OPENCV实现C++的OTSU自适应阈值分割的算法描述

cvWaitKey(0); #endif
return 0; } #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
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++) {
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;
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; } }
//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++) {
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);
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;
} }
#endif
return 0; }
threshold=cvThresholdOtsu(imgCb); } //cvThresholdOtsu(imgCb,imgCb); cvThreshold(imgCb,imgCb,threshold,255,CV_THRESH_BINARY); cvErode(imgCb,imgCb); cvDilate(imgCb,imgCb);
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);
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;
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) {
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) {
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);
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; }
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(@) // reference: Rafael C. Gonzalez. Digital Image Processing Using MATLAB int cvThresholdOtsu(IplImage* src) {