src/lib/imageProcessor.js

const sharp = require('sharp')

/**
 * 图片处理工具类
 * 专门处理长图检测、智能切割和重叠处理
 */
class ImageProcessor {
  constructor() {
    // 长图阈值：高度超过宽度*2.5倍（更合理的阈值，确保1080x4000等图片能被识别）
    this.LONG_IMAGE_RATIO = 2.5
    // 重叠像素范围
    this.OVERLAP_MIN = 50
    this.OVERLAP_MAX = 100
    // 默认重叠像素
    this.DEFAULT_OVERLAP = 75

    // 智能切割参数 - 针对模型识别效果优化
    this.OPTIMAL_SEGMENT_HEIGHT = 2500  // 理想片段高度
    this.MAX_SEGMENT_HEIGHT = 3000      // 最大片段高度
    this.MIN_SEGMENT_HEIGHT = 2000      // 最小片段高度
    this.MAX_SEGMENTS = 4               // 最大片段数量限制，避免API处理问题
  }

  /**
   * 检测是否为长图
   * @param {Buffer} imageBuffer - 图片缓冲区
   * @returns {Promise<{isLongImage: boolean, width: number, height: number, format: string}>}
   */
  async detectLongImage(imageBuffer) {
    try {
      const metadata = await sharp(imageBuffer).metadata()
      const { width, height, format } = metadata

      if (!width || !height) {
        throw new Error('无法获取图片尺寸信息')
      }

      const isLongImage = height > width * this.LONG_IMAGE_RATIO
      const threshold = width * this.LONG_IMAGE_RATIO

      console.log(`[长图检测] 图片尺寸: ${width}x${height} (${format}), 阈值: ${threshold}, 判断: ${isLongImage ? '是长图' : '非长图'}`)

      return {
        isLongImage,
        width,
        height,
        format,
        ratio: height / width,
        threshold
      }
    } catch (error) {
      throw new Error(`图片尺寸检测失败: ${error.message}`)
    }
  }

  /**
   * 计算最优片段高度
   * @param {number} width - 图片宽度
   * @param {number} height - 图片高度
   * @param {number} overlap - 重叠像素数
   * @returns {Object} 包含最优高度和片段数的对象
   */
  calculateOptimalSegmentHeight(width, height, overlap = this.DEFAULT_OVERLAP) {
    // 如果图片高度小于最大片段高度，直接返回原高度
    if (height <= this.MAX_SEGMENT_HEIGHT) {
      return {
        segmentHeight: height,
        segmentCount: 1,
        strategy: 'single_segment'
      }
    }

    // 计算理想的片段数量（基于理想高度）
    let idealSegmentCount = Math.ceil(height / this.OPTIMAL_SEGMENT_HEIGHT)

    // 如果片段数量超过最大限制，强制减少片段数量
    if (idealSegmentCount > this.MAX_SEGMENTS) {
      idealSegmentCount = this.MAX_SEGMENTS
      console.log(`[智能切割] 片段数量超限，强制限制为${this.MAX_SEGMENTS}个片段`)
    }

    // 计算每个片段的理想高度（不考虑重叠）
    const baseSegmentHeight = Math.floor(height / idealSegmentCount)

    // 检查是否在合理范围内
    if (baseSegmentHeight >= this.MIN_SEGMENT_HEIGHT && baseSegmentHeight <= this.MAX_SEGMENT_HEIGHT) {
      return {
        segmentHeight: baseSegmentHeight,
        segmentCount: idealSegmentCount,
        strategy: 'optimal_division'
      }
    }

    // 如果理想高度太小，减少片段数量
    if (baseSegmentHeight < this.MIN_SEGMENT_HEIGHT) {
      const adjustedSegmentCount = Math.max(1, Math.floor(height / this.MIN_SEGMENT_HEIGHT))
      const adjustedSegmentHeight = Math.floor(height / adjustedSegmentCount)

      return {
        segmentHeight: Math.min(adjustedSegmentHeight, this.MAX_SEGMENT_HEIGHT),
        segmentCount: adjustedSegmentCount,
        strategy: 'min_height_constraint'
      }
    }

    // 如果理想高度太大，增加片段数量
    if (baseSegmentHeight > this.MAX_SEGMENT_HEIGHT) {
      const adjustedSegmentCount = Math.ceil(height / this.MAX_SEGMENT_HEIGHT)
      const adjustedSegmentHeight = Math.floor(height / adjustedSegmentCount)

      return {
        segmentHeight: adjustedSegmentHeight,
        segmentCount: adjustedSegmentCount,
        strategy: 'max_height_constraint'
      }
    }

    // 兜底策略：使用最大高度
    return {
      segmentHeight: this.MAX_SEGMENT_HEIGHT,
      segmentCount: Math.ceil(height / this.MAX_SEGMENT_HEIGHT),
      strategy: 'fallback'
    }
  }

  /**
   * 计算切割参数（智能自适应版本）
   * @param {number} width - 图片宽度
   * @param {number} height - 图片高度
   * @param {number} overlap - 重叠像素数
   * @returns {Array<{top: number, left: number, width: number, height: number}>}
   */
  calculateCropRegions(width, height, overlap = this.DEFAULT_OVERLAP) {
    const regions = []

    // 计算最优片段高度
    const optimalConfig = this.calculateOptimalSegmentHeight(width, height, overlap)
    const { segmentHeight: baseSegmentHeight, segmentCount, strategy } = optimalConfig

    console.log(`[智能切割] 图片尺寸: ${width}x${height}, 策略: ${strategy}, 预计片段数: ${segmentCount}, 基础高度: ${baseSegmentHeight}`)

    let currentTop = 0
    let segmentIndex = 0

    while (currentTop < height && segmentIndex < segmentCount * 2) { // 防止无限循环
      const remainingHeight = height - currentTop
      let segmentHeight = Math.min(baseSegmentHeight, remainingHeight)

      // 如果不是第一个片段，需要向上扩展重叠区域
      if (segmentIndex > 0) {
        const overlapTop = Math.min(overlap, currentTop)
        currentTop = currentTop - overlapTop
        segmentHeight = Math.min(baseSegmentHeight + overlapTop, height - currentTop)
      }

      // 如果不是最后一个片段，需要向下扩展重叠区域
      const isLastSegment = (currentTop + segmentHeight >= height) || (segmentIndex >= segmentCount - 1)
      if (!isLastSegment) {
        const overlapBottom = Math.min(overlap, remainingHeight - segmentHeight)
        segmentHeight = Math.min(segmentHeight + overlapBottom, height - currentTop)
      }

      // 确保片段高度在合理范围内
      segmentHeight = Math.max(segmentHeight, this.MIN_SEGMENT_HEIGHT)
      segmentHeight = Math.min(segmentHeight, this.MAX_SEGMENT_HEIGHT)
      segmentHeight = Math.min(segmentHeight, height - currentTop) // 不能超出图片边界

      regions.push({
        top: currentTop,
        left: 0,
        width: width,
        height: segmentHeight,
        segmentIndex: segmentIndex,
        isFirst: segmentIndex === 0,
        isLast: currentTop + segmentHeight >= height,
        strategy: strategy
      })

      console.log(`[切割片段] 片段${segmentIndex + 1}: top=${currentTop}, height=${segmentHeight}, 范围=[${currentTop}, ${currentTop + segmentHeight}]`)

      // 移动到下一个片段的起始位置
      if (segmentIndex === 0) {
        // 第一个片段：直接移动基础高度
        currentTop += baseSegmentHeight
      } else {
        // 后续片段：移动基础高度减去重叠
        currentTop += Math.max(baseSegmentHeight - overlap, 1)
      }

      segmentIndex++

      // 如果已经覆盖了整个图片，退出循环
      if (currentTop >= height) {
        break
      }
    }

    console.log(`[切割完成] 实际生成${regions.length}个片段`)
    return regions
  }

  /**
   * 根据原图格式选择最优输出格式
   * @param {string} originalFormat - 原图格式
   * @returns {string} 最优输出格式
   */
  getOptimalOutputFormat(originalFormat) {
    // 格式映射表：原格式 -> 最优输出格式
    const formatMap = {
      'jpeg': 'jpeg',
      'jpg': 'jpeg',
      'png': 'png',
      'webp': 'webp',
      'gif': 'png', // GIF转PNG保持透明度
      'bmp': 'png', // BMP转PNG减小文件大小
      'tiff': 'png', // TIFF转PNG兼容性更好
      'svg': 'png'  // SVG转PNG用于显示
    }

    return formatMap[originalFormat?.toLowerCase()] || 'png'
  }

  /**
   * 应用最优格式转换
   * @param {Object} sharpInstance - Sharp实例
   * @param {string} format - 目标格式
   * @param {Object} options - 格式选项
   * @returns {Object} 配置后的Sharp实例
   */
  applyOptimalFormat(sharpInstance, format, options = {}) {
    switch (format) {
      case 'jpeg':
        return sharpInstance.jpeg({
          quality: options.quality || 85,
          progressive: true
        })
      case 'png':
        return sharpInstance.png({
          compressionLevel: options.compressionLevel || 6,
          progressive: true
        })
      case 'webp':
        return sharpInstance.webp({
          quality: options.quality || 80,
          effort: 4
        })
      default:
        return sharpInstance.png({ compressionLevel: 6 })
    }
  }

  /**
   * 切割长图为多个片段
   * @param {Buffer} imageBuffer - 原始图片缓冲区
   * @param {number} overlap - 重叠像素数
   * @returns {Promise<Array<{buffer: Buffer, metadata: Object}>>}
   */
  async cropLongImage(imageBuffer, overlap = this.DEFAULT_OVERLAP) {
    try {
      const detection = await this.detectLongImage(imageBuffer)

      if (!detection.isLongImage) {
        // 不是长图，返回原图
        return [{
          buffer: imageBuffer,
          metadata: {
            segmentIndex: 0,
            totalSegments: 1,
            isFirst: true,
            isLast: true,
            originalWidth: detection.width,
            originalHeight: detection.height,
            segmentWidth: detection.width,
            segmentHeight: detection.height
          }
        }]
      }

      const { width, height, format } = detection

      // 计算最优切割配置
      const optimalConfig = this.calculateOptimalSegmentHeight(width, height, overlap)
      const regions = this.calculateCropRegions(width, height, overlap)
      const segments = []

      // 选择最优输出格式
      const outputFormat = this.getOptimalOutputFormat(format)

      console.log(`检测到长图 ${width}x${height} (${format})，智能切割策略: ${optimalConfig.strategy}`)
      console.log(`切割配置: 目标高度=${optimalConfig.segmentHeight}px, 预计片段=${optimalConfig.segmentCount}个, 实际片段=${regions.length}个`)
      console.log(`输出格式: ${outputFormat}, 重叠像素: ${overlap}px`)

      for (const region of regions) {
        const sharpInstance = sharp(imageBuffer)
          .extract({
            left: region.left,
            top: region.top,
            width: region.width,
            height: region.height
          })

        // 应用最优格式
        const segmentBuffer = await this.applyOptimalFormat(sharpInstance, outputFormat)
          .toBuffer()

        segments.push({
          buffer: segmentBuffer,
          metadata: {
            segmentIndex: region.segmentIndex,
            totalSegments: regions.length,
            isFirst: region.isFirst,
            isLast: region.isLast,
            originalWidth: width,
            originalHeight: height,
            segmentWidth: region.width,
            segmentHeight: region.height,
            originalFormat: format,
            outputFormat: outputFormat,
            strategy: region.strategy,
            cropRegion: {
              top: region.top,
              left: region.left,
              width: region.width,
              height: region.height
            }
          }
        })

        const heightStatus = region.height <= 2500 ? '✓理想' : region.height <= 3000 ? '✓良好' : '⚠超限'
        console.log(`生成片段 ${region.segmentIndex + 1}/${regions.length}: ${region.width}x${region.height} (${heightStatus}) [${region.top}-${region.top + region.height}]`)
      }

      return segments
    } catch (error) {
      throw new Error(`长图切割失败: ${error.message}`)
    }
  }

  /**
   * 生成片段文件名
   * @param {string} originalName - 原始文件名
   * @param {number} segmentIndex - 片段索引
   * @param {number} totalSegments - 总片段数
   * @returns {string}
   */
  generateSegmentName(originalName, segmentIndex, totalSegments) {
    const nameWithoutExt = originalName.replace(/\.[^/.]+$/, '')
    const ext = originalName.includes('.') ? originalName.split('.').pop() : 'png'
    
    return `${nameWithoutExt}_part${segmentIndex + 1}of${totalSegments}.${ext}`
  }

  /**
   * 获取处理统计信息
   * @param {Array} segments - 切割后的片段数组
   * @returns {Object}
   */
  getProcessingStats(segments) {
    if (!segments || segments.length === 0) {
      return { totalSegments: 0, isLongImage: false }
    }

    const firstSegment = segments[0]
    const metadata = firstSegment.metadata

    return {
      totalSegments: segments.length,
      isLongImage: segments.length > 1,
      originalDimensions: {
        width: metadata.originalWidth,
        height: metadata.originalHeight
      },
      segmentDimensions: segments.map(segment => ({
        width: segment.metadata.segmentWidth,
        height: segment.metadata.segmentHeight,
        index: segment.metadata.segmentIndex
      }))
    }
  }
}

module.exports = new ImageProcessor()