879 lines
33 KiB
Vue
879 lines
33 KiB
Vue
<template>
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<Modal :is-open="isOpen" @close="cancel" title="Split spritesheet">
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<div class="space-y-6">
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<div class="flex flex-col space-y-4">
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<div class="flex items-center justify-center mb-4">
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<img :src="imageUrl" alt="Spritesheet" class="max-w-full max-h-48 sm:max-h-64 border border-gray-300 dark:border-gray-600 rounded-lg" :style="settingsStore.pixelPerfect ? { 'image-rendering': 'pixelated' } : {}" />
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</div>
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<div class="grid grid-cols-1 sm:grid-cols-2 gap-4">
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<div class="space-y-2">
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<label for="detection-method" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Detection Method</label>
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<select
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id="detection-method"
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v-model="detectionMethod"
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class="w-full px-3 py-2 border border-gray-300 dark:border-gray-600 dark:bg-gray-800 dark:text-gray-200 rounded-md shadow-sm focus:outline-none focus:ring-blue-500 focus:border-blue-500 dark:focus:ring-blue-400 dark:focus:border-blue-400"
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data-rybbit-event="spritesheet-detection-method"
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>
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<option value="irregular">Auto-detect</option>
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<option value="manual">Manual (specify rows and columns)</option>
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</select>
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</div>
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<div v-if="detectionMethod === 'auto' || detectionMethod === 'irregular'" class="space-y-2">
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<label for="sensitivity" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Detection Sensitivity</label>
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<input type="range" id="sensitivity" v-model="sensitivity" min="1" max="100" class="w-full dark:accent-blue-400" data-rybbit-event="spritesheet-sensitivity" />
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<div class="text-xs text-gray-500 dark:text-gray-400 flex justify-between">
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<span>Low</span>
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<span>High</span>
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</div>
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</div>
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<div v-if="detectionMethod === 'manual'" class="space-y-2">
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<label for="rows" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Rows</label>
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<input
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type="number"
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id="rows"
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v-model.number="rows"
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min="1"
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class="w-full px-3 py-2 border border-gray-300 dark:border-gray-600 dark:bg-gray-800 dark:text-gray-200 rounded-md shadow-sm focus:outline-none focus:ring-blue-500 focus:border-blue-500 dark:focus:ring-blue-400 dark:focus:border-blue-400"
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data-rybbit-event="spritesheet-rows"
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/>
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</div>
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<div v-if="detectionMethod === 'manual'" class="space-y-2">
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<label for="columns" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Columns</label>
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<input
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type="number"
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id="columns"
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v-model.number="columns"
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min="1"
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class="w-full px-3 py-2 border border-gray-300 dark:border-gray-600 dark:bg-gray-800 dark:text-gray-200 rounded-md shadow-sm focus:outline-none focus:ring-blue-500 focus:border-blue-500 dark:focus:ring-blue-400 dark:focus:border-blue-400"
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data-rybbit-event="spritesheet-columns"
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/>
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</div>
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</div>
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<div class="space-y-2">
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<div class="flex items-center">
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<input type="checkbox" id="remove-empty" v-model="removeEmpty" class="h-4 w-4 text-blue-600 dark:text-blue-400 focus:ring-blue-500 dark:focus:ring-blue-400 border-gray-300 dark:border-gray-600 rounded" data-rybbit-event="spritesheet-remove-empty" />
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<label for="remove-empty" class="ml-2 block text-sm text-gray-700 dark:text-gray-300"> Remove empty sprites (transparent/background color) </label>
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</div>
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</div>
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<div v-if="previewSprites.length > 0" class="space-y-2">
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<h3 class="text-sm font-medium text-gray-700 dark:text-gray-300">Preview ({{ previewSprites.length }} sprites)</h3>
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<div class="grid grid-cols-3 sm:grid-cols-6 md:grid-cols-8 gap-2 max-h-96 overflow-y-auto p-2 border border-gray-200 dark:border-gray-700 rounded-lg bg-white dark:bg-gray-800">
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<div v-for="(sprite, index) in previewSprites" :key="index" class="relative border border-gray-300 dark:border-gray-600 rounded bg-gray-100 dark:bg-gray-700 flex items-center justify-center" :style="{ width: '80px', height: '80px' }">
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<img :src="sprite.url" alt="Sprite preview" class="max-w-full max-h-full" :style="settingsStore.pixelPerfect ? { 'image-rendering': 'pixelated' } : {}" />
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</div>
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</div>
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</div>
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</div>
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<div class="flex flex-col-reverse sm:flex-row sm:justify-end space-y-3 space-y-reverse sm:space-y-0 sm:space-x-3">
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<button
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@click="cancel"
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class="px-4 py-2 border border-gray-300 dark:border-gray-600 rounded-md shadow-sm text-sm font-medium text-gray-700 dark:text-gray-300 bg-white dark:bg-gray-700 hover:bg-gray-50 dark:hover:bg-gray-600 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-blue-500 dark:focus:ring-offset-gray-800"
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data-rybbit-event="spritesheet-cancel"
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>
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Cancel
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</button>
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<button
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@click="confirm"
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class="px-4 py-2 border border-transparent rounded-md shadow-sm text-sm font-medium text-white bg-blue-600 hover:bg-blue-700 dark:bg-blue-500 dark:hover:bg-blue-600 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-blue-500 dark:focus:ring-offset-gray-800"
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:disabled="previewSprites.length === 0 || isProcessing"
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data-rybbit-event="spritesheet-split"
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>
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Split spritesheet
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</button>
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</div>
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</div>
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</Modal>
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</template>
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<script setup lang="ts">
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import { ref, watch, onUnmounted } from 'vue';
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import Modal from './utilities/Modal.vue';
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import { useSettingsStore } from '@/stores/useSettingsStore';
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import type { SpriteFile } from '@/types/sprites';
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interface SpritePreview {
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url: string;
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x: number;
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y: number;
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width: number;
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height: number;
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isEmpty: boolean;
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}
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const props = defineProps<{
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isOpen: boolean;
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imageUrl: string;
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imageFile: File | null | undefined;
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}>();
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const emit = defineEmits<{
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(e: 'close'): void;
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(e: 'split', sprites: SpriteFile[]): void; // Change from File[] to SpriteFile[]
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}>();
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// Get settings from store
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const settingsStore = useSettingsStore();
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// State
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const detectionMethod = ref<'manual' | 'auto' | 'irregular'>('irregular');
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const rows = ref(1);
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const columns = ref(1);
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const sensitivity = ref(50);
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const removeEmpty = ref(true);
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const previewSprites = ref<SpritePreview[]>([]);
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const isProcessing = ref(false);
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const imageElement = ref<HTMLImageElement | null>(null);
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const irregularWorker = ref<Worker | null>(null);
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// Cache for sprite detection results
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const detectionCache = new Map<string, SpritePreview[]>();
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// Generate cache key for current detection settings
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function getCacheKey(url: string, method: string, sensitivity: number, removeEmpty: boolean): string {
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return `${url}-${method}-${sensitivity}-${removeEmpty}`;
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}
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// Load the image when the component is mounted or the URL changes
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watch(() => props.imageUrl, loadImage, { immediate: true });
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function loadImage() {
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if (!props.imageUrl) return;
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const img = new Image();
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img.onload = () => {
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imageElement.value = img;
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// Set default rows and columns based on image dimensions
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// This is a simple heuristic - for pixel art, we might want to detect sprite size
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const aspectRatio = img.width / img.height;
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if (aspectRatio > 1) {
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// Landscape orientation - likely more columns than rows
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columns.value = Math.min(Math.ceil(Math.sqrt(aspectRatio * 4)), 8);
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rows.value = Math.ceil(4 / columns.value);
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} else {
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// Portrait orientation - likely more rows than columns
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rows.value = Math.min(Math.ceil(Math.sqrt(4 / aspectRatio)), 8);
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columns.value = Math.ceil(4 / rows.value);
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}
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// Generate initial preview
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generatePreview();
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};
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img.src = props.imageUrl;
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}
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// Generate preview of split sprites with caching
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async function generatePreview() {
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if (!imageElement.value) return;
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// Check cache first
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const cacheKey = getCacheKey(props.imageUrl, detectionMethod.value, sensitivity.value, removeEmpty.value);
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if (detectionCache.has(cacheKey)) {
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previewSprites.value = detectionCache.get(cacheKey)!;
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return;
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}
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isProcessing.value = true;
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previewSprites.value = [];
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try {
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const img = imageElement.value;
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if (detectionMethod.value === 'auto') {
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await autoDetectSprites(img);
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} else if (detectionMethod.value === 'irregular') {
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await detectIrregularSprites(img);
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} else {
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await splitSpritesheet(img, rows.value, columns.value);
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}
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// Cache results (limit cache size to prevent memory issues)
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if (detectionCache.size > 10) {
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const firstKey = detectionCache.keys().next().value;
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detectionCache.delete(firstKey || '');
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}
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detectionCache.set(cacheKey, previewSprites.value);
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} catch (error) {
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console.error('Error generating preview:', error);
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} finally {
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isProcessing.value = false;
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}
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}
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function getSpriteBoundingBox(ctx: CanvasRenderingContext2D, width: number, height: number) {
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const imageData = ctx.getImageData(0, 0, width, height);
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const data = imageData.data;
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let minX = width;
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let minY = height;
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let maxX = 0;
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let maxY = 0;
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let hasContent = false;
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// Scan through all pixels to find the bounding box
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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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const idx = (y * width + x) * 4;
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// Check if pixel is not transparent (alpha > 0)
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if (data[idx + 3] > 10) {
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// Allow some tolerance for compression artifacts
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minX = Math.min(minX, x);
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minY = Math.min(minY, y);
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maxX = Math.max(maxX, x);
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maxY = Math.max(maxY, y);
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hasContent = true;
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}
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}
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}
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// If no non-transparent pixels found, return null
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if (!hasContent) {
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return null;
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}
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// Return bounding box with a small padding, ensuring it stays within bounds
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const bx = Math.max(0, minX - 1);
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const by = Math.max(0, minY - 1);
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const bw = Math.min(width - bx, maxX - minX + 3); // +1 for inclusive bounds, +2 for padding
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const bh = Math.min(height - by, maxY - minY + 3);
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return {
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x: bx,
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y: by,
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width: Math.max(1, bw),
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height: Math.max(1, bh),
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};
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}
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// Split spritesheet manually based on rows and columns
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async function splitSpritesheet(img: HTMLImageElement, rows: number, columns: number) {
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const safeColumns = Number.isFinite(columns) && columns > 0 ? Math.floor(columns) : 1;
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const safeRows = Number.isFinite(rows) && rows > 0 ? Math.floor(rows) : 1;
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const spriteWidth = Math.max(1, Math.floor(img.width / safeColumns));
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const spriteHeight = Math.max(1, Math.floor(img.height / safeRows));
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const sprites: SpritePreview[] = [];
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// Create a canvas for processing the full sprite
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const canvas = document.createElement('canvas');
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const ctx = canvas.getContext('2d');
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// Create a second canvas for the cropped sprite
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const croppedCanvas = document.createElement('canvas');
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const croppedCtx = croppedCanvas.getContext('2d');
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if (!ctx || !croppedCtx) return;
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canvas.width = spriteWidth;
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canvas.height = spriteHeight;
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// Split the image into individual sprites
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for (let row = 0; row < safeRows; row++) {
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for (let col = 0; col < safeColumns; col++) {
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// Clear the canvas
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ctx.clearRect(0, 0, canvas.width, canvas.height);
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// Draw the portion of the spritesheet
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ctx.drawImage(img, col * spriteWidth, row * spriteHeight, spriteWidth, spriteHeight, 0, 0, spriteWidth, spriteHeight);
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// Check if the sprite is empty (all transparent or same color)
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const isEmpty = removeEmpty.value ? isCanvasEmpty(ctx, spriteWidth, spriteHeight) : false;
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// If we're not removing empty sprites or the sprite is not empty
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if (!removeEmpty.value || !isEmpty) {
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// Get bounding box of non-transparent pixels
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const boundingBox = getSpriteBoundingBox(ctx, spriteWidth, spriteHeight);
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let url;
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let x = 0; // Default position (will be updated if we have a bounding box)
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let y = 0;
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let width = spriteWidth;
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let height = spriteHeight;
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if (boundingBox) {
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// The key change: preserve the original position where the sprite was found
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x = boundingBox.x;
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y = boundingBox.y;
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width = boundingBox.width;
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height = boundingBox.height;
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// Set dimensions for the cropped sprite
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croppedCanvas.width = boundingBox.width;
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croppedCanvas.height = boundingBox.height;
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// Draw only the non-transparent part
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croppedCtx.clearRect(0, 0, croppedCanvas.width, croppedCanvas.height);
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croppedCtx.drawImage(canvas, boundingBox.x, boundingBox.y, boundingBox.width, boundingBox.height, 0, 0, boundingBox.width, boundingBox.height);
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// Convert to data URL
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url = croppedCanvas.toDataURL('image/png');
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} else {
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// No non-transparent pixels found, use the original sprite
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url = canvas.toDataURL('image/png');
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}
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sprites.push({
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url,
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x,
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y,
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width,
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height,
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isEmpty,
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});
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}
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}
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}
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previewSprites.value = sprites;
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}
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// Auto-detect sprites based on transparency/color differences
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async function autoDetectSprites(img: HTMLImageElement) {
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// This is a simplified implementation
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// A more sophisticated algorithm would analyze the image to find sprite boundaries
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// For now, we'll use a simple approach:
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// 1. Try to detect the sprite size by looking for repeating patterns
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// 2. Then use that size to split the spritesheet
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const canvas = document.createElement('canvas');
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const ctx = canvas.getContext('2d');
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if (!ctx) return;
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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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// Get image data for analysis
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const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
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const data = imageData.data;
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// Simple detection of sprite size based on transparency patterns
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// This is a very basic implementation and might not work for all spritesheets
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const { detectedWidth, detectedHeight } = detectSpriteSize(data, canvas.width, canvas.height);
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if (detectedWidth > 0 && detectedHeight > 0) {
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// Sanity thresholds to avoid absurdly tiny tiles/huge counts
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const MIN_TILE = 8;
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const MAX_SPRITES = 1024;
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if (detectedWidth < MIN_TILE || detectedHeight < MIN_TILE) {
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// Fallback if tile is too small
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const s = Math.max(1, Math.min(100, sensitivity.value));
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const divisor = 3 + Math.round(s / 20); // 3..8
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const estimatedSize = Math.max(MIN_TILE, Math.floor(Math.min(img.width, img.height) / divisor));
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const estimatedRows = Math.max(1, Math.floor(img.height / estimatedSize));
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const estimatedColumns = Math.max(1, Math.floor(img.width / estimatedSize));
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await splitSpritesheet(img, estimatedRows, estimatedColumns);
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return;
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}
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const detectedRows = Math.max(1, Math.floor(img.height / detectedHeight));
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const detectedColumns = Math.max(1, Math.floor(img.width / detectedWidth));
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// If the detected combination is unreasonably high, fallback to estimate
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if (detectedRows * detectedColumns > MAX_SPRITES) {
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const s = Math.max(1, Math.min(100, sensitivity.value));
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const divisor = 3 + Math.round(s / 20); // 3..8
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const estimatedSize = Math.max(MIN_TILE, Math.floor(Math.min(img.width, img.height) / divisor));
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const estimatedRows = Math.max(1, Math.floor(img.height / estimatedSize));
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const estimatedColumns = Math.max(1, Math.floor(img.width / estimatedSize));
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await splitSpritesheet(img, estimatedRows, estimatedColumns);
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return;
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}
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// Use the detected size to split the spritesheet
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await splitSpritesheet(img, detectedRows, detectedColumns);
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} else {
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// Fallback to manual splitting with a reasonable guess based on sensitivity
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const s = Math.max(1, Math.min(100, sensitivity.value));
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const divisor = 3 + Math.round(s / 20); // 3..8
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const estimatedSize = Math.max(8, Math.floor(Math.min(img.width, img.height) / divisor));
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const estimatedRows = Math.max(1, Math.floor(img.height / estimatedSize));
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const estimatedColumns = Math.max(1, Math.floor(img.width / estimatedSize));
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await splitSpritesheet(img, estimatedRows, estimatedColumns);
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}
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}
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// Helper function to detect sprite size based on transparency/color gutters and edge periodicity
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function detectSpriteSize(data: Uint8ClampedArray, width: number, height: number) {
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// Map sensitivity (1-100) to thresholds:
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// Higher sensitivity -> allows stricter background matching and lower gutter proportion thresholds
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const s = Math.max(1, Math.min(100, sensitivity.value));
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// Background/color thresholds
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const alphaBgThresh = Math.round(255 * (0.15 + (100 - s) * 0.001)); // 15%-25% depending on sensitivity
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const colorTol = Math.round(10 + (100 - s) * 0.8); // 10..90 Euclidean approx
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const gutterPropThresh = 0.92 - s * 0.004; // 0.92 down to ~0.52
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function getPixel(x: number, y: number) {
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const idx = (y * width + x) * 4;
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return [data[idx], data[idx + 1], data[idx + 2], data[idx + 3]] as [number, number, number, number];
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}
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// Estimate background color from corners (median of corners)
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const corners: [number, number, number, number][] = [getPixel(0, 0), getPixel(width - 1, 0), getPixel(0, height - 1), getPixel(width - 1, height - 1)];
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function median(arr: number[]) {
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const a = arr.slice().sort((a, b) => a - b);
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const mid = (a.length - 1) / 2;
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return (a[Math.floor(mid)] + a[Math.ceil(mid)]) / 2;
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}
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const bg = [median(corners.map(c => c[0])), median(corners.map(c => c[1])), median(corners.map(c => c[2])), median(corners.map(c => c[3]))] as [number, number, number, number];
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function isBg(r: number, g: number, b: number, a: number) {
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if (a <= alphaBgThresh) return true;
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const dr = r - bg[0];
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const dg = g - bg[1];
|
|
const db = b - bg[2];
|
|
// Use Manhattan distance approximation for speed
|
|
const manhattan = Math.abs(dr) + Math.abs(dg) + Math.abs(db);
|
|
// Normalize approx to 0..~765 and compare to scaled tolerance
|
|
return manhattan <= colorTol * 3;
|
|
}
|
|
|
|
// Sample stride to speed up scanning large sheets
|
|
const rowSample = Math.max(1, Math.floor(height / 64));
|
|
const colSample = Math.max(1, Math.floor(width / 64));
|
|
|
|
// Compute background proportion per column and row
|
|
const colBgProp: number[] = new Array(width).fill(0);
|
|
for (let x = 0; x < width; x++) {
|
|
let bgCount = 0;
|
|
let total = 0;
|
|
for (let y = 0; y < height; y += rowSample) {
|
|
const [r, g, b, a] = getPixel(x, y);
|
|
if (isBg(r, g, b, a)) bgCount++;
|
|
total++;
|
|
}
|
|
colBgProp[x] = total > 0 ? bgCount / total : 1;
|
|
}
|
|
|
|
const rowBgProp: number[] = new Array(height).fill(0);
|
|
for (let y = 0; y < height; y++) {
|
|
let bgCount = 0;
|
|
let total = 0;
|
|
for (let x = 0; x < width; x += colSample) {
|
|
const [r, g, b, a] = getPixel(x, y);
|
|
if (isBg(r, g, b, a)) bgCount++;
|
|
total++;
|
|
}
|
|
rowBgProp[y] = total > 0 ? bgCount / total : 1;
|
|
}
|
|
|
|
function extractRuns(bgProp: number[]): { emptyRuns: [number, number][]; segSizes: number[] } {
|
|
const emptyRuns: [number, number][] = [];
|
|
const segSizes: number[] = [];
|
|
let inEmpty = false;
|
|
let runStart = 0;
|
|
let lastSeparatorEnd = -1;
|
|
for (let i = 0; i < bgProp.length; i++) {
|
|
const isEmpty = bgProp[i] >= gutterPropThresh;
|
|
if (isEmpty && !inEmpty) {
|
|
inEmpty = true;
|
|
runStart = i;
|
|
if (lastSeparatorEnd >= 0) {
|
|
const seg = runStart - lastSeparatorEnd - 1;
|
|
if (seg > 0) segSizes.push(seg);
|
|
}
|
|
} else if (!isEmpty && inEmpty) {
|
|
inEmpty = false;
|
|
emptyRuns.push([runStart, i - 1]);
|
|
lastSeparatorEnd = i - 1;
|
|
}
|
|
}
|
|
if (inEmpty) {
|
|
emptyRuns.push([runStart, bgProp.length - 1]);
|
|
lastSeparatorEnd = bgProp.length - 1;
|
|
}
|
|
// Trailing segment after last empty run
|
|
if (lastSeparatorEnd >= 0 && lastSeparatorEnd < bgProp.length - 1) {
|
|
const seg = bgProp.length - 1 - lastSeparatorEnd;
|
|
if (seg > 0) segSizes.push(seg);
|
|
}
|
|
return { emptyRuns, segSizes };
|
|
}
|
|
|
|
function modeWithTolerance(values: number[], tol = 2): number {
|
|
if (values.length === 0) return 0;
|
|
values.sort((a, b) => a - b);
|
|
let bestCount = 0;
|
|
let bestVal = values[0];
|
|
for (let i = 0; i < values.length; i++) {
|
|
const base = values[i];
|
|
let count = 1;
|
|
for (let j = i + 1; j < values.length; j++) {
|
|
if (Math.abs(values[j] - base) <= tol) count++;
|
|
else break;
|
|
}
|
|
if (count > bestCount) {
|
|
bestCount = count;
|
|
bestVal = base;
|
|
}
|
|
}
|
|
return bestVal;
|
|
}
|
|
|
|
const colRuns = extractRuns(colBgProp);
|
|
const rowRuns = extractRuns(rowBgProp);
|
|
|
|
let detectedWidth = modeWithTolerance(colRuns.segSizes, 2);
|
|
let detectedHeight = modeWithTolerance(rowRuns.segSizes, 2);
|
|
|
|
// Fallback: use edge periodicity via autocorrelation if gutters not found
|
|
function edgeAutocorrLength(axis: 'x' | 'y'): number {
|
|
const maxLen = axis === 'x' ? width : height;
|
|
const otherLen = axis === 'x' ? height : width;
|
|
const sampleStepMajor = Math.max(1, Math.floor(maxLen / 512));
|
|
const sampleStepMinor = Math.max(1, Math.floor(otherLen / 64));
|
|
const energy: number[] = new Array(maxLen).fill(0);
|
|
if (axis === 'x') {
|
|
for (let x = 0; x < maxLen - 1; x += sampleStepMajor) {
|
|
let e = 0;
|
|
for (let y = 0; y < otherLen; y += sampleStepMinor) {
|
|
const [r1, g1, b1, a1] = getPixel(x, y);
|
|
const [r2, g2, b2, a2] = getPixel(x + 1, y);
|
|
e += Math.abs(r1 - r2) + Math.abs(g1 - g2) + Math.abs(b1 - b2) + Math.abs(a1 - a2);
|
|
}
|
|
energy[x] = e;
|
|
}
|
|
} else {
|
|
for (let y = 0; y < maxLen - 1; y += sampleStepMajor) {
|
|
let e = 0;
|
|
for (let x = 0; x < otherLen; x += sampleStepMinor) {
|
|
const [r1, g1, b1, a1] = getPixel(x, y);
|
|
const [r2, g2, b2, a2] = getPixel(x, y + 1);
|
|
e += Math.abs(r1 - r2) + Math.abs(g1 - g2) + Math.abs(b1 - b2) + Math.abs(a1 - a2);
|
|
}
|
|
energy[y] = e;
|
|
}
|
|
}
|
|
const minTile = Math.max(3, Math.floor(Math.min(maxLen / 32, 128)));
|
|
const maxTile = Math.max(minTile + 1, Math.floor(Math.min(maxLen / 2, 512)));
|
|
let bestLag = 0;
|
|
let bestVal = -Infinity;
|
|
for (let lag = minTile; lag <= maxTile; lag++) {
|
|
let sum = 0;
|
|
for (let i = 0; i + lag < energy.length; i++) {
|
|
const e1 = energy[i] || 0;
|
|
const e2 = energy[i + lag] || 0;
|
|
sum += e1 * e2;
|
|
}
|
|
if (sum > bestVal) {
|
|
bestVal = sum;
|
|
bestLag = lag;
|
|
}
|
|
}
|
|
return bestLag;
|
|
}
|
|
|
|
if (detectedWidth <= 0 || detectedWidth > width) {
|
|
const lagX = edgeAutocorrLength('x');
|
|
if (lagX > 0 && lagX <= width) detectedWidth = lagX;
|
|
}
|
|
if (detectedHeight <= 0 || detectedHeight > height) {
|
|
const lagY = edgeAutocorrLength('y');
|
|
if (lagY > 0 && lagY <= height) detectedHeight = lagY;
|
|
}
|
|
|
|
// Sanity checks
|
|
if (!Number.isFinite(detectedWidth) || detectedWidth <= 0 || detectedWidth > width) detectedWidth = 0;
|
|
if (!Number.isFinite(detectedHeight) || detectedHeight <= 0 || detectedHeight > height) detectedHeight = 0;
|
|
|
|
return { detectedWidth, detectedHeight };
|
|
}
|
|
|
|
// Detect irregular sprites using Web Worker
|
|
async function detectIrregularSprites(img: HTMLImageElement): Promise<void> {
|
|
const canvas = document.createElement('canvas');
|
|
const ctx = canvas.getContext('2d');
|
|
if (!ctx) throw new Error('Could not get canvas context');
|
|
|
|
canvas.width = img.width;
|
|
canvas.height = img.height;
|
|
ctx.drawImage(img, 0, 0);
|
|
|
|
const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
|
|
|
|
// Initialize worker lazily using Vite worker syntax
|
|
if (!irregularWorker.value) {
|
|
try {
|
|
irregularWorker.value = new Worker(new URL('../workers/irregularSpriteDetection.worker.ts', import.meta.url), { type: 'module' });
|
|
} catch (error) {
|
|
console.error('Failed to create worker:', error);
|
|
// Fallback to auto detection if worker fails
|
|
await autoDetectSprites(img);
|
|
return;
|
|
}
|
|
}
|
|
|
|
return new Promise<void>((resolve, reject) => {
|
|
const worker = irregularWorker.value!;
|
|
|
|
const handleMessage = async (e: MessageEvent) => {
|
|
clearTimeout(timeout);
|
|
worker.removeEventListener('message', handleMessage);
|
|
worker.removeEventListener('error', handleError);
|
|
|
|
if (e.data.type === 'spritesDetected') {
|
|
try {
|
|
await processDetectedSprites(img, e.data.sprites, e.data.backgroundColor);
|
|
resolve();
|
|
} catch (error) {
|
|
reject(error);
|
|
}
|
|
}
|
|
};
|
|
|
|
// Set timeout for worker processing
|
|
const timeout = setTimeout(() => {
|
|
worker.removeEventListener('message', handleMessage);
|
|
worker.removeEventListener('error', handleError);
|
|
console.warn('Worker timeout, falling back to auto detection');
|
|
// Fallback to auto detection
|
|
autoDetectSprites(img).then(resolve).catch(reject);
|
|
}, 30000); // 30 second timeout
|
|
|
|
const handleError = (error: ErrorEvent) => {
|
|
clearTimeout(timeout);
|
|
worker.removeEventListener('message', handleMessage);
|
|
worker.removeEventListener('error', handleError);
|
|
console.error('Worker error, falling back to auto detection:', error);
|
|
// Fallback to auto detection
|
|
autoDetectSprites(img).then(resolve).catch(reject);
|
|
};
|
|
|
|
worker.addEventListener('message', handleMessage);
|
|
worker.addEventListener('error', handleError);
|
|
|
|
try {
|
|
worker.postMessage({
|
|
type: 'detectIrregularSprites',
|
|
imageData,
|
|
sensitivity: sensitivity.value,
|
|
maxSize: 2048, // Limit processing size for performance
|
|
});
|
|
} catch (error) {
|
|
clearTimeout(timeout);
|
|
console.error('Failed to post message to worker:', error);
|
|
// Fallback to auto detection
|
|
autoDetectSprites(img).then(resolve).catch(reject);
|
|
}
|
|
});
|
|
}
|
|
|
|
// Process sprites detected by the worker (optimized)
|
|
async function processDetectedSprites(img: HTMLImageElement, detectedSprites: any[], backgroundColor?: [number, number, number, number]): Promise<void> {
|
|
if (!detectedSprites?.length) {
|
|
previewSprites.value = [];
|
|
return;
|
|
}
|
|
|
|
const sprites: SpritePreview[] = [];
|
|
const sourceCanvas = document.createElement('canvas');
|
|
const sourceCtx = sourceCanvas.getContext('2d');
|
|
const spriteCanvas = document.createElement('canvas');
|
|
const spriteCtx = spriteCanvas.getContext('2d');
|
|
|
|
if (!sourceCtx || !spriteCtx) return;
|
|
|
|
// Setup source canvas once
|
|
sourceCanvas.width = img.width;
|
|
sourceCanvas.height = img.height;
|
|
sourceCtx.drawImage(img, 0, 0);
|
|
|
|
// Process sprites in batches to avoid blocking
|
|
const batchSize = 50;
|
|
for (let i = 0; i < detectedSprites.length; i += batchSize) {
|
|
const batch = detectedSprites.slice(i, i + batchSize);
|
|
|
|
for (const sprite of batch) {
|
|
const { x, y, width, height } = sprite;
|
|
|
|
// Skip invalid sprites
|
|
if (width <= 0 || height <= 0) continue;
|
|
|
|
spriteCanvas.width = width;
|
|
spriteCanvas.height = height;
|
|
spriteCtx.clearRect(0, 0, width, height);
|
|
spriteCtx.drawImage(sourceCanvas, x, y, width, height, 0, 0, width, height);
|
|
|
|
// Remove background and make transparent for irregular sprites
|
|
if (detectionMethod.value === 'irregular' && backgroundColor) {
|
|
removeBackgroundFromSprite(spriteCtx, width, height, backgroundColor, sensitivity.value);
|
|
}
|
|
|
|
const isEmpty = removeEmpty.value ? isCanvasEmpty(spriteCtx, width, height) : false;
|
|
|
|
if (!removeEmpty.value || !isEmpty) {
|
|
sprites.push({
|
|
url: spriteCanvas.toDataURL('image/png'),
|
|
x,
|
|
y,
|
|
width,
|
|
height,
|
|
isEmpty,
|
|
});
|
|
}
|
|
}
|
|
|
|
// Yield control periodically for large batches
|
|
if (i > 0 && i % 100 === 0) {
|
|
await new Promise(resolve => setTimeout(resolve, 0));
|
|
}
|
|
}
|
|
|
|
previewSprites.value = sprites;
|
|
}
|
|
|
|
// Remove background color and make it transparent
|
|
function removeBackgroundFromSprite(ctx: CanvasRenderingContext2D, width: number, height: number, backgroundColor: [number, number, number, number], sensitivity: number): void {
|
|
const imageData = ctx.getImageData(0, 0, width, height);
|
|
const data = imageData.data;
|
|
|
|
const [bgR, bgG, bgB, bgA] = backgroundColor;
|
|
const colorTolerance = Math.round(50 - sensitivity * 0.45); // Same as worker
|
|
const alphaTolerance = Math.round(40 - sensitivity * 0.35);
|
|
|
|
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];
|
|
|
|
// Handle fully transparent pixels
|
|
if (a < 10) {
|
|
data[i + 3] = 0; // Make fully transparent
|
|
continue;
|
|
}
|
|
|
|
// Calculate color difference using Euclidean distance
|
|
const rDiff = r - bgR;
|
|
const gDiff = g - bgG;
|
|
const bDiff = b - bgB;
|
|
const aDiff = a - bgA;
|
|
|
|
const colorDistance = Math.sqrt(rDiff * rDiff + gDiff * gDiff + bDiff * bDiff);
|
|
const alphaDistance = Math.abs(aDiff);
|
|
|
|
// If pixel matches background color, make it transparent
|
|
if (colorDistance <= colorTolerance && alphaDistance <= alphaTolerance) {
|
|
data[i + 3] = 0; // Set alpha to 0 (transparent)
|
|
}
|
|
}
|
|
|
|
ctx.putImageData(imageData, 0, 0);
|
|
}
|
|
|
|
// Check if a canvas is empty (all transparent or same color)
|
|
function isCanvasEmpty(ctx: CanvasRenderingContext2D, width: number, height: number): boolean {
|
|
const imageData = ctx.getImageData(0, 0, width, height);
|
|
const data = imageData.data;
|
|
|
|
// Check if all pixels are transparent
|
|
let allTransparent = true;
|
|
let allSameColor = true;
|
|
|
|
// Reference values from first pixel
|
|
const firstR = data[0];
|
|
const firstG = data[1];
|
|
const firstB = data[2];
|
|
const firstA = data[3];
|
|
|
|
for (let i = 0; i < data.length; i += 4) {
|
|
const alpha = data[i + 3];
|
|
|
|
// Check transparency
|
|
if (alpha > 10) {
|
|
// Allow some tolerance for compression artifacts
|
|
allTransparent = false;
|
|
}
|
|
|
|
// Check if all pixels are the same color
|
|
if (data[i] !== firstR || data[i + 1] !== firstG || data[i + 2] !== firstB || Math.abs(data[i + 3] - firstA) > 10) {
|
|
allSameColor = false;
|
|
}
|
|
|
|
// Early exit if we've determined it's not empty
|
|
if (!allTransparent && !allSameColor) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
return allTransparent || allSameColor;
|
|
}
|
|
|
|
// Convert preview sprites to actual files
|
|
async function createSpriteFiles(): Promise<SpriteFile[]> {
|
|
const spriteFiles: SpriteFile[] = [];
|
|
|
|
for (let i = 0; i < previewSprites.value.length; i++) {
|
|
const sprite = previewSprites.value[i];
|
|
|
|
// Convert data URL to blob
|
|
const response = await fetch(sprite.url);
|
|
const blob = await response.blob();
|
|
|
|
// Create file from blob
|
|
const fileName = `sprite_${i + 1}.png`;
|
|
const file = new File([blob], fileName, { type: 'image/png' });
|
|
|
|
// Create sprite file with position information
|
|
spriteFiles.push({
|
|
file,
|
|
x: sprite.x,
|
|
y: sprite.y,
|
|
width: sprite.width,
|
|
height: sprite.height,
|
|
});
|
|
}
|
|
|
|
return spriteFiles;
|
|
}
|
|
|
|
// Actions
|
|
function cancel() {
|
|
emit('close');
|
|
}
|
|
|
|
async function confirm() {
|
|
if (previewSprites.value.length === 0) return;
|
|
|
|
isProcessing.value = true;
|
|
|
|
try {
|
|
const files = await createSpriteFiles();
|
|
emit('split', files);
|
|
emit('close');
|
|
} catch (error) {
|
|
console.error('Error creating sprite files:', error);
|
|
} finally {
|
|
isProcessing.value = false;
|
|
}
|
|
}
|
|
|
|
// Add these watchers to automatically update preview
|
|
watch([rows, columns, removeEmpty, detectionMethod, sensitivity], () => {
|
|
if (imageElement.value) {
|
|
generatePreview();
|
|
}
|
|
});
|
|
|
|
// Clean up worker and cache on component unmount
|
|
onUnmounted(() => {
|
|
if (irregularWorker.value) {
|
|
irregularWorker.value.terminate();
|
|
irregularWorker.value = null;
|
|
}
|
|
detectionCache.clear();
|
|
});
|
|
</script>
|