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sketch.js
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let video;
let poseNet;
let pose;
let skeleton;
let brain;
let poseLabel = "Y";
function setup() {
createCanvas(640, 480);
video = createCapture(VIDEO);
video.hide();
poseNet = ml5.poseNet(video, modelLoaded);
poseNet.on('pose', gotPoses);
let options = {
inputs: 34,
outputs: 4,
task: 'classification',
debug: true
}
brain = ml5.neuralNetwork(options);
const modelInfo = {
model: 'model/model.json',
metadata: 'model/model_meta.json',
weights: 'model/model.weights.bin',
};
brain.load(modelInfo, brainLoaded);
}
function brainLoaded() {
console.log('pose classification ready');
classifyPose();
}
function classifyPose() {
if (pose) {
let inputs = [];
for (let i = 0; i < pose.keypoints.length; i++) {
let x = pose.keypoints[i].position.x;
let y = pose.keypoints[i].position.y;
inputs.push(x);
inputs.push(y);
}
brain.classify(inputs, gotResult);
} else {
setTimeout(classifyPose, 100);
}
}
function gotResult(error, results) {
if (results[0].confidence > 0.75) {
poseLabel = results[0].label.toUpperCase();
}
//console.log(results[0].confidence);
classifyPose();
}
function gotPoses(poses) {
if (poses.length > 0) {
pose = poses[0].pose;
skeleton = poses[0].skeleton;
}
}
function modelLoaded() {
console.log('poseNet ready');
}
function draw() {
push();
translate(video.width, 0);
scale(-1, 1);
image(video, 0, 0, video.width, video.height);
if (pose) {
for (let i = 0; i < skeleton.length; i++) {
let a = skeleton[i][0];
let b = skeleton[i][1];
strokeWeight(2);
stroke(0);
line(a.position.x, a.position.y, b.position.x, b.position.y);
}
for (let i = 0; i < pose.keypoints.length; i++) {
let x = pose.keypoints[i].position.x;
let y = pose.keypoints[i].position.y;
fill(0);
stroke(255);
ellipse(x, y, 16, 16);
}
}
pop();
fill(255, 0, 255);
noStroke();
textSize(512);
textAlign(CENTER, CENTER);
text(poseLabel, width / 2, height / 2);
}