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<!DOCTYPE html>
<html>
<head>
<title>AI Night Vision Camera</title>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/coco-ssd"></script>
<style>
body {
margin: 0;
background: #000;
color: #fff;
font-family: monospace;
}
.container {
max-width: 1200px;
margin: 0 auto;
padding: 20px;
display: flex;
flex-direction: column;
align-items: center;
}
.video-container {
position: relative;
width: 640px;
height: 480px;
border: 2px solid #0f0;
border-radius: 8px;
overflow: hidden;
}
#video {
width: 100%;
height: 100%;
object-fit: cover;
transform: scaleX(-1);
}
#canvas {
position: absolute;
top: 0;
left: 0;
transform: scaleX(-1);
}
.controls {
margin-top: 20px;
display: flex;
gap: 10px;
}
button {
background: #0f0;
color: #000;
border: none;
padding: 10px 20px;
border-radius: 4px;
cursor: pointer;
font-weight: bold;
transition: all 0.3s;
}
button:hover {
background: #0f0;
box-shadow: 0 0 10px #0f0;
}
.detection-info {
margin-top: 20px;
padding: 10px;
background: rgba(0, 255, 0, 0.1);
border: 1px solid #0f0;
border-radius: 4px;
width: 100%;
max-width: 620px;
}
.stats {
display: flex;
justify-content: space-between;
margin-top: 10px;
font-size: 14px;
}
.night-vision {
filter: brightness(2) contrast(1.2) hue-rotate(120deg) grayscale(0.5);
}
.detection-box {
position: absolute;
border: 2px solid #0f0;
background: rgba(0, 255, 0, 0.1);
}
.detection-label {
position: absolute;
top: -25px;
left: 0;
background: #0f0;
color: #000;
padding: 2px 6px;
font-size: 12px;
border-radius: 2px;
}
</style>
</head>
<body>
<div class="container">
<div class="video-container">
<video id="video" autoplay playsinline></video>
<canvas id="canvas"></canvas>
</div>
<div class="controls">
<button onclick="toggleNightVision()">Toggle Night Vision</button>
<button onclick="toggleDetection()">Toggle Detection</button>
</div>
<div class="detection-info">
<div id="detections"></div>
<div class="stats">
<span id="fps">FPS: 0</span>
<span id="objects">Objects detected: 0</span>
</div>
</div>
</div>
<script>
let video = document.getElementById('video');
let canvas = document.getElementById('canvas');
let ctx = canvas.getContext('2d');
let model;
let isNightVision = false;
let isDetecting = false;
let lastTime = performance.now();
let frameCount = 0;
// Initialize camera and AI model
async function init() {
// Load COCO-SSD model
model = await cocoSsd.load();
// Setup camera
const constraints = {
video: {
width: 640,
height: 480,
facingMode: 'environment',
advanced: [{
exposureMode: 'manual',
exposureCompensation: 2
}]
}
};
const stream = await navigator.mediaDevices.getUserMedia(constraints);
video.srcObject = stream;
// Set canvas size
canvas.width = 640;
canvas.height = 480;
// Start detection loop
requestAnimationFrame(detect);
}
function toggleNightVision() {
isNightVision = !isNightVision;
video.className = isNightVision ? 'night-vision' : '';
}
function toggleDetection() {
isDetecting = !isDetecting;
}
async function detect() {
if (isDetecting) {
// Calculate FPS
const now = performance.now();
frameCount++;
if (now - lastTime >= 1000) {
document.getElementById('fps').textContent = `FPS: ${frameCount}`;
frameCount = 0;
lastTime = now;
}
// Detect objects
const predictions = await model.detect(video);
// Clear previous detections
ctx.clearRect(0, 0, canvas.width, canvas.height);
// Draw new detections
predictions.forEach(prediction => {
// Draw bounding box
ctx.strokeStyle = '#00ff00';
ctx.lineWidth = 2;
ctx.strokeRect(
prediction.bbox[0],
prediction.bbox[1],
prediction.bbox[2],
prediction.bbox[3]
);
// Draw label background
ctx.fillStyle = '#00ff00';
ctx.fillRect(
prediction.bbox[0],
prediction.bbox[1] - 20,
prediction.bbox[2],
20
);
// Draw label text
ctx.fillStyle = '#000000';
ctx.font = '16px monospace';
ctx.fillText(
`${prediction.class} ${Math.round(prediction.score * 100)}%`,
prediction.bbox[0] + 5,
prediction.bbox[1] - 5
);
});
// Update detection info
document.getElementById('objects').textContent =
`Objects detected: ${predictions.length}`;
document.getElementById('detections').innerHTML =
predictions.map(p =>
`Detected ${p.class} (${Math.round(p.score * 100)}% confidence)`
).join('<br>');
}
requestAnimationFrame(detect);
}
// Start application
init().catch(err => {
console.error('Error initializing camera:', err);
});
// Add image processing for better night vision
const imageProcessor = new ImageCapture(video.srcObject.getVideoTracks()[0]);
async function enhanceNightVision() {
if (isNightVision) {
try {
const photoCapabilities = await imageProcessor.getPhotoCapabilities();
await imageProcessor.setOptions({
brightness: photoCapabilities.brightness.max,
contrast: photoCapabilities.contrast.max,
saturation: 0,
sharpness: photoCapabilities.sharpness.max,
exposureMode: 'manual',
exposureCompensation: 2,
whiteBalanceMode: 'manual'
});
} catch (err) {
console.log('Night vision enhancement not supported');
}
}
}
</script>
</body>
</html> |