Mat get_image_BOF(Mat image, std::vector<KeyPoint> extracted_keypoints, int KNN)
{
cout << "Attempting to classify new image" << endl ;
cvtColor(image, image, CV_BGR2GRAY);
image.convertTo(image, CV_8U);
cout << "creating description matcher" << endl ;
Ptr<DescriptorMatcher> matcher (new FlannBasedMatcher()) ;
cout << "creating feature detector" << endl ;
Ptr<FeatureDetector> detector (new SiftFeatureDetector()) ;
cout << "creating descriptor extractor" << endl ;
Ptr<DescriptorExtractor> extractor (new SiftDescriptorExtractor()) ;
cout << "creating bag of words image descriptor extractor" << endl ;
BOWImgDescriptorExtractor bowDE(extractor, matcher) ;
cout << "setting vocabulary" << endl ;
bowDE.setVocabulary(loaded_dictionary) ;
cout << "keypoints size:" << extracted_keypoints.size() << endl ;
cout << "computing description matrix, image_type:" << image.type() << endl ;
Mat bowDescriptor ;
bowDE.compute(image, extracted_keypoints, bowDescriptor) ;
cout << "feature mat has been created successfully." << endl ;
imshow("bowDescriptor", bowDescriptor);
waitKey(0);
}
void SVMformat::bowExtractor(Mat img, Mat mask)
{
Ptr<DescriptorMatcher> matcher(new FlannBasedMatcher); //KNN
vector<KeyPoint> cornor;
int minHessian = 400; //SURF Hessian Threshold
Ptr<FeatureDetector> detector(new SurfFeatureDetector());
Ptr<DescriptorExtractor> extractor(new SurfDescriptorExtractor(minHessian,4,2,false));
BOWImgDescriptorExtractor bowDE(extractor,matcher);
bowDE.setVocabulary(dictionary);
Mat bowDescriptor;
detector->detect(img, cornor, mask);
if(cornor.size() < 10)
return;
bowDE.compute(img, cornor, bowDescriptor);
bowDescriptor = bowDescriptor*100;
bowDescSet.push_back(bowDescriptor); //have to convert to CV_8U before training
}
