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to train opencv's ml classes(they're all very similar there), you need a single Mat with the train features (each on a row of its own), and a Mat with one class label for each feature, those 2 Mat's go into a cv::TrainData container later.

TrainData
      features             labels
         image1              1
         image2              0
         image3              2
...

again, all features need to have the same size (needs some cropping/resize / procrustes), for detection you would use (-1,1) labels, for recognition e.g. person-id's

here's some (fairly pseudo) code:

Mat feats, labels; // start empty

for each image {
    Mat f = image.reshape(1,1); // flatten to a single row
    f.convertTo(f, CV_32F);     // ml needs float data
    feats.push_back(f);         // append at bottom

    labels.push_back(person_id); // an integer, this is, what you get back in the prediction
}

Ptr<ml::DTree> model = ml::DTree::create();
model->train(ml::TrainData::create(feats, ml::ROW_SAMPLE, labels));

for the prediction later, you'd treat your data in the same way:

Mat f = image.reshape(1,1); // flatten to a single row
f.convertTo(f, CV_32F);    // ml needs float data
prediction = model->predict(f);

to train opencv's ml classes(they're all very similar there), you need a single Mat with the train features (each on a row of its own), and a Mat with one class label for each feature, those 2 Mat's go into a cv::TrainData container later.

TrainData
      features             labels
         image1              1
         image2              0
         image3              2
...

again, all features need to have the same size (needs some cropping/resize / procrustes), for detection you would use (-1,1) labels, for recognition e.g. person-id's

here's some (fairly pseudo) code:

Mat feats, labels; // start empty

for each image {
    Mat f = image.reshape(1,1); // flatten to a single row
    f.convertTo(f, CV_32F);     // ml needs float data
    feats.push_back(f);         // append at bottom

    labels.push_back(person_id); // an integer, this is, what you get back in the prediction
}

Ptr<ml::DTree> model = ml::DTree::create();
model->train(ml::TrainData::create(feats, ml::ROW_SAMPLE, labels));

for the prediction later, you'd treat your data in the same way:

Mat f = image.reshape(1,1); // flatten to a single row
f.convertTo(f, CV_32F);    // ml needs float data
prediction = model->predict(f);

to train opencv's ml classes(they're all very similar there), you need a single Mat with the train features (each on a row of its own), and a Mat with one class label for each feature, those 2 Mat's go into a cv::TrainData container later.

TrainData
      features             labels
         image1              1
         image2              0
         image3              2
...

again, all features need to have the same size (needs some cropping/resize / procrustes), for detection a 2-class problem you would use (-1,1) labels, for recognition e.g. person-id's

here's some (fairly pseudo) code:

Mat feats, labels; // start empty

for each image {
    Mat f = image.reshape(1,1); // flatten to a single row
    f.convertTo(f, CV_32F);     // ml needs float data
    feats.push_back(f);         // append at bottom

    labels.push_back(person_id); // an integer, this is, what you get back in the prediction
}

Ptr<ml::DTree> model = ml::DTree::create();
model->train(ml::TrainData::create(feats, ml::ROW_SAMPLE, labels));

for the prediction later, you'd treat your data in the same way:

Mat f = image.reshape(1,1); // flatten to a single row
f.convertTo(f, CV_32F);    // ml needs float data
prediction = model->predict(f);

sidenote: this is just to get you started with the ml setup. you'll soon find, that a dtree might not be the optimal approach (SVM, maybe ?), that using e.g. HOG or LBPH features instead of images should be considered, etc.