Commit
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1
Parent(s):
54faa69
demo
Browse files- index.html +9 -14
- index.js +0 -76
index.html
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@@ -6,24 +6,19 @@
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<link rel="stylesheet" href="style.css" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>
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</head>
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<body>
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<h1>
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<
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d="M3.5 24.3a3 3 0 0 1-1.9-.8c-.5-.5-.8-1.2-.8-1.9V2.9c0-.7.3-1.3.8-1.9.6-.5 1.2-.7 2-.7h18.6c.7 0 1.3.2 1.9.7.5.6.7 1.2.7 2v18.6c0 .7-.2 1.4-.7 1.9a3 3 0 0 1-2 .8H3.6Zm0-2.7h18.7V2.9H3.5v18.7Zm2.7-2.7h13.3c.3 0 .5 0 .6-.3v-.7l-3.7-5a.6.6 0 0 0-.6-.2c-.2 0-.4 0-.5.3l-3.5 4.6-2.4-3.3a.6.6 0 0 0-.6-.3c-.2 0-.4.1-.5.3l-2.7 3.6c-.1.2-.2.4 0 .7.1.2.3.3.6.3Z">
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</path>
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</svg>
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Click to upload image
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<label id="example">(or try example)</label>
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</label>
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<label id="status">Loading model...</label>
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<input id="upload" type="file" accept="image/*" />
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<
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</body>
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</html>
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<link rel="stylesheet" href="style.css" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>CoreSole™ – Shoeprint Analysis and Matching with Vision AI</title>
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</head>
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<body>
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<h1>CoreSole™ Forensics AI – Image Embeddings for Shoeprint Recognition</h1>
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<p>Upload two images of shoeprints to get a similarity score using the CoreSole™ Vision AI model.</p>
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<p>This space is intended for demonstration purposes only. For production use and to index full shoe and shoeprint
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image collections, please contact <a href="https://coresole.com">CoreSole™</a>.</p>
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<iframe src="https://coreviz.io/tools/shoeprint-similarity/embed" width="100%" height="100%"
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style="min-height: 890px;">
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</iframe>
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</body>
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</html>
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index.js
DELETED
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import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.7.6';
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// Reference the elements that we will need
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const status = document.getElementById('status');
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const fileUpload = document.getElementById('upload');
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const imageContainer = document.getElementById('container');
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const example = document.getElementById('example');
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const EXAMPLE_URL = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg';
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// Create a new object detection pipeline
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status.textContent = 'Loading model...';
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const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
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status.textContent = 'Ready';
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example.addEventListener('click', (e) => {
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e.preventDefault();
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detect(EXAMPLE_URL);
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});
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fileUpload.addEventListener('change', function (e) {
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const file = e.target.files[0];
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if (!file) {
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return;
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}
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const reader = new FileReader();
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// Set up a callback when the file is loaded
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reader.onload = e2 => detect(e2.target.result);
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reader.readAsDataURL(file);
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});
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// Detect objects in the image
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async function detect(img) {
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imageContainer.innerHTML = '';
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imageContainer.style.backgroundImage = `url(${img})`;
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status.textContent = 'Analysing...';
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const output = await detector(img, {
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threshold: 0.5,
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percentage: true,
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});
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status.textContent = '';
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output.forEach(renderBox);
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}
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// Render a bounding box and label on the image
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function renderBox({ box, label }) {
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const { xmax, xmin, ymax, ymin } = box;
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// Generate a random color for the box
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const color = '#' + Math.floor(Math.random() * 0xFFFFFF).toString(16).padStart(6, 0);
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// Draw the box
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const boxElement = document.createElement('div');
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boxElement.className = 'bounding-box';
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Object.assign(boxElement.style, {
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borderColor: color,
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left: 100 * xmin + '%',
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top: 100 * ymin + '%',
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width: 100 * (xmax - xmin) + '%',
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height: 100 * (ymax - ymin) + '%',
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})
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// Draw label
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const labelElement = document.createElement('span');
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labelElement.textContent = label;
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labelElement.className = 'bounding-box-label';
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labelElement.style.backgroundColor = color;
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boxElement.appendChild(labelElement);
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imageContainer.appendChild(boxElement);
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}
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