point_cloud_pre_processing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "93aab015",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import laspy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ee4514a",
   "metadata": {},
   "outputs": [],
   "source": [
    "path= r'C:\\Users\\Rekha\\Documents\\Processed_pointclouds\\VelodyneData1_part1.laz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "387ce2c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "point_cloud = laspy.read(r'C:\\Users\\Rekha\\Documents\\Processed_pointclouds\\VelodyneData1_part1.laz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "00742831",
   "metadata": {},
   "outputs": [],
   "source": [
    "points = points = np.vstack((point_cloud.x, point_cloud.y, point_cloud.z)).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4dcf083d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#colors = np.vstack((point_cloud.red, point_cloud.green, point_cloud.blue)).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d64776f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = np.vstack((point_cloud.intensity, point_cloud.intensity, point_cloud.intensity)).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "23e2de3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(point_cloud.intensity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "909b72b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55690"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The decimation strategy, by setting a decimation factor\n",
    "factor=160\n",
    "decimated_points = points[::factor]\n",
    "decimated_colors = colors[::factor]\n",
    "len(decimated_points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad6f6a4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the number of voxels to create to fill the space including every point\n",
    "voxel_size=6\n",
    "nb_vox=np.ceil((np.max(points, axis=0) - np.min(points, axis=0))/voxel_size)\n",
    "#nb_vox.astype(int) #this gives you the number of voxels per axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "da112853",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3299"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the non empty voxels and keep a trace of indexes that we can relate to points in order to store points later on.\n",
    "# Also Sum and count the points in each voxel.\n",
    "non_empty_voxel_keys, inverse, nb_pts_per_voxel= np.unique(((points - np.min(points, axis=0)) // voxel_size).astype(int), axis=0, return_inverse=True, return_counts=True)\n",
    "idx_pts_vox_sorted=np.argsort(inverse)\n",
    "len(non_empty_voxel_keys) # if you need to display how many no-empty voxels you have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b426602b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here, we loop over non_empty_voxel_keys numpy array to\n",
    "#       > Store voxel indices as keys in a dictionnary\n",
    "#       > Store the related points as the value of each key\n",
    "#       > Compute each voxel barycenter and add it to a list\n",
    "#       > Compute each voxel closest point to the barycenter and add it to a list\n",
    "\n",
    "voxel_grid={}\n",
    "grid_barycenter,grid_candidate_center=[],[]\n",
    "last_seen=0\n",
    "\n",
    "for idx,vox in enumerate(non_empty_voxel_keys):\n",
    "  voxel_grid[tuple(vox)]=points[idx_pts_vox_sorted[last_seen:last_seen+nb_pts_per_voxel[idx]]]\n",
    "  grid_barycenter.append(np.mean(voxel_grid[tuple(vox)],axis=0))\n",
    "  grid_candidate_center.append(voxel_grid[tuple(vox)][np.linalg.norm(voxel_grid[tuple(vox)]-np.mean(voxel_grid[tuple(vox)],axis=0),axis=1).argmin()])\n",
    "  last_seen+=nb_pts_per_voxel[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9c99ece7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Step 4: Vizualise and export the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8325532a",
   "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"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.scatter(decimated_points[:,0], decimated_points[:,1], decimated_points[:,2], c = decimated_colors/255, s=0.01)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9539fc79",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define a function that takes as input an array of points, and a voxel size expressed in meters. It returns the sampled point cloud\n",
    "def grid_subsampling(points, voxel_size):\n",
    "\n",
    "  nb_vox=np.ceil((np.max(points, axis=0) - np.min(points, axis=0))/voxel_size)\n",
    "  non_empty_voxel_keys, inverse, nb_pts_per_voxel= np.unique(((points - np.min(points, axis=0)) // voxel_size).astype(int), axis=0, return_inverse=True, return_counts=True)\n",
    "  idx_pts_vox_sorted=np.argsort(inverse)\n",
    "  voxel_grid={}\n",
    "  grid_barycenter,grid_candidate_center=[],[]\n",
    "  last_seen=0\n",
    "\n",
    "  for idx,vox in enumerate(non_empty_voxel_keys):\n",
    "    voxel_grid[tuple(vox)]=points[idx_pts_vox_sorted[last_seen:last_seen+nb_pts_per_voxel[idx]]]\n",
    "    grid_barycenter.append(np.mean(voxel_grid[tuple(vox)],axis=0))\n",
    "    grid_candidate_center.append(voxel_grid[tuple(vox)][np.linalg.norm(voxel_grid[tuple(vox)]-np.mean(voxel_grid[tuple(vox)],axis=0),axis=1).argmin()])\n",
    "    last_seen+=nb_pts_per_voxel[idx]\n",
    "\n",
    "  return subsampled_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a46d2c80",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Execute the function, and store the results in the grid_sampled_point_cloud variable\n",
    "grid_sampled_point_cloud_variable = grid_subsampling(point_cloud, 6)\n",
    "\n",
    "#Save the variable to an ASCII file to open in a 3D Software\n",
    "%timeit np.savetxt(output_path+dataname+\"_sampled.xyz\", grid_sampled_point_cloud_variable, delimiter=\";\", fmt=\"%s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b205cf15",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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