博文
J2ME图像处理基础编程
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J2ME图像处理基础编程汇编如下，以便后续查找方便。大部分均来自网络启发思路，不敢独藏，拿来分享。
思路：程序将硬盘上的图像源文件读取到内存指定的区域中才能处理，对各像素点处理的关键是int类型的位移运算，特别是高位的alpha通道，不能破坏原有的值，所以，若要重新构造数据，则需要赋值全为1，即alpha=0xFF。新像素值p=((alpha<< 24) | (r<< 16) | (g<< 8) | b);
很多稍微复杂些的工作，均有其对应的套路，整理如下。
步骤如下：
setp 1.硬盘上的图像源文件读取到内存句柄中
public Image LoadImage(String strImgPath){
try{
img=Image.createImage(strImgPath);//"/test.jpg"
}catch(Exception ex){
System.out.print("Error:"+ex.toString());
}
return img;
}
try{
img=Image.createImage(strImgPath);//"/test.jpg"
}catch(Exception ex){
System.out.print("Error:"+ex.toString());
}
return img;
}
setp 2.内存句柄散列到指定数组，以便处理方便
public int[] getPixels(Image img)
{
int w = img.getWidth();
int h = img.getHeight();
int[] pixels = new int[w * h];
img.getRGB(pixels, 0, w, 0, 0, w, h);
return pixels;
}
{
int w = img.getWidth();
int h = img.getHeight();
int[] pixels = new int[w * h];
img.getRGB(pixels, 0, w, 0, 0, w, h);
return pixels;
}
step 3.各种具体的图像处理算法实施
public Image imageToGray(Image src) {
int srcW = src.getWidth();
int srcH = src.getHeight();
int[] srcPixels = getPixels(src);
int r = 0;
int g = 0;
int b = 0;
int a = 0;
int argb;
int temp;
public Image imageToGray(Image src) {
int srcW = src.getWidth();
int srcH = src.getHeight();
int[] srcPixels = getPixels(src);
int r = 0;
int g = 0;
int b = 0;
int a = 0;
int argb;
int temp;
for (int i = 0; i < srcH; i++)
{
for(int ii=0;ii<srcW;ii++)
{
argb = srcPixels[i*srcW+ii];
a = ((argb & 0xff000000) >> 24); // alpha channel
r = ((argb & 0x00ff0000) >> 16); // red channel
g = ((argb & 0x0000ff00) >> 8); // green channel
b = (argb & 0x000000ff); // blue channel
temp = (int)(.299*(double)r+.587*(double)g+.114*(double)b);
r = temp;
g = temp;
b = temp;
srcPixels[i*srcW+ii] = ((a << 24) | (r << 16) | (g << 8) | b);
}
}
return drawPixels(srcPixels, srcW, srcH);
}
step 4. 将处理后的数据数组中的像素值，还原为图像像素值RGB格式，以便显示等处理。
public Image drawPixels(int[] pixels, int w, int h)
{
Image image = Image.createRGBImage(pixels, w, h, true);
pixels = null;
return image;
}
{
Image image = Image.createRGBImage(pixels, w, h, true);
pixels = null;
return image;
}
////////////////////////////////////////////////////////////////////////////////
5.Robert边缘检测，可以生成类似于素描图的样式
public Image edgeDetectByRobert(Image src){
int srcW = src.getWidth();
int srcH = src.getHeight();
int[] srcPixels = getPixels(src);
int[] srcPixelsTemp = new int[srcW*srcH];
int r = 0;
int g = 0;
int b = 0;
int a = 0;
int argb,p0,p1,p2,p3;
int temp=0xFF;
int srcW = src.getWidth();
int srcH = src.getHeight();
int[] srcPixels = getPixels(src);
int[] srcPixelsTemp = new int[srcW*srcH];
int r = 0;
int g = 0;
int b = 0;
int a = 0;
int argb,p0,p1,p2,p3;
int temp=0xFF;
for (int i = 0; i < srcH-1; i++)
{
for(int ii=0;ii<srcW-1;ii++)
{
p0 = srcPixels[i*srcW+ii];
a = ((p0 & 0x00ff0000) >> 16);
p1=srcPixels[(i+1)*srcW+ii];
r = ((p1 & 0x00ff0000) >> 16);
p2 = srcPixels[i*srcW+ii+1];
g = ((p2 & 0x00ff0000) >> 16);
p3=srcPixels[(i+1)*srcW+ii+1];
b = ((p3 & 0x00ff0000) >> 16);
argb=(int)Math.abs(a-b)+(int)Math.abs(r-g);//高位的alpha通道，不能破坏原有的值
srcPixelsTemp[i*srcW+ii]= ((temp << 24) | (argb << 16) | (argb << 8) | argb);
}
}
return drawPixels(srcPixelsTemp, srcW, srcH);
}
6. 灰度化（所谓的黑白照片样式）
public Image imageToGray(Image src) {
int srcW = src.getWidth();
int srcH = src.getHeight();
int[] srcPixels = getPixels(src);
int r = 0;
int g = 0;
int b = 0;
int a = 0;
int argb;
int temp;
int srcW = src.getWidth();
int srcH = src.getHeight();
int[] srcPixels = getPixels(src);
int r = 0;
int g = 0;
int b = 0;
int a = 0;
int argb;
int temp;
for (int i = 0; i < srcH; i++)
{
for(int ii=0;ii<srcW;ii++)
{
argb = srcPixels[i*srcW+ii];
a = ((argb & 0xff000000) >> 24); // alpha channel
r = ((argb & 0x00ff0000) >> 16); // red channel
g = ((argb & 0x0000ff00) >> 8); // green channel
b = (argb & 0x000000ff); // blue channel
temp = (int)(.299*(double)r+.587*(double)g+.114*(double)b);
r = temp;
g = temp;
b = temp;
srcPixels[i*srcW+ii] = ((a << 24) | (r << 16) | (g << 8) | b);
}
}
return drawPixels(srcPixels, srcW, srcH);
}
////////////////////////////////////////////////////////////////////////////////
7. 边缘分割，可以生成类似于简笔画图的样式，需要先做二值化
public Image edgeDetectBy4Neighborhood(Image src){
int srcW = src.getWidth();
int srcH = src.getHeight();
int[] srcPixels = getPixels(src);
int[] srcPixelsTemp = new int[srcW*srcH];
int b = 0;
int c = 0;
int d = 0;
int e = 0,a=0;
int argb,p0,p1,p2,p3,p4;
int temp=0xFF;
int srcW = src.getWidth();
int srcH = src.getHeight();
int[] srcPixels = getPixels(src);
int[] srcPixelsTemp = new int[srcW*srcH];
int b = 0;
int c = 0;
int d = 0;
int e = 0,a=0;
int argb,p0,p1,p2,p3,p4;
int temp=0xFF;
for (int i = 1; i < srcH-1; i++)
{
for(int ii=1;ii<srcW-1;ii++)
{
p0 = srcPixels[i*srcW+ii];
a = ((p0 & 0x00ff0000) >> 16);
p1=srcPixels[(i-1)*srcW+ii];
b = ((p1 & 0x00ff0000) >> 16);
p2 = srcPixels[i*srcW+ii+1];
c = ((p2 & 0x00ff0000) >> 16);
p3=srcPixels[(i+1)*srcW+ii];
d = ((p3 & 0x00ff0000) >> 16);
p4=srcPixels[(i)*srcW+ii-1];
e = ((p4 & 0x00ff0000) >> 16);
//对二值化后的图像进行类似于简笔画的生成
if(a==0&&(b==255||c==255||d==255||e==255))
argb=0;
else
argb=255;
srcPixelsTemp[i*srcW+ii]= ((temp << 24) | (argb << 16) | (argb << 8) | argb);
}
}
return drawPixels(srcPixelsTemp, srcW, srcH);
}
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