图像处理------特殊灰度算法技巧 分类： 视频图像处理 2015-07-24 09:53 28人阅读 评论(0) 收藏

介绍几种特殊的灰度算法滤镜，将彩色图像转换为灰度图像。其中涉及到的有基于阈值的图

像二值化，弗洛伊德.斯坦德伯格抖动算法，基于阈值的部分灰度化

 

基础知识- 怎么把RGB转换为单色的[0 ~256]之间的灰度，最常用的转换公式如下：

Gray = 0.299 * red + 0.587 * green + 0.114 * blue;

 

1.       基于像素平均值的图像阈值二值化算法：

处理流程：

a.      首先将彩色图像转换为灰度图像

b.      计算灰度图像的算术平均值– M

c.      以M为阈值，完成对灰度图二值化( 大于阈值M,像素点赋值为白色，否则赋值为黑

色)

图像效果：

关键代码：

public BufferedImage filter(BufferedImage src, BufferedImage dest) {
    int width = src.getWidth();
       int height = src.getHeight();

       if ( dest == null )
           dest = createCompatibleDestImage( src, null );
       src = super.filter(src, dest);

       int[] inPixels = new int[width*height];
       int[] outPixels = new int[width*height];
       getRGB(src, 0, 0, width, height, inPixels );

       // calculate means of pixel
       int index = 0;
       double redSum = 0, greenSum = 0, blueSum = 0;
       double total = height * width;
       for(int row=0; row<height; row++) {
           int ta = 0, tr = 0, tg = 0, tb = 0;
           for(int col=0; col<width; col++) {
               index = row * width + col;
               ta = (inPixels[index] >> 24) & 0xff;
               tr = (inPixels[index] >> 16) & 0xff;
               tg = (inPixels[index] >> 8) & 0xff;
               tb = inPixels[index] & 0xff;
               redSum += tr;
               greenSum += tg;
               blueSum +=tb;
           }
       }
       int means = (int)(redSum / total);
       System.out.println(" threshold average value = " + means);

       // dithering
       for(int row=0; row<height; row++) {
        int ta = 0, tr = 0, tg = 0, tb = 0;
        for(int col=0; col<width; col++) {
            index = row * width + col;
            ta = (inPixels[index] >> 24) & 0xff;
               tr = (inPixels[index] >> 16) & 0xff;
               tg = (inPixels[index] >> 8) & 0xff;
               tb = inPixels[index] & 0xff;
               if(tr >=means) {
                tr = tg = tb = 255;
               } else {
                tr = tg = tb = 0;
               }
               outPixels[index] = (ta << 24) | (tr << 16) | (tg << 8) | tb;

        }
       }
       setRGB( dest, 0, 0, width, height, outPixels );
       return dest;
}

2.       基于错误扩散的Floyd-Steinberg抖动算法

关于什么是Floyd-Steinberg抖动，参见这里

http://en.wikipedia.org/wiki/Floyd–Steinberg_dithering

图像效果：

关键代码：

@Override
public BufferedImage filter(BufferedImage src, BufferedImage dest) {
    int width = src.getWidth();
       int height = src.getHeight();

       if ( dest == null )
        dest = createCompatibleDestImage( src, null );
       src = super.filter(src, dest);

       int[] inPixels = new int[width*height];
       int[] outPixels = new int[width*height];
       getRGB( src, 0, 0, width, height, inPixels );
       int index = 0;
       for(int row=0; row<height; row++) {
        for(int col=0; col<width; col++) {
            index = row * width + col;
               int r1 = (inPixels[index] >> 16) & 0xff;
               int g1 = (inPixels[index] >> 8) & 0xff;
               int b1 = inPixels[index] & 0xff;
               int cIndex = getCloseColor(r1, g1, b1);
               outPixels[index] = (255 << 24) | (COLOR_PALETTE[cIndex][0] << 16) | (COLOR_PALETTE[cIndex][1] << 8) | COLOR_PALETTE[cIndex][2];
               int er = r1 - COLOR_PALETTE[cIndex][0];
               int eg = g1 - COLOR_PALETTE[cIndex][1];
               int eb = b1 -  COLOR_PALETTE[cIndex][2];
               int k = 0;

               if(row + 1 < height && col - 1 > 0) {
                k = (row + 1) * width + col - 1;
                   r1 = (inPixels[k] >> 16) & 0xff;
                   g1 = (inPixels[k] >> 8) & 0xff;
                   b1 = inPixels[k] & 0xff;
                   r1 += (int)(er * kernelData[0]);
                   g1 += (int)(eg * kernelData[0]);
                   b1 += (int)(eb * kernelData[0]);
                   inPixels[k] = (255 << 24) | (clamp(r1) << 16) | (clamp(g1) << 8) | clamp(b1);
               }

               if(col + 1 < width) {
                k = row * width + col + 1;
                   r1 = (inPixels[k] >> 16) & 0xff;
                   g1 = (inPixels[k] >> 8) & 0xff;
                   b1 = inPixels[k] & 0xff;
                   r1 += (int)(er * kernelData[3]);
                   g1 += (int)(eg * kernelData[3]);
                   b1 += (int)(eb * kernelData[3]);
                   inPixels[k] = (255 << 24) | (clamp(r1) << 16) | (clamp(g1) << 8) | clamp(b1);
               }

               if(row + 1 < height) {
                k = (row + 1) * width + col;
                   r1 = (inPixels[k] >> 16) & 0xff;
                   g1 = (inPixels[k] >> 8) & 0xff;
                   b1 = inPixels[k] & 0xff;
                   r1 += (int)(er * kernelData[1]);
                   g1 += (int)(eg * kernelData[1]);
                   b1 += (int)(eb * kernelData[1]);
                   inPixels[k] = (255 << 24) | (clamp(r1) << 16) | (clamp(g1) << 8) | clamp(b1);
               }

               if(row + 1 < height && col + 1 < width) {
                k = (row + 1) * width + col + 1;
                   r1 = (inPixels[k] >> 16) & 0xff;
                   g1 = (inPixels[k] >> 8) & 0xff;
                   b1 = inPixels[k] & 0xff;
                   r1 += (int)(er * kernelData[2]);
                   g1 += (int)(eg * kernelData[2]);
                   b1 += (int)(eb * kernelData[2]);
                   inPixels[k] = (255 << 24) | (clamp(r1) << 16) | (clamp(g1) << 8) | clamp(b1);
               }
        }
       }
       setRGB( dest, 0, 0, width, height, outPixels );
       return dest;
}

3.       选择性灰度算法

计算选择的颜色与像素灰度颜色之间的几何距离值，跟阈值比较决定是否像素点为灰度

值，可以得到一些让你意想不到的图像处理效果！

图像效果 (Main Color = GREEN, 阈值 = 200)

原图：

处理以后

 关键代码：

public BufferedImage filter(BufferedImage src, BufferedImage dest) {
    int width = src.getWidth();
       int height = src.getHeight();

       if ( dest == null )
        dest = createCompatibleDestImage( src, null );

       int[] inPixels = new int[width*height];
       int[] outPixels = new int[width*height];
       getRGB( src, 0, 0, width, height, inPixels );
       int index = 0;
       for(int row=0; row<height; row++) {
        int ta = 0, tr = 0, tg = 0, tb = 0;
        for(int col=0; col<width; col++) {
            index = row * width + col;
            ta = (inPixels[index] >> 24) & 0xff;
               tr = (inPixels[index] >> 16) & 0xff;
               tg = (inPixels[index] >> 8) & 0xff;
               tb = inPixels[index] & 0xff;
               int gray = (int)(0.299 * (double)tr + 0.587 * (double)tg + 0.114 * (double)tb);
               double distance = getDistance(tr, tg, tb);
               if(distance < threshold) {
                double k = distance / threshold;
                int[] rgb = getAdjustableRGB(tr, tg, tb, gray, (float)k);
                tr = rgb[0];
                tg = rgb[1];
                tb = rgb[2];
                outPixels[index] = (ta << 24) | (tr << 16) | (tg << 8) | tb;
               } else {
                outPixels[index] = (ta << 24) | (gray << 16) | (gray << 8) | gray;
               }

        }
       }
       setRGB( dest, 0, 0, width, height, outPixels );
       return dest;
}

创建新的目标Image

public BufferedImage createCompatibleDestImage(BufferedImage src, ColorModel dstCM) {
    if ( dstCM == null )
        dstCM = src.getColorModel();
    return new BufferedImage(dstCM, dstCM.createCompatibleWritableRaster(src.getWidth(), src.getHeight()), dstCM.isAlphaPremultiplied(), null);
}

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