{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subpixel Matching - Part II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import os\n",
    "import sys\n",
    "\n",
    "sys.path.insert(0, os.path.abspath('/data/autocnet'))\n",
    "\n",
    "import autocnet\n",
    "# Enable the GPU\n",
    "autocnet.cuda(enable=True)\n",
    "\n",
    "from autocnet import CandidateGraph\n",
    "\n",
    "# The GPU based extraction library that contains SIFT extraction and matching\n",
    "import cudasift as cs\n",
    "# A method to resize the images on the fly.\n",
    "from scipy.misc import imresize\n",
    "\n",
    "%pylab inline\n",
    "figsize(16,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the CandidateGraph\n",
    "Just like the other notebooks this cell creates the candidate graph.  This also patches in functionality to the object on the fly.  To get a handle on what is going on, checkout the [Advanced 1. Extending the CandidateGraph](Advanced 1. Extending the CandidateGraph.ipynb) notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create the candidate graph and enable a GPU\n",
    "a = 'AS15-P-0111_CENTER_LRG_CROPPED.png'\n",
    "b = 'AS15-P-0112_CENTER_LRG_CROPPED.png'\n",
    "\n",
    "adj = {a:[b],\n",
    "       b:[a]}\n",
    "\n",
    "cg = CandidateGraph.from_adjacency(adj)\n",
    "\n",
    "# Define a function to do the feature extraction.\n",
    "def extract_features(self, arr, downsample_amount=None, **kwargs):\n",
    "    total_size = arr.shape[0] * arr.shape[1]\n",
    "    if not downsample_amount:\n",
    "        downsample_amount = math.ceil(total_size / 12500**2)\n",
    "    shape = (int(arr.shape[0] / downsample_amount), int(arr.shape[1] / downsample_amount))\n",
    "    # Downsample\n",
    "    arr = imresize(arr, shape, interp='lanczos')\n",
    "    \n",
    "    npts = max(arr.shape) / 3.5\n",
    "    sd = cs.PySiftData(npts)\n",
    "    cs.ExtractKeypoints(arr, sd, **kwargs)\n",
    "    kp, des = sd.to_data_frame()\n",
    "    kp = kp[['x', 'y', 'scale', 'sharpness', 'edgeness', 'orientation', 'score', 'ambiguity']]\n",
    "    kp['score'] = 0.0\n",
    "    kp['ambiguity'] = 0.0\n",
    "    \n",
    "    # Match the interface defined in the edge.\n",
    "    self.keypoints = kp\n",
    "    self.descriptors = des\n",
    "    \n",
    "    self['downsample_amount'] = downsample_amount\n",
    "\n",
    "# Import the class and update it.  This updates the current instance of the CandidateGraph\n",
    "from autocnet.graph.node import Node\n",
    "Node.extract_features = extract_features\n",
    "\n",
    "# Extract the features\n",
    "cg.extract_features(thresh=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Match and Find Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Match\n",
    "cg.match()\n",
    "\n",
    "# Apply outlier detection - see the above linked notebook if you are curious about what is going on here.\n",
    "for s,d,e in cg.edges_iter(data=True):\n",
    "    e.masks['ratio'] = e.matches.ambiguity <= e.matches.ambiguity.quantile(0.015)\n",
    "    e.masks['score'] = e.matches.score >= e.matches.score.quantile(0.85)\n",
    "    \n",
    "# Compute the F Matrix\n",
    "cg.compute_fundamental_matrices(clean_keys=['ratio', 'score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subpixel Refinement\n",
    "\n",
    "The last notebook looked at the results of subpixel refinement to explore the result quality.  In this notebook, subpixel refinement is simply applied using the built-in syntax sugar.\n",
    "\n",
    "**Caveat**: AutoCNet does not (yet?) natively support up and down sampling so the user has to manually rescale the correspondences.  The dev. team is thinking about if, and how best to implement multi-resolution tracking without adding lots of complexity.\n",
    "\n",
    "#### Subpixel Parameters :\n",
    "Since the Apollo Pan data was originally processed at a reduced resolution, the `max_x_shift` and `max_y_shift` parameters are allowed to be larger than the normal 1-pixel constraint.  Aslo, `clean_keys` are passed to subpixel register only the best matches currently found and `tiled` is set to true indicating that the entire image is **not** to be read into memory.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "# Scale the keypoints coordinates back to full resolution.\n",
    "for i, n in cg.nodes_iter(data=True):\n",
    "    n.keypoints.x *= n['downsample_amount']\n",
    "    n.keypoints.y *= n['downsample_amount']\n",
    "    \n",
    "cg.subpixel_register(clean_keys=['fundamental'], tiled=True, func=cv2.TM_CCORR_NORMED, max_x_shift=7, max_y_shift=7)\n",
    "\n",
    "# Scale the keypoint coordinates back to the reduced resolution\n",
    "for i, n in cg.nodes_iter(data=True):\n",
    "    n.keypoints.x /= n['downsample_amount']\n",
    "    n.keypoints.y /= n['downsample_amount']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Subpixel Results\n",
    "\n",
    "The subpixel matcher updates the masks with three new entries for (1) `shift`: has the correspondenced moved too far, (2) `threshold`: is the correlation good enough, and `subpixel`: the combination of (1) and (2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ratio</th>\n",
       "      <th>score</th>\n",
       "      <th>fundamental</th>\n",
       "      <th>shift</th>\n",
       "      <th>threshold</th>\n",
       "      <th>subpixel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ratio  score fundamental shift threshold subpixel\n",
       "0  False   True       False  True     False    False\n",
       "1  False   True       False  True     False    False\n",
       "2  False  False       False  True     False    False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cg.edge[0][1].masks.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_image</th>\n",
       "      <th>source_idx</th>\n",
       "      <th>destination_image</th>\n",
       "      <th>destination_idx</th>\n",
       "      <th>score</th>\n",
       "      <th>ambiguity</th>\n",
       "      <th>y_offset</th>\n",
       "      <th>x_offset</th>\n",
       "      <th>reference</th>\n",
       "      <th>correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296</td>\n",
       "      <td>0.985350</td>\n",
       "      <td>0.861263</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>6.3750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>291</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>0.816333</td>\n",
       "      <td>-1.2500</td>\n",
       "      <td>4.8750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>0.0</td>\n",
       "      <td>283</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1173</td>\n",
       "      <td>0.978444</td>\n",
       "      <td>0.890681</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>5.1875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>0.0</td>\n",
       "      <td>291</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1230</td>\n",
       "      <td>0.958613</td>\n",
       "      <td>0.936126</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>-2.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>0.0</td>\n",
       "      <td>309</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1175</td>\n",
       "      <td>0.977205</td>\n",
       "      <td>0.930651</td>\n",
       "      <td>-3.1875</td>\n",
       "      <td>2.3125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>0.0</td>\n",
       "      <td>338</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1295</td>\n",
       "      <td>0.971325</td>\n",
       "      <td>0.911120</td>\n",
       "      <td>-3.5625</td>\n",
       "      <td>6.7500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>0.0</td>\n",
       "      <td>345</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1290</td>\n",
       "      <td>0.971112</td>\n",
       "      <td>0.907802</td>\n",
       "      <td>-6.6875</td>\n",
       "      <td>-2.3125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>0.0</td>\n",
       "      <td>353</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1261</td>\n",
       "      <td>0.984677</td>\n",
       "      <td>0.789020</td>\n",
       "      <td>-6.0625</td>\n",
       "      <td>5.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>0.0</td>\n",
       "      <td>356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0.979318</td>\n",
       "      <td>0.899721</td>\n",
       "      <td>4.8125</td>\n",
       "      <td>-4.7500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>0.0</td>\n",
       "      <td>366</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1272</td>\n",
       "      <td>0.985263</td>\n",
       "      <td>0.876244</td>\n",
       "      <td>-5.4375</td>\n",
       "      <td>-1.1250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     source_image  source_idx  destination_image  destination_idx     score  \\\n",
       "17            0.0          17                1.0              296  0.985350   \n",
       "21            0.0          21                1.0              291  0.979893   \n",
       "283           0.0         283                1.0             1173  0.978444   \n",
       "291           0.0         291                1.0             1230  0.958613   \n",
       "309           0.0         309                1.0             1175  0.977205   \n",
       "338           0.0         338                1.0             1295  0.971325   \n",
       "345           0.0         345                1.0             1290  0.971112   \n",
       "353           0.0         353                1.0             1261  0.984677   \n",
       "356           0.0         356                1.0             1262  0.979318   \n",
       "366           0.0         366                1.0             1272  0.985263   \n",
       "\n",
       "     ambiguity  y_offset  x_offset  reference  correlation  \n",
       "17    0.861263    0.1250    6.3750        0.0     0.999713  \n",
       "21    0.816333   -1.2500    4.8750        0.0     0.999727  \n",
       "283   0.890681    3.0000    5.1875        0.0     0.999637  \n",
       "291   0.936126    3.0000   -2.0000        0.0     0.999747  \n",
       "309   0.930651   -3.1875    2.3125        0.0     0.999675  \n",
       "338   0.911120   -3.5625    6.7500        0.0     0.999822  \n",
       "345   0.907802   -6.6875   -2.3125        0.0     0.999785  \n",
       "353   0.789020   -6.0625    5.4375        0.0     0.999745  \n",
       "356   0.899721    4.8125   -4.7500        0.0     0.999745  \n",
       "366   0.876244   -5.4375   -1.1250        0.0     0.999751  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the `clean` method to get back the subpixel registered correspondences.\n",
    "matches, mask = cg.edge[0][1].clean(['subpixel'])\n",
    "matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_image</th>\n",
       "      <th>source_idx</th>\n",
       "      <th>destination_image</th>\n",
       "      <th>destination_idx</th>\n",
       "      <th>score</th>\n",
       "      <th>ambiguity</th>\n",
       "      <th>y_offset</th>\n",
       "      <th>x_offset</th>\n",
       "      <th>reference</th>\n",
       "      <th>correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296</td>\n",
       "      <td>0.985350</td>\n",
       "      <td>0.861263</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>6.3750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>291</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>0.816333</td>\n",
       "      <td>-1.2500</td>\n",
       "      <td>4.8750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>0.0</td>\n",
       "      <td>283</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1173</td>\n",
       "      <td>0.978444</td>\n",
       "      <td>0.890681</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>5.1875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>0.0</td>\n",
       "      <td>291</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1230</td>\n",
       "      <td>0.958613</td>\n",
       "      <td>0.936126</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>-2.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>0.0</td>\n",
       "      <td>293</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1235</td>\n",
       "      <td>0.985849</td>\n",
       "      <td>0.871732</td>\n",
       "      <td>-9.0000</td>\n",
       "      <td>-2.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>0.0</td>\n",
       "      <td>296</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1239</td>\n",
       "      <td>0.967968</td>\n",
       "      <td>0.927719</td>\n",
       "      <td>-7.6250</td>\n",
       "      <td>-4.5000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>0.0</td>\n",
       "      <td>298</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1238</td>\n",
       "      <td>0.964188</td>\n",
       "      <td>0.898417</td>\n",
       "      <td>-10.6875</td>\n",
       "      <td>13.1250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>0.0</td>\n",
       "      <td>309</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1175</td>\n",
       "      <td>0.977205</td>\n",
       "      <td>0.930651</td>\n",
       "      <td>-3.1875</td>\n",
       "      <td>2.3125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>0.0</td>\n",
       "      <td>338</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1295</td>\n",
       "      <td>0.971325</td>\n",
       "      <td>0.911120</td>\n",
       "      <td>-3.5625</td>\n",
       "      <td>6.7500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>0.0</td>\n",
       "      <td>345</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1290</td>\n",
       "      <td>0.971112</td>\n",
       "      <td>0.907802</td>\n",
       "      <td>-6.6875</td>\n",
       "      <td>-2.3125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>0.0</td>\n",
       "      <td>353</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1261</td>\n",
       "      <td>0.984677</td>\n",
       "      <td>0.789020</td>\n",
       "      <td>-6.0625</td>\n",
       "      <td>5.4375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>0.0</td>\n",
       "      <td>355</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1267</td>\n",
       "      <td>0.965001</td>\n",
       "      <td>0.915773</td>\n",
       "      <td>-14.0000</td>\n",
       "      <td>15.3750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>0.0</td>\n",
       "      <td>356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0.979318</td>\n",
       "      <td>0.899721</td>\n",
       "      <td>4.8125</td>\n",
       "      <td>-4.7500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>0.0</td>\n",
       "      <td>366</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1272</td>\n",
       "      <td>0.985263</td>\n",
       "      <td>0.876244</td>\n",
       "      <td>-5.4375</td>\n",
       "      <td>-1.1250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>0.0</td>\n",
       "      <td>386</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1248</td>\n",
       "      <td>0.984362</td>\n",
       "      <td>0.885029</td>\n",
       "      <td>15.8750</td>\n",
       "      <td>17.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     source_image  source_idx  destination_image  destination_idx     score  \\\n",
       "17            0.0          17                1.0              296  0.985350   \n",
       "21            0.0          21                1.0              291  0.979893   \n",
       "283           0.0         283                1.0             1173  0.978444   \n",
       "291           0.0         291                1.0             1230  0.958613   \n",
       "293           0.0         293                1.0             1235  0.985849   \n",
       "296           0.0         296                1.0             1239  0.967968   \n",
       "298           0.0         298                1.0             1238  0.964188   \n",
       "309           0.0         309                1.0             1175  0.977205   \n",
       "338           0.0         338                1.0             1295  0.971325   \n",
       "345           0.0         345                1.0             1290  0.971112   \n",
       "353           0.0         353                1.0             1261  0.984677   \n",
       "355           0.0         355                1.0             1267  0.965001   \n",
       "356           0.0         356                1.0             1262  0.979318   \n",
       "366           0.0         366                1.0             1272  0.985263   \n",
       "386           0.0         386                1.0             1248  0.984362   \n",
       "\n",
       "     ambiguity  y_offset  x_offset  reference  correlation  \n",
       "17    0.861263    0.1250    6.3750        0.0     0.999713  \n",
       "21    0.816333   -1.2500    4.8750        0.0     0.999727  \n",
       "283   0.890681    3.0000    5.1875        0.0     0.999637  \n",
       "291   0.936126    3.0000   -2.0000        0.0     0.999747  \n",
       "293   0.871732   -9.0000   -2.0000        0.0     0.999773  \n",
       "296   0.927719   -7.6250   -4.5000        0.0     0.999759  \n",
       "298   0.898417  -10.6875   13.1250        0.0     0.999724  \n",
       "309   0.930651   -3.1875    2.3125        0.0     0.999675  \n",
       "338   0.911120   -3.5625    6.7500        0.0     0.999822  \n",
       "345   0.907802   -6.6875   -2.3125        0.0     0.999785  \n",
       "353   0.789020   -6.0625    5.4375        0.0     0.999745  \n",
       "355   0.915773  -14.0000   15.3750        0.0     0.999711  \n",
       "356   0.899721    4.8125   -4.7500        0.0     0.999745  \n",
       "366   0.876244   -5.4375   -1.1250        0.0     0.999751  \n",
       "386   0.885029   15.8750   17.0000        0.0     0.999772  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the `clean` method to get back the subpixel registered correspondences.\n",
    "matches, mask = cg.edge[0][1].clean(['threshold'])\n",
    "matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4674d24a58>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA64AAACFCAYAAABbhaUaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4674d15ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cg.edge[0][1].plot(clean_keys=['threshold'], downsampling=True)"
   ]
  }
 ],
 "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": 2
}
