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