Learning Opencv--bow feature extraction function (feature point)

From:http://www.xuebuyuan.com/582331.html

Simple method of classifying by feature points:

First, train

1. Extract the feature of +/-sample, sift the number of features extracted from each image (assuming that each feature has 128 dimensions)

2. Use the clustering method (e.g K-means) to set the indefinite number of feature clusters to a fixed number (for example, 10) words that is bow (bag of Word)

(This article mainly completes the above work!) )

3.normalize, and make the histogram of these 10 classes e.g [0.1,0.2,0.7,0 ... 0];

4. Enter the 10 word of each image as Feature_instance and (manually tagged) label (+/-) into the SVM training

Second, predict

1. Extraction of test_img feature (e.g. 137)

2. For each feature distance from 10 classes (e.g. 128 Euclidean distance), determine which class the feature belongs to

3. Normalize, and make a histogram of these 10 classes e.g [0,0.2,0.2,0.6,0 ... 0];

4. Applying svm_predict to predict results

realization of feature clustering by OpenCV BOW

First, the general functions of OPENCV and bow are described here.

The main common interfaces are:

1. Feature Point extraction

Ptr<featuredetector> featuredetector::create (const string& Detectortype)

According to the above interface, test different feature points:

The detection results of two images before and after horizontal flipping of the same image are detected,

The coordinate type of the detected feature point is: Pt:int/float (related to the nature of KeyPoint)

The numbers were 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)//must be initialized with Initmodule_nonfree ()
"SURF" –surf (nonfree module) pt:float (num1:344 num2:342)
Ibid.
"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. Feature Descriptor Extraction

Ptr<descriptorextractor> descriptorextractor::create (const string& Descriptorextractortype)

//  

3. Descriptor Matching

ptr<descriptormatcher> Descriptormatcher = descriptormatcher::create (const string& DescriptorMatcherType)

Descriptormatchertype–descriptor matcher type. Now the following matcher types is supported://Bruteforce (it uses L2)//BRUTEFORCE-L1//bruteforce-hamming//Bru Teforce-hamming (2)//flannbased ptr<descriptormatcher> Descriptormatcher = Descriptormatcher::create (" Bruteforce ");

4.class Bowtrainer

Class Bowkmeanstrainer: Training:p ublic bowtrainer:kmeans algorithm

Bowkmeanstrainer:: Bowkmeanstrainer(int clustercount, const termcriteria& Termcrit=termcriteria (), int attempts=3, int flags=kmeans_pp_centers)¶

Parameter Same as
Kmeans

Code implementation:

1. Draw feature points.

2. Feature points Kmeans clusters, each of which represents a category.

 

#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 10void 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);p Uttext (keyP1, "drawkeypoints", Cvpoint (10,30), FONT_ Hershey_simplex, 1, scalar:: All ( -1));p Uttext (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);} Test Opencv:class bowtrainervoid bowkeams (const mat& img, const vector<keypoint>& keypoints, const MAT & Descriptors, mat& centers) {//bow Kmeans algorithm clustering; Bowkmeanstrainer Bowk (Clusternum, Cvtermcriteria (Cv_termcrit_eps + Cv_termcrit_iter, ten, 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 The first parameter is the node to be classified, the second parameter is the cluster center; Mat Democluster;img.copyto (Democluster);//For each class of KeypoiNT defines a color 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), C V_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: a color represents a type", Cvpoint (10,30), Font_hershey_simplex, 1, Scalar:: All (-1)); Imshow ("Keypoints clusrtering", Democluster);} int main () {cv::initmodule_nonfree ();//Sift/surf Create must be preceded by 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 EXSTR Actor 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);d etector->detect (Img2, keypoints2); cout << "IMG1:" << Keypoints1.size () << "points Img2:" <<keypoints2.size () << "points" << Endl << ">" &lt ;< 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 << "> The process of matching the << Endl;//draw and match Img1,img2 keypoints//is to match;drawandmatchkeypoints the descriptors of the feature points (IMG1,IMG2, KEYPOINTS1,KEYPOINTS2,DESCRIPTORS1,DESCRIPTORS2); Mat center;//extracts feature points to img1, and clusters//tests Opencv:class bowtrainerbowkeams (img1,keypoints1,descriptors1,center); Waitkey ();}

Implementing Drawkeypoints via QT:

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);p Uttext (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);//The position of the image in the window; Label->setpixmap (IMG); Label->resize (Label->pixmap ()->size ()); Label->show ();}

Since Initmodule_nonfree () is always in error, unable to extract sift and surf feature points,

And it is not possible to achieve clustering because the Kmeans algorithm that runs the/bow cluster: Bowkmeanstrainer bowk (Clusternum, Cvtermcriteria (cv_termcrit_eps + cv_termcrit_iter, 10 , 0.1), 3,2); always wrong, don't know how to solve ~~~~~ (>_<) ~ ~ ~ need to continue to learn

Learning Opencv--bow feature extraction function (feature point)