11/05/2014

Opencv gpu MOG2_GPU example source code (background subtraction)



refer to example source code
I also have introduced other background subtraction method in here.
http://feelmare.blogspot.kr/2014/04/opencv-study-background-subtractor-mog.html

..
#include < time.h>
#include < opencv2\opencv.hpp>
#include < opencv2\gpu\gpu.hpp>
#include < string>
#include < stdio.h>


#ifdef _DEBUG        
#pragma comment(lib, "opencv_core249d.lib")
#pragma comment(lib, "opencv_imgproc249d.lib")   //MAT processing
#pragma comment(lib, "opencv_gpu249d.lib")
#pragma comment(lib, "opencv_highgui249d.lib")
#else
#pragma comment(lib, "opencv_core249.lib")
#pragma comment(lib, "opencv_imgproc249.lib")
#pragma comment(lib, "opencv_gpu249.lib")
#pragma comment(lib, "opencv_highgui249.lib")
#endif   


#define RWIDTH 800
#define RHEIGHT 600

using namespace std;
using namespace cv;

int main()
{
 /////////////////////////////////////////////////////////////////////////
 gpu::MOG2_GPU pMOG2_g(30);
 pMOG2_g.history = 3000; //300;
 pMOG2_g.varThreshold =64; //128; //64; //32;//; 
 pMOG2_g.bShadowDetection = true;
 Mat Mog_Mask;
 gpu::GpuMat Mog_Mask_g;
 /////////////////////////////////////////////////////////////////////////


 VideoCapture cap("C:\\videoSample\\tracking\\sample.avi");//0);
 /////////////////////////////////////////////////////////////////////////
 Mat o_frame;
 gpu::GpuMat o_frame_gpu;
 gpu::GpuMat r_frame_gpu;
 gpu::GpuMat rg_frame_gpu;
 gpu::GpuMat r_frame_blur_gpu;
 /////////////////////////////////////////////////////////////////////////

 cap >> o_frame;
 if( o_frame.empty() )
   return 0; 
 vector< gpu::GpuMat> gpurgb(3);
 vector< gpu::GpuMat> gpurgb2(3);
 /////////////////////////////////////////////////////////////////////////


 unsigned long AAtime=0, BBtime=0;

 //Mat rFrame;
 Mat showMat_r_blur;
 Mat showMat_r;

 while(1)
 {
  /////////////////////////////////////////////////////////////////////////
  cap >> o_frame;
  if( o_frame.empty() )
   return 0;

  
  o_frame_gpu.upload(o_frame);
  gpu::resize(o_frame_gpu, r_frame_gpu, Size(RWIDTH, RHEIGHT) );
  AAtime = getTickCount();
  

  gpu::split(r_frame_gpu, gpurgb);
  gpu::blur(gpurgb[0], gpurgb2[0], Size(3,3) );
  gpu::blur(gpurgb[1], gpurgb2[1], Size(3,3) );
  gpu::blur(gpurgb[2], gpurgb2[2], Size(3,3) );
  gpu::merge(gpurgb2, r_frame_blur_gpu);
  //
  pMOG2_g.operator()(r_frame_blur_gpu, Mog_Mask_g,-1);
  //
  Mog_Mask_g.download(Mog_Mask);

  BBtime = getTickCount(); 
  float pt = (BBtime - AAtime)/getTickFrequency(); 
  float fpt = 1/pt;
  printf("gpu %.4lf / %.4lf \n",  pt, fpt );

  
  r_frame_gpu.download(showMat_r);
  //rg_frame_gpu.download(showMat_rg);
  r_frame_blur_gpu.download(showMat_r_blur);
  imshow("origin", showMat_r);
  //imshow("gray", showMat_rg);
  imshow("blur", showMat_r_blur);
  imshow("mog_mask", Mog_Mask);
  
  
  /////////////////////////////////////////////////////////////////////////

  if( waitKey(10) > 0)
   break;
 }

 return 0;
}
..

Opencv gpu 3 channel blur example

There is no 3 channel blur in gpu function.

gpu::blur is support CV_8UC1 and CV_8UC4  channel only.
gpu::gaussianblur is also not suitable often.

So one of idea is split channel.
split 3 channel and perform blur function for each channel.
and then merge to a blur 3channel image.

this is faster than cpu code(lager image will be faster).

In my case, the process takes cpu :0.0126sec gpu:0.0035sec in 800x600 image.

refer to example source code.


...
//gpu case
gpu::resize(o_frame_gpu, r_frame_gpu, Size(RWIDTH, RHEIGHT) );
vector< gpu::GpuMat> gpurgb(3);
vector< gpu::GpuMat> gpurgb2(3);
gpu::split(r_frame_gpu, gpurgb);
gpu::blur(gpurgb[0], gpurgb2[0], Size(3,3) );
gpu::blur(gpurgb[1], gpurgb2[1], Size(3,3) );
gpu::blur(gpurgb[2], gpurgb2[2], Size(3,3) );
gpu::merge(gpurgb2, r_frame_blur_gpu);

//cpu case
resize(o_frame, rFrame, Size(RWIDTH, RHEIGHT) );
blur(rFrame, blurFrame, Size(3,3));



...


11/03/2014

opencv randn(...) example

opencv randn is like in matlab.

The randn function make values of normal distribution random

in matlab
randn is usage like this..

randn()
>> 0.4663

randn(10,1)'
>>   -0.1465    1.0143    0.4669    1.5750   -1.1900    0.2689   -0.2967   -0.4877    0.5671    0.5632

to use mean 5, variance 3
5+3*rand(10,1)
>> 6.2932   12.5907    6.6214    1.6941    4.8522    3.1484    6.1745    4.5230    5.2183    5.6888


OK, now consider case of OpenCV
We will make mean 10 and variance 2 normal distribution random values and fill in 2x10 matrix.


cv::Mat matrix2xN(2, 10, CV_32FC1);
 randn(matrix2xN, 10, 2);
 for (int i = 0; i < 10; ++i)
 {
  cout << matrix2xN.at< float>(0, i) << " ";
  cout << matrix2xN.at< float>(1, i) << endl;
 }

OpenCV EMD(earth mover distance) example source code

EMD(earth mover distance) method is very good method to compare image similarity.
But processing time is slow.
For using the EMD compare, we should make signature value.
The EMD method compares two signatures value.

Firstly, we prepare histograms of 2 images.
And convert values of histrogram to signature.

A configuration of signature values is very simple.

bins value, x index, y index.
bins value, x index, y index.
bins value, x index, y index.
bins value, x index, y index.
bins value, x index, y index.
....

Of course this type is in case of 2d histogram.
More detail, see the source code.

In here I cannot explain earth mover distance algorithm.
please refer to internet information.

thank you.


origin images
 
result


...
#include < iostream>
#include < vector>

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


#ifdef _DEBUG           
#pragma comment(lib, "opencv_core249d.lib")   
#pragma comment(lib, "opencv_imgproc249d.lib")   //MAT processing   
#pragma comment(lib, "opencv_highgui249d.lib")   
#else   
#pragma comment(lib, "opencv_core249.lib")   
#pragma comment(lib, "opencv_imgproc249.lib")      
#pragma comment(lib, "opencv_highgui249.lib")   
#endif   


using namespace cv;   
using namespace std;   
  
  
  
int main()   
{   

 //read 2 images for histogram comparing   
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////////   
 Mat imgA, imgB;   
 imgA = imread(".\\image1.jpg");   
 imgB = imread(".\\image2.jpg");   


 imshow("img1", imgA);
 imshow("img2", imgB);


 //variables preparing   
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////////   
 int hbins = 30, sbins = 32;    
 int channels[] = {0,  1};   
 int histSize[] = {hbins, sbins};   
 float hranges[] = { 0, 180 };   
 float sranges[] = { 0, 255 };   
 const float* ranges[] = { hranges, sranges};    

 Mat patch_HSV;   
 MatND HistA, HistB;   

 //cal histogram & normalization   
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////////   
 cvtColor(imgA, patch_HSV, CV_BGR2HSV);   
 calcHist( &patch_HSV, 1, channels,  Mat(), // do not use mask   
  HistA, 2, histSize, ranges,   
  true, // the histogram is uniform   
  false );   
 normalize(HistA, HistA,  0, 1, CV_MINMAX);   


 cvtColor(imgB, patch_HSV, CV_BGR2HSV);   
 calcHist( &patch_HSV, 1, channels,  Mat(),// do not use mask   
  HistB, 2, histSize, ranges,   
  true, // the histogram is uniform   
  false );   
 normalize(HistB, HistB, 0, 1, CV_MINMAX);   

 //compare histogram   
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////////   
 int numrows = hbins * sbins;

 //make signature
 Mat sig1(numrows, 3, CV_32FC1);
 Mat sig2(numrows, 3, CV_32FC1);

 //fill value into signature
 for(int h=0; h< hbins; h++)
 {
  for(int s=0; s< sbins; ++s)
  {
   float binval = HistA.at< float>(h,s);
   sig1.at< float>( h*sbins + s, 0) = binval;
   sig1.at< float>( h*sbins + s, 1) = h;
   sig1.at< float>( h*sbins + s, 2) = s;

   binval = HistB.at< float>(h,s);
   sig2.at< float>( h*sbins + s, 0) = binval;
   sig2.at< float>( h*sbins + s, 1) = h;
   sig2.at< float>( h*sbins + s, 2) = s;
  }
 }

 //compare similarity of 2images using emd.
 float emd = cv::EMD(sig1, sig2, CV_DIST_L2); //emd 0 is best matching. 
 printf("similarity %5.5f %%\n", (1-emd)*100 );
 
 waitKey(0);   

 return 0;   
}  

...