I have a folder of images of a car from every angle. I want to use the bag of words approach to train the system in recognizing the car. Once the training is done, I want that if an image of that car is given it should be able to recognize it.
I have been trying to learn the BOW function in opencv in order to make this work and have come at a level where I do not know what to do now and some guidance would be appreciated.
Ptr<FeatureDetector> features = FeatureDetector::create("SIFT");
Ptr<DescriptorExtractor> descriptors = DescriptorExtractor::create("SIFT");
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
//defining terms for bowkmeans trainer
TermCriteria tc(MAX_ITER + EPS, 10, 0.001);
int dictionarySize = 1000;
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bowTrainer(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor bowDE(descriptors, matcher);
//training data now
Mat img = imread("c:\\1.jpg", 0);
Mat img2 = imread("c:\\2.jpg", 0);
vector<KeyPoint> keypoints, keypoints2;
descriptor->compute(img, keypoints, features);
descripto->compute(img2, keypoints2, features2);
Mat dictionary = bowTrainer.cluster();
This is all based on the BOW documentation.
I think at this stage my system is trained. and the next step is predicting.
this is where I dont know what to do. If I use SVM or NormalBayesClassifier they both use the terms train and predict.
How do I predict and train after this? any guidance would be much appreciated. How do I connect the training of the classifier to my `bowDE`` function?