Files
kenaito-media-picker/internal/probe/brightness.go
T
odboy 13bb195e1d refactor(classify): 重构图片明暗分类逻辑
- 移除固定的明暗阈值等级数组,改用动态分档机制
- 引入 neutralMargin 常量,当两分差幅小于 0.15 时视为中性图
- 修改 BrightnessName 函数,实现三分法分类:bright_N、dark_N、mid
- 新增 brightnessTier 辅助函数,将占比分档到最接近的 10%
- 完全透明的像素(alpha=0)不再参与明暗统计计算
- 更新错误提示信息,明暗分析失败时按中性 mid 归类
- 调整 README 文档描述,同步更新明暗分类规则说明
2025-04-01 21:07:48 +08:00

72 lines
1.7 KiB
Go

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。
// 完全透明的像素(alpha=0)不计入统计。
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, a := img.At(x, y).RGBA()
if a == 0 {
continue
}
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)
}