#dbow3#dbow3 is a simple lib so I assume you installed it in default directoryset( DBoW3_INCLUDE_DIRS "/usr/local/include")set( DBoW3_LIBS "/usr/local/lib/libDBoW3.a")add_executable( xxx src/xxx.cpp )target_link_libraries( xxx ${DBoW3_LIBS})
#include"DBoW3/DBoW3.h"//词袋支持头文件#include//opencv核心模块#include//gui模块#include//特征点头文件#include#include#includeusingnamespace cv;usingnamespace std;/***************************************************
* 本节演示了如何根据前面训练的字典计算相似性评分
* ************************************************/intmain(int argc,char**argv){if(argc !=2){
cout <<"Usage: 需要字典"<< endl;return1;}
string zidian_file = argv[1];
DBoW3::Vocabulary vocab(zidian_file);// read the images and database(读取图像和数据库)
cout <<"reading database"<< endl;//输出reading database(读取数据)//DBoW3::Vocabulary vocab("../src/vocabulary.yml.gz");//vocabulary.yml.gz路径//DBoW3::Vocabulary vocab("../src/vocab_larger.yml.gz"); // use large vocab if you want:if(vocab.empty()){
cerr <<"Vocabulary does not exist."<< endl;//输出Vocabulary does not existreturn1;}
cout <<"reading images... "<< endl;//输出reading images...
vector<Mat> images;for(int i =0; i <10; i++){
string path ="../data/"+to_string(i +1)+".png";//图像读取路径
images.push_back(imread(path));}// NOTE: in this case we are comparing images with a vocabulary generated by themselves, this may lead to overfit.// 这里我们用它们生成的字典比较它们本身的相似性，这可能会产生过拟合// detect ORB features
cout <<"detecting ORB features ... "<< endl;//输出detecting ORB features ...(正在检测ORB特征)
Ptr<Feature2D> detector =ORB::create();//默认图像500个特征点
vector<Mat> descriptors;//描述子 将10张图像提取ORB特征并存放在vector容器里for(Mat &image:images){
vector<KeyPoint> keypoints;//关键点
Mat descriptor;//描述子
detector->detectAndCompute(image,Mat(), keypoints, descriptor);//检测和计算
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;//输出comparing images with imagesfor(int i =0; i < images.size(); i++){
DBoW3::BowVector v1;//descriptors[i]表示图像i中所有的ORB描述子集合，函数transform()计算出用先前字典来描述的单词向量，每个向量中元素的值要么是0，表示图像i中没有这个单词；要么是该单词的权重//BoW描述向量中含有每个单词的ID和权重，两者构成了整个稀疏的向量//当比较两个向量时，DBoW3会为我们计算一个分数
vocab.transform(descriptors[i], v1);for(int j = i; j < images.size(); j++){
DBoW3::BowVector v2;
vocab.transform(descriptors[j], v2);double score = vocab.score(v1, v2);//p296式(11.9)
cout <<"image "<< i <<" vs image "<< j <<" : "<< score << endl;//输出一幅图像与另外一幅图像之间的相似度评分}
cout << endl;}// or with database//在进行数据库查询时，DBoW对上面的分数进行排序，给出最相似的结果
cout <<"comparing images with database "<< endl;
DBoW3::Database db(vocab,false,0);for(int i =0; i < descriptors.size(); i++)
db.add(descriptors[i]);
cout <<"database info: "<< db << endl;//输出database info(数据库信息)为for(int i =0; i < descriptors.size(); i++){
DBoW3::QueryResults ret;
db.query(descriptors[i], ret,4);// max result=4
cout <<"searching for image "<< i <<" returns "<< ret << endl << endl;}
cout <<"done."<< endl;}
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2.3.2.1 读取已有字典数据构建字典库
if(argc !=2){
cout <<"Usage: 需要字典"<< endl;return1;}
string zidian_file = argv[1];
DBoW3::Vocabulary vocab(zidian_file);// read the images and database(读取图像和数据库)
cout <<"reading database"<< endl;//输出reading database(读取数据)//DBoW3::Vocabulary vocab("../src/vocabulary.yml.gz");//vocabulary.yml.gz路径//DBoW3::Vocabulary vocab("../src/vocab_larger.yml.gz"); // use large vocab if you want:if(vocab.empty()){
cerr <<"Vocabulary does not exist."<< endl;//输出Vocabulary does not existreturn1;}
cout <<"reading images... "<< endl;//输出reading images...
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2.3.2.1.1 判断字典库是否已加载数据
if(vocab.empty()){}
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2.3.2.2 读取要进行回环检测的采集数据
cout <<"reading images... "<< endl;//输出reading images...
vector<Mat> images;for(int i =0; i <10; i++){
string path ="../data/"+to_string(i +1)+".png";//图像读取路径
images.push_back(imread(path));}
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2.3.2.3 提取每张图片的ORB特征点并计算描述子
// NOTE: in this case we are comparing images with a vocabulary generated by themselves, this may lead to overfit.// 这里我们用它们生成的字典比较它们本身的相似性，这可能会产生过拟合// detect ORB features
cout <<"detecting ORB features ... "<< endl;//输出detecting ORB features ...(正在检测ORB特征)
Ptr<Feature2D> detector =ORB::create();//默认图像500个特征点
vector<Mat> descriptors;//描述子 将10张图像提取ORB特征并存放在vector容器里for(Mat &image:images){
vector<KeyPoint> keypoints;//关键点
Mat descriptor;//描述子
detector->detectAndCompute(image,Mat(), keypoints, descriptor);//检测和计算
descriptors.push_back(descriptor);}
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2.3.2.4 图像之间的直接比较
// we can compare the images directly or we can compare one image to a database// images :
cout <<"comparing images with images "<< endl;//输出comparing images with imagesfor(int i =0; i < images.size(); i++){
DBoW3::BowVector v1;//descriptors[i]表示图像i中所有的ORB描述子集合，函数transform()计算出用先前字典来描述的单词向量，每个向量中元素的值要么是0，表示图像i中没有这个单词；要么是该单词的权重//BoW描述向量中含有每个单词的ID和权重，两者构成了整个稀疏的向量//当比较两个向量时，DBoW3会为我们计算一个分数
vocab.transform(descriptors[i], v1);for(int j = i; j < images.size(); j++){
DBoW3::BowVector v2;
vocab.transform(descriptors[j], v2);double score = vocab.score(v1, v2);//p296式(11.9)
cout <<"image "<< i <<" vs image "<< j <<" : "<< score << endl;//输出一幅图像与另外一幅图像之间的相似度评分}
cout << endl;}
// or with database//在进行数据库查询时，DBoW对上面的分数进行排序，给出最相似的结果
cout <<"comparing images with database "<< endl;
DBoW3::Database db(vocab,false,0);for(int i =0; i < descriptors.size(); i++)
db.add(descriptors[i]);
cout <<"database info: "<< db << endl;//输出database info(数据库信息)为for(int i =0; i < descriptors.size(); i++){
DBoW3::QueryResults ret;
db.query(descriptors[i], ret,4);// max result=4
cout <<"searching for image "<< i <<" returns "<< ret << endl << endl;}
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2.3.2.5.1 定义数据字典库
DBoW3::Database db(vocab,false,0);
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2.3.2.5.2 添加所要查询的图片的描述子
for(int i =0; i < descriptors.size(); i++)
db.add(descriptors[i]);
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2.3.2.5.3 输出字典库的信息
使用数据库查询时，DBoW对上面的分数进行排序，给出最相似的结果
cout <<"database info: "<< db << endl;//输出database info(数据库信息)为
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2.3.2.5.4 定义并输出查询结果
for(int i =0; i < descriptors.size(); i++){
DBoW3::QueryResults ret;
db.query(descriptors[i], ret,4);// max result=4
cout <<"searching for image "<< i <<" returns "<< ret << endl << endl;}