PPGFromVideoPython2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#setup\n",
    "import matplotlib.pyplot as plt\n",
    "import urllib2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import subprocess as sp\n",
    "import numpy\n",
    "import select\n",
    "import sys\n",
    "\n",
    "#set up ffmpeg\n",
    "FFMPEG_BIN = \"ffmpeg\"\n",
    "video = '556.mp4'\n",
    "width = 1920\n",
    "height = 1080\n",
    "command = [ FFMPEG_BIN,\n",
    "            '-i', video,\n",
    "            '-f', 'image2pipe',\n",
    "            '-pix_fmt', 'rgb24',\n",
    "            '-vcodec', 'rawvideo', '-']\n",
    "pipe = sp.Popen(command, stdout = sp.PIPE, bufsize=10**8)\n",
    "\n",
    "#return red value from each frame\n",
    "def captureFrame():\n",
    "    # read 1920*1080*3 bytes (= 1 frame)\n",
    "    raw_image = pipe.stdout.read(width*height*3)\n",
    "    # transform the byte read into a numpy array\n",
    "    image =  numpy.fromstring(raw_image, dtype='uint8')\n",
    "    frameTotalByteSize = width*height*3\n",
    "    if len(image) < frameTotalByteSize:\n",
    "        return False\n",
    "    image = image.reshape((width,height,3))\n",
    "    # throw away the data in the pipe's buffer.\n",
    "    pipe.stdout.flush()\n",
    "    totalRed = 0\n",
    "    for col in range(0, width):\n",
    "        for row in range(0, height):\n",
    "            totalRed += image[col][row][0]\n",
    "    return totalRed/1.0/width/height\n",
    "\n",
    "print 'Loading the video'\n",
    "rValueList = list()\n",
    "while True:\n",
    "    r = captureFrame()\n",
    "    if not r:\n",
    "        break\n",
    "    else:\n",
    "        rValueList.append(r)\n",
    "    print r\n",
    "print 'finished'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "file = open('temp', 'w')\n",
    "numpy.savetxt(file, rValueList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#debug purpose only\n",
    "file = open('temp', 'r')\n",
    "rValueList = file.read();\n",
    "rValueList = rValueList.split('\\n')\n",
    "rValueList =  rValueList[0:len(rValueList)-1]\n",
    "rValueList = [float(red) for red in rValueList]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#obtain rr intervals from red value\n",
    "def constructRRListFromRedValueList(redValueList, fps):\n",
    "    timePerFrame = 1.0/fps\n",
    "    rrList = list()\n",
    "    lastMaxIndex = False\n",
    "    for i in range(1, len(redValueList)-1):\n",
    "        previousSlope = redValueList[i] - redValueList[i-1]\n",
    "        afterSlope = redValueList[i+1] - redValueList[i]\n",
    "        # if at that point it is concave down = local max\n",
    "        if previousSlope*afterSlope < 0 and previousSlope > 0:\n",
    "            if lastMaxIndex:\n",
    "                rrInterval = (i-lastMaxIndex)*timePerFrame\n",
    "                rrList.append(rrInterval)\n",
    "            lastMaxIndex = i\n",
    "    return rrList\n",
    "rrList = constructRRListFromRedValueList(rValueList, 30.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Video Time:  29.3666666667\n",
      "RR Interval List:  [1.0, 0.3333333333333333, 0.5666666666666667, 0.3333333333333333, 0.5333333333333333, 0.9, 0.9333333333333333, 0.36666666666666664, 0.5, 0.9, 0.9333333333333333, 1.0333333333333332, 0.36666666666666664, 0.6, 0.3333333333333333, 0.5333333333333333, 0.36666666666666664, 0.6333333333333333, 1.0, 0.36666666666666664, 0.6, 0.3333333333333333, 0.5666666666666667, 0.36666666666666664, 0.5666666666666667, 0.36666666666666664, 0.6, 1.0, 0.3333333333333333, 0.5666666666666667, 0.3, 0.5666666666666667, 0.9333333333333333, 1.0, 1.0333333333333332, 0.3, 0.5666666666666667, 0.3333333333333333, 0.6333333333333333, 1.0333333333333332, 0.3333333333333333, 0.6666666666666666, 0.3333333333333333, 0.6, 0.36666666666666664, 0.6333333333333333, 0.2, 0.16666666666666666, 0.6, 0.36666666666666664, 0.5666666666666667]\n",
      "\n",
      "\n",
      "Time List:  [1.0, 1.3333333333333333, 1.9, 2.2333333333333334, 2.7666666666666666, 3.6666666666666665, 4.6, 4.966666666666666, 5.466666666666666, 6.366666666666666, 7.3, 8.333333333333332, 8.7, 9.299999999999999, 9.633333333333333, 10.166666666666666, 10.533333333333333, 11.166666666666666, 12.166666666666666, 12.533333333333333, 13.133333333333333, 13.466666666666667, 14.033333333333333, 14.4, 14.966666666666667, 15.333333333333334, 15.933333333333334, 16.933333333333334, 17.266666666666666, 17.833333333333332, 18.133333333333333, 18.7, 19.633333333333333, 20.633333333333333, 21.666666666666664, 21.966666666666665, 22.53333333333333, 22.866666666666664, 23.499999999999996, 24.53333333333333, 24.866666666666664, 25.53333333333333, 25.866666666666664, 26.466666666666665, 26.833333333333332, 27.466666666666665, 27.666666666666664, 27.833333333333332, 28.433333333333334, 28.8, 29.366666666666667]\n"
     ]
    },
    {
     "data": {
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KydNltY5RzyF1CEOeqTBCuiFiyDOGAcrp81+nvwDFnlM75+NTN1jEkICIPF9E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\nFCYMhmEYxij+PzEUAHqyCkdwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15fb411110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def constructTimeListFromRRList(rrList, beginT, endT):\n",
    "    #construct time array from the rr intervals\n",
    "    tList = list()\n",
    "    tList.append(float(rrList[0]))\n",
    "    for i in range(1, len(rrList)):\n",
    "        tList.append(tList[len(tList)-1] + float(rrList[i]))\n",
    "    if tList[len(tList)-1]<endT:\n",
    "        endT=tList[len(tList)-1]\n",
    "\n",
    "    #limit the time interval\n",
    "    beginIndex = 0\n",
    "    endIndex = 0\n",
    "    i = 0\n",
    "    while tList[i]<beginT:\n",
    "        i+=1\n",
    "    beginIndex = i\n",
    "    while tList[i]<endT:\n",
    "        i+=1\n",
    "    endIndex = i\n",
    "    tList = tList[beginIndex: endIndex]\n",
    "    rrList = rrList[beginIndex: endIndex]\n",
    "    return tList, rrList\n",
    "\n",
    "def plot(tList, rrList):\n",
    "    #plotting the graph\n",
    "    plt.plot(tList, rrList)\n",
    "    plt.xlabel('Time in Second')\n",
    "    plt.ylabel('RR Interval in Milisecond')\n",
    "    plt.title('Timve vs RR Interval')\n",
    "    plt.show()\n",
    "tList, rrList = constructTimeListFromRRList(rrList, 0, 50)\n",
    "total =0\n",
    "for r in rrList:\n",
    "    total+=r\n",
    "print 'Total Video Time: ', total\n",
    "print 'RR Interval List: ', rrList\n",
    "\n",
    "print '\\n'\n",
    "print 'Time List: ', tList\n",
    "plot(tList, rrList)"
   ]
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   "source": []
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