More stringent pedestrian detectionby Deep learning verification

More stringent pedestrian detection by HOG verification

Multi Objects tracking, first result

Pedestrian Detection by rough classifier.

Deep learning Library to work easily with opencv on window environment.

I was wrapping caffe library to use easy with opencv and window os environment(named MareDeepDLL).

MareDeepDll is referenced on
http://caffe.berkeleyvision.org/installation.html
https://initialneil.wordpress.com/


The dll is made on this environment
Window 10 64bit,
VS 2013 64bit
OpenCV 3.0 64bit
cuda 6.5 64bit
tbb 64bit
..
All dependency is as follows:

This code is example to use the dll
...

#include < iostream>  
#include < stdio.h>
#include < vector>
#include < time.h>

#include <  opencv2\opencv.hpp>    
#include <  opencv2\core.hpp>  
#include <  opencv2\highgui.hpp>   
#include <  opencv2\videoio.hpp>    
#include <  opencv2\imgproc.hpp>  



#include "DeepDll_B.h"

#ifdef _DEBUG            
#pragma comment(lib, "opencv_core300d.lib")  
#pragma comment(lib, "opencv_highgui300d.lib")    
#pragma comment(lib, "opencv_imgcodecs300d.lib")  
#pragma comment(lib, "opencv_imgproc300d.lib") //line, circle  
#else    
#pragma comment(lib, "opencv_core300.lib")  
#pragma comment(lib, "opencv_highgui300.lib")  
#pragma comment(lib, "opencv_imgcodecs300.lib")  
#pragma comment(lib, "opencv_imgproc300.lib") //line, circle  

//DEEP lib
#pragma comment(lib, "MareDeepDLL.lib")  
#endif   

using namespace cv;
using namespace std;

void main()
{

 //DEEP Class
 MareDeepDll_B cDeep;

 //load model and structure
 cDeep.SetNet("lenet_test-memory-1.prototxt", "lenet_iter_10000.caffemodel");
 //gpu using on
 cDeep.GPU_using();
 

 for (int i = 1; i <  14; ++i)
 {
  // time check..
  unsigned long AAtime = 0, BBtime = 0;
  AAtime = getTickCount();


  //make file name
  char str[256];  
  sprintf_s(str, "%d.jpg", i);
  printf("%s\n", str);
  

  //img load and preprocessing
  Mat img = imread(str);
  resize(img, img, Size(28, 28));
  cvtColor(img, img, CV_BGR2GRAY);  


  ////////////
  //classify
  vector< double> rV;
  //image and class num (caution!! class num is dependented by learning condition.) lenet is classify one number in 10 digits.
  rV = cDeep.eval(img, 10);
  /////////////

  //result out
  for (int i = 0; i <  rV.size(); i++) {
   printf("Probability to be Number %d is %.3f\n", i, rV[i]);   
  }

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

  //draw
  namedWindow("test", 0);
  imshow("test", img);
  waitKey(0);
 }

}

...
Lenet model was used to test the deep learning classification.
Many other models are introduced on github model zoo.
https://github.com/BVLC/caffe/wiki/Model-Zoo
You can apply other case, on code cDeep.SetNet("lenet_test-memory-1.prototxt", "lenet_iter_10000.caffemodel"); , first param means model structure and second param means the result of deep learning.

If you request to Google Plus to me, I will send the dll with the application code(project).