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];</span>
			}
		}
	}
	endLocalStatisticsSmooth();
}

效果图：磨皮强度为最大10.

Android平台Camera实时滤镜实现方法探讨(九)--磨皮算法探讨(一)

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