android快速实现毛玻璃效果

为了实现毛玻璃效果，本文采用的是StackBlur模糊算法，这种算法应用非常广泛，能得到非常良好的毛玻璃效果。在这里，我们使用的是它的Java实现代码FastBlur.java。

package com.example.user.baozoumanhua.text;import android.graphics.Bitmap;/** * Created by user on 2016/9/11. */publicclass FastBlurUtil {    publicstatic Bitmap doBlur(Bitmap sentBitmap, int radius, boolean canReuseInBitmap){        Bitmap bitmap;        if (canReuseInBitmap) {            bitmap = sentBitmap;        } else {            bitmap = sentBitmap.copy(sentBitmap.getConfig(), true);        }        if (radius < 1) {            return (null);        }        int w = bitmap.getWidth();        int h = bitmap.getHeight();        int[] pix = newint[w * h];        bitmap.getPixels(pix, 0, w, 0, 0, w, h);        int wm = w - 1;        int hm = h - 1;        int wh = w * h;        int div = radius + radius + 1;        int r[] = newint[wh];        int g[] = newint[wh];        int b[] = newint[wh];        int rsum, gsum, bsum, x, y, i, p, yp, yi, yw;        int vmin[] = newint[Math.max(w, h)];        int divsum = (div + 1) >> 1;        divsum *= divsum;        int dv[] = newint[256 * divsum];        for (i = 0; i < 256 * divsum; i++) {            dv[i] = (i / divsum);        }        yw = yi = 0;        int[][] stack = newint[div][3];        int stackpointer;        int stackstart;        int[] sir;        int rbs;        int r1 = radius + 1;        int routsum, goutsum, boutsum;        int rinsum, ginsum, binsum;        for (y = 0; y < h; y++) {            rinsum = ginsum = binsum = routsum = goutsum = boutsum = rsum = gsum = bsum = 0;            for (i = -radius; i <= radius; i++) {                p = pix[yi + Math.min(wm, Math.max(i, 0))];                sir = stack[i + radius];                sir[0] = (p & 0xff0000) >> 16;                sir[1] = (p & 0x00ff00) >> 8;                sir[2] = (p & 0x0000ff);                rbs = r1 - Math.abs(i);                rsum += sir[0] * rbs;                gsum += sir[1] * rbs;                bsum += sir[2] * rbs;                if (i > 0) {                    rinsum += sir[0];                    ginsum += sir[1];                    binsum += sir[2];                } else {                    routsum += sir[0];                    goutsum += sir[1];                    boutsum += sir[2];                }            }            stackpointer = radius;            for (x = 0; x < w; x++) {                r[yi] = dv[rsum];                g[yi] = dv[gsum];                b[yi] = dv[bsum];                rsum -= routsum;                gsum -= goutsum;                bsum -= boutsum;                stackstart = stackpointer - radius + div;                sir = stack[stackstart % div];                routsum -= sir[0];                goutsum -= sir[1];                boutsum -= sir[2];                if (y == 0) {                    vmin[x] = Math.min(x + radius + 1, wm);                }                p = pix[yw + vmin[x]];                sir[0] = (p & 0xff0000) >> 16;                sir[1] = (p & 0x00ff00) >> 8;                sir[2] = (p & 0x0000ff);                rinsum += sir[0];                ginsum += sir[1];                binsum += sir[2];                rsum += rinsum;                gsum += ginsum;                bsum += binsum;                stackpointer = (stackpointer + 1) % div;                sir = stack[(stackpointer) % div];                routsum += sir[0];                goutsum += sir[1];                boutsum += sir[2];                rinsum -= sir[0];                ginsum -= sir[1];                binsum -= sir[2];                yi++;            }            yw += w;        }        for (x = 0; x < w; x++) {            rinsum = ginsum = binsum = routsum = goutsum = boutsum = rsum = gsum = bsum = 0;            yp = -radius * w;            for (i = -radius; i <= radius; i++) {                yi = Math.max(0, yp) + x;                sir = stack[i + radius];                sir[0] = r[yi];                sir[1] = g[yi];                sir[2] = b[yi];                rbs = r1 - Math.abs(i);                rsum += r[yi] * rbs;                gsum += g[yi] * rbs;                bsum += b[yi] * rbs;                if (i > 0) {                    rinsum += sir[0];                    ginsum += sir[1];                    binsum += sir[2];                } else {                    routsum += sir[0];                    goutsum += sir[1];                    boutsum += sir[2];                }                if (i < hm) {                    yp += w;                }            }            yi = x;            stackpointer = radius;            for (y = 0; y < h; y++) {                // Preserve alpha channel: ( 0xff000000 & pix[yi] )                pix[yi] = (0xff000000 & pix[yi]) | (dv[rsum] << 16) | (dv[gsum] << 8) | dv[bsum];                rsum -= routsum;                gsum -= goutsum;                bsum -= boutsum;                stackstart = stackpointer - radius + div;                sir = stack[stackstart % div];                routsum -= sir[0];                goutsum -= sir[1];                boutsum -= sir[2];                if (x == 0) {                    vmin[y] = Math.min(y + r1, hm) * w;                }                p = x + vmin[y];                sir[0] = r[p];                sir[1] = g[p];                sir[2] = b[p];                rinsum += sir[0];                ginsum += sir[1];                binsum += sir[2];                rsum += rinsum;                gsum += ginsum;                bsum += binsum;                stackpointer = (stackpointer + 1) % div;                sir = stack[stackpointer];                routsum += sir[0];                goutsum += sir[1];                boutsum += sir[2];                rinsum -= sir[0];                ginsum -= sir[1];                binsum -= sir[2];                yi += w;            }        }        bitmap.setPixels(pix, 0, w, 0, 0, w, h);        return (bitmap);    }}

不过直接使用这种算法很容易造成内存溢出，对此，我们应该对原图进行缩放

int scaleRatio = 10;        int blurRadius = 8;Bitmap scaledBitmap = Bitmap.createScaledBitmap(bitmap,bitmap.getWidth() / scaleRatio,                bitmap.getHeight() / scaleRatio,                false);

可以看出使用该方法很简单，第一个参数就是原图，第二个和第三个就是缩放后的尺寸，第四个参数true表示返回边缘光滑的图片，反之则得到锯齿边缘的图片，

Bitmap my = FastBlurUtil.doBlur(scaledBitmap, 10, false);//10b表示的就是模糊效果，值 越大越模糊        iv.setImageBitmap(my);

这样就快速实现了毛玻璃效果