Android上掌纹识别第一步：基于OpenCV的6种肤色分割 源码和效果图

六种方法分别是：基于RGB分割，基于RG同道的分割，ycrcb+otsu（ostu可以参考http://blog.csdn.net/onezeros/article/details/6136770，

http://wenku.baidu.com/view/05c47e03bed5b9f3f90f1ce4.html），YCrCb空间，YUV空间，HSV空间。下一步就是通过JNI将这些检测移植到android上，最终目标是实现Android智能手机利用掌纹开关机。

环境是在qt下，.pro文件里增加如下代码：

INCLUDEPATH += /usr/include/opencv

LIBS += /usr/lib/libcv.so \
/usr/lib/libcvaux.so \
/usr/lib/libcxcore.so \
/usr/lib/libhighgui.so \
/usr/lib/libml.so

请看源码：

#include <iostream>
#include "cv.h"
#include "highgui.h"


void SkinRGB(IplImage* rgb,IplImage* _dst);
void cvSkinRG(IplImage* rgb,IplImage* gray);
void cvThresholdOtsu(IplImage* src, IplImage* dst);
void cvSkinOtsu(IplImage* src, IplImage* dst);
void cvSkinYCbCr(IplImage* img, IplImage* mask);
void cvSkinYUV(IplImage* src,IplImage* dst);
void cvSkinHSV(IplImage* src,IplImage* dst);






using namespace std;

// skin region location using rgb limitation



int main()
{
    IplImage *srcImg = cvLoadImage("/home/yan/download/testPalm4.jpg", 1);
    IplImage *dstRGB = cvCreateImage(cvGetSize(srcImg), 8, 3);
    IplImage *dstRG = cvCreateImage(cvGetSize(srcImg), 8, 1);
    IplImage* dst_crotsu=cvCreateImage(cvGetSize(srcImg),8,1);
    IplImage* dst_ycbcr=cvCreateImage(cvGetSize(srcImg),8,1);
    IplImage* dst_yuv=cvCreateImage(cvGetSize(srcImg),8,3);
    IplImage* dst_hsv=cvCreateImage(cvGetSize(srcImg),8,3);

    SkinRGB(srcImg, dstRGB);
    cvSaveImage("/home/yan/download/1_dstRGB.jpg", dstRGB);

    cvSkinRG(srcImg, dstRG);
    cvSaveImage("/home/yan/download/2_dstRG.jpg", dstRG);

    cvSkinOtsu(srcImg, dst_crotsu);
    cvSaveImage("/home/yan/download/3_dst_crotsu.jpg", dst_crotsu);

    cvSkinYCbCr(srcImg, dst_ycbcr);
    cvSaveImage("/home/yan/download/4_dst_ycbcr.jpg", dst_ycbcr);

    cvSkinYUV(srcImg, dst_yuv);
    cvSaveImage("/home/yan/download/5_dst_yuv.jpg", dst_yuv);

    cvSkinHSV(srcImg, dst_hsv);
    cvSaveImage("/home/yan/download/6_dst_hsv.jpg", dst_hsv);


    cvNamedWindow("srcImg", 1);
    cvShowImage("srcImg", srcImg);

    cvNamedWindow("dstRGB", 1);
    cvShowImage("dstRGB", dstRGB);

    cvNamedWindow("dstRG", 1);
    cvShowImage("dstRG", dstRG);

    cvNamedWindow("dstcrotsu", 1);
    cvShowImage("dstcrotsu", dst_crotsu);

    cvNamedWindow("dst_ycbcr", 1);
    cvShowImage("dst_ycbcr", dst_ycbcr);

    cvNamedWindow("dst_yuv", 1);
    cvShowImage("dst_yuv", dst_yuv);

    cvNamedWindow("dst_hsv", 1);
    cvShowImage("dst_hsv", dst_hsv);
    cvWaitKey(0);
    cout << "Hello World!" << endl;
    return 0;
}
void SkinRGB(IplImage* rgb,IplImage* _dst)
{
    cout<<"111"<<endl;
    assert(rgb->nChannels==3&& _dst->nChannels==3);

    static const int R=2;
    static const int G=1;
    static const int B=0;

    IplImage* dst=cvCreateImage(cvGetSize(_dst),8,3);
    cvZero(dst);

    for (int h=0;h<rgb->height;h++) {
        unsigned char* prgb=(unsigned char*)rgb->imageData+h*rgb->widthStep;
        unsigned char* pdst=(unsigned char*)dst->imageData+h*dst->widthStep;
        for (int w=0;w<rgb->width;w++) {
            if ((prgb[R]>95 && prgb[G]>40 && prgb[B]>20 &&
                 prgb[R]-prgb[B]>15 && prgb[R]-prgb[G]>15/*&&
                     !(prgb[R]>170&&prgb[G]>170&&prgb[B]>170)*/)||//uniform illumination
                    (prgb[R]>200 && prgb[G]>210 && prgb[B]>170 &&
                     abs(prgb[R]-prgb[B])<=15 && prgb[R]>prgb[B]&& prgb[G]>prgb[B])//lateral illumination
                    ) {
                memcpy(pdst,prgb,3);
            }
            prgb+=3;
            pdst+=3;
        }
    }
    cvCopyImage(dst,_dst);
    cvReleaseImage(&dst);
}

void cvSkinRG(IplImage* rgb,IplImage* gray)
{
    assert(rgb->nChannels==3&&gray->nChannels==1);

    const int R=2;
    const int G=1;
    const int B=0;

    double Aup=-1.8423;
    double Bup=1.5294;
    double Cup=0.0422;
    double Adown=-0.7279;
    double Bdown=0.6066;
    double Cdown=0.1766;
    for (int h=0; h<rgb->height; h++)
    {
        unsigned char* pGray=(unsigned char*)gray->imageData+h*gray->widthStep;
        unsigned char* pRGB=(unsigned char* )rgb->imageData+h*rgb->widthStep;
        for (int w=0; w<rgb->width; w++)
        {
            int s=pRGB[R]+pRGB[G]+pRGB[B];
            double r=(double)pRGB[R]/s;
            double g=(double)pRGB[G]/s;
            double Gup=Aup*r*r+Bup*r+Cup;
            double Gdown=Adown*r*r+Bdown*r+Cdown;
            double Wr=(r-0.33)*(r-0.33)+(g-0.33)*(g-0.33);
            if (g<Gup && g>Gdown && Wr>0.004)
            {
                *pGray=255;
            }
            else
            {
                *pGray=0;
            }
            pGray++;
            pRGB+=3;
        }
    }

}
void cvThresholdOtsu(IplImage* src, IplImage* dst)
{
    int height=src->height;
    int width=src->width;

    //histogram
    float histogram[256]= {0};
    for(int i=0; i<height; i++)
    {
        unsigned char* p=(unsigned char*)src->imageData+src->widthStep*i;
        for(int j=0; j<width; j++)
        {
            histogram[*p++]++;
        }
    }
    //normalize histogram
    int size=height*width;
    for(int i=0; i<256; i++)
    {
        histogram[i]=histogram[i]/size;
    }

    //average pixel value
    float avgValue=0;
    for(int i=0; i<256; i++)
    {
        avgValue+=i*histogram[i];
    }

    int threshold;
    float maxVariance=0;
    float w=0,u=0;
    for(int i=0; i<256; i++)
    {
        w+=histogram[i];
        u+=i*histogram[i];

        float t=avgValue*w-u;
        float variance=t*t/(w*(1-w));
        if(variance>maxVariance)
        {
            maxVariance=variance;
            threshold=i;
        }
    }
    cvThreshold(src,dst,threshold,255,CV_THRESH_BINARY);
}

void cvSkinOtsu(IplImage* src, IplImage* dst)
{
    assert(dst->nChannels==1&& src->nChannels==3);

    IplImage* ycrcb=cvCreateImage(cvGetSize(src),8,3);
    IplImage* cr=cvCreateImage(cvGetSize(src),8,1);
    cvCvtColor(src,ycrcb,CV_BGR2YCrCb);
    cvSplit(ycrcb,0,cr,0,0);

    cvThresholdOtsu(cr,cr);
    cvCopyImage(cr,dst);
    cvReleaseImage(&cr);
    cvReleaseImage(&ycrcb);
}
void cvSkinYCbCr(IplImage* img, IplImage* mask)
{
    CvSize imageSize = cvSize(img->width, img->height);
    IplImage *imgY = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
    IplImage *imgCr = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
    IplImage *imgCb = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);


    IplImage *imgYCrCb = cvCreateImage(imageSize, img->depth, img->nChannels);
    cvCvtColor(img,imgYCrCb,CV_BGR2YCrCb);
    cvSplit(imgYCrCb, imgY, imgCr, imgCb, 0);
    int y, cr, cb, l, x1, y1, value;
    unsigned char *pY, *pCr, *pCb, *pMask;

    pY = (unsigned char *)imgY->imageData;
    pCr = (unsigned char *)imgCr->imageData;
    pCb = (unsigned char *)imgCb->imageData;
    pMask = (unsigned char *)mask->imageData;
    cvSetZero(mask);
    l = img->height * img->width;
    for (int i = 0; i < l; i++){
        y  = *pY;
        cr = *pCr;
        cb = *pCb;
        cb -= 109;
        cr -= 152
                ;
        x1 = (819*cr-614*cb)/32 + 51;
        y1 = (819*cr+614*cb)/32 + 77;
        x1 = x1*41/1024;
        y1 = y1*73/1024;
        value = x1*x1+y1*y1;
        if(y<100)	(*pMask)=(value<700) ? 255:0;
        else		(*pMask)=(value<850)? 255:0;
        pY++;
        pCr++;
        pCb++;
        pMask++;
    }
    cvReleaseImage(&imgY);
    cvReleaseImage(&imgCr);
    cvReleaseImage(&imgCb);
    cvReleaseImage(&imgYCrCb);
}

void cvSkinYUV(IplImage* src,IplImage* dst)
{
    IplImage* ycrcb=cvCreateImage(cvGetSize(src),8,3);
    //IplImage* cr=cvCreateImage(cvGetSize(src),8,1);
    //IplImage* cb=cvCreateImage(cvGetSize(src),8,1);
    cvCvtColor(src,ycrcb,CV_BGR2YCrCb);
    //cvSplit(ycrcb,0,cr,cb,0);

    static const int Cb=2;
    static const int Cr=1;
    static const int Y=0;

    //IplImage* dst=cvCreateImage(cvGetSize(_dst),8,3);
    cvZero(dst);

    for (int h=0; h<src->height; h++)
    {
        unsigned char* pycrcb=(unsigned char*)ycrcb->imageData+h*ycrcb->widthStep;
        unsigned char* psrc=(unsigned char*)src->imageData+h*src->widthStep;
        unsigned char* pdst=(unsigned char*)dst->imageData+h*dst->widthStep;
        for (int w=0; w<src->width; w++)
        {
            if (pycrcb[Cr]>=133&&pycrcb[Cr]<=173&&pycrcb[Cb]>=77&&pycrcb[Cb]<=127)
            {
                memcpy(pdst,psrc,3);
            }
            pycrcb+=3;
            psrc+=3;
            pdst+=3;
        }
    }
    //cvCopyImage(dst,_dst);
    //cvReleaseImage(&dst);
}

void cvSkinHSV(IplImage* src,IplImage* dst)
{
    IplImage* hsv=cvCreateImage(cvGetSize(src),8,3);
    //IplImage* cr=cvCreateImage(cvGetSize(src),8,1);
    //IplImage* cb=cvCreateImage(cvGetSize(src),8,1);
    cvCvtColor(src,hsv,CV_BGR2HSV);
    //cvSplit(ycrcb,0,cr,cb,0);

    static const int V=2;
    static const int S=1;
    static const int H=0;

    //IplImage* dst=cvCreateImage(cvGetSize(_dst),8,3);
    cvZero(dst);

    for (int h=0; h<src->height; h++)
    {
        unsigned char* phsv=(unsigned char*)hsv->imageData+h*hsv->widthStep;
        unsigned char* psrc=(unsigned char*)src->imageData+h*src->widthStep;
        unsigned char* pdst=(unsigned char*)dst->imageData+h*dst->widthStep;
        for (int w=0; w<src->width; w++)
        {
            if (phsv[H]>=7&&phsv[H]<=29)
            {
                memcpy(pdst,psrc,3);
            }
            phsv+=3;
            psrc+=3;
            pdst+=3;
        }
    }
    //cvCopyImage(dst,_dst);
    //cvReleaseImage(&dst);
}

下面是效果图：

测试图片：

下图的贴图依次对应上面的六种方法：

从上面的结果对比图中可以清晰看的，ycrcb+ostu的效果无疑是最好的。其次是rgb和yuv方法。这个图片效果之所以这么好是因为测试图片拍摄的时候背景为白色。然后，遗憾的是，当背景色不纯的时候，比如有红也有黑，效果就很不理想了。实验发现，当背景为纯色，且是白色或黑色时，效果最好。

参考：

http://blog.sina.com.cn/s/blog_9ce5a1b501017otq.html

http://blog.csdn.net/scyscyao/article/details/5468577

http://wenku.baidu.com/view/05c47e03bed5b9f3f90f1ce4.html

http://blog.csdn.net/onezeros/article/details/6136770

--------------------------本掌纹是作者自己的，转载请注明作者yanzi1225627