Android图片高斯模糊的一些方法

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

public static void gaussBlur(int[] data, int width, int height, int radius,
			float sigma) {

		float pa = (float) (1 / (Math.sqrt(2 * Math.PI) * sigma));
		float pb = -1.0f / (2 * sigma * sigma);

		// generate the Gauss Matrix
		float[] gaussMatrix = new float[radius * 2 + 1];
		float gaussSum = 0f;
		for (int i = 0, x = -radius; x <= radius; ++x, ++i) {
			float g = (float) (pa * Math.exp(pb * x * x));
			gaussMatrix[i] = g;
			gaussSum += g;
		}

		for (int i = 0, length = gaussMatrix.length; i < length; ++i) {
			gaussMatrix[i] /= gaussSum;
		}

		// x direction
		for (int y = 0; y < height; ++y) {
			for (int x = 0; x < width; ++x) {
				float r = 0, g = 0, b = 0;
				gaussSum = 0;
				for (int j = -radius; j <= radius; ++j) {
					int k = x + j;
					if (k >= 0 && k < width) {
						int index = y * width + k;
						int color = data[index];
						int cr = (color & 0x00ff0000) >> 16;
						int cg = (color & 0x0000ff00) >> 8;
						int cb = (color & 0x000000ff);

						r += cr * gaussMatrix[j + radius];
						g += cg * gaussMatrix[j + radius];
						b += cb * gaussMatrix[j + radius];

						gaussSum += gaussMatrix[j + radius];
					}
				}

				int index = y * width + x;
				int cr = (int) (r / gaussSum);
				int cg = (int) (g / gaussSum);
				int cb = (int) (b / gaussSum);
				
				data[index] = cr << 16 | cg << 8 | cb | 0xff000000;
			}
		}

		// y direction
		for (int x = 0; x < width; ++x) {
			for (int y = 0; y < height; ++y) {
				float r = 0, g = 0, b = 0;
				gaussSum = 0;
				for (int j = -radius; j <= radius; ++j) {
					int k = y + j;
					if (k >= 0 && k < height) {
						int index = k * width + x;
						int color = data[index];
						int cr = (color & 0x00ff0000) >> 16;
						int cg = (color & 0x0000ff00) >> 8;
						int cb = (color & 0x000000ff);

						r += cr * gaussMatrix[j + radius];
						g += cg * gaussMatrix[j + radius];
						b += cb * gaussMatrix[j + radius];

						gaussSum += gaussMatrix[j + radius];
					}
				}

				int index = y * width + x;
				int cr = (int) (r / gaussSum);
				int cg = (int) (g / gaussSum);
				int cb = (int) (b / gaussSum);
				data[index] = cr << 16 | cg << 8 | cb | 0xff000000;
			}
		}
	}

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

public Bitmap blurBitmap(Bitmap bitmap){
		
		//Let's create an empty bitmap with the same size of the bitmap we want to blur
		Bitmap outBitmap = Bitmap.createBitmap(bitmap.getWidth(), bitmap.getHeight(), Config.ARGB_8888);
		
		//Instantiate a new Renderscript
		RenderScript rs = RenderScript.create(getApplicationContext());
		
		//Create an Intrinsic Blur Script using the Renderscript
		ScriptIntrinsicBlur blurScript = ScriptIntrinsicBlur.create(rs, Element.U8_4(rs));
		
		//Create the Allocations (in/out) with the Renderscript and the in/out bitmaps
		Allocation allIn = Allocation.createFromBitmap(rs, bitmap);
		Allocation allOut = Allocation.createFromBitmap(rs, outBitmap);
		
		//Set the radius of the blur
		blurScript.setRadius(25.f);
		
		//Perform the Renderscript
		blurScript.setInput(allIn);
		blurScript.forEach(allOut);
		
		//Copy the final bitmap created by the out Allocation to the outBitmap
		allOut.copyTo(outBitmap);
		
		//recycle the original bitmap
		bitmap.recycle();
		
		//After finishing everything, we destroy the Renderscript.
		rs.destroy();
		
		return outBitmap;
		
		
	}

（示例来源 https://gist.github.com/Mariuxtheone/903c35b4927c0df18cf8）

FastBlur

public class FastBlur {

    public static Bitmap doBlur(Bitmap sentBitmap, int radius, boolean canReuseInBitmap) {

        // Stack Blur v1.0 from
        // http://www.quasimondo.com/StackBlurForCanvas/StackBlurDemo.html
        //
        // Java Author: Mario Klingemann <mario at quasimondo.com>
        // http://incubator.quasimondo.com
        // created Feburary 29, 2004
        // Android port : Yahel Bouaziz <yahel at kayenko.com>
        // http://www.kayenko.com
        // ported april 5th, 2012

        // This is a compromise between Gaussian Blur and Box blur
        // It creates much better looking blurs than Box Blur, but is
        // 7x faster than my Gaussian Blur implementation.
        //
        // I called it Stack Blur because this describes best how this
        // filter works internally: it creates a kind of moving stack
        // of colors whilst scanning through the image. Thereby it
        // just has to add one new block of color to the right side
        // of the stack and remove the leftmost color. The remaining
        // colors on the topmost layer of the stack are either added on
        // or reduced by one, depending on if they are on the right or
        // on the left side of the stack.
        //
        // If you are using this algorithm in your code please add
        // the following line:
        //
        // Stack Blur Algorithm by Mario Klingemann <[email protected]>

        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);
    }

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

（示例来源 Android高级模糊技术）

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

mBlurImage.setOnTouchListener(new OnTouchListener() {

			private float mLastY;

			@Override
			public boolean onTouch(View v, MotionEvent event) {
				switch (event.getAction()) {
				case MotionEvent.ACTION_DOWN:
					mLastY = event.getY();
					break;
				case MotionEvent.ACTION_MOVE:
					float y = event.getY();
					float alphaDelt = (y - mLastY) / 1000;
					float alpha = mBlurImage.getAlpha() + alphaDelt;
					if (alpha > 1.0) {
						alpha = 1.0f;
					} else if (alpha < 0.0) {
						alpha = 0.0f;
					}
					mTextView.setText(String.valueOf(alpha));
					mBlurImage.setAlpha(alpha);
					break;
				case MotionEvent.ACTION_UP:
					break;
				}
				return true;
			}
		});

示例代码下载  http://download.csdn.net/detail/xu_fu/7628139