I'm experimenting people detector with opencv and HOGDescriptor c++ object: HOGDescriptor::getDefaultPeopleDetector(). Using the sample program peopledetect.cpp in the sample/cpp directory of the Opencv 2.4.3 repository and testing it against some of the INRIA dataset images.. it works quite well.
Now I want to try with some images I have to work with and, even if I try to change parameters.. it doesn't find anything.
I suppose it is because of the pedestrian in the image I have are much more smaller then the INRIA ones. So it should be better to train a new detector but before doing it..
Here my question:
Is it right? Is there a strict relationship between the images used for training and the detected ones? That means that HOG detector is not really scale invariant method..
In particular, what is the best size of the default HOGDescriptor::getDefaultPeopleDetector() ? Do I have to train a new detector for detect much smaller people?
Here is the peopledetect.cpp I'm using:
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <stdio.h>
#include <string.h>
#include <ctype.h>
#include <iostream>
using namespace cv;
using namespace std;
// static void help()
// {
// printf(
// "\nDemonstrate the use of the HoG descriptor using\n"
// " HOGDescriptor::hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());\n"
// "Usage:\n"
// "./peopledetect (<image_filename> | <image_list>.txt)\n\n");
// }
int main(int argc, char** argv)
{
std::cout << "OPENCV version: " << CV_MAJOR_VERSION << " " << CV_MINOR_VERSION << std::endl;
Mat img;
FILE* f = 0;
char _filename[1024];
if( argc == 1 )
{
printf("Usage: peopledetect (<image_filename> | <image_list>.txt)\n");
return 0;
}
img = imread(argv[1]);
if( img.data )
{
strcpy(_filename, argv[1]);
}
else
{
f = fopen(argv[1], "rt");
if(!f)
{
fprintf( stderr, "ERROR: the specified file could not be loaded\n");
return -1;
}
}
HOGDescriptor hog;
hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
namedWindow("people detector", 1);
for(;;)
{
char* filename = _filename;
if(f)
{
if(!fgets(filename, (int)sizeof(_filename)-2, f))
break;
//while(*filename && isspace(*filename))
// ++filename;
if(filename[0] == '#')
continue;
int l = (int)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).
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());
std::cout << "found: " << found.size() << std::endl;
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);
int c = waitKey(0) & 255;
if( c == 'q' || c == 'Q' || !f)
break;
}
if(f)
fclose(f);
return 0;
}
