mberg88.github.io/tfjs_main.html at master · mberg88/mberg88.github.io · GitHub

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<!doctype html>
<html lang="en">
  <head>
    <!-- Global site tag (gtag.js) - Google Analytics -->
    <script async src="https://www.googletagmanager.com/gtag/js?id=G-H0NW5Z2MYC"></script>
    <script>
      window.dataLayer = window.dataLayer || [];
      function gtag(){dataLayer.push(arguments);}
      gtag('js', new Date());

      gtag('config', 'G-H0NW5Z2MYC');
    </script>
    <title>Digit Recognition WebApp</title>
    <meta name="description" content="Simple Machine Learning Model into a WebApp using TensorFlow.js">
    <meta name="keywords" content="Machine Learning, TensorFlow.js">
    <meta name="author" content="Mattia Bergagio">
    <style>
      body {
        touch-action: none; /*https://developer.mozilla.org/en-US/docs/Web/CSS/touch-action*/
        font-family: "Roboto";
      }
      h1 {
        margin: 50px;
        font-size: 70px;
        text-align: center;
      }
      #paint {
        border:3px solid red;
        margin: auto;
      }
      #predicted {
        font-size: 60px;
        margin-top: 60px;
        text-align: center;
      }
      #number {
        border: 3px solid black;
        margin: auto;
        margin-top: 30px;
        text-align: center;
        vertical-align: middle;
      }
      #clear {
        margin: auto;
        margin-top: 70px;
        padding: 30px;
        text-align: center;
      }
    </style>
  </head>
  <body>
    <!--<script type="text/javascript" src="http://livejs.com/live.js"></script>-->
    <script src="https://code.jquery.com/jquery-2.2.4.min.js"></script>

	<!-- load TensorFlow.js -->
	<!-- https://www.tensorflow.org/js/tutorials/setup#usage_via_script_tag -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.5.2/dist/tf.min.js"></script>

	<!-- title -->
    <h1>Digit Recognition WebApp - copy of Aguayo's work</h1>
    <div id="paint">
      <canvas id="myCanvas"></canvas>
    </div>
    <div id="predicted">
      Recognized digit
      <div id="number"></div>
      <button id="clear">Clear</button>
    </div>

	<!-- Place your code in the script tag below. You can also use an external .js file -->
    <script>

	// There is no 'import' statement: 'tf' is available on the index-page
    // because of the script tag above

    var isMobile = /Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(navigator.userAgent);
    if (isMobile) {
      $('#paint').css({'width': '60%'});
      $('#number').css({'width': '30%', 'font-size': '240px'});
      $('#clear').css({'font-size': '50px'});
    } else {
      $('#paint').css({'width': '300px'});
      $('#number').css({'width': '150px', 'font-size': '120px'});
      $('#clear').css({'font-size': '35px'});
    }

    var cw = $('#paint').width();
    $('#paint').css({'height': cw + 'px'});

    cw = $('#number').width();
    $('#number').css({'height': cw + 'px'});

    // From https://www.html5canvastutorials.com/labs/html5-canvas-paint-application/
    var canvas = document.getElementById('myCanvas');
    var context = canvas.getContext('2d');

    var compuetedStyle = getComputedStyle(document.getElementById('paint'));
    canvas.width = parseInt(compuetedStyle.getPropertyValue('width'));
    canvas.height = parseInt(compuetedStyle.getPropertyValue('height'));

    var mouse = {x: 0, y: 0};

    canvas.addEventListener('mousemove', function(e) {
      mouse.x = e.pageX - this.offsetLeft;
      mouse.y = e.pageY - this.offsetTop;
    }, false);

    context.lineWidth = isMobile ? 60 : 25;
    context.lineJoin = 'round';
    context.lineCap = 'round';
    context.strokeStyle = '#0000FF';

    // hook mouse events to draw into the Canvas
    canvas.addEventListener('mousedown', function(e) {
      context.moveTo(mouse.x, mouse.y);
      context.beginPath();
      canvas.addEventListener('mousemove', onPaint, false);
    }, false);

    // Once we can draw, fetch the image upon mouse up
    canvas.addEventListener('mouseup', function() {
      $('#number').html('<img id="spinner" src="https://media.giphy.com/media/3oEjI6SIIHBdRxXI40/giphy.gif"/>');
      canvas.removeEventListener('mousemove', onPaint, false);
      var img = new Image();
      img.onload = function() {
		// scale the fetched image down to 28 by 28 pixels so that it matches the trained model
        context.drawImage(img, 0, 0, 28, 28);

		// data is a 1D array with RGBA values
		// Our model only takes 0 to 1 values (or 0 from 255 in grayscale)
		// Since we are drawing Blue into the canvas, slice the array in chunks of 4 and take every 3rd element,
		// so use data[i + 2]
        data = context.getImageData(0, 0, 28, 28).data;

		// keep data into an input array and pass it to a
		// predict function defined later
        var input = [];
        for(var i = 0; i < data.length; i += 4) {
          input.push(data[i + 2] / 255);
        }
        predict(input);
      };
      img.src = canvas.toDataURL('image/png');
    }, false);

    // draw into Canvas
    var onPaint = function() {
      context.lineTo(mouse.x, mouse.y);
      context.stroke();
    };

    // load the trained model
	// https://www.tensorflow.org/js/tutorials/conversion/import_keras#step_2_load_the_model_into_tensorflowjs
	// load*Model(...) fetches model.json, and then makes additional HTTP(S) requests
	// to obtain the sharded weight files referenced in the model.json weight manifest.
	// This approach allows all of these files to be cached by the browser
	// (and perhaps by additional caching servers on the internet),
	// because the model.json and the weight shards are each smaller than the typical cache file size limit.
	// Thus a model is likely to load more quickly on subsequent occasions
    tf.loadLayersModel('model/model.json').then(function(model) {
      window.model = model;
    });

    // http://bencentra.com/code/2014/12/05/html5-canvas-touch-events.html
    // Set up touch events for mobile etc
	// Add the touch action to disable scrolling
    canvas.addEventListener('touchstart', function (e) {
      var touch = e.touches[0];
      canvas.dispatchEvent(new MouseEvent('mousedown', {
        clientX: touch.clientX,
        clientY: touch.clientY
      }));
    }, false);
    canvas.addEventListener('touchend', function (e) {
      canvas.dispatchEvent(new MouseEvent('mouseup', {}));
    }, false);
    canvas.addEventListener('touchmove', function (e) {
      var touch = e.touches[0];
      canvas.dispatchEvent(new MouseEvent('mousemove', {
        clientX: touch.clientX,
        clientY: touch.clientY
      }));
    }, false);

    // predict function
    var predict = function(input) {
      if (window.model) {
	    // once we have the data, feed it into the model upon mouseup
        window.model.predict([tf.tensor(input).reshape([1, 28, 28, 1])]).array().then(function(scores){
          scores = scores[0];
          predicted = scores.indexOf(Math.max(...scores));
          $('#number').html(predicted);
        });
      } else {
        // The model takes a bit to load. If we are too fast, wait
        setTimeout(function(){predict(input)}, 50);
      }
    }

    // clear drawing
    $('#clear').click(function(){
      context.clearRect(0, 0, canvas.width, canvas.height);
      $('#number').html('');
    });
    </script>
  </body>
</html>