android 磨皮原理,Android平台Camera实时滤镜实现方法探讨(九)–磨皮算法探讨(一)

上一篇开头提到了一些可用于磨皮的去噪算法，下面我们实现这些算法并且观察效果，咱不考虑实时性的问题

该算法利用图像局部统计特性进行滤波处理，例如NXM像素的灰度图，首先计算点(i,j)所在窗口内(大小为(2n+1)(2m+1))的平均值m(i,j)

以及均方差：

得到加性去噪后的结果为：

其中：

1.根据原文提出的优化方法，首先是建立两个积分图，如图所示，点4的积分即为Sum(Ra)+Sum(Rb)+Sum(Rc)+Sum(Rd)。积分图的建立算法可以参考这篇文章进行简单优化，然后即可根据积分图计算公式中的m值和v值。

例如半径为r的窗口的m(i,j)为Integral(i+r,j+r) + Integral(i-r-1,j-r-1)-Integral(i+r,j-r-1)-Integral(i-r-1,j+r)。代码如下，分别求1次方和平方的积分图。

void MagicBeauty::initIntegral(uint8_t* inputMatrix){

LOGE(“initIntegral start”);

if(mIntegralMatrix == NULL)

mIntegralMatrix = new uint64_t[mImageWidth * mImageHeight];

if(mIntegralMatrixSqr == NULL)

mIntegralMatrixSqr = new uint64_t[mImageWidth * mImageHeight];

uint64_t *columnSum = new uint64_t[mImageWidth];

uint64_t *columnSumSqr = new uint64_t[mImageWidth];

columnSum[0] = inputMatrix[0];

columnSumSqr[0] = inputMatrix[0] * inputMatrix[0];

mIntegralMatrix[0] = columnSum[0];

mIntegralMatrixSqr[0] = columnSumSqr[0];

for(int i = 1;i < mImageWidth;i++){

columnSum[i] = inputMatrix[i];

columnSumSqr[i] = inputMatrix[i] * inputMatrix[i];

mIntegralMatrix[i] = columnSum[i];

mIntegralMatrix[i] += mIntegralMatrix[i-1];

mIntegralMatrixSqr[i] = columnSumSqr[i];

mIntegralMatrixSqr[i] += mIntegralMatrixSqr[i-1];

}

for (int i = 1;i < mImageHeight; i++){

int offset = i * mImageWidth;

columnSum[0] += inputMatrix[offset];

columnSumSqr[0] += inputMatrix[offset] * inputMatrix[offset];

mIntegralMatrix[offset] = columnSum[0];

mIntegralMatrixSqr[offset] = columnSumSqr[0];

// other columns

for(int j = 1; j < mImageWidth; j++){

columnSum[j] += inputMatrix[offset+j];

columnSumSqr[j] += inputMatrix[offset+j] * inputMatrix[offset+j];

mIntegralMatrix[offset+j] = mIntegralMatrix[offset+j-1] + columnSum[j];

mIntegralMatrixSqr[offset+j] = mIntegralMatrixSqr[offset+j-1] + columnSumSqr[j];

}

}

delete[] columnSum;

delete[] columnSumSqr;

LOGE(“initIntegral end”);

}

2.根据网上抄来的RGB肤色检测计算肤色区域

void MagicBeauty::initSkinMatrix(){

LOGE(“start – initSkinMatrix”);

if(mSkinMatrix == NULL)

mSkinMatrix = new uint8_t[mImageWidth * mImageHeight];

for(int i = 0; i < mImageHeight; i++){

for(int j = 0; j < mImageWidth; j++){

int offset = i*mImageWidth+j;

ARGB RGB;

BitmapOperation::convertIntToArgb(mImageData_rgb[offset],&RGB);

if ((RGB.blue>95 && RGB.green>40 && RGB.red>20 &&

RGB.blue-RGB.red>15 && RGB.blue-RGB.green>15)||//uniform illumination

(RGB.blue>200 && RGB.green>210 && RGB.red>170 &&

abs(RGB.blue-RGB.red)<=15 && RGB.blue>RGB.red&& RGB.green>RGB.red))//lateral illumination

mSkinMatrix[offset] = 255;

else

mSkinMatrix[offset] = 0;

}

}

LOGE(“end – initSkinMatrix”);

}

3.根据公式对RGB通道或者将RGB通道转化为YCbCr格式单独对Y通道进行滤波

void MagicBeauty::startLocalStatisticsSmooth(float sigema){

if(mIntegralMatrix == NULL || mIntegralMatrixSqr == NULL ||

mImageData_yuv_y == NULL || mSkinMatrix == NULL || mImageData_yuv == NULL){

LOGE(“not init correctly”);

return;

}

int radius = mImageWidth > mImageHeight ? mImageWidth * 0.02 : mImageHeight * 0.02;

LOGE(“startSmooth”);

for(int i = 1; i < mImageHeight; i++){

for(int j = 1; j < mImageWidth; j++){

int offset = i * mImageWidth + j;

if(mSkinMatrix[offset] == 255){

int iMax = i + radius >= mImageHeight-1 ? mImageHeight-1 : i + radius;

int jMax = j + radius >= mImageWidth-1 ? mImageWidth-1 :j + radius;

int iMin = i – radius <= 1 ? 1 : i – radius;

int jMin = j – radius <= 1 ? 1 : j – radius;

int squar = (iMax – iMin + 1)*(jMax – jMin + 1);

int i4 = iMax*mImageWidth+jMax;

int i3 = (iMin-1)*mImageWidth+(jMin-1);

int i2 = iMax*mImageWidth+(jMin-1);

int i1 = (iMin-1)*mImageWidth+jMax;

float m = (mIntegralMatrix[i4]

+ mIntegralMatrix[i3]

– mIntegralMatrix[i2]

– mIntegralMatrix[i1]) / squar;

float v = (mIntegralMatrixSqr[i4]

+ mIntegralMatrixSqr[i3]

– mIntegralMatrixSqr[i2]

– mIntegralMatrixSqr[i1]) / squar – m*m;

float k = v / (v + sigema);

mImageData_yuv[offset*3] = m – k * m + k * mImageData_yuv_y[offset];

}

}

}

endLocalStatisticsSmooth();

}

效果图：磨皮强度为最大10.