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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Enhanced Object Detection</title>
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<link href="https://unpkg.com/material-components-web@latest/dist/material-components-web.min.css" rel="stylesheet">
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<style>
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body { font-family: 'Roboto', sans-serif; margin: 2em; color: #3d3d3d; background: #f0f2f5; }
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h1 { color: #007f8b; text-align: center; }
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.container { max-width: 1200px; margin: 0 auto; background: white; padding: 20px; border-radius: 12px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); }
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.controls { display: flex; gap: 20px; flex-wrap: wrap; margin-bottom: 20px; padding: 15px; background: #e6fcfd; border-radius: 8px; align-items: center; }
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.control-group { display: flex; flex-direction: column; min-width: 200px; }
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label { font-weight: bold; font-size: 0.9em; margin-bottom: 5px; color: #007f8b; }
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input[type=range] { width: 100%; }
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.upload-btn { background: #007f8b; color: white; padding: 10px 20px; border-radius: 25px; cursor: pointer; display: inline-block; font-weight: bold; text-align: center; }
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.upload-btn:hover { background: #006069; }
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input[type="file"] { display: none; }
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.image-grid {
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display: block;
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column-count: 3;
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column-gap: 20px;
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}
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@media (max-width: 900px) {
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.image-grid { column-count: 2; }
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}
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@media (max-width: 600px) {
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.image-grid { column-count: 1; }
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}
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.detect-card {
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position: relative;
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border-radius: 8px;
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overflow: hidden;
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box-shadow: 0 2px 4px rgba(0,0,0,0.1);
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background: #000;
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cursor: pointer;
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transition: transform 0.2s;
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z-index: 1;
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break-inside: avoid-column;
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margin-bottom: 20px;
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}
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.detect-card:hover { transform: scale(1.01); }
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.detect-card img { display: block; width: 100%; height: auto; transition: opacity 0.3s; }
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.detect-card.processing { pointer-events: none; }
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.detect-card.processing img { opacity: 0.6; filter: grayscale(50%); }
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.inference-panel {
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position: absolute;
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bottom: 0;
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left: 0;
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width: 100%;
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background: rgba(255, 255, 255, 0.95);
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padding: 15px;
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box-sizing: border-box;
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transform: translateY(100%);
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transition: transform 0.3s cubic-bezier(0.4, 0.0, 0.2, 1);
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z-index: 50;
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border-top: 3px solid #007f8b;
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}
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.detect-card.processing .inference-panel {
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transform: translateY(0);
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}
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.inference-status {
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display: flex;
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justify-content: space-between;
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font-weight: bold;
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color: #007f8b;
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margin-bottom: 8px;
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font-size: 0.9rem;
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}
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.progress-track {
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width: 100%;
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height: 6px;
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background: #e0e0e0;
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border-radius: 3px;
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overflow: hidden;
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}
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.progress-bar {
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height: 100%;
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background: #007f8b;
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width: 30%;
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border-radius: 3px;
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animation: loading 1.5s infinite ease-in-out;
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}
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@keyframes loading {
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0% { transform: translateX(-100%); }
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100% { transform: translateX(400%); }
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}
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.highlighter { position: absolute; border: 2px solid; border-radius: 4px; z-index: 10; pointer-events: none; }
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.label-tag { position: absolute; padding: 2px 6px; color: white; font-size: 11px; font-weight: bold; border-radius: 4px; pointer-events: none; z-index: 11; white-space: nowrap; box-shadow: 0 1px 2px rgba(0,0,0,0.2); }
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#loader { position: fixed; top: 50%; left: 50%; transform: translate(-50%, -50%); padding: 20px; background: white; border-radius: 8px; box-shadow: 0 0 20px rgba(0,0,0,0.2); display: none; z-index: 1000; font-weight: bold; }
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</style>
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</head>
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<body>
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<div class="container">
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<h1>Smart Object Recognition</h1>
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<div class="controls">
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<div class="control-group">
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<label for="imageUpload" class="upload-btn">📂 Upload Image</label>
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<input type="file" id="imageUpload" accept="image/*">
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</div>
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<div class="control-group">
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<label>Confidence Threshold: <span id="confValue">50</span>%</label>
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<input type="range" id="confidenceSlider" min="10" max="90" value="50">
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<small>Increase to remove weak guesses.</small>
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</div>
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<div class="control-group">
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<label>Overlap Fix (NMS): <span id="overlapValue">30</span>%</label>
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<input type="range" id="overlapSlider" min="0" max="100" value="30">
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<small>Lower value = Fewer overlapping boxes.</small>
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</div>
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</div>
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<div id="loader">Loading AI Model...</div>
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<div class="image-grid" id="imageContainer">
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<div class="detect-card">
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<img src="https://assets.codepen.io/9177687/coupledog.jpeg" crossorigin="anonymous" />
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</div>
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<div class="detect-card">
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<img src="https://assets.codepen.io/9177687/doggo.jpeg" crossorigin="anonymous" />
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</div>
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<div class="detect-card">
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<img src="https://tse3.mm.bing.net/th/id/OIP.mIJJ36cXpVujF1wnZnd4VQHaE8?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
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</div>
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<div class="detect-card">
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<img src="https://images.pexels.com/photos/23409055/pexels-photo-23409055/free-photo-of-cars-on-street-in-town.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=1" crossorigin="anonymous" />
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</div>
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<div class="detect-card">
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<img src="https://tse4.mm.bing.net/th/id/OIP.bWwaHeR-aoBb3esBRaAEEgHaE8?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
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</div>
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<div class="detect-card">
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<img src="https://tse4.mm.bing.net/th/id/OIP.vs_d1C-7n4PoNv0GVlaVDwHaFj?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
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</div>
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<div class="detect-card">
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<img src="https://tse4.mm.bing.net/th/id/OIP.V1zVa5IUI22o0i6gG4or2QHaLH?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
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</div>
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<div class="detect-card">
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<img src="https://th.bing.com/th/id/R.2be55af1ab4a38df1a9b54bf6b68a8bd?rik=f7wiKdc8N42mgA&pid=ImgRaw&r=0" crossorigin="anonymous" />
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</div>
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<div class="detect-card">
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<img src="https://images.pexels.com/photos/20625972/pexels-photo-20625972.jpeg?cs=srgb&dl=pexels-saturnus99-20625972.jpg&fm=jpg" crossorigin="anonymous" />
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</div>
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<div class="detect-card">
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<img src="https://tse4.mm.bing.net/th/id/OIP.ZMnNqw1GVTa9HpHvsTYcjQAAAA?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
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</div>
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<div class="detect-card">
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<img src="https://bestbackpacklab.com/wp-content/uploads/2021/05/children-1536x864.jpg" crossorigin="anonymous" />
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</div>
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<div class="detect-card">
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<img src="https://tse1.explicit.bing.net/th/id/OIP.za2l0WGKXbR4Qkj8phu2UwHaE8?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
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</div>
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<div class="detect-card">
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<img src="https://tse2.mm.bing.net/th/id/OIP.bcOP7ZTpLAyyl3tKpdk5gAHaFB?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
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</div>
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<div class="detect-card">
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<img src="https://tse3.mm.bing.net/th/id/OIP.PC6Fr2mEuGUsEiaNfCSOaAHaE7?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
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</div>
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<div class="detect-card">
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<img src="https://tse3.mm.bing.net/th/id/OIF.EABwKojMHBX0uEfpxor95w?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
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</div>
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<div class="detect-card">
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<img src="https://tse4.mm.bing.net/th/id/OIP.qliYrfiREN-ydW4DxWYfSgHaE7?w=626&h=417&rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
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</div>
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</div>
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</div>
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<script type="module">
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import { ObjectDetector, FilesetResolver } from "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.2";
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const loader = document.getElementById("loader");
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let objectDetector;
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let runningMode = "IMAGE";
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let confidenceThreshold = 0.5;
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let overlapThreshold = 0.3;
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const initializeObjectDetector = async () => {
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loader.style.display = "block";
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const vision = await FilesetResolver.forVisionTasks("https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.2/wasm");
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objectDetector = await ObjectDetector.createFromOptions(vision, {
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baseOptions: {
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modelAssetPath: `https://storage.googleapis.com/mediapipe-models/object_detector/efficientdet_lite2/float16/1/efficientdet_lite2.tflite`,
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delegate: "GPU"
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},
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scoreThreshold: 0.2,
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runningMode: runningMode
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});
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loader.style.display = "none";
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console.log("Model Loaded: EfficientDet-Lite2");
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};
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initializeObjectDetector();
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function filterDetections(detections, iouLimit) {
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detections.sort((a, b) => b.categories[0].score - a.categories[0].score);
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const selected = [];
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const active = new Array(detections.length).fill(true);
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for (let i = 0; i < detections.length; i++) {
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if (!active[i]) continue;
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if (detections[i].categories[0].score < confidenceThreshold) continue;
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selected.push(detections[i]);
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const boxA = detections[i].boundingBox;
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for (let j = i + 1; j < detections.length; j++) {
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if (!active[j]) continue;
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const boxB = detections[j].boundingBox;
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const x1 = Math.max(boxA.originX, boxB.originX);
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const y1 = Math.max(boxA.originY, boxB.originY);
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const x2 = Math.min(boxA.originX + boxA.width, boxB.originX + boxB.width);
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const y2 = Math.min(boxA.originY + boxA.height, boxB.originY + boxB.height);
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if (x2 < x1 || y2 < y1) continue;
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const intersection = (x2 - x1) * (y2 - y1);
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const areaA = boxA.width * boxA.height;
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const areaB = boxB.width * boxB.height;
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const union = areaA + areaB - intersection;
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const iou = intersection / union;
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if (iou > iouLimit) {
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active[j] = false;
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}
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}
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}
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return selected;
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}
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async function handleClick(event) {
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if (!objectDetector) return;
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const img = event.target;
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const card = img.parentNode;
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let infoPanel = card.querySelector('.inference-panel');
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if (!infoPanel) {
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infoPanel = document.createElement('div');
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infoPanel.className = 'inference-panel';
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infoPanel.innerHTML = `
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<div class="inference-status">
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<span>Running Inference...</span>
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<span>Please wait</span>
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</div>
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<div class="progress-track">
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<div class="progress-bar"></div>
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</div>
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`;
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card.appendChild(infoPanel);
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}
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card.classList.add('processing');
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card.querySelectorAll('.highlighter, .label-tag').forEach(el => el.remove());
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await new Promise(resolve => requestAnimationFrame(() => setTimeout(resolve, 50)));
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try {
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const predictions = objectDetector.detect(img);
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const filteredDetections = filterDetections(predictions.detections, overlapThreshold);
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displayDetections(filteredDetections, img);
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} catch(e) {
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console.error(e);
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alert("Error running model");
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} finally {
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card.classList.remove('processing');
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}
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}
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function displayDetections(detections, img) {
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const ratioX = img.width / img.naturalWidth;
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const ratioY = img.height / img.naturalHeight;
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detections.forEach(detection => {
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const box = detection.boundingBox;
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const category = detection.categories[0];
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const score = Math.round(category.score * 100);
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const color = getColorForLabel(category.categoryName);
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const highlighter = document.createElement("div");
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highlighter.className = "highlighter";
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highlighter.style.left = `${box.originX * ratioX}px`;
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highlighter.style.top = `${box.originY * ratioY}px`;
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highlighter.style.width = `${box.width * ratioX}px`;
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highlighter.style.height = `${box.height * ratioY}px`;
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highlighter.style.borderColor = color;
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highlighter.style.backgroundColor = color + "20";
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const label = document.createElement("div");
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label.className = "label-tag";
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label.innerText = `${category.categoryName} ${score}%`;
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label.style.backgroundColor = color;
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const topPos = (box.originY * ratioY) - 25;
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label.style.left = `${box.originX * ratioX}px`;
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label.style.top = `${topPos > 0 ? topPos : (box.originY * ratioY)}px`;
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img.parentNode.appendChild(highlighter);
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img.parentNode.appendChild(label);
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});
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}
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function getColorForLabel(label) {
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let hash = 0;
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for (let i = 0; i < label.length; i++) {
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hash = label.charCodeAt(i) + ((hash << 5) - hash);
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}
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const c = (hash & 0x00FFFFFF).toString(16).toUpperCase();
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return "#" + "00000".substring(0, 6 - c.length) + c;
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}
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const imageContainer = document.getElementById("imageContainer");
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imageContainer.addEventListener('click', (e) => {
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if (e.target.tagName === 'IMG') handleClick(e);
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});
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document.getElementById('imageUpload').addEventListener('change', (e) => {
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const file = e.target.files[0];
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if (!file) return;
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const reader = new FileReader();
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reader.onload = (event) => {
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const div = document.createElement('div');
|
|
|
div.className = 'detect-card';
|
|
|
const img = document.createElement('img');
|
|
|
img.src = event.target.result;
|
|
|
div.appendChild(img);
|
|
|
imageContainer.insertBefore(div, imageContainer.firstChild);
|
|
|
};
|
|
|
reader.readAsDataURL(file);
|
|
|
});
|
|
|
|
|
|
document.getElementById('confidenceSlider').addEventListener('input', (e) => {
|
|
|
confidenceThreshold = e.target.value / 100;
|
|
|
document.getElementById('confValue').innerText = e.target.value;
|
|
|
});
|
|
|
|
|
|
document.getElementById('overlapSlider').addEventListener('input', (e) => {
|
|
|
overlapThreshold = e.target.value / 100;
|
|
|
document.getElementById('overlapValue').innerText = e.target.value;
|
|
|
});
|
|
|
</script>
|
|
|
|
|
|
</body>
|
|
|
</html>
|
|
|
|
