{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a4318247",
   "metadata": {},
   "source": [
    "# Shape measures\n",
    "\n",
    "Mesurer la géométrie, en particulier sur les objets linéraires, peut-être utile (étudier la qualité, comparer les formes, etc.) et est toujours abordé dans la littérature. \n",
    "\n",
    "- Les mesures intrinsèques qui concernent la géométrie de la ligne\n",
    "- Shape measures to describe the similarity of polygons "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa21a4e4",
   "metadata": {},
   "source": [
    "## Let's start by defining our environment\n",
    "\n",
    "The first task is only useful for the online notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "79a45624",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "# Import de tracklib\n",
    "module_path = os.path.abspath(os.path.join('../../..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab181195",
   "metadata": {},
   "source": [
    "The following imports are necessary for all of the examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "57f6a1cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Matplotlib to create visualizations\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import tracklib library\n",
    "import tracklib as tkl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9d518cd",
   "metadata": {},
   "source": [
    "## Hausdorff distance between two tracks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006b0977",
   "metadata": {},
   "source": [
    "## Mesures de caractérisation des lignes\n",
    "\n",
    "La comparaison directe des géométries de la ligne originale et de son homologue généralisé. Quantifier l'écart positionnel produit par la généralisation.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a23316d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.5 7.5\n",
      "index max: 3\n",
      "8 8\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tkl.ObsTime.setReadFormat(\"4Y-2M-2D 2h:2m:2s\")\n",
    "trace1 = tkl.Track([], 1)\n",
    "\n",
    "trace1.addObs(tkl.Obs(tkl.ENUCoords(0, 0, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:00:00\")))\n",
    "trace1.addObs(tkl.Obs(tkl.ENUCoords(10, 0, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:00:12\")))\n",
    "trace1.addObs(tkl.Obs(tkl.ENUCoords(10, 10, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:00:40\")))\n",
    "trace1.addObs(tkl.Obs(tkl.ENUCoords(20, 10, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:01:50\")))\n",
    "trace1.addObs(tkl.Obs(tkl.ENUCoords(20, 20, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:02:10\")))\n",
    "trace1.addObs(tkl.Obs(tkl.ENUCoords(30, 20, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:02:35\")))\n",
    "trace1.addObs(tkl.Obs(tkl.ENUCoords(30, 30, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:02:43\")))\n",
    "trace1.addObs(tkl.Obs(tkl.ENUCoords(40, 30, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:02:55\")))\n",
    "trace1.addObs(tkl.Obs(tkl.ENUCoords(60, 30, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:03:25\")))\n",
    "        \n",
    "trace1.plot('k-')\n",
    "        \n",
    "# 25%\n",
    "borneinf = min(int(trace1.bbox().getDx()), int(trace1.bbox().getDy())) / 4 \n",
    "print(borneinf, 7.5)\n",
    "        \n",
    "ind1 = tkl.compareWithDouglasPeuckerSimplification(trace1, borneinf)\n",
    "print(ind1, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26a84a49",
   "metadata": {},
   "source": [
    "### compareWithDouglasPeuckerSimplification\n",
    "\n",
    "retourne le nombre de points de la ligne la plus généralisée \n",
    "    avec Douglas Peucker et qui respecte une qualité donnée avec threshold."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff6f23e1",
   "metadata": {},
   "source": [
    "### averageOffsetDistance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad2b47aa",
   "metadata": {},
   "source": [
    "## Radial distance\n",
    "\n",
    "Radial signature and radial distance. The source code to compute radial signature and radial distance are detailed in:\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\" style=\"padding:1em\">\n",
    "Yann Méneroux, Ibrahim Maidaneh Abdi, Arnaud Le Guilcher, Ana-Maria Olteanu-Raimond. Is the radial distance really a distance? An analysis of its properties and interest for the matching of polygon features. International Journal of Geographical Information Science, 2023, 37 (2), pp.438 - 475. ⟨10.1080/13658816.2022.2123487⟩. ⟨hal-03790024⟩</div>\n",
    "\n",
    "Datasets and scripts for radial distance analysis are available on Zenodo: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7006944.svg)](https://doi.org/10.5281/zenodo.7006944)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "acd92645",
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Radial distance between two simple building polygons:  0.0915944791404684\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "param = tkl.TrackFormat({'ext': 'WKT',\n",
    "                        'id_wkt': 0,\n",
    "                        'separator': \"#\", \n",
    "                        'header': 1, \n",
    "                        'doublequote': False})\n",
    "TRACES = tkl.TrackReader.readFromFile(\"../../../data/wkt/twobatis.wkt\", param)\n",
    "\n",
    "bati1 = TRACES[0]\n",
    "bati2 = TRACES[1]\n",
    "C = bati2.getEnclosedPolygon().centroid()\n",
    "bati2.rotate(math.pi/10, tkl.ENUCoords(C[0], C[1]))\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplots_adjust(top=1.3, wspace=0.2, hspace=0.5)\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# \n",
    "ax1 = plt.subplot2grid((2, 2), (0, 0))\n",
    "\n",
    "bati1.plot('b-', append=ax1)\n",
    "bati2.plot('r-', append=ax1)\n",
    "plt.xlim([bati1.bbox().getXmin()-5, bati1.bbox().getXmax()+5])\n",
    "plt.title('Two similar polygons')\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "ax2 = plt.subplot2grid((2, 2), (0, 1))\n",
    "\n",
    "sign1 = tkl.computeRadialSignature(bati1)\n",
    "R1 = sign1.getAnalyticalFeature('r')\n",
    "plt.plot(R1, color=\"royalblue\", linestyle='--')\n",
    "\n",
    "sign2 = tkl.computeRadialSignature(bati2)\n",
    "R2 = sign2.getAnalyticalFeature('r')\n",
    "plt.plot(R2, color=\"red\", linestyle='-.')\n",
    "\n",
    "plt.title('Non-normalized radial signatures')\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "ax3 = plt.subplot2grid((2, 2), (1, 0))\n",
    "\n",
    "# radial distance unilateral\n",
    "sign1.op('r1 = r >> 54')\n",
    "sign1.createAnalyticalFeature(\"r2\", sign2[\"r\"])\n",
    "sign1.op(tkl.Operator.CORRELATOR, \"r1\", \"r2\", \"rho\")\n",
    "dt = sign1.op(\"ARGMAX(rho)\")[0]\n",
    "sign1.op('r1 = r1 >> ' + str(dt))\n",
    "\n",
    "R3 = sign1.getAnalyticalFeature('r1')\n",
    "R4 = sign1.getAnalyticalFeature('r2')\n",
    "plt.plot(R3, color=\"royalblue\", linestyle='--')\n",
    "plt.plot(R4, color=\"red\", linestyle='-.')\n",
    "\n",
    "plt.title('Radial signature with optimal shift')\n",
    "\n",
    "# ===============================================================\n",
    "\n",
    "T = sign1.op(tkl.Operator.L2, 'r1', 'r2')\n",
    "print ('Radial distance between two simple building polygons: ', T)\n"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
