调用opencv自带的行人检测 我用的是opencv3.3.0+vs2015

#include "opencv2/imgproc/imgproc.hpp"  
#include "opencv2/objdetect/objdetect.hpp"  
#include "opencv2/highgui/highgui.hpp"  


#include  
#include  
#include  
#include  


using namespace cv;
using namespace std;




int main()
{


Mat img;


vector found, found_filtered;
string namepic = "timg.jpg";
img = imread(namepic);
if (!img.data)
{
printf("No data!--Exiting the program \n");
return -1;
}
namedWindow("people detector", 1);
//imshow("people detector",img);  


HOGDescriptor hog;//(cv::Size(64,64), cv::Size(16,16), cv::Size(8,8), cv::Size(8,8), 9);  
hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());






cout << namepic << endl;


fflush(stdout);    //刷新  


  // run the detector with default parameters. to get a higher hit-rate  
  // (and more false alarms, respectively), decrease the hitThreshold and  
  // groupThreshold (set groupThreshold to 0 to turn off the grouping completely).  
hog.detectMultiScale(img, found, 0, Size(8, 8), Size(32, 32), 1.05, 2);
size_t i, j;
for (i = 0; i < found.size(); i++)
{
Rect r = found[i];
for (j = 0; j < found.size(); j++)
if (j != i && (r & found[j]) == r)
break;
if (j == found.size())
found_filtered.push_back(r);
}
for (i = 0; i < found_filtered.size(); i++)
{
Rect r = found_filtered[i];
// the HOG detector returns slightly larger rectangles than the real objects.  
// so we slightly shrink the rectangles to get a nicer output.  
r.x += cvRound(r.width*0.1);
r.width = cvRound(r.width*0.8);
r.y += cvRound(r.height*0.07);
r.height = cvRound(r.height*0.8);
rectangle(img, r.tl(), r.br(), cv::Scalar(0, 255, 0), 3);
}
imshow("people detector", img);
waitKey(0);
return 0;
}