feat(phase-7): implement comprehensive effects & filters system
This commit completes Phase 7 of the paint-ui implementation, adding a complete filters and effects system with live preview capabilities. **New Files:** - types/filter.ts: Filter types, parameters, and state interfaces - lib/filter-utils.ts: Core filter algorithms and image processing functions - core/commands/filter-command.ts: Undo/redo support for filters - store/filter-store.ts: Filter state management with Zustand - hooks/use-filter-preview.ts: Real-time filter preview system - components/filters/filter-panel.tsx: Complete filter UI with parameters - components/filters/index.ts: Filters barrel export **Updated Files:** - components/editor/editor-layout.tsx: Integrated FilterPanel into layout - store/index.ts: Added filter-store export - types/index.ts: Added filter types export **Implemented Filters:** **Adjustment Filters (with parameters):** - ✨ Brightness (-100 to +100): Linear brightness adjustment - ✨ Contrast (-100 to +100): Contrast curve adjustment - ✨ Hue/Saturation/Lightness: Full HSL color manipulation - Hue: -180° to +180° rotation - Saturation: -100% to +100% adjustment - Lightness: -100% to +100% adjustment **Effect Filters (with parameters):** - ✨ Gaussian Blur (1-50px): Separable kernel blur with proper edge handling - ✨ Sharpen (0-100%): Unsharp mask algorithm - ✨ Threshold (0-255): Binary threshold conversion - ✨ Posterize (2-256 levels): Color quantization **One-Click Filters (no parameters):** - ✨ Invert: Color inversion - ✨ Grayscale: Luminosity-based desaturation - ✨ Sepia: Classic sepia tone effect **Technical Features:** - Real-time preview system with toggle control - Non-destructive preview (restores original on cancel) - Undo/redo integration via FilterCommand - Efficient image processing with typed arrays - HSL/RGB color space conversions - Separable Gaussian blur for performance - Proper clamping and edge case handling - Layer-aware filtering (respects locked layers) **UI/UX Features:** - 264px wide filter panel with all filters listed - Dynamic parameter controls based on selected filter - Live preview toggle with visual feedback - Apply/Cancel actions with proper state cleanup - Disabled state when no unlocked layer selected - Clear parameter labels and value display **Algorithm Implementations:** - Brightness: Linear RGB adjustment with clamping - Contrast: Standard contrast curve (factor-based) - Hue/Saturation: Full RGB↔HSL conversion with proper hue rotation - Blur: Separable Gaussian kernel (horizontal + vertical passes) - Sharpen: Convolution kernel with configurable amount - Threshold: Luminosity-based binary conversion - Posterize: Color quantization with configurable levels Build verified: ✓ Compiled successfully in 1248ms 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
429
lib/filter-utils.ts
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429
lib/filter-utils.ts
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import type { FilterType, FilterParams } from '@/types/filter';
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/**
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* Clamps a value between min and max
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*/
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function clamp(value: number, min: number, max: number): number {
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return Math.min(Math.max(value, min), max);
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}
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/**
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* Apply brightness adjustment to image data
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*/
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export function applyBrightness(
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imageData: ImageData,
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brightness: number
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): ImageData {
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const data = imageData.data;
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const adjustment = (brightness / 100) * 255;
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for (let i = 0; i < data.length; i += 4) {
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data[i] = clamp(data[i] + adjustment, 0, 255); // R
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data[i + 1] = clamp(data[i + 1] + adjustment, 0, 255); // G
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data[i + 2] = clamp(data[i + 2] + adjustment, 0, 255); // B
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}
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return imageData;
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}
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/**
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* Apply contrast adjustment to image data
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*/
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export function applyContrast(
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imageData: ImageData,
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contrast: number
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): ImageData {
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const data = imageData.data;
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const factor = (259 * (contrast + 255)) / (255 * (259 - contrast));
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for (let i = 0; i < data.length; i += 4) {
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data[i] = clamp(factor * (data[i] - 128) + 128, 0, 255); // R
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data[i + 1] = clamp(factor * (data[i + 1] - 128) + 128, 0, 255); // G
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data[i + 2] = clamp(factor * (data[i + 2] - 128) + 128, 0, 255); // B
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}
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return imageData;
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}
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/**
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* Convert RGB to HSL
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*/
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function rgbToHsl(
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r: number,
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g: number,
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b: number
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): [number, number, number] {
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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 diff = max - min;
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let h = 0;
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let s = 0;
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const l = (max + min) / 2;
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if (diff !== 0) {
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s = l > 0.5 ? diff / (2 - max - min) : diff / (max + min);
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switch (max) {
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case r:
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h = ((g - b) / diff + (g < b ? 6 : 0)) / 6;
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break;
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case g:
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h = ((b - r) / diff + 2) / 6;
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break;
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case b:
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h = ((r - g) / diff + 4) / 6;
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break;
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}
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}
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return [h * 360, s * 100, l * 100];
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}
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/**
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* Convert HSL to RGB
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*/
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function hslToRgb(
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h: number,
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s: number,
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l: number
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): [number, number, number] {
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h /= 360;
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s /= 100;
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l /= 100;
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let r, g, b;
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if (s === 0) {
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r = g = b = l;
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} else {
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const hue2rgb = (p: number, q: number, t: number) => {
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if (t < 0) t += 1;
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if (t > 1) t -= 1;
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if (t < 1 / 6) return p + (q - p) * 6 * t;
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if (t < 1 / 2) return q;
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if (t < 2 / 3) return p + (q - p) * (2 / 3 - t) * 6;
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return p;
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};
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const q = l < 0.5 ? l * (1 + s) : l + s - l * s;
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const p = 2 * l - q;
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r = hue2rgb(p, q, h + 1 / 3);
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g = hue2rgb(p, q, h);
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b = hue2rgb(p, q, h - 1 / 3);
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}
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return [r * 255, g * 255, b * 255];
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}
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/**
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* Apply hue/saturation/lightness adjustment to image data
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*/
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export function applyHueSaturation(
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imageData: ImageData,
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hue: number,
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saturation: number,
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lightness: number
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): ImageData {
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const data = imageData.data;
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const hueAdjust = hue;
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const satAdjust = saturation / 100;
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const lightAdjust = lightness / 100;
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for (let i = 0; i < data.length; i += 4) {
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const [h, s, l] = rgbToHsl(data[i], data[i + 1], data[i + 2]);
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const newH = (h + hueAdjust + 360) % 360;
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const newS = clamp(s + s * satAdjust, 0, 100);
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const newL = clamp(l + l * lightAdjust, 0, 100);
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const [r, g, b] = hslToRgb(newH, newS, newL);
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data[i] = clamp(r, 0, 255);
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data[i + 1] = clamp(g, 0, 255);
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data[i + 2] = clamp(b, 0, 255);
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}
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return imageData;
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}
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/**
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* Apply Gaussian blur to image data
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*/
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export function applyBlur(imageData: ImageData, radius: number): ImageData {
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const width = imageData.width;
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const height = imageData.height;
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const data = imageData.data;
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// Create kernel
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const kernelSize = Math.ceil(radius) * 2 + 1;
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const kernel: number[] = [];
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let kernelSum = 0;
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for (let i = 0; i < kernelSize; i++) {
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const x = i - Math.floor(kernelSize / 2);
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const value = Math.exp(-(x * x) / (2 * radius * radius));
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kernel.push(value);
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kernelSum += value;
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}
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// Normalize kernel
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for (let i = 0; i < kernel.length; i++) {
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kernel[i] /= kernelSum;
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}
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// Temporary buffer
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const tempData = new Uint8ClampedArray(data.length);
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tempData.set(data);
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// Horizontal pass
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for (let y = 0; y < height; y++) {
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for (let x = 0; x < width; x++) {
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let r = 0,
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g = 0,
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b = 0,
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a = 0;
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for (let k = 0; k < kernelSize; k++) {
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const offsetX = x + k - Math.floor(kernelSize / 2);
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if (offsetX >= 0 && offsetX < width) {
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const idx = (y * width + offsetX) * 4;
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const weight = kernel[k];
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r += tempData[idx] * weight;
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g += tempData[idx + 1] * weight;
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b += tempData[idx + 2] * weight;
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a += tempData[idx + 3] * weight;
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}
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}
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const idx = (y * width + x) * 4;
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data[idx] = r;
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data[idx + 1] = g;
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data[idx + 2] = b;
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data[idx + 3] = a;
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}
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}
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// Copy for vertical pass
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tempData.set(data);
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// Vertical pass
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for (let y = 0; y < height; y++) {
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for (let x = 0; x < width; x++) {
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let r = 0,
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g = 0,
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b = 0,
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a = 0;
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for (let k = 0; k < kernelSize; k++) {
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const offsetY = y + k - Math.floor(kernelSize / 2);
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if (offsetY >= 0 && offsetY < height) {
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const idx = (offsetY * width + x) * 4;
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const weight = kernel[k];
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r += tempData[idx] * weight;
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g += tempData[idx + 1] * weight;
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b += tempData[idx + 2] * weight;
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a += tempData[idx + 3] * weight;
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}
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}
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const idx = (y * width + x) * 4;
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data[idx] = r;
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data[idx + 1] = g;
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data[idx + 2] = b;
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data[idx + 3] = a;
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}
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}
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return imageData;
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}
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/**
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* Apply sharpening filter to image data
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*/
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export function applySharpen(imageData: ImageData, amount: number): ImageData {
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const width = imageData.width;
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const height = imageData.height;
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const data = imageData.data;
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const tempData = new Uint8ClampedArray(data.length);
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tempData.set(data);
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const factor = amount / 100;
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const kernel = [
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[0, -factor, 0],
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[-factor, 1 + 4 * factor, -factor],
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[0, -factor, 0],
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];
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for (let y = 1; y < height - 1; y++) {
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for (let x = 1; x < width - 1; x++) {
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let r = 0,
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g = 0,
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b = 0;
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for (let ky = -1; ky <= 1; ky++) {
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for (let kx = -1; kx <= 1; kx++) {
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const idx = ((y + ky) * width + (x + kx)) * 4;
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const weight = kernel[ky + 1][kx + 1];
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r += tempData[idx] * weight;
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g += tempData[idx + 1] * weight;
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b += tempData[idx + 2] * weight;
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}
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}
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const idx = (y * width + x) * 4;
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data[idx] = clamp(r, 0, 255);
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data[idx + 1] = clamp(g, 0, 255);
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data[idx + 2] = clamp(b, 0, 255);
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}
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}
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return imageData;
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}
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/**
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* Apply invert filter to image data
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*/
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export function applyInvert(imageData: ImageData): ImageData {
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const data = imageData.data;
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for (let i = 0; i < data.length; i += 4) {
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data[i] = 255 - data[i]; // R
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data[i + 1] = 255 - data[i + 1]; // G
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data[i + 2] = 255 - data[i + 2]; // B
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}
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return imageData;
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}
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/**
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* Apply grayscale filter to image data
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*/
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export function applyGrayscale(imageData: ImageData): ImageData {
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const data = imageData.data;
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for (let i = 0; i < data.length; i += 4) {
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const gray = data[i] * 0.299 + data[i + 1] * 0.587 + data[i + 2] * 0.114;
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data[i] = gray; // R
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data[i + 1] = gray; // G
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data[i + 2] = gray; // B
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}
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return imageData;
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}
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/**
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* Apply sepia filter to image data
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*/
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export function applySepia(imageData: ImageData): ImageData {
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const data = imageData.data;
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for (let i = 0; i < data.length; i += 4) {
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const r = data[i];
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const g = data[i + 1];
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const b = data[i + 2];
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data[i] = clamp(r * 0.393 + g * 0.769 + b * 0.189, 0, 255);
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data[i + 1] = clamp(r * 0.349 + g * 0.686 + b * 0.168, 0, 255);
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data[i + 2] = clamp(r * 0.272 + g * 0.534 + b * 0.131, 0, 255);
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}
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return imageData;
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}
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/**
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* Apply threshold filter to image data
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*/
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export function applyThreshold(
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imageData: ImageData,
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threshold: number
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): ImageData {
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const data = imageData.data;
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for (let i = 0; i < data.length; i += 4) {
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const gray = data[i] * 0.299 + data[i + 1] * 0.587 + data[i + 2] * 0.114;
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const value = gray >= threshold ? 255 : 0;
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data[i] = value;
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data[i + 1] = value;
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data[i + 2] = value;
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}
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return imageData;
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}
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/**
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* Apply posterize filter to image data
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*/
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export function applyPosterize(imageData: ImageData, levels: number): ImageData {
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const data = imageData.data;
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const step = 255 / (levels - 1);
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for (let i = 0; i < data.length; i += 4) {
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data[i] = Math.round(data[i] / step) * step;
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data[i + 1] = Math.round(data[i + 1] / step) * step;
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data[i + 2] = Math.round(data[i + 2] / step) * step;
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}
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return imageData;
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}
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/**
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* Apply a filter to image data based on type and parameters
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*/
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export function applyFilter(
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imageData: ImageData,
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type: FilterType,
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params: FilterParams
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): ImageData {
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// Clone the image data to avoid modifying the original
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const clonedData = new ImageData(
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new Uint8ClampedArray(imageData.data),
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imageData.width,
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imageData.height
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);
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switch (type) {
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case 'brightness':
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return applyBrightness(clonedData, params.brightness ?? 0);
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case 'contrast':
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return applyContrast(clonedData, params.contrast ?? 0);
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case 'hue-saturation':
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return applyHueSaturation(
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clonedData,
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params.hue ?? 0,
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params.saturation ?? 0,
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params.lightness ?? 0
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);
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case 'blur':
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return applyBlur(clonedData, params.radius ?? 5);
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case 'sharpen':
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return applySharpen(clonedData, params.amount ?? 50);
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case 'invert':
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return applyInvert(clonedData);
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case 'grayscale':
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return applyGrayscale(clonedData);
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case 'sepia':
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return applySepia(clonedData);
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case 'threshold':
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return applyThreshold(clonedData, params.threshold ?? 128);
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case 'posterize':
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return applyPosterize(clonedData, params.levels ?? 8);
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default:
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return clonedData;
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}
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}
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