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with right hand.", + "short": "Retrieve the dinner knife from the cutlery basket" + } +} \ No newline at end of file diff --git a/cookbooks/cosmos3/generator/action/assets/egocentric_hand_action_example/human_annotation/ESCALE_000374.json b/cookbooks/cosmos3/generator/action/assets/egocentric_hand_action_example/human_annotation/ESCALE_000374.json new file mode 100644 index 00000000..6f2a25fe --- /dev/null +++ b/cookbooks/cosmos3/generator/action/assets/egocentric_hand_action_example/human_annotation/ESCALE_000374.json @@ -0,0 +1 @@ +{"num_frames": 122, "coordinate_frame": "camera", "left_hand": {"hand_keypoints": [[[-0.06701853104071631, 0.12378401603270461, 0.340427271156313], [-0.04531910884197288, 0.11400347661398236, 0.3465005754937809], [-0.028162020816793643, 0.11443916233202067, 0.35621689541084267], [-0.01705082707451311, 0.11610291893977376, 0.36961227547387576], [-0.013747331536173135, 0.12044082036805825, 0.3916361215802251], [-0.041358337194672856, 0.09233575205336031, 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0.18820847227756232, 0.385754111591309], [0.37639719392647913, -0.15473391425192742, 0.4086442409142781, 0.8169409112978386, 0.14483160840182308, 0.1863620601328293, 0.3848650747039102], [0.37655098894180883, -0.15422520742901843, 0.41200216790259053, 0.8152779598173788, 0.1453402736337992, 0.18531280306198816, 0.3842957933765736]]} \ No newline at end of file diff --git a/cookbooks/cosmos3/generator/action/assets/egocentric_hand_action_example/videos/ESCALE_000374.mp4 b/cookbooks/cosmos3/generator/action/assets/egocentric_hand_action_example/videos/ESCALE_000374.mp4 new file mode 100644 index 00000000..783742ef Binary files /dev/null and b/cookbooks/cosmos3/generator/action/assets/egocentric_hand_action_example/videos/ESCALE_000374.mp4 differ diff --git a/cookbooks/cosmos3/generator/action/finetune/README.md b/cookbooks/cosmos3/generator/action/finetune/README.md index 52436be6..c8f9855c 100644 --- a/cookbooks/cosmos3/generator/action/finetune/README.md +++ b/cookbooks/cosmos3/generator/action/finetune/README.md @@ -6,6 +6,11 @@ This example demonstrates supervised fine-tuning (SFT) of [Cosmos3-Nano](https:/ | --- | --- | --- | --- | | Policy-DROID SFT | `launch_sft_action_policy_droid.sh` | Cosmos3-Nano | [Cosmos3-DROID](https://huggingface.co/datasets/nvidia/Cosmos3-DROID) success split | +For a runnable egocentric hand-pose data conversion example, see +[`README_egocentric_hand_action.md`](./README_egocentric_hand_action.md). It +converts a sample video and 3D hand-pose annotation pair into the raw 57D hand +Action format used by the dataset path. + The recipe uses `[job].task = "vfm"` with the registered `action_policy_droid_nano` experiment. It trains a DROID policy model with `joint_pos` 8-D actions, proprioceptive state, `concat_view` 480p video, chunk length 32, episode-shuffle streaming, and the optional `keep_ranges_1_0_1.json` window filter. ## Prerequisites diff --git a/cookbooks/cosmos3/generator/action/finetune/README_egocentric_hand_action.md b/cookbooks/cosmos3/generator/action/finetune/README_egocentric_hand_action.md new file mode 100644 index 00000000..62c5884e --- /dev/null +++ b/cookbooks/cosmos3/generator/action/finetune/README_egocentric_hand_action.md @@ -0,0 +1,173 @@ +# Egocentric Hand Action Data Processing + +This example converts an egocentric hand-pose annotation sample into a raw +57-dimensional action array. Each action row describes the camera motion, both +wrist motions, and five fingertip positions for one transition between +consecutive video frames. + +The script expects one sample in this layout: + +```text +example_root/ + videos/.mp4 + captions/.json + cameras/.json + human_annotation/.json +``` + +The checked-in example sample is `ESCALE_000374`. + +## Input Schema + +The converter is intentionally small and expects the following JSON fields. +All pose arrays must have the same first dimension `N`, matching +`human_annotation/.json["num_frames"]`. + +| File | Field | Shape / Type | Meaning | +| --- | --- | --- | --- | +| `human_annotation/.json` | `num_frames` | integer | Number of annotated pose frames. | +| `human_annotation/.json` | `left_hand.hand_keypoints` | `[N, 21, 3]` | Left-hand 3D keypoints in camera coordinates, meters. Joint `0` is the wrist. | +| `human_annotation/.json` | `right_hand.hand_keypoints` | `[N, 21, 3]` | Right-hand 3D keypoints in camera coordinates, meters. Joint `0` is the wrist. | +| `human_annotation/.json` | `left_ee_pose` | `[N, 7]` | Left wrist pose as `[qx, qy, qz, qw, x, y, z]` in camera coordinates. | +| `human_annotation/.json` | `right_ee_pose` | `[N, 7]` | Right wrist pose as `[qx, qy, qz, qw, x, y, z]` in camera coordinates. | +| `cameras/.json` | `camera.pose_world2cam` | `[N, 7]` | Camera world-to-camera pose as `[qx, qy, qz, qw, x, y, z]`; the script inverts it to camera-to-world. | +| `cameras/.json` | `camera.focal_length` | `[2]` | `[fx, fy]`; not used by the converter, but included for visualization checks. | +| `cameras/.json` | `camera.principal_point` | `[2]` | `[cx, cy]`; not used by the converter, but included for visualization checks. | +| `cameras/.json` | `camera.distortion` | `[4]` | Distortion coefficients; not used by the converter, but included for visualization checks. | +| `captions/.json` | `vlm_pipeline.long` or `vlm_pipeline.medium` | string | Optional caption copied into the output metadata. | +| `videos/.mp4` | video file | mp4 | Used only for frame-count reporting by this script. | + +By default, wrist translation comes from keypoint `0` in +`hand_keypoints`. Pass `--wrist-position-source ee_pose` if your source should +use the translation stored in `left_ee_pose` and `right_ee_pose` instead. + +## Setup + +Install or clone `cosmos-framework` first so `cosmos_framework` is importable. +For a local checkout next to this repo: + +```bash +git clone https://github.com/NVIDIA/cosmos-framework.git ~/projects/cosmos-framework +``` + +Then run the converter from the `cosmos` repo root: + +```bash +cd ~/projects/cosmos + +PYTHONPATH=~/projects/cosmos-framework python \ + cookbooks/cosmos3/generator/action/finetune/data_processing_for_egocentric_hand_action.py \ + --output-dir /tmp/egocentric_hand_action_example +``` + +If you installed `cosmos-framework` into the active Python environment, omit +the `PYTHONPATH=...` prefix. The script defaults to the checked-in +`egocentric_hand_action_example` asset and its single sample, `ESCALE_000374`. + +Key output lines for `ESCALE_000374`: + +```text +sample_id: ESCALE_000374 +pose frames: 122 +video frames: 123 +wrist position source: keypoint (left keypoint-vs-ee mean 0.0435 m, right 0.0435 m) +raw action: (121, 57) -> /tmp/egocentric_hand_action_example/ESCALE_000374_raw_action_57d.npy +round-trip check against source annotations: + ... + fingertip camera L2 max/mean: right 4.2e-05/2.0e-05, left 3.1e-05/1.5e-05 +metadata: /tmp/egocentric_hand_action_example/ESCALE_000374_metadata.json +``` + +Small numerical differences across dependency versions are acceptable; the +roundtrip fingertip errors should remain below `1e-4` meters. + +## 57D Action Layout + +The raw action is saved as `_raw_action_57d.npy` with shape +`[num_pose_frames - 1, 57]`. + +Each row is: + +```text +[camera(9), right_wrist(9), right_fingertips(15), left_wrist(9), left_fingertips(15)] +``` + +Pose blocks are `[translation(3), rot6d(6)]`. The `rot6d` block is the first two +columns of the relative rotation matrix, following the convention implemented by +`cosmos_framework.data.vfm.action.pose_utils.pose_abs_to_rel`. + +Fingertip blocks contain five 3D fingertip positions expressed in the +corresponding wrist frame at the future frame. + +The script also writes `_metadata.json`. It records the output +paths, action shape, frame counts, wrist-position diagnostics, the roundtrip +verification metrics, and the copied caption text. + +## Coordinate Conventions + +The input camera pose in this example is `pose_world2cam`; the script inverts it +to camera-to-world before computing relative camera motion. Hand keypoints and +wrist poses are in the camera frame. + +The script assumes the wrist-local frame already follows this convention: + +```text ++X: thumb side toward pinky side ++Y: outward from the palm ++Z: wrist toward fingertips +``` + +If your source data uses a different wrist-local frame, edit the +`WRIST_FRAME_ALIGN` matrix in the script. Keep it as identity for data already +in this convention. + +## Model-Space Action + +By default the script writes only the raw 57D action. To also write the padded +model-space action, pass normalization stats from the matching training setup: + +```bash +PYTHONPATH=~/projects/cosmos-framework python \ + cookbooks/cosmos3/generator/action/finetune/data_processing_for_egocentric_hand_action.py \ + --output-dir /tmp/egocentric_hand_action_example \ + --normalizer-stats /path/to/action_stats.json \ + --normalizer-stats-key global_raw \ + --action-normalization quantile_rot \ + --max-action-dim 64 +``` + +Use normalization stats from the same dataset/checkpoint configuration you plan +to train or run. Do not mix unrelated action statistics. + +The stats JSON is loaded with +`cosmos_framework.data.vfm.action.action_processing.load_action_stats`. It must +contain the keys required by the selected normalization method, either at the +top level or under `--normalizer-stats-key`. For `quantile` and `quantile_rot`, +provide `q01` and `q99` arrays of length `57`. For `meanstd`, provide `mean` +and `std`; for `minmax`, provide `min` and `max`. + +## Downstream Use + +This script is a data-conversion example for one sample. For training, run the +same conversion over your dataset, compute normalization statistics over the raw +57D actions, and connect those actions to the SFT dataset pipeline used by the +action policy recipe. + +The surrounding cookbook entry point is +[`README.md`](./README.md). The canonical training implementation and config +reference live in +[`cosmos-framework`](https://github.com/NVIDIA/cosmos-framework), especially the +Cosmos3 Action policy post-training and SFT docs linked from the parent README. + +## Verification + +The script runs a roundtrip check by default: + +1. Encode source annotations into raw 57D action. +2. Decode the camera and wrist relative pose blocks back to absolute poses. +3. Transform the fingertip blocks back into camera coordinates. +4. Report pose and fingertip errors against the original source annotations. + +The roundtrip check validates the geometry and indexing in the conversion. It +does not validate that unrelated source conventions, such as a different wrist +axis definition, are semantically correct; use `WRIST_FRAME_ALIGN` for that. diff --git a/cookbooks/cosmos3/generator/action/finetune/data_processing_for_egocentric_hand_action.py b/cookbooks/cosmos3/generator/action/finetune/data_processing_for_egocentric_hand_action.py new file mode 100644 index 00000000..f6c6d8f7 --- /dev/null +++ b/cookbooks/cosmos3/generator/action/finetune/data_processing_for_egocentric_hand_action.py @@ -0,0 +1,459 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: OpenMDW-1.1 +"""Encode egocentric hand-pose annotations into the raw 57D Action format. + +This script is intentionally source-neutral. It demonstrates the hand-pose +action contract using a folder layout like: + + example_root/ + videos/.mp4 + captions/.json + cameras/.json + human_annotation/.json + +The raw 57D layout is: + + [camera, right_wrist, right_fingertips, left_wrist, left_fingertips] + +where camera and wrist pose blocks are [translation(3), rot6d(6)], and each +fingertip block is 5 * xyz in the frame-local wrist coordinate frame. +""" + +from __future__ import annotations + +import argparse +import json +import subprocess +from pathlib import Path +from typing import Any + +import numpy as np +import torch + +try: + from cosmos_framework.data.vfm.action.action_processing import ( + ActionProcessor, + ActionNormalizationMethod, + load_action_stats, + resolve_action_normalization, + ) + from cosmos_framework.data.vfm.action.pose_utils import ( + build_abs_pose_from_components, + pose_abs_to_rel, + pose_rel_to_abs, + ) +except ModuleNotFoundError as exc: + if exc.name != "cosmos_framework": + raise + raise ModuleNotFoundError( + "This script requires cosmos-framework. Install it and activate its environment, " + "or run with PYTHONPATH pointing to a cosmos-framework checkout." + ) from exc + +NUM_JOINTS = 21 +WRIST = 0 +THUMB_TIP = 4 +INDEX_TIP = 8 +MIDDLE_TIP = 12 +RING_TIP = 16 +PINKY_TIP = 20 +FINGERTIP_JOINTS = (THUMB_TIP, INDEX_TIP, MIDDLE_TIP, RING_TIP, PINKY_TIP) +DEFAULT_EXAMPLE_ROOT = Path(__file__).resolve().parents[1] / "assets" / "egocentric_hand_action_example" + +# Source-specific wrist-frame correction applied as: +# wrist_pose_for_action = wrist_pose_from_source @ WRIST_FRAME_ALIGN +# +# The ESCALE example is already in the convention used by this script, so this +# stays identity. If your data uses a different wrist-local coordinate frame, +# edit the 3x3 rotation block so the transformed wrist frame has: +# +X: from thumb side toward pinky side +# +Y: outward from the palm +# +Z: from wrist toward fingertips +# Keep the translation column zero unless your wrist origin needs a known offset. +WRIST_FRAME_ALIGN = np.eye(4, dtype=np.float32) # [4,4] + + +def _as_float32_array(value: Any, name: str, shape_suffix: tuple[int, ...] | None = None) -> np.ndarray: + array = np.asarray(value, dtype=np.float32) # [...] + if shape_suffix is not None and array.shape[-len(shape_suffix) :] != shape_suffix: + raise ValueError(f"{name} must end with shape {shape_suffix}, got {array.shape}") + return array # [...] + + +def build_pose_matrix(translation: np.ndarray, quat_xyzw: np.ndarray) -> np.ndarray: + translation = _as_float32_array(translation, "translation", (3,)) # [T,3] + quat_xyzw = _as_float32_array(quat_xyzw, "quat_xyzw", (4,)) # [T,4] + return build_abs_pose_from_components(translation, quat_xyzw, "quat_xyzw").astype(np.float32) # [T,4,4] + + +def invert_pose_matrix(pose: np.ndarray) -> np.ndarray: + pose = _as_float32_array(pose, "pose", (4, 4)) # [T,4,4] + return np.linalg.inv(pose).astype(np.float32) # [T,4,4] + + +def build_fingertips_in_wrist_frame(hand_keypoints: np.ndarray, wrist_cam: np.ndarray) -> np.ndarray: + hand_keypoints = _as_float32_array(hand_keypoints, "hand_keypoints", (NUM_JOINTS, 3)) # [T+1,21,3] + wrist_cam = _as_float32_array(wrist_cam, "wrist_cam", (4, 4)) # [T+1,4,4] + future_fingertips = hand_keypoints[1:, FINGERTIP_JOINTS, :] # [T,5,3] + ones = np.ones((*future_fingertips.shape[:-1], 1), dtype=np.float32) # [T,5,1] + future_fingertips_h = np.concatenate([future_fingertips, ones], axis=-1) # [T,5,4] + wrist_inv = invert_pose_matrix(wrist_cam[1:]) # [T,4,4] + fingertips_wrist = np.einsum("tij,tnj->tni", wrist_inv, future_fingertips_h)[..., :3] # [T,5,3] + return fingertips_wrist.reshape(fingertips_wrist.shape[0], -1).astype(np.float32) # [T,15] + + +def build_hand_wrist_cam( + hand_keypoints: np.ndarray, + ee_pose: np.ndarray, + wrist_position_source: str, +) -> np.ndarray: + hand_keypoints = _as_float32_array(hand_keypoints, "hand_keypoints", (NUM_JOINTS, 3)) # [T+1,21,3] + ee_pose = _as_float32_array(ee_pose, "ee_pose", (7,)) # [T+1,7] + wrist_quat = ee_pose[:, :4] # [T+1,4] + if wrist_position_source == "keypoint": + wrist_pos = hand_keypoints[:, WRIST, :] # [T+1,3] + elif wrist_position_source == "ee_pose": + wrist_pos = ee_pose[:, 4:] # [T+1,3] + else: + raise ValueError(f"Unsupported wrist_position_source: {wrist_position_source!r}") + wrist_cam = build_pose_matrix(wrist_pos, wrist_quat) # [T+1,4,4] + return wrist_cam @ WRIST_FRAME_ALIGN[None, :, :] # [T+1,4,4] + + +def build_raw_57d_action( + camera_world2cam: np.ndarray, + left_keypoints: np.ndarray, + right_keypoints: np.ndarray, + left_ee_pose: np.ndarray, + right_ee_pose: np.ndarray, + wrist_position_source: str, + pose_convention: str, +) -> np.ndarray: + camera_world2cam = _as_float32_array(camera_world2cam, "camera_world2cam", (7,)) # [T+1,7] + cam_w2c = build_pose_matrix(camera_world2cam[:, 4:], camera_world2cam[:, :4]) # [T+1,4,4] + cam_c2w = invert_pose_matrix(cam_w2c) # [T+1,4,4] + + right_wrist_cam = build_hand_wrist_cam(right_keypoints, right_ee_pose, wrist_position_source) # [T+1,4,4] + left_wrist_cam = build_hand_wrist_cam(left_keypoints, left_ee_pose, wrist_position_source) # [T+1,4,4] + right_wrist_world = cam_c2w @ right_wrist_cam # [T+1,4,4] + left_wrist_world = cam_c2w @ left_wrist_cam # [T+1,4,4] + + cam_rel = pose_abs_to_rel(cam_c2w, rotation_format="rot6d", pose_convention=pose_convention) # [T,9] + right_wrist_rel = pose_abs_to_rel( + right_wrist_world, + rotation_format="rot6d", + pose_convention=pose_convention, + ) # [T,9] + left_wrist_rel = pose_abs_to_rel( + left_wrist_world, + rotation_format="rot6d", + pose_convention=pose_convention, + ) # [T,9] + right_fingertips = build_fingertips_in_wrist_frame(right_keypoints, right_wrist_cam) # [T,15] + left_fingertips = build_fingertips_in_wrist_frame(left_keypoints, left_wrist_cam) # [T,15] + + return np.concatenate( + [cam_rel, right_wrist_rel, right_fingertips, left_wrist_rel, left_fingertips], + axis=-1, + ).astype(np.float32) # [T,57] + + +def decompose_raw_57d_action(action: np.ndarray) -> dict[str, np.ndarray]: + action = _as_float32_array(action, "action", (57,)) # [T,57] + return { + "camera": action[:, 0:9], # [T,9] + "right_wrist": action[:, 9:18], # [T,9] + "right_fingertips": action[:, 18:33].reshape(action.shape[0], len(FINGERTIP_JOINTS), 3), # [T,5,3] + "left_wrist": action[:, 33:42], # [T,9] + "left_fingertips": action[:, 42:57].reshape(action.shape[0], len(FINGERTIP_JOINTS), 3), # [T,5,3] + } + + +def _pose_error_metrics(pred: np.ndarray, target: np.ndarray) -> dict[str, float]: + pred = _as_float32_array(pred, "pred", (4, 4)) # [T,4,4] + target = _as_float32_array(target, "target", (4, 4)) # [T,4,4] + trans_l2 = np.linalg.norm(pred[:, :3, 3] - target[:, :3, 3], axis=-1) # [T] + rot_delta = np.swapaxes(pred[:, :3, :3], -1, -2) @ target[:, :3, :3] # [T,3,3] + trace = np.trace(rot_delta, axis1=1, axis2=2) # [T] + cos_angle = np.clip((trace - 1.0) / 2.0, -1.0, 1.0) # [T] + rot_deg = np.degrees(np.arccos(cos_angle)) # [T] + return { + "translation_l2_max": float(trans_l2.max(initial=0.0)), + "translation_l2_mean": float(trans_l2.mean() if trans_l2.size else 0.0), + "rotation_deg_max": float(rot_deg.max(initial=0.0)), + "rotation_deg_mean": float(rot_deg.mean() if rot_deg.size else 0.0), + } + + +def verify_raw_57d_roundtrip( + action: np.ndarray, + camera_world2cam: np.ndarray, + left_keypoints: np.ndarray, + right_keypoints: np.ndarray, + left_ee_pose: np.ndarray, + right_ee_pose: np.ndarray, + wrist_position_source: str, + pose_convention: str, +) -> dict[str, Any]: + """Decode the raw action and compare against the original pose arrays.""" + parts = decompose_raw_57d_action(action) + cam_w2c = build_pose_matrix(camera_world2cam[:, 4:], camera_world2cam[:, :4]) # [T+1,4,4] + target_cam_c2w = invert_pose_matrix(cam_w2c) # [T+1,4,4] + target_right_wrist_cam = build_hand_wrist_cam( + right_keypoints, + right_ee_pose, + wrist_position_source, + ) # [T+1,4,4] + target_left_wrist_cam = build_hand_wrist_cam( + left_keypoints, + left_ee_pose, + wrist_position_source, + ) # [T+1,4,4] + target_right_wrist_world = target_cam_c2w @ target_right_wrist_cam # [T+1,4,4] + target_left_wrist_world = target_cam_c2w @ target_left_wrist_cam # [T+1,4,4] + + pred_cam_c2w = pose_rel_to_abs( + parts["camera"], + rotation_format="rot6d", + pose_convention=pose_convention, + initial_pose=target_cam_c2w[0], + normalize_rotation=False, + ).astype(np.float32) # [T+1,4,4] + pred_right_wrist_world = pose_rel_to_abs( + parts["right_wrist"], + rotation_format="rot6d", + pose_convention=pose_convention, + initial_pose=target_right_wrist_world[0], + normalize_rotation=False, + ).astype(np.float32) # [T+1,4,4] + pred_left_wrist_world = pose_rel_to_abs( + parts["left_wrist"], + rotation_format="rot6d", + pose_convention=pose_convention, + initial_pose=target_left_wrist_world[0], + normalize_rotation=False, + ).astype(np.float32) # [T+1,4,4] + + pred_cam_w2c = invert_pose_matrix(pred_cam_c2w) # [T+1,4,4] + pred_right_wrist_cam = pred_cam_w2c @ pred_right_wrist_world # [T+1,4,4] + pred_left_wrist_cam = pred_cam_w2c @ pred_left_wrist_world # [T+1,4,4] + + right_fingertips_h = np.concatenate( + [parts["right_fingertips"], np.ones((*parts["right_fingertips"].shape[:-1], 1), dtype=np.float32)], + axis=-1, + ) # [T,5,4] + left_fingertips_h = np.concatenate( + [parts["left_fingertips"], np.ones((*parts["left_fingertips"].shape[:-1], 1), dtype=np.float32)], + axis=-1, + ) # [T,5,4] + pred_right_tips_cam = np.einsum("tij,tnj->tni", pred_right_wrist_cam[1:], right_fingertips_h)[..., :3] # [T,5,3] + pred_left_tips_cam = np.einsum("tij,tnj->tni", pred_left_wrist_cam[1:], left_fingertips_h)[..., :3] # [T,5,3] + target_right_tips_cam = right_keypoints[1:, FINGERTIP_JOINTS, :] # [T,5,3] + target_left_tips_cam = left_keypoints[1:, FINGERTIP_JOINTS, :] # [T,5,3] + right_tip_l2 = np.linalg.norm(pred_right_tips_cam - target_right_tips_cam, axis=-1) # [T,5] + left_tip_l2 = np.linalg.norm(pred_left_tips_cam - target_left_tips_cam, axis=-1) # [T,5] + + return { + "camera_c2w": _pose_error_metrics(pred_cam_c2w, target_cam_c2w), + "right_wrist_world": _pose_error_metrics(pred_right_wrist_world, target_right_wrist_world), + "left_wrist_world": _pose_error_metrics(pred_left_wrist_world, target_left_wrist_world), + "right_fingertips_camera_l2_max": float(right_tip_l2.max(initial=0.0)), + "right_fingertips_camera_l2_mean": float(right_tip_l2.mean() if right_tip_l2.size else 0.0), + "left_fingertips_camera_l2_max": float(left_tip_l2.max(initial=0.0)), + "left_fingertips_camera_l2_mean": float(left_tip_l2.mean() if left_tip_l2.size else 0.0), + } + + +def load_example(root: Path, sample_id: str) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]: + annotation = json.loads((root / "human_annotation" / f"{sample_id}.json").read_text()) + camera = json.loads((root / "cameras" / f"{sample_id}.json").read_text()) + caption = json.loads((root / "captions" / f"{sample_id}.json").read_text()) + return annotation, camera, caption + + +def get_video_frame_count(video_path: Path) -> int | None: + if not video_path.exists(): + return None + cmd = [ + "ffprobe", + "-v", + "error", + "-select_streams", + "v:0", + "-show_entries", + "stream=nb_frames", + "-of", + "default=nw=1:nk=1", + str(video_path), + ] + try: + output = subprocess.check_output(cmd, text=True).strip() + except (OSError, subprocess.CalledProcessError): + return None + return int(output) if output.isdigit() else None + + +def preprocess_model_action( + raw_action: np.ndarray, + stats_path: Path, + stats_key: str, + normalization_method: ActionNormalizationMethod, + max_action_dim: int, +) -> np.ndarray: + raw_action = _as_float32_array(raw_action, "raw_action", (57,)) # [T,57] + stats_np = load_action_stats(str(stats_path), stats_key=stats_key) + stats: dict[str, torch.Tensor] = {} + for key, value in stats_np.items(): + stats[key] = torch.from_numpy(value).float() # [D] + normalizer = resolve_action_normalization(normalization_method, stats) + processor = ActionProcessor(max_action_dim=max_action_dim) + raw_action_tensor = torch.from_numpy(raw_action).float() # [T,57] + processed = processor.preprocess_action( + {}, + raw_action_tensor, + action_normalizer=normalizer, + ) # dict + processed_action = processed["action"] # [T,D_model] + return processed_action.detach().cpu().numpy().astype(np.float32) # [T,D_model] + + +def pick_default_sample_id(root: Path) -> str: + candidates = sorted((root / "human_annotation").glob("*.json")) + if not candidates: + raise FileNotFoundError(f"No annotation JSON files found under {root / 'human_annotation'}") + return candidates[0].stem + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--example-root", type=Path, default=DEFAULT_EXAMPLE_ROOT) + parser.add_argument("--sample-id", type=str, default=None) + parser.add_argument("--output-dir", type=Path, default=Path("hand_pose_57d_outputs")) + parser.add_argument("--pose-convention", choices=["backward_framewise", "backward_anchored"], default="backward_framewise") + parser.add_argument("--wrist-position-source", choices=["keypoint", "ee_pose"], default="keypoint") + parser.add_argument("--normalizer-stats", type=Path, default=None) + parser.add_argument("--normalizer-stats-key", type=str, default="global_raw") + parser.add_argument( + "--action-normalization", + choices=["quantile", "quantile_rot", "meanstd", "minmax"], + default="quantile_rot", + ) + parser.add_argument("--max-action-dim", type=int, default=64) + parser.add_argument("--skip-roundtrip-check", action="store_true") + args = parser.parse_args() + + root = args.example_root + sample_id = args.sample_id or pick_default_sample_id(root) + annotation, camera, caption = load_example(root, sample_id) + + num_frames = int(annotation["num_frames"]) + left_keypoints = _as_float32_array(annotation["left_hand"]["hand_keypoints"], "left_hand.hand_keypoints", (NUM_JOINTS, 3)) # [N,21,3] + right_keypoints = _as_float32_array(annotation["right_hand"]["hand_keypoints"], "right_hand.hand_keypoints", (NUM_JOINTS, 3)) # [N,21,3] + left_ee_pose = _as_float32_array(annotation["left_ee_pose"], "left_ee_pose", (7,)) # [N,7] + right_ee_pose = _as_float32_array(annotation["right_ee_pose"], "right_ee_pose", (7,)) # [N,7] + camera_world2cam = _as_float32_array(camera["camera"]["pose_world2cam"], "camera.pose_world2cam", (7,)) # [N,7] + + if not all(array.shape[0] == num_frames for array in [left_keypoints, right_keypoints, left_ee_pose, right_ee_pose, camera_world2cam]): + raise ValueError("All pose arrays must have num_frames rows.") + + left_wrist_pos_delta = np.linalg.norm(left_keypoints[:, WRIST, :] - left_ee_pose[:, 4:], axis=1) # [N] + right_wrist_pos_delta = np.linalg.norm(right_keypoints[:, WRIST, :] - right_ee_pose[:, 4:], axis=1) # [N] + raw_action = build_raw_57d_action( + camera_world2cam=camera_world2cam, + left_keypoints=left_keypoints, + right_keypoints=right_keypoints, + left_ee_pose=left_ee_pose, + right_ee_pose=right_ee_pose, + wrist_position_source=args.wrist_position_source, + pose_convention=args.pose_convention, + ) # [N-1,57] + + args.output_dir.mkdir(parents=True, exist_ok=True) + raw_path = args.output_dir / f"{sample_id}_raw_action_57d.npy" + np.save(raw_path, raw_action) + + model_action_path = None + model_action = None + if args.normalizer_stats is not None: + model_action = preprocess_model_action( + raw_action, + args.normalizer_stats, + args.normalizer_stats_key, + args.action_normalization, + args.max_action_dim, + ) # [N-1,D_model] + model_action_path = args.output_dir / f"{sample_id}_model_action_{args.max_action_dim}d.npy" + np.save(model_action_path, model_action) + + roundtrip = None + if not args.skip_roundtrip_check: + roundtrip = verify_raw_57d_roundtrip( + action=raw_action, + camera_world2cam=camera_world2cam, + left_keypoints=left_keypoints, + right_keypoints=right_keypoints, + left_ee_pose=left_ee_pose, + right_ee_pose=right_ee_pose, + wrist_position_source=args.wrist_position_source, + pose_convention=args.pose_convention, + ) + + video_frames = get_video_frame_count(root / "videos" / f"{sample_id}.mp4") + caption_text = caption.get("vlm_pipeline", {}).get("long") or caption.get("vlm_pipeline", {}).get("medium") + + metadata = { + "sample_id": sample_id, + "num_pose_frames": num_frames, + "num_action_frames": int(raw_action.shape[0]), + "video_frames": video_frames, + "raw_action_shape": list(raw_action.shape), + "raw_action_path": str(raw_path), + "model_action_path": str(model_action_path) if model_action_path else None, + "layout": "[camera(9), right_wrist(9), right_fingertips(15), left_wrist(9), left_fingertips(15)]", + "pose_convention": args.pose_convention, + "wrist_position_source": args.wrist_position_source, + "wrist_frame_align": WRIST_FRAME_ALIGN.tolist(), + "wrist_keypoint_vs_ee_translation_l2_m": { + "left_mean": float(left_wrist_pos_delta.mean()), + "left_max": float(left_wrist_pos_delta.max()), + "right_mean": float(right_wrist_pos_delta.mean()), + "right_max": float(right_wrist_pos_delta.max()), + }, + "roundtrip_check": roundtrip, + "caption": caption_text, + } + metadata_path = args.output_dir / f"{sample_id}_metadata.json" + metadata_path.write_text(json.dumps(metadata, indent=2) + "\n") + + print(f"sample_id: {sample_id}") + print(f"pose frames: {num_frames}") + print(f"video frames: {video_frames}") + if video_frames is not None and abs(video_frames - num_frames) > 1: + print("warning: video and pose frame counts differ by more than one frame") + print( + "wrist position source: " + f"{args.wrist_position_source} " + f"(left keypoint-vs-ee mean {float(left_wrist_pos_delta.mean()):.4f} m, " + f"right {float(right_wrist_pos_delta.mean()):.4f} m)" + ) + print(f"raw action: {raw_action.shape} -> {raw_path}") + if model_action_path is not None: + print(f"model action: {model_action.shape} -> {model_action_path}") + if roundtrip is not None: + print("round-trip check against source annotations:") + print(f" camera c2w: {roundtrip['camera_c2w']}") + print(f" right wrist world: {roundtrip['right_wrist_world']}") + print(f" left wrist world: {roundtrip['left_wrist_world']}") + print( + " fingertip camera L2 max/mean: " + f"right {roundtrip['right_fingertips_camera_l2_max']:.3e}/" + f"{roundtrip['right_fingertips_camera_l2_mean']:.3e}, " + f"left {roundtrip['left_fingertips_camera_l2_max']:.3e}/" + f"{roundtrip['left_fingertips_camera_l2_mean']:.3e}" + ) + print(f"metadata: {metadata_path}") + + +if __name__ == "__main__": + main()