Soft Cascade Training
=======================

.. highlight:: cpp

Soft Cascade Detector Training
--------------------------------------------


softcascade::Octave
-------------------
.. ocv:class:: softcascade::Octave : public Algorithm

Public interface for soft cascade training algorithm. ::

    class Octave : public Algorithm
    {
    public:

        enum {
            // Direct backward pruning. (Cha Zhang and Paul Viola)
            DBP = 1,
            // Multiple instance pruning. (Cha Zhang and Paul Viola)
            MIP = 2,
            // Originally proposed by L. Bourdev and J. Brandt
            HEURISTIC = 4 };

        virtual ~Octave();
        static cv::Ptr<Octave> create(cv::Rect boundingBox, int npositives, int nnegatives, int logScale, int shrinkage);

        virtual bool train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth) = 0;
        virtual void setRejectThresholds(OutputArray thresholds) = 0;
        virtual void write( cv::FileStorage &fs, const FeaturePool* pool, InputArray thresholds) const = 0;
        virtual void write( CvFileStorage* fs, string name) const = 0;

    };



softcascade::Octave::~Octave
---------------------------------------
Destructor for Octave.

.. ocv:function:: softcascade::Octave::~Octave()


softcascade::Octave::train
--------------------------

.. ocv:function:: bool softcascade::Octave::train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth)

    :param dataset an object that allows communicate for training set.

    :param pool an object that presents feature pool.

    :param weaks a number of weak trees should be trained.

    :param treeDepth a depth of resulting weak trees.



softcascade::Octave::setRejectThresholds
----------------------------------------

.. ocv:function:: void softcascade::Octave::setRejectThresholds(OutputArray thresholds)

    :param thresholds an output array of resulted rejection vector. Have same size as number of trained stages.


softcascade::Octave::write
--------------------------

.. ocv:function:: void softcascade::Octave::train(cv::FileStorage &fs, const FeaturePool* pool, InputArray thresholds) const
.. ocv:function:: void softcascade::Octave::train( CvFileStorage* fs, string name) const

    :param fs an output file storage to store trained detector.

    :param pool an object that presents feature pool.

    :param dataset a rejection vector that should be included in detector xml file.

    :param name a name of root node for trained detector.


softcascade::FeaturePool
------------------------
.. ocv:class:: softcascade::FeaturePool

Public interface for feature pool. This is a hight level abstraction for training random feature pool. ::

    class FeaturePool
    {
    public:

        virtual int size() const = 0;
        virtual float apply(int fi, int si, const Mat& channels) const = 0;
        virtual void write( cv::FileStorage& fs, int index) const = 0;
        virtual ~FeaturePool();

    };

softcascade::FeaturePool::size
------------------------------

Returns size of feature pool.

.. ocv:function:: int softcascade::FeaturePool::size() const



softcascade::FeaturePool::~FeaturePool
--------------------------------------

FeaturePool destructor.

.. ocv:function:: softcascade::FeaturePool::~FeaturePool()



softcascade::FeaturePool::write
-------------------------------

Write specified feature from feature pool to file storage.

.. ocv:function:: void softcascade::FeaturePool::write( cv::FileStorage& fs, int index) const

    :param fs an output file storage to store feature.

    :param index an index of feature that should be stored.


softcascade::FeaturePool::apply
-------------------------------

Compute feature on integral channel image.

.. ocv:function:: float softcascade::FeaturePool::apply(int fi, int si, const Mat& channels) const

    :param fi an index of feature that should be computed.

    :param si an index of sample.

    :param fs a channel matrix.