autocnet_reprojective_error.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from autocnet.examples import get_path\n",
    "from autocnet.graph.network import CandidateGraph\n",
    "\n",
    "from IPython.display import display\n",
    "\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate a 2 image adjacenecy graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Point to the adjacency Graph\n",
    "adjacency = get_path('three_image_adjacency.json')\n",
    "basepath = get_path('Apollo15')\n",
    "cg = CandidateGraph.from_adjacency(adjacency, basepath=basepath)\n",
    "\n",
    "cg.extract_features()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cg.match()\n",
    "cg.symmetry_checks()\n",
    "cg.ratio_checks()\n",
    "cg.compute_fundamental_matrices(clean_keys=['symmetry', 'ratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "err = cg.edge[0][1].compute_fundamental_error(clean_keys=['fundamental'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff7b6a31208>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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baL1J6ihmhmZz77jk1Y1spxej/vO4nCKip6Kvc9RNAJcD93SXfESs7pp2NnDXoGtIkuqr\n89//S4DzgW0RsbUaeztwbkSczNyumx3Am2sllCTVUueomy8BschVNw0eR5LUNF8ZK0mFs+glqXAW\nvSQVzqKXpMJZ9JJUOItekgpn0UtS4Sx6SSrc6L2hh6Sha/P9lDas3cuF1XptvsfOM4lFX8Ny/TBI\nUj/cdSNJhbPoJalwFr0kFc6il6TC+cdYSSvGcn16WulH+/iMXpIKZ9FLUuEsekkqnEUvSYUbWtFH\nxOkRcV9EbI+IjcNaR5K0f0Mp+og4APgo8CrgBODciDhhGGtJkvZvWIdXngJsz8wHASJiGjgT+OYw\nFuv3kCzfN0ZSt+XskDYO7RzWrps1wPe6Lu+sxiRJLYvMbH6jEa8FTsvMN1WXzwdOycw/7pqzHlhf\nXXwhcF/jQfbtSODRFtdrwihmhtHMPYqZwdxtWimZn5+ZRy01aVi7bnYCz+u6fAzwUPeEzNwEbBrS\n+vsVEZszc2I51h7UKGaG0cw9ipnB3G0atczD2nXzNeD4iDg2Ip4FnANcP6S1JEn7MZRn9Jm5NyLe\nCtwMHABckZl3D2MtSdL+De1NzTLzJuCmYW2/pmXZZVTTKGaG0cw9ipnB3G0aqcxD+WOsJGnl8C0Q\nJKlwxRZ9RBwREbdExP3V11X7mHdBNef+iLiga/xzEXFHRNwdEZdVr/ZdsZkj4tkRcWNE3FtlvmTY\neZvIXY2/LyK+FxGzLWTd71tzRMRBEXFNdf1XImK867q3VeP3RcRpw87aRO6IeG5EfD4iZiPiIyOS\n+bciYktEbKu+vnxEcp8SEVur0x0RcXabufcrM4s8AR8ANlbnNwLvX2TOEcCD1ddV1flV1XW/UH0N\n4FrgnJWcGXg2MFXNeRbwReBVI3JfvxhYDcwOOecBwAPAcdV9dAdwwoI5fwRcVp0/B7imOn9CNf8g\n4NhqOwe0dP/WyX0o8JvAxcBH2sjbQOYXAb9UnT8J2DUiuZ8NHFidXw08Mn95uU/FPqNn7i0XrqrO\nXwWctcic04BbMvOxzHwcuAU4HSAzf1jNOZC5f/A2/pgxcObMfDIzPw+Qmf8LfJ251y+0oe59fXtm\nPtxCzp++NUd1H82/NUe37u/l08ArIiKq8enMfDozvw1sr7bXhoFzZ+YTmfkl4MctZZ1XJ/M3MnP+\ndTd3AwdHxEGtpK6X+8nM3FuNH0w7ndGTkou+M18e1dejF5mz37dqiIibmftf+UfM/YMOW+3MABFx\nOPA7wK1DyrlQI7lb0EuGn86pfmj3AM/t8bbDUif3cmkq8+8B38jMp4eUc6FauSPiNyLibmAbcHFX\n8S+rkf7M2Ij4T+AXF7nqHb1uYpGxn/4vnJmnRcTBwCeBlzP3LLSWYWeOiAOBq4EPZ/Wmck0Ydu6W\n9JJhX3OWM3+d3MulduaIOBF4P3Bqg7mWUit3Zn4FODEifgW4KiL+PTPb/m3qZ4x00WfmK/d1XUTs\njojVmflwRMzvL1toJzDZdfkYYGbBGj+OiOuZ+3WtdtG3kHkTcH9m/m3drN3auK9bsORbc3TN2Vn9\np3kY8FiPtx2WOrmXS63MEXEMcB3w+sx8YPhxfybTvIHu68y8JyKeYO5vDJuHF7c3Je+6uR6YP7Lj\nAuCzi8y5GTg1IlZVR4qcCtwcEWNVYc0/Qz4DuHclZ66y/iVzD7o/bSFrt1q5W9TLW3N0fy+/D/xX\nzv117XrgnOqIi2OB44GvjkDu5TJw5mrX443A2zLzv1tLPKdO7mOrviAins/cmzXuaCf2Epb7r8HD\nOjG3z+xW4P7q6xHV+ATwT13zLmLuD2vbgTdUYx3m/sHvZO6PQX9HC389r5n5GOZ+fbwH2Fqd3rTS\n7+tq/APMPUv6v+rru4eY9QzgW8wdWfGOauw9wGuq8wcD/1Jl/CpwXNdt31Hd7j5aOqKpodw7mHvG\nOVvdvyes5MzAO4Enuh7HW4GjV/p9DZxf9cVW5g6GOKvNx8j+Tr4yVpIKV/KuG0kSFr0kFc+il6TC\nWfSSVDiLXpIKZ9FLUuEsekkqnEUvSYX7f4bLWNHsWtJ2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff830715e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "err.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff830715d30>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff7b6ac3080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "err = cg.edge[0][1].compute_fundamental_error(clean_keys=['symmetry'])\n",
    "err.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff7b6975f28>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff7b6ac3a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "err = cg.edge[0][1].compute_fundamental_error()\n",
    "err.hist()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "AutoCNet",
   "language": "python",
   "name": "autocnet"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}