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

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  
#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")  

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

 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);  

  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()));

  namedWindow("test", 0);
  imshow("test", img);


Lenet model was used to test the deep learning classification.
Many other models are introduced on github 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).


  1. Hii, can you post the training process? I have some problems to set training parameters

    1. I plan to post the code about learning method of deep learning.
      However, it is difficult for the parameter settings.
      because it require understanding of deep learning algorithm.
      I think tensor flow is good to study deep learning.
      I plan to post later about tensor flow.

      Can you open your learning data and parameter?
      Then I also try learning.

      Thank you.

  2. ์•ˆ๋…•ํ•˜์„ธ์š”? ๋ธ”๋กœ๊ทธ ๋ฐ openCV๊ฐ•์ขŒ ์œ ์šฉํ•˜๊ฒŒ ์ž˜๋ณด๊ณ ์žˆ์Šต๋‹ˆ๋‹ค!! ๋‹ค๋ฆ„์ด์•„๋‹ˆ๋ผ caffe์„ค์น˜์— ์˜๋ฌธ์ ์ด ์žˆ์Šต๋‹ˆ๋‹ค. ์ผ๋‹จ github์—์„œ ๊ตฌ์„ฑ์š”์†Œ๋“ค์„ ๋ฐ›์•˜๋Š”๋ฐ caffe๋ฅผ ์ถ”๊ฐ€ํ•˜๋ ค๋ฉด opencv์„ค์น˜ํ• ๋•Œ ์‚ฌ์šฉํ•œ cmakeํ”„๋กœ๊ทธ๋žจ์—์„œ ๊ฒฝ๋กœ์ง€์ •ํ•ด์ฃผ๊ณ  ๋˜‘๊ฐ™์ด ์ง„ํ–‰ํ•˜๋ฉด ๋˜๋‚˜์š”? ๊ทธ๋ฆฌ๊ณ  include ๋””๋ ‰ํ† ๋ฆฌ๋Š” ๋ฐ”๋กœ ์ฐพ์•˜๋Š”๋ฐ caffe_libs๋Š” ๋ฌด์Šจ ํŒŒ์ผ๋กœ ์ง€์ •ํ•ด์ค˜์•ผํ• ์ง€ ๋ชจ๋ฅด๊ฒ ๋„ค์š”.. ์•„๋ฌด์ชผ๋ก ๋‹ต๋ณ€๊ธฐ๋‹ค๋ฆฌ๊ฒ ์Šต๋‹ˆ๋‹ค.

  3. ์•ˆ๋…•ํ•˜์„ธ์š”. ์ €๋„ opencv ๋นŒ๋“œํ• ๋•Œ caffe๋ฅผ ๋„ฃ์–ด์„œ ํ•ด๋ณธ ์ ์ด ์—†์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ๋ฐ˜๋Œ€๋กœ caffe ๋นŒ๋“œํ• ๋•Œ, opencv๋ฅผ ํฌํ•จ์‹œ์ผœ์„œ ๋นŒ๋“œํ•œ ์ ์€ ์žˆ์Šต๋‹ˆ๋‹ค.
    ๊ทธ๋•Œ ๋‚จ๊ธด ๊ธ€์€ http://study.marearts.com/2015/10/deep-learning-library-to-work-easily.html, ์ฐธ๊ณ ํ•˜์„ธ์š”.
    ์—ฌ๊ธฐ https://initialneil.wordpress.com ์†Œ๊ฐœ๋œ ๋ฐฉ๋ฒ•์„ ๋”ฐ๋ผ ํ–ˆ์Šต๋‹ˆ๋‹ค.
    ๊ทธ๋Ÿฐ๋ฐ ์ด๊ฒŒ 2015๋…„ ์ž๋ฃŒ๋ผ์„œ ๋” ์ตœ์‹  ์ž๋ฃŒ๋ฅผ ์ฐพ์•„ ๋ณด๋Š”๊ฒŒ ์ข‹์„ ๊ฒƒ ๊ฐ™์Šต๋‹ˆ๋‹ค.
    ์ €๋„ opencv์— caffe๋ฅผ ๋„ฃ์–ด์„œ ๋นŒ๋“œ๋ฅผ ํ•ด๋ณด๊ณ  ์‹ถ์Šต๋‹ˆ๋‹ค.
    ์„ฑ๊ณตํ•˜๋ฉด ์ ˆ์ฐจ๋ฅผ ๋ธ”๋กœ๊ทธ์— ์†Œ๊ฐœํ•˜๊ฒ ์Šต๋‹ˆ๋‹ค.
    changhun ๋‹˜๋„ ์ข‹์€ ์ •๋ณด ์žˆ์œผ๋ฉด ์•Œ๋ ค์ฃผ์„ธ์š”.