/**
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* 数据拆分工具类
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* 提供随机分配、种子生成等功能
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*/
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import { ElMessage } from "element-plus";
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import { generateRandomSeed } from "./randomSeedUtils";
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/**
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* 使用Fisher-Yates洗牌算法进行随机分配
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* @param {Array} instances - 图片数组
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* @param {number} trainCount - 训练集数量
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* @param {number} valCount - 验证集数量
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* @param {number} testCount - 测试集数量
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* @param {number} seed - 随机种子
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* @returns {Object} { assignments: Map, seed: number }
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*/
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export const assignInstancesRandomly = (
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instances,
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trainCount,
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valCount,
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testCount,
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seed = null
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) => {
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// 设置随机种子
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const randomSeed = seed || generateRandomSeed();
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const assignments = new Map();
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// 伪随机数生成器（确保可重现）
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let rng = randomSeed;
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const random = () => {
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rng = (rng * 9301 + 49297) % 233280;
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return rng / 233280;
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};
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// 创建分配数组
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const assignments_array = [
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...Array(trainCount).fill("train"),
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...Array(valCount).fill("val"),
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...Array(testCount).fill("test"),
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];
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// 不自动补充剩余图片，允许部分图片保持未分配状态
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const total = instances.length;
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const assigned = trainCount + valCount + testCount;
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console.log(`[splitUtils] 分配信息: 总图片${total}张，预期分配${assigned}张，未分配${total - assigned}张`)
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// Fisher-Yates 洗牌算法
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for (let i = assignments_array.length - 1; i > 0; i--) {
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const j = Math.floor(random() * (i + 1));
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[assignments_array[i], assignments_array[j]] = [
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assignments_array[j],
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assignments_array[i],
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];
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}
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// 随机选择要分配的图片
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const shuffledInstances = [...instances];
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for (let i = shuffledInstances.length - 1; i > 0; i--) {
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const j = Math.floor(random() * (i + 1));
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[shuffledInstances[i], shuffledInstances[j]] = [shuffledInstances[j], shuffledInstances[i]];
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}
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// 只分配指定数量的图片，剩余的保持未分配
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for (let i = 0; i < assigned && i < shuffledInstances.length; i++) {
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assignments.set(shuffledInstances[i].id, assignments_array[i]);
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}
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return { assignments, seed: randomSeed };
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};
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/**
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* 生成拆分ID
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* @returns {string} 格式: split_YYYYMMDDHHMMSSSS
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*/
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export const generateSplitId = () => {
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const now = new Date();
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// 格式化日期部分 YYYYMMDD
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const year = now.getFullYear();
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const month = (now.getMonth() + 1).toString().padStart(2, "0");
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const day = now.getDate().toString().padStart(2, "0");
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const dateStr = `${year}${month}${day}`;
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// 格式化时间部分 HHMMSSSS
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const hours = now.getHours().toString().padStart(2, "0");
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const minutes = now.getMinutes().toString().padStart(2, "0");
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const seconds = now.getSeconds().toString().padStart(2, "0");
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const milliseconds = now.getMilliseconds().toString().padStart(3, "0");
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const timeStr = `${hours}${minutes}${seconds}${milliseconds}`;
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return `split_${dateStr}${timeStr}`;
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};
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/**
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* 生成拆分名称
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* @returns {string} 格式: split_YYMMDDHHmmss (年月日时分秒)
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*/
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export const generateSplitName = () => {
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const now = new Date();
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// 使用年月日时分秒格式，简洁且唯一
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const year = now.getFullYear().toString().slice(-2); // 取后两位年份
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const month = (now.getMonth() + 1).toString().padStart(2, "0");
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const day = now.getDate().toString().padStart(2, "0");
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const hours = now.getHours().toString().padStart(2, "0");
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const minutes = now.getMinutes().toString().padStart(2, "0");
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const seconds = now.getSeconds().toString().padStart(2, "0");
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// 格式: split_231215143025 (年月日时分秒)
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const timeStr = `${year}${month}${day}${hours}${minutes}${seconds}`;
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return `split_${timeStr}`;
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};
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/**
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* 验证拆分数据的有效性
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* @param {Object} splitData - 拆分数据
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* @param {number} totalInstances - 总实例数
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* @returns {Object} { isValid: boolean, errors: string[] }
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*/
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export const validateSplitData = (splitData, totalInstances) => {
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const errors = [];
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if (!splitData.name || splitData.name.trim() === "") {
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errors.push("拆分名称不能为空");
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}
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const totalCount =
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splitData.trainCount + splitData.valCount + splitData.testCount;
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if (totalCount > totalInstances) {
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errors.push(`总数量 ${totalCount} 超过了实例总数 ${totalInstances}`);
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}
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if (totalCount === 0) {
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errors.push("至少需要分配一个实例");
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ElMessage({
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message: "至少需要分配一个实例",
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type: "warning",
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});
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}
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return {
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isValid: errors.length === 0,
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errors,
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};
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};
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/**
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* 计算拆分比例
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* @param {number} trainCount - 训练集数量
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* @param {number} valCount - 验证集数量
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* @param {number} testCount - 测试集数量
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* @param {number} totalInstances - 总实例数
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* @returns {Object} { trainRatio, valRatio, testRatio }
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*/
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export const calculateSplitRatios = (
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trainCount,
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valCount,
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testCount,
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totalInstances
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) => {
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if (totalInstances === 0)
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return { trainRatio: 0, valRatio: 0, testRatio: 0 };
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return {
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trainRatio: Math.round((trainCount / totalInstances) * 100),
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valRatio: Math.round((valCount / totalInstances) * 100),
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testRatio: Math.round((testCount / totalInstances) * 100),
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};
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};
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/**
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* 根据比例计算数量
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* @param {number} ratio - 比例 (0-100)
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* @param {number} totalInstances - 总实例数
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* @returns {number} 计算得出的数量
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*/
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export const calculateCountFromRatio = (ratio, totalInstances) => {
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return Math.floor((ratio / 100) * totalInstances);
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};
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/**
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* 计算数据集分配统计
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* @param {Map} assignments - 分配映射
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* @returns {Object} { trainCount, valCount, testCount, total }
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*/
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export const calculateDatasetCounts = (assignments) => {
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const assignmentValues = Array.from(assignments.values());
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const trainCount = assignmentValues.filter(v => v === 'train').length;
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const valCount = assignmentValues.filter(v => v === 'val').length;
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const testCount = assignmentValues.filter(v => v === 'test').length;
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return {
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trainCount,
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valCount,
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testCount,
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total: trainCount + valCount + testCount
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};
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};
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/**
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* 计算图像分布统计
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* @param {Array} images - 图片数组
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* @param {Map} assignments - 分配映射
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* @returns {Object} { train: Set, val: Set, test: Set }
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*/
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export const calculateImageDistribution = (images, assignments) => {
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const imagesByDataset = { train: new Set(), val: new Set(), test: new Set() };
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images.forEach(image => {
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const assignment = assignments.get(image.id);
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if (assignment && imagesByDataset[assignment]) {
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imagesByDataset[assignment].add(image.id);
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}
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});
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return imagesByDataset;
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};
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/**
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* 计算百分比
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* @param {number} value - 数值
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* @param {number} total - 总数
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* @returns {number} 百分比 (0-100)
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*/
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export const calculatePercentage = (value, total) => {
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return total > 0 ? Math.round(value / total * 100) : 0;
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};
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/**
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* 计算分配图片的实例统计
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* @param {Array} allImages - 所有图片数据
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* @param {Map} assignments - 分配映射（图片ID -> 数据集类型）
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* @returns {Object} { trainInstances, valInstances, testInstances, totalInstances }
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*/
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export const calculateInstanceCounts = (allImages, assignments) => {
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const counts = { train: 0, val: 0, test: 0 };
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// 遍历分配的图片，统计各数据集的实例数量
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assignments.forEach((datasetType, imageId) => {
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const image = allImages.find(img => img.id === imageId);
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if (image && counts[datasetType] !== undefined) {
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counts[datasetType] += image.annotationCount || 0;
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}
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});
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return {
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trainInstances: counts.train,
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valInstances: counts.val,
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testInstances: counts.test,
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totalInstances: counts.train + counts.val + counts.test
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};
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};
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