// Cloned by Abdelshafa Abdala on 20 Nov 2022 from World "CharRecognition_UsingCNN (clone by Dheera(21261395))" by Dheera 
// Please leave this clone trail here.
 
const PIXELS=28,
PIXELSSQUARED=PIXELS*PIXELS,


NOTRAIN=124800,
NOTEST=20800,


noinput=PIXELSSQUARED,
nohidden=64,
nooutput=10,


learningrate=.1;

let do_training=!0;

const TRAINPERSTEP=15,
TESTPERSTEP=5,

ZOOMFACTOR=7,
ZOOMPIXELS=7*PIXELS,

canvaswidth=PIXELS+ZOOMPIXELS+50,
canvasheight=3*ZOOMPIXELS+100,


DOODLE_THICK=18,
DOODLE_BLUR=3;

let mnist,

mycnn,
mycnnTrain,
mycnnModel,doodle,
demo,

alphabets=["A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","Q","R","S","T","U","V","W","X","Y","Z"],
trainrun=1,
train_index=0,
testrun=1,
test_index=0,
total_tests=0,
total_correct=0,
doodle_exists=!1,
demo_exists=!1,
mousedrag=!1;

var train_inputs,test_inputs,demo_inputs,doodle_inputs,thehtml;
function randomWeight()
{
return AB.randomFloatAtoB(-.5,.5)}

AB.headerCSS({"max-height":"95vh"}),
thehtml="<hr> <h1> 1. Doodle </h1> Top row: Doodle (left) and shrunk (right). <br>  Draw your doodle in top LHS. <button onclick='wipeDoodle();' class='normbutton' >Clear and Re-draw</button> <br> ",

AB.msg(thehtml,1),
thehtml="<hr> <h1> 2. Training </h1> Middle row: Training image magnified (left) and original (right). <br>   <button onclick='do_training = false;' class='normbutton' >Pause training</button>  <button onclick='do_training = true;' class='normbutton' >Resume training</button> <br> ",AB.msg(thehtml,3),thehtml="<h3> Hidden tests </h3> ",
AB.msg(thehtml,5),
thehtml="<hr> <h1> 3. Demo </h1> Bottom row: Test image magnified (left) and  original (right). <br> The network is <i>not</i> trained on any of these images. <br>  <button onclick='makeDemo();' class='normbutton' >Demo test image</button> <br> ",
AB.msg(thehtml,7);
const bluespan="<span style='font-weight:bold; font-size:large; color:darkblue'> ";
function setup(){createCanvas(canvaswidth,canvasheight),
(doodle=createGraphics(ZOOMPIXELS,ZOOMPIXELS)).pixelDensity(1),

wipeDoodle(),AB.loadingScreen(),$.getScript("/uploads/codingtrain/matrix.js",

function(){$.getScript("/uploads/dheera0704/convnet3.js",

function(){$.getScript("/uploads/dheera0704/mnistDhe.js",

function(){console.log("All JS Files loaded");

let t=[];

t.push({type:"input",out_sx:28,out_sy:28,out_depth:1}),
t.push({type:"conv",sx:5,filters:8,stride:1,pad:2,activation:"relu"}),
t.push({type:"pool",sx:2,stride:2}),
t.push({type:"conv",sx:5,filters:16,stride:1,pad:2,activation:"relu"}),
t.push({type:"pool",sx:3,stride:3}),
t.push({type:"softmax",num_classes:26}),

(mycnnModel=new convnetjs.Net).makeLayers(t),
mycnnTrain=new convnetjs.SGDTrainer(mycnnModel,
{method:"adadelta",momentum:.9,batch_size:10,l2_decay:.001}),loadData()})})})}

function loadData(){loadMNIST(
    
function(t){mnist=t;
let e=0;
for(;e<NOTRAIN;e++)rotateImage(mnist.train_images[e]);
for(e=0;e<NOTEST;e++)rotateImage(mnist.test_images[e]);
console.log("All data loaded into Emnist object."),
console.log(mnist),AB.removeLoading()})}
function getImage(t){let e=createImage(PIXELS,PIXELS);e.loadPixels();
for(let n=0;n<PIXELSSQUARED;n++)
{
let o=t[n],s=4*n;e.pixels[s+0]=o,e.pixels[s+1]=o,e.pixels[s+2]=o,e.pixels[s+3]=255}
return e.updatePixels(),e}
function getInputs(t){
let e=[];
for(
let n=0;n<PIXELSSQUARED;n++){
let o=t[n];e[n]=o/255}
return e}
function rotateImage(t){
for(let e=0;e<PIXELS;e++)
for(let n=e;n<PIXELS;n++)

{
let o=e*PIXELS+n,s=n*PIXELS+e,i=t[o];t[o]=t[s],t[s]=i}}
function trainit(t)
{
let e=mnist.train_images[train_index],n=mnist.train_labels[train_index];
if(t)
{
var o=getImage(e);
image(o,0,
ZOOMPIXELS+50,
ZOOMPIXELS,
ZOOMPIXELS),
image(o,
ZOOMPIXELS+50,
ZOOMPIXELS+50,
PIXELS,PIXELS)}

let s=getInputs(e);train_inputs=s;

{
let t=getmycnnInputs(s);

mycnnTrain.train(t,n)}thehtml=" trainrun: "+trainrun+"<br> no: "+train_index,
AB.msg(thehtml,4),++train_index==NOTRAIN&&(train_index=0,

console.log("finished trainrun: "+trainrun),trainrun++)}

function getmycnnInputs(t)

{for(
var e=new convnetjs.Vol(28,28,1,0),
n=0;n<PIXELSSQUARED;n++)e.w[n]=t[n];
return e}
function testit()
{
let t=mnist.test_images[test_index],
e=mnist.test_labels[test_index],
n=getInputs(t),o=getmycnnInputs(n);
test_inputs=n;

let s=findMax(mycnnModel.forward(o).w);
var i=getImage(t);
image(i,0,ZOOMPIXELS+50,

ZOOMPIXELS,
ZOOMPIXELS),
image(i,ZOOMPIXELS+50,
ZOOMPIXELS+50,
PIXELS,PIXELS),
total_tests++,s==e&&total_correct++;

let a=total_correct/total_tests*100;
thehtml=" testrun: "+testrun+"<br> no: "+total_tests+" <br>  correct: "+total_correct+"<br>  score: "+bluespan+a.toFixed(2)+"</span>",
AB.msg(thehtml,6),++test_index==NOTEST&&(
    
console.log("finished testrun: "+testrun+" score: "+a.toFixed(2)),
testrun++,test_index=0,total_tests=0,total_correct=0)}

function find12(t){let e=0,n=0,o=0,s=0;for(let i=0;i<t.length;

i++)t[i]>o?(n=e,s=o,e=i,o=t[i]):t[i]>s&&(n=i,s=t[i]);

return[e,n]}
function findMax(t){let e=0,n=0;for(let o=0;o<t.length;o++)t[o]>n&&(e=o,n=t[o]);
return e}
function draw(){
if(void 0!==mnist){
if(background("black"),strokeWeight(1),stroke("green"),
rect(0,0,ZOOMPIXELS,ZOOMPIXELS),textSize(10),
textAlign(CENTER),text("DOODLE HERE",
ZOOMPIXELS/2,ZOOMPIXELS/2),do_training){
for(
let t=0;t<TRAINPERSTEP;t++)trainit(0===t);

for(let t=0;t<TESTPERSTEP;t++)testit()}
if(demo_exists&&(drawDemo(),guessDemo()),
doodle_exists&&(drawDoodle(),guessDoodle()),mouseIsPressed){
    var t=ZOOMPIXELS+20;
    
    mouseX<t&&mouseY<t&&pmouseX<t&&pmouseY<t&&(mousedrag=!0,
    doodle_exists=!0,
    
    doodle.stroke("red"),strokeJoin(ROUND),doodle.strokeWeight(DOODLE_THICK),doodle.line(mouseX,mouseY,pmouseX,pmouseY))}
    
    else mousedrag&&(mousedrag=!1,
    doodle.filter(BLUR,DOODLE_BLUR))}}
    
    function makeDemo(){demo_exists=!0;
    
    var t=AB.randomIntAtoB(0,NOTEST-1);demo=mnist.test_images[t];
    
    var e=mnist.test_labels[t];thehtml="Test image no: "+t+"<br>Classification: "+alphabets[e-1]+"<br>",AB.msg(thehtml,8)}
    
    function drawDemo(){
var t=getImage(demo);image(t,0,canvasheight-ZOOMPIXELS,ZOOMPIXELS,ZOOMPIXELS),image(t,ZOOMPIXELS+50,
canvasheight-ZOOMPIXELS,PIXELS,PIXELS)}
function guessDemo(){let t=getInputs(demo);
demo_inputs=t;
let e=getmycnnInputs(t),n=findMax(mycnnModel.forward(e).w);thehtml=" We classify it as: "+bluespan+alphabets[n-1]+"</span>",AB.msg(thehtml,9)}
function drawDoodle(){
    let t=doodle.get();image(t,0,0,ZOOMPIXELS,ZOOMPIXELS),image(t,ZOOMPIXELS+20,0,PIXELS,PIXELS)}
    function guessDoodle(){let t=doodle.get();t.resize(PIXELS,PIXELS),t.loadPixels();let e=[];
    for(let n=0;n<PIXELSSQUARED;n++)e[n]=t.pixels[4*n]/255;doodle_inputs=e;
    
    let n=getmycnnInputs(e),
    o=find12(mycnnModel.forward(n).w);thehtml=" Our 1st Guess is: "+bluespan+alphabets[o[0]-1]+"</span> <br> Our 2nd Guess is: "+bluespan+alphabets[o[1]-1]+"</span>",AB.msg(thehtml,2)}
    function wipeDoodle(){doodle_exists=!1,doodle.background("black")}
    function showInputs(t){var e="";for(let n=0;n<t.length;n++){n%PIXELS==0&&(e+="\n"),e=e+" "+t[n].toFixed(2)}console.log(e)}