app.js

const express = require('express'),
  app = express(),
  logger = require('morgan'),
  errorHandler = require('errorhandler'),
  debug = require('debug')('server'),
  socketio = require('socket.io'),
  http = require('http'),
  cookieParser = require('cookie-parser'),
  bodyParser = require('body-parser'),
  utils = require('./app/src/utils'),
  consts = require('./consts'),
  axios = require('axios'),
  BASE_PATH = `${__dirname}/app`,
  ENV = process.env.NODE_ENV || 'development',
  DEFAULT_PORT = 3001,
  SOCKET_PORT = 8000,
  EMOTION_FPS = 5; // return the analysed data after n frames

/* Configuration */
app.set('views', `${BASE_PATH}/views`)
app.engine('html', require('ejs').renderFile);
app.set('view engine', 'html');
app.use(bodyParser.json())
app.use(bodyParser.urlencoded({ extended: false }))
app.use(cookieParser())
app.use('/assets', express.static(`${BASE_PATH}/public`))

if (ENV === 'development') {
  console.log('DEVELOPMENT env')
  app.use(errorHandler({dumpExceptions: true, showStack: true}))
  app.use(logger('dev'))
} else {
  console.log('PRODUCTION env')
  app.use(errorHandler())
  app.use(logger())
}

// app.locals = {
//   accEmotions: {
//     anger: 0,
//     contempt: 0,
//     disgust: 0,
//     fear: 0,
//     happiness: 0,
//     neutral: 0,
//     sadness: 0,
//     surprise: 0
//   },
//   lecturer: {
//     overall: {
//       emotion: '',
//       emotionData: []
//     },
//     lectures: [
//       {
//         date: '',
//         emotion: '',
//         emotionData: [],
//         series: [
//           {
//             time: '',
//             emotion: '',
//             emotionData: []
//           }
//         ]
//       }
//     ]
//   }
// }

app.locals = {
  accEmotions: {
    anger: 0,
    contempt: 0,
    disgust: 0,
    fear: 0,
    happiness: 0,
    neutral: 0,
    sadness: 0,
    surprise: 0
  },
  lecturer: {
    overall: {
      emotion: ''
    },
    lecture: {
        accFrames: EMOTION_FPS,
        date: '',
        emotion: '',
        timeline: [],
        count: 0,
        avgcount: 0
      }
  }
}

/**
 * Get port from environment and use it for Express.
 */
const PORT = utils.normalizePort(process.env.PORT || DEFAULT_PORT)
app.set('port', PORT)

/**
 * Create HTTP server.
 */
const server = http.createServer(app)

/**
 * Listen on provided port, on all network interfaces.
 */

server.listen(PORT)

/**
 * Server event handling
 */
server.on('error', (err) => {
  throw err
})
server.on('listening', (err) => {
  let addr = server.address()
  let bind = typeof addr === 'string' ?
    'pipe ' + addr :
    'port ' + addr.port
  debug('DeepEval is alive on ' + bind)
})

/**
 * Init websockets
 */
const io = socketio(server);
io.listen(SOCKET_PORT);
console.log('Listening on SOCKET_PORT ', SOCKET_PORT);

io.on('connection', (client) => {
  client.on('imagePost', (imgData) => {
    // console.log('timestamp:', imgData.timestamp)

    axios({
      method: 'post',
      url: consts.endpoints.faceAPI,
      data: imgData.uri,
      headers: {
            'Content-Type': 'application/octet-stream',
            'Ocp-Apim-Subscription-Key': process.env.SUB_KEY
        },
      params: {
        'returnFaceId': 'true',
        'returnFaceLandmarks': 'false',
        'returnFaceAttributes': 'headPose,emotion,blur,exposure,noise',
      },
      })
      .then(function (response) {
        let faceData = response.data
        if (faceData.length <= 0) {
          // console.log(faceData.length)
          return;
        }
        // Only include attentive faces
        // let attentiveFaces = faceData.filter((item) => {
        //   return parseInt(item['headPose']['pitch']) == 0;
        // });
        let attentiveFaces = faceData
        app.locals.lecturer.lecture.count++;

        // console.log(attentiveFaces)
        // console.log(app.locals.lecturer.lecture.count)
        let maxEmotionMapping = {
          anger: 1,
          sadness: 2,
          disgust: 3,
          fear: 4,
          neutral: 5,
          contempt: 6,
          surprise: 7,
          happiness: 8,
        }
        let maximumEmotion = 0
        let sum = {
          anger: 0,
          contempt: 0,
          disgust: 0,
          fear: 0,
          happiness: 0,
          neutral: 0,
          sadness: 0,
          surprise: 0,
        }
        let max = 0

        function avg(count, x1, x2) {
          return ((count-1)*x1+x2)/2
        }

        attentiveFaces.forEach(elem => {
          for (let prop in elem['faceAttributes']['emotion']) {
            app.locals.accEmotions[prop] = avg(app.locals.lecturer.lecture.count, app.locals.accEmotions[prop], elem['faceAttributes']['emotion'][prop])
            // console.log(prop)
            sum[prop] = sum[prop] + elem['faceAttributes']['emotion'][prop]
            if(elem['faceAttributes']['emotion'][prop] > max) {
              max = elem['faceAttributes']['emotion'][prop]
              maximumEmotion = maxEmotionMapping[prop]
            }
          }
        });
        console.log(require('util').inspect(plotData, { depth: null }));


        if (--app.locals.lecturer.lecture.accFrames < 0) {
          let plotData = {
            emotion: maximumEmotion,
            timestamp: imgData.timestamp
          }

          app.locals.lecturer.lecture.timeline.append(plotData)
          app.locals.lecturer.lecture.avgcount++;
          app.locals.lecturer.lecture.emotion = app.locals.lecturer.overall.emotion = avg(app.locals.lecturer.lecture.avgcount, app.locals.lecturer.lecture.emotion, maximumEmotion)



          // Reset the accumlated frames
          app.locals.lecturer.lecture.accFrames = EMOTION_FPS

          // send back off to client
          client.emit('results', plotData)
        }

      })
      .catch(function (error) {
        // console.error(error);
      });
  });
});


// const websocket = socketio(server)

app.get('/', function (req, res) {
  res.render('index')
})

module.exports = app