// Based on https://editor.p5js.org/ml5/sketches/FeatureExtractor_Image_Classification

// Image recognition using transfer learning. Uses ml5.js, p5.js, MobileNet. 

// thank you to Golan and Sophia for helping me work out issues in the program :) 

// Tarot Card readings from : https://raw.githubusercontent.com/dariusk/corpora/master/data/divination/tarot_interpretations.json

​

let featureExtractor;

let classifier;

let video;

​

​

// TRAINING PICTURES

let n0 = 0;

let n1 = 0;

let n2 = 0;

let n3 = 0;

let n4 = 0;

let n5 = 0;

let n6 = 0;

let n7 = 0;

let n8 = 0;

let n9 = 0;

//ORDER OF KEYBOARD

let nQ = 0;

let nW = 0;

let nE = 0;

let nR = 0;

let nT = 0;

let nY = 0;

let nU = 0;

let nI = 0;

let nO = 0;

let nP = 0;

let nA = 0;

let nS = 0;

let nD = 0;

let nF = 0;

let nG = 0;

let nH = 0;

let nJ = 0;

let nK = 0;

let nL = 0;

let nZ = 0;

let nX = 0;

let nC = 0;

let nV = 0;

let nSamplesB = 0;

let nN = 0;

let nM = 0;

​

​

​

let bMobileNetIsLoaded = false;

let trainingLoss = NaN;

let currentLabel = "";

let currentConfidence = "";

​

let fortuneJSON;

​

 let cardPickedText = "PSYCHIC";

 let  fortuneText = "EYE";

 let  cautionText = "FORTUNES";

​

let isDejaPicked = false;

let oldCard = "";

let currCard;

​

let fortune = "";

let cardChosen = 0;

let fortuneCt;

let shadowArray;

let luckyNum;

let shadowNum;

​

//let fortuneArg = "";

​

let psychicWords;

let psychicFluff;

let psychicCaution;

​

const TRAINING_NOT_STARTED = -1;

const TRAINING_STARTED = 0;

const TRAINING_FINISHED = 1;

let systemStatus = TRAINING_NOT_STARTED;

​

// what does the psychic say 

thinkingJargon = ["Aaaah ", "Ooh that's interesting, ", "I seeee ", "hmmmmm ", "Well, well, well, ", "The spirits tell of "]

fluffingJargon = ["The ancestors say ", "", "", "", "I see you that must ", "The cards say "]

cautionJargon = ["but BEWARE ", "the Cards Caution Against "]

​

​

//-----------------------------------------

// asynchronous

function preload() {

  let url = 'https://raw.githubusercontent.com/dariusk/corpora/master/data/divination/tarot_interpretations.json';

  fortuneJSON = loadJSON(url);

}

//-----------------------------------------

function setup() {

  createCanvas(1200, 1200);

​

  print("D:");

  print(fortuneJSON);

​

  video = createCapture(VIDEO);

  video.size(320, 240);

  video.hide(); // hide the video's DOM object.

​

  // Extract the already learned features from MobileNet

  featureExtractor = ml5.featureExtractor('MobileNet',

    theModelReadyCallbackFunction);

​

​

  // Create a new classifier using those features, 

  // and provide it with the video object

  const options = {

    numLabels: 6

  };

  classifier = featureExtractor.classification(video, options);

}

​

//-----------------------------------------

function draw() {

  background(0);

  tint(255, 255, 255);

  image(video, 0, 350, 320, 240);

  textAlign(CENTER);

  textFont('Georgia', 30)

  fill(255);

  text(cardPickedText, width/2, height/4);

  text(fortuneText, width/2, height/4+60);

  text(cautionText, width/2, height/4+120);

​

  drawStatusIndicators();

}

​

//-----------------------------------------

function drawStatusIndicators() {

  let n = 230;

  fill('white');

textFont('Georgia', 14)

  text("MobileNet done loading: " + bMobileNetIsLoaded, 15, n+20);

​

  text("#A:" + nA, 15, n+35);

  text("#B:" + nSamplesB, 15, n+ 50);

  text("#C:" + nC, 15, n+65);

​

  text("#D:" + nD, 15, n+80);

  text("#E:" + nE, 15, n+95);

  text("#F:" + nF, 15, n+110);

  text("#G:" + nG, 15, n+125);

  text("#H:" + nH, 15, n+140);

  text("#I:" + nI, 15, n+155);

  text("#J:" + nJ, 15, n+170);

  text("#K:" + nK, 15, n+185);

  text("#L:" + nL, 15, n+200);

  text("#M:" + nM, 15, n+215);

  text("#N:" + nN, 15, n+230);

​

  text("#O:" + nO, 15,n+ 245);

  text("#P:" + nP, 15,n+ 260);

  text("#Q:" + nQ, 15,n+ 275);

  text("#R:" + nR, 15,n+ 290);

  text("#S:" + nS, 15,n+ 305);

​

  text("#T:" + nT, 15,n+ 320);

  text("#U:" + nU, 15, n+335);

​

​

​

  if (systemStatus === TRAINING_NOT_STARTED) {

    text("TRAINING_NOT_STARTED", 15, n+350);

  } else if (systemStatus === TRAINING_STARTED) {

    text("TRAINING_STARTED", 15, n+365);

  } else if (systemStatus === TRAINING_FINISHED) {

    text("TRAINING_FINISHED", 15, n+380);

  }

​

  if (systemStatus >= 0) {

    text("trainingLoss = " + trainingLoss, 15, n+95);

  } else {

    if ((nA > 0) && (nSamplesB > 0) && (nC > 0) && (nD > 0) && (nE > 0) && (nF > 0) && (nG > 0)) {// && (nH > 0) && (nI > 0) && (nJ > 0) && (nK > 0) && (nL > 0) && (nM > 0) && (nN > 0) && (nO > 0) && (nP > 0) && (nQ > 0) && (nR > 0) && (nS > 0) && (nT > 0) && (nU > 0)) {

      text("Press t to start training", 15, 95);

    } else {

      text("Add samples by pressing keys a,b,c", 15, n+400);

    }

  }

​

  if (systemStatus === TRAINING_FINISHED) {

    text("Label: " + currentLabel, 15, n+110);

    text("Confidence: " + currentConfidence, 15,n+ 125); //nf(currentConfidence,1,6)

  }

}

​

​

//----------------------------------------- train the program

function keyPressed() {

  switch (key) {

 // save image reference:     

//       let imgPath = "/dataset/" + label + "/"

// let filename = imgPath + str(i) + ".jpg"

// classifier.addImage(filename, label);

//       let imgPath = "/card01/" + label + "/"

// let filename = imgPath + str(i) + ".jpg"

// classifier.addImage(filename, label);

    case 'A': // correlate to each card set letter and number // do by list order

      //thank you sophia and izzy for saving instructions

      classifier.addImage('A'); // correlate for different cards

      nA++;

      break;

    case 'B': // correlate to each card set letter and number 

      classifier.addImage('B');

      nSamplesB++;

      break;

    case 'C':

      classifier.addImage('C');

      nC++;

      break;

      //

    case 'D':

      classifier.addImage('D');

      nD++;

      break;

​

    case 'E':

      classifier.addImage('E');

      nE++;

      break;

​

    case 'F':

      classifier.addImage('F');

      nF++;

      break;

​

    case 'G':

      classifier.addImage('G');

      nG++;

      break;

​

​

//     case 'H':

//       classifier.addImage('H');

//       nH++;

//       break;

​

//     case 'I':

//       classifier.addImage('I');

//       nI++;

//       break;

​

//     case 'J':