exBRISK.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting the SIFT keypoint detector and feature descriptor error function\n",
    "This document contains an experiment in which different keypoint detector and feature descriptor combinations are used to detect and uniquely describe common points of interest in image pairs. The goal of this experiment is to explore the effects that varying the values of the different input parameters of the detector/descriptors have on the frequency of inlier matches when computing a 3D relationship between images based upon epipolar geometry."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis\n",
    "\n",
    "There exists an optimal set of descriptor/detector parameters that can minimize the number of outlier match pairs computer over a set of images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experimental Design\n",
    "1. Select three image sets:\n",
    "    1. Nadir Facing Projective Camera.\n",
    "    2. Nadir Facing Fisheye Camera.\n",
    "    3. Oblique Camera.\n",
    "3. Define a bound and a step size for the different parameters.\n",
    "4. Load the images and initialize the Descriptor/Detector to the openCV initial values.\n",
    "5. For each image in the image set compute the keypoints and descriptors.\n",
    "6. For each pair of consecutive images, match corresponding keypoints.\n",
    "7. Estimate the fundamental matrix between the image pair and compute the inliers and outliers for the image pair.\n",
    "8. Sum the the number of inliers, outliers and total computed matches for the entire sequence.\n",
    "9. Select one parameter and vary it by the step size.\n",
    "10. Repeat steps 5 - 9 while varying the selected parameter until it reaches the bounds.\n",
    "11. Reset the parameter to its initial value.\n",
    "12. Select a new parameter to vary and repeat 10.\n",
    "13. Perform 3 - 12 for each image type.\n",
    "\n",
    "This experiment will result in a function for each parameter that corresponds to its behaviour about the point corresponding to the openCV initial values.\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment\n",
    "To begin we import the necessary python packages including the custom built image processing modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import image as im\n",
    "import matching as mt\n",
    "import imageUtility as ut\n",
    "import transforms as tn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select three image sets\n",
    "The following three image datasets are used:\n",
    "* __Nadir Facing Perspective Camera:__ Glacier images from the Sensefly eBee RTK\n",
    "* __Nadir Facing Fisheye Camera:__ Images of the Quatre Fourches River in Wood Buffalo National Park, Alberta, Canada\n",
    "* __Oblique Perspective Camera:__ Images of an office space\n",
    "\n",
    "In this experiment we operate on the raw, unprojected images. Therefore we do not account for any lense distortion nor the internal imaging geometry.\n",
    "\n",
    "### Define a bound and a step size for the different parameters\n",
    "In this experiment we will explore the following parameters:\n",
    "\n",
    "| Parameter | Description | Initial Value | Step Size | Bounds |\n",
    "| --- | --- | --- | --- | --- |\n",
    "|# Features | _desc_ | 10000| 0 | - |\n",
    "|# Octave Layers | _desc_ | 3| 1 | 1 - 5 |\n",
    "|Contrast Threshold | _desc_ | 0.04| 0.01 | 0.01 - 0.08 |\n",
    "|Edge Threshold | _desc_ | 10| 1 | 5 - 15 |\n",
    "|Sigma | _desc_ | 1.6 | 0.1 | 1.0 - 2.0 | \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "thresh=30\n",
    "octaves=3\n",
    "patternScale=1.0\n",
    "\n",
    "imgDir = {'glacier':{'dir':'/home/doopy/Documents/View3D/View3D_0_1/exp1/glacier/','ext':'.JPG'},\n",
    "            'wbnp':{'dir':'/home/doopy/Documents/View3D/View3D_0_1/exp1/wbnp/','ext':'.JPG'},\n",
    "            'desk':{'dir':'/home/doopy/Documents/View3D/View3D_0_1/exp1/desk/','ext':'.png'}\n",
    "           }\n",
    "\n",
    "imgRange = range(1,10)\n",
    "\n",
    "imageParams = {'scale':0.15,\n",
    "          'kp':'brisk',\n",
    "          'thresh':thresh,\n",
    "          'octaves':octaves,\n",
    "          'patternScale':patternScale\n",
    "         }\n",
    "\n",
    "params = {'thresh':{'step':5,'iv':10,'bdy':50},\n",
    "        'octaves':{'step':1,'iv':1,'bdy':10},\n",
    "        'patternScale':{'step':0.1,'iv':0.5,'bdy':1.5}\n",
    "         }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Open Images and Initialize the Descriptor/Detector to the openCV initial values\n",
    "A custom function is created, using the image class, which creates an image object for each image in the image set, and compute the keypoints and feature descriptors corresponding to the default openCV values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def openImages(imgRange,imgDir,imgParams,imgSet):\n",
    "    \n",
    "    images = []\n",
    "\n",
    "\n",
    "    for i in imgRange:\n",
    "        \n",
    "        images.append(im.image(imgDir[imgSet]['dir'] + str(i) + imgDir[imgSet]['ext'],imgParams))\n",
    "        \n",
    "    return images\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Results\n",
    "A custom function is created, using pyplot, which creates a line plots for each parameter variation. The x-axis corresponds to the parameter value, and the y axis correponds to the number of matches. Two curves are plotted:\n",
    "1. total number of matches.\n",
    "2. number of outlier matches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plotResults(results):\n",
    "    \n",
    "    plt.figure(1,figsize=(12,12))\n",
    "    i = 0\n",
    "    \n",
    "    for param in results.keys():\n",
    "        \n",
    "        values = np.array(results[param]['value'])\n",
    "        inlier = np.array(results[param]['inlierCount'])\n",
    "        outlier = np.array(results[param]['outlierCount'])\n",
    "        \n",
    "        spVal = 221+i\n",
    "        \n",
    "        plt.subplot(spVal)\n",
    "        plt.plot(values,outlier,label='outlier')\n",
    "        plt.plot(values,outlier+inlier,label='total matches')\n",
    "        plt.legend()\n",
    "        plt.title(param)\n",
    "\n",
    "        i+=1\n",
    "        \n",
    "    plt.subplots_adjust(left=0.10, right=0.95, hspace=0.8, wspace=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment\n",
    "A custom function is created to generate the experimental results. The function can be applied to any keypoint detector and descriptor pair available in the image and matching toolbox."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def computeExperiment(imageSet,params,imageParams):\n",
    "    \n",
    "    results = {}\n",
    "        \n",
    "    for parameter in params.keys():\n",
    "\n",
    "        print '*********** ' + parameter + ' ************'\n",
    "        \n",
    "        results[parameter] = {'value':[],'inlierCount':[],'outlierCount':[]}\n",
    "        \n",
    "        imgParams = {key: value for key, value in imageParams.items()}\n",
    "        \n",
    "        imgParams[parameter] = params[parameter]['iv']\n",
    "        print imgParams\n",
    "        \n",
    "        for j in np.arange(imgParams[parameter],params[parameter]['bdy'],params[parameter]['step']):\n",
    "\n",
    "            imgParams[parameter] = j\n",
    "\n",
    "            print '*********** ' + str(j) + ' ***********'\n",
    "\n",
    "            for img in imageSet:\n",
    "\n",
    "                img.computeKP(imgParams)\n",
    "\n",
    "            img1 = imageSet[0]\n",
    "            inlierSum = 0\n",
    "            outlierSum = 0\n",
    "\n",
    "            for img2 in imageSet[1:]:\n",
    "\n",
    "                F = tn.fundamental(img1,img2,imgParams)\n",
    "\n",
    "                inlierSum += F.inlierCount()\n",
    "                outlierSum += F.outlierCount()\n",
    "\n",
    "                img1 = img2   \n",
    "\n",
    "            results[parameter]['value'].append(imgParams[parameter])\n",
    "            results[parameter]['inlierCount'].append(inlierSum)\n",
    "            results[parameter]['outlierCount'].append(outlierSum)\n",
    "            \n",
    "    return results\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Glacier Images\n",
    "_Open and initialize the images_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "imgSet = openImages(imgRange,imgDir,imageParams,'glacier')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Compute the experiment._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*********** octaves ************\n",
      "{'thresh': 30, 'patternScale': 1.0, 'scale': 0.15, 'octaves': 1, 'kp': 'brisk'}\n",
      "*********** 1 ***********\n",
      "*********** 2 ***********\n",
      "*********** 3 ***********\n",
      "*********** 4 ***********\n",
      "*********** 5 ***********\n",
      "*********** 6 ***********\n",
      "*********** 7 ***********\n",
      "*********** 8 ***********\n",
      "*********** 9 ***********\n",
      "*********** patternScale ************\n",
      "{'thresh': 30, 'patternScale': 0.5, 'scale': 0.15, 'octaves': 3, 'kp': 'brisk'}\n",
      "*********** 0.5 ***********\n",
      "*********** 0.6 ***********\n",
      "*********** 0.7 ***********\n",
      "*********** 0.8 ***********\n",
      "*********** 0.9 ***********\n",
      "*********** 1.0 ***********\n",
      "*********** 1.1 ***********\n",
      "*********** 1.2 ***********\n",
      "*********** 1.3 ***********\n",
      "*********** 1.4 ***********\n",
      "*********** thresh ************\n",
      "{'thresh': 10, 'patternScale': 1.0, 'scale': 0.15, 'octaves': 3, 'kp': 'brisk'}\n",
      "*********** 10 ***********\n",
      "*********** 15 ***********\n",
      "*********** 20 ***********\n",
      "*********** 25 ***********\n",
      "*********** 30 ***********\n",
      "*********** 35 ***********\n",
      "*********** 40 ***********\n",
      "*********** 45 ***********\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'kp': 'brisk', 'octaves': 3, 'patternScale': 1.0, 'scale': 0.15, 'thresh': 30}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = computeExperiment(imgSet,params,imageParams)\n",
    "imageParams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Plot the results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ubJw3e/bsqKSkJCosLIy+8IUvRMuWLct4fK393Wn6kSSp41q5sqk3ydXvgeWr5eveauWr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fedfe8510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotResults(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Display results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'octaves': {'inlierCount': [3527,\n",
       "   3527,\n",
       "   3527,\n",
       "   3527,\n",
       "   3527,\n",
       "   3527,\n",
       "   3527,\n",
       "   3527,\n",
       "   3527],\n",
       "  'outlierCount': [199, 199, 199, 199, 199, 199, 199, 199, 199],\n",
       "  'value': [1, 2, 3, 4, 5, 6, 7, 8, 9]},\n",
       " 'patternScale': {'inlierCount': [3527,\n",
       "   3527,\n",
       "   3527,\n",
       "   3527,\n",
       "   3527,\n",
       "   3527,\n",
       "   3527,\n",
       "   3527,\n",
       "   3527,\n",
       "   3527],\n",
       "  'outlierCount': [199, 199, 199, 199, 199, 199, 199, 199, 199, 199],\n",
       "  'value': [0.5,\n",
       "   0.59999999999999998,\n",
       "   0.69999999999999996,\n",
       "   0.79999999999999993,\n",
       "   0.89999999999999991,\n",
       "   0.99999999999999989,\n",
       "   1.0999999999999999,\n",
       "   1.1999999999999997,\n",
       "   1.2999999999999998,\n",
       "   1.3999999999999999]},\n",
       " 'thresh': {'inlierCount': [3527, 3527, 3527, 3527, 3527, 3527, 3527, 3527],\n",
       "  'outlierCount': [199, 199, 199, 199, 199, 199, 199, 199],\n",
       "  'value': [10, 15, 20, 25, 30, 35, 40, 45]}}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wood Buffalo Images\n",
    "_Open and initialize the images_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imgSet = openImages(imgRange,imgDir,imageParams,'wbnp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Compute the experiment._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*********** octaves ************\n",
      "{'thresh': 30, 'patternScale': 1.0, 'scale': 0.15, 'octaves': 1, 'kp': 'brisk'}\n",
      "*********** 1 ***********\n",
      "*********** 2 ***********\n",
      "*********** 3 ***********\n",
      "*********** 4 ***********\n",
      "*********** 5 ***********\n",
      "*********** 6 ***********\n",
      "*********** 7 ***********\n",
      "*********** 8 ***********\n",
      "*********** 9 ***********\n",
      "*********** patternScale ************\n",
      "{'thresh': 30, 'patternScale': 0.5, 'scale': 0.15, 'octaves': 3, 'kp': 'brisk'}\n",
      "*********** 0.5 ***********\n",
      "*********** 0.6 ***********\n",
      "*********** 0.7 ***********\n",
      "*********** 0.8 ***********\n",
      "*********** 0.9 ***********\n",
      "*********** 1.0 ***********\n",
      "*********** 1.1 ***********\n",
      "*********** 1.2 ***********\n",
      "*********** 1.3 ***********\n",
      "*********** 1.4 ***********\n",
      "*********** thresh ************\n",
      "{'thresh': 10, 'patternScale': 1.0, 'scale': 0.15, 'octaves': 3, 'kp': 'brisk'}\n",
      "*********** 10 ***********\n",
      "*********** 15 ***********\n",
      "*********** 20 ***********\n",
      "*********** 25 ***********\n",
      "*********** 30 ***********\n",
      "*********** 35 ***********\n",
      "*********** 40 ***********\n",
      "*********** 45 ***********\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'kp': 'brisk', 'octaves': 3, 'patternScale': 1.0, 'scale': 0.15, 'thresh': 30}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = computeExperiment(imgSet,params,imageParams)\n",
    "imageParams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Plot Results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fJXWzrsTYrjY3WgRMj4enA49ljZ8GDAL2Aw4Eqru4DUmS+hvjq6SikEtzo4XA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\nAAAAAAAAAAAAAAAAYA3+H7zeB9pF0Fh0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fee03c990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotResults(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'octaves': {'inlierCount': [380, 380, 380, 380, 380, 380, 380, 380, 380],\n",
       "  'outlierCount': [8, 8, 8, 8, 8, 8, 8, 8, 8],\n",
       "  'value': [1, 2, 3, 4, 5, 6, 7, 8, 9]},\n",
       " 'patternScale': {'inlierCount': [380,\n",
       "   380,\n",
       "   380,\n",
       "   380,\n",
       "   380,\n",
       "   380,\n",
       "   380,\n",
       "   380,\n",
       "   380,\n",
       "   380],\n",
       "  'outlierCount': [8, 8, 8, 8, 8, 8, 8, 8, 8, 8],\n",
       "  'value': [0.5,\n",
       "   0.59999999999999998,\n",
       "   0.69999999999999996,\n",
       "   0.79999999999999993,\n",
       "   0.89999999999999991,\n",
       "   0.99999999999999989,\n",
       "   1.0999999999999999,\n",
       "   1.1999999999999997,\n",
       "   1.2999999999999998,\n",
       "   1.3999999999999999]},\n",
       " 'thresh': {'inlierCount': [380, 380, 380, 380, 380, 380, 380, 380],\n",
       "  'outlierCount': [8, 8, 8, 8, 8, 8, 8, 8],\n",
       "  'value': [10, 15, 20, 25, 30, 35, 40, 45]}}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Office Images\n",
    "_Open and initialize the images_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "imageParams['scale']=1\n",
    "imgSet = openImages(imgRange,imgDir,imageParams,'desk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imgSet[0].showImage()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Compute Experiment_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*********** octaves ************\n",
      "{'thresh': 30, 'patternScale': 1.0, 'scale': 1, 'octaves': 1, 'kp': 'brisk'}\n",
      "*********** 1 ***********\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object has no attribute '__getitem__'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-b5b89ad2fe43>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcomputeExperiment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimgSet\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mimageParams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mimageParams\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-11-d32804c5ba0a>\u001b[0m in \u001b[0;36mcomputeExperiment\u001b[1;34m(imageSet, params, imageParams)\u001b[0m\n\u001b[0;32m     32\u001b[0m                 \u001b[0mF\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfundamental\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mimg2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mimgParams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 34\u001b[1;33m                 \u001b[0minlierSum\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minlierCount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     35\u001b[0m                 \u001b[0moutlierSum\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutlierCount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/doopy/Documents/View3D/View3D_0_1/transforms.pyc\u001b[0m in \u001b[0;36minlierCount\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     41\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0minlierCount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 43\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbitmask\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbitmask\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     44\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0moutlierCount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'NoneType' object has no attribute '__getitem__'"
     ]
    }
   ],
   "source": [
    "results = computeExperiment(imgSet,params,imageParams)\n",
    "imageParams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9d9s/2yFJ7Z4FhCRJykkbtm3g/EfPZ+uOrcy4bAa9u/TOdkiScAyEJEnKQYs+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\nZozQhbp5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22e3b34e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotResults(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'contrastThreshold': {'inlierCount': [2196,\n",
       "   2055,\n",
       "   1883,\n",
       "   1735,\n",
       "   1570,\n",
       "   1418,\n",
       "   1260,\n",
       "   1108],\n",
       "  'outlierCount': [482, 397, 358, 305, 278, 251, 232, 188],\n",
       "  'value': [0.01,\n",
       "   0.02,\n",
       "   0.029999999999999999,\n",
       "   0.040000000000000001,\n",
       "   0.050000000000000003,\n",
       "   0.060000000000000005,\n",
       "   0.069999999999999993,\n",
       "   0.080000000000000002]},\n",
       " 'edgeThreshold': {'inlierCount': [1281,\n",
       "   1417,\n",
       "   1563,\n",
       "   1619,\n",
       "   1664,\n",
       "   1735,\n",
       "   1771,\n",
       "   1817,\n",
       "   1847,\n",
       "   1870,\n",
       "   1929],\n",
       "  'outlierCount': [209, 247, 248, 276, 307, 305, 330, 347, 349, 364, 343],\n",
       "  'value': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]},\n",
       " 'nOctaveLayers': {'inlierCount': [1089, 1597, 1735, 1877, 1798],\n",
       "  'outlierCount': [216, 283, 305, 325, 330],\n",
       "  'value': [1, 2, 3, 4, 5]},\n",
       " 'sigma': {'inlierCount': [1705,\n",
       "   1766,\n",
       "   1757,\n",
       "   1737,\n",
       "   1791,\n",
       "   1770,\n",
       "   1735,\n",
       "   1760,\n",
       "   1762,\n",
       "   1671,\n",
       "   1688],\n",
       "  'outlierCount': [383, 382, 384, 358, 315, 322, 305, 305, 271, 275, 261],\n",
       "  'value': [1.0,\n",
       "   1.1000000000000001,\n",
       "   1.2000000000000002,\n",
       "   1.3000000000000003,\n",
       "   1.4000000000000004,\n",
       "   1.5000000000000004,\n",
       "   1.6000000000000005,\n",
       "   1.7000000000000006,\n",
       "   1.8000000000000007,\n",
       "   1.9000000000000008,\n",
       "   2.0000000000000009]}}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}