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"cells": [
{
"cell_type": "markdown",
"id": "ee215408-e60b-4209-a09a-f84a8fd5ffa1",
"metadata": {},
"source": [
"# HOT3D Data Reader\n",
"\n",
"This notebook is adapted from the HOT3D official tutorial. It supports both **Aria** and **Quest3** devices and uses only the **MANO** hand model (no UmeTrack or object assets required).\n",
"\n",
"## Sections\n",
"- **Section 0**: Initialization (update the two paths, then run)\n",
"- **Section 1**: Image streams + camera calibration\n",
"- **Section 2.a**: Device / headset pose trajectory\n",
"- **Section 2.b**: Hand wrist trajectory\n",
"- **Section 2.b.a**: Hand landmarks (skeleton) + mesh\n",
"- **Section 2.c**: Object poses (requires assets folder, skipped automatically otherwise)\n",
"- **Section 3.b**: Hand 2D bounding boxes\n",
"- **Section 4**: Eye gaze (Aria only, skipped automatically on Quest3)\n",
"- **Section 5**: Hand keypoint reprojection onto image\n",
"\n",
"```\n",
"Hot3dDataProvider\n",
"|- device_data_provider -> image data + camera calibration\n",
"|- device_pose_data_provider -> device pose\n",
"|- mano_hand_data_provider -> hand pose (MANO)\n",
"|- object_pose_data_provider -> object pose\n",
"|- hand_box2d_data_provider -> hand 2D bounding boxes\n",
"|- object_box2d_data_provider -> object 2D bounding boxes\n",
"```\n",
"\n",
"> All pose data is in **world coordinates** (meters)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "97bcb96c-4910-4a60-9a41-c3b34ee7ac7b",
"metadata": {},
"outputs": [],
"source": [
"# Section 0: DataProvider initialization\n",
"#\n",
"# Only the two paths below need to be updated; everything else adapts automatically\n",
"# to Aria or Quest3.\n",
"\n",
"import os\n",
"import sys\n",
"\n",
"# Make sure the hot3d package is importable from this notebook\n",
"notebook_dir = os.path.abspath('')\n",
"if notebook_dir not in sys.path:\n",
" sys.path.insert(0, notebook_dir)\n",
"\n",
"from dataset_api import Hot3dDataProvider\n",
"from data_loaders.mano_layer import MANOHandModel\n",
"from data_loaders.headsets import Headset\n",
"from projectaria_tools.core.stream_id import StreamId\n",
"\n",
"# ── Update these two paths ───────────────────────────────────────\n",
"sequence_path = \"/home/zhang/3D_Reconstruct/HOT3D/hot3d/hot3d/dataset/P0002_2f137f83\"\n",
"mano_model_path = \"/home/zhang/Downloads/mano_v1_2/models\"\n",
"# ────────────────────────────────────────────────────────────────\n",
"\n",
"# Load MANO hand model (requires smplx: pip install smplx)\n",
"mano_hand_model = MANOHandModel(mano_model_path)\n",
"\n",
"# Initialize the data provider.\n",
"# object_library=None: no assets folder needed when only reading hand data.\n",
"hot3d_data_provider = Hot3dDataProvider(\n",
" sequence_folder=sequence_path,\n",
" object_library=None,\n",
" mano_hand_model=mano_hand_model,\n",
")\n",
"\n",
"# Auto-detect device type and pick the corresponding primary camera stream_id\n",
"device_type = hot3d_data_provider.get_device_type()\n",
"print(f\"Device type: {device_type}\")\n",
"\n",
"if device_type == Headset.Aria:\n",
" main_stream_id = StreamId(\"214-1\") # Aria: RGB camera\n",
"else:\n",
" main_stream_id = StreamId(\"1201-1\") # Quest3: SLAM Left camera\n",
"\n",
"print(f\"Primary stream_id: {main_stream_id}\")\n",
"print(f\"Data statistics: {hot3d_data_provider.get_data_statistics()}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "24dae021-1cfe-440a-a5c8-6f44851219e7",
"metadata": {},
"outputs": [],
"source": [
"# Utility functions for Rerun visualization\n",
"\n",
"import rerun as rr\n",
"import numpy as np\n",
"from projectaria_tools.core.sophus import SE3\n",
"from projectaria_tools.utils.rerun_helpers import ToTransform3D\n",
"\n",
"\n",
"def log_image(image: np.array, label: str, static=False) -> None:\n",
" rr.log(label, rr.Image(image), static=static)\n",
"\n",
"\n",
"def log_pose(pose: SE3, label: str, static=False) -> None:\n",
" rr.log(label, ToTransform3D(pose, False), static=static)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7447d83-bf07-4bfb-8e4f-9d548617cde7",
"metadata": {},
"outputs": [],
"source": [
"# Section 1: Image streams + camera calibration\n",
"\n",
"from tqdm import tqdm\n",
"from projectaria_tools.core.sensor_data import TimeDomain, TimeQueryOptions\n",
"\n",
"device_data_provider = hot3d_data_provider.device_data_provider\n",
"image_stream_ids = device_data_provider.get_image_stream_ids()\n",
"\n",
"# Timestamp retrieval differs between Aria and Quest3\n",
"if device_type == Headset.Aria:\n",
" timestamps = device_data_provider.get_sequence_timestamps(\n",
" stream_id=main_stream_id,\n",
" time_domain=TimeDomain.TIME_CODE,\n",
" )\n",
"else:\n",
" timestamps = device_data_provider.get_sequence_timestamps()\n",
"\n",
"print(f\"Sequence : {os.path.basename(os.path.normpath(sequence_path))}\")\n",
"print(f\"Streams : {image_stream_ids}\")\n",
"print(f\"Frames : {len(timestamps)}\")\n",
"\n",
"rr.init(\"Device images\")\n",
"rec = rr.memory_recording()\n",
"\n",
"# Sample one frame every 200 timestamps to keep the visualization fast\n",
"for timestamp_ns in tqdm(timestamps[::200]):\n",
" for stream_id in image_stream_ids:\n",
" image_stream_label = device_data_provider.get_image_stream_label(stream_id)\n",
" image_data = device_data_provider.get_image(timestamp_ns, stream_id)\n",
" if image_data is not None:\n",
" log_image(label=f\"img/{image_stream_label}\", image=image_data)\n",
"\n",
"# Print calibration parameters for each camera stream\n",
"for stream_id in image_stream_ids:\n",
" [extrinsics, intrinsics] = device_data_provider.get_camera_calibration(stream_id)\n",
" label = device_data_provider.get_image_stream_label(stream_id)\n",
" print(f\"\\n[{label}] intrinsics: {intrinsics}\")\n",
"\n",
"rr.notebook_show()"
]
},
{
"cell_type": "markdown",
"id": "1d199e9c-7359-43b2-8ecd-807db548def0",
"metadata": {},
"source": [
"# GT Data Provider API\n",
"\n",
"All GT data providers share the same query interface:\n",
"```python\n",
"result = provider.get_X_at_timestamp(\n",
" timestamp_ns=timestamp_ns,\n",
" time_query_options=TimeQueryOptions.CLOSEST,\n",
" time_domain=TimeDomain.TIME_CODE,\n",
")\n",
"```\n",
"- If the exact timestamp is not found, the **closest** sample is returned.\n",
"- The delta time (`dt`) between the queried and returned timestamp is also available.\n",
"\n",
"Available providers:\n",
"```\n",
"|- device_pose_data_provider -> device/headset pose\n",
"|- mano_hand_data_provider -> hand pose (MANO)\n",
"|- object_pose_data_provider -> object pose\n",
"|- hand_box2d_data_provider -> hand 2D bbox + visibility\n",
"|- object_box2d_data_provider -> object 2D bbox + visibility\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a7c473c3-174f-4cff-9d58-0650b580da72",
"metadata": {},
"outputs": [],
"source": [
"# Section 2.a: Device / headset pose trajectory\n",
"\n",
"device_pose_provider = hot3d_data_provider.device_pose_data_provider\n",
"\n",
"rr.init(\"Device/Headset trajectory\")\n",
"rec = rr.memory_recording()\n",
"\n",
"pose_translations = []\n",
"for timestamp_ns in tqdm(timestamps):\n",
" rr.set_time_nanos(\"synchronization_time\", int(timestamp_ns))\n",
" rr.set_time_sequence(\"timestamp\", timestamp_ns)\n",
"\n",
" if device_pose_provider is None:\n",
" continue\n",
" result = device_pose_provider.get_pose_at_timestamp(\n",
" timestamp_ns=timestamp_ns,\n",
" time_query_options=TimeQueryOptions.CLOSEST,\n",
" time_domain=TimeDomain.TIME_CODE,\n",
" )\n",
" if result is None:\n",
" continue\n",
"\n",
" T_world_device = result.pose3d.T_world_device\n",
" log_pose(pose=T_world_device, label=\"world/device\")\n",
" pose_translations.append(T_world_device.translation()[0])\n",
"\n",
"rr.log(\"world/device_trajectory\", rr.LineStrips3D([pose_translations]), static=True)\n",
"rr.notebook_show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5253618-2ca2-4d11-a605-aa5aa19c54eb",
"metadata": {},
"outputs": [],
"source": [
"# Section 2.b: Hand wrist trajectory (MANO only)\n",
"\n",
"hand_data_provider = hot3d_data_provider.mano_hand_data_provider\n",
"if hand_data_provider is None:\n",
" print(\"MANO hand data provider not initialized. Check mano_model_path and smplx installation.\")\n",
"\n",
"rr.init(\"Hand wrist trajectory\")\n",
"rec = rr.memory_recording()\n",
"\n",
"left_traj, right_traj = [], []\n",
"for timestamp_ns in tqdm(timestamps):\n",
" rr.set_time_nanos(\"synchronization_time\", int(timestamp_ns))\n",
" rr.set_time_sequence(\"timestamp\", timestamp_ns)\n",
"\n",
" if hand_data_provider is None:\n",
" continue\n",
" result = hand_data_provider.get_pose_at_timestamp(\n",
" timestamp_ns=timestamp_ns,\n",
" time_query_options=TimeQueryOptions.CLOSEST,\n",
" time_domain=TimeDomain.TIME_CODE,\n",
" )\n",
" if result is None:\n",
" continue\n",
"\n",
" for hand_pose in result.pose3d_collection.poses.values():\n",
" label = hand_pose.handedness_label()\n",
" T_world_wrist = hand_pose.wrist_pose\n",
" log_pose(pose=T_world_wrist, label=f\"world/hand/{label}\")\n",
" if hand_pose.is_left_hand():\n",
" left_traj.append(T_world_wrist.translation()[0])\n",
" else:\n",
" right_traj.append(T_world_wrist.translation()[0])\n",
"\n",
"if left_traj:\n",
" rr.log(\"world/left_hand_traj\", rr.LineStrips3D([left_traj]), static=True)\n",
"if right_traj:\n",
" rr.log(\"world/right_hand_traj\", rr.LineStrips3D([right_traj]), static=True)\n",
"rr.notebook_show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0a013c63-65e6-4c38-b378-141c8c7ae3ae",
"metadata": {},
"outputs": [],
"source": [
"# Section 2.b.a: Hand landmarks (skeleton) and mesh\n",
"#\n",
"# Left hand -> landmark line strips (skeleton)\n",
"# Right hand -> triangular mesh\n",
"\n",
"from data_loaders.hand_common import LANDMARK_CONNECTIVITY\n",
"\n",
"hand_data_provider = hot3d_data_provider.mano_hand_data_provider\n",
"\n",
"rr.init(\"Hand Landmark / Mesh\")\n",
"rec = rr.memory_recording()\n",
"\n",
"for timestamp_ns in tqdm(timestamps[:300]):\n",
" rr.set_time_nanos(\"synchronization_time\", int(timestamp_ns))\n",
" rr.set_time_sequence(\"timestamp\", timestamp_ns)\n",
"\n",
" if hand_data_provider is None:\n",
" continue\n",
" result = hand_data_provider.get_pose_at_timestamp(\n",
" timestamp_ns=timestamp_ns,\n",
" time_query_options=TimeQueryOptions.CLOSEST,\n",
" time_domain=TimeDomain.TIME_CODE,\n",
" )\n",
" if result is None:\n",
" continue\n",
"\n",
" for hand_pose in result.pose3d_collection.poses.values():\n",
" label = hand_pose.handedness_label()\n",
"\n",
" if hand_pose.is_left_hand():\n",
" # Skeleton: connected landmark line strips\n",
" landmarks = hand_data_provider.get_hand_landmarks(hand_pose)\n",
" points = [\n",
" [landmarks[i].numpy().tolist() for i in conn]\n",
" for conn in LANDMARK_CONNECTIVITY\n",
" ]\n",
" rr.log(f\"world/{label}/joints\", rr.LineStrips3D(points, radii=0.002))\n",
"\n",
" else:\n",
" # Mesh: vertices, triangle indices, and vertex normals\n",
" verts = hand_data_provider.get_hand_mesh_vertices(hand_pose)\n",
" triangles, normals = hand_data_provider.get_hand_mesh_faces_and_normals(hand_pose)\n",
" rr.log(\n",
" f\"world/{label}/mesh\",\n",
" rr.Mesh3D(\n",
" vertex_positions=verts,\n",
" vertex_normals=normals,\n",
" triangle_indices=triangles,\n",
" ),\n",
" )\n",
"\n",
"rr.notebook_show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a909427f-d8c5-40a7-8eba-8702de2a313b",
"metadata": {},
"outputs": [],
"source": [
"# Section 2.c: Object poses\n",
"# Requires object_library (assets folder). Skipped automatically if not available.\n",
"\n",
"if hot3d_data_provider._object_library is None:\n",
" print(\"Skipping Section 2.c: object_library=None (no assets folder loaded)\")\n",
"else:\n",
" from data_loaders.loader_object_library import ObjectLibrary\n",
" object_library = hot3d_data_provider._object_library\n",
" object_pose_data_provider = hot3d_data_provider.object_pose_data_provider\n",
" object_cache_status = {}\n",
"\n",
" rr.init(\"Object pose\")\n",
" rec = rr.memory_recording()\n",
"\n",
" for timestamp_ns in tqdm(timestamps[100:300]):\n",
" rr.set_time_nanos(\"synchronization_time\", int(timestamp_ns))\n",
" rr.set_time_sequence(\"timestamp\", timestamp_ns)\n",
"\n",
" result = object_pose_data_provider.get_pose_at_timestamp(\n",
" timestamp_ns=timestamp_ns,\n",
" time_query_options=TimeQueryOptions.CLOSEST,\n",
" time_domain=TimeDomain.TIME_CODE,\n",
" )\n",
" if result is None:\n",
" continue\n",
"\n",
" object_uids = object_pose_data_provider.object_uids_with_poses\n",
" logging_status = {x: False for x in object_uids}\n",
"\n",
" for object_uid, object_pose3d in result.pose3d_collection.poses.items():\n",
" object_name = object_library.object_id_to_name_dict[object_uid] + \"_\" + str(object_uid)\n",
" log_pose(pose=object_pose3d.T_world_object, label=f\"world/objects/{object_name}\")\n",
" logging_status[object_uid] = True\n",
" if object_uid not in object_cache_status:\n",
" object_cache_status[object_uid] = True\n",
" asset_path = ObjectLibrary.get_cad_asset_path(\n",
" object_library_folderpath=object_library.asset_folder_name,\n",
" object_id=object_uid,\n",
" )\n",
" rr.log(f\"world/objects/{object_name}\", rr.Asset3D(path=asset_path))\n",
"\n",
" for object_uid, displayed in logging_status.items():\n",
" if not displayed:\n",
" object_name = object_library.object_id_to_name_dict[object_uid] + \"_\" + str(object_uid)\n",
" rr.log(f\"world/objects/{object_name}\", rr.Clear.recursive())\n",
" object_cache_status.pop(object_uid, None)\n",
"\n",
" rr.notebook_show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f31214ce-108e-403e-b66f-2c224ab450f1",
"metadata": {},
"outputs": [],
"source": [
"# Section 3: 2D Bounding Boxes\n",
"# Bbox data is queried by TIMESTAMP + STREAM_ID and contains an amodal bbox and a visibility ratio."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b841145d-a114-4693-a0d4-492f9629bbbe",
"metadata": {},
"outputs": [],
"source": [
"# Section 3.a: Object 2D bounding boxes\n",
"# Requires object_library. Skipped automatically if not available.\n",
"\n",
"if hot3d_data_provider._object_library is None:\n",
" print(\"Skipping Section 3.a: object_library=None\")\n",
"else:\n",
" import matplotlib.pyplot as plt\n",
" object_library = hot3d_data_provider._object_library\n",
" object_box2d_data_provider = hot3d_data_provider.object_box2d_data_provider\n",
" object_uids = list(object_box2d_data_provider.object_uids)\n",
" color_map = plt.get_cmap(\"viridis\")\n",
" object_box2d_colors = color_map(np.linspace(0, 1, len(object_uids)))\n",
"\n",
" rr.init(\"Object bounding boxes\")\n",
" rec = rr.memory_recording()\n",
"\n",
" stream_id = main_stream_id\n",
" for timestamp_ns in tqdm(timestamps[100:200]):\n",
" rr.set_time_nanos(\"synchronization_time\", int(timestamp_ns))\n",
" rr.set_time_sequence(\"timestamp\", timestamp_ns)\n",
"\n",
" result = object_box2d_data_provider.get_bbox_at_timestamp(\n",
" stream_id=stream_id,\n",
" timestamp_ns=timestamp_ns,\n",
" time_query_options=TimeQueryOptions.CLOSEST,\n",
" time_domain=TimeDomain.TIME_CODE,\n",
" )\n",
" if result is None or result.box2d_collection is None:\n",
" continue\n",
"\n",
" for object_uid in result.box2d_collection.object_uid_list:\n",
" object_name = object_library.object_id_to_name_dict[object_uid]\n",
" ab = result.box2d_collection.box2ds[object_uid]\n",
" bbox = ab.box2d\n",
" if bbox is None:\n",
" continue\n",
" rr.log(\n",
" f\"{stream_id}_raw/bbox/{object_name}\",\n",
" rr.Boxes2D(mins=[bbox.left, bbox.top], sizes=[bbox.width, bbox.height],\n",
" colors=object_box2d_colors[object_uids.index(object_uid)]),\n",
" )\n",
" rr.log(f\"visibility/{object_name}\", rr.Scalar(ab.visibility_ratio))\n",
" image_data = device_data_provider.get_image(timestamp_ns, stream_id)\n",
" if image_data is not None:\n",
" log_image(label=f\"{stream_id}_raw\", image=image_data)\n",
"\n",
" rr.notebook_show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d1daf98-2df6-4d0a-ae38-331060353b99",
"metadata": {},
"outputs": [],
"source": [
"# Section 3.b: Hand 2D bounding boxes\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from data_loaders.loader_hand_poses import LEFT_HAND_INDEX, RIGHT_HAND_INDEX\n",
"\n",
"hand_box2d_data_provider = hot3d_data_provider.hand_box2d_data_provider\n",
"hand_uids = [LEFT_HAND_INDEX, RIGHT_HAND_INDEX]\n",
"hand_names = {LEFT_HAND_INDEX: \"left\", RIGHT_HAND_INDEX: \"right\"}\n",
"color_map = plt.get_cmap(\"viridis\")\n",
"hand_box2d_colors = color_map(np.linspace(0, 1, 2))\n",
"\n",
"rr.init(\"Hand bounding boxes\")\n",
"rec = rr.memory_recording()\n",
"\n",
"stream_id = main_stream_id\n",
"if stream_id not in hand_box2d_data_provider.stream_ids:\n",
" print(f\"stream_id {stream_id} has no hand bbox data. Available: {hand_box2d_data_provider.stream_ids}\")\n",
"\n",
"for timestamp_ns in tqdm(timestamps[100:200]):\n",
" rr.set_time_nanos(\"synchronization_time\", int(timestamp_ns))\n",
" rr.set_time_sequence(\"timestamp\", timestamp_ns)\n",
"\n",
" result = hand_box2d_data_provider.get_bbox_at_timestamp(\n",
" stream_id=stream_id,\n",
" timestamp_ns=timestamp_ns,\n",
" time_query_options=TimeQueryOptions.CLOSEST,\n",
" time_domain=TimeDomain.TIME_CODE,\n",
" )\n",
" if result is None or result.box2d_collection is None:\n",
" continue\n",
"\n",
" for i, hand_uid in enumerate(hand_uids):\n",
" ab = result.box2d_collection.box2ds[hand_uid]\n",
" bbox = ab.box2d\n",
" if bbox is None:\n",
" continue\n",
" hand_name = hand_names[hand_uid]\n",
" rr.log(\n",
" f\"{stream_id}_raw/bbox/{hand_name}\",\n",
" rr.Boxes2D(mins=[bbox.left, bbox.top], sizes=[bbox.width, bbox.height],\n",
" colors=hand_box2d_colors[i]),\n",
" )\n",
" rr.log(f\"visibility/{hand_name}\", rr.Scalar(ab.visibility_ratio))\n",
" image_data = device_data_provider.get_image(timestamp_ns, stream_id)\n",
" if image_data is not None:\n",
" log_image(label=f\"{stream_id}_raw\", image=image_data)\n",
"\n",
"rr.notebook_show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d8bad34e-a8ab-437d-84ff-1c046ca35d51",
"metadata": {},
"outputs": [],
"source": [
"# Section 4: Eye gaze (Aria only, skipped automatically on Quest3)\n",
"\n",
"if device_type != Headset.Aria:\n",
" print(f\"Skipping Section 4: eye gaze is Aria-only (current device: {device_type})\")\n",
"else:\n",
" from projectaria_tools.core.calibration import FISHEYE624\n",
"\n",
" rr.init(\"Eye Gaze reprojection\")\n",
" rec = rr.memory_recording()\n",
"\n",
" stream_id = StreamId(\"214-1\")\n",
" for timestamp_ns in tqdm(timestamps[100:120]):\n",
" rr.set_time_nanos(\"synchronization_time\", int(timestamp_ns))\n",
" rr.set_time_sequence(\"timestamp\", timestamp_ns)\n",
"\n",
" eye_gaze = device_data_provider.get_eye_gaze(timestamp_ns)\n",
" if eye_gaze is None:\n",
" continue\n",
"\n",
" proj = device_data_provider.get_eye_gaze_in_camera(\n",
" stream_id, timestamp_ns, camera_model=FISHEYE624\n",
" )\n",
" if proj is None or not proj.any():\n",
" continue\n",
"\n",
" rr.log(f\"{stream_id}/eye-gaze\", rr.Points2D(proj, radii=20))\n",
" image_data = device_data_provider.get_image(timestamp_ns, stream_id)\n",
" if image_data is not None:\n",
" log_image(label=str(stream_id), image=image_data)\n",
"\n",
" rr.notebook_show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b4d6ac37-e3b3-401a-83b7-e09a42a183a3",
"metadata": {},
"outputs": [],
"source": [
"# Section 5: Hand keypoint reprojection onto image\n",
"#\n",
"# Projects 3D world-space hand landmarks through the camera extrinsics + intrinsics\n",
"# to obtain 2D pixel coordinates, then overlays them on the image.\n",
"\n",
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"from typing import Any, Optional\n",
"from data_loaders.HeadsetPose3dProvider import HeadsetPose3dProvider\n",
"from data_loaders.loader_hand_poses import Handedness, HandPose3dCollection\n",
"from projectaria_tools.core.calibration import CameraCalibration\n",
"from projectaria_tools.core.sophus import SE3\n",
"\n",
"# Use a mid-sequence frame; clamp so the index is always valid\n",
"frame_idx = min(420, len(timestamps) - 1)\n",
"timestamp_ns = timestamps[frame_idx]\n",
"image_streamid = main_stream_id # Aria -> 214-1, Quest3 -> 1201-1\n",
"\n",
"image_stream_label = device_data_provider.get_image_stream_label(image_streamid)\n",
"image_data = device_data_provider.get_image(timestamp_ns, image_streamid)\n",
"\n",
"\n",
"def get_hand_poses(ts):\n",
" if hand_data_provider is None:\n",
" return None\n",
" result = hand_data_provider.get_pose_at_timestamp(\n",
" timestamp_ns=ts,\n",
" time_query_options=TimeQueryOptions.CLOSEST,\n",
" time_domain=TimeDomain.TIME_CODE,\n",
" )\n",
" return result.pose3d_collection if result else None\n",
"\n",
"\n",
"def get_camera_pose(ts, stream_id):\n",
" \"\"\"Returns (T_world_camera, intrinsics) or None.\"\"\"\n",
" if device_pose_provider is None:\n",
" return None\n",
" result = device_pose_provider.get_pose_at_timestamp(\n",
" timestamp_ns=ts,\n",
" time_query_options=TimeQueryOptions.CLOSEST,\n",
" time_domain=TimeDomain.TIME_CODE,\n",
" )\n",
" if result is None:\n",
" return None\n",
" [T_device_camera, intrinsics] = device_data_provider.get_camera_calibration(stream_id)\n",
" T_world_camera = result.pose3d.T_world_device @ T_device_camera\n",
" return T_world_camera, intrinsics\n",
"\n",
"\n",
"hand_data = get_hand_poses(timestamp_ns)\n",
"camera_pose = get_camera_pose(timestamp_ns, image_streamid)\n",
"\n",
"if hand_data is None or camera_pose is None or image_data is None:\n",
" print(\"Missing hand data, camera pose, or image — cannot project.\")\n",
"else:\n",
" T_world_camera, intrinsics = camera_pose\n",
" plt.figure(figsize=(10, 8))\n",
" plt.imshow(image_data, interpolation=\"nearest\")\n",
" plt.title(f\"{image_stream_label} frame={frame_idx}\")\n",
"\n",
" for hand_pose in hand_data.poses.values():\n",
" label = hand_pose.handedness_label()\n",
" landmarks = hand_data_provider.get_hand_landmarks(hand_pose)\n",
"\n",
" # Gather all 3D points along the skeleton connectivity\n",
" all_pts_3d = [\n",
" landmarks[idx].numpy()\n",
" for conn in LANDMARK_CONNECTIVITY\n",
" for idx in conn\n",
" ]\n",
"\n",
" # Project each 3D world point into the camera image plane\n",
" projected = []\n",
" for pt_world in all_pts_3d:\n",
" pt_cam = T_world_camera.inverse() @ pt_world\n",
" pt_2d = intrinsics.project(pt_cam)\n",
" if pt_2d is not None:\n",
" projected.append(pt_2d)\n",
"\n",
" color = 'r' if hand_pose.handedness == Handedness.Right else 'b'\n",
" print(f\"{label} hand: {len(projected)} keypoints visible in image\")\n",
" if projected:\n",
" plt.scatter([p[0] for p in projected], [p[1] for p in projected],\n",
" s=3, c=color, label=label)\n",
"\n",
" plt.legend()\n",
" plt.axis('off')\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"id": "88f7a32a",
"metadata": {},
"source": [
"#\n",
"# Segmentation Code Sample\n",
"#\n",
"Written by Zhonghao Zhang (FKZZddd): \n",
"This script was copied and adapted from the original script provided by Meta for HOT3D dataset. \n",
"The original script can be found at HOT3D/hot3d/hot3d/render_3d.py in the HOT3D repository.\n",
"\n",
"Used for segmentation of hand meshes by restoring the hands pose and shape data on sense.\n",
"setup_hand_at_timestamp is the main function that segments hands and adds hand meshes to the scene\n",
"This script was tested on BOTH Aria and Quest3. By modifying sequence_folder=\"./dataset/P0002_2f137f83\". The script can detect the device type and load the corresponding data for rendering.\n",
"#\n",
"# Code Sample\n",
"#\n",
"Installation:\n",
"- pip install pyrender trimesh\n",
"#\n",
"Details:\n",
"Demonstrate how to use PyRender to render HOT3D meshes (objects, hands) for a given timestamp & stream_id\n",
"- As OpenGL rendering is rectilinear, color, segmentation and depth buffer are also rectilinear rendering\n",
"- We then show how to map the rectilinear image back to the original fisheye image (but do not we are loosing some field of view)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b11b22eb",
"metadata": {},
"outputs": [],
"source": [
"from typing import Dict, List, Optional, Tuple\n",
"\n",
"import numpy as np\n",
"\n",
"try:\n",
" import trimesh\n",
" from pyrender import (\n",
" IntrinsicsCamera,\n",
" Mesh,\n",
" Node,\n",
" OffscreenRenderer,\n",
" RenderFlags,\n",
" Scene,\n",
" )\n",
"except ImportError:\n",
" print(\"trimesh or pyrender modules are missing. Please install them.\")\n",
"\n",
"from data_loaders.HandDataProviderBase import HandDataProviderBase\n",
"from data_loaders.headsets import Headset\n",
"from data_loaders.loader_object_library import load_object_library, ObjectLibrary\n",
"from dataset_api import Hot3dDataProvider\n",
"from PIL import Image\n",
"from projectaria_tools.core.calibration import (\n",
" CameraCalibration,\n",
" distort_by_calibration,\n",
" FISHEYE624,\n",
" LINEAR,\n",
")\n",
"from projectaria_tools.core.sensor_data import TimeDomain, TimeQueryOptions\n",
"from projectaria_tools.core.sophus import SE3\n",
"from projectaria_tools.core.stream_id import StreamId\n",
"from tqdm import tqdm\n",
"\n",
"# Matrix transform to change Aria camera pose to PyRender coordinate system\n",
"# PyRender: +Z = back, +Y = up, +X = right\n",
"# Aria: +Z = forward, +Y = down, +X = right\n",
"T_ARIA_OPENGL = SE3.from_matrix(\n",
" np.array(\n",
" [\n",
" [1.0, 0.0, 0.0, 0.0],\n",
" [0.0, -1.0, 0.0, 0.0],\n",
" [0.0, 0.0, -1.0, 0.0],\n",
" [0.0, 0.0, 0.0, 1.0],\n",
" ]\n",
" )\n",
")\n",
"\n",
"ACCEPTABLE_TIME_DELTA = 0 # To retrieve exact GT\n",
"\n",
"\n",
"def load_meshes_scene(\n",
" hot3d_data_provider: Hot3dDataProvider,\n",
") -> Dict[str, Mesh]:\n",
" \"\"\"\n",
" Load all meshes in the scene and hash them by object_uid\n",
" \"\"\"\n",
"\n",
" object_library = hot3d_data_provider.object_library\n",
" object_library_folderpath = object_library.asset_folder_name\n",
"\n",
" object_pose_data_provider = hot3d_data_provider.object_pose_data_provider\n",
" object_uids = object_pose_data_provider.object_uids_with_poses\n",
"\n",
" #\n",
" # Load all meshes in the scene and store them in a dict\n",
" #\n",
" meshes: Dict[str, Mesh] = {}\n",
" for object_uid in tqdm(object_uids):\n",
" object_cad_asset_filepath = ObjectLibrary.get_cad_asset_path(\n",
" object_library_folderpath=object_library_folderpath,\n",
" object_id=object_uid,\n",
" )\n",
" # Load the mesh, merge its component\n",
" scene = trimesh.load_mesh(\n",
" object_cad_asset_filepath,\n",
" process=True,\n",
" merge_primitives=True,\n",
" file_type=\"glb\",\n",
" )\n",
" # Represent the scene by a single mesh\n",
" glb_mesh = scene.to_mesh()\n",
" # Store the resulting mesh in the dict\n",
" meshes[object_uid] = Mesh.from_trimesh(glb_mesh)\n",
"\n",
" return meshes\n",
"\n",
"\n",
"def setup_objects_at_timestamp(\n",
" scene: Scene,\n",
" meshes: Dict[str, Mesh],\n",
" hot3d_data_provider: Hot3dDataProvider,\n",
" timestamp_ns: int,\n",
") -> Dict[str, Node]:\n",
" \"\"\"\n",
" Setup object meshes in the scene for the specified timestamp\n",
" \"\"\"\n",
"\n",
" object_pose_data_provider = hot3d_data_provider.object_pose_data_provider\n",
"\n",
" pyrender_node_meshes = {}\n",
" object_poses_with_dt = None\n",
" if object_pose_data_provider is not None:\n",
" object_poses_with_dt = object_pose_data_provider.get_pose_at_timestamp(\n",
" timestamp_ns=timestamp_ns,\n",
" time_query_options=TimeQueryOptions.CLOSEST,\n",
" time_domain=TimeDomain.TIME_CODE,\n",
" acceptable_time_delta=ACCEPTABLE_TIME_DELTA,\n",
" )\n",
" if object_poses_with_dt is not None:\n",
" objects_pose3d_collection = object_poses_with_dt.pose3d_collection\n",
" for (\n",
" object_uid,\n",
" object_pose3d,\n",
" ) in objects_pose3d_collection.poses.items():\n",
" transform = object_pose3d.T_world_object.to_matrix()\n",
" pyrender_node_meshes[object_uid] = scene.add(\n",
" meshes[object_uid], pose=transform\n",
" )\n",
"\n",
" return pyrender_node_meshes\n",
"\n",
"\n",
"def get_camera_calibration(\n",
" hot3d_data_provider: Hot3dDataProvider,\n",
" timestamp_ns: int,\n",
" stream_id: StreamId,\n",
" camera_model=LINEAR,\n",
") -> Optional[Tuple[SE3, CameraCalibration]]:\n",
" \"\"\"\n",
" Return the camera calibration\n",
" \"\"\"\n",
" device_data_provider = hot3d_data_provider.device_data_provider\n",
" if hot3d_data_provider.get_device_type() is Headset.Aria:\n",
" return device_data_provider.get_online_camera_calibration(\n",
" stream_id=stream_id,\n",
" timestamp_ns=timestamp_ns,\n",
" camera_model=camera_model,\n",
" )\n",
" elif hot3d_data_provider.get_device_type() is Headset.Quest3:\n",
" return device_data_provider.get_camera_calibration(\n",
" stream_id=stream_id,\n",
" camera_model=camera_model,\n",
" )\n",
" else:\n",
" return None\n",
"\n",
"\n",
"def setup_camera_at_timestamp(\n",
" scene: Scene,\n",
" hot3d_data_provider: Hot3dDataProvider,\n",
" timestamp_ns: int,\n",
" stream_id: StreamId,\n",
") -> Tuple[Node, List[int]]:\n",
" \"\"\"\n",
" Setup a rectilinear camera for the specified stream_id and timestamp\n",
" \"\"\"\n",
"\n",
" device_data_provider = hot3d_data_provider.device_data_provider\n",
" device_pose_provider = hot3d_data_provider.device_pose_data_provider\n",
"\n",
" [T_device_camera, intrinsics] = get_camera_calibration(\n",
" hot3d_data_provider=hot3d_data_provider,\n",
" stream_id=stream_id,\n",
" timestamp_ns=timestamp_ns,\n",
" camera_model=LINEAR,\n",
" )\n",
"\n",
" headset_pose3d_with_dt = None\n",
" if device_data_provider is not None:\n",
" headset_pose3d_with_dt = device_pose_provider.get_pose_at_timestamp(\n",
" timestamp_ns=timestamp_ns,\n",
" time_query_options=TimeQueryOptions.CLOSEST,\n",
" time_domain=TimeDomain.TIME_CODE,\n",
" acceptable_time_delta=ACCEPTABLE_TIME_DELTA,\n",
" )\n",
" if headset_pose3d_with_dt is not None:\n",
" headset_pose3d = headset_pose3d_with_dt.pose3d\n",
" focal_lengths = intrinsics.get_focal_lengths()\n",
" principal_point = intrinsics.get_principal_point()\n",
" camera = IntrinsicsCamera(\n",
" focal_lengths[0],\n",
" focal_lengths[0],\n",
" principal_point[0],\n",
" principal_point[1],\n",
" znear=0.05,\n",
" zfar=100.0,\n",
" name=None,\n",
" )\n",
"\n",
" camera_pose = (\n",
" (headset_pose3d.T_world_device @ T_device_camera) @ T_ARIA_OPENGL\n",
" ).to_matrix()\n",
"\n",
" camera_node = scene.add(camera, pose=camera_pose)\n",
" return [camera_node, intrinsics.get_image_size().tolist()]\n",
"\n",
"\n",
"def setup_hand_at_timestamp(\n",
" scene: Scene,\n",
" hot3d_data_provider: Hot3dDataProvider,\n",
" timestamp_ns: int,\n",
" hand_data_provider: HandDataProviderBase,\n",
") -> Dict[str, Mesh]:\n",
" \"\"\"\n",
" Add hand meshes to the scene for the specified timestamp\n",
" \"\"\"\n",
"\n",
" pyrender_node_meshes = {}\n",
"\n",
" if hand_data_provider is None:\n",
" return []\n",
"\n",
" hand_poses_with_dt = hand_data_provider.get_pose_at_timestamp(\n",
" timestamp_ns=timestamp_ns,\n",
" time_query_options=TimeQueryOptions.CLOSEST,\n",
" time_domain=TimeDomain.TIME_CODE,\n",
" acceptable_time_delta=ACCEPTABLE_TIME_DELTA,\n",
" )\n",
" if hand_poses_with_dt is not None:\n",
" hand_pose_collection = hand_poses_with_dt.pose3d_collection\n",
"\n",
" for hand_pose_data in hand_pose_collection.poses.values():\n",
" handedness_label = hand_pose_data.handedness_label()\n",
"\n",
" hand_mesh_vertices = hand_data_provider.get_hand_mesh_vertices(\n",
" hand_pose_data\n",
" )\n",
"\n",
" [hand_triangles, hand_vertex_normals] = (\n",
" hand_data_provider.get_hand_mesh_faces_and_normals(hand_pose_data)\n",
" )\n",
"\n",
" pyrender_node_meshes[handedness_label] = scene.add(\n",
" Mesh.from_trimesh(\n",
" trimesh.Trimesh(\n",
" vertices=hand_mesh_vertices,\n",
" normals=hand_vertex_normals,\n",
" faces=hand_triangles,\n",
" )\n",
" )\n",
" )\n",
" return pyrender_node_meshes\n",
"\n",
"\n",
"def offscreen_render(\n",
" scene: Scene,\n",
" resolution: List[int], # [width, height]\n",
") -> Tuple[np.ndarray, np.ndarray]:\n",
" \"\"\"\n",
" Return COLOR and DEPTH images\n",
" \"\"\"\n",
" renderer = OffscreenRenderer(resolution[0], resolution[1])\n",
" color, depth = renderer.render(scene) # , flags=RenderFlags.RGBA)\n",
"\n",
" nm = {\n",
" node: 20 * (i + 1) for i, node in enumerate(scene.mesh_nodes)\n",
" } # Node->Seg Id map\n",
" seg = renderer.render(scene, RenderFlags.SEG, nm)[0]\n",
"\n",
" renderer.delete()\n",
" return [color, depth, seg]\n",
"\n",
"\n",
"def distort_rendering(\n",
" image: np.ndarray,\n",
" hot3d_data_provider: Hot3dDataProvider,\n",
" timestamp_ns: int,\n",
" stream_id: StreamId,\n",
") -> np.ndarray:\n",
" \"\"\"\n",
" Map a rectilinear image to the native Fisheye camera model.\n",
" - Do notice that we are loosing some field of view.\n",
" \"\"\"\n",
"\n",
" # Retrieve the camera model we want to distort to\n",
" [T_device_camera, intrinsics_raw] = get_camera_calibration(\n",
" hot3d_data_provider=hot3d_data_provider,\n",
" stream_id=stream_id,\n",
" timestamp_ns=timestamp_ns,\n",
" camera_model=FISHEYE624,\n",
" )\n",
"\n",
" # Retrieve the camera model we used for rendering\n",
" [T_device_camera, intrinsics_linear] = get_camera_calibration(\n",
" hot3d_data_provider=hot3d_data_provider,\n",
" stream_id=stream_id,\n",
" timestamp_ns=timestamp_ns,\n",
" camera_model=LINEAR,\n",
" )\n",
"\n",
" re_distorted_image = distort_by_calibration(\n",
" image,\n",
" intrinsics_raw,\n",
" intrinsics_linear,\n",
" )\n",
"\n",
" return re_distorted_image\n",
"\n",
"\n",
"\n",
"#The main function \n",
"#Set object_library=None because object meshes are unnecessary for project.\n",
"hot3d_data_provider = Hot3dDataProvider(\n",
" # sequence_folder=\"./data_loaders/tests/data_sample/Aria/P0003_c701bd11\",\n",
" sequence_folder=\"./dataset/P0002_2f137f83\",\n",
" object_library=None,\n",
")\n",
"print(f\"data_provider statistics: {hot3d_data_provider.get_data_statistics()}\")\n",
"\n",
"scene_meshes = {}\n",
"\n",
"# Define timestamps and stream ids that need rendering\n",
"# Default attempts all stream ids and a timestamp in the middle of the sequence\n",
"\n",
"#total timestamps/2\n",
"timestamps = hot3d_data_provider.device_data_provider.get_sequence_timestamps()\n",
"timestamp_list = [timestamps[len(timestamps) // 2]]\n",
"stream_id_list = (\n",
" [StreamId(\"1201-1\"), StreamId(\"1201-2\"), StreamId(\"214-1\")]\n",
" if hot3d_data_provider.get_device_type() is Headset.Aria\n",
" else [StreamId(\"1201-1\"), StreamId(\"1201-2\")]\n",
")\n",
"\n",
"# Main rendering loop\n",
"print(f\"Rendering for: {stream_id_list}\")\n",
"for stream_id in stream_id_list:\n",
" for timestamp_ns in timestamp_list:\n",
" # Initialize the scene\n",
" scene = Scene(ambient_light=np.array([1.0, 1.0, 1.0, 1.0]))\n",
"\n",
" # Add hands into scene, the main function that do segmentation\n",
" setup_hand_at_timestamp(\n",
" scene=scene,\n",
" hot3d_data_provider=hot3d_data_provider,\n",
" timestamp_ns=timestamp_ns,\n",
" hand_data_provider=hot3d_data_provider.umetrack_hand_data_provider,\n",
" )\n",
"\n",
" # Setup camera rendering (for the specific stream_id and timestamp)\n",
" camera_node_and_resolution = setup_camera_at_timestamp(\n",
" scene=scene,\n",
" hot3d_data_provider=hot3d_data_provider,\n",
" timestamp_ns=timestamp_ns,\n",
" stream_id=stream_id,\n",
" )\n",
"\n",
" print(camera_node_and_resolution)\n",
" # Setup off screen rendering (to save rendering buffer to disk as image)\n",
" [color, depth, seg] = offscreen_render(scene, camera_node_and_resolution[1])\n",
"\n",
" camera_node_and_resolution = setup_camera_at_timestamp(\n",
" scene=scene,\n",
" hot3d_data_provider=hot3d_data_provider,\n",
" timestamp_ns=timestamp_ns,\n",
" stream_id=stream_id,\n",
" )\n",
" \n",
" im = Image.fromarray(color)\n",
" im.save(f\"render_native_{stream_id}_{timestamp_ns}.png\")\n",
"\n",
" # im = Image.fromarray(distorted_seg)\n",
" im = Image.fromarray(seg)\n",
" im.save(f\"seg_ref_{stream_id}_{timestamp_ns}.png\")\n",
"\n",
" # Save \"depth\" buffer\n",
" # im = Image.fromarray(depth)\n",
" # im.save(f\"depth_ref_{stream_id}_{timestamp_ns}.tiff\")\n",
"\n",
" image_data_raw = hot3d_data_provider.device_data_provider.get_image(\n",
" timestamp_ns, stream_id\n",
" )\n",
" if image_data_raw is not None:\n",
" im = Image.fromarray(image_data_raw)\n",
" im.save(f\"image_ref_{stream_id}_{timestamp_ns}.png\")"
]
}
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|