File size: 3,243 Bytes
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// Function to start the webcam
function startWebcam() {
const video = document.getElementById('video');
if (video) {
navigator.mediaDevices.getUserMedia({ video: true })
.then(stream => {
video.srcObject = stream;
})
.catch(error => {
console.error("Error accessing the webcam:", error);
});
} else {
console.error("Video element not found.");
}
}
// Call the startWebcam function once the DOM is fully loaded
document.addEventListener('DOMContentLoaded', (event) => {
startWebcam();
});
// Function to start real-time detection
function startDetection() {
const video = document.getElementById('video');
const canvas = document.getElementById('overlay');
const ctx = canvas.getContext('2d');
const realTimeOutputDiv = document.getElementById('realTimeOutput');
setInterval(() => {
ctx.drawImage(video, 0, 0, canvas.width, canvas.height);
// Convert the captured frame to base64
const imageData = canvas.toDataURL('image/png');
fetch('/predict', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({ image: imageData }),
})
.then(response => response.json())
.then(data => {
ctx.clearRect(0, 0, canvas.width, canvas.height);
if (data.prediction && data.bbox) {
const { x, y, w, h } = data.bbox;
// Draw bounding box
ctx.strokeStyle = 'red';
ctx.lineWidth = 2;
ctx.strokeRect(x, y, w, h);
// Display prediction label
ctx.font = '16px Arial';
ctx.fillStyle = 'red';
ctx.fillText(data.prediction, x, y - 10);
realTimeOutputDiv.innerHTML = `Real-Time Prediction: ${data.prediction}`;
} else {
realTimeOutputDiv.innerHTML = 'No face detected';
}
})
.catch(error => {
console.error('Error:', error);
});
}, 1000); // Capture and send frame every second
}
// Function to upload an image
function uploadImage() {
const fileInput = document.getElementById('imageUpload');
const uploadOutputDiv = document.getElementById('uploadOutput');
if (fileInput && fileInput.files.length > 0) {
const file = fileInput.files[0];
const formData = new FormData();
formData.append('image', file);
fetch('/predict', {
method: 'POST',
body: formData,
})
.then(response => response.json())
.then(data => {
if (data.prediction) {
uploadOutputDiv.innerHTML = `Uploaded Image Prediction: ${data.prediction}`;
} else {
uploadOutputDiv.innerHTML = 'No face detected';
}
})
.catch(error => {
console.error('Error:', error);
});
} else {
alert('Please select an image before uploading.');
}
}
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