// 图片优化脚本 - 使用sharp压缩PNG图片
const sharp = require('sharp');
const fs = require('fs');
const path = require('path');

// 需要优化的大图片列表 (> 500KB)
const LARGE_IMAGES = [
  'CoverImage.png',
  'BasicImage.png',
  'teams-image.png',
  'hand-background.png',
  'basic-auth.png',
  'BgMusicCard.png',
  'Landing2.png',
  'Landing3.png',
  'Landing1.png',
  'smart-home.png',
  'automotive-background-card.png'
];

const IMG_DIR = path.join(__dirname, 'src/assets/img');
const BACKUP_DIR = path.join(IMG_DIR, 'original-backup');

// 确保备份目录存在
if (!fs.existsSync(BACKUP_DIR)) {
  fs.mkdirSync(BACKUP_DIR, { recursive: true });
}

console.log('🎨 开始优化图片...');
console.log('================================\n');

let totalBefore = 0;
let totalAfter = 0;
let optimizedCount = 0;

async function optimizeImage(filename) {
  const srcPath = path.join(IMG_DIR, filename);
  const backupPath = path.join(BACKUP_DIR, filename);

  if (!fs.existsSync(srcPath)) {
    console.log(`⚠️  跳过: ${filename} (文件不存在)`);
    return;
  }

  try {
    // 获取原始大小
    const beforeStats = fs.statSync(srcPath);
    const beforeSize = beforeStats.size;
    totalBefore += beforeSize;

    // 备份原始文件
    if (!fs.existsSync(backupPath)) {
      fs.copyFileSync(srcPath, backupPath);
    }

    // 读取图片元数据
    const metadata = await sharp(srcPath).metadata();

    // 优化策略：
    // 1. 如果宽度 > 2000px，缩放到 2000px
    // 2. 压缩质量到 85
    // 3. 使用 pngquant 算法压缩
    let pipeline = sharp(srcPath);

    if (metadata.width > 2000) {
      pipeline = pipeline.resize(2000, null, {
        withoutEnlargement: true,
        fit: 'inside'
      });
    }

    // PNG优化
    pipeline = pipeline.png({
      quality: 85,
      compressionLevel: 9,
      adaptiveFiltering: true,
      force: true
    });

    // 保存优化后的图片
    await pipeline.toFile(srcPath + '.tmp');

    // 替换原文件
    fs.renameSync(srcPath + '.tmp', srcPath);

    // 获取优化后的大小
    const afterStats = fs.statSync(srcPath);
    const afterSize = afterStats.size;
    totalAfter += afterSize;

    // 计算节省的大小
    const saved = beforeSize - afterSize;
    const percent = Math.round((saved / beforeSize) * 100);

    if (saved > 0) {
      optimizedCount++;
      console.log(`✅ ${filename}`);
      console.log(`   ${Math.round(beforeSize/1024)} KB → ${Math.round(afterSize/1024)} KB`);
      console.log(`   节省: ${Math.round(saved/1024)} KB (-${percent}%)\n`);
    } else {
      console.log(`ℹ️  ${filename} - 已经是最优化状态\n`);
    }

  } catch (error) {
    console.error(`❌ ${filename} 优化失败:`, error.message);
  }
}

async function main() {
  // 依次优化每个图片
  for (const img of LARGE_IMAGES) {
    await optimizeImage(img);
  }

  console.log('================================');
  console.log('📊 优化总结:\n');
  console.log(`   优化前总大小: ${Math.round(totalBefore/1024/1024)} MB`);
  console.log(`   优化后总大小: ${Math.round(totalAfter/1024/1024)} MB`);

  const totalSaved = totalBefore - totalAfter;
  const totalPercent = Math.round((totalSaved / totalBefore) * 100);

  console.log(`   节省空间: ${Math.round(totalSaved/1024/1024)} MB (-${totalPercent}%)`);
  console.log(`   成功优化: ${optimizedCount}/${LARGE_IMAGES.length} 个文件\n`);
  console.log('✅ 图片优化完成！');
  console.log(`📁 原始文件已备份到: ${BACKUP_DIR}\n`);
}

main().catch(console.error);