opencv/modules/softcascade/doc/softcascade_training.rst

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Soft Cascade Training
=======================
.. highlight:: cpp
Soft Cascade Detector Training
--------------------------------------------
softcascade::Octave
-------------------
.. ocv:class:: softcascade::Octave : public Algorithm
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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 };
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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;
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virtual void write( CvFileStorage* fs, String name) const = 0;
};
softcascade::Octave::~Octave
---------------------------------------
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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
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.. 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.
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softcascade::FeaturePool
------------------------
.. ocv:class:: softcascade::FeaturePool
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Public interface for feature pool. This is a hight level abstraction for training random feature pool. ::
class FeaturePool
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{
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
------------------------------
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Returns size of feature pool.
.. ocv:function:: int softcascade::FeaturePool::size() const
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softcascade::FeaturePool::~FeaturePool
--------------------------------------
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FeaturePool destructor.
.. ocv:function:: softcascade::FeaturePool::~FeaturePool()
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softcascade::FeaturePool::write
-------------------------------
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Write specified feature from feature pool to file storage.
.. ocv:function:: void softcascade::FeaturePool::write( cv::FileStorage& fs, int index) const
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:param fs an output file storage to store feature.
:param index an index of feature that should be stored.
softcascade::FeaturePool::apply
-------------------------------
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Compute feature on integral channel image.
.. ocv:function:: float softcascade::FeaturePool::apply(int fi, int si, const Mat& channels) const
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:param fi an index of feature that should be computed.
:param si an index of sample.
:param fs a channel matrix.