2016-3-13FindingOptimalTimeWindow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding the Optimal Time Window for Detecting RR Interval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we refer to the time window as the frame. Each frame is 0.033 sec front and back (so 0.67 total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "fps = 30.323427803"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def readFromFile(fileName):\n",
    "    l =numpy.loadtxt(fileName)\n",
    "    return l[0], l[1], l[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def detectPeak(colorL, frameThreshold):\n",
    "    peakL = list()\n",
    "    colorlistLen = len(colorL)\n",
    "    for i in range(1, colorlistLen-1):\n",
    "        biggerThanPrevious = colorL[i] > colorL[i-1]\n",
    "        biggerThanAfter = colorL[i] > colorL[i+1]\n",
    "        #if it is a local maximum, then check if it is a RR peak by comparing its maxima interval to frameThreshold\n",
    "        if biggerThanPrevious and biggerThanAfter:\n",
    "            for k in range(2, frameThreshold+1):\n",
    "                if i-k >= 0:\n",
    "                    if not colorL[i] > colorL[i-k]:\n",
    "                        #this is not a rr peak\n",
    "                        break\n",
    "                if i+k < colorlistLen:\n",
    "                    if not colorL[i] > colorL[i+k]:\n",
    "                        #this is not a rr peak\n",
    "                        break\n",
    "            else:\n",
    "                #this IS a rr peak, store the array index\n",
    "                peakL.append(i)\n",
    "    return peakL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def constructRRIntervalList(peakL):\n",
    "    return [ (peakL[i]-peakL[i-1])/fps for i in range(1, len(peakL))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function defines the criteria for being a true RR interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def isTrueRRInterval(rrInterval):\n",
    "    if 0.54 <= rrInterval and rrInterval <= 1.0:\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function detect the amount of errors in a list of RR interval, as judged by function isTrueRRInterval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def getPercentError(rrL):\n",
    "    errorNum = 0\n",
    "    for rrInterval in rrL:\n",
    "        if (not isTrueRRInterval(rrInterval)):\n",
    "            errorNum += 1\n",
    "    return 100.0*errorNum/len(rrL) if len(rrL) else 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function analyze the rgb txt file of a PPG video and return important information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def analyzeVideoAndFindPercentErrorList(fileName):\n",
    "    rErrorL = []\n",
    "    gErrorL = []\n",
    "    bErrorL = []\n",
    "    rL, gL, bL = readFromFile(fileName)\n",
    "    for frameThreshold in range(1, 26):\n",
    "        rRRL = constructRRIntervalList(detectPeak(rL, frameThreshold))\n",
    "        gRRL = constructRRIntervalList(detectPeak(gL, frameThreshold))\n",
    "        bRRL = constructRRIntervalList(detectPeak(bL, frameThreshold))\n",
    "        rErrorL.append(getPercentError(rRRL))\n",
    "        gErrorL.append(getPercentError(gRRL))\n",
    "        bErrorL.append(getPercentError(bRRL))\n",
    "    return rErrorL, gErrorL, bErrorL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "videoList = [\"/home/mong/Desktop/map/map/videos/VID_20150123_172926_F.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/VID_20150123_173259_Q.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/VID_20150123_173329_F.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/VID_20150123_173358_Q.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/VID_20150123_173427_F.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/VID_20150123_173457_Q.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/VID_20150123_173556_Q.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/VID_20150123_173629_F.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/VID_20150123_173700_Q.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/VID_20150123_173757_Q.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/VID_20150123_173028_F.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/VID_20150123_172857_Q.mp4.txt\"]\n",
    "videoList = [\"/home/mong/Desktop/map/map/videos/160x120.VID_20150123_172926_F.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/160x120.VID_20150123_173259_Q.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/160x120.VID_20150123_173329_F.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/160x120.VID_20150123_173358_Q.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/160x120.VID_20150123_173427_F.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/160x120.VID_20150123_173457_Q.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/160x120.VID_20150123_173556_Q.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/160x120.VID_20150123_173629_F.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/160x120.VID_20150123_173700_Q.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/160x120.VID_20150123_173757_Q.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/160x120.VID_20150123_173028_F.mp4.txt\",\n",
    "    \"/home/mong/Desktop/map/map/videos/160x120.VID_20150123_172857_Q.mp4.txt\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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PP8lw7YULdlejlFJK2Ss+HtaskcayhQvDF1/I4v+QEGn2/sADdlcoPDagrQhd4fiLZs4s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\nZJQ5nwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f727a9b66d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10, 6)\n",
    "rAvgErrorL = [0 for i in range(0, 25)]\n",
    "gAvgErrorL = [0 for i in range(0, 25)]\n",
    "bAvgErrorL = [0 for i in range(0, 25)]\n",
    "goodVideoL = {}\n",
    "for video in videoList:\n",
    "    rErrorL, gErrorL, bErrorL = analyzeVideoAndFindPercentErrorList(video)\n",
    "    #find the errors less than .1\n",
    "    for i in range(0, len(rErrorL)):\n",
    "        if rErrorL[i] < 0.1:\n",
    "            goodVideoL[video+\"(red frame \" + str((i+1)*2)+\")\"] = rErrorL[i]\n",
    "    for i in range(0, len(gErrorL)):\n",
    "        if gErrorL[i] < 0.1:\n",
    "            goodVideoL[video+\"(green frame \" + str((i+1)*2)+\")\"] = gErrorL[i]\n",
    "    for i in range(0, len(bErrorL)):\n",
    "        if bErrorL[i] < 0.1:\n",
    "            goodVideoL[video+\"(blue frame \" + str((i+1)*2)+\")\"] = bErrorL[i]\n",
    "    for i in range(0, 25):\n",
    "        rAvgErrorL[i] += rErrorL[i]\n",
    "        gAvgErrorL[i] += gErrorL[i]\n",
    "        bAvgErrorL[i] += bErrorL[i]\n",
    "rAvgErrorL = [totalError/len(videoList) for totalError in rAvgErrorL]\n",
    "gAvgErrorL = [totalError/len(videoList) for totalError in gAvgErrorL]\n",
    "bAvgErrorL = [totalError/len(videoList) for totalError in bAvgErrorL]\n",
    "frameThresholdL = [frameThreshold*2 for frameThreshold in range(1, 26)]\n",
    "plt.plot(frameThresholdL, rAvgErrorL, color='red')\n",
    "plt.plot(frameThresholdL, gAvgErrorL, color='green')\n",
    "plt.plot(frameThresholdL, bAvgErrorL, color='blue')\n",
    "plt.xlabel(\"Frame around the peak (each frame 0.0333s)\")\n",
    "plt.xlim(2, 50)\n",
    "plt.ylim(0, 101)\n",
    "plt.ylabel(\"Percent Error\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the good videos with percent errors less than 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160x120.VID_20150123_172926_F.mp4.txt(green frame 18) with percent error of 0.0\n",
      "160x120.VID_20150123_172926_F.mp4.txt(green frame 14) with percent error of 0.0\n",
      "160x120.VID_20150123_172926_F.mp4.txt(green frame 10) with percent error of 0.0\n",
      "160x120.VID_20150123_172926_F.mp4.txt(green frame 12) with percent error of 0.0\n",
      "160x120.VID_20150123_172926_F.mp4.txt(green frame 20) with percent error of 0.0\n",
      "160x120.VID_20150123_172926_F.mp4.txt(green frame 16) with percent error of 0.0\n",
      "160x120.VID_20150123_172926_F.mp4.txt(green frame 8) with percent error of 0.0\n"
     ]
    }
   ],
   "source": [
    "for goodVideo in goodVideoL:\n",
    "    print (goodVideo.split(\"/\")[-1], \"with percent error of\", goodVideoL[goodVideo])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ideal frame around and time around is as follow\n",
      "for red: 14 which is 0.467 seconds\n",
      "for green: 16 which is 0.533 seconds\n",
      "for blue: 30 which is 1.000 seconds\n"
     ]
    }
   ],
   "source": [
    "def minimum(l):\n",
    "    minIndex = 0\n",
    "    minimum = l[minIndex];\n",
    "    for curIndex in range (1, len(l)):\n",
    "        if l[curIndex] < l[minIndex]:\n",
    "            minIndex = curIndex\n",
    "            minimum = l[curIndex]\n",
    "    return minimum, minIndex\n",
    "rLowestErrorPercentage, rIndex = minimum(rAvgErrorL)\n",
    "gLowestErrorPercentage, gIndex = minimum(gAvgErrorL)\n",
    "bLowestErrorPercentage, bIndex = minimum(bAvgErrorL)\n",
    "print(\"The ideal frame around and time around is as follow\")\n",
    "print(\"for red: {0:2d} which is {1:.3f} seconds\".format((rIndex*2+2), (rIndex*2+2)/30))\n",
    "print(\"for green: {0:2d} which is {1:.3f} seconds\".format(gIndex*2+2, (gIndex*2+2)/30))\n",
    "print(\"for blue: {0:2d} which is {1:.3f} seconds\".format(bIndex*2+2, (bIndex*2+2)/30))\n"
   ]
  }
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