DBoW2在windows上的vs工程搭建方法

2023-05-16

注释：

蓝体字是opencv249专用的修改；

黑体字是opencv3需要的修改

环境：

vs2012 32bit

叙述比较简略。不明之处可以留言

1. 配置opencv 2.4.9

需要surf支持，应该可以用orb替换

（本方法orb替换后仍然有pFirstBlock == pHead错误，建议使用opencv3）

（或者是能够使用extra module，cmake成功生成lib应该也可以）

#1.配置opencv3.0.0/3.1.0/3.2.0等

如果需要使用surf，在opencv的cmake过程需要包含opencv的extra module（非免费开放的库）

2. CMAKE DLib

需要opencv的支持

需要boost库支持（git）

生成debug和release的静态库，类型Mtd/Mt

3. 新建vs工程

4.移动文件

将DBoW2-master 中的src文件夹下的cpp文件包含进工程

将demo代码添加进来（main函数）

5.包含文件

头文件包含：

DBoW2-master的include

DLib-master的include

Opencv的include

根据编译结果灵活修改

库文件包含：

DLib-master的cmake出来的lib

Opencv的lib（opencv3以后需要自己cmake，或者是使用opencv_worldxxx.lib）可以参考opencv的配置。

6.修改代码

#1添加预编译头 #include”stdfx.h”

#2 修改FORB.cpp

void FORB::toMat8U(const std::vector<TDescriptor>&descriptors, cv::Mat &mat)

std::copy(d, d + FORB::L, p);

改成

std::copy(d, d + FORB::L,stdext::unchecked_array_iterator<unsigned char*>(p));

#3将demo中的image文件夹放到新建的vs工程中。

7.opencv3适用的main函数见9

修改main函数（opencv249版本，（pFirstBlock == pHead错误）有时候会发生）

loadFeatures

voidloadFeatures(vector<vector<vector<float> > > &features)

{

features.clear();

features.reserve(NIMAGES);

///cv::Ptr<cv::xfeatures2d::SURF>surf = cv::xfeatures2d::SURF::create(400, 4, 2, EXTENDED_SURF);// chenhui

cv::SURF surf( 400, 4, 2, EXTENDED_SURF );//chenhui

cout << "Extracting SURFfeatures..." << endl;

for(int i = 0; i < NIMAGES; ++i)

{

stringstream ss;

ss << "images/image"<< i << ".png";

cv::Mat image = cv::imread(ss.str(), 0);

cv::Mat mask;

vector<cv::KeyPoint> keypoints;

vector<float> descriptors;

/surf->detectAndCompute(image,mask, keypoints, descriptors); /// chenhui

///surf.compute(image, mask,keypoints, descriptors); /// chenhui

surf.operator()(image, mask, keypoints,descriptors);

features.push_back(vector<vector<float> >());

changeStructure(descriptors,features.back(), surf.descriptorSize());

}

8. 总体属性

#1属性 -> C/C++ -> 代码生成-> 代码生成：Mtd/Mt（dubug和release两个版本）；

对于opencv249，C/C++ 命令行->其他选项，输入（/D "_AFXDLL"）可能可以解决（pFirstBlock == pHead错误）

9. demo的代码

/**
 * File: Demo.cpp
 * Date: November 2011
 * Author: Dorian Galvez-Lopez
 * Description: demo application of DBoW2
 * License: see the LICENSE.txt file
 */

/**
 * This is a modified version of TemplatedVocabulary.h from DBoW2 (see below).
 * Added functions: Save and Load from text files without using cv::FileStorage.
 * Date: August 2015
 * Raúl Mur-Artal
 */

/*
修改后的 DBow2 demo 
修改内容：
	#1 使用orb提取特征
	#2 需要使用由 Raúl Mur-Artal 修改的 TemplatedVocabulary.h （在orb_slam2的工程中提供）
	#3 包含生成词袋和测试词袋的代码
修改时间：2017.03.22
修改说明：
	#1 生成的词袋没有英文的提示信息，全部都是数据。
	如果看过voc.save和voc.saveToTextFile 生成的文件，就知道这两个结果的差异就是save出来的含有英文的注释，而saveToTextFile是纯数据。
	恰恰orb_slam2中所使用的词袋是 saveToTextFile 出来的，使用save的结果就读不了了（原因显而易见）。
*/

#include "stdafx.h"
#include <iostream>
#include <vector>

// DBoW2
#include "DBoW2.h" // defines Surf64Vocabulary and Surf64Database
#include <DUtils/DUtils.h>
#include <DVision/DVision.h>


// OpenCV
#include "opencv2/stitching/detail/matchers.hpp"
#include <opencv2/opencv.hpp>


// 调整到何时的路径，或者使用其他的方法加载静态库
#pragma comment( lib, "vfw32.lib" )   
#pragma comment( lib, "comctl32.lib" ) 
#pragma comment( lib, "C:\\ippicvmt.lib" ) 

#if 1
// debug
#pragma comment( lib, "IlmImfd.lib" ) 
#pragma comment( lib, "libjasperd.lib" )
#pragma comment( lib, "libjpegd.lib" )
#pragma comment( lib, "libpngd.lib" )
#pragma comment( lib, "libtiffd.lib" )
#pragma comment( lib, "libwebpd.lib" )
#pragma comment( lib, "zlibd.lib" )
#pragma comment( lib, "opencv_core300d.lib" )
#pragma comment( lib, "opencv_highgui300d.lib" )
#pragma comment( lib, "opencv_imgproc300d.lib" )
#pragma comment( lib, "opencv_calib3d300d.lib" )
#pragma comment( lib, "opencv_features2d300d.lib" ) 
#pragma comment( lib, "opencv_flann300d.lib" )
#pragma comment( lib, "opencv_imgcodecs300d.lib" )
#pragma comment( lib, "opencv_objdetect300d.lib" )
#pragma comment( lib, "opencv_hal300d.lib" )
#pragma comment( lib, "opencv_photo300d.lib" )
#pragma comment( lib, "opencv_shape300d.lib" )
#pragma comment( lib, "opencv_superres300d.lib" )
#pragma comment( lib, "opencv_video300d.lib" )
#pragma comment( lib, "opencv_stitching300d.lib" )
#pragma comment( lib, "opencv_videoio300d.lib" )


#else
// release
#pragma comment( lib, "C:\\IlmImf.lib" )
#pragma comment( lib, "C:\\libjasper.lib" )
#pragma comment( lib, "C:\\libjpeg.lib" )
#pragma comment( lib, "C:\\libpng.lib" )
#pragma comment( lib, "C:\\libtiff.lib" )
#pragma comment( lib, "C:\\libwebp.lib" )
#pragma comment( lib, "C:\\zlib.lib" )
#pragma comment( lib, "C:\\opencv_core300.lib" )
#pragma comment( lib, "C:\\opencv_highgui300.lib" )
#pragma comment( lib, "C:\\opencv_imgproc300.lib" )
#pragma comment( lib, "C:\\opencv_calib3d300.lib" )
#pragma comment( lib, "C:\\opencv_features2d300.lib" ) 
#pragma comment( lib, "C:\\opencv_flann300.lib" )
#pragma comment( lib, "C:\\opencv_imgcodecs300.lib" )
#pragma comment( lib, "C:\\opencv_objdetect300.lib" )
#pragma comment( lib, "C:\\opencv_hal300.lib" )
#pragma comment( lib, "C:\\opencv_photo300.lib" )
#pragma comment( lib, "C:\\opencv_shape300.lib" )
#pragma comment( lib, "C:\\opencv_superres300.lib" )
#pragma comment( lib, "C:\\opencv_video300.lib" )
#pragma comment( lib, "C:\\opencv_stitching300.lib" )
#pragma comment( lib, "C:\\opencv_videoio300.lib" )

#endif


using namespace DBoW2;
using namespace DUtils;
using namespace std;
using namespace cv::detail;

// - - - - - --- - - - -- - - - - -

/// ORB Vocabulary
typedef DBoW2::TemplatedVocabulary<DBoW2::FORB::TDescriptor, DBoW2::FORB> ORBVocabulary;


// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
void extractORBFeatures(cv::Mat &image, vector<vector<cv::Mat> > &features, FeaturesFinder *finder) ;
void changeStructureORB( const cv::Mat &descriptor,vector<bool> &mask, vector<cv::Mat> &out);
void isInImage(vector<cv::KeyPoint> &keys, float &cx, float &cy, float &rMin, float &rMax, vector<bool> &mask);
void createVocabularyFile(ORBVocabulary &voc, std::string &fileName, const vector<vector<cv::Mat> > &features);
void test(vector<vector<cv::Mat> > &features);
// ----------------------------------------------------------------------------
	

const int N_IMAGES = 31; // 图的数量

int main()
{

	vector<std::string> filenames;
	for(int i = 0; i < N_IMAGES; ++i)
	{
		stringstream ss;
		ss << "AAA/ccc" << i << ".bmp"; // 根据文件所在位置和文件名设置

		filenames.push_back(ss.str());
	}

    // initialze     ORBextractor
    int nLevels = 5;//6;
	// chenhui
	cv::Ptr<cv::detail::FeaturesFinder> finder;
	finder = new OrbFeaturesFinder(cv::Size(5,5), 5000, 1.05f, 3);

    int nImages = filenames.size();

    vector<vector<cv::Mat > > features;
    features.clear();
    features.reserve(nImages);

   
    cv::Mat image;
    cout << "> Extracting Features from " << nImages << " images..." << endl;
    for(int i = 0; i < nImages; ++i)
    {
        std::cout << "Processing the " << i <<" image " << std::endl;
		cv::Mat src = cv::imread(filenames[i], CV_LOAD_IMAGE_UNCHANGED);
        imshow("View", src);
        cv::waitKey(1);
        if (!src.empty())
        {
            //cv_ptr = cv_bridge::toCvShare(i);
            //cv::cvtColor(src, image, CV_RGB2GRAY); // 单通道图像就不需要再转
			image = src;
			extractORBFeatures(image, features, finder);
        }

    }

    //bag.close();

    cout << "... Extraction done!" << endl;

    // Creating the Vocabulary
    // define vocabulary
    const int k = 10; // branching factor
    const WeightingType weight = IDF;
    const ScoringType score = L1_NORM;
    ORBVocabulary voc(k, nLevels, weight, score);

    std::string vociName = "vociOmni.txt";

    createVocabularyFile(voc, vociName, features);

    cout << "\n--- START TEST ---\n" << endl;
    
	test(features);

	cout << "--- THE END ---" << endl;

    return 0;
}


// ----------------------------------------------------------------------------

void extractORBFeatures(cv::Mat &image, vector<vector<cv::Mat> > &features, FeaturesFinder *finder) 
{
    vector<cv::KeyPoint> keypoints;
    cv::Mat descriptorORB;


    // extract
	ImageFeatures features300; 
	(*finder)(image, features300);

	descriptorORB = features300.descriptors.getMat(cv::ACCESS_RW);
	keypoints = features300.keypoints;


    // reject features outside region of interest
    vector<bool> mask;

    float cx = 0; float cy = 0;
    float rMin = 0; float rMax = 0;
    isInImage(keypoints, cx, cy, rMin, rMax, mask);

    // create descriptor vector for the vocabulary
    features.push_back(vector<cv::Mat>());
    changeStructureORB(descriptorORB, mask, features.back());
	imshow("ORBFeature", features.back().back());
}

// ----------------------------------------------------------------------------

void changeStructureORB( const cv::Mat &descriptor,vector<bool> &mask, vector<cv::Mat> &out) {
    for (int i = 0; i < descriptor.rows; i++) {
        if(mask[i]) {
            out.push_back(descriptor.row(i));
        }
    }
}

// ----------------------------------------------------------------------------

void isInImage(vector<cv::KeyPoint> &keys, float &cx, float &cy, float &xMax, float &yMax, vector<bool> &mask) {
    int N = keys.size();
    mask = vector<bool>(N, false);
	mask = vector<bool>(N, true); 
    int num = 0;
  //  for(int i=0; i<N; i++) {
  //      cv::KeyPoint kp = keys[i];

  //      float u = kp.pt.x;
  //      float v = kp.pt.y;

  //      //if(u>20 && u<320-20 && v>20 && v<240-20)
		//if(u>cx && u<xMax && v>cy && v<yMax)
  //      {
  //          mask[i] = true;
  //          num ++;
  //      }
  //  }
    std::cout << "In image number " << num << std::endl;

}

// ----------------------------------------------------------------------------

void createVocabularyFile(ORBVocabulary &voc, std::string &fileName, const vector<vector<cv::Mat> > &features)
{

    cout << "> Creating vocabulary. May take some time ..." << endl;
    voc.create(features);
    cout << "... done!" << endl;

    cout << "> Vocabulary information: " << endl
        << voc << endl << endl;

    // save the vocabulary to disk
    cout << endl << "> Saving vocabulary..." << endl;
	voc.saveToTextFile(fileName); // orb_slam2支持的save
	voc.save(fileName); // 最基本的save
	cout << "... saved to file: " << fileName << endl;
}


void test(vector<vector<cv::Mat> > &features)
{
  // branching factor and depth levels 
  const int k = 9;
  const int L = 3;
  const WeightingType weight = IDF;
  const ScoringType score = L1_NORM;

  ORBVocabulary voc(k, L, weight, score); // 读取当前图像

 
  cout << "Creating a small " << k << "^" << L << " vocabulary..." << endl;
  voc.create(features);

  cout << "... done!" << endl;

  cout << "Vocabulary information: " << endl
  << voc << endl << endl;

  // lets do something with this vocabulary
  cout << "Matching images against themselves (0 low, 1 high): " << endl;
  BowVector v1, v2;
  for(int i = 0; i < N_IMAGES; i++)
  {
    voc.transform(features[i], v1); 
    for(int j = 0; j < N_IMAGES; j++)
    {
      voc.transform(features[j], v2);
      
      double score = voc.score(v1, v2); 
      cout << "Image " << i << " vs Image " << j << ": " << score << endl;
    }
  }

  // save the vocabulary to disk
  cout << endl << "Saving vocabulary..." << endl;
  voc.save("small_voc.yml.gz");
  cout << "Done" << endl;
}

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