refactoring and better mapping concept

This commit is contained in:
SallarShayegan
2025-02-09 21:00:53 +01:00
parent 8dc9641ee0
commit 269f4afe11

View File

@ -1,10 +1,11 @@
let img;
let morphs = [];
const orderedMorphs = [5,6,0,4,1,2,3];
let count = 7;
let size = 10;
let countH = 400;
let countV = 260;
let countH = 800;
let countV = 520;
// Load the image
function preload() {
@ -14,14 +15,6 @@ function preload() {
}
function setup() {
/*
createCanvas(26*size, 40*size);
imageMode(CORNER);
for (let i = 0; i < 26; i++) {
for (let j = 0; j < 40; j++) {
image(morphs[1], i*size, j*size, size, size);
}
}*/
pixelDensity(1);
@ -29,80 +22,87 @@ function setup() {
img.resize(0, countH);
img.filter(GRAY);
createCanvas(countV, countH);
//createCanvas(countV*size, countH*size);
image(img, 0, 0);
// load the pixels of the canvas
// this is a 1-dimensional integer array with the rgba values of
// the pixels
loadPixels();
let pixels2d = new Array(countH);
for(let i = 0; i < countH; i++){
pixels2d[i] = new Array(countV);
}
// create an array containing only the grayscale of every pixel
// sins the picture is gray, the first 3 values (rgb) for every picture are similar
// we need the value at indexes 0,4,7,...
let i = 0;
let j = 0;
let str1 = "";
let pixels1d = [];
for (let i = 0; i < pixels.length; i += 4)
pixels1d[i/4] = pixels[i];
/*
let averages = [];
let sum = 0;
for (let i = 0; i < pixels1d.length; i++) {
sum += pixels1d[i];
if (i%10 == 0) {
averages[i/10] = sum/10;
sum = 0;
}
}
*/
pixels1d.push(pixels[i]);
// combine every 10 values into one by calculating their average
// put the result in a 2d array, the rows are the pixelrows of the image
// the columns are greyscale averages of every 10 pixelcolumns of the picture
let averages = new Array(countH);
for (let i = 0; i < averages.length; i++)
averages[i] = new Array(countV/10);
let sum = 0;
let sum2 = [];
for(let k = 0; k < pixels1d.length; k++) {
//str1 += str(pixels1d[k]%count);
sum += pixels1d[k];
if (j%10 == 0) {
averages[i][j/10] = sum/10;
averages[i][Math.floor(j/10)] = sum/10;
sum = 0;
}
pixels2d[i][j] = pixels1d[k]%count;
j++;
if (k%countV == 0 && k>0) {
//str1 += '\n';
i++;
j = 0;
}
}
let sums = []
// combine every 10 rows into one row by calculating the average of the values of every column
// put the result into a new array 'average2'
let sums = new Array(countV/10).fill(0);
let averages2 = new Array(countH/10);
for (let i = 0; i < averages2.length; i++)
averages2[i] = new Array(countV/10);
for (let i = 0; i < averages.length; i++) {
for (let j = 0; j < averages[0].length; j++) {
for (let j = 0; j < averages2[0].length; j++) {
sums[j] += averages[i][j];
if (i%10 == 0) {
averages2[i/10][j] = Math.round(sums[j]/10);
if (!averages2[i/10][j]) averages2[i/10][j] = 0;
averages2[i/10][j] %= 7;
// let maxVal = 255; // Da Graustufen 0-255 sind
// averages2[i/10][j] = Math.floor((averages2[i/10][j] / maxVal) * 6);
sums[j] = 0;
}
}
}
console.log(averages2);
console.log(pixels1d);
console.log(pixels2d);
// 1. Finde Min- und Max-Wert
let minGray = Infinity;
let maxGray = -Infinity;
//saveStrings(str1.split('\n'), 'data.txt');
//resizeCanvas(countV*10, countH*10)
fill(255);
//rect(0,0,countV,countH);
for (let i = 0; i < averages2.length; i++) {
for (let j = 0; j < averages2[i].length; j++) {
let val = averages2[i][j];
if (val < minGray) minGray = val;
if (val > maxGray) maxGray = val;
}
}
// 2. Skaliere die Werte basierend auf minGray und maxGray
for (let i = 0; i < averages2.length; i++) {
for (let j = 0; j < averages2[i].length; j++) {
let normalized = (averages2[i][j] - minGray) / (maxGray - minGray); // auf 01 skalieren
averages2[i][j] = Math.round(normalized * 6)%7; // auf 06 mappen
}
}
console.log(averages2);
resizeCanvas(countV*2, countH);
image(img, 0, 0);
imageMode(CORNER);
for (let i = 0; i < countV/10; i++) {
for (let j = 0; j < countH/10; j++) {
image(morphs[averages2[j][i]], i*size, j*size, size, size);
for (let i = 0; i < countH/10; i++) {
for (let j = 0; j < countV/10; j++) {
image(morphs[orderedMorphs[averages2[i][j]]], j*size+countV+size, i*size, size, size);
}
}