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收集整理的這篇文章主要介紹了
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
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from：http://blog.csdn.net/onezeros/article/details/6136770
?otsu算法選擇使類間方差最大的灰度值為閾值，具有很好的效果
算法具體描述見otsu論文，或?qū)_雷斯著名的數(shù)字圖像處理那本書
這里給出程序流程：
1、計算直方圖并歸一化histogram
2、計算圖像灰度均值avgValue.
3、計算直方圖的零階w[i]和一級矩u[i]
4、計算并找到最大的類間方差（between-class variance）
variance[i]=(avgValue*w[i]-u[i])*(avgValue*w[i]-u[i])/(w[i]*(1-w[i]))
對應(yīng)此最大方差的灰度值即為要找的閾值
5、用找到的閾值二值化圖像
我在代碼中做了一些優(yōu)化，所以算法描述的某些地方跟程序并不一致
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otsu代碼，先找閾值，繼而二值化
[cpp]?view plaincopy
?? ?? ?? void?cvThresholdOtsu(IplImage*?src,?IplImage*?dst)?? {?? ????int?height=src->height;?? ????int?width=src->width;?????? ?????? ?????? ????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++]++;?? ????????}?? ????}?? ?????? ????int?size=height*width;?? ????for(int?i=0;i<256;i++)?{?? ????????histogram[i]=histogram[i]/size;?? ????}?? ?????? ?????? ????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);?? }?? ?
更多情況下我們并不需要對每一幀都是用otsu尋找閾值，于是可以先找到閾值，然后用找到的閾值處理后面的圖像。下面這個函數(shù)重載了上面的，返回值就是閾值。只做了一點改變
[cpp]?view plaincopy
?? ?? ?? int?cvThresholdOtsu(IplImage*?src)?? {?? ????int?height=src->height;?? ????int?width=src->width;?????? ?? ?????? ????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++]++;?? ????????}?? ????}?? ?????? ????int?size=height*width;?? ????for(int?i=0;i<256;i++)?{?? ????????histogram[i]=histogram[i]/size;?? ????}?? ?? ?????? ????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;?? }?? ?
我在手的自動檢測中使用這個方法，效果很好。
下面是使用上述兩個函數(shù)的簡單的主程序，可以試運行一下，如果處理視頻，要保證第一幀時，手要在圖像中。
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[cpp]?view plaincopy
#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);?? ????????}?? ?????????? ????????cvThreshold(imgCb,imgCb,threshold,255,CV_THRESH_BINARY);?? ????????cvErode(imgCb,imgCb);?? ????????cvDilate(imgCb,imgCb);?? ?????????? ????????cvShowImage("object",imgCb);?? ????????cvReleaseImage(&imgCb);?? ?? ????????if?(cvWaitKey(3)==27){?? ????????????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;?? }?? ?
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效果圖：
1、膚色cb分量
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2、otsu自適應(yīng)閾值分割效果
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3、開運算后效果
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