feat: K-means color palette extraction with average brand color
Algorithm improvements: - Implemented K-means clustering (3-6 clusters based on image size) - Extract complete color palette from logo (5-7 representative colors) - Calculate average RGB of palette colors as final brand color - Sort palette by saturation (most vibrant colors first) Filters applied: - Skip whites (RGB > 235), blacks (RGB < 20), grays (saturation < 0.2) - Filter palette by lightness (0.25-0.80 range) - 10 iterations K-means with convergence check UI improvements: - Show extracted color palette as color chips - Display final brand color (average of palette) - Labeled as 'Colore Brand (media palette)' - Hover effect on palette colors with tooltips - Console logging for debugging Benefits: - More accurate color extraction from complex logos - Avoids picking background colors (white/black) - Represents true brand identity (not just most common) - Works better with gradients and multi-color logos - Average creates balanced, professional brand color
This commit is contained in:
d.viti
274
shop-mode.html
274
shop-mode.html
@@ -170,18 +170,23 @@
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</div>
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</div>
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<div
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<div
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x-show="logoColor"
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x-show="logoColor"
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class="mt-3 flex items-center gap-2"
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<div x-show="logoColor" class="mt-3">
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>
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<div class="flex items-center gap-2 mb-2">
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<span class="text-xs text-gray-600"
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<span class="text-xs font-semibold text-gray-700">Colore Brand (media palette):</span>
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>Colore estratto:</span
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<div :style="'background-color: ' + logoColor" class="w-10 h-10 rounded-lg border-2 border-gray-300 shadow-sm"></div>
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>
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<span class="text-xs font-mono font-bold" x-text="logoColor"></span>
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<div
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</div>
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:style="'background-color: ' + logoColor"
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<div x-show="colorPalette.length > 0" class="mt-2">
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class="w-8 h-8 rounded border-2 border-gray-300"
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<span class="text-xs text-gray-600">Palette estratta:</span>
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></div>
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<div class="flex gap-1 mt-1 flex-wrap">
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<span
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<template x-for="color in colorPalette" :key="color">
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class="text-xs font-mono"
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<div :style="'background-color: ' + color"
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x-text="logoColor"
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:title="color"
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class="w-8 h-8 rounded border border-gray-300 shadow-sm cursor-pointer hover:scale-110 transition-transform"></div>
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</template>
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</div>
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</div>
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</div>
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></span>
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></span>
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</div>
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</div>
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</div>
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</div>
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@@ -675,6 +680,7 @@
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},
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},
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logoColor: null,
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logoColor: null,
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colorPalette: [],
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cliente: {
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cliente: {
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nome: "",
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nome: "",
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@@ -725,131 +731,153 @@
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reader.readAsDataURL(file);
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reader.readAsDataURL(file);
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},
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},
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extractDominantColor(imageData) {
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const img = new Image();
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img.onload = () => {
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const canvas = document.createElement("canvas");
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const ctx = canvas.getContext("2d");
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canvas.width = img.width;
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canvas.height = img.height;
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ctx.drawImage(img, 0, 0);
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try {
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extractDominantColor(imageData) {
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const imgData = ctx.getImageData(
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const img = new Image();
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0,
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img.onload = () => {
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0,
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const canvas = document.createElement("canvas");
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canvas.width,
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const ctx = canvas.getContext("2d");
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canvas.height,
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canvas.width = img.width;
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);
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canvas.height = img.height;
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const data = imgData.data;
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ctx.drawImage(img, 0, 0);
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// Collect color palette with counts
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try {
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const colorMap = {};
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const imgData = ctx.getImageData(0, 0, canvas.width, canvas.height);
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for (let i = 0; i < data.length; i += 16) {
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const data = imgData.data;
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// Sample every 4 pixels for better coverage
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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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const a = data[i + 3];
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// Skip transparent pixels
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// Step 1: Collect all valid colors
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if (a < 125) continue;
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const colors = [];
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for (let i = 0; i < data.length; i += 16) {
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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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const a = data[i + 3];
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// Skip whites (> 240 in all channels)
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// Skip transparent
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if (r > 240 && g > 240 && b > 240) continue;
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if (a < 125) continue;
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// Skip blacks (< 15 in all channels)
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// Skip whites (> 235)
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if (r < 15 && g < 15 && b < 15) continue;
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if (r > 235 && g > 235 && b > 235) continue;
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// Skip grays (low saturation: when R≈G≈B)
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// Skip blacks (< 20)
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const maxChan = Math.max(r, g, b);
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if (r < 20 && g < 20 && b < 20) continue;
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const minChan = Math.min(r, g, b);
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const saturation =
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maxChan === 0
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? 0
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: (maxChan - minChan) / maxChan;
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if (saturation < 0.15) continue; // Skip low saturation (grays)
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// Quantize colors to reduce variations (group similar colors)
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// Calculate saturation to skip grays
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const qr = Math.round(r / 10) * 10;
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const max = Math.max(r, g, b);
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const qg = Math.round(g / 10) * 10;
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const min = Math.min(r, g, b);
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const qb = Math.round(b / 10) * 10;
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const saturation = max === 0 ? 0 : (max - min) / max;
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const rgb = `${qr},${qg},${qb}`;
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if (saturation < 0.2) continue; // Skip grays
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colorMap[rgb] = (colorMap[rgb] || 0) + 1;
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}
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// Build palette with color scores
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colors.push({ r, g, b });
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const palette = [];
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}
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for (const [rgb, count] of Object.entries(
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colorMap,
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)) {
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const [r, g, b] = rgb
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.split(",")
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.map(Number);
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// Calculate HSL for better color selection
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if (colors.length === 0) {
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const max = Math.max(r, g, b);
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this.logoColor = "#10b981";
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const min = Math.min(r, g, b);
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this.colorPalette = [];
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const l = (max + min) / 2 / 255; // Lightness 0-1
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console.log("No valid colors found, using fallback");
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const s =
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return;
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max === min
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}
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? 0
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: (max - min) / (max + min); // Saturation 0-1
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// Skip too light (> 0.85) or too dark (< 0.20)
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// Step 2: K-means clustering to extract palette (5-7 colors)
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if (l > 0.85 || l < 0.2) continue;
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const numClusters = Math.min(6, Math.max(3, Math.floor(colors.length / 50)));
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const palette = this.kMeansClustering(colors, numClusters);
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// Calculate color "score" based on:
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// Step 3: Filter palette colors by lightness
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// - Saturation (higher is better)
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const filteredPalette = palette.filter(color => {
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// - Frequency (more common is better)
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const max = Math.max(color.r, color.g, color.b);
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// - Ideal lightness (0.4-0.6 is best)
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const min = Math.min(color.r, color.g, color.b);
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const lightnessScore =
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const l = (max + min) / 2 / 255;
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1 - Math.abs(l - 0.5) * 2; // Peaks at 0.5
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return l > 0.25 && l < 0.80; // Keep mid-range lightness
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const score =
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});
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s * 2 + count / 100 + lightnessScore;
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palette.push({
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if (filteredPalette.length === 0) {
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r,
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this.logoColor = "#10b981";
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g,
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this.colorPalette = [];
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b,
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console.log("No suitable colors in palette, using fallback");
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count,
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return;
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saturation: s,
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}
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lightness: l,
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score,
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});
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}
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// Sort by score (best colors first)
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// Step 4: Calculate average color from palette
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palette.sort((a, b) => b.score - a.score);
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const avgR = Math.round(filteredPalette.reduce((sum, c) => sum + c.r, 0) / filteredPalette.length);
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const avgG = Math.round(filteredPalette.reduce((sum, c) => sum + c.g, 0) / filteredPalette.length);
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const avgB = Math.round(filteredPalette.reduce((sum, c) => sum + c.b, 0) / filteredPalette.length);
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// Get the best color from palette
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// Store results
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if (palette.length > 0) {
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this.logoColor = this.rgbToHex(avgR, avgG, avgB);
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const bestColor = palette[0];
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this.colorPalette = filteredPalette.map(c => this.rgbToHex(c.r, c.g, c.b));
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this.logoColor = this.rgbToHex(
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bestColor.r,
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console.log("Extracted palette:", this.colorPalette);
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bestColor.g,
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console.log("Average brand color:", this.logoColor);
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bestColor.b,
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} catch (err) {
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);
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console.error("Error extracting color:", err);
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console.log(
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this.logoColor = "#10b981";
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"Extracted color:",
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this.colorPalette = [];
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this.logoColor,
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}
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"from palette of",
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};
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palette.length,
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img.src = imageData;
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"colors",
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},
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);
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} else {
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kMeansClustering(colors, k) {
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this.logoColor = "#10b981"; // Fallback emerald
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// Initialize centroids randomly
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console.log(
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let centroids = [];
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"No suitable colors found, using fallback",
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const shuffled = [...colors].sort(() => Math.random() - 0.5);
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);
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for (let i = 0; i < k; i++) {
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}
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centroids.push({ ...shuffled[i % shuffled.length] });
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} catch (err) {
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}
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console.error("Error extracting color:", err);
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this.logoColor = "#10b981"; // Fallback emerald
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// K-means iterations
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}
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for (let iter = 0; iter < 10; iter++) {
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};
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// Assign colors to nearest centroid
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img.src = imageData;
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const clusters = Array(k).fill(null).map(() => []);
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},
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colors.forEach(color => {
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let minDist = Infinity;
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let closestIdx = 0;
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centroids.forEach((centroid, idx) => {
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const dist = Math.sqrt(
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Math.pow(color.r - centroid.r, 2) +
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Math.pow(color.g - centroid.g, 2) +
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Math.pow(color.b - centroid.b, 2)
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);
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if (dist < minDist) {
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minDist = dist;
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closestIdx = idx;
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}
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});
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clusters[closestIdx].push(color);
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});
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// Update centroids
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const newCentroids = clusters.map(cluster => {
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if (cluster.length === 0) return centroids[0]; // Fallback
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const avgR = Math.round(cluster.reduce((sum, c) => sum + c.r, 0) / cluster.length);
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const avgG = Math.round(cluster.reduce((sum, c) => sum + c.g, 0) / cluster.length);
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const avgB = Math.round(cluster.reduce((sum, c) => sum + c.b, 0) / cluster.length);
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return { r: avgR, g: avgG, b: avgB };
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});
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// Check convergence
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const converged = centroids.every((c, i) =>
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c.r === newCentroids[i].r &&
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c.g === newCentroids[i].g &&
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c.b === newCentroids[i].b
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);
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centroids = newCentroids;
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if (converged) break;
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}
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// Sort by saturation (most saturated first)
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return centroids.sort((a, b) => {
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const satA = (Math.max(a.r, a.g, a.b) - Math.min(a.r, a.g, a.b)) / Math.max(a.r, a.g, a.b);
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const satB = (Math.max(b.r, b.g, b.b) - Math.min(b.r, b.g, b.b)) / Math.max(b.r, b.g, b.b);
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return satB - satA;
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});
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},
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rgbToHex(r, g, b) {
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rgbToHex(r, g, b) {
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return (
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return (
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