氛围灯动态屏保取色方案二

news/2025/10/22 11:23:34/文章来源:https://www.cnblogs.com/LiuZhen/p/19157538
氛围灯动态屏保取色方案二

hue查找：整体偏差不会很大，但是对于亮度较高存在误差，精准度不够

lab查找：整体一般，但是精准度较好，不过算法复杂，增加耗时

hue色相查找存在误差，在有限的256色中，匹配的规则需要调整

这里使用lab算法提高精准度

RGB转Lab

fun rGBToLab(r: Int, g: Int, b: Int): DoubleArray {// 处理负值（如-2563864）val labR = if (r < 0) 0f else r / 255fval labG = if (g < 0) 0f else g / 255fval labB = if (b < 0) 0f else b / 255f// 线性化处理（sRGB转换）val rLin = if (labR <= 0.04045) labR / 12.92 else ((labR + 0.055) / 1.055).pow(2.4)val gLin = if (labG <= 0.04045) labG / 12.92 else ((labG + 0.055) / 1.055).pow(2.4)val bLin = if (labB <= 0.04045) labB / 12.92 else ((labB + 0.055) / 1.055).pow(2.4)// XYZ转换（D65标准光源）val x = 0.4124564 * rLin + 0.3575761 * gLin + 0.1804375 * bLinval y = 0.2126729 * rLin + 0.7151522 * gLin + 0.0721750 * bLinval z = 0.0193339 * rLin + 0.1191920 * gLin + 0.9503041 * bLin// Lab转换val xn = 0.95047fval yn = 1.0fval zn = 1.08883fval fX = x / xnval fY = y / ynval fZ = z / znval l = 116f * fY.pow(1 / 3.0) - 16val a = 500f * (fX.pow(1 / 3.0) - fY.pow(1 / 3.0))val b = 200f * (fY.pow(1 / 3.0) - fZ.pow(1 / 3.0))return doubleArrayOf(l, a, b)}
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对rgb进行转换，这里是简化版，如果需要精确要求极高，需要完善

处理流程跟方案一一样，使用集合缓存

接着使用 ΔE76色差公式 查找色值（另外还有ΔEab欧式距离算法，CIEDE2000色差公式，CIE76）

fun deltaE76(lab1: DoubleArray, lab2: DoubleArray): Double {val (l1, a1, b1) = lab1val (l2, a2, b2) = lab2// 计算中间变量val c1 = sqrt(a1.pow(2) + b1.pow(2))val c2 = sqrt(a2.pow(2) + b2.pow(2))val cAvg = (c1 + c2) / 2val g = 0.5 * (1 - sqrt(cAvg.pow(7) / (cAvg.pow(7) + 25f.pow(7))))val a1p = a1 * (1 + g)val a2p = a2 * (1 + g)val c1p = sqrt(a1p.pow(2) + b1.pow(2))val c2p = sqrt(a2p.pow(2) + b2.pow(2))val h1p = Math.toDegrees(kotlin.math.atan2(b1, a1p)).let { if (it < 0) it + 360 else it }val h2p = Math.toDegrees(kotlin.math.atan2(b2, a2p)).let { if (it < 0) it + 360 else it }// 色差计算val deltaLp = l2 - l1val deltaCp = c2p - c1pval deltaHp = when {c1p * c2p == 0.0 -> 0.0abs(h2p - h1p) <= 180 -> h2p - h1ph2p <= h1p -> h2p - h1p + 360else -> h2p - h1p - 360}val deltaHpS = 2 * sqrt(c1p * c2p) * sin(Math.toRadians(deltaHp) / 2)// 加权计算val lAvg = (l1 + l2) / 2val cpAvg = (c1p + c2p) / 2val hpAvg = when {c1p * c2p == 0.0 -> h1p + h2pabs(h1p - h2p) > 180 -> (h1p + h2p + 360) / 2else -> (h1p + h2p) / 2}.let { if (it >= 360) it - 360 else it }val t = 1 - 0.17 * cos(Math.toRadians(hpAvg - 30)) +0.24 * cos(Math.toRadians(2 * hpAvg)) +0.32 * cos(Math.toRadians(3 * hpAvg + 6)) -0.20 * cos(Math.toRadians(4 * hpAvg - 63))val sl = 1 + (0.015 * (lAvg - 50).pow(2)) / sqrt(20 + (lAvg - 50).pow(2))val sc = 1 + 0.045 * cpAvgval sh = 1 + 0.015 * cpAvg * tval deltaTheta = 30 * exp(-((hpAvg - 275) / 25).pow(2))val rc = 2 * sqrt(cpAvg.pow(7) / (cpAvg.pow(7) + 25f.pow(7)))val rt = -rc * sin(2 * Math.toRadians(deltaTheta))return sqrt((deltaLp / sl).pow(2) +(deltaCp / sc).pow(2) +(deltaHpS / sh).pow(2) +rt * (deltaCp / sc) * (deltaHpS / sh))}
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var minIndex = 0var minDistance = Double.MAX_VALUEvar minLab = doubleArrayOf()tableLabPalette.forEachIndexed { index, lab ->val distance = ColorEabLabUtils.deltaE76(lab, list)if (distance < minDistance) {minDistance = distanceminLab = labminIndex = index}}
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image

从上面图中结果可以明显看到，大部分图片中，hue原方案精准度对比lab查找，是有差距的

但是缺点也明显，在某些图片上表现不好，可以看到原方案hue更精准

image

经过大量调整算法，优化算法，测试下来发现，如果在不通过底层显示屏直接转换色值情况下，都存在误差

而刚好这两种查找结果有一定互补作用，那是否可以取各自的优势，结合结果，达到一个平衡

private fun findLabColor(list: DoubleArray, hue: Float): Int {// 寻找最近颜色（简化版）var minIndex = 0var minDistance = Double.MAX_VALUEvar minLab = doubleArrayOf()tableLabPalette.forEachIndexed { index, lab ->val distance = ColorEabLabUtils.deltaE76(lab, list)if (distance < minDistance) {minDistance = distanceminLab = labminIndex = index}}return if (minDistance < 26) {tableColorList[minIndex]} else {findColor(hue).colorTip}}
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在lab精度不够时，可能结果偏差较大，但是hue偏差不会很大，hue只是精度不够，所以这里兼容两种方案

在lab误差较大时，直接采用hue方案计算结果，这样直接弥补了自身的缺点，比如图一中误差小，采用lab方案，提高了精准度，而在图二精度不够时，采用hue，避免误差偏差较大

image

最终方案是融合方案，不过精度任然存在误差，但本身转换过程结果只有256色，所以不可能达到完美，相比原图取色结果精度是较高

import android.graphics.Bitmap
import android.graphics.Color
import androidx.palette.graphics.Palette
import androidx.palette.graphics.Palette.Swatch
import java.lang.Math.toDegrees
import java.lang.Math.toRadians
import kotlin.math.abs
import kotlin.math.atan2
import kotlin.math.cos
import kotlin.math.exp
import kotlin.math.pow
import kotlin.math.sin
import kotlin.math.sqrtobject ColorEabLabUtils {fun getPerceptuallyDominantColor(bitmap: Bitmap): Int {val palette = Palette.from(bitmap).maximumColorCount(24).clearFilters().generate()val swatches = palette.swatchesif (swatches.isEmpty()) return Color.WHITEvar bestSwatch: Swatch? = nullvar maxScore = 0ffor (swatch in swatches) {val hsl = swatch.getHsl()val saturation = hsl[1] // 饱和度 (0-1)val luminance = hsl[2] // 亮度 (0-1)val population = swatch.population// 评分公式：人口占比 * 饱和度 * 亮度因子// 亮度因子确保避免过暗或过亮的颜色（0.1-0.9为理想范围）val luminanceFactor = 1f - abs(luminance - 0.5f) * 1.8fval score = population * saturation * luminanceFactorif (score > maxScore) {maxScore = scorebestSwatch = swatch}}return bestSwatch?.rgb ?: palette.getDominantColor(Color.WHITE)}// RGB转Lab *********
    fun rGBToLab(r: Int, g: Int, b: Int): DoubleArray {// 处理负值（如-2563864）val labR = if (r < 0) 0f else r / 255fval labG = if (g < 0) 0f else g / 255fval labB = if (b < 0) 0f else b / 255f// 线性化处理（sRGB转换）val rLin = if (labR <= 0.04045) labR / 12.92 else ((labR + 0.055) / 1.055).pow(2.4)val gLin = if (labG <= 0.04045) labG / 12.92 else ((labG + 0.055) / 1.055).pow(2.4)val bLin = if (labB <= 0.04045) labB / 12.92 else ((labB + 0.055) / 1.055).pow(2.4)// XYZ转换（D65标准光源）val x = 0.4124564 * rLin + 0.3575761 * gLin + 0.1804375 * bLinval y = 0.2126729 * rLin + 0.7151522 * gLin + 0.0721750 * bLinval z = 0.0193339 * rLin + 0.1191920 * gLin + 0.9503041 * bLin// Lab转换val xn = 0.95047fval yn = 1.0fval zn = 1.08883fval fX = x / xnval fY = y / ynval fZ = z / znval l = 116f * fY.pow(1 / 3.0) - 16val a = 500f * (fX.pow(1 / 3.0) - fY.pow(1 / 3.0))val b = 200f * (fY.pow(1 / 3.0) - fZ.pow(1 / 3.0))return doubleArrayOf(l, a, b)}// ΔEab算法
    fun deltaEab(lab1: DoubleArray, lab2: DoubleArray): Double {val dL = lab1[0] - lab2[0]val da = lab1[1] - lab2[1]val db = lab1[2] - lab2[2]return sqrt(dL.pow(2) + da.pow(2) + db.pow(2))}// 改进的RGB转Lab（包含伽马校正和D65白点）
    fun rgbToLab(r: Int, g: Int, b: Int): DoubleArray {// 处理负值（如-2563864）val labR = if (r < 0) 0f else r / 255fval labG = if (g < 0) 0f else g / 255fval labB = if (b < 0) 0f else b / 255f// sRGB伽马校正val rLin = if (labR <= 0.04045) labR / 12.92 else ((labR + 0.055) / 1.055).pow(2.4)val gLin = if (labG <= 0.04045) labG / 12.92 else ((labG + 0.055) / 1.055).pow(2.4)val bLin = if (labB <= 0.04045) labB / 12.92 else ((labB + 0.055) / 1.055).pow(2.4)// D65标准光源XYZ转换val x = 0.4124564 * rLin + 0.3575761 * gLin + 0.1804375 * bLinval y = 0.2126729 * rLin + 0.7151522 * gLin + 0.0721750 * bLinval z = 0.0193339 * rLin + 0.1191920 * gLin + 0.9503041 * bLin// Lab转换（包含阈值处理）val xn = 0.95047val yn = 1.0val zn = 1.08883val fX = x / xnval fY = y / ynval fZ = z / znval l = if (fY > 0.008856) 116 * fY.pow(1 / 3.0) - 16 else 903.3 * fYval a = 500 * (fX.pow(1 / 3.0) - fY.pow(1 / 3.0))val b = 200 * (fY.pow(1 / 3.0) - fZ.pow(1 / 3.0))return doubleArrayOf(l, a, b)}// 改进的ΔE76色差公式（包含亮度权重）  *********
    fun deltaE76(lab1: DoubleArray, lab2: DoubleArray): Double {val (l1, a1, b1) = lab1val (l2, a2, b2) = lab2// 计算中间变量val c1 = sqrt(a1.pow(2) + b1.pow(2))val c2 = sqrt(a2.pow(2) + b2.pow(2))val cAvg = (c1 + c2) / 2val g = 0.5 * (1 - sqrt(cAvg.pow(7) / (cAvg.pow(7) + 25f.pow(7))))val a1p = a1 * (1 + g)val a2p = a2 * (1 + g)val c1p = sqrt(a1p.pow(2) + b1.pow(2))val c2p = sqrt(a2p.pow(2) + b2.pow(2))val h1p = Math.toDegrees(kotlin.math.atan2(b1, a1p)).let { if (it < 0) it + 360 else it }val h2p = Math.toDegrees(kotlin.math.atan2(b2, a2p)).let { if (it < 0) it + 360 else it }// 色差计算val deltaLp = l2 - l1val deltaCp = c2p - c1pval deltaHp = when {c1p * c2p == 0.0 -> 0.0abs(h2p - h1p) <= 180 -> h2p - h1ph2p <= h1p -> h2p - h1p + 360else -> h2p - h1p - 360}val deltaHpS = 2 * sqrt(c1p * c2p) * sin(Math.toRadians(deltaHp) / 2)// 加权计算val lAvg = (l1 + l2) / 2val cpAvg = (c1p + c2p) / 2val hpAvg = when {c1p * c2p == 0.0 -> h1p + h2pabs(h1p - h2p) > 180 -> (h1p + h2p + 360) / 2else -> (h1p + h2p) / 2}.let { if (it >= 360) it - 360 else it }val t = 1 - 0.17 * cos(Math.toRadians(hpAvg - 30)) +0.24 * cos(Math.toRadians(2 * hpAvg)) +0.32 * cos(Math.toRadians(3 * hpAvg + 6)) -0.20 * cos(Math.toRadians(4 * hpAvg - 63))val sl = 1 + (0.015 * (lAvg - 50).pow(2)) / sqrt(20 + (lAvg - 50).pow(2))val sc = 1 + 0.045 * cpAvgval sh = 1 + 0.015 * cpAvg * tval deltaTheta = 30 * exp(-((hpAvg - 275) / 25).pow(2))val rc = 2 * sqrt(cpAvg.pow(7) / (cpAvg.pow(7) + 25f.pow(7)))val rt = -rc * sin(2 * Math.toRadians(deltaTheta))return sqrt((deltaLp / sl).pow(2) +(deltaCp / sc).pow(2) +(deltaHpS / sh).pow(2) +rt * (deltaCp / sc) * (deltaHpS / sh))}/*** CIEDE2000色差公式实现* 更符合人眼感知的颜色差异计算* @param lab1 第一个颜色的Lab值* @param lab2 第二个颜色的Lab值* @return Double 色差值（ΔE越小颜色越接近）*/fun deltaE2000(lab1: DoubleArray, lab2: DoubleArray): Double {val (l1, a1, b1) = lab1val (l2, a2, b2) = lab2// 步骤1：计算色度val c1 = sqrt(a1.pow(2) + b1.pow(2))val c2 = sqrt(a2.pow(2) + b2.pow(2))val cAvg = (c1 + c2) / 2.0// 步骤2：计算补偿因子val g = 0.5 * (1.0 - sqrt(cAvg.pow(7) / (cAvg.pow(7) + 25.0.pow(7))))val a1Prime = a1 * (1.0 + g)val a2Prime = a2 * (1.0 + g)val c1Prime = sqrt(a1Prime.pow(2) + b1.pow(2))val c2Prime = sqrt(a2Prime.pow(2) + b2.pow(2))val cBarPrime = (c1Prime + c2Prime) / 2.0// 步骤3：计算色调角val h1Prime = toDegrees(atan2(b1, a1Prime)).let { if (it < 0) it + 360.0 else it }val h2Prime = toDegrees(atan2(b2, a2Prime)).let { if (it < 0) it + 360.0 else it }// 步骤4：计算基本差异val deltaLPrime = l2 - l1val deltaCPrime = c2Prime - c1Prime// 步骤5：计算色调差var deltaHPrime = 0.0if (c1Prime * c2Prime != 0.0) {val deltaHPrimeRaw = when {abs(h2Prime - h1Prime) <= 180.0 -> h2Prime - h1Primeh2Prime <= h1Prime -> h2Prime - h1Prime + 360.0else -> h2Prime - h1Prime - 360.0}deltaHPrime = 2.0 * sqrt(c1Prime * c2Prime) * sin(toRadians(deltaHPrimeRaw) / 2.0)}// 步骤6：计算加权平均值val lBar = (l1 + l2) / 2.0val cBar = (c1 + c2) / 2.0// 步骤7：计算色调平均值val hBarPrime = when {abs(h1Prime - h2Prime) > 180.0 -> (h1Prime + h2Prime + 360.0) / 2.0else -> (h1Prime + h2Prime) / 2.0}// 步骤8：计算补偿项val t = 1.0 - 0.17 * cos(toRadians(hBarPrime - 30.0)) +0.24 * cos(toRadians(2.0 * hBarPrime)) +0.32 * cos(toRadians(3.0 * hBarPrime + 6.0)) -0.20 * cos(toRadians(4.0 * hBarPrime - 63.0))// 步骤9：计算旋转项val deltaTheta = 30.0 * exp(-((hBarPrime - 275.0) / 25.0).pow(2))val rc = 2.0 * sqrt(cBarPrime.pow(7) / (cBarPrime.pow(7) + 25.0.pow(7)))val rt = -rc * sin(2.0 * toRadians(deltaTheta))// 步骤10：计算权重因子val sl = 1.0 + (0.015 * (lBar - 50.0).pow(2) / sqrt(20.0 + (lBar - 50.0).pow(2)))val sc = 1.0 + 0.045 * cBarPrimeval sh = 1.0 + 0.015 * cBarPrime * t// 步骤11：最终色差计算val term1 = (deltaLPrime / sl).pow(2)val term2 = (deltaCPrime / sc).pow(2)val term3 = (deltaHPrime / sh).pow(2)val term4 = rt * (deltaCPrime / sc) * (deltaHPrime / sh)return sqrt(term1 + term2 + term3 + term4)}// 优化的伽马校正函数fun preciseGammaExpand(c: Double) = if (c <= 0.04045) {c / 12.92} else {((c + 0.055) / 1.055).pow(2.4)}// 优化的Lab转换函数fun preciseF(t: Double) = if (t > 0.008856) {t.pow(1.0 / 3.0)} else {(7.787 * t) + (16.0 / 116.0)}/*** 优化的RGB转Lab转换方法* 使用更精确的浮点数计算和标准化处理*/fun rgbToLabOptimized(r: Int, g: Int, b: Int): DoubleArray {// 使用更精确的归一化val rNormalized = r / 255.0val gNormalized = g / 255.0val bNormalized = b / 255.0// 应用伽马校正val rLinear = preciseGammaExpand(rNormalized)val gLinear = preciseGammaExpand(gNormalized)val bLinear = preciseGammaExpand(bNormalized)// 使用D65标准光源的精确XYZ转换矩阵val x = 0.4124564 * rLinear + 0.3575761 * gLinear + 0.1804375 * bLinearval y = 0.2126729 * rLinear + 0.7151522 * gLinear + 0.0721750 * bLinearval z = 0.0193339 * rLinear + 0.1191920 * gLinear + 0.9503041 * bLinear// 使用CIE标准参考白点val refX = 0.95047val refY = 1.00000val refZ = 1.08883// 计算相对值val xRelative = x / refXval yRelative = y / refYval zRelative = z / refZval fx = preciseF(xRelative)val fy = preciseF(yRelative)val fz = preciseF(zRelative)// 计算Lab值val l = (116.0 * fy) - 16.0val a = 500.0 * (fx - fy)val bLab = 200.0 * (fy - fz)return doubleArrayOf(l, a, bLab)}/*** 完整的CIEDE2000色差公式实现* 包含所有补偿项和旋转项*/fun deltaE2000Complete(lab1: DoubleArray, lab2: DoubleArray): Double {val (l1, a1, b1) = lab1val (l2, a2, b2) = lab2// 步骤1：计算色度值val c1 = sqrt(a1.pow(2) + b1.pow(2))val c2 = sqrt(a2.pow(2) + b2.pow(2))// 步骤2：计算平均值val lBar = (l1 + l2) / 2.0val cBar = (c1 + c2) / 2.0// 步骤3：计算补偿因子val g = 0.5 * (1.0 - sqrt(cBar.pow(7) / (cBar.pow(7) + 25.0.pow(7))))// 步骤4：计算调整后的a'值val a1Prime = a1 * (1.0 + g)val a2Prime = a2 * (1.0 + g)// 步骤5：计算调整后的色度值val c1Prime = sqrt(a1Prime.pow(2) + b1.pow(2))val c2Prime = sqrt(a2Prime.pow(2) + b2.pow(2))val cBarPrime = (c1Prime + c2Prime) / 2.0// 步骤6：计算色调角val h1Prime = toDegrees(atan2(b1, a1Prime)).let {if (it < 0) it + 360.0 else it}val h2Prime = toDegrees(atan2(b2, a2Prime)).let {if (it < 0) it + 360.0 else it}// 步骤7：计算基本差异val deltaLPrime = l2 - l1val deltaCPrime = c2Prime - c1Prime// 步骤8：计算色调差var deltaHPrime = 0.0if (c1Prime != 0.0 && c2Prime != 0.0) {val deltaHPrimeRaw = when {abs(h2Prime - h1Prime) <= 180.0 -> h2Prime - h1Primeh2Prime <= h1Prime -> h2Prime - h1Prime + 360.0else -> h2Prime - h1Prime - 360.0}deltaHPrime = 2.0 * sqrt(c1Prime * c2Prime) * sin(toRadians(deltaHPrimeRaw) / 2.0)}// 步骤9：计算色调平均值val hBarPrime = when {abs(h1Prime - h2Prime) > 180.0 -> (h1Prime + h2Prime + 360.0) / 2.0else -> (h1Prime + h2Prime) / 2.0}// 步骤10：计算补偿项val t = 1.0 - 0.17 * cos(toRadians(hBarPrime - 30.0)) +0.24 * cos(toRadians(2.0 * hBarPrime)) +0.32 * cos(toRadians(3.0 * hBarPrime + 6.0)) -0.20 * cos(toRadians(4.0 * hBarPrime - 63.0))// 步骤11：计算旋转项val deltaTheta = 30.0 * exp(-((hBarPrime - 275.0) / 25.0).pow(2))val rc = 2.0 * sqrt(cBarPrime.pow(7) / (cBarPrime.pow(7) + 25.0.pow(7)))val rt = -rc * sin(2.0 * toRadians(deltaTheta))// 步骤12：计算权重因子val sl = 1.0 + (0.015 * (lBar - 50.0).pow(2) / sqrt(20.0 + (lBar - 50.0).pow(2)))val sc = 1.0 + 0.045 * cBarPrimeval sh = 1.0 + 0.015 * cBarPrime * t// 步骤13：最终色差计算val term1 = (deltaLPrime / sl).pow(2)val term2 = (deltaCPrime / sc).pow(2)val term3 = (deltaHPrime / sh).pow(2)val term4 = rt * (deltaCPrime / sc) * (deltaHPrime / sh)return sqrt(term1 + term2 + term3 + term4)}}
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color_rgb_256.json

 

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