RenderScript使用教程（四）實現美顏功能

前言

最近在做一個相機APP，通過OpenGL實現了實時美顏功能，但是網上很難找到通過RS實現美顏的例子，因爲相機預覽通過OpenGL實現美顏後，用戶點擊拍照後的數據依然是原始數據，並沒有美顏，所以我通過同樣的算法用RS實現了一遍。有需要的同學可以參考一下

Java調用代碼

    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代碼

// 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);
             }

             // 對綠色通道進行模糊操作
            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);

             // 線性混合
             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);
}

相機美顏的原理

1.取出綠色通道，對綠色通道進行模糊處理，例如高斯模糊，得到模糊後的值sampleColor
2.用原圖綠色通道值減去sampleColor，加上0.5（即128），1+2兩個步驟即PS中的高反差保留
3.對上述結果值進行3-5次強光處理，此步驟可以使得噪聲更加突出
4.計算原圖的灰度值，公式爲0.299R + 0.587G + 0.114*B
5.將灰度值作爲閾值，用來排除非皮膚部分，根據灰度值計算，將原圖與1-3後的結果圖合成
6.對混合後結果增加亮度
7.以灰度值作爲透明度將原圖與混合後結果進行濾色、柔光等混合，並調節飽和度

參考文檔