rokasma's profile - activity

hahhhahaha... I know how to track an object, but i don't know how to determine if object is comming in or out

Hi,

I am writing a code for moving objects detection, but now i want to count in/out objects. Now i can count olny how many objects cross the line. What is the best way to do that?

For detection I use frames substraction (first frame with current frame).

what is the best way to count people? Now i write a code that counts by coordinates (when people cross the line it is counted)

Yes, exactly

Hello,

I have people detection algorithm and it works quite good and now I want to count in/out people. What is the best way to do that?

Hi,

When the input comes from webcam, the detection works fine. But when from video file, it works very slow (video file is playing very slow).

Here is my code:

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int main (int argc, const char * argv[])
{
    VideoCapture cap("video.avi");
    cap.set(CV_CAP_PROP_FRAME_WIDTH, 320);
    cap.set(CV_CAP_PROP_FRAME_HEIGHT, 240);    
    if (!cap.isOpened())
        return -1;

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

    namedWindow("video capture", CV_WINDOW_AUTOSIZE);
    while (true)
    {
        cap >> img;
        if (!img.data)
            continue;

        vector<Rect> found, found_filtered;
        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];
            r.x += cvRound(r.width*0.1);
        r.width = cvRound(r.width*0.8);
        r.y += cvRound(r.height*0.06);
        r.height = cvRound(r.height*0.9);
        rectangle(img, r.tl(), r.br(), cv::Scalar(0,255,0), 2);
    }
        imshow("video capture", img);
        if (waitKey(20) >= 0)
            break;
    }
    return 0;
}

What do you mean windows headers?

Hello,

I am trying to train my hog algorithm and I have one problem. In svm_common.h file I have some ambiguity.

typedef struct word {
  FNUM    wnum;                /* word number */
  FVAL    weight;              /* word weight */
} WORD;

typedef struct svector {
  WORD    *words;    /* Visual Studio 2012 says that WORD is ambiguous */

Where is the problem?

Hello,

I have a code for human detection, but it works not as good as I want. I read some articles that is possible to train this detector. I was searching for hour to find some good tutorials for training. Maybe you can help me.

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int main (int argc, const char * argv[])
{
    VideoCapture cap(CV_CAP_ANY);
    cap.set(CV_CAP_PROP_FRAME_WIDTH, 320);
    cap.set(CV_CAP_PROP_FRAME_HEIGHT, 240);    
    if (!cap.isOpened())
        return -1;

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

    namedWindow("video capture", CV_WINDOW_AUTOSIZE);
    while (true)
    {
        cap >> img;
        if (!img.data)
            continue;

        vector<Rect> found, found_filtered;
        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];
            r.x += cvRound(r.width*0.1);
        r.width = cvRound(r.width*0.8);
        r.y += cvRound(r.height*0.06);
        r.height = cvRound(r.height*0.9);
        rectangle(img, r.tl(), r.br(), cv::Scalar(0,255,0), 2);
    }
        imshow("video capture", img);
        if (waitKey(20) >= 0)
            break;
    }
    return 0;
}

Hello, Can anyone explaine me in simple words what these parameters:

0, Size(6,6), Size(32,32), 1.05, 2

in this function does?

do hog.detectMultiScale(img, found, 0, Size(6,6), Size(32,32), 1.05, 2);

Hello, I want to recognize and track human from viedo stream. What is the best way for this?

I want to recognize and detect people from video stream

Hi, I am new with OpenCV. I wrote a code for human full body detection, but it works not very well. Sometimes when I stay infront of camera, it does not recognise me. Maybe I done some mistakes in my code. I hope You understand me.

#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;

/** Function Headers */ 

void detectAndDisplay( Mat frame );

/** Global variables */ 
String body_cascade_name = "haarcascade_fullbody.xml";
CascadeClassifier body_cascade; 
string window_name = "Capture - Face detection"; 
RNG rng(12345);

/** @function main */
int main( int argc, const char** argv ) 
{ 
CvCapture* capture;
Mat frame;

//-- 1. Load the cascades 

if( !body_cascade.load( body_cascade_name ) )
{ 
    printf("--(!)Error loading\n"); return -1; 
};

//-- 2. Read the video stream 
capture = cvCaptureFromCAM( -1 ); 
cvSetCaptureProperty( capture, CV_CAP_PROP_FRAME_WIDTH, 180 );
cvSetCaptureProperty( capture, CV_CAP_PROP_FRAME_HEIGHT, 180 );

if( capture ) 
{ 
    while( true ) 
    { 
        frame = cvQueryFrame( capture );

        //-- 3. Apply the classifier to the frame 
        if( !frame.empty() ) 
        { 
            detectAndDisplay( frame ); 
        } else { 
            printf(" --(!) No captured frame -- Break!"); 
            break; 
        }
        int c = waitKey(100); 
        if( (char)c == 'c' ) 
        { 
            break; 
        } 
    } 
} 
return 0; 
}

/** @function detectAndDisplay */ 
void detectAndDisplay( Mat frame ) 
{ 
    vector<Rect> bodys;
    Mat frame_gray;

    cvtColor( frame, frame_gray, CV_BGR2GRAY ); 
    equalizeHist( frame_gray, frame_gray );
    //-- detect body */
    body_cascade.detectMultiScale(frame_gray, bodys, 1.1, 2, 18|9, Size(3,7));
    for( int j = 0; j < bodys.size(); j++ ) 
        { 
            Point center( bodys[j].x + bodys[j].width*0.5, bodys[j].y+ + bodys[j].height*0.5 ); 
            ellipse( frame, center, Size( bodys[j].width*0.5, bodys[j].height*0.5), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );
        } 
    imshow( window_name, frame ); 
}