12章

来源：互联网发布：公司网络营销策划书编辑：程序博客网时间：2024/06/06 18:17

12章有三块程序，首先是创建字典feature_training.cpp

#include "DBoW3/DBoW3.h"#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/features2d/features2d.hpp>#include <iostream>#include <vector>#include <string>using namespace cv;using namespace std;/*************************************************** * 本节演示了如何根据data/目录下的十张图训练字典 * ************************************************/int main( int argc, char** argv ){    // read the image     cout<<"reading images... "<<endl;    vector<Mat> images;     for ( int i=0; i<10; i++ )    {        string path = "./data/"+to_string(i+1)+".png";        images.push_back( imread(path) );    }    // detect ORB features    cout<<"detecting ORB features ... "<<endl;    Ptr< Feature2D > detector = ORB::create();    vector<Mat> descriptors;    for ( Mat& image:images )    {        vector<KeyPoint> keypoints;         Mat descriptor;        detector->detectAndCompute( image, Mat(), keypoints, descriptor );        descriptors.push_back( descriptor );    }    // create vocabulary     cout<<"creating vocabulary ... "<<endl;    //简单易用，创建一个字典对象    DBoW3::Vocabulary vocab;    //调用create()函数，并用上方检测出来的描述子数组创建    vocab.create( descriptors );    cout<<"vocabulary info: "<<vocab<<endl;    //save()函数输出字典压缩包，格式为vocabulary.yml.gz    vocab.save( "vocabulary.yml.gz" );    cout<<"done"<<endl;    return 0;}

用法很简单，创建字典对象，create()进数据，然后输出为.yml.gz文件即可。

loop_closure.cpp

#include "DBoW3/DBoW3.h"#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/features2d/features2d.hpp>#include <iostream>#include <vector>#include <string>using namespace cv;using namespace std;/*************************************************** * 本节演示了如何根据前面训练的字典计算相似性评分 * ************************************************/int main( int argc, char** argv ){    // read the images and database      cout<<"reading database"<<endl;    DBoW3::Vocabulary vocab("./vocabulary.yml.gz");    // DBoW3::Vocabulary vocab("./vocab_larger.yml.gz");  // use large vocab if you want:     if ( vocab.empty() )    {        cerr<<"Vocabulary does not exist."<<endl;        return 1;    }    cout<<"reading images... "<<endl;    vector<Mat> images;     for ( int i=0; i<10; i++ )    {        string path = "./data/"+to_string(i+1)+".png";        images.push_back( imread(path) );    }    // NOTE: in this case we are comparing images with a vocabulary generated by themselves, this may leed to overfitting.      // detect ORB features    cout<<"detecting ORB features ... "<<endl;    Ptr< Feature2D > detector = ORB::create();    vector<Mat> descriptors;    for ( Mat& image:images )    {        vector<KeyPoint> keypoints;         Mat descriptor;        detector->detectAndCompute( image, Mat(), keypoints, descriptor );        descriptors.push_back( descriptor );    }    // we can compare the images directly or we can compare one image to a database     // images :    cout<<"comparing images with images "<<endl;    for ( int i=0; i<images.size(); i++ )    {        //创建一个词袋描述向量，用于描述图像        DBoW3::BowVector v1;        /**         * 先看一下函数定义：         * virtual void transform(const  cv::Mat & features, BowVector &v)         * 其实就是将图像转化成描述向量，用于相似性比较，v1为承接向量         */        vocab.transform( descriptors[i], v1 );        //由于双向比较所以用了双循环，也就是image1与image10相似性和image10与image1相似性为两个比较。        for ( int j=i; j<images.size(); j++ )        {            //同样的方法取得另外一张图的描述向量，可以看出，由于j从=i开始，所以会比较到自身            DBoW3::BowVector v2;            vocab.transform( descriptors[j], v2 );            //核心功能，score()函数，传入两个词袋描述向量，然后取得相似性得分            double score = vocab.score(v1, v2);            cout<<"image "<<i<<" vs image "<<j<<" : "<<score<<endl;        }        cout<<endl;    }    /**     * 图像与图像之间的检测比较简单：     * 根据图像的描述子，调用transform()函数将图像转化成描述向量，     * 根据描述向量，调用score()函数求两张图像相似性得分。     */    // or with database     cout<<"comparing images with database "<<endl;    //用字典创建一个基础数据集    DBoW3::Database db( vocab, false, 0);    //加入所有需要匹配的图像描述子    for ( int i=0; i<descriptors.size(); i++ )        db.add(descriptors[i]);    cout<<"database info: "<<db<<endl;    //输出匹配结果    for ( int i=0; i<descriptors.size(); i++ )    {        //创建一个结果队列对象，用于承接匹配结果        DBoW3::QueryResults ret;        //利用数据集的query()函数输出每一张图，以数据集为匹配依据，与输入的所有图像去匹配时的相似性结果，参数设置为输出最相似的四个。        db.query( descriptors[i], ret, 4);      // max result=4        cout<<"searching for image "<<i<<" returns "<<ret<<endl<<endl;    }    cout<<"done."<<endl;}/** * 与数据集匹配时一次性可以匹配到所有的图像匹配结果，并能够排序输出， * 做法就是： * 1、用字典创建一个数据集： DBoW3::Database db( vocab, false, 0) * 2、把图像全都add()进去： db.add(descriptors[i]); * 3、利用query()函数输出各个图的匹配结果： db.query( descriptors[i], ret, 4);*/

后面还有个产生更多更大词典的gen_vocab_large.cpp
没什么新的，只是多读入了很多图，然后创建了更大的描述子数组，左后的生成词典的过程没有一点变化。

#include "DBoW3/DBoW3.h"#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/features2d/features2d.hpp>#include <iostream>#include <vector>#include <string>using namespace cv;using namespace std;int main( int argc, char** argv ){    string dataset_dir = argv[1];    ifstream fin ( dataset_dir+"/associate.txt" );    if ( !fin )    {        cout<<"please generate the associate file called associate.txt!"<<endl;        return 1;    }    vector<string> rgb_files, depth_files;    vector<double> rgb_times, depth_times;    while ( !fin.eof() )    {        string rgb_time, rgb_file, depth_time, depth_file;        fin>>rgb_time>>rgb_file>>depth_time>>depth_file;        rgb_times.push_back ( atof ( rgb_time.c_str() ) );        depth_times.push_back ( atof ( depth_time.c_str() ) );        rgb_files.push_back ( dataset_dir+"/"+rgb_file );        depth_files.push_back ( dataset_dir+"/"+depth_file );        if ( fin.good() == false )            break;    }    fin.close();    cout<<"generating features ... "<<endl;    vector<Mat> descriptors;    Ptr< Feature2D > detector = ORB::create();    int index = 1;    for ( string rgb_file:rgb_files )    {        Mat image = imread(rgb_file);        vector<KeyPoint> keypoints;         Mat descriptor;        detector->detectAndCompute( image, Mat(), keypoints, descriptor );        descriptors.push_back( descriptor );        cout<<"extracting features from image " << index++ <<endl;    }    cout<<"extract total "<<descriptors.size()*500<<" features."<<endl;    // create vocabulary     cout<<"creating vocabulary, please wait ... "<<endl;    DBoW3::Vocabulary vocab;    vocab.create( descriptors );    cout<<"vocabulary info: "<<vocab<<endl;    vocab.save( "vocab_larger.yml.gz" );    cout<<"done"<<endl;    return 0;}

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