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RepoFile.js
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RepoFile.js
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let video;
let poseNet;
let poses = [];
let currentModel;
let currentPostureIndex = 0;
const postureLabels = ["PostureA", "PostureB", "PostureC", "PostureD", "PostureE"];
let poseLabel = "";
let poseTimer = 300;
const poseTimerThreshold = 300;
const posesPerStep = 2;
let stepsCompleted = 0;
const totalSteps = 3;
function setup() {
createCanvas(640, 480);
video = createCapture(VIDEO);
video.hide();
currentModel = ml5.neuralNetwork();
loadModel(currentModel, "modelAB");
poseNet = ml5.poseNet(video, modelReady);
poseNet.on('pose', function(results) {
poses = results.length;
});
}
function modelReady() {
console.log('PoseNet model is ready!');
}
function loadModel(model, folderName) {
const modelInfo = {
model: `${folderName}/model.json`,
metadata: `${folderName}/model_meta.json`,
weights: `${folderName}/model.weights.bin`,
};
model.neuralNetwork.load(modelInfo, function() {
console.log(`${folderName} Model loaded successfully!`);
});
}
function draw() {
image(video, 0, 0, width, height);
drawPose();
classifyPose();
fill(255);
textSize(24);
textAlign(CENTER, TOP);
text(`Current Pose: ${poseLabel}`, width / 2, 10);
text(`Pose Timer: ${poseTimer}`, width / 2, 50);
}
function drawPose() {
for (let i = 0; i < poses.length; i++) {
let pose = poses[i].pose;
for (let j = 0; j < pose.keypoints.length; j++) {
let keypoint = pose.keypoints[j];
fill(255, 0, 0);
ellipse(keypoint.position.x, keypoint.position.y, 10, 10);
}
}
}
function classifyPose() {
if (poses.length > 0) {
let inputs = [];
let pose = poses[0].pose; // Assuming the first detected pose
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);
}
currentModel.classify(inputs, gotResult);
} else {
setTimeout(classifyPose, 100);
}
}
function gotResult(error, results) {
if (results[0].confidence > 0.75) {
poseLabel = results[0].label.toUpperCase();
if (poseLabel === postureLabels[currentPostureIndex]) {
console.log(`Correct Pose: ${poseLabel}`);
if (poseTimer > 0) {
poseTimer--;
}
// Check if the timer has reached 0
if (poseTimer === 0) {
currentPostureIndex++;
if (currentPostureIndex < postureLabels.length) {
console.log(`Switching to ${postureLabels[currentPostureIndex]}`);
poseTimer = poseTimerThreshold;
if (currentPostureIndex === posesPerStep) {
stepsCompleted++;
console.log(`Step ${stepsCompleted} completed`);
if (stepsCompleted < totalSteps) {
currentModel = ml5.neuralNetwork();
loadModel(currentModel, `newModelForStep${stepsCompleted}`);
currentPostureIndex = 0;
} else {
// Alert when all postures are completed
alert("All postures completed!");
noLoop();
return;
}
}
}
}
}
}
classifyPose();
}
function checkPosture() {
}