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### training an SVM with 68 Point2f data.

I am looking into the opencv SVM implementation, and have a couple of questions.

My data is the landmark points from dlib, which I have as std::vector<cv::Point2f> resultPnts;

this vector contains 68 * Point2f.

Each of my training samples is one of these vectors. So for example, with the label '1', I might have 200 vectors of 68 points, and the same for label '2'.

In the SVM example, the Mat for training is set up as follows:

int labels[4] = { 1, -1, -1, -1 };
float trainingData[4][2] = { { 501, 10 },{ 255, 10 },{ 501, 255 },{ 10, 501 } };
Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
Mat labelsMat(4, 1, CV_32SC1, labels);


in my example, should I:

int labels[27200] = {1,1,1,2,2,2... }; // 400 * 68 labels
float trainingData[27200][2] = { { 501, 10 },{ 255, 10 },......  }; // 400 * 68 points
Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
Mat labelsMat(4, 1, CV_32SC1, labels);


or is there a cleaner way?

Additionally, Is it possible to return the 'percentage of a label' ? For example, if the result is half way between '1' and '2' labels, it would return 50% for each. Or is it just a 'on' or 'off' classifier?

thanks!