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
[cpp] view plain copy // 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
[cpp] view plain copy // 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。
[cpp] view plain copy #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
转自：http://blog.csdn.net/onezeros/article/details/6136770
