SuppressionNotebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import gdal\n",
    "import json\n",
    "sys.path.insert(0, os.path.abspath('..'))\n",
    "\n",
    "from unittest.mock import MagicMock\n",
    "from unittest.mock import Mock\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import plio\n",
    "from autocnet.cg import cg\n",
    "from autocnet.examples import get_path\n",
    "from autocnet.graph.network import CandidateGraph\n",
    "from autocnet.graph.edge import Edge\n",
    "from autocnet.graph import edge\n",
    "from autocnet.graph import node\n",
    "from scipy.spatial import ConvexHull\n",
    "from autocnet.matcher import suppression_funcs as spf\n",
    "\n",
    "from osgeo import ogr\n",
    "\n",
    "from IPython.display import display\n",
    "\n",
    "%pylab inline\n",
    "%matplotlib inline\n",
    "fig=plt.figure(figsize=(25, 25), dpi= 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "adjacency = get_path('suppression_adjacency.json')\n",
    "basepath = get_path('Apollo15')\n",
    "cang1 = CandidateGraph.from_adjacency(adjacency, basepath=basepath)\n",
    "\n",
    "#Apply SIFT to extract features\n",
    "cang1.extract_features(method='sift')\n",
    "\n",
    "#Match\n",
    "cang1.match_features(k=2)\n",
    "\n",
    "#Apply outlier detection\n",
    "cang1.symmetry_checks()\n",
    "cang1.ratio_checks(clean_keys = ['symmetry'], single=True)\n",
    "\n",
    "destination_df = pd.DataFrame(cang1.edge[0][1].destination._keypoints)\n",
    "source_df = pd.DataFrame(cang1.edge[0][1].source._keypoints)\n",
    "\n",
    "cang1.apply_func_to_edges(\"compute_fundamental_matrix\", clean_keys=['ratio', 'symmetry'])\n",
    "\n",
    "#print(\"Length of the error array: \" + len(cang1.edge[0][1].fundamental_matrix.error).__str__())\n",
    "\n",
    "cang1.suppress(suppression_func = spf.error, clean_keys = ['ratio', 'symmetry'], k = 15)\n",
    "\n",
    "matches_df = cang1.edge[0][1].matches\n",
    "matches_df['destination x'] = destination_df['x']\n",
    "matches_df['destination y'] = destination_df['y']\n",
    "matches_df['source x'] = source_df['x']\n",
    "matches_df['source y'] = source_df['y']\n",
    "matches_df['symmetry'] = cang1.edge[0][1].masks['symmetry']\n",
    "matches_df['ratio'] = cang1.edge[0][1].masks['ratio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig=plt.figure(figsize=(15, 15), dpi= 30)\n",
    "cang1.edge[0][1].plot(clean_keys = ['suppression'], scatter_kwargs = {'color':'r', 's':150},\n",
    "                      line_kwargs = {'marker':'o'})\n",
    "cang1.edge[0][1].plot(clean_keys = ['fundamental'], scatter_kwargs = {'color':'b', 's':50, 'alpha':0.5}, \n",
    "                      line_kwargs = {'linewidth':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cang1.edge[0][1].fundamental_matrix.mask.sum()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}