图像处理------简单脸谱检测算法 分类： 视频图像处理 2015-07-24 10:11 42人阅读 评论(0) 收藏

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。

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

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