[22c804]: samples / tapi / hog.cpp  Maximize  Restore  History

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#include <iostream>
#include <fstream>
#include <string>
#include <sstream>
#include <iomanip>
#include <stdexcept>
#include <opencv2/core/ocl.hpp>
#include <opencv2/core/utility.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/objdetect.hpp>
#include <opencv2/imgproc.hpp>
using namespace std;
using namespace cv;
class App
{
public:
App(CommandLineParser& cmd);
void run();
void handleKey(char key);
void hogWorkBegin();
void hogWorkEnd();
string hogWorkFps() const;
void workBegin();
void workEnd();
string workFps() const;
string message() const;
// This function test if gpu_rst matches cpu_rst.
// If the two vectors are not equal, it will return the difference in vector size
// Else if will return
// (total diff of each cpu and gpu rects covered pixels)/(total cpu rects covered pixels)
double checkRectSimilarity(Size sz,
std::vector<Rect>& cpu_rst,
std::vector<Rect>& gpu_rst);
private:
App operator=(App&);
//Args args;
bool running;
bool make_gray;
double scale;
double resize_scale;
int win_width;
int win_stride_width, win_stride_height;
int gr_threshold;
int nlevels;
double hit_threshold;
bool gamma_corr;
int64 hog_work_begin;
double hog_work_fps;
int64 work_begin;
double work_fps;
string img_source;
string vdo_source;
string output;
int camera_id;
bool write_once;
};
int main(int argc, char** argv)
{
const char* keys =
"{ h help | false | print help message }"
"{ i input | | specify input image}"
"{ c camera | -1 | enable camera capturing }"
"{ v video | 768x576.avi | use video as input }"
"{ g gray | false | convert image to gray one or not}"
"{ s scale | 1.0 | resize the image before detect}"
"{ o output | | specify output path when input is images}";
CommandLineParser cmd(argc, argv, keys);
if (cmd.has("help"))
{
cout << "Usage : hog [options]" << endl;
cout << "Available options:" << endl;
cmd.printMessage();
return EXIT_SUCCESS;
}
App app(cmd);
try
{
app.run();
}
catch (const Exception& e)
{
return cout << "error: " << e.what() << endl, 1;
}
catch (const exception& e)
{
return cout << "error: " << e.what() << endl, 1;
}
catch(...)
{
return cout << "unknown exception" << endl, 1;
}
return EXIT_SUCCESS;
}
App::App(CommandLineParser& cmd)
{
cout << "\nControls:\n"
<< "\tESC - exit\n"
<< "\tm - change mode GPU <-> CPU\n"
<< "\tg - convert image to gray or not\n"
<< "\to - save output image once, or switch on/off video save\n"
<< "\t1/q - increase/decrease HOG scale\n"
<< "\t2/w - increase/decrease levels count\n"
<< "\t3/e - increase/decrease HOG group threshold\n"
<< "\t4/r - increase/decrease hit threshold\n"
<< endl;
make_gray = cmd.has("gray");
resize_scale = cmd.get<double>("s");
vdo_source = cmd.get<string>("v");
img_source = cmd.get<string>("i");
output = cmd.get<string>("o");
camera_id = cmd.get<int>("c");
win_width = 48;
win_stride_width = 8;
win_stride_height = 8;
gr_threshold = 8;
nlevels = 13;
hit_threshold = 1.4;
scale = 1.05;
gamma_corr = true;
write_once = false;
cout << "Group threshold: " << gr_threshold << endl;
cout << "Levels number: " << nlevels << endl;
cout << "Win width: " << win_width << endl;
cout << "Win stride: (" << win_stride_width << ", " << win_stride_height << ")\n";
cout << "Hit threshold: " << hit_threshold << endl;
cout << "Gamma correction: " << gamma_corr << endl;
cout << endl;
}
void App::run()
{
running = true;
VideoWriter video_writer;
Size win_size(win_width, win_width * 2);
Size win_stride(win_stride_width, win_stride_height);
// Create HOG descriptors and detectors here
HOGDescriptor hog(win_size, Size(16, 16), Size(8, 8), Size(8, 8), 9, 1, -1,
HOGDescriptor::L2Hys, 0.2, gamma_corr, cv::HOGDescriptor::DEFAULT_NLEVELS);
hog.setSVMDetector( HOGDescriptor::getDaimlerPeopleDetector() );
while (running)
{
VideoCapture vc;
UMat frame;
if (vdo_source!="")
{
vc.open(vdo_source.c_str());
if (!vc.isOpened())
throw runtime_error(string("can't open video file: " + vdo_source));
vc >> frame;
}
else if (camera_id != -1)
{
vc.open(camera_id);
if (!vc.isOpened())
{
stringstream msg;
msg << "can't open camera: " << camera_id;
throw runtime_error(msg.str());
}
vc >> frame;
}
else
{
imread(img_source).copyTo(frame);
if (frame.empty())
throw runtime_error(string("can't open image file: " + img_source));
}
UMat img_aux, img;
Mat img_to_show;
// Iterate over all frames
while (running && !frame.empty())
{
workBegin();
// Change format of the image
if (make_gray) cvtColor(frame, img_aux, COLOR_BGR2GRAY );
else frame.copyTo(img_aux);
// Resize image
if (abs(scale-1.0)>0.001)
{
Size sz((int)((double)img_aux.cols/resize_scale), (int)((double)img_aux.rows/resize_scale));
resize(img_aux, img, sz);
}
else img = img_aux;
img.copyTo(img_to_show);
hog.nlevels = nlevels;
vector<Rect> found;
// Perform HOG classification
hogWorkBegin();
hog.detectMultiScale(img.getMat(ACCESS_READ), found, hit_threshold, win_stride,
Size(0, 0), scale, gr_threshold);
hogWorkEnd();
// Draw positive classified windows
for (size_t i = 0; i < found.size(); i++)
{
Rect r = found[i];
rectangle(img_to_show, r.tl(), r.br(), Scalar(0, 255, 0), 3);
}
putText(img_to_show, "Mode: CPU", Point(5, 25), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
putText(img_to_show, "FPS (HOG only): " + hogWorkFps(), Point(5, 65), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
putText(img_to_show, "FPS (total): " + workFps(), Point(5, 105), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
imshow("opencv_hog", img_to_show);
if (vdo_source!="" || camera_id!=-1) vc >> frame;
workEnd();
if (output!="" && write_once)
{
if (img_source!="") // wirte image
{
write_once = false;
imwrite(output, img_to_show);
}
else //write video
{
if (!video_writer.isOpened())
{
video_writer.open(output, VideoWriter::fourcc('x','v','i','d'), 24,
img_to_show.size(), true);
if (!video_writer.isOpened())
throw std::runtime_error("can't create video writer");
}
if (make_gray) cvtColor(img_to_show, img, COLOR_GRAY2BGR);
else cvtColor(img_to_show, img, COLOR_BGRA2BGR);
video_writer << img.getMat(ACCESS_READ);
}
}
handleKey((char)waitKey(3));
}
}
}
void App::handleKey(char key)
{
switch (key)
{
case 27:
running = false;
break;
case 'm':
case 'M':
ocl::setUseOpenCL(!cv::ocl::useOpenCL());
cout << "Switched to " << (ocl::useOpenCL() ? "OpenCL enabled" : "CPU") << " mode\n";
break;
case 'g':
case 'G':
make_gray = !make_gray;
cout << "Convert image to gray: " << (make_gray ? "YES" : "NO") << endl;
break;
case '1':
scale *= 1.05;
cout << "Scale: " << scale << endl;
break;
case 'q':
case 'Q':
scale /= 1.05;
cout << "Scale: " << scale << endl;
break;
case '2':
nlevels++;
cout << "Levels number: " << nlevels << endl;
break;
case 'w':
case 'W':
nlevels = max(nlevels - 1, 1);
cout << "Levels number: " << nlevels << endl;
break;
case '3':
gr_threshold++;
cout << "Group threshold: " << gr_threshold << endl;
break;
case 'e':
case 'E':
gr_threshold = max(0, gr_threshold - 1);
cout << "Group threshold: " << gr_threshold << endl;
break;
case '4':
hit_threshold+=0.25;
cout << "Hit threshold: " << hit_threshold << endl;
break;
case 'r':
case 'R':
hit_threshold = max(0.0, hit_threshold - 0.25);
cout << "Hit threshold: " << hit_threshold << endl;
break;
case 'c':
case 'C':
gamma_corr = !gamma_corr;
cout << "Gamma correction: " << gamma_corr << endl;
break;
case 'o':
case 'O':
write_once = !write_once;
break;
}
}
inline void App::hogWorkBegin()
{
hog_work_begin = getTickCount();
}
inline void App::hogWorkEnd()
{
int64 delta = getTickCount() - hog_work_begin;
double freq = getTickFrequency();
hog_work_fps = freq / delta;
}
inline string App::hogWorkFps() const
{
stringstream ss;
ss << hog_work_fps;
return ss.str();
}
inline void App::workBegin()
{
work_begin = getTickCount();
}
inline void App::workEnd()
{
int64 delta = getTickCount() - work_begin;
double freq = getTickFrequency();
work_fps = freq / delta;
}
inline string App::workFps() const
{
stringstream ss;
ss << work_fps;
return ss.str();
}

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