demo_usage/argoverse_forecasting_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Argoverse-forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a simple tutorial that will show you how to interact with the Argoverse-forecasting dataset using our python package. See [github page](https://github.com/argoai/argoverse-api) for instructions on how to install the package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Argoverse dataset can be download at [https://www.argoverse.org](https://www.argoverse.org)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to create argoverse loader. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of sequences: 5\n"
     ]
    }
   ],
   "source": [
    "from argoverse.data_loading.argoverse_forecasting_loader import ArgoverseForecastingLoader\n",
    "\n",
    "##set root_dir to the correct path to your dataset folder\n",
    "root_dir = '../../forecasting_sample/data/'\n",
    "\n",
    "afl = ArgoverseForecastingLoader(root_dir)\n",
    "\n",
    "print('Total number of sequences:',len(afl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seq : /data/workspace/argoverse-api/demo_usage/../../forecasting_sample/data/3861.csv\n",
      "        ----------------------\n",
      "        || City: PIT\n",
      "        || # Tracks: 5\n",
      "        ----------------------\n"
     ]
    }
   ],
   "source": [
    "print(afl[4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to go through each log in our dataset is by iterating through our data loader. For example, we can see statistics for each log with simple iteration and printing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seq : /data/workspace/argoverse-api/demo_usage/../../forecasting_sample/data/3700.csv\n",
      "        ----------------------\n",
      "        || City: PIT\n",
      "        || # Tracks: 32\n",
      "        ----------------------\n",
      "Seq : /data/workspace/argoverse-api/demo_usage/../../forecasting_sample/data/4791.csv\n",
      "        ----------------------\n",
      "        || City: MIA\n",
      "        || # Tracks: 60\n",
      "        ----------------------\n",
      "Seq : /data/workspace/argoverse-api/demo_usage/../../forecasting_sample/data/2645.csv\n",
      "        ----------------------\n",
      "        || City: MIA\n",
      "        || # Tracks: 24\n",
      "        ----------------------\n",
      "Seq : /data/workspace/argoverse-api/demo_usage/../../forecasting_sample/data/3828.csv\n",
      "        ----------------------\n",
      "        || City: MIA\n",
      "        || # Tracks: 45\n",
      "        ----------------------\n",
      "Seq : /data/workspace/argoverse-api/demo_usage/../../forecasting_sample/data/3861.csv\n",
      "        ----------------------\n",
      "        || City: PIT\n",
      "        || # Tracks: 5\n",
      "        ----------------------\n"
     ]
    }
   ],
   "source": [
    "for argoverse_forecasting_data in (afl):\n",
    "    print(argoverse_forecasting_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also get all the track_ids for a sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['00000000-0000-0000-0000-000000000000', '00000000-0000-0000-0000-000000014129', '00000000-0000-0000-0000-000000014364', '00000000-0000-0000-0000-000000014617', '00000000-0000-0000-0000-000000014661', '00000000-0000-0000-0000-000000014669', '00000000-0000-0000-0000-000000014676', '00000000-0000-0000-0000-000000014684', '00000000-0000-0000-0000-000000014685', '00000000-0000-0000-0000-000000014686', '00000000-0000-0000-0000-000000014691', '00000000-0000-0000-0000-000000014692', '00000000-0000-0000-0000-000000014708', '00000000-0000-0000-0000-000000014720', '00000000-0000-0000-0000-000000014723', '00000000-0000-0000-0000-000000014751', '00000000-0000-0000-0000-000000014762', '00000000-0000-0000-0000-000000014786', '00000000-0000-0000-0000-000000014787', '00000000-0000-0000-0000-000000014789', '00000000-0000-0000-0000-000000014799', '00000000-0000-0000-0000-000000014801', '00000000-0000-0000-0000-000000014803', '00000000-0000-0000-0000-000000014811', '00000000-0000-0000-0000-000000014816', '00000000-0000-0000-0000-000000014818', '00000000-0000-0000-0000-000000014824', '00000000-0000-0000-0000-000000014827', '00000000-0000-0000-0000-000000014828', '00000000-0000-0000-0000-000000014831', '00000000-0000-0000-0000-000000014833', '00000000-0000-0000-0000-000000014834']\n"
     ]
    }
   ],
   "source": [
    "argoverse_forecasting_data = afl[0]\n",
    "print(argoverse_forecasting_data.track_id_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from argoverse.visualization.visualize_sequences import viz_sequence\n",
    "seq_path = f\"{root_dir}/2645.csv\"\n",
    "viz_sequence(afl.get(seq_path).seq_df, show=True)\n",
    "seq_path = f\"{root_dir}/3828.csv\"\n",
    "viz_sequence(afl.get(seq_path).seq_df, show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using map_api"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting candidate centerlines for the agent's trajectory is a simple function call. Below we use the first 2 secs of the trajectory to compute candidate centerlines for the next 3 secs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from argoverse.map_representation.map_api import ArgoverseMap\n",
    "\n",
    "avm = ArgoverseMap()\n",
    "\n",
    "obs_len = 20\n",
    "\n",
    "index = 2\n",
    "seq_path = afl.seq_list[index]\n",
    "agent_obs_traj = afl.get(seq_path).agent_traj[:obs_len]\n",
    "candidate_centerlines = avm.get_candidate_centerlines_for_traj(agent_obs_traj, afl[index].city, viz=True)\n",
    "\n",
    "index = 3\n",
    "seq_path = afl.seq_list[index]\n",
    "agent_obs_traj = afl.get(seq_path).agent_traj[:obs_len]\n",
    "candidate_centerlines = avm.get_candidate_centerlines_for_traj(agent_obs_traj, afl[index].city, viz=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So is getting the lane direction of the trajectory's coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = 2\n",
    "seq_path = afl.seq_list[index]\n",
    "agent_traj = afl.get(seq_path).agent_traj\n",
    "lane_direction = avm.get_lane_direction(agent_traj[0], afl[index].city, visualize=True)\n",
    "\n",
    "index = 3\n",
    "seq_path = afl.seq_list[index]\n",
    "agent_traj = afl.get(seq_path).agent_traj\n",
    "lane_direction = avm.get_lane_direction(agent_traj[0], afl[index].city, visualize=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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