opencv hog svm java_opencv+hog+svm行人检测自己训练

单昊穹

2023-12-01

#include

using namespace std;

using namespace cv;

//常量定义

char IMG_PATH_TEXT[]="E:/INRIAPerson/color/img_path.txt"; //图像路径

//char IMG_PATH_TEXT[]="E:/研究生课件/机器视觉/Project/Data/pictures"; //图像路径

//const int START_FRAME=243; //开始跟踪的帧243 || 413

//const int END_FRAME=290; //结束跟踪的帧290 || 480

const int WINHEIGHT=180; //图像高度

const int WINWIDTH=320; //图像宽度

const int ROIWIDTH=160; //ROI高度

const int ROIHEIGHT=160; //ROI宽度

const Scalar green(0,255,0);

const Scalar red(0,0,255);

const Scalar blue(255,0,0);

const string DETECT_WINNAME="Detect Window";

const string TRACKER_WINNAME="Track Window";

Mat img(WINHEIGHT,WINWIDTH,CV_8UC3); //the Mat storage the image

Mat blackboard(WINHEIGHT,WINWIDTH,CV_8UC3);

const cv::Rect WHOLEIMG(0,0,WINWIDTH-2,WINHEIGHT-2);

const int STATE_NUM=4;

const int MEASURE_NUM=2;

//使用HOG在img图像中检测行人位置

//返回的矩阵vector为矩阵位置

//返回检测时间

void DetectPedestrian(vector<:rect>& found_filtered,const HOGDescriptor& hog,

double* time=NULL)

{

found_filtered.clear ();

vector<:rect> found;

//hog detector get the cv::Rect with pedestrian

if(time!=NULL)

*time=(double)getTickCount();

hog.detectMultiScale(img, found, 0, Size(8,8), Size(32,32), 1.05, 2);

if(time!=NULL)

{

*time=(double)getTickCount()-*time;

*time /= getTickFrequency();

}

//if the cv::Rect overlap

size_t i, j;

for( i = 0; i < found.size(); i++ )

{

cv::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++ )

{

cv::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);

//found_filtered[i]=r;

cv::rectangle (img,r,green,3);

}

std::cout<

}

//重载函数，在img的一个子区域中检测行人位置

//该子区域由一个矩形确定

//返回的矩阵vector为矩阵位置

//返回检测时间

void DetectPedestrian(vector<:rect>& found_filtered,const HOGDescriptor& hog,

const cv::Rect& roi,double* time=NULL)

{

//CV_Assert(WHOLEIMG.contains(roi.br()) && WHOLEIMG.contains(roi.tl()));

found_filtered.clear ();

vector<:rect> found;

//hog detector get the cv::Rect with pedestrian

if(time!=NULL)

*time=(double)getTickCount();

hog.detectMultiScale(img(roi), found, 0, Size(8,8), Size(32,32), 1.05, 2);

if(time!=NULL)

{

*time=(double)getTickCount()-*time;

*time /= getTickFrequency();

}

Point pt=roi.tl();

//if the cv::Rect overlap

size_t i, j;

for( i = 0; i < found.size(); i++ )

{

found[i]+=pt;

cv::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++ )

{

cv::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);

//found_filtered[i]=r;

cv::rectangle (img,r,green,3);

}

std::cout<

}

void InitialKalmanFilter(KalmanFilter& kf,double x,double y,

double delta_x,double delta_y)

{

kf.transitionMatrix=(Mat_(STATE_NUM, STATE_NUM) <<

1,0,1,0,

0,1,0,1,

0,0,1,0,

0,0,0,1 );

kf.statePost=(Mat_(STATE_NUM,1)<<

delta_x,

delta_y );

kf.statePre=(Mat_(STATE_NUM,1)<<

delta_x,

delta_y );

//setIdentity: 缩放的单位对角矩阵;

// !< measurement matrix (H) 观测模型

setIdentity(kf.measurementMatrix);

// !< process noise covariance matrix (Q)

// wk 是过程噪声，并假定其符合均值为零，协方差矩阵为Qk(Q)的多元正态分布;

setIdentity(kf.processNoiseCov,Scalar::all(25));

// !< measurement noise covariance matrix (R)

//vk 是观测噪声，其均值为零，协方差矩阵为Rk,且服从正态分布;

setIdentity(kf.measurementNoiseCov,Scalar::all(25));

// !< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)

// A代表F: transitionMatrix

//预测估计协方差矩阵;

setIdentity(kf.errorCovPost,Scalar::all(25));

}

//获得矩形的质心坐标

Point2f GetCentroid(const cv::Rect& r)

{

Point tl=r.tl();

Point br=r.br();

return Point2f((float)((tl.x+br.x)/2.0),(float)((tl.y+br.y)/2.0));

}

string DescripRect(const cv::Rect r)

{

char buf[128];

sprintf(buf,"This rectangle width: %d , height %d , tl: (%d,%d) ,br: (%d,%d)",

r.width,r.height,r.tl ().x,r.tl ().y,r.br ().x,r.br ().y);

return string(buf);

}

cv::Rect GetROI(const Point2f& centroid)

{

Point tl((int)(centroid.x-ROIWIDTH/2),(int)(centroid.y-ROIHEIGHT/2));

return (cv::Rect(tl.x,tl.y,ROIWIDTH,ROIHEIGHT) & WHOLEIMG);

}

int main()

{

double time;

ofstream fout("data_recorder.txt");

int count=0; //frame count

string img_path; // img path

ifstream fin(IMG_PATH_TEXT); //the text storage the image path

vector<:rect> pedestrian_location;

HOGDescriptor hog; //HoG detector

hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());

cv::Rect preFrame;

cv::Rect curFrame;

int count_tmp=-1;

bool start_track=false; //是否开始跟踪？

int loss_frame=0;

KalmanFilter kf(STATE_NUM,MEASURE_NUM);

vector pedestrian_centroid; //存储质心变化

vector pedestrian_centroid_pre; //预测的质心变化

vector state_post;

//while(getline (fin,img_path) && count<=END_FRAME)

const string video_name = "E:/研究生课件/机器视觉/Project/Data/VID_20141228_151213.mp4";

VideoCapture video(video_name);

Mat frame;

VideoWriter videowriter;

videowriter.open("Test_LX.avi",CV_FOURCC('M','J','P','G'),

10,Size(WINWIDTH,WINHEIGHT),true);

VideoWriter boardwriter;

boardwriter.open("Test_LX_board.avi",CV_FOURCC('M','J','P','G'),

10,Size(WINWIDTH,WINHEIGHT),true);

while(1)

{

//BEGIN !

//Read the image from disk

//img=imread (img_path,1);

video >> frame;

if(frame.empty ())

break;

cv::resize (frame, img, Size(WINWIDTH,WINHEIGHT));

blackboard.setTo (0);

//绘制行人质心

for (size_t k=0;k

{

cv::circle(blackboard ,pedestrian_centroid[k],3,green);

if(k!=0)

cv::line(blackboard ,pedestrian_centroid[k],pedestrian_centroid[k-1],

green,2);

}

for (size_t k=0;k

{

cv::circle(blackboard ,pedestrian_centroid_pre[k],3,red);

if(k!=0)

cv::line(blackboard ,pedestrian_centroid_pre[k],

pedestrian_centroid_pre[k-1],red,2);

}

for (size_t k=0;k

{

cv::circle(blackboard ,state_post[k],4,blue,-1);

if(k!=0)

cv::line(blackboard ,state_post[k],state_post[k-1],blue,2);

}

char buf[128];

sprintf(buf,"frame: %3d",count);

putText (blackboard ,string(buf),cv::Point(10,20),