#include <iostream>
#include <fstream>
#include <string>
#include <sstream>
#include <iomanip>
#include <stdexcept>
class App
{
public:
void run();
void handleKey(char key);
void hogWorkBegin();
void hogWorkEnd();
string hogWorkFps() const;
void workBegin();
void workEnd();
string workFps() const;
private:
App operator=(App&);
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;
double hog_work_fps;
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 | | print help message }"
"{ i input | | specify input image}"
"{ c camera | -1 | enable camera capturing }"
"{ v video | vtest.avi | use video as input }"
"{ g gray | | convert image to gray one or not}"
"{ s scale | 1.0 | resize the image before detect}"
"{ o output | output.avi | specify output path when input is images}";
if (cmd.has("help"))
{
cmd.printMessage();
return EXIT_SUCCESS;
}
App app(cmd);
try
{
app.run();
}
{
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;
}
{
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 = samples::findFileOrKeep(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;
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;
Size win_size(win_width, win_width * 2);
Size win_stride(win_stride_width, win_stride_height);
HOGDescriptor hog(win_size,
Size(16, 16),
Size(8, 8),
Size(8, 8), 9, 1, -1,
hog.setSVMDetector( HOGDescriptor::getDaimlerPeopleDetector() );
while (running)
{
if (vdo_source!="")
{
vc.
open(vdo_source.c_str());
throw runtime_error(string("can't open video file: " + vdo_source));
vc >> frame;
}
else if (camera_id != -1)
{
{
stringstream msg;
msg << "can't open camera: " << camera_id;
throw runtime_error(msg.str());
}
vc >> frame;
}
else
{
throw runtime_error(string("can't open image file: " + img_source));
}
UMat img_aux, img, img_to_show;
while (running && !frame.
empty())
{
workBegin();
if (make_gray)
cvtColor(frame, img_aux, COLOR_BGR2GRAY );
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, 0, 0, INTER_LINEAR_EXACT);
}
else img = img_aux;
hog.nlevels = nlevels;
vector<Rect> found;
hogWorkBegin();
hog.detectMultiScale(img, found, hit_threshold, win_stride,
Size(0, 0), scale, gr_threshold);
hogWorkEnd();
for (size_t i = 0; i < found.size(); i++)
{
}
putText(img_to_show, ocl::useOpenCL() ?
"Mode: OpenCL" :
"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!="")
{
write_once = false;
}
else
{
{
video_writer.
open(output, VideoWriter::fourcc(
'x',
'v',
'i',
'd'), 24,
img_to_show.
size(),
true);
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;
}
}
}
}
}
void App::handleKey(char key)
{
switch (key)
{
case 27:
running = false;
break;
case 'm'
case 'M'
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'
cout <<
"Scale: " <<
scale << endl;
break;
case 'q'
case 'Q'
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()
{
}
inline void App::hogWorkEnd()
{
hog_work_fps = freq / delta;
}
inline string App::hogWorkFps() const
{
stringstream ss;
ss << hog_work_fps;
return ss.str();
}
inline void App::workBegin()
{
}
inline void App::workEnd()
{
work_fps = freq / delta;
}
inline string App::workFps() const
{
stringstream ss;
ss << work_fps;
return ss.str();
}
如果陣列沒有元素,則返回 true。
int64_t int64
T get(const String &name, bool space_delete=true) const
按名稱訪問引數。
定義 utility.hpp:956
bool has(const String &name) const
檢查命令列中是否提供了欄位。
virtual const char * what() const noexcept override
void copyTo(OutputArray m) const
將矩陣複製到另一個矩陣。
用於指定影像或矩形大小的模板類。
Definition types.hpp:335
int cols
number of columns in the matrix; -1 when the matrix has more than 2 dimensions
Definition mat.hpp:2650
MatSize size
矩陣的維度大小;可透過各種格式訪問
Definition mat.hpp:2671
int rows
number of rows in the matrix; -1 when the matrix has more than 2 dimensions
Definition mat.hpp:2647
cv::getTickFrequency
如果矩陣資料為 NULL,則返回 true
void copyTo(OutputArray m) const
將矩陣內容複製到“m”。
Class for video capturing from video files, image sequences or cameras.
Definition videoio.hpp:772
virtual bool open(const String &filename, int apiPreference=CAP_ANY)
Opens a video file or a capturing device or an IP video stream for video capturing.
virtual bool isOpened() const
如果影片捕獲已初始化,則返回 true。
影片寫入類。
定義 videoio.hpp:1071
virtual bool open(const String &filename, int fourcc, double fps, Size frameSize, bool isColor=true)
初始化或重新初始化影片寫入器。
virtual bool isOpened() const
如果影片寫入器已成功初始化,則返回 true。
cv::getTickCount
int64 getTickCount()
void imshow(const String &winname, InputArray mat)
在指定視窗中顯示影像。
int waitKey(int delay=0)
等待按鍵按下。
CV_EXPORTS_W bool imwrite(const String &filename, InputArray img, const std::vector< int > ¶ms=std::vector< int >())
將影像儲存到指定檔案。
CV_EXPORTS_W Mat imread(const String &filename, int flags=IMREAD_COLOR_BGR)
從檔案載入影像。
void cvtColor(InputArray src, OutputArray dst, int code, int dstCn=0, AlgorithmHint hint=cv::ALGO_HINT_DEFAULT)
將影像從一個顏色空間轉換為另一個顏色空間。
void rectangle(InputOutputArray img, Point pt1, Point pt2, const Scalar &color, int thickness=1, int lineType=LINE_8, int shift=0)
繪製一個簡單、粗或填充的矩形。
void putText(InputOutputArray img, const String &text, Point org, int fontFace, double fontScale, Scalar color, int thickness=1, int lineType=LINE_8, bool bottomLeftOrigin=false)
繪製文字字串。
int main(int argc, char *argv[])
定義 highgui_qt.cpp:3
void scale(cv::Mat &mat, const cv::Mat &range, const T min, const T max)
Definition quality_utils.hpp:90
Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector.
定義 objdetect.hpp:403
@ DEFAULT_NLEVELS
預設 nlevels 值。
Definition objdetect.hpp:407