How to run *.run file in Linux ? (simple tip)

Just change mode
and run!
'./' means sh command


# chmod a+x install_test.run
# ./install_test.run

Linux is not easy to me.....

Mac computers that use OpenCL and OpenGL graphics

link to
https://support.apple.com/en-us/HT202823

There is no product use nvidia graphic card now(2015.12).
So apple's computer can not use cuda library.

Many machine learning related library use CUDA. cuDNN, cuBlas..
Now I use MAC book pro 2014 retina 15'' that embedded geforce gt 750m.
But I have to concern whether I buy apple notebook or not for next notebook.

I like apple products. but for my study, I need nvidia graphic card for using cuda.

Opencv Condensation(particle filter) example source code

Dear Jihad anwar

Here is source code you asking.
I don't remember equation about particle filter well now.
But source code is still run well. ^^
I hope to help your study.

Note. Opencv 2.4.9 and 32 bit.

Thank you.

#include < iostream>
#include < vector>

#include <  stdio.h>      
#include <  opencv2\opencv.hpp>    
#include < opencv2\legacy\legacy.hpp>

#ifdef _DEBUG           
#pragma comment(lib, "opencv_core249d.lib")   
#pragma comment(lib, "opencv_imgproc249d.lib")   //MAT processing   
#pragma comment(lib, "opencv_objdetect249d.lib") //HOGDescriptor   
//#pragma comment(lib, "opencv_gpu249d.lib")   
//#pragma comment(lib, "opencv_features2d249d.lib")   
#pragma comment(lib, "opencv_highgui249d.lib")   
#pragma comment(lib, "opencv_ml249d.lib")   
//#pragma comment(lib, "opencv_stitching249d.lib");   
//#pragma comment(lib, "opencv_nonfree249d.lib");   
#pragma comment(lib, "opencv_video249d.lib")
#pragma comment(lib, "opencv_legacy249d.lib")
#else   
#pragma comment(lib, "opencv_core249.lib")   
#pragma comment(lib, "opencv_imgproc249.lib")   
#pragma comment(lib, "opencv_objdetect249.lib")   
//#pragma comment(lib, "opencv_gpu249.lib")   
//#pragma comment(lib, "opencv_features2d249.lib")   
#pragma comment(lib, "opencv_highgui249.lib")   
#pragma comment(lib, "opencv_ml249.lib")   
//#pragma comment(lib, "opencv_stitching249.lib");   
//#pragma comment(lib, "opencv_nonfree249.lib");   
#pragma comment(lib, "opencv_video249d.lib")   
#endif   



using namespace cv;
using namespace std;

// (1) functions for calculating the likelihood
float calc_likelihood (IplImage * img, int x, int y)
{
 float b, g, r;
 float dist = 0.0, sigma = 50.0;

 b = img->imageData[img->widthStep * y + x * 3];       //B
 g = img->imageData[img->widthStep * y + x * 3 + 1];   //G
 r = img->imageData[img->widthStep * y + x * 3 + 2];   //R
 dist = sqrt (b * b + g * g + (255.0 - r) * (255.0 - r));

 return 1.0 / (sqrt (2.0 * CV_PI) * sigma) * expf (-dist * dist / (2.0 * sigma * sigma));
}

void ProccTimePrint( unsigned long Atime , string msg)   
{   
 unsigned long Btime=0;   
 float sec, fps;   
 Btime = getTickCount();   
 sec = (Btime - Atime)/getTickFrequency();   
 fps = 1/sec;   
 printf("%s %.4lf(sec) / %.4lf(fps) \n", msg.c_str(),  sec, fps );   
}   



int main ()
{

 float TakeTime;   
 unsigned long Atime, Btime;   

 int i, c;
 double w = 0.0, h = 0.0;
 CvCapture *capture = 0;
 IplImage *frame = 0;

 int n_stat = 4;
 int n_particle = 1000;
 vector<  float > vx(n_particle);
 vector<  float > vy(n_particle);

 CvConDensation *cond = 0;
 CvMat *lowerBound = 0;
 CvMat *upperBound = 0;

 int xx, yy;

 // (2)you want to create a capture structure with respect to the camera with the specified number by the command argument
 
 capture = cvCreateCameraCapture(0);

 // (3)The one frame captured, and obtains the capture size.
 frame = cvQueryFrame (capture);
 w = frame->width;
 h = frame->height;

 cvNamedWindow ("Condensation", CV_WINDOW_AUTOSIZE);

 // (4)Condensation create a structure.
 cond = cvCreateConDensation (n_stat, 0, n_particle);

 // (5) it will specify the minimum and maximum values of the state vector can be taken for each dimension.
 lowerBound = cvCreateMat (4, 1, CV_32FC1);
 upperBound = cvCreateMat (4, 1, CV_32FC1);

 cvmSet (lowerBound, 0, 0, 0.0);
 cvmSet (lowerBound, 1, 0, 0.0);
 cvmSet (lowerBound, 2, 0, -10); //-10.0);
 cvmSet (lowerBound, 3, 0, -10); //-10.0);
 cvmSet (upperBound, 0, 0, w);
 cvmSet (upperBound, 1, 0, h);
 cvmSet (upperBound, 2, 0, 10); //10.0);
 cvmSet (upperBound, 3, 0, 10); //10.0);

 // (6)Condensation Initialize the structure
 cvConDensInitSampleSet (cond, lowerBound, upperBound);

 // (7)ConDensation Specify the dynamics of the state vector in the algorithm
 cond->DynamMatr[0] = 1.0;
 cond->DynamMatr[1] = 0.0;
 cond->DynamMatr[2] = 1.0;
 cond->DynamMatr[3] = 0.0;
 cond->DynamMatr[4] = 0.0;
 cond->DynamMatr[5] = 1.0;
 cond->DynamMatr[6] = 0.0;
 cond->DynamMatr[7] = 1.0;
 cond->DynamMatr[8] = 0.0;
 cond->DynamMatr[9] = 0.0;
 cond->DynamMatr[10] = 1.0;
 cond->DynamMatr[11] = 0.0;
 cond->DynamMatr[12] = 0.0;
 cond->DynamMatr[13] = 0.0;
 cond->DynamMatr[14] = 0.0;
 cond->DynamMatr[15] = 1.0;

 // (8)re-set the noise parameters.

 while (1) {
  frame = cvQueryFrame (capture);
  Atime = getTickCount(); //시작 시간 
  float a=0,b=0,c=0,d=0,e=0;

  // (9) It calculates the likelihood for each particle.
  for (i = 0; i <  n_particle; i++) {
   xx = (int) (cond->flSamples[i][0]);
   yy = (int) (cond->flSamples[i][1]);

   vx[i] = cond->flSamples[i][0];
   vy[i] = cond->flSamples[i][1];


   if (xx <  0 || xx >= w || yy <  0 || yy >= h) {
    cond->flConfidence[i] = 0.0;
   }
   else {
    cond->flConfidence[i] = calc_likelihood (frame, xx, yy);
    cvCircle (frame, cvPoint (xx, yy), 1, CV_RGB (0, 0, 255), -1);   
    
   }
  }

  
  // (10)  estimate the state of the next model
  cvConDensUpdateByTime (cond);
  printf("crrection \n");
  
  ProccTimePrint( Atime , "time :"); //처리시간 출력  

  cv::Point statePt(cond->State[0], cond->State[1]);
  cvCircle (frame, statePt, 5, CV_RGB (255, 255, 255), 5);  
  printf("-----------\n");
  
  cvShowImage ("Condensation", frame);
  
  if (cvWaitKey (10) > 10 )
   break;

 }

 cvDestroyWindow ("Condensation");

 cvReleaseCapture (&capture);
 cvReleaseConDensation (&cond);
 cvReleaseMat (&lowerBound);
 cvReleaseMat (&upperBound);

 return 0;
}

Opencv Hog+SVM example of pedestrian detection

source code

#include < iostream>    
#include "opencv2\objdetect\objdetect.hpp"
#include "opencv2\highgui\highgui.hpp"
#include "opencv2\imgproc\imgproc.hpp"
#include "opencv2\cudaobjdetect.hpp"
#include "opencv2\cudaimgproc.hpp"
#include "opencv2\cudawarping.hpp"


#ifdef _DEBUG               
#pragma comment(lib, "opencv_core300d.lib")       
#pragma comment(lib, "opencv_highgui300d.lib")    
#pragma comment(lib, "opencv_imgcodecs300d.lib")  
#pragma comment(lib, "opencv_objdetect300d.lib")  
#pragma comment(lib, "opencv_imgproc300d.lib")  
#pragma comment(lib, "opencv_videoio300d.lib")    
#pragma comment(lib, "opencv_cudaobjdetect300d.lib")  
#pragma comment(lib, "opencv_cudawarping300d.lib")
#pragma comment(lib, "opencv_cudaimgproc300d.lib")

#else       
#pragma comment(lib, "opencv_core300.lib")       
#pragma comment(lib, "opencv_highgui300.lib")    
#pragma comment(lib, "opencv_imgcodecs300.lib")    
#pragma comment(lib, "opencv_objdetect300.lib")  
#pragma comment(lib, "opencv_imgproc300.lib")  
#pragma comment(lib, "opencv_videoio300.lib")    
#pragma comment(lib, "opencv_cudaobjdetect300.lib")
#pragma comment(lib, "opencv_cudawarping300.lib")
#pragma comment(lib, "opencv_cudaimgproc300.lib")
#endif        

using namespace std;
using namespace cv;


void main()
{


 cv::Ptr< cv::cuda::HOG> d_hog = cv::cuda::HOG::create(Size(48, 96)); //Size(64, 128));// 
 d_hog->setSVMDetector(d_hog->getDefaultPeopleDetector());
 

 //video loading
 Mat img; 
 VideoCapture cap("M:\\____videoSample____\\tracking\\TownCentreXVID.avi");

 //loading test
 cap >> img;
 if (img.empty())
  return;

 //window
 namedWindow("pedestrian", 0);

 //processing time check
 unsigned long AAtime = 0, BBtime = 0;
 
 //resize
 double scale = float(800) / img.cols;
 cuda::GpuMat GpuImg, rGpuImg;
 GpuImg.upload(img);
 cuda::resize(GpuImg, rGpuImg, Size(GpuImg.cols * scale, GpuImg.rows * scale));
 Mat rInimg;
 rGpuImg.download(rInimg);

 while (1)
 {
  //time check
  AAtime = getTickCount();

  //loading
  cap >> img;
  if (img.empty())
   break;

  //resize
  GpuImg.upload(img);
  cuda::resize(GpuImg, rGpuImg, Size(GpuImg.cols * scale, GpuImg.rows * scale));
  rGpuImg.download(rInimg);
  cuda::cvtColor(rGpuImg, rGpuImg, CV_BGR2GRAY);
  
  
  vector< Point> found_locations;
  d_hog->detect(rGpuImg, found_locations);

  std::vector< cv::Rect> found_locations_rect;
  d_hog->detectMultiScale(rGpuImg, found_locations_rect);


  for (int i = 0; i < found_locations_rect.size(); ++i)
  {

   cv::rectangle(rInimg, found_locations_rect[i], CvScalar(0, 0, 255), 1);   
  }


  imshow("pedestrian", rInimg);
  waitKey(10);

  //time check
  BBtime = getTickCount();
  double s_time = (BBtime - AAtime) / getTickFrequency();
  double fps_time = 1/s_time;
  printf("%.2lf sec / %.2lf fps \n", s_time, fps_time); 

 }


}

illegal trash dumping detection (test1)