Bitmap模糊处理

1.FastBlur处理

直接上代码，该FastBlur在github上有大篇幅的使用，算法暂时不讨论。主要讲一些模糊的逻辑

public static 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 = new int[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[] = new int[wh];
        int g[] = new int[wh];
        int b[] = new int[wh];
        int rsum, gsum, bsum, x, y, i, p, yp, yi, yw;
        int vmin[] = new int[Math.max(w, h)];

        int divsum = (div + 1) >> 1;
        divsum *= divsum;
        int dv[] = new int[256 * divsum];
        for (i = 0; i < 256 * divsum; i++) {
            dv[i] = (i / divsum);
        }

        yw = yi = 0;

        int[][] stack = new int[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);
    }

如果本来使用的图片就是很大，很占内存，所以很容易就造成OOM，因此尽管是虚化，模糊处理，我们也要对图片进行一定的处理后，才进行模糊处理展示。

获取bitmap时：

BitmapFactory.Options options = new BitmapFactory.Options();
options.inSampleSize = 16;//提前缩小
options.inPreferredConfig = Bitmap.Config.RGB_565;//改分辨率每单位width只占1字节，上篇文章有介绍
Uri uri = Uri.parse(localFile);//localFile为本地文件路径
Bitmap bmp = BitmapFactory.decodeFile(uri.toString(), options);

bitmap更改大小处理

方法一：createScaledBitmap

int scaleRatio = 10;
int blurRadius = 8;
Bitmap scaledBitmap = Bitmap.createScaledBitmap(originBitmap,
    originBitmap.getWidth() / scaleRatio,
    originBitmap.getHeight() / scaleRatio,
    false);
Bitmap blurBitmap = FastBlur.doBlur(scaledBitmap, blurRadius, true);
imageView.setScaleType(ImageView.ScaleType.CENTER_CROP);
imageView.setImageBitmap(blurBitmap);

方法二：Canvas

private Bitmap newBlur(Bitmap bkg, SimpleDraweeView view) {
        float scaleFactor = 20;//图片缩放比例；
        int radius = 15;//模糊程度
        Bitmap bitmap = null;
        try {
            bkg = zoomBitmap(bkg);
            if (bkg == null) return null;
            Bitmap overlay = Bitmap.createBitmap(
                    (int) (getX() / scaleFactor),
                    (int) (getY() / scaleFactor),
                    Bitmap.Config.RGB_565);
            Canvas canvas = new Canvas(overlay);
            canvas.translate(-view.getLeft() / scaleFactor, -view.getTop() / scaleFactor);
            canvas.scale(1 / scaleFactor, 1 / scaleFactor);
            Paint paint = new Paint();
            paint.setFlags(Paint.FILTER_BITMAP_FLAG);
            canvas.drawBitmap(bkg, 0, 0, paint);
            bitmap = FastBlur.doBlur(overlay, radius, true);
        } catch (Exception e) {
            e.printStackTrace();
        }
        return bitmap;
    }

其中blurRadius为模糊处理的虚化程度，不断对该数值的增大，会造成CPU的紧张，通过简单的多次使用，默认最大为25。当然越小的话对CPU负担越不重。

因此我们改为对scaleRatio做文章。

我们对scaleRatio数值更改也可达到目的：增大scaleRatio缩放比，使用一样更小的bitmap去虚化可以得到更好的模糊效果，而且有利于占用内存的减小

因此更改scaleRatio为100最好，哈哈，当然。很模糊。

分析

分析从时间效率和CPU占用两方面考虑

1.时间效率

long start = System.currentTimeMillis();
Bitmap scaledBitmap, blurBitmap;
int scaleRatio = 10;
int loopCount = 100
for (int i=0; i<loopCount; i++) {
  scaledBitmap = Bitmap.createScaledBitmap(originBitmap,
      originBitmap.getWidth() / scaleRatio,
      originBitmap.getHeight() / scaleRatio,
      false);
  blurBitmap = FastBlur.doBlur(scaledBitmap, 8, true);
}
Log.i("blurtime", String.valueOf(System.currentTimeMillis() - start));

获得scaleRatio越大，BlurTime越小，时间消耗越小

2.CPU

从MemoryMonitors分析得出，scaleRatio越大，CPU消耗越少

2.高斯模糊