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 MATLAB
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);
}
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 MATLAB
int cvThresholdOtsu(IplImage* src)
{
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;
}
}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 process
CvCapture* 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){//esc
break;
}
}cvReleaseCapture(&capture);
#else //single image process
const 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);
#endif
return 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
