squart.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Squart Page</title>
</head>
<body>
<div>온라인 헬스 케어 서비스 (스쿼트)</div>
<div>
<script src = "./squart.js"></script>
<sapn id = "user_count">0</sapn>
</div>
<button type="button" onclick="init()">Start</button>
<div><canvas id="canvas"></canvas></div>
<div id="label-container"></div>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.3.1/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@teachablemachine/pose@0.8/dist/teachablemachine-pose.min.js"></script>
<script type="text/javascript">
// More API functions here:
// https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/pose
// the link to your model provided by Teachable Machine export panel
const URL = "https://teachablemachine.withgoogle.com/models/xymjZj4q-/"; // 임시 URI - stand , squart, bent(허리 굽은 자세) 학습.
let model, webcam, ctx, labelContainer, maxPredictions;
async function init() {
const modelURL = URL + "model.json";
const metadataURL = URL + "metadata.json";
// load the model and metadata
// Refer to tmImage.loadFromFiles() in the API to support files from a file picker
// Note: the pose library adds a tmPose object to your window (window.tmPose)
model = await tmPose.load(modelURL, metadataURL);
maxPredictions = model.getTotalClasses();
// Convenience function to setup a webcam
const size = 200;
const flip = true; // whether to flip the webcam
webcam = new tmPose.Webcam(size, size, flip); // width, height, flip
await webcam.setup(); // request access to the webcam
await webcam.play();
window.requestAnimationFrame(loop);
// append/get elements to the DOM
const canvas = document.getElementById("canvas");
canvas.width = size; canvas.height = size;
ctx = canvas.getContext("2d");
labelContainer = document.getElementById("label-container");
for (let i = 0; i < maxPredictions; i++) { // and class labels
labelContainer.appendChild(document.createElement("div"));
}
}
async function loop(timestamp) {
webcam.update(); // update the webcam frame
await predict();
window.requestAnimationFrame(loop);
}
// 상태
let status = "stand" ;
// 갯수 count
let count = 0;
// 잘못된 경우 count
let wrong_count = 0;
async function predict() {
// Prediction #1: run input through posenet
// estimatePose can take in an image, video or canvas html element
const { pose, posenetOutput } = await model.estimatePose(webcam.canvas);
// Prediction 2: run input through teachable machine classification model
const prediction = await model.predict(posenetOutput);
if (prediction[0].probability.toFixed(2) > 0.9) // 서있는 상태
{
if (status == "squart")
{
count++;
console.log(`올바른 스쿼트 : ${count}`)
}
status = "stand";
}
else if (prediction[1].probability.toFixed(2) > 1.00) // 스쿼트 자세
{
status = "squart";
}
else if (prediction[2].probability.toFixed(2) == 1.00) // 굽은 자세(잘못된 케이스)
{
if (status == "squart" || status == "stand") // 굽은 자세로 잘못 수행하면,
{
wrong_count++;
console.log(`잘못된 자세 : ${wrong_count}`)
}
status = "bent";
}
for (let i = 0; i < maxPredictions; i++) {
const classPrediction =
prediction[i].className + ": " + prediction[i].probability.toFixed(2);
labelContainer.childNodes[i].innerHTML = classPrediction;
}
// finally draw the poses
drawPose(pose);
}
function drawPose(pose) {
if (webcam.canvas) {
ctx.drawImage(webcam.canvas, 0, 0);
// draw the keypoints and skeleton
if (pose) {
const minPartConfidence = 0.5;
tmPose.drawKeypoints(pose.keypoints, minPartConfidence, ctx);
tmPose.drawSkeleton(pose.keypoints, minPartConfidence, ctx);
}
}
}
</script>
</body>
</html>