exSIFT.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Experiment\n",
    "To begin we import the necessary python packages including the custom built image processing modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "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": {
    "deletable": true,
    "editable": true
   },
   "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": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "nOctaveLayers = 3\n",
    "contrastThreshold = 0.04\n",
    "edgeThreshold = 10\n",
    "sigma = 1.6\n",
    "\n",
    "imgDir = {'glacier':{'dir':'/home/dennis/Documents/View3D/images/glacier/','ext':'.JPG'},\n",
    "            'wbnp':{'dir':'/home/dennis/Documents/View3D/images/wbnp/','ext':'.JPG'},\n",
    "            'desk':{'dir':'/home/dennis/Documents/View3D/images/cathedral/','ext':'.JPG'}\n",
    "         }\n",
    "\n",
    "imgRange = range(1,9)\n",
    "\n",
    "imageParams = {'scale':0.15,\n",
    "          'kp':'sift',\n",
    "          'nOctaveLayers':nOctaveLayers,\n",
    "          'contrastThreshold':contrastThreshold,\n",
    "          'edgeThreshold':edgeThreshold,\n",
    "          'sigma':sigma\n",
    "         }\n",
    "\n",
    "params = {'nOctaveLayers':{'step':1,'iv':1,'bdy':6},\n",
    "        'contrastThreshold':{'step':0.01,'iv':0.01,'bdy':0.09},\n",
    "        'edgeThreshold':{'step':1,'iv':5,'bdy':16},\n",
    "        'sigma':{'step':0.1,'iv':1.0,'bdy':2.1},\n",
    "         }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": 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": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "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": {
    "deletable": true,
    "editable": true
   },
   "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": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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",
    "        total = inlier+outlier\n",
    "        \n",
    "        spVal = 221+i\n",
    "        \n",
    "        plt.subplot(spVal)\n",
    "        plt.plot(values,1.0*outlier/total.max(),label='outlier')\n",
    "        plt.plot(values,1.0*(outlier+inlier)/(total.max()),label='total matches')\n",
    "        plt.plot(values,1.0*outlier/(total),label='wi')\n",
    "        #plt.plot(values,outlier+inlier,label='epsi')\n",
    "        #plt.plot(values,outlier+inlier,label='metric')\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": {
    "deletable": true,
    "editable": true
   },
   "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": 20,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": 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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Glacier Images\n",
    "_Open and initialize the images_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "imgSet = openImages(imgRange,imgDir,imageParams,'glacier')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "_Compute the experiment._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*********** sigma ************\n",
      "{'scale': 0.15, 'kp': 'sift', 'edgeThreshold': 10, 'sigma': 1.0, 'nOctaveLayers': 3, 'contrastThreshold': 0.04}\n",
      "*********** 1.0 ***********\n",
      "*********** 1.1 ***********\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",
      "*********** 2.000000000000001 ***********\n",
      "*********** nOctaveLayers ************\n",
      "{'scale': 0.15, 'kp': 'sift', 'edgeThreshold': 10, 'sigma': 1.6, 'nOctaveLayers': 1, 'contrastThreshold': 0.04}\n",
      "*********** 1 ***********\n",
      "*********** 2 ***********\n",
      "*********** 3 ***********\n",
      "*********** 4 ***********\n",
      "*********** 5 ***********\n",
      "*********** contrastThreshold ************\n",
      "{'scale': 0.15, 'kp': 'sift', 'edgeThreshold': 10, 'sigma': 1.6, 'nOctaveLayers': 3, 'contrastThreshold': 0.01}\n",
      "*********** 0.01 ***********\n",
      "*********** 0.02 ***********\n",
      "*********** 0.03 ***********\n",
      "*********** 0.04 ***********\n",
      "*********** 0.05 ***********\n",
      "*********** 0.060000000000000005 ***********\n",
      "*********** 0.06999999999999999 ***********\n",
      "*********** 0.08 ***********\n",
      "*********** edgeThreshold ************\n",
      "{'scale': 0.15, 'kp': 'sift', 'edgeThreshold': 5, 'sigma': 1.6, 'nOctaveLayers': 3, 'contrastThreshold': 0.04}\n",
      "*********** 5 ***********\n",
      "*********** 6 ***********\n",
      "*********** 7 ***********\n",
      "*********** 8 ***********\n",
      "*********** 9 ***********\n",
      "*********** 10 ***********\n",
      "*********** 11 ***********\n",
      "*********** 12 ***********\n",
      "*********** 13 ***********\n",
      "*********** 14 ***********\n",
      "*********** 15 ***********\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'contrastThreshold': 0.04,\n",
       " 'edgeThreshold': 10,\n",
       " 'kp': 'sift',\n",
       " 'nOctaveLayers': 3,\n",
       " 'scale': 0.15,\n",
       " 'sigma': 1.6}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = computeExperiment(imgSet,params,imageParams)\n",
    "imageParams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "_Plot the results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwYAAALICAYAAADFU+ekAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fede3446150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotResults(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "_Display results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'contrastThreshold': {'inlierCount': [20895,\n",
       "   20783,\n",
       "   19788,\n",
       "   17851,\n",
       "   16507,\n",
       "   14400,\n",
       "   12592,\n",
       "   10819],\n",
       "  'outlierCount': [634, 330, 286, 782, 363, 535, 503, 603],\n",
       "  'value': [0.01,\n",
       "   0.02,\n",
       "   0.03,\n",
       "   0.04,\n",
       "   0.05,\n",
       "   0.060000000000000005,\n",
       "   0.06999999999999999,\n",
       "   0.08]},\n",
       " 'edgeThreshold': {'inlierCount': [13940,\n",
       "   15321,\n",
       "   16502,\n",
       "   17271,\n",
       "   17841,\n",
       "   17851,\n",
       "   18365,\n",
       "   18665,\n",
       "   18861,\n",
       "   18902,\n",
       "   19245],\n",
       "  'outlierCount': [547, 686, 482, 431, 380, 782, 550, 502, 481, 599, 367],\n",
       "  'value': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]},\n",
       " 'nOctaveLayers': {'inlierCount': [6550, 13700, 17851, 21238, 22849],\n",
       "  'outlierCount': [216, 382, 782, 528, 466],\n",
       "  'value': [1, 2, 3, 4, 5]},\n",
       " 'sigma': {'inlierCount': [19561,\n",
       "   21541,\n",
       "   23353,\n",
       "   22695,\n",
       "   21092,\n",
       "   20305,\n",
       "   17851,\n",
       "   16220,\n",
       "   14845,\n",
       "   13793,\n",
       "   12488],\n",
       "  'outlierCount': [1195, 435, 964, 814, 1102, 519, 782, 587, 557, 374, 378],\n",
       "  'value': [1.0,\n",
       "   1.1,\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",
       "   2.000000000000001]}}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Wood Buffalo Images\n",
    "_Open and initialize the images_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "imgSet = openImages(imgRange,imgDir,imageParams,'wbnp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "_Compute the experiment._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*********** sigma ************\n",
      "{'scale': 0.15, 'kp': 'sift', 'edgeThreshold': 10, 'sigma': 1.0, 'nOctaveLayers': 3, 'contrastThreshold': 0.04}\n",
      "*********** 1.0 ***********\n",
      "*********** 1.1 ***********\n",
      "*********** 1.2 ***********\n",
      "*********** 1.3 ***********\n",
      "*********** 1.4 ***********\n",
      "*********** 1.5 ***********\n",
      "*********** 1.6 ***********\n",
      "*********** 1.7 ***********\n",
      "*********** 1.8 ***********\n",
      "*********** 1.9 ***********\n",
      "*********** 2.0 ***********\n",
      "*********** nOctaveLayers ************\n",
      "{'scale': 0.15, 'kp': 'sift', 'edgeThreshold': 10, 'sigma': 1.6, 'nOctaveLayers': 1, 'contrastThreshold': 0.04}\n",
      "*********** 1 ***********\n",
      "*********** 2 ***********\n",
      "*********** 3 ***********\n",
      "*********** 4 ***********\n",
      "*********** 5 ***********\n",
      "*********** contrastThreshold ************\n",
      "{'scale': 0.15, 'kp': 'sift', 'edgeThreshold': 10, 'sigma': 1.6, 'nOctaveLayers': 3, 'contrastThreshold': 0.01}\n",
      "*********** 0.01 ***********\n",
      "*********** 0.02 ***********\n",
      "*********** 0.03 ***********\n",
      "*********** 0.04 ***********\n",
      "*********** 0.05 ***********\n",
      "*********** 0.06 ***********\n",
      "*********** 0.07 ***********\n",
      "*********** 0.08 ***********\n",
      "*********** edgeThreshold ************\n",
      "{'scale': 0.15, 'kp': 'sift', 'edgeThreshold': 5, 'sigma': 1.6, 'nOctaveLayers': 3, 'contrastThreshold': 0.04}\n",
      "*********** 5 ***********\n",
      "*********** 6 ***********\n",
      "*********** 7 ***********\n",
      "*********** 8 ***********\n",
      "*********** 9 ***********\n",
      "*********** 10 ***********\n",
      "*********** 11 ***********\n",
      "*********** 12 ***********\n",
      "*********** 13 ***********\n",
      "*********** 14 ***********\n",
      "*********** 15 ***********\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'contrastThreshold': 0.04,\n",
       " 'edgeThreshold': 10,\n",
       " 'kp': 'sift',\n",
       " 'nOctaveLayers': 3,\n",
       " 'scale': 0.15,\n",
       " 'sigma': 1.6}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = computeExperiment(imgSet,params,imageParams)\n",
    "imageParams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "_Plot Results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAALICAYAAADv4xYLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIiIiIiIiIiIiIiISAT/B8guDxcjbMjRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22dde97090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotResults(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "_Results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'contrastThreshold': {'inlierCount': [1859,\n",
       "   1734,\n",
       "   1631,\n",
       "   1439,\n",
       "   1196,\n",
       "   958,\n",
       "   738,\n",
       "   583],\n",
       "  'outlierCount': [207, 210, 143, 162, 170, 154, 105, 72],\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': [1305,\n",
       "   1346,\n",
       "   1363,\n",
       "   1338,\n",
       "   1429,\n",
       "   1439,\n",
       "   1442,\n",
       "   1452,\n",
       "   1452,\n",
       "   1455,\n",
       "   1462],\n",
       "  'outlierCount': [119, 168, 174, 224, 153, 162, 172, 166, 176, 181, 176],\n",
       "  'value': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]},\n",
       " 'nOctaveLayers': {'inlierCount': [779, 1140, 1439, 1525, 1502],\n",
       "  'outlierCount': [91, 146, 162, 200, 247],\n",
       "  'value': [1, 2, 3, 4, 5]},\n",
       " 'sigma': {'inlierCount': [1510,\n",
       "   1371,\n",
       "   1401,\n",
       "   1480,\n",
       "   1457,\n",
       "   1394,\n",
       "   1439,\n",
       "   1360,\n",
       "   1300,\n",
       "   1241,\n",
       "   1232],\n",
       "  'outlierCount': [421, 321, 284, 255, 182, 190, 162, 127, 143, 158, 131],\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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Office Images\n",
    "_Open and initialize the images_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "imageParams['scale']=1\n",
    "imgSet = openImages(imgRange,imgDir,imageParams,'desk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "imgSet[0].showImage()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "_Compute Experiment_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*********** sigma ************\n",
      "{'scale': 1, 'kp': 'sift', 'edgeThreshold': 10, 'sigma': 1.0, 'nOctaveLayers': 3, 'contrastThreshold': 0.04}\n",
      "*********** 1.0 ***********\n",
      "*********** 1.1 ***********\n",
      "*********** 1.2 ***********\n",
      "*********** 1.3 ***********\n",
      "*********** 1.4 ***********\n",
      "*********** 1.5 ***********\n",
      "*********** 1.6 ***********\n",
      "*********** 1.7 ***********\n",
      "*********** 1.8 ***********\n",
      "*********** 1.9 ***********\n",
      "*********** 2.0 ***********\n",
      "*********** nOctaveLayers ************\n",
      "{'scale': 1, 'kp': 'sift', 'edgeThreshold': 10, 'sigma': 1.6, 'nOctaveLayers': 1, 'contrastThreshold': 0.04}\n",
      "*********** 1 ***********\n",
      "*********** 2 ***********\n",
      "*********** 3 ***********\n",
      "*********** 4 ***********\n",
      "*********** 5 ***********\n",
      "*********** contrastThreshold ************\n",
      "{'scale': 1, 'kp': 'sift', 'edgeThreshold': 10, 'sigma': 1.6, 'nOctaveLayers': 3, 'contrastThreshold': 0.01}\n",
      "*********** 0.01 ***********\n",
      "*********** 0.02 ***********\n",
      "*********** 0.03 ***********\n",
      "*********** 0.04 ***********\n",
      "*********** 0.05 ***********\n",
      "*********** 0.06 ***********\n",
      "*********** 0.07 ***********\n",
      "*********** 0.08 ***********\n",
      "*********** edgeThreshold ************\n",
      "{'scale': 1, 'kp': 'sift', 'edgeThreshold': 5, 'sigma': 1.6, 'nOctaveLayers': 3, 'contrastThreshold': 0.04}\n",
      "*********** 5 ***********\n",
      "*********** 6 ***********\n",
      "*********** 7 ***********\n",
      "*********** 8 ***********\n",
      "*********** 9 ***********\n",
      "*********** 10 ***********\n",
      "*********** 11 ***********\n",
      "*********** 12 ***********\n",
      "*********** 13 ***********\n",
      "*********** 14 ***********\n",
      "*********** 15 ***********\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'contrastThreshold': 0.04,\n",
       " 'edgeThreshold': 10,\n",
       " 'kp': 'sift',\n",
       " 'nOctaveLayers': 3,\n",
       " 'scale': 1,\n",
       " 'sigma': 1.6}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = computeExperiment(imgSet,params,imageParams)\n",
    "imageParams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAALICAYAAADv4xYLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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,
    "deletable": true,
    "editable": true
   },
   "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"
   ]
  }
 ],
 "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
}