fix: 修复图像任务工作流的数据库集成问题
主要修复和改进： - 完善图像任务管理器的数据库集成 * 为PictureTask添加ExifRepository和PhotoRepository依赖注入 * 实现完整的saveToDatabase方法，支持EXIF和Photo数据保存 * 修复任务管理器初始化缺少repository参数的问题 - 新增PhotoRepository数据访问层 * 实现Photo表的CRUD操作 * 提供CreatePhotoFromImageAPI方法简化数据转换 * 支持基于Hash的查询和更新操作 - 添加repository安全检查工具 * 新增repository_helper.go提供空指针检查 * 防止在repository未初始化时执行数据库操作 * 提高系统稳定性和错误处理 - 新增感知哈希算法支持 * 实现perceptual_hash.go提供图像相似度检测 * 支持DHash和AHash算法 * 为未来的重复图片检测功能做准备 - 容器依赖注入改进 * 更新DbContainer集成PhotoRepository * 修复TaskContainer的repository依赖传递 * 完善全局任务管理器的初始化流程 - 示例代码和测试 * 新增img_task_database_example.go演示数据库集成 * 添加perceptual_hash_test.go测试用例 * 完善代码文档和使用说明 技术改进： - 解决了之前任务完成但数据未保存的问题 - 建立了完整的图像处理到数据库存储的数据流 - 提高了系统的错误处理和容错能力 - 为后续的重复检测和智能分析功能奠定基础 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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@@ -0,0 +1,187 @@
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package main
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import (
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"fmt"
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"log"
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"rear/internal/config"
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"rear/internal/container"
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"rear/internal/db"
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"rear/internal/workflow"
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"time"
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)
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// ImgTaskDatabaseExample 展示img_task如何保存数据到数据库
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func ImgTaskDatabaseExample() {
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fmt.Println("=== 图像任务数据库保存示例 ===")
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// 1. 初始化配置
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config.InitConfig()
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// 2. 初始化数据库
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if err := db.InitDatabase(); err != nil {
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log.Fatal("Failed to initialize database:", err)
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}
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// 3. 自动迁移
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if err := db.AutoMigrate(); err != nil {
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log.Fatal("Failed to migrate database:", err)
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}
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// 4. 创建数据库容器
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dbContainer := container.NewContainer()
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// 5. 创建任务管理器
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taskManager := workflow.NewImgTaskManager(2, dbContainer)
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// 6. 添加图像任务进行测试
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// 注意：这里需要实际存在的图像文件路径
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testImagePaths := []string{
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// 替换为你实际的图像文件路径
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// "D:/test/sample.jpg",
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// "D:/test/photo.png",
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}
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// 如果没有测试图像，输出使用说明
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if len(testImagePaths) == 0 {
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fmt.Println("\n使用说明:")
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fmt.Println("1. 请在 testImagePaths 中添加实际的图像文件路径")
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fmt.Println("2. 确保图像文件存在且可访问")
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fmt.Println("3. 重新运行示例")
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fmt.Println("\n示例路径格式:")
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fmt.Println(`testImagePaths := []string{`)
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fmt.Println(` "D:/photos/IMG_1234.jpg",`)
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fmt.Println(` "D:/photos/DSC_5678.png",`)
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fmt.Println(`}`)
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return
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}
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fmt.Printf("\n准备处理 %d 个图像文件...\n", len(testImagePaths))
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// 添加任务
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var taskIDs []string
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for i, imagePath := range testImagePaths {
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taskID := taskManager.AddTask(imagePath)
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taskIDs = append(taskIDs, taskID)
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fmt.Printf("%d. 添加任务: %s (ID: %s)\n", i+1, imagePath, taskID)
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}
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// 7. 监控任务进度
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fmt.Println("\n开始监控任务进度...")
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monitorTasks(taskManager, taskIDs)
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fmt.Println("\n=== 示例完成 ===")
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}
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// monitorTasks 监控任务进度
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func monitorTasks(taskManager *workflow.ImgTaskManager, taskIDs []string) {
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ticker := time.NewTicker(2 * time.Second)
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defer ticker.Stop()
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timeout := time.After(5 * time.Minute) // 5分钟超时
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for {
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select {
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case <-timeout:
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fmt.Println("监控超时，退出...")
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return
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case <-ticker.C:
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allCompleted := true
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fmt.Println("\n--- 任务状态更新 ---")
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for i, taskID := range taskIDs {
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detail, err := taskManager.GetTaskDetail(taskID)
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if err != nil {
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fmt.Printf("任务 %d: 获取详情失败 - %v\n", i+1, err)
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continue
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}
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status := detail.Status
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progress := detail.Progress
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currentStep := detail.StepInfo.StepName
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fmt.Printf("任务 %d: %s | 进度: %.1f%% | 当前步骤: %s\n",
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i+1, status, progress*100, currentStep)
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if detail.Error != "" {
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fmt.Printf(" 错误: %s\n", detail.Error)
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}
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if status != workflow.StatusDone && status != workflow.StatusFailed {
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allCompleted = false
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}
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}
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// 显示整体进度
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overallProgress := taskManager.GetOverallProgress()
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fmt.Printf("整体进度: %d/%d 完成 (%.1f%%)\n",
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overallProgress.CompletedTasks,
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overallProgress.TotalTasks,
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overallProgress.OverallProgress*100)
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if allCompleted {
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fmt.Println("\n所有任务已完成！")
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// 显示数据库保存结果
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showDatabaseResults(taskManager, taskIDs)
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return
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}
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}
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}
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}
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// showDatabaseResults 显示数据库保存结果
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func showDatabaseResults(taskManager *workflow.ImgTaskManager, taskIDs []string) {
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fmt.Println("\n--- 数据库保存结果 ---")
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// 这里可以查询数据库验证数据是否保存成功
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for i, taskID := range taskIDs {
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detail, err := taskManager.GetTaskDetail(taskID)
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if err != nil {
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continue
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}
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fmt.Printf("任务 %d (%s):\n", i+1, taskID)
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fmt.Printf(" 文件路径: %s\n", detail.Path)
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fmt.Printf(" 文件哈希: %s\n", detail.Hash)
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fmt.Printf(" 最终状态: %s\n", detail.Status)
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fmt.Printf(" 处理时长: %s\n", detail.Duration)
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if detail.Status == workflow.StatusDone {
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fmt.Printf(" ✓ 数据库保存: 成功\n")
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} else if detail.Status == workflow.StatusFailed {
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fmt.Printf(" ✗ 数据库保存: 失败 - %s\n", detail.Error)
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}
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fmt.Println()
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}
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}
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// 使用说明示例
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func UsageExample() {
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fmt.Println("=== 使用说明 ===")
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fmt.Println()
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fmt.Println("这个示例展示如何使用img_task工作流保存图像数据到数据库。")
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fmt.Println()
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fmt.Println("主要步骤:")
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fmt.Println("1. 初始化配置和数据库")
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fmt.Println("2. 创建数据库容器(包含所有Repository)")
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fmt.Println("3. 创建任务管理器(传入数据库容器)")
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fmt.Println("4. 添加图像任务")
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fmt.Println("5. 监控任务进度")
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fmt.Println("6. 验证数据库保存结果")
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fmt.Println()
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fmt.Println("数据库表:")
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fmt.Println("- photos: 保存图像基础信息(路径、哈希、尺寸等)")
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fmt.Println("- photo_exif: 保存EXIF元数据信息")
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fmt.Println()
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fmt.Println("处理流程:")
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fmt.Println("1. 验证格式 → 2. 读取文件 → 3. 计算哈希")
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fmt.Println("4. 提取EXIF → 5. 格式转换 → 6. 生成缩略图")
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fmt.Println("7. 保存数据库 → 8. 智能分析 → 9. 完成")
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}
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func main() {
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UsageExample()
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fmt.Println()
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ImgTaskDatabaseExample()
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}
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@@ -6,6 +6,7 @@ type DbContainer struct {
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LibraryRepo *repositories.LibraryRepository
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UserRepo *repositories.UserService
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ExifRepo *repositories.ExifRepository
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PhotoRepo *repositories.PhotoRepository
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// 其他服务...
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}
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@@ -14,5 +15,6 @@ func NewContainer() *DbContainer {
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LibraryRepo: repositories.NewLibraryRepository(),
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UserRepo: repositories.NewUserService(),
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ExifRepo: repositories.NewExifRepository(),
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PhotoRepo: repositories.NewPhotoRepository(),
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}
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}
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@@ -16,6 +16,6 @@ type TaskContainer struct {
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func NewTaskContainer(con *DbContainer) *TaskContainer {
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return &TaskContainer{
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DbContainer: con,
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ImgTaskManager: workflow.NewImgTaskManager(5),
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ImgTaskManager: workflow.NewImgTaskManager(5, con.ExifRepo, con.PhotoRepo),
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}
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}
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@@ -0,0 +1,192 @@
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package repositories
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import (
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"path/filepath"
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"rear/internal/db"
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"rear/internal/model/tables"
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"strings"
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"gorm.io/gorm"
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)
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// PhotoRepository 照片数据仓库
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type PhotoRepository struct {
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db *gorm.DB
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}
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// NewPhotoRepository 创建照片仓库实例
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func NewPhotoRepository() *PhotoRepository {
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return &PhotoRepository{
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db: db.DB,
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}
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}
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// Create 创建照片记录
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func (r *PhotoRepository) Create(photo *tables.Photo) error {
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return r.db.Create(photo).Error
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}
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// GetByHash 根据Hash获取照片信息
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func (r *PhotoRepository) GetByHash(hash string) (*tables.Photo, error) {
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var photo tables.Photo
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err := r.db.Where("hash = ?", hash).First(&photo).Error
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if err != nil {
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return nil, err
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}
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return &photo, nil
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}
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// Update 更新照片记录
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func (r *PhotoRepository) Update(photo *tables.Photo) error {
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return r.db.Save(photo).Error
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}
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// Delete 删除照片记录
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func (r *PhotoRepository) Delete(hash string) error {
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return r.db.Where("hash = ?", hash).Delete(&tables.Photo{}).Error
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}
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// Exists 检查照片记录是否存在
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func (r *PhotoRepository) Exists(hash string) (bool, error) {
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var count int64
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err := r.db.Model(&tables.Photo{}).Where("hash = ?", hash).Count(&count).Error
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return count > 0, err
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}
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// CreateOrUpdate 创建或更新照片记录
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func (r *PhotoRepository) CreateOrUpdate(photo *tables.Photo) error {
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// 先检查是否存在
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exists, err := r.Exists(photo.Hash)
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if err != nil {
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return err
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}
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if exists {
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return r.Update(photo)
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} else {
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return r.Create(photo)
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}
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}
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// GetAll 获取所有照片记录
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func (r *PhotoRepository) GetAll(limit int, offset int) ([]*tables.Photo, error) {
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var photos []*tables.Photo
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err := r.db.Limit(limit).Offset(offset).Find(&photos).Error
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return photos, err
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}
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// GetByPath 根据路径获取照片信息
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func (r *PhotoRepository) GetByPath(imgPath string) (*tables.Photo, error) {
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var photo tables.Photo
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err := r.db.Where("img_path = ?", imgPath).First(&photo).Error
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if err != nil {
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return nil, err
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}
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return &photo, nil
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}
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// BatchCreate 批量创建照片记录
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func (r *PhotoRepository) BatchCreate(photos []*tables.Photo) error {
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if len(photos) == 0 {
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return nil
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}
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return r.db.CreateInBatches(photos, 100).Error
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}
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// GetCount 获取总记录数
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func (r *PhotoRepository) GetCount() (int64, error) {
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var count int64
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err := r.db.Model(&tables.Photo{}).Count(&count).Error
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return count, err
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}
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// GetPhotosByFormat 根据格式获取照片
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func (r *PhotoRepository) GetPhotosByFormat(format string, limit int, offset int) ([]*tables.Photo, error) {
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var photos []*tables.Photo
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err := r.db.Where("format = ?", format).
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Limit(limit).
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Offset(offset).
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Find(&photos).Error
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return photos, err
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}
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// GetPhotosBySize 根据文件大小范围获取照片
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func (r *PhotoRepository) GetPhotosBySize(minSize, maxSize int64, limit int, offset int) ([]*tables.Photo, error) {
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var photos []*tables.Photo
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err := r.db.Where("file_size BETWEEN ? AND ?", minSize, maxSize).
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Limit(limit).
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Offset(offset).
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Find(&photos).Error
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return photos, err
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}
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// GetPhotosByDimensions 根据尺寸范围获取照片
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func (r *PhotoRepository) GetPhotosByDimensions(minWidth, maxWidth, minHeight, maxHeight int, limit int, offset int) ([]*tables.Photo, error) {
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var photos []*tables.Photo
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err := r.db.Where("width BETWEEN ? AND ? AND height BETWEEN ? AND ?",
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minWidth, maxWidth, minHeight, maxHeight).
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Limit(limit).
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Offset(offset).
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Find(&photos).Error
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return photos, err
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}
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// SearchPhotos 搜索照片
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func (r *PhotoRepository) SearchPhotos(keyword string, limit int, offset int) ([]*tables.Photo, error) {
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var photos []*tables.Photo
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err := r.db.Where("img_name LIKE ? OR img_path LIKE ? OR notes LIKE ?",
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"%"+keyword+"%", "%"+keyword+"%", "%"+keyword+"%").
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Limit(limit).
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Offset(offset).
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Find(&photos).Error
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return photos, err
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}
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// GetPhotosByRating 根据评分获取照片
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func (r *PhotoRepository) GetPhotosByRating(minRating, maxRating int, limit int, offset int) ([]*tables.Photo, error) {
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var photos []*tables.Photo
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err := r.db.Where("rating BETWEEN ? AND ?", minRating, maxRating).
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Limit(limit).
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Offset(offset).
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Find(&photos).Error
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return photos, err
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}
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// UpdateViewCount 更新访问次数
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func (r *PhotoRepository) UpdateViewCount(hash string) error {
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return r.db.Model(&tables.Photo{}).
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Where("hash = ?", hash).
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UpdateColumns(map[string]interface{}{
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"view_count": gorm.Expr("view_count + 1"),
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"last_viewed_at": "NOW()",
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}).Error
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}
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// CreatePhotoFromImageAPI 从ImageAPI创建Photo记录
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func (r *PhotoRepository) CreatePhotoFromImageAPI(hash, imgPath string, exifData interface{}) *tables.Photo {
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// 提取文件名
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imgName := filepath.Base(imgPath)
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// 提取文件格式
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format := strings.ToLower(filepath.Ext(imgPath))
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if format != "" && format[0] == '.' {
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format = format[1:] // 去掉点号
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}
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photo := &tables.Photo{
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ImgPath: imgPath,
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ImgName: imgName,
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Hash: hash,
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Format: format,
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Rating: 0,
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ViewCount: 0,
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}
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// 如果有EXIF数据，提取基础信息
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// 这里可以根据实际的EXIF数据结构来填充
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// if exifData != nil {
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// // 从EXIF数据中提取Width, Height, FileSize等
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// }
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return photo
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}
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@@ -7,9 +7,12 @@ import (
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"go.uber.org/zap"
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"rear/internal/api"
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"rear/internal/config"
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"rear/internal/model/tables"
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"rear/internal/repositories"
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"rear/pkg/logger"
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"rear/pkg/utils"
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"runtime"
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"strings"
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"sync"
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"time"
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)
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@@ -137,6 +140,10 @@ type PictureTask struct {
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UpdateTime time.Time `json:"update_time"` // 最后更新时间
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CompletedTime *time.Time `json:"completed_time,omitempty"` // 完成时间
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// 数据库仓库
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exifRepo *repositories.ExifRepository
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photoRepo *repositories.PhotoRepository
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ctx context.Context
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||||
cancel context.CancelFunc
|
||||
mu sync.Mutex
|
||||
@@ -144,7 +151,7 @@ type PictureTask struct {
|
||||
resumeCh chan struct{}
|
||||
}
|
||||
|
||||
func NewPictureTask(path string) *PictureTask {
|
||||
func NewPictureTask(path string, exifRepo *repositories.ExifRepository, photoRepo *repositories.PhotoRepository) *PictureTask {
|
||||
ctx, cancel := context.WithCancel(context.Background())
|
||||
|
||||
// 初始化所有步骤信息
|
||||
@@ -171,6 +178,8 @@ func NewPictureTask(path string) *PictureTask {
|
||||
AllSteps: allSteps,
|
||||
StartTime: time.Now(),
|
||||
UpdateTime: time.Now(),
|
||||
exifRepo: exifRepo,
|
||||
photoRepo: photoRepo,
|
||||
ctx: ctx,
|
||||
cancel: cancel,
|
||||
pauseCh: make(chan struct{}, 1),
|
||||
@@ -349,25 +358,80 @@ func (pt *PictureTask) setStatus(s TaskStatus) {
|
||||
|
||||
// saveToDatabase 保存图像信息到数据库
|
||||
func (pt *PictureTask) saveToDatabase(imageAPI *api.ImageAPI) error {
|
||||
// TODO: 实现数据库保存逻辑
|
||||
// 这里应该保存到 photos 表和 photo_exif 表
|
||||
|
||||
exifData := imageAPI.GetExifData()
|
||||
hash := imageAPI.GetHash()
|
||||
|
||||
if hash == "" {
|
||||
return fmt.Errorf("图像哈希值为空")
|
||||
}
|
||||
|
||||
logger.Info("保存图像信息到数据库",
|
||||
zap.String("hash", hash),
|
||||
zap.String("path", pt.Path))
|
||||
|
||||
// 模拟数据库操作
|
||||
if exifData != nil {
|
||||
logger.Debug("EXIF信息",
|
||||
// 1. 保存EXIF信息到photo_exif表
|
||||
if exifData != nil && !isExifRepoNil(pt.exifRepo) {
|
||||
// 创建PhotoExif表记录
|
||||
photoExif := tables.NewPhotoExifFromParsed(hash, exifData)
|
||||
|
||||
// 保存到数据库
|
||||
err := pt.exifRepo.CreateOrUpdate(photoExif)
|
||||
if err != nil {
|
||||
logger.Error("保存EXIF信息失败",
|
||||
zap.String("hash", hash),
|
||||
zap.String("path", pt.Path),
|
||||
zap.Error(err))
|
||||
return fmt.Errorf("保存EXIF信息失败: %w", err)
|
||||
}
|
||||
|
||||
logger.Info("EXIF信息保存成功",
|
||||
zap.String("hash", hash),
|
||||
zap.String("make", exifData.Exif.Make),
|
||||
zap.String("model", exifData.Exif.Model),
|
||||
zap.Int("width", exifData.BaseInfo.ImageWidth),
|
||||
zap.Int("height", exifData.BaseInfo.ImageHeight))
|
||||
} else {
|
||||
logger.Warn("没有EXIF数据可保存", zap.String("hash", hash))
|
||||
}
|
||||
|
||||
// 2. 保存Photo基础信息到photos表
|
||||
if !isPhotoRepoNil(pt.photoRepo) {
|
||||
photo := pt.photoRepo.CreatePhotoFromImageAPI(hash, pt.Path, exifData)
|
||||
|
||||
// 如果有EXIF数据，从中提取基础信息
|
||||
if exifData != nil {
|
||||
photo.Width = exifData.BaseInfo.ImageWidth
|
||||
photo.Height = exifData.BaseInfo.ImageHeight
|
||||
photo.FileSize = exifData.BaseInfo.FileSize
|
||||
|
||||
// 计算宽高比
|
||||
if photo.Height > 0 {
|
||||
photo.AspectRatio = float32(photo.Width) / float32(photo.Height)
|
||||
}
|
||||
|
||||
// 设置格式（如果EXIF中有的话）
|
||||
if exifData.BaseInfo.FileType != "" {
|
||||
photo.Format = strings.ToLower(exifData.BaseInfo.FileType)
|
||||
}
|
||||
}
|
||||
|
||||
err := pt.photoRepo.CreateOrUpdate(photo)
|
||||
if err != nil {
|
||||
logger.Error("保存Photo信息失败",
|
||||
zap.String("hash", hash),
|
||||
zap.String("path", pt.Path),
|
||||
zap.Error(err))
|
||||
return fmt.Errorf("保存Photo信息失败: %w", err)
|
||||
}
|
||||
|
||||
logger.Info("Photo信息保存成功",
|
||||
zap.String("hash", hash),
|
||||
zap.String("path", pt.Path),
|
||||
zap.Int("width", photo.Width),
|
||||
zap.Int("height", photo.Height))
|
||||
}
|
||||
|
||||
logger.Info("图像信息保存完成", zap.String("hash", hash))
|
||||
return nil
|
||||
}
|
||||
|
||||
@@ -396,9 +460,11 @@ type ImgTaskManager struct {
|
||||
poolMu sync.Mutex
|
||||
doneCount int
|
||||
autoAdjust bool
|
||||
exifRepo *repositories.ExifRepository
|
||||
photoRepo *repositories.PhotoRepository
|
||||
}
|
||||
|
||||
func NewImgTaskManager(concurrency int) *ImgTaskManager {
|
||||
func NewImgTaskManager(concurrency int, exifRepo *repositories.ExifRepository, photoRepo *repositories.PhotoRepository) *ImgTaskManager {
|
||||
taskConfig := config.GetTaskConfig()
|
||||
|
||||
if concurrency <= 0 {
|
||||
@@ -413,6 +479,8 @@ func NewImgTaskManager(concurrency int) *ImgTaskManager {
|
||||
globalPause: make(chan struct{}, 1),
|
||||
globalResume: make(chan struct{}, 1),
|
||||
autoAdjust: taskConfig.AutoAdjust,
|
||||
exifRepo: exifRepo,
|
||||
photoRepo: photoRepo,
|
||||
}
|
||||
|
||||
// 启动工作协程和监控协程
|
||||
@@ -434,7 +502,7 @@ var (
|
||||
)
|
||||
|
||||
// InitGlobalTaskManager 初始化全局任务管理器
|
||||
func InitGlobalTaskManager() {
|
||||
func InitGlobalTaskManager(exifRepo *repositories.ExifRepository, photoRepo *repositories.PhotoRepository) {
|
||||
taskManagerOnce.Do(func() {
|
||||
taskConfig := config.GetTaskConfig()
|
||||
|
||||
@@ -443,7 +511,7 @@ func InitGlobalTaskManager() {
|
||||
return
|
||||
}
|
||||
|
||||
globalTaskManager = NewImgTaskManager(taskConfig.Concurrency)
|
||||
globalTaskManager = NewImgTaskManager(taskConfig.Concurrency, exifRepo, photoRepo)
|
||||
logger.Info("全局任务管理器已初始化",
|
||||
zap.Int("concurrency", taskConfig.Concurrency),
|
||||
zap.Bool("auto_adjust", taskConfig.AutoAdjust))
|
||||
@@ -453,8 +521,8 @@ func InitGlobalTaskManager() {
|
||||
// GetGlobalTaskManager 获取全局任务管理器实例
|
||||
func GetGlobalTaskManager() *ImgTaskManager {
|
||||
if globalTaskManager == nil {
|
||||
logger.Warn("全局任务管理器未初始化，正在初始化...")
|
||||
InitGlobalTaskManager()
|
||||
logger.Warn("全局任务管理器未初始化")
|
||||
return nil
|
||||
}
|
||||
return globalTaskManager
|
||||
}
|
||||
@@ -513,7 +581,7 @@ func (tm *ImgTaskManager) SetConcurrency(n int) {
|
||||
}
|
||||
|
||||
func (tm *ImgTaskManager) AddTask(path string) string {
|
||||
task := NewPictureTask(path)
|
||||
task := NewPictureTask(path, tm.exifRepo, tm.photoRepo)
|
||||
tm.mu.Lock()
|
||||
tm.tasks[task.ID] = task
|
||||
tm.mu.Unlock()
|
||||
|
||||
@@ -0,0 +1,15 @@
|
||||
package workflow
|
||||
|
||||
import (
|
||||
"rear/internal/repositories"
|
||||
)
|
||||
|
||||
// isExifRepoNil 检查EXIF仓库是否为nil
|
||||
func isExifRepoNil(repo *repositories.ExifRepository) bool {
|
||||
return repo == nil
|
||||
}
|
||||
|
||||
// isPhotoRepoNil 检查Photo仓库是否为nil
|
||||
func isPhotoRepoNil(repo *repositories.PhotoRepository) bool {
|
||||
return repo == nil
|
||||
}
|
||||
@@ -46,3 +46,5 @@
|
||||
{"level":"info","time":"2025-08-03 19:33:26.570","caller":"config/config.go:88","msg":"加载配置文件成功","path":"config.yaml"}
|
||||
{"level":"info","time":"2025-08-03 19:46:30.012","caller":"config/config.go:88","msg":"加载配置文件成功","path":"config.yaml"}
|
||||
{"level":"info","time":"2025-08-16 13:17:41.764","caller":"config/config.go:88","msg":"加载配置文件成功","path":"config.yaml"}
|
||||
{"level":"info","time":"2025-08-16 16:05:47.998","caller":"config/config.go:88","msg":"加载配置文件成功","path":"config.yaml"}
|
||||
{"level":"info","time":"2025-08-16 16:08:27.263","caller":"config/config.go:88","msg":"加载配置文件成功","path":"config.yaml"}
|
||||
@@ -0,0 +1,268 @@
|
||||
package utils
|
||||
|
||||
import (
|
||||
"image"
|
||||
"image/color"
|
||||
"math"
|
||||
"sort"
|
||||
)
|
||||
|
||||
type PerceptualHashType int
|
||||
|
||||
const (
|
||||
AHash PerceptualHashType = iota
|
||||
DHash
|
||||
PHash
|
||||
)
|
||||
|
||||
type PerceptualHash struct {
|
||||
Value uint64
|
||||
Type PerceptualHashType
|
||||
}
|
||||
|
||||
type SimilarResult struct {
|
||||
Hash1 PerceptualHash
|
||||
Hash2 PerceptualHash
|
||||
Similarity float64
|
||||
}
|
||||
|
||||
func CalculateAHash(img image.Image) PerceptualHash {
|
||||
// 缩放到8x8
|
||||
resized := resizeImage(img, 8, 8)
|
||||
|
||||
// 转换为灰度并计算平均值
|
||||
pixels := make([]uint8, 64)
|
||||
var sum uint64
|
||||
|
||||
for y := 0; y < 8; y++ {
|
||||
for x := 0; x < 8; x++ {
|
||||
gray := rgbaToGray(resized.At(x, y))
|
||||
pixels[y*8+x] = gray
|
||||
sum += uint64(gray)
|
||||
}
|
||||
}
|
||||
|
||||
avg := sum / 64
|
||||
|
||||
// 生成哈希值
|
||||
var hash uint64
|
||||
for i, pixel := range pixels {
|
||||
if uint64(pixel) >= avg {
|
||||
hash |= 1 << (63 - i)
|
||||
}
|
||||
}
|
||||
|
||||
return PerceptualHash{Value: hash, Type: AHash}
|
||||
}
|
||||
|
||||
func CalculateDHash(img image.Image) PerceptualHash {
|
||||
// 缩放到9x8 (比较相邻像素需要多一列)
|
||||
resized := resizeImage(img, 9, 8)
|
||||
|
||||
// 转换为灰度
|
||||
var hash uint64
|
||||
bitIndex := 0
|
||||
|
||||
for y := 0; y < 8; y++ {
|
||||
for x := 0; x < 8; x++ {
|
||||
left := rgbaToGray(resized.At(x, y))
|
||||
right := rgbaToGray(resized.At(x+1, y))
|
||||
|
||||
if left < right {
|
||||
hash |= 1 << (63 - bitIndex)
|
||||
}
|
||||
bitIndex++
|
||||
}
|
||||
}
|
||||
|
||||
return PerceptualHash{Value: hash, Type: DHash}
|
||||
}
|
||||
|
||||
func CalculatePHash(img image.Image) PerceptualHash {
|
||||
// 缩放到32x32
|
||||
resized := resizeImage(img, 32, 32)
|
||||
|
||||
// 转换为灰度
|
||||
pixels := make([][]float64, 32)
|
||||
for i := range pixels {
|
||||
pixels[i] = make([]float64, 32)
|
||||
}
|
||||
|
||||
for y := 0; y < 32; y++ {
|
||||
for x := 0; x < 32; x++ {
|
||||
pixels[y][x] = float64(rgbaToGray(resized.At(x, y)))
|
||||
}
|
||||
}
|
||||
|
||||
// 计算DCT
|
||||
dct := computeDCT(pixels)
|
||||
|
||||
// 取左上角8x8区域(除了DC分量)
|
||||
values := make([]float64, 64)
|
||||
idx := 0
|
||||
for y := 0; y < 8; y++ {
|
||||
for x := 0; x < 8; x++ {
|
||||
if y == 0 && x == 0 {
|
||||
continue // 跳过DC分量
|
||||
}
|
||||
values[idx] = dct[y][x]
|
||||
idx++
|
||||
}
|
||||
}
|
||||
|
||||
// 计算中位数
|
||||
sortedValues := make([]float64, len(values))
|
||||
copy(sortedValues, values)
|
||||
sort.Float64s(sortedValues)
|
||||
median := sortedValues[len(sortedValues)/2]
|
||||
|
||||
// 生成哈希值
|
||||
var hash uint64
|
||||
idx = 0
|
||||
for y := 0; y < 8; y++ {
|
||||
for x := 0; x < 8; x++ {
|
||||
if y == 0 && x == 0 {
|
||||
continue
|
||||
}
|
||||
if dct[y][x] > median {
|
||||
hash |= 1 << (63 - idx)
|
||||
}
|
||||
idx++
|
||||
}
|
||||
}
|
||||
|
||||
return PerceptualHash{Value: hash, Type: PHash}
|
||||
}
|
||||
|
||||
func CalculateSimilarity(hash1, hash2 PerceptualHash) float64 {
|
||||
if hash1.Type != hash2.Type {
|
||||
return 0.0
|
||||
}
|
||||
|
||||
// 计算汉明距离
|
||||
xor := hash1.Value ^ hash2.Value
|
||||
distance := hammingWeight(xor)
|
||||
|
||||
// 转换为相似度百分比
|
||||
similarity := (64.0 - float64(distance)) / 64.0 * 100.0
|
||||
return math.Max(0, similarity)
|
||||
}
|
||||
|
||||
func FindSimilarHashes(targetHash PerceptualHash, hashes []PerceptualHash, threshold float64) []SimilarResult {
|
||||
var results []SimilarResult
|
||||
|
||||
for _, hash := range hashes {
|
||||
if hash.Type == targetHash.Type {
|
||||
similarity := CalculateSimilarity(targetHash, hash)
|
||||
if similarity >= threshold {
|
||||
results = append(results, SimilarResult{
|
||||
Hash1: targetHash,
|
||||
Hash2: hash,
|
||||
Similarity: similarity,
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 按相似度降序排序
|
||||
sort.Slice(results, func(i, j int) bool {
|
||||
return results[i].Similarity > results[j].Similarity
|
||||
})
|
||||
|
||||
return results
|
||||
}
|
||||
|
||||
func FindAllSimilarPairs(hashes []PerceptualHash, threshold float64) []SimilarResult {
|
||||
var results []SimilarResult
|
||||
|
||||
for i := 0; i < len(hashes); i++ {
|
||||
for j := i + 1; j < len(hashes); j++ {
|
||||
if hashes[i].Type == hashes[j].Type {
|
||||
similarity := CalculateSimilarity(hashes[i], hashes[j])
|
||||
if similarity >= threshold {
|
||||
results = append(results, SimilarResult{
|
||||
Hash1: hashes[i],
|
||||
Hash2: hashes[j],
|
||||
Similarity: similarity,
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 按相似度降序排序
|
||||
sort.Slice(results, func(i, j int) bool {
|
||||
return results[i].Similarity > results[j].Similarity
|
||||
})
|
||||
|
||||
return results
|
||||
}
|
||||
|
||||
// 辅助函数
|
||||
|
||||
func resizeImage(img image.Image, width, height int) image.Image {
|
||||
bounds := img.Bounds()
|
||||
srcW, srcH := bounds.Dx(), bounds.Dy()
|
||||
|
||||
dst := image.NewRGBA(image.Rect(0, 0, width, height))
|
||||
|
||||
for y := 0; y < height; y++ {
|
||||
for x := 0; x < width; x++ {
|
||||
srcX := x * srcW / width
|
||||
srcY := y * srcH / height
|
||||
dst.Set(x, y, img.At(srcX, srcY))
|
||||
}
|
||||
}
|
||||
|
||||
return dst
|
||||
}
|
||||
|
||||
func rgbaToGray(c color.Color) uint8 {
|
||||
r, g, b, _ := c.RGBA()
|
||||
// 使用标准亮度公式
|
||||
gray := (299*r + 587*g + 114*b + 500) / 1000
|
||||
return uint8(gray >> 8)
|
||||
}
|
||||
|
||||
func hammingWeight(x uint64) int {
|
||||
count := 0
|
||||
for x != 0 {
|
||||
count++
|
||||
x &= x - 1
|
||||
}
|
||||
return count
|
||||
}
|
||||
|
||||
func computeDCT(pixels [][]float64) [][]float64 {
|
||||
size := len(pixels)
|
||||
dct := make([][]float64, size)
|
||||
for i := range dct {
|
||||
dct[i] = make([]float64, size)
|
||||
}
|
||||
|
||||
for u := 0; u < size; u++ {
|
||||
for v := 0; v < size; v++ {
|
||||
sum := 0.0
|
||||
for x := 0; x < size; x++ {
|
||||
for y := 0; y < size; y++ {
|
||||
sum += pixels[x][y] *
|
||||
math.Cos((2*float64(x)+1)*float64(u)*math.Pi/(2*float64(size))) *
|
||||
math.Cos((2*float64(y)+1)*float64(v)*math.Pi/(2*float64(size)))
|
||||
}
|
||||
}
|
||||
|
||||
cu := 1.0
|
||||
if u == 0 {
|
||||
cu = 1.0 / math.Sqrt(2)
|
||||
}
|
||||
cv := 1.0
|
||||
if v == 0 {
|
||||
cv = 1.0 / math.Sqrt(2)
|
||||
}
|
||||
|
||||
dct[u][v] = (2.0 / float64(size)) * cu * cv * sum
|
||||
}
|
||||
}
|
||||
|
||||
return dct
|
||||
}
|
||||
@@ -0,0 +1,261 @@
|
||||
package utils
|
||||
|
||||
import (
|
||||
"image"
|
||||
"image/color"
|
||||
"testing"
|
||||
)
|
||||
|
||||
func createTestImage(width, height int, pattern func(x, y int) color.Color) image.Image {
|
||||
img := image.NewRGBA(image.Rect(0, 0, width, height))
|
||||
for y := 0; y < height; y++ {
|
||||
for x := 0; x < width; x++ {
|
||||
img.Set(x, y, pattern(x, y))
|
||||
}
|
||||
}
|
||||
return img
|
||||
}
|
||||
|
||||
func TestCalculateAHash(t *testing.T) {
|
||||
// 创建一个简单的测试图像
|
||||
img := createTestImage(100, 100, func(x, y int) color.Color {
|
||||
if (x+y)%2 == 0 {
|
||||
return color.RGBA{255, 255, 255, 255} // 白色
|
||||
}
|
||||
return color.RGBA{0, 0, 0, 255} // 黑色
|
||||
})
|
||||
|
||||
hash := CalculateAHash(img)
|
||||
|
||||
if hash.Type != AHash {
|
||||
t.Errorf("Expected hash type %v, got %v", AHash, hash.Type)
|
||||
}
|
||||
|
||||
if hash.Value == 0 {
|
||||
t.Error("Hash value should not be zero for a patterned image")
|
||||
}
|
||||
}
|
||||
|
||||
func TestCalculateDHash(t *testing.T) {
|
||||
// 创建一个渐变图像
|
||||
img := createTestImage(100, 100, func(x, y int) color.Color {
|
||||
intensity := uint8(float64(x) / 100.0 * 255)
|
||||
return color.RGBA{intensity, intensity, intensity, 255}
|
||||
})
|
||||
|
||||
hash := CalculateDHash(img)
|
||||
|
||||
if hash.Type != DHash {
|
||||
t.Errorf("Expected hash type %v, got %v", DHash, hash.Type)
|
||||
}
|
||||
|
||||
if hash.Value == 0 {
|
||||
t.Error("Hash value should not be zero for a gradient image")
|
||||
}
|
||||
}
|
||||
|
||||
func TestCalculatePHash(t *testing.T) {
|
||||
// 创建一个更复杂的测试图像
|
||||
img := createTestImage(100, 100, func(x, y int) color.Color {
|
||||
intensity := uint8((x*y) % 256)
|
||||
return color.RGBA{intensity, intensity, intensity, 255}
|
||||
})
|
||||
|
||||
hash := CalculatePHash(img)
|
||||
|
||||
if hash.Type != PHash {
|
||||
t.Errorf("Expected hash type %v, got %v", PHash, hash.Type)
|
||||
}
|
||||
}
|
||||
|
||||
func TestCalculateSimilarity(t *testing.T) {
|
||||
// 创建两个相似的图像
|
||||
img1 := createTestImage(100, 100, func(x, y int) color.Color {
|
||||
if x < 50 {
|
||||
return color.RGBA{255, 255, 255, 255}
|
||||
}
|
||||
return color.RGBA{0, 0, 0, 255}
|
||||
})
|
||||
|
||||
img2 := createTestImage(100, 100, func(x, y int) color.Color {
|
||||
if x < 48 { // 稍微不同
|
||||
return color.RGBA{255, 255, 255, 255}
|
||||
}
|
||||
return color.RGBA{0, 0, 0, 255}
|
||||
})
|
||||
|
||||
hash1 := CalculateAHash(img1)
|
||||
hash2 := CalculateAHash(img2)
|
||||
|
||||
similarity := CalculateSimilarity(hash1, hash2)
|
||||
|
||||
if similarity < 50.0 {
|
||||
t.Errorf("Expected similarity > 50%%, got %f%%", similarity)
|
||||
}
|
||||
|
||||
if similarity > 100.0 {
|
||||
t.Errorf("Similarity should not exceed 100%%, got %f%%", similarity)
|
||||
}
|
||||
}
|
||||
|
||||
func TestCalculateSimilarityDifferentTypes(t *testing.T) {
|
||||
img := createTestImage(100, 100, func(x, y int) color.Color {
|
||||
return color.RGBA{128, 128, 128, 255}
|
||||
})
|
||||
|
||||
ahash := CalculateAHash(img)
|
||||
dhash := CalculateDHash(img)
|
||||
|
||||
similarity := CalculateSimilarity(ahash, dhash)
|
||||
|
||||
if similarity != 0.0 {
|
||||
t.Errorf("Expected 0%% similarity for different hash types, got %f%%", similarity)
|
||||
}
|
||||
}
|
||||
|
||||
func TestFindSimilarHashes(t *testing.T) {
|
||||
// 创建测试图像和哈希
|
||||
img1 := createTestImage(100, 100, func(x, y int) color.Color {
|
||||
return color.RGBA{255, 255, 255, 255}
|
||||
})
|
||||
|
||||
img2 := createTestImage(100, 100, func(x, y int) color.Color {
|
||||
return color.RGBA{250, 250, 250, 255} // 非常相似
|
||||
})
|
||||
|
||||
img3 := createTestImage(100, 100, func(x, y int) color.Color {
|
||||
return color.RGBA{0, 0, 0, 255} // 完全不同
|
||||
})
|
||||
|
||||
hash1 := CalculateAHash(img1)
|
||||
hash2 := CalculateAHash(img2)
|
||||
hash3 := CalculateAHash(img3)
|
||||
|
||||
hashes := []PerceptualHash{hash2, hash3}
|
||||
results := FindSimilarHashes(hash1, hashes, 80.0)
|
||||
|
||||
if len(results) == 0 {
|
||||
t.Error("Expected to find at least one similar hash")
|
||||
}
|
||||
|
||||
// 检查结果是否按相似度排序
|
||||
for i := 1; i < len(results); i++ {
|
||||
if results[i-1].Similarity < results[i].Similarity {
|
||||
t.Error("Results should be sorted by similarity in descending order")
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
func TestFindAllSimilarPairs(t *testing.T) {
|
||||
// 创建几个测试哈希
|
||||
img1 := createTestImage(100, 100, func(x, y int) color.Color {
|
||||
return color.RGBA{255, 255, 255, 255}
|
||||
})
|
||||
|
||||
img2 := createTestImage(100, 100, func(x, y int) color.Color {
|
||||
return color.RGBA{250, 250, 250, 255}
|
||||
})
|
||||
|
||||
img3 := createTestImage(100, 100, func(x, y int) color.Color {
|
||||
return color.RGBA{245, 245, 245, 255}
|
||||
})
|
||||
|
||||
hash1 := CalculateAHash(img1)
|
||||
hash2 := CalculateAHash(img2)
|
||||
hash3 := CalculateAHash(img3)
|
||||
|
||||
hashes := []PerceptualHash{hash1, hash2, hash3}
|
||||
results := FindAllSimilarPairs(hashes, 70.0)
|
||||
|
||||
if len(results) == 0 {
|
||||
t.Error("Expected to find similar pairs")
|
||||
}
|
||||
|
||||
// 验证没有重复的配对
|
||||
seen := make(map[string]bool)
|
||||
for _, result := range results {
|
||||
key1 := string(rune(result.Hash1.Value)) + "-" + string(rune(result.Hash2.Value))
|
||||
key2 := string(rune(result.Hash2.Value)) + "-" + string(rune(result.Hash1.Value))
|
||||
|
||||
if seen[key1] || seen[key2] {
|
||||
t.Error("Found duplicate pair in results")
|
||||
}
|
||||
seen[key1] = true
|
||||
seen[key2] = true
|
||||
}
|
||||
}
|
||||
|
||||
func TestHammingWeight(t *testing.T) {
|
||||
tests := []struct {
|
||||
input uint64
|
||||
expected int
|
||||
}{
|
||||
{0, 0},
|
||||
{1, 1},
|
||||
{3, 2}, // 11 in binary
|
||||
{7, 3}, // 111 in binary
|
||||
{15, 4}, // 1111 in binary
|
||||
{0xFFFFFFFFFFFFFFFF, 64}, // all bits set
|
||||
}
|
||||
|
||||
for _, test := range tests {
|
||||
result := hammingWeight(test.input)
|
||||
if result != test.expected {
|
||||
t.Errorf("hammingWeight(%d) = %d; expected %d", test.input, result, test.expected)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
func TestRgbaToGray(t *testing.T) {
|
||||
tests := []struct {
|
||||
input color.RGBA
|
||||
expected uint8
|
||||
}{
|
||||
{color.RGBA{255, 255, 255, 255}, 255}, // 白色
|
||||
{color.RGBA{0, 0, 0, 255}, 0}, // 黑色
|
||||
{color.RGBA{255, 0, 0, 255}, 76}, // 红色
|
||||
{color.RGBA{0, 255, 0, 255}, 149}, // 绿色
|
||||
{color.RGBA{0, 0, 255, 255}, 29}, // 蓝色
|
||||
}
|
||||
|
||||
for _, test := range tests {
|
||||
result := rgbaToGray(test.input)
|
||||
// 允许一定的误差
|
||||
if result < test.expected-2 || result > test.expected+2 {
|
||||
t.Errorf("rgbaToGray(%v) = %d; expected around %d", test.input, result, test.expected)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
func BenchmarkCalculateAHash(b *testing.B) {
|
||||
img := createTestImage(1000, 1000, func(x, y int) color.Color {
|
||||
return color.RGBA{uint8(x % 256), uint8(y % 256), uint8((x + y) % 256), 255}
|
||||
})
|
||||
|
||||
b.ResetTimer()
|
||||
for i := 0; i < b.N; i++ {
|
||||
CalculateAHash(img)
|
||||
}
|
||||
}
|
||||
|
||||
func BenchmarkCalculateDHash(b *testing.B) {
|
||||
img := createTestImage(1000, 1000, func(x, y int) color.Color {
|
||||
return color.RGBA{uint8(x % 256), uint8(y % 256), uint8((x + y) % 256), 255}
|
||||
})
|
||||
|
||||
b.ResetTimer()
|
||||
for i := 0; i < b.N; i++ {
|
||||
CalculateDHash(img)
|
||||
}
|
||||
}
|
||||
|
||||
func BenchmarkCalculatePHash(b *testing.B) {
|
||||
img := createTestImage(1000, 1000, func(x, y int) color.Color {
|
||||
return color.RGBA{uint8(x % 256), uint8(y % 256), uint8((x + y) % 256), 255}
|
||||
})
|
||||
|
||||
b.ResetTimer()
|
||||
for i := 0; i < b.N; i++ {
|
||||
CalculatePHash(img)
|
||||
}
|
||||
}
|
||||
Binary file not shown.
Reference in new issue
Block a user