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<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>AI Vehicle Tracking System</title>
<script src="https://cdn.tailwindcss.com"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@3.18.0/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/coco-ssd@2.2.2/dist/coco-ssd.min.js"></script>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
<style>
.video-container {
position: relative;
width: 100%;
height: 0;
padding-bottom: 56.25%;
background-color: #1a202c;
border-radius: 0.5rem;
overflow: hidden;
}
.video-container video, .video-container canvas {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
object-fit: cover;
}
.plate-highlight {
position: absolute;
border: 2px solid #3B82F6;
background-color: rgba(59, 130, 246, 0.2);
z-index: 10;
}
.processing-overlay {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
background-color: rgba(0, 0, 0, 0.7);
display: flex;
justify-content: center;
align-items: center;
z-index: 20;
color: white;
font-size: 1.5rem;
border-radius: 0.5rem;
}
.pulse {
animation: pulse 2s infinite;
}
@keyframes pulse {
0% { opacity: 0.6; }
50% { opacity: 1; }
100% { opacity: 0.6; }
}
.result-card {
transition: all 0.3s ease;
}
.result-card:hover {
transform: translateY(-5px);
box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1);
}
</style>
</head>
<body class="bg-gray-100 min-h-screen">
<div class="container mx-auto px-4 py-8">
<!-- Header -->
<header class="mb-8 text-center">
<h1 class="text-4xl font-bold text-blue-600 mb-2">
<i class="fas fa-car-alt mr-2"></i>AI Vehicle Tracking System
</h1>
<p class="text-gray-600">Real-time car tracking with license plate recognition powered by Ultralytics and Claude AI</p>
</header>
<!-- Main Content -->
<div class="grid grid-cols-1 lg:grid-cols-3 gap-8">
<!-- Video Feed Section -->
<div class="lg:col-span-2">
<div class="bg-white rounded-xl shadow-lg p-6">
<div class="flex justify-between items-center mb-4">
<h2 class="text-xl font-semibold text-gray-800">
<i class="fas fa-video mr-2 text-blue-500"></i>Live Camera Feed
</h2>
<div class="flex space-x-2">
<button id="startBtn" class="bg-green-500 hover:bg-green-600 text-white px-4 py-2 rounded-lg flex items-center">
<i class="fas fa-play mr-2"></i> Start
</button>
<button id="stopBtn" disabled class="bg-red-500 hover:bg-red-600 text-white px-4 py-2 rounded-lg flex items-center">
<i class="fas fa-stop mr-2"></i> Stop
</button>
<button id="uploadBtn" class="bg-blue-500 hover:bg-blue-600 text-white px-4 py-2 rounded-lg flex items-center">
<i class="fas fa-upload mr-2"></i> Upload
</button>
<input type="file" id="fileInput" accept="image/*,video/*" class="hidden">
</div>
</div>
<div class="video-container" id="videoContainer">
<video id="videoFeed" autoplay muted playsinline class="hidden"></video>
<canvas id="canvasOutput"></canvas>
<div id="processingOverlay" class="processing-overlay hidden">
<div class="text-center">
<i class="fas fa-cog fa-spin text-4xl mb-2 text-blue-400"></i>
<p class="pulse">Processing frame...</p>
</div>
</div>
</div>
<div class="mt-4 grid grid-cols-2 gap-4">
<div class="bg-gray-50 p-4 rounded-lg">
<h3 class="font-medium text-gray-700 mb-2">
<i class="fas fa-chart-line mr-2 text-blue-500"></i>Detection Stats
</h3>
<div class="space-y-2">
<div class="flex justify-between">
<span class="text-gray-600">Vehicles Detected:</span>
<span id="vehicleCount" class="font-semibold">0</span>
</div>
<div class="flex justify-between">
<span class="text-gray-600">License Plates:</span>
<span id="plateCount" class="font-semibold">0</span>
</div>
<div class="flex justify-between">
<span class="text-gray-600">Processing Time:</span>
<span id="processingTime" class="font-semibold">0ms</span>
</div>
</div>
</div>
<div class="bg-gray-50 p-4 rounded-lg">
<h3 class="font-medium text-gray-700 mb-2">
<i class="fas fa-sliders-h mr-2 text-blue-500"></i>Settings
</h3>
<div class="space-y-3">
<div>
<label class="block text-sm text-gray-600 mb-1">Confidence Threshold</label>
<input type="range" id="confidenceSlider" min="0.1" max="0.9" step="0.1" value="0.5" class="w-full">
<div class="flex justify-between text-xs text-gray-500">
<span>10%</span>
<span id="confidenceValue">50%</span>
<span>90%</span>
</div>
</div>
<div class="flex items-center">
<input type="checkbox" id="enableOCR" checked class="mr-2">
<label for="enableOCR" class="text-sm text-gray-600">Enable OCR Processing</label>
</div>
</div>
</div>
</div>
</div>
</div>
<!-- Results Section -->
<div class="lg:col-span-1">
<div class="bg-white rounded-xl shadow-lg p-6 h-full">
<div class="flex justify-between items-center mb-4">
<h2 class="text-xl font-semibold text-gray-800">
<i class="fas fa-clipboard-list mr-2 text-blue-500"></i>Detection Results
</h2>
<button id="clearResults" class="text-gray-500 hover:text-gray-700">
<i class="fas fa-trash-alt"></i>
</button>
</div>
<div id="resultsContainer" class="space-y-4 max-h-[calc(100vh-250px)] overflow-y-auto pr-2">
<div class="text-center py-10 text-gray-400" id="emptyResults">
<i class="fas fa-car-side text-4xl mb-3"></i>
<p>No detections yet. Start the camera or upload media to begin.</p>
</div>
</div>
</div>
</div>
</div>
<!-- API Status -->
<div class="mt-8 bg-white rounded-xl shadow-lg p-6">
<h2 class="text-xl font-semibold text-gray-800 mb-4">
<i class="fas fa-plug mr-2 text-blue-500"></i>API Status
</h2>
<div class="grid grid-cols-1 md:grid-cols-3 gap-4">
<div class="bg-gray-50 p-4 rounded-lg">
<div class="flex items-center mb-2">
<div id="ultralyticsStatus" class="w-3 h-3 rounded-full bg-gray-400 mr-2"></div>
<span class="font-medium">Ultralytics Model</span>
</div>
<p class="text-sm text-gray-600">Vehicle detection and license plate extraction</p>
</div>
<div class="bg-gray-50 p-4 rounded-lg">
<div class="flex items-center mb-2">
<div id="claudeStatus" class="w-3 h-3 rounded-full bg-gray-400 mr-2"></div>
<span class="font-medium">Claude API</span>
</div>
<p class="text-sm text-gray-600">OCR processing for license plates</p>
</div>
<div class="bg-gray-50 p-4 rounded-lg">
<div class="flex items-center mb-2">
<div id="systemStatus" class="w-3 h-3 rounded-full bg-gray-400 mr-2"></div>
<span class="font-medium">System Status</span>
</div>
<p class="text-sm text-gray-600" id="systemStatusText">Initializing...</p>
</div>
</div>
</div>
</div>
<!-- Result Card Template -->
<template id="resultCardTemplate">
<div class="result-card bg-gray-50 rounded-lg p-4 border border-gray-200">
<div class="flex justify-between items-start mb-2">
<div>
<span class="font-semibold text-blue-600 detection-type">Vehicle</span>
<span class="text-xs bg-blue-100 text-blue-800 px-2 py-1 rounded-full ml-2 confidence">85%</span>
</div>
<span class="text-xs text-gray-500 timestamp">12:34:56 PM</span>
</div>
<div class="flex mb-3">
<div class="w-16 h-16 bg-gray-200 rounded-md overflow-hidden mr-3 thumbnail-container">
<img src="" alt="Detection" class="w-full h-full object-cover thumbnail">
</div>
<div class="flex-1">
<div class="text-sm mb-1">
<span class="text-gray-600">Plate:</span>
<span class="font-medium ml-1 plate-number">Not detected</span>
</div>
<div class="text-sm">
<span class="text-gray-600">Make/Model:</span>
<span class="font-medium ml-1 vehicle-model">Unknown</span>
</div>
</div>
</div>
<div class="flex justify-end space-x-2">
<button class="text-xs bg-blue-50 text-blue-600 px-3 py-1 rounded hover:bg-blue-100 view-btn">
<i class="fas fa-search mr-1"></i> View
</button>
<button class="text-xs bg-gray-100 text-gray-600 px-3 py-1 rounded hover:bg-gray-200 export-btn">
<i class="fas fa-download mr-1"></i> Export
</button>
</div>
</div>
</template>
<!-- Modal for detailed view -->
<div id="detailModal" class="fixed inset-0 bg-black bg-opacity-50 flex items-center justify-center z-50 hidden">
<div class="bg-white rounded-lg w-full max-w-2xl max-h-[90vh] overflow-auto">
<div class="p-4 border-b flex justify-between items-center">
<h3 class="text-lg font-semibold">Detection Details</h3>
<button id="closeModal" class="text-gray-500 hover:text-gray-700">
<i class="fas fa-times"></i>
</button>
</div>
<div class="p-6">
<div class="grid grid-cols-1 md:grid-cols-2 gap-6">
<div>
<h4 class="font-medium text-gray-700 mb-2">Detection Image</h4>
<img id="modalImage" src="" alt="Detection" class="w-full rounded-lg border border-gray-200">
</div>
<div>
<h4 class="font-medium text-gray-700 mb-2">Details</h4>
<div class="space-y-3">
<div>
<label class="block text-sm text-gray-500">Detection Type</label>
<p id="modalType" class="font-medium">Vehicle</p>
</div>
<div>
<label class="block text-sm text-gray-500">Confidence</label>
<p id="modalConfidence" class="font-medium">85%</p>
</div>
<div>
<label class="block text-sm text-gray-500">License Plate</label>
<p id="modalPlate" class="font-medium">ABC123</p>
</div>
<div>
<label class="block text-sm text-gray-500">Vehicle Make/Model</label>
<p id="modalModel" class="font-medium">Toyota Camry</p>
</div>
<div>
<label class="block text-sm text-gray-500">Timestamp</label>
<p id="modalTimestamp" class="font-medium">12:34:56 PM</p>
</div>
</div>
</div>
</div>
<div class="mt-6 pt-4 border-t">
<h4 class="font-medium text-gray-700 mb-2">Raw Data</h4>
<pre id="modalRawData" class="bg-gray-50 p-3 rounded text-xs overflow-x-auto"></pre>
</div>
</div>
<div class="p-4 border-t flex justify-end space-x-3">
<button class="px-4 py-2 border border-gray-300 rounded-lg hover:bg-gray-50">Export as JSON</button>
<button class="px-4 py-2 bg-blue-600 text-white rounded-lg hover:bg-blue-700">Save to Database</button>
</div>
</div>
</div>
<script>
// DOM Elements
const videoFeed = document.getElementById('videoFeed');
const canvasOutput = document.getElementById('canvasOutput');
const videoContainer = document.getElementById('videoContainer');
const startBtn = document.getElementById('startBtn');
const stopBtn = document.getElementById('stopBtn');
const uploadBtn = document.getElementById('uploadBtn');
const fileInput = document.getElementById('fileInput');
const processingOverlay = document.getElementById('processingOverlay');
const resultsContainer = document.getElementById('resultsContainer');
const emptyResults = document.getElementById('emptyResults');
const confidenceSlider = document.getElementById('confidenceSlider');
const confidenceValue = document.getElementById('confidenceValue');
const enableOCR = document.getElementById('enableOCR');
const clearResults = document.getElementById('clearResults');
const vehicleCount = document.getElementById('vehicleCount');
const plateCount = document.getElementById('plateCount');
const processingTime = document.getElementById('processingTime');
const ultralyticsStatus = document.getElementById('ultralyticsStatus');
const claudeStatus = document.getElementById('claudeStatus');
const systemStatus = document.getElementById('systemStatus');
const systemStatusText = document.getElementById('systemStatusText');
const detailModal = document.getElementById('detailModal');
const closeModal = document.getElementById('closeModal');
const modalImage = document.getElementById('modalImage');
const modalType = document.getElementById('modalType');
const modalConfidence = document.getElementById('modalConfidence');
const modalPlate = document.getElementById('modalPlate');
const modalModel = document.getElementById('modalModel');
const modalTimestamp = document.getElementById('modalTimestamp');
const modalRawData = document.getElementById('modalRawData');
// State variables
let stream = null;
let model = null;
let isProcessing = false;
let detectionHistory = [];
let currentDetections = [];
let confidenceThreshold = 0.5;
let ctx = canvasOutput.getContext('2d');
let animationId = null;
let currentMediaType = null; // 'camera' or 'file'
// Initialize
document.addEventListener('DOMContentLoaded', async () => {
// Set up canvas to match video container aspect ratio
resizeCanvas();
// Event listeners
startBtn.addEventListener('click', startCamera);
stopBtn.addEventListener('click', stopCamera);
uploadBtn.addEventListener('click', () => fileInput.click());
fileInput.addEventListener('change', handleFileUpload);
confidenceSlider.addEventListener('input', updateConfidenceThreshold);
clearResults.addEventListener('click', clearDetectionHistory);
closeModal.addEventListener('click', () => detailModal.classList.add('hidden'));
// Initialize models
await initializeModels();
// Update system status
updateSystemStatus();
});
// Resize canvas to match container
function resizeCanvas() {
const containerWidth = videoContainer.clientWidth;
const containerHeight = videoContainer.clientHeight;
canvasOutput.width = containerWidth;
canvasOutput.height = containerHeight;
}
// Initialize AI models
async function initializeModels() {
try {
systemStatusText.textContent = "Loading Ultralytics model...";
ultralyticsStatus.classList.remove('bg-gray-400', 'bg-red-500');
ultralyticsStatus.classList.add('bg-yellow-500');
// In a real implementation, we would load the actual Ultralytics model here
// For this demo, we'll use COCO-SSD as a placeholder
model = await cocoSsd.load();
ultralyticsStatus.classList.remove('bg-yellow-500');
ultralyticsStatus.classList.add('bg-green-500');
systemStatusText.textContent = "Models loaded successfully";
systemStatus.classList.remove('bg-gray-400');
systemStatus.classList.add('bg-green-500');
// Simulate Claude API connection
setTimeout(() => {
claudeStatus.classList.remove('bg-gray-400');
claudeStatus.classList.add('bg-green-500');
}, 1500);
} catch (error) {
console.error("Error loading models:", error);
ultralyticsStatus.classList.remove('bg-yellow-500');
ultralyticsStatus.classList.add('bg-red-500');
systemStatusText.textContent = "Error loading models";
systemStatus.classList.remove('bg-gray-400');
systemStatus.classList.add('bg-red-500');
}
}
// Update system status UI
function updateSystemStatus() {
// This would be more comprehensive in a real implementation
const statusElements = [
{ element: ultralyticsStatus, condition: model !== null },
{ element: claudeStatus, condition: true }, // Simulated as connected
{ element: systemStatus, condition: model !== null }
];
statusElements.forEach(item => {
if (item.condition) {
item.element.classList.remove('bg-gray-400', 'bg-red-500', 'bg-yellow-500');
item.element.classList.add('bg-green-500');
}
});
}
// Start camera feed
async function startCamera() {
try {
currentMediaType = 'camera';
stream = await navigator.mediaDevices.getUserMedia({
video: { width: 1280, height: 720, facingMode: 'environment' },
audio: false
});
videoFeed.srcObject = stream;
videoFeed.classList.remove('hidden');
startBtn.disabled = true;
stopBtn.disabled = false;
uploadBtn.disabled = true;
// Start processing frames
processVideo();
} catch (error) {
console.error("Error accessing camera:", error);
alert("Could not access the camera. Please ensure you've granted camera permissions.");
}
}
// Stop camera feed
function stopCamera() {
if (stream) {
stream.getTracks().forEach(track => track.stop());
stream = null;
}
if (animationId) {
cancelAnimationFrame(animationId);
animationId = null;
}
videoFeed.classList.add('hidden');
startBtn.disabled = false;
stopBtn.disabled = true;
uploadBtn.disabled = false;
isProcessing = false;
// Clear canvas
ctx.clearRect(0, 0, canvasOutput.width, canvasOutput.height);
}
// Handle file upload
function handleFileUpload(event) {
const file = event.target.files[0];
if (!file) return;
currentMediaType = 'file';
const fileURL = URL.createObjectURL(file);
if (file.type.startsWith('image/')) {
processImageFile(fileURL);
} else if (file.type.startsWith('video/')) {
processVideoFile(fileURL);
}
// Reset file input
event.target.value = '';
}
// Process image file
function processImageFile(fileURL) {
const img = new Image();
img.onload = async () => {
// Set canvas dimensions to match image
canvasOutput.width = img.width;
canvasOutput.height = img.height;
// Draw image to canvas
ctx.drawImage(img, 0, 0, img.width, img.height);
// Process the image
await detectObjects(canvasOutput);
};
img.src = fileURL;
}
// Process video file
function processVideoFile(fileURL) {
videoFeed.src = fileURL;
videoFeed.classList.remove('hidden');
startBtn.disabled = true;
stopBtn.disabled = false;
uploadBtn.disabled = true;
videoFeed.onloadedmetadata = () => {
// Set canvas dimensions to match video
canvasOutput.width = videoFeed.videoWidth;
canvasOutput.height = videoFeed.videoHeight;
// Start processing
processVideo();
};
videoFeed.play();
}
// Process video frames
function processVideo() {
if (!isProcessing) {
isProcessing = true;
processingOverlay.classList.remove('hidden');
}
// Draw video frame to canvas
ctx.drawImage(videoFeed, 0, 0, canvasOutput.width, canvasOutput.height);
// Detect objects in the frame
detectObjects(canvasOutput).then(() => {
if (stream || currentMediaType === 'file') {
animationId = requestAnimationFrame(processVideo);
} else {
isProcessing = false;
processingOverlay.classList.add('hidden');
}
});
}
// Detect objects in frame
async function detectObjects(canvas) {
if (!model) return;
const startTime = performance.now();
try {
// Get predictions from model
const predictions = await model.detect(canvas);
// Filter predictions based on confidence threshold and relevant classes
const relevantClasses = ['car', 'truck', 'bus', 'motorcycle'];
const vehiclePredictions = predictions.filter(
p => relevantClasses.includes(p.class) && p.score >= confidenceThreshold
);
// Clear previous detections
currentDetections = [];
// Process each vehicle detection
for (const prediction of vehiclePredictions) {
const { bbox, class: className, score } = prediction;
const [x, y, width, height] = bbox;
// Draw bounding box
ctx.strokeStyle = '#3B82F6';
ctx.lineWidth = 2;
ctx.strokeRect(x, y, width, height);
// Draw label background
ctx.fillStyle = '#3B82F6';
const textWidth = ctx.measureText(`${className} ${Math.round(score * 100)}%`).width;
ctx.fillRect(x, y - 20, textWidth + 10, 20);
// Draw label text
ctx.fillStyle = 'white';
ctx.font = '14px Arial';
ctx.fillText(`${className} ${Math.round(score * 100)}%`, x + 5, y - 5);
// Simulate license plate detection (in a real app, this would use Ultralytics)
const hasPlate = Math.random() > 0.3; // 70% chance of detecting a plate
let plateText = null;
let plateBbox = null;
if (hasPlate && enableOCR.checked) {
// Simulate plate position (bottom center of vehicle)
const plateWidth = width * 0.6;
const plateHeight = height * 0.15;
const plateX = x + (width - plateWidth) / 2;
const plateY = y + height - plateHeight * 0.8;
plateBbox = [plateX, plateY, plateWidth, plateHeight];
// Draw plate bounding box
ctx.strokeStyle = '#10B981';
ctx.lineWidth = 2;
ctx.strokeRect(plateX, plateY, plateWidth, plateHeight);
// Simulate OCR with Claude API (in a real app, this would make an API call)
plateText = simulateClaudeOCR(canvas, plateBbox);
// Draw plate text
ctx.fillStyle = '#10B981';
ctx.font = '12px Arial';
ctx.fillText(plateText || 'Processing...', plateX + 5, plateY + 15);
}
// Save detection data
const detection = {
type: className,
confidence: score,
bbox: [x, y, width, height],
plate: plateText ? {
text: plateText,
bbox: plateBbox
} : null,
timestamp: new Date().toLocaleTimeString(),
imageData: canvas.toDataURL('image/jpeg', 0.7)
};
currentDetections.push(detection);
}
// Update stats
updateDetectionStats(vehiclePredictions.length, currentDetections.filter(d => d.plate).length);
// Add to history and update UI
if (currentDetections.length > 0) {
addToDetectionHistory(currentDetections);
}
// Update processing time
const endTime = performance.now();
processingTime.textContent = `${Math.round(endTime - startTime)}ms`;
} catch (error) {
console.error("Detection error:", error);
} finally {
processingOverlay.classList.add('hidden');
isProcessing = false;
}
}
// Simulate Claude API OCR processing
function simulateClaudeOCR(canvas, bbox) {
// In a real implementation, this would:
// 1. Extract the license plate region from the canvas
// 2. Send to Claude API for OCR processing
// 3. Return the recognized text
// For demo purposes, generate random plate numbers
const letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ';
const numbers = '0123456789';
let plateText = '';
// Random format: 3 letters + 3 numbers (e.g., ABC123)
for (let i = 0; i < 3; i++) {
plateText += letters.charAt(Math.floor(Math.random() * letters.length));
}
for (let i = 0; i < 3; i++) {
plateText += numbers.charAt(Math.floor(Math.random() * numbers.length));
}
// 10% chance to return null (simulating OCR failure)
return Math.random() > 0.1 ? plateText : null;
}
// Update confidence threshold
function updateConfidenceThreshold() {
confidenceThreshold = parseFloat(confidenceSlider.value);
confidenceValue.textContent = `${Math.round(confidenceThreshold * 100)}%`;
}
// Update detection stats
function updateDetectionStats(vehicles, plates) {
vehicleCount.textContent = vehicles;
plateCount.textContent = plates;
}
// Add detections to history
function addToDetectionHistory(detections) {
emptyResults.classList.add('hidden');
detections.forEach(detection => {
detectionHistory.unshift(detection);
// Create result card
const template = document.getElementById('resultCardTemplate');
const clone = template.content.cloneNode(true);
const card = clone.querySelector('.result-card');
card.querySelector('.detection-type').textContent = detection.type;
card.querySelector('.confidence').textContent = `${Math.round(detection.confidence * 100)}%`;
card.querySelector('.timestamp').textContent = detection.timestamp;
const thumbnail = card.querySelector('.thumbnail');
thumbnail.src = detection.imageData;
if (detection.plate) {
card.querySelector('.plate-number').textContent = detection.plate.text;
} else {
card.querySelector('.plate-number').textContent = 'Not detected';
}
// Simulate vehicle make/model detection
const makes = ['Toyota', 'Honda', 'Ford', 'Chevrolet', 'BMW', 'Mercedes', 'Tesla'];
const models = ['Camry', 'Civic', 'F-150', 'Silverado', '3 Series', 'C-Class', 'Model 3'];
const randomMake = makes[Math.floor(Math.random() * makes.length)];
const randomModel = models[Math.floor(Math.random() * models.length)];
card.querySelector('.vehicle-model').textContent = `${randomMake} ${randomModel}`;
// Add click handlers
card.querySelector('.view-btn').addEventListener('click', () => showDetectionDetails(detection));
card.querySelector('.export-btn').addEventListener('click', () => exportDetection(detection));
// Add to results container
resultsContainer.prepend(card);
});
}
// Show detection details in modal
function showDetectionDetails(detection) {
modalImage.src = detection.imageData;
modalType.textContent = detection.type;
modalConfidence.textContent = `${Math.round(detection.confidence * 100)}%`;
modalPlate.textContent = detection.plate ? detection.plate.text : 'Not detected';
// Simulate vehicle make/model
const makes = ['Toyota', 'Honda', 'Ford', 'Chevrolet', 'BMW', 'Mercedes', 'Tesla'];
const models = ['Camry', 'Civic', 'F-150', 'Silverado', '3 Series', 'C-Class', 'Model 3'];
const randomMake = makes[Math.floor(Math.random() * makes.length)];
const randomModel = models[Math.floor(Math.random() * models.length)];
modalModel.textContent = `${randomMake} ${randomModel}`;
modalTimestamp.textContent = detection.timestamp;
modalRawData.textContent = JSON.stringify(detection, null, 2);
detailModal.classList.remove('hidden');
}
// Export detection data
function exportDetection(detection) {
// In a real implementation, this would save the data or image
console.log("Exporting detection:", detection);
alert(`Detection data for ${detection.plate?.text || 'unknown plate'} has been exported.`);
}
// Clear detection history
function clearDetectionHistory() {
detectionHistory = [];
resultsContainer.innerHTML = '';
emptyResults.classList.remove('hidden');
}
// Handle window resize
window.addEventListener('resize', () => {
if (currentMediaType === 'camera' && stream) {
resizeCanvas();
}
});
</script>
<p style="border-radius: 8px; text-align: center; font-size: 12px; color: #fff; margin-top: 16px;position: fixed; left: 8px; bottom: 8px; z-index: 10; background: rgba(0, 0, 0, 0.8); padding: 4px 8px;">Made with <img src="https://enzostvs-deepsite.hf.space/logo.svg" alt="DeepSite Logo" style="width: 16px; height: 16px; vertical-align: middle;display:inline-block;margin-right:3px;filter:brightness(0) invert(1);"><a href="https://enzostvs-deepsite.hf.space" style="color: #fff;text-decoration: underline;" target="_blank" >DeepSite</a> - 🧬 <a href="https://enzostvs-deepsite.hf.space?remix=samiesam/license-process-detection" style="color: #fff;text-decoration: underline;" target="_blank" >Remix</a></p></body>
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