没日没夜的改论文生活终于要告一段落了，比起改论文，学OpenCV就是一件幸福的事情。OpenCV的发展越来越完善了，已经可以直接使用BOW函数来进行对象分类了。
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)
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)
// 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)
// 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 BOWTrainer:Kmeans算法训练
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。
#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：
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