opencv Normbal bayes predict probability output zero - c++

Question

I have some training data that consists of a number of features extracted from images and different class labels. I have managed to train Normal Bayes classifiers using OpenCV3 in C++. And I am able to pass new test data into the classifiers to obtain the predicted class labels using predict() function.
However, I don't want to simply get the predicted class label, I also wish to know the probabilities for each class label per test data using predictProb() function of class NormalBayesClassifier.
There is a predictProb() function that seems to be able to return the probabilities per class label:
virtual float cv::ml::NormalBayesClassifier::predictProb
( InputArray inputs,
OutputArray outputs,
OutputArray outputProbs,
int flags = 0
) const
However, when i tested the code, I always get a vector of 0s or a mixture of 0s and Inf for different test images as the predict probabilities even though I do get a correct prediction. I tried adding RAW_OUTPUT to the flags and the result is the same.
int N=4;
vector<string> loc;
loc.push_back("1.jpg");
loc.push_back("2.jpg");
loc.push_back("3.jpg");
loc.push_back("4.jpg");
loc.push_back("5.jpg");
loc.push_back("6.jpg");
Ptr<ml::NormalBayesClassifier> rt = cv::ml::NormalBayesClassifier::create();
Mat img,features,dictionary;
vector<cv::KeyPoint> keyPoints;
Mat X;
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<SURF> detector = SURF::create(400,4,2,1,1);
Ptr<DescriptorExtractor> extractor = detector;
FileStorage fs("Bag-Of-Features.yml", FileStorage::READ);
fs["dictionary"] >> dictionary;
fs.release();
Ptr<BOWImgDescriptorExtractor> bowDE=makePtr<BOWImgDescriptorExtractor>(extractor, matcher);
bowDE->setVocabulary(dictionary);
for(int i=0;i<4;i++)
{
img=imread(loc[i]);
detector->detect(img,keyPoints);
bowDE->compute(img,keyPoints,features);
int rows = features.rows;
int cols = features.cols;
//cout << "r"<< rows << "c "<< cols ;
X.push_back(features);
}
Mat_<int> Y(N,1);
Y << 0,0, 1,1 ;
rt->train(X, ml::ROW_SAMPLE, Y);
rt->save("classifier.yml");
/////////prediction/////////////
Mat features1;
vector<cv::KeyPoint> keyPoints1;
Mat r,p;
Mat inp;
Mat R1,P1;
for (int i=0;i<2;i++)
{
inp=imread(loc[4+i]);
//inp.convertTo(inp,CV_8U);
detector->detect(inp, keyPoints1);
bowDE->compute(inp, keyPoints1, features1);
//features1.convertTo(features1,CV_32F);
rt->predictProb(features1,r,p);
R1.push_back(r);
P1.push_back(p);
}
cout << "Probability"<<P1 <<endl ;
return 0;
}
Ouput:
Probability[0, 0;inf, 0]

Answer 1

There are two conditions that need to be met for the Bayes Classifier to be able to output probabilities:
1) You must train it with multiple samples that pair to the same response value.
2) The training samples should be normalized.
I had this same question, and after scouring the internet for answers finally this link helped me conclude the above statement:
Opencv3 Bayes Classifier predictProb giving strange results