终于到了有实际应用的功能了，有2张图片，里面各有一个人脸，我的目的是比较这两个人脸的相似度，这里用到了facedetect的功能，还有图像转换，图像剪切，以及直方图的比较。具体流程是:
1。分别用facedetect功能将两张图片中的人脸检测出来
2。将人脸部分的图片剪切出来，存到两张只有人脸的图片里。
3。将这两张人脸图片转换成单通道的图像
4。使用直方图比较这两张单通道的人脸图像，得出相似度。
这里对图的要求还是比较高的，光线和姿势不能有差别，脸的垂直或者左右角度偏差就会影响比较，但和两张图片的大小关系不大，本人觉得较适合于证件照的对比。
下面是代码，其中haarcascade_frontalface_alt.xml是opencv里facedetect例子用的样本。 比较的是srcImage和targetImage对应的文件.
还有下面是IplImage和Mat混用，纯当熟悉这两个类了。
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#include"opencv/cv.hpp"#include"opencv2/objdetect/objdetect.hpp"#include"opencv2/highgui/highgui.hpp"#include"opencv2/imgproc/imgproc.hpp"#include<iostream>#include<stdio.h>usingnamespacestd;usingnamespacecv;StringcascadeName="D:\\OpenCV-2.4.2\\data\\haarcascades\\haarcascade_frontalface_alt.xml";IplImage*cutImage(IplImage*src,CvRectrect){cvSetImageROI(src,rect);IplImage*dst=cvCreateImage(cvSize(rect.width,rect.height),src->depth,src->nChannels);cvCopy(src,dst,0);cvResetImageROI(src);returndst;}IplImage*detect(Mat&img,CascadeClassifier&cascade,doublescale){inti=0;doublet=0;vector<Rect>faces;Matgray,smallImg(cvRound(img.rows/scale),cvRound(img.cols/scale),CV_8UC1);cvtColor(img,gray,CV_BGR2GRAY);resize(gray,smallImg,smallImg.size(),0,0,INTER_LINEAR);equalizeHist(smallImg,smallImg);t=(double)cvGetTickCount();cascade.detectMultiScale(smallImg,faces,1.3,2,CV_HAAR_SCALE_IMAGE,Size(30,30));t=(double)cvGetTickCount()-t;printf("detectiontime=%gms\n",t/((double)cvGetTickFrequency()*1000.));for(vector<Rect>::const_iteratorr=faces.begin();r!=faces.end();r++,i++){IplImage*temp=cutImage(&(IplImage(img)),cvRect(r->x,r->y,r->width,r->height));returntemp;}returnNULL;}//画直方图用intHistogramBins=256;floatHistogramRange1[2]={0,255};float*HistogramRange[1]={&HistogramRange1[0]};intCompareHist(IplImage*image1,IplImage*image2){IplImage*srcImage;IplImage*targetImage;if(image1->nChannels!=1){srcImage=cvCreateImage(cvSize(image1->width,image1->height),image1->depth,1);cvCvtColor(image1,srcImage,CV_BGR2GRAY);}else{srcImage=image1;}if(image2->nChannels!=1){targetImage=cvCreateImage(cvSize(image2->width,image2->height),srcImage->depth,1);cvCvtColor(image2,targetImage,CV_BGR2GRAY);}else{targetImage=image2;}CvHistogram*Histogram1=cvCreateHist(1,&HistogramBins,CV_HIST_ARRAY,HistogramRange);CvHistogram*Histogram2=cvCreateHist(1,&HistogramBins,CV_HIST_ARRAY,HistogramRange);cvCalcHist(&srcImage,Histogram1);cvCalcHist(&targetImage,Histogram2);cvNormalizeHist(Histogram1,1);cvNormalizeHist(Histogram2,1);//CV_COMP_CHISQR,CV_COMP_BHATTACHARYYA这两种都可以用来做直方图的比较，值越小，说明图形越相似printf("CV_COMP_CHISQR:%.4f\n",cvCompareHist(Histogram1,Histogram2,CV_COMP_CHISQR));printf("CV_COMP_BHATTACHARYYA:%.4f\n",cvCompareHist(Histogram1,Histogram2,CV_COMP_BHATTACHARYYA));//CV_COMP_CORREL,CV_COMP_INTERSECT这两种直方图的比较，值越大，说明图形越相似printf("CV_COMP_CORREL:%.4f\n",cvCompareHist(Histogram1,Histogram2,CV_COMP_CORREL));printf("CV_COMP_INTERSECT:%.4f\n",cvCompareHist(Histogram1,Histogram2,CV_COMP_INTERSECT));cvReleaseHist(&Histogram1);cvReleaseHist(&Histogram2);if(image1->nChannels!=1){cvReleaseImage(&srcImage);}if(image2->nChannels!=1){cvReleaseImage(&targetImage);}return0;}StringsrcImage="d:\\ldh1.jpg";StringtargetImage="d:\\ldh5.jpg";intmain(intargc,char*argv[]){CascadeClassifiercascade;namedWindow("image1");namedWindow("image2");if(!cascade.load(cascadeName)){return-1;}MatsrcImg,targetImg;IplImage*faceImage1;IplImage*faceImage2;srcImg=imread(srcImage);targetImg=imread(targetImage);faceImage1=detect(srcImg,cascade,1);if(faceImage1==NULL){return-1;}//cvSaveImage("d:\\face.jpg",faceImage1,0);faceImage2=detect(targetImg,cascade,1);if(faceImage2==NULL){return-1;}//cvSaveImage("d:\\face1.jpg",faceImage2,0);imshow("image1",Mat(faceImage1));imshow("image2",Mat(faceImage2));CompareHist(faceImage1,faceImage2);cvWaitKey(0);cvReleaseImage(&faceImage1);cvReleaseImage(&faceImage2);return0;}
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