open3d_lab.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, 'utils.py')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# /!\\ Before running the lab make sure every additional libraries is installed \n",
    "\n",
    "\n",
    "\n",
    "# Import local libraries\n",
    "from utils import *\n",
    "import time\n",
    "import open3d as o3d\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading of one LiDAR scan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_frame = 0\n",
    "actor = 'ego_vehicle'\n",
    "\n",
    "points = get_point_cloud(n_frame, actor)\n",
    "\n",
    "pcd = o3d.geometry.PointCloud()\n",
    "pcd.points = o3d.utility.Vector3dVector(points)\n",
    "\n",
    "flag_display = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Voxel Down sampling\n",
    "\n",
    "To reduce the number of points and accelerate the computation use the function _voxel_down_sample()_ of open3D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "downpcd = pcd.voxel_down_sample(voxel_size=0.0005)\n",
    "if flag_display:\n",
    "    o3d.visualization.draw_geometries([downpcd])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normals estimation\n",
    "We would like to detect opstacles, to do that the first step is to estimate normals to obtain the local orientation of the point cloud.\n",
    "\n",
    "Use the function _estimate_normals()_ of open3D to estimate this normals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ToDo estimate the normals, radius is the size of the neighborhood of the sensor, max_nn is the maximum number of nearest neighbors\n",
    "o3d.geometry.PointCloud.estimate_normals( downpcd, search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=3,  max_nn=30))\n",
    "\n",
    "\n",
    "if flag_display:\n",
    "    o3d.visualization.draw_geometries([downpcd], point_show_normal=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "downpcd.colors = o3d.utility.Vector3dVector(np.abs(np.array(downpcd.normals)))\n",
    "\n",
    "if flag_display:\n",
    "    o3d.visualization.draw_geometries([downpcd])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ground and objects segmentation\n",
    "\n",
    "Based on the height of each point, the local orientation of the points cloud arround this point and eventually the variation of this orientation filter the ground to obtain a new points cloud whith only the ground points and another one with only the objects on top of this ground.\n",
    "\n",
    "For both of these new points clouds you can also filter the points corresponding to the roof of the ego vehicle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ground_filtering(pcd):\n",
    "    # Assuming 'normals' is your array of normal vectors\n",
    "    z_components = np.asarray(pcd.normals)[:, 2]\n",
    "\n",
    "    # Plot histogram of z-components for threshold determination\n",
    "    # plt.hist(z_components, bins=50)\n",
    "    # plt.xlabel('z-component of Normal Vectors')\n",
    "    # plt.ylabel('Frequency')\n",
    "    # plt.title('Distribution of z-components of Normal Vectors')\n",
    "    # plt.show()\n",
    "\n",
    "    # Experiment with different thresholds\n",
    "    threshold = 0.8  # Adjust this value based on the histogram\n",
    "    pcd_ground = pcd.select_by_index(np.where((z_components < -threshold) | (z_components > threshold))[0])\n",
    "    pcd_ground = pcd_ground.select_by_index(np.where(np.asarray(pcd_ground.points)[:, 2] <0.1)[0])\n",
    "    return pcd_ground\n",
    "\n",
    "pcd_ground = ground_filtering(downpcd)\n",
    "\n",
    "if flag_display:\n",
    "    o3d.visualization.draw_geometries([pcd_ground])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objects_filtering(pcd):\n",
    "    z_components = np.asarray(pcd.normals)[:, 2]\n",
    "    height = np.asarray(pcd.points)[:, 2]\n",
    "    threshold = 0.8  # Adjust this value based on the histogram\n",
    "    #first level of filtering based on spatial height of points\n",
    "    pcd_objects = pcd.select_by_index(np.where(height > 0.5)[0])\n",
    "    #z-components of filtered point cloud\n",
    "    z_components = np.asarray(pcd_objects.normals)[:, 2]\n",
    "    pcd_objects = pcd_objects.select_by_index(np.where((z_components > -threshold) & (z_components < threshold))[0])\n",
    "\n",
    "    return pcd_objects\n",
    "\n",
    "pcd_objects = objects_filtering(downpcd)\n",
    "\n",
    "if flag_display:\n",
    "    o3d.visualization.draw_geometries([pcd_objects])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering\n",
    "\n",
    "Now that the points of the objects are not connected to the ground anymore we can regroup all the points of the same object on one cluster.\n",
    "\n",
    "To do that, use the function _cluster_dbscan()_ of open3D to obtain the cluster id for each point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The points cloud has 61 clusters\n",
      "[Open3D WARNING] GLFW Error: WGL: Failed to make context current: The handle is invalid. \n",
      "[Open3D WARNING] GLFW Error: WGL: Failed to make context current: The requested transformation operation is not supported. \n",
      "[Open3D WARNING] GLFW Error: WGL: Failed to make context current: The requested transformation operation is not supported. \n",
      "[Open3D WARNING] GLFW Error: WGL: Failed to make context current: The requested transformation operation is not supported. \n",
      "[Open3D WARNING] GLFW Error: WGL: Failed to make context current: The handle is invalid. \n",
      "[Open3D WARNING] GLFW Error: WGL: Failed to make context current: The requested transformation operation is not supported. \n"
     ]
    }
   ],
   "source": [
    "def clustering(pcd_objects):\n",
    "    \n",
    "    #eps: maximum distance between two samples for one to be considered as in the neighborhood of the other.\n",
    "    #min_points: number of samples in a neighborhood for a point to be considered as a core point.\n",
    "\n",
    "    cluster_ids = np.array(pcd_objects.cluster_dbscan(eps=0.5, min_points=10, print_progress=True))\n",
    "    \n",
    "    cluster_nb = cluster_ids.max()\n",
    "    colors = plt.get_cmap(\"tab20\")(cluster_ids / (cluster_nb if cluster_nb > 0 else 1))\n",
    "    colors[cluster_ids < 0] = 0\n",
    "    pcd_objects.colors = o3d.utility.Vector3dVector(colors[:, :3])\n",
    "    return pcd_objects, cluster_ids\n",
    "\n",
    "pcd_objects, cluster_ids = clustering(pcd_objects)\n",
    "\n",
    "print(\"The points cloud has\", cluster_ids.max() + 1, \"clusters\")\n",
    "\n",
    "if flag_display:\n",
    "    o3d.visualization.draw_geometries([pcd_objects])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bounding Boxes estimation\n",
    "Estimate for each of these clusters its bounding box. You can use the function _get_axis_aligned_bounding_box()_ or _get_oriented_bounding_box()_ of open3D.\n",
    "\n",
    "What are the limits of these functions ?\n",
    "\n",
    "You can keep only the small bounding boxes correponding to the small objects (such as vehicles or pedestrians and not buildings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cluster_boxes(pcd_objects, cluster_ids):\n",
    "    \n",
    "    # Bounding boxes\n",
    "    boxes = []\n",
    "    for cluster_id in np.unique(cluster_ids):\n",
    "        if cluster_id == -1:\n",
    "            continue\n",
    "        cluster_points = pcd_objects.select_by_index(np.where(cluster_ids == cluster_id)[0])\n",
    "        box = cluster_points.get_axis_aligned_bounding_box()\n",
    "        if box.volume() < 5:  # Adjust the volume threshold based on your requirements\n",
    "            boxes.append(box)\n",
    "    return boxes\n",
    "\n",
    "boxes = cluster_boxes(pcd_objects, cluster_ids)\n",
    "\n",
    "obj_to_display = boxes\n",
    "obj_to_display.append(pcd_objects)\n",
    "\n",
    "if flag_display:\n",
    "    o3d.visualization.draw_geometries(obj_to_display)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Objects detection and tracking on LiDAR scans\n",
    "\n",
    "Applied the previous functions on the different scan to estimate the bounding boxes.\n",
    "\n",
    "### Bounding boxes association\n",
    "\n",
    "Create a function to associate the bounding boxes of the current time to the one of the previous time based the shortest distances of there centers.\n",
    "\n",
    "### Bounding boxes orientation estimation\n",
    "Based on these association estimate the orientation of there motion to correct the orientation of the bounding boxes.\n",
    "\n",
    "### Points cloud mapping\n",
    "Build a map by accumulating the different points cloud and removing the moving objects (keep the cluster id and add a new one for the ground points)\n",
    "\n",
    "### Iterative Closest Point tracking\n",
    "To better associate the clusters you can also use the function _icp()_ of open3D on each cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/ULNmzQK1Kq3JwcFBtu2EhAQAj76mX6lSJdlt0aJFyMrKQlpaWrHW+TQzZszAmTNn4O7ujiZNmuCLL754ZuC+efMmMjMzixx3Xl4erly58sxtP0tISAiqVauGSZMmQQhRYHn+vi2sjlq1ahXY92ZmZqhSpUqh23qe50pSUhL69OmDChUqwMbGBpUqVUKrVq0AoMCxUiokJASXLl3CwYMHAQDr169HZmam9NFcYmIihBD4/PPPCzxPJk6cCODRFzgAZa+tohhiH3t5eWHEiBFYtGgRHB0dERQUhAULFui9b14EPKeJ9Hb16lWkpaWhWrVqRfaxtLTEvn37sGfPHmzevBnR0dFYsWIF2rRpg+3btz9zFiN/HYZW1P/ScnNzFdVkCEVtp7A/MqVl7ty5+O233/Drr7+ifv36smWurq6y+5GRkejTp0+J1FHUvinLffasbef/R2DmzJkF9l2+J88Lep7xdOvWDS1atMC6deuwfft2zJw5E9OnT8fatWvRoUOHZz6+JOXPNvXp0wcbNmx47vVpNJoCgfvxbRWnPX/f5ubmom3btkhNTcWnn36KWrVqwdraGv/++y/69Omj98x4z549MWbMGCxbtgxNmzbFsmXL4ODggI4dOwL43/Nk1KhRCAoKKnQdT3tPLSlF7eNZs2ZJx3H79u0YOnQowsPDcejQoSKD7IuMoYn09ssvvwBAkS/8fCYmJggICEBAQABmz56NqVOnYvz48dizZw8CAwMNfqXd/P/x5xNCIDExEXXr1pXaHBwccPfu3QKP/eeff2QfWxSnNg8PD+zcuRP37t2TzTadP39eWm4IHh4eOHXqFPLy8mRvcs+7nT///BOjRo3CsGHDpP8VP27Hjh2y+/knulatWhVnzpwpk5qLkv+/+ceP34ULFwBA+ijHw8MD8fHxBR6rb035H7VqtdoCJ9U/j6c9B11dXTFo0CAMGjQIN27cQMOGDfHVV18VGZoqVaoEKyurIsdtYmJSYIZGX++//z6mTJmCL7/8Em+//bZsWf6+jY+PR5s2bWTL4uPjDf58KMzp06dx4cIFLFmyBL1795ban3yeF5ebmxtat26NVatW4fPPP8eOHTvQp08fabY8//3F3Nz8mc8TJa+top4fhtzHvr6+8PX1xWeffYaDBw+iWbNmiIiIwJQpUxSv40XBj+dIL7t378bkyZPh5eVV6B/YfKmpqQXa8v8XnpWVBQCwtrYGgEJDjD5+/vln2XlWq1evxvXr12V/SKpWrYpDhw4hOztbatu0aVOBjyaKU1vHjh2Rm5uLb7/9Vtb+zTffQKVSGex//x07dkRycjJWrFghteXk5GD+/PmwsbGRPl4ojuvXr6Nbt25o3rw5Zs6cWWifwMBA2S1/5qlr1644efJkodfmyf9ffUnU/DTXrl2T1aPT6fDzzz+jfv36cHFxkWo6cuQIYmJipH4ZGRn44Ycf4OnpCR8fn2Jts1GjRqhatSq+/vprpKenF1ief92j4rK2ti7w/MvNzS3wEYmTkxPc3Nyk11VhTE1N0a5dO2zYsEH2cXVKSgqWLVuG5s2bQ6vV6lVnYdv67LPPEBsbi40bN8qWNW7cGE5OToiIiJDVu3XrVpw7dw7BwcEGqeFZ9QHyWT0hBObOnfvc6w4JCcGNGzfw0Ucf4eHDh7L3SCcnJ7zxxhtYuHAhrl+/XuCxjz9PlLy2inqPMsQ+1ul0yMnJkbX5+vrCxMTkqc+zFxlnmuiZtm7divPnzyMnJwcpKSnYvXs3duzYAQ8PD2zcuPGpF7OcNGkS9u3bh+DgYHh4eODGjRv47rvvUKVKFekE3KpVq8Le3h4RERGwtbWFtbU1/Pz84OXlpVe9FSpUQPPmzdG3b1+kpKRgzpw5qFatmuyyCB9++CFWr16N9u3bo1u3brh48SJ+/fVX2YnZxa3trbfeQuvWrTF+/HhcvnwZ9erVw/bt27FhwwYMGzaswLr1NWDAACxcuBB9+vTB8ePH4enpidWrV+PAgQOYM2fOU88xK8rQoUNx8+ZNjBkzRnYSK/DoBPXHZ+meNHr0aKxevRrvvfce+vXrh0aNGiE1NRUbN25EREQE6tWrVyI1P02NGjUQFhaGo0ePwtnZGT/99BNSUlIQGRkp9Rk7dix+++03dOjQAUOHDkWFChWwZMkSXLp0CWvWrCny46CimJiYYNGiRejQoQNq166Nvn37onLlyvj333+xZ88eaLVa/P7778UeS6NGjbBz507Mnj0bbm5u8PLyQs2aNVGlShW8++67qFevHmxsbLBz504cPXoUs2bNeur6pkyZgh07dqB58+YYNGgQzMzMsHDhQmRlZWHGjBnFru9pQkJCMHny5AI/8WFubo7p06ejb9++aNWqFXr27Cl9Hd7T0xPDhw83aB2FqVWrFqpWrYpRo0bh33//hVarxZo1axSdT/YsXbt2xaBBg7Bhwwa4u7ujZcuWsuULFixA8+bN4evri/79++PVV19FSkoKYmJicPXqVZw8eRKAstfW096jnncf7969G0OGDMF7772HGjVqICcnB7/88gtMTU3RtWvX595P5VIZfGOPyon8Sw7k39RqtXBxcRFt27YVc+fOlX2dN9+TX83dtWuX6NSpk3BzcxNqtVq4ubmJnj17igsXLsget2HDBuHj4yPMzMwKvbhlYYq65MBvv/0mxo0bJ5ycnISlpaUIDg6WfX0436xZs6QLFTZr1kwcO3aswDqfVlthF7e8d++eGD58uHBzcxPm5uaievXqT7245ZOKuhTCk1JSUkTfvn2Fo6OjUKvVwtfXt9DLIii95ED+pR0Kuym5uOXt27fFkCFDROXKlaWLBIaGhopbt24Vq+aivuZe1E/kFHZZjMcvblm3bl2h0WhErVq1Cv3adP7FLe3t7YWFhYVo0qRJkRe3fPLx+bU+OYYTJ06ILl26iIoVKwqNRiM8PDxEt27dxK5du6Q+RV3sMX88j38t/vz586Jly5bC0tJSurhlVlaWGD16tKhXr550gcp69eqJ7777rsAYC/PXX3+JoKAgYWNjI6ysrETr1q3FwYMHCx1fcS858KTH30eeHO+KFStEgwYNhEajERUqVHjqhRcLU9jrqDjPobNnz4rAwEBhY2MjHB0dRf/+/aVLfzx+XJVecuBx7733ngAgxowZU+jyixcvit69ewsXFxdhbm4uKleuLN58802xevVqWT8lr62i3qOEeL59/Pfff4t+/fqJqlWrCgsLC1GhQgXRunVrsXPnzmLtixeJSogyPOuUiMjAPD09UadOHWzatKmsSyGiFwzPaSIiIiJSgKGJiIiISAGGJiIiIiIFeE4TERERkQKcaSIiIiJSgKGJiIiISAFe3NJA8vLycO3aNdja2hr8Z0GIiIioZAghcO/ePbi5uT3zorYMTQZy7do1g/1mExEREZWuK1euPPNHiBmaDCT/ZyCuXLlisN9uIiIiopKl0+ng7u6u6OecGJoMJP8jOa1Wy9BERERUzig5tYYnghMREREpwNBEREREpABDExEREZECDE1ERERECjA0ERERESnA0ERERESkAEMTERERkQIMTUREREQKMDQRERERKcDQRERERKQAQxMRERGRAgxNRERERAowNBEREREpwNBEREREpABDExEREZECZmVdACnjOXbzM/tcnhZcCpUQERG9nDjTRERERKQAQxMRERGRAgxNRERERAowNBEREREpwNBEREREpABDExEREZECDE1ERERECjA0ERERESnA0ERERESkAEMTERERkQIMTUREREQKMDQRERERKcDQRERERKQAQxMRERGRAgxNRERERAowNBEREREpwNBEREREpABDExEREZECDE1ERERECpRpaNq3bx/eeustuLm5QaVSYf369bLlQghMmDABrq6usLS0RGBgIBISEmR9UlNTERISAq1WC3t7e4SFhSE9PV3W59SpU2jRogUsLCzg7u6OGTNmFKhl1apVqFWrFiwsLODr64stW7YYfLxERERUfpVpaMrIyEC9evWwYMGCQpfPmDED8+bNQ0REBA4fPgxra2sEBQXhwYMHUp+QkBDExcVhx44d2LRpE/bt24cBAwZIy3U6Hdq1awcPDw8cP34cM2fOxBdffIEffvhB6nPw4EH07NkTYWFhOHHiBDp37ozOnTvjzJkzJTd4IiIiKldUQghR1kUAgEqlwrp169C5c2cAj2aZ3NzcMHLkSIwaNQoAkJaWBmdnZ0RFRaFHjx44d+4cfHx8cPToUTRu3BgAEB0djY4dO+Lq1atwc3PD999/j/HjxyM5ORlqtRoAMHbsWKxfvx7nz58HAHTv3h0ZGRnYtGmTVM/rr7+O+vXrIyIiQlH9Op0OdnZ2SEtLg1arNdRukXiO3fzMPpenBRt8u0RERC+y4vz9Ntpzmi5duoTk5GQEBgZKbXZ2dvDz80NMTAwAICYmBvb29lJgAoDAwECYmJjg8OHDUp+WLVtKgQkAgoKCEB8fjzt37kh9Ht9Ofp/87RARERGZlXUBRUlOTgYAODs7y9qdnZ2lZcnJyXBycpItNzMzQ4UKFWR9vLy8Cqwjf5mDgwOSk5Ofup3CZGVlISsrS7qv0+mKMzwiIiIqZ4x2psnYhYeHw87OTrq5u7uXdUlERERUgow2NLm4uAAAUlJSZO0pKSnSMhcXF9y4cUO2PCcnB6mpqbI+ha3j8W0U1Sd/eWHGjRuHtLQ06XblypXiDpGIiIjKEaMNTV5eXnBxccGuXbukNp1Oh8OHD8Pf3x8A4O/vj7t37+L48eNSn927dyMvLw9+fn5Sn3379uHhw4dSnx07dqBmzZpwcHCQ+jy+nfw++dspjEajgVarld2IiIjoxVWmoSk9PR2xsbGIjY0F8Ojk79jYWCQlJUGlUmHYsGGYMmUKNm7ciNOnT6N3795wc3OTvmHn7e2N9u3bo3///jhy5AgOHDiAIUOGoEePHnBzcwMA9OrVC2q1GmFhYYiLi8OKFSswd+5cjBgxQqrjk08+QXR0NGbNmoXz58/jiy++wLFjxzBkyJDS3iVERERkpMr0RPBjx46hdevW0v38IBMaGoqoqCiMGTMGGRkZGDBgAO7evYvmzZsjOjoaFhYW0mOWLl2KIUOGICAgACYmJujatSvmzZsnLbezs8P27dsxePBgNGrUCI6OjpgwYYLsWk5NmzbFsmXL8Nlnn+G///0vqlevjvXr16NOnTqlsBeIiIioPDCa6zSVd7xOExERUfnzQlyniYiIiMiYMDQRERERKcDQRERERKQAQxMRERGRAgxNRERERAowNBEREREpwNBEREREpABDExEREZECDE1ERERECjA0ERERESnA0ERERESkAEMTERERkQIMTUREREQKMDQRERERKcDQRERERKQAQxMRERGRAgxNRERERAowNBEREREpwNBEREREpABDExEREZECDE1ERERECjA0ERERESnA0ERERESkAEMTERERkQIMTUREREQKMDQRERERKcDQRERERKQAQxMRERGRAgxNRERERAowNBEREREpwNBEREREpABDExEREZECDE1ERERECjA0ERERESnA0ERERESkAEMTERERkQIMTUREREQKMDQRERERKcDQRERERKQAQxMRERGRAgxNRERERAowNBEREREpwNBEREREpABDExEREZECDE1ERERECjA0ERERESnA0ERERESkAEMTERERkQIMTUREREQKMDQRERERKcDQRERERKQAQxMRERGRAgxNRERERAowNBEREREpwNBEREREpIBRh6bc3Fx8/vnn8PLygqWlJapWrYrJkydDCCH1EUJgwoQJcHV1haWlJQIDA5GQkCBbT2pqKkJCQqDVamFvb4+wsDCkp6fL+pw6dQotWrSAhYUF3N3dMWPGjFIZIxEREZUPRh2apk+fju+//x7ffvstzp07h+nTp2PGjBmYP3++1GfGjBmYN28eIiIicPjwYVhbWyMoKAgPHjyQ+oSEhCAuLg47duzApk2bsG/fPgwYMEBartPp0K5dO3h4eOD48eOYOXMmvvjiC/zwww+lOl4iIiIyXirx+LSNkXnzzTfh7OyMxYsXS21du3aFpaUlfv31Vwgh4ObmhpEjR2LUqFEAgLS0NDg7OyMqKgo9evTAuXPn4OPjg6NHj6Jx48YAgOjoaHTs2BFXr16Fm5sbvv/+e4wfPx7JyclQq9UAgLFjx2L9+vU4f/68olp1Oh3s7OyQlpYGrVZr4D0BeI7d/Mw+l6cFG3y7REREL7Li/P026pmmpk2bYteuXbhw4QIA4OTJk9i/fz86dOgAALh06RKSk5MRGBgoPcbOzg5+fn6IiYkBAMTExMDe3l4KTAAQGBgIExMTHD58WOrTsmVLKTABQFBQEOLj43Hnzp1Ca8vKyoJOp5PdiIiI6MVlVtYFPM3YsWOh0+lQq1YtmJqaIjc3F1999RVCQkIAAMnJyQAAZ2dn2eOcnZ2lZcnJyXBycpItNzMzQ4UKFWR9vLy8Cqwjf5mDg0OB2sLDw/Hll18aYJRERERUHhj1TNPKlSuxdOlSLFu2DH/99ReWLFmCr7/+GkuWLCnr0jBu3DikpaVJtytXrpR1SURERFSCjHqmafTo0Rg7dix69OgBAPD19cU///yD8PBwhIaGwsXFBQCQkpICV1dX6XEpKSmoX78+AMDFxQU3btyQrTcnJwepqanS411cXJCSkiLrk38/v8+TNBoNNBrN8w+SiIiIygWjnmnKzMyEiYm8RFNTU+Tl5QEAvLy84OLigl27dknLdTodDh8+DH9/fwCAv78/7t69i+PHj0t9du/ejby8PPj5+Ul99u3bh4cPH0p9duzYgZo1axb60RwRERG9fIw6NL311lv46quvsHnzZly+fBnr1q3D7Nmz8c477wAAVCoVhg0bhilTpmDjxo04ffo0evfuDTc3N3Tu3BkA4O3tjfbt26N///44cuQIDhw4gCFDhqBHjx5wc3MDAPTq1QtqtRphYWGIi4vDihUrMHfuXIwYMaKshk5ERERGxqg/nps/fz4+//xzDBo0CDdu3ICbmxs++ugjTJgwQeozZswYZGRkYMCAAbh79y6aN2+O6OhoWFhYSH2WLl2KIUOGICAgACYmJujatSvmzZsnLbezs8P27dsxePBgNGrUCI6OjpgwYYLsWk5ERET0cjPq6zSVJ7xOExERUfnzwlyniYiIiMhYMDQRERERKcDQRERERKQAQxMRERGRAgxNRERERAowNBEREREpwNBEREREpABDExEREZECDE1ERERECjA0ERERESnA0ERERESkAEMTERERkQIMTUREREQKMDQRERERKcDQRERERKQAQxMRERGRAgxNRERERAowNBEREREpwNBEREREpABDExEREZECDE1ERERECjA0ERERESnA0ERERESkAEMTERERkQIMTUREREQKMDQRERERKcDQRERERKQAQxMRERGRAgxNRERERAowNBEREREpwNBEREREpABDExEREZECDE1ERERECjA0ERERESnA0ERERESkAEMTERERkQIMTUREREQK6BWa/v77b0PXQURERGTU9ApN1apVQ+vWrfHrr7/iwYMHhq6JiIiIyOjoFZr++usv1K1bFyNGjICLiws++ugjHDlyxNC1ERERERkNvUJT/fr1MXfuXFy7dg0//fQTrl+/jubNm6NOnTqYPXs2bt68aeg6iYiIiMrUc50IbmZmhi5dumDVqlWYPn06EhMTMWrUKLi7u6N37964fv26oeokIiIiKlPPFZqOHTuGQYMGwdXVFbNnz8aoUaNw8eJF7NixA9euXUOnTp0MVScRERFRmTLT50GzZ89GZGQk4uPj0bFjR/z888/o2LEjTEweZTAvLy9ERUXB09PTkLXSM3iO3fzMPpenBZdCJURERC8evULT999/j379+qFPnz5wdXUttI+TkxMWL178XMURERERGQu9QlNCQsIz+6jVaoSGhuqzeiIiIiKjo9c5TZGRkVi1alWB9lWrVmHJkiXPXRQRERGRsdErNIWHh8PR0bFAu5OTE6ZOnfrcRREREREZG71CU1JSEry8vAq0e3h4ICkp6bmLIiIiIjI2eoUmJycnnDp1qkD7yZMnUbFixecuioiIiMjY6BWaevbsiaFDh2LPnj3Izc1Fbm4udu/ejU8++QQ9evQwdI1EREREZU6vb89NnjwZly9fRkBAAMzMHq0iLy8PvXv35jlNRERE9ELSKzSp1WqsWLECkydPxsmTJ2FpaQlfX194eHgYuj4iIiIio6BXaMpXo0YN1KhRw1C1EBERERktvc5pys3NxeLFi9GrVy8EBgaiTZs2spsh/fvvv3j//fdRsWJFaUbr2LFj0nIhBCZMmABXV1dYWloiMDCwwMU3U1NTERISAq1WC3t7e4SFhSE9PV3W59SpU2jRogUsLCzg7u6OGTNmGHQcREREVL7pNdP0ySefICoqCsHBwahTpw5UKpWh6wIA3LlzB82aNUPr1q2xdetWVKpUCQkJCXBwcJD6zJgxA/PmzcOSJUvg5eWFzz//HEFBQTh79iwsLCwAACEhIbh+/Tp27NiBhw8fom/fvhgwYACWLVsGANDpdGjXrh0CAwMRERGB06dPo1+/frC3t8eAAQNKZGxERERUvqiEEKK4D3J0dJR+pLckjR07FgcOHMCff/5Z6HIhBNzc3DBy5EiMGjUKAJCWlgZnZ2dERUWhR48eOHfuHHx8fHD06FE0btwYABAdHY2OHTvi6tWrcHNzw/fff4/x48cjOTkZarVa2vb69etx/vx5RbXqdDrY2dkhLS0NWq3WAKOXU/JjvErwB3uJiIj+pzh/v/X6eE6tVqNatWp6FVccGzduROPGjfHee+/ByckJDRo0wI8//igtv3TpEpKTkxEYGCi12dnZwc/PDzExMQCAmJgY2NvbS4EJAAIDA2FiYoLDhw9LfVq2bCkFJgAICgpCfHw87ty5U2htWVlZ0Ol0shsRERG9uPQKTSNHjsTcuXOhxyRVsfz999/4/vvvUb16dWzbtg0ff/wxhg4dKv2+XXJyMgDA2dlZ9jhnZ2dpWXJyMpycnGTLzczMUKFCBVmfwtbx+DaeFB4eDjs7O+nm7u7+nKMlIiIiY6bXOU379+/Hnj17sHXrVtSuXRvm5uay5WvXrjVIcXl5eWjcuLF07acGDRrgzJkziIiIQGhoqEG2oa9x48ZhxIgR0n2dTsfgRERE9ALTKzTZ29vjnXfeMXQtBbi6usLHx0fW5u3tjTVr1gAAXFxcAAApKSlwdXWV+qSkpKB+/fpSnxs3bsjWkZOTg9TUVOnxLi4uSElJkfXJv5/f50kajQYajUbPkREREVF5o1doioyMNHQdhWrWrBni4+NlbRcuXJAuounl5QUXFxfs2rVLCkk6nQ6HDx/Gxx9/DADw9/fH3bt3cfz4cTRq1AgAsHv3buTl5cHPz0/qM378eDx8+FCaNduxYwdq1qwp+6YeERERvbz0OqcJeDRbs3PnTixcuBD37t0DAFy7dq3A9Y+ex/Dhw3Ho0CFMnToViYmJWLZsGX744QcMHjwYAKBSqTBs2DBMmTIFGzduxOnTp9G7d2+4ubmhc+fOAB7NTLVv3x79+/fHkSNHcODAAQwZMgQ9evSAm5sbAKBXr15Qq9UICwtDXFwcVqxYgblz58o+fiMiIqKXm14zTf/88w/at2+PpKQkZGVloW3btrC1tcX06dORlZWFiIgIgxT32muvYd26dRg3bhwmTZoELy8vzJkzByEhIVKfMWPGICMjAwMGDMDdu3fRvHlzREdHS9doAoClS5diyJAhCAgIgImJCbp27Yp58+ZJy+3s7LB9+3YMHjwYjRo1gqOjIyZMmMBrNBEREZFEr+s0de7cGba2tli8eDEqVqyIkydP4tVXX8XevXvRv3//AlfkfhnwOk1ERETlT3H+fus10/Tnn3/i4MGDsusaAYCnpyf+/fdffVZJREREZNT0OqcpLy8Pubm5BdqvXr0KW1vb5y6KiIiIyNjoFZratWuHOXPmSPdVKhXS09MxceLEEv9pFSIiIqKyoNfHc7NmzUJQUBB8fHzw4MED9OrVCwkJCXB0dMRvv/1m6BqJiIiIypxeoalKlSo4efIkli9fjlOnTiE9PR1hYWEICQmBpaWloWskIiIiKnN6hSbg0e+3vf/++4ashYiIiMho6RWafv7556cu7927t17FEBERERkrvULTJ598Irv/8OFDZGZmQq1Ww8rKiqGJiIiIXjh6fXvuzp07slt6ejri4+PRvHlznghORERELyS9f3vuSdWrV8e0adMKzEIRERERvQgMFpqARyeHX7t2zZCrJCIiIjIKep3TtHHjRtl9IQSuX7+Ob7/9Fs2aNTNIYURERETGRK/Q1LlzZ9l9lUqFSpUqoU2bNpg1a5Yh6iIiIiIyKnqFpry8PEPXQURERGTUDHpOExEREdGLSq+ZphEjRijuO3v2bH02QURERGRU9ApNJ06cwIkTJ/Dw4UPUrFkTAHDhwgWYmpqiYcOGUj+VSmWYKomIiIjKmF6h6a233oKtrS2WLFkCBwcHAI8ueNm3b1+0aNECI0eONGiRRERERGVNr3OaZs2ahfDwcCkwAYCDgwOmTJnCb88RERHRC0mv0KTT6XDz5s0C7Tdv3sS9e/eeuygiIiIiY6NXaHrnnXfQt29frF27FlevXsXVq1exZs0ahIWFoUuXLoaukYiIiKjM6XVOU0REBEaNGoVevXrh4cOHj1ZkZoawsDDMnDnToAUSERERGQO9QpOVlRW+++47zJw5ExcvXgQAVK1aFdbW1gYtjoiIiMhYPNfFLa9fv47r16+jevXqsLa2hhDCUHURERERGRW9QtPt27cREBCAGjVqoGPHjrh+/ToAICwsjJcbICIioheSXqFp+PDhMDc3R1JSEqysrKT27t27Izo62mDFERERERkLvc5p2r59O7Zt24YqVarI2qtXr45//vnHIIURERERGRO9ZpoyMjJkM0z5UlNTodFonrsoIiIiImOjV2hq0aIFfv75Z+m+SqVCXl4eZsyYgdatWxusOCIiIiJjodfHczNmzEBAQACOHTuG7OxsjBkzBnFxcUhNTcWBAwcMXSMRERFRmdNrpqlOnTq4cOECmjdvjk6dOiEjIwNdunTBiRMnULVqVUPXSERERFTmij3T9PDhQ7Rv3x4REREYP358SdREREREZHSKPdNkbm6OU6dOlUQtREREREZLr4/n3n//fSxevNjQtRAREREZLb1OBM/JycFPP/2EnTt3olGjRgV+c2727NkGKY6IiIjIWBQrNP3999/w9PTEmTNn0LBhQwDAhQsXZH1UKpXhqiMiIiIyEsUKTdWrV8f169exZ88eAI9+NmXevHlwdnYukeKIiIiIjEWxzmkSQsjub926FRkZGQYtiIiIiMgY6XUieL4nQxQRERHRi6pYoUmlUhU4Z4nnMBEREdHLoFjnNAkh0KdPH+lHeR88eICBAwcW+Pbc2rVrDVchERERkREoVmgKDQ2V3X///fcNWgwRERGRsSpWaIqMjCypOoiIiIiM2nOdCE5ERET0smBoIiIiIlKAoYmIiIhIAYYmIiIiIgUYmoiIiIgUYGgiIiIiUoChiYiIiEgBhiYiIiIiBRiaiIiIiBRgaCIiIiJSgKGJiIiISAGGJiIiIiIFylVomjZtGlQqFYYNGya1PXjwAIMHD0bFihVhY2ODrl27IiUlRfa4pKQkBAcHw8rKCk5OThg9ejRycnJkffbu3YuGDRtCo9GgWrVqiIqKKoURERERUXlRbkLT0aNHsXDhQtStW1fWPnz4cPz+++9YtWoV/vjjD1y7dg1dunSRlufm5iI4OBjZ2dk4ePAglixZgqioKEyYMEHqc+nSJQQHB6N169aIjY3FsGHD8OGHH2Lbtm2lNj4iIiIybuUiNKWnpyMkJAQ//vgjHBwcpPa0tDQsXrwYs2fPRps2bdCoUSNERkbi4MGDOHToEABg+/btOHv2LH799VfUr18fHTp0wOTJk7FgwQJkZ2cDACIiIuDl5YVZs2bB29sbQ4YMwbvvvotvvvmmTMZLRERExqdchKbBgwcjODgYgYGBsvbjx4/j4cOHsvZatWrhlVdeQUxMDAAgJiYGvr6+cHZ2lvoEBQVBp9MhLi5O6vPkuoOCgqR1FCYrKws6nU52IyIioheXWVkX8CzLly/HX3/9haNHjxZYlpycDLVaDXt7e1m7s7MzkpOTpT6PB6b85fnLntZHp9Ph/v37sLS0LLDt8PBwfPnll3qPi4iIiMoXo55punLlCj755BMsXboUFhYWZV2OzLhx45CWlibdrly5UtYlERERUQky6tB0/Phx3LhxAw0bNoSZmRnMzMzwxx9/YN68eTAzM4OzszOys7Nx9+5d2eNSUlLg4uICAHBxcSnwbbr8+8/qo9VqC51lAgCNRgOtViu7ERER0YvLqENTQEAATp8+jdjYWOnWuHFjhISESP82NzfHrl27pMfEx8cjKSkJ/v7+AAB/f3+cPn0aN27ckPrs2LEDWq0WPj4+Up/H15HfJ38dREREREZ9TpOtrS3q1Kkja7O2tkbFihWl9rCwMIwYMQIVKlSAVqvFf/7zH/j7++P1118HALRr1w4+Pj744IMPMGPGDCQnJ+Ozzz7D4MGDodFoAAADBw7Et99+izFjxqBfv37YvXs3Vq5cic2bN5fugImIiMhoGXVoUuKbb76BiYkJunbtiqysLAQFBeG7776TlpuammLTpk34+OOP4e/vD2tra4SGhmLSpElSHy8vL2zevBnDhw/H3LlzUaVKFSxatAhBQUFlMSQiIiIyQiohhCjrIl4EOp0OdnZ2SEtLK5HzmzzHGmbW6/K0YIOsh4iI6EVQnL/fRn1OExEREZGxYGgiIiIiUoChiYiIiEgBhiYiIiIiBRiaiIiIiBRgaCIiIiJSgKGJiIiISAGGJiIiIiIFGJqIiIiIFGBoIiIiIlKAoYmIiIhIAYYmIiIiIgUYmoiIiIgUYGgiIiIiUoChiYiIiEgBhiYiIiIiBRiaiIiIiBRgaCIiIiJSgKGJiIiISAGGJiIiIiIFGJqIiIiIFGBoIiIiIlKAoYmIiIhIAYYmIiIiIgUYmoiIiIgUYGgiIiIiUoChiYiIiEgBhiYiIiIiBRiaiIiIiBRgaCIiIiJSgKGJiIiISAGGJiIiIiIFzMq6ACpdnmM3P7PP5WnBpVAJERFR+cKZJiIiIiIFGJqIiIiIFGBoIiIiIlKAoYmIiIhIAYYmIiIiIgUYmoiIiIgUYGgiIiIiUoChiYiIiEgBhiYiIiIiBRiaiIiIiBRgaCIiIiJSgKGJiIiISAGGJiIiIiIFGJqIiIiIFGBoIiIiIlKAoYmIiIhIAYYmIiIiIgUYmoiIiIgUYGgiIiIiUoChiYiIiEgBhiYiIiIiBYw6NIWHh+O1116Dra0tnJyc0LlzZ8THx8v6PHjwAIMHD0bFihVhY2ODrl27IiUlRdYnKSkJwcHBsLKygpOTE0aPHo2cnBxZn71796Jhw4bQaDSoVq0aoqKiSnp4REREVI4YdWj6448/MHjwYBw6dAg7duzAw4cP0a5dO2RkZEh9hg8fjt9//x2rVq3CH3/8gWvXrqFLly7S8tzcXAQHByM7OxsHDx7EkiVLEBUVhQkTJkh9Ll26hODgYLRu3RqxsbEYNmwYPvzwQ2zbtq1Ux0tERETGSyWEEGVdhFI3b96Ek5MT/vjjD7Rs2RJpaWmoVKkSli1bhnfffRcAcP78eXh7eyMmJgavv/46tm7dijfffBPXrl2Ds7MzACAiIgKffvopbt68CbVajU8//RSbN2/GmTNnpG316NEDd+/eRXR0tKLadDod7OzskJaWBq1Wa/Cxe47dbPB1FuXytOBS2xYREVFZKs7fb6OeaXpSWloaAKBChQoAgOPHj+Phw4cIDAyU+tSqVQuvvPIKYmJiAAAxMTHw9fWVAhMABAUFQafTIS4uTurz+Dry++SvozBZWVnQ6XSyGxEREb24yk1oysvLw7Bhw9CsWTPUqVMHAJCcnAy1Wg17e3tZX2dnZyQnJ0t9Hg9M+cvzlz2tj06nw/379wutJzw8HHZ2dtLN3d39ucdIRERExqvchKbBgwfjzJkzWL58eVmXAgAYN24c0tLSpNuVK1fKuiQiIiIqQWZlXYASQ4YMwaZNm7Bv3z5UqVJFandxcUF2djbu3r0rm21KSUmBi4uL1OfIkSOy9eV/u+7xPk9+4y4lJQVarRaWlpaF1qTRaKDRaJ57bERERFQ+GPVMkxACQ4YMwbp167B79254eXnJljdq1Ajm5ubYtWuX1BYfH4+kpCT4+/sDAPz9/XH69GncuHFD6rNjxw5otVr4+PhIfR5fR36f/HUQERERGfVM0+DBg7Fs2TJs2LABtra20jlIdnZ2sLS0hJ2dHcLCwjBixAhUqFABWq0W//nPf+Dv74/XX38dANCuXTv4+Pjggw8+wIwZM5CcnIzPPvsMgwcPlmaKBg4ciG+//RZjxoxBv379sHv3bqxcuRKbN5feN9aIiIjIuBl1aPr+++8BAG+88YasPTIyEn369AEAfPPNNzAxMUHXrl2RlZWFoKAgfPfdd1JfU1NTbNq0CR9//DH8/f1hbW2N0NBQTJo0Serj5eWFzZs3Y/jw4Zg7dy6qVKmCRYsWISgoqMTHSERERMourVPWl8QpV9dpMma8ThMREZH+yio0vbDXaSIiIiIqKwxNRERERAowNBEREREpwNBEREREpABDExEREZECDE1ERERECjA0ERERESnA0ERERESkAEMTERERkQIMTUREREQKMDQRERERKcDQRERERKQAQxMRERGRAgxNRERERAowNBEREREpwNBEREREpIBZWRdA9LLyHLv5mX0uTwsuhUqIiEgJhiaiEqAkEBERUfnCj+eIiIiIFGBoIiIiIlKAoYmIiIhIAYYmIiIiIgV4IjiREeM37IiIjAdnmoiIiIgU4EwTUTHxcgJERC8nzjQRERERKcDQRERERKQAQxMRERGRAjyniQp4mb+xxfOViIioKJxpIiIiIlKAoYmIiIhIAYYmIiIiIgUYmoiIiIgUYGgiIiIiUoDfniOil97L/I1RIlKOoYmonOMffCKi0sHQRESkAMMpETE00UuDF64kIqLnwRPBiYiIiBRgaCIiIiJSgKGJiIiISAGe00RERER6e5m+JMGZJiIiIiIFONNELwR+M46IiEoaQxPRS+Blmj4nIiopDE1ERAbCcEpUuBfl0wCe00RERESkAGeaiOiF9qL8D5eIyh5nmoiIiIgU4EwTGT3OFJQOno9DRE/i+68cQxPphX9giYjoZcOP54iIiIgU4EwTESlmbDOM5fGjA2Pbh0SkHEMTlZjy+AeNjMfL/PxhsKLS8DK/xvTF0PSEBQsWYObMmUhOTka9evUwf/58NGnSpKzLInqh8M2aiMojhqbHrFixAiNGjEBERAT8/PwwZ84cBAUFIT4+Hk5OTmVdHlG5wEBUOjgb9eLha8f4MTQ9Zvbs2ejfvz/69u0LAIiIiMDmzZvx008/YezYsWVcHRFR8ZTHP8IMemTMGJr+X3Z2No4fP45x48ZJbSYmJggMDERMTEwZVkZE9PIwtqBnqBBnbOMi/TA0/b9bt24hNzcXzs7OsnZnZ2ecP3++QP+srCxkZWVJ99PS0gAAOp2uROrLy8oskfUSEVHRXhm+qqxLoMeUxN/Y/HUKIZ7Zl6FJT+Hh4fjyyy8LtLu7u5dBNURERC8+uzklt+579+7Bzs7uqX0Ymv6fo6MjTE1NkZKSImtPSUmBi4tLgf7jxo3DiBEjpPt5eXlITU1FxYoVoVKpDFqbTqeDu7s7rly5Aq1Wa9B1GwOOr/x70cf4oo8PePHHyPGVfyU1RiEE7t27Bzc3t2f2ZWj6f2q1Go0aNcKuXbvQuXNnAI+C0K5duzBkyJAC/TUaDTQajazN3t6+RGvUarUv7IsB4PheBC/6GF/08QEv/hg5vvKvJMb4rBmmfAxNjxkxYgRCQ0PRuHFjNGnSBHPmzEFGRob0bToiIiJ6eTE0PaZ79+64efMmJkyYgOTkZNSvXx/R0dEFTg4nIiKilw9D0xOGDBlS6MdxZUmj0WDixIkFPg58UXB85d+LPsYXfXzAiz9Gjq/8M4YxqoSS79gRERERveRMyroAIiIiovKAoYmIiIhIAYYmIiIiIgUYmoiIiIgUYGgyAl999RWaNm0KKysrxRfIFEJgwoQJcHV1haWlJQIDA5GQkCDrk5qaipCQEGi1Wtjb2yMsLAzp6eklMIKnK24dly9fhkqlKvS2atX/fgeqsOXLly8vjSEVoM++fuONNwrUP3DgQFmfpKQkBAcHw8rKCk5OThg9ejRycnJKciiFKu74UlNT8Z///Ac1a9aEpaUlXnnlFQwdOlT6jcZ8ZXkMFyxYAE9PT1hYWMDPzw9Hjhx5av9Vq1ahVq1asLCwgK+vL7Zs2SJbruQ1WZqKM74ff/wRLVq0gIODAxwcHBAYGFigf58+fQocq/bt25f0MIpUnPFFRUUVqN3CwkLWx9iOH1C8MRb2fqJSqRAc/L8fHDamY7hv3z689dZbcHNzg0qlwvr165/5mL1796Jhw4bQaDSoVq0aoqKiCvQp7uu62ASVuQkTJojZs2eLESNGCDs7O0WPmTZtmrCzsxPr168XJ0+eFG+//bbw8vIS9+/fl/q0b99e1KtXTxw6dEj8+eefolq1aqJnz54lNIqiFbeOnJwccf36ddntyy+/FDY2NuLevXtSPwAiMjJS1u/x8ZcmffZ1q1atRP/+/WX1p6WlSctzcnJEnTp1RGBgoDhx4oTYsmWLcHR0FOPGjSvp4RRQ3PGdPn1adOnSRWzcuFEkJiaKXbt2ierVq4uuXbvK+pXVMVy+fLlQq9Xip59+EnFxcaJ///7C3t5epKSkFNr/wIEDwtTUVMyYMUOcPXtWfPbZZ8Lc3FycPn1a6qPkNVlaiju+Xr16iQULFogTJ06Ic+fOiT59+gg7Oztx9epVqU9oaKho37697FilpqaW1pBkiju+yMhIodVqZbUnJyfL+hjT8ROi+GO8ffu2bHxnzpwRpqamIjIyUupjTMdwy5YtYvz48WLt2rUCgFi3bt1T+//999/CyspKjBgxQpw9e1bMnz9fmJqaiujoaKlPcfeZPhiajEhkZKSi0JSXlydcXFzEzJkzpba7d+8KjUYjfvvtNyGEEGfPnhUAxNGjR6U+W7duFSqVSvz7778Gr70ohqqjfv36ol+/frI2JS+00qDvGFu1aiU++eSTIpdv2bJFmJiYyN7cv//+e6HVakVWVpZBalfCUMdw5cqVQq1Wi4cPH0ptZXUMmzRpIgYPHizdz83NFW5ubiI8PLzQ/t26dRPBwcGyNj8/P/HRRx8JIZS9JktTccf3pJycHGFrayuWLFkitYWGhopOnToZulS9FHd8z3pvNbbjJ8TzH8NvvvlG2NraivT0dKnNmI7h45S8D4wZM0bUrl1b1ta9e3cRFBQk3X/efaYEP54rhy5duoTk5GQEBgZKbXZ2dvDz80NMTAwAICYmBvb29mjcuLHUJzAwECYmJjh8+HCp1WqIOo4fP47Y2FiEhYUVWDZ48GA4OjqiSZMm+OmnnyDK4LJjzzPGpUuXwtHREXXq1MG4ceOQmZkpW6+vr6/sivRBQUHQ6XSIi4sz/ECKYKjnUlpaGrRaLczM5NfULe1jmJ2djePHj8tePyYmJggMDJReP0+KiYmR9QceHYv8/kpek6VFn/E9KTMzEw8fPkSFChVk7Xv37oWTkxNq1qyJjz/+GLdv3zZo7UroO7709HR4eHjA3d0dnTp1kr2GjOn4AYY5hosXL0aPHj1gbW0tazeGY6iPZ70GDbHPlOAVwcuh5ORkACjw8y7Ozs7SsuTkZDg5OcmWm5mZoUKFClKf0mCIOhYvXgxvb280bdpU1j5p0iS0adMGVlZW2L59OwYNGoT09HQMHTrUYPUroe8Ye/XqBQ8PD7i5ueHUqVP49NNPER8fj7Vr10rrLewY5y8rLYY4hrdu3cLkyZMxYMAAWXtZHMNbt24hNze30H17/vz5Qh9T1LF4/PWW31ZUn9Kiz/ie9Omnn8LNzU32B6h9+/bo0qULvLy8cPHiRfz3v/9Fhw4dEBMTA1NTU4OO4Wn0GV/NmjXx008/oW7dukhLS8PXX3+Npk2bIi4uDlWqVDGq4wc8/zE8cuQIzpw5g8WLF8vajeUY6qOo16BOp8P9+/dx586d537eK8HQVELGjh2L6dOnP7XPuXPnUKtWrVKqyLCUju953b9/H8uWLcPnn39eYNnjbQ0aNEBGRgZmzpxpsD+4JT3GxwOEr68vXF1dERAQgIsXL6Jq1ap6r1ep0jqGOp0OwcHB8PHxwRdffCFbVtLHkIpv2rRpWL58Ofbu3Ss7WbpHjx7Sv319fVG3bl1UrVoVe/fuRUBAQFmUqpi/vz/8/f2l+02bNoW3tzcWLlyIyZMnl2FlJWPx4sXw9fVFkyZNZO3l+RgaC4amEjJy5Ej06dPnqX1effVVvdbt4uICAEhJSYGrq6vUnpKSgvr160t9bty4IXtcTk4OUlNTpcc/D6Xje946Vq9ejczMTPTu3fuZff38/DB58mRkZWUZ5LeJSmuM+fz8/AAAiYmJqFq1KlxcXAp88yMlJQUAys0xvHfvHtq3bw9bW1usW7cO5ubmT+1v6GNYGEdHR5iamkr7Ml9KSkqR43FxcXlqfyWvydKiz/jyff3115g2bRp27tyJunXrPrXvq6++CkdHRyQmJpbqH9znGV8+c3NzNGjQAImJiQCM6/gBzzfGjIwMLF++HJMmTXrmdsrqGOqjqNegVquFpaUlTE1Nn/t5oYjBzo6i51bcE8G//vprqS0tLa3QE8GPHTsm9dm2bVuZnQiubx2tWrUq8I2rokyZMkU4ODjoXau+DLWv9+/fLwCIkydPCiH+dyL449/8WLhwodBqteLBgweGG8Az6Du+tLQ08frrr4tWrVqJjIwMRdsqrWPYpEkTMWTIEOl+bm6uqFy58lNPBH/zzTdlbf7+/gVOBH/aa7I0FXd8Qggxffp0odVqRUxMjKJtXLlyRahUKrFhw4bnrre49Bnf43JyckTNmjXF8OHDhRDGd/yE0H+MkZGRQqPRiFu3bj1zG2V5DB8HhSeC16lTR9bWs2fPAieCP8/zQlGtBlsT6e2ff/4RJ06ckL5Wf+LECXHixAnZ1+tr1qwp1q5dK92fNm2asLe3Fxs2bBCnTp0SnTp1KvSSAw0aNBCHDx8W+/fvF9WrVy+zSw48rY6rV6+KmjVrisOHD8sel5CQIFQqldi6dWuBdW7cuFH8+OOP4vTp0yIhIUF89913wsrKSkyYMKHEx1OY4o4xMTFRTJo0SRw7dkxcunRJbNiwQbz66quiZcuW0mPyLznQrl07ERsbK6Kjo0WlSpXK7JIDxRlfWlqa8PPzE76+viIxMVH2FeecnBwhRNkew+XLlwuNRiOioqLE2bNnxYABA4S9vb30TcUPPvhAjB07Vup/4MABYWZmJr7++mtx7tw5MXHixEIvOfCs12RpKe74pk2bJtRqtVi9erXsWOW/B927d0+MGjVKxMTEiEuXLomdO3eKhg0biurVq5dqgNd3fF9++aXYtm2buHjxojh+/Ljo0aOHsLCwEHFxcVIfYzp+QhR/jPmaN28uunfvXqDd2I7hvXv3pL91AMTs2bPFiRMnxD///COEEGLs2LHigw8+kPrnX3Jg9OjR4ty5c2LBggWFXnLgafvMEBiajEBoaKgAUOC2Z88eqQ/+/3o2+fLy8sTnn38unJ2dhUajEQEBASI+Pl623tu3b4uePXsKGxsbodVqRd++fWVBrLQ8q45Lly4VGK8QQowbN064u7uL3NzcAuvcunWrqF+/vrCxsRHW1taiXr16IiIiotC+paG4Y0xKShItW7YUFSpUEBqNRlSrVk2MHj1adp0mIYS4fPmy6NChg7C0tBSOjo5i5MiRsq/sl5bijm/Pnj2FPqcBiEuXLgkhyv4Yzp8/X7zyyitCrVaLJk2aiEOHDknLWrVqJUJDQ2X9V65cKWrUqCHUarWoXbu22Lx5s2y5ktdkaSrO+Dw8PAo9VhMnThRCCJGZmSnatWsnKlWqJMzNzYWHh4fo37+/Qf8YFVdxxjds2DCpr7Ozs+jYsaP466+/ZOsztuMnRPGfo+fPnxcAxPbt2wusy9iOYVHvEfljCg0NFa1atSrwmPr16wu1Wi1effVV2d/EfE/bZ4agEqIMvqNNREREVM7wOk1ERERECjA0ERERESnA0ERERESkAEMTERERkQIMTUREREQKMDQRERERKcDQRERERKQAQxMRUSHOnz+P119/HRYWFmXy+2Mlae/evVCpVLh7925Zl0JUrjA0EdEL44svvjBYwJk4cSKsra0RHx+PXbt2FdqnT58+UKlUmDZtmqx9/fr1UKlUBqmjtGVnZ8PR0bHAmPJNnjwZzs7OePjw4XNtx5DHiqi0MDQRERXi4sWLaN68OTw8PFCxYsUi+1lYWGD69Om4c+eOQbefnZ1t0PUppVar8f777yMyMrLAMiEEoqKi0Lt3b5ibm5dBdQWV1X6ilxNDE1EZuHz5MlQqVYHbG2+88dTH/f7773jttddgYWEBR0dHvPPOO9KyO3fuoHfv3nBwcICVlRU6dOiAhIQEaXlUVBTs7e2xadMm1KxZE1ZWVnj33XeRmZmJJUuWwNPTEw4ODhg6dChyc3Olx3l6emLy5Mno2bMnrK2tUblyZSxYsEBWV1JSEjp16gQbGxtotVp069YNKSkp0vL8WYVffvkFnp6esLOzQ48ePXDv3j2pT15eHsLDw+Hl5QVLS0vUq1cPq1evlpbnf6S0a9cuNG7cGFZWVmjatCni4+Ol8X355Zc4efKktD+joqIK3Y95eXmYNGkSqlSpAo1Gg/r16yM6OlparlKpcPz4cUyaNAkqlQpffPFFkcckMDAQLi4uCA8PL7IPAKxZswa1a9eGRqOBp6cnZs2aJVuev5979+4NrVaLAQMG6H3MfvnlFzRu3Bi2trZwcXFBr169cOPGjafW97iwsDBcuHAB+/fvl7X/8ccf+PvvvxEWFgYAWLRoEby9vWFhYYFatWrhu+++k/W/evUqevbsiQoVKsDa2hqNGzfG4cOHn3qslD6XFi1aBC8vL1hYWAAAVq9eDV9fX1haWqJixYoIDAxERkaG4jETKWLQX7IjIkVycnJkvyZ/4sQJUbFiRfH5558X+ZhNmzYJU1NTMWHCBHH27FkRGxsrpk6dKi1/++23hbe3t9i3b5+IjY0VQUFBolq1aiI7O1sIIURkZKQwNzcXbdu2FX/99Zf4448/RMWKFUW7du1Et27dRFxcnPj999+FWq0Wy5cvl9br4eEhbG1tRXh4uIiPjxfz5s0Tpqam0o+C5ubmivr164vmzZuLY8eOiUOHDolGjRrJfmxz4sSJwsbGRnTp0kWcPn1a7Nu3T7i4uIj//ve/Up8pU6aIWrVqiejoaHHx4kURGRkpNBqN2Lt3rxDifz/w6efnJ/bu3Svi4uJEixYtRNOmTYUQj36QdOTIkaJ27drSfs3MzCx0X86ePVtotVrx22+/ifPnz4sxY8YIc3NzceHCBSGEENevXxe1a9cWI0eOFNevXy/yh65DQ0NFp06dxNq1a4WFhYW4cuWKEEKIdevWicffXo8dOyZMTEzEpEmTRHx8vIiMjBSWlpayHxz18PAQWq1WfP311yIxMVEkJibqfcwWL14stmzZIi5evChiYmKEv7+/6NChg7Q8f1/euXOn0HEJIcRrr70m+vbtK2vr3bu3tL9//fVX4erqKtasWSP+/vtvsWbNGlGhQgURFRUlhHj0K/avvvqqaNGihfjzzz9FQkKCWLFihTh48GCRx0rpc8na2lq0b99e/PXXX+LkyZPi2rVrwszMTMyePVtcunRJnDp1SixYsKBMfqCcXmwMTURl7P79+8LPz0+8+eabIjc3t8h+/v7+IiQkpNBlFy5cEADEgQMHpLZbt24JS0tLsXLlSiHEo9AEQCQmJkp9PvroI2FlZSX74xIUFCQ++ugj6b6Hh4do3769bHvdu3eX/ghv375dmJqaiqSkJGl5XFycACCOHDkihHj0h87KykrodDqpz+jRo4Wfn58QQogHDx4IKysrcfDgQdl2wsLCRM+ePYUQ//tDv3PnTmn55s2bBQBx//59aTv16tUrdB89zs3NTXz11Veyttdee00MGjRIul+vXj0xceLEp64nPzQJIcTrr78u+vXrJ4QoGJp69eol2rZtK3vs6NGjhY+Pj3Tfw8NDdO7cWdZH32P2pKNHjwoA0mOUhKaIiAhhY2MjPUan0wkrKyuxaNEiIYQQVatWFcuWLZM9ZvLkycLf318IIcTChQuFra2tuH37dqHrL+xYKX0umZubixs3bkh9jh8/LgCIy5cvFzkeIkPgx3NEZaxfv364d+8eli1bBhOTRy9JGxsb6TZw4EAAQGxsLAICAgpdx7lz52BmZgY/Pz+prWLFiqhZsybOnTsntVlZWaFq1arSfWdnZ3h6esLGxkbW9uRHOf7+/gXu56/33LlzcHd3h7u7u7Tcx8cH9vb2sm17enrC1tZWuu/q6iptJzExEZmZmWjbtq1s7D///DMuXrwo23bdunVl6wBQrI+edDodrl27hmbNmsnamzVrJqu3uKZPn44lS5YUuo5z584Vur2EhATZx2qNGzcu8Fh9jtnx48fx1ltv4ZVXXoGtrS1atWoF4NFHX0r17NkTubm5WLlyJQBgxYoVMDExQffu3ZGRkYGLFy8iLCxMdrymTJkiHa/Y2Fg0aNAAFSpUULxNpc8lDw8PVKpUSbpfr149BAQEwNfXF++99x5+/PFHg59jRgQAZmVdANHLbMqUKdi2bRuOHDkiCxSxsbHSv7VaLQDA0tLyubf35Mm7KpWq0La8vLzn3paSbedvJz09HQCwefNmVK5cWdZPo9EUuZ78b6iVRL3F1bJlSwQFBWHcuHHo06ePXuuwtrYu0FbcY5aRkYGgoCAEBQVh6dKlqFSpEpKSkhAUFFSsk6a1Wi3effddREZGol+/foiMjES3bt1gY2MjnWP0448/yoI6AJiamgIwzPO1KE/uJ1NTU+zYsQMHDx7E9u3bMX/+fIwfPx6HDx+Gl5dXidVBLx/ONBGVkTVr1mDSpElYuXKlbCYBAKpVqybdnJycADyaYSnqq+/e3t7IycnB4cOHpbbbt28jPj4ePj4+z13roUOHCtz39vaWtn3lyhVcuXJFWn727FncvXtX8bZ9fHyg0WiQlJQkG3u1atVksw7PolarZTM3hdFqtXBzc8OBAwdk7QcOHHjufTVt2jT8/vvviImJkbV7e3sXur0aNWpIIcNQzp8/j9u3b2PatGlo0aIFatWqVayZuMeFhYVh//792LRpEw4ePCidAO7s7Aw3Nzf8/fffBY5XfkipW7cuYmNjkZqaWui6CztWz/NcUqlUaNasGb788kucOHECarUa69at02vcREXhTBNRGThz5gx69+6NTz/9FLVr10ZycjKAR39Iivo4Y+LEiQgICEDVqlXRo0cP5OTkYMuWLfj0009RvXp1dOrUCf3798fChQtha2uLsWPHonLlyujUqdNz13vgwAHMmDEDnTt3xo4dO7Bq1Sps3rwZwKNvj/n6+iIkJARz5sxBTk4OBg0ahFatWhX6cVNhbG1tMWrUKAwfPhx5eXlo3rw50tLScODAAWi1WoSGhipaj6enJy5duoTY2FhUqVIFtra2BWaqAGD06NGYOHEiqlativr16yMyMhKxsbFYunSp8p1SiPz9MG/ePFn7yJEj8dprr2Hy5Mno3r07YmJi8O233xb4tpkhvPLKK1Cr1Zg/fz4GDhyIM2fOYPLkyXqtq2XLlqhWrRp69+6NWrVqoWnTptKyL7/8EkOHDoWdnR3at2+PrKwsHDt2DHfu3MGIESPQs2dPTJ06FZ07d0Z4eDhcXV1x4sQJuLm5wd/fv9Bjpe9z6fDhw9i1axfatWsHJycnHD58GDdv3pSCPZGhcKaJqAwcO3YMmZmZmDJlClxdXaVbly5dinzMG2+8gVWrVmHjxo2oX78+2rRpgyNHjkjLIyMj0ahRI7z55pvw9/eHEAJbtmwxyPV0Ro4ciWPHjqFBgwaYMmUKZs+ejaCgIACP/oe/YcMGODg4oGXLlggMDMSrr76KFStWFGsbkydPxueff47w8HB4e3ujffv22Lx5c7E+XunatSvat2+P1q1bo1KlSvjtt98K7Td06FCMGDECI0eOhK+vL6Kjo7Fx40ZUr169WDUXZtKkSQU+LmzYsCFWrlyJ5cuXo06dOpgwYQImTZqk98d4T1OpUiVERUVh1apV8PHxwbRp0/D111/rtS6VSoV+/frhzp076Nevn2zZhx9+iEWLFiEyMhK+vr5o1aoVoqKipOOlVquxfft2ODk5oWPHjvD19cW0adOkmbXCjpW+zyWtVot9+/ahY8eOqFGjBj777DPMmjULHTp00GvcREVRCSFEWRdBRMbL09MTw4YNw7Bhw8q6FCKiMsWZJiIiIiIFGJqIiIiIFODHc0REREQKcKaJiIiISAGGJiIiIiIFGJqIiIiIFGBoIiIiIlKAoYmIiIhIAYYmIiIiIgUYmoiIiIgUYGgiIiIiUoChiYiIiEiB/wO/Q9fyALojxgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[26], line 27\u001b[0m\n\u001b[0;32m     24\u001b[0m downpcd\u001b[38;5;241m.\u001b[39mcolors \u001b[38;5;241m=\u001b[39m o3d\u001b[38;5;241m.\u001b[39mutility\u001b[38;5;241m.\u001b[39mVector3dVector(np\u001b[38;5;241m.\u001b[39mabs(np\u001b[38;5;241m.\u001b[39marray(downpcd\u001b[38;5;241m.\u001b[39mnormals)))\n\u001b[0;32m     26\u001b[0m \u001b[38;5;66;03m# Filter ground points\u001b[39;00m\n\u001b[1;32m---> 27\u001b[0m pcd_ground \u001b[38;5;241m=\u001b[39m \u001b[43mground_filtering\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdownpcd\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     30\u001b[0m \u001b[38;5;66;03m# Filter objects points\u001b[39;00m\n\u001b[0;32m     31\u001b[0m pcd_objects \u001b[38;5;241m=\u001b[39m objects_filtering(downpcd)\n",
      "Cell \u001b[1;32mIn[22], line 6\u001b[0m, in \u001b[0;36mground_filtering\u001b[1;34m(pcd)\u001b[0m\n\u001b[0;32m      3\u001b[0m z_components \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(pcd\u001b[38;5;241m.\u001b[39mnormals)[:, \u001b[38;5;241m2\u001b[39m]\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# Plot histogram of y-components\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhist\u001b[49m\u001b[43m(\u001b[49m\u001b[43mz_components\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mz-component of Normal Vectors\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      8\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFrequency\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\enric\\Desktop\\open3d_lab\\myenv\\Lib\\site-packages\\matplotlib\\pyplot.py:3440\u001b[0m, in \u001b[0;36mhist\u001b[1;34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, data, **kwargs)\u001b[0m\n\u001b[0;32m   3415\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mhist)\n\u001b[0;32m   3416\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhist\u001b[39m(\n\u001b[0;32m   3417\u001b[0m     x: ArrayLike \u001b[38;5;241m|\u001b[39m Sequence[ArrayLike],\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3438\u001b[0m     BarContainer \u001b[38;5;241m|\u001b[39m Polygon \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mlist\u001b[39m[BarContainer \u001b[38;5;241m|\u001b[39m Polygon],\n\u001b[0;32m   3439\u001b[0m ]:\n\u001b[1;32m-> 3440\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhist\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   3441\u001b[0m \u001b[43m        \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3442\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3443\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3444\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdensity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdensity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3445\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3446\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcumulative\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcumulative\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3447\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbottom\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbottom\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3448\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhisttype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhisttype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3449\u001b[0m \u001b[43m        \u001b[49m\u001b[43malign\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malign\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3450\u001b[0m \u001b[43m        \u001b[49m\u001b[43morientation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morientation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3451\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrwidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrwidth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3452\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3453\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3454\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3455\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstacked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstacked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3456\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3457\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3458\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\enric\\Desktop\\open3d_lab\\myenv\\Lib\\site-packages\\matplotlib\\__init__.py:1473\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1470\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m   1471\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1472\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1473\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1474\u001b[0m \u001b[43m            \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1475\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1476\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1478\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1479\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[0;32m   1480\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
      "File \u001b[1;32mc:\\Users\\enric\\Desktop\\open3d_lab\\myenv\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:7061\u001b[0m, in \u001b[0;36mAxes.hist\u001b[1;34m(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[0;32m   7059\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   7060\u001b[0m     height \u001b[38;5;241m=\u001b[39m top\n\u001b[1;32m-> 7061\u001b[0m bars \u001b[38;5;241m=\u001b[39m \u001b[43m_barfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbins\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mboffset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   7062\u001b[0m \u001b[43m                \u001b[49m\u001b[43malign\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcenter\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   7063\u001b[0m \u001b[43m                \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mbottom_kwarg\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mbottom\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   7064\u001b[0m patches\u001b[38;5;241m.\u001b[39mappend(bars)\n\u001b[0;32m   7065\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m stacked:\n",
      "File \u001b[1;32mc:\\Users\\enric\\Desktop\\open3d_lab\\myenv\\Lib\\site-packages\\matplotlib\\__init__.py:1473\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1470\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m   1471\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1472\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1473\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1474\u001b[0m \u001b[43m            \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1475\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1476\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1478\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1479\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[0;32m   1480\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
      "File \u001b[1;32mc:\\Users\\enric\\Desktop\\open3d_lab\\myenv\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:2597\u001b[0m, in \u001b[0;36mAxes.bar\u001b[1;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[0;32m   2595\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:  \u001b[38;5;66;03m# horizontal\u001b[39;00m\n\u001b[0;32m   2596\u001b[0m         r\u001b[38;5;241m.\u001b[39msticky_edges\u001b[38;5;241m.\u001b[39mx\u001b[38;5;241m.\u001b[39mappend(l)\n\u001b[1;32m-> 2597\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_patch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2598\u001b[0m     patches\u001b[38;5;241m.\u001b[39mappend(r)\n\u001b[0;32m   2600\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m xerr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m yerr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\enric\\Desktop\\open3d_lab\\myenv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2414\u001b[0m, in \u001b[0;36m_AxesBase.add_patch\u001b[1;34m(self, p)\u001b[0m\n\u001b[0;32m   2412\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m p\u001b[38;5;241m.\u001b[39mget_clip_path() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2413\u001b[0m     p\u001b[38;5;241m.\u001b[39mset_clip_path(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch)\n\u001b[1;32m-> 2414\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_patch_limits\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2415\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_children\u001b[38;5;241m.\u001b[39mappend(p)\n\u001b[0;32m   2416\u001b[0m p\u001b[38;5;241m.\u001b[39m_remove_method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_children\u001b[38;5;241m.\u001b[39mremove\n",
      "File \u001b[1;32mc:\\Users\\enric\\Desktop\\open3d_lab\\myenv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2436\u001b[0m, in \u001b[0;36m_AxesBase._update_patch_limits\u001b[1;34m(self, patch)\u001b[0m\n\u001b[0;32m   2433\u001b[0m \u001b[38;5;66;03m# Get all vertices on the path\u001b[39;00m\n\u001b[0;32m   2434\u001b[0m \u001b[38;5;66;03m# Loop through each segment to get extrema for Bezier curve sections\u001b[39;00m\n\u001b[0;32m   2435\u001b[0m vertices \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m-> 2436\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcurve\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miter_bezier\u001b[49m\u001b[43m(\u001b[49m\u001b[43msimplify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m   2437\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# Get distance along the curve of any extrema\u001b[39;49;00m\n\u001b[0;32m   2438\u001b[0m \u001b[43m    \u001b[49m\u001b[43m_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdzeros\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mcurve\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxis_aligned_extrema\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2439\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# Calculate vertices of start, end and any extrema in between\u001b[39;49;00m\n",
      "File \u001b[1;32mc:\\Users\\enric\\Desktop\\open3d_lab\\myenv\\Lib\\site-packages\\matplotlib\\path.py:445\u001b[0m, in \u001b[0;36mPath.iter_bezier\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    443\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m BezierSegment(np\u001b[38;5;241m.\u001b[39marray([first_vert])), code\n\u001b[0;32m    444\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m code \u001b[38;5;241m==\u001b[39m Path\u001b[38;5;241m.\u001b[39mLINETO:  \u001b[38;5;66;03m# \"CURVE2\"\u001b[39;00m\n\u001b[1;32m--> 445\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m BezierSegment(np\u001b[38;5;241m.\u001b[39marray([prev_vert, verts])), code\n\u001b[0;32m    446\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m code \u001b[38;5;241m==\u001b[39m Path\u001b[38;5;241m.\u001b[39mCURVE3:\n\u001b[0;32m    447\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m BezierSegment(np\u001b[38;5;241m.\u001b[39marray([prev_vert, verts[:\u001b[38;5;241m2\u001b[39m],\n\u001b[0;32m    448\u001b[0m                                   verts[\u001b[38;5;241m2\u001b[39m:]])), code\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize Open3D Visualizer\n",
    "if flag_display:\n",
    "    vis = o3d.visualization.Visualizer()\n",
    "    vis.create_window()\n",
    "\n",
    "for n_frame in range(nb_frames):\n",
    "    \n",
    "    # Read point cloud\n",
    "    points = get_point_cloud(n_frame, actor)\n",
    "    pcd = o3d.geometry.PointCloud()\n",
    "    pcd.points = o3d.utility.Vector3dVector(points)\n",
    "    \n",
    "    # Voxel down sampling\n",
    "    downpcd = pcd.voxel_down_sample(voxel_size=0.005)\n",
    "    \n",
    "    # Transform points cloud from the LiDAR to the world frame\n",
    "    tf = get_tf_lidar2world(actor, n_frame)\n",
    "    downpcd.transform(tf)\n",
    "    \n",
    "    # Estimate normals\n",
    "    downpcd.estimate_normals(\n",
    "        search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=3,\n",
    "                                                          max_nn=15))\n",
    "    downpcd.colors = o3d.utility.Vector3dVector(np.abs(np.array(downpcd.normals)))\n",
    "    \n",
    "    # Filter ground points\n",
    "    pcd_ground = ground_filtering(downpcd)\n",
    "    \n",
    "    \n",
    "    # Filter objects points\n",
    "    pcd_objects = objects_filtering(downpcd)\n",
    "    \n",
    "    # Clustering\n",
    "    pcd_objects, cluster_ids = clustering(pcd_objects)\n",
    "    \n",
    "    if flag_display:\n",
    "        # Clear previous geometries and add new point cloud to visualizer\n",
    "        vis.clear_geometries()\n",
    "        vis.add_geometry(pcd_objects)\n",
    "        \n",
    "        # Update the visualizer to show the new point cloud\n",
    "        vis.poll_events()\n",
    "        vis.update_renderer()\n",
    "\n",
    "        # Wait before updating the points cloud (adjust time as needed)\n",
    "        time.sleep(0.1)\n",
    "\n",
    "if flag_display:\n",
    "    # Close the visualizer\n",
    "    vis.destroy_window()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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