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kmeans.js
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kmeans.js
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const { createCanvas, loadImage } = require('node-canvas')
const MAX_ITERS = 50;
// HELPER FUNCTIONS
function RGBAToLAB(rgba) {
// https://github.com/antimatter15/rgb-lab
// since album art is a jpg, a = 255 for all pixels. we can disregard rgba[3]
var r = rgba[0] / 255,
g = rgba[1] / 255,
b = rgba[2] / 255,
x, y, z;
r = (r > 0.04045) ? Math.pow((r + 0.055) / 1.055, 2.4) : r / 12.92;
g = (g > 0.04045) ? Math.pow((g + 0.055) / 1.055, 2.4) : g / 12.92;
b = (b > 0.04045) ? Math.pow((b + 0.055) / 1.055, 2.4) : b / 12.92;
x = (r * 0.4124 + g * 0.3576 + b * 0.1805) / 0.95047;
y = (r * 0.2126 + g * 0.7152 + b * 0.0722) / 1.00000;
z = (r * 0.0193 + g * 0.1192 + b * 0.9505) / 1.08883;
x = (x > 0.008856) ? Math.pow(x, 1 / 3) : (7.787 * x) + 16 / 116;
y = (y > 0.008856) ? Math.pow(y, 1 / 3) : (7.787 * y) + 16 / 116;
z = (z > 0.008856) ? Math.pow(z, 1 / 3) : (7.787 * z) + 16 / 116;
return [(116 * y) - 16, 500 * (x - y), 200 * (y - z)]
}
function LABtoRGBA(lab) {
// https://github.com/antimatter15/rgb-lab
// since album art is a jpg, a = 1 for all pixels.
var y = (lab[0] + 16) / 116,
x = lab[1] / 500 + y,
z = y - lab[2] / 200,
r, g, b;
x = 0.95047 * ((x * x * x > 0.008856) ? x * x * x : (x - 16 / 116) / 7.787);
y = 1.00000 * ((y * y * y > 0.008856) ? y * y * y : (y - 16 / 116) / 7.787);
z = 1.08883 * ((z * z * z > 0.008856) ? z * z * z : (z - 16 / 116) / 7.787);
r = x * 3.2406 + y * -1.5372 + z * -0.4986;
g = x * -0.9689 + y * 1.8758 + z * 0.0415;
b = x * 0.0557 + y * -0.2040 + z * 1.0570;
r = (r > 0.0031308) ? (1.055 * Math.pow(r, 1 / 2.4) - 0.055) : 12.92 * r;
g = (g > 0.0031308) ? (1.055 * Math.pow(g, 1 / 2.4) - 0.055) : 12.92 * g;
b = (b > 0.0031308) ? (1.055 * Math.pow(b, 1 / 2.4) - 0.055) : 12.92 * b;
return [
Math.max(0, Math.min(1, r)) * 255,
Math.max(0, Math.min(1, g)) * 255,
Math.max(0, Math.min(1, b)) * 255,
1
]
}
function deltaE(labA, labB) {
// https://github.com/antimatter15/rgb-lab
// original code, broken link: https://github.com/THEjoezack/ColorMine/blob/master/ColorMine/ColorSpaces/Comparisons/Cie94Comparison.cs
var deltaL = labA[0] - labB[0];
var deltaA = labA[1] - labB[1];
var deltaB = labA[2] - labB[2];
var c1 = Math.sqrt(labA[1] * labA[1] + labA[2] * labA[2]);
var c2 = Math.sqrt(labB[1] * labB[1] + labB[2] * labB[2]);
var deltaC = c1 - c2;
var deltaH = deltaA * deltaA + deltaB * deltaB - deltaC * deltaC;
deltaH = deltaH < 0 ? 0 : Math.sqrt(deltaH);
var sc = 1.0 + 0.045 * c1;
var sh = 1.0 + 0.015 * c1;
var deltaLKlsl = deltaL / (1.0);
var deltaCkcsc = deltaC / (sc);
var deltaHkhsh = deltaH / (sh);
var i = deltaLKlsl * deltaLKlsl + deltaCkcsc * deltaCkcsc + deltaHkhsh * deltaHkhsh;
return i < 0 ? 0 : Math.sqrt(i);
}
// KMEANS ALGORITHM
async function kmeans(albumURL) {
const start = performance.now()
console.log("Running kmeans on " + albumURL)
var image = await loadImage(albumURL)
var width = image.width
var height = image.height
// draw image to server-side canvas and get RGBA pixel data
const canvas = createCanvas(width, height);
const ctx = canvas.getContext('2d');
ctx.drawImage(image, 0, 0, width, height);
const imageData = ctx.getImageData(0, 0, width, height);
// convert RGBA to LAB to group colors based on human perception
const pixelDataRGBA = imageData.data; // [R, G, B, A, R, G, B, A, ..., R, G, B, A]
var pixelDataLAB = [] // [[L, A, B], [L, A, B], ... [L, A, B]]
for (var i = 0; i < pixelDataRGBA.length; i += 4) {
var pixel = [
pixelDataRGBA[i],
pixelDataRGBA[i + 1],
pixelDataRGBA[i + 2],
pixelDataRGBA[i + 3]
]
pixelDataLAB.push(RGBAToLAB(pixel))
}
// select k random pixels
var centroids = []
for (var i = 0; i < 5; i++) {
while (true) {
var random = Math.floor(Math.random() * pixelDataLAB.length)
if (centroids.includes(pixelDataLAB[random]) === false) {
centroids.push(pixelDataLAB[random])
break
}
}
}
// repeat until centroids no longer change
var newCentroids = []
var iters = 0
while (true) {
// for each point, determine euclidean distance to each centroid and add it to the closest centroid cluster
iters++
var clusters = [[], [], [], [], []]
for (var i = 0; i < pixelDataLAB.length; i++) {
var distances = []
for (var j = 0; j < 5; j++) {
distances.push(deltaE(pixelDataLAB[i], centroids[j]))
}
var minIndex = distances.indexOf(Math.min(...distances))
clusters[minIndex].push(pixelDataLAB[i])
}
// check for empty clusters
if (clusters.some(x => x.length == 0)) {
var numEmpty = clusters.filter(x => x.length == 0).length
if (numEmpty > 2) {
console.log('More then three empty clusters found. Restarting k-means.');
return kmeans(albumURL);
} else {
const maxLengthIndex = clusters.reduce((maxIndex, arr, currentIndex, array) => {
return arr.length > array[maxIndex].length ? currentIndex : maxIndex;
}, 0);
const maxCluster = [...clusters[maxLengthIndex]];
const newClusters = clusters.map(arr => (arr.length === 0 ? maxCluster : arr));
clusters = newClusters
}
}
// push the mean of each cluster to new centroids
for (var i = 0; i < 5; i++) {
var mean = []
// find mean of LAB values
for (var j = 0; j < 3; j++) {
var values = clusters[i].map((x) => x[j])
mean.push(values.reduce((acc, val) => acc + val, 0) / values.length)
}
newCentroids.push(mean)
}
if (iters === MAX_ITERS || JSON.stringify(newCentroids) === JSON.stringify(centroids)) {
break;
} else {
centroids = newCentroids
newCentroids = []
}
}
// convert LAB to RGBA strings for CSS attribute
var result = []
for (var i = 0; i < 5; i++) {
var rgba = LABtoRGBA(centroids[i])
result.push("rgba(" + rgba.join(", ") + ")")
}
const end = performance.now()
console.log("Time elapsed: " + (end - start) + "ms")
return result
}
module.exports = { kmeans }