feat(classify): 添加图片明暗分类功能

- 修改 ImageDir 函数增加 darkScore 和 lightScore 参数
- 实现 BrightnessName 函数计算明暗等级
- 定义暗色和亮色阈值等级数组
- 在 organize.go 中集成图片明暗分析逻辑
- 使用 ITU-R BT.601 公式计算像素亮度
- 为大图实现自动采样加速分析
- 更新 README.md 文档说明明暗分类算法
- 在目录结构中体现明暗等级命名规则

internal/classify/brightness.go

@@ -0,0 +1,35 @@
+package classify
+
+import "fmt"
+
+// 暗色阈值等级（从高到低）
+var darkThresholds = []int{100, 95, 85, 75, 65, 55}
+
+// 亮色阈值等级（从高到低）
+var brightThresholds = []int{85, 75, 65, 55, 45, 35, 25, 15}
+
+// BrightnessName 按主导分判定明暗等级，返回如 "dark_100" / "bright_85"。
+// 亮色主导（lightScore > darkScore）归 bright，否则归 dark，每张图必归亮暗之一。
+func BrightnessName(darkScore, lightScore float64) string {
+	darkLowest := darkThresholds[len(darkThresholds)-1]
+	brightLowest := brightThresholds[len(brightThresholds)-1]
+
+	if lightScore > darkScore {
+		picked := brightLowest
+		for _, t := range brightThresholds {
+			if lightScore >= float64(t)/100.0 {
+				picked = t
+				break
+			}
+		}
+		return fmt.Sprintf("bright_%d", picked)
+	}
+	picked := darkLowest
+	for _, t := range darkThresholds {
+		if darkScore >= float64(t)/100.0 {
+			picked = t
+			break
+		}
+	}
+	return fmt.Sprintf("dark_%d", picked)
+}

internal/classify/classify.go

@@ -35,12 +35,14 @@ func (o Orientation) String() string {
 	}
 }
 
-// ImageDir 返回图片归类子目录名，如 "图片_横屏_1080P" / "图片_GIF"
-func ImageDir(ext string, width, height int) string {
+// ImageDir 返回图片归类子目录名，如 "图片_横屏_1080P-dark_100" / "图片_GIF"。
+// darkScore/lightScore 用于明暗分类（GIF 不做明暗分类，忽略这两项）。
+func ImageDir(ext string, width, height int, darkScore, lightScore float64) string {
 	if strings.EqualFold(ext, ".gif") {
 		return "图片_GIF"
 	}
-	return resolutionDir("图片", ext, width, height)
+	dir := resolutionDir("图片", ext, width, height)
+	return dir + "-" + BrightnessName(darkScore, lightScore)
 }
 
 // VideoDir 返回视频归类子目录名，如 "视频_竖屏_4k" / "视频_横屏_GIF"

internal/organize/organize.go

@@ -129,7 +129,16 @@ func (o *Organizer) ProcessImages(images []scanner.File) {
 			continue
 		}
 		o.record(f, "image", res)
-		o.add(f, classify.ImageDir(f.Ext, res.Width, res.Height))
+		darkScore, lightScore := 0.0, 0.0
+		if !strings.EqualFold(f.Ext, ".gif") {
+			dark, light, berr := probe.AnalyzeBrightness(f.Path)
+			if berr != nil {
+				fmt.Printf("=== 明暗分析失败(按默认dark_55归类): %s \n", f.Path)
+			} else {
+				darkScore, lightScore = dark, light
+			}
+		}
+		o.add(f, classify.ImageDir(f.Ext, res.Width, res.Height, darkScore, lightScore))
 		success++
 	}
 	fmt.Printf("=== 图片处理完毕，成功: %d, 读取异常: %d, 忽略: %d \n\n", success, fail, ignore)

internal/probe/brightness.go

@@ -0,0 +1,67 @@
+package probe
+
+import (
+	"image"
+	"math"
+	"os"
+
+	_ "image/gif"
+	_ "image/jpeg"
+	_ "image/png"
+
+	_ "golang.org/x/image/bmp"
+	_ "golang.org/x/image/tiff"
+	_ "golang.org/x/image/webp"
+)
+
+// AnalyzeBrightness 打开并解码图片，分析明暗程度。
+// darkScore: 暗色像素（亮度 < 80）占比；lightScore: 亮色像素（亮度 > 175）占比。
+func AnalyzeBrightness(filePath string) (darkScore float64, lightScore float64, err error) {
+	f, err := os.Open(filePath)
+	if err != nil {
+		return 0, 0, err
+	}
+	defer f.Close()
+	img, _, err := image.Decode(f)
+	if err != nil {
+		return 0, 0, err
+	}
+	dark, light := analyzeBrightness(img)
+	return dark, light, nil
+}
+
+// analyzeBrightness 统计暗色/亮色像素占比（ITU-R BT.601 亮度公式），大图自动采样加速
+func analyzeBrightness(img image.Image) (darkScore float64, lightScore float64) {
+	bounds := img.Bounds()
+	totalPixels := bounds.Dx() * bounds.Dy()
+	if totalPixels == 0 {
+		return 0, 0
+	}
+
+	darkCount, lightCount, sampleCount := 0, 0, 0
+	sampleStep := 1
+	if totalPixels > 1_000_000 {
+		sampleStep = int(math.Sqrt(float64(totalPixels) / 100_000))
+	}
+
+	for y := bounds.Min.Y; y < bounds.Max.Y; y += sampleStep {
+		for x := bounds.Min.X; x < bounds.Max.X; x += sampleStep {
+			r, g, b, _ := img.At(x, y).RGBA()
+			r8 := uint8(r >> 8)
+			g8 := uint8(g >> 8)
+			b8 := uint8(b >> 8)
+			brightness := 0.299*float64(r8) + 0.587*float64(g8) + 0.114*float64(b8)
+			if brightness < 80 {
+				darkCount++
+			} else if brightness > 175 {
+				lightCount++
+			}
+			sampleCount++
+		}
+	}
+
+	if sampleCount == 0 {
+		return 0, 0
+	}
+	return float64(darkCount) / float64(sampleCount), float64(lightCount) / float64(sampleCount)
+}