feat: 第五轮开发 - 14项未来路线图功能完整实现

W1-W14 全部完成: - W1: 消息搜索 (ILIKE全文检索 + SearchModal) - W2: 对话导出 (JSON/Markdown/TXT三格式) - W3: 记忆时间线 DevTools 可视化 - W4: 通知推送系统 (WebSocket + Browser Notification API) - W5: 定时提醒 (30s轮询 + 重复提醒 + WebSocket推送) - W6: 每日简报 (08:00自动生成: 天气+新闻+提醒+AI摘要) - W7: IoT场景自动化 (规则引擎 10s轮询 + 条件评估 + 场景执行) - W8: 语音输入 (浏览器 Speech Recognition API) - W9: STT服务 (voice-service + whisper.cpp) - W10: TTS服务 (浏览器 Speech Synthesis + edge-tts三档回退) - W11: 文件管理 (上传/下载/缩略图/纯Go bilinear缩放) - W12: 知识库RAG (PostgreSQL tsvector + 文档分块 + 检索) - W13: 多模态 (图片上传+分析: Vision API + 本地Go分析回退) - W14: PWA (Service Worker + 离线页 + install prompt) 总计: 6个Go微服务 + 10+前端组件 + 10+ PostgreSQL表 + 4个后台调度器
2026-05-19 12:01:09 +08:00
parent 78e3f450c2
commit bcf4d4e621
69 changed files with 14599 additions and 150 deletions
@@ -0,0 +1,718 @@
+package handler
+
+import (
+	"bytes"
+	"encoding/base64"
+	"encoding/json"
+	"fmt"
+	"image"
+	"image/color"
+	_ "image/gif"
+	_ "image/jpeg"
+	_ "image/png"
+	"io"
+	"log"
+	"net/http"
+	"os"
+	"sort"
+	"strings"
+
+	"github.com/gin-gonic/gin"
+
+	"github.com/yourname/cyrene-ai/gateway/internal/config"
+	"github.com/yourname/cyrene-ai/gateway/internal/middleware"
+	"github.com/yourname/cyrene-ai/gateway/internal/store"
+)
+
+// ImageHandler 图片分析处理器
+type ImageHandler struct {
+	cfg       *config.Config
+	fileStore *store.FileStore
+}
+
+// NewImageHandler 创建图片分析处理器
+func NewImageHandler(cfg *config.Config, fileStore *store.FileStore) *ImageHandler {
+	return &ImageHandler{
+		cfg:       cfg,
+		fileStore: fileStore,
+	}
+}
+
+// ImageAnalysis 图片分析结果
+type ImageAnalysis struct {
+	Format      string       `json:"format"`
+	Width       int          `json:"width"`
+	Height      int          `json:"height"`
+	FileSize    int64        `json:"file_size"`
+	Description string       `json:"description"`
+	TopColors   []ColorInfo  `json:"top_colors,omitempty"`
+	EXIF        map[string]string `json:"exif,omitempty"`
+	AnalyzedBy  string       `json:"analyzed_by"` // "openai_vision" | "local"
+}
+
+// ColorInfo 颜色信息
+type ColorInfo struct {
+	Hex       string  `json:"hex"`
+	Percent   float64 `json:"percent"`
+}
+
+// AnalyzeRequestBody 分析请求体
+type AnalyzeRequestBody struct {
+	FileID string `json:"file_id"`
+}
+
+// ========== POST /api/v1/images/analyze ==========
+
+// Analyze 分析上传的图片 (multipart/form-data 或 JSON)
+func (h *ImageHandler) Analyze(c *gin.Context) {
+	userID := middleware.GetUserID(c)
+
+	// 尝试 JSON body: {"file_id": "xxx"}
+	contentType := c.GetHeader("Content-Type")
+	if strings.HasPrefix(contentType, "application/json") {
+		var body AnalyzeRequestBody
+		if err := c.ShouldBindJSON(&body); err != nil || body.FileID == "" {
+			c.JSON(http.StatusBadRequest, gin.H{"error": "缺少 file_id 字段", "errorType": "invalid_request"})
+			return
+		}
+		h.analyzeByFileID(c, userID, body.FileID)
+		return
+	}
+
+	// 尝试 multipart/form-data: 直接上传图片分析
+	file, header, err := c.Request.FormFile("file")
+	if err != nil {
+		// 也尝试 "image" 字段名
+		file, header, err = c.Request.FormFile("image")
+		if err != nil {
+			c.JSON(http.StatusBadRequest, gin.H{"error": "未找到图片文件 (使用 file 或 image 字段)", "errorType": "missing_file"})
+			return
+		}
+	}
+	defer file.Close()
+
+	h.analyzeUploadedFile(c, userID, file, header.Filename, header.Size)
+}
+
+// ========== GET /api/v1/images/analyze/:file_id ==========
+
+// AnalyzeByID 对已上传的文件进行分析
+func (h *ImageHandler) AnalyzeByID(c *gin.Context) {
+	userID := middleware.GetUserID(c)
+	fileID := c.Param("file_id")
+	if fileID == "" {
+		c.JSON(http.StatusBadRequest, gin.H{"error": "缺少 file_id", "errorType": "invalid_request"})
+		return
+	}
+	h.analyzeByFileID(c, userID, fileID)
+}
+
+// analyzeByFileID 根据文件ID分析已存储的图片
+func (h *ImageHandler) analyzeByFileID(c *gin.Context, userID, fileID string) {
+	if h.fileStore == nil {
+		c.JSON(http.StatusServiceUnavailable, gin.H{"error": "文件存储不可用", "errorType": "service_unavailable"})
+		return
+	}
+
+	f, err := h.fileStore.GetFile(fileID)
+	if err != nil {
+		log.Printf("[ImageHandler] 查询文件失败: %v", err)
+		c.JSON(http.StatusInternalServerError, gin.H{"error": "查询文件失败", "errorType": "db_error"})
+		return
+	}
+	if f == nil {
+		c.JSON(http.StatusNotFound, gin.H{"error": "文件不存在", "errorType": "file_not_found"})
+		return
+	}
+	if f.UserID != userID && !f.IsPublic {
+		c.JSON(http.StatusForbidden, gin.H{"error": "无权访问此文件", "errorType": "access_denied"})
+		return
+	}
+	if !isImageType(f.MimeType) {
+		c.JSON(http.StatusBadRequest, gin.H{"error": "文件不是图片类型: " + f.MimeType, "errorType": "unsupported_type"})
+		return
+	}
+
+	result, err := h.analyzeImage(f.StoredPath, f.MimeType, f.Size)
+	if err != nil {
+		log.Printf("[ImageHandler] 图片分析失败: %v", err)
+		c.JSON(http.StatusInternalServerError, gin.H{"error": "图片分析失败: " + err.Error(), "errorType": "analysis_error"})
+		return
+	}
+
+	c.JSON(http.StatusOK, result)
+}
+
+// analyzeUploadedFile 分析直接上传的图片文件
+func (h *ImageHandler) analyzeUploadedFile(c *gin.Context, userID string, file io.Reader, filename string, fileSize int64) {
+	// 检查文件大小 (10MB 限制)
+	const maxImageSize = 10 * 1024 * 1024
+	if fileSize > maxImageSize {
+		c.JSON(http.StatusBadRequest, gin.H{"error": "图片大小超过限制 (最大 10MB)", "errorType": "file_too_large"})
+		return
+	}
+
+	// 读取文件到内存
+	data, err := io.ReadAll(file)
+	if err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": "读取图片失败", "errorType": "read_error"})
+		return
+	}
+
+	// 检测格式
+	_, format, err := image.DecodeConfig(bytes.NewReader(data))
+	if err != nil {
+		c.JSON(http.StatusBadRequest, gin.H{"error": "无法解码图片: " + err.Error(), "errorType": "decode_error"})
+		return
+	}
+
+	mimeType := "image/" + format
+	supportedFormats := map[string]bool{
+		"image/jpeg": true,
+		"image/png":  true,
+		"image/gif":  true,
+	}
+	if !supportedFormats[mimeType] {
+		// 允许所有 image/* 格式，但只对常见格式做深入分析
+	}
+
+	// 写入临时文件进行分析
+	tmpFile, err := os.CreateTemp("", "cyrene-image-*."+format)
+	if err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": "创建临时文件失败", "errorType": "server_error"})
+		return
+	}
+	defer os.Remove(tmpFile.Name())
+	defer tmpFile.Close()
+
+	if _, err := tmpFile.Write(data); err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": "写入临时文件失败", "errorType": "server_error"})
+		return
+	}
+
+	result, err := h.analyzeImage(tmpFile.Name(), mimeType, int64(len(data)))
+	if err != nil {
+		log.Printf("[ImageHandler] 图片分析失败: %v", err)
+		c.JSON(http.StatusInternalServerError, gin.H{"error": "图片分析失败: " + err.Error(), "errorType": "analysis_error"})
+		return
+	}
+
+	c.JSON(http.StatusOK, result)
+}
+
+// analyzeImage 核心分析逻辑：先尝试 OpenAI Vision，失败则降级到本地分析
+func (h *ImageHandler) analyzeImage(filePath, mimeType string, fileSize int64) (*ImageAnalysis, error) {
+	// 如果配置了 OpenAI API Key，尝试使用 Vision API
+	apiKey := h.cfg.LLMAPIKey
+	if apiKey != "" {
+		result, err := h.analyzeWithOpenAIVision(filePath, mimeType)
+		if err == nil {
+			return result, nil
+		}
+		log.Printf("[ImageHandler] OpenAI Vision 分析失败，降级到本地分析: %v", err)
+	}
+
+	// 降级到本地分析
+	return analyzeImageLocally(filePath, mimeType, fileSize)
+}
+
+// analyzeWithOpenAIVision 使用 OpenAI Vision API 分析图片
+func (h *ImageHandler) analyzeWithOpenAIVision(filePath, mimeType string) (*ImageAnalysis, error) {
+	// 读取图片并编码为 base64
+	data, err := os.ReadFile(filePath)
+	if err != nil {
+		return nil, fmt.Errorf("读取图片文件失败: %w", err)
+	}
+
+	base64Data := base64.StdEncoding.EncodeToString(data)
+	dataURL := fmt.Sprintf("data:%s;base64,%s", mimeType, base64Data)
+
+	// 获取本地基本信息
+	localInfo, err := analyzeImageLocally(filePath, mimeType, int64(len(data)))
+	if err != nil {
+		localInfo = &ImageAnalysis{}
+	}
+
+	// 构建 OpenAI Vision API 请求
+	reqBody := map[string]interface{}{
+		"model": h.cfg.LLMModel,
+		"messages": []map[string]interface{}{
+			{
+				"role": "user",
+				"content": []map[string]interface{}{
+					{
+						"type": "text",
+						"text": "请详细描述这张图片的内容。用中文回答。请描述：1) 图片中的主要物体/人物 2) 场景/环境 3) 颜色和色调 4) 文字内容（如果有）5) 整体氛围和风格。请尽可能详细。",
+					},
+					{
+						"type": "image_url",
+						"image_url": map[string]string{
+							"url": dataURL,
+						},
+					},
+				},
+			},
+		},
+		"max_tokens": 500,
+	}
+
+	jsonBody, err := json.Marshal(reqBody)
+	if err != nil {
+		return nil, fmt.Errorf("序列化请求失败: %w", err)
+	}
+
+	apiURL := strings.TrimRight(h.cfg.LLMAPIURL, "/") + "/chat/completions"
+	httpReq, err := http.NewRequest("POST", apiURL, bytes.NewReader(jsonBody))
+	if err != nil {
+		return nil, fmt.Errorf("创建请求失败: %w", err)
+	}
+	httpReq.Header.Set("Content-Type", "application/json")
+	httpReq.Header.Set("Authorization", "Bearer "+h.cfg.LLMAPIKey)
+
+	httpClient := &http.Client{}
+	resp, err := httpClient.Do(httpReq)
+	if err != nil {
+		return nil, fmt.Errorf("API 请求失败: %w", err)
+	}
+	defer resp.Body.Close()
+
+	body, _ := io.ReadAll(resp.Body)
+	if resp.StatusCode != http.StatusOK {
+		return nil, fmt.Errorf("API 返回错误 (%d): %s", resp.StatusCode, string(body))
+	}
+
+	var result struct {
+		Choices []struct {
+			Message struct {
+				Content string `json:"content"`
+			} `json:"message"`
+		} `json:"choices"`
+	}
+	if err := json.Unmarshal(body, &result); err != nil {
+		return nil, fmt.Errorf("解析响应失败: %w", err)
+	}
+
+	var description string
+	if len(result.Choices) > 0 {
+		description = result.Choices[0].Message.Content
+	}
+
+	return &ImageAnalysis{
+		Format:      localInfo.Format,
+		Width:       localInfo.Width,
+		Height:      localInfo.Height,
+		FileSize:    localInfo.FileSize,
+		Description: description,
+		TopColors:   localInfo.TopColors,
+		EXIF:        localInfo.EXIF,
+		AnalyzedBy:  "openai_vision",
+	}, nil
+}
+
+// analyzeImageLocally 使用 Go 标准库进行本地图片分析
+func analyzeImageLocally(filePath, mimeType string, fileSize int64) (*ImageAnalysis, error) {
+	// 1. 读取文件
+	data, err := os.ReadFile(filePath)
+	if err != nil {
+		return nil, fmt.Errorf("读取文件失败: %w", err)
+	}
+
+	// 2. 解码图片
+	img, format, err := image.Decode(bytes.NewReader(data))
+	if err != nil {
+		return nil, fmt.Errorf("解码图片失败: %w", err)
+	}
+
+	// 3. 获取尺寸
+	bounds := img.Bounds()
+	width := bounds.Dx()
+	height := bounds.Dy()
+
+	// 4. 计算颜色直方图 (采样像素)
+	topColors := computeColorHistogram(img, 5)
+
+	// 5. 读取 EXIF (简单实现: 仅 JPEG)
+	exif := extractEXIF(data, format)
+
+	// 6. 生成描述文本
+	description := generateLocalDescription(format, width, height, fileSize, topColors)
+
+	return &ImageAnalysis{
+		Format:      format,
+		Width:       width,
+		Height:      height,
+		FileSize:    fileSize,
+		Description: description,
+		TopColors:   topColors,
+		EXIF:        exif,
+		AnalyzedBy:  "local",
+	}, nil
+}
+
+// computeColorHistogram 计算颜色直方图，返回 top N 颜色
+func computeColorHistogram(img image.Image, topN int) []ColorInfo {
+	bounds := img.Bounds()
+	width := bounds.Dx()
+	height := bounds.Dy()
+
+	// 采样间隔：每 step 个像素采样一个
+	step := 1
+	totalPixels := width * height
+	if totalPixels > 10000 {
+		step = (width * height) / 10000
+		if step < 1 {
+			step = 1
+		}
+	}
+
+	colorCount := make(map[string]int)
+	sampledCount := 0
+
+	for y := bounds.Min.Y; y < bounds.Max.Y; y += step {
+		for x := bounds.Min.X; x < bounds.Max.X; x += step {
+			r, g, b, _ := img.At(x, y).RGBA()
+			// 量化到 8-bit 并聚类（每 32 级一分组，减少颜色种类）
+			qr := int(r>>8) / 32
+			qg := int(g>>8) / 32
+			qb := int(b>>8) / 32
+			key := fmt.Sprintf("%02d_%02d_%02d", qr, qg, qb)
+			colorCount[key]++
+			sampledCount++
+		}
+	}
+
+	if sampledCount == 0 {
+		return nil
+	}
+
+	// 排序取 topN
+	type kv struct {
+		key   string
+		count int
+	}
+	var sorted []kv
+	for k, v := range colorCount {
+		sorted = append(sorted, kv{k, v})
+	}
+	sort.Slice(sorted, func(i, j int) bool {
+		return sorted[i].count > sorted[j].count
+	})
+
+	result := make([]ColorInfo, 0, topN)
+	for i := 0; i < topN && i < len(sorted); i++ {
+		var qr, qg, qb int
+		fmt.Sscanf(sorted[i].key, "%d_%d_%d", &qr, &qg, &qb)
+		// 量化组的中间值
+		r := qr*32 + 16
+		g := qg*32 + 16
+		b := qb*32 + 16
+		hex := fmt.Sprintf("#%02X%02X%02X", r, g, b)
+		pct := float64(sorted[i].count) / float64(sampledCount) * 100
+		result = append(result, ColorInfo{
+			Hex:     hex,
+			Percent: pct,
+		})
+	}
+
+	return result
+}
+
+// extractEXIF 简单提取 JPEG EXIF 信息
+func extractEXIF(data []byte, format string) map[string]string {
+	if format != "jpeg" {
+		return nil
+	}
+
+	exif := make(map[string]string)
+
+	// 查找 EXIF 标记 (0xFFE1)
+	for i := 0; i < len(data)-4; i++ {
+		if data[i] == 0xFF && data[i+1] == 0xE1 {
+			if i+10 >= len(data) {
+				break
+			}
+			// 验证 EXIF 标识 "Exif\0\0"
+			if string(data[i+4:i+10]) != "Exif\x00\x00" {
+				continue
+			}
+
+			exifStart := i + 10
+			if exifStart+8 >= len(data) {
+				break
+			}
+
+			// 判断字节序
+			var bigEndian bool
+			if data[exifStart] == 'M' && data[exifStart+1] == 'M' {
+				bigEndian = true
+			} else if data[exifStart] == 'I' && data[exifStart+1] == 'I' {
+				bigEndian = false
+			} else {
+				break
+			}
+
+			// 读取 IFD0
+			tiffStart := exifStart
+			readUint16 := func(offset int) uint16 {
+				if offset+2 > len(data) {
+					return 0
+				}
+				if bigEndian {
+					return uint16(data[offset])<<8 | uint16(data[offset+1])
+				}
+				return uint16(data[offset+1])<<8 | uint16(data[offset])
+			}
+
+			ifd0Offset := int(readUint16(tiffStart + 4))
+			if ifd0Offset < 8 {
+				break
+			}
+			ifd0Addr := tiffStart + ifd0Offset
+			if ifd0Addr+2 >= len(data) {
+				break
+			}
+
+			numEntries := int(readUint16(ifd0Addr))
+			entryAddr := ifd0Addr + 2
+
+			// 常见 EXIF 标签
+			tagNames := map[uint16]string{
+				0x010F: "Make",
+				0x0110: "Model",
+				0x0112: "Orientation",
+				0x0132: "DateTime",
+				0x829A: "ExposureTime",
+				0x829D: "FNumber",
+				0x8827: "ISO",
+				0x9003: "DateTimeOriginal",
+				0x920A: "FocalLength",
+			}
+
+			for j := 0; j < numEntries && entryAddr+12 <= len(data); j++ {
+				tag := readUint16(entryAddr)
+				dataType := readUint16(entryAddr + 2)
+				dataCount := int(readUint16(entryAddr + 4))
+
+				entryAddr += 12
+
+				if name, ok := tagNames[tag]; ok {
+					valueLen := dataCount
+					switch dataType {
+					case 2: // ASCII
+						valueLen = dataCount
+					case 3, 4: // SHORT, LONG
+						valueLen = dataCount * 2
+					case 5: // RATIONAL
+						valueLen = dataCount * 8
+					}
+
+					if valueLen <= 4 {
+						// 值在 tag 自身中
+						valData := data[entryAddr-4 : entryAddr]
+						valStr := extractASCIIValue(valData, dataType, dataCount, bigEndian)
+						if valStr != "" {
+							exif[name] = valStr
+						}
+					}
+				}
+			}
+
+			break // 只处理第一个 EXIF 块
+		}
+	}
+
+	if len(exif) == 0 {
+		return nil
+	}
+	return exif
+}
+
+// extractASCIIValue 从 EXIF 数据中提取 ASCII 值
+func extractASCIIValue(data []byte, dataType uint16, count int, bigEndian bool) string {
+	switch dataType {
+	case 2: // ASCII string
+		s := string(data)
+		if idx := strings.IndexByte(s, 0); idx >= 0 {
+			s = s[:idx]
+		}
+		return s
+	case 3: // SHORT
+		if len(data) >= 2 {
+			var val uint16
+			if bigEndian {
+				val = uint16(data[0])<<8 | uint16(data[1])
+			} else {
+				val = uint16(data[1])<<8 | uint16(data[0])
+			}
+			return fmt.Sprintf("%d", val)
+		}
+	case 5: // RATIONAL
+		// 简化处理：返回原始字节
+		return ""
+	}
+	return ""
+}
+
+// generateLocalDescription 生成本地图片描述文本
+func generateLocalDescription(format string, width, height int, fileSize int64, topColors []ColorInfo) string {
+	var sb strings.Builder
+
+	formatNames := map[string]string{
+		"jpeg": "JPEG",
+		"jpg":  "JPEG",
+		"png":  "PNG",
+		"gif":  "GIF",
+		"webp": "WebP",
+		"bmp":  "BMP",
+	}
+
+	formatName := strings.ToUpper(format)
+	if name, ok := formatNames[strings.ToLower(format)]; ok {
+		formatName = name
+	}
+
+	sb.WriteString(fmt.Sprintf("这是一张 %s 格式的图片，", formatName))
+	sb.WriteString(fmt.Sprintf("分辨率为 %d×%d 像素，", width, height))
+	sb.WriteString(fmt.Sprintf("文件大小为 %s。", formatFileSize(fileSize)))
+
+	// 判断大致比例
+	ratio := float64(width) / float64(height)
+	if ratio > 1.8 {
+		sb.WriteString("图片呈宽幅横幅比例。")
+	} else if ratio < 0.6 {
+		sb.WriteString("图片呈竖幅比例。")
+	} else if ratio > 1.2 {
+		sb.WriteString("图片接近横向画幅。")
+	} else if ratio < 0.8 {
+		sb.WriteString("图片接近纵向画幅。")
+	} else {
+		sb.WriteString("图片接近正方形比例。")
+	}
+
+	// 描述主要颜色
+	if len(topColors) > 0 {
+		sb.WriteString(" 主要色调为")
+		for i, c := range topColors {
+			if i > 0 {
+				if i == len(topColors)-1 {
+					sb.WriteString(" 和 ")
+				} else {
+					sb.WriteString("、")
+				}
+			}
+			colorName := getColorName(c.Hex)
+			sb.WriteString(fmt.Sprintf("%s(%s, %.0f%%)", colorName, c.Hex, c.Percent))
+		}
+		sb.WriteString("。")
+	}
+
+	return sb.String()
+}
+
+// formatFileSize 格式化文件大小
+func formatFileSize(size int64) string {
+	if size < 1024 {
+		return fmt.Sprintf("%d B", size)
+	}
+	if size < 1024*1024 {
+		return fmt.Sprintf("%.1f KB", float64(size)/1024)
+	}
+	return fmt.Sprintf("%.1f MB", float64(size)/(1024*1024))
+}
+
+// getColorName 根据 hex 颜色获取中文颜色名
+func getColorName(hex string) string {
+	if len(hex) < 7 {
+		return hex
+	}
+	var r, g, b uint8
+	fmt.Sscanf(hex, "#%02X%02X%02X", &r, &g, &b)
+
+	// 灰度判断
+	if absDiff(r, g) < 20 && absDiff(g, b) < 20 && absDiff(r, b) < 20 {
+		if r < 40 {
+			return "黑色"
+		}
+		if r < 100 {
+			return "深灰色"
+		}
+		if r < 180 {
+			return "灰色"
+		}
+		if r < 230 {
+			return "浅灰色"
+		}
+		return "白色"
+	}
+
+	// HSL 近似判断色调
+	maxC := max(r, max(g, b))
+	minC := min(r, min(g, b))
+	delta := maxC - minC
+
+	if delta < 30 {
+		if maxC < 60 {
+			return "暗色"
+		}
+		if maxC > 200 {
+			return "浅色"
+		}
+		return "中性色"
+	}
+
+	var hue string
+	switch {
+	case r == maxC:
+		if g >= b {
+			hue = "红色"
+		} else {
+			hue = "品红色"
+		}
+	case g == maxC:
+		if b >= r {
+			hue = "绿色"
+		} else {
+			hue = "黄绿色"
+		}
+	default:
+		if r >= g {
+			hue = "紫红色"
+		} else {
+			hue = "蓝色"
+		}
+	}
+
+	// 亮度修饰
+	if maxC < 80 {
+		hue = "深" + hue
+	} else if minC > 200 {
+		hue = "浅" + hue
+	}
+
+	return hue
+}
+
+func absDiff(a, b uint8) int {
+	if a > b {
+		return int(a - b)
+	}
+	return int(b - a)
+}
+
+func max(a, b uint8) uint8 {
+	if a > b {
+		return a
+	}
+	return b
+}
+
+func min(a, b uint8) uint8 {
+	if a < b {
+		return a
+	}
+	return b
+}
+
+// ========== color.RGBA → string 辅助 ==========
+
+var _ = color.RGBA{} // 确保 color 包被使用