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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>Human body part 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> (the first click may take a second to warm up) to try and recognize any humans in the image using the power of Machine Learning! Notice how we can understand the different parts of the body as shown by the different colours in the mask. Very useful.</p>

      <div class="classifyOnClick">
        <img src="https://cdn.glitch.com/ff4f00ae-20e2-4bdc-8771-2642ee05ae93%2Fjj.jpg?v=1581963497215" width="100%" crossorigin="anonymous" title="Click to get classification!" />
      </div>

      <div class="classifyOnClick">
        <img src="https://cdn.glitch.com/ff4f00ae-20e2-4bdc-8771-2642ee05ae93%2Fwalk.jpg?v=1581963497392" width="100%" crossorigin="anonymous" title="Click to get classification!" />
      </div>

      <h2>Demo: Webcam continuous classification</h2>
      <p>Try this out using your webcam. Stand a few feet away from your webcam for a nice full body shot and see the results in real time! Note, you must be on <a href="https://codepen.io/jasonmayes/pen/QWbNeJd">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="webcam">
        <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/body-pix" title="View TensorFlow.js BodyPix 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 bodypix model to recognize body parts in images -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/body-pix@2.0"></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;
}

button {
  z-index: 1000;
  position: relative;
}

.removed {
  display: none;
}

.invisible {
  opacity: 0.2;
}

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

.webcam {
  position: relative;
}

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

.webcam 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;
}

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

.classifyOnClick {
  z-index: 0;
  position: relative;
}

.classifyOnClick canvas, .webcam canvas.overlay {
  opacity: 0.66;
  position: absolute;
  top: 0;
  left: 0;
  z-index: 2;
}
              
            
!

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

// An object to configure parameters to set for the bodypix model.
// See github docs for explanations.
const bodyPixProperties = {
  architecture: 'MobileNetV1',
  outputStride: 16,
  multiplier: 0.75,
  quantBytes: 4
};

// An object to configure parameters for detection. I have raised
// the segmentation threshold to 90% confidence to reduce the
// number of false positives.
const segmentationProperties = {
  flipHorizontal: false,
  internalResolution: 'high',
  segmentationThreshold: 0.9
};


// This array will hold the colours we wish to use to highlight different body parts we find.
// RGBA (Red, Green, Blue, and Alpha (transparency) channels can be specified).
const colourMap = [];

// Left_face
colourMap.push({r: 244, g: 67, b: 54, a: 255});
// Right_face
colourMap.push({r: 183, g: 28, b: 28, a: 255});
// left_upper_arm_front
colourMap.push({r: 233, g: 30, b: 99, a: 255});
// left_upper_arm_back  
colourMap.push({r: 136, g: 14, b: 79, a: 255});
// right_upper_arm_front
colourMap.push({r: 233, g: 30, b: 99, a: 255});
// 	right_upper_arm_back
colourMap.push({r: 136, g: 14, b: 79, a: 255});
// 	left_lower_arm_front
colourMap.push({r: 233, g: 30, b: 99, a: 255});
// 	left_lower_arm_back
colourMap.push({r: 136, g: 14, b: 79, a: 255});
// right_lower_arm_front
colourMap.push({r: 233, g: 30, b: 99, a: 255});
// right_lower_arm_back
colourMap.push({r: 136, g: 14, b: 79, a: 255});
// left_hand 
colourMap.push({r: 156, g: 39, b: 176, a: 255});
// right_hand
colourMap.push({r: 156, g: 39, b: 176, a: 255});
// torso_front
colourMap.push({r: 63, g: 81, b: 181, a: 255}); 
// torso_back 
colourMap.push({r: 26, g: 35, b: 126, a: 255});
// left_upper_leg_front
colourMap.push({r: 33, g: 150, b: 243, a: 255});
// left_upper_leg_back
colourMap.push({r: 13, g: 71, b: 161, a: 255});
// right_upper_leg_front
colourMap.push({r: 33, g: 150, b: 243, a: 255});
// right_upper_leg_back
colourMap.push({r: 13, g: 71, b: 161, a: 255});
// left_lower_leg_front
colourMap.push({r: 0, g: 188, b: 212, a: 255});
// left_lower_leg_back
colourMap.push({r: 0, g: 96, b: 100, a: 255});
// right_lower_leg_front
colourMap.push({r: 0, g: 188, b: 212, a: 255});
// right_lower_leg_back
colourMap.push({r: 0, g: 188, b: 212, a: 255});
// left_feet
colourMap.push({r: 255, g: 193, b: 7, a: 255});
// right_feet
colourMap.push({r: 255, g: 193, b: 7, a: 255});


// A function to render returned segmentation data to a given canvas context.
function processSegmentation(canvas, segmentation) {
  var ctx = canvas.getContext('2d');
  
  var imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
  var data = imageData.data;
  
  let n = 0;
  for (let i = 0; i < data.length; i += 4) {
    if (segmentation.data[n] !== -1) {
      data[i] = colourMap[segmentation.data[n]].r;     // red
      data[i + 1] = colourMap[segmentation.data[n]].g; // green
      data[i + 2] = colourMap[segmentation.data[n]].b; // blue
      data[i + 3] = colourMap[segmentation.data[n]].a; // alpha
    } else {
      data[i] = 0;    
      data[i + 1] = 0;
      data[i + 2] = 0;
      data[i + 3] = 0;
    }
    n++;
  }
  
  ctx.putImageData(imageData, 0, 0);
}



// Let's load the model with our parameters defined above.
// Before we can use bodypix 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.
var modelHasLoaded = false;
var model = undefined;

model = bodyPix.load(bodyPixProperties).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.segmentPerson 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.segmentPersonParts(event.target, segmentationProperties).then(function(segmentation) {
    console.log(segmentation);
    
    // Lets create a canvas to render our findings.
    var canvas = document.createElement('canvas');
    canvas.width = segmentation.width;
    canvas.height = segmentation.height;

    processSegmentation(canvas, segmentation);

    // Add our canvas to the DOM.
    event.target.parentNode.appendChild(canvas);
  });
}



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

var previousSegmentationComplete = true;

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


// This function will repeatidly call itself when the browser is ready to process
// the next frame from webcam.
function predictWebcam() {
  if (previousSegmentationComplete) {
    // Copy the video frame from webcam to a tempory canvas in memory only (not in the DOM).
    videoRenderCanvasCtx.drawImage(video, 0, 0);
    previousSegmentationComplete = false;
    // Now classify the canvas image we have available.
    model.segmentPersonParts(videoRenderCanvas, segmentationProperties).then(function(segmentation) {
      processSegmentation(webcamCanvas, segmentation);
      previousSegmentationComplete = true;
    });
  }

  // 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.addEventListener('loadedmetadata', function() {
      // Update widths and heights once video is successfully played otherwise
      // it will have width and height of zero initially causing classification
      // to fail.
      webcamCanvas.width = video.videoWidth;
      webcamCanvas.height = video.videoHeight;
      videoRenderCanvas.width = video.videoWidth;
      videoRenderCanvas.height = video.videoHeight;
    });
    
    video.srcObject = stream;
    
    video.addEventListener('loadeddata', predictWebcam);
  });
}


// Lets create a canvas to render our findings to the DOM.
var webcamCanvas = document.createElement('canvas');
webcamCanvas.setAttribute('class', 'overlay');
liveView.appendChild(webcamCanvas);

// We will also create a tempory canvas to render to that is in memory only
// to store frames from the web cam stream for classification.
var videoRenderCanvas = document.createElement('canvas');
var videoRenderCanvasCtx = videoRenderCanvas.getContext('2d');

// 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');
}
              
            
!
999px

Console