学步园

以前做算法实现一直用的说matlab，最近做目标跟踪的算法，开始使用Opencv，发现Opencv真的很强大，集合了很多算法，封装了很多函数，使用方法跟matlab很类似，但又比matlab的效率高。下面说我使用hog算法进行人体检测的代码，跟Opencv中的peopledetect.cpp类似，不过我修改了一些代码，可以检测连续的视频序列。

代码如下：

#include <fstream>
#include <string>
#include <cv.h>
#include <highgui.h>
#include <ml.h>
#include <iostream>
#include <fstream>
#include <string>
#include <vector>
#include "cvaux.h"
#include <iostream>
#include <stdio.h>
#include <string.h>
#include <ctype.h>


using namespace cv;
using namespace std;


int main()
{
Mat img0,img1;
Mat img2;
//Mat img3;
int i;
FILE* f = 0;
char _filename[1024];

//imshow("hello",img3);
//waitKey(0);
HOGDescriptor hog;
//CvLatentSvmDetector* detector = cvLoadLatentSvmDetector("result.xml");
CvSVM svm;
svm.load("result.xml");
//hog.load("result.xml");
//int sv_num= svm.get_support_vector_count(); 
hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
//hog.setSVMDetector(detector);

Mat img4;
for(i=46;i<180;i++)
{
char file[1024];
sprintf(file,"D:\\My Documents\\Visual Studio 2008\\Projects\\cc\\CS-%d.bmp",i);
char file4[1024];
sprintf(file4,"D:\\My Documents\\Visual Studio 2008\\Projects\\cc\\CS-%d.bmp",i-1);
Mat img3;
img3 = imread(file4);
img4 = imread(file,1);
//cvSub((CvMat)img2,(CvMat)img3,img);
//img = img4-img3;
Mat img(img4.rows,img4.cols,CV_8UC1);
for(int n=0;n<img4.rows;n++)
{
for(int j=0;j<img4.cols;j++)
{
short e,d;
short temp;
e = *(img4.data+n*img4.step+j*3);
d = *(img3.data+n*img3.step+j*3);
//c = *(img2.data+i);
temp = e-d;
if(abs(temp)<3)
temp=0;
*(img.data+n*img.step+j) = (uchar)e;
}
}
//cv::subtract
//char* filename = _filename;
char* filename = file;
if(0)
{
if(!fgets(filename, (int)sizeof(_filename)-2, f))
break;
//while(*filename && isspace(*filename))
// ++filename;
if(filename[0] == '#')
continue;
int l = strlen(filename);
while(l > 0 && isspace(filename[l-1]))
--l;
filename[l] = '\0';
img = imread(filename);
}
printf("%s:\n", filename);
if(!img.data)
continue;


fflush(stdout);
vector<Rect> found, found_filtered;
double t = (double)getTickCount();
// 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).
int can = img.channels();
hog.detectMultiScale(img, found, 0, Size(8,8), Size(32,32), 1.05, 2);
t = (double)getTickCount() - t;
printf("tdetection time = %gms\n", t*1000./cv::getTickFrequency());
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*1);
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);
//int c = waitKey(0) & 255;
//if( c == 'q' || c == 'Q' || !f)
//break;
waitKey(1);
//cvDestoryWindow("people detector");
}
if(f)
fclose(f);
return 0;
}

其中hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());得到svm的检测算子。

hog.detectMultiScale(img, found, 0, Size(8,8), Size(32,32), 1.05, 2);得到检测的结果。