mirror of
https://github.com/tiennm99/WPlace-AutoBOT.git
synced 2026-10-11 14:13:15 +00:00
MiraiSeori
00d69fafbe
- Deleted extensive CSS rules for the edit panel, including overlay, container, header, main area, toolbar, and minimap styles. - Cleaned up unused styles to streamline the codebase and improve maintainability.
3019 lines
92 KiB
JavaScript
3019 lines
92 KiB
JavaScript
// ==UserScript==
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// @name WPlace Image Processor
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// @namespace http://tampermonkey.net/
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// @version 2025-09-16.1
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// @description Image processing and color management for WPlace AutoBot
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// @author Wbot
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// @match https://wplace.live/*
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// @grant none
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// ==/UserScript==
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/**
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* ImageProcessor - Handles image loading, processing, color conversion, and dithering for WPlace AutoBot
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* Extracted from Auto-Image.js for better modularity and reusabilit
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*/
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class ImageProcessor {
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constructor(imageSrc = null) {
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this.imageSrc = imageSrc;
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this.img = null;
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this.canvas = null;
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this.ctx = null;
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// Dithering buffers
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this._ditherWorkBuf = null;
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this._ditherEligibleBuf = null;
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// Color cache for performance optimization
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this._colorCache = new Map();
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this._labCache = new Map();
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this._hsvCache = new Map();
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this._hslCache = new Map();
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this._xyzCache = new Map();
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this._luvCache = new Map();
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this._yuvCache = new Map();
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this._oklabCache = new Map();
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this._lchCache = new Map();
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// Configuration constants
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this.TRANSPARENCY_THRESHOLD = 128;
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this.WHITE_THRESHOLD = 230;
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this.COLOR_CACHE_LIMIT = 15000;
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}
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/**
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* Load image from source
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* @returns {Promise<void>}
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*/
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async load() {
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if (!this.imageSrc) {
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throw new Error('No image source provided');
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}
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return new Promise((resolve, reject) => {
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this.img = new Image();
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this.img.crossOrigin = 'anonymous';
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this.img.onload = () => {
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this.canvas = document.createElement('canvas');
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this.ctx = this.canvas.getContext('2d');
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this.canvas.width = this.img.width;
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this.canvas.height = this.img.height;
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this.ctx.drawImage(this.img, 0, 0);
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resolve();
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};
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this.img.onerror = reject;
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this.img.src = this.imageSrc;
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});
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}
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/**
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* Get image dimensions
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* @returns {{width: number, height: number}}
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*/
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getDimensions() {
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if (!this.canvas) {
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throw new Error('Image not loaded. Call load() first.');
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}
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return {
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width: this.canvas.width,
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height: this.canvas.height,
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};
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}
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/**
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* Get pixel data from the image
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* @returns {Uint8ClampedArray} RGBA pixel data
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*/
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getPixelData() {
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if (!this.ctx) {
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throw new Error('Image not loaded. Call load() first.');
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}
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return this.ctx.getImageData(0, 0, this.canvas.width, this.canvas.height).data;
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}
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/**
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* Resize image to new dimensions with specified resampling method
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* @param {number} newWidth - Target width
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* @param {number} newHeight - Target height
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* @param {string} method - Resampling method: 'nearest', 'bilinear', 'box', 'median', 'dominant'
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* @returns {Uint8ClampedArray} Resized image data
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*/
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resize(newWidth, newHeight, method = 'nearest') {
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if (!this.canvas || !this.ctx) {
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throw new Error('Image not loaded. Call load() first.');
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}
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const tempCanvas = document.createElement('canvas');
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const tempCtx = tempCanvas.getContext('2d');
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tempCanvas.width = newWidth;
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tempCanvas.height = newHeight;
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// Use the specified resampling method
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const resampledCanvas = this.resampleImage(this.canvas, newWidth, newHeight, method);
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this.canvas.width = newWidth;
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this.canvas.height = newHeight;
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this.ctx.clearRect(0, 0, newWidth, newHeight);
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this.ctx.drawImage(resampledCanvas, 0, 0);
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return this.ctx.getImageData(0, 0, newWidth, newHeight).data;
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}
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/**
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* Resample image using specified method (adapted from wplace_helper)
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* @param {HTMLCanvasElement|HTMLImageElement} source - Source image/canvas
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* @param {number} dstW - Destination width
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* @param {number} dstH - Destination height
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* @param {string} method - Resampling method
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* @returns {HTMLCanvasElement} Resampled canvas
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*/
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resampleImage(source, dstW, dstH, method = 'nearest') {
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const srcW = source.width;
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const srcH = source.height;
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const factor = srcW / dstW;
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const isInteger = Math.abs(factor - Math.round(factor)) < 1e-6;
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// For non-integer factors, only nearest and bilinear are available
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if (!isInteger) {
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if (method === 'bilinear') {
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return this.resampleBilinear(source, dstW, dstH);
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}
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return this.resampleNearest(source, dstW, dstH);
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}
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const intFactor = Math.max(1, Math.round(factor));
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switch (method) {
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case 'bilinear':
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return this.resampleBilinear(source, dstW, dstH);
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case 'box':
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return this.resampleBox(source, dstW, dstH, intFactor);
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case 'median':
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return this.resampleMedian(source, dstW, dstH, intFactor);
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case 'dominant':
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return this.resampleDominant(source, dstW, dstH, intFactor);
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case 'nearest':
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default:
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return this.resampleNearest(source, dstW, dstH);
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}
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}
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/**
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* Nearest neighbor resampling
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*/
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resampleNearest(source, dstW, dstH) {
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const canvas = document.createElement('canvas');
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const ctx = canvas.getContext('2d', { willReadFrequently: true });
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canvas.width = dstW;
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canvas.height = dstH;
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ctx.imageSmoothingEnabled = false;
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ctx.clearRect(0, 0, dstW, dstH);
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ctx.drawImage(source, 0, 0, dstW, dstH);
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return canvas;
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}
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/**
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* Bilinear resampling
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*/
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resampleBilinear(source, dstW, dstH) {
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const canvas = document.createElement('canvas');
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const ctx = canvas.getContext('2d', { willReadFrequently: true });
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canvas.width = dstW;
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canvas.height = dstH;
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ctx.imageSmoothingEnabled = true;
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ctx.clearRect(0, 0, dstW, dstH);
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ctx.drawImage(source, 0, 0, dstW, dstH);
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return canvas;
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}
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/**
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* Box filter resampling (average of pixels in block)
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*/
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resampleBox(source, dstW, dstH, factor) {
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const srcCanvas = document.createElement('canvas');
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const srcCtx = srcCanvas.getContext('2d', { willReadFrequently: true });
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srcCanvas.width = source.width;
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srcCanvas.height = source.height;
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srcCtx.drawImage(source, 0, 0);
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const srcData = srcCtx.getImageData(0, 0, source.width, source.height).data;
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const dstCanvas = document.createElement('canvas');
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const dstCtx = dstCanvas.getContext('2d', { willReadFrequently: true });
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dstCanvas.width = dstW;
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dstCanvas.height = dstH;
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const dstImageData = dstCtx.createImageData(dstW, dstH);
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const dstData = dstImageData.data;
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const sw = source.width;
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const sh = source.height;
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for (let y = 0; y < dstH; y++) {
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const sy0 = y * factor;
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const sy1 = Math.min(sh, sy0 + factor);
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for (let x = 0; x < dstW; x++) {
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const sx0 = x * factor;
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const sx1 = Math.min(sw, sx0 + factor);
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let r = 0, g = 0, b = 0, a = 0, cnt = 0;
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for (let yy = sy0; yy < sy1; yy++) {
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let p = (yy * sw + sx0) * 4;
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for (let xx = sx0; xx < sx1; xx++) {
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r += srcData[p];
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g += srcData[p + 1];
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b += srcData[p + 2];
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a += srcData[p + 3];
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cnt++;
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p += 4;
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}
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}
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const q = (y * dstW + x) * 4;
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dstData[q] = Math.min(255, Math.max(0, Math.round(r / cnt)));
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dstData[q + 1] = Math.min(255, Math.max(0, Math.round(g / cnt)));
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dstData[q + 2] = Math.min(255, Math.max(0, Math.round(b / cnt)));
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dstData[q + 3] = Math.min(255, Math.max(0, Math.round(a / cnt)));
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}
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}
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dstCtx.putImageData(dstImageData, 0, 0);
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return dstCanvas;
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}
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/**
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* Median filter resampling (median color in block)
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*/
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resampleMedian(source, dstW, dstH, factor) {
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const srcCanvas = document.createElement('canvas');
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const srcCtx = srcCanvas.getContext('2d', { willReadFrequently: true });
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srcCanvas.width = source.width;
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srcCanvas.height = source.height;
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srcCtx.drawImage(source, 0, 0);
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const srcData = srcCtx.getImageData(0, 0, source.width, source.height).data;
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const dstCanvas = document.createElement('canvas');
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const dstCtx = dstCanvas.getContext('2d', { willReadFrequently: true });
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dstCanvas.width = dstW;
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dstCanvas.height = dstH;
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const dstImageData = dstCtx.createImageData(dstW, dstH);
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const dstData = dstImageData.data;
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const sw = source.width;
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const sh = source.height;
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const rHist = new Uint32Array(16);
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const gHist = new Uint32Array(16);
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const bHist = new Uint32Array(16);
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const aHist = new Uint32Array(16);
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const binToByte = (bin) => (bin * 17) | 0;
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for (let y = 0; y < dstH; y++) {
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const sy0 = y * factor;
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const sy1 = Math.min(sh, sy0 + factor);
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for (let x = 0; x < dstW; x++) {
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rHist.fill(0); gHist.fill(0); bHist.fill(0); aHist.fill(0);
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const sx0 = x * factor;
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const sx1 = Math.min(sw, sx0 + factor);
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for (let yy = sy0; yy < sy1; yy++) {
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let p = (yy * sw + sx0) * 4;
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for (let xx = sx0; xx < sx1; xx++) {
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rHist[srcData[p] >> 4]++;
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gHist[srcData[p + 1] >> 4]++;
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bHist[srcData[p + 2] >> 4]++;
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aHist[srcData[p + 3] >> 4]++;
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p += 4;
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}
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}
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const half = ((sx1 - sx0) * (sy1 - sy0)) >> 1;
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const medianFrom = (hist) => {
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let acc = 0;
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for (let i = 0; i < 16; i++) {
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acc += hist[i];
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if (acc > half) return binToByte(i);
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}
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return binToByte(15);
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};
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const q = (y * dstW + x) * 4;
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dstData[q] = medianFrom(rHist);
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dstData[q + 1] = medianFrom(gHist);
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dstData[q + 2] = medianFrom(bHist);
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dstData[q + 3] = medianFrom(aHist);
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}
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}
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dstCtx.putImageData(dstImageData, 0, 0);
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return dstCanvas;
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}
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/**
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* Dominant color resampling (most frequent color in block)
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*/
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resampleDominant(source, dstW, dstH, factor) {
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const srcCanvas = document.createElement('canvas');
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const srcCtx = srcCanvas.getContext('2d', { willReadFrequently: true });
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srcCanvas.width = source.width;
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srcCanvas.height = source.height;
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srcCtx.drawImage(source, 0, 0);
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const srcData = srcCtx.getImageData(0, 0, source.width, source.height).data;
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const dstCanvas = document.createElement('canvas');
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const dstCtx = dstCanvas.getContext('2d', { willReadFrequently: true });
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dstCanvas.width = dstW;
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dstCanvas.height = dstH;
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const dstImageData = dstCtx.createImageData(dstW, dstH);
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const dstData = dstImageData.data;
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const sw = source.width;
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const sh = source.height;
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const counts = new Uint32Array(4096);
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for (let y = 0; y < dstH; y++) {
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const sy0 = y * factor;
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const sy1 = Math.min(sh, sy0 + factor);
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for (let x = 0; x < dstW; x++) {
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counts.fill(0);
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const sx0 = x * factor;
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const sx1 = Math.min(sw, sx0 + factor);
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let bestIdx = 0, bestCount = -1;
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for (let yy = sy0; yy < sy1; yy++) {
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let p = (yy * sw + sx0) * 4;
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for (let xx = sx0; xx < sx1; xx++) {
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const r = srcData[p] >> 4;
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const g = srcData[p + 1] >> 4;
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const b = srcData[p + 2] >> 4;
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const idx = (r << 8) | (g << 4) | b;
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const c = (counts[idx] = (counts[idx] + 1) >>> 0);
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if (c > bestCount) {
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bestCount = c;
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bestIdx = idx;
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}
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p += 4;
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}
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}
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const r = ((bestIdx >> 8) & 0xF) * 17;
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const g = ((bestIdx >> 4) & 0xF) * 17;
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const b = (bestIdx & 0xF) * 17;
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const q = (y * dstW + x) * 4;
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dstData[q] = r;
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dstData[q + 1] = g;
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dstData[q + 2] = b;
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dstData[q + 3] = 255;
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}
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}
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dstCtx.putImageData(dstImageData, 0, 0);
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return dstCanvas;
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}
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/**
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* Generate preview with resizing
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* @param {number} width - Preview width
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* @param {number} height - Preview height
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* @returns {string} Base64 data URL of the preview
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*/
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generatePreview(width, height) {
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if (!this.canvas) {
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throw new Error('Image not loaded. Call load() first.');
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}
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const previewCanvas = document.createElement('canvas');
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const previewCtx = previewCanvas.getContext('2d');
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previewCanvas.width = width;
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previewCanvas.height = height;
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previewCtx.imageSmoothingEnabled = false;
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previewCtx.drawImage(this.canvas, 0, 0, width, height);
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return previewCanvas.toDataURL();
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}
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// =============================================
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// COLOR PROCESSING METHODS
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// =============================================
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/**
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* Convert RGB to LAB color space
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* @param {number} r - Red value (0-255)
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* @param {number} g - Green value (0-255)
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* @param {number} b - Blue value (0-255)
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* @returns {number[]} LAB values [L, a, b]
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* @private
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*/
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_rgbToLab(r, g, b) {
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// Normalize RGB values
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let x = r / 255.0;
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let y = g / 255.0;
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let z = b / 255.0;
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// Apply gamma correction
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x = x > 0.04045 ? Math.pow((x + 0.055) / 1.055, 2.4) : x / 12.92;
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y = y > 0.04045 ? Math.pow((y + 0.055) / 1.055, 2.4) : y / 12.92;
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z = z > 0.04045 ? Math.pow((z + 0.055) / 1.055, 2.4) : z / 12.92;
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// Convert to XYZ using sRGB matrix
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let X = x * 0.4124564 + y * 0.3575761 + z * 0.1804375;
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let Y = x * 0.2126729 + y * 0.7151522 + z * 0.0721750;
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let Z = x * 0.0193339 + y * 0.1191920 + z * 0.9503041;
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// Normalize for D65 illuminant
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X /= 0.95047;
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Y /= 1.00000;
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Z /= 1.08883;
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// Convert to LAB
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X = X > 0.008856 ? Math.pow(X, 1/3) : (7.787 * X + 16/116);
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Y = Y > 0.008856 ? Math.pow(Y, 1/3) : (7.787 * Y + 16/116);
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Z = Z > 0.008856 ? Math.pow(Z, 1/3) : (7.787 * Z + 16/116);
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const L = 116 * Y - 16;
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const a = 500 * (X - Y);
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const b2 = 200 * (Y - Z);
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return [L, a, b2];
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}
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/**
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* Get LAB values with caching
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* @param {number} r - Red value
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* @param {number} g - Green value
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* @param {number} b - Blue value
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* @returns {number[]} Cached LAB values
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* @private
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*/
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_getLab(r, g, b) {
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const key = (r << 16) | (g << 8) | b;
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let v = this._labCache.get(key);
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if (!v) {
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v = this._rgbToLab(r, g, b);
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this._labCache.set(key, v);
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}
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return v;
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}
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/**
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* Convert RGB to HSV color space
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* @param {number} r - Red value (0-255)
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* @param {number} g - Green value (0-255)
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* @param {number} b - Blue value (0-255)
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* @returns {number[]} HSV values [H (0-360), S (0-1), V (0-1)]
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* @private
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*/
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_rgbToHsv(r, g, b) {
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r /= 255;
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g /= 255;
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b /= 255;
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const max = Math.max(r, g, b);
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const min = Math.min(r, g, b);
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const delta = max - min;
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let h = 0;
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const s = max === 0 ? 0 : delta / max;
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const v = max;
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if (delta !== 0) {
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if (max === r) {
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h = ((g - b) / delta) % 6;
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} else if (max === g) {
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h = (b - r) / delta + 2;
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} else {
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h = (r - g) / delta + 4;
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}
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h *= 60;
|
|
if (h < 0) h += 360;
|
|
}
|
|
|
|
return [h, s, v];
|
|
}
|
|
|
|
/**
|
|
* Convert HSV to RGB color space
|
|
* @param {number} h - Hue value (0-360)
|
|
* @param {number} s - Saturation value (0-1)
|
|
* @param {number} v - Value/Brightness (0-1)
|
|
* @returns {number[]} RGB values [R, G, B] (0-255)
|
|
* @private
|
|
*/
|
|
_hsvToRgb(h, s, v) {
|
|
const c = v * s;
|
|
const x = c * (1 - Math.abs(((h / 60) % 2) - 1));
|
|
const m = v - c;
|
|
|
|
let r = 0, g = 0, b = 0;
|
|
|
|
if (h >= 0 && h < 60) {
|
|
r = c; g = x; b = 0;
|
|
} else if (h >= 60 && h < 120) {
|
|
r = x; g = c; b = 0;
|
|
} else if (h >= 120 && h < 180) {
|
|
r = 0; g = c; b = x;
|
|
} else if (h >= 180 && h < 240) {
|
|
r = 0; g = x; b = c;
|
|
} else if (h >= 240 && h < 300) {
|
|
r = x; g = 0; b = c;
|
|
} else if (h >= 300 && h < 360) {
|
|
r = c; g = 0; b = x;
|
|
}
|
|
|
|
return [
|
|
Math.round((r + m) * 255),
|
|
Math.round((g + m) * 255),
|
|
Math.round((b + m) * 255)
|
|
];
|
|
}
|
|
|
|
/**
|
|
* Convert RGB to HSL color space
|
|
* @param {number} r - Red value (0-255)
|
|
* @param {number} g - Green value (0-255)
|
|
* @param {number} b - Blue value (0-255)
|
|
* @returns {number[]} HSL values [H (0-360), S (0-1), L (0-1)]
|
|
* @private
|
|
*/
|
|
_rgbToHsl(r, g, b) {
|
|
r /= 255;
|
|
g /= 255;
|
|
b /= 255;
|
|
|
|
const max = Math.max(r, g, b);
|
|
const min = Math.min(r, g, b);
|
|
const delta = max - min;
|
|
|
|
let h = 0;
|
|
let s = 0;
|
|
const l = (max + min) / 2;
|
|
|
|
if (delta !== 0) {
|
|
s = l > 0.5 ? delta / (2 - max - min) : delta / (max + min);
|
|
|
|
if (max === r) {
|
|
h = ((g - b) / delta) % 6;
|
|
} else if (max === g) {
|
|
h = (b - r) / delta + 2;
|
|
} else {
|
|
h = (r - g) / delta + 4;
|
|
}
|
|
h *= 60;
|
|
if (h < 0) h += 360;
|
|
}
|
|
|
|
return [h, s, l];
|
|
}
|
|
|
|
/**
|
|
* Convert HSL to RGB color space
|
|
* @param {number} h - Hue value (0-360)
|
|
* @param {number} s - Saturation value (0-1)
|
|
* @param {number} l - Lightness value (0-1)
|
|
* @returns {number[]} RGB values [R, G, B] (0-255)
|
|
* @private
|
|
*/
|
|
_hslToRgb(h, s, l) {
|
|
const c = (1 - Math.abs(2 * l - 1)) * s;
|
|
const x = c * (1 - Math.abs(((h / 60) % 2) - 1));
|
|
const m = l - c / 2;
|
|
|
|
let r = 0, g = 0, b = 0;
|
|
|
|
if (h >= 0 && h < 60) {
|
|
r = c; g = x; b = 0;
|
|
} else if (h >= 60 && h < 120) {
|
|
r = x; g = c; b = 0;
|
|
} else if (h >= 120 && h < 180) {
|
|
r = 0; g = c; b = x;
|
|
} else if (h >= 180 && h < 240) {
|
|
r = 0; g = x; b = c;
|
|
} else if (h >= 240 && h < 300) {
|
|
r = x; g = 0; b = c;
|
|
} else if (h >= 300 && h < 360) {
|
|
r = c; g = 0; b = x;
|
|
}
|
|
|
|
return [
|
|
Math.round((r + m) * 255),
|
|
Math.round((g + m) * 255),
|
|
Math.round((b + m) * 255)
|
|
];
|
|
}
|
|
|
|
/**
|
|
* Convert RGB to XYZ color space
|
|
* @param {number} r - Red value (0-255)
|
|
* @param {number} g - Green value (0-255)
|
|
* @param {number} b - Blue value (0-255)
|
|
* @returns {number[]} XYZ values [X, Y, Z]
|
|
* @private
|
|
*/
|
|
_rgbToXyz(r, g, b) {
|
|
// Normalize RGB values
|
|
let x = r / 255.0;
|
|
let y = g / 255.0;
|
|
let z = b / 255.0;
|
|
|
|
// Apply gamma correction
|
|
x = x > 0.04045 ? Math.pow((x + 0.055) / 1.055, 2.4) : x / 12.92;
|
|
y = y > 0.04045 ? Math.pow((y + 0.055) / 1.055, 2.4) : y / 12.92;
|
|
z = z > 0.04045 ? Math.pow((z + 0.055) / 1.055, 2.4) : z / 12.92;
|
|
|
|
// Convert to XYZ using sRGB matrix
|
|
const X = x * 0.4124564 + y * 0.3575761 + z * 0.1804375;
|
|
const Y = x * 0.2126729 + y * 0.7151522 + z * 0.0721750;
|
|
const Z = x * 0.0193339 + y * 0.1191920 + z * 0.9503041;
|
|
|
|
return [X, Y, Z];
|
|
}
|
|
|
|
/**
|
|
* Convert XYZ to RGB color space
|
|
* @param {number} x - X value
|
|
* @param {number} y - Y value
|
|
* @param {number} z - Z value
|
|
* @returns {number[]} RGB values [R, G, B] (0-255)
|
|
* @private
|
|
*/
|
|
_xyzToRgb(x, y, z) {
|
|
// Convert XYZ to linear RGB
|
|
let r = x * 3.2404542 + y * -1.5371385 + z * -0.4985314;
|
|
let g = x * -0.9692660 + y * 1.8760108 + z * 0.0415560;
|
|
let b = x * 0.0556434 + y * -0.2040259 + z * 1.0572252;
|
|
|
|
// Apply gamma correction
|
|
r = r > 0.0031308 ? 1.055 * Math.pow(r, 1.0 / 2.4) - 0.055 : 12.92 * r;
|
|
g = g > 0.0031308 ? 1.055 * Math.pow(g, 1.0 / 2.4) - 0.055 : 12.92 * g;
|
|
b = b > 0.0031308 ? 1.055 * Math.pow(b, 1.0 / 2.4) - 0.055 : 12.92 * b;
|
|
|
|
// Clamp and convert to 0-255 range
|
|
return [
|
|
Math.round(Math.max(0, Math.min(1, r)) * 255),
|
|
Math.round(Math.max(0, Math.min(1, g)) * 255),
|
|
Math.round(Math.max(0, Math.min(1, b)) * 255)
|
|
];
|
|
}
|
|
|
|
/**
|
|
* Convert RGB to LUV color space
|
|
* @param {number} r - Red value (0-255)
|
|
* @param {number} g - Green value (0-255)
|
|
* @param {number} b - Blue value (0-255)
|
|
* @returns {number[]} LUV values [L, u, v]
|
|
* @private
|
|
*/
|
|
_rgbToLuv(r, g, b) {
|
|
const [X, Y, Z] = this._rgbToXyz(r, g, b);
|
|
|
|
// Reference white D65
|
|
const Xn = 0.95047;
|
|
const Yn = 1.00000;
|
|
const Zn = 1.08883;
|
|
|
|
const yr = Y / Yn;
|
|
const L = yr > 0.008856 ? 116 * Math.pow(yr, 1/3) - 16 : 903.3 * yr;
|
|
|
|
const denom = X + 15 * Y + 3 * Z;
|
|
const denomN = Xn + 15 * Yn + 3 * Zn;
|
|
|
|
const up = denom === 0 ? 0 : (4 * X) / denom;
|
|
const vp = denom === 0 ? 0 : (9 * Y) / denom;
|
|
const upN = (4 * Xn) / denomN;
|
|
const vpN = (9 * Yn) / denomN;
|
|
|
|
const u = 13 * L * (up - upN);
|
|
const v = 13 * L * (vp - vpN);
|
|
|
|
return [L, u, v];
|
|
}
|
|
|
|
/**
|
|
* Convert LUV to RGB color space
|
|
* @param {number} l - L value
|
|
* @param {number} u - u value
|
|
* @param {number} v - v value
|
|
* @returns {number[]} RGB values [R, G, B] (0-255)
|
|
* @private
|
|
*/
|
|
_luvToRgb(l, u, v) {
|
|
// Reference white D65
|
|
const Xn = 0.95047;
|
|
const Yn = 1.00000;
|
|
const Zn = 1.08883;
|
|
|
|
const upN = (4 * Xn) / (Xn + 15 * Yn + 3 * Zn);
|
|
const vpN = (9 * Yn) / (Xn + 15 * Yn + 3 * Zn);
|
|
|
|
const Y = l > 8 ? Yn * Math.pow((l + 16) / 116, 3) : Yn * l / 903.3;
|
|
|
|
const up = u / (13 * l) + upN;
|
|
const vp = v / (13 * l) + vpN;
|
|
|
|
const X = Y * 9 * up / (4 * vp);
|
|
const Z = Y * (12 - 3 * up - 20 * vp) / (4 * vp);
|
|
|
|
return this._xyzToRgb(X, Y, Z);
|
|
}
|
|
|
|
/**
|
|
* Convert RGB to YUV color space
|
|
* @param {number} r - Red value (0-255)
|
|
* @param {number} g - Green value (0-255)
|
|
* @param {number} b - Blue value (0-255)
|
|
* @returns {number[]} YUV values [Y, U, V]
|
|
* @private
|
|
*/
|
|
_rgbToYuv(r, g, b) {
|
|
const Y = 0.299 * r + 0.587 * g + 0.114 * b;
|
|
const U = -0.14713 * r - 0.28886 * g + 0.436 * b;
|
|
const V = 0.615 * r - 0.51499 * g - 0.10001 * b;
|
|
|
|
return [Y, U, V];
|
|
}
|
|
|
|
/**
|
|
* Convert YUV to RGB color space
|
|
* @param {number} y - Y (luminance) value
|
|
* @param {number} u - U (chrominance) value
|
|
* @param {number} v - V (chrominance) value
|
|
* @returns {number[]} RGB values [R, G, B] (0-255)
|
|
* @private
|
|
*/
|
|
_yuvToRgb(y, u, v) {
|
|
const r = y + 1.13983 * v;
|
|
const g = y - 0.39465 * u - 0.58060 * v;
|
|
const b = y + 2.03211 * u;
|
|
|
|
return [
|
|
Math.round(Math.max(0, Math.min(255, r))),
|
|
Math.round(Math.max(0, Math.min(255, g))),
|
|
Math.round(Math.max(0, Math.min(255, b)))
|
|
];
|
|
}
|
|
|
|
/**
|
|
* Convert RGB to Oklab color space (improved LAB)
|
|
* @param {number} r - Red value (0-255)
|
|
* @param {number} g - Green value (0-255)
|
|
* @param {number} b - Blue value (0-255)
|
|
* @returns {number[]} Oklab values [L, a, b]
|
|
* @private
|
|
*/
|
|
_rgbToOklab(r, g, b) {
|
|
// Normalize RGB
|
|
r /= 255;
|
|
g /= 255;
|
|
b /= 255;
|
|
|
|
// Apply gamma correction
|
|
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;
|
|
|
|
// Linear RGB to Oklab
|
|
const l = 0.4122214708 * r + 0.5363325363 * g + 0.0514459929 * b;
|
|
const m = 0.2119034982 * r + 0.6806995451 * g + 0.1073969566 * b;
|
|
const s = 0.0883024619 * r + 0.2817188376 * g + 0.6299787005 * b;
|
|
|
|
const l_ = Math.cbrt(l);
|
|
const m_ = Math.cbrt(m);
|
|
const s_ = Math.cbrt(s);
|
|
|
|
return [
|
|
0.2104542553 * l_ + 0.7936177850 * m_ - 0.0040720468 * s_,
|
|
1.9779984951 * l_ - 2.4285922050 * m_ + 0.4505937099 * s_,
|
|
0.0259040371 * l_ + 0.7827717662 * m_ - 0.8086757660 * s_
|
|
];
|
|
}
|
|
|
|
/**
|
|
* Convert Oklab to RGB color space
|
|
* @param {number} l - L value
|
|
* @param {number} a - a value
|
|
* @param {number} b - b value
|
|
* @returns {number[]} RGB values [R, G, B] (0-255)
|
|
* @private
|
|
*/
|
|
_oklabToRgb(l, a, b) {
|
|
const l_ = l + 0.3963377774 * a + 0.2158037573 * b;
|
|
const m_ = l - 0.1055613458 * a - 0.0638541728 * b;
|
|
const s_ = l - 0.0894841775 * a - 1.2914855480 * b;
|
|
|
|
const l3 = l_ * l_ * l_;
|
|
const m3 = m_ * m_ * m_;
|
|
const s3 = s_ * s_ * s_;
|
|
|
|
let r = 4.0767416621 * l3 - 3.3077115913 * m3 + 0.2309699292 * s3;
|
|
let g = -1.2684380046 * l3 + 2.6097574011 * m3 - 0.3413193965 * s3;
|
|
let b2 = -0.0041960863 * l3 - 0.7034186147 * m3 + 1.7076147010 * s3;
|
|
|
|
// Apply gamma correction
|
|
r = r > 0.0031308 ? 1.055 * Math.pow(r, 1.0 / 2.4) - 0.055 : 12.92 * r;
|
|
g = g > 0.0031308 ? 1.055 * Math.pow(g, 1.0 / 2.4) - 0.055 : 12.92 * g;
|
|
b2 = b2 > 0.0031308 ? 1.055 * Math.pow(b2, 1.0 / 2.4) - 0.055 : 12.92 * b2;
|
|
|
|
return [
|
|
Math.round(Math.max(0, Math.min(1, r)) * 255),
|
|
Math.round(Math.max(0, Math.min(1, g)) * 255),
|
|
Math.round(Math.max(0, Math.min(1, b2)) * 255)
|
|
];
|
|
}
|
|
|
|
/**
|
|
* Convert LAB to LCH color space (cylindrical representation)
|
|
* @param {number} l - L value
|
|
* @param {number} a - a value
|
|
* @param {number} b - b value
|
|
* @returns {number[]} LCH values [L, C, H]
|
|
* @private
|
|
*/
|
|
_labToLch(l, a, b) {
|
|
const c = Math.sqrt(a * a + b * b);
|
|
let h = Math.atan2(b, a) * 180 / Math.PI;
|
|
if (h < 0) h += 360;
|
|
|
|
return [l, c, h];
|
|
}
|
|
|
|
/**
|
|
* Convert LCH to LAB color space
|
|
* @param {number} l - L value
|
|
* @param {number} c - C (chroma) value
|
|
* @param {number} h - H (hue) value (0-360)
|
|
* @returns {number[]} LAB values [L, a, b]
|
|
* @private
|
|
*/
|
|
_lchToLab(l, c, h) {
|
|
const hRad = h * Math.PI / 180;
|
|
const a = c * Math.cos(hRad);
|
|
const b = c * Math.sin(hRad);
|
|
|
|
return [l, a, b];
|
|
}
|
|
|
|
/**
|
|
* Find closest color in palette using specified algorithm
|
|
* @param {number} r - Red value
|
|
* @param {number} g - Green value
|
|
* @param {number} b - Blue value
|
|
* @param {Array} palette - Array of RGB arrays [[r,g,b], ...]
|
|
* @param {string} algorithm - 'legacy' or 'lab'
|
|
* @param {Object} options - Additional options for color matching
|
|
* @returns {number[]} Closest RGB color [r, g, b]
|
|
*/
|
|
findClosestPaletteColor(r, g, b, palette, algorithm = 'lab', options = {}) {
|
|
if (!palette || palette.length === 0) {
|
|
return [0, 0, 0];
|
|
}
|
|
|
|
const { enableChromaPenalty = false, chromaPenaltyWeight = 0.15 } = options;
|
|
|
|
if (algorithm === 'legacy') {
|
|
let menorDist = Infinity;
|
|
let cor = [0, 0, 0];
|
|
for (let i = 0; i < palette.length; i++) {
|
|
const [pr, pg, pb] = palette[i];
|
|
const rmean = (pr + r) / 2;
|
|
const rdiff = pr - r;
|
|
const gdiff = pg - g;
|
|
const bdiff = pb - b;
|
|
const dist = Math.sqrt(
|
|
(((512 + rmean) * rdiff * rdiff) >> 8) +
|
|
4 * gdiff * gdiff +
|
|
(((767 - rmean) * bdiff * bdiff) >> 8)
|
|
);
|
|
if (dist < menorDist) {
|
|
menorDist = dist;
|
|
cor = [pr, pg, pb];
|
|
}
|
|
}
|
|
return cor;
|
|
}
|
|
|
|
// HSV algorithm
|
|
if (algorithm === 'hsv') {
|
|
const [ht, st, vt] = this._rgbToHsv(r, g, b);
|
|
let best = null;
|
|
let bestDist = Infinity;
|
|
|
|
for (let i = 0; i < palette.length; i++) {
|
|
const [pr, pg, pb] = palette[i];
|
|
const [hp, sp, vp] = this._rgbToHsv(pr, pg, pb);
|
|
const dh = Math.min(Math.abs(ht - hp), 360 - Math.abs(ht - hp)) / 360; // Normalize hue distance
|
|
const ds = st - sp;
|
|
const dv = vt - vp;
|
|
const dist = dh * dh + ds * ds + dv * dv;
|
|
|
|
if (dist < bestDist) {
|
|
bestDist = dist;
|
|
best = palette[i];
|
|
if (bestDist === 0) break;
|
|
}
|
|
}
|
|
return best || [0, 0, 0];
|
|
}
|
|
|
|
// HSL algorithm
|
|
if (algorithm === 'hsl') {
|
|
const [ht, st, lt] = this._rgbToHsl(r, g, b);
|
|
let best = null;
|
|
let bestDist = Infinity;
|
|
|
|
for (let i = 0; i < palette.length; i++) {
|
|
const [pr, pg, pb] = palette[i];
|
|
const [hp, sp, lp] = this._rgbToHsl(pr, pg, pb);
|
|
const dh = Math.min(Math.abs(ht - hp), 360 - Math.abs(ht - hp)) / 360; // Normalize hue distance
|
|
const ds = st - sp;
|
|
const dl = lt - lp;
|
|
const dist = dh * dh + ds * ds + dl * dl;
|
|
|
|
if (dist < bestDist) {
|
|
bestDist = dist;
|
|
best = palette[i];
|
|
if (bestDist === 0) break;
|
|
}
|
|
}
|
|
return best || [0, 0, 0];
|
|
}
|
|
|
|
// XYZ algorithm
|
|
if (algorithm === 'xyz') {
|
|
const [xt, yt, zt] = this._rgbToXyz(r, g, b);
|
|
let best = null;
|
|
let bestDist = Infinity;
|
|
|
|
for (let i = 0; i < palette.length; i++) {
|
|
const [pr, pg, pb] = palette[i];
|
|
const [xp, yp, zp] = this._rgbToXyz(pr, pg, pb);
|
|
const dx = xt - xp;
|
|
const dy = yt - yp;
|
|
const dz = zt - zp;
|
|
const dist = dx * dx + dy * dy + dz * dz;
|
|
|
|
if (dist < bestDist) {
|
|
bestDist = dist;
|
|
best = palette[i];
|
|
if (bestDist === 0) break;
|
|
}
|
|
}
|
|
return best || [0, 0, 0];
|
|
}
|
|
|
|
// LUV algorithm
|
|
if (algorithm === 'luv') {
|
|
const [lt, ut, vt] = this._rgbToLuv(r, g, b);
|
|
let best = null;
|
|
let bestDist = Infinity;
|
|
|
|
for (let i = 0; i < palette.length; i++) {
|
|
const [pr, pg, pb] = palette[i];
|
|
const [lp, up, vp] = this._rgbToLuv(pr, pg, pb);
|
|
const dl = lt - lp;
|
|
const du = ut - up;
|
|
const dv = vt - vp;
|
|
const dist = dl * dl + du * du + dv * dv;
|
|
|
|
if (dist < bestDist) {
|
|
bestDist = dist;
|
|
best = palette[i];
|
|
if (bestDist === 0) break;
|
|
}
|
|
}
|
|
return best || [0, 0, 0];
|
|
}
|
|
|
|
// YUV algorithm
|
|
if (algorithm === 'yuv') {
|
|
const [yt, ut, vt] = this._rgbToYuv(r, g, b);
|
|
let best = null;
|
|
let bestDist = Infinity;
|
|
|
|
for (let i = 0; i < palette.length; i++) {
|
|
const [pr, pg, pb] = palette[i];
|
|
const [yp, up, vp] = this._rgbToYuv(pr, pg, pb);
|
|
const dy = yt - yp;
|
|
const du = ut - up;
|
|
const dv = vt - vp;
|
|
const dist = dy * dy + du * du + dv * dv;
|
|
|
|
if (dist < bestDist) {
|
|
bestDist = dist;
|
|
best = palette[i];
|
|
if (bestDist === 0) break;
|
|
}
|
|
}
|
|
return best || [0, 0, 0];
|
|
}
|
|
|
|
// Oklab algorithm
|
|
if (algorithm === 'oklab') {
|
|
const [lt, at, bt] = this._rgbToOklab(r, g, b);
|
|
let best = null;
|
|
let bestDist = Infinity;
|
|
|
|
for (let i = 0; i < palette.length; i++) {
|
|
const [pr, pg, pb] = palette[i];
|
|
const [lp, ap, bp] = this._rgbToOklab(pr, pg, pb);
|
|
const dl = lt - lp;
|
|
const da = at - ap;
|
|
const db = bt - bp;
|
|
const dist = dl * dl + da * da + db * db;
|
|
|
|
if (dist < bestDist) {
|
|
bestDist = dist;
|
|
best = palette[i];
|
|
if (bestDist === 0) break;
|
|
}
|
|
}
|
|
return best || [0, 0, 0];
|
|
}
|
|
|
|
// LCH algorithm
|
|
if (algorithm === 'lch') {
|
|
const [Lt, at, bt] = this._getLab(r, g, b);
|
|
const [lt, ct, ht] = this._labToLch(Lt, at, bt);
|
|
let best = null;
|
|
let bestDist = Infinity;
|
|
|
|
for (let i = 0; i < palette.length; i++) {
|
|
const [pr, pg, pb] = palette[i];
|
|
const [Lp, ap, bp] = this._getLab(pr, pg, pb);
|
|
const [lp, cp, hp] = this._labToLch(Lp, ap, bp);
|
|
const dl = lt - lp;
|
|
const dc = ct - cp;
|
|
const dh = Math.min(Math.abs(ht - hp), 360 - Math.abs(ht - hp)); // Circular hue distance
|
|
const dist = dl * dl + dc * dc + (dh * dh) / 360; // Normalize hue
|
|
|
|
if (dist < bestDist) {
|
|
bestDist = dist;
|
|
best = palette[i];
|
|
if (bestDist === 0) break;
|
|
}
|
|
}
|
|
return best || [0, 0, 0];
|
|
}
|
|
|
|
// LAB algorithm (default)
|
|
const [Lt, at, bt] = this._getLab(r, g, b);
|
|
const targetChroma = Math.sqrt(at * at + bt * bt);
|
|
let best = null;
|
|
let bestDist = Infinity;
|
|
|
|
for (let i = 0; i < palette.length; i++) {
|
|
const [pr, pg, pb] = palette[i];
|
|
const [Lp, ap, bp] = this._getLab(pr, pg, pb);
|
|
const dL = Lt - Lp;
|
|
const da = at - ap;
|
|
const db = bt - bp;
|
|
let dist = dL * dL + da * da + db * db;
|
|
|
|
if (enableChromaPenalty && targetChroma > 20) {
|
|
const candChroma = Math.sqrt(ap * ap + bp * bp);
|
|
if (candChroma < targetChroma) {
|
|
const chromaDiff = targetChroma - candChroma;
|
|
dist += chromaDiff * chromaDiff * chromaPenaltyWeight;
|
|
}
|
|
}
|
|
|
|
if (dist < bestDist) {
|
|
bestDist = dist;
|
|
best = palette[i];
|
|
if (bestDist === 0) break;
|
|
}
|
|
}
|
|
|
|
return best || [0, 0, 0];
|
|
}
|
|
|
|
/**
|
|
* Check if a pixel is considered white based on threshold
|
|
* @param {number} r - Red value
|
|
* @param {number} g - Green value
|
|
* @param {number} b - Blue value
|
|
* @param {number} threshold - White threshold (default: 230)
|
|
* @returns {boolean} True if pixel is white
|
|
*/
|
|
isWhitePixel(r, g, b, threshold = this.WHITE_THRESHOLD) {
|
|
return r >= threshold && g >= threshold && b >= threshold;
|
|
}
|
|
|
|
/**
|
|
* Resolve target RGB to closest available color with caching
|
|
* @param {number[]} targetRgb - Target RGB array [r, g, b]
|
|
* @param {Array} availableColors - Available colors with id and rgb properties
|
|
* @param {Object} options - Matching options
|
|
* @returns {Object} {id: number|null, rgb: number[]}
|
|
*/
|
|
resolveColor(targetRgb, availableColors, options = {}) {
|
|
const {
|
|
exactMatch = false,
|
|
algorithm = 'lab',
|
|
enableChromaPenalty = false,
|
|
chromaPenaltyWeight = 0.15,
|
|
whiteThreshold = this.WHITE_THRESHOLD
|
|
} = options;
|
|
|
|
if (!availableColors || availableColors.length === 0) {
|
|
return { id: null, rgb: targetRgb };
|
|
}
|
|
|
|
const cacheKey = `${targetRgb[0]},${targetRgb[1]},${targetRgb[2]}|${algorithm}|${enableChromaPenalty ? 'c' : 'nc'}|${chromaPenaltyWeight}|${exactMatch ? 'exact' : 'closest'}`;
|
|
|
|
if (this._colorCache.has(cacheKey)) {
|
|
return this._colorCache.get(cacheKey);
|
|
}
|
|
|
|
// Check for exact match
|
|
if (exactMatch) {
|
|
const match = availableColors.find(
|
|
(c) => c.rgb[0] === targetRgb[0] && c.rgb[1] === targetRgb[1] && c.rgb[2] === targetRgb[2]
|
|
);
|
|
const result = match ? { id: match.id, rgb: [...match.rgb] } : { id: null, rgb: targetRgb };
|
|
this._colorCache.set(cacheKey, result);
|
|
return result;
|
|
}
|
|
|
|
// Check for white pixel matching
|
|
if (
|
|
targetRgb[0] >= whiteThreshold &&
|
|
targetRgb[1] >= whiteThreshold &&
|
|
targetRgb[2] >= whiteThreshold
|
|
) {
|
|
const whiteEntry = availableColors.find(
|
|
(c) => c.rgb[0] >= whiteThreshold && c.rgb[1] >= whiteThreshold && c.rgb[2] >= whiteThreshold
|
|
);
|
|
if (whiteEntry) {
|
|
const result = { id: whiteEntry.id, rgb: [...whiteEntry.rgb] };
|
|
this._colorCache.set(cacheKey, result);
|
|
return result;
|
|
}
|
|
}
|
|
|
|
// Find nearest color
|
|
let bestId = availableColors[0].id;
|
|
let bestRgb = [...availableColors[0].rgb];
|
|
let bestScore = Infinity;
|
|
|
|
if (algorithm === 'legacy') {
|
|
for (let i = 0; i < availableColors.length; i++) {
|
|
const c = availableColors[i];
|
|
const [r, g, b] = c.rgb;
|
|
const rmean = (r + targetRgb[0]) / 2;
|
|
const rdiff = r - targetRgb[0];
|
|
const gdiff = g - targetRgb[1];
|
|
const bdiff = b - targetRgb[2];
|
|
const dist = Math.sqrt(
|
|
(((512 + rmean) * rdiff * rdiff) >> 8) +
|
|
4 * gdiff * gdiff +
|
|
(((767 - rmean) * bdiff * bdiff) >> 8)
|
|
);
|
|
if (dist < bestScore) {
|
|
bestScore = dist;
|
|
bestId = c.id;
|
|
bestRgb = [...c.rgb];
|
|
if (dist === 0) break;
|
|
}
|
|
}
|
|
} else {
|
|
const [Lt, at, bt] = this._getLab(targetRgb[0], targetRgb[1], targetRgb[2]);
|
|
const targetChroma = Math.sqrt(at * at + bt * bt);
|
|
const penaltyWeight = enableChromaPenalty ? chromaPenaltyWeight : 0;
|
|
|
|
for (let i = 0; i < availableColors.length; i++) {
|
|
const c = availableColors[i];
|
|
const [r, g, b] = c.rgb;
|
|
const [L2, a2, b2] = this._getLab(r, g, b);
|
|
const dL = Lt - L2;
|
|
const da = at - a2;
|
|
const db = bt - b2;
|
|
let dist = dL * dL + da * da + db * db;
|
|
|
|
if (penaltyWeight > 0 && targetChroma > 20) {
|
|
const candChroma = Math.sqrt(a2 * a2 + b2 * b2);
|
|
if (candChroma < targetChroma) {
|
|
const cd = targetChroma - candChroma;
|
|
dist += cd * cd * penaltyWeight;
|
|
}
|
|
}
|
|
|
|
if (dist < bestScore) {
|
|
bestScore = dist;
|
|
bestId = c.id;
|
|
bestRgb = [...c.rgb];
|
|
if (dist === 0) break;
|
|
}
|
|
}
|
|
}
|
|
|
|
const result = { id: bestId, rgb: bestRgb };
|
|
this._colorCache.set(cacheKey, result);
|
|
|
|
// Limit cache size
|
|
if (this._colorCache.size > this.COLOR_CACHE_LIMIT) {
|
|
const firstKey = this._colorCache.keys().next().value;
|
|
this._colorCache.delete(firstKey);
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
// =============================================
|
|
// DITHERING METHODS (Floyd-Steinberg)
|
|
// =============================================
|
|
|
|
/**
|
|
* Ensure dithering buffers are properly sized
|
|
* @param {number} n - Number of pixels
|
|
* @returns {Object} {work: Float32Array, eligible: Uint8Array}
|
|
* @private
|
|
*/
|
|
_ensureDitherBuffers(n) {
|
|
if (!this._ditherWorkBuf || this._ditherWorkBuf.length !== n * 3) {
|
|
this._ditherWorkBuf = new Float32Array(n * 3);
|
|
}
|
|
if (!this._ditherEligibleBuf || this._ditherEligibleBuf.length !== n) {
|
|
this._ditherEligibleBuf = new Uint8Array(n);
|
|
}
|
|
return { work: this._ditherWorkBuf, eligible: this._ditherEligibleBuf };
|
|
}
|
|
|
|
/**
|
|
* Apply Floyd-Steinberg dithering to image data
|
|
* @param {Uint8ClampedArray} imageData - RGBA image data
|
|
* @param {number} width - Image width
|
|
* @param {number} height - Image height
|
|
* @param {Array} palette - Color palette for dithering
|
|
* @param {Object} options - Dithering options
|
|
* @returns {Object} {data: Uint8ClampedArray, totalValidPixels: number}
|
|
*/
|
|
applyFloydSteinbergDithering(imageData, width, height, palette, options = {}) {
|
|
const {
|
|
paintTransparentPixels = false,
|
|
paintWhitePixels = true,
|
|
transparencyThreshold = this.TRANSPARENCY_THRESHOLD,
|
|
whiteThreshold = this.WHITE_THRESHOLD,
|
|
algorithm = 'lab',
|
|
enableChromaPenalty = false,
|
|
chromaPenaltyWeight = 0.15,
|
|
mask = null
|
|
} = options;
|
|
|
|
const data = new Uint8ClampedArray(imageData);
|
|
const n = width * height;
|
|
const { work, eligible } = this._ensureDitherBuffers(n);
|
|
let totalValidPixels = 0;
|
|
|
|
// Initialize working buffers
|
|
for (let y = 0; y < height; y++) {
|
|
for (let x = 0; x < width; x++) {
|
|
const idx = y * width + x;
|
|
const i4 = idx * 4;
|
|
const r = data[i4];
|
|
const g = data[i4 + 1];
|
|
const b = data[i4 + 2];
|
|
const a = data[i4 + 3];
|
|
const masked = mask && mask[idx];
|
|
|
|
const isEligible =
|
|
!masked &&
|
|
(paintTransparentPixels || a >= transparencyThreshold) &&
|
|
(paintWhitePixels || !this.isWhitePixel(r, g, b, whiteThreshold));
|
|
|
|
eligible[idx] = isEligible ? 1 : 0;
|
|
work[idx * 3] = r;
|
|
work[idx * 3 + 1] = g;
|
|
work[idx * 3 + 2] = b;
|
|
|
|
if (!isEligible) {
|
|
data[i4 + 3] = 0; // Make ineligible pixels transparent
|
|
}
|
|
}
|
|
}
|
|
|
|
// Error diffusion function
|
|
const diffuse = (nx, ny, er, eg, eb, factor) => {
|
|
if (nx < 0 || nx >= width || ny < 0 || ny >= height) return;
|
|
const nidx = ny * width + nx;
|
|
if (!eligible[nidx]) return;
|
|
const base = nidx * 3;
|
|
work[base] = Math.min(255, Math.max(0, work[base] + er * factor));
|
|
work[base + 1] = Math.min(255, Math.max(0, work[base + 1] + eg * factor));
|
|
work[base + 2] = Math.min(255, Math.max(0, work[base + 2] + eb * factor));
|
|
};
|
|
|
|
// Apply Floyd-Steinberg dithering
|
|
for (let y = 0; y < height; y++) {
|
|
for (let x = 0; x < width; x++) {
|
|
const idx = y * width + x;
|
|
if (!eligible[idx]) continue;
|
|
|
|
const base = idx * 3;
|
|
const r0 = work[base];
|
|
const g0 = work[base + 1];
|
|
const b0 = work[base + 2];
|
|
const i4 = idx * 4;
|
|
const a = data[i4 + 3];
|
|
|
|
// If painting transparent pixels AND this pixel is transparent, keep it transparent
|
|
if (paintTransparentPixels && a < transparencyThreshold) {
|
|
data[i4] = 0;
|
|
data[i4 + 1] = 0;
|
|
data[i4 + 2] = 0;
|
|
data[i4 + 3] = 0;
|
|
totalValidPixels++;
|
|
continue;
|
|
}
|
|
|
|
const [nr, ng, nb] = this.findClosestPaletteColor(
|
|
r0, g0, b0, palette, algorithm, { enableChromaPenalty, chromaPenaltyWeight }
|
|
);
|
|
|
|
data[i4] = nr;
|
|
data[i4 + 1] = ng;
|
|
data[i4 + 2] = nb;
|
|
data[i4 + 3] = 255;
|
|
totalValidPixels++;
|
|
|
|
// Calculate and diffuse error
|
|
const er = r0 - nr;
|
|
const eg = g0 - ng;
|
|
const eb = b0 - nb;
|
|
|
|
diffuse(x + 1, y, er, eg, eb, 7 / 16);
|
|
diffuse(x - 1, y + 1, er, eg, eb, 3 / 16);
|
|
diffuse(x, y + 1, er, eg, eb, 5 / 16);
|
|
diffuse(x + 1, y + 1, er, eg, eb, 1 / 16);
|
|
}
|
|
} return { data, totalValidPixels };
|
|
}
|
|
|
|
/**
|
|
* Apply simple color quantization without dithering
|
|
* @param {Uint8ClampedArray} imageData - RGBA image data
|
|
* @param {number} width - Image width
|
|
* @param {number} height - Image height
|
|
* @param {Array} palette - Color palette for quantization
|
|
* @param {Object} options - Quantization options
|
|
* @returns {Object} {data: Uint8ClampedArray, totalValidPixels: number}
|
|
*/
|
|
applySimpleQuantization(imageData, width, height, palette, options = {}) {
|
|
const {
|
|
paintTransparentPixels = false,
|
|
paintWhitePixels = true,
|
|
transparencyThreshold = this.TRANSPARENCY_THRESHOLD,
|
|
whiteThreshold = this.WHITE_THRESHOLD,
|
|
algorithm = 'lab',
|
|
enableChromaPenalty = false,
|
|
chromaPenaltyWeight = 0.15
|
|
} = options;
|
|
|
|
const data = new Uint8ClampedArray(imageData);
|
|
let totalValidPixels = 0;
|
|
|
|
for (let i = 0; i < data.length; i += 4) {
|
|
const r = data[i];
|
|
const g = data[i + 1];
|
|
const b = data[i + 2];
|
|
const a = data[i + 3];
|
|
|
|
const isTransparent = a < transparencyThreshold;
|
|
|
|
const isEligible =
|
|
(paintTransparentPixels || !isTransparent) &&
|
|
(paintWhitePixels || !this.isWhitePixel(r, g, b, whiteThreshold));
|
|
|
|
if (!isEligible) {
|
|
data[i + 3] = 0; // Make ineligible pixels transparent
|
|
continue;
|
|
}
|
|
|
|
// If painting transparent pixels AND this pixel is transparent, keep it transparent
|
|
if (paintTransparentPixels && isTransparent) {
|
|
data[i] = 0;
|
|
data[i + 1] = 0;
|
|
data[i + 2] = 0;
|
|
data[i + 3] = 0;
|
|
totalValidPixels++;
|
|
continue;
|
|
}
|
|
|
|
const [nr, ng, nb] = this.findClosestPaletteColor(
|
|
r, g, b, palette, algorithm, { enableChromaPenalty, chromaPenaltyWeight }
|
|
);
|
|
|
|
data[i] = nr;
|
|
data[i + 1] = ng;
|
|
data[i + 2] = nb;
|
|
data[i + 3] = 255;
|
|
totalValidPixels++;
|
|
}
|
|
|
|
return { data, totalValidPixels };
|
|
}
|
|
|
|
// =============================================
|
|
// UTILITY METHODS
|
|
// =============================================
|
|
|
|
/**
|
|
* Extract available colors from DOM elements
|
|
* @returns {Array|null} Array of color objects with id and rgb properties
|
|
*/
|
|
extractAvailableColors() {
|
|
const colorElements = document.querySelectorAll('.color-option, [data-color-id]');
|
|
if (colorElements.length === 0) return null;
|
|
|
|
const colors = [];
|
|
colorElements.forEach((element) => {
|
|
const colorId = element.dataset.colorId || element.getAttribute('data-color-id');
|
|
const computedStyle = window.getComputedStyle(element);
|
|
const backgroundColor = computedStyle.backgroundColor;
|
|
|
|
if (backgroundColor && colorId) {
|
|
const rgbMatch = backgroundColor.match(/rgb\((\d+),\s*(\d+),\s*(\d+)\)/);
|
|
if (rgbMatch) {
|
|
colors.push({
|
|
id: parseInt(colorId),
|
|
rgb: [parseInt(rgbMatch[1]), parseInt(rgbMatch[2]), parseInt(rgbMatch[3])]
|
|
});
|
|
}
|
|
}
|
|
});
|
|
|
|
return colors.length > 0 ? colors : null;
|
|
}
|
|
|
|
/**
|
|
* Create file uploader for images
|
|
* @returns {Promise<string>} Promise resolving to image data URL
|
|
*/
|
|
createImageUploader() {
|
|
return new Promise((resolve) => {
|
|
const input = document.createElement('input');
|
|
input.type = 'file';
|
|
input.accept = 'image/png,image/jpeg';
|
|
input.onchange = () => {
|
|
if (input.files && input.files[0]) {
|
|
const fr = new FileReader();
|
|
fr.onload = () => resolve(fr.result);
|
|
fr.readAsDataURL(input.files[0]);
|
|
} else {
|
|
resolve(null);
|
|
}
|
|
};
|
|
input.click();
|
|
});
|
|
}
|
|
|
|
/**
|
|
* Extract available colors from DOM palette
|
|
* @param {Object} colorMap - The color map object (optional, will use window.CONFIG if not provided)
|
|
* @returns {Array|null} Array of available colors or null if none found
|
|
*/
|
|
extractAvailableColors(colorMap = null) {
|
|
const colorElements = document.querySelectorAll('.tooltip button[id^="color-"]');
|
|
if (colorElements.length === 0) {
|
|
console.log('❌ No color elements found on page');
|
|
return null;
|
|
}
|
|
|
|
// Use provided colorMap or fallback to window.CONFIG
|
|
const effectiveColorMap = colorMap || (window.CONFIG?.COLOR_MAP);
|
|
|
|
// Separate available and unavailable colors
|
|
const availableColors = [];
|
|
const unavailableColors = [];
|
|
|
|
Array.from(colorElements).forEach((el) => {
|
|
const id = Number.parseInt(el.id.replace('color-', ''));
|
|
if (id === 0) return; // Skip transparent color
|
|
|
|
const rgbStr = el.style.backgroundColor.match(/\d+/g);
|
|
if (!rgbStr || rgbStr.length < 3) {
|
|
console.warn(`Skipping color element ${el.id} — cannot parse RGB`);
|
|
return;
|
|
}
|
|
const rgb = rgbStr.map(Number);
|
|
|
|
// Find color name from COLOR_MAP
|
|
let name = `Unknown Color ${id}`;
|
|
if (effectiveColorMap) {
|
|
const colorInfo = Object.values(effectiveColorMap).find((color) => color.id === id);
|
|
name = colorInfo ? colorInfo.name : name;
|
|
}
|
|
|
|
const colorData = { id, name, rgb };
|
|
|
|
// Check if color is available (no SVG overlay means available)
|
|
if (!el.querySelector('svg')) {
|
|
availableColors.push(colorData);
|
|
} else {
|
|
unavailableColors.push(colorData);
|
|
}
|
|
});
|
|
|
|
// Console log detailed color information
|
|
console.log('=== CAPTURED COLORS STATUS ===');
|
|
console.log(`Total available colors: ${availableColors.length}`);
|
|
console.log(`Total unavailable colors: ${unavailableColors.length}`);
|
|
console.log(`Total colors scanned: ${availableColors.length + unavailableColors.length}`);
|
|
|
|
if (availableColors.length > 0) {
|
|
console.log('\n--- AVAILABLE COLORS ---');
|
|
availableColors.forEach((color, index) => {
|
|
console.log(
|
|
`${index + 1
|
|
}. ID: ${color.id}, Name: "${color.name}", RGB: (${color.rgb[0]}, ${color.rgb[1]}, ${color.rgb[2]})`
|
|
);
|
|
});
|
|
}
|
|
|
|
if (unavailableColors.length > 0) {
|
|
console.log('\n--- UNAVAILABLE COLORS ---');
|
|
unavailableColors.forEach((color, index) => {
|
|
console.log(
|
|
`${index + 1
|
|
}. ID: ${color.id}, Name: "${color.name}", RGB: (${color.rgb[0]}, ${color.rgb[1]}, ${color.rgb[2]}) [LOCKED]`
|
|
);
|
|
});
|
|
}
|
|
|
|
console.log('=== END COLOR STATUS ===');
|
|
|
|
return availableColors;
|
|
}
|
|
|
|
/**
|
|
* Clear all caches to free memory
|
|
*/
|
|
clearCaches() {
|
|
this._colorCache.clear();
|
|
this._labCache.clear();
|
|
this._hsvCache?.clear();
|
|
this._hslCache?.clear();
|
|
this._xyzCache?.clear();
|
|
this._luvCache?.clear();
|
|
this._yuvCache?.clear();
|
|
this._oklabCache?.clear();
|
|
this._lchCache?.clear();
|
|
this._ditherWorkBuf = null;
|
|
this._ditherEligibleBuf = null;
|
|
}
|
|
|
|
/**
|
|
* Clean up resources
|
|
*/
|
|
cleanup() {
|
|
this.clearCaches();
|
|
if (this.canvas) {
|
|
this.canvas.width = 0;
|
|
this.canvas.height = 0;
|
|
}
|
|
this.img = null;
|
|
this.canvas = null;
|
|
this.ctx = null;
|
|
}
|
|
|
|
/**
|
|
* Apply pre-blur to canvas (before downscaling)
|
|
* @param {HTMLCanvasElement} canvas - Source canvas
|
|
* @param {string} mode - Blur mode: 'none', 'box', 'gaussian', 'kuwahara'
|
|
* @param {number} radius - Blur radius
|
|
* @returns {HTMLCanvasElement} Blurred canvas
|
|
*/
|
|
applyPreBlur(canvas, mode, radius) {
|
|
const r = Math.max(0, Math.floor(radius));
|
|
if (!mode || mode === 'none' || r <= 0) return canvas;
|
|
|
|
if (mode === 'box') return this.boxBlur(canvas, r);
|
|
if (mode === 'gaussian') return this.gaussianBlur(canvas, r);
|
|
if (mode === 'kuwahara') return this.kuwaharaFilter(canvas, r);
|
|
|
|
return canvas;
|
|
}
|
|
|
|
/**
|
|
* Box blur filter
|
|
* @param {HTMLCanvasElement} canvas - Source canvas
|
|
* @param {number} r - Blur radius
|
|
* @returns {HTMLCanvasElement} Blurred canvas
|
|
*/
|
|
boxBlur(canvas, r) {
|
|
const w = canvas.width, h = canvas.height;
|
|
if (r <= 0 || w === 0 || h === 0) return canvas;
|
|
|
|
const ctx = canvas.getContext('2d', { willReadFrequently: true });
|
|
const imgData = ctx.getImageData(0, 0, w, h);
|
|
const src = imgData.data;
|
|
const tmp = new Uint8ClampedArray(src.length);
|
|
const win = 2 * r + 1;
|
|
|
|
// Horizontal pass
|
|
for (let y = 0; y < h; y++) {
|
|
let rs = 0, gs = 0, bs = 0, as = 0;
|
|
const yoff = y * w * 4;
|
|
|
|
// Initialize window
|
|
for (let k = -r; k <= r; k++) {
|
|
const xx = Math.max(0, k);
|
|
const p = yoff + xx * 4;
|
|
rs += src[p];
|
|
gs += src[p + 1];
|
|
bs += src[p + 2];
|
|
as += src[p + 3];
|
|
}
|
|
|
|
for (let x = 0; x < w; x++) {
|
|
const i = yoff + x * 4;
|
|
tmp[i] = (rs / win) | 0;
|
|
tmp[i + 1] = (gs / win) | 0;
|
|
tmp[i + 2] = (bs / win) | 0;
|
|
tmp[i + 3] = (as / win) | 0;
|
|
|
|
const x0 = Math.max(0, x - r);
|
|
const x1 = Math.min(w - 1, x + r + 1);
|
|
const p0 = yoff + x0 * 4;
|
|
const p1 = yoff + x1 * 4;
|
|
rs += src[p1] - src[p0];
|
|
gs += src[p1 + 1] - src[p0 + 1];
|
|
bs += src[p1 + 2] - src[p0 + 2];
|
|
as += src[p1 + 3] - src[p0 + 3];
|
|
}
|
|
}
|
|
|
|
// Vertical pass
|
|
const out = new Uint8ClampedArray(src.length);
|
|
for (let x = 0; x < w; x++) {
|
|
let rs = 0, gs = 0, bs = 0, as = 0;
|
|
const xoff = x * 4;
|
|
|
|
// Initialize window
|
|
for (let k = -r; k <= r; k++) {
|
|
const yy = Math.max(0, k);
|
|
const p = yy * w * 4 + xoff;
|
|
rs += tmp[p];
|
|
gs += tmp[p + 1];
|
|
bs += tmp[p + 2];
|
|
as += tmp[p + 3];
|
|
}
|
|
|
|
for (let y = 0; y < h; y++) {
|
|
const i = y * w * 4 + xoff;
|
|
out[i] = (rs / win) | 0;
|
|
out[i + 1] = (gs / win) | 0;
|
|
out[i + 2] = (bs / win) | 0;
|
|
out[i + 3] = (as / win) | 0;
|
|
|
|
const y0 = Math.max(0, y - r);
|
|
const y1 = Math.min(h - 1, y + r + 1);
|
|
const p0 = y0 * w * 4 + xoff;
|
|
const p1 = y1 * w * 4 + xoff;
|
|
rs += tmp[p1] - tmp[p0];
|
|
gs += tmp[p1 + 1] - tmp[p0 + 1];
|
|
bs += tmp[p1 + 2] - tmp[p0 + 2];
|
|
as += tmp[p1 + 3] - tmp[p0 + 3];
|
|
}
|
|
}
|
|
|
|
imgData.data.set(out);
|
|
ctx.putImageData(imgData, 0, 0);
|
|
return canvas;
|
|
}
|
|
|
|
/**
|
|
* Gaussian blur (approximated with multiple box blurs)
|
|
* @param {HTMLCanvasElement} canvas - Source canvas
|
|
* @param {number} r - Blur radius
|
|
* @returns {HTMLCanvasElement} Blurred canvas
|
|
*/
|
|
gaussianBlur(canvas, r) {
|
|
// Approximate Gaussian with 3 box blur passes
|
|
let result = canvas;
|
|
for (let i = 0; i < 3; i++) {
|
|
result = this.boxBlur(result, Math.max(1, r));
|
|
}
|
|
return result;
|
|
}
|
|
|
|
/**
|
|
* Kuwahara filter - edge-preserving smoothing filter
|
|
* @param {HTMLCanvasElement} canvas - Source canvas
|
|
* @param {number} r - Radius (size of regions)
|
|
* @returns {HTMLCanvasElement} Filtered canvas
|
|
*/
|
|
kuwaharaFilter(canvas, r) {
|
|
const w = canvas.width | 0;
|
|
const h = canvas.height | 0;
|
|
if (r <= 0 || w === 0 || h === 0) return canvas;
|
|
|
|
const ctx = canvas.getContext('2d', { willReadFrequently: true });
|
|
const img = ctx.getImageData(0, 0, w, h);
|
|
const s = img.data;
|
|
const out = new Uint8ClampedArray(s.length);
|
|
|
|
// Build integral images for fast region sum calculation
|
|
const sw = w + 1;
|
|
const sh = h + 1;
|
|
const N = sw * sh;
|
|
const iR = new Float64Array(N);
|
|
const iG = new Float64Array(N);
|
|
const iB = new Float64Array(N);
|
|
const iC = new Float64Array(N);
|
|
const iL = new Float64Array(N);
|
|
const iL2 = new Float64Array(N);
|
|
|
|
const idx = (x, y) => y * sw + x;
|
|
|
|
// Build integral images
|
|
for (let y = 0; y < h; y++) {
|
|
for (let x = 0; x < w; x++) {
|
|
const p = (y * w + x) * 4;
|
|
const a = s[p + 3] | 0;
|
|
const c = (a >= 128) ? 1 : 0;
|
|
const r0 = s[p] | 0;
|
|
const g0 = s[p + 1] | 0;
|
|
const b0 = s[p + 2] | 0;
|
|
const lum = 0.299 * r0 + 0.587 * g0 + 0.114 * b0;
|
|
|
|
const ii = idx(x + 1, y + 1);
|
|
const left = idx(x, y + 1);
|
|
const up = idx(x + 1, y);
|
|
const upleft = idx(x, y);
|
|
|
|
iR[ii] = iR[left] + iR[up] - iR[upleft] + (c ? r0 : 0);
|
|
iG[ii] = iG[left] + iG[up] - iG[upleft] + (c ? g0 : 0);
|
|
iB[ii] = iB[left] + iB[up] - iB[upleft] + (c ? b0 : 0);
|
|
iC[ii] = iC[left] + iC[up] - iC[upleft] + c;
|
|
iL[ii] = iL[left] + iL[up] - iL[upleft] + (c ? lum : 0);
|
|
iL2[ii] = iL2[left] + iL2[up] - iL2[upleft] + (c ? lum * lum : 0);
|
|
}
|
|
}
|
|
|
|
const rectSum = (ii, x0, y0, x1, y1) => {
|
|
const a = idx(x0, y0);
|
|
const b = idx(x1, y0);
|
|
const c = idx(x0, y1);
|
|
const d = idx(x1, y1);
|
|
return ii[d] - ii[b] - ii[c] + ii[a];
|
|
};
|
|
|
|
const clampByte = (v) => (v < 0 ? 0 : v > 255 ? 255 : v | 0);
|
|
|
|
// Process each pixel
|
|
for (let y = 0; y < h; y++) {
|
|
for (let x = 0; x < w; x++) {
|
|
const p = (y * w + x) * 4;
|
|
const a = s[p + 3] | 0;
|
|
|
|
// Skip transparent pixels
|
|
if (a < 128) {
|
|
out[p] = s[p];
|
|
out[p + 1] = s[p + 1];
|
|
out[p + 2] = s[p + 2];
|
|
out[p + 3] = a;
|
|
continue;
|
|
}
|
|
|
|
// Define 4 regions (quadrants)
|
|
const x0 = Math.max(0, x - r);
|
|
const x1 = x;
|
|
const x2 = Math.min(w - 1, x + r);
|
|
const y0 = Math.max(0, y - r);
|
|
const y1 = y;
|
|
const y2 = Math.min(h - 1, y + r);
|
|
|
|
const regions = [
|
|
{ x0: x0, y0: y0, x1: x1, y1: y1 }, // Top-left
|
|
{ x0: x1, y0: y0, x1: x2, y1: y1 }, // Top-right
|
|
{ x0: x0, y0: y1, x1: x1, y1: y2 }, // Bottom-left
|
|
{ x0: x1, y0: y1, x1: x2, y1: y2 } // Bottom-right
|
|
];
|
|
|
|
let bestVar = 1e20;
|
|
let mR = s[p];
|
|
let mG = s[p + 1];
|
|
let mB = s[p + 2];
|
|
|
|
// Find region with minimum variance
|
|
for (let k = 0; k < 4; k++) {
|
|
const rx0 = regions[k].x0;
|
|
const ry0 = regions[k].y0;
|
|
const rx1 = regions[k].x1;
|
|
const ry1 = regions[k].y1;
|
|
|
|
const ex0 = rx0;
|
|
const ey0 = ry0;
|
|
const ex1 = rx1 + 1;
|
|
const ey1 = ry1 + 1;
|
|
|
|
const cnt = rectSum(iC, ex0, ey0, ex1, ey1);
|
|
if (cnt <= 0) continue;
|
|
|
|
const sumL = rectSum(iL, ex0, ey0, ex1, ey1);
|
|
const sumL2 = rectSum(iL2, ex0, ey0, ex1, ey1);
|
|
const meanL = sumL / cnt;
|
|
const variance = (sumL2 / cnt) - meanL * meanL;
|
|
|
|
if (variance < bestVar) {
|
|
bestVar = variance;
|
|
const sr = rectSum(iR, ex0, ey0, ex1, ey1);
|
|
const sg = rectSum(iG, ex0, ey0, ex1, ey1);
|
|
const sb = rectSum(iB, ex0, ey0, ex1, ey1);
|
|
mR = clampByte((sr / cnt) | 0);
|
|
mG = clampByte((sg / cnt) | 0);
|
|
mB = clampByte((sb / cnt) | 0);
|
|
}
|
|
}
|
|
|
|
out[p] = mR;
|
|
out[p + 1] = mG;
|
|
out[p + 2] = mB;
|
|
out[p + 3] = 255;
|
|
}
|
|
}
|
|
|
|
img.data.set(out);
|
|
ctx.putImageData(img, 0, 0);
|
|
return canvas;
|
|
}
|
|
|
|
/**
|
|
* Apply unsharp mask (sharpening)
|
|
* @param {HTMLCanvasElement} canvas - Source canvas
|
|
* @param {number} amount - Sharpen amount (0-300, percentage)
|
|
* @param {number} radius - Blur radius for mask
|
|
* @param {number} threshold - Threshold for sharpening (0-64)
|
|
* @returns {HTMLCanvasElement} Sharpened canvas
|
|
*/
|
|
applyUnsharpMask(canvas, amount, radius, threshold) {
|
|
const w = canvas.width, h = canvas.height;
|
|
if (w === 0 || h === 0 || amount <= 0 || radius <= 0) return canvas;
|
|
|
|
const ctx = canvas.getContext('2d', { willReadFrequently: true });
|
|
const imgData = ctx.getImageData(0, 0, w, h);
|
|
const orig = imgData.data;
|
|
|
|
// Create blurred version
|
|
const blurCanvas = document.createElement('canvas');
|
|
blurCanvas.width = w;
|
|
blurCanvas.height = h;
|
|
const blurCtx = blurCanvas.getContext('2d', { willReadFrequently: true });
|
|
blurCtx.drawImage(canvas, 0, 0);
|
|
|
|
const blurred = this.gaussianBlur(blurCanvas, radius);
|
|
const blurImgData = blurred.getContext('2d').getImageData(0, 0, w, h);
|
|
const blurData = blurImgData.data;
|
|
|
|
const k = amount / 100;
|
|
const clampByte = (v) => Math.min(255, Math.max(0, v));
|
|
|
|
for (let i = 0; i < orig.length; i += 4) {
|
|
const dr = orig[i] - blurData[i];
|
|
const dg = orig[i + 1] - blurData[i + 1];
|
|
const db = orig[i + 2] - blurData[i + 2];
|
|
|
|
if (Math.abs(dr) >= threshold) orig[i] = clampByte(orig[i] + k * dr);
|
|
if (Math.abs(dg) >= threshold) orig[i + 1] = clampByte(orig[i + 1] + k * dg);
|
|
if (Math.abs(db) >= threshold) orig[i + 2] = clampByte(orig[i + 2] + k * db);
|
|
}
|
|
|
|
ctx.putImageData(imgData, 0, 0);
|
|
return canvas;
|
|
}
|
|
|
|
// =============================================
|
|
// MULTI-ALGORITHM DITHERING SYSTEM
|
|
// =============================================
|
|
|
|
/**
|
|
* Apply dithering with multiple algorithm support
|
|
* @param {Uint8ClampedArray} imageData - RGBA image data
|
|
* @param {number} width - Image width
|
|
* @param {number} height - Image height
|
|
* @param {Array} palette - Color palette for dithering
|
|
* @param {Object} options - Dithering options
|
|
* @returns {Object} {data: Uint8ClampedArray, totalValidPixels: number}
|
|
*/
|
|
applyDithering(imageData, width, height, palette, options = {}) {
|
|
const {
|
|
method = 'floyd',
|
|
strength = 0.5,
|
|
posterizeLevels = 0,
|
|
paintTransparentPixels = false,
|
|
paintWhitePixels = true,
|
|
transparencyThreshold = this.TRANSPARENCY_THRESHOLD,
|
|
whiteThreshold = this.WHITE_THRESHOLD,
|
|
algorithm = 'lab',
|
|
enableChromaPenalty = false,
|
|
chromaPenaltyWeight = 0.15,
|
|
mask = null
|
|
} = options;
|
|
|
|
// Apply posterization if requested
|
|
let workingData = new Uint8ClampedArray(imageData);
|
|
if (posterizeLevels >= 2) {
|
|
workingData = this._applyPosterize(workingData, width, height, posterizeLevels);
|
|
}
|
|
|
|
// Error diffusion methods
|
|
const errorDiffusionMethods = {
|
|
'floyd': this._getFloydSteinbergKernel(),
|
|
'falsefloydsteinberg': this._getFalseFloydSteinbergKernel(),
|
|
'atkinson': this._getAtkinsonKernel(),
|
|
'jarvis': this._getJarvisKernel(),
|
|
'stucki': this._getStuckiKernel(),
|
|
'burkes': this._getBurkesKernel(),
|
|
'sierra': this._getSierraKernel(),
|
|
'twosierra': this._getTwoSierraKernel(),
|
|
'sierralite': this._getSierraLiteKernel()
|
|
};
|
|
|
|
// Ordered dithering methods
|
|
const orderedMethods = ['bayer2', 'bayer4', 'bayer8', 'random'];
|
|
|
|
if (errorDiffusionMethods[method]) {
|
|
return this._applyErrorDiffusion(workingData, width, height, palette, errorDiffusionMethods[method], options);
|
|
} else if (orderedMethods.includes(method)) {
|
|
return this._applyOrderedDither(workingData, width, height, palette, method, strength, options);
|
|
} else {
|
|
// Fallback to Floyd-Steinberg
|
|
console.warn(`Unknown dithering method: ${method}, falling back to Floyd-Steinberg`);
|
|
return this._applyErrorDiffusion(workingData, width, height, palette, this._getFloydSteinbergKernel(), options);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Apply posterization (color quantization by levels)
|
|
* @private
|
|
*/
|
|
_applyPosterize(data, width, height, levels) {
|
|
const result = new Uint8ClampedArray(data);
|
|
const step = 256 / levels;
|
|
|
|
for (let i = 0; i < result.length; i += 4) {
|
|
for (let c = 0; c < 3; c++) {
|
|
const value = result[i + c];
|
|
result[i + c] = Math.floor(value / step) * step;
|
|
}
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
/**
|
|
* Generic error diffusion dithering
|
|
* @private
|
|
*/
|
|
_applyErrorDiffusion(imageData, width, height, palette, kernel, options) {
|
|
const data = new Uint8ClampedArray(imageData);
|
|
const n = width * height;
|
|
const { work, eligible } = this._ensureDitherBuffers(n);
|
|
let totalValidPixels = 0;
|
|
|
|
const {
|
|
paintTransparentPixels = false,
|
|
paintWhitePixels = true,
|
|
transparencyThreshold = this.TRANSPARENCY_THRESHOLD,
|
|
whiteThreshold = this.WHITE_THRESHOLD,
|
|
algorithm = 'lab',
|
|
enableChromaPenalty = false,
|
|
chromaPenaltyWeight = 0.15,
|
|
mask = null
|
|
} = options;
|
|
|
|
// Initialize working buffers
|
|
for (let y = 0; y < height; y++) {
|
|
for (let x = 0; x < width; x++) {
|
|
const idx = y * width + x;
|
|
const i4 = idx * 4;
|
|
const r = data[i4];
|
|
const g = data[i4 + 1];
|
|
const b = data[i4 + 2];
|
|
const a = data[i4 + 3];
|
|
const masked = mask && mask[idx];
|
|
|
|
const isEligible =
|
|
!masked &&
|
|
(paintTransparentPixels || a >= transparencyThreshold) &&
|
|
(paintWhitePixels || !this.isWhitePixel(r, g, b, whiteThreshold));
|
|
|
|
eligible[idx] = isEligible ? 1 : 0;
|
|
work[idx * 3] = r;
|
|
work[idx * 3 + 1] = g;
|
|
work[idx * 3 + 2] = b;
|
|
|
|
if (!isEligible) {
|
|
data[i4 + 3] = 0;
|
|
}
|
|
}
|
|
}
|
|
|
|
// Error diffusion function
|
|
const diffuse = (nx, ny, er, eg, eb, factor) => {
|
|
if (nx < 0 || nx >= width || ny < 0 || ny >= height) return;
|
|
const nidx = ny * width + nx;
|
|
if (!eligible[nidx]) return;
|
|
const base = nidx * 3;
|
|
work[base] = Math.min(255, Math.max(0, work[base] + er * factor));
|
|
work[base + 1] = Math.min(255, Math.max(0, work[base + 1] + eg * factor));
|
|
work[base + 2] = Math.min(255, Math.max(0, work[base + 2] + eb * factor));
|
|
};
|
|
|
|
// Apply error diffusion
|
|
for (let y = 0; y < height; y++) {
|
|
for (let x = 0; x < width; x++) {
|
|
const idx = y * width + x;
|
|
if (!eligible[idx]) continue;
|
|
|
|
const base = idx * 3;
|
|
const r0 = work[base];
|
|
const g0 = work[base + 1];
|
|
const b0 = work[base + 2];
|
|
const i4 = idx * 4;
|
|
const a = data[i4 + 3];
|
|
|
|
if (paintTransparentPixels && a < transparencyThreshold) {
|
|
data[i4] = 0;
|
|
data[i4 + 1] = 0;
|
|
data[i4 + 2] = 0;
|
|
data[i4 + 3] = 0;
|
|
totalValidPixels++;
|
|
continue;
|
|
}
|
|
|
|
const [nr, ng, nb] = this.findClosestPaletteColor(
|
|
r0, g0, b0, palette, algorithm, { enableChromaPenalty, chromaPenaltyWeight }
|
|
);
|
|
|
|
data[i4] = nr;
|
|
data[i4 + 1] = ng;
|
|
data[i4 + 2] = nb;
|
|
data[i4 + 3] = 255;
|
|
totalValidPixels++;
|
|
|
|
const er = r0 - nr;
|
|
const eg = g0 - ng;
|
|
const eb = b0 - nb;
|
|
|
|
// Apply error diffusion kernel (kernel is an object with numbered properties)
|
|
for (let k = 0; kernel[k]; k++) {
|
|
const { dx, dy, weight } = kernel[k];
|
|
diffuse(x + dx, y + dy, er, eg, eb, weight / kernel.divisor);
|
|
}
|
|
}
|
|
}
|
|
|
|
return { data, totalValidPixels };
|
|
}
|
|
|
|
/**
|
|
* Apply ordered (threshold) dithering
|
|
* @private
|
|
*/
|
|
_applyOrderedDither(imageData, width, height, palette, method, strength, options) {
|
|
const data = new Uint8ClampedArray(imageData);
|
|
let totalValidPixels = 0;
|
|
|
|
const {
|
|
paintTransparentPixels = false,
|
|
paintWhitePixels = true,
|
|
transparencyThreshold = this.TRANSPARENCY_THRESHOLD,
|
|
whiteThreshold = this.WHITE_THRESHOLD,
|
|
algorithm = 'lab',
|
|
enableChromaPenalty = false,
|
|
chromaPenaltyWeight = 0.15
|
|
} = options;
|
|
|
|
// Get threshold matrix
|
|
let matrix;
|
|
if (method === 'bayer2') matrix = this._getBayer2x2();
|
|
else if (method === 'bayer4') matrix = this._getBayer4x4();
|
|
else if (method === 'bayer8') matrix = this._getBayer8x8();
|
|
else if (method === 'random') matrix = null; // Random doesn't use matrix
|
|
|
|
const matrixSize = matrix ? matrix.length : 0;
|
|
|
|
for (let y = 0; y < height; y++) {
|
|
for (let x = 0; x < width; x++) {
|
|
const i = (y * width + x) * 4;
|
|
const r = data[i];
|
|
const g = data[i + 1];
|
|
const b = data[i + 2];
|
|
const a = data[i + 3];
|
|
|
|
const isTransparent = a < transparencyThreshold;
|
|
const isEligible =
|
|
(paintTransparentPixels || !isTransparent) &&
|
|
(paintWhitePixels || !this.isWhitePixel(r, g, b, whiteThreshold));
|
|
|
|
if (!isEligible) {
|
|
data[i + 3] = 0;
|
|
continue;
|
|
}
|
|
|
|
if (paintTransparentPixels && isTransparent) {
|
|
data[i] = 0;
|
|
data[i + 1] = 0;
|
|
data[i + 2] = 0;
|
|
data[i + 3] = 0;
|
|
totalValidPixels++;
|
|
continue;
|
|
}
|
|
|
|
// Get threshold value
|
|
let threshold;
|
|
if (method === 'random') {
|
|
threshold = Math.random();
|
|
} else {
|
|
const mx = x % matrixSize;
|
|
const my = y % matrixSize;
|
|
threshold = matrix[my][mx];
|
|
}
|
|
|
|
// Apply strength modulation
|
|
threshold = threshold * strength;
|
|
|
|
// Add threshold noise to colors
|
|
const noise = (threshold - 0.5) * 64 * strength;
|
|
const nr = Math.max(0, Math.min(255, r + noise));
|
|
const ng = Math.max(0, Math.min(255, g + noise));
|
|
const nb = Math.max(0, Math.min(255, b + noise));
|
|
|
|
const [pr, pg, pb] = this.findClosestPaletteColor(
|
|
nr, ng, nb, palette, algorithm, { enableChromaPenalty, chromaPenaltyWeight }
|
|
);
|
|
|
|
data[i] = pr;
|
|
data[i + 1] = pg;
|
|
data[i + 2] = pb;
|
|
data[i + 3] = 255;
|
|
totalValidPixels++;
|
|
}
|
|
}
|
|
|
|
return { data, totalValidPixels };
|
|
}
|
|
|
|
// Dithering kernel definitions
|
|
_getFloydSteinbergKernel() {
|
|
return {
|
|
divisor: 16,
|
|
0: { dx: 1, dy: 0, weight: 7 },
|
|
1: { dx: -1, dy: 1, weight: 3 },
|
|
2: { dx: 0, dy: 1, weight: 5 },
|
|
3: { dx: 1, dy: 1, weight: 1 }
|
|
};
|
|
}
|
|
|
|
_getFalseFloydSteinbergKernel() {
|
|
return {
|
|
divisor: 8,
|
|
0: { dx: 1, dy: 0, weight: 3 },
|
|
1: { dx: 0, dy: 1, weight: 3 },
|
|
2: { dx: 1, dy: 1, weight: 2 }
|
|
};
|
|
}
|
|
|
|
_getAtkinsonKernel() {
|
|
return {
|
|
divisor: 8,
|
|
0: { dx: 1, dy: 0, weight: 1 },
|
|
1: { dx: 2, dy: 0, weight: 1 },
|
|
2: { dx: -1, dy: 1, weight: 1 },
|
|
3: { dx: 0, dy: 1, weight: 1 },
|
|
4: { dx: 1, dy: 1, weight: 1 },
|
|
5: { dx: 0, dy: 2, weight: 1 }
|
|
};
|
|
}
|
|
|
|
_getJarvisKernel() {
|
|
return {
|
|
divisor: 48,
|
|
0: { dx: 1, dy: 0, weight: 7 },
|
|
1: { dx: 2, dy: 0, weight: 5 },
|
|
2: { dx: -2, dy: 1, weight: 3 },
|
|
3: { dx: -1, dy: 1, weight: 5 },
|
|
4: { dx: 0, dy: 1, weight: 7 },
|
|
5: { dx: 1, dy: 1, weight: 5 },
|
|
6: { dx: 2, dy: 1, weight: 3 },
|
|
7: { dx: -2, dy: 2, weight: 1 },
|
|
8: { dx: -1, dy: 2, weight: 3 },
|
|
9: { dx: 0, dy: 2, weight: 5 },
|
|
10: { dx: 1, dy: 2, weight: 3 },
|
|
11: { dx: 2, dy: 2, weight: 1 }
|
|
};
|
|
}
|
|
|
|
_getStuckiKernel() {
|
|
return {
|
|
divisor: 42,
|
|
0: { dx: 1, dy: 0, weight: 8 },
|
|
1: { dx: 2, dy: 0, weight: 4 },
|
|
2: { dx: -2, dy: 1, weight: 2 },
|
|
3: { dx: -1, dy: 1, weight: 4 },
|
|
4: { dx: 0, dy: 1, weight: 8 },
|
|
5: { dx: 1, dy: 1, weight: 4 },
|
|
6: { dx: 2, dy: 1, weight: 2 },
|
|
7: { dx: -2, dy: 2, weight: 1 },
|
|
8: { dx: -1, dy: 2, weight: 2 },
|
|
9: { dx: 0, dy: 2, weight: 4 },
|
|
10: { dx: 1, dy: 2, weight: 2 },
|
|
11: { dx: 2, dy: 2, weight: 1 }
|
|
};
|
|
}
|
|
|
|
_getBurkesKernel() {
|
|
return {
|
|
divisor: 32,
|
|
0: { dx: 1, dy: 0, weight: 8 },
|
|
1: { dx: 2, dy: 0, weight: 4 },
|
|
2: { dx: -2, dy: 1, weight: 2 },
|
|
3: { dx: -1, dy: 1, weight: 4 },
|
|
4: { dx: 0, dy: 1, weight: 8 },
|
|
5: { dx: 1, dy: 1, weight: 4 },
|
|
6: { dx: 2, dy: 1, weight: 2 }
|
|
};
|
|
}
|
|
|
|
_getSierraKernel() {
|
|
return {
|
|
divisor: 32,
|
|
0: { dx: 1, dy: 0, weight: 5 },
|
|
1: { dx: 2, dy: 0, weight: 3 },
|
|
2: { dx: -2, dy: 1, weight: 2 },
|
|
3: { dx: -1, dy: 1, weight: 4 },
|
|
4: { dx: 0, dy: 1, weight: 5 },
|
|
5: { dx: 1, dy: 1, weight: 4 },
|
|
6: { dx: 2, dy: 1, weight: 2 },
|
|
7: { dx: -1, dy: 2, weight: 2 },
|
|
8: { dx: 0, dy: 2, weight: 3 },
|
|
9: { dx: 1, dy: 2, weight: 2 }
|
|
};
|
|
}
|
|
|
|
_getTwoSierraKernel() {
|
|
return {
|
|
divisor: 16,
|
|
0: { dx: 1, dy: 0, weight: 4 },
|
|
1: { dx: 2, dy: 0, weight: 3 },
|
|
2: { dx: -2, dy: 1, weight: 1 },
|
|
3: { dx: -1, dy: 1, weight: 2 },
|
|
4: { dx: 0, dy: 1, weight: 3 },
|
|
5: { dx: 1, dy: 1, weight: 2 },
|
|
6: { dx: 2, dy: 1, weight: 1 }
|
|
};
|
|
}
|
|
|
|
_getSierraLiteKernel() {
|
|
return {
|
|
divisor: 4,
|
|
0: { dx: 1, dy: 0, weight: 2 },
|
|
1: { dx: 0, dy: 1, weight: 1 },
|
|
2: { dx: 1, dy: 1, weight: 1 }
|
|
};
|
|
}
|
|
|
|
// Bayer matrices for ordered dithering
|
|
_getBayer2x2() {
|
|
return [
|
|
[0/4, 2/4],
|
|
[3/4, 1/4]
|
|
];
|
|
}
|
|
|
|
_getBayer4x4() {
|
|
return [
|
|
[0/16, 8/16, 2/16, 10/16],
|
|
[12/16, 4/16, 14/16, 6/16],
|
|
[3/16, 11/16, 1/16, 9/16],
|
|
[15/16, 7/16, 13/16, 5/16]
|
|
];
|
|
}
|
|
|
|
_getBayer8x8() {
|
|
return [
|
|
[0/64, 32/64, 8/64, 40/64, 2/64, 34/64, 10/64, 42/64],
|
|
[48/64, 16/64, 56/64, 24/64, 50/64, 18/64, 58/64, 26/64],
|
|
[12/64, 44/64, 4/64, 36/64, 14/64, 46/64, 6/64, 38/64],
|
|
[60/64, 28/64, 52/64, 20/64, 62/64, 30/64, 54/64, 22/64],
|
|
[3/64, 35/64, 11/64, 43/64, 1/64, 33/64, 9/64, 41/64],
|
|
[51/64, 19/64, 59/64, 27/64, 49/64, 17/64, 57/64, 25/64],
|
|
[15/64, 47/64, 7/64, 39/64, 13/64, 45/64, 5/64, 37/64],
|
|
[63/64, 31/64, 55/64, 23/64, 61/64, 29/64, 53/64, 21/64]
|
|
];
|
|
}
|
|
|
|
// ====== POST-PROCESSING METHODS ======
|
|
|
|
/**
|
|
* Apply edge overlay to the image
|
|
* @param {Uint8ClampedArray} imageData - RGBA pixel data
|
|
* @param {number} width - Image width
|
|
* @param {number} height - Image height
|
|
* @param {string} algorithm - Edge detection algorithm: 'sobel', 'prewitt', 'roberts'
|
|
* @param {number} threshold - Edge detection threshold (0-255, default 60)
|
|
* @param {number} thickness - Edge line thickness (1-6, default 1)
|
|
* @param {boolean} thinEdges - Apply non-maximum suppression (default false)
|
|
* @returns {Uint8ClampedArray} Image data with edge overlay applied
|
|
*/
|
|
applyEdgeOverlay(imageData, width, height, algorithm = 'sobel', threshold = 60, thickness = 1, thinEdges = false) {
|
|
// Detect edges with optional NMS thinning
|
|
const edges = this._detectEdges(imageData, width, height, algorithm, threshold, thinEdges);
|
|
const result = new Uint8ClampedArray(imageData);
|
|
|
|
// Apply thickness by dilating edges (if thickness > 1)
|
|
const processed = this._dilateEdges(edges, width, height, thickness);
|
|
|
|
for (let i = 0; i < processed.length; i++) {
|
|
if (processed[i] > 128) {
|
|
const idx = i * 4;
|
|
result[idx] = 0; // R
|
|
result[idx + 1] = 0; // G
|
|
result[idx + 2] = 0; // B
|
|
result[idx + 3] = 255; // A (keep alpha)
|
|
}
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
/**
|
|
* Detect edges using specified algorithm
|
|
* @private
|
|
*/
|
|
_detectEdges(imageData, width, height, algorithm, threshold = 60, applyNMS = false) {
|
|
const gray = new Uint8ClampedArray(width * height);
|
|
const magnitude = new Float32Array(width * height);
|
|
const direction = applyNMS ? new Int8Array(width * height) : null;
|
|
|
|
// Convert to grayscale
|
|
for (let i = 0; i < imageData.length; i += 4) {
|
|
const idx = i / 4;
|
|
gray[idx] = Math.round(0.299 * imageData[i] + 0.587 * imageData[i + 1] + 0.114 * imageData[i + 2]);
|
|
}
|
|
|
|
// Apply edge detection and store magnitude + direction
|
|
switch (algorithm) {
|
|
case 'sobel':
|
|
this._sobelEdgeDetection(gray, magnitude, direction, width, height);
|
|
break;
|
|
case 'prewitt':
|
|
this._prewittEdgeDetection(gray, magnitude, direction, width, height);
|
|
break;
|
|
case 'roberts':
|
|
this._robertsEdgeDetection(gray, magnitude, direction, width, height);
|
|
break;
|
|
case 'laplacian':
|
|
this._laplacianEdgeDetection(gray, magnitude, width, height);
|
|
break;
|
|
default:
|
|
this._sobelEdgeDetection(gray, magnitude, direction, width, height);
|
|
}
|
|
|
|
const edges = new Uint8ClampedArray(width * height);
|
|
|
|
// Apply non-maximum suppression if requested
|
|
if (applyNMS && direction) {
|
|
for (let y = 1; y < height - 1; y++) {
|
|
for (let x = 1; x < width - 1; x++) {
|
|
const i = y * width + x;
|
|
const mag = magnitude[i];
|
|
if (mag < threshold) continue;
|
|
|
|
const dir = direction[i];
|
|
let m1 = 0, m2 = 0;
|
|
|
|
// Compare with neighbors along gradient direction
|
|
if (dir === 0) { // Horizontal
|
|
m1 = magnitude[i - 1];
|
|
m2 = magnitude[i + 1];
|
|
} else { // Vertical
|
|
m1 = magnitude[i - width];
|
|
m2 = magnitude[i + width];
|
|
}
|
|
|
|
// Keep only if local maximum
|
|
if (mag >= m1 && mag >= m2) {
|
|
edges[i] = 255;
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
// Simple thresholding
|
|
for (let i = 0; i < edges.length; i++) {
|
|
edges[i] = magnitude[i] > threshold ? 255 : 0;
|
|
}
|
|
}
|
|
|
|
return edges;
|
|
}
|
|
|
|
/**
|
|
* Sobel edge detection
|
|
* @private
|
|
*/
|
|
_sobelEdgeDetection(gray, magnitude, direction, width, height) {
|
|
const sobelX = [-1, 0, 1, -2, 0, 2, -1, 0, 1];
|
|
const sobelY = [-1, -2, -1, 0, 0, 0, 1, 2, 1];
|
|
|
|
for (let y = 1; y < height - 1; y++) {
|
|
for (let x = 1; x < width - 1; x++) {
|
|
let gx = 0, gy = 0;
|
|
let idx = 0;
|
|
for (let ky = -1; ky <= 1; ky++) {
|
|
for (let kx = -1; kx <= 1; kx++) {
|
|
const pixelIdx = (y + ky) * width + (x + kx);
|
|
gx += sobelX[idx] * gray[pixelIdx];
|
|
gy += sobelY[idx] * gray[pixelIdx];
|
|
idx++;
|
|
}
|
|
}
|
|
const i = y * width + x;
|
|
magnitude[i] = Math.abs(gx) + Math.abs(gy);
|
|
|
|
// Store gradient direction for NMS
|
|
if (direction) {
|
|
const ax = Math.abs(gx);
|
|
const ay = Math.abs(gy);
|
|
direction[i] = ax >= ay ? 0 : 1; // 0=horizontal, 1=vertical
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Prewitt edge detection
|
|
* @private
|
|
*/
|
|
_prewittEdgeDetection(gray, magnitude, direction, width, height) {
|
|
const prewittX = [-1, 0, 1, -1, 0, 1, -1, 0, 1];
|
|
const prewittY = [1, 1, 1, 0, 0, 0, -1, -1, -1];
|
|
|
|
for (let y = 1; y < height - 1; y++) {
|
|
for (let x = 1; x < width - 1; x++) {
|
|
let gx = 0, gy = 0;
|
|
let idx = 0;
|
|
for (let ky = -1; ky <= 1; ky++) {
|
|
for (let kx = -1; kx <= 1; kx++) {
|
|
const pixelIdx = (y + ky) * width + (x + kx);
|
|
gx += prewittX[idx] * gray[pixelIdx];
|
|
gy += prewittY[idx] * gray[pixelIdx];
|
|
idx++;
|
|
}
|
|
}
|
|
const i = y * width + x;
|
|
magnitude[i] = Math.abs(gx) + Math.abs(gy);
|
|
|
|
if (direction) {
|
|
const ax = Math.abs(gx);
|
|
const ay = Math.abs(gy);
|
|
direction[i] = ax >= ay ? 0 : 1;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Roberts edge detection
|
|
* @private
|
|
*/
|
|
_robertsEdgeDetection(gray, magnitude, direction, width, height) {
|
|
for (let y = 0; y < height - 1; y++) {
|
|
for (let x = 0; x < width - 1; x++) {
|
|
const idx = y * width + x;
|
|
const gx = gray[idx] - gray[idx + width + 1];
|
|
const gy = gray[idx + 1] - gray[idx + width];
|
|
magnitude[idx] = Math.abs(gx) + Math.abs(gy);
|
|
|
|
if (direction) {
|
|
const ax = Math.abs(gx);
|
|
const ay = Math.abs(gy);
|
|
direction[idx] = ax >= ay ? 0 : 1;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Laplacian edge detection
|
|
* @private
|
|
*/
|
|
_laplacianEdgeDetection(gray, magnitude, width, height) {
|
|
// Laplacian kernel for edge detection
|
|
const laplacianKernel = [0, -1, 0, -1, 4, -1, 0, -1, 0];
|
|
|
|
for (let y = 1; y < height - 1; y++) {
|
|
for (let x = 1; x < width - 1; x++) {
|
|
let sum = 0;
|
|
let idx = 0;
|
|
for (let ky = -1; ky <= 1; ky++) {
|
|
for (let kx = -1; kx <= 1; kx++) {
|
|
const pixelIdx = (y + ky) * width + (x + kx);
|
|
sum += laplacianKernel[idx] * gray[pixelIdx];
|
|
idx++;
|
|
}
|
|
}
|
|
magnitude[y * width + x] = Math.abs(sum);
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Dilate edges to make them thicker
|
|
* @private
|
|
*/
|
|
_dilateEdges(edges, width, height, thickness = 1) {
|
|
const t = Math.max(1, Math.min(6, thickness || 1));
|
|
// WPlace uses thickness - 1 for dilation count
|
|
// thickness=1 means no dilation (keep original edges)
|
|
// thickness=2 means dilate 1 time, etc.
|
|
const dilationCount = t - 1;
|
|
|
|
if (dilationCount <= 0) return new Uint8ClampedArray(edges);
|
|
|
|
const dilated = new Uint8ClampedArray(edges);
|
|
const tmp = new Uint8ClampedArray(edges.length);
|
|
|
|
for (let k = 0; k < dilationCount; k++) {
|
|
tmp.fill(0);
|
|
for (let y = 0; y < height; y++) {
|
|
for (let x = 0; x < width; x++) {
|
|
const i = y * width + x;
|
|
if (dilated[i] > 128) {
|
|
tmp[i] = 255;
|
|
continue;
|
|
}
|
|
// Check 4-connected neighbors
|
|
if ((x > 0 && dilated[i - 1] > 128) ||
|
|
(x + 1 < width && dilated[i + 1] > 128) ||
|
|
(y > 0 && dilated[i - width] > 128) ||
|
|
(y + 1 < height && dilated[i + width] > 128)) {
|
|
tmp[i] = 255;
|
|
}
|
|
}
|
|
}
|
|
dilated.set(tmp);
|
|
}
|
|
|
|
return dilated;
|
|
}
|
|
|
|
/**
|
|
* Apply outline to the image
|
|
* @param {Uint8ClampedArray} imageData - RGBA pixel data
|
|
* @param {number} width - Image width
|
|
* @param {number} height - Image height
|
|
* @param {number} thickness - Outline thickness in pixels (0-10)
|
|
* @returns {Uint8ClampedArray} Image data with outline applied
|
|
*/
|
|
applyOutline(imageData, width, height, thickness) {
|
|
// Morphological dilation approach from wplace_helper
|
|
const t = Math.max(0, Math.floor(thickness || 0));
|
|
if (t <= 0) return new Uint8ClampedArray(imageData);
|
|
|
|
const w = width;
|
|
const h = height;
|
|
const data = new Uint8ClampedArray(imageData);
|
|
|
|
// Create binary mask: 1 = opaque (alpha >= 128), 0 = transparent
|
|
const mask = new Uint8Array(w * h);
|
|
for (let i = 0, p = 0; i < mask.length; i++, p += 4) {
|
|
mask[i] = data[p + 3] >= 128 ? 1 : 0;
|
|
}
|
|
|
|
// Dilate the mask t times
|
|
const dil = new Uint8Array(mask);
|
|
const tmp = new Uint8Array(mask.length);
|
|
|
|
function dilateOnce() {
|
|
tmp.fill(0);
|
|
for (let y = 0; y < h; y++) {
|
|
for (let x = 0; x < w; x++) {
|
|
const i = y * w + x;
|
|
if (dil[i]) {
|
|
tmp[i] = 1;
|
|
continue;
|
|
}
|
|
// Check 4-connected neighbors
|
|
if ((x > 0 && dil[i - 1]) ||
|
|
(x + 1 < w && dil[i + 1]) ||
|
|
(y > 0 && dil[i - w]) ||
|
|
(y + 1 < h && dil[i + w])) {
|
|
tmp[i] = 1;
|
|
}
|
|
}
|
|
}
|
|
dil.set(tmp);
|
|
}
|
|
|
|
// Apply dilation t times
|
|
for (let k = 0; k < t; k++) {
|
|
dilateOnce();
|
|
}
|
|
|
|
// Draw outline where dilated but not original
|
|
for (let y = 0; y < h; y++) {
|
|
for (let x = 0; x < w; x++) {
|
|
const i = y * w + x;
|
|
if (dil[i] && !mask[i]) {
|
|
const p = i * 4;
|
|
data[p] = 0; // R
|
|
data[p + 1] = 0; // G
|
|
data[p + 2] = 0; // B
|
|
data[p + 3] = 255; // A
|
|
}
|
|
}
|
|
}
|
|
|
|
return data;
|
|
}
|
|
|
|
/**
|
|
* Simplify regions by merging small color areas
|
|
* @param {Uint8ClampedArray} imageData - RGBA pixel data
|
|
* @param {number} width - Image width
|
|
* @param {number} height - Image height
|
|
* @param {number} minArea - Minimum area in pixels (0-1000)
|
|
* @returns {Uint8ClampedArray} Simplified image data
|
|
*/
|
|
simplifyRegions(imageData, width, height, minArea) {
|
|
if (minArea <= 0) return new Uint8ClampedArray(imageData);
|
|
|
|
const thr = Math.max(1, Math.floor(minArea || 0));
|
|
if (width === 0 || height === 0) return new Uint8ClampedArray(imageData);
|
|
|
|
const result = new Uint8ClampedArray(imageData);
|
|
const visited = new Uint8Array(width * height);
|
|
const qx = new Int32Array(width * height);
|
|
const qy = new Int32Array(width * height);
|
|
let qs = 0, qe = 0;
|
|
|
|
function idx(x, y) { return (y * width + x) << 2; }
|
|
|
|
for (let y = 0; y < height; y++) {
|
|
for (let x = 0; x < width; x++) {
|
|
const li = y * width + x;
|
|
if (visited[li]) continue;
|
|
|
|
const p = li << 2;
|
|
const a = result[p + 3] | 0;
|
|
if (a < 128) {
|
|
visited[li] = 1;
|
|
continue;
|
|
}
|
|
|
|
const r0 = result[p] | 0;
|
|
const g0 = result[p + 1] | 0;
|
|
const b0 = result[p + 2] | 0;
|
|
const key = ((r0 << 16) | (g0 << 8) | b0) >>> 0;
|
|
|
|
qs = 0;
|
|
qe = 0;
|
|
qx[qe] = x;
|
|
qy[qe] = y;
|
|
qe++;
|
|
|
|
const comp = [];
|
|
const borderColors = {};
|
|
visited[li] = 1;
|
|
|
|
while (qs < qe) {
|
|
const cx0 = qx[qs];
|
|
const cy0 = qy[qs];
|
|
qs++;
|
|
const pi = idx(cx0, cy0);
|
|
comp.push(pi);
|
|
|
|
const neighbors = [[1, 0], [-1, 0], [0, 1], [0, -1]];
|
|
for (let k = 0; k < 4; k++) {
|
|
const nx = cx0 + neighbors[k][0];
|
|
const ny = cy0 + neighbors[k][1];
|
|
if (nx < 0 || ny < 0 || nx >= width || ny >= height) continue;
|
|
|
|
const l2 = ny * width + nx;
|
|
if (visited[l2]) continue;
|
|
|
|
const p2 = idx(nx, ny);
|
|
const a2 = result[p2 + 3] | 0;
|
|
if (a2 < 128) {
|
|
visited[l2] = 1;
|
|
continue;
|
|
}
|
|
|
|
const r = result[p2] | 0;
|
|
const g = result[p2 + 1] | 0;
|
|
const b = result[p2 + 2] | 0;
|
|
const k2 = ((r << 16) | (g << 8) | b) >>> 0;
|
|
|
|
if (k2 === key) {
|
|
visited[l2] = 1;
|
|
qx[qe] = nx;
|
|
qy[qe] = ny;
|
|
qe++;
|
|
} else {
|
|
borderColors[k2] = (borderColors[k2] || 0) + 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (comp.length > 0 && comp.length < thr) {
|
|
let bestKey = -1;
|
|
let bestCnt = -1;
|
|
for (const ks in borderColors) {
|
|
const c = borderColors[ks] | 0;
|
|
if (c > bestCnt) {
|
|
bestCnt = c;
|
|
bestKey = Number(ks) | 0;
|
|
}
|
|
}
|
|
|
|
if (bestKey >= 0) {
|
|
const nr = (bestKey >>> 16) & 255;
|
|
const ng = (bestKey >>> 8) & 255;
|
|
const nb = bestKey & 255;
|
|
for (let i = 0; i < comp.length; i++) {
|
|
const p3 = comp[i];
|
|
result[p3] = nr;
|
|
result[p3 + 1] = ng;
|
|
result[p3 + 2] = nb;
|
|
result[p3 + 3] = 255;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
/**
|
|
* Flood fill to find connected regions
|
|
* @private
|
|
*/
|
|
|
|
/**
|
|
* Erode edges by shrinking them inward
|
|
* @param {Uint8ClampedArray} imageData - RGBA pixel data
|
|
* @param {number} width - Image width
|
|
* @param {number} height - Image height
|
|
* @param {number} amount - Erosion amount in pixels (0-20)
|
|
* @returns {Uint8ClampedArray} Eroded image data
|
|
*/
|
|
erodeEdges(imageData, width, height, amount) {
|
|
if (amount <= 0) return new Uint8ClampedArray(imageData);
|
|
|
|
const result = new Uint8ClampedArray(imageData);
|
|
|
|
for (let iteration = 0; iteration < amount; iteration++) {
|
|
const temp = new Uint8ClampedArray(result);
|
|
for (let y = 1; y < height - 1; y++) {
|
|
for (let x = 1; x < width - 1; x++) {
|
|
const idx = (y * width + x) * 4;
|
|
const alpha = temp[idx + 3];
|
|
|
|
// Check if any neighbor is transparent
|
|
let hasTransparentNeighbor = false;
|
|
for (let dy = -1; dy <= 1 && !hasTransparentNeighbor; dy++) {
|
|
for (let dx = -1; dx <= 1; dx++) {
|
|
const nidx = ((y + dy) * width + (x + dx)) * 4;
|
|
if (temp[nidx + 3] < 128) {
|
|
hasTransparentNeighbor = true;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (hasTransparentNeighbor && alpha > 128) {
|
|
result[idx + 3] = 0; // Make transparent
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
/**
|
|
* Apply mode filter (median-like filtering) - optimized from wplace_helper
|
|
* @param {Uint8ClampedArray} imageData - RGBA pixel data
|
|
* @param {number} width - Image width
|
|
* @param {number} height - Image height
|
|
* @param {number} radius - Filter radius (1-10)
|
|
* @returns {Uint8ClampedArray} Filtered image data
|
|
*/
|
|
applyModeFilter(imageData, width, height, radius) {
|
|
if (radius <= 0 || width === 0 || height === 0) return new Uint8ClampedArray(imageData);
|
|
|
|
const n = radius | 0;
|
|
if (n <= 1) return new Uint8ClampedArray(imageData);
|
|
|
|
const left = Math.floor((n - 1) / 2);
|
|
const right = n - left - 1;
|
|
const result = new Uint8ClampedArray(imageData);
|
|
const out = new Uint8ClampedArray(imageData);
|
|
|
|
for (let y = 0; y < height; y++) {
|
|
for (let x = 0; x < width; x++) {
|
|
const i = (y * width + x) * 4;
|
|
const a = imageData[i + 3] | 0;
|
|
|
|
// Skip transparent pixels
|
|
if (a < 128) {
|
|
out[i] = imageData[i];
|
|
out[i + 1] = imageData[i + 1];
|
|
out[i + 2] = imageData[i + 2];
|
|
out[i + 3] = a;
|
|
continue;
|
|
}
|
|
|
|
const counts = {};
|
|
let best = 0;
|
|
let bR = imageData[i];
|
|
let bG = imageData[i + 1];
|
|
let bB = imageData[i + 2];
|
|
|
|
for (let dy = -left; dy <= right; dy++) {
|
|
const yy = y + dy;
|
|
if (yy < 0 || yy >= height) continue;
|
|
|
|
for (let dx = -left; dx <= right; dx++) {
|
|
const xx = x + dx;
|
|
if (xx < 0 || xx >= width) continue;
|
|
|
|
const j = (yy * width + xx) * 4;
|
|
if ((imageData[j + 3] | 0) < 128) continue;
|
|
|
|
const key = ((imageData[j] | 0) << 16) | ((imageData[j + 1] | 0) << 8) | (imageData[j + 2] | 0);
|
|
const c = (counts[key] || 0) + 1;
|
|
counts[key] = c;
|
|
|
|
if (c > best) {
|
|
best = c;
|
|
bR = imageData[j];
|
|
bG = imageData[j + 1];
|
|
bB = imageData[j + 2];
|
|
}
|
|
}
|
|
}
|
|
|
|
out[i] = bR;
|
|
out[i + 1] = bG;
|
|
out[i + 2] = bB;
|
|
out[i + 3] = 255;
|
|
}
|
|
}
|
|
|
|
// Copy result back
|
|
for (let i = 0; i < out.length; i += 4) {
|
|
result[i] = out[i];
|
|
result[i + 1] = out[i + 1];
|
|
result[i + 2] = out[i + 2];
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
// ====== COLOR CORRECTION METHODS ======
|
|
|
|
/**
|
|
* Apply color correction to the image
|
|
* @param {Uint8ClampedArray} imageData - RGBA pixel data
|
|
* @param {number} brightness - Brightness adjustment (-100 to 100)
|
|
* @param {number} contrast - Contrast adjustment (-100 to 100)
|
|
* @param {number} saturation - Saturation adjustment (-100 to 100)
|
|
* @param {number} hue - Hue rotation (-180 to 180 degrees)
|
|
* @param {number} gamma - Gamma correction (0.5 to 3.0)
|
|
* @returns {Uint8ClampedArray} Color-corrected image data
|
|
*/
|
|
applyColorCorrection(imageData, brightness = 0, contrast = 0, saturation = 0, hue = 0, gamma = 1.0) {
|
|
const result = new Uint8ClampedArray(imageData);
|
|
|
|
for (let i = 0; i < result.length; i += 4) {
|
|
let r = result[i];
|
|
let g = result[i + 1];
|
|
let b = result[i + 2];
|
|
|
|
// Brightness
|
|
if (brightness !== 0) {
|
|
r = Math.max(0, Math.min(255, r + (brightness * 2.55)));
|
|
g = Math.max(0, Math.min(255, g + (brightness * 2.55)));
|
|
b = Math.max(0, Math.min(255, b + (brightness * 2.55)));
|
|
}
|
|
|
|
// Contrast
|
|
if (contrast !== 0) {
|
|
const factor = (contrast + 100) / 100;
|
|
r = Math.max(0, Math.min(255, (r - 128) * factor + 128));
|
|
g = Math.max(0, Math.min(255, (g - 128) * factor + 128));
|
|
b = Math.max(0, Math.min(255, (b - 128) * factor + 128));
|
|
}
|
|
|
|
// Saturation
|
|
if (saturation !== 0) {
|
|
const [h, s, l] = this._rgbToHsl(r, g, b);
|
|
const newS = Math.max(0, Math.min(100, s + saturation));
|
|
[r, g, b] = this._hslToRgb(h, newS, l);
|
|
}
|
|
|
|
// Hue rotation
|
|
if (hue !== 0) {
|
|
const [h, s, l] = this._rgbToHsl(r, g, b);
|
|
const newH = (h + hue) % 360;
|
|
[r, g, b] = this._hslToRgb(newH, s, l);
|
|
}
|
|
|
|
// Gamma correction
|
|
if (gamma !== 1.0) {
|
|
const invGamma = 1.0 / gamma;
|
|
r = Math.pow(r / 255, invGamma) * 255;
|
|
g = Math.pow(g / 255, invGamma) * 255;
|
|
b = Math.pow(b / 255, invGamma) * 255;
|
|
}
|
|
|
|
result[i] = Math.round(r);
|
|
result[i + 1] = Math.round(g);
|
|
result[i + 2] = Math.round(b);
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
/**
|
|
* Convert RGB to HSL
|
|
* @private
|
|
*/
|
|
_rgbToHsl(r, g, b) {
|
|
r /= 255;
|
|
g /= 255;
|
|
b /= 255;
|
|
const max = Math.max(r, g, b);
|
|
const min = Math.min(r, g, b);
|
|
let h = 0, s = 0;
|
|
const l = (max + min) / 2;
|
|
|
|
if (max !== min) {
|
|
const d = max - min;
|
|
s = l > 0.5 ? d / (2 - max - min) : d / (max + min);
|
|
switch (max) {
|
|
case r: h = ((g - b) / d + (g < b ? 6 : 0)) / 6; break;
|
|
case g: h = ((b - r) / d + 2) / 6; break;
|
|
case b: h = ((r - g) / d + 4) / 6; break;
|
|
}
|
|
}
|
|
|
|
return [h * 360, s * 100, l * 100];
|
|
}
|
|
|
|
/**
|
|
* Convert HSL to RGB
|
|
* @private
|
|
*/
|
|
_hslToRgb(h, s, l) {
|
|
h /= 360;
|
|
s /= 100;
|
|
l /= 100;
|
|
|
|
let r, g, b;
|
|
if (s === 0) {
|
|
r = g = b = l;
|
|
} else {
|
|
const hue2rgb = (p, q, t) => {
|
|
if (t < 0) t += 1;
|
|
if (t > 1) t -= 1;
|
|
if (t < 1/6) return p + (q - p) * 6 * t;
|
|
if (t < 1/2) return q;
|
|
if (t < 2/3) return p + (q - p) * (2/3 - t) * 6;
|
|
return p;
|
|
};
|
|
const q = l < 0.5 ? l * (1 + s) : l + s - l * s;
|
|
const p = 2 * l - q;
|
|
r = hue2rgb(p, q, h + 1/3);
|
|
g = hue2rgb(p, q, h);
|
|
b = hue2rgb(p, q, h - 1/3);
|
|
}
|
|
|
|
return [Math.round(r * 255), Math.round(g * 255), Math.round(b * 255)];
|
|
}
|
|
|
|
/**
|
|
* Color matching helper
|
|
* @private
|
|
*/
|
|
_colorMatch(color1, color2, tolerance = 10) {
|
|
return Math.abs(color1[0] - color2[0]) <= tolerance &&
|
|
Math.abs(color1[1] - color2[1]) <= tolerance &&
|
|
Math.abs(color1[2] - color2[2]) <= tolerance;
|
|
}
|
|
}
|
|
|
|
|
|
// Create global instance
|
|
window.WPlaceImageProcessor = ImageProcessor;
|
|
|
|
// Create global instance for Auto-Image.js compatibility
|
|
window.globalImageProcessor = new ImageProcessor();
|
|
|
|
// Legacy compatibility - expose key methods globally for backward compatibility
|
|
window.ImageProcessor = ImageProcessor;
|
|
|
|
console.log('✅ WPlace Image Processor loaded and ready');
|