android图片特效处理之模糊效果

这篇将讲到图片特效处理的模糊效果。跟前面一样是对像素点进行处理，算法是通用的，但耗时会更长，至于为什么，看了下面的代码你就会明白。

算法：

一、简单算法：将像素点周围八个点包括自身一共九个点的RGB值分别相加后平均，作为当前像素点的RGB值，即可实现效果。

举例：

ABC

DEF

GHI

假如当前点是E，那么会有：

E.r = (A.r + B.r + C.r + D.r + E.r + F.r + G.r + H.r + I.r) /9  // r表示的是E像素点RGB值的R值

E.r = (A.r + B.r + C.r + D.r + E.r + F.r + G.r + H.r + I.r) / 9 // r表示的是E像素点RGB值的R值

E像素点的GB值类似。

二、采用高斯模糊：

高斯矩阵：

int[] gauss = new int[] { 1, 2, 1, 2, 4, 2, 1, 2, 1 };

int[] gauss = new int[] { 1, 2, 1, 2, 4, 2, 1, 2, 1 };算法是：将九个点的RGB值分别与高斯矩阵中的对应项相乘的和，然后再除以一个相应的值作为当前像素点的RGB值。

举例：（还是上面的九个点）
假如当前点是E，那么会有：

int delta = 16;
E.r =( A.r * gauss[0] + B.r * gauss[1] + C.r * gauss[2] + D.r * gauss[3] + E.r * gauss[4] + F.r * gauss[5] + G.r * gauss[6] + H.r * gauss[7] + I.r * gauss[8]) / delta

int delta = 16; E.r =( A.r * gauss[0] + B.r * gauss[1] + C.r * gauss[2] + D.r * gauss[3] + E.r * gauss[4] + F.r * gauss[5] + G.r * gauss[6] + H.r * gauss[7] + I.r * gauss[8]) / deltaE像素点的GB值类似，delta的取值貌似没有规定值，可以自己设置任意值，但要想达到效果，能设的值很少，下面图片是值为16的效果。
处理效果：

原图片：

处理后：

两种处理方式的代码：

/**
     * 模糊效果
     * @param bmp
     * @return
     */
    private Bitmap blurImage(Bitmap bmp)
    {
        int width = bmp.getWidth();
        int height = bmp.getHeight();
        Bitmap bitmap = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565);

        int pixColor = 0;

        int newR = 0;
        int newG =  0;
        int newB = 0;

        int newColor = 0;

        int[][] colors = new int[9][3];
        for (int i = 1, length = width - 1; i < length; i++)
        {
            for (int k = 1, len = height - 1; k < len; k++)
            {
                for (int m = 0; m < 9; m++)
                {
                    int s = 0;
                    int p = 0;
                    switch(m)
                    {
                    case 0:
                        s = i - 1;
                        p = k - 1;
                        break;
                    case 1:
                        s = i;
                        p = k - 1;
                        break;
                    case 2:
                        s = i + 1;
                        p = k - 1;
                        break;
                    case 3:
                        s = i + 1;
                        p = k;
                        break;
                    case 4:
                        s = i + 1;
                        p = k + 1;
                        break;
                    case 5:
                        s = i;
                        p = k + 1;
                        break;
                    case 6:
                        s = i - 1;
                        p = k + 1;
                        break;
                    case 7:
                        s = i - 1;
                        p = k;
                        break;
                    case 8:
                        s = i;
                        p = k;
                    }
                    pixColor = bmp.getPixel(s, p);
                    colors[m][0] = Color.red(pixColor);
                    colors[m][1] = Color.green(pixColor);
                    colors[m][2] = Color.blue(pixColor);
                }

                for (int m = 0; m < 9; m++)
                {
                    newR += colors[m][0];
                    newG += colors[m][1];
                    newB += colors[m][2];
                }

                newR = (int) (newR / 9F);
                newG = (int) (newG / 9F);
                newB = (int) (newB / 9F);

                newR = Math.min(255, Math.max(0, newR));
                newG = Math.min(255, Math.max(0, newG));
                newB = Math.min(255, Math.max(0, newB));

                newColor = Color.argb(255, newR, newG, newB);
                bitmap.setPixel(i, k, newColor);

                newR = 0;
                newG = 0;
                newB = 0;
            }
        }

        return bitmap;
    }

    /**
     * 柔化效果(高斯模糊)(优化后比上面快三倍)
     * @param bmp
     * @return
     */
    private Bitmap blurImageAmeliorate(Bitmap bmp)
    {
        long start = System.currentTimeMillis();
        // 高斯矩阵
        int[] gauss =  new int[] { 1, 2, 1, 2, 4, 2, 1, 2, 1 };

        int width = bmp.getWidth();
        int height = bmp.getHeight();
        Bitmap bitmap = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565);

        int pixR =  0;
        int pixG = 0;
        int pixB =  0;

        int pixColor =  0;

        int newR =  0;
        int newG = 0;
        int newB =  0;

        int delta =  16; // 值越小图片会越亮，越大则越暗

        int idx =  0;
        int[] pixels = new int[width * height];
        bmp.getPixels(pixels, 0, width, 0, 0, width, height);
        for (int i = 1, length = height - 1; i < length; i++)
        {
            for (int k = 1, len = width - 1; k < len; k++)
            {
                idx = 0;
                for (int m = -1; m <= 1; m++)
                {
                    for (int n = -1; n <= 1; n++)
                    {
                        pixColor = pixels[(i + m) * width + k + n];
                        pixR = Color.red(pixColor);
                        pixG = Color.green(pixColor);
                        pixB = Color.blue(pixColor);

                        newR = newR + (int) (pixR * gauss[idx]);
                        newG = newG + (int) (pixG * gauss[idx]);
                        newB = newB + (int) (pixB * gauss[idx]);
                        idx++;
                    }
                }

                newR /= delta;
                newG /= delta;
                newB /= delta;

                newR = Math.min(255, Math.max(0, newR));
                newG = Math.min(255, Math.max(0, newG));
                newB = Math.min(255, Math.max(0, newB));

                pixels[i * width + k] = Color.argb(255, newR, newG, newB);

                newR = 0;
                newG = 0;
                newB = 0;
            }
        }

        bitmap.setPixels(pixels, 0, width, 0, 0, width, height);
        long end = System.currentTimeMillis();
        Log.d("may",  "used time="+(end - start));
        return bitmap;
    }

/** * 模糊效果 * @param bmp * @return */ private Bitmap blurImage(Bitmap bmp) { int width = bmp.getWidth(); int height = bmp.getHeight(); Bitmap bitmap = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565); int pixColor = 0; int newR = 0; int newG = 0; int newB = 0; int newColor = 0; int[][] colors = new int[9][3]; for (int i = 1, length = width - 1; i < length; i++) { for (int k = 1, len = height - 1; k < len; k++) { for (int m = 0; m < 9; m++) { int s = 0; int p = 0; switch(m) { case 0: s = i - 1; p = k - 1; break; case 1: s = i; p = k - 1; break; case 2: s = i + 1; p = k - 1; break; case 3: s = i + 1; p = k; break; case 4: s = i + 1; p = k + 1; break; case 5: s = i; p = k + 1; break; case 6: s = i - 1; p = k + 1; break; case 7: s = i - 1; p = k; break; case 8: s = i; p = k; } pixColor = bmp.getPixel(s, p); colors[m][0] = Color.red(pixColor); colors[m][1] = Color.green(pixColor); colors[m][2] = Color.blue(pixColor); } for (int m = 0; m < 9; m++) { newR += colors[m][0]; newG += colors[m][1]; newB += colors[m][2]; } newR = (int) (newR / 9F); newG = (int) (newG / 9F); newB = (int) (newB / 9F); newR = Math.min(255, Math.max(0, newR)); newG = Math.min(255, Math.max(0, newG)); newB = Math.min(255, Math.max(0, newB)); newColor = Color.argb(255, newR, newG, newB); bitmap.setPixel(i, k, newColor); newR = 0; newG = 0; newB = 0; } } return bitmap; } /** * 柔化效果(高斯模糊)(优化后比上面快三倍) * @param bmp * @return */ private Bitmap blurImageAmeliorate(Bitmap bmp) { long start = System.currentTimeMillis(); // 高斯矩阵 int[] gauss = new int[] { 1, 2, 1, 2, 4, 2, 1, 2, 1 }; int width = bmp.getWidth(); int height = bmp.getHeight(); Bitmap bitmap = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565); int pixR = 0; int pixG = 0; int pixB = 0; int pixColor = 0; int newR = 0; int newG = 0; int newB = 0; int delta = 16; // 值越小图片会越亮，越大则越暗 int idx = 0; int[] pixels = new int[width * height]; bmp.getPixels(pixels, 0, width, 0, 0, width, height); for (int i = 1, length = height - 1; i < length; i++) { for (int k = 1, len = width - 1; k < len; k++) { idx = 0; for (int m = -1; m <= 1; m++) { for (int n = -1; n <= 1; n++) { pixColor = pixels[(i + m) * width + k + n]; pixR = Color.red(pixColor); pixG = Color.green(pixColor); pixB = Color.blue(pixColor); newR = newR + (int) (pixR * gauss[idx]); newG = newG + (int) (pixG * gauss[idx]); newB = newB + (int) (pixB * gauss[idx]); idx++; } } newR /= delta; newG /= delta; newB /= delta; newR = Math.min(255, Math.max(0, newR)); newG = Math.min(255, Math.max(0, newG)); newB = Math.min(255, Math.max(0, newB)); pixels[i * width + k] = Color.argb(255, newR, newG, newB); newR = 0; newG = 0; newB = 0; } } bitmap.setPixels(pixels, 0, width, 0, 0, width, height); long end = System.currentTimeMillis(); Log.d("may", "used time="+(end - start)); return bitmap; }

 

在优化后的代码中要注意了，pixels数组不能超过规定的大小，也就是说图片的尺寸不能太大，否则会栈内存溢出。