ml::KNearest->findNearest() inconsistent result when category label changes - c++

Question

I am using OpenCV 3.4.1.
I am working on a video classification project and am trying to use KNearest to classify between 2 categories. I have 8 areas of interest in each video frame. To make decision on each frame, each KNearest is done on the pixel values on each area. The majority win (and favor to one category if it is a tie). So, I have 8 sets of training data (one for each area of interest).
Problem: The response generated from the knn model changed when I labelled the categories differently.
The training data sets are organized as rows of:
[category label], data0, data1, data2....etc. (different dimensions for each
training set)
where dataX = pixel data of a frame (1 row = 1 frame)
Then, I build the model by:
Ptr<TrainData> tdata = TrainData::loadFromCSV(filename, 0, 0, -1, String("cat"));
Mat raw = tdata->getTrainSamples();
Mat res = tdata->getResponses();
PCA pca(raw, noArray(), PCA::DATA_AS_ROW, 0.99);
Mat knnIn = pca.project(raw);
Ptr<ml::KNearest> knn = ml::KNearest::create();
knn->train(knnIn, ml::ROW_SAMPLE, res);
After that, testing data is passed to the pca and knn to get the response.
To testing it, I put 1 set of testing data to the 8 knn models.
If I use 0 & 1 as the [category label] in the training data sets, the 8 responses from KNN are 1,1,1,1,1,0,0,1.
If I change the label to 2 & 1 instead (replace all '0' by '2' in the first column of the training data), the 8 responses becomes 1,1,1,1,1,1,1,1, while I am expecting 1,1,1,1,1,2,2,1.
Some observations:
no editing error on the training data(while changing the category labels).
The KNN model isClassifier()=true. DefaultK=10. AlgorithmType=1 (BRUTE_FORCE).
The result is consistent with the same training data set and testing data set.
I don't see any pattern on the difference responses for the 2 label sets (after using different
training data sets and testing data sets).
Please shed some light. Thank you very much!