bin_reading.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(124668, 2)"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting ITTIK sweep\n",
    "dir_ittik = 'D:\\KPRNET\\ITTIK\\odo_sweep_coordinates'\n",
    "seq = '00'\n",
    "sweep_nr = '0000.bin'\n",
    "path_ittik = os.path.join(dir_ittik, seq, sweep_nr)\n",
    "\n",
    "f = open(path_ittik, \"r\")\n",
    "sweep = np.fromfile(f, dtype=np.uint16)\n",
    "sweep = sweep.reshape(-1, 2)\n",
    "sweep.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#f_kitti = open(r\"D:\\KPRNET\\unzipped\\dataset\\sequences\\00\\velodyne\\000000.bin\", \"r\")\n",
    "#sweep_kitti = np.fromfile(f_kitti, dtype=np.float32)\n",
    "\n",
    "#sweep_kitti = sweep_kitti.reshape(-1,4)\n",
    "#sweep_kitti.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.2897942e+01, 2.2989739e-02, 1.9979945e+00, 7.9999998e-02],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sweep_kitti[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_kitti = open(r\"D:\\KPRNET\\unzipped\\dataset\\sequences\\00\\labels\\000000.label\", \"r\")\n",
    "np_labels = np.fromfile(labels_kitti, dtype=np.uint32)\n",
    "\n",
    "# Only getting semantic labels (lower half of int32)\n",
    "sem_labels = np_labels & 0xFFFF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,  10,  40,  44,  48,  50,  51,  52,  60,  70,  71,  72,\n",
       "        80,  81,  99, 255], dtype=uint32)"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(sem_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping labels to pixels in visualized image\n",
    "vis = np.zeros(( sweep[:, 1].max() +1, sweep[:, 0].max() +1 ))\n",
    "\n",
    "for i in range(sweep.shape[0]):\n",
    "    u,v = sweep[i]\n",
    "    label = sem_labels[i]\n",
    "    vis[v][u] = label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x17d1e733310>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 30000x5000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10), dpi=2000)\n",
    "plt.imshow(vis, cmap = \"tab20c\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "kprnet2",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}