NeuralNetwork returns result as an empty array

[kokoxuya]

Hii! The array returned from my brain.classify is empty although my inputs are correct :'))

let troo = false;
let video;
let poseNet;
let pose;
let poses = [];
let skeleton;
let beiande;
let currlabel = 0;
let endofexercise = 0;
let elmo = 6;

//let x = document.createElement("a");

let brain;
let state = 'waiting';
let targetLabel;
let testvid;

let currat = 0;

function keyPressed() {
beiande = key;
if (key == 'r')
{
setTimeout(function() {
console.log('collecting');
state = 'collecting';
setTimeout(function() {
console.log('not collecting');
state = 'waiting';
}, 10000); //wait then run
}, 2000);
setTimeout(function()
{
brain.saveData("traindata");
},13000);
}
if (key =='n')
{
brain.loadData('traindata.json',function()
{
brain.normalizeData();
brain.train(
{epochs:50},
function(){
console.log('model trained!:)');
});
});
brain.save();
console.log('done');
}
}

function gotPoses(results)
{
poses = results;
if (poses.length > 0)
{
skeleton = poses[0].skeleton;
if (state == 'collecting')
{
setTimeout(function()
{
if (currlabel == 144)
{
//console.log('start of new exercise');
currlabel = 0;
}
else
{
currlabel += 6;
}
addData(results);
},400);
}
}
}

let req = [[11,5,7],[12,6,8],[5,7,9],[6,8,10],[11,13,15],[12,14,16],[5,11,13],[6,12,14]];
function addData(poses)
{
if (poses.length > 0)
{
let pose = poses[0].pose;
let keyp = pose.keypoints;
let inval = [];
for(let i = 0; i < req.length; i++)
{
let secval = [];
for (let ii = 0; ii < 3; ii++)
{
secval.push([keyp[req[i][ii]].position.x,keyp[req[i][ii]].position.y]);
}
inval.push(secval);
}

//nose, eye, leftshoulder,rightshoulder
let secval = [];
secval.push([keyp[0].position.x,keyp[0].position.y]);
secval.push([keyp[2].position.x,keyp[2].position.y]);
secval.push([keyp[5].position.x,keyp[5].position.y]);
secval.push([keyp[6].position.x,keyp[6].position.y]);

inval.push(secval);

let compy = calang(inval);
console.log(compy.length);
brain.addData(compy,[currlabel]);

}
}

function setup() {
createCanvas(640, 480);
video = createCapture(VIDEO);
video.hide();
poseNet = ml5.poseNet(video, function(){console.log('posenet ready')});
poseNet.on('pose', gotPoses);
//console.log('yay3');

let options = {
inputs: 9,
outputs: 25,
task: 'classification',
debug: true
}
brain = ml5.neuralNetwork(options);

const modelInfo = {
model: 'model1/model.json',
metadata: 'model1/model_meta.json',
weights: 'model1/model.weights.bin',
};

//console.log('yay4s');
brain.load(modelInfo, function()
{
console.log('model ready!:)');
});
}

function calang(myvalues)
{
let currval = myvalues[myvalues.length - 1];
//nose, eye, leftshould,rightshould
let t1 = currval[3][0] - currval[2][0];
t1/=2;
let t2 = currval[1][0] - currval[0][0];
t1 -= t2;

let pushin = [currval[1],[currval[3][0] - t1,currval[3][1]],currval[3]];
myvalues.pop();
myvalues.push(pushin);

//console.log('atp ok');
let retval = [];
for (let i = 0;i < (myvalues.length); i++)
{
let currang = 0;
currval = myvalues[i];

//up,down,side
let y = currval[0][1]- currval[1][1];
let x = currval[0][0] - currval[1][0]; 

currang = atan2(y,x);

y = currval[2][1] - currval[1][1];
x = currval[2][0] - currval[1][0];

currang -= (atan2(y,x));
retval.push(currang);

}
//console.log('checkpojnt 1');
return retval;
}

let count = 0;
function classifyPose()
{
if (poses.length > 0)
{
let pose = poses[0].pose;
let keyp = pose.keypoints;
let inval = [];
for(let i = 0; i < req.length; i++)
{
let secval = [];
for (let ii = 0; ii < 3; ii++)
{
secval.push([keyp[req[i][ii]].position.x,keyp[req[i][ii]].position.y]);
}
inval.push(secval);
}

//console.log('pls work:\')');
//nose, eye, leftshoulder,rightshoulder
let secval = [];
secval.push([keyp[0].position.x,keyp[0].position.y]);
secval.push([keyp[2].position.x,keyp[2].position.y]);
secval.push([keyp[5].position.x,keyp[5].position.y]);
secval.push([keyp[6].position.x,keyp[6].position.y]);

//console.log('jiuming');
inval.push(secval);

let compy = calang(inval);
console.log(compy.length);
console.log('compy',compy);
brain.classify(compy,gotResults)

}
}

function gotResults(error,results)
{
console.log(error);
console.log('results',results);
console.log(results[0].label);
let t1 = results[0].label;
let t2 = results[0].confidence;
if (t1 == currat && t2 > 0.75)
{
currat += elmo;
updateCount();
}
}

function draw() {
translate(video.width,0);
scale(-1,1);
image(video, 0, 0, video.width, video.height);
drawKeypoints();
drawSkeleton();
classifyPose();
}

function drawKeypoints() {
for (let i = 0; i < poses.length; i += 1)
{
const pose = poses[i].pose;
for (let j = 0; j < pose.keypoints.length; j += 1)
{
const keypoint = pose.keypoints[j];
if (keypoint.score > 0.2) {
fill(255, 0, 0);
noStroke();
ellipse(keypoint.position.x, keypoint.position.y, 10, 10);
}
}
}
}

function drawSkeleton()
{
for (let i = 0; i < poses.length; i+=1)
{
const pose = poses[i].pose.keypoints;
let tt = [];
for (let j = 0; j < req.length; j+=1)
{
for (let i = 0; i < 3; i++)
{
tt.push(pose[req[j][i]]);
}
}
stroke(255, 0, 0);
line(tt[0].position.x, tt[0].position.y, tt[1].position.x, tt[1].position.y);
line(tt[1].position.x,tt[1].position.y,tt[2].position.x,tt[2].position.y);
}
}

function updateCount(poseLabel){
fill(0,0,255);
noStroke();
textSize(100);
textAlign(TOP);
text(count,width-60,height-420);
fill(0,255,255);
textSize(40);
textAlign(BOTTOM);
text("Confidence level =",width-300,height-50);
text(Math.round(poseLabelConfidence100)/100, width-80, height-50);
if (currat == (625)){
count++;
count = 0;

updateDisplay();
console.log("count is:",count);

}
}

function updateDisplay(){
document.getElementById('exercise-count').innerText= count;
// document.getElementById('confidence-level').innerText= Math.round(poseLabelConfidence*100)/100;
}