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。
#include "opencv/cv.hpp" #include "opencv2/objdetect/objdetect.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" #include <iostream> #include <stdio.h> using namespace std; using namespace cv; String cascadeName = "D:\\OpenCV-2.4.2\\data\\haarcascades\\haarcascade_frontalface_alt.xml"; IplImage* cutImage(IplImage* src, CvRect rect) { cvSetImageROI(src, rect); IplImage* dst = cvCreateImage(cvSize(rect.width, rect.height), src->depth, src->nChannels); cvCopy(src,dst,0); cvResetImageROI(src); return dst; } IplImage* detect( Mat& img, CascadeClassifier& cascade, double scale) { int i = 0; double t = 0; vector<Rect> faces; Mat gray, 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( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) ); for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ ) { IplImage* temp = cutImage(&(IplImage(img)), cvRect(r->x, r->y, r->width, r->height)); return temp; } return NULL; } //画直方图用 int HistogramBins = 256; float HistogramRange1[2]={0,255}; float *HistogramRange[1]={&HistogramRange1[0]}; int CompareHist(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); } return 0; } String srcImage = "d:\\ldh1.jpg"; String targetImage = "d:\\ldh5.jpg"; int main(int argc, char* argv[]) { CascadeClassifier cascade; namedWindow("image1"); namedWindow("image2"); if( !cascade.load( cascadeName ) ) { return -1; } Mat srcImg, 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); return 0; }