// 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 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)}