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HTML

              
                <!DOCTYPE html>
<html lang="en">
  <head>
    <title>Multiple object detection using 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>Multiple object detection using 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: Classifying 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! Notice how in this demo we not only know if the object is in the image, but also its position in the image. Very useful.</p>

      <div class="classifyOnClick">
        <img src="https://cdn.glitch.com/74418d0b-3465-49a2-8c71-a721b7734473%2Fdogs_flickr_publicdomain.jpg?v=1579294514974" width="100%" crossorigin="anonymous" title="Click to get classification!" />
      </div>
      <div class="classifyOnClick">
        <img src="https://cdn.glitch.com/74418d0b-3465-49a2-8c71-a721b7734473%2Fcats_flickr_publicdomain.jpg?v=1579294753947" 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 to get a real-time classification! You must be on <a href="https://codepen.io/jasonmayes/pen/qBEJxgg">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 id="liveView" class="videoView">
        <button id="webcamButton">Enable Webcam</button>
        <video id="webcam" autoplay></video>
      </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/coco-ssd" title="View TensorFlow.js COCO-SSD on our GitHub">an easy to use JavaScript class</a> made by the TensorFlow.js team to do the hard work for you. No machine learning knowledge is needed to use this. View the link to learn more about fine tuning this machine learning model. See our other tutorials if you want to load a model directly yourself, or recognize a custom object using your own data.
      </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>
    
    <!-- Load the coco-ssd model to use to recognize things in images -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/coco-ssd"></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;
}

h2 {
  clear: both;
}

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%;
}

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

.videoView p, .classifyOnClick p {
  position: absolute;
  padding: 5px;
  background-color: rgba(255, 111, 0, 0.85);
  color: #FFF;
  border: 1px dashed rgba(255, 255, 255, 0.7);
  z-index: 2;
  font-size: 12px;
  margin: 0;
}

.highlighter {
  background: rgba(0, 255, 0, 0.25);
  border: 1px dashed #fff;
  z-index: 1;
  position: absolute;
}

.classifyOnClick {
  z-index: 0;
}

.classifyOnClick img {
  width: 100%;
}
              
            
!

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
 ********************************************************************/

const demosSection = document.getElementById('demos');

var model = undefined;

// Before we can use COCO-SSD class we must wait for it to finish
// loading. Machine Learning models can be large and take a moment to
// get everything needed to run.
cocoSsd.load().then(function (loadedModel) {
  model = loadedModel;
  // 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 (!model) {
    console.log('Wait for model to load before clicking!');
    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.detect(event.target).then(function (predictions) {
    // Lets write the predictions to a new paragraph element and
    // add it to the DOM.
    console.log(predictions);
    for (let n = 0; n < predictions.length; n++) {
      // Description text
      const p = document.createElement('p');
      p.innerText = predictions[n].class  + ' - with ' 
          + Math.round(parseFloat(predictions[n].score) * 100) 
          + '% confidence.';
      // Positioned at the top left of the bounding box.
      // Height is whatever the text takes up.
      // Width subtracts text padding in CSS so fits perfectly.
      p.style = 'left: ' + predictions[n].bbox[0] + 'px;' + 
          'top: ' + predictions[n].bbox[1] + 'px; ' + 
          'width: ' + (predictions[n].bbox[2] - 10) + 'px;';

      const highlighter = document.createElement('div');
      highlighter.setAttribute('class', 'highlighter');
      highlighter.style = 'left: ' + predictions[n].bbox[0] + 'px;' +
          'top: ' + predictions[n].bbox[1] + 'px;' +
          'width: ' + predictions[n].bbox[2] + 'px;' +
          'height: ' + predictions[n].bbox[3] + 'px;';

      event.target.parentNode.appendChild(highlighter);
      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://tensorflow-js-image-classification.glitch.me/
********************************************************************/

const video = document.getElementById('webcam');
const liveView = document.getElementById('liveView');

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

// Keep a reference of all the child elements we create
// so we can remove them easilly on each render.
var children = [];


// 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');
}


// Enable the live webcam view and start classification.
function enableCam(event) {
  if (!model) {
    console.log('Wait! Model not loaded yet.')
    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);
  });
}


function predictWebcam() {
  // Now let's start classifying the stream.
  model.detect(video).then(function (predictions) {
    // Remove any highlighting we did previous frame.
    for (let i = 0; i < children.length; i++) {
      liveView.removeChild(children[i]);
    }
    children.splice(0);
    
    // Now lets loop through predictions and draw them to the live view if
    // they have a high confidence score.
    for (let n = 0; n < predictions.length; n++) {
      // If we are over 66% sure we are sure we classified it right, draw it!
      if (predictions[n].score > 0.66) {
        const p = document.createElement('p');
        p.innerText = predictions[n].class  + ' - with ' 
            + Math.round(parseFloat(predictions[n].score) * 100) 
            + '% confidence.';
        // Draw in top left of bounding box outline.
        p.style = 'left: ' + predictions[n].bbox[0] + 'px;' +
            'top: ' + predictions[n].bbox[1] + 'px;' + 
            'width: ' + (predictions[n].bbox[2] - 10) + 'px;';

        // Draw the actual bounding box.
        const highlighter = document.createElement('div');
        highlighter.setAttribute('class', 'highlighter');
        highlighter.style = 'left: ' + predictions[n].bbox[0] + 'px; top: '
            + predictions[n].bbox[1] + 'px; width: ' 
            + predictions[n].bbox[2] + 'px; height: '
            + predictions[n].bbox[3] + 'px;';

        liveView.appendChild(highlighter);
        liveView.appendChild(p);
        
        // Store drawn objects in memory so we can delete them next time around.
        children.push(highlighter);
        children.push(p);
      }
    }
    
    // Call this function again to keep predicting when the browser is ready.
    window.requestAnimationFrame(predictWebcam);
  });
}

              
            
!
999px

Console