webduino-module-facenet/test.html at master · webduinoio/webduino-module-facenet · GitHub

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<!DOCTYPE html>
<html>

<head>
  <script src="./tfjs@0.14.1.js"></script>
  <script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.1.0/js/bootstrap.min.js"></script>
  <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.3.1/jquery.min.js"></script>
  <style>
    #wrapper {
            text-align: center;
            margin: 10px auto;
        }

        #overlay {
            width: 550px;
        }

        #output_image {
            width: 550px;
        }
        .inline {
            display: inline-block;
        }
    </style>
</head>

<body>
  <center style="padding:10px; margin:5px">
    <h4>
            <span id="status">Model Loading ...</span>
        </h4>
  </center>
  <center style="padding:10px">
    <button type="button" id='start' class="btn btn-outline-primary" onclick='startVideo()'>Start Video</button>
  </center>
  <div class="inline">
    <center><span id="original_video"></span></center>
    <video id='video' width="500px"></video>
  </div>
  <div class="inline">
    <!--        <center><span id="emotion_video"></span></center>-->
    <canvas id="canvas" src="" style=" margin-top:10px;" />
  </div>
  <div id="thank">
    <center style="padding:10px; margin:5px">
      <div>Maintained by <a href='http://mirlab.org/users/kevin.hsiao/'>Kevin Hsiao </a> and special thanks <a href="mailto:haochun.fu@mirlab.org" target="_top">Haochun Fu</a></div>
    </center>
  </div>
</body>
<script>
var constraints = {
  video: true
};

var $body = document.querySelector('body');
//    var status = document.getElementById('status');
var emotion_labels = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
var emotion_colors = ["#ff0000", "#00a800", "#ff4fc1", "#ffe100", "#306eff", "#ff9d00", "#7c7c7c"];
var offset_x = 15;
var offset_y = 40;

loadModel('https://127.0.0.1:8080/mobilenetv1_models/model.json');
// create model
async function loadModel(path) {
  model = await tf.loadModel(path)
}

function createVideoElement() {
  let $video = document.createElement('video')
  $video.style.maxWidth = '100vw'
  $video.style.width = '100vw'
  $video.style.maxHeight = '0vh'
  $body.appendChild($video)
  return $video
}

function handleError(error) {
  if (error.name === 'DevicesNotFoundError') {
    alert('No camera detected. <br> Do you have any camera connected?');
  } else if (error.name === 'NotAllowedError') {
    alert('You have to allow camera access in order to run this experiment.');
  } else if (navigator.userAgent.indexOf('Chrome') > -1) {
    alert('Error. <br> Enable experimental features on chrome://flags/#enable-experimental-web-platform-features');
  } else {
    alert('Error. <br> Does your browser supports FaceDetector API?');
  }
  console.error(error)
}

function createCanvas(video) {

  const canvas = document.getElementById('canvas')
  const videoCompStyle = window.getComputedStyle(video)
  canvas.width = videoCompStyle.width.replace('px', '')
  canvas.height = videoCompStyle.height.replace('px', '')
  //        canvas.style.display = 'none'
  document.querySelector('body').appendChild(canvas)

  return canvas
}

function createDrawFunction() {

  const faceDetector = new window.FaceDetector({
    maxDetectedFaces: 3
  })
  let isDetectingFaces = false
  let faces = []
  let hideTimeout

  return async function draw(canvas, video) {
    window.requestAnimationFrame(() => draw(canvas, video))
    const context = canvas.getContext('2d')
    const videoCompStyle = window.getComputedStyle(video)
    const videoWidth = videoCompStyle.width.replace('px', '')
    const videoHeight = videoCompStyle.height.replace('px', '')
    context.drawImage(video, 0, 0, videoWidth, videoHeight)
    //            context.clearRect(0, 0, canvas.width, canvas.height);
    //            clearTimeout(hideTimeout)
    if (faces.length) {

      //                let canvas = document.getElementById('canvas')
      let ctx = context;
      //                let scale = 1;

      ctx.lineWidth = 4;
      ctx.font = "20px Arial"
      ctx.fillText('Result', 0, 0);
      for (var i = 0; i < faces.length; i++) {
        ctx.beginPath();
        var item = faces[i].boundingBox;
        //                    console.log(item)
        let s_x = Math.floor(item.x - offset_x / 2);
        let s_y = Math.floor(item.y - offset_y / 2);
        let s_w = Math.floor(item.width + offset_x);
        let s_h = Math.floor(item.height + offset_y);
        let cT = ctx.getImageData(s_x, s_y, s_w, s_h);
        console.log(s_x, s_y, s_w, s_h);
        cT = preprocess(cT);
        z = model.predict(cT)

        let index = z.argMax(1).dataSync()[0]
        let label = emotion_labels[index];

        ctx.strokeStyle = emotion_colors[index];
        ctx.rect(s_x, s_y, s_w, s_h);
        ctx.stroke();
        ctx.fillStyle = emotion_colors[index];
        ctx.fillText(label, s_x, s_y);
        ctx.closePath();
      }
    } else {
      console.log('NO FACE')
      //                status.innerHTML = "NO FACE";
    }

    if (isDetectingFaces) {
      return
    }

    isDetectingFaces = true
    faces = await faceDetector.detect(canvas)
    isDetectingFaces = false
    var status = document.getElementById('status');
    status.innerHTML = "Running the model ... ";
  }
}

function preprocess(imgData) {
  return tf.tidy(() => {
    let tensor = tf.fromPixels(imgData).toFloat();

    tensor = tensor.resizeBilinear([100, 100])

    tensor = tf.cast(tensor, 'float32')
    const offset = tf.scalar(255.0);
    // Normalize the image
    const normalized = tensor.div(offset);
    //We add a dimension to get a batch shape
    const batched = normalized.expandDims(0)
    return batched
  })
}


function playCameraOnVideo(video) {
  return navigator.mediaDevices.getUserMedia({
      video: {
        facingMode: 'user',
        frameRate: 60
      },
      audio: false
    })
    .then(srcObject => video.srcObject = srcObject)
    .then(() => video.play())
}

async function main(video) {
  const video_canvas = createCanvas(video)
  const draw = createDrawFunction()
  draw(video_canvas, video)
}

function startVideo() {
  var x = document.getElementById("thank");
  x.style.display = "none";
  let elem = document.getElementById('start');
  elem.parentNode.removeChild(elem);
  var status = document.getElementById('status');
  status.innerHTML = "Initializing the camera ... ";

  var ori = document.getElementById("original_video");
  //        var emo = document.getElementById("emotion_video");
  ori.innerHTML = "Original: "
  //        emo.innerHTML = "Result : "
  playCameraOnVideo(video)
    .then(() => main(video))
    .catch(handleError)

}
</script>

</html>