图像处理之简单脸谱检测算法（Simple Face Detection Algorithm）
介绍基于皮肤检测之后的，寻找最大连通区域，完成脸谱检测的算法。大致的算法步骤如下：
原图如下：
每步处理以后的效果：
程序运行，加载选择图像以后的截屏如下：
截屏中显示图片，是适当放缩以后，代码如下：
Image scaledImage = rawImg.getScaledInstance(200, 200, Image.SCALE_FAST); // Java Image API， rawImage is source image g2.drawImage(scaledImage, 0, 0, 200, 200, null);
第一步：图像预处理，预处理的目的是为了减少图像中干扰像素，使得皮肤检测步骤可以得
到更好的效果，最常见的手段是调节对比度与亮度，也可以高斯模糊。关于怎么调节亮度与
对比度可以参见这里：http://blog.csdn.net/jia20003/article/details/7385160
这里调节对比度的算法很简单，源代码如下：
package com.gloomyfish.face.detection; import java.awt.image.BufferedImage; public class ContrastFilter extends AbstractBufferedImageOp { private double nContrast = 30; public ContrastFilter() { System.out.println("Contrast Filter"); } @Override public BufferedImage filter(BufferedImage src, BufferedImage dest) { int width = src.getWidth(); int height = src.getHeight(); double contrast = (100.0 + nContrast) / 100.0; contrast *= contrast; 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; int ta = 0, tr = 0, tg = 0, tb = 0; for(int row=0; row<height; row++) { 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; // adjust contrast - red, green, blue tr = adjustContrast(tr, contrast); tg = adjustContrast(tg, contrast); tb = adjustContrast(tb, contrast); // output RGB pixel outPixels[index] = (ta << 24) | (tr << 16) | (tg << 8) | tb; } } setRGB( dest, 0, 0, width, height, outPixels ); return dest; } public int adjustContrast(int color, double contrast) { double result = 0; result = color / 255.0; result -= 0.5; result *= contrast; result += 0.5; result *=255.0; return clamp((int)result); } public static int clamp(int c) { if (c < 0) return 0; if (c > 255) return 255; return c; } }
注意：第一步不是必须的，如果图像质量已经很好，可以直接跳过。
第二步：皮肤检测，采用的是基于RGB色彩空间的统计结果来判断一个像素是否为skin像
素，如果是皮肤像素，则设置像素为黑色，否则为白色。给出基于RGB色彩空间的五种皮
肤检测统计方法，最喜欢的一种源代码如下：
package com.gloomyfish.face.detection; import java.awt.image.BufferedImage; /** * this skin detection is absolutely good skin classification, * i love this one very much * * this one should be always primary skin detection * from all five filters * * @author zhigang * */ public class SkinFilter4 extends AbstractBufferedImageOp { @Override 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; // detect skin method... double sum = tr + tg + tb; if (((double)tb/(double)tg<1.249) && ((double)sum/(double)(3*tr)>0.696) && (0.3333-(double)tb/(double)sum>0.014) && ((double)tg/(double)(3*sum)<0.108)) { tr = tg = tb = 0; } else { tr = tg = tb = 255; } outPixels[index] = (ta << 24) | (tr << 16) | (tg << 8) | tb; } } setRGB(dest, 0, 0, width, height, outPixels); return dest; } }
第三步：寻找最大连通区域
使用连通组件标记算法，寻找最大连通区域，关于什么是连通组件标记算法，可以参见这里
http://blog.csdn.net/jia20003/article/details/7483249，里面提到的连通组件算法效率不高，所
以这里我完成了一个更具效率的版本，主要思想是对像素数据进行八邻域寻找连通，然后合
并标记。源代码如下：
package com.gloomyfish.face.detection; import java.util.Arrays; import java.util.HashMap; /** * fast connected component label algorithm * * @date 2012-05-23 * @author zhigang * */ public class FastConnectedComponentLabelAlg { private int bgColor; private int[] labels; private int[] outData; private int dw; private int dh; public FastConnectedComponentLabelAlg() { bgColor = 255; // black color } public int[] doLabel(int[] inPixels, int width, int height) { dw = width; dh = height; int nextlabel = 1; int result = 0; labels = new int[dw * dh/2]; outData = new int[dw * dh]; for(int i=0; i<labels.length; i++) { labels[i] = i; } // we need to define these two variable arrays. int[] fourNeighborhoodPixels = new int[8]; int[] fourNeighborhoodLabels = new int[8]; int[] knownLabels = new int[4]; int srcrgb = 0, index = 0; boolean existedLabel = false; for(int row = 0; row < height; row ++) { for(int col = 0; col < width; col++) { index = row * width + col; srcrgb = inPixels[index] & 0x000000ff; if(srcrgb == bgColor) { result = 0; // which means no labeled for this pixel. } else { // we just find the eight neighborhood pixels. fourNeighborhoodPixels[0] = getPixel(inPixels, row-1, col); // upper cell fourNeighborhoodPixels[1] = getPixel(inPixels, row, col-1); // left cell fourNeighborhoodPixels[2] = getPixel(inPixels, row+1, col); // bottom cell fourNeighborhoodPixels[3] = getPixel(inPixels, row, col+1); // right cell // four corners pixels fourNeighborhoodPixels[4] = getPixel(inPixels, row-1, col-1); // upper left corner fourNeighborhoodPixels[5] = getPixel(inPixels, row-1, col+1); // upper right corner fourNeighborhoodPixels[6] = getPixel(inPixels, row+1, col-1); // left bottom corner fourNeighborhoodPixels[7] = getPixel(inPixels, row+1, col+1); // right bottom corner // get current possible existed labels fourNeighborhoodLabels[0] = getLabel(outData, row-1, col); // upper cell fourNeighborhoodLabels[1] = getLabel(outData, row, col-1); // left cell fourNeighborhoodLabels[2] = getLabel(outData, row+1, col); // bottom cell fourNeighborhoodLabels[3] = getLabel(outData, row, col+1); // right cell // four corners labels value fourNeighborhoodLabels[4] = getLabel(outData, row-1, col-1); // upper left corner fourNeighborhoodLabels[5] = getLabel(outData, row-1, col+1); // upper right corner fourNeighborhoodLabels[6] = getLabel(outData, row+1, col-1); // left bottom corner fourNeighborhoodLabels[7] = getLabel(outData, row+1, col+1); // right bottom corner knownLabels[0] = fourNeighborhoodLabels[0]; knownLabels[1] = fourNeighborhoodLabels[1]; knownLabels[2] = fourNeighborhoodLabels[4]; knownLabels[3] = fourNeighborhoodLabels[5]; existedLabel = false; for(int k=0; k<fourNeighborhoodLabels.length; k++) { if(fourNeighborhoodLabels[k] != 0) { existedLabel = true; break; } } if(!existedLabel) { result = nextlabel; nextlabel++; } else { int found = -1, count = 0; for(int i=0; i<fourNeighborhoodPixels.length; i++) { if(fourNeighborhoodPixels[i] != bgColor) { found = i; count++; } } if(count == 1) { result = (fourNeighborhoodLabels[found] == 0) ? nextlabel : fourNeighborhoodLabels[found]; } else { result = (fourNeighborhoodLabels[found] == 0) ? nextlabel : fourNeighborhoodLabels[found]; for(int j=0; j<knownLabels.length; j++) { if(knownLabels[j] != 0 && knownLabels[j] != result && knownLabels[j] < result) { result = knownLabels[j]; } } boolean needMerge = false; for(int mm = 0; mm < knownLabels.length; mm++ ) { if(knownLabels[0] != knownLabels[mm] && knownLabels[mm] != 0) { needMerge = true; } } // merge the labels now.... if(needMerge) { int minLabel = knownLabels[0]; for(int m=0; m<knownLabels.length; m++) { if(minLabel > knownLabels[m] && knownLabels[m] != 0) { minLabel = knownLabels[m]; } } // find the final label number... result = (minLabel == 0) ? result : minLabel; // re-assign the label number now... if(knownLabels[0] != 0) { setData(outData, row-1, col, result); } if(knownLabels[1] != 0) { setData(outData, row, col-1, result); } if(knownLabels[2] != 0) { setData(outData, row-1, col-1, result); } if(knownLabels[3] != 0) { setData(outData, row-1, col+1, result); } } } } } outData[index] = result; // assign to label } } // post merge each labels now for(int row = 0; row < height; row ++) { for(int col = 0; col < width; col++) { index = row * width + col; mergeLabels(index); } } // labels statistic HashMap<Integer, Integer> labelMap = new HashMap<Integer, Integer>(); for(int d=0; d<outData.length; d++) { if(outData[d] != 0) { if(labelMap.containsKey(outData[d])) { Integer count = labelMap.get(outData[d]); count+=1; labelMap.put(outData[d], count); } else { labelMap.put(outData[d], 1); } } } // try to find the max connected component Integer[] keys = labelMap.keySet().toArray(new Integer[0]); Arrays.sort(keys); int maxKey = 1; int max = 0; for(Integer key : keys) { if(max < labelMap.get(key)){ max = labelMap.get(key); maxKey = key; } System.out.println( "Number of " + key + " = " + labelMap.get(key)); } System.out.println("maxkey = " + maxKey); System.out.println("max connected component number = " + max); return outData; } private void mergeLabels(int index) { int row = index / dw; int col = index % dw; // get current possible existed labels int min = getLabel(outData, row, col); if(min == 0) return; if(min > getLabel(outData, row-1, col) && getLabel(outData, row-1, col) != 0) { min = getLabel(outData, row-1, col); } if(min > getLabel(outData, row, col-1) && getLabel(outData, row, col-1) != 0) { min = getLabel(outData, row, col-1); } if(min > getLabel(outData, row+1, col) && getLabel(outData, row+1, col) != 0) { min = getLabel(outData, row+1, col); } if(min > getLabel(outData, row, col+1) && getLabel(outData, row, col+1) != 0) { min = getLabel(outData, row, col+1); } if(min > getLabel(outData, row-1, col-1) && getLabel(outData, row-1, col-1) != 0) { min = getLabel(outData, row-1, col-1); } if(min > getLabel(outData, row-1, col+1) && getLabel(outData, row-1, col+1) != 0) { min = getLabel(outData, row-1, col+1); } if(min > getLabel(outData, row+1, col-1) && getLabel(outData, row+1, col-1) != 0) { min = getLabel(outData, row+1, col-1); } if(min > getLabel(outData, row+1, col+1) && getLabel(outData, row+1, col+1) != 0) { min = getLabel(outData, row+1, col+1); } if(getLabel(outData, row, col) == min) return; outData[index] = min; // eight neighborhood pixels if((row -1) >= 0) { mergeLabels((row-1)*dw + col); } if((col-1) >= 0) { mergeLabels(row*dw+col-1); } if((row+1) < dh) { mergeLabels((row + 1)*dw+col); } if((col+1) < dw) { mergeLabels((row)*dw+col+1); } if((row-1)>= 0 && (col-1) >=0) { mergeLabels((row-1)*dw+col-1); } if((row-1)>= 0 && (col+1) < dw) { mergeLabels((row-1)*dw+col+1); } if((row+1) < dh && (col-1) >=0) { mergeLabels((row+1)*dw+col-1); } if((row+1) < dh && (col+1) < dw) { mergeLabels((row+1)*dw+col+1); } } private void setData(int[] data, int row, int col, int value) { if(row < 0 || row >= dh) { return; } if(col < 0 || col >= dw) { return; } int index = row * dw + col; data[index] = value; } private int getLabel(int[] data, int row, int col) { // handle the edge pixels if(row < 0 || row >= dh) { return 0; } if(col < 0 || col >= dw) { return 0; } int index = row * dw + col; return (data[index] & 0x000000ff); } private int getPixel(int[] data, int row, int col) { // handle the edge pixels if(row < 0 || row >= dh) { return bgColor; } if(col < 0 || col >= dw) { return bgColor; } int index = row * dw + col; return (data[index] & 0x000000ff); } /** * binary image data: * * 255, 0, 0, 255, 0, 255, 255, 0, 255, 255, 255, * 255, 0, 0, 255, 0, 255, 255, 0, 0, 255, 0, * 255, 0, 0, 0, 255, 255, 255, 255, 255, 0, 0, * 255, 255, 0, 255, 255, 255, 0, 255, 0, 0, 255 * 255, 255, 0, 0, 0, 0, 255, 0, 0, 0, 0 * * height = 5, width = 11 * @param args */ public static int[] imageData = new int[]{ 255, 0, 0, 255, 0, 255, 255, 0, 255, 255, 255, 255, 0, 0, 255, 0, 255, 255, 0, 0, 255, 0, 255, 0, 0, 0, 255, 255, 255, 255, 255, 0, 0, 255, 255, 0, 255, 255, 255, 0, 255, 0, 0, 255, 255, 255, 0, 0, 0, 0, 255, 0, 0, 0, 0 }; public static void main(String[] args) { FastConnectedComponentLabelAlg ccl = new FastConnectedComponentLabelAlg(); int[] outData = ccl.doLabel(imageData, 11, 5); for(int i=0; i<5; i++) { System.out.println("--------------------"); for(int j = 0; j<11; j++) { int index = i * 11 + j; if(j != 0) { System.out.print(","); } System.out.print(outData[index]); } System.out.println(); } } }
找到最大连通区域以后，对最大连通区域数据进行扫描，找出最小点，即矩形区域左上角坐
标，找出最大点，即矩形区域右下角坐标。知道这四个点坐标以后，在原图上打上红色矩形
框，标记出脸谱位置。寻找四个点坐标的实现代码如下：
private void getFaceRectangel() { int width = resultImage.getWidth(); int height = resultImage.getHeight(); int[] inPixels = new int[width*height]; getRGB(resultImage, 0, 0, width, height, inPixels); int index = 0; int ta = 0, tr = 0, tg = 0, tb = 0; for(int row=0; row<height; row++) { 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 == tg && tg == tb && tb == 0) { // face skin if(minY > row) { minY = row; } if(minX > col) { minX = col; } if(maxY < row) { maxY = row; } if(maxX < col) { maxX = col; } } } } }
缺点：
此算法不支持多脸谱检测，不支持裸体中的脸谱检测，但是根据人脸的
生物学特征可以进一步细化分析，支持裸体人脸检测。
写本文章的目的：本例为图像处理综合运行的一个简单实例。同时人脸检
测也是个感兴趣的话题。
