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 };
nt[] 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]) / delta
E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：
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。
转自：http://blog.csdn.net/sjf0115/article/details/7266998
时间： 2024-10-11 12:06:46