RenderScript使用教程（四）实现美颜功能

前言

最近在做一个相机APP，通过OpenGL实现了实时美颜功能，但是网上很难找到通过RS实现美颜的例子，因为相机预览通过OpenGL实现美颜后，用户点击拍照后的数据依然是原始数据，并没有美颜，所以我通过同样的算法用RS实现了一遍。有需要的同学可以参考一下

Java调用代码

    public static Bitmap beauty(Context context, Bitmap inBitmap) {
        // Creates a RS context.
        RenderScript mRS = RenderScript.create(context);

        // Creates the input Allocation and copies all Bitmap contents into it.
        Allocation inAllocation = Allocation.createFromBitmap(mRS, inBitmap);

        // Defines the output Type, which will be a RGBA pixel.
        // The Allocation will be composed by four unsigned chars (0-255) for each pixel,
        // so that R-G-B-A values can be stored.
        // It is necessary to use a Type-based approach whenever there is a multi-dimensional sizing (X,Y).
        int bitmapWidth = inBitmap.getWidth();
        int bitmapHeight = inBitmap.getHeight();

        Type.Builder outType = new Type.Builder(mRS, Element.RGBA_8888(mRS)).setX(bitmapWidth).setY(bitmapHeight);

        // Creates the output Allocation wherein to store the conversion result.
        Allocation outAllocation = Allocation.createTyped(mRS, outType.create(), Allocation.USAGE_SCRIPT);

        // Creates the conversion script wrapper.
        ScriptC_beauty processScript = new ScriptC_beauty(mRS);

        // Binds the inAllocation variable with the actual Allocation.
        processScript.set_inAllocation(inAllocation);

        // Performs the conversion. RS kernel will use outAllocation size for its iterations.
        processScript.forEach_beauty(outAllocation);

        // Creates output Bitmap, matching input one size.
        Bitmap outBitmap = Bitmap.createBitmap(bitmapWidth, bitmapHeight, inBitmap.getConfig());

        // Copy calculation result to the output Bitmap.
        outAllocation.copyTo(outBitmap);

        mRS.destroy();
        return outBitmap;
    }

RenderScript代码

// Needed directive for RS to work
#pragma version(1)
// Change java_package_name directive to match your Activity's package path
#pragma rs java_package_name(com.dong.opencamera)

rs_allocation inAllocation;
static const float beautyParam = 0.5f;
static const float4 weight = {0.299f, 0.587f, 0.114f, 0.0f};

static float hardLight(float color) {
    if (color <= 0.5) {
        color = color * color * 2.0;
    } else {
        color = 1.0 - ((1.0 - color) * (1.0 - color) * 2.0);
    }
    return color;
}

uchar4 __attribute__((kernel)) beauty(uint32_t x, uint32_t y) {
            const uint32_t imageWidth = rsAllocationGetDimX(inAllocation);
            const uint32_t imageHeight = rsAllocationGetDimY(inAllocation);
            float4 in = rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x, y));

            //process border
            if(x < 10 || y < 10 || x >= (imageWidth - 10) || y >= (imageHeight - 10)) {
                    return rsPackColorTo8888(in);
             }

             // 对绿色通道进行模糊操作
            float4 sampleColor = in.g * 20.0;
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x, y - 10));
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x, y + 10));
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x - 10, y));
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x + 10, y));
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x + 5, y - 8));
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x + 5, y + 8));
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x - 5, y + 8));
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x - 5, y - 8));
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x + 8, y - 5));
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x + 8, y - 5));
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x - 8, y + 5));
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x - 8, y - 5));
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x, y - 6)) * 2.0;
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x, y + 6)) * 2.0;
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x + 6, y)) * 2.0;
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x - 6, y)) * 2.0;
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x - 4, y - 4)) * 2.0;
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x - 4, y + 4))* 2.0;
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x + 4, y - 4)) * 2.0;
            sampleColor += rsUnpackColor8888(rsGetElementAt_uchar4(inAllocation, x + 4, y + 4))* 2.0;

            sampleColor = sampleColor / 48.0;
            float highPass = in.g - sampleColor.g + 0.5;
            highPass = clamp(highPass, 0.0, 1.0);

             for (int i = 0; i < 5; i++) {
                   highPass = hardLight(highPass);
             }
             highPass = clamp(highPass, 0.0, 1.0);
             float luminance = dot(in, weight);
             float alpha = pow(luminance, beautyParam);

             alpha = clamp(alpha, 0.0, 1.0);

             float3 offset = (in.rgb - (float3){highPass, highPass, highPass}) * alpha * 0.1;
             float3 smoothColor = in.rgb + offset;
             smoothColor.r = clamp(smoothColor.r, 0.0, 1.0);
             smoothColor.g = clamp(smoothColor.g, 0.0, 1.0);
             smoothColor.b = clamp(smoothColor.b, 0.0, 1.0);

             // 线性混合
             float4 out;
             out.r = mix(smoothColor.r, max(in.r,smoothColor.r), alpha);
             out.g = mix(smoothColor.g, max(in.g,smoothColor.g), alpha);
             out.b = mix(smoothColor.b, max(in.b,smoothColor.b), alpha);
             out.a = in.a;

            return rsPackColorTo8888(out);
}

相机美颜的原理

1.取出绿色通道，对绿色通道进行模糊处理，例如高斯模糊，得到模糊后的值sampleColor
2.用原图绿色通道值减去sampleColor，加上0.5（即128），1+2两个步骤即PS中的高反差保留
3.对上述结果值进行3-5次强光处理，此步骤可以使得噪声更加突出
4.计算原图的灰度值，公式为0.299R + 0.587G + 0.114*B
5.将灰度值作为阈值，用来排除非皮肤部分，根据灰度值计算，将原图与1-3后的结果图合成
6.对混合后结果增加亮度
7.以灰度值作为透明度将原图与混合后结果进行滤色、柔光等混合，并调节饱和度

参考文档