//Simple structure forked from Andreas Refsgaard. 

//requires this code uploaded to the Arduino -->

https://github.com/j3nsykes/creativeML2020/tree/master/PhysicalTeachableMachines/ML5_neuralNet/Arduino/AnalogInput_Serial/Serial_arrayInputs

//Adapted by j3nsykes to train an array of incoming inputs. 

​

*/

// The neural network is the brain

let brain;

let label;

​

// Declare a "SerialPort" object

let serial;

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

​

let inputs = []; // all your inputs in an array

let totalInputs = 8; //how many incoming inputs? scale to your delight. 

let x = 0.0;

let y = 0.0;

let z = 0.0;

let f;

let cresult = '';

let displayResults=false;

​

function preload() {

  f = loadFont('SourceCodePro-Medium.ttf');

  //  f = loadFont('Nunito-Bold.ttf');

​

}

​

function setup() {

  let canvas = createCanvas(500, 500);

  // Only when clicking on the canvas

  //canvas.mousePressed(addData);

​

  // Create the model

  const options = {

    inputs: totalInputs, //update the variable to scale

    outputs: ['label'], // amount of label outputs

    debug: true,

    task: 'classification'

  }

  brain = ml5.neuralNetwork(options);

​

  // Train Model button

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

​

  background(0);

​

  // Instantiate our SerialPort object

  serial = new p5.SerialPort();

​

  // Get a list the ports available

  // You should have a callback defined to see the results

  serial.list();

​

  // Assuming our Arduino is connected, let's open the connection to it

  //!!!!!!! CHECK THIS !!!!

  // Change this to the name of your arduino's serial port

  //serial.open("/dev/tty.usbmodem144101");

  //serial.open("/dev/tty.usbmodem21956001");

  serial.open("/dev/tty.SLAB_USBtoUART");

​

  // Here are the callbacks that you can register

  // When we connect to the underlying server

  serial.on('connected', serverConnected);

​

  // When we get a list of serial ports that are available

  serial.on('list', gotList);

  // OR

  //serial.onList(gotList);

​

  // When we some data from the serial port

  serial.on('data', getInputs);

  // OR

  //serial.onData(gotData);

​

  // When or if we get an error

  serial.on('error', gotError);

  // OR

  //serial.onError(gotError);

​

  // When our serial port is opened and ready for read/write

  serial.on('open', gotOpen);

  // OR

  //serial.onOpen(gotOpen);

​

  serial.on('close', gotClose);

​

  // Callback to get the raw data, as it comes in for handling yourself

  //serial.on('rawdata', gotRawData);

  // OR

  //serial.onRawData(gotRawData);

​

}

​

// Add a data record

function addData() {

  // Get frequency

  label = select('#label').value();

​

​

  // Add data

  //retirve array of data

  let data = getItem('myData');

  // console.log(inputs); //this slows process down significantly use with caution!

​

  brain.addData(data, {

    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);

}

​

// Classify

function classify() {

  console.log('classify');

​

  let data = getItem('myData');

  brain.classify(data, gotResults);

​

}

​

// When the model is trained

function finishedTraining() {

  classify();

}

​

// Got a result

function gotResults(error, results) {

  if (error) {

    console.error(error);

    return;

  }

​

  // Show classification

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

cresult=results[0].label;

  displayResults=true;

  // Predict again

  classify();

}

​

function serverConnected() {

  print("Connected to Server");

}

​

// Got the list of ports

function gotList(thelist) {

  print("List of Serial Ports:");

  // theList is an array of their names

  for (let i = 0; i < thelist.length; i++) {

    // Display in the console

    print(i + " " + thelist[i]);

  }

}

​

// Connected to our serial device

function gotOpen() {

  print("Serial Port is Open");

}

​

function gotClose() {

  print("Serial Port is Closed");

  latestData = "Serial Port is Closed";

}

​

//Oh, here is an error, let's log it

function gotError(theerror) {

  print(theerror);

}

​

// There is data available to work with from the serial port

function getInputs() {

  let currentString = serial.readLine(); // read the incoming string

  trim(currentString); // remove any trailing whitespace

  if (!currentString) return; // if the string is empty, do no more

  //console.log(currentString); // print the string

​

  //seperate values in the array

  let splitVal = splitTokens(currentString, ',');

​

​

  //parse strings into floats. 

  splitVal = float(splitVal);

  //console.log(splitVal);

​

​

  //get the individual values for visualisation

  x = splitVal[0];

  y = splitVal[1];

  z = splitVal[2];

​

  //TODO make this an array so it can be scaled 

​

  //only push  totalInputs at one a time. 

  let inputs = [];

  for (let i = 0; i < totalInputs; i++) {

    inputs.push(splitVal[i]);

  }

​

  //console.log(inputs);

  storeItem('myData', inputs); //store array info

​

}

​

// We got raw from the serial port

function gotRawData(thedata) {

  print("gotRawData" + thedata);

}

​

​

function draw() {

​

  if (mouseIsPressed) {

​

    addData();

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

    background('#99d8d0');

    textSize(32);

    textAlign(LEFT);

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

  } else {

    background(0);

  }

if(displayResults){

    background('#99d8d0');

    textSize(72);

    textAlign(LEFT);

    text(cresult, width/2-25, height/2);

​

}

}