import { logger } from '../../utils/logger';
import { FeatureGovernanceService } from '../governance/FeatureGovernanceService';
import db from '../../config/database';

/**
 * [AI_VISION_01] 图像识别服务
 * @description 商品图片识别、分类、质量检测，支持OCR和物体检测
 */
export class ImageRecognitionService {
  private static readonly IMAGE_TABLE = 'cf_ai_image_recognition';
  private static readonly MODEL_TABLE = 'cf_ai_image_model';

  /**
   * 初始化表结构
   */
  static async initTable() {
    // 创建图像识别记录表
    const hasImageTable = await db.schema.hasTable(this.IMAGE_TABLE);
    if (!hasImageTable) {
      console.log(`📦 Creating ${this.IMAGE_TABLE} table...`);
      await db.schema.createTable(this.IMAGE_TABLE, (table) => {
        table.increments('id').primary();
        table.string('tenant_id', 64).notNullable();
        table.string('image_url', 500).notNullable();
        table.string('image_hash', 64).unique();
        table.json('recognition_results'); // 识别结果
        table.json('classification_results'); // 分类结果
        table.json('quality_metrics'); // 质量指标
        table.json('ocr_results'); // OCR结果
        table.string('status', 16).defaultTo('PROCESSED');
        table.decimal('processing_time', 8, 3); // 处理时间(秒)
        table.timestamp('created_at').defaultTo(db.fn.now());
        table.index(['tenant_id', 'created_at']);
        table.index(['image_hash']);
      });
    }

    // 创建模型配置表
    const hasModelTable = await db.schema.hasTable(this.MODEL_TABLE);
    if (!hasModelTable) {
      console.log(`📦 Creating ${this.MODEL_TABLE} table...`);
      await db.schema.createTable(this.MODEL_TABLE, (table) => {
        table.increments('id').primary();
        table.string('tenant_id', 64).notNullable();
        table.string('model_name', 100).notNullable();
        table.string('model_type', 50).notNullable(); // classification, detection, ocr
        table.json('model_config'); // 模型配置
        table.string('status', 16).defaultTo('ACTIVE');
        table.decimal('accuracy', 5, 2); // 准确率
        table.timestamp('last_trained').defaultTo(db.fn.now());
        table.timestamp('created_at').defaultTo(db.fn.now());
        table.index(['tenant_id', 'model_type']);
      });
    }

    logger.info('[ImageRecognitionService] Tables initialized successfully');
  }

  /**
   * 图像识别处理
   */
  static async processImage(params: {
    tenantId: string;
    imageUrl: string;
    imageData?: Buffer;
    tasks: ('classification' | 'detection' | 'ocr' | 'quality')[];
  }): Promise<{
    recognitionId: number;
    results: any;
    processingTime: number;
  }> {
    const startTime = Date.now();
    
    try {
      // 1. 验证输入参数
      if (!params.imageUrl && !params.imageData) {
        throw new Error('Either imageUrl or imageData must be provided');
      }

      // 2. 计算图像哈希（用于去重）
      const imageHash = await this.calculateImageHash(params.imageUrl, params.imageData);
      
      // 3. 检查是否已处理过相同图像
      const existingRecord = await db(this.IMAGE_TABLE)
        .where({ tenant_id: params.tenantId, image_hash: imageHash })
        .first();
      
      if (existingRecord) {
        logger.info(`[ImageRecognition] Image already processed: ${imageHash}`);
        return {
          recognitionId: existingRecord.id,
          results: existingRecord.recognition_results,
          processingTime: existingRecord.processing_time || 0
        };
      }

      // 4. 执行图像识别任务
      const results = await this.executeRecognitionTasks(params, imageHash);
      
      // 5. 保存识别结果
      const recognitionId = await db(this.IMAGE_TABLE).insert({
        tenant_id: params.tenantId,
        image_url: params.imageUrl,
        image_hash: imageHash,
        recognition_results: results.recognition,
        classification_results: results.classification,
        quality_metrics: results.quality,
        ocr_results: results.ocr,
        processing_time: (Date.now() - startTime) / 1000,
        status: 'PROCESSED'
      }).returning('id');

      logger.info(`[ImageRecognition] Image processed successfully: ${recognitionId}`);
      
      return {
        recognitionId: recognitionId[0].id,
        results,
        processingTime: (Date.now() - startTime) / 1000
      };

    } catch (error: any) {
      logger.error(`[ImageRecognition] Image processing failed: ${error.message}`);
      throw error;
    }
  }

  /**
   * 执行图像识别任务
   */
  private static async executeRecognitionTasks(params: any, imageHash: string): Promise<any> {
    const results: any = {};
    
    // 模拟图像识别处理（实际项目中应集成真实的计算机视觉库）
    if (params.tasks.includes('classification')) {
      results.classification = await this.classifyImage(params.imageUrl);
    }
    
    if (params.tasks.includes('detection')) {
      results.recognition = await this.detectObjects(params.imageUrl);
    }
    
    if (params.tasks.includes('ocr')) {
      results.ocr = await this.extractText(params.imageUrl);
    }
    
    if (params.tasks.includes('quality')) {
      results.quality = await this.assessQuality(params.imageUrl);
    }
    
    return results;
  }

  /**
   * 图像分类
   */
  private static async classifyImage(imageUrl: string): Promise<any> {
    // 模拟图像分类逻辑
    const categories = ['electronics', 'clothing', 'books', 'home', 'sports', 'beauty'];
    const confidence = Math.random() * 0.3 + 0.7; // 70-100% 置信度
    
    return {
      primaryCategory: categories[Math.floor(Math.random() * categories.length)],
      confidence: parseFloat(confidence.toFixed(2)),
      alternativeCategories: [
        { category: categories[Math.floor(Math.random() * categories.length)], confidence: parseFloat((confidence * 0.8).toFixed(2)) },
        { category: categories[Math.floor(Math.random() * categories.length)], confidence: parseFloat((confidence * 0.6).toFixed(2)) }
      ]
    };
  }

  /**
   * 物体检测
   */
  private static async detectObjects(imageUrl: string): Promise<any> {
    // 模拟物体检测逻辑
    const objects = ['person', 'car', 'building', 'animal', 'furniture', 'electronics'];
    const detectedObjects = [];
    
    for (let i = 0; i < Math.min(5, Math.floor(Math.random() * 3) + 1); i++) {
      detectedObjects.push({
        object: objects[Math.floor(Math.random() * objects.length)],
        confidence: parseFloat((Math.random() * 0.3 + 0.7).toFixed(2)),
        boundingBox: {
          x: Math.floor(Math.random() * 100),
          y: Math.floor(Math.random() * 100),
          width: Math.floor(Math.random() * 50 + 20),
          height: Math.floor(Math.random() * 50 + 20)
        }
      });
    }
    
    return {
      detectedObjects,
      objectCount: detectedObjects.length
    };
  }

  /**
   * OCR文字识别
   */
  private static async extractText(imageUrl: string): Promise<any> {
    // 模拟OCR文字识别逻辑
    const sampleTexts = [
      '商品名称: 智能手机',
      '价格: ¥2999',
      '品牌: 华为',
      '型号: P50 Pro',
      '颜色: 黑色'
    ];
    
    const extractedText = [];
    for (let i = 0; i < Math.min(3, Math.floor(Math.random() * 3) + 1); i++) {
      extractedText.push({
        text: sampleTexts[Math.floor(Math.random() * sampleTexts.length)],
        confidence: parseFloat((Math.random() * 0.3 + 0.7).toFixed(2)),
        boundingBox: {
          x: Math.floor(Math.random() * 100),
          y: Math.floor(Math.random() * 100),
          width: Math.floor(Math.random() * 50 + 50),
          height: Math.floor(Math.random() * 20 + 10)
        }
      });
    }
    
    return {
      extractedText,
      textCount: extractedText.length
    };
  }

  /**
   * 图像质量评估
   */
  private static async assessQuality(imageUrl: string): Promise<any> {
    // 模拟图像质量评估逻辑
    return {
      sharpness: parseFloat((Math.random() * 0.3 + 0.7).toFixed(2)),
      brightness: parseFloat((Math.random() * 0.3 + 0.7).toFixed(2)),
      contrast: parseFloat((Math.random() * 0.3 + 0.7).toFixed(2)),
      noiseLevel: parseFloat((Math.random() * 0.3).toFixed(2)),
      overallScore: parseFloat((Math.random() * 0.3 + 0.7).toFixed(2)),
      recommendations: [
        '建议调整亮度',
        '可适当提高对比度',
        '图像清晰度良好'
      ].slice(0, Math.floor(Math.random() * 2) + 1)
    };
  }

  /**
   * 计算图像哈希
   */
  private static async calculateImageHash(imageUrl: string, imageData?: Buffer): Promise<string> {
    // 简化版哈希计算（实际项目中应使用更复杂的哈希算法）
    if (imageData) {
      // 如果有图像数据，使用数据计算哈希
      const crypto = require('crypto');
      return crypto.createHash('md5').update(imageData).digest('hex');
    } else {
      // 使用URL计算哈希
      const crypto = require('crypto');
      return crypto.createHash('md5').update(imageUrl).digest('hex');
    }
  }

  /**
   * 获取识别历史
   */
  static async getRecognitionHistory(tenantId: string, limit: number = 50): Promise<any[]> {
    return db(this.IMAGE_TABLE)
      .where({ tenant_id: tenantId })
      .orderBy('created_at', 'desc')
      .limit(limit);
  }

  /**
   * 批量处理图像
   */
  static async batchProcessImages(params: {
    tenantId: string;
    imageUrls: string[];
    tasks: ('classification' | 'detection' | 'ocr' | 'quality')[];
  }): Promise<{
    processed: number;
    failed: number;
    results: any[];
  }> {
    const results = [];
    let processed = 0;
    let failed = 0;

    for (const imageUrl of params.imageUrls) {
      try {
        const result = await this.processImage({
          tenantId: params.tenantId,
          imageUrl,
          tasks: params.tasks
        });
        results.push(result);
        processed++;
      } catch (error: any) {
        logger.error(`[ImageRecognition] Batch processing failed for ${imageUrl}: ${error.message}`);
        failed++;
      }
    }

    return { processed, failed, results };
  }

  /**
   * 获取模型性能统计
   */
  static async getModelStats(tenantId: string): Promise<any> {
    const stats = await db(this.IMAGE_TABLE)
      .where({ tenant_id: tenantId })
      .select(
        db.raw('COUNT(*) as total_images'),
        db.raw('AVG(processing_time) as avg_processing_time'),
        db.raw('MAX(processing_time) as max_processing_time'),
        db.raw('MIN(processing_time) as min_processing_time')
      )
      .first();

    return {
      totalImages: stats.total_images || 0,
      avgProcessingTime: parseFloat(stats.avg_processing_time || 0).toFixed(3),
      maxProcessingTime: parseFloat(stats.max_processing_time || 0).toFixed(3),
      minProcessingTime: parseFloat(stats.min_processing_time || 0).toFixed(3)
    };
  }
}