', new / 'frozen-code/scripts')
+shutil.copytree(snapshot / 'docs', new / 'frozen-code/docs')
+shutil.copytree(snapshot / 'tests', new / 'frozen-code/tests')
+release = new / 'H2O/shared-v1.0'
+for directory in ('data', 'doc', 'script', 'script/provenance/r1'):
+ (release / directory).mkdir(parents=True, exist_ok=True)
+history = release / 'script/provenance/r1'
+shutil.copytree(old / 'H2O/shared-v1.0/script', history / 'script')
+shutil.copytree(old / 'frozen-code/docs', history / 'docs')
+for name in ('full.log', 'full.status', 'run-full.sh'):
+ shutil.copy2(old / name, history / name)
+records = []
+for record in sorted((old / 'H2O/shared-v1.0/doc').glob('*.build.json')):
+ build = json.loads(record.read_text())
+ seq = build['sequence_id']
+ assert Path(seq).name == seq and seq not in ('.', '..')
+ assert set(build['views']) == {f'cam{i}' for i in range(5)}, seq
+ data = old / 'H2O/shared-v1.0/data' / seq
+ frames = {}
+ for cam in build['views']:
+ meta = json.loads((data / cam / 'metainfo.json').read_text())
+ frames[cam] = sum(1 for _ in (data / cam / 'camera/frames.jsonl').open())
+ assert frames[cam] > 0
+ shutil.copytree(data, release / 'data' / seq, copy_function=os.link)
+ shutil.copy2(record, release / 'doc' / record.name)
+ records.append({'sequence_id': seq, 'frames_by_camera': frames,
+ 'original_script_sha256': build['scripts']})
+manifest = {'time': datetime.datetime.now().astimezone().isoformat(),
+ 'source_run': str(old), 'destination_run': str(new),
+ 'reason': 'User requested four archive workers, ten threads per encoder, no CPU quota',
+ 'reuse': 'Hard-linked immutable completed views; frames and component source SHA256 rechecked during resume',
+ 'excluded': 'Unpublished .tmp directories; historical 62-sequence output; failed initial full run',
+ 'original_runtime_provenance': 'script/provenance/r1', 'sequences': records}
+(release / 'doc/RESTART_PROVENANCE.json').write_text(json.dumps(manifest, indent=2) + '\n')
+for source, target in (('/tmp/h2o-prepare-w4-c10.py', 'prepare-w4-c10.py'),
+ ('/tmp/h2o-reconfigure-w4-c10.sh', 'reconfigure-w4-c10.sh'),
+ ('/tmp/h2o-run-w4-c10.sh', 'run-full.sh')):
+ shutil.copy2(source, release / 'script' / target)
+shutil.copy2('/tmp/h2o-run-w4-c10.sh', new / 'run-full.sh')
+shutil.copy2('/tmp/h2o-w4-c10-frozen-tests.log', history.parent / 'worker4-codec10-regression.log')
+print(f'REUSED_SEQUENCES={len(records)} REUSED_VIEWS={sum(len(r["frames_by_camera"]) for r in records)}', flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/worker4-codec10-final-regression.log b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/worker4-codec10-final-regression.log
new file mode 100644
index 0000000000000000000000000000000000000000..1567daf867ed1a6ddda45dc8e52f659325c17b2c
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/worker4-codec10-final-regression.log
@@ -0,0 +1,29 @@
+Converted subject1/h1/0
+Converted subject2/h1/0
+Converted subject3/h1/0
+Converted subject4/h1/0
+Converted subject2/h1/0
+Converted subject1/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Archive subject2_v1_1.tar.gz: 1 sequences
+Converted subject3/h1/0
+Archive subject3_v1_1.tar.gz: 1 sequences
+Converted subject4/h1/0
+Archive subject4_v1_1.tar.gz: 1 sequences
+Converted subject4/h1/0
+Converted subject3/h1/0
+Converted subject1/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Converted subject2/h1/0
+Archive subject2_v1_1.tar.gz: 1 sequences
+Archive subject3_v1_1.tar.gz: 1 sequences
+Archive subject4_v1_1.tar.gz: 1 sequences
+Converted subject2/h1/0
+Converted subject1/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Archive subject2_v1_1.tar.gz: 1 sequences
+Converted subject3/h1/0
+Archive subject3_v1_1.tar.gz: 1 sequences
+Converted subject4/h1/0
+Archive subject4_v1_1.tar.gz: 1 sequences
+PASS H2O four archive workers, codec 1/4/10, RGB FFV1-only, depth and persistent QC
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/worker4-codec10-regression.log b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/worker4-codec10-regression.log
new file mode 100644
index 0000000000000000000000000000000000000000..5382e73ffa63d0b26e08739134fe21ab7b25155a
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/provenance/worker4-codec10-regression.log
@@ -0,0 +1,29 @@
+Converted subject1/h1/0
+Converted subject2/h1/0
+Converted subject3/h1/0
+Converted subject4/h1/0
+Converted subject2/h1/0
+Converted subject1/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Archive subject2_v1_1.tar.gz: 1 sequences
+Converted subject3/h1/0
+Archive subject3_v1_1.tar.gz: 1 sequences
+Converted subject4/h1/0
+Archive subject4_v1_1.tar.gz: 1 sequences
+Converted subject1/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Converted subject4/h1/0
+Converted subject2/h1/0
+Archive subject2_v1_1.tar.gz: 1 sequences
+Converted subject3/h1/0
+Archive subject3_v1_1.tar.gz: 1 sequences
+Archive subject4_v1_1.tar.gz: 1 sequences
+Converted subject1/h1/0
+Archive subject1_v1_1.tar.gz: 1 sequences
+Converted subject2/h1/0
+Archive subject2_v1_1.tar.gz: 1 sequences
+Converted subject3/h1/0
+Archive subject3_v1_1.tar.gz: 1 sequences
+Converted subject4/h1/0
+Archive subject4_v1_1.tar.gz: 1 sequences
+PASS H2O four archive workers, codec 1/4/10, RGB FFV1-only, depth and persistent QC
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/reconfigure-w4-c10.sh b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/reconfigure-w4-c10.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0a6af7e0bec22e511e06d11ee33c8429cdd2a9bd
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/reconfigure-w4-c10.sh
@@ -0,0 +1,11 @@
+#!/usr/bin/env bash
+set -euo pipefail
+cd /home/xuzhefeng/Projects/uni-hoi-dataset
+test "$(cat /tmp/h2o-w4-c10-frozen-tests.exit)" = 0
+test ! -e /data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10
+scope=h2o-full-r1-20260929.scope
+test "$(systemctl --user show --no-pager "$scope" -p ActiveState --value)" = active
+printf '\nRECONFIGURE=%s REASON=user_requested_worker4_codec10 CPU_LIMIT=none\n' "$(date -Is)" >> /data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260929-r1/full.status
+systemctl --user stop "$scope"
+.venv/bin/python /tmp/h2o-prepare-w4-c10.py
+systemd-run --user --scope --unit=h2o-full-w4c10-20260930 -p CPUAccounting=yes /bin/bash /data/xuzhefeng/Datasets/uni-hoi-h2o-full-20260930-w4-c10/run-full.sh
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/render_dexycb_validation.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/render_dexycb_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8bb71e9ad9c5a44f85e5c50cee467a00512aa1d
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/render_dexycb_validation.py
@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+"""Audit a finished Dex-YCB release and render persisted modalities for manual review."""
+from collections import Counter
+import hashlib
+import json
+from pathlib import Path
+import sys
+
+from render_hoi4d_validation import render
+
+
+def inspect(root):
+ data, doc, scripts = root / 'data', root / 'doc', root / 'script'
+ sequences = sorted(p for p in data.iterdir() if p.is_dir())
+ assert len(sequences) == 1000, f'expected 1000 sequences, got {len(sequences)}'
+ assert len(list(doc.glob('*.build.json'))) == 1000
+ assert not list(data.glob('*.tmp-*')), 'unfinished temporary sequences'
+ config = json.loads((scripts / 'config.json').read_text())
+ assert all(config.get(k) is None for k in ('limit', 'max_frames', 'subjects', 'views'))
+ hashes = {p.name: hashlib.sha256(p.read_bytes()).hexdigest() for p in scripts.glob('*.py')}
+ subjects, splits, availability = Counter(), Counter(), Counter()
+ selected = []
+ for seq in sequences:
+ views = sorted(p for p in seq.iterdir() if p.is_dir())
+ assert len(views) == 8, (seq, len(views))
+ build = json.loads((doc / (seq.name + '.build.json')).read_text())
+ assert set(build['views']) == {p.name for p in views}, seq
+ assert all(hashes.get(k) == v for k, v in build['scripts'].items()), seq
+ subject = seq.name.split('_')[0]
+ subjects[subject] += 1
+ if subjects[subject] == 1:
+ selected.append(views[0])
+ if subject.endswith('subject-09'):
+ selected.append(views[-1]) # one more camera, different camera
+ if seq.name == '20201015-subject-09_20201015_150838':
+ selected.append(views[0]) # source-labelled left-hand sequence
+ for view in views:
+ meta = json.loads((view / 'metainfo.json').read_text())
+ assert meta['sequence_id'] == seq.name and meta['camera_id'] == view.name, view
+ assert meta['dataset_id'] == 'dex-ycb' and meta['release_id'] == 'shared-v1.0', view
+ assert meta['num_frames'] > 0 and meta.get('view_type') == 'exo', view
+ assert (view / 'camera/frames.jsonl').is_file(), view
+ assert meta['components']['rgb']['available'], view
+ splits[meta['split']] += 1
+ for key, part in meta['components'].items():
+ if part.get('available'):
+ availability[key] += 1
+ for ref in ('data_ref', 'valid_ref'):
+ if part.get(ref):
+ assert (view / part[ref]).is_file(), (view, key, ref)
+ assert len(subjects) == 10 and set(subjects.values()) == {100}, subjects
+ assert availability['depth'] == availability['rgb'] == 8000, availability
+ report = dict(sequences=len(sequences), views=sum(splits.values()), subjects=dict(subjects),
+ splits=dict(splits), available_views=dict(availability), config=config,
+ published_script_sha256=hashes, sampled_views=[str(v.relative_to(root)) for v in selected],
+ scope='Structure and file refs checked for all views; persisted QC and HTML only for sampled views. No manual visual approval yet.')
+ destination = doc / 'validation'
+ destination.mkdir(exist_ok=True)
+ (destination / 'STRUCTURAL_AUDIT.json').write_text(json.dumps(report, ensure_ascii=False, indent=2) + '\n')
+ for view in selected:
+ render(view, destination)
+ return report
+
+
+if __name__ == '__main__':
+ print(json.dumps(inspect(Path(sys.argv[1]).expanduser().resolve()), ensure_ascii=False), flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/render_h2o_validation.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/render_h2o_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..b89e7c0827f45ec7f854ff8651234cb9a4223f79
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/render_h2o_validation.py
@@ -0,0 +1,97 @@
+#!/usr/bin/env python3
+"""Render stored H2O RGB-D, native camera-space hands/objects, and OBJ assets for review."""
+import argparse
+import html
+import json
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageDraw
+import torch
+
+from render_hoi4d_validation import mesh_preview, picture, snapshot
+from warehouse_v1_0 import unpack_depth, validate_view
+
+
+def load(view, item, n):
+ if not item.get('available') or not item.get('data_ref', '').endswith('.pt'):
+ return None, None
+ values = torch.load(view / item['data_ref'], map_location='cpu', weights_only=True).numpy()
+ valid = torch.load(view / item['valid_ref'], map_location='cpu', weights_only=True).numpy() if item.get('valid_ref') else None
+ return (values, valid) if values.ndim and values.shape[0] == n else (None, None)
+
+
+def render(view, destination):
+ meta = json.loads((view / 'metainfo.json').read_text())
+ if meta['dataset_id'] != 'H2O':
+ raise ValueError('H2O view required')
+ n = meta['num_frames']
+ height, width = reversed(meta['camera']['image_size'])
+ parts = meta['components']
+ frames = [json.loads(line) for line in (view / 'camera/frames.jsonl').read_text().splitlines()]
+ assert len(frames) == n
+ selected = {0, n // 4, n // 2, 3 * n // 4, n - 1}
+ numeric = {}
+ for name, item in parts.items():
+ if item.get('data_ref', '').endswith('.pt') and item.get('available'):
+ values, valid = load(view, item, n)
+ if values is not None:
+ numeric[name] = (values, valid)
+ if valid is not None:
+ indices = np.flatnonzero(valid.reshape(n, -1).any(axis=1))
+ if len(indices):
+ selected.add(int(indices[0]))
+ selected = set(sorted(selected)[:12]) | {n - 1}
+ rgb = snapshot(view / parts['rgb']['data_ref'], 'rgb24', (height, width, 3), np.uint8, selected)
+ depth = snapshot(view / parts['depth']['data_ref'], 'gray16le', (height, 2 * width), ' 0:
+ u, v = intrinsic[i, 0, 0] * xyz[0] / xyz[2] + intrinsic[i, 0, 2], intrinsic[i, 1, 1] * xyz[1] / xyz[2] + intrinsic[i, 1, 2]
+ if 0 <= u < width and 0 <= v < height:
+ draw.ellipse((u-4, v-4, u+4, v+4), fill=color)
+ legend.append(name + ' ' + color)
+ panels = ['' + picture(photo) + 'RGB + 原生 3D 关键点投影（仅预览：' + html.escape(', '.join(legend) or '当前帧无可投影点') + '） ']
+ if i in depth:
+ z = unpack_depth(depth[i])
+ luminance = (np.clip(np.nan_to_num(z, nan=0), 0, 3) * 85).astype(np.uint8)
+ panels.append('' + picture(Image.fromarray(luminance)) + '深度 Z，黑=无效，白≥3 m；不改源深度 ')
+ details = {'frame': frames[i]}
+ for name, (values, valid) in numeric.items():
+ details[name] = {'value': values[i].tolist(), 'valid': None if valid is None else valid[i].tolist()}
+ pages.append('帧 ' + str(i) + ' / ' + str(n) + ' ' + ''.join(panels) + '
原帧号、全部数值标注及有效性 ' + html.escape(json.dumps(details, ensure_ascii=False, indent=2)) + ' ' + html.escape(name) + ' ' + mesh_preview(view / item['data_ref']) + '源 OBJ 静态预览，不代表物体姿态投影 ' + html.escape(title) + ' ' + html.escape(title) + ' 预览只作人工核查；仅显示真实存在的标注，不合成分割。
' + ''.join(pages) + '源网格 ' + ''.join(assets) + '
全部组件元数据 ' + html.escape(json.dumps(meta, ensure_ascii=False, indent=2)) + ' 持久化 QC ' + html.escape(json.dumps(report, ensure_ascii=False, indent=2)) + ' H2O 试点预览 ')
+ return destination / (title + '.html')
+
+
+if __name__ == '__main__':
+ p = argparse.ArgumentParser()
+ p.add_argument('views', nargs='+', type=Path)
+ p.add_argument('--out', type=Path, required=True)
+ args = p.parse_args()
+ for view in args.views:
+ print(render(view, args.out), flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/render_hoi4d_source_review.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/render_hoi4d_source_review.py
new file mode 100644
index 0000000000000000000000000000000000000000..79d7a58744572343670e9cbaecc4cc54ea07f1c1
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/render_hoi4d_source_review.py
@@ -0,0 +1,108 @@
+#!/usr/bin/env python3
+"""Evidence-only HTML: native HOI4D pose boxes over persisted RGB and native masks."""
+import argparse
+import html
+import json
+from pathlib import Path
+import shutil
+import zipfile
+
+import numpy as np
+from PIL import Image, ImageDraw
+
+from render_hoi4d_validation import picture, snapshot
+
+
+def source_parts(view):
+ rows = []
+ for source in view.glob('annotation/object/*/assets/result.json'):
+ def visit(node):
+ if isinstance(node, list):
+ for child in node:
+ visit(child)
+ return
+ if node.get('objs'):
+ rows.append({'source_part_id': node['id'], 'source_part_name': node['name'],
+ 'source_meshes': node['objs'], 'source_locator': str(source.relative_to(view))})
+ for child in node.get('children', []):
+ visit(child)
+ visit(json.loads(source.read_text()))
+ return rows
+
+
+def render(view, z, output):
+ output.mkdir(parents=True, exist_ok=True)
+ meta = json.loads((view / 'metainfo.json').read_text())
+ seq = meta['sampling']['selection']['source_sequence']
+ w, h = meta['camera']['image_size']
+ chosen = sorted({0, meta['num_frames'] // 2, meta['num_frames'] - 1})
+ frames = [json.loads(line) for line in (view / 'camera/frames.jsonl').read_text().splitlines()]
+ rgb = snapshot(view / meta['components']['rgb']['data_ref'], 'rgb24', (h, w, 3), np.uint8, chosen)
+ segment = meta['components'].get('segment', {})
+ masks = (snapshot(view / segment['data_ref'], 'gray16le', (h, w), '' + html.escape(seq) + f' / frame {native_id} ' + picture(image)
+ + '仅验收：伪彩=原生分割码，黄框=上游 2dBox；颜色和框不写回 shared，空间重叠不能代替身份依据。
'
+ + '上游原记录与编码对照 '
+ + html.escape(json.dumps(note, ensure_ascii=False, indent=2)) + ' HOI4D source correspondence review 源姿态／原生分割／RGB 的证据图（非真值修改） '
+ page += ''.join(panels) + 'CAD result.json 原生部件→OBJ；不等同逐帧姿态对应 '
+ page += html.escape(json.dumps(parts, ensure_ascii=False, indent=2)) + ' {html.escape(name)} 源依据对照（非物体几何验收通过） ')
+ shutil.copyfile(Path(__file__), args.out / 'renderer.py')
+
+
+if __name__ == '__main__':
+ main()
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/render_hoi4d_validation.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/render_hoi4d_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7889a1b63b004e046b52fe4e4da0b2b4651b768
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/render_hoi4d_validation.py
@@ -0,0 +1,182 @@
+#!/usr/bin/env python3
+"""Standalone, offline HTML review of every persisted component in selected views."""
+import argparse
+import base64
+import html
+import io
+import json
+import random
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageDraw
+import torch
+
+from warehouse_v1_0 import video_frames, unpack_depth, validate_view
+
+
+def snapshot(video, fmt, shape, dtype, indices):
+ return {i: frame.copy() for i, frame in enumerate(video_frames(video, fmt, shape, dtype)) if i in indices}
+
+
+def picture(image):
+ image.thumbnail((800, 450)) # Previews only; released tensors and videos are not resized.
+ output = io.BytesIO()
+ image.save(output, format='PNG')
+ return '' + picture(image) + 'RGB + 原生 2D 手关键点（若有） ']
+ if i in depth:
+ z = unpack_depth(depth[i])
+ luminance = (np.clip(np.nan_to_num(z, nan=0), 0, 3) * 85).astype(np.uint8)
+ panels.append('' + picture(Image.fromarray(luminance)) + '深度 Z（黑=无效，白≥3 m；仅预览） ')
+ if i in masks:
+ mask = masks[i]
+ colors = np.zeros((*mask.shape, 3), dtype=np.uint8)
+ for value in np.unique(mask):
+ if value not in (0, 65535):
+ colors[mask == value] = preview_color(int(value), meta['dataset_id'])
+ legend = '；'.join(f'{int(value)}=' + ('手' if value == 255 and meta['dataset_id'] == 'dex-ycb' else '物体' if meta['dataset_id'] == 'dex-ycb' else '区域') + f' #{bytes(preview_color(int(value), meta["dataset_id"])).hex()}' for value in np.unique(mask) if value not in (0, 65535))
+ panels.append('' + picture(Image.fromarray(colors)) + '原生分割整数编码的伪彩色预览（' + html.escape(legend) + '） ')
+ active = (mask != 0) & (mask != 65535)
+ overlay = rgb[i].copy()
+ overlay[active] = ((overlay[active].astype(np.uint16) + colors[active]) // 2).astype(np.uint8)
+ panels.append('' + picture(Image.fromarray(overlay)) + '原生分割叠加 RGB（仅预览） ')
+ details = {'frame': frames[i], 'segment_labels': labels.get(i, [])}
+ for name, component in components.items():
+ ref = component.get('data_ref', '')
+ if component.get('available') and ref.endswith('.pt'):
+ values = torch.load(view / ref, map_location='cpu', weights_only=True)
+ if values.ndim and values.shape[0] == n:
+ details[name] = {'value': values[i].tolist()}
+ if component.get('valid_ref', '').endswith('.pt'):
+ valid = torch.load(view / component['valid_ref'], map_location='cpu', weights_only=True)
+ details[name]['valid'] = valid[i].tolist()
+ sections.append('帧 ' + str(i) + ' / ' + str(n) + ' ' + ''.join(panels)
+ + '
帧号、来源、全部数值组件与有效性 '
+ + html.escape(json.dumps(details, ensure_ascii=False, indent=2)) + ' ' + html.escape(name) + ' ' + mesh_preview(view / ref)
+ + '仅展示网格原生形状；未验证尺度／位姿对应。 ' + html.escape(title) + ' ' + html.escape(title) + ' 预览图仅供人工审核，不是训练输入；未提供的模态在 metainfo 中保留原因。
' + ''.join(sections) + '原始物体模型（独立预览） ' + ''.join(assets) + '
全部组件元信息及原始来源 ' + html.escape(json.dumps(meta, ensure_ascii=False, indent=2)) + ' 持久化 QC 结果 ' + html.escape(json.dumps(result, ensure_ascii=False, indent=2)) + ' ' + html.escape(meta['dataset_id']) + ' review ' + html.escape(meta['dataset_id']) + ' 抽样人工审核 选择视图；所有预览均来自已保存的 shared 数据。
')
+ print(destination / (title + '.html'), 'selected_frames', sorted(selected), flush=True)
+
+
+def main():
+ p = argparse.ArgumentParser(description=__doc__)
+ p.add_argument('views', nargs='+', type=Path)
+ p.add_argument('--out', type=Path)
+ args = p.parse_args()
+ for view in args.views:
+ render(view, args.out or view.parents[2] / 'doc/validation')
+
+
+if __name__ == '__main__':
+ main()
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/run-full.sh b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/run-full.sh
new file mode 100644
index 0000000000000000000000000000000000000000..5b9267d75a5fb7c4d1729397219e7f8ca2a688c4
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/run-full.sh
@@ -0,0 +1,26 @@
+#!/usr/bin/env bash
+set -u
+base="$HOME/Datasets/uni-hoi-h2o-full-20260930-w4-c10"
+raw="$HOME/Datasets/Hand-Datasets-Raw/H2O"
+cd "$HOME/Projects/uni-hoi-dataset" || exit 1
+mkdir -p "$base/scratch"
+export TMPDIR="$base/scratch" PYTHONUNBUFFERED=1
+for n in 1 2 3 4; do test -s "$raw/raw/data/subject${n}_v1_1.tar.gz" || exit 2; done
+test ! -e "$base/full.status" || exit 3
+cg=$(awk -F: '$1=="0" {print $3}' /proc/self/cgroup)
+if [[ -f "/sys/fs/cgroup$cg/cpu.max" ]]; then
+ read -r quota period < "/sys/fs/cgroup$cg/cpu.max"
+else
+ quota=max
+ period=controller_disabled
+fi
+printf 'START=%s\nWORKERS=4 CODEC_THREADS=10 SUBJECTS=1,2,3,4 CASA=false\nCPU_LIMIT=none\nCGROUP=%s\nCPU_MAX=%s %s\n' "$(date -Is)" "$cg" "$quota" "$period" > "$base/full.status"
+if [[ "$quota" != max ]]; then
+ printf 'EXIT=4 ERROR=UNEXPECTED_CPU_QUOTA\n' >> "$base/full.status"
+ exit 4
+fi
+/usr/bin/time -v .venv/bin/python "$base/frozen-code/scripts/convert_h2o_v1.0.py" --root "$raw" --out "$base" --release-id shared-v1.0 --workers 4 --codec-threads 10 --mano-dir "$HOME/Projects/WiLoR/mano_data" > "$base/full.log" 2>&1
+code=$?
+printf 'EXIT=%s END=%s\n' "$code" "$(date -Is)" >> "$base/full.status"
+if [[ -f "/sys/fs/cgroup$cg/cpu.stat" ]]; then cat "/sys/fs/cgroup$cg/cpu.stat" >> "$base/full.status"; fi
+exit "$code"
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/warehouse_v1_0.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/warehouse_v1_0.py
new file mode 100644
index 0000000000000000000000000000000000000000..f705011bcea587bd320e1e228f99bed259b32b3a
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/provenance/previous-run/script/warehouse_v1_0.py
@@ -0,0 +1,931 @@
+"""Shared I/O for CONTRACT.md v1.0. No annotation synthesis."""
+import argparse
+from contextlib import contextmanager
+from functools import lru_cache
+import hashlib
+import json
+import math
+import shlex
+from pathlib import Path
+import pickle
+import random
+import shutil
+import subprocess
+import tempfile
+import time
+
+import numpy as np
+from PIL import Image
+import torch
+
+SCHEMA_PATH = next(p for p in (Path(__file__).resolve().parents[1] / 'docs/schema.json',
+ Path(__file__).resolve().parents[1] / 'doc/schema.json') if p.is_file())
+SCHEMA = json.loads(SCHEMA_PATH.read_text())
+RAW_ROOT = Path.home() / 'Datasets/Hand-Datasets-Raw'
+
+
+def json_write(path, value):
+ path = Path(path)
+ path.parent.mkdir(parents=True, exist_ok=True)
+ path.write_text(json.dumps(value, ensure_ascii=False, indent=2, allow_nan=False) + '\n')
+
+
+def jsonl_write(path, rows):
+ path = Path(path)
+ path.parent.mkdir(parents=True, exist_ok=True)
+ with path.open('w') as out:
+ for row in rows:
+ out.write(json.dumps(row, ensure_ascii=False, allow_nan=False) + '\n')
+
+
+def sha256(path):
+ with Path(path).open('rb') as f:
+ return hashlib.file_digest(f, 'sha256').hexdigest()
+
+
+def locator(path, member=None, data=None, **extra):
+ result = {'file': str(Path(path).resolve())}
+ if member is not None:
+ result['member'] = member
+ if data is not None:
+ result['sha256'] = hashlib.sha256(data).hexdigest()
+ return result | extra
+
+
+def source(method='Copy source annotation; normalize storage only'):
+ return {'kind': 'format_conversion', 'method': method}
+
+
+def run(args, **kwargs):
+ return subprocess.run(args, check=True, stderr=subprocess.PIPE, **kwargs)
+
+
+@lru_cache(maxsize=1)
+def ffversion():
+ return run(['ffmpeg', '-version'], stdout=subprocess.PIPE).stdout.decode().splitlines()[0]
+
+
+def probe(path):
+ return json.loads(run(['ffprobe', '-v', 'error', '-select_streams', 'v:0',
+ '-show_streams', '-of', 'json', str(path)],
+ stdout=subprocess.PIPE).stdout)['streams'][0]
+
+
+def decode_args(path, pixel_format):
+ return ['ffmpeg', '-v', 'error', '-nostdin', '-filter_threads', '1', '-threads', '1', '-noautorotate',
+ '-i', str(path), '-map', '0:v:0', '-vsync', '0', '-threads', '1',
+ '-f', 'rawvideo', '-pix_fmt', pixel_format]
+
+
+def decoded_digest(path, pixel_format):
+ start = time.monotonic()
+ with tempfile.TemporaryFile() as err:
+ proc = subprocess.Popen(decode_args(path, pixel_format) + ['pipe:1'],
+ stdout=subprocess.PIPE, stderr=err)
+ digest, size = hashlib.sha256(), 0
+ with proc.stdout:
+ while chunk := proc.stdout.read(1024 * 1024):
+ digest.update(chunk)
+ size += len(chunk)
+ if proc.wait():
+ err.seek(0)
+ raise RuntimeError(err.read().decode(errors='replace'))
+ return digest.hexdigest(), size, time.monotonic() - start
+
+
+def video_frames(path, pixel_format, shape, dtype):
+ frame_bytes = int(np.prod(shape)) * np.dtype(dtype).itemsize
+ with tempfile.TemporaryFile() as err:
+ proc = subprocess.Popen(decode_args(path, pixel_format) + ['pipe:1'], stdout=subprocess.PIPE, stderr=err)
+ completed = False
+ try:
+ while True:
+ data = proc.stdout.read(frame_bytes)
+ if not data:
+ break
+ if len(data) != frame_bytes:
+ raise ValueError('truncated decoded video frame')
+ yield np.frombuffer(data, dtype=dtype).reshape(shape)
+ if proc.wait():
+ err.seek(0)
+ raise ValueError(err.read().decode(errors='replace'))
+ completed = True
+ finally:
+ proc.stdout.close()
+ if not completed and proc.poll() is None:
+ proc.terminate()
+ proc.wait()
+
+
+def storage(path, profile, parameters, source_lossy):
+ info = probe(path)
+ if info['codec_name'] == 'h264':
+ frames = json.loads(run(['ffprobe', '-v', 'error', '-threads', '1', '-select_streams', 'v:0',
+ '-show_entries', 'frame=key_frame', '-of', 'json', str(path)], stdout=subprocess.PIPE).stdout)['frames']
+ parameters = parameters | {'keyframe_indices': [i for i, f in enumerate(frames) if f['key_frame']]}
+ return {'profile': profile, 'codec': info['codec_name'],
+ 'pixel_format': info['pix_fmt'], 'encoded_size': [info['width'], info['height']],
+ 'decoder': {'name': 'ffmpeg', 'version': ffversion(), 'autorotate': False,
+ 'color_space': info.get('color_space', 'unknown'),
+ 'color_range': info.get('color_range', 'unknown'),
+ 'color_primaries': info.get('color_primaries', 'unknown'),
+ 'color_transfer': info.get('color_transfer', 'unknown')},
+ 'encoder_parameters': {'version': ffversion()} | parameters,
+ 'source_lossy': source_lossy, 'storage_fps': 30, 'timebase_role': 'index_only'}
+
+
+def encode_raw(raw, target, width, height, pixel_format, profile, codec='ffv1', extra=(), threads=1):
+ start = time.monotonic()
+ run(['ffmpeg', '-v', 'error', '-nostdin', '-filter_threads', '1', '-y', '-f', 'rawvideo',
+ '-pixel_format', pixel_format, '-video_size', f'{width}x{height}', '-framerate', '30',
+ '-i', str(raw), '-an', '-c:v', codec, '-threads', str(threads), *extra, str(target)])
+ digest, size, decode_seconds = decoded_digest(target, pixel_format)
+ if size != Path(raw).stat().st_size or digest != sha256(raw):
+ raise ValueError(f'lossless roundtrip failed: {target}')
+ config = storage(target, profile, {'name': codec, 'arguments': list(extra), 'threads': threads}, False)
+ return config, {'bytes': target.stat().st_size, 'encode_seconds': time.monotonic() - start - decode_seconds,
+ 'decode_seconds': decode_seconds, 'decoded_sha256': digest, 'all_frames_exact': True}
+
+
+def measure_rgb_access(video, raw, width, height, count):
+ index = count // 2
+ start = time.monotonic()
+ data = run(['ffmpeg', '-v', 'error', '-nostdin', '-filter_threads', '1', '-threads', '1', '-noautorotate',
+ '-i', str(video), '-vf', f'select=eq(n\\,{index})', '-frames:v', '1', '-threads', '1',
+ '-f', 'rawvideo', '-pix_fmt', 'rgb24', 'pipe:1'], stdout=subprocess.PIPE).stdout
+ with raw.open('rb') as inp:
+ inp.seek(index * width * height * 3)
+ if data != inp.read(width * height * 3):
+ raise ValueError('exact indexed RGB read failed')
+ return {'random_access_strategy': 'decode from first keyframe; select exact presentation index',
+ 'random_frame_index': index, 'random_frame_read_seconds': time.monotonic() - start}
+
+
+def jpeg_sample_indices(sequence, camera, count):
+ seed = int.from_bytes(hashlib.sha256(f'{sequence}/{camera}/20260920'.encode()).digest()[:8], 'big')
+ indices = {0, count - 1, count // 4, count // 2, 3 * count // 4}
+ indices.update(random.Random(seed).sample([i for i in range(count) if i not in indices],
+ min(count, 16) - len(indices)))
+ return sorted(indices), seed
+
+
+def check_jpeg_copy(target, paths, hashes, size, sequence, camera, full):
+ packets = json.loads(run(['ffprobe', '-v', 'error', '-select_streams', 'v:0',
+ '-show_packets', '-show_data_hash', 'sha256', '-of', 'json', str(target)],
+ stdout=subprocess.PIPE).stdout)['packets']
+ if [p['data_hash'].split(':', 1)[1].lower() for p in packets] != hashes:
+ raise ValueError('JPEG packet hashes/order/count differ')
+ info = probe(target)
+ if [info['width'], info['height']] != list(size):
+ raise ValueError('JPEG container dimensions differ')
+ indices, seed = jpeg_sample_indices(sequence, camera, len(paths))
+ if full:
+ indices = list(range(len(paths)))
+ select = '+'.join(f'eq(n\\,{i})' for i in indices)
+ frame_bytes = size[0] * size[1] * 3
+ # Spool selected frames to disk, keeping RAM bounded for full-view audits.
+ with tempfile.TemporaryFile() as decoded:
+ run(decode_args(target, 'rgb24') + ([] if full else ['-vf', 'select=' + select]) + ['pipe:1'], stdout=decoded)
+ decoded.seek(0)
+ for i in indices:
+ expected = run(decode_args(paths[i], 'rgb24') + ['pipe:1'], stdout=subprocess.PIPE).stdout
+ if len(expected) != frame_bytes or decoded.read(frame_bytes) != expected:
+ raise ValueError(f'JPEG decoded pixels differ at frame {i}')
+ if decoded.read(1):
+ raise ValueError('JPEG selected pixel count differs')
+ return {'policy': 'jpeg-packets-all-rgb-sample16/v1', 'seed': seed,
+ 'selected_frame_indices': indices, 'pixel_frames_checked': len(indices),
+ 'packet_frames_checked': len(paths), 'jpeg_packets_exact': True,
+ 'packet_count_order_dimensions_exact': True, 'sampled_pixels_exact': True,
+ 'all_frames_exact': len(indices) == len(paths), 'full_view_audit': full}
+
+
+def rgb_video(paths, target, work, video=None, count=None, sequence="", camera="", full_jpeg=False,
+ ffv1_first=False, codec_threads=1):
+ """JPEG packet proof plus deterministic pixels; re-encodings compare all pixels."""
+ target.parent.mkdir(parents=True, exist_ok=True)
+ raw = work / 'rgb.raw'
+ packets = []
+ has_missing = video is None and any(p is None for p in paths)
+ if has_missing:
+ present = next((p for p in paths if p is not None), None)
+ if present is None:
+ raise ValueError('no source RGB image to establish the pixel format/grid')
+ original = probe(present)
+ width, height = original['width'], original['height']
+ with raw.open('wb') as dest:
+ for path in paths:
+ if path is None:
+ dest.write(bytes(width * height * 3))
+ else:
+ with Image.open(path) as im:
+ if im.mode != 'RGB' or im.size != (width, height):
+ raise ValueError('source RGB grid/type changes within view')
+ run(decode_args(path, 'rgb24') + ['pipe:1'], stdout=dest)
+ lossy, count = 'unknown', len(paths)
+ input_args = None
+ elif video is None:
+ if not paths:
+ raise ValueError('RGB timeline must contain existing source images')
+ suffix = paths[0].suffix.lower()
+ if suffix not in ('.jpg', '.jpeg', '.png'):
+ raise ValueError('unsupported source RGB format')
+ images = work / 'rgb_source'
+ images.mkdir()
+ dimensions = None
+ for i, path in enumerate(paths):
+ with Image.open(path) as im:
+ if im.mode != 'RGB' or (dimensions is not None and dimensions != im.size):
+ raise ValueError('RGB must be homogeneous 8-bit RGB, with no silent color/size conversion')
+ dimensions = im.size
+ if im.format == 'PNG' and path.read_bytes()[24] != 8:
+ raise ValueError('high bit depth RGB needs a registered schema')
+ dest = images / f'{i:08d}{suffix}'
+ shutil.copyfile(path, dest)
+ packets.append(sha256(path))
+ width, height = dimensions
+ input_args = ['-framerate', '30', '-i', str(images / ('%08d' + suffix))]
+ count = len(paths)
+ original = probe(paths[0])
+ lossy = suffix in ('.jpg', '.jpeg')
+ else:
+ original = probe(video)
+ if int(original.get('bits_per_raw_sample') or 8) > 8 or any(
+ x in original.get('pix_fmt', '') for x in ('10', '12', '16', 'f32')):
+ raise ValueError('high bit depth RGB needs a registered schema')
+ width, height = original['width'], original['height']
+ input_args = ['-i', str(video)]
+ lossy = 'unknown'
+ limits = ['-frames:v', str(count)] if count else []
+ records = []
+ if video is None and not has_missing and suffix in ('.jpg', '.jpeg'):
+ start = time.monotonic()
+ try:
+ run(['ffmpeg', '-v', 'error', '-nostdin', '-y', *input_args, '-map', '0:v:0',
+ *limits, '-c:v', 'copy', '-an', str(target)])
+ encode_seconds = time.monotonic() - start
+ validation = check_jpeg_copy(target, paths, packets, (width, height), sequence, camera, full_jpeg)
+ cfg = storage(target, 'mkv/mjpeg_copy/v1', {'stream_copy': True}, lossy)
+ cfg['decoder']['source_color_metadata'] = {k: original.get(k, 'unknown') for k in
+ ('color_space', 'color_range', 'color_primaries', 'color_transfer')}
+ records.append(validation | {'profile': cfg['profile'], 'bytes': target.stat().st_size,
+ 'encode_seconds': encode_seconds, 'validation_seconds': time.monotonic() - start - encode_seconds})
+ return cfg, [width, height], records, count
+ except (subprocess.CalledProcessError, ValueError) as exc:
+ records.append({'profile': 'mkv/mjpeg_copy/v1', 'rejected': str(exc)})
+ target.unlink(missing_ok=True)
+ if not has_missing:
+ run(['ffmpeg', '-v', 'error', '-nostdin', '-y', '-threads', '1', '-noautorotate',
+ *input_args, '-map', '0:v:0', *limits, '-vsync', '0', '-threads', '1',
+ '-f', 'rawvideo', '-pix_fmt', 'rgb24', str(raw)])
+ frame_bytes = width * height * 3
+ actual, remainder = divmod(raw.stat().st_size, frame_bytes)
+ if remainder or not actual or (count is not None and actual != count):
+ raise ValueError('source RGB frame count mismatch')
+ expected_hash = sha256(raw)
+ can_copy = video is not None
+ if can_copy:
+ start = time.monotonic()
+ try:
+ run(['ffmpeg', '-v', 'error', '-nostdin', '-y', *input_args, '-map', '0:v:0',
+ *limits, '-c:v', 'copy', '-an', str(target)])
+ digest, size, seconds = decoded_digest(target, 'rgb24')
+ if digest != expected_hash or size != raw.stat().st_size:
+ raise ValueError('stream-copy RGB bytes/order/count mismatch')
+ cfg = storage(target, 'mkv/source_copy/v1',
+ {'stream_copy': True}, lossy)
+ if video:
+ cfg['storage_fps'] = original.get('avg_frame_rate')
+ cfg['preserve_source_pts'] = True
+ records.append({'profile': cfg['profile'], 'all_frames_exact': True,
+ 'decoded_sha256': digest,
+ 'bytes': target.stat().st_size, 'decode_seconds': seconds,
+ 'encode_seconds': time.monotonic() - start - seconds})
+ records[-1].update(measure_rgb_access(target, raw, width, height, actual))
+ return cfg, [width, height], records, actual
+ except (subprocess.CalledProcessError, ValueError) as exc:
+ records.append({'profile': 'stream_copy', 'rejected': str(exc)})
+ target.unlink(missing_ok=True)
+ candidates = []
+ for profile, codec, params in [
+ ('mkv/ffv1_rgb/v1', 'ffv1', ['-level', '3', '-pix_fmt', 'bgr0']),
+ ('mkv/h264_rgb_lossless/v1', 'libx264rgb', ['-crf', '0', '-preset', 'medium', '-g', '30'])]:
+ path = work / (codec + '.mkv')
+ try:
+ cfg, record = encode_raw(raw, path, width, height, 'rgb24', profile, codec, params,
+ codec_threads if codec == 'ffv1' else 1)
+ cfg['source_lossy'] = lossy
+ cfg['decoder']['source_color_metadata'] = {k: original.get(k, 'unknown') for k in
+ ('color_space', 'color_range', 'color_primaries', 'color_transfer')}
+ record['profile'] = profile
+ record.update(measure_rgb_access(path, raw, width, height, actual))
+ records.append(record)
+ candidates.append((path.stat().st_size, path, cfg))
+ if ffv1_first and codec == 'ffv1':
+ break # Keep the H.264 fallback only if the preferred exact encoder fails.
+ except subprocess.CalledProcessError as exc:
+ records.append({'profile': profile, 'rejected': exc.stderr.decode(errors='replace')})
+ if not candidates:
+ raise ValueError('no supported lossless RGB encoder')
+ _, chosen, cfg = min(candidates, key=lambda x: x[0])
+ shutil.move(chosen, target)
+ return cfg, [width, height], records, actual
+
+
+def pack_depth(depth):
+ bits = np.ascontiguousarray(depth, dtype='> 16).astype(' 0)
+ arr[~good] = np.nan
+ out.write(pack_depth(arr).tobytes())
+ vo.write(good.astype(np.uint8).tobytes())
+ else:
+ if not np.issubdtype(arr.dtype, np.integer) or arr.min() < 0 or arr.max() > 65535:
+ raise ValueError('invalid segmentation region codes')
+ out.write(arr.astype(' vertices:
+ raise ValueError('empty or invalid mesh')
+ def dependency(parent, name):
+ dep = (parent / name).resolve()
+ if not dep.is_relative_to(asset_root) or not dep.is_file():
+ raise ValueError(f'missing or escaping asset dependency: {name}')
+ return dep
+ dependencies = []
+ for name in materials:
+ mtl = dependency(path.parent, name)
+ dependencies.append(mtl)
+ for line in mtl.read_text().splitlines():
+ fields = shlex.split(line, comments=True)
+ if fields and (fields[0].startswith('map_') or fields[0] in ('bump', 'norm', 'disp', 'decal')):
+ dependencies.append(dependency(mtl.parent, fields[-1]))
+ return {'vertices': vertices, 'faces': faces, 'sha256': sha256(path),
+ 'dependencies': {p.relative_to(asset_root).as_posix(): sha256(p) for p in dependencies}}
+
+
+def mesh_asset(view, oid, directory, obj, loc):
+ dest = view.path / f'annotation/object/{oid}/assets'
+ shutil.copytree(directory, dest)
+ ref = (dest / Path(obj).relative_to(directory)).relative_to(view.path).as_posix()
+ mesh_check = check_mesh(dest / Path(obj).relative_to(directory), dest)
+ view.build['validation'].append({'component': f'mesh_{oid}', **mesh_check})
+ view.component(f'mesh_{oid}', 'mesh/obj/v1', ref, ['vertex'], loc, valid=True, unit='m')
+ p = view.path / f'annotation/object/{oid}/metainfo.json'
+ info = json.loads(p.read_text())
+ info.update(has_model=True, mesh_ref=ref)
+ json_write(p, info)
+
+
+def safe_member(root, name):
+ path = (Path(root) / name).resolve()
+ if not path.is_relative_to(Path(root).resolve()):
+ raise ValueError(f'unsafe archive path: {name}')
+ return path
+
+
+class ChumpyStub:
+ def __setstate__(self, state):
+ self.__dict__.update(state)
+
+
+class ManoUnpickler(pickle.Unpickler):
+ def find_class(self, module, name):
+ if module.startswith('chumpy'):
+ return ChumpyStub
+ return super().find_class(module, name)
+
+
+def mano_models(directory):
+ result = {}
+ if directory is None:
+ return result
+ for side in ('left', 'right'):
+ path = Path(directory).expanduser() / f'MANO_{side.upper()}.pkl'
+ if not path.exists():
+ continue
+ with path.open('rb') as f:
+ data = ManoUnpickler(f, encoding='latin1').load()
+ result[side] = {'components': np.asarray(data['hands_components']),
+ 'mean': np.asarray(data['hands_mean']), 'sha256': sha256(path),
+ 'file': path.name}
+ return result
+
+
+def model_profile(models):
+ identity = json.dumps({side: d['sha256'] for side, d in sorted(models.items())}).encode()
+ return {'version': 'sha256:' + hashlib.sha256(identity).hexdigest(), 'flat_hand_mean': True,
+ 'left_shapedirs_correction': 'not_applied',
+ 'models': {side: {'file': d['file'], 'sha256': d['sha256']} for side, d in models.items()}}
+
+
+def pca_to_axis_angle(coeffs, model):
+ coeffs = np.asarray(coeffs)
+ if coeffs.shape[-1:] != (45,) or not np.isfinite(coeffs).all():
+ raise ValueError('invalid MANO PCA coefficients')
+ return coeffs @ model['components'] + model['mean']
+
+
+def parser(dataset, description):
+ p = argparse.ArgumentParser(description=description)
+ p.add_argument('--root', f'--{dataset.lower()}-root', dest='root', type=Path, default=RAW_ROOT / dataset)
+ p.add_argument('--out', type=Path, default=Path('converted'))
+ p.add_argument('--release-id', default='v1.0')
+ p.add_argument('--limit', type=int, help='Maximum sequences to convert')
+ p.add_argument('--max-frames', type=int, help='First N source frame indices per view (recorded as sampling)')
+ p.add_argument('--mano-dir', type=Path)
+ p.add_argument('--views', help='Comma-separated source camera IDs')
+ p.add_argument('--jpeg-full-check', action='store_true', help='Full decoded RGB check for selected JPEG pilot/final views')
+ return p
+
+
+def roots(root):
+ root = Path(root).expanduser()
+ return [p for p in (root / 'raw/data', root / 'raw', root) if p.is_dir()]
+
+
+def find_file(root, name):
+ return next((p / name for p in roots(root) if (p / name).exists()), None)
+
+
+def release(args, dataset):
+ if not args.release_id or Path(args.release_id).name != args.release_id or args.release_id in ('.', '..'):
+ raise ValueError('release-id must be a single directory name')
+ if args.limit is not None and args.limit < 1 or args.max_frames is not None and args.max_frames < 1:
+ raise ValueError('limits must be positive')
+ out = args.out.expanduser().resolve() / dataset / args.release_id
+ raw = args.root.expanduser().resolve()
+ if out == raw or out.is_relative_to(raw):
+ raise ValueError('output must not modify the raw source tree')
+ (out / 'data').mkdir(parents=True, exist_ok=True)
+ (out / 'doc').mkdir(exist_ok=True)
+ (out / 'script').mkdir(exist_ok=True)
+ scripts = list(Path(__file__).resolve().parent.glob('*.py'))
+ existing = {p.name for p in (out / 'script').glob('*.py')}
+ if existing and existing != {p.name for p in scripts}:
+ raise ValueError('converter files changed; choose a new --release-id')
+ copies = [(SCHEMA_PATH.parent / name, out / 'doc' / name) for name in ('CONTRACT.md', 'schema.json')]
+ copies += [(path, out / 'script' / path.name) for path in scripts]
+ # Check every existing file before changing any published definition or script.
+ for src, dest in copies:
+ if dest.exists() and sha256(dest) != sha256(src):
+ raise ValueError('converter or contract changed within an existing release; choose a new --release-id')
+ for src, dest in copies:
+ if src.resolve() != dest.resolve():
+ shutil.copyfile(src, dest)
+ json_write(out / 'script/config.json', {k: str(v) if isinstance(v, Path) else v for k, v in vars(args).items()})
+ return out
+
+
+@contextmanager
+def sequence_dir(out, sequence):
+ if Path(sequence).name != sequence:
+ raise ValueError('sequence ID must be a single path component')
+ final = out / 'data' / sequence
+ with tempfile.TemporaryDirectory(prefix=sequence + '.tmp-', dir=out / 'data') as wd:
+ tmp = Path(wd)
+ yield tmp
+ if final.exists():
+ if any((final / child.name).exists() for child in tmp.iterdir()):
+ raise FileExistsError('view already published; use a new release-id to preserve it')
+ for child in list(tmp.iterdir()):
+ child.rename(final / child.name)
+ else:
+ tmp.rename(final)
+
+
+def build_record(out, sequence, reports):
+ path = out / 'doc' / (sequence + '.build.json')
+ previous = json.loads(path.read_text()) if path.exists() else {}
+ config = json.loads((out / 'script/config.json').read_text())
+ for report in reports.values():
+ report['configuration'] = config
+ json_write(path, {'sequence_id': sequence, 'storage_revision': SCHEMA['storage_revision'],
+ 'scripts': {p.name: sha256(p) for p in (out / 'script').glob('*.py')},
+ 'runtime': {'torch': torch.__version__, 'ffmpeg': ffversion()},
+ 'views': previous.get('views', {}) | reports})
+
+
+def check_view_type(meta):
+ # The pinned ARCTIC contract makes view_type optional; adapters still supply known views.
+ if 'view_type' not in meta:
+ return
+ if meta['view_type'] not in ('ego', 'exo', 'unknown'):
+ raise ValueError('view_type must be ego/exo/unknown')
+ expected = {'dex-ycb': 'exo', 'EPIC-KITCHENS-VISOR': 'ego', 'HOI4D': 'ego'}.get(meta['dataset_id'])
+ if meta['dataset_id'] == 'H2O':
+ expected = 'ego' if meta['camera_id'] == 'cam4' else 'exo' if meta['camera_id'] in ('cam0', 'cam1', 'cam2', 'cam3') else 'unknown'
+ if expected and meta['view_type'] not in (expected, 'unknown'):
+ raise ValueError('view_type contradicts known source capture setup')
+
+
+def check_valid_policy(component, count):
+ policy = component.get('valid_policy', {})
+ for field in ('revision', 'meaning', 'source_rule', 'source_locator', 'scope', 'numerical_checks',
+ 'official_qa', 'local_excluded_frame_indices', 'local_exclusion_reasons', 'visibility_independent'):
+ if field not in policy:
+ raise ValueError(f'valid_policy missing {field}')
+ if policy['revision'] != SCHEMA['validity_revision'] or policy['meaning'] != SCHEMA['annotation_validity']['meaning']:
+ raise ValueError('outdated annotation validity semantics')
+ if not policy['source_rule'] or not policy['numerical_checks'] or policy['scope'] != component['valid_axes']:
+ raise ValueError('incomplete validity rule/scope')
+ if policy['official_qa'].get('status') != 'not_provided' or not policy['official_qa'].get('reason'):
+ raise ValueError('official QA requires a verified adapter; supervision acceptance paused')
+ excluded = policy['local_excluded_frame_indices']
+ if any(type(i) is not int or not 0 <= i < count for i in excluded):
+ raise ValueError('invalid local exclusion frame index')
+ if excluded:
+ raise ValueError('local exclusions require a reviewed rule/report; not supported by these adapters')
+ if policy['visibility_independent'] is not True:
+ raise ValueError('visibility must remain independent of validity')
+
+
+def validate_view(path):
+ """Validate the persisted contract, not a rendering of the source labels."""
+ path = Path(path)
+ meta = json.loads((path / 'metainfo.json').read_text())
+ n = meta['num_frames']
+ check_view_type(meta)
+ if meta['schema_version'] != SCHEMA['schema_version'] or meta['hand_slots'] != ['left', 'right'] or n < 1:
+ raise ValueError('invalid view header')
+ def resolve(ref):
+ if Path(ref).is_absolute() or '\\' in ref:
+ raise ValueError('component reference must be relative POSIX')
+ dest = safe_member(path, ref)
+ if not dest.is_file():
+ raise ValueError(f'referenced file missing: {ref}')
+ return dest
+ def tensor(ref):
+ def cpu_storage(storage, location):
+ if location != 'cpu':
+ raise ValueError('PT storage must be on CPU')
+ return storage
+ value = torch.load(resolve(ref), map_location=cpu_storage, weights_only=True)
+ if not isinstance(value, torch.Tensor) or value.device.type != 'cpu':
+ raise ValueError('PT must contain only a CPU Tensor')
+ return value.numpy()
+ frames = [json.loads(line) for line in resolve(meta['sampling']['frames_ref']).read_text().splitlines()]
+ if len(frames) != n or [f['frame_index'] for f in frames] != list(range(n)):
+ raise ValueError('frame index/count mismatch')
+ for f in frames:
+ if not all(k in f for k in ('source_frame_id', 'timestamp_ns', 'clock_domain', 'source_locator')):
+ raise ValueError('incomplete frame provenance')
+ entities = {'hand_left', 'hand_right'}
+ parts = {}
+ for p in (path / 'annotation/object').glob('*/metainfo.json'):
+ obj = json.loads(p.read_text())
+ if obj['object_id'] != p.parent.name or obj['identity_scope'] not in ('frame', 'subsequence', 'sequence'):
+ raise ValueError('invalid object identity')
+ entities.add(obj['object_id'])
+ if obj.get('mesh_ref'):
+ if not resolve(obj['mesh_ref']).is_relative_to(p.parent.resolve()):
+ raise ValueError('mesh must belong to its object')
+ if obj.get('articulation_ref'):
+ art = json.loads(resolve(obj['articulation_ref']).read_text())
+ part_ids = {part['part_id'] for part in art['parts']}
+ if len(part_ids) != len(art['parts']):
+ raise ValueError('duplicate part ID')
+ parents = {part['part_id']: part['parent_id'] for part in art['parts']}
+ for part_id in parents:
+ visited, cur = set(), part_id
+ while cur is not None:
+ if cur in visited or cur not in parents:
+ raise ValueError('invalid or cyclic part hierarchy')
+ visited.add(cur)
+ cur = parents[cur]
+ parts[obj['object_id']] = part_ids
+ video_stats = {}
+ for key, component in meta['components'].items():
+ if not component.get('available'):
+ if not component.get('reason'):
+ raise ValueError('unavailable component needs a reason')
+ continue
+ schema = SCHEMA['components'][component['schema_id']]
+ for required in ('axes', 'provenance', 'source_locator'):
+ if required not in component:
+ raise ValueError(f'{key}: {required} missing')
+ for required in schema.get('required_metadata', []):
+ if required not in component:
+ raise ValueError(f'{key}: {required} missing')
+ if component['provenance']['kind'] == 'derived':
+ raise ValueError('v1.0 must not synthesize annotations')
+ if component['data_ref'].startswith('annotation/') and schema.get('valid_shape'):
+ if not component.get('valid_ref') or not component.get('valid_axes'):
+ raise ValueError(f'{key}: annotation validity reference/axes missing')
+ check_valid_policy(component, n)
+ data_path = resolve(component['data_ref'])
+ valid = tensor(component['valid_ref']) if component.get('valid_ref', '').endswith('.pt') else None
+ valid_shape = schema.get('valid_shape')
+ if valid is not None and valid_shape and (len(valid_shape) != valid.ndim or any(
+ isinstance(s, int) and valid.shape[i] != s for i, s in enumerate(valid_shape))):
+ raise ValueError(f'{key}: validity schema shape mismatch')
+ if valid is not None and (valid.dtype != bool or valid.shape[0] != n):
+ raise ValueError(f'{key}: invalid validity tensor')
+ if component['schema_id'] == 'mesh/obj/v1':
+ objroot = next((parent for parent in data_path.parents if parent.parent.name == 'object'), None)
+ if objroot is None:
+ raise ValueError('mesh must be owned by an object')
+ check_mesh(data_path, objroot)
+ if data_path.suffix == '.pt':
+ arr = tensor(component['data_ref'])
+ if arr.dtype != np.float32 or arr.shape[0] != n:
+ raise ValueError(f'{key}: float dtype or timeline mismatch')
+ shape = schema.get('shape')
+ if shape and (len(shape) != arr.ndim or any(isinstance(s, int) and arr.shape[i] != s for i, s in enumerate(shape))):
+ raise ValueError(f'{key}: schema shape mismatch')
+ if valid is not None:
+ if arr.shape[:valid.ndim] != valid.shape or not np.isfinite(arr[valid]).all() or not np.isnan(arr[~valid]).all():
+ raise ValueError(f'{key}: invalid finite/NaN/validity values')
+ if component['schema_id'] in ('object/rigid/v1', 'transform/se3/v1'):
+ check_transform(arr[valid])
+ if data_path.suffix == '.mkv':
+ cfg = component['storage']
+ if cfg['profile'] not in SCHEMA['storage_profiles']:
+ raise ValueError('unregistered video profile')
+ for field in SCHEMA['video_metadata']['required']:
+ if field not in cfg:
+ raise ValueError('video storage declaration incomplete')
+ w, h = meta['camera']['image_size']
+ if cfg['encoded_size'] != [w * (2 if key == 'depth' else 1), h]:
+ raise ValueError('encoded raster differs from logical RGB grid')
+ actual = probe(data_path)
+ if (actual['codec_name'] != cfg['codec'] or actual['pix_fmt'] != cfg['pixel_format'] or
+ [actual['width'], actual['height']] != cfg['encoded_size']):
+ raise ValueError('video storage metadata differs from file')
+ if key == 'rgb':
+ fmt, bpp = 'rgb24', 3
+ else:
+ fmt, bpp = 'gray16le', 2
+ digest, size, _ = decoded_digest(data_path, fmt)
+ w, h = cfg['encoded_size']
+ if size != n * w * h * bpp:
+ raise ValueError('raster timeline mismatch')
+ video_stats[key] = {'decoded_sha256': digest, 'frames': n}
+ if key == 'depth':
+ validity = component['valid_storage']
+ vp = resolve(component['valid_ref'])
+ actual_valid = probe(vp)
+ if (validity['profile'] != 'mkv/bool_ffv1/v1' or actual_valid['codec_name'] != 'ffv1'
+ or actual_valid['pix_fmt'] != 'gray' or [actual_valid['width'], actual_valid['height']] != [w // 2, h]):
+ raise ValueError('invalid depth validity video')
+ count = 0
+ for encoded, mask in zip(video_frames(data_path, 'gray16le', (h, w), ' 0)).all() or not np.isnan(depth[~good]).all():
+ raise ValueError('invalid depth/validity semantics')
+ count += 1
+ if count != n:
+ raise ValueError('depth validity timeline mismatch')
+ if component['schema_id'].startswith('segment/') and component.get('mapping_ref'):
+ rows = [json.loads(line) for line in resolve(component['mapping_ref']).read_text().splitlines()]
+ if len(rows) != n or [row['frame_index'] for row in rows] != list(range(n)):
+ raise ValueError('segmentation mapping timeline mismatch')
+ for row in rows:
+ labels = row['labels']
+ if len({a['mask_value'] for a in labels}) != len(labels):
+ raise ValueError('duplicate region mapping')
+ for label in labels:
+ if label['entity_id'] is not None and label['entity_id'] not in entities:
+ raise ValueError('unresolved segmentation entity')
+ if label['part_id'] is not None and label['part_id'] not in parts.get(label['entity_id'], set()):
+ raise ValueError('unresolved segmentation part')
+ if component['schema_id'] == 'segment/uint16_labels/v1':
+ width, height = meta['camera']['image_size']
+ for i, mask in enumerate(video_frames(data_path, 'gray16le', (height, width), ' 0 and meta.get('view_type') == 'exo', view
+ assert (view / 'camera/frames.jsonl').is_file(), view
+ assert meta['components']['rgb']['available'], view
+ splits[meta['split']] += 1
+ for key, part in meta['components'].items():
+ if part.get('available'):
+ availability[key] += 1
+ for ref in ('data_ref', 'valid_ref'):
+ if part.get(ref):
+ assert (view / part[ref]).is_file(), (view, key, ref)
+ assert len(subjects) == 10 and set(subjects.values()) == {100}, subjects
+ assert availability['depth'] == availability['rgb'] == 8000, availability
+ report = dict(sequences=len(sequences), views=sum(splits.values()), subjects=dict(subjects),
+ splits=dict(splits), available_views=dict(availability), config=config,
+ published_script_sha256=hashes, sampled_views=[str(v.relative_to(root)) for v in selected],
+ scope='Structure and file refs checked for all views; persisted QC and HTML only for sampled views. No manual visual approval yet.')
+ destination = doc / 'validation'
+ destination.mkdir(exist_ok=True)
+ (destination / 'STRUCTURAL_AUDIT.json').write_text(json.dumps(report, ensure_ascii=False, indent=2) + '\n')
+ for view in selected:
+ render(view, destination)
+ return report
+
+
+if __name__ == '__main__':
+ print(json.dumps(inspect(Path(sys.argv[1]).expanduser().resolve()), ensure_ascii=False), flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/render_h2o_validation.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/render_h2o_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..b89e7c0827f45ec7f854ff8651234cb9a4223f79
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/render_h2o_validation.py
@@ -0,0 +1,97 @@
+#!/usr/bin/env python3
+"""Render stored H2O RGB-D, native camera-space hands/objects, and OBJ assets for review."""
+import argparse
+import html
+import json
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageDraw
+import torch
+
+from render_hoi4d_validation import mesh_preview, picture, snapshot
+from warehouse_v1_0 import unpack_depth, validate_view
+
+
+def load(view, item, n):
+ if not item.get('available') or not item.get('data_ref', '').endswith('.pt'):
+ return None, None
+ values = torch.load(view / item['data_ref'], map_location='cpu', weights_only=True).numpy()
+ valid = torch.load(view / item['valid_ref'], map_location='cpu', weights_only=True).numpy() if item.get('valid_ref') else None
+ return (values, valid) if values.ndim and values.shape[0] == n else (None, None)
+
+
+def render(view, destination):
+ meta = json.loads((view / 'metainfo.json').read_text())
+ if meta['dataset_id'] != 'H2O':
+ raise ValueError('H2O view required')
+ n = meta['num_frames']
+ height, width = reversed(meta['camera']['image_size'])
+ parts = meta['components']
+ frames = [json.loads(line) for line in (view / 'camera/frames.jsonl').read_text().splitlines()]
+ assert len(frames) == n
+ selected = {0, n // 4, n // 2, 3 * n // 4, n - 1}
+ numeric = {}
+ for name, item in parts.items():
+ if item.get('data_ref', '').endswith('.pt') and item.get('available'):
+ values, valid = load(view, item, n)
+ if values is not None:
+ numeric[name] = (values, valid)
+ if valid is not None:
+ indices = np.flatnonzero(valid.reshape(n, -1).any(axis=1))
+ if len(indices):
+ selected.add(int(indices[0]))
+ selected = set(sorted(selected)[:12]) | {n - 1}
+ rgb = snapshot(view / parts['rgb']['data_ref'], 'rgb24', (height, width, 3), np.uint8, selected)
+ depth = snapshot(view / parts['depth']['data_ref'], 'gray16le', (height, 2 * width), ' 0:
+ u, v = intrinsic[i, 0, 0] * xyz[0] / xyz[2] + intrinsic[i, 0, 2], intrinsic[i, 1, 1] * xyz[1] / xyz[2] + intrinsic[i, 1, 2]
+ if 0 <= u < width and 0 <= v < height:
+ draw.ellipse((u-4, v-4, u+4, v+4), fill=color)
+ legend.append(name + ' ' + color)
+ panels = ['' + picture(photo) + 'RGB + 原生 3D 关键点投影（仅预览：' + html.escape(', '.join(legend) or '当前帧无可投影点') + '） ']
+ if i in depth:
+ z = unpack_depth(depth[i])
+ luminance = (np.clip(np.nan_to_num(z, nan=0), 0, 3) * 85).astype(np.uint8)
+ panels.append('' + picture(Image.fromarray(luminance)) + '深度 Z，黑=无效，白≥3 m；不改源深度 ')
+ details = {'frame': frames[i]}
+ for name, (values, valid) in numeric.items():
+ details[name] = {'value': values[i].tolist(), 'valid': None if valid is None else valid[i].tolist()}
+ pages.append('帧 ' + str(i) + ' / ' + str(n) + ' ' + ''.join(panels) + '
原帧号、全部数值标注及有效性 ' + html.escape(json.dumps(details, ensure_ascii=False, indent=2)) + ' ' + html.escape(name) + ' ' + mesh_preview(view / item['data_ref']) + '源 OBJ 静态预览，不代表物体姿态投影 ' + html.escape(title) + ' ' + html.escape(title) + ' 预览只作人工核查；仅显示真实存在的标注，不合成分割。
' + ''.join(pages) + '源网格 ' + ''.join(assets) + '
全部组件元数据 ' + html.escape(json.dumps(meta, ensure_ascii=False, indent=2)) + ' 持久化 QC ' + html.escape(json.dumps(report, ensure_ascii=False, indent=2)) + ' H2O 试点预览 ')
+ return destination / (title + '.html')
+
+
+if __name__ == '__main__':
+ p = argparse.ArgumentParser()
+ p.add_argument('views', nargs='+', type=Path)
+ p.add_argument('--out', type=Path, required=True)
+ args = p.parse_args()
+ for view in args.views:
+ print(render(view, args.out), flush=True)
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/render_hoi4d_source_review.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/render_hoi4d_source_review.py
new file mode 100644
index 0000000000000000000000000000000000000000..79d7a58744572343670e9cbaecc4cc54ea07f1c1
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/render_hoi4d_source_review.py
@@ -0,0 +1,108 @@
+#!/usr/bin/env python3
+"""Evidence-only HTML: native HOI4D pose boxes over persisted RGB and native masks."""
+import argparse
+import html
+import json
+from pathlib import Path
+import shutil
+import zipfile
+
+import numpy as np
+from PIL import Image, ImageDraw
+
+from render_hoi4d_validation import picture, snapshot
+
+
+def source_parts(view):
+ rows = []
+ for source in view.glob('annotation/object/*/assets/result.json'):
+ def visit(node):
+ if isinstance(node, list):
+ for child in node:
+ visit(child)
+ return
+ if node.get('objs'):
+ rows.append({'source_part_id': node['id'], 'source_part_name': node['name'],
+ 'source_meshes': node['objs'], 'source_locator': str(source.relative_to(view))})
+ for child in node.get('children', []):
+ visit(child)
+ visit(json.loads(source.read_text()))
+ return rows
+
+
+def render(view, z, output):
+ output.mkdir(parents=True, exist_ok=True)
+ meta = json.loads((view / 'metainfo.json').read_text())
+ seq = meta['sampling']['selection']['source_sequence']
+ w, h = meta['camera']['image_size']
+ chosen = sorted({0, meta['num_frames'] // 2, meta['num_frames'] - 1})
+ frames = [json.loads(line) for line in (view / 'camera/frames.jsonl').read_text().splitlines()]
+ rgb = snapshot(view / meta['components']['rgb']['data_ref'], 'rgb24', (h, w, 3), np.uint8, chosen)
+ segment = meta['components'].get('segment', {})
+ masks = (snapshot(view / segment['data_ref'], 'gray16le', (h, w), '' + html.escape(seq) + f' / frame {native_id} ' + picture(image)
+ + '仅验收：伪彩=原生分割码，黄框=上游 2dBox；颜色和框不写回 shared，空间重叠不能代替身份依据。
'
+ + '上游原记录与编码对照 '
+ + html.escape(json.dumps(note, ensure_ascii=False, indent=2)) + ' HOI4D source correspondence review 源姿态／原生分割／RGB 的证据图（非真值修改） '
+ page += ''.join(panels) + 'CAD result.json 原生部件→OBJ；不等同逐帧姿态对应 '
+ page += html.escape(json.dumps(parts, ensure_ascii=False, indent=2)) + ' {html.escape(name)} 源依据对照（非物体几何验收通过） ')
+ shutil.copyfile(Path(__file__), args.out / 'renderer.py')
+
+
+if __name__ == '__main__':
+ main()
diff --git a/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/render_hoi4d_validation.py b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/render_hoi4d_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7889a1b63b004e046b52fe4e4da0b2b4651b768
--- /dev/null
+++ b/H2O/shared-v1.1/script/v1_0_snapshot/script/provenance/previous-run/script/render_hoi4d_validation.py
@@ -0,0 +1,182 @@
+#!/usr/bin/env python3
+"""Standalone, offline HTML review of every persisted component in selected views."""
+import argparse
+import base64
+import html
+import io
+import json
+import random
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageDraw
+import torch
+
+from warehouse_v1_0 import video_frames, unpack_depth, validate_view
+
+
+def snapshot(video, fmt, shape, dtype, indices):
+ return {i: frame.copy() for i, frame in enumerate(video_frames(video, fmt, shape, dtype)) if i in indices}
+
+
+def picture(image):
+ image.thumbnail((800, 450)) # Previews only; released tensors and videos are not resized.
+ output = io.BytesIO()
+ image.save(output, format='PNG')
+ return '' + picture(image) + 'RGB + 原生 2D 手关键点（若有） ']
+ if i in depth:
+ z = unpack_depth(depth[i])
+ luminance = (np.clip(np.nan_to_num(z, nan=0), 0, 3) * 85).astype(np.uint8)
+ panels.append('' + picture(Image.fromarray(luminance)) + '深度 Z（黑=无效，白≥3 m；仅预览） ')
+ if i in masks:
+ mask = masks[i]
+ colors = np.zeros((*mask.shape, 3), dtype=np.uint8)
+ for value in np.unique(mask):
+ if value not in (0, 65535):
+ colors[mask == value] = preview_color(int(value), meta['dataset_id'])
+ legend = '；'.join(f'{int(value)}=' + ('手' if value == 255 and meta['dataset_id'] == 'dex-ycb' else '物体' if meta['dataset_id'] == 'dex-ycb' else '区域') + f' #{bytes(preview_color(int(value), meta["dataset_id"])).hex()}' for value in np.unique(mask) if value not in (0, 65535))
+ panels.append('' + picture(Image.fromarray(colors)) + '原生分割整数编码的伪彩色预览（' + html.escape(legend) + '） ')
+ active = (mask != 0) & (mask != 65535)
+ overlay = rgb[i].copy()
+ overlay[active] = ((overlay[active].astype(np.uint16) + colors[active]) // 2).astype(np.uint8)
+ panels.append('' + picture(Image.fromarray(overlay)) + '原生分割叠加 RGB（仅预览） ')
+ details = {'frame': frames[i], 'segment_labels': labels.get(i, [])}
+ for name, component in components.items():
+ ref = component.get('data_ref', '')
+ if component.get('available') and ref.endswith('.pt'):
+ values = torch.load(view / ref, map_location='cpu', weights_only=True)
+ if values.ndim and values.shape[0] == n:
+ details[name] = {'value': values[i].tolist()}
+ if component.get('valid_ref', '').endswith('.pt'):
+ valid = torch.load(view / component['valid_ref'], map_location='cpu', weights_only=True)
+ details[name]['valid'] = valid[i].tolist()
+ sections.append('帧 ' + str(i) + ' / ' + str(n) + ' ' + ''.join(panels)
+ + '
帧号、来源、全部数值组件与有效性 '
+ + html.escape(json.dumps(details, ensure_ascii=False, indent=2)) + ' ' + html.escape(name) + ' ' + mesh_preview(view / ref)
+ + '仅展示网格原生形状；未验证尺度／位姿对应。 ' + html.escape(title) + ' ' + html.escape(title) + ' 预览图仅供人工审核，不是训练输入；未提供的模态在 metainfo 中保留原因。
' + ''.join(sections) + '原始物体模型（独立预览） ' + ''.join(assets) + '
全部组件元信息及原始来源 ' + html.escape(json.dumps(meta, ensure_ascii=False, indent=2)) + ' 持久化 QC 结果 ' + html.escape(json.dumps(result, ensure_ascii=False, indent=2)) + ' ' + html.escape(meta['dataset_id']) + ' review ' + html.escape(meta['dataset_id']) + ' 抽样人工审核 选择视图；所有预览均来自已保存的 shared 数据。
')
+ print(destination / (title + '.html'), 'selected_frames', sorted(selected), flush=True)
+
+
+def main():
+ p = argparse.ArgumentParser(description=__doc__)
+ p.add_argument('views', nargs='+', type=Path)
+ p.add_argument('--out', type=Path)
+ args = p.parse_args()
+ for view in args.views:
+ render(view, args.out or view.parents[2] / 'doc/validation')
+
+
+if __name__ == '__main__':
+ main()
).decode() + ')
'
+
+
+def preview_color(value, dataset_id):
+ # Dex-YCB reserves 255 for the native hand mask. The old mod-240 palette
+ # gave it the same color as YCB object 15; keep data codes untouched.
+ if dataset_id == 'dex-ycb' and value == 255:
+ return (255, 32, 64)
+ return tuple((value * factor) % 240 + 15 for factor in (89, 61, 131))
+
+
+def mesh_preview(path):
+ """Project the whole source asset; bound drawing cost without favoring early faces."""
+ vertices, faces, referenced = [], [], set()
+ rng = random.Random(0)
+ face_count = 0
+ with path.open(errors='replace') as stream:
+ for line in stream:
+ if line.startswith('v '):
+ vertices.append([float(v) for v in line.split()[1:4]])
+ elif line.startswith('f '):
+ face = []
+ for token in line.split()[1:]:
+ index = int(token.split('/')[0])
+ face.append(index - 1 if index > 0 else len(vertices) + index)
+ if len(face) < 3:
+ continue
+ referenced.update(face)
+ face_count += 1
+ # ponytail: bounded static preview; use a full OBJ viewer if 5000 faces miss fine detail.
+ if len(faces) < 5000:
+ faces.append(face)
+ else:
+ pick = rng.randrange(face_count)
+ if pick < len(faces):
+ faces[pick] = face
+ if not vertices or not faces:
+ return 'OBJ 未提供可绘制的顶点/面'
+ if min(referenced) < 0 or max(referenced) >= len(vertices):
+ raise ValueError(f'invalid OBJ face index: {path}')
+ xyz = np.asarray(vertices, dtype=np.float64)
+ if not np.isfinite(xyz).all():
+ raise ValueError(f'nonfinite OBJ vertex: {path}')
+ # Fixed orthonormal isometric basis: geometry only, not an inferred camera pose.
+ projected = np.column_stack(((xyz[:, 0] - xyz[:, 2]) / np.sqrt(2),
+ (xyz[:, 0] + xyz[:, 2] - 2 * xyz[:, 1]) / np.sqrt(6)))
+ used = projected[np.fromiter(referenced, dtype=np.int64)]
+ lo, hi = used.min(axis=0), used.max(axis=0)
+ extent = (hi - lo).max()
+ if extent <= 0:
+ return 'OBJ 所有面投影到同一点'
+ projected = (projected - (lo + hi) / 2) * (380 / extent) + 215
+ image = Image.new('RGB', (430, 430), 'white')
+ draw = ImageDraw.Draw(image)
+ for face in faces:
+ polygon = [tuple(projected[i]) for i in face]
+ draw.line(polygon + [polygon[0]], fill='#205caa', width=1)
+ return picture(image)
+
+
+def render(view, destination):
+ meta = json.loads((view / 'metainfo.json').read_text())
+ n = meta['num_frames']
+ width, height = meta['camera']['image_size']
+ components = meta['components']
+ frames = [json.loads(line) for line in (view / 'camera/frames.jsonl').read_text().splitlines()]
+ assert len(frames) == n
+ selected = {0, n // 4, n // 2, 3 * n // 4, n - 1}
+ for item in components.values():
+ ref = item.get('valid_ref', '')
+ if ref.endswith('.pt'):
+ valid = torch.load(view / ref, map_location='cpu', weights_only=True).numpy()
+ if valid.shape[0] == n:
+ matching = np.flatnonzero(valid.reshape(n, -1).any(axis=1))
+ if len(matching):
+ selected.add(int(matching[0]))
+ selected = set(sorted(selected)[:12]) | {n - 1}
+ rgb = snapshot(view / components['rgb']['data_ref'], 'rgb24', (height, width, 3), np.uint8, selected)
+ depth = (snapshot(view / components['depth']['data_ref'], 'gray16le', (height, 2 * width), '