学习OpenCV——BOW特征提取函数（特征点篇）

简单的通过特征点分类的方法：

一、train

1.提取+/- sample的feature，每幅图提取出的sift特征个数不定（假设每个feature有128维）

2.利用聚类方法（e.g K-means）将不定数量的feature聚类为固定数量的(比如10个)words即BOW（bag of word）

（本篇文章主要完成以上的工作！）

3.normalize，并作这10个类的直方图e.g [0.1，0.2，0.7，0...0];

4.将each image的这10个word作为feature_instance 和 (手工标记的) label（+/-）进入SVM训练

二、predict

1. 提取test_img的feature（如137个）

2. 分别求each feature与10个类的距离(e.g. 128维欧氏距离)，确定该feature属于哪个类
3. normalize，并作这10个类的直方图e.g [0，0.2，0.2，0.6，0...0];

4. 应用SVM_predict进行结果预测

通过OpenCV实现feature聚类 BOW

首先在此介绍一下OpenCV的特征描述符与BOW的通用函数。

主要的通用接口有：

1.特征点提取

Ptr<FeatureDetector> FeatureDetector::create(const string& detectorType)

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Ptr<FeatureDetector> FeatureDetector::create(const string& detectorType)

// "FAST" – FastFeatureDetector

// "STAR" – StarFeatureDetector

// "SIFT" – SIFT (nonfree module)//必须使用 initModule_nonfree()初始化

// "SURF" – SURF (nonfree module)//同上;

// "ORB" – ORB

// "MSER" – MSER

// "GFTT" – GoodFeaturesToTrackDetector

// "HARRIS" – GoodFeaturesToTrackDetector with Harris detector enabled

// "Dense" – DenseFeatureDetector

// "SimpleBlob" – SimpleBlobDetector

根据以上接口，测试不同的特征点：

对同一幅图像进行水平翻转前后的两幅图像检测特征点检测结果，

检测到的特征点的坐标类型为：pt: int / float（与keyPoint的性质有关）

数量分别为num1, num2,

"FAST" – FastFeatureDetector pt:int (num1:615 num2:618)

"STAR" – StarFeatureDetector pt:int (num1:43 num2:42 )

"SIFT" – SIFT (nonfree module) pt:float(num1:155 num2:135) //必须使用 initModule_nonfree()初始化

"SURF" – SURF (nonfree module) pt:float(num1:344 num2:342) //同上;

"ORB" – ORB pt:float(num1:496 num2:497)

"MSER" – MSER pt:float(num1:51 num2:45 )

"GFTT" – GoodFeaturesToTrackDetector pt:int (num1:744 num2:771)

"HARRIS" – GoodFeaturesToTrackDetector with Harris detector enabled pt:float(num1:162 num2:160)

"Dense" – DenseFeatureDetector pt:int (num1:3350 num2:3350)

2.特征描述符提取

Ptr<DescriptorExtractor> DescriptorExtractor::create(const string& descriptorExtractorType)

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// Ptr<DescriptorExtractor> DescriptorExtractor::create(const string& descriptorExtractorType)

// "SIFT" – SIFT

// "SURF" – SURF

// "ORB" – ORB

// "BRIEF" – BriefDescriptorExtractor

3.描述符匹配

Ptr<DescriptorMatcher> descriptorMatcher = DescriptorMatcher::create(const string& descriptorMatcherType)

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// descriptorMatcherType – Descriptor matcher type.

// Now the following matcher types are supported:

// BruteForce (it uses L2 )

// BruteForce-L1

// BruteForce-Hamming

// BruteForce-Hamming(2)

// FlannBased

Ptr<DescriptorMatcher> descriptorMatcher = DescriptorMatcher::create( "BruteForce" );

4.class BOWTrainer

class BOWKmeansTrainer::public[b] BOWTrainer:Kmeans算法训练[/b]

BOWKMeansTrainer ::BOWKmeansTrainer(int clusterCount, const TermCriteria& termcrit=TermCriteria(), int attempts=3, int flags=KMEANS_PP_CENTERS)¶

parameter same as Kmeans

代码实现：

1.画特征点。

2.特征点Kmeans聚类，每一种颜色代表一个类别。

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#include "opencv2/highgui/highgui.hpp"

#include "opencv2/calib3d/calib3d.hpp"

#include "opencv2/imgproc/imgproc.hpp"

#include "opencv2/features2d/features2d.hpp"

#include "opencv2/nonfree/nonfree.hpp"

#include <iostream>

using namespace cv;

using namespace std;

#define ClusterNum 10

void DrawAndMatchKeypoints(const Mat& Img1,const Mat& Img2,const vector<KeyPoint>& Keypoints1,

const vector<KeyPoint>& Keypoints2,const Mat& Descriptors1,const Mat& Descriptors2)

{

Mat keyP1,keyP2;

drawKeypoints(Img1,Keypoints1,keyP1,Scalar::all(-1),0);

drawKeypoints(Img2,Keypoints2,keyP2,Scalar::all(-1),0);

putText(keyP1, "drawKeyPoints", cvPoint(10,30), FONT_HERSHEY_SIMPLEX, 1 ,Scalar :: all(-1));

putText(keyP2, "drawKeyPoints", cvPoint(10,30), FONT_HERSHEY_SIMPLEX, 1 ,Scalar :: all(-1));

imshow("img1 keyPoints",keyP1);

imshow("img2 keyPoints",keyP2);

Ptr<DescriptorMatcher> descriptorMatcher = DescriptorMatcher::create( "BruteForce" );

vector<DMatch> matches;

descriptorMatcher->match( Descriptors1, Descriptors2, matches );

Mat show;

drawMatches(Img1,Keypoints1,Img2,Keypoints2,matches,show,Scalar::all(-1),CV_RGB(255,255,255),Mat(),4);

putText(show, "drawMatchKeyPoints", cvPoint(10,30), FONT_HERSHEY_SIMPLEX, 1 ,Scalar :: all(-1));

imshow("match",show);

}

//测试OpenCV：class BOWTrainer

void BOWKeams(const Mat& img, const vector<KeyPoint>& Keypoints,

const Mat& Descriptors, Mat& centers)

{

//BOW的kmeans算法聚类;

BOWKMeansTrainer bowK(ClusterNum,

cvTermCriteria (CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 10, 0.1),3,2);

centers = bowK.cluster(Descriptors);

cout<<endl<<"< cluster num: "<<centers.rows<<" >"<<endl;

Ptr<DescriptorMatcher> descriptorMatcher = DescriptorMatcher::create( "BruteForce" );

vector<DMatch> matches;

descriptorMatcher->match(Descriptors,centers,matches);//const Mat& queryDescriptors, const Mat& trainDescriptors第一个参数是待分类节点，第二个参数是聚类中心;

Mat demoCluster;

img.copyTo(demoCluster);

//为每一类keyPoint定义一种颜色

Scalar color[]={CV_RGB(255,255,255),

CV_RGB(255,0,0),CV_RGB(0,255,0),CV_RGB(0,0,255),

CV_RGB(255,255,0),CV_RGB(255,0,255),CV_RGB(0,255,255),

CV_RGB(123,123,0),CV_RGB(0,123,123),CV_RGB(123,0,123)};

for (vector<DMatch>::iterator iter=matches.begin();iter!=matches.end();iter++)

{

cout<<"< descriptorsIdx:"<<iter->queryIdx<<" centersIdx:"<<iter->trainIdx

<<" distincs:"<<iter->distance<<" >"<<endl;

Point center= Keypoints[iter->queryIdx].pt;

circle(demoCluster,center,2,color[iter->trainIdx],-1);

}

putText(demoCluster, "KeyPoints Clustering: 一种颜色代表一种类型",

cvPoint(10,30), FONT_HERSHEY_SIMPLEX, 1 ,Scalar :: all(-1));

imshow("KeyPoints Clusrtering",demoCluster);

}

int main()

{

cv::initModule_nonfree();//使用SIFT/SURF create之前，必须先initModule_<modulename>();

cout << "< Creating detector, descriptor extractor and descriptor matcher ...";

Ptr<FeatureDetector> detector = FeatureDetector::create( "SIFT" );

Ptr<DescriptorExtractor> descriptorExtractor = DescriptorExtractor::create( "SIFT" );

Ptr<DescriptorMatcher> descriptorMatcher = DescriptorMatcher::create( "BruteForce" );

cout << ">" << endl;

if( detector.empty() || descriptorExtractor.empty() )

{

cout << "Can not create detector or descriptor exstractor or descriptor matcher of given types" << endl;

return -1;

}

cout << endl << "< Reading images..." << endl;

Mat img1 = imread("D:/demo0.jpg");

Mat img2 = imread("D:/demo1.jpg");

cout<<endl<<">"<<endl;

//detect keypoints;

cout << endl << "< Extracting keypoints from images..." << endl;

vector<KeyPoint> keypoints1,keypoints2;

detector->detect( img1, keypoints1 );

detector->detect( img2, keypoints2 );

cout <<"img1:"<< keypoints1.size() << " points img2:" <<keypoints2.size()

<< " points" << endl << ">" << endl;

//compute descriptors for keypoints;

cout << "< Computing descriptors for keypoints from images..." << endl;

Mat descriptors1,descriptors2;

descriptorExtractor->compute( img1, keypoints1, descriptors1 );

descriptorExtractor->compute( img2, keypoints2, descriptors2 );

cout<<endl<<"< Descriptoers Size: "<<descriptors2.size()<<" >"<<endl;

cout<<endl<<"descriptor's col: "<<descriptors2.cols<<endl

<<"descriptor's row: "<<descriptors2.rows<<endl;

cout << ">" << endl;

//Draw And Match img1,img2 keypoints

//匹配的过程是对特征点的descriptors进行match;

DrawAndMatchKeypoints(img1,img2,keypoints1,keypoints2,descriptors1,descriptors2);

Mat center;

//对img1提取特征点，并聚类

//测试OpenCV：class BOWTrainer

BOWKeams(img1,keypoints1,descriptors1,center);

waitKey();

}



通过Qt实现DrawKeypoints：

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void Qt_test1::on_DrawKeypoints_clicked()

{

//initModule_nonfree();

Ptr<FeatureDetector> detector = FeatureDetector::create( "FAST" );

vector<KeyPoint> keypoints;

detector->detect( src, keypoints );

Mat DrawKeyP;

drawKeypoints(src,keypoints,DrawKeyP,Scalar::all(-1),0);

putText(DrawKeyP, "drawKeyPoints", cvPoint(10,30),

FONT_HERSHEY_SIMPLEX, 0.5 ,Scalar :: all(255));

cvtColor(DrawKeyP, image, CV_RGB2RGBA);

QImage img = QImage((const unsigned char*)(image.data),

image.cols, image.rows, QImage::Format_RGB32);

QLabel *label = new QLabel(this);

label->move(50, 50);//图像在窗口中所处的位置;

label->setPixmap(QPixmap::fromImage(img));

label->resize(label->pixmap()->size());

label->show();

}



由于initModule_nonfree()总是出错，无法对SIFT与SURF特征点提取，

而且无法实现聚类因为运行/BOW的kmeans算法聚类：BOWKMeansTrainer bowK(ClusterNum, cvTermCriteria (CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 10, 0.1),3,2);总是出错，不知道咋解决~~~~~(>_<)~~~~ 需要继续学习