{Documentation} Add sample data links.

Summary:
This diff adds gen2 sample data link to the notebook and tutorials.

It also slightly changed how `notebook_show()` is called, it should be at the end of the cell block.

Reviewed By: kongchen1992

Differential Revision: D84948289

fbshipit-source-id: b0b91de7015a5882e343f30d34c857ffb261d174

Author: YLouWashU

examples/Gen2/python_notebooks/Tutorial_1_vrs_data_provider_basics.ipynb

@@ -25,7 +25,12 @@
     "- Retrieve data using either sequential (index-based) or temporal (timestamp-based) access APIs.   \n",
     "- Learn about timing domains and time query options\n",
     "\n",
-    "**Prerequisites:** Basic Python knowledge and a general understanding of multimodal sensor data."
+    "**Prerequisites:** \n",
+    "- Basic Python knowledge and a general understanding of multimodal sensor data.\n",
+    "- Download Aria Gen2 sample data from [link](https://www.projectaria.com/async/sample/download/?bucket=core&filename=aria_gen2_sample_data_1.vrs)\n",
+    "\n",
+    "**Note on Visualization**\n",
+    "If visualization window is not showing up, this is due to `Rerun` lib's caching issue. Just rerun the specific code cell."
    ]
   },
   {
@@ -148,7 +153,6 @@
     "# Visualize with Rerun\n",
     "import rerun as rr\n",
     "rr.init(\"rerun_viz_query_by_index\")\n",
-    "rr.notebook_show()\n",
     "\n",
     "# Get number of samples in stream\n",
     "num_samples = vrs_data_provider.get_num_data(rgb_stream_id)\n",
@@ -169,7 +173,9 @@
     "    # Process image data\n",
     "    if image_data.is_valid():\n",
     "        rr.set_time_nanos(\"device_time\", timestamp_ns)\n",
-    "        rr.log(\"camera_rgb\", rr.Image(image_data.to_numpy_array()))\n"
+    "        rr.log(\"camera_rgb\", rr.Image(image_data.to_numpy_array()))\n",
+    "\n",
+    "rr.notebook_show()"
    ]
   },
   {
@@ -231,7 +237,6 @@
     "from projectaria_tools.core.sensor_data import TimeDomain, TimeQueryOptions\n",
     "\n",
     "rr.init(\"rerun_viz_query_by_timestamp\")\n",
-    "rr.notebook_show()\n",
     "\n",
     "# Get time bounds for RGB images\n",
     "first_timestamp_ns = vrs_data_provider.get_first_time_ns(rgb_stream_id, TimeDomain.DEVICE_TIME)\n",
@@ -270,7 +275,9 @@
     "            rr.set_time_nanos(\"device_time\", capture_time_ns)\n",
     "            rr.log(label, rr.Image(image_data.to_numpy_array()))\n",
     "\n",
-    "    query_timestamp_ns = query_timestamp_ns + int(1e9) # 1 second\n"
+    "    query_timestamp_ns = query_timestamp_ns + int(1e9) # 1 second\n",
+    "\n",
+    "rr.notebook_show()"
    ]
   }
  ],

examples/Gen2/python_notebooks/Tutorial_2_device_calibration.ipynb

@@ -25,7 +25,11 @@
     "- Multi-sensor coordination and sensor poses, and the concept of the \"Device\" frame. \n",
     "\n",
     "**Pre-requisite:**  \n",
-    "Familiarity with VRS basics from `Tutorial_1_vrs_Data_provider_basics.ipynb`."
+    "- Familiarity with VRS basics from `Tutorial_1_vrs_Data_provider_basics.ipynb`.\n",
+    "- Download Aria Gen2 sample data from [link](https://www.projectaria.com/async/sample/download/?bucket=core&filename=aria_gen2_sample_data_1.vrs)\n",
+    "\n",
+    "**Note on Visualization**\n",
+    "If visualization window is not showing up, this is due to `Rerun` lib's caching issue. Just rerun the specific code cell."
    ]
   },
   {
@@ -315,7 +319,6 @@
     "import rerun as rr\n",
     "\n",
     "rr.init(\"rerun_viz_image_undistortion\")\n",
-    "rr.notebook_show()\n",
     "\n",
     "# We already obtained RGB camera calibration as `camera_alib`.\n",
     "# Now, create a linear camera model that is similar to camera_calib\n",
@@ -350,7 +353,8 @@
     "        dstCalib=linear_camera_model,\n",
     "        srcCalib=camera_calib,\n",
     "    )\n",
-    "    rr.log(\"undistorted_camera_rgb\", rr.Image(undistorted_image))"
+    "    rr.log(\"undistorted_camera_rgb\", rr.Image(undistorted_image))\n",
+    "rr.notebook_show()"
    ]
   },
   {
@@ -448,7 +452,6 @@
     "\n",
     "\n",
     "rr.init(\"rerun_viz_imu_rectification\")\n",
-    "rr.notebook_show()\n",
     "\n",
     "imu_label = \"imu-right\"\n",
     "imu_calib = device_calib.get_imu_calib(imu_label)\n",
@@ -489,7 +492,9 @@
     "    _plot_imu_signals(imu_data.accel_msec2, imu_data.gyro_radsec, \"imu_right\")\n",
     "\n",
     "    # Plot compensated IMU readings in a separate plot\n",
-    "    _plot_imu_signals(compensated_accel, compensated_gyro, \"imu_right_compensated\")\n"
+    "    _plot_imu_signals(compensated_accel, compensated_gyro, \"imu_right_compensated\")\n",
+    "\n",
+    "rr.notebook_show()\n"
    ]
   },
   {
@@ -532,7 +537,6 @@
     ")\n",
     "\n",
     "rr.init(\"rerun_viz_sensor_extrinsics\")\n",
-    "rr.notebook_show()\n",
     "\n",
     "# Obtain a glass outline for visualization. This outline uses factory calibration extrinsics if possible, uses CAD extrinsics if factory calibration is not available.\n",
     "glass_outline = AriaGlassesOutline(device_calib, use_cad_calib=False)\n",
@@ -575,7 +579,9 @@
     "                focal_length=float(camera_calibration.get_focal_lengths()[0]),\n",
     "            ),\n",
     "            static=True,\n",
-    "        )\n"
+    "        )\n",
+    "\n",
+    "rr.notebook_show()"
    ]
   }
  ],

examples/Gen2/python_notebooks/Tutorial_3_sequential_access_multi_sensor_data.ipynb

@@ -20,7 +20,11 @@
     "4. Apply frame rate subsampling for efficient processing\n",
     "\n",
     "**Prerequisites**\n",
-    "- Complete Tutorial 1 (VrsDataProvider Basics) to understand basic data provider concepts"
+    "- Complete Tutorial 1 (VrsDataProvider Basics) to understand basic data provider concepts\n",
+    "- Download Aria Gen2 sample data from [link](https://www.projectaria.com/async/sample/download/?bucket=core&filename=aria_gen2_sample_data_1.vrs)\n",
+    "\n",
+    "**Note on Visualization**\n",
+    "If visualization window is not showing up, this is due to `Rerun` lib's caching issue. Just rerun the specific code cell."
    ]
   },
   {
@@ -223,7 +227,6 @@
     "print(f\"Start visualizing customized sensor data queue\")\n",
     "\n",
     "rr.init(\"rerun_viz_customized_sensor_data_queue\")\n",
-    "rr.notebook_show()\n",
     "\n",
     "for sensor_data in vrs_data_provider.deliver_queued_sensor_data(customized_deliver_options):\n",
     "    stream_id = sensor_data.stream_id()\n",
@@ -234,7 +237,9 @@
     "\n",
     "    # Visualize\n",
     "    rr.set_time_nanos(\"device_time\", device_time_ns)\n",
-    "    rr.log(stream_label, rr.Image(image_data_and_record[0].to_numpy_array()))\n"
+    "    rr.log(stream_label, rr.Image(image_data_and_record[0].to_numpy_array()))\n",
+    "\n",
+    "rr.notebook_show()"
    ]
   },
   {

examples/Gen2/python_notebooks/Tutorial_4_on_device_eyetracking_handtracking.ipynb

@@ -24,7 +24,11 @@
     "\n",
     "**Prerequisites**\n",
     "- Complete Tutorial 1 (VrsDataProvider Basics) to understand basic data provider concepts\n",
-    "- Complete Tutorial 2 (Device Calibration) to understand how to properly use calibration in Aria data. "
+    "- Complete Tutorial 2 (Device Calibration) to understand how to properly use calibration in Aria data.\n",
+    "- Download Aria Gen2 sample data from [link](https://www.projectaria.com/async/sample/download/?bucket=core&filename=aria_gen2_sample_data_1.vrs)\n",
+    "\n",
+    "**Note on Visualization**\n",
+    "If visualization window is not showing up, this is due to `Rerun` lib's caching issue. Just rerun the specific code cell, or restart the python kernel. "
    ]
   },
   {
@@ -207,7 +211,6 @@
     "rgb_camera_calib = device_calib.get_camera_calib(rgb_camera_label)\n",
     "\n",
     "rr.init(\"rerun_viz_et_in_cameras\")\n",
-    "rr.notebook_show()\n",
     "\n",
     "# Set up a sensor queue with only RGB image + EyeGaze\n",
     "deliver_options = vrs_data_provider.get_default_deliver_queued_options()\n",
@@ -249,7 +252,9 @@
     "\n",
     "        # Plot Eyegaze overlay on top of camera images\n",
     "        rr.set_time_nanos(\"device_time\", device_time_ns)\n",
-    "        plot_eyegaze_in_camera(eyegaze_data = eye_gaze, camera_label = rgb_camera_label, camera_calib = rgb_camera_calib, T_device_cpf = T_device_cpf)\n"
+    "        plot_eyegaze_in_camera(eyegaze_data = eye_gaze, camera_label = rgb_camera_label, camera_calib = rgb_camera_calib, T_device_cpf = T_device_cpf)\n",
+    "\n",
+    "rr.notebook_show()"
    ]
   },
   {
@@ -536,7 +541,6 @@
     "slam_stream_ids = [vrs_data_provider.get_stream_id_from_label(label) for label in slam_camera_labels]\n",
     "\n",
     "rr.init(\"rerun_viz_ht_in_cameras\")\n",
-    "rr.notebook_show()\n",
     "\n",
     "# Set up a sensor queue with only RGB images.\n",
     "# Handtracking data will be queried with interpolated API.\n",
@@ -576,7 +580,9 @@
     "\n",
     "# Wait for rerun to buffer 1 second of data\n",
     "import time\n",
-    "time.sleep(1)"
+    "time.sleep(1)\n",
+    "\n",
+    "rr.notebook_show()"
    ]
   },
   {

examples/Gen2/python_notebooks/Tutorial_5_on_device_vio.ipynb

@@ -23,7 +23,12 @@
     "\n",
     "**Prerequisites**\n",
     "- Complete Tutorial 1 (VrsDataProvider Basics) to understand basic data provider concepts\n",
-    "- Complete Tutorial 2 (Device Calibration) to understand how to properly use calibration in Aria data. "
+    "- Complete Tutorial 2 (Device Calibration) to understand how to properly use calibration in Aria data.\n",
+    "- Download Aria Gen2 sample data from [link](https://www.projectaria.com/async/sample/download/?bucket=core&filename=aria_gen2_sample_data_1.vrs)\n",
+    "\n",
+    "\n",
+    "**Note on visualization:**\n",
+    "If visualization window is not showing up, this is due to `Rerun` lib's caching issue. Just rerun the specific code cell."
    ]
   },
   {
@@ -301,7 +306,6 @@
     "print(\"\\n=== Visualizing on-device VIO trajectory + HandTracking in 3D view ===\")\n",
     "\n",
     "rr.init(\"rerun_viz_vio_trajectory\")\n",
-    "rr.notebook_show()\n",
     "\n",
     "device_calib = vrs_data_provider.get_device_calibration()\n",
     "handtracking_stream_id = vrs_data_provider.get_stream_id_from_label(\"handtracking\")\n",
@@ -388,7 +392,9 @@
     "    # For visualization purpose, also plot the hand tracking results\n",
     "    interpolated_hand_pose = vrs_data_provider.get_interpolated_hand_pose_data(handtracking_stream_id, vio_data.capture_timestamp_ns, TimeDomain.DEVICE_TIME)\n",
     "    if interpolated_hand_pose is not None:\n",
-    "        plot_hand_pose_data_3d(hand_pose_data = interpolated_hand_pose)\n"
+    "        plot_hand_pose_data_3d(hand_pose_data = interpolated_hand_pose)\n",
+    "\n",
+    "rr.notebook_show()"
    ]
   },
   {

examples/Gen2/python_notebooks/Tutorial_6_timestamp_alignment_in_aria_gen2.ipynb

@@ -22,7 +22,12 @@
     "\n",
     "**Prerequisites**\n",
     "- Complete Tutorial 1 (VrsDataProvider Basics) to understand basic data provider concepts\n",
-    "- Complete Tutorial 3 (Sequential Access multi-sensor data) to understand how to create a queue of sensor data from VRS file."
+    "- Complete Tutorial 3 (Sequential Access multi-sensor data) to understand how to create a queue of sensor data from VRS file.\n",
+    "- Download Aria Gen2 sample data: [host recording](https://www.projectaria.com/async/sample/download/?bucket=core&filename=aria_gen2_sample_data_host_1.vrs) and [client recording](https://www.projectaria.com/async/sample/download/?bucket=core&filename=aria_gen2_sample_data_client_1.vrs)\n",
+    "\n",
+    "\n",
+    "**Note on visualization:**\n",
+    "If visualization window is not showing up, this is due to `Rerun` lib's caching issue. Just rerun the specific code cell, or restart the Python kernel. "
    ]
   },
   {
@@ -164,7 +169,6 @@
     "\n",
     "print(\"=== Single VRS timestamp-based query visualization examples ===\")\n",
     "rr.init(\"rerun_viz_single_vrs_timestamp_based_query\")\n",
-    "rr.notebook_show()\n",
     "\n",
     "# Select RGB and SLAM stream IDs to visualize\n",
     "all_labels = vrs_data_provider.get_device_calibration().get_camera_labels()\n",
@@ -202,7 +206,9 @@
     "        rr.log(single_slam_label, rr.Image(slam_image_data.to_numpy_array()))\n",
     "\n",
     "    # Increment query timestamp\n",
-    "    current_timestamp_ns += target_period_ns\n"
+    "    current_timestamp_ns += target_period_ns\n",
+    "\n",
+    "rr.notebook_show()"
    ]
   },
   {
@@ -279,7 +285,6 @@
    "source": [
     "print(\"======= Multi-VRS time mapping example: Query APIs ======\")\n",
     "rr.init(\"rerun_viz_multi_vrs_time_mapping\")\n",
-    "rr.notebook_show()\n",
     "\n",
     "# Set up sensor queue options in host VRS, only turn on RGB stream\n",
     "host_deliver_options = host_data_provider.get_default_deliver_queued_options()\n",
@@ -328,7 +333,9 @@
     "    rr.set_time_nanos(\"device_time\", converted_client_timestamp_ns)\n",
     "\n",
     "    # Plot client image\n",
-    "    rr.log(\"rgb_image_in_client\", rr.Image(client_image_data.to_numpy_array()))"
+    "    rr.log(\"rgb_image_in_client\", rr.Image(client_image_data.to_numpy_array()))\n",
+    "\n",
+    "rr.notebook_show()"
    ]
   }
  ],
@@ -348,7 +355,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.11"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,

examples/Gen2/python_notebooks/Tutorial_7_mps_data_provider_basics.ipynb

@@ -26,7 +26,11 @@
     "\n",
     "**Prerequisites**\n",
     "- Complete Tutorial 1 (VrsDataProvider Basics) to understand basic data provider concepts\n",
-    "- Complete Tutorial 2 (Device Calibration) to understand how to properly use calibration in Aria data."
+    "- Complete Tutorial 2 (Device Calibration) to understand how to properly use calibration in Aria data.\n",
+    "- Download Aria Gen2 sample data: [VRS](https://www.projectaria.com/async/sample/download/?bucket=core&filename=aria_gen2_sample_data_1.vrs) and [MPS output zip file](https://www.projectaria.com/async/sample/download/?bucket=core&filename=aria_gen2_sample_data_1_mps_output.zip).\n",
+    "\n",
+    "**Note on Visualization**\n",
+    "If visualization window is not showing up, this is due to `Rerun` lib's caching issue. Just rerun the specific code cell."
    ]
   },
   {
@@ -372,7 +376,6 @@
     "\n",
     "# Initialize Rerun\n",
     "rr.init(\"MPS SLAM Visualization\")\n",
-    "rr.notebook_show()  # open the in-notebook viewer first, then stream logs\n",
     "\n",
     "# Set up the 3D scene\n",
     "rr.log(\"world\", rr.ViewCoordinates.RIGHT_HAND_Z_UP, static=True)\n",
@@ -463,7 +466,9 @@
     "            radii=5e-3,\n",
     "        ),\n",
     "        static=False,\n",
-    "    )"
+    "    )\n",
+    "    \n",
+    "rr.notebook_show()  "
    ]
   }
  ],
@@ -483,7 +488,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.11"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,

website/docs-research-tools/projectariatools/pythontutorials/calibration.mdx

@@ -24,7 +24,11 @@ Most sensors in Aria glasses are calibrated both extrinsically and intrinsically
 - Multi-sensor coordination and sensor poses, and the concept of the "Device" frame.
 
 **Pre-requisite:**
-Familiarity with VRS basics from `Tutorial_1_vrs_Data_provider_basics.ipynb`.
+- Familiarity with VRS basics from `Tutorial_1_vrs_Data_provider_basics.ipynb`.
+- Download Aria Gen2 sample data from [link](https://www.projectaria.com/async/sample/download/?bucket=core&filename=aria_gen2_sample_data_1.vrs)
+
+**Note on Visualization**
+If visualization window is not showing up, this is due to `Rerun` lib's caching issue. Just rerun the specific code cell.
 
 ### Obtaining Device Calibration Content
 
@@ -223,7 +227,6 @@ import rerun as rr
 from projectaria_tools.core import calibration
 
 rr.init("rerun_viz_image_undistortion")
-rr.notebook_show()
 
 # We already obtained RGB camera calibration as `camera_calib`.
 # Now, create a linear camera model that is similar to camera_calib
@@ -259,6 +262,8 @@ for i in range(first_few):
         srcCalib=camera_calib,
     )
     rr.log("undistorted_camera_rgb", rr.Image(undistorted_image))
+
+rr.notebook_show()
 ```
 
 ### IMU Intrinsics: Measurement Rectification
@@ -347,7 +352,6 @@ def _plot_imu_signals(accel_data, gyro_data, rerun_plot_label):
 
 
 rr.init("rerun_viz_imu_rectification")
-rr.notebook_show()
 
 imu_label = "imu-right"
 imu_calib = device_calib.get_imu_calib(imu_label)
@@ -389,6 +393,8 @@ for i in range(0, first_few, 50):
 
     # Plot compensated IMU readings in a separate plot
     _plot_imu_signals(compensated_accel, compensated_gyro, "imu_right_compensated")
+
+rr.notebook_show()
 ```
 
 ### 5. Accessing Sensor Extrinsics
@@ -419,7 +425,6 @@ from projectaria_tools.utils.rerun_helpers import (
 )
 
 rr.init("rerun_viz_sensor_extrinsics")
-rr.notebook_show()
 
 # Obtain a glass outline for visualization. This outline uses factory calibration extrinsics if possible, uses CAD extrinsics if factory calibration is not available.
 glass_outline = AriaGlassesOutline(device_calib, use_cad_calib=False)
@@ -463,4 +468,5 @@ for sensor in sensor_labels:
             ),
             static=True,
         )
+rr.notebook_show()
 ```