图像处理之特殊灰度算法技巧
介绍几种特殊的灰度算法滤镜，将彩×××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×××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> 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> 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> 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> 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); }