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Tutorial
javascript
Building a Real-Time Object Detection Web App with TensorFlow.js and p5.js
Create a new file called sketch.js in your project folder. We’ll use p5.js to access the webcam and display the video on a canvas:
let video;
let detector;
let detections = [];
function setup() {
// Create the canvas to match the video dimensions
createCanvas(640, 480);
// Capture video from the webcam
video = createCapture(VIDEO);
video.size(640, 480);
video.hide();
// Load the pre-trained COCO-SSD model
cocoSsd.load().then(model => {
detector = model;
console.log("Model Loaded!");
// Begin detecting objects every frame
detectObjects();
});
}
function detectObjects() {
detector.detect(video.elt).then(results => {
detections = results;
// Continue detection in a loop
detectObjects();
});
}
function draw() {
// Draw the video
image(video, 0, 0);
// Draw detection boxes and labels if available
if (detections) {
for (let i = 0; i < detections.length; i++) {
let object = detections[i];
stroke(0, 255, 0);
strokeWeight(2);
noFill();
rect(object.bbox[0], object.bbox[1], object.bbox[2], object.bbox[3]);
noStroke();
fill(0, 255, 0);
textSize(16);
text(object.class, object.bbox[0] + 4, object.bbox[1] + 16);
}
}
}Feb 12, 2025
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