otsu自适应阈值分割的算法描述和opencv实现,及其在肤色检测中的应用

 otsu算法选择使类间方差最大的灰度值为阈值，具有很好的效果
算法具体描述见otsu论文，或冈萨雷斯著名的数字图像处理那本书
这里给出程序流程：
1、计算直方图并归一化histogram
2、计算图像灰度均值avgValue.
3、计算直方图的零阶w[i]和一级矩u[i]
4、计算并找到最大的类间方差（between-class variance）
variance[i]=(avgValue*w[i]-u[i])*(avgValue*w[i]-u[i])/(w[i]*(1-w[i]))
对应此最大方差的灰度值即为要找的阈值
5、用找到的阈值二值化图像

我在代码中做了一些优化，所以算法描述的某些地方跟程序并不一致

 

otsu代码，先找阈值，继而二值化

// implementation of otsu algorithm// author: onezeros(@yahoo.cn)// reference: Rafael C. Gonzalez. Digital Image Processing Using MATLABvoid cvThresholdOtsu(IplImage* src, IplImage* dst){int height=src->height;int width=src->width;//histogramfloat 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 histogramint size=height*width;for(int i=0;i<256;i++) {histogram[i]=histogram[i]/size;}//average pixel valuefloat 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);}

 

更多情况下我们并不需要对每一帧都是用otsu寻找阈值，于是可以先找到阈值，然后用找到的阈值处理后面的图像。下面这个函数重载了上面的，返回值就是阈值。只做了一点改变

// implementation of otsu algorithm// author: onezeros(@yahoo.cn)// reference: Rafael C. Gonzalez. Digital Image Processing Using MATLABint cvThresholdOtsu(IplImage* src){int height=src->height;int width=src->width;//histogramfloat 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 histogramint size=height*width;for(int i=0;i<256;i++) {histogram[i]=histogram[i]/size;}//average pixel valuefloat 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;}}return threshold;}

 

我在手的自动检测中使用这个方法，效果很好。

下面是使用上述两个函数的简单的主程序，可以试运行一下，如果处理视频，要保证第一帧时，手要在图像中。

 #include <cv.h>#include <cxcore.h>#include <highgui.h>#pragma comment(lib,"cv210d.lib")#pragma comment(lib,"cxcore210d.lib")#pragma comment(lib,"highgui210d.lib")#include <iostream>using namespace std;int main(int argc, char** argv){#ifdef VIDEO //video processCvCapture* capture=cvCreateCameraCapture(-1);if (!capture){cout<<"failed to open camera"<<endl;exit(0);}int threshold=-1;IplImage* img;while (img=cvQueryFrame(capture)){cvShowImage("video",img);cvCvtColor(img,img,CV_RGB2YCrCb);IplImage* imgCb=cvCreateImage(cvGetSize(img),8,1);cvSplit(img,NULL,NULL,imgCb,NULL);if (threshold<0){threshold=cvThresholdOtsu(imgCb);}//cvThresholdOtsu(imgCb,imgCb);cvThreshold(imgCb,imgCb,threshold,255,CV_THRESH_BINARY);cvErode(imgCb,imgCb);cvDilate(imgCb,imgCb);cvShowImage("object",imgCb);cvReleaseImage(&imgCb);if (cvWaitKey(3)==27){//escbreak;}}cvReleaseCapture(&capture); #else //single image processconst char* filename=(argc>=2?argv[1]:"cr.jpg");IplImage* img=cvLoadImage(filename,CV_LOAD_IMAGE_GRAYSCALE);cvThresholdOtsu(img,img);cvShowImage( "src", img );char buf[256];sprintf_s(buf,256,"%s.otsu.jpg",filename);cvSaveImage(buf,img);cvErode(img,img);cvDilate(img,img);cvShowImage( "dst", img );sprintf_s(buf,256,"%s.otsu.processed.jpg",filename);cvSaveImage(buf,img);cvWaitKey(0);#endifreturn 0;}

 

 

效果图：

1、肤色cb分量

 

2、otsu自适应阈值分割效果

 

3、开运算后效果