直方图反向投影式通过给定的直方图信息，在图像找到相应的像素分布区域，opencv提供两种算法，一个是基于像素的，一个是基于块的。
使用方法不写了，可以参考一下几个网站：
测试例子1：灰度直方图反向投影
灰度直方图反向投影 IplImage * image= cvLoadImage("22.jpg"); IplImage * image2= cvLoadImage("2.jpg"); int hist_size=256; float range[] = {0,255}; float* ranges[]={range}; IplImage* gray_plane = cvCreateImage(cvGetSize(image),8,1); cvCvtColor(image,gray_plane,CV_BGR2GRAY); CvHistogram* gray_hist = cvCreateHist(1,&hist_size,CV_HIST_ARRAY,ranges,1); cvCalcHist(&gray_plane,gray_hist,0,0); //cvNormalizeHist(gray_hist,1.0); IplImage* gray_plane2 = cvCreateImage(cvGetSize(image2),8,1); cvCvtColor(image2,gray_plane2,CV_BGR2GRAY); //CvHistogram* gray_hist2 = cvCreateHist(1,&hist_size,CV_HIST_ARRAY,ranges,1); //cvCalcHist(&gray_plane2,gray_hist2,0,0); //cvNormalizeHist(gray_hist2,1.0); IplImage* dst = cvCreateImage(cvGetSize(gray_plane2),IPL_DEPTH_8U,1); cvCalcBackProject(&gray_plane2, dst ,gray_hist); cvEqualizeHist(dst,dst); //产生的图像太暗，做了一些直方图均衡 cvNamedWindow( "dst"); cvShowImage("dst",dst); cvNamedWindow( "src"); cvShowImage( "src", image2 ); cvNamedWindow( "templ"); cvShowImage( "templ", image ); cvWaitKey();
效果图：
第一个图为源图像，中间的那个小图像是产生用于反向投影的直方图的图像，最后的用直方图均衡化后的结果图像，可以看到，苹果的像素位置几被找到了。
测试例子2：彩色直方图反向投影测试
彩色图像直方图反向投影 IplImage*src= cvLoadImage("myhand2.jpg", 1); IplImage*templ=cvLoadImage("myhand3.jpg",1); cvNamedWindow( "Source" ); cvShowImage( "Source", src ); IplImage* h_plane2 = cvCreateImage( cvGetSize(src), 8, 1 ); IplImage* s_plane2 = cvCreateImage( cvGetSize(src), 8, 1 ); IplImage* v_plane2 = cvCreateImage( cvGetSize(src), 8, 1); IplImage* planes2[] = { h_plane2, s_plane2,v_plane2 }; IplImage* hsv2 = cvCreateImage( cvGetSize(src), 8, 3 ); cvCvtColor( src, hsv2, CV_BGR2HSV ); cvSplit( hsv2, h_plane2, s_plane2, v_plane2, 0 ); printf("h%d",h_plane2->widthStep); printf("s%d",h_plane2->widthStep); printf("v%d",h_plane2->widthStep); IplImage* h_plane = cvCreateImage( cvGetSize(templ), 8, 1 ); IplImage* s_plane = cvCreateImage( cvGetSize(templ), 8, 1 ); IplImage* v_plane = cvCreateImage( cvGetSize(templ), 8, 1); IplImage* planes[] = { h_plane, s_plane,v_plane }; IplImage* hsv = cvCreateImage( cvGetSize(templ), 8, 3 ); cvCvtColor( templ, hsv, CV_BGR2HSV ); cvSplit( hsv, h_plane, s_plane, v_plane, 0 ); printf("h%d\n",h_plane->widthStep); printf("s%d\n",s_plane->widthStep); printf("v%d\n",v_plane->widthStep); int h_bins = 16, s_bins = 16,v_bins=16; int hist_size[] = {h_bins, s_bins,v_bins}; float h_ranges[] = {0,255}; float s_ranges[] = {0,255}; float v_ranges[] = {0,255}; float* ranges[] = { h_ranges, s_ranges,v_ranges}; CvHistogram* hist; hist = cvCreateHist( 3, hist_size, CV_HIST_ARRAY, ranges, 1 ); cvCalcHist( planes, hist, 0, 0 ); //1.double a=1.f; //2.cvNormalizeHist(hist,a); //templ's hist is just calculate IplImage*back_project=cvCreateImage(cvGetSize(src),8,1);//!!归一，把改成，就弹出对话框，说planes的steps不是一致的！ cvZero(back_project); //但是我去掉归一，改成就可以显示 //NOW we begin calculate back project cvCalcBackProject(planes2,back_project,hist); cvNamedWindow( "back_project" ); cvShowImage( "back_project", back_project ); cvWaitKey(0);
测试结果：
手的肤色位置基本找到了，但是有一个问题，在做直方图反向的时候，直方图分级是16等分，并不是256等分，下图是32等分和8等分的图像效果：
32等分 
8等分
程序里面使用了SHV分量，也算是肤色检测的一个实例，里面的颜色区分很明显，所有采用大一点的区域统计，能更好的找到肤色的位置，如果采用很细的颜色区分，光照的影响也会考虑进去了。
测试例子3：基于块的直方图投影
这种方法速度很慢，模版图像别弄的太大了。
基于块的图像直方图反向投影 IplImage*src= cvLoadImage("2.jpg", 1); IplImage*templ=cvLoadImage("22.jpg",1); cvNamedWindow( "Source" ); cvShowImage( "Source", src ); IplImage* h_plane2 = cvCreateImage( cvGetSize(src), 8, 1 ); IplImage* s_plane2 = cvCreateImage( cvGetSize(src), 8, 1 ); IplImage* v_plane2 = cvCreateImage( cvGetSize(src), 8, 1); IplImage* planes2[] = { h_plane2, s_plane2,v_plane2 }; IplImage* hsv2 = cvCreateImage( cvGetSize(src), 8, 3 ); cvCvtColor( src, hsv2, CV_BGR2HSV ); cvSplit( hsv2, h_plane2, s_plane2, v_plane2, 0 ); printf("h%d",h_plane2->widthStep); printf("s%d",h_plane2->widthStep); printf("v%d",h_plane2->widthStep); IplImage* h_plane = cvCreateImage( cvGetSize(templ), 8, 1 ); IplImage* s_plane = cvCreateImage( cvGetSize(templ), 8, 1 ); IplImage* v_plane = cvCreateImage( cvGetSize(templ), 8, 1); IplImage* planes[] = { h_plane, s_plane,v_plane }; IplImage* hsv = cvCreateImage( cvGetSize(templ), 8, 3 ); cvCvtColor( templ, hsv, CV_BGR2HSV ); cvSplit( hsv, h_plane, s_plane, v_plane, 0 ); printf("h%d\n",h_plane->widthStep); printf("s%d\n",s_plane->widthStep); printf("v%d\n",v_plane->widthStep); int h_bins = 16, s_bins = 16,v_bins=16; int hist_size[] = {h_bins, s_bins,v_bins}; float h_ranges[] = {0,255}; float s_ranges[] = {0,255}; float v_ranges[] = {0,255}; float* ranges[] = { h_ranges, s_ranges,v_ranges}; CvHistogram* hist; hist = cvCreateHist( 3, hist_size, CV_HIST_ARRAY, ranges, 1 ); cvCalcHist( planes, hist, 0, 0 ); CvSize temp ; temp.height = src->height - templ->height + 1; temp.width = src->width - templ->width + 1; IplImage*back_project=cvCreateImage(temp,IPL_DEPTH_32F,1);//!!归一，把改成，就弹出对话框，说planes的steps不是一致的！ cvZero(back_project); //但是我去掉归一，改成就可以显示 cvCalcBackProjectPatch(planes2, back_project, cvGetSize(templ), hist,CV_COMP_INTERSECT ,1); cvNamedWindow( "back_project" ); cvShowImage( "back_project", back_project ); cvWaitKey(0);
测试图像：
当模版图像小雨目标的时候，作为区域检测器，测试如下：可以找到手区域
当模版等于目标的时候，测试如下：输出图像，较亮的部分就是人的头部大致位置
基于块的反向，速度太慢了。