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(2)

//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 #include #include

#pragma comment(lib,\#pragma comment(lib,\#pragma comment(lib,\

#include using namespace std;

int main(int argc, char** argv) {

#ifdef VIDEO //video process

CvCapture* capture=cvCreateCameraCapture(-1); if (!capture) {

cout<<\ exit(0); }

int threshold=-1; IplImage* img;

while (img=cvQueryFrame(capture)) {

cvShowImage(\

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(\ cvReleaseImage(&imgCb);

if (cvWaitKey(3)==27) //esc {

break; } }

cvReleaseCapture(&capture);

#else //single image process

const char* filename=(argc>=2?argv[1]:\

IplImage* img=cvLoadImage(filename,CV_LOAD_IMAGE_GRAYSCALE);

cvThresholdOtsu(img,img); cvShowImage( \ char buf[256];

sprintf_s(buf,256,\ cvSaveImage(buf,img);

cvErode(img,img); cvDilate(img,img); cvShowImage( \

sprintf_s(buf,256,\ cvSaveImage(buf,img);

cvWaitKey(0); #endif

return 0; }

#include #include #include

#pragma comment(lib,\#pragma comment(lib,\#pragma comment(lib,\#include

using namespace std; int main(int argc, char** argv) {

#ifdef VIDEO //video process

CvCapture* capture=cvCreateCameraCapture(-1); if (!capture) {

cout<<\ exit(0); }

int threshold=-1; IplImage* img;

while (img=cvQueryFrame(capture)) {

cvShowImage(\

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(\ cvReleaseImage(&imgCb); if (cvWaitKey(3)==27) //esc {

break; } }

cvReleaseCapture(&capture);

#else //single image process

const char* filename=(argc>=2?argv[1]:\

IplImage* img=cvLoadImage(filename,CV_LOAD_IMAGE_GRAYSCALE); cvThresholdOtsu(img,img); cvShowImage( \ char buf[256];

sprintf_s(buf,256,\ cvSaveImage(buf,img); cvErode(img,img); cvDilate(img,img); cvShowImage( \

sprintf_s(buf,256,\ cvSaveImage(buf,img); cvWaitKey(0); #endif

return 0; }