void train(const QList<Mat> &images, const QList<float> &labels)

This is a pure, virtual function. Train the classifier using the provided images and labels.

• function definition:

  virtual void train(const QList<Mat> &images, const QList<float> &labels) = 0
  

• parameters:

  Parameter	Type	Descriptions
  images	const QList<Mat> &	Training images
  labels	const QList<float> &	Training labels

• output: (void)

• example:

  // Create data for a 2-class classification problem
  QList<Mat> images = QList<Mat>() << Template("training_pic1.jpg").m()
                                   << Template("training_pic2.jpg").m()
                                   << Template("training_pic3.jpg").m()
                                   << Template("training_pic4.jpg").m();
  
  QList<float> labels = QList<float>() << 0 << 0 << 1 << 1;
  
  Classifier *classifier = Classifier::make("Classifier");
  rep->train(images, labels);
  

float classify(const Mat &image) const

This is a pure virtual function. Classify a provided input image.

• function description:

  virtual float classify(const Mat &image) const = 0
  

• parameters:

  Parameter	Type	Description
  image	const Mat &	Input image to be classified

• output: (float) Returns the classification value of the image. The value can be a confidence, a regression, or a class. In 2-class classification is it often a confidence which has been normalized such that 0 is the inflection point. Values below zero represent a negative classification and values above represent a positive classification.

• example: Classifier *classifier = Classifier::make("2ClassClassifier"); // assume classifier is already trained

  Template p1("pos_image1.jpg"); // positive sample
  Template n1("neg_image1.jpg"); // negative sample
  
  classifier->classify(p1); // returns confidence > 0
  classifier->classify(n1); // returns confidence < 0