//Adaptted from serial stream example by from Andreas Refsgaard.

//https://editor.p5js.org/AndreasRef/sketches/zMEfp6YP

​

//adapted by j3nsykes to work with webUSB and remove need for p5serial app running.

need this arduino code to work -->

https://github.com/j3nsykes/creativeML2020/tree/master/PhysicalTeachableMachines/ML5_neuralNet/Arduino/AnalogInput_WebUSB/webUSB_singleInputsend

*/

​

// The neural network is the brain

let brain;

var sensorData = 0;

var data = 0; //give initial values. 

let sensorValueInt = 0;

let latestData = "waiting for data"; // you'll use this to write incoming data to the canvas

​

function setup() {

  let canvas = createCanvas(256, 256);

  // Only when clicking on the canvas

  //canvas.mousePressed(addData);

​

  // Create the model

  const options = {

    inputs: ['sensorValue'],

    outputs: ['label'], // TODO: support ['label']

    debug: true,

    task: 'classification'

  }

  brain = ml5.neuralNetwork(options);

​

  // Train Model button

  select('#train').mousePressed(trainModel);

​

  background(0);

​

​

​

}

​

// Add a data record

function addData() {

  // Get frequency

  let label = select('#label').value();

  // Add data

  brain.addData({

    sensorValue: sensorValueInt

  }, {

    label

  });

}

​

// Train the model

function trainModel() {

  // ml5 will normalize data to a range between 0 and 1 for you.

  brain.normalizeData();

  // Train the model

  // Epochs: one cycle through all the training data

  brain.train({

    epochs: 100

  }, finishedTraining);

}

​

// When the model is trained

function finishedTraining() {

  brain.classify([sensorValueInt], gotResults);

}

​

// Got a result

function gotResults(error, results) {

  if (error) {

    console.error(error);

    return;

  }

​

  // Show classification

  select('#classification').html(results[0].label);

​

  // Predict again

  brain.classify([sensorValueInt], gotResults);

}

​

​

​

​

// There is data available to work with 

function gotData() {

  // console.log(sensorData);

  if (sensorData != 0) { //allow for Serial to start stream at beginning. 

    data = JSON.parse(sensorData);

    console.log(data.sensor);

    latestData = data.sensor; // save it for the draw method

      //parse string into int

  sensorValueInt = int(latestData);

  }

​

​

}

​

​

​

​

function draw() {

  //maybe add setinterval to control more?

gotData();//get the webUSB serial information!!!

​

  if (mouseIsPressed) {

​

    addData();

    let label = select('#label').value();

    background(0, 255, 0);

    text("Adding training data for " + label + ": " + sensorValueInt, 10, 50);

  } else {

    background(0);

  }

}