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