services/calcolatore_prezzi_software
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feat: Add back button to return to index.html in both modes
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d.viti
2025-10-14 00:46:43 +02:00
parent d759bec45d
commit e5a72183b5
2 changed files with 262 additions and 155 deletions
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17
project-mode.html
View File

@@ -104,9 +104,17 @@
>
<div class="container mx-auto p-4 max-w-7xl">
<!-- Header -->
<div
class="glass rounded-2xl p-8 mb-6 animate-slide-in text-center"
<div class="glass rounded-2xl p-8 mb-6 animate-slide-in">
<div class="flex items-center justify-between mb-4">
<a
href="index.html"
class="bg-gradient-to-r from-gray-500 to-gray-600 text-white font-semibold py-2 px-4 rounded-lg hover:from-gray-600 hover:to-gray-700 transition-all flex items-center gap-2 shadow-lg"
>
<i class="fas fa-arrow-left"></i>
<span>Torna al Menu</span>
</a>
</div>
<div class="text-center">
<h1
class="text-4xl font-bold text-gray-800 flex items-center justify-center gap-3"
>
@@ -114,10 +122,11 @@
Calcolatore Prezzi Software Pro
</h1>
<p class="text-gray-600 mt-2">
Sistema professionale per preventivi sviluppo software in
Italia
Sistema professionale per preventivi sviluppo software
in Italia
</p>
</div>
</div>
<!-- Notifiche Toast -->
<div

188
shop-mode.html
View File

@@ -82,9 +82,17 @@
>
<div class="container mx-auto p-4 max-w-7xl">
<!-- Header -->
<div
class="glass rounded-2xl p-8 mb-6 animate-slide-in text-center"
<div class="glass rounded-2xl p-8 mb-6 animate-slide-in">
<div class="flex items-center justify-between mb-4">
<a
href="index.html"
class="bg-gradient-to-r from-gray-500 to-gray-600 text-white font-semibold py-2 px-4 rounded-lg hover:from-gray-600 hover:to-gray-700 transition-all flex items-center gap-2 shadow-lg"
>
<i class="fas fa-arrow-left"></i>
<span>Torna al Menu</span>
</a>
</div>
<div class="text-center">
<h1
class="text-4xl font-bold text-gray-800 flex items-center justify-center gap-3"
>
@@ -95,6 +103,7 @@
Sistema di preventivi basato su articoli e quantità
</p>
</div>
</div>
<!-- Notifiche Toast -->
<div
@@ -170,17 +179,32 @@
</div>
<div x-show="logoColor" class="mt-3">
<div class="flex items-center gap-2 mb-2">
<span class="text-xs font-semibold text-gray-700">Colore Brand (media palette):</span>
<div :style="'background-color: ' + logoColor" class="w-10 h-10 rounded-lg border-2 border-gray-300 shadow-sm"></div>
<span class="text-xs font-mono font-bold" x-text="logoColor"></span>
<span class="text-xs font-semibold text-gray-700"
>Colore Brand (media palette):</span
>
<div
:style="'background-color: ' + logoColor"
class="w-10 h-10 rounded-lg border-2 border-gray-300 shadow-sm"
></div>
<span
class="text-xs font-mono font-bold"
x-text="logoColor"
></span>
</div>
<div x-show="colorPalette.length > 0" class="mt-2">
<span class="text-xs text-gray-600">Palette estratta:</span>
<span class="text-xs text-gray-600"
>Palette estratta:</span
>
<div class="flex gap-1 mt-1 flex-wrap">
<template x-for="color in colorPalette" :key="color">
<div :style="'background-color: ' + color"
<template
x-for="color in colorPalette"
:key="color"
>
<div
:style="'background-color: ' + color"
:title="color"
class="w-8 h-8 rounded border border-gray-300 shadow-sm cursor-pointer hover:scale-110 transition-transform"></div>
class="w-8 h-8 rounded border border-gray-300 shadow-sm cursor-pointer hover:scale-110 transition-transform"
></div>
</template>
</div>
</div>
@@ -727,8 +751,7 @@
reader.readAsDataURL(file);
},
extractDominantColor(imageData) {
extractDominantColor(imageData) {
const img = new Image();
img.onload = () => {
const canvas = document.createElement("canvas");
@@ -738,7 +761,12 @@ extractDominantColor(imageData) {
ctx.drawImage(img, 0, 0);
try {
const imgData = ctx.getImageData(0, 0, canvas.width, canvas.height);
const imgData = ctx.getImageData(
0,
0,
canvas.width,
canvas.height,
);
const data = imgData.data;
// Step 1: Collect all valid colors
@@ -761,7 +789,8 @@ extractDominantColor(imageData) {
// Calculate saturation to skip grays
const max = Math.max(r, g, b);
const min = Math.min(r, g, b);
const saturation = max === 0 ? 0 : (max - min) / max;
const saturation =
max === 0 ? 0 : (max - min) / max;
if (saturation < 0.2) continue; // Skip grays
colors.push({ r, g, b });
@@ -770,40 +799,87 @@ extractDominantColor(imageData) {
if (colors.length === 0) {
this.logoColor = "#10b981";
this.colorPalette = [];
console.log("No valid colors found, using fallback");
console.log(
"No valid colors found, using fallback",
);
return;
}
// Step 2: K-means clustering to extract palette (5-7 colors)
const numClusters = Math.min(6, Math.max(3, Math.floor(colors.length / 50)));
const palette = this.kMeansClustering(colors, numClusters);
const numClusters = Math.min(
6,
Math.max(3, Math.floor(colors.length / 50)),
);
const palette = this.kMeansClustering(
colors,
numClusters,
);
// Step 3: Filter palette colors by lightness
const filteredPalette = palette.filter(color => {
const max = Math.max(color.r, color.g, color.b);
const min = Math.min(color.r, color.g, color.b);
const filteredPalette = palette.filter(
(color) => {
const max = Math.max(
color.r,
color.g,
color.b,
);
const min = Math.min(
color.r,
color.g,
color.b,
);
const l = (max + min) / 2 / 255;
return l > 0.25 && l < 0.80; // Keep mid-range lightness
});
return l > 0.25 && l < 0.8; // Keep mid-range lightness
},
);
if (filteredPalette.length === 0) {
this.logoColor = "#10b981";
this.colorPalette = [];
console.log("No suitable colors in palette, using fallback");
console.log(
"No suitable colors in palette, using fallback",
);
return;
}
// Step 4: Calculate average color from palette
const avgR = Math.round(filteredPalette.reduce((sum, c) => sum + c.r, 0) / filteredPalette.length);
const avgG = Math.round(filteredPalette.reduce((sum, c) => sum + c.g, 0) / filteredPalette.length);
const avgB = Math.round(filteredPalette.reduce((sum, c) => sum + c.b, 0) / filteredPalette.length);
const avgR = Math.round(
filteredPalette.reduce(
(sum, c) => sum + c.r,
0,
) / filteredPalette.length,
);
const avgG = Math.round(
filteredPalette.reduce(
(sum, c) => sum + c.g,
0,
) / filteredPalette.length,
);
const avgB = Math.round(
filteredPalette.reduce(
(sum, c) => sum + c.b,
0,
) / filteredPalette.length,
);
// Store results
this.logoColor = this.rgbToHex(avgR, avgG, avgB);
this.colorPalette = filteredPalette.map(c => this.rgbToHex(c.r, c.g, c.b));
this.logoColor = this.rgbToHex(
avgR,
avgG,
avgB,
);
this.colorPalette = filteredPalette.map((c) =>
this.rgbToHex(c.r, c.g, c.b),
);
console.log("Extracted palette:", this.colorPalette);
console.log("Average brand color:", this.logoColor);
console.log(
"Extracted palette:",
this.colorPalette,
);
console.log(
"Average brand color:",
this.logoColor,
);
} catch (err) {
console.error("Error extracting color:", err);
this.logoColor = "#10b981";
@@ -811,22 +887,28 @@ extractDominantColor(imageData) {
}
};
img.src = imageData;
},
},
kMeansClustering(colors, k) {
kMeansClustering(colors, k) {
// Initialize centroids randomly
let centroids = [];
const shuffled = [...colors].sort(() => Math.random() - 0.5);
const shuffled = [...colors].sort(
() => Math.random() - 0.5,
);
for (let i = 0; i < k; i++) {
centroids.push({ ...shuffled[i % shuffled.length] });
centroids.push({
...shuffled[i % shuffled.length],
});
}
// K-means iterations
for (let iter = 0; iter < 10; iter++) {
// Assign colors to nearest centroid
const clusters = Array(k).fill(null).map(() => []);
const clusters = Array(k)
.fill(null)
.map(() => []);
colors.forEach(color => {
colors.forEach((color) => {
let minDist = Infinity;
let closestIdx = 0;
@@ -834,7 +916,7 @@ kMeansClustering(colors, k) {
const dist = Math.sqrt(
Math.pow(color.r - centroid.r, 2) +
Math.pow(color.g - centroid.g, 2) +
Math.pow(color.b - centroid.b, 2)
Math.pow(color.b - centroid.b, 2),
);
if (dist < minDist) {
minDist = dist;
@@ -846,21 +928,31 @@ kMeansClustering(colors, k) {
});
// Update centroids
const newCentroids = clusters.map(cluster => {
const newCentroids = clusters.map((cluster) => {
if (cluster.length === 0) return centroids[0]; // Fallback
const avgR = Math.round(cluster.reduce((sum, c) => sum + c.r, 0) / cluster.length);
const avgG = Math.round(cluster.reduce((sum, c) => sum + c.g, 0) / cluster.length);
const avgB = Math.round(cluster.reduce((sum, c) => sum + c.b, 0) / cluster.length);
const avgR = Math.round(
cluster.reduce((sum, c) => sum + c.r, 0) /
cluster.length,
);
const avgG = Math.round(
cluster.reduce((sum, c) => sum + c.g, 0) /
cluster.length,
);
const avgB = Math.round(
cluster.reduce((sum, c) => sum + c.b, 0) /
cluster.length,
);
return { r: avgR, g: avgG, b: avgB };
});
// Check convergence
const converged = centroids.every((c, i) =>
const converged = centroids.every(
(c, i) =>
c.r === newCentroids[i].r &&
c.g === newCentroids[i].g &&
c.b === newCentroids[i].b
c.b === newCentroids[i].b,
);
centroids = newCentroids;
@@ -869,11 +961,17 @@ kMeansClustering(colors, k) {
// Sort by saturation (most saturated first)
return centroids.sort((a, b) => {
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);
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);
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);
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);
return satB - satA;
});
},
},
rgbToHex(r, g, b) {
return (