prepare_mcap_file.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize trajectories in Foxglove"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import geojson\n",
    "from shapely.geometry import LineString\n",
    "import numpy as np\n",
    "\n",
    "from mcap_protobuf.writer import Writer\n",
    "from foxglove_schemas_protobuf.CompressedImage_pb2 import CompressedImage\n",
    "from foxglove_schemas_protobuf.LocationFix_pb2 import LocationFix\n",
    "\n",
    "from google.protobuf.timestamp_pb2 import Timestamp\n",
    "from google.protobuf.duration_pb2 import Duration\n",
    "\n",
    "from utils import write_mcap_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single GPS trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "trajectory_path = pathlib.Path(\"data/example_trajectory.geojson\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "trajectory_path_as_geojson = geojson.loads(trajectory_path.read_text())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "gps_path = LineString(trajectory_path_as_geojson[\"features\"][0][\"geometry\"][\"coordinates\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n"
     ]
    }
   ],
   "source": [
    "num_samples = len(gps_path.coords)\n",
    "print(num_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "timesteps = np.arange(0, num_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "gps_lon, gps_lat  = gps_path.coords.xy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpolate for smooth visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "FPS = 30\n",
    "timesteps_for_viz = np.arange(0, num_samples, 1/FPS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gps_lon_interp = np.interp(timesteps_for_viz, timesteps, gps_lon)\n",
    "gps_lat_interp = np.interp(timesteps_for_viz, timesteps, gps_lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "<LINESTRING (9.223 49.145, 9.223 49.145, 9.223 49.145, 9.223 49.145, 9.223 4...>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gps_path_for_viz = LineString(np.c_[gps_lon_interp, gps_lat_interp])\n",
    "gps_path_for_viz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create an array of LocationFix messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "gps_message_array = list()\n",
    "\n",
    "for t, gps_lon, gps_lat in zip(timesteps_for_viz, gps_lon_interp, gps_lat_interp):\n",
    "\n",
    "    timestamp_pb2 = Timestamp(seconds=int(math.floor(t)), nanos=int(t*1e9 % 1e9))\n",
    "    loc_fix_msg = LocationFix(timestamp=timestamp_pb2, frame_id=\"world\", longitude=gps_lon, latitude=gps_lat, altitude=0)\n",
    "\n",
    "    gps_message_array.append(loc_fix_msg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Object trajectories (cut-in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import create_cut_in_trajectories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle_data = {}\n",
    "\n",
    "df_veh_1, df_veh_2 = create_cut_in_trajectories()\n",
    "\n",
    "vehicle_data[\"V1\"] = df_veh_1\n",
    "vehicle_data[\"V2\"] = df_veh_2\n",
    "\n",
    "vehicle_colors = {}\n",
    "vehicle_colors[\"V1\"] = \"blue\"\n",
    "vehicle_colors[\"V2\"] = \"red\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "        x         y     theta\n",
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   ],
   "source": [
    "vehicle_data[\"V2\"].head()"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
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     "execution_count": 16,
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_veh_2[\"y\"])\n",
    "plt.xlabel(\"time\")\n",
    "plt.ylabel(\"y [m]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Foxglove entity bounding boxes\n",
    "\n",
    "This is later visualized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import create_vehicle_cuboid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from foxglove_schemas_protobuf.SceneEntity_pb2 import SceneEntity\n",
    "from foxglove_schemas_protobuf.SceneUpdate_pb2 import SceneUpdate\n",
    "from foxglove_schemas_protobuf.CubePrimitive_pb2 import CubePrimitive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scene_updates = list()\n",
    "\n",
    "timesteps = df_veh_1.index.array\n",
    "\n",
    "for i, time in enumerate(timesteps):\n",
    "\n",
    "    scene_entities = list()\n",
    "\n",
    "    timestamp_pb2 = Timestamp(seconds=int(math.floor(t)), nanos=int(t*1e9 % 1e9))\n",
    "\n",
    "    for veh_id, veh_df in vehicle_data.items():\n",
    "\n",
    "        veh_sample = veh_df.loc[time]\n",
    "\n",
    "        cube = create_vehicle_cuboid(veh_sample[\"x\"], veh_sample[\"y\"], 0.0, veh_sample[\"theta\"], vehicle_colors[veh_id], alpha=0.5)\n",
    "        assert isinstance(cube, CubePrimitive)\n",
    "\n",
    "        scene_entity_foxglove = SceneEntity(id=veh_id, frame_id=\"world\", timestamp=timestamp_pb2, frame_locked=False, cubes=[cube])\n",
    "\n",
    "        scene_entities.append(scene_entity_foxglove)\n",
    "    \n",
    "    scene_update_foxglove = SceneUpdate(entities=scene_entities)\n",
    "    scene_updates.append(scene_update_foxglove)\n",
    "\n",
    "len(scene_updates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HD map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from foxglove_schemas_protobuf.LinePrimitive_pb2 import LinePrimitive\n",
    "from foxglove_schemas_protobuf.Point3_pb2 import Point3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "LANE_WIDTH = 4.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "right_marker_line = LineString([(-100, -LANE_WIDTH/2), (500,-LANE_WIDTH/2)])\n",
    "center_marker_line = LineString([(-100, LANE_WIDTH/2), (500,LANE_WIDTH/2)])\n",
    "left_marker_line = LineString([(-100, LANE_WIDTH+LANE_WIDTH/2), (500, LANE_WIDTH + LANE_WIDTH/2)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def line_string_to_lineprimitive(linestring: LineString) -> LinePrimitive:\n",
    "    return LinePrimitive(type=0, thickness=0.05, scale_invariant=False, \n",
    "                                   points=[Point3(x=coord[0], y=coord[1], z=0) for coord in linestring.coords])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "right_lane_marking = line_string_to_lineprimitive(right_marker_line)\n",
    "center_lane_marking = line_string_to_lineprimitive(center_marker_line)\n",
    "left_lane_marking = line_string_to_lineprimitive(left_marker_line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_entity = SceneEntity(id=\"hd-map\", frame_id=\"world\", timestamp=Timestamp(seconds=0, nanos=0), frame_locked=False, \n",
    "                         lines=[right_lane_marking, center_lane_marking, left_lane_marking])\n",
    "map_scene_update_foxglove = SceneUpdate(entities=[map_entity])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create frame transformation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from foxglove_schemas_protobuf.FrameTransforms_pb2 import FrameTransforms\n",
    "from foxglove_schemas_protobuf.FrameTransform_pb2 import FrameTransform\n",
    "from foxglove_schemas_protobuf.Vector3_pb2 import Vector3\n",
    "from foxglove_schemas_protobuf.Quaternion_pb2 import Quaternion\n",
    "\n",
    "from scipy.spatial.transform import Rotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame_transform_pb2 = FrameTransform(timestamp=Timestamp(seconds=0, nanos=0), \n",
    "               parent_frame_id=\"world\", child_frame_id=\"world\",\n",
    "               translation=Vector3(x=0, y=0, z=0), rotation=Quaternion(x=0.0, y=0.0, z=0.0, w=1.0))\n",
    "frame_transforms_pb2 = FrameTransforms(transforms=[frame_transform_pb2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame_transforms_foxglove = list()\n",
    "\n",
    "VEHICLE_HEIGHT = 1.5\n",
    "\n",
    "for t, ego_sample in vehicle_data[\"V1\"].iterrows():\n",
    "\n",
    "    rot = Rotation.from_euler(\"zyx\", [ego_sample[\"theta\"], 0, 0], degrees=False)\n",
    "    quat = rot.as_quat()\n",
    "    quat_foxglove = Quaternion(x=quat[0], y=quat[1], z=quat[2], w=quat[3])\n",
    "\n",
    "    timestamp_pb2 = Timestamp(seconds=int(math.floor(t)), nanos=int(t*1e9 % 1e9))\n",
    "\n",
    "    frame_transform_pb2 = FrameTransform(timestamp=timestamp_pb2, \n",
    "               parent_frame_id=\"world\", child_frame_id=\"V1\",\n",
    "               translation=Vector3(x=ego_sample[\"x\"], y=ego_sample[\"y\"], z= VEHICLE_HEIGHT/2),\n",
    "               rotation=quat_foxglove)\n",
    "    frame_transforms_pb2 = FrameTransforms(transforms=[frame_transform_pb2])\n",
    "\n",
    "    frame_transforms_foxglove.append(frame_transforms_pb2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import write_mcap_file, TopicWithData\n",
    "\n",
    "from foxglove_schemas_protobuf.KeyValuePair_pb2 import KeyValuePair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "write_mcap_file(\"bboxes.mcap\", [\n",
    "    TopicWithData(\"/tf\", timesteps, frame_transforms_foxglove),\n",
    "    TopicWithData(\"/objects\", timesteps, scene_updates),\n",
    "    # state over time\n",
    "    TopicWithData(\"/enum_topic\", [0.0, 3.0, 7.0, 10.0], [KeyValuePair(key=\"state\", value=\"CRUISING\"), \n",
    "                                                   KeyValuePair(key=\"state\", value=\"CHANGING LANE\"), \n",
    "                                                   KeyValuePair(key=\"state\", value=\"CRUISING\"),\n",
    "                                                   KeyValuePair(key=\"state\", value=\"CRUISING\")\n",
    "                                                   ]),\n",
    "    TopicWithData(\"/semantic_map\", [0], [map_scene_update_foxglove]),\n",
    "    TopicWithData(\"/gps_location\", timesteps_for_viz, gps_message_array)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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