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HTML

              
                <!DOCTYPE html>
<html lang="en">
  <head>
    <title>Classifying images using a pre trained model in TensorFlow.js</title>
    <meta charset="utf-8">
    <meta http-equiv="X-UA-Compatible" content="IE=edge">
    <meta name="viewport" content="width=device-width, initial-scale=1">
    <meta name="author" content="Jason Mayes">
    
    <!-- Import the webpage's stylesheet -->
    <link rel="stylesheet" href="/style.css">
  </head>
  <body>
    <h1>Classifying images using a pre trained model in TensorFlow.js</h1>
    
    <header class="note"> 
      <h2>Difficulty: Easy</h2>
    </header>

    
    <h2>How to use</h2>
    <p>Please wait for the model to load before trying the demos below at which point they will become visible when ready to use.</p>
    
    <section id="demos" class="invisible">
      <h2>Demo: Classify Images</h2>
      <p><em>Click on an image below</em> to try and recognize what is in the image using the power of Machine Learning!</p>

      <div class="classifyOnClick">
        <img src="https://cdn.glitch.com/5bf7c54b-c36f-4009-a191-186909fb788e%2Fdog_flickr_publicdomain.jpg?v=1579209396930" width="100%" crossorigin="anonymous" title="Click to get classification!" />
      </div>
      <div class="classifyOnClick">
        <img src="https://cdn.glitch.com/5bf7c54b-c36f-4009-a191-186909fb788e%2Fcat_flickr_publicdomain.jpg?v=1579215444151" width="100%" crossorigin="anonymous" title="Click to get classification!" />
      </div>


      <h2>Demo: Webcam continuous classification</h2>
      <p>Hold some objects up close to your webcam (avoid having lots of stuff in the shot at the same time for best results) to get a real time classification! You must be on <a href="https://codepen.io/jasonmayes/pen/Jjompww">the https version of the website</a> for this to work. When ready click "enable webcam" below and accept access to the webcam when the browser asks (check the top left of your window)</p>
     
      <div class="webcam">
        <button id="webcamButton">Enable Webcam</button>
        <video id="webcam" autoplay></video>
        <p id="webcamPredictions"></p>
      </div>
    </section>
    
    <footer class="note"> 
      <p>
        <em>Please note:</em> This demo loads our desired machine learning model via <a href="https://github.com/tensorflow/tfjs-models/tree/master/mobilenet" title="View TensorFlow.js MobileNet on our GitHub">an easy to use JavaScript class</a> made by the TensorFlow.js team to do the hardwork for you. No machine learning knowledge is needed to use this. See our other tutorials if you want to load a model directly yourself, or recognize a custom object using your own data.
      </p>
      <p>
         Want to recognize more than one thing in an image? Check out our <a href="https://codepen.io/jasonmayes/pen/qBEJxgg">Multiple Object Detection example</a>.
      </p>
    </footer>
    
    <!-- Import TensorFlow.js library -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js" type="text/javascript"></script>
    
    <!-- Import pre-trained MobileNet model so that we can recognize things in images  -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/mobilenet@2.0.4"></script>
    
    <!-- Import the page's JavaScript to do some stuff -->
    <script src="/script.js" defer></script>
  </body>
</html>
              
            
!

CSS

              
                /**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */

/******************************************************
 * Stylesheet by Jason Mayes 2020.
 *
 * Got questions? Reach out to me on social:
 * Twitter: @jason_mayes
 * LinkedIn: https://www.linkedin.com/in/creativetech
 *****************************************************/


body {
  font-family: helvetica, arial, sans-serif;
  margin: 2em;
  color: #3D3D3D;
}

h1 {
  font-style: italic;
  color: #FF6F00;
}

em {
  font-weight: bold;
}

video {
  clear: both;
  display: block;
}

section {
  opacity: 1;
  transition: opacity 500ms ease-in-out;
}

header, footer {
  clear: both;
}

.removed {
  display: none;
}

.invisible {
  opacity: 0.2;
}

.note {
  font-style: italic;
  font-size: 130%;
}

.webcam, .classifyOnClick {
  position: relative;
  float: left;
  width: 48%;
  margin: 2% 1%;
  cursor: pointer;
}

.webcam p, .classifyOnClick p {
  position: absolute;
  bottom: 0;
  left: 0;
  padding: 5px;
  background-color: #FF6F00;
  color: #FFF;
  border: 1px dashed rgba(255, 255, 255, 0.7);
  margin: 0 0 10px 0;
}

.classifyOnClick {
  z-index: 0;
}
              
            
!

JS

              
                /**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */

/********************************************************************
 * Demo created by Jason Mayes 2020.
 *
 * Got questions? Reach out to me on social:
 * Twitter: @jason_mayes
 * LinkedIn: https://www.linkedin.com/in/creativetech
 ********************************************************************/

// Grab various useful DOM elements from the webpage we need later.
const video = document.getElementById('webcam');
const webcamPredictions = document.getElementById('webcamPredictions');
const demosSection = document.getElementById('demos');

// Keep track of model and status.
var modelHasLoaded = false;
var model = undefined;

// Before we can use MobileNet we must wait for it to finish loading. 
// Machine Learning models can be large and take a moment to get 
// everything they need to run.
mobilenet.load().then(function (loadedModel) {
  model = loadedModel;
  modelHasLoaded = true;
  // Show demo section now model is ready to use.
  demosSection.classList.remove('invisible');
});


/********************************************************************
// Demo 1: Grab a bunch of images from the page and classify them
// upon click.
********************************************************************/

// In this demo, we have put all our clickable images in divs with the 
// CSS class 'classifyOnClick'. Lets get all the elements that have
// this class.
const imageContainers = document.getElementsByClassName('classifyOnClick');

// Now let's go through all of these and add a click event listener.
for (let i = 0; i < imageContainers.length; i++) {
  // Add event listener to the child element whichis the img element.
  imageContainers[i].children[0].addEventListener('click', handleClick);
}


// When an image is clicked, let's classify it and display results!
function handleClick(event) {
  if (!modelHasLoaded) {
    return;
  }
  
  // We can call model.classify as many times as we like with
  // different image data each time. This returns a promise
  // which we wait to complete and then call a function to
  // print out the results of the prediction.
  model.classify(event.target).then(function (predictions) {
    // Lets write the predictions to a new paragraph element and
    // add it to the DOM.
    const p = document.createElement('p');
    p.innerText = 'We think this image contains a: ' + predictions[0].className 
        + ' - with ' + Math.round(parseFloat(predictions[0].probability) * 100) 
        + '% confidence.';

    event.target.parentNode.appendChild(p);
  });
}



/********************************************************************
// Demo 2: Continuously grab image from webcam stream and classify it.
// Note: You must access the demo on https for this to work:
// https://codepen.io/jasonmayes/pen/Jjompww
********************************************************************/

// Check if webcam access is supported.
function hasGetUserMedia() {
  return !!(navigator.mediaDevices &&
    navigator.mediaDevices.getUserMedia);
}


function predictWebcam() {
  // Now let's start classifying the stream.
  model.classify(video).then(function (predictions) {
    webcamPredictions.innerText = 'We think this image contains a: ' + predictions[0].className 
        + ' - with ' + Math.round(parseFloat(predictions[0].probability) * 100) 
        + '% confidence.';
    // Call this function again to keep predicting when the browser is ready.
    window.requestAnimationFrame(predictWebcam);
  });
}


// Enable the live webcam view and start classification.
function enableCam(event) {
  if (!modelHasLoaded) {
    return;
  }
  
  // Hide the button.
  event.target.classList.add('removed');  
  
  // getUsermedia parameters.
  const constraints = {
    video: true
  };

  // Activate the webcam stream.
  navigator.mediaDevices.getUserMedia(constraints).then(function(stream) {
    video.srcObject = stream;
    video.addEventListener('loadeddata', predictWebcam);
  });
}


// If webcam supported, add event listener to button for when user
// wants to activate it.
if (hasGetUserMedia()) {
  const enableWebcamButton = document.getElementById('webcamButton');
  enableWebcamButton.addEventListener('click', enableCam);
} else {
  console.warn('getUserMedia() is not supported by your browser');
}
              
            
!
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