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<!doctype html>
<html lang="en">
<head>
<!-- Basic Page Needs
–––––––––––––––––––––––––––––––––––––––––––––––––– -->
<meta charset="utf-8">
<title>U-Net Skin Detector | 123mpozzi</title>
<meta name="description" content="Detect human skin pixels directly from your browser using deep learning!">
<meta name="author" content="123mpozzi">
<!-- Mobile Specific Metas
–––––––––––––––––––––––––––––––––––––––––––––––––– -->
<meta name="viewport" content="width=device-width, initial-scale=1">
<!-- FONT
–––––––––––––––––––––––––––––––––––––––––––––––––– -->
<link href="https://fonts.googleapis.com/css?family=Raleway:400,300,600" rel="stylesheet" type="text/css">
<!-- CSS
–––––––––––––––––––––––––––––––––––––––––––––––––– -->
<link rel="stylesheet" href="css/normalize.css">
<link rel="stylesheet" href="css/skeleton.css">
<link rel="stylesheet" href="css/styles.css">
<!-- Favicon
–––––––––––––––––––––––––––––––––––––––––––––––––– -->
<link rel="icon" type="image/png" href="images/favicon.png">
<!-- JS
–––––––––––––––––––––––––––––––––––––––––––––––––– -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js"></script>
<script src="main.js"></script>
</head>
<body>
<!-- Primary Page Layout
–––––––––––––––––––––––––––––––––––––––––––––––––– -->
<div class="container">
<div class="row">
<a href="https://github.com/123mpozzi/skin-detect-live" class="github-corner" aria-label="View source on GitHub" style="position: fixed; top: 0; right: 0; z-index: 5;" target="_blank" ><svg width="7rem" height="7rem" viewBox="0 0 250 250" style="fill:#151513; color:#fff; position: absolute; top: 0; border: 0; right: 0;" aria-hidden="true"><path d="M0,0 L115,115 L130,115 L142,142 L250,250 L250,0 Z"></path><path d="M128.3,109.0 C113.8,99.7 119.0,89.6 119.0,89.6 C122.0,82.7 120.5,78.6 120.5,78.6 C119.2,72.0 123.4,76.3 123.4,76.3 C127.3,80.9 125.5,87.3 125.5,87.3 C122.9,97.6 130.6,101.9 134.4,103.2" fill="currentColor" style="transform-origin: 130px 106px;" class="octo-arm"></path><path d="M115.0,115.0 C114.9,115.1 118.7,116.5 119.8,115.4 L133.7,101.6 C136.9,99.2 139.9,98.4 142.2,98.6 C133.8,88.0 127.5,74.4 143.8,58.0 C148.5,53.4 154.0,51.2 159.7,51.0 C160.3,49.4 163.2,43.6 171.4,40.1 C171.4,40.1 176.1,42.5 178.8,56.2 C183.1,58.6 187.2,61.8 190.9,65.4 C194.5,69.0 197.7,73.2 200.1,77.6 C213.8,80.2 216.3,84.9 216.3,84.9 C212.7,93.1 206.9,96.0 205.4,96.6 C205.1,102.4 203.0,107.8 198.3,112.5 C181.9,128.9 168.3,122.5 157.7,114.1 C157.9,116.9 156.7,120.9 152.7,124.9 L141.0,136.5 C139.8,137.7 141.6,141.9 141.8,141.8 Z" fill="currentColor" class="octo-body"></path></svg></a><style>.github-corner:hover .octo-arm{animation:octocat-wave 560ms ease-in-out}@keyframes octocat-wave{0%,100%{transform:rotate(0)}20%,60%{transform:rotate(-25deg)}40%,80%{transform:rotate(10deg)}}@media (max-width:500px){.github-corner:hover .octo-arm{animation:none}.github-corner .octo-arm{animation:octocat-wave 560ms ease-in-out}}</style>
<div id="navbar" class="row u-full-width">
<a href="index.html">Try Rule-based</a>
<a href="probability.html">Try Statistical</a>
</div>
<div id="page">
<h5 id="heading">U-Net skin detector</h5>
<p id="subheading"><i>on colored images</i></p>
<div class="main-wrapper">
<div class="padder"></div>
<div>
<p class="li-header">
<b>Limitations</b>
</p>
<ul>
<li>Image must be hosted on the same website (use Wikipedia, Imgur, Flickr)</li>
<li>Resize image to 352x352</li>
<li>Webpage may freeze, do not refresh</li>
</ul>
<p>
<b>Performance depends on your hardware</b>
<br />
Also take note that <i>filtered images</i> are bad because the colors are altered
</p>
</div>
<p id="info">READY - Waiting input</p>
<div class="u-full-width search-form" >
<div id="url-container" class="six columns" >
<input id="name" name="name" class="two-thirds column" type="search"
placeholder="Paste an image URL">
<span class="padder-horiz"></span>
<span id="randomness" class="one columns"
onclick="insertRandom()"></span>
</div>
<button id="run" class="one-half column button-primary"
onclick="skinDetectTf(webWorker)">Detect Skin</button>
</div>
<div id="slider-container" class="u-full-width">
<img id="imgbox-ori" alt="Waiting for detection..." title="Original image" crossorigin='anonymous' />
<img id="imgbox" alt="Waiting for detection..." title="Detected skin pixels" crossorigin='anonymous' />
<input type="range" min="0" max="100" value="50" id="slider" oninput="slide()">
</div>
</div>
</div>
<div class="row u-full-width">
<hr />
<p style="text-align: center;">
Styled with Skeleton - Powered by Tensorflow.js
</p>
</div>
</div>
</div>
<!-- Tensorflow.js
–––––––––––––––––––––––––––––––––––––––––––––––––– -->
<script type="text/javascript">
const info_elem = document.getElementById("info");
const img_pred = document.getElementById("imgbox");
const img_ori = document.getElementById("imgbox-ori");
let model;
const modelURL = 'models/web_model-20210428-155148/model.json';
let webWorker = null;
/** Whether to print memory usage in main steps */
const debugMemory = false;
// Utilities
/** Print GPU memory usage (tensors, bytes) if debugMemory flag is set */
function printMemory() {
if (debugMemory) self.postMessage({ table: tf.memory() });
}
// Load Model
async function loadModel() {
info('Loading model...');
return await tf.loadGraphModel(modelURL);
}
/** Warmup the model before using real data to get a faster 1st prediction */
async function warmupModel() {
info('Model warmup...');
const inputShape = [1, square_size, square_size, 3];
const warmupResult = model.execute({ 'feature' : tf.zeros(inputShape) });
warmupResult.dataSync(); // we don't care about the result
warmupResult.dispose();
}
/**
* Init the execution.
* Route the execution between main thread mode and web workers mode,
* giving priority to the latter.
*/
async function main() {
// Check if can use web workers
if (canIUseOffscreenCanvas()) {
info('Using web workers')
webWorker = initWorker('unet.js'); // init the web worker and update the local var
return
}
else { // cannot use web workers: use main thread
info('Using main thread')
model = await loadModel();
warmupModel(model);
canRun = true;
info('Waiting input', 'ready');
printMemory();
return
}
}
// Skin Detect
/**
* Run init skin detection tasks.
* Route the execution between main thread mode and web workers mode,
* giving priority to the latter.
*/
async function skinDetectTf(webWorker) {
if (webWorker !== null) {
skinDetect(webWorker);
return
}
else { // cannot use web workers: use main thread
skinDetectMain();
return
}
}
/** Init skin detection: init tfjs, check URL validity, and fetch the image */
async function skinDetectMain() {
if (!canRun || running) return; // prevent users from spamming clicks on "Skin Detect" button
running = true;
document.getElementById("slider-container").style.visibility = "hidden";
info('Using backend: ' + tf.getBackend());
// This fixes GPU memory leak (bytes), but inference is slower
if (tf.getBackend() === 'webgl')
tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);
// Check URL
info('Checking URL...');
if (!isValidHttpUrl(document.getElementById("name").value)) {
info('Invalid URL. Does it start with https:// ?');
running = false; // early stop
return;
}
info('Running script...');
// Fetch image
info('Fetching image...');
const img_url = document.getElementById("name").value;
getMeta(img_ori, img_url, null);
}
/**
* Run the skin detection tasks.
* Override skinDetectContinue() to route the execution between
* main thread mode and web workers mode, giving priority to the latter
*/
async function skinDetectContinue(webWorker) {
if (webWorker !== null) {
skinDetectContinueWorker(webWorker);
return
}
else { // cannot use web workers: use main thread
skinDetectContinueMain();
return
}
}
/** After \<img> src is loaded, run the skin detection */
async function skinDetectContinueMain() {
// remove previous event listener or it will lag after setting img src
img_ori.removeEventListener("load", onLoad);
img_ori.removeEventListener("error", onError);
try {
// Start Preprocessing
info('Preprocessing...')
const tensor = tf.tidy(() => {
// Now that image has dimensions, finally get image pixels
const squared_image = imageToSquare(img_ori, square_size);
let result = tf.browser.fromPixels(squared_image); // img in 0-255
// Normalize to 0-1
result = result.cast('float32').div(tf.scalar(255));
return result;
});
printMemory();
// Save pre-processed original image
const canvas_ori = document.createElement('canvas');
canvas_ori.width = tensor.shape[1]
canvas_ori.height = tensor.shape[0]
await tf.browser.toPixels(tensor, canvas_ori);
let ori_data = canvas_ori.toDataURL() // will return the base64 encoding
// Predict
const outcome = tf.tidy(() => {
info('Feeding model...')
// insert image into 4D tensor and place in dict as the feature image
const outputs = model.execute({ 'feature' : tensor.expandDims(0) });
// Extract image from 4D tensor and Binarize
return outputs.squeeze(0).round();
});
tensor.dataSync();
tf.dispose(tensor)
// Get resulting image data
info('Encoding prediction...')
const image_width = outcome.shape[1];
const image_height = outcome.shape[0];
// Draw tensor to canvas to later get the base64 encoding
// (used to update src of \<img> elements)
const canvas = document.createElement('canvas');
canvas.width = image_width;
canvas.height = image_height;
await tf.browser.toPixels(outcome, canvas);
outcome.dataSync(); // clean GPU
tf.dispose(outcome);
const img_data = canvas.toDataURL() // will return the base64 encoding
updateSlider([img_data, ori_data, image_width, image_height]);
} catch (_) {
info('Predict error, refresh and retry', 'critical');
}
// clean memory
//tf.dispose(model); // if wanting to completely clean memory
printMemory();
}
/** After \<img> src is loaded, run the skin detection */
async function skinDetectContinueWorker(webWorker) {
// remove previous event listener or it will lag after setting img src
const img_ori = document.getElementById("imgbox-ori");
img_ori.removeEventListener("load", onLoad);
img_ori.removeEventListener("error", onError);
// Now that \<img> has dimensions, finally get image pixels
const squared_image = imageToSquare(img_ori, square_size);
// canvas to tensor
const from_pixels = tf.browser.fromPixels(squared_image); // img in 0-255
const data_shape = from_pixels.shape;
const data = await from_pixels.data();
from_pixels.dispose();
let id = 1;
// pass image data to web worker
const context = {
square_size: square_size,
from_pixels: [data, data_shape],
};
// Request skin detection
webWorker.postMessage({
...context,
id,
});
}
main();
</script>
<!-- End Document
–––––––––––––––––––––––––––––––––––––––––––––––––– -->
</body>
</html>