include "stdafx.h"

include <opencv cv.h="">

include <opencv highgui.h="">

include <opencv2 nonfree="" features2d.hpp="">

#include "stdafx.h"
#include <opencv/cv.h>
#include <opencv/highgui.h>
#include <opencv2/nonfree/features2d.hpp>

using namespace cv;
using namespace std;
 
define std;
#define DICTIONARY_BUILD 0 // set DICTIONARY_BUILD 1 to do Step 1, otherwise it goes to step 2
 
2
int _tmain(int argc, _TCHAR* argv[])
{ 
 
int minHessian = 400; //Hessian Threshold
 
if Threshold
#if DICTIONARY_BUILD == 1
 
1
//Step 1 - Obtain the set of bags of features.
 //to store the input file names
 
char filename[100]={}; 
 
//to store the current input image
Mat input; 
 
 //To store the keypoints that will be extracted by SURF
 vector<KeyPoint> keypoints;
 //To store the SURF descriptor of current image
 Mat descriptor;
 //To store all the descriptors that are extracted from all the images.
 Mat featuresUnclustered;
 //The SURF feature extractor and descriptor
 SurfDescriptorExtractor detector(minHessian,4,2,false);
 //I select 20 (1000/50) images from 1000 images to extract feature descriptors and build the vocabulary
 for(int f=1;f<25;f++){
  //create the file name of an image
 sprintf(filename,"C:\\harshada\\OpenCV BoFSURF\\Release\\image\\%i.jpg",f);
 //open the file
  input = imread(filename, CV_LOAD_IMAGE_GRAYSCALE); //Load as grayscale
 //Mat img =cv::imread("C:\\harshada\\OpenCV BoFSURF\\Release\\image\\22.jpg");
 //cv::cvtColor(img,img,CV_BGR2GRAY);
 if(input.empty())
 {
  cout << "Error: Image cannot be loaded !" << endl;
 system("Pause");
  return -1;
 }
  //detect feature points
 detector.detect(input, keypoints);
  //compute the descriptors for each keypoint
 detector.compute(input, keypoints,descriptor);
  //put the all feature descriptors in a single Mat object
 featuresUnclustered.push_back(descriptor);
 //print the percentage
  printf("%i percent done\n",f/10);
 }
 //Construct BOWKMeansTrainer
 //the number of bags
 int dictionarySize=200;
 //define Term Criteria
 TermCriteria tc(CV_TERMCRIT_ITER,100,0.001);
 //retries number
 int retries=1;
 //necessary flags
 int flags=KMEANS_PP_CENTERS;
 //Create the BoW (or BoF) trainer
 BOWKMeansTrainer bowTrainer(dictionarySize,tc,retries,flags);
 bowTrainer.add(featuresUnclustered);
 if (featuresUnclustered.type() != CV_32F)
 
{
featuresUnclustered.convertTo(featuresUnclustered, CV_32F);
}
Mat vocabulary = bowTrainer.cluster();
//cluster the feature vectors
//Mat dictionary=bowTrainer.cluster(featuresUnclustered); 
 
//store the vocabulary
FileStorage fs("C:\harshada\OpenCV BoFSURF\Release\image\dictionary.yml", fs("C:\\harshada\\OpenCV BoFSURF\\Release\\image\\dictionary.yml", FileStorage::WRITE);
fs << "vocabulary" << vocabulary;
fs.release();
 
else
 
fs.release();
#else
//Step 2 - Obtain the BoF descriptor for given image/video frame.
 //prepare BOW descriptor extractor from the dictionary
 Mat dictionary;
 FileStorage fs("C:\\harshada\\OpenCV BoFSURF\\Release\\image\\dictionary.yml", FileStorage::READ);
 fs["vocabulary"] >> dictionary;
 fs.release();
 //create a nearest neighbor matcher
 Ptr<DescriptorMatcher> matcher(new FlannBasedMatcher);
 //create SURF feature point extracter
 Ptr<FeatureDetector> detector(new SurfFeatureDetector(minHessian,4,2,false));
 //create SURF descriptor extractor
 Ptr<DescriptorExtractor> extractor(new SurfDescriptorExtractor(minHessian,4,2,false));
 //create BoF (or BoW) descriptor extractor
 BOWImgDescriptorExtractor bowDE(extractor,matcher);
 //Set the dictionary with the vocabulary we created in the first step
 bowDE.setVocabulary(dictionary);
 //To store the image file name
 char * filename = new char[100];
 //To store the image tag name - only for save the descriptor in a file
 char * imageTag = new char[10];
 //open the file to write the resultant descriptor
 FileStorage fs1("C:\\harshada\\OpenCV BoFSURF\\Release\\image\\descriptor.yml", FileStorage::WRITE);
 //the image file with the location.
 sprintf(filename,"C:\\harshada\\OpenCV BoFSURF\\Release\\image\\8.jpg");
 //read the image
 Mat img=imread(filename,CV_LOAD_IMAGE_GRAYSCALE);
 //To store the keypoints that will be extracted by SURF
 vector<KeyPoint> keypoints;
 //Detect SURF keypoints (or feature points)
 detector->detect(img,keypoints);
 //To store the BoW (or BoF) representation of the image
 Mat bowDescriptor;
 //extract BoW (or BoF) descriptor from given image
 bowDE.compute(img,keypoints,bowDescriptor);
 cv::imshow("matcher",bowDescriptor);
 //prepare the yml (some what similar to xml) file
 sprintf(imageTag,"img1");
 //write the new BoF descriptor to the file
 fs1 << imageTag << bowDescriptor;
 //You may use this descriptor for classifying the image.
 //release the file storage
 fs1.release();
#endif
printf("\ndone\n");
return 0;
}
 
endif
 
printf("\ndone\n"); 
return 0;
 
} 
i want to extract similar image from vocabulary that was created in above code.this code run successfully creating two descriptors file.My question is that I don't understand that file "descriptr.yml" contains extracted features of query image or matched features of query image with vocabulary.
Please help