高斯模糊
高斯模糊就是将指定像素变换为其与周边像素加权平均后的值,权重就是高斯分布函数计算出来的值。
一种实现
点击打开链接<-这里是一片关于高斯模糊算法的介绍,我们需要首先根据高斯分布函数计算权重值,为了提高效率我们采用一维高斯分布函数,然后处理图像的时候在横向和纵向进行两次计算得到结果。下面是一种实现
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public static void gaussBlur(int[] data, int width, int height, int radius,
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float sigma) {
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float pa = (float) (1 / (Math.sqrt(2 * Math.PI) * sigma));
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float pb = -1.0f / (2 * sigma * sigma);
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float[] gaussMatrix = new float[radius * 2 + 1];
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float gaussSum = 0f;
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for (int i = 0, x = -radius; x <= radius; ++x, ++i) {
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float g = (float) (pa * Math.exp(pb * x * x));
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gaussMatrix[i] = g;
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gaussSum += g;
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}
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for (int i = 0, length = gaussMatrix.length; i < length; ++i) {
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gaussMatrix[i] /= gaussSum;
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}
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for (int y = 0; y < height; ++y) {
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for (int x = 0; x < width; ++x) {
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float r = 0, g = 0, b = 0;
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gaussSum = 0;
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for (int j = -radius; j <= radius; ++j) {
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int k = x + j;
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if (k >= 0 && k < width) {
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int index = y * width + k;
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int color = data[index];
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int cr = (color & 0x00ff0000) >> 16;
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int cg = (color & 0x0000ff00) >> 8;
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int cb = (color & 0x000000ff);
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r += cr * gaussMatrix[j + radius];
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g += cg * gaussMatrix[j + radius];
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b += cb * gaussMatrix[j + radius];
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gaussSum += gaussMatrix[j + radius];
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}
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}
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int index = y * width + x;
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int cr = (int) (r / gaussSum);
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int cg = (int) (g / gaussSum);
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int cb = (int) (b / gaussSum);
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data[index] = cr << 16 | cg << 8 | cb | 0xff000000;
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}
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}
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for (int x = 0; x < width; ++x) {
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for (int y = 0; y < height; ++y) {
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float r = 0, g = 0, b = 0;
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gaussSum = 0;
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for (int j = -radius; j <= radius; ++j) {
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int k = y + j;
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if (k >= 0 && k < height) {
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int index = k * width + x;
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int color = data[index];
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int cr = (color & 0x00ff0000) >> 16;
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int cg = (color & 0x0000ff00) >> 8;
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int cb = (color & 0x000000ff);
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r += cr * gaussMatrix[j + radius];
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g += cg * gaussMatrix[j + radius];
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b += cb * gaussMatrix[j + radius];
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gaussSum += gaussMatrix[j + radius];
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}
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}
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int index = y * width + x;
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int cr = (int) (r / gaussSum);
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int cg = (int) (g / gaussSum);
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int cb = (int) (b / gaussSum);
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data[index] = cr << 16 | cg << 8 | cb | 0xff000000;
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}
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}
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}
实际测试会发现这种计算方式是很耗时间的,而且模糊半径越大,从原理也可以看到计算量是平方增长的,所以计算时间也越长。
RenderScript
RenderScript是Android在API 11之后加入的,用于高效的图片处理,包括模糊、混合、矩阵卷积计算等,代码示例如下
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public Bitmap blurBitmap(Bitmap bitmap){
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Bitmap outBitmap = Bitmap.createBitmap(bitmap.getWidth(), bitmap.getHeight(), Config.ARGB_8888);
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RenderScript rs = RenderScript.create(getApplicationContext());
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ScriptIntrinsicBlur blurScript = ScriptIntrinsicBlur.create(rs, Element.U8_4(rs));
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Allocation allIn = Allocation.createFromBitmap(rs, bitmap);
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Allocation allOut = Allocation.createFromBitmap(rs, outBitmap);
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blurScript.setRadius(25.f);
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blurScript.setInput(allIn);
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blurScript.forEach(allOut);
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allOut.copyTo(outBitmap);
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bitmap.recycle();
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rs.destroy();
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return outBitmap;
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}
(示例来源 https://gist.github.com/Mariuxtheone/903c35b4927c0df18cf8)
FastBlur
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public class FastBlur {
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public static Bitmap doBlur(Bitmap sentBitmap, int radius, boolean canReuseInBitmap) {
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Bitmap bitmap;
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if (canReuseInBitmap) {
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bitmap = sentBitmap;
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} else {
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bitmap = sentBitmap.copy(sentBitmap.getConfig(), true);
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}
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if (radius < 1) {
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return (null);
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}
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int w = bitmap.getWidth();
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int h = bitmap.getHeight();
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int[] pix = new int[w * h];
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bitmap.getPixels(pix, 0, w, 0, 0, w, h);
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int wm = w - 1;
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int hm = h - 1;
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int wh = w * h;
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int div = radius + radius + 1;
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int r[] = new int[wh];
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int g[] = new int[wh];
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int b[] = new int[wh];
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int rsum, gsum, bsum, x, y, i, p, yp, yi, yw;
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int vmin[] = new int[Math.max(w, h)];
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int divsum = (div + 1) >> 1;
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divsum *= divsum;
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int dv[] = new int[256 * divsum];
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for (i = 0; i < 256 * divsum; i++) {
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dv[i] = (i / divsum);
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}
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yw = yi = 0;
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int[][] stack = new int[div][3];
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int stackpointer;
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int stackstart;
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int[] sir;
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int rbs;
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int r1 = radius + 1;
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int routsum, goutsum, boutsum;
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int rinsum, ginsum, binsum;
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for (y = 0; y < h; y++) {
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rinsum = ginsum = binsum = routsum = goutsum = boutsum = rsum = gsum = bsum = 0;
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for (i = -radius; i <= radius; i++) {
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p = pix[yi + Math.min(wm, Math.max(i, 0))];
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sir = stack[i + radius];
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sir[0] = (p & 0xff0000) >> 16;
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sir[1] = (p & 0x00ff00) >> 8;
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sir[2] = (p & 0x0000ff);
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rbs = r1 - Math.abs(i);
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rsum += sir[0] * rbs;
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gsum += sir[1] * rbs;
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bsum += sir[2] * rbs;
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if (i > 0) {
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rinsum += sir[0];
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ginsum += sir[1];
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binsum += sir[2];
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} else {
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routsum += sir[0];
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goutsum += sir[1];
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boutsum += sir[2];
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}
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}
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stackpointer = radius;
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for (x = 0; x < w; x++) {
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r[yi] = dv[rsum];
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g[yi] = dv[gsum];
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b[yi] = dv[bsum];
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rsum -= routsum;
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gsum -= goutsum;
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bsum -= boutsum;
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stackstart = stackpointer - radius + div;
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sir = stack[stackstart % div];
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routsum -= sir[0];
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goutsum -= sir[1];
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boutsum -= sir[2];
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if (y == 0) {
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vmin[x] = Math.min(x + radius + 1, wm);
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}
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p = pix[yw + vmin[x]];
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sir[0] = (p & 0xff0000) >> 16;
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sir[1] = (p & 0x00ff00) >> 8;
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sir[2] = (p & 0x0000ff);
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rinsum += sir[0];
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ginsum += sir[1];
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binsum += sir[2];
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rsum += rinsum;
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gsum += ginsum;
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bsum += binsum;
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stackpointer = (stackpointer + 1) % div;
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sir = stack[(stackpointer) % div];
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routsum += sir[0];
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goutsum += sir[1];
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boutsum += sir[2];
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rinsum -= sir[0];
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ginsum -= sir[1];
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binsum -= sir[2];
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yi++;
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}
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yw += w;
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}
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for (x = 0; x < w; x++) {
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rinsum = ginsum = binsum = routsum = goutsum = boutsum = rsum = gsum = bsum = 0;
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yp = -radius * w;
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for (i = -radius; i <= radius; i++) {
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yi = Math.max(0, yp) + x;
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sir = stack[i + radius];
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sir[0] = r[yi];
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sir[1] = g[yi];
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sir[2] = b[yi];
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rbs = r1 - Math.abs(i);
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rsum += r[yi] * rbs;
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gsum += g[yi] * rbs;
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bsum += b[yi] * rbs;
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if (i > 0) {
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rinsum += sir[0];
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ginsum += sir[1];
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binsum += sir[2];
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} else {
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routsum += sir[0];
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goutsum += sir[1];
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boutsum += sir[2];
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}
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if (i < hm) {
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yp += w;
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}
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}
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yi = x;
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stackpointer = radius;
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for (y = 0; y < h; y++) {
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pix[yi] = (0xff000000 & pix[yi]) | (dv[rsum] << 16) | (dv[gsum] << 8) | dv[bsum];
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rsum -= routsum;
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gsum -= goutsum;
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bsum -= boutsum;
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stackstart = stackpointer - radius + div;
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sir = stack[stackstart % div];
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routsum -= sir[0];
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goutsum -= sir[1];
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boutsum -= sir[2];
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if (x == 0) {
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vmin[y] = Math.min(y + r1, hm) * w;
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}
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p = x + vmin[y];
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sir[0] = r[p];
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sir[1] = g[p];
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sir[2] = b[p];
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rinsum += sir[0];
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ginsum += sir[1];
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binsum += sir[2];
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rsum += rinsum;
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gsum += ginsum;
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bsum += binsum;
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stackpointer = (stackpointer + 1) % div;
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sir = stack[stackpointer];
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routsum += sir[0];
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goutsum += sir[1];
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boutsum += sir[2];
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rinsum -= sir[0];
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ginsum -= sir[1];
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binsum -= sir[2];
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yi += w;
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}
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}
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bitmap.setPixels(pix, 0, w, 0, 0, w, h);
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return (bitmap);
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}
这里的方法也可以实现高斯模糊的效果,但使用了特殊的算法,比第一种可以快很多,但比起RenderScript还是慢一些
实现YAHOO天气的动态模糊效果
YAHOO天气中的背景会随着手指上滑模糊程度加深,实际使用中发现怎么都达不到那样流畅的效果,因为手势刷新的速度很快,每一帧都去重新模糊计算一遍,还是会有延迟,造成页面卡顿。后来在一次偶然的开发中发现其实不需要每一帧都重新去模糊一遍,而是将图片最大程度模糊一次,之后和原图叠加,通过改变叠加的模糊图片的alpha值来达到不同程度的模糊效果。下面是一个例子,可以看到随着模糊图片alpha值的变化,叠加后产生不同程度的模糊效果。
随滑动变换alpha值的代码如下
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mBlurImage.setOnTouchListener(new OnTouchListener() {
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private float mLastY;
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@Override
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public boolean onTouch(View v, MotionEvent event) {
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switch (event.getAction()) {
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case MotionEvent.ACTION_DOWN:
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mLastY = event.getY();
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break;
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case MotionEvent.ACTION_MOVE:
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float y = event.getY();
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float alphaDelt = (y - mLastY) / 1000;
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float alpha = mBlurImage.getAlpha() + alphaDelt;
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if (alpha > 1.0) {
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alpha = 1.0f;
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} else if (alpha < 0.0) {
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alpha = 0.0f;
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}
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mTextView.setText(String.valueOf(alpha));
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mBlurImage.setAlpha(alpha);
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break;
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case MotionEvent.ACTION_UP:
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break;
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}
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return true;
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}
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});
示例代码下载 http://download.csdn.net/detail/xu_fu/7628139